From 70979df977071b532f5f9f1de1c60c27126e83fc Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 11 Jan 2022 15:42:14 -0700 Subject: [PATCH 01/35] Adding Cost Impact Added a notebook to display cost impact estimates and added function stubs to scaffolding for calculating cost impact. --- .gitignore | 3 + viz_scripts/auxiliary_files/cost.csv | 15 + .../cost_and_time_impact_estimates.ipynb | 1393 +++++++++++++++++ viz_scripts/scaffolding.py | 62 + 4 files changed, 1473 insertions(+) create mode 100644 viz_scripts/auxiliary_files/cost.csv create mode 100644 viz_scripts/cost_and_time_impact_estimates.ipynb diff --git a/.gitignore b/.gitignore index 6a0eab5..7270470 100644 --- a/.gitignore +++ b/.gitignore @@ -133,3 +133,6 @@ dmypy.json # Pyre type checker .pyre/ +.DS_Store +.DS_Store +viz_scripts/.DS_Store diff --git a/viz_scripts/auxiliary_files/cost.csv b/viz_scripts/auxiliary_files/cost.csv new file mode 100644 index 0000000..2de3113 --- /dev/null +++ b/viz_scripts/auxiliary_files/cost.csv @@ -0,0 +1,15 @@ +mode,C($/PMT),($)/trip,D(time/PMT),(time)/PMT +"Car, drove alone",0,,, +"Car, with others",0,,, +Taxi/Uber/Lyft,0,,, +Bus,0.855,,, +Free Shuttle,0,,, +Train,0.855,,, +Scooter share,0.0041,,, +Pilot ebike,0,,, +Bikeshare,0.09,,, +Walk,0,,, +Skate board,0,,, +Regular Bike,0,,, +Not a Trip,0,,, +No Travel,0,,, \ No newline at end of file diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb new file mode 100644 index 0000000..f99cf2f --- /dev/null +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -0,0 +1,1393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2518a96d", + "metadata": {}, + "source": [ + "# Based on Issue #31: Add cost and time estimates as well\n", + "We want to add simple cost and time estimates to assess the broader impacts of programs.\n", + "We will do so by creating simple distance-based maps for each metric - e.g.\n", + "\n", + "cost_per_mile = { \"drove_alone\": ..., \"shared_ride\": ...., \"pilot_ebike\": ....\n", + "}\n", + "\n", + "We can then compute the overall impact of the metric by pseudo code similar to:\n", + "\n", + "for trip in trips:\n", + " cost_impact_trip = (cost_per_mile[“ebike”] – cost_per_mile[trip.replaced_mode]) * trip.length_in_miles\n", + " cost_impact_trips.append(cost_impact_trip)\n", + "\n", + "cost_impact_overall = sum(cost_impact_trips)\n", + "\n", + "Of course, we could also use pandas if that works better - e.g. something like:\n", + "\n", + "cost_impact_trips = trips.apply(lambda trip_row: (cost_per_mile[\"ebike\"] - cost_per_mile[trip_row.replaced_mode]) * trip_row.length__in_miles\n", + "cost_impact_overall = cost_impact_trips.sum()\n" + ] + }, + { + "cell_type": "markdown", + "id": "0a308acb", + "metadata": {}, + "source": [ + "Shankari K. suggested following the process outlined in the energy_calculations notebook reproduced below." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b9624fe3", + "metadata": {}, + "outputs": [], + "source": [ + "# user defined modules\n", + "import scaffolding\n", + "from plots import *\n", + "\n", + "# external packages\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "29424542", + "metadata": {}, + "outputs": [], + "source": [ + "# global configurations\n", + "sns.set_style('whitegrid')\n", + "sns.set()\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e55a1a1f", + "metadata": {}, + "outputs": [], + "source": [ + "# external variables (run mapping_dictionaries notebook before running this cell)\n", + "%store -r df_EI \n", + "%store -r df_C\n", + "\n", + "%store -r dic_re\n", + "%store -r dic_pur\n", + "%store -r dic_fuel\n", + "\n", + "# convert a dictionary to a defaultdict\n", + "dic_pur = defaultdict(lambda: 'Other',dic_pur)\n", + "dic_re = defaultdict(lambda: 'Other',dic_re)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ef7dd45c", + "metadata": {}, + "outputs": [], + "source": [ + "# Scaffolding Inputs (None -> get all data)\n", + "year = None\n", + "month = None\n", + "program = None" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9beff67f", + "metadata": {}, + "outputs": [], + "source": [ + "# Define time series for year and month\n", + "tq = scaffolding.get_time_query(year, month)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6a7cdcde", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[UUID('576e37c7-ab7e-4c03-add7-02486bc3f42e'),\n", + " UUID('8b563348-52b3-4e3e-b046-a0aaf4fcea15'),\n", + " UUID('5079bb93-c9cf-46d7-a643-dfc86bb05605'),\n", + " UUID('feabfccd-dd6c-4e8e-8517-9d7177042483'),\n", + " UUID('113aef67-400e-4e21-a29f-d04e50fc42ea'),\n", + " UUID('c8b9fe22-86f8-449a-b64f-c18a8d20eefc'),\n", + " UUID('e7b24d99-324d-4d6d-b247-9edc87d3c848'),\n", + " UUID('1044195f-af9e-43d4-9407-60594e5e9938'),\n", + " UUID('898b1a5e-cdd4-4a0c-90e4-942fa298e456'),\n", + " UUID('1d292b85-c549-409a-a10d-746e957582a0'),\n", + " UUID('cb3222a7-1e72-4a92-8b7b-2c4795402497'),\n", + " UUID('efdbea3b-eef6-48fc-9558-7585f4ad6f24'),\n", + " UUID('960835ac-9d8a-421d-8b8a-bf816f8a4b92')]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded all confirmed trips of length 3492\n" + ] + }, + { + "data": { + "text/html": [ + "
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sourceend_tsend_fmt_timeend_locraw_tripstart_tsstart_fmt_timestart_locdurationdistance...end_local_dt_monthend_local_dt_dayend_local_dt_hourend_local_dt_minuteend_local_dt_secondend_local_dt_weekdayend_local_dt_timezone_iduser_idmetadata_write_ts
0DwellSegmentationTimeFilter1.604364e+092020-11-02T17:45:22.115000-07:00{'type': 'Point', 'coordinates': [-104.9409405...5fa139609ae96f3a5fcdef311.604364e+092020-11-02T17:39:08.049000-07:00{'type': 'Point', 'coordinates': [-104.9398732...374.066000384.730231...1121745220America/Denver600533265e173ffb99e076251d292b85-c549-409a-a10d-746e957582a01.604402e+09
1DwellSegmentationTimeFilter1.604604e+092020-11-05T12:12:12-07:00{'type': 'Point', 'coordinates': [-105.0670666...5fa4690763a5e0e8d90c7fa41.604601e+092020-11-05T11:30:56.952000-07:00{'type': 'Point', 'coordinates': [-104.9479963...2475.04800013765.915676...1151212123America/Denver600533265e173ffb99e076261d292b85-c549-409a-a10d-746e957582a01.604610e+09
2DwellSegmentationTimeFilter1.604604e+092020-11-05T12:27:22-07:00{'type': 'Point', 'coordinates': [-105.080878,...5fa4690763a5e0e8d90c7fa81.604604e+092020-11-05T12:22:21.130739-07:00{'type': 'Point', 'coordinates': [-105.0670666...300.8692611508.223413...1151227223America/Denver600533265e173ffb99e076271d292b85-c549-409a-a10d-746e957582a01.604610e+09
3DwellSegmentationTimeFilter1.604606e+092020-11-05T12:47:29.017000-07:00{'type': 'Point', 'coordinates': [-105.0827029...5fa4690763a5e0e8d90c7faa1.604605e+092020-11-05T12:42:19.793043-07:00{'type': 'Point', 'coordinates': [-105.080878,...309.223957434.038504...1151247293America/Denver600533265e173ffb99e076281d292b85-c549-409a-a10d-746e957582a01.604610e+09
4DwellSegmentationTimeFilter1.604610e+092020-11-05T13:54:28.880000-07:00{'type': 'Point', 'coordinates': [-105.0824703...5fa4771a533f6ebf89c7c5e31.604610e+092020-11-05T13:52:57.667396-07:00{'type': 'Point', 'coordinates': [-105.0827029...91.212605333.230154...1151354283America/Denver600533265e173ffb99e076291d292b85-c549-409a-a10d-746e957582a01.604614e+09
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5 rows × 33 columns

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sourceend_tsend_fmt_timeend_locraw_tripstart_tsstart_fmt_timestart_locdurationdistance...end_local_dt_minuteend_local_dt_secondend_local_dt_weekdayend_local_dt_timezone_iduser_idmetadata_write_tsmode_confirmpurpose_confirmreplaced_mode
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1DwellSegmentationTimeFilter1.604604e+092020-11-05T12:12:12-07:00{'type': 'Point', 'coordinates': [-105.0670666...5fa4690763a5e0e8d90c7fa41.604601e+092020-11-05T11:30:56.952000-07:00{'type': 'Point', 'coordinates': [-104.9479963...2475.04800013765.915676...12123America/Denver600533265e173ffb99e076261d292b85-c549-409a-a10d-746e957582a01.604610e+09trainpersonal_medsame_mode
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4DwellSegmentationTimeFilter1.604610e+092020-11-05T13:54:28.880000-07:00{'type': 'Point', 'coordinates': [-105.0824703...5fa4771a533f6ebf89c7c5e31.604610e+092020-11-05T13:52:57.667396-07:00{'type': 'Point', 'coordinates': [-105.0827029...91.212605333.230154...54283America/Denver600533265e173ffb99e076291d292b85-c549-409a-a10d-746e957582a01.604614e+09not_a_triptransit_transfersame_mode
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5 rows × 36 columns

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" + ], + "text/plain": [ + " source end_ts \\\n", + "0 DwellSegmentationTimeFilter 1.604364e+09 \n", + "1 DwellSegmentationTimeFilter 1.604604e+09 \n", + "2 DwellSegmentationTimeFilter 1.604604e+09 \n", + "3 DwellSegmentationTimeFilter 1.604606e+09 \n", + "4 DwellSegmentationTimeFilter 1.604610e+09 \n", + "\n", + " end_fmt_time \\\n", + "0 2020-11-02T17:45:22.115000-07:00 \n", + "1 2020-11-05T12:12:12-07:00 \n", + "2 2020-11-05T12:27:22-07:00 \n", + "3 2020-11-05T12:47:29.017000-07:00 \n", + "4 2020-11-05T13:54:28.880000-07:00 \n", + "\n", + " end_loc \\\n", + "0 {'type': 'Point', 'coordinates': [-104.9409405... \n", + "1 {'type': 'Point', 'coordinates': [-105.0670666... \n", + "2 {'type': 'Point', 'coordinates': [-105.080878,... \n", + "3 {'type': 'Point', 'coordinates': [-105.0827029... \n", + "4 {'type': 'Point', 'coordinates': [-105.0824703... \n", + "\n", + " raw_trip start_ts start_fmt_time \\\n", + "0 5fa139609ae96f3a5fcdef31 1.604364e+09 2020-11-02T17:39:08.049000-07:00 \n", + "1 5fa4690763a5e0e8d90c7fa4 1.604601e+09 2020-11-05T11:30:56.952000-07:00 \n", + "2 5fa4690763a5e0e8d90c7fa8 1.604604e+09 2020-11-05T12:22:21.130739-07:00 \n", + "3 5fa4690763a5e0e8d90c7faa 1.604605e+09 2020-11-05T12:42:19.793043-07:00 \n", + "4 5fa4771a533f6ebf89c7c5e3 1.604610e+09 2020-11-05T13:52:57.667396-07:00 \n", + "\n", + " start_loc duration \\\n", + "0 {'type': 'Point', 'coordinates': [-104.9398732... 374.066000 \n", + "1 {'type': 'Point', 'coordinates': [-104.9479963... 2475.048000 \n", + "2 {'type': 'Point', 'coordinates': [-105.0670666... 300.869261 \n", + "3 {'type': 'Point', 'coordinates': [-105.080878,... 309.223957 \n", + "4 {'type': 'Point', 'coordinates': [-105.0827029... 91.212605 \n", + "\n", + " distance ... end_local_dt_minute end_local_dt_second \\\n", + "0 384.730231 ... 45 22 \n", + "1 13765.915676 ... 12 12 \n", + "2 1508.223413 ... 27 22 \n", + "3 434.038504 ... 47 29 \n", + "4 333.230154 ... 54 28 \n", + "\n", + " end_local_dt_weekday end_local_dt_timezone _id \\\n", + "0 0 America/Denver 600533265e173ffb99e07625 \n", + "1 3 America/Denver 600533265e173ffb99e07626 \n", + "2 3 America/Denver 600533265e173ffb99e07627 \n", + "3 3 America/Denver 600533265e173ffb99e07628 \n", + "4 3 America/Denver 600533265e173ffb99e07629 \n", + "\n", + " user_id metadata_write_ts mode_confirm \\\n", + "0 1d292b85-c549-409a-a10d-746e957582a0 1.604402e+09 walk \n", + "1 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 train \n", + "2 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 skateboard \n", + "3 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 not_a_trip \n", + "4 1d292b85-c549-409a-a10d-746e957582a0 1.604614e+09 not_a_trip \n", + "\n", + " purpose_confirm replaced_mode \n", + "0 meal same_mode \n", + "1 personal_med same_mode \n", + "2 transit_transfer bus \n", + "3 transit_transfer same_mode \n", + "4 transit_transfer same_mode \n", + "\n", + "[5 rows x 36 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Just expand the user_input feature to multiple features for each entry\n", + "expanded_ct = scaffolding.expand_userinputs(labeled_ct)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f948dc57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2374, 36)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Removes some rows that don't show a change from another mode to pilot e-bike + name same_mode as confirmed_mode\n", + "expanded_ct = scaffolding.data_quality_check(expanded_ct)\n", + "expanded_ct.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9495e947", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2374, 41)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Mapping new labels with dictionaries\n", + "expanded_ct['Trip_purpose']= expanded_ct['purpose_confirm'].map(dic_pur)\n", + "expanded_ct['Mode_confirm']= expanded_ct['mode_confirm'].map(dic_re)\n", + "expanded_ct['Replaced_mode']= expanded_ct['replaced_mode'].map(dic_re)\n", + "\n", + "#Mapping fuel\n", + "expanded_ct['Mode_confirm_fuel']= expanded_ct['Mode_confirm'].map(dic_fuel)\n", + "expanded_ct['Replaced_mode_fuel']= expanded_ct['Replaced_mode'].map(dic_fuel)\n", + "expanded_ct.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9e27d0e9", + "metadata": {}, + "outputs": [], + "source": [ + "# Just a meters to miles conversion at this point\n", + "scaffolding.unit_conversions(expanded_ct)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "e5330285", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Based on 2374 confirmed trips from 12 users\n", + "of 3492 total trips from 13 users (67.98%)\n" + ] + } + ], + "source": [ + "file_suffix = scaffolding.get_file_suffix(year, month, program)\n", + "quality_text = scaffolding.get_quality_text(participant_ct_df, expanded_ct)" + ] + }, + { + "cell_type": "markdown", + "id": "e0420cf9", + "metadata": {}, + "source": [ + "### This is where I need to make changes to include the cost and time impact..." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "9448046e", + "metadata": {}, + "outputs": [], + "source": [ + "expanded_ct = scaffolding.energy_intensity(expanded_ct, df_EI, 'distance','Replaced_mode', 'Mode_confirm')\n", + "expanded_ct = scaffolding.energy_impact_kWH(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "69f6ae3d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data=expanded_ct.loc[(expanded_ct['distance_miles'] <= 40)].sort_values(by=['Energy_Impact(kWH)'], ascending=False) \n", + "x='Energy_Impact(kWH)'\n", + "y='distance_miles'\n", + "legend ='Mode_confirm'\n", + "plot_title=\"Sketch of Energy Impact (kWH) by Travel Mode Selected\\n%s\" % quality_text\n", + "file_name ='sketch_distance_energy_impact%s.png' % file_suffix\n", + "distancevsenergy(data,x,y,legend,plot_title,file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84f69f84", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index c8858a6..f1b5580 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -137,6 +137,39 @@ def energy_intensity(df,df1,distance,col1,col2): return df +def cost(df, df_cost, dist_m, rep_m, mode): + """ + Calculates the cost of the CanBikeCO E-bike pilot program + + Parameters: + df - CanBikeCO data input + df_cost - dataframe defining cost ($/PMT) for each mode + dist_m - feature name in df of feature with distance in miles + rep_m - feature name in df of feature with replaced mode + mode - feature name in df of feature with confirmed mode + + Returns: + df with appended cost feature for each trip in $$$ for both mode and replaced mode (float) + """ + + +def time(df, df_dur, dist_m, rep_m, mode) + """ + Calculates the duration of the CanBikeCO E-bike pilot program + + Parameters: + df - CanBikeCO data input + df_cost - dataframe defining duration/PMT for each mode + dist_m - feature name in df of feature with distance in miles + rep_m - feature name in df of feature with replaced mode + mode - feature name in df of feature with confirmed mode + + Returns: + df with appended duration feature for each trip in time for both mode and replaced mode (timeDelta?) + """ + + + def energy_impact_kWH(df,distance,col1,col2): """ Inputs: df = dataframe with data @@ -207,3 +240,32 @@ def CO2_impact_lb(df,distance,col1,col2): df['CO2_Impact(lb)'] = round((df[col1+'_lb_CO2'] - df[col2+'_lb_CO2']),3) return df + + +def cost_impact(df, dist_m, rep_m, mode): + """ + Calculates the cost impact of the CanBikeCO E-bike program + + Parameters: + df - CanBikeCO data input + dist_m - feature name in df of feature with distance in miles + rep_m - feature name in df of feature with replaced mode + mode - feature name in df of feature with confirmed mode + + Returns: + df with appended cost impact feature for each trip in $$$ (float) + """ + +def time_impact(df, dist_m, rep_m, mode): + """ + Calculates the time impact of the CanBikeCO E-bike program + + Parameters: + df - CanBikeCO data input + dist_m - feature name in df of feature with distance in miles + rep_m - feature name in df of feature with replaced mode + mode - feature name in df of feature with confirmed mode + + Returns: + df with appended time impact feature for each trip in $$$ (float) + """ \ No newline at end of file From f6b8f46909f867f2ef26c265a7a8bbe572d2b574 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 11 Jan 2022 15:52:29 -0700 Subject: [PATCH 02/35] Create tests.py Created a separate file for testing functions in scaffolding. --- viz_scripts/tests.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 viz_scripts/tests.py diff --git a/viz_scripts/tests.py b/viz_scripts/tests.py new file mode 100644 index 0000000..e69de29 From 840ff24d899862f9df8d36aeb6689b166ddda4d6 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 11:42:43 -0700 Subject: [PATCH 03/35] Added Cost and Time Calculations by Mode --- viz_scripts/tests.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/viz_scripts/tests.py b/viz_scripts/tests.py index e69de29..f3043c2 100644 --- a/viz_scripts/tests.py +++ b/viz_scripts/tests.py @@ -0,0 +1,20 @@ +import pandas as pd +import numpy as np +import scaffolding + +def test_energy_intensity(): + + # Inputs + dummy_data = pd.DataFrame({ + + }) + + + + + + + +def test_energy_impact_kWH(): + # + None \ No newline at end of file From 1fa759acdfe4746ee89304102ed46150a5200ac0 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 11:43:25 -0700 Subject: [PATCH 04/35] Same as before? --- viz_scripts/scaffolding.py | 90 +++++++++++++++++++++++++++++--------- 1 file changed, 69 insertions(+), 21 deletions(-) diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index f1b5580..46c4a49 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -107,25 +107,35 @@ def unit_conversions(df): df['distance_miles']= df["distance"]*0.00062 #meters to miles def energy_intensity(df,df1,distance,col1,col2): - """ Inputs: - df = dataframe with data - df = dataframe with energy factors + """Inputs: + df = dataframe with data from CanBikeCO + df1 = dataframe with energy factors distance = distance in meters col1 = Replaced_mode col2= Mode_confirm - """ + + # Create a copy of the energy_factors dataframe df1 = df1.copy() + + # Create a replaced mode column in df1 same as mode df1[col1] = df1['mode'] + + # Pair energy intensity with mode dic_ei_factor = dict(zip(df1[col1],df1['energy_intensity_factor'])) + + # Pair CO2_factor with mode dic_CO2_factor = dict(zip(df1[col1],df1['CO2_factor'])) + + # Pair (KWH)/trip with mode dic_ei_trip = dict(zip(df1[col1],df1['(kWH)/trip'])) + # Create new features in data for replaced mode df['ei_'+col1] = df[col1].map(dic_ei_factor) df['CO2_'+col1] = df[col1].map(dic_CO2_factor) df['ei_trip_'+col1] = df[col1].map(dic_ei_trip) - + # Create new features in data for confirmed mode df1[col2] = df1[col1] dic_ei_factor = dict(zip(df1[col2],df1['energy_intensity_factor'])) dic_ei_trip = dict(zip(df1[col2],df1['(kWH)/trip'])) @@ -137,37 +147,75 @@ def energy_intensity(df,df1,distance,col1,col2): return df -def cost(df, df_cost, dist_m, rep_m, mode): +def cost(data, cost, dist, repm, mode): """ Calculates the cost of the CanBikeCO E-bike pilot program Parameters: - df - CanBikeCO data input - df_cost - dataframe defining cost ($/PMT) for each mode - dist_m - feature name in df of feature with distance in miles - rep_m - feature name in df of feature with replaced mode - mode - feature name in df of feature with confirmed mode + data - CanBikeCO data input + cost - dataframe defining cost ($/PMT) for each mode + dist - feature name in data of feature with distance in miles + repm - feature name in data of feature with replaced mode + mode - feature name in data of feature with confirmed mode Returns: - df with appended cost feature for each trip in $$$ for both mode and replaced mode (float) + data with appended cost feature for each trip in $$$ for both mode and replaced mode (float) """ + + # Create a copy of the cost dataframe + cost = cost.copy() + + # Create a replaced mode column in cost same as mode + cost[repm] = cost['mode'] + + # Pair cost with mode + dic_cost__trip = dict(zip(cost[repm],cost['C($/PMT)'])) + # Create new features in data for replaced mode + data['cost__trip_'+repm] = data[repm].map(dic_cost__trip) + + # Create new features in data for confirmed mode + cost[mode] = cost[repm] + dic_cost__trip = dict(zip(cost[mode],cost['C($/PMT)'])) + data['cost__trip_'+mode] = data[mode].map(dic_cost__trip) + + return data -def time(df, df_dur, dist_m, rep_m, mode) - """ - Calculates the duration of the CanBikeCO E-bike pilot program + +def cost(data, dura, dist, repm, mode): + """ + Calculates the cost of the CanBikeCO E-bike pilot program Parameters: - df - CanBikeCO data input - df_cost - dataframe defining duration/PMT for each mode - dist_m - feature name in df of feature with distance in miles - rep_m - feature name in df of feature with replaced mode - mode - feature name in df of feature with confirmed mode + data - CanBikeCO data input + dura - dataframe defining duration ((1/speed)/PMT) for each mode + dist - feature name in data of feature with distance in miles + repm - feature name in data of feature with replaced mode + mode - feature name in data of feature with confirmed mode Returns: - df with appended duration feature for each trip in time for both mode and replaced mode (timeDelta?) + data with appended cost feature for each trip in $$$ for both mode and replaced mode (float) """ + + # Create a copy of the dura dataframe + dura = dura.copy() + + # Create a replaced mode column in dura same as mode + dura[repm] = dura['mode'] + + # Pair dura with mode + dic_dura__trip = dict(zip(dura[repm],dura['C($/PMT)'])) + # Create new features in data for replaced mode + data['dura__trip_'+repm] = data[repm].map(dic_dura__trip) + + # Create new features in data for confirmed mode + dura[mode] = dura[repm] + dic_dura__trip = dict(zip(dura[mode],dura['C($/PMT)'])) + data['dura__trip_'+mode] = data[mode].map(dic_dura__trip) + + return data + def energy_impact_kWH(df,distance,col1,col2): From 18d4124d38ec1872b75dc92c3bdd1195af3ad920 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 11:54:15 -0700 Subject: [PATCH 05/35] Added time_impact and cost_impact functions. --- viz_scripts/scaffolding.py | 51 +++++++++++++++++++++++++++----------- 1 file changed, 36 insertions(+), 15 deletions(-) diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 46c4a49..b875c96 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -219,13 +219,21 @@ def cost(data, dura, dist, repm, mode): def energy_impact_kWH(df,distance,col1,col2): - """ Inputs: + """ + Purpose: + Calculates energy intensity for each mode + by fuel type, then calculates the diference + between the energy intensity of replaced and + confirmed modes. + + Inputs: df = dataframe with data distance = distance in miles col1 = Replaced_mode col2= Mode_confirm """ - + + conditions_col1 = [(df['Replaced_mode_fuel'] =='gasoline'), (df['Replaced_mode_fuel'] == 'diesel'), (df['Replaced_mode_fuel'] == 'electric')] @@ -290,30 +298,43 @@ def CO2_impact_lb(df,distance,col1,col2): return df -def cost_impact(df, dist_m, rep_m, mode): +def cost_impact(data, dist, repm, mode): """ Calculates the cost impact of the CanBikeCO E-bike program Parameters: - df - CanBikeCO data input - dist_m - feature name in df of feature with distance in miles - rep_m - feature name in df of feature with replaced mode + data - CanBikeCO data input + dist - feature name in df of feature with distance in miles + repm - feature name in df of feature with replaced mode mode - feature name in df of feature with confirmed mode Returns: - df with appended cost impact feature for each trip in $$$ (float) + data with appended cost impact feature for each trip in $$$ (float) """ - -def time_impact(df, dist_m, rep_m, mode): + + data[mode+'_cost'] = data[dist] * data['cost__trip_mode'] + data[repm+'_cost'] = data[dist] * data['cost__trip_repm'] + data['Cost_Impact($)'] = round((data[mode+'_cost'] - data[repm+'_cost']),2) + + return data + + +def time_impact(data, dist, repm, mode): """ - Calculates the time impact of the CanBikeCO E-bike program + Calculates the cost impact of the CanBikeCO E-bike program Parameters: - df - CanBikeCO data input - dist_m - feature name in df of feature with distance in miles - rep_m - feature name in df of feature with replaced mode + data - CanBikeCO data input + dist - feature name in df of feature with distance in miles + repm - feature name in df of feature with replaced mode mode - feature name in df of feature with confirmed mode Returns: - df with appended time impact feature for each trip in $$$ (float) - """ \ No newline at end of file + data with appended time impact feature for each trip in $$$ (float) + """ + + data[mode+'_dura'] = data[dist] * data['dura__trip_mode'] + data[repm+'_dura'] = data[dist] * data['dura__trip_repm'] + data['Cost_Impact($)'] = round((data[mode+'_dura'] - data[repm+'_dura']),3) + + return data \ No newline at end of file From e9e1dfdd102e2542e707562ff676dc061f997d5e Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 11:59:29 -0700 Subject: [PATCH 06/35] cost_time.csv updates Added cost_time.csv, read in through the mapping_dictionaries.ipynb and added the function call in cost_time_impact_estimates. --- viz_scripts/auxiliary_files/cost.csv | 15 --------------- viz_scripts/auxiliary_files/cost_time.csv | 15 +++++++++++++++ viz_scripts/cost_and_time_impact_estimates.ipynb | 2 +- viz_scripts/mapping_dictionaries.ipynb | 2 ++ 4 files changed, 18 insertions(+), 16 deletions(-) delete mode 100644 viz_scripts/auxiliary_files/cost.csv create mode 100644 viz_scripts/auxiliary_files/cost_time.csv diff --git a/viz_scripts/auxiliary_files/cost.csv b/viz_scripts/auxiliary_files/cost.csv deleted file mode 100644 index 2de3113..0000000 --- a/viz_scripts/auxiliary_files/cost.csv +++ /dev/null @@ -1,15 +0,0 @@ -mode,C($/PMT),($)/trip,D(time/PMT),(time)/PMT -"Car, drove alone",0,,, -"Car, with others",0,,, -Taxi/Uber/Lyft,0,,, -Bus,0.855,,, -Free Shuttle,0,,, -Train,0.855,,, -Scooter share,0.0041,,, -Pilot ebike,0,,, -Bikeshare,0.09,,, -Walk,0,,, -Skate board,0,,, -Regular Bike,0,,, -Not a Trip,0,,, -No Travel,0,,, \ No newline at end of file diff --git a/viz_scripts/auxiliary_files/cost_time.csv b/viz_scripts/auxiliary_files/cost_time.csv new file mode 100644 index 0000000..66b0696 --- /dev/null +++ b/viz_scripts/auxiliary_files/cost_time.csv @@ -0,0 +1,15 @@ +mode,C($/PMT),($)/trip,D(hours/PMT),(hours)/trip +"Car, drove alone",0,0,0,0 +"Car, with others",0,0,0,0 +Taxi/Uber/Lyft,0,0,0,0 +Bus,0.855,0,0,0 +Free Shuttle,0,0,0,0 +Train,0.855,0,0,0 +Scooter share,0.0041,0,0,0 +Pilot ebike,0,0,0,0 +Bikeshare,0.09,0,0,0 +Walk,0,0,0,0 +Skate board,0,0,0,0 +Regular Bike,0,0,0,0 +Not a Trip,0,0,0,0 +No Travel,0,0,0,0 \ No newline at end of file diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index f99cf2f..58b76f6 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -1327,7 +1327,7 @@ "metadata": {}, "outputs": [], "source": [ - "expanded_ct = scaffolding.energy_intensity(expanded_ct, df_EI, 'distance','Replaced_mode', 'Mode_confirm')\n", + "expanded_ct = scaffolding.cost(expanded_ct, df_EI, 'distance','Replaced_mode', 'Mode_confirm')\n", "expanded_ct = scaffolding.energy_impact_kWH(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" ] }, diff --git a/viz_scripts/mapping_dictionaries.ipynb b/viz_scripts/mapping_dictionaries.ipynb index e7c4264..43eb969 100644 --- a/viz_scripts/mapping_dictionaries.ipynb +++ b/viz_scripts/mapping_dictionaries.ipynb @@ -20,6 +20,7 @@ "df_pur= pd.read_csv(r'auxiliary_files/purpose_labels.csv')\n", "df_re = pd.read_csv(r'auxiliary_files/mode_labels.csv')\n", "df_EI = pd.read_csv(r'auxiliary_files/energy_intensity.csv')\n", + "df_CT = pd.read_csv(r'auxiliary_files/cost_time.csv')\n", "\n", "#dictionaries:\n", "dic_pur = dict(zip(df_pur['purpose_confirm'],df_pur['bin_purpose'])) # bin purpose\n", @@ -35,6 +36,7 @@ "outputs": [], "source": [ "%store df_EI \n", + "%store df_CT\n", "%store dic_re \n", "%store dic_pur \n", "%store dic_fuel " From dca163d723ba3c475546ba3aa2d2b74bfeee6364 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 12:04:30 -0700 Subject: [PATCH 07/35] Debugging --- .../cost_and_time_impact_estimates.ipynb | 71 +++++++++++++------ viz_scripts/mapping_dictionaries.ipynb | 28 ++++++-- viz_scripts/scaffolding.py | 4 +- 3 files changed, 77 insertions(+), 26 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 58b76f6..26a2107 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -36,10 +36,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "b9624fe3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting to database URL db\n" + ] + } + ], "source": [ "# user defined modules\n", "import scaffolding\n", @@ -54,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "29424542", "metadata": {}, "outputs": [], @@ -68,14 +76,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "e55a1a1f", "metadata": {}, "outputs": [], "source": [ "# external variables (run mapping_dictionaries notebook before running this cell)\n", "%store -r df_EI \n", - "%store -r df_C\n", + "%store -r df_CT\n", "\n", "%store -r dic_re\n", "%store -r dic_pur\n", @@ -88,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "ef7dd45c", "metadata": {}, "outputs": [], @@ -101,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "id": "9beff67f", "metadata": {}, "outputs": [], @@ -112,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "id": "6a7cdcde", "metadata": {}, "outputs": [ @@ -639,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "id": "c50baf4a", "metadata": {}, "outputs": [ @@ -898,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "c2dd6e2a", "metadata": {}, "outputs": [ @@ -1230,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "id": "f948dc57", "metadata": {}, "outputs": [ @@ -1240,7 +1248,7 @@ "(2374, 36)" ] }, - "execution_count": 31, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1253,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 11, "id": "9495e947", "metadata": {}, "outputs": [ @@ -1263,7 +1271,7 @@ "(2374, 41)" ] }, - "execution_count": 35, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1282,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 12, "id": "9e27d0e9", "metadata": {}, "outputs": [], @@ -1293,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 13, "id": "e5330285", "metadata": {}, "outputs": [ @@ -1322,13 +1330,36 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 17, "id": "9448046e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'cost__trip_mode'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2888\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2889\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_CT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost_impact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/src/app/saved-notebooks/scaffolding.py\u001b[0m in \u001b[0;36mcost_impact\u001b[0;34m(data, dist, repm, mode)\u001b[0m\n\u001b[1;32m 313\u001b[0m \"\"\"\n\u001b[1;32m 314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost__trip_mode'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 316\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrepm\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost__trip_repm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Cost_Impact($)'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrepm\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2897\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2898\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2893\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'" + ] + } + ], "source": [ - "expanded_ct = scaffolding.cost(expanded_ct, df_EI, 'distance','Replaced_mode', 'Mode_confirm')\n", - "expanded_ct = scaffolding.energy_impact_kWH(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" + "expanded_ct = scaffolding.cost(expanded_ct, df_CT, 'distance','Replaced_mode', 'Mode_confirm')\n", + "expanded_ct = scaffolding.cost_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" ] }, { diff --git a/viz_scripts/mapping_dictionaries.ipynb b/viz_scripts/mapping_dictionaries.ipynb index 43eb969..0733074 100644 --- a/viz_scripts/mapping_dictionaries.ipynb +++ b/viz_scripts/mapping_dictionaries.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "available-fusion", "metadata": {}, "outputs": [], @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "obvious-chapter", "metadata": {}, "outputs": [], @@ -30,10 +30,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "younger-indication", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stored 'df_EI' (DataFrame)\n", + "Stored 'df_CT' (DataFrame)\n", + "Stored 'dic_re' (dict)\n", + "Stored 'dic_pur' (dict)\n", + "Stored 'dic_fuel' (dict)\n" + ] + } + ], "source": [ "%store df_EI \n", "%store df_CT\n", @@ -41,6 +53,14 @@ "%store dic_pur \n", "%store dic_fuel " ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53b56bce", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index b875c96..8e88ed2 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -312,8 +312,8 @@ def cost_impact(data, dist, repm, mode): data with appended cost impact feature for each trip in $$$ (float) """ - data[mode+'_cost'] = data[dist] * data['cost__trip_mode'] - data[repm+'_cost'] = data[dist] * data['cost__trip_repm'] + data[mode+'_cost'] = data[dist] * data['cost__trip_'+mode] + data[repm+'_cost'] = data[dist] * data['cost__trip_'+repm] data['Cost_Impact($)'] = round((data[mode+'_cost'] - data[repm+'_cost']),2) return data From 545fdc08915bb2360623020e5018189656b57444 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 12:06:53 -0700 Subject: [PATCH 08/35] debugging --- .../cost_and_time_impact_estimates.ipynb | 126 ++++++++++-------- viz_scripts/scaffolding.py | 2 +- 2 files changed, 70 insertions(+), 58 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 26a2107..c02492f 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -36,18 +36,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 49, "id": "b9624fe3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting to database URL db\n" - ] - } - ], + "outputs": [], "source": [ "# user defined modules\n", "import scaffolding\n", @@ -62,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 50, "id": "29424542", "metadata": {}, "outputs": [], @@ -76,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 51, "id": "e55a1a1f", "metadata": {}, "outputs": [], @@ -96,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 52, "id": "ef7dd45c", "metadata": {}, "outputs": [], @@ -109,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 53, "id": "9beff67f", "metadata": {}, "outputs": [], @@ -120,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 54, "id": "6a7cdcde", "metadata": {}, "outputs": [ @@ -647,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 55, "id": "c50baf4a", "metadata": {}, "outputs": [ @@ -906,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 56, "id": "c2dd6e2a", "metadata": {}, "outputs": [ @@ -1238,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 57, "id": "f948dc57", "metadata": {}, "outputs": [ @@ -1248,7 +1240,7 @@ "(2374, 36)" ] }, - "execution_count": 10, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1261,7 +1253,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 58, "id": "9495e947", "metadata": {}, "outputs": [ @@ -1271,7 +1263,7 @@ "(2374, 41)" ] }, - "execution_count": 11, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1290,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 59, "id": "9e27d0e9", "metadata": {}, "outputs": [], @@ -1301,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 60, "id": "e5330285", "metadata": {}, "outputs": [ @@ -1330,55 +1322,67 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 62, "id": "9448046e", "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'cost__trip_mode'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2888\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2889\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_CT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost_impact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/src/app/saved-notebooks/scaffolding.py\u001b[0m in \u001b[0;36mcost_impact\u001b[0;34m(data, dist, repm, mode)\u001b[0m\n\u001b[1;32m 313\u001b[0m \"\"\"\n\u001b[1;32m 314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m 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"\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2889\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2893\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'" - ] - } - ], + "outputs": [], "source": [ "expanded_ct = scaffolding.cost(expanded_ct, df_CT, 'distance','Replaced_mode', 'Mode_confirm')\n", - "expanded_ct = scaffolding.cost_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" + "# expanded_ct = scaffolding.cost_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" ] }, { "cell_type": "code", - "execution_count": 39, - "id": "69f6ae3d", + "execution_count": 63, + "id": "ecaff572", "metadata": {}, "outputs": [ { "data": { - "image/png": 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iskhVn0xUmYCt9ZqVOK+JmNVjABalcTpVo134OzWDuuX1Mar6gYh8i42FtoS1oNVMA8zqE7+ezbDxmXyJ8SymaPcNn5sT28cCnYD/pYp4WQXexNY1f5twZY4zCTheRNqHADWIBVraBXtpA8XrTHtRHKSmKbZOLr7mbyzmcv1VCithWtI8m7OALSh+Nmf67E2SiUxjgYtE5BBVfTlNnVTP9LexMdBcVVM+b0TkZ0x57k1J6/NhGcrvOE4lcOXPcRyAL0RkDLZGbTY2Ib4ACyjyWLKyqs4TkX2xsOAvi8gBIbjINcCdIrIZ5paUja3x2UtV+4bdvwV6i0gfbFL6q6r+KiIXAU+JyFNYQIgibL3J8AxdLFOiqq+HIAbPiMgl2JvsCzBlY0glm20iIj1SlE+vYOCMi7DgLoVYwIlF2IT8YODyKKBDGoZigXpeEpE7MBeqS7BrVuJNe+jf/4Z2b64GS09l2FZEHsAm3Htg7sT/jFnd3gTOCGv+5mPn1iDRxuOh/I0w1hSLvthBVS8JbsA/AP2DZW458HkUKCTBR5hlojMplD8AVb0/TOQfFZHFqjo6tvkDzI1xQ2KWv3BvTMWsq8m1TJWhC+Y6l86lL055fRzxMDb2P9SK5TjMCFVdKCKTgavEghAVYmNzIbBOou5ssWBIt2FWzpGJ5q7BrtUrwYI9FwsGsi8wTFXHVVLGfEzxKYthmLvpayJyFVAQ5JmLRddFVb8SkReB+0Pgld+wQFJLE23djkXDfVtE7sZewrTGopuOV9VS0UYDDwWL6ShsvV8LzG13B8LazQo8e5NkItObwOvA0yJyHeaS3QYLghQFF/oWODg8YxabSKoi8m9gRPAgmYIpiNti9+vJqloQtt0mInOx/yeHY+vPHcdZTXjAF8dxwKxC7bCccW9gbkBfAd2iN95JVPU3bI1OO+B5sciHg7GgDtHEdziW3y2eh+++cIxHsLfmp4T2nsb+8XfEFKHHw/c51XB+fbFJzFDM0pAF7F0Fq0xHitfgxD8HVKQRVR2PTdLXB57ArJ4XAT+T2n0rvu9MTJnbAAuQcBYWoS+HkhaHiEhxqWpwj8pyETbxH4W5RV5PyXxoZ2Hj5F5sbHxJwgoUgorsjfXTdcBrod24i+ypmOXtLWx8JQPPRG0twSa1ZbrAquqQIMeI8MIjInLnXExpxexDbIxVJb9fxAFYFNxUrnNJyuvjiGgsPFIN8qXjH5hS/Thm2RsVvqdiBKZQTNRETsbwAqQHpkw9iF3zazGrYlWtqmUS1kHvgyk3D2MvwmZgkYPjbrgDsGfa0FBvLCXXLRJeCvUIbd0R6g/GvCvKSrlyHzbGrgr7PIAFydlfVZ+LtZ/Jszd5fuXKFKytfbG+Pwfr/xso6XJ7IZZC4xXsnouCVZ2BjcHjMdf2Ydgz673YvkOxQDanYmOkKTaOHcdZTWQVFVWnF4XjOI5TU4gljH8fU2zfSWwbiSV73/0vlqkdpgQcWobbWI0gIn2xtWQbaXGqgTUGEYncrvcJLwqqq93TsUn+RpoilYvjOI5Te3G3T8dxnLUUEbkVy3P4O5Yu40rsjf27sTrbYa6Dh1F6DV1dZzRmRTqO1AFFaprTMGtYtSh+QRHvAFyGuUy64uc4jlPHcOXPcRxn7aUBtnarNbZe8A3gvISL4EuYG+R9cTcxZ1Uk1FMwxXlNZCFwdjW2dw3mjvku9qLAcRzHqWO426fjOI7jOI7jOE4dwAO+OI7jOI7jOI7j1AFc+XMcx3Ecx3Ecx6kD+Jo/x3HWGkIuq6tjRcuw3Fd3q+qDNSJUCkTkTEymrBo6fg6Wy/AQYJtQ/D8sd+DkWL1tgX8B2wMtsciSbwBXhlQeUb106wNWqmoyFx8hL9lkYGfWsCifItIGC8e/K5YWYS8sVH8nVe1Sg6KlJOQZXAScqKrD0tTZADgdC+LyY4btFgFnqWqqVBDViogMxILMbIrlj9tzdR8zU0RkSyxVQQ8smfz7SfnCmDkP2A9Lrr4AS2J+qar+iuM4zlqEW/4cx1nbWAj0DJ9DsZxaD4jIP2pUqjWLRlhS7clYJMtjgTxgvIh0jtVrjqVhuADYH1Os9wFeFZH4y8GeKT5zsZxfqTgZS8S9JnI5liD7aOw8PsZykQ2oQZmqygbYtWtXgX16YjkvVysisiFwP5Z77m+YkromsS1wEDA1fFLRGct1Nxx75lwIdAc+CMq54zjOWoNb/hzHWdvIV9WJsd9jRWQXoA/wdA3JtKaxDNhcVRdEBSIyFpvcngmcCKCqH1AyEfk4EfkFs/5tjylGJPobEemKRRAdnjywiLQAbsSUzzUxfUJHYJKqvhorKzflgYg0DEnm12pEpJGqLkte09XIlkAO8Iiqpk1mHsn1F8kU5yVVHRNkeA4b10nGAx1VNT8qEJGPAQUOx5K/r3HUYJ86jrMG48qf4zi1gUVAbvRDRJoAtwL7Yq5ms4BXMTetP2P1TsLcuTYHlgBfAaer6ldhe0PgOsxKtAHwbWjj1VgbDTDXyWOBQmwi+FMmQovI3sDNmCVqITAKuEhVF4ftewLvYK6JZwAHArOB21T1vnTtqmoB5poWL1spIl+F8yiLeeFv/TLqHI3110sptl0PTMAsshkhIo2wfHtHAhsCvwIjVPXSsD0HS03wf1hai+nAjar6dKyNYZjb3qXY9dgCy4E4KHY9i2L1i4AZqtou2jdy+xSRAcCjmHVncPh7k4j8HMo7A7cD3TCF+v8wReAeLJ/iPMzFtoRyLCK9w3l0Av4AHg/18mJ1DsfGxKaY5fa8cvquHfBF+PmOiGWtUNWs2Pg5ABs/ewPPACcl3T5FZBxmzX0Dc9Fsjbk2nqKqM2PHuxQ4CdgEG7OfAANU9fcUsl1DsZv2Z0G2E4FxmMX5WMzi3AuYAuwjIu2BO4KsWaHuuao6PdZuUeiXTTCLbRFwi6reJiInhGOuBzyP3c9plfZEWpR0df5IUTZVRJZSzv2Uyr029MuZqtoq/F4XuA2zQK6H3eOvq+rA2D6dsGfaHqHov6Hd38P2PUl/rct8zjmOU7dwt0/HcdY6RKRe+KwjIsdi7mQvxKo0xqwNl2MK05XYZOjZWBt7AP8Gngx1/g+zgjWPtfMcNrm8CXP3mgy8KCI7xurcgrk5Xg8cA2wGnJ/BOWyDTeDmYtaDq7EcbKly8f0H+AxzPRsH3Csi3co7RuJ4DTCl5esU27JFpL7Y7PwW7Dw/StNOFtAPGKOqSxPbtscm9xdUQK4szCXwNOBebAJ8NSUtMNdh1/JBTFGYADwlIkcnmmuL5T28kWKFfWQ4Bpir4yfYJLkn1p9lMRx4OcgUX7f4WNh2OKagPIetI/wVOAKYBDwuIpvEzrM/pox8FM7hWuAUTNGL6uyMTdg/w5TIF4GR5cj4GzbuwCb9kVtunIdDm73C93T0BM7CFIWTMOvv6Jh8x2OK4e2Y0nYapog3SdPeQ0Emgow9gVdi22/DXtz0w5TrBthLg62Bgdi91x54V0TWS7R9PtAUu85PA0NEZHDY5+wg5zHAOWWcb6UJY70xKe6nSnA7sBtwLtavl2EKbXSsLbEx3xBz4x6Auau+FBvbESWudYbPOcdx6hBu+XMcZ22jJbZ+Lc5dqvp49ENV52ATU8CURczSMF5E2qrqT5jV5nNVvTnWzouxff4OHAzsqarvhuI3RKQDpoj0E5GWwKnA1ar6r7Df62Q2IbwKmAH0CpY6RGQ+8IyI9FTVD2N1h6vqDaHOOEwRPYw0CloaLgdakNoV81Vs0gkWGOagMiwiu2MWlxEptt0N3Kuq04NFKhP2wyy0vVX1xVj54wBh0n8OcEPUB8DrQbG6hpKup+sBu6rqtLBvNvZSQIBvVXWiiPwJzM/Q7fEuVb0z+iEiO4Svt6nqY6EsC1Noxqnq5aHsI0wJPBS4P9QZAjyuqqfH2luBKfI3q+o8zFV2KtBfVYuA14JCFJ13KVR1hYhE7pRfpzmvZ1U1k6TuGwC7qOqMIN8M7J45QFX/i90zbySszs+XIdsvIhLdC5+r6peh3UhZnKiqkXKIiJyKKfAdVPX7UDYJ+B4YRExRBqap6qBQ5y1MgRwIbBZZ94M1rC/2QqPaCOPqTmAaZimtKt2w++aZWNmTse9XA78DB6rqyiDD55gnwkGUVKhLXGsRuYAynnOO49Q93PLnOM7axkKga/jsBvwTOEFE4lFAEZHjROQTEVlMCHYSNnUIfz8FdhKRO0RkDxFJujnug024JsQsjfUwy0QUFXI77G38mGinoDSNoXy6AS9Eil9gFJAfzivOqglmcBGchilgGSEiB2PK38WqqimqnIVFOzwOs6a8FlxeU3E05lL6euIYR2FKVlpFJQ17Y8pYuglpJ8zCkgxO8gzQQSzSZcSPkeIXiBSPjPsqwStpyuMurZE74ttRgaouBOZQHPSmA6bUjEyMpbex8dMp1OsGvBgUv4i0ylUFSHceST6OFD8AVZ2AuSBGVuZPgYNE5FoR6RbccatTrm5Bhu9jMvyCWb2S98TYWJ1C7OXO/+Ju3di1WR2Bh27GrJjHxV12q8CnwIUicnp4uZRkH+wlRmFs7PwA/Ejxsygi2aflPeccx6ljuPLnOM7aRr6qTgmfCap6F+ZyeVnkGiYifTHL0YeYRaAHxS5+DQFU9S3MRXEPzJVyrojcF7NKtMLWn+UlPtdg67EI28EmyHGSv1PRBluLuIqgCM7DLFhxkmuOVkbnUR5iwVmeAR5Q1aGp6qjqNFWdpKpPYhbAnTAX1GRb9TBXx1GRBSKU52KWrVuB7LCGaZ2wuYmINCtDxJaY62I62oS/sxLl0e8WsbJU/QQZ9lUKksdMdZyVKcqi8ui4kQvrq5QcSz+E8vh4qsxYKo9055Ek1bFmU3wNHsFcEvtjrq2zROT6KiiBSblK3ROxepncE5W+TzJFRE7Hon2eoKqTqqnZMzH32qsAFZFp4WVKRCvgYko/izaneOxEJJ8p5T3nHMepY7jbp+M4tYGvsQAlWwDzMYVvUsLF7m/JnYLr3mMisj7mRnkHFvnxktDOTCyKaDqiIBcbhPrEfpfHb8l6YRLdMtFWpQlWhFcwK8lZmeyjqjOC++nmKTb/HVif0lE+m2DWtdvDJ84ILBfjlmkOOY9i5SIVkWK4AcXBaMACkkA19VUa0uU3rCiRjKdgaw6TRErg75QeO5mMpfLI9DxSHWsDwjUIFrY7gDtEZFNsTd2N2H3y72qQ6zdsLVuS1qze65wRYsF47saCMj1TXv3ACkoHTyqhyIaAMmcDZ4e1hBdha1o/V9WvsXN/gdQu23MTv0td63Kec47j1DFc+XMcpzYQuc39HP42wiZdcY4hDWGN4AMichjFSdHHYkElFqvqt2l2/QJYDvTG1t9E64F6ZyDzJKCviFwWc/08DHsuj0+/W2aIJaZ+HVO8jk64l5a1n2AK6A8pNh+NKSjjEuWLsYikcTbElMTLiLlEpmAscJGIHKKpk8F/CSzFFPrrYuX9ganh2q3pKKYgtVPV/5RRbzLQS0Qujbl+HpZB+1W1cEbsHFsTi4jsiil/pdaWqurPwC0iciLF90xVmQQcLyLtVfWHIMPGwC6Yxb3GCOsHnwLuUdXbKrDrL1gAm6idbMzVOSWq+rmIXIg9rzpiL7bGYs+4/yVcgitEmuec4zh1DFf+HMdZ26gnIj3C9/pYBMsrsOiTkSXuTSyQxuXYhPIgzGq1ChG5FnsDPw57e74TFjX0klgbrwNvisitWHj0dYAdgYaqeqmqzhORB4FrRSQ/1BmIrZsrjxswK9BoEbkfs5zdioV4/7DMPctBLHXCa5hL5JnA9hJSAAArVPWTUO82bI3hJMxlbmvM6vAdiYAuIfBIH2BYMhiMWv6zcYn67cLXL8pxj4v6+WkRuQ7LLdgG2ENVB6nqfBEZClwR+ngKphAdhCmjazyqWigi5wNPiMg62LVZiVlX+wBHhMipt2LXYqSIPIxN+E/K4BA/YbkdTxCRhUCeqk6phKizgZfFUhE0DPJ8HIK9ICIPYFaoidja272ArTCXxOpgWGjrNRG5CijAlL65wAPVdIwSiEhjbCyBrQ9cR0SOCL9fVdWlIrI15pb5LRaQqUesiTmq+l0Zh3gBOENEPsEC15xMsUt0JMP4UFSYv+gAACAASURBVO9LzHI3EEvJECnd14Tvr4jII1h/bIwFShqmquPKOL/ynnOO49QxXPlzHGdtozm2lg9s3csMzOUsHmjkAWxi/U9sEvsmtoYtHglxMhZa/SigWWjnGiyKH6paFN6QX4ZFm2yLTXw/xVy/Ii7CcgxeheX5exJzffxXWSehql+JyIFYGonnMTes4aG9qtIayx0IJVMUgJ1nu/B9CuYOegrWTz9hQWduVtUlif0OxPo+VZTPShP6uS+2bvMczK30Vyx8f8RVmJJ6GsV5/o5V1WqVZXWiqs+ESKOXYeH2CzBl4GWC5U5Vp4S1XjdjysYULPdhmVFdVXW5iAzEokK+i43HZAqATPgQeAsYil2HcdjYiG8fiEXebIhdh4GqOppqIEQu3Qe7fx6mOM/fYaq6utw+N6B0MKHod3ssqEp3bOzvgAWfifMYlnohHdeGY9yAXed7MCXvzFidD0Mb7bBx8QkW2fMXWJVTsEdo40HMs2EmZhGcTtmU+ZxzHKfukVVUVF1LGhzHcRzHWRuRkORdVY8or67jOI6z9uLRPh3HcRzHcRzHceoArvw5juM4juM4juPUAdzt03Ecx3Ecx3Ecpw7glj/HcRzHcRzHcZw6gEf7dBynTiMivYFbsATxv6pquxR1NsQiiO6ERe5bgOXiu0xVp6VpdycsWuMCVW2V2LYpFlFxXyy0+2vA2ao6O1ZnEHAEsD0WWfFL4FpVfaOc89kP2EZVh5Z78qX37QYcpKrXVGLfPYF3gO1U9cs0dTpgUVeHhsTW5bXZDss3eGiaHIDVSkgvMAhLNfG4qg5Y3cfMFBHpgkWI7ImlVyglX2XGaW1ARF4CpqjqtbGyxlj006OxvvgV+LeqDg7bBwCPpmnyQVUdVMbxGmARWf+BpW34H3BuMr2GiOyLRdbshOUD/QC4RFU1VudU4Mrw85pkHsiQ2mGRqv4zUX4vlnImk1QgjuM4q3DLn+M4dRYRyQEeBz7DEi/3TVO1MTaRvhI4AAudLsDbIrJuinazsJDupRKQi0g9TNnbGjgRy/vVBcttlhOrejmm+ERK4HTgvyLSq5zT2g9LmVAZumET5tVFh9B+qT5Lw2+YslPlpPflEZSra7HrtiuWemJNYldgNyx0/+9p6lRonNYGRKQ7lm/w7lhZDvAq0Bu7jw4Abkzs+go2tuKf6L55rZzD3oXlX7wGOBxYDLwlIpvFZOgcjjET6AecjqWfeTPkekREtsFeAl0d2ron5BSMt3EoqRPcDwGOEZEty5HVcRynBG75cxynLtMGe3P/tKqmVTBU9XsSubxE5H/AVExpfD6xy7FYPrpHKJknDWwiuDXQMbLGiIhiCmhf4LlQb2dVnRvb700R2Qqb0L+Y4fmttYhIQ1VdTsncjKuTjuHvvar6Z7pKItJIVZf9RTLFuVtV7wwypEzgXolxukYQu9aV4WxgTCIP4EAsJ5/ErOnj4jup6hwSL2dE5DgseX1a5U9ENsEUv1NU9ZFQ9jaWs/FCivP39cPygv5DVfNDvWnYfb5rOMbewFhVfShsPyyUfRPaGIpZAxck5VDVH0Ny+NOA89PJ6ziOk8SVP8dxai0i0h+zgnQAZmNWvqtVNT/h9jVGRMDcKq/JsPl54W/9xDGbAbdib/p3TLHfjsCMuBueqn4uIrOAgwnKX0Lxi/gE2DOdQCJyDWEiKCJRNK/HIvfADPrj7sS+76rqniLSEbM+7Aq0xCyS/wHuUtXCdPIkZNsTeCn8/CH09wxVbRe7Ft2BweHvTSLyBAm3TxH5EeujhcAZQFNgDHC6qi4MdXIxt7z+mBI+D5gEHKmqK1PINgw4IfxcGGTbK/x+B7McnYFNzJ8BThKRHYF/YRajFZil6TxVnRXabBdkPxrYH7MQ/Ym5/T0pIhdhlqZc7CXBpWX1Zab9nIKU4zROOvfa0C+dVLVL+L0JloB9Tyxh+K/Yi5MrY/vshlnZugLLMIXzPFVdFLYPIMW1Bq4XkUsxxWoT7Pp+AgxQ1ZSWznCv9QWOSWz6P2Bk3I26PIK18AjgeVVdUUbV7YAc4K2oICSnfx+7fyPlLxdYGil+gcjVOSv8rY/1UcTSUIaIHAW0AP5dhiyjgKtF5MIqjA/HceoYrvw5jlMrCWvfnsEUnAuxtXPXY8rLqZhL1mHY5PQCYALwSzltZmMTv42AG4AZoZ04VwHfqOrooCAkaQiUUkAwBWLrFOVxegJfl7H9IWw9WNyFdU6QPZP++BemPPYM+0YWsI0BBZ4CFmEK7LVAI0zJyoSPsX6+Dev337BzjjMcuD+0XdaawKMxN9iBmPV2cDj3fmH7pZhCcAmm1GwIHIRdu1RcD/wMXIH13TKsn3cO2x/GFJahwHIRWR+zJH2Drftqiq0bfVNEuiQUzFuxfjscU0oeC+tBNwu/O2Nj6RNgRBnnnDEZjtPK8Dh2zU/Brs/mFFtMEZFdgbHAaEyRaon1S4vwO06Jay0ixwOXARcDX4V99waalCHPLkGeD2Iy1MfWPL4kIk9hY20ldp//swyr7t+xtYHDy+oA7P6F0vfwCmAzEWmsqkuBJ4EzRORi4EFsjNwOfIv1EdhawatEZHtMIdwHGCoijbBxc5KqFpQhywfYy43tMIui4zhOubjy5zhObeU6YJyqRhad/waLzs0icoOq/iIin4RtqqqZuBfeh63BA3Pz2jeyaACIHeAMzJqRjunAqSLSUlXnhf02whSstBYHEfk/bFKb1sUrnNNvwIoU55NJf/wY2imxr6qOJUxYw3rG8dj6soFkqPyp6p/BvRXgE1X9MUW1uyLXxnCsdmmaawQcrKqLQ70lwBMisrWqfoOtXXxaVR+L7TOyDNm+E5Hvws/JsXajKs8mrFu3hK/7R8qEiEzFrIuHU1KBeFtVLwt1JmFKUC/M7bcAuw69MWW9WpQ/yhmnVaAbcLSqRhbccYnttwAfqOqRUYGIzATGikinRCCg5LU+CXhDVe+L1SnPTbUzMDeytgZaYnObi7Ax2wtoi62Ra4JZg1NxFGYNf7ucY04Pf7sSLNnhnuiKKXDrYha/T0TkEOBZrF/AFL/9I8uiqr4rIqMoVtweVNX3ReRq4FNVXWVdTMNXQAF2XVz5cxwnIzzgi+M4tY7gwrUzNvGK8wz23OtZaqfMuAmbaPXDLGpviEjr2PY7gWGq+kUZbTyNRf57WETaBgUncj9N+ZY/BH64G7hTVd+pqNBV7Q8RaSgi14rIdExBzcNc+9qHADbVRabWqTcjBS3wPDbx7hp+fwoMEJGLRGT7MDmvTrm6YYrKKiuSqn4E/IgFZYkzNlbnT2zcvJuw6EzHlP/qorxxWlk+xV4WDBCRtvENYtE1ewIjRaRe9MFeFORhilqcZJ9+ChwUxlk3KRn8KB0bAkn36GheswDop6pvqurDmIttPxHZItlIsBb2xZT8sixthHt7AnCbiHQOVuCbMFdqCPewiGyL3evPYxa93kGmVyUEfAntRW6uG6vqoOBaew5wvoisKyIjRWS+iHwoFiAmLks+ZoHdsCyZHcdx4rjy5zhObaQVtuZmVqI8+r1eZRpV1Z9UdbKqPodF1VwXs/QhIgdia+JuD5O2dTEXsazwu0FoYx7mKtgDc8f7AVvr82oKeRGRzbGJ8lgqH9ihqv1xK+ay+SDmPtkVcyeEYje46qDU+aehxFquEIBlMeYCCibbvdi6y8+An0WkRKj8KsrVJkVZVC/Zl0n31ZVpyqqtH8sap1XkSCx9yR3ADBH5VET+Hra1wFxN78OUveizAht7mybaSvbfI5jbZ3/MgjpLRK4vRwlsSGlreRQcZYKq5sXKI4veNpTmQKyPynP5jBiA3bNTsLF4KPbiJw8L8gLmSjxNVU9S1bGq+iK2JrA9FuF3Fao6U1V/DT9vBf6jqtMxF/J6WN+Nxtxuk6ygeu9Bx3FqOa78OY5TG5mLTcQ2SJRH1o/5VJFgxfkOW/cEFlK/KTANm4AuwNYvrRe+Xxjb9xXsbf+2wKaq2je0U8LdUkQ2AF7HlMSjyrNKlEFV+6MfFm1ysKq+pZbPLL+cfSpDUflVgMR5hDVSTbF1hKjqclW9Si1nYwfMwjlURA6oJrl+S8oQaE01jK3qJMU4TUUUaTMZFKaEIhuUlAGYa2VPLOXEiyLSElNoi7C0BV1TfB5JtF2iT1W1UFXvUNWtMTfN2zBlcGAZcs8nkTYkrLebkaJuZP1NFRjlKOAnYmsHy0JVp6vqTlhu0I7YmrsGwMcxhbMjZs2M77cgyFbK+gggIj2xdY7Ri5W9gEdUdQn2MqNz3GoYWJc1bMw5jrNm48qf4zi1jqAk/Y/iACAR/bHJ34dVPYaItMIUvh9C0XPYZC3+eQwLmrIX8ERCxnxV/TqstfsbNlkcFmu/KWYNBDgkTGozoZQVqQL9sTIcO2lJaETMwhKsMUdlKE9SNpLyVYJ9Q/9EHIYpE6VSIISoqhdg8qey+lSGScD+IdokACLSFWjHX5CTsCKkGKepmI29HIjnmGtKGnfgoKhNxIK1NAY2CwrKRCy9wpQUn19TtZWm/Z9V9RbMHbasa6bARpFVPcbLwG7BnTPi79hYL+GSHdxVDwVGqGqmLx8iOb9XVcWU4f5YYKCIGdga3fixWmJj5MdkW8E1+U7gikRQmsbhb6nAN8HltDGWysNxHCcjPOCL4zi1lauB10XkUSyQxnaYK9Z/VLXMqJ5JROR8zF3rPWyi3B7Lt7cCeAAs2AqJaKFi6Q3yVHVconwItm5oMbY263LgBlX9NlbteSwi5wBgi/hapXKC03wLtBYLqf8lFhDjRzLrj+j4/xTLXfZnmNy+iUUunI5ZGc7ALB0VJQr4MkhERmCBMcpaH5mOZcAroR/bYME8XlDVrwFE5AVM2f0k1D0C+3/3XiWOlYrbsfxqr4vIrRRH+/wCC79f7YSJ/t/CzxZYZMkjAIJ7Z0bjNBWqWigiY4BzRWQGZsU7n1gaAhFpjlmhH8eUjQahzu8U56W7CAvuUoi9DFmEWfEOBi5X1bRKiog8gI2tiViah72wyLUXl9EtEzCX0u0oqfgPwXJtjhKR+zC3yVsxK9pPiTZ6YYpVSpdPEbkKuEpV68XKzsZSaMwMMl6KXfu48vdvYLRYuozh4RgXYy9AnkpxqOOxMfporOxd4BIRWRi2f5JQDLtgLz0yslg6juOAW/4cx6mlqOobmHWqCxaV7xwslcGZZe2Xhs8wC8S9wH8xZe0jLBF7hRTJwGbY+rlXsLQF52jp/IL7YhPbpzDLXPxTFiMxC+JgYDKWny/T/ngfmzj/E7NuRQrDWWHbvZj73pdknuJhFao6A7PCHYZN3F8qe4+0jMDy7z2MpV94DcsPF/EB0AcLuDEGCzZyeHBXrTJqCcL3wtwlh2P98j4WVTNVGo/qYFssYM+zmAvnnrHfEVUZp2di1+S+sP9wSka+XI4pOP8EXsSs2kuB/cKaS1R1PLAHsD5m6X4JUwh/pvz1nB+GfR/FLN59gYGqOjrdDkGZ/BJbsxcvn4EFWVkXe4lyfZD3rBTNHGW76KcptoHNk5LrDhthrpmvY+vynsVyJK5yKVXVMdgayei6/RtTGPdMWkGDlfUm7DkQd0u9FrMgPotZb49PyHEAFjxoHo7jOBmSVVRUIS8Hx3Ecx6kxQjqK51T1ghoWxVkDEJFzsXx4nWpalr+S4Ho9A7hEVZ+saXkcx1l7cMuf4ziO4zhrKw8C64vIPjUtyF9MP8wtt7pyQzqOU0dw5c9xHMdxnLWSEGjmBFIERKnlZGEWz9URdddxnFqMu306juM4juM4juPUAWpTtM8GWC6h34DK5sJyHMdxHMdxHMdZW8nBImFPJpamKaI2KX9dsWhrjuM4juM4juM4dZndSZF7tjYpf78BLFiwhMJCd2WtCi1bNmXevMU1LYZTi/Ax5VQnPp6c6sTHk1Pd+JhyqpOKjqfs7CxatGgCQTdKUpuUvwKAwsIiV/6qAe9Dp7rxMeVUJz6enOrEx5NT3fiYcqqTSo6nlMvgPNqn4ziO4ziO4zhOHcCVP8dxHMdxHMdxnDqAK3+O4ziO4ziO4zh1gNq05s9xHMdxHMdxajVFRUUsXryQ5cuXUFjo2c1qO3PnZlNQUFiqPDe3Ps2btyInp2LqnCt/juM4juM4jrOW8Mcfc6hfP5d27TajXr1csrKyalokZzWSm5tDXl5JJb+oqIgFC+bzxx9zWW+9DSvUnrt9Oo7jOI7jOM5awsqVy2nTpg25ufVd8aujZGVl0aLFeuTlrazwvq78OY7jOI7jOM5aRHa2T+HrOpVV/H3kOI7jOI7jOI7j1AFqVPkTkatFpEhEOoXfHUTkQxGZGv5uVZPyOY7jOGsWOTnZFBV58mTHcZw1if3224dDDz2Yww7rS58+vXj11VcBuOeeu3nttdcAuPfeexgyZHC5bY0d+xZffPF5peWYNm1aym2nnTaIn376CYABA05g3LhxlTrG2k6NBXwRkZ2BHsBPseJ/A/eq6pMicizwALB3TcjnOI7jrDlkZRexpP5cvln6GUWzC+nYeEea5rWCAndgcRzHyYRpk2cx+aUfWLxgBU1bNKDroe3Zqmvramv/9tuHstVWW/HNN19z7LHH0LNnT84886wKt/P222+z7bbbst1221ebbAD33/9Atba3tlIj/zVFpAFwL3A6UBTKNgB2BoaHasOBnUVk/ZqQ0XEcx1lzWFJ/Hv+dN4oZy6bz07LveWPe8yzOnVvTYjmO46wVTJs8i/eHT2XxghUALF6wgveHT2Xa5FnVfqytt96GJk2a8Msvv3D55Zfx9NNPlapTUFDAkCGD6dOnF3369GLIkMEUFBQwYcJ43nnnbR5++CEOP7wvY8aMKbXvnDlzOPfcczjqqCPp27c3Dz5YUql7+eWXOOGE4zjwwP1LHDudVfDVV1/lyCP78/vvv5fbdm2gpix/1wFPquoPIhKVbQrMVNUCAFUtEJFfQ/mcTBtu2bJpdctaJ1l//WY1LYJTy/Ax5VSFd+a8X6ps6tIv2L91nxqQxqlt+PPJqW5W55iaOzeb3NycCu0z+eUfyM8rmSsuP6+QyS//wDa7bFRlmbKysqhXz+SaOHEiK1asYMstNyc7O4ucHCvPycle9f3ZZ59h6lTlhRdeAGDgwIE8//xz/OMf/+Dvf/87nTp14thjj015rMsvv5TTTz+drl27snLlSgYMGMCOO+7ArrvuSlZWFgsWzOfpp59m7ty59OnTh+7du9GxY8cSMmZn2/dhwx5hwoQJPPbYMJo1a8aJJ56Ytu2aJN31zsnJrvBY+8uVPxHpCXQFLlkd7c+bt5jCQl8PUhXWX78Zc+YsqmkxnFqEjymnKuTkZJNVVNpRJYssFixYQn5+6eS3jpMp/nxyqpvVPaYKCgpL5X0rj8XzV6Qtr2hbqSgqKuLss8+mfv36NG3alDvuuJNGjZpQWFi0St6CgsJV3ydM+IBevXqTlWVKTe/efRg7diz9+h1ZYp8kS5cu5aOPPmLevHmrypYsWcrUqdPo1q0HRUVF9OlzGHl5BTRv3oI99vgbH344kS222IqioiLy863dwsIi7rrrbtq0acN9991Pbm59Fi5cVGbbNUWqPH8RBQWFpcZadnZWmcawmrD8/Q3oCERWv02A14FzgY1FJCdY/XKAjYCfa0BGx3EcZw2hoKCQLRptzdSlX1JE8cs9abw9+ctd8XMcxymPpi0arHL5TJZXF9Gav0woKioqlaogk9QFUcCvESNGkpubW6njROywww58+OEH/Prrr2y2WbsKt7228pev+VPVW1R1I1Vtp6rtgF+A/VV1JPApcHSoejTwiapm7PLpOI7j1E4arWjBQa36s3XTHdm66fYc2LI/TVa2rGmxHMdx1gq6Htqeerklp/31crPpemj7GpGnZ89dGD16NHl5eeTl5TFmzBh69DDrWpMmTVi0KLXltEmTJnTu3JmHH35oVdlvv/3G3LnF6sKYMaMBmD9/PuPHv0/Xrt1StrXrrrtx5ZVXcdpppzJ9+rSM2q4NrGlh0k4FzhKRqcBZ4bfjOI5TxykqyqLBshZ0yu7JHq32p9Hy9SgqrFyCW8dxnLrGVl1bs/vRHVZZ+pq2aMDuR3eo1mifFaFfv36IdKBfv8Pp1+9wRDpwxBH9ADj00F68+uoraQO+3HrrYL777jv69u1N3769ueCC8/nzz2JlsU2bNhx//LEcc8zRnHzyQDp06JBWju7de3DDDTdx5pln8vXXX5fbdm0gqxblS2oH/OBr/qqOr39wqhsfU0514uPJqU58PDnVzeoeU7Nm/YRIx9XWvrNmUdaaP9Vvad26bYmy2Jq/9sCPyX3WNMuf4ziO4ziO4ziOsxpw5c9xHMdxHMdxHKcO4Mqf4ziO4ziO4zhOHcCVP8dxHMdxHMdxnDqAK3+O4ziO4ziO4zh1AFf+HMdxHMdxHMdx6gCu/DmO4ziO4ziOU2ny8vK45567OfjgA+nd+1AOPfQQhgy5lby8vL9clsMP78vy5csBeOKJx5k3b96qbffeew9DhgyuUvszZ87k2ZEjKcwvpKjA0svtt98+TJs2rUrt/lXUq2kBHMdxHCcTsrKA3HyW5i8hKyuLWpSn1nEcZ63miisuZ8WKFYwc+RxNmjQhLy+P0aNfIC9vJbm5uRm1kZ+fT716VVdNRo16YdX3J554nB49etKyZcsqtxsx85dfeGbEM+yz28EANGiS2fmVR3Wdf3m48uc4juOs+dTLZ1bWT3z654cU/lnEjs2607qoPVn5/m/McRwnE75b8g1TFo1nScEimuQ0o0uz3diiydZVbnfGjB95++2xvPXW2zRp0gSA3Nxc+vXrD8DUqVO54YbrWLZsGStWrKBfv/4cd9zxAFx++WU0adKYGTN+YsGC+Ywc+Vza44wc+QxTp07liiuu5IsvPufoo49i+PBn2G677bj++uvo2LEj/fr1p1Onbfjoo8k88cQTzJ49m/POO4f69eszePAQAGbPns1ppw3il19+YZNNNuX22++gUaNGpY43fvz7DB16BwUFhay3Xguuvvoa2rbdjOuvv55ff5vJiaf+g4032pQbrroViuD11//LNddcxZw5cxgw4ET+8Y9jAPjhhx+49dabWbDgD/Ly8jjuuOPo2/cwADp12obzzjuf9957j86dO7Pbbrtz0003UFhYSH5+PoMGnUrv3r2qfI3i+H9Nx3EcZ41nYc5sxs9/Y9XvD/4Yy54tDmI92tagVI7jOGsH3y35hvEL36SgKB+AJQWLGL/wTYAqK4DffPMNbdu2pXnz5im3b7zxxjz00CPUr1+fpUuXcNRRR7LLLruyxRZbAPDpp58xbNhjNG7cuMzj9OjRgyeeeByAiRMnssMOOzJp0kS22247Jk2ayAknDChRf9CgUxk16jluv30oW2211aryr776khEjRtKsWTNOOWUgr7zyMkcc0a/EvvPmzePSSy9h2LDH2GKLLRk1ahQXX3wRw4c/w3lnXcw9/x7KQ/c+vqp+EbBs2TKeemo4M2fOpE+fXvTp04f69Rtw8cUXcsstg9l8881ZsmQJRx7Zjx122JHNN9/c9i0qYtiwxwA466wzOO64E+jVqxdFRUUsWrSo/AtQQVz5cxzHcdZo6tXL5vtl35Yqn7rsK3Zr0I78/MIakMpxHGftYcqi8asUv4iConymLBpfZeWvPA/8ZcuWcd111zJ1qpKVlc2cOXNQ1VXK33777Veu4gfQtu1mLF++nN9//51JkyZyzjnn8sAD/+bggw9h5cqVtG2b2cvAXXfdjXXWWQeA7bffnp9//rlUnS+++BwRYYsttgSgb9++3HDDdSxdsoTc+qVDpmQBBx54EGDK7jrrNOf332dRVFTI999/z4UXnr+q7sqVK/n+++9XKX+9e/deta1bt+489NCD/Pbbr/Ts2ZPtt98ho3OqCK78OY7jOGs0hYVFNM1Zp1T5OjnNy510OI7jOGbpq0h5Rdhmm6356aefWLhwYUrr3513DqVVq1bceONN1KtXj4EDT2blyhWrtmei+EV0796d9957l3nz5tG1a1duvPF63nvvXbp1655xG/Xr11/1PTs7m/z8/FJ1bE15VulyoH7jerYGPdCwSS5kQYMGDVaV5eRkU1BQQFYWrLvuuiXWISaJn/9xxx3P3/62JxMnfshNN93ELrvswvnnn5fxuWWCR/t0HMdx1mgKC4to17AD9bOK/2HXy8ply4bbUlDgVj/HcZzyaJLTrELlFWGzzdqx5557cd1117BkyRIACgoKeOKJJ1i6dAmLFi1iww3bUK9ePaZNm8bHH/+vzPYuvfQS3nrrrZTbunfvwUMP/Ycdd9wJgJ122omHH36IHj16pKzfpEnTSrlO7rDDjqh+y/fffw/AmDGj2XrrrWnSpAlNmzVj6fIlNN+gMeu2bkyj5vXTttOuXXsaNWrEiy++uKrs+++/Z/HixSnr//jjj7Rt25b+/Y/k2GOP5Ysvvqiw7OXhlj/HcRxnjaf+8nU4YL3+LCiYQ3ZOFuvQkvormuGGP8dxnPLp0my3Emv+AHKy6tGl2W7V0v5NN93EfffdR//+R5Cbm0thYSG7774Hubn1GTRoEJdeegkvv/wSm266KZ07dymzra+//mpVsJQk3bv34NJLL1ml7HXv3oNnn32W7t1TK3/HHHMsV155OQ0bNlwV8CUT1ltvPW6++RYuvvhC8vMLWG+9Ftxyy60AdOjQgXbt2nPYEb1p335z7rhjaNp26tWrxz333Mstt9zCo48+QmFhAS1btuJf/7o9Zf2nnnqSjz6aRG5uLvXr1+fSSy/PWOZMyapFobLbAT/Mm7eYwsJac041wvrrN2POnOpfYOrUXXxMOdWJjyenOvHx5FQ3q3tMzZr1EyIdK7zf6or2WZ0sXPgH559/Pg899HBNi7LGkJubQ15eQcptqt/SunXJtY7Z2Vm0bNkUoD3wY3Ift/w5juM4juM4Ti1n/DUboAAAIABJREFUiyZbr3HKXpLmzdd1xW8142v+HMdxHMdxHMdx6gCu/DmO4ziO4ziO49QBXPlzHMdxHMdxHMepA7jy5ziO4ziO4ziOUwdw5c9xHMdxHMdxHKcO4Mqf4ziO4ziO4zhOHcCVP8dxHMdxHMdxKsVdd93Jddddu+r3uHHj6NRpG6ZPn7aq7PTTT2PUqFEp9585cya77bbLqt+dOm3D0qVLVp/AdRxX/hzHcRzHcRynljN35kI+e/s7Jr+qfPb2d8ydubBa2u3atSuTJ09e9XvKlMlsv/32q8oKCgr45JOP6datW7Ucz6karvw5juM4juM4Ti1m7syFzPhiFiuX5wOwcnk+M76YVS0K4E477czMmb8wd+5cAKZMmcIpp5zK5MkfAfDNN9/QpEkTRowYzpFH9ueww/py0kkn8uuvM8tst7CwkFtvvYULL7yAlStXVllOx3Dlz3Ecx3Ecx3FqMTN1LoWFRSXKCguLmKlzq9x2w4YN6dSpE5MnT2bJkiUsW7aU3XffnW+//RaAyZM/olu37px88kCeeWYkzz//AgceeDC333572jZXrFjJ+eefR3Z2NoMHD6F+/fpVltMx6tW0AI7jOI7jOI7jrD4ii1+m5RWlW7fuTJ78EU2bNmHnnXcmJyeHzTbbjOnTpzF58mT23Xdf3n//fUaMeJqlS5dSUFBQZnuDBp3CgQceyIkn/l+1yOcU45Y/x3Ecx3Ecx6nF1G+Y2t6TrryiROv+pkyZQpcuXQHo3LkLkyZN4pNPPmaTTTZl8OBbGDz4NkaPfpHrrruBFStWpG2vW7duTJgwnqVLl1aLfE4xNaL8ichoEflMRD4RkfdFZMdQ/qOIfCsin4bP/jUhn+M4juM4juPUFjaWVmRnZ5Uoy87OYmNpVS3t77jjTvz660zefPMNuna1wC6dO3fh6aefolmzZjRv3pzc3FxatWpFYWEhI0c+U2Z7p59+Oj177sKpp57C4sWLq0VGx6gpt88TVHUhgIj0Bh4Bdg7bjlDVL2tILsdxHMdxHMepVbTauDlga/9WLs+nfsN6bCytVpVXlQYNGrDddtsze/YsNthgAwA6derE7Nmz2W+//enQoQP77bc/ffr0YsMN29ClSxf+978pZbZ50kkn06BBA04++SQeeOABmjdfF4qgqKiIrIQi62ROVlFRUfm1ViMicjxwtqp2EZEfgUMqqfy1A36YN29xqQWtTsVYf/1mzJmzqKbFcGoRPqac6sTHk1Od+HhyqpvVPaZmzfoJkY6rrf01lcK8QhbNX0FhQSE5udk0W68hWTm1XwnMzc0hLy/1GknVb2ndum2JsuzsLFq2bArQHvgxuU+NBXwRkYeA/YAs4IDYpqdEJAsYD1ymqn9UpN1wsk4VWX/9ZjUtglPL8DHlVCc+npzqxMeTU92szjE1d242ubk5q639NZH8lQUsnLscgtGqIK+QP+ctp8WGjcnJqf0hTNJd75yc7AqPtRpT/lT1ZAAROQ4YAhwE7K6qP4tIA2AocA9wbEXadctf1fG3oE5142PKqU58PDnViY8np7pZ3WOqoKAwrSWotlKQX7hK8YsozC+kIK+w1s/7y7L8FRQUlhprMctfSmpcVVbVJ4C9RKSlqv4cylYA9wG71qhwjuM4juM4juPUKMlgNQBZWVlk1X6vz2rnL1f+RKSpiGwa+30oMB9YLiLNQ1kWcBTw6V8tn+M4juM4juM4aw5ZOVk0XqdkovemLRqAB36pMDXh9tkEeFZEmgAFmOJ3KNAaGCX/z959h0dVpQ8c/947Lb2HEEKLGC4iRYqAIFjBRkcsa/dnW10FC9gVy9qwIIq7uooooggqRdHdFVwRRJrSMZdOQgghCellyr3390cgGBMgIRMmgffzPD5mzm1vhpuZeeec8x5NswE2YDNwdwDiE0IIIYQQQjQirjAHjiB7RcEXu3pKFHtpCCc8+dN1PQvoc4TN3U5kLEIIIYQQQoimQbUrqPZTq9iNvwV8zp8QQghRG4rDwB2UT1b5XhTHqVXsQAghGrP//OffXHnlSEaNGsGQIVcwfvy4BrlOaurv/Pvf3zXIuQ9ZuXIlV101ukGvEUgBq/YphBBC1JbpKmd16WL2lO8EINHVit5hF6KWBwc4MiGEOLVlZ2fz/PPPMWvWFyQmJmJZFrqe2iDXSk1NZfHixVx66WV1Ptbn82G3n5jUxzTNgwVpGt/QVEn+hBBCNGqKAllGWmXiB5DpTifDtYPWSics6+Qu8y2EEP6gbN+KbfUqKCmG0DCMnmdjtUup93lzcrKx2+1ERUVVXEdR6NDhjMrta9eu5bXXXqWkpASABx98iH79+rFhwwZeeukFSkvLCAkJ5pFHHqNz584AzJs3j2nTpgIKrVq14umnJ2Cz2Xj77bcoKSlh1KgR9OjRk8cee5z169fxxhtvUFJSDMA999zLeeedR0ZGBldfPZprr/0Ly5cvZ/DgwVx99TWVcZWVlfH444+ybds27HYHycltee21NwAwDB/PPPM0a9euQ1EUJk58lXbt2pGTk824ceMoKSnG7XYzYMB5PPjgQwBMmfI2aWlplJWVkp6ezrRpH7Nu3Vree+89PB43DoeD8eMfoWvXrvV+zutDkj8hhBCNms2mkuHeVa093b2D5KDO+HyS/AkhxNEo27diW7oExfBVNJQUY1u6BAPqnQBqWgc6d+7MwIEXcfbZvejevTtDhgwlKiqKgoJ8xoy5l0mTJtOtWzcMw6C4uBiv18P994/hueee55xz+rJ8+S/cf/8Yvvvu3+zatZtJk15n1qwviI+P5623JvPCC3/ntdde529/u5fFixfzxhuTACgsLOTZZ5/hH/94l/j4eLKzs7nmmquYM2ceAPn5+Zx2Wjvuuedv1eL++eefKSwsZP78bwAoKCio3LZ9+3aef/7vPP30M7z77j95771/8vLLEwkPj2DKlCmEhITi9Xq58847WLp0Ceee2x+AX39dzezZXxIdHU1aWhrvvvtP3n33X4SFhbFt21buuutOFi78oV7Pd31J8ieEEKJRMwyTBGdr0sp3VGlPdLbFMCTxE0KIY7GtXnU48TtIMXzYVq/CV8/kT1VVJk9+m61bt7J69SoWLVrEhx9OZc6cuaxdu4527U6nW7eKmo42m43IyEi2bNmCw+HgnHP6AtCnzzk4HA527tzFqlUr6d9/APHx8QCMHn0Vo0aNqPHaa9euISMjg7vuuuOPvxlpaWlER0fjcrm49NJLazxW0zR27tzJ888/x9lnn82AAedVbmvbti1nnNERgK5du7J48Y8AGIbBq6++ytq1a7Esi9zcHFJTUyuTv/79BxAdHQ3AsmU/k56ezk033VB5XsMwyMnJIS4uro7Psv9I8ieEEKKRUygraEaMPYkDRgYAUbbmuAsTUSJARn0KIcQxHBwSWev245CSkkJKSgrXXvsXhg4dzMqVq3A4HDXua1lWjfPhFEWpcduR5s5ZFrRv356PPppebVtGRgbBwcFHPLZVq1bMn/8Ny5f/wtKlS3jzzUmVPYZOp6tyP1W14fNVFBn7+OOPKCws5LPPZuJyuZgw4WncbnflviEhIVV+x379zuXFF1+q8fqBItU+hRBCNGo2m8KHP+1n+YrTiS4aRHTRQNb8egZv/ScTVRb4FUKIYwsNq1t7HWRlZbF27drKx/v27SMvL4+WLZPo1u0stm/fVrndMAwKCgo47bRkPB4PK1euAGDlyhX4fD7atm1Dnz59WLLkJ3JysgH44ovZ9OlzTkW4oWEUFRVVXuuss85i9+7dlecB2LBhQ63mgu/btw9VVbnooosZP/4R8vLyqgz9rElRUSHx8fG4XC6ysrL44YcjD+Hs27cvP/+8lG3btlaJLdCk508IIUSj5vOZXNShGY/O2ciXqw+3Txh8BoZhBi4wIYRoIoyeZ1ed8wdYNjtGz7Prf27Dx5Qpb5OZuReXy4VpWtx7732VwyYnTZrMxIkvU1pahqoqPPTQOM45py9vvPFmlYIvr78+CYfDyemnpzBmzP3cfvttVBR8aclTT00AoE+fPkyb9iEjR46gZ8+Kgi9vvTWF1157lZdeegmv10vLli2ZMuWdY8a9desW3nijosCLaRrcdtvtNGvWjF27dh3xmOuuu54HHniAK68cSfPmifTpc6Sly6FNm7a8+OLLPPXUk5SXu/F6vXTr1q2yqE2gKCdRlbS2wM7c3GJM86T5nQIiPj6c7OyiY+8oRC3JPSXqq9Bn8uHyND5dmYYFjOqWxF3nJhPpkAEson7k9Un4W0PfU1lZaWhahzof11DVPkXDcjhseL01r22r66kkJLSu0qaqCrGxYQDJwK4/HyM9f0IIIRq9CLvKff2TuaZHS1wuOxE2UOV7PiGEqDWrXUq9i7uIpk++MhVCCNEkKJZFQrCdlIRwSfyEEEKI4yDJnxBCiEZHURTMchPLW7HIuxBCCCHqT4Z9CiGEaFTMMpM1/05j+5psQsKd9B3VjtjkMCxJAoUQQoh6kZ4/IYQQjYaqKKxfuIftv2WDBaWFHhZO+53SXE+gQxPCrxSHgTeoCMtVLkuWCCFOGOn5E0II0WgYZSbbfttftdGC/H2lBMc5AxOUEH5mBJewvPAHsjwZOBUnvSMvIF5pA4Z8Jy+EaFjyKiOEEKLRUFSF8Jigau0Olw1FVcjzmKTllshEQNFkKXaTNcU/k+XJAMBjeViS/x/KHUdfXFoIIfxBkj8hhBCNhupS6Hl52yrD4Jq1DSciIYTpv2Yw5B/LuPj1n/hgZTpFPlngXTQ9pt1DWvmOau1FZn4AohHCP7xeL2+//RZXXHEZw4YNYciQwUyc+DJer7dBr9upU0dKS0sa9Bp1tXLlSq66anSgwzgiGfYphBCi0TAti9hWoVx8a0fy95XiCLIR3zqcNcUlvPnDtsr9/vnTDlrFhHDJ6TFYsuyDaEJU00GkPYYC34Eq7cFKSIAiEqeK+ev28tr3W8gsKCcxMogHB7ZnaNcWfjn3E088jtvtZtasLwgNDcXr9TJ37hy8Xg8Oh6NW5/D5fNjt/klN/Hmuk408K0IIIRoXp0JMcihRLUNQbQqqXWHO/G3Vdvt63V6u6BCH1ys9gKLpsDw2zom8kO8PzMGwDACSg9sTZsYEODJxMpu/bi+Pz9tI+cHXy70F5Tw+byNAvRPA3bt38cMPi1i48AdCQ0MBcDgcjB59FQBbtmzh+eefpaysDLfbzejRV3HDDTcC8PjjjxEaGsLu3Wnk5R1g1qwvjnqt77//nsmTJxEREcmAAQOqbOvUqSMPPPAgP/30Ez169ODaa//Cs88+Q3p6OmBx8823MmzYMObPn8/Chd8zefJbQEWiOHDgRXzyyackJSUxdeoH/Pe//8UwfDRrlsAzzzxDXFx8tVgefngcO3fuwuv10KpVa5577nkiIyOr7Tdv3jymTZsKKLRq1Yqnn55AbGwsc+fOYcGCBURERLBt21bCwyOYNGlS5bUOxWGaBvHxzY4YR13JsE8hhBCNjmlaqDYFlIo1/1pHV+8VaRMXiiJz/0QTFOqO44qYv3Bh9BAujbmSbq4B4K1d74gQx+O177dUJn6HlHtNXvt+S73P/fvvv9O6desaEx+ApKQk3n9/KrNnf8nMmZ8ze/Ystm/fXrl97dp1vPHGpGMmfrm5uUyY8BSTJ7/NjBmf1tijaFkW06Z9xL333seLL75ASkoKc+bM5b333ueNN15j69atDBw4kN9++5W8vDwAli5dQnLyaSQlJfH11/NJS0vj008/Y/bsL+nffwATJ75SYzyPPPIYs2bNZs6ceZx++ulMnfpBtX22bt3KpEmv89577zNnzlxSUlJ44YW/V27ftGkjDz00jnnzvqZdu3bMmDEDoEocc+bMOWocdSU9f0IIIRoXE3J3FbPhhz3YnTa6XtyKC1LiWLAhk/1FbgBiQ530Pz0On8z7E02QZYG9PJQoQgMdijhFZBaU16m9Lo419L6srIxnn32GLVt0FEUlOzsbXddp164dAIMGDSIk5NjDntevX8cZZ3QkOTkZgCuvHM3rr79WZZ9hw4ZV/rx8+S+MGzcegPj4eAYMOI+VK1eQkpLCBRdcyIIF33D99Tcwd+5chg8fAcCPP/6PTZs2MXr0KAAMwyAsLLzGeObPn8eCBd/g9XopKyujTZu21fZZuXIF/fsPID6+osdu9OirGDVqROX2s87qRmJiIgBdunTll1+WVYtDURR8Pt8R46grSf6EEEI0KnlpJSz68PfKx3u35jPons7c0KcNhmVVfHBWFdrEBGGaMuFPCCGOJTEyiL01JHqJkdWrK9dVx45nkJaWRkFBQY29f2++OYm4uDj+/vcXsNvt3H77bXg87srttUn8oKJX71j+fK4/jw459Hj48OG8/PJLDB48hNWrV/Hiiy9VXuOOO+5k5MhRR73Or7+u5vPPZ/LJJ58SExPDggXfMHv27BpjPlIMAC6Xq/Jnm03FMIxqcTgcNrxe41i/eq3JsM8GVGpYbMkrY3t+OW75gCKEEMdkUxQ2Ls6o1p6xMZehnZpzRkI4nVtGckmHeJoHyTA5IYSojQcHtifIUfVjf5BD5cGB7et97jZt2nL++Rfw7LMTKCmpqLxpGAbTp0+ntLSEoqIimjdPxG63s3XrVn777dejnu/RRx9h4cKF1dq7dj2L1NTf2b17FwBffnn0YaJ9+pzD7NmzAMjJyWbJkp/o1asXAD169KS4uJhJk97gwgsvIjg4GIDzz7+QmTNnUlBQsfSKx+MhNTW12rmLiooICwsnKioKj8fDnDlfHSGGPixZ8hM5OdkAfPHFbPr0OeeocdcljuMhPX8NJNvtY/xXG9m4txCA/ilxPHmpRrTTFuDIhBCiEVPAGVz9rckZbCfMrtC9eRjx8eFkZxcFIDghhGiaDhV1aahqny+88ALvvPMOV111JQ6HA9M06d9/AA6HkzvvvJNHH32Eb775mlatWtGjR8+jnmvz5k385S/XVWuPjY3l6aef4W9/u4eIiEguvfTSo57n0Ucf49lnJzBixHDAYuzYBzj99JTK7cOGDeettybz8cfTK9uGDh1Kfn4eN998EwCWZXLNNdfSoUOHKuc+99z+fP311wwZcgUJCc0588wz2bBhQ7UYTj89hTFj7uf222+jouBLS556asJR4/5zHIoCpllzHMdDqU0XahPRFtiZm1sc8GFAqqowddUepvy4vUr780M7cmn7uEZfllw+WAl/k3tK1EXpfjcL3l5f+Vppd6hccV8XXFEVPX1yPwlFUcDhBVQsT/0GMcn9JPytoe+prKw0NK3+SUBjVFCQz4MPPsj771cvnnKqOtqwT11PJSGhdZU2VVWIjQ0DSAZ2/fkY6flrACawZFtOtfZVaflc3qEZhiEFCoQQ4khCm7m44m9dSf/9AHa7SlKHaIJjXJimvHYKsBxeMqztbCr8FYcaRI/wvkT6mmMZUvlViKYuMjJKEr8GJnP+GoAKXKhVX4ejd9toSfyEEOIYPEUGC6duZuuqLDb/vJdv31lPWa772AeKk56iQBa7WFHwI8VGEXnebBYemEep48CxDxZCCCHJX0MwTYtLzkjg7LbRlW2XntmcXq2jAhiVEEI0foqikL7pAOUlXkoLPJQVefF5TH5fmolNlZ6dU53iMNlcsqZae6YnXdZ8FEKIWgjIsE9N0+ZSMQ7VBIqBe3VdX6tpWnvgIyAWyAVu1HV9ayBirK84l403RnYio9CNTVVIDHPhlPclIYQ4KlVVKMqtXo68IKcMUIBGPmlaNCxTIVgNJZ+qPX1BajAYcm8IIcSxBKrn7yZd17vqut4NeBWYerD9n8AUXdfbA1OAdwMUn1+4FIXTIoNoEy6JnxBC1IZhmLTtGlet/Yy+iZiWDJs/1Zk+ha5hvVE4/KYapAbTzJ7U6IupCSFEYxCQnj9d1wv+8DASMDVNawZ0BwYebP8MeFvTtHhd17NPdIxCCCECI7JFMOddp7Hqm52YPouzBrUi/rRw+XAvAAh1x3J57NXk+vZhVxzE2ppjKw8NdFhCCNEkBKzap6Zp7wODqBjHcynQCsjQdd0A0HXd0DRt78H2Wid/B0ubinqKjw8PdAjiJCP3lKiL+IRwkrvEgQWhUa7q2+V+OsVF0JJWhx/W83aQ+0n4W0PeUzk5Kg5H41k3evTo0Xg8HrxeL7t27SIlpWItvY4dO/Liiy8e8/jPPvsMt9vNzTff3MCRNl1H+ve22dQ632sBS/50Xb8NQNO0G4CJwJP+OG9jWOevqZM1j4S/yT0l6qM021PlsdxPwp/kfhL+1tD3lGGYR1z3LRA+/XQmABkZGVx99Wi++OKrym1er4HP58NuP3LKceWVV1XuK6o72jp/hmFWu9f+sM5fjQK+zp+u69M1TXsP2AMkaZpmO9jrZwNaAOmBjVAIIYQQQoimTdk4G9v/noPCDIhIwrjgSaxOoxvkWoMGXczIkaNYsWIFLVu2ZMyYMYwbN46SkmLcbjcDBpzHgw8+BMCUKW9TWlrKuHHjmTt3DgsWLCAiIoJt27YSHh7BpEmTiIurvoSaOD4nPPnTNC0MiNZ1Pf3g4yHAAWA/sBa4Fvjk4P/XyHw/IYQQQgghjp+ycTa2BWNRfGUVDYV7sC0YiwENlgBmZ2fz4YfTAHC73UyZMoWQkFC8Xi933nkHS5cu4dxz+1c7btOmjXz55RwSExN5+umnmDFjBmPGjG2QGE9Fgej5CwVma5oWChhUJH5DdF23NE27C/hI07SngDzgxgDE1+gpClL4QAghhBDiFGOzHV/5eNv/njuc+B2k+Mqw/e85fA2U/A0dOqzyZ8MwePXVV1m7di2WZZGbm0NqamqNyd9ZZ3UjMTERgC5duvLLL8saJL5T1QlP/nRdzwL6HGFbKtD7xEbUdBzwGGzILCKrsJxOLSJIiQnGIYvaCiGEEEKc3CwoyXazc20O0R0MLMNCqUsiWJhRt3Y/CAkJqfz5448/orCwkM8+m4nL5WLChKdxu901HudyHS7yZbOpGIbMBfSngM/5E7VT4DW5b9Y69KziyraXRnbi4tNisaQbUAghhBDipFWUWc53/9gAwNltosnfX0ZUs+DaJ4ARSVC4p+b2E6CoqJD4+HhcLhdZWVn88MMPXH311Sfk2qKqQC3yLupoW05JlcQP4OV/6xRKZSQhhBAnGcVh4AspxhNUgOLwBTocIQJKVRTWLfpT/UPLwlNe+78N44InsezBVU9hD8a4wC/F9o/puuuuZ82aNVx55Uiee+5Z+vSpcRCgOAGk56+JKK0hySsq9+GTZS2EEEKcRMzgMvYZu8gq2UuoLZwIezTNXC1R3cHHPliIk5ACGJ4avuy3al8Hwuo0GgMatNpnUlISS5dWzM/7738XVtnWokUSM2d+XuNx99zzt8qfhw8fwfDhI474WNSfJH9NRLu4UFx2FbfPrGwb3bMl0S4bSP4nhBDiJGCzKezwpLK2cEVlW5g9gnOiwohQQmSagzglGZZF5wtbkTV1c5V2R5C9TgUArU6jG6y4i2g6ZNhnE5EU6mDazT3p2y6WhAgXd593Grf0bi2JnxBCiJOG4SxjY9GvVdqKfYUUGQXYbPKRRZy6YtqEMuj2jrRoH4XdqRLZLATVLkX/RN1Jz18TYVlwemQQrw8/E7dpEWpTZLkHIYQQJw1FAUP10SXibFRFJa10B/s9mQA4FAeGYR7jDEKcxFSIah3KeTe1JzNztyR+4rhJ8tfE2IAQVRI/IYQQJ5fyoHwW5cyj3KxYi6xDWBccqpNcz35i7AlYbnnjE8IwLRn0JepFxlAIIYQQIqAUh8EvBYsqEz+A1OL1tAvpwKDYEdhKwgIYnRBCnDwk+RNCCCFEQBk2Dwe82dU3WAr20ogTH5AQQpykJPkTQgghREDZTCdxzubV2kPV8ABEA6qqoDgMVFtALi+EEA1Gkj8hhBBNhqUqlNew7qlo2iyPjXMiLiTEVjG8U0GhZ8S5BPuiTngspquc7azj++LZ/OpdhDe4AEVqawhxVP/5z7+58sqRjBo1giFDrmD8+HGV2zp16khpaUmtz1VYWMjUqR8cVxxz587h/vvHHtexx2vKlLeZOPGVE3rN+pCCL+KEUxSwbHZ8hoXDpoDhkwI2Qoij8gEbs4r519JdGJbF//Vty1mJYTjkU/lJw1EWwaWRV1FKEU7FhdMTimmc2H9f1WaxoXwVW0s3AVDoyyejfDeXxVyDrTzkhMYihL/pPy9m2azpFOXmEB4bR9+rbkDrd169z5udnc3zzz/HrFlfkJiYiGVZ6HrqcZ+vqKiIqVM/4NZb/6/esfmbz+fDbm/a6VPTjl40OaoKxT6VdTsKMUxw2hW6JYfjRBJAIcSRpeaUcvsnv1U+/nV3Hu9d351uCVII5KTidhJCLACBWNjB6yhjW2HVhbQ9lociM48oJPkTTZf+82IWfTAFn8cNQFFuNos+mAJQ7wQwJycbu91OVFRFT72iKHTocEa1/UzTZOLEV8jJyeHvf3+BN9+cxOrVq/F6vURHR/Hcc8/TokUSzz//HEVFRYwaNYKgoGBmzPiU7OxsXnjh72RmZuJ2l3PZZZdzxx131hhPUVERY8eOIS0tjaioKF588SUSEhIwDIPXX3+Nn39eCkC/fufywAMPYrPZWLDgGz75ZDperxeAhx4aR58+5wAwaNDFjBw5ihUrVtCyZUvGj3+Yp556ku3bt5GY2ILo6GhiY2Pr9RyeSDLsU5xQpmpnza5iDi3X5PFZrNlZhNLEv0URQjQcm01l7tq91do/W5UuC38Lv1JQsSnV349URSb/iaZt2azplYnfIT6Pm2Wzptf73JrWgc6dOzNw4EXcf/9Ypk//mPz8/Cr7uN0eHnzwAVRV5ZVXJuJ0Orntttv5/PNZfPXVHC677Apef/11AJ544knCw8P58ss5zJjxKQCPPvoI1113PTNnfs6sWbNZunQJy5YtqzGeNWu9fd1MAAAgAElEQVR+Y8yYsXz11Rx69uzJSy+9CMDs2bPR9VRmz/6C2bO/IDX1d2bPng1UJIKffjqTL774iokTX+Pxxx+rcs7s7Gw+/HAazz33PP/85zuEhoYyf/43vPTSy6xevarez+GJJJ+4xQlV7rWq9fB5fBZew0LeWoUQNbMID6r+dhUR5JC5WMKvbO4gukecw8qCnyrbYhxxhFvRAYxKiPorys2pU3tdqKrK5Mlvs3XrVlavXsWiRYv48MOpzJkzl8jIit7AO++8g8suu4xbbrm18rglS5Ywc+anlJaWYhhHnstdWlrK6tWrePHFA5VtJSWl7Nixnb59+1bbv1u37iQnJwMwatSVjBgxDIDly39h2LDhOBxOAIYPH8GiRYu45pprSE9PY9y4yezfn4XdbicnJ4ecnGzi4uIBGDp0WOX5V65cyaOPPg5AdHQ0F1888Liet0CR5E+cUC579U9qDpuCXVWwAjHGRwjR6BmGxeAuifyWlsf5WjMURWGxvp+rerbE55MXDuE/lgUtlfZExMSw35NBhD2aODURxe0KdGhC1Et4bBxFudWXUwmPjfPbNVJSUkhJSeHaa//C0KGDWblyFQMHViRGvXr14uefl3L11dcQEhLC3r0ZvPLKS8ycOYuWLVuyZs0aHn54XI3ntQ72GsycOQuHw1GnmCzLQjn4LeEffz7k0ONx48Yxbtw4LrroYkzTpGfP7rjdnsr9QkJCqpyzKZPxMuKEslkGnVuHcuhPT1Wga9swOMo3PkIIYVoWvZJj+deSHby7eDtdWp34KpDCP1QVDFcp5a48cHkDHU51XjsR7ua0V3rSzHsaqjs40BEJUW99r7oBu7Pqlxh2p4u+V91Q73NnZWWxdu3aysf79u0jLy+Pli2TKtvuvvtuzjmnL3fddQfFxcUUF5fgcDiIi4vDNE1mzfq8ct+wsFDKy8vx+XwAhIaG0qNHDz744P3KfTIzM8nJqWFtUGDt2jXs3r0LgHnz5tKrV28AzjmnL3PnzsXr9eL1epk3bx59+vQBoKiokKSklgB89dWXeDyeGs8N0Lt3H+bOnQNAfn4+ixYtrPVz1RjUuudP07RRQJSu6x8cfJwMzAA6AouA/9N1Pf8opxAC07SIcin01SLw+CxcdgVFqn0KIY7CblfZtLeQact2VbZ9uiKNxIggOsa1kN6/psRmslfdwYq8HzEsH2H2CC6IGoyjrPEt5G6a8sYkTh6Hiro0RLVPw/AxZcrbZGbuxeVyYZoW9957H2ec0bHKfv/3f7fhcrm47bb/491332XQoEsYPnwozZsn0rNnT379dTUAkZFRXHHFYEaMGE5ERAQzZnzKyy+/wssvv1w5hDMkJJTnnnu+cljmH/Xs2ZMpU6awbdu2yoIvAKNHjyY9fTejR48CoF+/flx55WgAHn74UcaMuZdmzRLo2bNnZfGamtx111958sknGDp0MElJSfTt26/ez+GJpNS261LTtDXAx7quv3Hw8TdAe2AqcCfwra7r9zRUoLXQFtiZm1ssL9j1FB8fTnZ2UaDDECcRuadEfTgcNsbN28j3v1f9lrd3cgzvXdMVj0dGDjQV5UF5fJv7eZW2GEczLggbiuUJzEwUeX0S/tbQ91RWVhqa1qHBzi8aF4fDhvcI69vqeioJCa2rtKmqQmxsGEAysOvPx9Rl2OdpwAYATdMigUHA/bquvwQ8Dgypw7lEE1NmWqQVuTngMVBVqbAghDhxTNOiS3z1xOCsBLuMGmhCFAUs1aBbZB/OiuxF6MEF3Q949+NV3cc4WgghhD/U9Wu2Q2+z5wEGcGiQ6x6ger+rOCnsKfHy0Jfr2Z5dQpjLzoTBHTm3bZRU5xRCnBCKApcm5PN5pIs9BRVJQrNwByNbFjb5ifenCkWBsqADLD7wHaVGCQ7FwVmRvdGLN2JhYTedgQ5RCCFOCXVJ/tYB12mathy4DfifruuHvqprDez3d3Ai8MpNi8fnb2J7dgkAxW4f475azxe396F1uLxZCyEans9nkhzsYWa3zeiOjlgotPel0iKoLXky369JMJ0efsz7llKj4r3Ea3lZnf8zPaPOJdaWAOV1q+AnhBDi+NQl+XsM+Bq4CSimYtjnIcOBFX6MSzQS+W6DzXsLq7RZFqTnl0ryJ4RoUIoCysHawMVx3WnuLSJpyVMolg+j30MUN+sV4AhFbXkopdQortJmYRFuiyLUHY+F9OAKIcSJUOvkT9f1pZqmtaaiyMv2P1X2nAps83dwIvDCnCrNI4LYV1hepT0+TNY8EkI0HMtnkbWtkM1LMgmNctL5gpYYLS/FcVV/IsJc5JXKfL+mxEkwLjUIt1n1vSSEMBm6K4QQJ1Cd1vnTdb1I1/VfgQJN01pommY/2P6trutbGiRCEVDhdpW/Dz8Tl/3wrXJrv7a0jQwKYFRCiJOZoijs3ZTP4hlbyE4rYtf6XBZMWU95ng+3Egoh0ZL4NTGqJ4j+UYNQ/zBbvFfkeTi94QGMSgghTj11KviiadrlwNPAWYAN6AX8pmnae8BPuq5/4v8QRSBZFnRpFsqXd/QmPb+c6BAnrSKcOBWp+CmEaBiW12LtwvQqbabPIndPMYmRsrh7U2RZFpGeFgyO/QtlZhEuNQSnNwyMOn0HLYRopAYNuhiXy4XT6cTj8dC9ew+eeOJJHA6Zz9vY1PpVV9O0G4H5QCpwx5+O3Qr8n39DE42GBQnBDnomhtMu0iWJnxCiQSkK2J3V355sdkkUmjLLAnt5KOGe5jjLIyTxE+Ik8/rrk/jyyznMnTufbdu2sXDhwmMfJE64urzyPg5M1HX9JuDPPXybgI5+i0oIIcSpyw49r2hbpckVaiemZWhg4hFCiJPAntxyFm04wDe/5rBowwH25JYf+6Dj4Ha78XjcRERE8Pjjj/HppzMqt/3x8ezZsxgyZDCjRo1gxIjh7Nixo0HiEVXVZdhnG+D7I2wrByJqcxJN02KB6UA7wE1FoZg7dV3P1jRt18FzHbobH9Z1/T91iFEIIUQTZ1kQmxzGZXd3Jn3zAUIinCR1iMYeKquLCiHE8diTW86G3cUYB+dLl3lMNuyuqMDbMtY/dRweeGAsTqeT9PR0+vbtR79+/fj22wVH3P+1115lzpx5JCYm4vF4MAzDL3GIo6tL8pcOdAN+qGFbT2pf7dMCXtF1/UcATdMmAi9xeNjolbqub6xDXEIIIU42CoQnBtEpKQnLorIipIw6F0KIutP3llYmfocYVkW7v5K/11+fREpKCm63m7FjxzB9+sdH3b9Xr948+eTjXHDBhQwYcB6tWrXySxzi6Ooy7PMD4GlN064Hgg+2KZqmXQSMB/5Vm5Poun7gUOJ30HIqehWFEEKIKkzTwrIsFEVhT4mHeZuz+WDpDtKKPJIICiFELZV5zDq114fL5eK8885n2bJl2Gw2TPPwNdxud+XPb745mTFjxlJWVsatt97MkiU/+T0WUV1dev5eBloBHwGH+mWXUVH1811d1yfX9eKapqnAX6koJHPIDE3TFGAp8Nif1hMUQghxCtpV6Ob6D1dS7q34EOGyq3x8y9mcFiFrjgohxLEEO9UaE73gGopr1ZdpmqxevYq2bdsSExPDxo0VA/qys7NZuXIl3bt3x+fzsXfvXjp37kLnzl1IT0/n999/p3//AX6PR1RVl0XeLeAeTdNeBy4C4oADwA/1WOPvLaAYePvg4/66rqdrmuYCJh1sv74uJ4yNDTvOUMQfxcfL2kvCv+SeEvXxj+WbKhM/ALfP5IvfMpg4umsAoxKNjWVZ5HlzyPfmEaQGEeOMJ8gWfMzj5PVJ+FtD3lM5OSoOR93mQJ/ZKow1Owsx/pD/2dSK9rqeqyaKovDAA2NxuVx4vV5SUlK4996/YZom9913H6NGjaBt27Z07doFm03FZlN48snHKSoqQlEUEhMTGTfuIb/EcjI60vNis6l1vtcUK0Ar5Wqa9irQBRii67q7hu2dgfm6rifX8pRtgZ25ucWYpqz+Wx/x8eFkZxcFOowTQlUVVFXB5/P/sAdx2Kl0Twn/s9tVnvw2lW827KvS3j8ljskjO8nfr6hUGpTDLwWLKPIVYGJyWrBGV9e5KN4jrzUmr0/C3xr6nsrKSkPTOtT5uD255eh7SynzmAQ7VbQWIX6b7ycajsNhw+utuRiOrqeSkNC6SpuqKoc6w5KBXX8+5qg9f5qm1Wn5Bl3XN9dmP03T/g70AK44lPhpmhYK2HVdLzg47PMaYG1dri9Endgd7C3wUFhmkBTtJMwJliEfIoVobHw+k2FdW1RL/q7p2UoSP1HJCillvyeDeFdzTg87g3zvAbaXpHJ6cEfCSAh0eEIEXMvYIEn2xDGHfW6kojrnsSgH9ztmX62maWcCjwFbgGWapgHsBB4EvtQ0zXbwPJuBu2txbXEScJuQ7/YR7rQRYjsBVRzsdlZuL8Tjq7i99xd46JAUQnxwRZl5IUTjkhDhYvwlGnPXZmCYMOysRJKi5EOMOMjpY1nBIrI8GZVNKaFnEu9sTrlVhkwIEUKICsdK/i7w9wV1Xd9ERbJYk27+vp5o/HYXe3hy/iZ+zyyidUwwfx/WiTNiQ2jIIcmlHrMy8Ttka2YZzdqHY3l9DXZdIUTdqarCgg37mL4ijfPax6MqCu8u3sG+AjcPnX8ahvTYn/JKlPwqiR/AtpLNdIvsQ7gaFaCohBCi8Tlq8qfr+uITFYg4NRUbJvfNXMvegnIA0g6UcdeM3/jijt7EuepSjLaupEa8EE2FqiqkHSij1GPw3cbDQz9355bIcg8CAJPq82EsLOIdCQR5Ims1hEk0bWa5Sd7eUnxug8jmIQRHO+TfXYgaNOSnayGOKavIU5n4HVLiMdhb4CauWcPdniFOBaddqdL7l9I8GMt3uNfv0IdKGQYqRGD5fCZDuiTy3aZ9NI8IQlVgb0E5o7u3lDl/AoAwogi1hVFiFFe2tQpKJsJIwDDkG4KTnVFmsvD9zRTsLwNAURUu+2snwprL0HAh/uxYBV/2A5four5G07RsjjH/T9f1Zv4MTpz8woPsuOwq7j99gIsMbuDvJXw+zm4Xwb58D0VlBi1inIQfLPiiKGCodg6U+FCAmDA7qmE06DBUIcTRdUkM493ru/Ojno1pWZynxdMpQWZyBZzTS4mSj8/yEq5GY3MHB+QLM8Xt4qKo4WwpX0+WJ4M2QSm0dWoY5ZL4nQoOZJRUJn4Almmx8utdXHRbByy5BYSo4lifsKcAWX/4WT79Cr9qFmzn8cs78NT8w4Vi7z6/HUnhzga/tuLzkhShokbZ8PlMrIOjhgzVzi9bCys/wKgK9GkfgeqTuYBCBEpGoYe/fbYGr1Hxh/nFbxlMv+VsUqToS8BYTg8Z5ja8eCjyFbCndBcXxw7HWRYZkHjs5WF0UvtyZqiJYtgwy+Ujy6nCXVL9/bkkrxzLZ4FDsj8h/uhYc/6e+cPPExo8GnHqsWBgShxn3N6bPfllJES4aBMRhP0EzckzTavKupA2m8LOHHeVb65NC/ble2kZocoakkIEgM2mMH9DZmXiB2CYFp+tSufZyzQZ+hkgxbZcNhetpdhXSLQjlu7R57CuaAW9gwZi+gLzgds0AVPFku+qTymxSaGH684f1PHcFihORaZunCCDBl2My+XC6XTi8Xjo3r0HTzzxJF999SVut5sbb7yJuXPnsHjxYt54Y1K9r3fzzTdx8823cP7559c/+FOMzPkTAWcH2ka4aBvhCnQoKIqCp4YPkh6fiaLYkM5vIQJBocRd/Zv9Uo8hH+wCxAgq5X+53+KzvADkeXNZX7Ca5JAUTJsPfEdeVF0IfwuJdTLotjNZMW8HZYUeOvZPIrlHvLw+/Enhgm848Oab+Pbtw968OTFjxhBxxWC/nf/11yeRkpKCYRjceOMNLFy4kKuvvsZv5/c3n8+H3X7qpUJ1+o01TbsKGAEkAdXG2ui63stPcQkRED6fSev4ILILvVXaW0S7MAwZ9ilEIBiGyfCuLZi7dm+V9mvObiXLPARIqVVYmfgdUmwUEuWIRfU6kH8VcSJZCkS1DuGSuzthGRY2l4opmV8VhQu+IXvCBKzyiiJ7vsxMsidMAPBrAgjgdrvxeNxEREQwZcrblJaWMm7c+Cr7ZGZmMmbMfdx6661ceullTJ36Af/9738xDB/NmiXwzDPPEBcXzw8/LOKttyajqiqGYfDYY0/Qq1dFurF69So++OB9srP3c8kll3L//Q8AMG3ah3z33XcYhg+Xy8WTTz5Fhw5nANCpU0ceeOBBfvrpJ3r06MEtt9zKK6+8zJYtW3C73fTq1Yvx4x/GZjvm0uVNVq2TP03TXgLGA6uAbYCnoYISIpBCbBbdk8PYllWOqsDpCcG4FFO+QRQigDrEhvDBjT2Y+vMufKbFrX3bcmZ8SKDDOiUpNhOn3Un3yHMoN8vQizdgWAZ2xU60PR7THegIxSnLBopNkcSvBgfefLMy8TvEKi/nwJtv+i35e+CBsTidTtLT0+nbtx/9+vVj7do11fZLTU3lkUfG88QTT9GzZ0++/no+aWlpfPrpZ6iqysyZM5k48RVefnkib7/9Nk888SQ9evTEMAzKyg4X9snMzOSjjz6mpKSEyy67hJEjR9KmTVuGDh3GzTffAsAvvyzj2Wef4dNPZx7+vS2LadM+AuCpp56kZ8+zefbZ5zBNk4cfHs+cOV9x5ZWj/fKcNEZ16fm7FXhc1/UXGyoYIRoDyzAJUaFbq2AADJ+BJV9jC3HimVCe78Vd6iUkpmKwyZCuLVCw8FkmhtRyOOEsu4+txlo27F8NQLg9krOj+rM870d6R52PozRcBscL0Qj59u2rU/vxODTs0+12M3bsGKZP/7jaPlu26IwdO4YpU96hXbt2APz44//YtGkTo0ePAsAwDMLCwgHo3bs3EydOZNCgQfTvP4CUlJTKc11yySWoqkp4eDinnXYa6enptGnTls2bN/Gvf71HQUEBiqKye/euKjEMGzas8ucff/wfGzZs4KOPPgSgvLychIQEvz0njVFdkj8v8GtDBSJEY2P4qi8aLIQ4QQxI/SmT9T/sAcDuVDn7Ro0Hv0ul1GMwsnsSYU47HWOl9+9EKrHlsqFgdeXjIl8Be8p2Myz+emzuEBkhIUQjZW/eHF9mZo3t/uZyuTjvvPNZvPhHOnXqVGVbQkJzSkpKWLVqVWXyZ1kWd9xxJyNHjqp2rocffoQtW7awYsUKHnjgfm666abKXjmn83CtCFW14fMZeL0e7r9/LB99NJ2OHTuyf/9+Lrzw/CrnDAk5/L5hWRaTJ79Fq1at/PPLNwFqHfZ9E7hN0zT5nlUIIUSDKslxVyZ+AD6Pyaa5uxjVKRGPYTJzVTrrMwqx2+vyNibqq9gsqta2z70H1XCCIf8WQjRWMWPGoARVLdehBAURM2aM369lmiarV6+ibdu21bZFRkby/vsf8PXX85k2raK37fzzL2TmzJkUFBQA4PF4SE1NBWDnzp20b9+eG264gcGDB7Nx48ajXtvt9mAYBs0PJrUzZ3521P3PP/8CPvjgfQyj4gv/vLw89uzZc9Rjmrpa9/zpuv6KpmmvAqmapi0G8v+0i6Xr+sN+jU4IIcQpqSS/+sSxwpwyBrRszQer0gBYui2HG3smnejQTmmhani1tgRnC2yGFHkRojE7NK+vIat9Hprz5/V6SUlJ4a67/sonn0yvtl94eDj/+te/uPvuuykrK+Ovf72b/Pw8br75JgAsy+Saa66lQ4cOvPHG66Sl7cZmsxEeHsGzzz531BjCwsL429/u5ZprriIxMZFzz+1/1P0feeRRXnvtVUaNGoGiKDgcTh555BFatmx5/E9EI6dYtRyjoWnadcBHgAlkU73gi6Xr+mn+Da9O2gI7c3OLZS22eoqPDyc7u/q3u0IcL7mnRF0VZZTz3T83VGmLaxVGz2vbsMm7h9377OQX2rm7bxt5zT9BbDYFHAab3avZVPwbAKG2MC6MHoajrHpS2FTI65Pwt4a+p7Ky0tC0Dg12ftG4OBw2vN6apyLpeioJCa2rtKmqQmxsGEAysOvPx9Rlzt9LwOfAXbquy6ukEEKIBuMKt9PjsrasW5iGz2sSERdMpwFJ7LavJVNdT1RyOIMjhmJ6JfFraIoKpc5cNpeuweN20zHsLNoFnYHHdBNMBEqZM9AhCiGEqKW6JH8RwFRJ/ERToKgKBR4Dm6IQZlepbQ+3EKJxcIbbiYgP4qyLW4MCnnIfZrMidlibACg1i8gythKh9pCevwZW5szj37lfYB2s45npTufCmMFEuU/eYVFCCHGyqsvs7C+BCxoqECH8pdBrMnVlOoPf+YXR76/kh50HkM4BIZoYBZq1j6B11xhatI8ksl8ZK4P+jWEdHvqy37MXVZUaZA1JVRUyPDsrE79DNhT/is0RoKCEEEIct7r0/P0HeEnTtObAD1Qv+IKu69/6KzAhjoeiKCzamsM7i3cAUOY1GP/lBj68sQed4kMDHJ0Qoi4sy8Ieaqv4z6ngy/NW2Z4cpGEYUmakIVkWqEr1jwp2xX5wRIUk30II0ZTUJfk7VCv11oP//ZkF2OodkRD14LEsPl+dXq19+a4DdEkIk+FhQjRRcUoSZ4Z35/eitVhYaKGdSbC3wvIFOrKTm2VZJLnasF5ZifGHJ7tTWE/Mckn8hBCiqalL8pfcYFEI4Sd2BdrEhLA9u6RKe4vIYEn8hGjCypQistx76BrZC4A9ZbsoDSoilKBjHCnqy+WO5LLY0aS5t+ExPbQNSiHUG4u8ogohRNNTl3X+dtd234MLwX8ATNB1Pe14AhPieCgW3Nn/NH7enovbVzEcrGVUEGe3jgpwZEKI42WzqWwv20yOZz85nv2V7aml6+jtHITPJ0M/G5JlgbMskvZqTxQbGOV/ngEohDjVDRp0MSEhIXz11VxUVa1smzLlH6SkpBz12ClT3uaOO+7A4Tj+ysH33XcvGRkVi7Pruk5KSvuDSx7E8d57/6rcb//+/Tz88Hg+/HDacV+rqatLz19dqMBNwNuAJH/ihGoXFcTnt/Via3YJLruK1iyMGKeMSBaiKVOV6vXJ1DrVLBN1odosTLsX1XBg+iqGd8roiaZJVRQs00JRFUypfC0aUGlpKV9/PZ9hw4bX6bh//OMdbrnllnolf5Mnv1X5c6dOHZkxYwYhIVVrPfh8Ppo1a3ZKJ37QcMkfyCxwESCWZZEU6iQpVNaeEuJkYBgmySHt2VKysUrVyfZhZ+IrlV4/fzOCS1hbspI9xbuIdzSne3g/HGURgQ5LHAdficHW5Vmk/55Hq44xpPRJwB4iX5qcqsrWZVP8fRpmgRs10kXYwNYEd4332/nvvvse3nlnCpdffnm1RC4tbTfPPDOBAwfysNttjBkzlnPP7c/zzz8HwHXXXYeqKnz44UdERBx+vfH5fNx991/Jz8/H7S6nc+fOPP30hFonijfffBNnnXUWGzasx+l08cQTT3L11aNZunQZUJEo/vWvd7Ns2TIKCvIZM2YsAwcO8tMz0jg1ZPInhBBC1JuqKuwvz6RP9Pns92RiWRbNXIlklqWj2RKl4qcfKU6DJQX/4YC3Ynhthns3ud5sLou6CsXt//mViqJgOT2Ahep1Se+iP/ngx+k6BzIq5sDnZ5WSuTWfC287Q8rznYLK1mVTOG87eCteL80Cd8Vj8FsCeOaZnTjzzDOZOfNzbrjhhirbHn54PFdeeRWjRo1i+/Zt3HTTjcyf/w1PPPEkM2d+VmNPHYDNZuOVVyYSFRWFZVk89tijfPXVV1x99TW1jmvbtm28++6/sNvtZGRkVNuuqiozZnzKzp07uf76v9C9ew9iY2Pr/gQ0EZL8CSGEaNQURSHfm8uOIp0oRwygsKNUp2VQWxRXoKM7uZQrxZWJX2WbWUoJhYT5u7iO3cc+ZTdrCpZhWiZdw3uTZGsHXllA0B9K892Vid8hOXuKKc3zEBInI2NONcXfp1UmfpW8JsXfp/m19+/ee8dw6603M2rUyMq2kpISUlNTGTFiBADt2p1Ohw4dWL9+Heeff/QlxE3T5MMPp7J06RJM06SwsJCgoLq9Fl1++RXY7UdOeUaOHAVAcnIyZ5zRkfXr13HBBRfW6RpNiSR/QgghGjXDMGkX0hGPOwRbSQssLCKCskgJScLnll4/f7IpdlRUTKo+r3bF/wlZgW0/Px/4vvLxioIfGRAdRBxt/X6tU5Fqq3l4p2qTWTmnIrPAXaf245WcnEz//gP46KOPKtusI841Pfa9uGDBAtas+Y2PP/6E0NBQ3nvvXXbvrnUNSgBCQkLqsLeFopzcfyMy8FsIIUSjZxTHEmIkYQSnYwan4TKaY5T479tqUcHpDaFreK8qbe2COxBk+HfOn82msqt8S7X2LaUbsNlP7g9eJ0pwpIN23av+jbTr0YygSOlZPRWpkTUPkzhSe33cffc9fPbZZ5SUVPQ8h4WF0aFDB+bNmwvAjh070HWdLl26ABAaGkpRUXGN5yoqKiQqKvrgPkV8++0Cv8c7d+4cAHbv3kVqaiqdO3fx+zUaE+n5E0II0agpikKZ7QDblG8xD87vU5RNxKjDUNVEmSfmR6ahkGzvRHxMIoVGPqG2cCKsOPD4b5KY4jRx2ws5zdWBWGczNhetpdgoBCDSFo0lnbl+YanQ7fI2tO4US3ZaEfGtw4ltEypf+5+iwga2rjLnDwCHStjA1n6/VvPmzRkyZAgffTStsu3ll1/hmWcm8PHHH2O323jxxZeIiYkB4Kabbub//u8WXC5XtYIvQ4cO43//+4Fhw4bQrFkC3bv3wO32b2+lw+Hg+uuvIz8/j6efnnBSz/cDUI7cFVuzg2v4tQRaAet0XS85wn5tgL26rnvrHWXttAV25uYWyweBeoqPDyc7uyjQYYiTiNxTJz9VVRrstdfptLG89Ed2uTdVaW/pTKF/2CW43b4Gua7wP8vlIdPcwebitSgopIR1JMwWwY5L0KgAACAASURBVLK8HzAtk0tjrsRZHnlCYzoVXp8a8u9TVNfQ91RWVhqa1qHOxzV0tc+mqFOnjqxcuarGYjONhcNhw+s1atym66kkJFRN4CvWNwwDSAZ2/fmYOvX8aZp2N/AE0BywgLOB3zRN+wr4Sdf1SYeDqf2i8EIIIZqmXLfB6vR8tu0vpndyDGc2CyPYz3OKTNNCUUxa2rriKYrHssAVnouiFmKa0k1UX4rDoNxWjGH5CCEC3P4dFnho+oxlQR6ZLM//sXLb6vyf6R19HhfGDsZpBuNwh8kC8g1AEj8BFVU9T/VkT9Qh+dM0bRzwHPAy8D/ghz9s/hG4FphU/chq54kFpgPtADewDbhT1/VsTdPaAx8BsUAucKOu61trG6MQQogTp8hncv+X6/k9s+Ib7mm/7GbsRadzXbekik/69aQApbke8jNLaRHSjXUlZTz5vY5pgdY8lFeu7o33z9XrRJ1YTg/ry39hW9nvAETZYzgv6gpsZfX/FlwByvO8ZG4rAKBF+yh2h++ott++8j0khZ0G5S5J/BoZxQTTZ6E6Ffm3ESeljRs3BzqEE64uI7/vAZ7Sdf1pYMmftulA+1qexwJe0XVd03W9C7AdeOngtn8CU3Rdbw9MAd6tQ3xCCCFOoB0HyioTv0P+sXgHOeX1H4apKJCXVsrXb65jyedbWfyhjrk4m/v6JAOg7yvh+415BAXJ1PX6yGd/ZeIHkO87QGrZOlQ/TPErO+Dl68nrWDl/Jyvn72T+pLW0ya9eSCHIFoLNkKUHGhPFgpIsN7vX5vL7T5ms+3c6Zrl80SLEyaAuyV9z4NcjbDOhdgsA6bp+QNf1H//QtBxoo2laM6A78NnB9s+A7pqmSf+0EEI0Qt4aFlf3GKZfhphZPvjlq+38sbuhMKuM9s7DlelWbC+s93VOZYqicMC3v1p7hnsXlq1+CbzNprJl+T5M3+F/QNNnkbG6lBjH4bd1u+Lg9JAzMNxS4bOxsLwWG/6bwYK317N8zg7SNh0gJiGUdd/vQa1FaX4hRONWl69MtwHnAYtq2DYAqHO/qaZpKvBXYD4VBWQydF03AHRdNzRN23uwPbu25zw4wVHUU3x8eKBDECcZuadOPh1Vlaj/Z+/O4+Mq68WPf845s+/Z16ZJ22Sa7ju0QGlBKqBQy2LZ1QuCevVexYt4uSp6f9drEfUKgguIKLggFVCQrdIiZSkUutF1uiRptjZ7Mpl9zvL7I2lKmrTNJOmS5Hm/Xrxonpk555nJyZnzPc/zfL8OM+2Ro3m9ls/MZ1KeB6t5aENHna0xosFEn3blI2vePzYlE7fbjlscWoMWDOfCMXkpxtlKSPd4kaWhpYWMhfsGkPGQxseyrqA2VgUG5FjzybblQSpluE4BcX46qurDZna9Vd/zc7A5St2+duLhJCZJwZtpP4O9GzlO5THV3CxjHuI5VhhZjvf7VhQ55WMtleDvp8DP/X5/AvhLd1u23++/FbgT+HxKe+7yMyAEPATMHsTr+xDZPoduLGQ+Gw1kk4JqSOiGgUUGXe0/E9TZQBxTo5MDePyWefx2w0F21AdZXJbJwgkZ1DaH8JiGFjhIkkT5eXls/2fd0TZZorN74O/i8kyWFWg0N3cOx/LCMctlyaTUMZV9ka5MqmnmDMpsM2hp7jeR90k5jQSWYDskEyz5eDrP1XfS0Xw0LXvZOTmoHRZyj6wUSUBT55k9N4jz01GSBM11feutNVQGmXxuLomkKj6rATjVx5Sm6cfN/iiMPifK9qlpep9j7SPZPvs14OAvEAj82u/3pwHfAb7X3fwSEAG+GwgE/jjQbQH4/f4fAaXAFYFAQPf7/TVAgd/vV7pH/RQgH6hJZbuCMBZIJoVAQ5yG9q6RkXSXiakFDgxVpLwfKkkallwlY4aqG1S3hinNcfHy9sM8/nYVX19Wxo2z8tC0wX+QhmHgPy8XSZEIbDiMK83KrCuKOGjS+fGnp5GdroGzHZMik1TFWqTBkhIWZpjPw58xA83QsBtuiA4u26dTT2B/cx1SSzMAJkni05/7OBs+iNJYEWLmJUV48sSo0dnMMMCX0/d3lFPioWCyD8kszo+CMNKltFI+EAjc7/f7fwksBDKBVmBDIBDoSGU7fr//+8Bc4BOBQCDeve1Gv9+/la6sob/v/v+WQCAw4CmfgjAWSBJ0RPWewA+gNaTS2KmS4xS1nAbLsCQISs0EtTa8SjoePROSw5vyfjTadTjIlpoOqDn6NfDbt6v4+ORs0sxDG/2TrTIlMzLIyHOhGjr7O+Lcu34fLeEEiizxy8+WMT97qO9gbProTQ5DlbGoQ6+tZ4Qi1GZNJeSScdkNsjoqsWzeiG9pIcWX5+NMOEQB9xHAk2tnyvn5PVM/PVl2Zl0yDluaWQR+wnEtW/YxrFYrZrOFZDLJZz7zWa655ppTsq+6ujpWrryWt956Z8jbmjZtCqWlZciyRCKR4GMfu4R///evAvDQQz9j4sRJXHbZZTz88ENEIhHuuusbQ97nmZZymrRAINAJrBnsDv1+/1TgHmAv8I7f7weoDAQCK4AvAL/z+/3fAdqAWwa7H0EYrWRZpjnUdy1UYzBBntsmgr9BkEw62+Mb2RfZ0dM2xTWbctMCDFUkODgRWer7+ViHOOXziI66KK/+6ujvxGSWuWf5RL7+2m403eD5TW0surJQlHtIgWzSiFtCRI0QCmbsuhs5PvTROMmAD7dG2PyPwz1tcy4sYr69gjxTDmvaX+VC3+XYYmlD3pdwaklmienLCig9Jwc1oeFMt4BJlHoYDZor9lG75T0S4RAWp4vC2eeQOaF02Lb/k5/8lNLSUvbt28e1117N4sWLyc4+e+7QaZqGovRdO/eHP/wBh8NJNBpl+fIrWLp0KTNmzOTLX/7KGejlqZdKnb/vA5mBQOCOfh77JdAUCAS+fbLtBAKBndB/uqhAILAHOGegfRKEkUiSJCRp8EV3dV0n3WWmvrV3AJjlMZOUFDoSBiZZwmWTQUwDHZCYKci+jh292naFtjAxoxyz6jlDvRpedrUVa9tOpGgbenopIVcZGkNPGDA5102aw0zbR5K+3HZBCelWBWMINyJkSWLrmupebWpSx96a7EkyE4rpIC5JB0yWJULmFg7H6wiq7QST7ZS7Z5JpLUCKW0++gROId6psfq2uV9vm9Y1MvGM6m+KbmO6Zy6FENROkdAwxfHTWMySw+kxYUx8jEM5SzRX7qNrwBrrWdV2QCIeo2vAGwLAGgAClpaV4PF4aGhrIzs6msrKS++77AW1t7SSTSW6++WZWrLgKgH/8Yw0PPvgAVquVZcs+zoMPPsDGje/T1tbea3TvRKN9d999F5WVVSSTCcaNK+L//b//wev1snHjRu677wfMnTuXHTt2cPvtX2DJkiXH7XcsFkPTNNzuru/9//qve5g6dSo33HBjr+ft3buXu+++i3vu+RazZs3kgQce4IMPPiCZTFBaWsZ3vvMdHI6h10o9VVL5q76ervV+/XkT+G/gpMGfIIxVkgS1oSTvVLTQGk6wuDSLsnQ7SooDS4YBaXaZLI+ZpmDXBbfPaSLLY+WdPe09l8I2s8y8CS4RAA6AZvT/GamGymiY+GnXWnG8+hXkqq4veiQJz9VP0J67dMjTuErcFh6+YQ7r9jTS2BnjgtIsZuV7hhT4AWCA1s9aPkPTUbpHG6+dn0siIUb9Bipp76Ayso+qyD4cipPJrhnsD+/C4/FiZWjBXzKm9Y3DDQiaZAocxTTFD5FnHQ9JEfgJwplQu+W9nsDvCF1Tqd3y3rAHf5s3byYtzcfkyX5UVeXuu+9i1aofMmHCBMLhMCtXXsvMmbPwer1897v38sc//onx44t54onfDWp/3/zmPaSldc0qePDBB/jNbx7ja1+7E4B9+/bx7W/fyz33fOu4r7/xxq7grrr6ICtXXkdJSclxn/vuuxu4775V/OhHP2bixEn86le/xO1289RTfwbgJz/5MY8++mjP1NGzUSrBXz5Qd5zH6rsfFwThOOrDSW757fsEuwtg//rtKn514xzm5KZensRQNcrzbEzKsWMYBjaLzLaDoV7XXrGkTjCq4RkN0csp5sCDS3ET0o5mzPKa0rEzOtK/W1t3Hw38AAwD06vfwH7TGiLy0KbhGQaUeq1MPLcIDbAOYVT7o3QMZl48jrW/PVqAXJIlHEUuitvtfHv5BIrzE2IN0gApCnTqIfKsBWSas2lTW3i37Z/M851HwkgMMfQDh8+C02cl3H40s6fTZ0HOMKiOVpBjzSfLlIfRd8a6IAinQSLcN4vridoH4847v4phGNTU1PDAAw9iNls4cGA/FRUV3HXX14/uM5GgoqICRZEpL5/C+PHFAKxYcRU//OF9Ke/3+ef/xosv/p1kMkk0Gu3ZHsD48eOZNWvWCV9/ZNpnMBjk1ls/x9q1r3HxxR/r87x33nmHt99+i0ce+XXPdNbXX3+dcDjEmjWvdr+3JN1L2s5aqQR/h+kqwv56P4/NIYVafIIwFm2qae8J/I742ev7eeT6WYMaXdLVo5P2DB3iat+r4IRmIFlEdraTilu4KG0528MbORSvpcA6nmnOeYPOeni2keL95OQKHUbWYjA8y/OQDQMZGM4lpxklLi65dQo719dhdiqUL8nGyIvw/amZaFIH1dEq8qzivuNAJOwdRBJhQlqQhJ4gzZLBDM/8rgyf0tCnJ8lWiSWfL+GD52ppOBAiZ6KLsivdhBzNZOt55FnHIUVsYpKuIJwhFqer30DP4hy++thH1vy9+uorfPObd/P3v7+EYRj4fD6eeea5Ps9ft24tUj/rxgEURUHXj87siMfj/T5v06YP+POfn+L3v/8j6enpvPji31m9enXP43b7wIuIejweFi5cxIYNG/oN/oqLi9m/fz87d+4gO/sioCsz9be+9W3OOefcAe/nTEvla/9p4Dt+v/8TH230+/2X0zXd86nh7JggjCaSBPF+klKEExr6MERmuqpTnNX33r3PYRKB3wCZoi7mWpZyue86ZpsXo0TP3vn6qdLSJoHce32fPvlK4pasM9SjAZIhrdjJhZ/1M+fmDN6wr2Zt23O80f4C77avZ7J7Bqoo83BSsj3J4Xgd2zs3URHZi11x0BxvIMOSiUvxYIkNPdOnZNbZ634f3/VNzLvbgXNlPe9aXiSshcgwZ2OPp4lzkSCcQYWzz0FWeo/5yIqJwtnDn2rj4x+/lEWLFvHYY7+muLgEu93O888/3/N4RUUFoVCIGTNmsmvXTqqrDwLw17/+tec5mZmZqKra89hLL73Y7746Oztxudz4fD4SiQTPPffsoPudTCbYunVLr5HDj8rPz+fRR3/NT3/6f7z88ssALF26lN/97nfEYjEAwuEwBw4cGHQfTodURv6+A8wCXvD7/S3AISAPSKcr+6dY7ycIx2EYMKfIhyJLaB8ZGrnt/BJssjTkiyLDMMhwKJQXOKhojGExSUzOd2BBQ1waD5yuSqBaRt1nFnJNQrn2jyiv3o3UUY0+ZQWJRd8gaYyMZA4GUBXbj0/OxxovAQOS9oMcjBxgupKHpo2239jwaqOR99rX9/y8uWMD83znkdRVsuWCIU/T1a0xDunVJPQ46XY3hj1GdXAfACbJhIfMrr8tQRDOmCPr+k5lts+P+upXv8anP30tt956Kw899DCrVq3i8cd/g65rZGRk8uMf/4TMzEy+8517+dKXvojPl8aSJUswmUzYbHZkWeab3/xPPv/528jLy2fBggX97uf88y/ghRde4IorPkFOTi5Tp05l+/btKfX1xhtv7Cn1MG/ePK67buVxn5ubm8tjj/2G22+/nVgsyq233sbPf/4w1133aSRJRpIkvvjFLzFx4sSU+nA6Salm3fL7/R8HlgIZQAuwNhAI/OMU9C1VxUBlS0tIpLofoqwsN01NnSd/opAaCQKtUR59q5LmUILPnDuec4t82FPN+HICsiyBLCMBmqr13w0JFEVG143T9rcijqkzT5LApnWg6FFi5kzUERL4AZhMMjvD+/nDG1Fe3t4CwEXl6Xz2YhvTnWVi9O8EFEXmAFtoV1s5FK8lqoUByLONY65nEZbw0NZ8SmaNjbF11MSO3unOsuTiNadxMLKfS7Ouxhz2DWkfp5o4PwnD7VQfUw0N1fj9k0/Z9k+ncDiM09k10+a5557l2Wef5cknf3+Ge3V2MZsVksn+r+kCgT3k5BT1apNliYwMF0AJUHXsawZT5+9V4NVUXycIY54B/jQ79y+fim6ACWPYp0HpugF6/ycIAMmk0B7VqWuN47YrFKZbkDVVTMcaAwwDorIXZO+Iq46gaToHapy8vL22p23d7lYWlExiypQR9mZOI0mSiFhaaQodplPrYJJzMqqeZHfoQxyyA7vh4fhni4GJKaFegR9AU+IwU92zKXfMxhLxjLTDTRCE0+j3v3+SNWteRdO07uyf3zvTXRr1Ug7+/H6/FSgAbMc+FggEdg1HpwRhNJN0A4XTf/0tyxKHOlT2HY4C0BpKUt8a59xJblEOQjirmc0m1u9t7dO+dlcL188cRyIx1BBmdEpYg7zW8hxJI4mOTnuyFb9rOunmLCY7Z6JFhl7n8Xh3juyGG7MI/ARBOIk77vgCd9zxhTPdjTEllSLv+cAjwGX9PCzRdS07DN8kgiCcCoaicKAh3KstqRmEEzqOYcr4OJpJJo2IqYOQ3oFdcuDS0yE5OrKBnu10XWP2eBdrd/dOKr1gohtdzPjslyRBQo5Q7pmJhIQimfiw4332h3dxRdZ1KBEPxjCEZjbdTaG1mNp4VU9bliUXuzE6yqScrc7E9H1BEEaHVEb+fk1XSYc7gV2AqNYjCCOMJEl97tQfL82ycJQsQy37ebflaKWbUsdUplsWQVLc8zrVVNXgIn8mr+xoYkdtV6ryslwnl03NRj3O2taxLmZr47Xm59GMrs/HIluZ5T2HD4MfIKs2Ul3vfzxGUmGecwkFtkqqYwcosBZTYJ4AMXFj5FTREwaNB4Ls/6CRrHEuJs7NxuQW5yFBEAYmleDvPODzgUDg6VPVGUEQBkCSUA0Dc9847sRUlbI8O7vrIj1NNrOMwyxhiFmfJ5Q0R3i/7c1ebfsiOym1TcVG+hnq1diSbbbzwPXlVDSHMTCYkOHCh12sV+2HokjsDm/tCfwAEnqckNbJud6lSAlLyqN+R0aaNE1Hc4SI6CHMkgWr5kaO2yiUplBsn4aq6hgx8Us5VSRJYv+7DWz9RzUA9Xvb2buxgU98ZQayXUzhEATh5FIJ/hqB6KnqiCAIJyZJsL8jzm/erqK6NcJ188dx4YR0XKaBfeEbBmQ6ZeZOcNPQnsBlV8h0mTDEer+TUkmi9RMhJ4xE38XPwrCSDIh3qqgJHY/XxuwMO5mZXZn0RIjRP9lsMMlSTqG9mJ2dm2lJdk2XVXWVLMahpRgxa1Gd+kA7DZVBCqd6sU1I0GFrpDF+iPGOSWRZCyFuPW42OmH4qFGND1+v6dUW7UzS0RglbfzoqU0qCMKpk8ptou8Ad/v9fs+p6owgCMdXG0rwmcffZ82uBvYc7uS7L+zihZ0NRI3uEg8DYGg6dkljYqaZLLskEr0MkM1wkW7uXRDdLJlxSuJ0eCzVgOpQgg8bw7TEtSFNKzZUgz1vHOKvP9rC3x/Yxiu/2EEiKAKMEzEcETrkRuJGFM3QmJd2AVNdswAotpaiJVL7fRhJgzeeDLDhmQNUbG5i/ZP7OfBqiLpQLXaTg0OxGmKKKJNwukiARN/foZi+L5xJ11+/kquvXsGVV36SmTOnc/XVK7j66hV861v3pLSdL37xDqqrq3t+Pnz4cE/NvWnTphCJ9M5bcP75i6irqwNg2bKPsW/fviG+k6Nuu+1f2L9/Hw8//BD33//DAb9u7drXuOKKT3LNNVdRWVnJ6tVn34TJVEb+rgKKgIN+v/99oP2Yx41AIHD8qoiCIAxJoDFM/Jh6Zo+9XUVnTCXdZeHisizSzAO7nyOKYqfGSChc4L2UzaG3qIlVkmHO5lzvUkwxhxh9+oiEYfDnrfX87PUDGAa4rCZ+eeNs/Gn2QW2vszHGtrVHyzsEm2JseaWa7NtEMpH+mEwyLUYb24MfENKCjLdPJGnEu0bnzAW4tcyUj9dwS5zmmlCvtgPvtTJ/0SS2aOuY7zufiB7CTubwvRHhuBS7wsxLxrH55YM9bU6fBU/24P7GBGE4/OlPfwagrq6OlSuv5ZlnnhvUdn7xi1/1+nndurUsXbp0yP0bKFVVMZlMBINBGhoamTSplFdfTa263erVT/PlL3+Zj3/8UjZu3Mjq1au59tpPn6IeD04qwV8mcKSYjxnIOsFzBWFY9ZOnZMwx91MM3m5WCMZVHn2rErfNzOxCD9nWkVO8eyRRok7OsS1jnjOBrJkwoooI/I5R3RHnwXVHa76F4ir/+dwOnvzMPJz9HL8n09kS69NWF2gj3BjGZJVRxW+gl5i1jWCijQL7eGRk9oQ+JN9WhNfkI1sfj6qmftOn38QwBkhG1+8zooXJNucPtevCABmGwcT5WXiz7FRubSaj0Mn46RnINjHyJ5zcS5Uv8fC2n3E4cphcRy7/OvMrXF5y+bDvR1VVvvSlL9Le3k48HmP69Once+93MZst/PKXv2D37t088MCDRKNRrr9+JXfe+XUWL76QZcs+xsMP/4LS0lIA1q1bxze+cfeA9/v3v7/A1q1baGxs5Oabb+GGG24EoLKykvvu+wFtbe0kk0luvvlmVqy4CugaUbzzzq+zfv165s6dy1e+8m+sX/8Gixdf0O8+YrEYl166jNWrnyErqysU+t///T6ZmZm0tbWxadMmqqqqeOqpp2htbaGuro6rr15BUdF4/u//fjqUj3XYDPgqMRAInL7QWxC6Nen17Gj9kLAaZnrGDMZbJoIxNhe1T852keWy0hSK97Rdv2Acv3m7CoAt1W1ous75Jel4BrgOUEiNnpSQklYRchxHQ2ffYK2mLUoooeG0p35Twp3ed0VlbrGHyEtVmJxmbEsLiJnFRS8AJo2g2o5TcdOSaGJPaBtzfeexteM9JjnKBz3a78qw4ct1EOtMEoskwYCiWT4a7BWgQ5o5A3vSi5hLcPpIZomsUjd55V5R6kEYsJcqX+J/Nv43Ma3rPH0ocoj/2fjfAMMeACqKwg9/eD8+nw/DMLjnnv/k2WefZeXK67j99ju4447b+cMffs/u3bu54ILFLF58YZ9tBINBDh8+RFlZ2YD329LSwu9+9yTNzc1ce+3VzJ07j4kTJ3L33XexatUPmTBhAuFwmJUrr2XmzFlMmDAB6Lqp8tvf/q5nO+vWreWGG27qdx82m43lyz/F6tVP86Uv/SuRSIRXXnmZ5577GxkZGezevZvPfvZzLFmyhI0bN/KjH93P00+vTvETPLXEEIFw1mrU6/ji67fTFm8DQJZkHl7yC/z26We4Z2dGls3E47fMZUNlK/ubQxRnOFm3p5GOaBKATJeVjmiSuo4YngzHGe6tMBYVePtOPZuY5cIYZHzmzrExdXE+O9fXA+D0WZkzJxPtlSo0A+RCF9JU35ifFQCg2SOQMIhqERyKk4uzPsl7respc03Dqgw+K6rkVln8H3nE1AiJoITeaiLoOcwOvZJieylZ0jh0Ue7kjBjMSK4wdj287Wc9gd8RMS3Gw9t+NuzBn67rPP74b3jrrTfRdZ1gMIjN1nUzT5ZlVq26j2uuuYq8vDyeeOL3/W7jzTfXc8EFi0+6r48ud73qqqsByMzMZPHiC3n//Y2YTAoVFRXcddfXe56XSCSoqKjoCf6WL1/e67GdO3cye/bs4+7zuuuu55ZbbuL22+/ghReeZ+HCRWRkZJy0r2eLlII/v9/vBpYDZdA3yV0gEPjGMPVLENjWsqUn8APQDZ1f73yE+xb8BFkfmzWkcuwmVkzNpiacxk2PbSSc6Ep+UZTuoDjTyVMbq1lUIkoPCGdGmsPMl5ZM5LG3KomrOrkeGzedW8SOQ0FyB3FcSiaJqR8roHRBDom6MLbWGNo/qjky9KrtbsU6K4NEYowngXFFaU8205JswixbsMpW2hJtzPKdg0WyYgq7BjUyZzijtGiHaIk3YVccuNI8aL4Y46RxFFGETfNATAR+gjASHI4cTql9KF588UW2bNnME0/8HqfTySOP/IqDB4+uU62rq0WWZYLBILFYDJfL1Wcb69atZeXK63t+Tk9Pp729HYejK6utqqqEQiHS0vr/bjEMA0mSMAwDn893wnWIDsfRG+bvvruBefPmoyjHP7fl5eUxbdp0Xn99HU899RT33vvd4z73bDTguWF+v38isA/4OfBfwC3AV4H/AG4FrjkVHRTGJkmSaI8fm1MIWmItqGO8KJ1hwDinmSf+ZQH3XjGFey6fzJ2XlLK9tp2p+R7yXdYz3UXhLCbLBqotTNzagWQZ3r8liyLzfmULn188gS9fNIlLpuRw/6sB4qqOabBTkSVwZlqQI3G09w7BR0Y7onlihFuSIGIECWmdWGUbMT3Krs5tWGULJky49HT0QUR+igJNyXrqYzUcjBzgg/a32R3ahlmyYDFs2GJpIEb8BGHEyHXkptQ+FJ2dQXy+NJxOJ52dnbz00os9j3V0dHD33d/g/vt/xKWXXsZ3v3tvn9cnkwm2b9/OnDlzetoWLlzI008fzZz5l7+sZsaMGdjtR2ec/O1vfwWgtbWVt956k/nzF1BcXILdbuf555/veV5FRQWhUO9EVke8/vo6li696KTv8YYbbmTVqlWYTAqzZs3q9zkul4tQ6OzLhpzKyN//AR8A1wJh4HJgG7AS+EH3/wVhWBiGwdyseX3aryu7HiuOlAsUjzaGAUVOM7llmdR3Jtjb0MnSsmzKMu0cbwmUSJojYFY5oO1kW8t76OhkWnI4z7MMJTo89cG8VhOXz8jney/s6mkryezadgxp0OsMZFkmOcGNstsOjd3lZjOsJMu8jPUM95ojOTqQcAAAIABJREFUwvvtb9OSaATAa0qj1DWFqB7Fa07HCKU+S0KSIGproyq4jw61jXH2Ekyyme3BD5jkLMeriDXFgjDS/OvMr/Ra8wdgU2z868yvDPu+rrxyOa+/vo7ly68gOzuHOXPmEo935Sv49re/xYoVVzFnzlxmzpzFrbf+C3/+81OsXHldz+vfffdd5s6dh8l09Fvj7rv/k1WrfsCKFZ9CliVyc/P4wQ/u67XfvLw8brnlJpqamrjtts/3rBd86KGHWbVqFY8//ht0XSMjI5Mf//gnffptGAYbNmzgrrt6J5lZvfppXnnl5Z6f77jjC3z60yuxWi29RiePVVZWRnFxCZ/61JWUlEw4axK+SP1m8uqH3+8/DNwGvASowKJAIPBu92P/BlwXCAQWnaqODkAxUNnSEhKLn4coK6urgPKZpksqu0Lb+MX2XxBMdHCj/yYW5yzFgaitlgpJgoPBBJtr2lFkiblFPgqc5tMaCJ4tx9RYF7I2sKa199SXSfZyZpkXo2tDj6IUBd6p6aSmLUJrOInDohBJaBRnOLioNAPTIM/NiiLTZDTTVCshN6tggJ5pIqPQIN+cSSIxNtc+SRLUmvbwXvs/e7WXu2aQYc4mTylGi6Yecmu2CC+1PkXSSPS0lThKiWhhSp1TyDUmoqdYL/BsJs5PwnA71cdUQ0M1fv/klF93urJ9DtX3vvddFi1axCWXLDut+922bRuPPPIrHn745yd9bm1tLTfffCMvvfRKr9HHU8FsVkgm+1/eEAjsISenqFebLEtkZLgASoCqY1+TyreCDQgGAgHd7/e3Ah/N7bwDmJnCtgThpGTDxDTnXB5Y9DAaKjbD1X/aceGE9rfHueXx90l0Z/tzWBQeun42k9Ls2AeRfl8YuYJa36nU1fEKZtgXgmYZ8vY1DfK9Nmrborxb0YyqGVwxK5+STCfhuIrXPLhpghaLQmv8ADXpFaSnTQYkWqRddGoZjLdfPmaDP0WRaYzX9WlvTTbjd85ACw1urLVTb+0V+AFURfYz17eIdHM2ekicNwZDQkKLacgmGcksZmIIp9/lJZeflcHesc7UGrqZM2cOKPB76KGf8dxzz3LXXd845YHfqZDKN8NeYHz3v7cAX/D7/S8BGl1r/uqHuW+CAIBJt2LCOuaneg6Gokj86f2ansAPIJLQeGVnA7MLvSwrzRAj5WOIU+lbHD3bkoesmYctVb/XKjNrnJeJ2U403cBlNfH37fVcMDGTeXmpF2fXYjpVe5oJVWUxcUIuofw69klbAUgng2gogSJJ6PLYu5hWVZ0CawmV0X292ovsEzBF3OgpnjMlCVRrGB2Neb7zqI9VUx+rAcAkmci3FmEOe8SZeBC0qM62NTUc2NSI02flvGsn4RvnEJ+lIIxAX/7yV/jyl4d/uuzpkkrw9xQwC3gS+DbwKhAE9O7tfHa4OycIwtAYSDT2U3utI5pk9eZazhmfhtsk7uKPFR4jkxK7n8poAACbbGe2axF6dPiOgaQGN/9mIzMKfPicZtbvbSKpGZw3YRBpsDXYsPoA9Xu7RyzfhrJFuUy7+AL2ahspdU6hYXsbna1Rys4pHFtF380aMSWIExfnp1/CO63r0NEYb5tIgTwJPZl64NdpbWBt6/NoRtf0olLnFAptxdTGqpjjOQ9z2DvmAuzhICGxfW0tyZjG+BmZ1OxqZc2jO7nyzllYfWMzc7UgCGdOKkXef/KRf7/r9/unAZcCdmBdIBDYcQr6JwjCEOiazg0LithQ0dqrffY4Hw+9vq97NFUEf2OFlLAw27qYyfaZJKU4VtlKR7IVpy2JPZkG2tATeWTYTdxy7ngee7uqpy3daWFiRupJZcIt8aOBX7e9G5q4cEEZ5fYCpISZ9sauYzsZVZFsYyT7pCVBk1RLSA2iSAr10Ro+kX0thipj1V0Y8dR/j4Ylwdvt/+gJ/AD2hXexJOMyyh2zceuZIvAbLNVgwqwMwsEIhmHgPyebnesPEWyMkiWCP2GQdF1HlkXypbFssEuhBhz8+f3+xcDmQCAQAggEAjXAo92Pufx+/+JAILB+UL0QBOGUmZXnZtVV03n0zQoUWWL5rALW7GrgC4sn4jXL4oJurEkq2M0edsfeoCp2dLrgQt9F5OtlQz8edIMb5xVSnOHkua31zCjwsGJWPhnW1AOzfqckGxClkUppK/b4JSiKhKYZyCZ5TIz7SZJEwhLGqtuwylYiWph8exEHowcok+aiD7LkoSolCWv9pD7XZdyJnKF1eqwzDBqqj96AC3fEmbo4F+QxcrNCGHYWi41Dh+rJysrGbDYjjfW0x2OQYRi0tbViNqe+Xj+VaZ+vAwuBjf085u9+XJzJBOEsY5MlLpmYxtS8WWypaWPtnkaum1fIgiIxhWusisjtvQI/gPc73uST6eOQY0NfvO42yVxWlsHlk7OQMNC0wR1ornQr3mw7HUfKOwD5k720OGsIai00GQeYWOInFkqiWGTUMbB+VXd20ppoJqKHCaud2BQHmZZswoQwJBW01JK8SJJB0homZLSzMG0pNdFKamNVPY+7ZO8wv4OxRVFkWmr6JlqKRmIUlGWhjYlbFsJw8/myCIU6OHjwILqugziORjVFkdG0vivzzWYLXm9myttL5VviRLcVXEAk5b0LgnBa6Drk2hQ+OTmLK8qzUdWxmR1R6JI4JpMjgGok0VAZrklEhgFGP19WqZAtEouumkTFliaaa0PkTvCQV+pll9G1ZjEs1ZNddA7xuDYmAj/ZpNOcbKFdbaEt2UJMi+F3TSOux7qS+SRSDfwgaGlgbcvz6N0pfyY5yxlnL+FwrI5FvouwJlzisnIIDMNAMff9qzKZTV1XYIMcqRXGNkmScLt9uN2+M90V4TQY7tIhJ/ym6J7queQjTbf5/f5Lj3maDfgEsH2gO/X7/T8CrqarNt/0I+sF/X5/FRDr/g/g7kAg8OpAtysIwol1jcAM/FJOkrq+ZERG0MGTLBqGpCMlTm9txRNxyz5MkhnVSPa05VgKsGhnV/bBZEjj1Ud34M6wkZbj4OCOFna+Vc9F/34ujbZnyLdMRMdAO1s+2FMsbu2kNlxFfayaNHMGk93TqAzvp9hRSpo5A6NvbqcTs8fpSLYw23suIS3I3tBO9od3syxzBXPtizElHeJvfwAkQ8LQDSST1GcNjq4b+HJcNFS1Yxz5LCXIHu8b9Ii4IAjCUJzsNuE5wJFcpgZwLV0F3j8qAewB7kphv38FHgDe7Oexa0TyGEE485piKhsPtlHTFmXhhAzKMx1YZLGuYKAk2aDT0sQHwTeJaGEmO2dQYi5HSljPdNcwxZ0sy1jBxuB6WhNNjLdPZIbzHIzo8M/cj+oGEVXHY1FINbVFrDOJrhl0NEZ7Tf0kZKbAMwlTrIC4MTaGTmSzzqaOd2hI1KEZKhEtTHOikXL3TNwmN1LUkVI5HN0SY1NoPTXRCgDSzBnM8S1kU/s7JLQEroQ95VIRY41kSERa4rTWh6nf107uBC+F09KQzL3Pk7oM/nMK6WyJYOjgyXSgK4iZeoIgnBEnDP4CgcD9wP0Afr+/ElgRCAS2DnWngUDgre5tDnVTgiCcAm0Jjdv/uIXatq4L7sferuI7nyznwokZIknMAMUsHfyj5bmeC/Ktne9iuA1K5dnoZ3jWrWEY2KLpXOi8Et2VRNYsGMNY7gG6Roz3tEa494VdHGgKs6A4jXsum0yBY+AhoM1lxumzEm6P97RZHSYcLgd/W5tHJFbLQ9ekjYlpzAlLiDyjgExrNjbZRkVkL82JBsySGafkRU9xFKnZONQT+AG0JVsIqUE8Jp9Y5zcAekJn59p69mw4BMDEOdldNyh2QNGcjF4jgIYBmgTOLCeSBJpuiMBPEIQzJpVSDyWnsiMf8Qe/3y8BbwH3BAKBviulTyAjw3VqejXGZGWlXoxZGD027TjUE/gd8eC6/TR2xlk2JZc549NS3uZYO6Z2BSv6jMTsCW9jev5snKbR/1nsb+zk809uJprsGpnbWNXGN57dzh9vO5dM98BGP5tiQc791AS2rKmmtT6ML8fB7EuKiKk6RWke4kmNtLTUS0iMNCE1yFst75HQ4zQlDqMaKvN959OpduA1p5PtyAHHwLfXnmhDjyaZ61tEUk+yI7gJHZ2meAMXpH+MXHsuints5W8b6PlJU3U6W2K0t8Y5XNnRcyNs/6ZGZi8rIvDuYcoX5WF3pZ6BTxhdxtp3nnBqDefxlEqph6sBXyAQeKz75xLgD8AUYC1wa6qBWj8uCAQCNX6/3wr8FHgIuCmVDbS0hMQahSEa7oWlwsgTjR87uxuiia6L+G/9bQePXDcTawqppcfiMaVY+o5w2WQ70Xicxthh4nocr5KGJeYdleesPYeCPYHfEXsbQgQOBzFitgFtQzMMmqqDzLp4HJqqo5hk2hsj1ETglR2H+eE1M0b9cSVJkHB2UGgvRjd0yl0zqYzuY2fnFmZ5zsGrZqX0GSTtnaxpfYa43rVA0KW4me09l00d7zDOVowjlkVreGzlbxvw+Slh0FoTZuebdTQc7GTyuXlk5LvYv6kRgMaqINnFHsKRBKFo/CQbE0azsfidJ5w6qR5PsiydcDAslcRu3wI8H/n5Z0AmsAqYA3w/hW31q7t2IIFAIA78HDhvqNsUBCF1k7Kc2M297/wvn1XAP3Y1sqs+SCQ5+oKV4eaTMvGYemdim++7gE2hN1nT/FfeaH2ZF5ufJmRrOEM9PLWOPX6OtKXypWNyyGQUunlz9T52rK/nrb/spzOuUqPGaAnHWbu7AYtldI9QJR1B1re8ytuta9nQ9jrbOt+nxDEJBQUf4zESA59GqygSQb2Fqe7ZlDjKkJAIaZ3E9RiFtmJKLOUiCUk/JAkiTXFee2w3657cg9VhZv7lJex6u560XAeKqeuo9uU4mDQ3G0MSn6EgCGevVPJCT6A7o6ff7/cCy+haA/ii3++vpisI/NfBdsTv9zsBUyAQ6Oie9nkdMOT1hYIgpC7PYeGJz87j0berqGgOc2FZFuG4yoGmEPOL03BZZGpCCfY2hrCaFCbnuMgcRBHv0UyO27nIu5x2vYm4HiPNlIVKgqro/p7naIbKxvY3Weq9EiOaajqUs1uhz8YnZ+Tx9w8P9bTden4JWc6BT4czDMgudfPJr8yksqYDX5ZCo3cXRRziZ1Oms2FH3xHq0USWJaqi++hQ23raWhKNNCcameFexAub2rl+prNPhsn+SGadsNGBGgJdNogbMeb7zmdj+5vEtCiLnMtQY8NV6GN0SQQ1Xv75DrTutaVV21tIxDWKytNpqu4kLddBIq4yaX421rTR9XcsCMLok1pRoKNLlC+kqzrNa90/1wJZA92I3+9/ELgKyAVe8/v9LcAVwDN+v1+hq1j8LuBLKfZPEIRhYBgGxR4r37tsMu/XdfD9l3bTEIwzLs3OPZdNpqo9xmd++z7J7lGCXI+Vx26eS7Yt1VPK6CbH7KRT1PWDCocte/s8pz3ZgiYnkFPOhXl2y7Ao3HROEdMKvITiKm6riQyXhaZwAq9vYNM+oetLx5lhJd9i4aCxi0ptN0kjQStr+cSCy0lh9vGII8vQEKvr096ebMWcmMizWypYMT0f60k+A0kCPapxaGsUNaFgtuYwZXohVYmdZFvzGGedKAK/E+hsjvYEfkfU721n3uXFSBJMXZKPw2dFsozig1EQhFEjlSu1bcCNfr//XeA24PXu6ZkARUDjQDcUCAT+Dfi3fh6anUJ/BEE4xUwYLCz08NhNcwnFVXLdVuwmmf94bWdP4AdwOBhnQ1UbZZlO8r02vP0UNRbAbepbkLfQXoxJszHa8lUaBgSjSeraongdZg51xPj5Pw9QkGbn0etmMdDrZD2qs/HvFezb2IDTZ2P21ZcSyH6LDr2VQ9puJisTGY2VsmUZopZ2yk0zmOaeQ2uyiS0d7wGQayni3397gI+VZ2NTpKP1445DQWL/poaejKDJuErLhwZps7Mpdk/CrWaf8vczEimKDKpOWp6DjEInLbXhnsesDhOSBEXTM1Cc4nwnCMLIkUrwdw/wAvAZIETXtM8jPgW8N4z9EgThbGFAjt1Ejr3rdBHXDeqOyQQKcKApxK/eqAAJHr1pDoXOoRU1l2UJWe4qMD9aEqI41XTOTVvCpvZ3SBoJcqz5zPKcCzEZyaIhqaZR814BmjrjPPnuwV5tSU0nrulYTCe/YJaR2Lq2lr3vda2LDLXFeeexOs79t/l8YH8Vq2wblWvUJAli9g5ieggZhb2hnaRZMpjrXURropW9VS7SXHGumV1w0sAPQI1rfUpBqAmNND2dNC0LLSmCF+gKuJOdOrpuYHeZORRopr0xjCTB3MvGUbmtlX0bu+5zn3PlBHLLvIyyAXtBEMaAVEo9vOX3+4uAMuDAMZk9fwPs7/+VgiCMJjZZ4vr54/j+y3t6tY9Pd9IU6poM8ON/7OPKmXmU5bjIt6d+dSSZTDSGVA63x8l0mcn1mUEdBeu7EiaKTOXkZBWgGknshouoFuX9+Fo6tFYm2acyzjLprCgEPxxKMvuWYVjqzxpwiTM1rrPv/WMmlRigN1uRx8mMs0wdldM+dWeI6ug+9oS2Y5EsTPfMI2kkyTBn4oqV43VrPHxt4Umnex6hWBSQ6FVbTlYk0uzpaAkR+AHocYO9GxvY+UY9JovMtAsLySlx0d4YxjCgqbad6UsKyJ/kw5Nlx5FhwRiFx54gCKNfSmf9QCDQGQgENh1b0iEQCLwUCAT6LmYRBKGHIWs06fU0aLXocvJMd2fQDMPg4rJMvnbxJDx2EwU+O/+xzM8rOw/3POfDug621QX57xf30JJMbUqebFLYezjGnroI7WGV/Q1RtlSFkU2jYz2hqhooYTfWSDoJI8GrLX/hYGw/7clWPgi+yd7kNuRRcj2e7bJw5yVlpDnMSBIs8WcxMdvF7obQgF6vmCRcaX0DYbfVzWTpkzzzzugrSSDLErXxKj4MfkBCjxPSOtnQ9jouxU1Mi5FhMjPBaxtQ4CdZVOLWdpK2MCUzc3o9Nn56NpIuhq2OqN/dxtY1NSTjGtHOJO//vZJgUxyXz97znFgkTt5UH/ZMEfgJgjBynfBqyu/3fwlYHQgEmrr/fSJGIBD4xfB1TRBGjxAd/HHfEzy998/ohs5lxZdxe/mX8EoZZ7prg+I2ydw4O58rpufSFEry+d9vIhg7OjI3vziNnXUdbKlpZ39ThDW7m/hEeRaeftL/H0s1JJJE8GQ2YsJKPJxBOKoR0wwcJhlNMwaU3XAkCOptqEbvGwG7Q1spy5iBNMBaeGczQ4enNlbzqdkFOK0mPqhq5cG1+3lw5awBvt5gxtJC3nlmf88U4uxiNwlD4o7HKnnks1PRtNG1WlK26OwL7urT3pg4hN8xAwZYPs6wR2jRG2hLtrIn9CFzvefhP38SasTAbDVhyIyqKcaDokKkI0FTrJPGqmCfhw9XdDBpXgah9q5p7jaXRXxmgiCMeCe7lf4Q8AHQ1P3vEzEAEfwJQj+2tW3mqcCfen5+ueplytPLuSLv2hEbyBgGuBUZk8vCDQuKeOTNCnQDSrNdzBufzqpXuqaFShJYFJlX9jSxckbuSdcBtml1PFb5/9jStBmTZOKmyZ9jlmc5uuFhd0OcdKeJTJcJYxRMA5WlvkN8JtnMaBlW8FkVPjUrn5+/UdHT5rWbmZTpGNDrJUWiemcLC66cgJbUkRWJUFucpKrx6OemEHJsAi45Rb0/MwxdwqW4aU+29mp3Ki5McfuAUtsYzghBvZ2Q2klMj7A08zI+aH+bNE8WNj0NDYMBz70dpZKdGut+u5uOxiiSBNOXFFI0JZ3qXUc/d1ealUg40f1vOzaXBXWEnq8FQRCOOGHwFwgE5P7+LQjCwCmKzIZDb/dpX1O9hisKrgZtZF/o2xWJz80v5ONTc9hS086O+iD3rwkAMHucD5tF4ZnNtVw7r5CgauBWjv9+JVnn6cCf2NK0GQDVUPnt7ke5b9F0Gjt8NLQnaGhP4LErzCpyoKsjO8ujlww8Jh9B9ehM+nme81GSNvTRcHVuGFwzK58cj42/baunMM3B+aUZJPWBjdYZMkxbWsgrv9zRk9jEm+9glw9+s66Gh26ZhWYaBZ9TN8OSoE1qpcRRRr6tiL3hnbQnW3EoTvIsRWjhk58rZJtGm97K4XgdFtlKob2E/aEAc3wLCalBbKSdhndydpOQaDjQzvxPjkdSQE8a7HjjECUzM3uCP4fXQtG0DMx2iZzxPmSrgjrA41YQBOFsdrJpn4tT2JYRCATeHGJ/BGHU0XWdqRlTeanqpV7tc7LmIBkyxii4yJeBcS4LTWl2WsNJFhSnM3OclzlFaWw52E5DZ4ymzgSWEwR+ABEjzD9rX+/TXhXai65O7fk5GNUIqRKKZMJhMtBSXFd4tpDiNi7yLKdRryOkdpBjKcStZY6qqWVmWeL5Dw9htyjsOhTk+W31ZLms/OFz80mznPyeoifPxpVfm0VdTSeKVaHDBrWNHXzl4km8ty9G2SgpECRZEzRSQ0QLIUsyTfEGZnoWIBkyPlM6cth18m1IEJJbaUu0YJVtVET2slffwcK0pUS1CE7JexreydlP1sFkhZZDR2+6zLqkgGTMYMlNfiRZIi3PidktY3QPkmoi8BMEYZQ42bTPf9J13jtyxfbRK5JjcocBXcXZBUH4CMOAc7IW4U/zE2jrGhHLd+Zz+fgrjnuRr8kJOrQ27LIDB+4hlUw4XQzdwGcz8/b+JpZOzmFnXQfPba7jhnOKCEZVrCYZmySdcJqrVXIwPXM662rW9WrPthWidvR+XSSusac2zKQ8B/leBX2EBoBy3E4uk7rKWsRHwC86RXWhBJsOtvVqawrFOdASZl6e++QbkCQUs4TFLNMQjFHTofL4OwfRdIMHr5/ZUw5kJJMk6JCbqQwFiGgR2pLNjLdPoj5WzXTruRC2DGg7SUcHb7X+g5AaREZmmmcODfFDdGod+EwZOBJjdNRPhUhbHMUsY/OY0TWdzrbeyYJaDnVQPC0P2XH0hsRIOO8KgiCk6mTB3/SP/DuPrpIOrwDP0lXUPRu4Gvg48C+nooOCMBqkyzn8aOFPqY5UoRsa450luI4z/apZr+f/tvyYDYc2kOfM4575/8VU5+wRsQ5sgtfGdz85hQ/rg5Tne5iY7eLn/zyAIktcMCnjpOsbZV3hX6bcxtamrbTGuqZfXVCwmBLnVKo7jj7PZVOIJbruxB84HCHb48VsltBVdcResI30AOZ4FElCkSW0Y9/fAGs0xNoSvPizD1G7f98Oj4VvX1zKt14P8MrOw5xXNPIDGt0WpS3ZjGpopJkz8LumsbljAzM889AlbUBpuRWbQaceYYH3AhoS9ezu3MaHwQ+Y7zsfwzBwy150dWRlzJW7jxF9kH/UsiyhdurUBtrY884h2hsilJ2Tw4yLCvo8V1N1zDZ5QGsqBUEQRrKTrfnbeeTffr//f4EnAoHAt4552it+v/9/gK8Crw1/FwVhdHDipdwx84TPScpxVn3wv2xu7Frzdih8iK+98VWeuOT35JqKTkc3h8QwDPIdZjImpLO5PsiT71VzYWkWt15QwgT3wEYv8pViHrvod9SGq7EqVsbZx2NTPLjH6RxuT+BxmLCYZAJ14e59Qlw12FQdYlqRC49dIhkduaU0Rptcl4Xr5o/jD+9V97RdXJ6NzSQjnWQkWJYkdr5R3xP4AUSCCYqDGmkOMx2RJCaTTCIxci/ZJQmqk/vZ1PFOT1tNtJKZ3vkoKJhUGyebcCjZErRJTbQlW1AkE5mWHBZnXMo/W15CNVTyrIXooZGTPVZGItqWoGpbM4crOpi+dBzpRc6U5hZJKrTWhWk7FObAlkbyS33kTvCwZ8NhiqZmoCgS2kcK37vT7GCWYZRljxUEQThWKrcBL+b4GT/foCv4EwRhCNrVlp7A7wjVUKkJV5PrPfuDvyOsssTCQi8Li2YiSZCZ7qSpqXPAr/eSgdfZXQbDAEPVSLdCTpGT9ys66Ywevdj3OkxIkkE8qbPpQJAFpV6cNjPJmAgAzwZOk8xSfxYFPjutkQQemxmXVWHNrgamXjiBE43/SUBHY7Rve0jF57Bw1ZwCksmRfbGu22JIqsRs77mYJBMVkb20JBoxDIMsSx565MQjpJIkEZSaaUjU05popi52kHH2EsqcU8m3jiPTkoMjmjViEgjpCYPq7S3sfucQDo+F0nm5vPvcARZePZG08c6Tvl6WQdZkDld1sP6pvRgaTF9SQGN1JznFHqwOE821nZSfX8jBHQ1EOxOk5brIm5hOUgR+giCMAalk8GwFlh/nsRXdjwuCMAQ22Y7P6uvT7rGM0EQNuo4xTBdUhgHJeIIZ491kesyYFYm8NCtl+Q4qG2I9z+uIqKgjYIrsWKHrOofao4TiKm6rCQN4ecdhNN3gJPl/0DEonZ/dp73In8ZtF5SQ1AY8e/SsJFl0YnInbpOPdHMmQbWDYvsknIobp+LCGut7LjiW7gwR0cOYMGOT7SxMW0p9rIaoFmGmdwHuZPaImVIsyxLVW1t4968VdDRGObS/g3ee2c+0CwvY+UYdinziX7Ye1and2sb6P+6lsTLIpZ+fTtG0dLa+VsO48nRq97SRO8FLRoELTYLimblMOX88ZfMKSY7U+eKCIAgpSmXkbxXwkN/vLwae5+iav+XAZcCXh713gjDGeKR0/nPePXzz7bt7soBeOv5SiuwlZ7hnp5ckQVTvmkbqUOSeqYGGAVIywbRCO5ohE4yp7K4NE4odHQmUpa6ZW+ZjpnUJZ4ZhwNxxPr74py0cbO0axfPazdxzqf+kQYmiyCRjGtOXFLDn3cOYzDJTzi8g2BjhL3UNLJuagyzLMEJXasXMbbzXtp7WZBMOxckc70I6k0H8rmmkSbkY+kkiW0eUpuQholqE5kQjPks6jfF6JrumY2Dg1jNRkyMnOtbiBtv/WdurTdcNknFQ2joMAAAgAElEQVQNi91E/3nmuiQ7NZoqgyRiGu50GzvW11OxrZmLP1NO5dZm1ERXuzfHRnph1wiiqhuAgaKISlaCIIwdAw7+AoHAz/1+fx1wD13TP02ACmwFrgoEAn89NV0UhLHDMAzm+hbyu0uepCZc/f/Ze8/4OLLzTvc5VdXVOQNoZBBEYg5DDsnJeRRG0siSZUl7Jdvyyr62bHnXu7YlB8kj6+76+u7a+5Mt2Za18lpWtJWsmZE0mpwThzmCJEiQyLnRuSud+6E5IDFgmOEgcVTPFxIHVdWnugvV51/v+/5f4t4ELYFW/PLyNu9vFQwExycKTOUNCoaN4zjcsjKJ/7wwkVW2EAI8qjZL+NXGvUh5VgC6wm/ZUO3T+OpHtnBiPI/tSNqqQyRfR5sHgOxkicHjadbcUI9tORx+doD2LSksR/LUsTF+ZVvTAs9+YVD8JiPGIC2BNppkK8fzh3h+8nFur34XOj7UQuCS+6temwk5TsHOU3ZKaIqKgkJCr8aRDgm9Git39Qg/AEUF3adRzJqvGVdYe0vDBdstKLagmDUxSxYogjOHJpAS1txYz+FnB5keLZKoD6LpKmtvacCf8LyxnKclQFFExazGctNQXVxc5p83ZP3V3d39I+BHXV1dClANjHV3d7t3JxeXeUSRKo2elTTGVlYG3qCGKSmVxWBYiaE4V1f3lYzl8KVnTvGD3QMAtNeE+NTbu9jZn+bmltmujlJCULW5vitGulBZLCpCEAloKEJe1iTDZXGJ6Qq1ER+PHh3lse5R3r62lrXVQfRLpPIJAXXtMXp2j7HvsT4AdL9GoiFIo+UnHvBcle6uQgimGedAZhdlp4QudDbHdnAws4eSXSAqay75Zy+EZFIZpjt7gBFjCF3R2RTdxrQ5hbAF9f5m/IXE1fc3oMK297TyyFcPzwwFojoNnVG8cc+c98RMW+x5pI/R0xnq2qK0b0nRcW2KvY/00bLOC4BQBLVtERpWxWf69i1nzIzNyT1jTA3lab82RbIliNCuLhHv4uKyvLki3+ezgm9knufi4uLyJpDC4WjhAH/5yl/Qn+vnjqY7+Y21v0lS1C711F43+4eyM8IP4MRojgf2DbG2PoyhKOivefJv2xIFg9qYF8uupHApSKyya/ayECiKwGeOg1ApqrE3tJAezJv88j/vJFOyAPjBnkH+x/vXc1tr/KLHkRKKWYPNdzfj2BIhKsIpP1XGpyq8a3Mcx7n6Uj5tvchTkw9hOGUADGnwSvp5NkauJaCGkIVLfzU7wTyWZbIi2EFLoINxY5i90zvpCq0j6anBX45zNfYklxISLUHu+Z0NDPVMEwjrpNoiqAFllvCTpiQzXGJiIEdtawSjZNGze4yp4QJb37mC9i01OLYkENFJ1AVIdUZAXf59+6yCw0/+bj/lfOVv5MyhSba/dyWt11ZdNXWbLi4uy5+rq+mPi4vLRRm2+vlPT34SS1YWDo+ceZiyXeIzmz6PKj1LPLvLI4Tg8FBmzviu01O8a0Md/+/PjvLxG1qpD8w9F6NQnvn/Vbjmfd0IAba3SEFm0YUXrxlG2ouTw+a3J/Ef/i7qi18Ejx/frZ8h23gnpvC/rv0PD2dnhN+rfOHxE2z/1a34LxL9s22HWG2Ag08O0N9daRRf1xZjzS31fKjGTyI+iRDVXG01f7ZisC58DZY0OZE/SsHOYUsLvxIgbCcvGfVzgjkmzRFydg5VqJRtg4SnmglzDL8SJCISSGuZ5zVeAikgmPLSUZua0wZECMAU9B2c5JUf92KWK5/7xjuaKEwbTA7msS0H1aMQq/Fz98fX4k1oV40Qnh4uzAi/V9n90Gma1ycQuhv9c3FxmR+u3m8IFxeXWZzJnZ4Rfq/y9MDTpO2JJZrRG0NKSWcqPGd8U1MUv0fhgf3D/MVD3Zg/xw/Ai75JfjL1HR6e/AEPTnybE3IfaNbld3yTCAH+3odRn/g8FKcgM4h6/28RnNz7uo8xp8k7YNrOJaMxUlbS/mpawlx7TyvXvnMFNStC2LrCx766j9Hx6FXn9imCJQylSMJTRViNsS68mSo9hYJCUq1Fli/+oEbxG5iU8ap+Yp4kQTWMV9WRQtLq76BKT0Hh6unndyleK/wUR5AbLnNy1xi9+8bZfHcz9Z0VN9SDTw3QtrkaBGi6SqotQnVHGD1+9Qg/4MIp/j/H9zsXF5eFwRV/Li5vEcKeucIp4UvgVa6exeDG+jDvWHcuTbUlGeCXtjbx2fsrNUAvnpok8yYbeiuqiilUCo6CVDWUq+QuKHSL59KPzqQKAuzLvkRRnV7w19YxUPf885xxtedRlMvY77/K2rowPs/sN/sTt7QR1C7+AQgBIyczTAzkZ8Zyk2WOPdbPP/7yah46MA7l8kX3X24Iv8Fp4zhPjP2Yx8YfZLB8hpgnSVugi+tjd+IpX9zYSdMElmYgcfAqPjShYcgyQTWMLnRqfU3ohau0JcxlsAsOz3+3hx9/cT8HnuinaU2Ck3vHaF6TQCgC23IQimDVjjoiVT5CKe8FHzYsd6J1AbzB2QlZ17y9GdV3ldykXFxcrgrctE8Xl7cIKwIrubH+Jp4dfGZm7A+3fIqwiM95ir5ciXtUPnVXBx/c2ki2ZIOAz/zoEGcmCwC0VgUIeK58IaSoKr2TJmfGK30BBbClLYxf2Mu/HkgxmLbmtlMtkCOoLGxNkC00nHgrysjBWeMy2vS6r63GkJevf2wb397ZR/9UgQ9d28TWhuhl9hcUswanD05w+uC5CHa8NkA25/CuzQmKD9yP58abMSOJKzm1RaUgKmnN6yNbmbamOJE/QkAN0OxrI1CqumRrB8dfwHBKCFRUNKQsEVFj2Nho0oO/GL0qBc/lEAj2PdLHmbOffylv8vIDp9h+70qGTkxT3RRCOpLatgj+qI7wimX/t3wxtIDCOz+xYY7hi1vv5+LiMp+44s/F5S1CgAh/uOmP+UDHCaaNNM3BFhq9K64a4fcqQUWwNhmgaEu+9OypGeHn1RQ+9+61F60Pez2UHWaEH1Qyqg6cybN9ZQhpLXz65JtBs70kPSkmzNleW2VZ4LRyiEatHWHoC/LalqNgb/sEyolHwDr7/oVqMFtufd0LbSklK8I6f3pXOxJwXkcrDikl1c1zI9ptW2r49qEptnWF6IjHcA7uh+tvff0ntBToBkfz+zlVOAZAlZ7imuh1HMsfoj2w5pLCTw0VKckyAgWP4iFdniKsR3BkJQoeUELY9vLLf3XKkvRQgXLeJJYK4E/qlScub+gYDid3j80ZtwwHb0AjXOWjc0ctWnD5RcecsiQzUsQ0bKI1frxR7bJ/L56Iyurb6txWDy4uLguGK/5cXN5ChIiyPrgFgmcHri7dNwu/KvjkTa3cu6GO6ZJFU8xPbeDyi6dLYV5AcJRNBwdQNRWQ2Mt0wSVNleujd/D41APk7SwKCusiWzhVOM5IeRCi0KKsXbAowXRkA+Fffgh17DCoOmb1OnJ64xs+zhvpvygElHIm1/1CG90vDmOULNq31lCYNvjI1ib+7sWT3NMaobhrN9oNty3rBx1FbZqErCLhqUIIwYHMLvJ2jlq9AV36L2pZo4ZKmMLEtA00RcOwDcJ6BMMp4UHHpwYge+megEuBU5Y8/fWjjJ7OzYzd+WurSa4MXbrO05LkxsoUMwahhJdgXCeW8jM5VJi1neYRrLqhDn9ibguI5YBTcnjya92M91fOX9EE7/zEegLV3svv60g32ufi4rJguOLPxcVl2eJVBB3xc26Sb3Zt779AY/F4SMMBDg0UEQjaUj78irMsF1+eYoS3RT/AlBhm2priZKGbtFlJBT2S20NTYuWCGX5IKcn426G5fUGOf+HXhFDSx8/+4SArNlTh8aoce2mEmk1J/umZHj5+80rKz9yPd+1arGUs/AgUkEiiWgJFCA5N7+Wa6HUczR3glsTd2LkLm7zoXigJk7ydw6v4EFLDpoAiFQJqBI/wIPNe7GXocZsZKc4SfgDPf6+Hd/3exouvPBw49PgAh58Zmhm6/v1t3PShTh74m30z0eKGrjgNXXE8UW3ZCv7JgfyM8ANwLMnOB3q59WOrkGJ5ztnFxeXnA1f8ubi4/NygODZbVoY5cCaHYUliAY3VjUFeODo9Ez2YzJlsa4+gs0zTQMs6psdk9/QLs4a9qp/DhV10+jahlYIX2fnqI1jl5Zb/q4sXftBDKW/SuLWG7oDDM/sn6KqPsKmzC6dl5VJP86Iouk1aTtFXOoUjJUm9mrWRzQyV+ugIrsVTjGJfJHZV0nNkrTQCwXBpAE3RqNHrAYkiBMJWsOzlJ/yAmTYM51PMmUhbXrRpeTFtzBJ+AC/96BTv/f1N3PtfN5EZK6H7VMI1PtDEshV+AKXs3F6j02NFHMtBeJZfiq6Li8vPD674c3FxeWsiYLxoMVEwqQ7pJL0a0pEEFIcd7WFsCboCR4aKc5be/ZNlmpJePAoI21p2BhJJtY6AGqRgn3PB7Aiu4eWpZ/AqfjqULcsycnlFKNC0Ps6ecgOlnMGXjo1wtDsLwL6+NJ6PbiFfWL59/kqeacZLowTUEIOlPo7nD3F9/HbqfE1ERAK78JpedhIKkwaZyQIyUGY03kePPEBnaB0prQ5DltHQ8EgdI68u0VldnkiNH0URs67Dru0pFJ9yUdFmXOBztC0H03DwxT0kwxd3Q11shBBYRRucilHLa88o3jD3AUzn9hSqruAstxuKi4vLzxWu+HNxcXnLIQU8f2aaP/rhQYqmTdir8b9+aSObUiGklEjTQgCOquBR5z6FV4TgUF+eouGwvSOC5ljLSkyppQB3x9/PgHWSklPCrwY4mtuPg82oMUiXb+uymu+bxXEkOcXhL585MWv8po4qHLl8oyjCY5Ox04BktDyEXw2wPrKVvtIpNoa3IQq+WaJBUQRDh6d56pvdM2Mrt9Zx83taeDr3INV6ipJdoEpPYWSWj/BTVQXHkbNEnS+q8fZPrOflH/WQGSvRuT1F1w11l4zWhZJedJ+KUTonAuO1AXyRi/c+XAqkKenZOcreR/qQjmTNTXWsuqkexXvuWgxVebn9V1bxwg96KOctuq6rpXNHyhV+Li4uS44r/lxcXN5yjBRM/uD7+2cMXrJli//yvX3828e3k9TPLZpt26Ex6aV/sjwT3VMExIIa/RMVV8vuwQIranyENQfLWj4LN6UYwO8JsTf7EqY8l2K2ItBBSWTwiADSXH4OiFeCbTs0xf28bW2Knx2quJ3e2llNZyq0rJt4F/U0x6YPMWaMkPLWU+dtJGNNowsdnxLktfrcKti8/MBJ1r2jhkA9GGlB98OTtI+3sqluB5PmGB2BtYh8EJZBnZ9jSiZO5zhzcJLqphD1q+Nogco1J4FQysvtH1+DtCSKd3bET1EEmBKjaKJ6FDS/BxGAt//mOp77Xg8T/TkauuJsu7cVlo/OBWCyL8+un56e+fngU4NEawI0bozP3EekgOqOCO/+vU04jkS9RMTTxcXFZTFxxZ+Li8tbjtGsMcfZM1O0ODlZZFAVBDwqTVEvuhBojsV1HRHGsyYOAq9H4cR5zoL5ks1ExoSIjt+jgGks9ulclKSopzO4nsO5PQCsDHaRtaYRHhgo97E5cANKyX+Zo1wNCEqGwwe2NvKLWxqxHYlhOaQL5vJdUPsMDmR34lP8SOkwWDpD1pqmLbCKltAGyM9ty2FbDvf8WTu2YlG0CyhCEFtdhTPlkNSrKFh5PI4PexnU+SkCjj0/wt5H+wDo2TVK9AU/d//GWoR+XjRWAaHPrs8TAqy8Sc+ec/V94WSAxtVV+JI6d/76amzDQfUqLHVgVwgByBlRp6rKTM/B8zmxc5TmTUlsee6zkVKCBxSWd32ii4vLzxdLIv66urr+J/B+YAWwvru7++DZ8U7ga0ASmAB+ubu7+/hSzNHFxeXqpTqkoykC67zQSsircXIsR89Ynh/uGeDdG+r4+I2t1Ps1FNsiFVSwhMoLxzOzjlUT0xnPmnh1lSP9ObaujOAYCyMAhRCgqliOxKMI5GXqDYWh0+5fg67qCAT9pV56ykcJqiGaA22cMo7QLq5ZdjWLAI6AyZKNRxHEveol01SNrEnjqMVYX5qq9giPT2X53pEhPn/v2rOL8+WFokBRzZLwVFOyi9xa9Q6GSwMczu0lqIYIWTEu1FFErS1RcHKMF0coOHma/SsJxjUI2gjhIeVpwLmAaFwKzLzN/if6Z41NjxTJjJWINsx+4KAoYGYdMuNFvH4PoaTOmcOjs7bJThSwSjboClKhUhu44GdxcYSE/HiZ4RPT6AGN2rYoWqhynSYbQhzfOXv+VS0h5LJsOuHi4uIym6WK/P078AXgmdeM/wPwpe7u7m90dXV9BPgycPtiT87FxeXqpjbg4U/ftZr/9uMjmLbE51H4ndva+e6uPm7sqAbggf1DrKmPsLY+wsqoD68CmuKwrjnI0YECli2pjen4PAqWLTEth0LZIVe2CamVJ/nzKaqEgJJU2Hsii2lLdE2wuTWMjn3JqEHWnmbP9IuzxvJ2Dp/i52S+m/bwRjCWV97cpGHzxadO8uCBISI+D59+exe3tMbxXEDISUPy2D8dmenzdnLXKNtubmB/VZAXT05wS1uCcnmxz+DSlP1pHhm/H8OpTKw7f5Cbk28jVAwTUauxirM/D+GAUTYQjgohSUANUOWt4UhmH2sim1B0SUCJILML08bjSpBSIC8g2C80lh8xeOSrhynlTYSADbc3Ut8ZZbh3ctZ2lumgXaAdy1KQHijysy8fnPnZF/Lwzt9ejxZSqV8VI1LtIzNWSQ33hz10bq99S9XZuri4vHVZEvHX3d39LEBXV9fMWFdXVw1wDXDX2aFvA1/s6uqq7u7uHlv0Sbq4uFy1CGBTQ5Tfvq19Jvr3z8/3csfqGh4/OjKzXd6w2X0mTboqSFdNkASQ9Al2dEaYzNmMpMsMTZXpqA9yuC83c/T+jE1AV4kFVLDmpyWEVDV2H8/wakafYUl2n8yyoz18ydfwK3MdEANqkLJTIqU3IGx1ecUjhOCbO/t4YH8l5W+6aPJHPzzI1z92LasSc1NUM8PFOQ2+jzw7yCf/4yr++pVekBWDnuVkpOFgc3PibiSSk4VuThWOczCzm22RO5mY8NF4Xk92K2fz7L8eZ7Q3izegccMH26haEyYrp1kV2UDJKZL0VCPy/mUVWdKDKquur+PIc+dSNwMxnUjNbIEqbHj2345TylfqUqWEfY/1k2pdixDnencKAd6AdtFm94uJkLDrJ72zxko5k7HTOerWRtGCKnf/3+vIjBaRjiSa8qP4lododXFxcbkcy6nmrwkY6O7utgG6u7vtrq6uwbPjr1v8JZPLxwr6aqa6OrzUU3B5i7HY11TCkeRNh796uJuh6RLv3lhHtmTRN1mc2canqZQtm4MDGTRFIRPWWVUbIeH3EPLZeD2C8YzJoTNZHAlhv4qmCE6OVJ74B7wq17ZHiIc8bzr9cHS6zGtLuUxbgqpSHb943Z7p+Lhe3s4Lk08gkehCZ2NkG4eze7mr5j0k9YV93y3HpOQU0YUXXfVedvuRTIn79w/NGe+ZKHBTV82c8Yme3JwxKSUJXePuNSmyY3kC0QCh2PKIio2WBpkoj1FyioyWhmgLrcKvBOkv9TI6pWKUJJtbKp+JUbL4ydf3M3o6i9evUS5YPP5/unnPH6wjXB8ha2XQhU5Yi+JLLr/azWvvaaWqMcyxl4epXRlh1fX1VDWEMMoWU4N5inlz5rxeSyFjUN0cZ+zMFN6AzsoNtUSrg0uWxnv+/amYMy44Z8d0Zm2Xaowsytxcrk7cdZTLfDKf19NyEn/zwsREzk29eJNUV4cZG8su9TRc3kIs1TXVGfXypQ9swHQkedvh9/5tPwBeTeEjO1p4oWec69qqABjPlckZJj85OMwHrmmgWlcJezWsoIZp6UQCGvGQhwOnz4mRQtlmZNqgZNh4nDcZAVS1WZEQAFUBaduXfe/qlA7enaynTAFN8WDZFrdH3osz7WWMhXvfbX+ePbnnGCifpspTy7WRm/GWopdMh7WBtuogu06nZ40n/Z4Lnmc44SMY85JPn8vtXLmxCtWrcF11GUdAPlOiaM5tqr3YiIDByfIxDmX34kiHjtBqxspDtPg7CKlRPvuNfv7n+9bPnKdddNjxKw2IYB22tPHg4dhTk0yO5mloCOJT/ejCR3bKIruAn+PFUBSBtCRCvXhktX59lKaNMRxZackxNpLl2LPD7H2kYgSjehRueH87L/x7D+Z5LRzCCS/RpiBVTRGkANN2GB+fK/QXg9fen4QQbLi9kee+e+K8MUg2h9zvRpfXhbuOcplP3uj1pCjiksGw5ZSn0Ac0dHV1qQBn/60/O+7i4uJyRWiAXxFUeVS++MGN/O2HN/Ff7upkulDmjtUpHj40RCKo83j3KF5V5WvP9/LU8XG60yUcwyLmkayp91Ef97Dz+DSF8msS0yQcGShgKhpCu/LnaYq0Wd987mYtBGxoCSHsy4tK6Qi0UohgqQZvIU6wXI0oXz4Kd/5r5UhzqnyMCWcYIS7/AE3oFk9N/4QzpZPY0mbEGODRyX/H0UuX3M8D/Nc7O/Fq575+NjfFWJ2a2xQbQNUFN32wg67ttdS0hNl0ZxNdO+qoHvoxKw79PZqmonmXR02jIXJU6SluStzJ5uh2Tua78StBinaB3EQ9971rDcnz5uqtsvCHdHRFx68EyDkZVt4SxlsjsTDxK35ELnCJV1w4nLLkxAujPPSlg7zwbz2Upyr1eq9FSrBsCRLMaZvylElVY4i1N9UDYJsOu392mjU3VH4WimDLO1oI1/hwHAfTcbCWgXvp+UgpqV8d48YPdhBLBahrj/LO396AP7G8+g26uLi4XAnLJvLX3d092tXVtRf4MPCNs//ucev9XFzeegghKJDBwSEsYosWrY9qCtc1RuiN+on4NMZyBh/e1sJotsRTx8b4wJZGHFmpBfzXnX388nXNJHweokiEImip9nNq9FzaqKYKJFAybEYzJhMZg43NQeQV1AFKRxLV4YauCGVT4vMIhGMj52FdLHSLgsigCBWvHQJztljqNY7zh8/+PmPFMbyql09v/SNuqrodRV78K6IosqTN2Zb3JadIjgxhLp2C2Rn3891f386piQIBXaUtGSCsXfhZpKKr5KfLVDWFqO+IkUuXKGYNlPwo/lwvjk/DcCRLbWkqAwVOF09yonAEr+KjK7Se6+N3cCi3h63hO4j5AtR6tZlpakEDS5hYjoktHVShElRDlJ0SyboADg6isDSpngI4/NQgh58ZBGB6tMhgd5p3/95GtOBcoe2UHA4/O8zh5wbRPCob72ikZkUEx5YceX6I3FSZlZuraOiKofs1fHHPsqrRvBDCI2hYH6NhTRyhgLOsKi5dXFxcrpwlifx1dXX9TVdXVz/QCDza1dV16OyvfhP4ZFdX1zHgk2d/dnFxeQthiCJPjv+MX338I3z00Q/zwOD3KCnZStPnRUA6UO3T6B7OMTxd5Gsv9PK/Hj3OezfVc/++QbyagioEpybynBov8KGv7qQ3U8axJY1xD6sbg0T8GnVxL6sbg5wcLlAX9zI6bZAp2hTNK18iSkciLAufsMGyLuic+EZxfAWezD3ATyf/jR9PfJs95aeQ3nOtKkoix5+9+BnGipXnbGW7zOdeuo8h49JJF5rQUS7wFaKLy7cikFJSF/BwfVOUTanQRYUfQH6yxO6HTlPIGGQmipRyJs99/wQZdSWTXR9mtC+95CksiiIYNvsoOgWiWpwpc4IXp55ECodqZQV9IyoCcd72YComZadIySlRcorYWNiORUiNoGs+vNKPvdjuJ7YkP1JmpDtD46o4XdtTM78yyzbTI8VZmytCIA1JbqxM7/5xHEtiFC12PtiLY0vitZWoZbIhhCesEarzoce0ZS/8XkVKkIrEcWWfi4vLW4ilcvv8XeB3LzB+FNi++DNycXFZLI5kD/K5l+6b+fmvdv8VhjTAgVvqbychUhffeZ4IqIIPb23gxd4p8mWbX9jcwInRHHv7Jvj9t3Xxv585ybs31jNVMBjLlbnvwSP8w4c24rUsagIK8WCQnpECh/tyNCR8aKogf7aeyZFQREERAr8mcObJDfRKUBQ4UT7ChHnO4fRU8Rj1/iZSahvYCmlzijPZM3P2HSoM0hBtveixdSPA5sh17Mo8NzPWGVyH155fkwPdq5GfNtjz8Lk5CgGy6VoeGhnn7c0hzLKNWMIWAbYvT7o4yVC5n6gW5/rEbexKv8CUMUnQXsnR8TKrOoIzUVwZKCIQCBRsx8Kj6JTtEn41gBAKQoKRW9yvZ0XCsRdG2f2zs++zgBt+sYNS3uT0wUpLBvU8kS5MSI/kmRzM49jQtb2W8f4cp882QB8+OU3L+iSRah83fbhjUR81K0JgGw6KKpBL/WTAxcXFZZmxbNI+XVxc3vqoqsLjfY/OGX+6/2n8mp8n+p/kL677S6QDISVa6QS+QCR0lXu6qnj36hqmDYvaiI+ArvKlJ05wc0c1QV3l9EQl0nFwMEPOcPB6VWzbQRUOq+r8ZOI+TgwXyBYrwk9VoGQ6HO7PAxAJqGxsurI00HlBdejLn5ozPFweIBFI4SlECGsRagI1jBZmN62u9s913jwfxxG0aKupStaSszMElBBhJzEnpfTNIqVk9WtaCqy9uYF0Waeutg7dp2FZEnuJojOKKjlc2M+4OULZLjJgZRgtD7I+shVN6Pz3+/v503vWnEvfDecpOSVyZhYHSdwTJ2/n8AovUko0oVGcXvzzKEwa54QfgISX7z/JbR9dxemDk8RqA0RS/krz8zGD9HAeoSjEaoIMHJ8inPCTao1w5vAk0pGEkz4iSR9v/8T6RVtpKELgGJKT+8bY90gfwajOtntXEm3wu7E7FxcXl7O4z8RcXFwWDSklDeGmOePV/momS5McnDjAk0OP8cGH388Xj/w1U3pAsO4AACAASURBVHL0AkeZz/mAbTuEVIXrmqPcs76O//GLG9jWmmAib/KtlyuL4TX1EULnRZakBNuwCOmC2piOX1eojnjY2BrmxPC5nnSZgk2m5CyZfT22SoOvZc5wVIszag7gBPKERJT7tn8Ov1apLxMIfnvj71Dvbb788S2NYKmalNlGuJwCc/4NMRxLkpsqs/09K9nyjhVsf89K0sMFNCl4+NAwul9DKku3tDe8OaJ6nCq9mrWRzawObcSUJopQcApV/Kc7OmkIVNSPxwcSUIRKSAsTViOcKhwnpEZwkGiKB2OJDAILmbluqeZZc6ObP9zJHb+2GhyYOJWjmDHwBj3se+wM+UyZ+vYYUkiKOYNYjZ9wwkdjVxw1pC6a8LPyDoceHeDRrxwmO1Zi/a2NTA4X+NmXD1KcXHonWBcXF5flghv5c3FxWTQcR3Jz3S18p/tbpMsVq3+/5mdzzWYePVOJCBasAkWryA96vk/OzPGpDX+KcNQ5bRDmG+FIWsM6RtDDwf5pvvXSaQCSQZ3P3bMa73kCLmM5DGXKhLwadUEP9ZEgQlV4uSeHYc2eZN6wKVuCgK4S1EFai1fI5TiStsAaBkq9TJ01Z2n0raBoF/ApcY7k97DOt4PVbORf7vwmw8UhYt44tZ4GVLk8nA39ER1vQOOl+0/OjLWsS1L2SMZyBohKqu1SIPwGUrGJa0m8woemeDht9lDvayKiJtDVKgJxgay0a8T05LEck7JTwsFGEzrN/pXk7AwRLYbX8WMsUZfzSJUP1aNgm+cchoIxL9GUn5g3QGnCZPjkNEIIevaM4tFVrn9fO8997zirb6ynri1CZkKndmWUaLUfJaggF6m2T5qSJ/75CFNnH7xMDuWpaQnTsSXF8VdGmBrKU5eILcpcXFxcXJY7rvhzcXFZVGrVJv7xtq9yLHOUidI4pmPylQNfAeCamms4kT7XW+uRMw/zq2t+hYMTBxktjbE+uY6O4Cp8cuGa5+qK4MOb67mts5pMyaQ+4iWmqzPC80zO4BPf3sNIpowi4BO3tvO+jXVENEFDQuf40GxTDF1TONxXSQOtT3jpSHmxjcVLA1ULQa6P3cWwcQYEjJdHGCqMsOtEDbesWUVZy6KX4ySVWpLB2spOyyhHznEcVqyvIlkfJDdVJhjzEk76yBVt7tlQh20vzWSFgJKa5Xj+MNPmFM3+lYSVKM2BlRTsLFFZjTjfxEg3oKghDIWgx4/tL1Oy82iKD1148aBj5JeuZYUnonLXx9bw9HeOUcgYRKp83PLhToQKY8eyZCdLKIpg/5N9rL6+nlP7x5gazhOvD6L7VFSPQqIxiOpfhIQiB/ITZfKTZfwRHY9PnRF+rzJ6OkvLuiQAut9d6ri4uLi8intHdHFxWXSqlDqqYnVMywl2TbzM9trtrK9az1BhiG8e+ebMdlFvlCcGnpgRhwB/sv1PuKn6dnRn4WzwVaAh6KEhWIl+vSr8TAmf/8kRRjKVhuOOhC8+cYJY0MOGughtiSDFskP/ZBlVEbSm/Iyfl043OFkmFdPxaRqaYy1ad4IzgzpnchGi0RK2tYJjPfDVpwboavBSG0qgaBLHWqLU1MtglR1e+GEPq66rI5TwYVsOr/y4l23vaaU9pSxam5DXYgeznCmcIqiGKdlFXkk/x+rwBpp8bVRpdYjCOddTLWRQFiVMxUR6JaYp8GVD6CEbBYFPDWJlL++SOh8oirjgeyYlRJv93PO7GzDyNt6QBkj2PzpA94vDSEeieRR2/EIbz3//BFve3kJ6tEBta4RoTaDSa1Ff+GtIIDi9b4IXf9AzM7b6hjoaV8XpPzr1mo0FsVSAeP3S9Ep0cXFxWY644s/FxWXJiIokd1S/g7tS95B10nx25x/P+v3vbPokf7fvS7PGvnboa3TdsJpGdeViThWAnGmzt2+uG0e6YPJb39rDP//qVuoSGiuqIyAUdp/KzmkKL4BXerLsaA/DIhnBhL06f/p/+rDOW/R7NQWvt8xY1mJceZp1/m0o5aXpK3cpFFVQ1Rhi1097Z8ZCcS9ev8a0U1iSKKVQIG9nkTiMlAexpc22+M28MvUcKwNd+Iwwr37qms+mQJ6sOY1X8eFXg1geg7JTwC/9OIqFKCyCOLErpi5DJ9JEqvwkm4IovtlROilBeAUeodJ/eJJDzwwSiOjc8P529j3WR3ayxMm9Y9S3x3AcqG6OEK8LoKoOqlfBXqCnGdKS5MfLWIZDMOZl7yOz3WmPPDfEzR/qnCX+Oq6tobo5RMvGxJzzdHFxcfl5xhV/Li4uS8qrpisBIvzZ1s9zPNtNujzFinArE+VxJkuTs7bPGBlKdqESnltkgrrKxqYo+14jAL2awkTe4MBghsZYpYF8UzyI9prehQGviiIEpi0xbNA9GkJKnAWuA0z5NT77ni7u+9FRHAmqIvjj9zSjetMMZnXO6EcIqCE6lS0489BU/o0iFIHhSHRFzOltqKiC2rYo0Ro/QyemSdQHSdQFQYGHDkyy9tYaKC/ufE1/hqnyBBkzjUTSFuykr3CKJv8KFDRsoyI2hICikqNQKpA2pzhQeIVVofV0BNdg6TbCVtEdHXOB6/yckqT/0CTDPdMkG0MMHU9z7KVhbvhwx5y/I0UR9O4b5+X7Ky6x06NFRk5m2PbuVl780Umy4yWa1iSIJH0kGoPoQRUpWDDh55QlL3zvBANHKzXCHp/K9ves5LnvHp8VOfdHdG79SBdjZ7LUtEZINodQFiES6eLi4nK14Yo/FxeXZUOYONeEd8DZkj6PegJN0bCccxGyd7S+g5gnMWs/IWDSGSVtpKnyVhMR8QVJqdSBz7xzNb/1zT2M5coIAR+6tomXT02iCBjNlilbDg0xPxJJfZWHSEFjMmcQC3qIhzzkyjYCKBgOL53IEfGrrGsO45HGgjX1FsCdXXFafqOLoUyWaMgB7xkGB1uIV4+CAz3FI6xMdqEWwotm1AEwVrL4/t5Bnjw2zk3tST5wTQM1vnNfTZqukKgPcuzFLP6Qh0KmTENnDI9XpW/cXHSxqvocSsLEq/poDXYS0sLsnHqORn8LMU8SfzmKeTYcqQQNPEIj5Avi8dTT6Hs/T6Z/SoO/mYASQlXAzC1cVEoApUmTU/vHkTbUtcfY91gfDZ1xLMMhP14mmPJWzksKjEKlX+Kxl4ZnHcdxJEbJRlEELesTtG6sIlzjw5JnW2ws4OWSHirMCD8As2TTs3uUxtUJ+g5XHgzpfo1QtZeoz0/t6uiSpQK7uLi4XA244s/FxWXZ0uRp429u/Ru+cuArjBXHuL35dm6ov5EqpXZmwSmFw0vp5/jzl+6jaBVJ+pL8xQ1/SYdvzYIIwBVhnW//2rXsHphmaLrE40dH2dc/zUd3tPDI4RFuX1VDybSRMsihwXFu7aghGdYxLQdFQM9IkbbaAKfHSwBkijav9ExzTVsEXbNxrIVRMx7LQ3OgioBHYzRbJDPVSjKZ47TzCgAxT4Iho49qbwN6KbIgc3gtRUfyBz88yKHBDAA9YzleODXJP35oEwG1ErV5Vdx1bE+d7ZUnEUJglm0+et0KbNNacCfYWXPW0oyVh7GlRckp0p07xLWxGxgxBokpCUyzMhEnnGPSmiBtTBBQQ0S0GGgON0TuomBniKlxzOzCOqoaGZsHv7h/xsFT0QQ77m3jhR+cYMs7VmBbTsW4ZsJk90OnmRzK09iV4Lr3tfPcd0+QGT9nXqSogq7ra+m6vg4tqGAtwBvulBwyYxVjmXC1D6EL8lOlOdtNDRfYdFczfYcnqWoKcf0vtqP5FaTEFX4uLi4ul8EVfy4uLssXKVjl28Rf7Pj/yNjTBJUgARmbFZkatQb4zPN/giUr0cGJ0gR//Pyn+afb/oUQ82/vLiVEPQpbGqM8WjC5qbOaO9ekeO7EOP1TRQzLQRGCkuVQNB2OjWW4ozOFUKBQtNnUGqZ7ME+mcC6aaViSdM4i7FfxK3LBFrAhRSXsq6I1ZrIz9zi9pUr7BE14aAm0M21OIZDU+9qhtPAGJIPZ8ozwe5Xu4Syn0kXWJit1cI7p0P3iMM1r4iAFCMhOlPDFvRyUJbakQjiOgyUXPgQoQmUEghq9FlOajBsjtARayVhpqvU6RCGABFRNcrJwnMPZPRjSACotNjZGrkUVKiEtgpNbuPpKaUpyY2WGT06z5W0tTAzm6Nk9hmNJxvuyFYMWXSFU5cPOOzz05YOUz16P3S8Nk5sqsfWdLTz+L0cBCER0mtcl8IRVJAsjtK2czUP/cJDCdOX9itUGuONjq4jXBedsu3JTNa3XJGlaG0d4BCiLJ/5dXFxcrnZc8efi4rLs0e0gVQTBAfmaHLOR4siM8HuV8eI4k8YEhqfMicwxHOnQFmmnRm2Yt0ViRFPY0hzjvgcPc3AgQ2cqxH++s4ODA9MYlkOuZLK3f5rqsM6nf3QATQh+/cZWWhIBpnIXMHoRlabwvrAKzsIZwUgpoajRGVpHja8ORzoIBLvSz6MJjWti13PSPES7vhHHWGCjDHHhmizjvPYNekglFPfy1LePE074KGYNVE1hy0c6efLlPj56TQPKIpR2qarAFhaO4yBxyFlZUt56MtYUAkGYBJasnJLpzxE149yQvBMpHXZNv0B/qZfO0FqqvSkcS7BQHSpkQdJ7YJwTu0ZJrYiixBR0v0ZVY4jx/hxG0aJlXZLG1XEUr2CqtzAj/F5l4FiajXc0seWdLfjDOjWtYbSgumDZnYoiOPbSyIzwA0gPFxg8Nk3zxgTXva+NnQ+cwjIdmtcmWH1THTYS4XVr+lxcXFzeKK74c3FxuapJ+qoQiFmiMKJH8Hg0fuOJ/zhjGBPyhPj7275Mg9Y6b6/dEtL561/cwJmpIr3jBbJFC79HZWtLgs89eIh7NzVQFfJybDhL31SRZ0+M862Pb2NVQ4CjA+f6krWm/IylyyTC+qKZV5btMq+kn5s1FtZr8Aid/ZmdNNa04DUTCxpRSQZ07lhdw2NHRmfGbumsRjtPc1pS0ralBn9Yp+/wJPUdUVZuqubxkSm6akNkJgoEogvvUmoFskyZE+TtLIZjEPckMGyDqJZAEQrW2do9IzDNU+M/JWNV6tTag6vZFruJx8d/jC1tVFvHLF7qla6MV+v7Dj0zwNRQgY4tKYZ6pslMFNG9Ki3rqxjvz9F2TQ3hpI/0SJFItQ89MHcZoGoKHp9K+/WpRan/FAhGezNzxif6sqzYUkXLNQnqV8VwbDljMOPi4uLicmW4/scuLi5XNbWeRn5/yx+giMrtTFd0Prfjz3ll5JVZTqE5M8cPT/6AIauXCWcIMU93v7imsLE6yLbWOF6PwOtR+e8/OUJVyMsNbZUm031TldV+3rB5+XQaC5sbVsdY1RhkdWOQYtmiLuGrpIwuQvoiQFLUElLP1fYJBBsj23gl/SwSiY2N6c3Ny2spFwnNxT0Kd69O8bt3tPPeTQ188vZ2mhN+Yv5zKadCCp7/3gn6u6fo3FZDIKLzyD8dZqMvwD0bUpglc040eL7x6ILR8iDPTz3OzvSz9Jd6KTtlFEXBkRK/FT27nUQIyU2Ju7g+dhua8HAif4SinafZt5KYlqBUuMyLXQFOWXJm9yTPf78HpKBrRx17HjlDXXuUkVMZqprC+EIad398LaoOY30Z+o5M8uAX9qEogobO2enRG+9oxBfXF834xzkr8F9L45oEtu1UHGr9Cp6QK/xcXFxc3ixu5M/FxeWqRpUab6t9Fxvv2sxUeZKUP0VKb+CRMw/P2fbkdA9/s/8L7B3by6e2fpqbq+5AkfNzG6zzadzaUcWRkTxdqTBBr0q6aPDdVwZmbVcoWxwbzbLrzBQBXWNjY4x4SMe2HZoTOs4i9f5Tyn7ujP0CU4yQt7N4FT8708+SsdKsDHRiSYuMPUKK0BW/hs+ewj+6C2VgJ05qHUbdDvJa9czvpZRsbIhwZqrAeK5E3jDZsTLJv+8d4BM3rEAFpC1Jj1RSEwfO6+MmChbHhk1Wr6qhvEAmOa9i63liVHFX9bspWSWemXqEnsJR1oe2EtND2GmV4lSJkYkiekTFVwd+X4i31byXn458n5yd45rYdYjslb+XF0IIsIsOYyez2GbFvOXErlH6jkyy4fYmTh+coKEzRjCmU7syim05jA1MUcgVSDbqNK/pZNdPe7nxQx2M9ebIjBZJNoaI1geQYvGK6KSUNKyOseq6WrpfHEYogg23N5Jsnt/3y8XFxcXFFX8uLi5vARSpUa+1UK+1AGBbktsb7+AnvT+Ztd21tdfyjSPfoGyX+fOXPsfX7+6c1zTQhEflxqYoedtBUwQPHxtjb985m/rWqiBTBROfR8XnUfnD7+/no9e18IFrGmgIeBZN+L2KUvIT8SYpUmC0PIhX8bE+shVbWmSsKVR55W6UGibBV76A8sr/rrwWoLTdif22v6UkwjPbjWbLfOflPrauSDCWLfH5B48gBLx/UwP1QQ8CaF6X5PjLI7OOH4x5eW/DOI6sQVPErAb284q/zLAxxECpl1FjiEZfK3dV38tPR76HGvagFv2kh4pMDRXwR3WOPz2GFlBY9Z4wptdgXWgLCU8VSi40r/FJRQhKkyaDx9P0d08xeDzN2psa0Lwqg8fSKAroPo3mNQlUj8K//j87iVT52fauFcRSgvRIHikl1U1hJFDdEaamMzxvab5CCEC+7uMpXoWN72xmzS31CAGeoIpr3Oni4uIy/7hpny4uLm9J1kY38qmtnyaiRwhoAf7Dqv9Af66fonWu4GqoMDTvryulJKAIdOD2jio++641/NLWJj5xaxtvW5viGy/2Uhf1ky+bAHzn5TM8cniU7skiiyv9Kuh2kMnyGKPlIbyKj+O5QwyXBpg20wQ8QWzfleUp+gtnUHZ9ddaY0vMo/syJWWOOlEzkDX52aJjdZypCWcrKOIBQBY2dcVrWJUGAN6Cx9Z0r8OoS/eC/nk0pXbhcwJKSZdQYJGtnaA10oqs6A8VetkZvolzykh0xeeo73fQeHOeF7/cQq/fTeXMCY0wFIWgNdhAnNb+1kzac2jnGg3+7j5cfPIXHq7L5zmYOPT1A0+pKD0whBOtuaaBcsHj8axXXzsx4kce/fhR/oOKkWsqbNHTFUPXKUmA+5qg4kB8uM3YsQ7qvgJm1L+brMweJRAuqqAFX+Lm4uLgsFG7kz8XF5S2JVwZ4R+293Ji6BYMS9734WQ5MHJi1TY1vbp3RfBIQgg0NUZ46Nsr9+wYI6Bp/fM8apgpljo3kZ7azpeSnh4bZ3BxnTSpEUlfnHEvRFMq2gi0lfk2Abc3LYl1aCut8Oxjz9dNXOklXaD1hLYItbZ6fegyP8HJ75F5E2fuGjisc44JqQh07QEzVmQ6vRUpojvtpqwrQM35OZN61JkVtqFL35whJpNpHKOFjy9tXYJs2QkgixhE8w3vAq2KVLFiAzE/Fb6IIhbbgatqloDt/ANOxCGh+VgQ72dstaTUMNt3ZTDjpI9KkUbQK2GkFTwiQCj6CmKW5n+cVY0py42XKeQuPT8XKOpw+MEEw6iVS5cMyHMJJH/WrYiiqYOc/9M7a3bEluckSiioQQhBN+bHn4UKStiQ3WmZqII8EhnumSY8WWHtTPan2KJ7QPL4HLi4uLi5XjHrfffct9Rzmixjwn4tFw+338yYJBr0UCsblN3RxeZ0s1TUlJej4CIgQKxLNPN7/OJZjIRD8zsZPsjV+HQoXX5SWlBxT9jiqKtDklfW9i+kq29qS3NxZzV1rUqjA8dE83375DADvu6aRnb1T+Dwqj3ePkIr6aYr5Uc+LlghN5fiIQfdggcEpg6G0QX1VAOHMj+IRtkbYSdAQbOJUqZvBch/H8gcxpUnZKdLkb8Vrv8H6K08AfWQ3Yrrv3Fi0CRGsRn30Myhr78XQongVwfaOajRFwaMK3rGulnhAJ+T3UB2opJ3Gq4MEQgJVU4hUealNZqn62fuZ3PwJ/M2bMUr2TKRwvlBVQVFPM14eJmtPU7BzNAdWkjYmaNBXMTCmEskLAn4I1gt6902QG7RJrQ6i+Bz0iMCjasicb17mIwQYUyb9h9OMnclxcs8oG+9oZnqsiFG0KBdMaldGqe+IsubmejxhFSEFPa+MzjR5f5XObbXkM0VaN9Si+NR5+c7seWGMZ759jP7uKQa6p6hri2GbNmbZwR/QCCTe2MODxcT9znOZb9xrymU+eaPXkxCCQEAH+AKQfu3v3cifi4vLWx4pYZV/I1+/61sMF4aIeeOkPA2oFzF7EQJ6jWN8/qU/52TmJCsjK/nT7Z+lVe+8ooVyTFOIJQOoqsJQ3uDwcJbr2pJc0xxneLrI4aEM926q58cHhvjy0yfZ0LCJ6vOifwUThtPnbvymLekeKrC21ottz48AlBIsx+R08cQc90xTlBjznCKmVqGXI6/LBbIsguhv+2v0g99AOfZTqN0IdRvgif8Gtok6dQxqmwDoGcvx5LExWpIBfrR3kIm8wc7eSf7+gxvxAJmxIg995ShSgm06+IIqd7/vuxQSKaIC7AVwSDX8GUbLg1jSYtqcJGNNoyoaq8LrmcwKamw/B57u5dBEia4ba2i7NYJQBOVhBb2hksKol8Pzk8prw1RfnqnhApbhMNA9yea7W3jxRz1svquFnQ+eIlYToKEr9v+zd99xct31vf9f31On78xs703SqstWs+UqbGPLDgYMpgVDLmn0knKTAJdfuI+bCxcIIZTUG4fQLqEZU2JwxR13VUtatS3avrO708tp398fK9aW5SLbShDwfT4efth7zpkzZ2aPZ/e93+/388Gp+rgVHyOioYU1tr2un7u/Prh0qp519SSawjT0dBFoEJyB+ZVu3mPnrSMnbTv40CRbf6uXwYen6NlQjxCqEbuiKMrZQIU/RVF+Y6RFM+lo8+IXz/OLaE7O8cf3/hELtcXqksfyx/jjez/Ev13+NRLUv+Tn9/2AppDB727t4MhCA1+66wiOL/nw1Sv5wa4JAIYypZN+STYMDa926sXmSh5ShNH1p879clluhOWRNRwq71vaFtFjzLuz7Mk/hi4MdtS/HruSAsAVNcZroxTdAm2Rduq1lpOCYcHqIL7h9wiVZmF6Hzx509NeWGTpP+dLLuPZCuPZp9ZjTuSqVHxJyNLY+8AElYK7tM+t+Ryf6YW6FO3+6RcVEQGUFxx8NyBWb4P57IvRdBOKQZFABmhCo9lup9lup+gXCAyfmUyIme8O0nVegrqiyZ7bJvBrrbReIbFkCEOYaJ6Bewb+8B84kp0/HuHozlkAwnGTjTu62X/fBH0bGgn8ACtssPriNh7+wTEWpsoIAde8bz3RJpvmgQTXfnADuZkK4ZhJojmMsAU+8nn/H3gxnJp/6vdALgbL5t4E8XRIBT9FUZSzhAp/iqIozzBdmVoKfr+QrWWZqkySCL/08PcLuoSBZIg/3zHA39xxmM/cOrhUrfKCvnrqQzpVL2DXVJH/2DtJT32Ui5c1ks1LnBNtDVpSNo6EY9MOtiHoqLcxgpe3DjDwBWvsLaTNRoaqg9SbTcSMOI9lHwTAlx5Plh5nU/QSKhWXfzv0L3z78LcAiBgRvrD9S/RZK086Z8loxGzdiL7nW0vbZMdWaqmBpa+XNUVPuZarVjeTqzikrRCzo4VT9s+NFRnYVI9b9dB17QXDb1CTPPbDYYb3ZACoawxx+e+uxniWtWhVO0fNrRHWIhT8PIdK+9lQtxkdHVNESVsGLde2UK1bYDr0JJs2rWXo5iy95S5E3EfDwi2fmTVu+anyUvADqBRchndniCQs4vUhWvrqSDZHyM2USTSGWZgqIyXsueM4F751+WIfwgaLcMNLm7Z8OqJJi1jKprhQW9oWSVjohmDVha1YdcZ/cidGRVEU5XSpap+KoijPEDcT6OLkX951oVNn1S19rWmCBTnDkeoBFuTMaVc0fLqWsMlVq5uxjMWP4jVtCf5ixwpMIbj9yBzv//dd/PTJaf7x3mO899+fIBQJiIU1UjGDjnqbhw7lmck5HJ+r8dDhPL728v+eJxybdn+A7ZHXYIsQj2TvI8Bf2l/0C8wGE8y440vBD6DslfnUY/+HmnZyddBAQrH/Oty3fA//FX+J99p/oXLNP1LR00vHJEMmf7FjJZ3pMDHb4M1bOkEI/AAs26BzdZpnau5J4EuJ0E6v1uf88eJS8APIzVY5cP8kz+w/L8KL6xx9PCpBmageY2PyPLLuAq12B7mMRe3JLMXRAH9/krUL29mrP0D/NRE0Q0PTBG7+pbfI+AXtxIUV5mun7MuMFWnqiZNqjlDJ1yjna8yMFEi3RIkmF9fWVYruGRvZe0Gm4JW/v5q2FUmEgJb+Oq54x2p6NzVip00V/BRFUc4iauRPURTlGZrMNt634f18ftffLm1774b30Wi0glxcE7gz/zAfffAjlL0yESPC/9r2V5ybOA/k6adAQ8CO5fVs6TyfkuvTFLGwNcg7AV+86+SWCNmyy57xHP2NUTZ3ptk5Ujppv5QwV/SIhXQipkC+jJ6BUoLrSBpCrafs6wz3cKC4k02JC7mw7QIemHhwad/h7CEqfglbRE56jCMiOOnzEPXnPevIZHPM5LHhec7rrScRMvjZ4Cy9DRHa4haaphOKGAyc18LhR6cRmmDlBa2gQcHx6azTqbnPP+qnaYL58dIp2ycOZVl/ZcdJfwZ19CIFJ4cjaziBg62HCCRE9AhRWcdj3x5mYapEtegSTdqs0ds4/9wryYVmIJzCduKcGtdOX1ANyIwUyU6Xae5LUN9xaqGdtuVJ2lemeOLWEcYPZtl6bS+N3Qn2/myM/o2N7LlrjLXb2wleYuwSAvxygOcG2DETqb3wecyEwcU3LCdwJJqlIYV8yc+vKIqi/OdR4U9RFOUZNKlzTftr2NBwDtPlKZoizXTYPWgnCsTMBTN85MEPL/UMLHtlPvrgR/jUxZ9itpxhXf16WowOfP+Ff/mVEtK2Ttp+aqRRIp+z/P53fgo7PwAAIABJREFUHh+jMxUmFtEIpEah8lTw8QLJnpEilqGxqS9G4LjPeo7TFfcauDB9Obtyj+BJlxWxNeS9HGZQx/iC5EPnvY+Hbn4YXy6ODG5p3kJMr3vOtgvPNSXVFoI/e+VyHhya56GhBd6xrZttPSksIXAcl7qmCAcfmeLcq7qQAQzvzdCzvp+fT+ZZ3RRF1wW+/+znhsW1Zw1d8VO2d61Jg/5UJRIRrTLnzFINKriBQ9adx9RMYrpJm93F7B6X3nMaWB1tZX6yyM7bjhN4Eq1mYSdtLELUqi/qLT75/XEl9359kJmR4tK2rdf2cMH1/Tx007HF19EZY9VFbVSLLude0c3UkTy77jjOxh1dtPQlCMcsLr1hgMb+U1/v6V0ETDyZ5ec3HcVzApp64lz05uUY0ReexioFCFucUjBIURRFOXuoVg/KKVSJYuVM+1W8p3QMkno9HaFuUnoD+tP+VjZWHeb7x2466XhPenQluvjcE5/jpyM/YVlDL65eJamnebFNyMOGRixsc/+Rp6Ypxm2Djd0pSjWfB47OMZYtMzibZ0tvklIlQBPQmgoxlXVwfUkybiE0DUN7GWUWA42oGUPXNerMFEPlw+TcBUKFzXzzwXmW9xTY0XkVtwz9hN5ELx/d8jES4tQpmqf1mnWNgcYor1zZSFPCJmzo6AJ0XSNwA1p66ihnHYQuGDi/hVDEpCEdJqILAilfsGplKGYifUnm+GKwauqJs/HqbsSJoi+aJqkYeQzNQhcGo6Wj9MaWUQ2qRLQYQTbJbV/Yx/H988yNF+nd0IhuaISiJpE+j1QkDcXI813CCypMV9l9x9hJ26aG8mx9VS/LtjTRu76eSNzm7q8f5NjOWTpWpkg2hxk7uEDvhgaSzRE6N6SINdq8pHnIQGXO4fYb9xOc+MNFKetQztboXPvy17qerX4VP5+Us5u6p5QzSbV6UBRF+SVL2/WE9BBV/6lhHkuzECdCXtEt8tjMY+yc3slfbPkwvebK5zrVswoCyY6VDdRF1nHzrnGaEyFWtSb4l/uO8c5L+/nELQd4z/Zl/N3PjnA0U+QTr12LrZkcfNrUxmotoOAH1McMXk6HNa0WIpdpp0SWhEgxmTH585+O0hC3sGQdXfVNfOWKr9FoNxOSL7IX4DNkqj5ffugYP9ozSU9DlA9fNcDapij5uSp22KRteRIJeK7P/GQJ2R7CdzSk8cLL14UpWH9lBwPbWvC9gEjSQj7tYX6kiIaOHkiklJyb2sZQ8RB1VoqQSFL1fDZ/IInuWIzeU+H4/nlWXdyMF/jYMQejWPfcT/48NKHhV30MWyfwTg2wvheQOV5k9niB1r46jJDOss3NHH50monDOfrOaaChI0aqNYqdMBYHXU8z62sInJKPEGBGdQIpKcydOnQ58uQ8W2sBwn5pgVJRFEU5e6iCL4qiKC9Svd7Mx8//n1jaYgVFUzP5w/V/yI+P/XjpmFwthxu47J3bg6NVcbUaBRYItNObihnRNa7oS/Phq1cy0BxntlDjdy/s5Z/vPcbFyxp4bHgegJ2jWcazFf7150eJhJ/65VzXBaahMZKpIUwLzbaWioi8GFJKhB/mQ/82wQe+PMUnf3SciuuzY12K+kiYBS9DT7j/ZQc/KeCL9xzjW4+NUXZ89k/k+b2vPs7xfA07ZDD44CSZ8SJzYwUOPzqNaes8OrxAKG6ddpsLKcBKGITTJwc/I+zj4lH2CxT9HLZm4wUO/bEBwnqEo2MCO1Sl2phhZ/JW6l9bAMPHCGtYnS7hyksb7XRzHo989yg//vxuHvruUSIJi9gzmqH3bWjAdXyevG+C+759GDts0NKXAEA3BVLARW9aTrjeXBrFfCGaJhA+7P7pcb7/qcf5/qef4OA9U0hXEoqfWqwm3RpBO81zK4qiKGc3NfKnKIryYknB1uRFfO3K/8dcNYPUfD75yCcZL44vHbI6vZrbhm/DCRzm/Cn++vG/5snMPra1bePda99Lk9bxwk8jJc22QWudzdcfHmEiW2VLT4rz++r59K2LjbtNXVD1ApY1xdg/nWVNY4q2dIjZXA1dX6w+ufd4kWLV59zeBFHLx3NeXE/AlQ0RPnrNSj53x2Gqrs+15zTzW+emcIIsjqyRIUudHUKrhV7c+/g081WfW/ZNnrTN8QOG5sp0zQf0b2piZN8cvhvQu6GByaM5ulfG0YT2sqf6B7qDKSwSRgoZBJSDEgKNQAZYfoqkqzP4o3ka+pex/Zxu7i/eyvnbr0YkHUK11HMtcXx+juSurxwkN7u4bnRod4a58RI7/mAtT9w6wtx4id5zGmgfSFPOVdm0o5vHfzJCfrZKsjmMZgh61jYQbwsTyNMvrVKddxjaNYdX80m3RqlripCdLrPr9lHS7RHqu2Is39LE4UdnADAsjQuuX35SWFYURVF+danwpyiK8lJIQaPWRmOkjSol/tuad/DlJ/8VS7O4tv9a7jp+F3ErTmesk//z6CfZm9kLwD1j9zBeGOdPN/8pIT1Ek9FOiFP73P2CLuCS7hRr376Johtw+/5pPnPb4NL+N23upFT1cPyAnw3OsH1ZM+NzVXqaw+wZKrC6K8bekcX6k7uH82xdXoemuS+4Ru7pbE1w3eomLlmZoOxVMcM58sExhCdY8DKYxRhm7BBrQhvBeWk/VkxdkIpYzJdOXtcQsXQS9RZ3fuUAiYYwuiG4/cv72f7WAYxkCCme/3UIH2pFj0BKQgnz1PkusTKFIM+8m8EQJhE9RkyPU/RzxIwUD311mtHd87T211FXiCCnIqxu3YCQLoZpwEusqVPI1JaC3y/kMxVymQqlnENTd4KjT8wihCCeDpFui6KbGgiJFTa45t3riTVbBPKFo6chNcp5h1rRw615DO/JLE7vFHDh65fxwPeOgITjT87TvKKOc3+ri4FtLTgVn1i9jRkzkGoxvaIoyq+Fsy78DQwMDAPVE/8A/Png4OCtv7QLUhRFeQEholzecDWbt29huHiMbx/6Np3xTt4y8BaaIy1Lwe8XjuSOsHN2J3eN3sUbB97IssRymqxWwvLZKzQGgSRp6iRNnYuWNxKyDBbKDumoRbbsko5afOa2QX57axc13+PQfJb9cwusb6tjfP6pNVyOJ6m6AbZhoGsS6Z9+U3gpIRkJOFZ8hAaa0TWDodIhyn4Je2E1dVaNcjhHhJdWGKTO1PnwjpX89+/tWdq2vqMO09DQEZx3bR/5uQq+G7B8UzOGpbNQcOkNwDA0PO/UEBRUJDPH8kwP5wlFTeqawjT2xdFDiwlQCKjKKq50MYSJKx0W3Ay60IkZdRjSpukVPq0XNjLxYI1yrkZoAWIdCeJmDFEMv6jXKHzwXYluCwzr2YfSLNugqTvB3rsXC7/svus4V/zOaqoVl0jCoqknQbzJJhCLPRRf6PnKCw7Tx3IgBXXNEWZHi1x4/TJ+9rWD1MoeYwcXaOqOMzNcoKEjThAEoAsijTa/KF+jgp+iKMqvj7Mu/J1w/eDg4L5f9kUoiqKcLikhIRvYEG1g9ZZ1uIFDWCYYrh455VjjRDP2w9nDZCoZCk6B24Zv44MbP0iT3UKK5ud8npVpGyFSHJ0tAoJ1bXX8eO8kyYhFxNa57h+f6rv35i2d7FjTQipqkC15mIbA1ASZgsfwTIWNvXGMwD3tAKhXI3Tba3g4fyuOdLA0m265nQPFANk8wkJ1GXFDnFaLi2eSUtKasPjTKweouD6WoTGTr/JH397NN397M8WFKtE6GyGgVvGwwgY9ndHFZvDPsuZP0wRTx3Lc+++HlraF4yaX/vYAdZ1hpAQZKxEEPoEMsLUQKb2enJel6pept5r46fT3yIbmkSHJsqvXETxgEUnFiRr12KXkaU+1FEJQnq3xwHcOMz9Rpn0gxXmv6WXlBS0cfHBq6bgVW5pZmC6RaAix6sJWDjwwSeBJPNcnmrS59LcHSLSE8ILnH+2zbYPibI0Hvn2YXKZCz9oGInUWj90yxKarepgZybPqwjZ23T6K7wdoukZdc4S2lUlVLVtRFOXX3Nka/hRFUX4lSQmGG8ZgcVSo3e7ktf3XcfPR7y8d88YVb+TO0TsBmK/O0xZr4+DCQe6fuJ9cNcc7Vv0+KZqe9fy+D8sSNgOpMDUJh2aKvGptM68/t4233vjIScfe9MQ4a9sTHJwqcH5PA6taErh+QMXxqbkBjx9bnAaqywDffeGm8FJC0mtnrXYdw9l5MjnB/TnJlrXzLEiDO/dWuLi/RE+9hqi++PV/maLLX982iPa0US0hoOYFJFsizE+UcKs+9e0xNB10F2QQPGtgET7svG30pG2VgktupkK6JwpIqtIjkAGa0Cm4OQpejha7g2pQhkDQH1mF0GCqNs6Ryl4uPr8XLeIQcusJ5OkVQDFOVNR0qz4rz2/l4M+nGB9c4O6v1XjF21fRf24jnhMggwAjZHLfNw/RsTJFffviVOB4OkS8PkQ4ZREI+bzBTzqSuZEiQ7szJBrCbNzRzd3fGGTw4Sn6zm3Esg2K2Sq6rpGoX/z+rLqgDc0UJBpDCEsVdVEURfl1d7aGv28MDAwI4H7gI4ODg6f0qHgu9fUvr+Kcsqix8SU2CFaU5/Cbe0/F+dCmD3JVz5Uczh7GDVwenXqUI9nFEcHWaOvSuq1juWMUnALDpaOs6Ok/rbO31i+GhNv3T58yDVAiMTSNh47N8/WHRvnX39nMsqYoUwuLawAdT7JQ8rB0QUNdiFjo1EqPz6aNOCEjTC6Rpdh/AN+z0TPb+Ic7R7FEBN+ssb65h6gRwws8HN8hYr5wD7wBV2LqAvdpI4dXrW6hTtd4ck+G1v4kIgnZmTKJ+hC2VgAZetZ7q5St4dZO7fyu6YJUKspkZYyJ6ihz7ix5N8v6uk04voMbOET1OBl3koRZh64Z+NJnwZlDS3ikY/WkrdNr6zA9nOPJR6aZOJKjqSdO74YG1l/ZwcM3HWN+sky15J4o1uKjGwb3fWuQldtaOfL4DK3LkzR0xrjgdcto6olj2c/9vSnlavieZORIhnu/+dRI56FHprjkTSu47cYnGdqdYeOVXUgJ0aRNteTwqvetp215EtM+W38V+K/zm/v5pPxnUfeUciadyfvpbPzEv3hwcPD4wMCADfwt8CXghtN98Nxc8UUVMlBO1dgYZ3a28Mu+DOXXiLqnLAbsDTQ3tXPjwX/ikalHCBth3jzwZhpCDXzlwFcAWJVexbcHv02ulmMoM8pDMw/y6MwjbG0+j00NW0iJpudcf9VdZ9MUt5kp1Ja2veacdnRNcGRmsbH51x8e5Ybzu2htNJnPBpRrPq4XEASC+YJHreaD7y01+H4+mmfwP749xsq2ZgYnywxlRmhPhpnMVhmb92lPzTA6N8ZXB7/K4MJBfqv3VWxvuYyEeO41gQ2W4Ma3b+YTtxxgaK7M1eta+L0LerF9wfItLRzfP0+l6NC+IkVzb4KaLzGlfNZ7S9MEF79pOdNDeYb3zpHPVNANjfrOGPOFHAVylP0SVb9CT3QZR0oHGIiuR9d0jEoEzarh4eMHPkkjTV90gDorhZ+zmeWF72Xhw73fPMTMyOKxs6MFpo7mufC3e1l5QSt77hoj8ANMW0cIcCoO67Z3UCv51HfESLdFuOIPVhMISS7/9GXwT3sOCbPHijx001GqJZdlm5pYf1kHe+5aXC9YKbhUy4sVaUJRA88NaOpJEE6Y2HUmfhCQzVdOOe9vGvX5pJxp6p5SzqQXez9pmnjewTBxNi/kHhgYWAf8cHBwsPc0Du8BhlT4e/nUh5Zypql76imB5jHnT+NSYyg3xOee+Bzz1Xku6biE1mgrtw3fxqcv+Qz/tu/LPDD5wNLjzm85n+v6r2MgsYaEePa+cpNllx/snWLveI5LVzTQlYrwyZ8cZCK3GByuWtNCxfH4w0v7WCg6DDTFGZ2p0dEQJggkFScgHTdJhgSe8/zTQIWAJ6ZLfOwH+5jO1+hriPK2bd18+qeDfOLNLazptrCweM33Xk9wohnCG5a/gXcPfBCC5+4bUA0kDx/PcXimyM+PzZEp1Pjb166jyReYlkEQSAxTY9cdI2y+ppdYyqRYeca1SlgYLfPwzceollxWbG0mUR+ioStOuMHEi+aoBGVqfhVN08m5C2hCwxYhGu1WpvaVMAKb+IoAx6hgCB1DWITKKeQLTPfUNAG+pJrzuPmzO0/Zf/nvD6CFA+YOORx5fIb1l7XT0BFlanSehtYU8xMlGrsShOrNZw36uq6BK/H9AKfkcfNnd520f/VFbYwdnCefWfyeX/ym5dz3rcNse10/jZ0xIimLQH/el/AbR30+KWeauqeUM+llhL9eYPiZ+8+q8DcwMBAFjMHBwdyJaZ9/BaweHBy87jQe3oMKf2eE+tBSzjR1Tz07X3MYqR1htDDK/eP3k3fyvG3V26i3G7nh1t8+5fh3b3g3mfIc7x74wHMGKE0T6LrGsWyVN/zzQ/gnPg81AR+5ZhX/+5YDfO13t7JQcgmQdKRCNETCTGcdqk5AruSxqT+OrQsC9wX6GGiCu4fmGZmvMJmt8OM9k6zriPFn1yWIhAPy7gLFgsuf3POnAOhC51s7vktaPHdBm/2ZMr/zlcdO2ratN83vJlMc+tkEsBg8L3/Hauo7o8RTFvnCye0hyrMOP/7C7pO2bXlVL/3bGhHhKlVRphpUMIWFlFALKsw607SHurHzdZSjGUxhEczbyIYylmYTqiTxT51FehLhwcyxPHvvHifdGqGxM8EjPx46afrpK985QKQdHvryJNNDedJtUS58Yz+lfIV0UxzN0uBZGqprmiAoBUwN5fHdAN3QMEM6U8dyPHnvxNJxkYRFz/oG9t8/gR0xuOoP1hIEknizjX8W/bw/m6jPJ+VMU/eUciad6fB3tk37bAa+NzAwoAM6sB94zy/3khRFUf5z6IFFn7ma3sblrEmvxRIhkqKBo5XBZz1eIrnpyPd407K3YGkWU5VJYkacZrMVIRc/zoNAEgQ+HTGTf3n7Jn64exIpYXVbgq/+fISLlzega4LZUpVcxSNmGxQqBdrrwhyaqOEFUKoFaGGdFxwgCiRbOuoIWZCvVvjotb2s6TIQdoap6gy2HmZlwxoMzcALPCzdQhPPf9aJ/KnTGx8enucv37mStvY4bs0nng4RjlsITaBpJ4dgIRanWD7TgQcmWLa1EVfzsEUIgU7eXUDTNCJ6lBarDasa5SefP8BFb+lD9hUIJ20CYRISIZ6vwKamQVCGuckibtVn+eYmZkeLPP7TEa794AYWpkrkpqvMHs9jNgQYQYTiwuLrNG0d3dSIJ0PoUf2UCqIaUJl3qRY9hA61ksfCdJncTJmOlSmauhMci89SKSwG9Xh9CCuks3JbCwPntxBuMAkCVPBTFEVRgLMs/A0ODh4Dzv1lX4eiKMp/JeGbNNIBcrGiZnuog3UN69mbearn3dqGtYzkRzA1k4os8e67/pDZyiya0Hjnunfxmo7rsXiqwqaQsKo+QnV1E/983xA37xrnomX1vGd7P4OTBTIFh6htkK+6jM6X8aRkWXuYbNYHAY4bIISOqQkMESCfpZ0CQFTXOL81xcZlgnl3Blc6TFRHmayOcVFqB7myy2uXvZbvHvouf7j2naS1hucNUk1x+5RtGzqSeAIiMQs7HGBaGkIAvlz899NICaHYqcVRokkbP1zFlw4V36Hql4mbSXxfEhAQNqLsv2mOcy7v4rEfjHLJu3oJYlVCWoj0jdfinf8+Sh1X4GjRk793EuaPlRl8eArT1mnpq0O3NJItYaaH8uRnKyQawoSiJi3L6qhO1gjVG3SvXRydW3tJG+GoCYZ1ckCTUMu6zB0v4lR9FibLzIzmuej65RTmK0ujf9WSS0t/HUO7Mmi6YMu1PSTbo0gZ4LrB877XiqIoym+es2ra58vUg5r2eUao6QrKmabuqRdvXk5z5/htPDDxAKvrVxM1o9y470bed877OTC3nzuP33nS8Z+79HNkqzm64z102X1oJ0YCNQ2mqj7Fmg9I7jmUoTFuM5Qp0RS3aU+F+ad7jvGOC3o4tytJzfWJWhamrvHYkTwAfc0hutLW864DnAsqBKFphquDmMKiPdTL5HSY9iaPkGYwtjBNd7ifENHnPAfAWMnlG48e57uPLxYtaYzbvPvSfhojFhujYYIAhC7QdLAsnWRLmGzRAwF4i03r/UrA7f/0JPm56on3QHDNe9dh9zmApBpUEAhqQY24kcD1JVZgMrm7SHHCxwwZNK23iLUYNN70R4jDPwHAe92/sdB6xdK1apqgOFXllr/fu1QkR9MF572mD98JcB0fw9KxQjoNXVEWJstkpyr0b2xkfqpEtM4m1RrGE/KkdhVCwPxwidtv3M8vhgI7VqYIxUwqBYfOVenFqaQSoimbeEOIcs6hrimMnTRUr74XSX0+KWeauqeUM+nXfdqnoiiKAqRFM2/qejvXdL+Kx2YeZf/8k3z24r+hO97Dl3Z98ZTjDywc4J/3/DMAn7jgk2xLbUdKSRBAk6XTaOuMFBz6GiOML1SJWDqGLkiETIYyJZrrQgRSYhgaNd9HioDGOoPZnMex6SqxsEkiZIHrnPLcAAkR4t7BKHZoA64I+PZQld/aaOKJAk8UnmRb6nKM6vMHP4C4pZMpVPmjK5bjBZJSzeMztw7y+TefQzHnoGnglD2SLVHQBYGU3DeYx9AEA+0R6sMCwnDlO9ewMFnGrfmkWiOE2n2k0AkIiOox3MBDaODIGnh1VI0cofVlEq1pskc89JgkPHtkKfgB6Lu+gtl5Ja4X4BY8hnfNMT9RZPPVPcyMFhjekyHwJflMlUS9TduKeob3Zth56whXv2cdqZYoSDDCBu1rU/h+gCslyMX1gsW5Gr4bEI6bTB/L8/Q5oGMHF9h6bS+PPTHLmkvamRsrEakzaeiKY8Q0ok2LI6Yq+CmKoijPR4U/RVGUs1QQSKKk2N5wJZc1XYXvSxytwur0avbP7z/pWFt/arrkZx7/NF+5fAMxkkvbhIS+hEXU0mmOh3G8gMl8hQ/ftJe/eu1aNEATkvmSx0yhxkLZIQjgnM4ko1POYlP4iRJblyXwa6cGQFMILlsdYyibw/UMXr1NY07uJqUtZ9aZZLI2TipYRkx7/jV/DRGDV65u4eM/enKp398N53Vx/+FZzt3aQ2WhRiwdIvAlmiaWqm96geTJ4yW2LItjA1pYo75vsdS1Fq9RliVyzhyGsAhrEWxhY2khBIKcE+BrJSJGDNnk0EgMO+oS/7tXnXRtMt5GICGoSn76D/uW1tmN7JvnvNf00dqXYP8Dk5iWRrotitDADi+2WKiVPOIpm3RbDDMMnrc4H1MTAulIHvnhEEO7MsDiKN/6V3RQmKsytDuz9Py+J2noWnxNyZYwqeYIekxTgU9RFEU5bSr8KYqinOWkBP9EELKCMH++6cP88X0fYq46h0Bw/YrreWzqqQqZ2Vp2cURLgNQCZt0JKl6F5lArjVaMlKUzXfWQSD73xnOoOC51YYNiNUAIQaHmMZ2vUXV9YiGDvuYoEUuj4gSUnYBTV+UtEpUwfWlBLsgg0dH8Fu7K/AeWZrN7ZjehWsCFbSsJa8/dLkFKOL5Q4j3bl+FLiaVr3D04Q0siRKCLpSIvhiGRgUTXTz5XtujRnjDwA38pFEnpIYA6I40hTIZKh+iI9BAhhqZpxBxBIaLhSxfLsLFaITkzCVYUnNLiSQwbb/3bmDtawKkFXPGO1Yw+OcfuOxenp+66fZTL3raStdvbSbdGWZgpk2qKUNcUIRw3iaVsdFNDTwi8YLFlQy3r8tgtI1TyDj3rGwh8ycjeOcYOLtDQGWfg/Oal8Cc0gRXS2XptL6G4hRnT8H2pgp+iKIryoqjwpyiK8iumy+rnxsu+wlRlAtMw+MLOz7M781Rrg8u7Liep11MLyvxw5Hv8333/F0969Nf187+3fZImrYOOsEHMMnD9gHDKYq7oEkiYL1a5Ze8kl61s4vb904RMnbCpoWnQ2WSiawKep+WBKIfxLcnD2Z9RCcqEtQidxgr+cs//4rqOv2D3VIHz2uoQz6hrqekSxyzjAVesbuKGf3mUqrs4OmbpGu+4sJeZmkujreM6HpouMDXtlDXeui+561/303dOI20rU1BfIufm8TyNkuMSmOP0RJeTc7PE7ASlkocvXOJumopZwBYhKIXJxwaI33ALxsSjIH285i0cGm3iwIPHmR0tYIZ0rvy9NSzf2ozn+Dz+kxGkEPiuJJ+pYFsGgR9g2IJX3LCSUJ2BJyWaEOQnKuy7ZxwkdK5Oc+CBSR798RCbr+lh8kgOp+IxO5Kne+1iP8dYymbbdf0kWyMYUYHvP/XHAEVRFEV5MVT4UxRF+RUjJSRIkwinQUjevf49fPaJzzJaGGFH9w7eNvDf0AKDocp+/mHvPyw97mjuKP+07x/46DkfRwtM6jSoaBqZokuh6lF2fG7ZN40GNCdCPHRsnh1rW5grOoRNg3zVoSMdQkMsFTgByDoBg7NFqm5Af2OEZCxKOStIhtuYLc3xTwc+jhu4BG6CwzNF2tIa3eHYUyNzlsN+dycH5nYhkSwLr+Gb79rAT3bl8WXA2rY6nhiZR5CmPhLGMDU8JwD75GqfMVsjfzTL1JE8U0fyrLmkjYFX11EKsviaR2BK9Fors36Z5ngKAwOvWIH6KmIhhZW20b0Q/on3OB/uRyzrpzzr8PNvHiU3c4yuNWl61tXz6H8M88RtI1xw3TI8R7Lp6h40DYZ2lWhbnsQKG9gREyOkI2xwnYCF8TLTR3NYYYO6xgh77x5j9MA8F75uGQ987whHHp+ha3WaI4/P0NAZJxQzef1HNqEZAs0SJ0aA/2vuMUVRFOXXkwp/iqIov8qkYEVoHV+88O+pyRoxkUDIxXV1E6XxUw5/cPJBSuuLxEkBEAa6YiYzhuDwbJnLVjaRK7v88bd2c9lAE41RmyOzRdpSkmOZMh2pCKahEdE1UqbGXM270UibAAAbOUlEQVTnvd/axdHZxemRlq7x5d/ZzIrEGv7Hgx+m4BaImlH+cOXH+Pa9HpetDBjOztGaNDCri60pZuU4+4s7l67xSOVJ+vQkppHGROdzdxwmU6xx6UATlaKLaeuYto5XW5ymurE3hnQlY0/M8PhPhpfOs//+SQa2N9CW6iTrzjPmDJMKG5TzLViJCDlvAbvBIJ/1SaQEphbBLz21JlHTwCtJHvr+UTLHiwAcfWIWf33A5b+zisd/OoKUkuxUifquOMLQSDZHCEVNIgkTI6ojtcXv0eB9k+z52djSuZt7E6zY2syhR6aZHs6Tbo2iGQLfC2joiNG7oQFs0OViH0M1vVNRFEU5E1T4UxRF+TVgyQgWkZMqRDaFm085bn3DesJaBE70f8vKWY7mjwCSle39VGv1HJ4p8enr1xE2DSbzFVIRCz+QRG0DP5AUSw6HCjVWNMcZXygvBT8Axw/4/F2H+eL1m/j77V9l3/Q4mZzF4wcs3rwlzRfuPMSnBhqpBCWMSIDpxDheO3rKdZaNYfaN69xzKEPE0vmTKwcImRq+6zM2OEv78jTp9jBSSiJaQG6mzO5bhk86h6YJPFyCoExIC7Myeg7Tzhg+LoFj4mVNaHCpr0tjGAInH156rF8JGHxkhuE9GRo6Y2y4vJORffMM7Z5leM8c517Zxfmv7cMKG0TqLNyqjx3SSTWHidSZ6KZgbnTxfUnUh9j/wMRJ1zY9lKd7TT2wuJ4vCCSbruzGtHXi9TbyxEifoiiKopxJKvwpiqL8muqLLue6/tfx/aM3AZCyU7x/wwcxgsWSLdP+GO+/9z1kKpml/X+3/R85r6eL4fkKI5kSGoL2VIjDM0VSYYuqG6BrGkOZEgtlh41dKd66tZNvPHJ86XlH5ytU/YAOq5lyIsaRsTmylQJ/fdsgH3l1J6H4BIVKil3+rVyQupxG2cJI5chJ115vtPG287u5dEUTqaiFLuD4fJn2eIxlm5oxLQ239lQT80RTiERjiPxsdekc669sw4nnOVjYS1uok3qrkXqjhVDIZuroLPF4FCldNF3gl2wgQAiQVUl5oUYkYeG7AUcfn2X6WJ4L37CM+vYYo/szi83kowa1kouma5imhmYKEi0Ripkqs6NFnrx3nHLeIRw32Xx1Dw/94NhJrzEIJEITdK+tZ8X5zUQb7cUiPWf6RlAURVGUE1T4UxRF+TUVIc67Vr6fV/e+hrJXpi3SQVLUI+XiqNjPRu9YCn4AC7UFfjj8fap+lc5YF1t7LiWbr2Ou5NCVjuD6AQiJEIKfH5ujryHK2vY6Lh1o4rJVzXzilgMcnS3xhk3tRPXFkauVjRFCmyuMZGu8PZTEjAzRaPWSWXDJMM2Uc5zuUD+Hy/vJefMAxPQ6GsQyRqSktW5xauj+yTzJiEXQoCFqAbWKhxkyltb8CVvjlb+/hvGDC8xPFOlclyLWC8JwWRM7h7yfxZUOMa0ezzfQl5fwxn1Cmobhh3D8AM2DctahuFDDMDVi6RCXvHkFj/7HELOjRXxPEorqrLmoncALMAyNALDCBqGowcJkmTu/fADPCTBDOluu6WHXHccp5x0KCzXi6RCF+cVwGk3aJBpDXPuB9UQa7VMK1yiKoijKfwYV/hRFUX6NmdKm21oO1uLXv5hKqGmCQ9lDpxx/JHuEQAbcfORmlid/wl+d/3ksY7EvYMjQqLo++8Zy7FjbygNHMviBJFOs8YlbDvKlt5zDyFyZC3pTS8/j1wTNdpJEm8CTDoVKPTNzGjPGzyGASlDGFQ5X1F1HXs4jpSQuUoxmBE+MzPC9J8bQhOANmzu5Z3CGNekorVIj8BcD1tNDkx7R6N5UT+/2GK50kTJgwc1j6TZpo56CXyQQAi3j4UckkUYNQ9g4ZfBLAVMjBZyKRzhuUc47SAGRuMW265ZxdOc0SIjUhTh+YI7uNfWYcYPFcTrJztuOk2qJ0LEyzfCeDG7V57FbhllzSTs7bxslP1vmnFd2svfucVr6Eqy+pA27zsD3pQp+iqIoyn8ZFf4URVF+A3lewJVdV3H32N0nbT+36Vy+fuDrABzOHmamdoxAl1RxqQt3M1WwEUJw98EZ3n5BD4enC7TUhSnWPEYXKoQsHcnJvfcs3yAiGhlxJ5j1hpjSD+AGi43iE0aKol8gRpR47ak1io5X5aad4+xY04IvJd98ZJT5ksPvX9SL8EDKxX5/z+zz50ULuH4NJ6ix4M6RMhuwsEDTSGlpxp4okrBCRN0YVsQgKNj45YA7btxPbrYCgBnS2fbafsoFh1jSppSt0T6QpjhfRdME8XQYK2wiA3jw5iOM7c8uPf+GyzuJ14cozFVxqj6avliwpX9zE80DCdpWJxG6IJBStWtQFEVR/sup8KcoivIban1yI+/d8F5u3HcjEsmr+1/NRHGxIfwvHMsd5XNPfA6ApJ3kv2/+Mwbi/fQ19uIHAYaus3c8B4CpC76/c5y7D85ww7YeWqImCWMx/HheQHMsQkk3mClpJPQkq+PnENbCTFQmaJINpMVTP5Ja62za6kJ8/eHRpW3b+upZKLt06RbhmIGmL05B/QXDECA0dBFBoNFqd1D2S/jCR/o+YS0GzdN4080Yto6shBBCMnUkR2N3nBVbm0EIMmMFRvfPsfridgLXx635LEyXQUrq2+OE4iZmVGP+eOmk4Aew//4J1lzcxq47jmPaOkjJxh1dNPbGF0dDNZCqkouiKIryS6LCn6Ioym+oCHHe0PlWXtm+g0D43Hz0Jr4z9J2l/b2JXsaf1i4iW8ty79g9tMeOcn7qzZQdQUNM8PEfHaO/MUbUMmiM2RyYKvDFuw5z/cZ2zu1MIaSGoQmiboo2q5OWUBt+4FPxy5T9El65kbGyS8bycfRBmkLNxLQ2rljdzOaeNAen8qxqTRAEkuG5Euf1RkEIZHDyqJ8MOwg0JAFhPYL0A9AFNb9Cym6gMg2z4RE2tvehBwH5sQq6odG+IkljZ5yxg/M8/pMRWpfV0b4iiWlpmHGTe791mLWXtFPXFMYKa5gJgyB49umagb9YxEU3Nba/dYBkewTd1ghU4FMURVHOAir8KYqi/AYLAkhQDxJe1/sG2mLt3Hn8DjY3b6Ej3sHHHvwfJx0/nB/G8R1i1o/40q4vcU7jOfy/932AoNzJX9y0l7dt6+VHeyY5Pq/x1vO62DeZJ2LpNEdtDk/6bOjpYt6dxtXyuJ5gbsHCqdQxuVDhjgOT/NWbunjX7b/H/3feX5IIdfPlB4bpTkf4zmNjVByfL7zlHPyoAQUHYWjIEwHMiLmUZZGsO4cudGwtTESLECaKYZgIR0e40Fe/AtsxOXjPFAd/PkUpW6N/UyMbLusk2RLht967jh9/aQ8dK9PopqCUreG7AS19dYQSBlhiKfTVNUewIwa1srf0/qy/rJ2OVSl6NzViRjQkqOCnKIqinDX0j3/847/sazhTksCHKhVH9UZ6maJRm3LZ+WVfhvJrRN1TvxpsEWFZbIAdna9iTWI907VJbhu99aRjru69msPZw1i6xYH5A0yWJslUphloamRrTyufumWYmhewpTtFU9ym5gX8/T1HWdmaoDUZwg8EdVacXC7EkQmDfMni4GSRkfkyR2fLXL7B5Oq+y/nIfR/lrWtfQ2cyxVCmRH9TjPddtow7npymvzVO2JEYuk64zqRa9fCsCoH0sbUQJb/IVG2cqBEnpIUxdYtSxsXQDcJ2iOFHsgghaOyKUd8e4+CDUzR1x4k3hCnMVQnHTXIzZVqXJQnFLJZvbiJUbyC1k0cadVvQu64RTRNousbGHV10rV8sBKOZJx+rnL3U55Nypql7SjmTXuz9JIQgErEAPg9kn7lfO3OXpiiKovyqkxI8zycIJKsT63jX+ndhaRaa0Liq+yq8wOPq3qu5dfipUPjQ5EME+AyX9lF2PFoSId55aR/feGiUVMRiJlfjpp3jSC1ACMnxTJW2ZIT1bSnmiw77Jwv8YNcEr9+SpsgUhin4m8s+Q8nNc+eBGV63sYNtfWk+d/shHj++gC4Wf7gFUiIDiR5zkCIgEBIQJIwUPeFllPwCgQg4ctcClayHFTZwp3RCMZPjB+eJpULYYYPVF7Uxe7xIrM4ingrRf24TjV1xdEOjNF+lVvOftfmelGDVGay/upPLfn8VbWuTCEuFPkVRFOXspaZ9KoqiKM8qTIw3dr2N7e2XkXezlN0Klm7y6cc+TdkrLx3XEe/ADVxqYpzPvvHVaFaOO2b+gY9c+zailiRiL7Zk8HxJQbroJni+JBm2uH5jJz/cPcEHXtlBT+8UcTNJJSgTtiJ0xltIRo7z4Zv2AmDpGp+6fh2WFAS+j2kZSMARFbLuAp70iOkxDCwQENLCGBiAwA4bjB/NUJrxaeyOs/bSdnbfcZy1l3SQbIng1nx8T6KbAhB0rEozsm+O8cEFNlzeSVCVYD97sFucBqqmnCiKoihnPxX+FEVRlOcWCJpEB01WB1hQEQW6E90M54cBMDSDG1bdwHB+mLgd5mO7X0cgAwDObT6H3fOH+dib1hH1m3D8gENTRXoaomRKNRqiNrmywzd+byMPVf6DjnAPU5VxklY9ZVmiKSZ5y9ZOrlzdTL7q0RS3cT2ffdMFXtGexHMlQoD0BXEjCYFgrDRKc7QJQ5ik9SY86dK9IY0IecyPasyMZCku1FixtZn+jU3E0hbjBxdoH0iDADtiMnkkx+HHppk6mmPzNT1Uiy6hmIlh67/Eb4SiKIqivHwq/CmKoiinLSzj/MmGP+Pa/muZKc+QDqXJlDNkyhlyTm4p+AFMl8f5zqHv8OYBnXUNBpXCajwpKbseFdcnZOhUPR9dRNgaewVj3iEa7GZ25h7m/PR2Ftw5eprrKfgLLMxbZIo1vvf4GH0NUS5rT6KdyGICDROdYlXQYHWSqy7QGE4jKxrEBKGoweCDc7QuS5JoCDE2uIBT9WlblsB1AgJf4lRcYg0WsyNFqiWXzpVpJo/kkHIxEFpRg0CN7imKoii/4tSaP0VRFOVFickUG6MXcGXrNXSGe9jYuBlP+tw+cvvSMREjgkTiBz6a0Hh46mEsTdAQtdh9PIsQsHs8S7HmUXZ9AidB0hlgX+EJzk2eR0yP81jufmYqWVqSLjVm+eJdR5jIVdnQmSQ40Uhe0zSKrsNM3qPsOdi6SXO4CUvYlLUiujBYmKhi2Dr5TBU7atDcE8e0NOyIBYGkY1WaWDrMN//nIxBAteQh/v/27j5Gruq84/j33jszOzO7s++v9q5tWHtPEuKY2KQJiJBUBiIrGJWUqIWm/JGoCW2joiqkqqoCERJqSkgrNbEgEgqhqeQmQW1VUqUtiaBKFNqEEAhxkqdOYmNj/LK7tvdldmfmzr23f8yAjAUFL2PP7M7v889qzpnRPrv66c4859x7x4ee4RyFgSydfR3Evho/ERFZ/bTzJyIiK+JXM4ylNgBw85aPsBwu8cQLTzDZM8n1m6/n/mfvZ/fkbh4//Di7LtpFOuVj9e/sC70T9BayeF6W2WIZPA+fDDu836PgL3B0NqFKyMxCyPbxCbZMPMlYd45cJk065TNdDRlOpwGYjQ9SyPWSjvpYLNVu+jIUd+Dly0RLMdOHFlk/1UupWCVIBeS7O8jWd/KyhQ6O/uo03/3H/UDt+r1CX4Y4gffd7Mh1p0nSzfoPi4iINJZ2/kRE5E0b9Mb4s61/yUPXfoXdk7t57OBj3LjlRsI4ZK48x/ahy/C8mK0b8pyKn+UzT/8B1eAo2UxEkiSUw4jlMALPZ2G+m69//yTOv4bFYoZqBGO5Ldx5Yw9XTA7wd9/Zz/FiBb/+DtY5s5mfz/2cODVPterR6eeplhNyXp7ikRm6etMszYfEcUQQeAQpn7AcEy5VmTm8yPJC7RbagxNdZDvTjG7uYeM7+8kOqPETEZG1RTt/IiLSEF4cMOptZHxsE5cOXsqzM88ykh/hhs0f4vb/+hSzpdlXPD/xytz33G3c9rbPUwnTZFIBMwtlhro7+O0dE3ihjxuq8thP5tg60cPh4tP8yzODZNMBfuDjUdupO/6DeYJL3sNS13FGuofojtKEnVVO/bpMoW8dqfQ8iReQK+QIS1UWT5YpzpXJFWp3BU1nU7z1ijHc5aPk+tJESUL86n+iiIjIqqadPxERaahqNWHE28i1Q9dz9eB1TKQv4rKRy17xnJH8CPtOPsNgbph8Jks2HRBGEYfnTxPFFeZLJTJ+QDbIEXgBSfYY/fksG/tz3LZzC/tenMNPeXgedGQDdox04ZfGyfq1rbri8iJR1xJhpUqmM0smG1ApV4mjmP/88j6qlQg/8OgeyLJx2wCXXreBTG+KKNG1fSIisnZp509ERM4rP05z69v+mKm+Kb596NtM9k4y2TvJgz95kPuu+gJR5FONIvYdO01v30GWKdHbE+B1nGJ6cZhtEz2E5TTL1X3c8+Fx7v7nI+zaup7lJKHL81j31gJJbpmkmCaJE+Zmi+S7uigFSwQVH89LEVUjnv/ZDE89eohCf5bhjd1k8ilSXX7tLp6Rmj4REVn71PyJiMh51+MN8qH1N3P1+LUcmD/AXOU0d1x+J0cXpsmR4tDcNIeTHxJHfZTnXmRD12amF07RnwLfH2H2NIxndpAO4NO73sI//egIpyYHGAIqyxHZjoTRdECq1EF1aZlMf5poOkWSS4gTWFoMiSpwxY2bGdlUINeXphon+vIGERFpK2r+RETkgkiShEIywDu6BgA4Eb/An/zok5xYOgHARGGCiwduoBgWOVk+Rj6dJ/KWOLp8gI5sD/lUN5VSmsFcB5/6wATHT0cA9I13EiQh6aiDcjGmqzcHCex//DS/cX0fZKBrNMvUWI6kflpnNVbbJyIi7aflmj/n3BTwMDAAzAK3mNn+5lYlIiKNNuyPs+f9D/CL0/tYCBfoTHWyGC4yWhjl0V8/ysfe/jGSuIPFeBHPK3GsOEchPcJQV4HAjwmCWgNXSQUUSgFx6BP4CYunSpw6FrLhkn78jPfy7l6i6/lERKTNteINXx4A9pjZFLAH+FKT6xERkfNkgFGu7NvJe9ddxXDnMNkgy1x5jo++/aPMV+b52v6vglcim+rieOkQi+FJojihXAkY6a/v/OXTRGWPIO0TxxHZQppKKWF0Sw9JK77LiYiINElLvS0654aB7cDe+tBeYLtzbqh5VYmIyPmUJNAZ9uMy2/jN8Z1sG9zG0eJRjheP88GLd9GT6ef+5+5lU+84vbkClbhKKYRqHNZeXwI/8CgvhYQV6OhOMTRVwMt4Tf7LREREWovXSqfBOOd2AH9vZpecMfYz4CNm9vTrvHwTcOA8liciIhfIyaWTHCke4bnpnzKzfJItfVP0pAYZzFxM4NVO78x2LpKqFMjEPqVilVQ6oH+0q9mli4iItIKLgINnD7bcNX9v1uzsIrEu5H9ThoYKTE8vNLsMWUOUKTl3aUbZxPDYeorJIoGXp1TOEMUJfhATByWyS91EYUQIeFmfiEQ5k3Om45M0mjIljXSuefJ9j4GB114IbanTPoHDwHrnXABQ/7muPi4iIm3Gr6YpRH10pVLk0iG5bJmBQsygnyeVSRGnfOI1t4wpIiJyfrRU82dmJ4BngJvqQzcBPzaz6eZVJSIizRaXAnJxhs4oR1++j3IIy+Ww2WWJiIisKq24Xnor8LBz7k7gFHBLk+sRERERERFZ9Vqu+TOzXwDvbnYdIiIiIiIia0lLnfYpIiIiIiIi54eaPxERERERkTag5k9ERERERKQNqPkTERERERFpA2r+RERERERE2oCaPxERERERkTag5k9ERERERKQNtNz3/L0JAYDve82uY03Q/1EaTZmSRlKepJGUJ2k0ZUoa6VzydMZzg1eb95IkaUBJLeFK4LvNLkJERERERKTJ3gt87+zBtdT8dQDvAo4CUZNrERERERERudACYAz4IVA+e3ItNX8iIiIiIiLyGnTDFxERERERkTag5k9ERERERKQNqPkTERERERFpA2r+RERERERE2oCaPxERERERkTag5k9ERERERKQNqPkTERERERFpA2r+RERERERE2kCq2QVI63HOvR/4DnCbmX2xPpYHHgJ2AFXgdjP7ZtOKlJbnnNsD7ATKwCK1PD1Vn1Oe5Jw556aAh4EBYBa4xcz2N7cqWS2ccwPAV4FJaselXwKfMLNpZUveDOfcXcBngK1m9lPlSVbKOZcF/ha4GigBT5rZxxuZKe38ySs45wrAXwPfOmvqdmDBzDYDu4EHnXNdF7o+WVW+Re2NcBvwV8DXzphTnmQlHgD2mNkUsAf4UpPrkdUlAe41M2dm7wB+BXy2PqdsyYo457YD7wEOnTGsPMlK3Uut6Zsys63AHfXxhmVKzZ+c7W+AzwEzZ43/DrXgUV9peArYdWFLk9XEzL5pZmH94ZPAuHPupWOO8iTnxDk3DGwH9taH9gLbnXNDzatKVhMzO2lmT5wx9N/ARmVLVso510Htg/gfUVtc0LFKVqy+CH4LcIeZJQBmdrzRmVLzJy9zzu0Ces3skVeZ3gA8f8bjQ8DEBSlM1oJPAv9mZnH9sfIk52oCOGJmEUD954soN7IC9YWoPwT+FWVLVu5u4B/M7MAZY8qTrNQktVM673LOPeWce8I5dyUNzpSu+WsjzrmnqX3oftVpaqe/XHPhKpLV7HXyNPLSQco597vAzcBVF6o2EZHX8QVq1yJ/EXhnk2uRVcg5dznwLuDPm12LrBkp4GLgx2b2aefcu4FHgQ83+pdImzCz7a81V19ZGAN+4JwDGAR2O+f6zexuajszG4Hp+ks2AI+f34qllf1/eXqJc+4G4B5gp5kdP2NKeZJzdRhY75wLzCxyzgXAuvq4yBvmnLsP2ALsNrPYOadsyUq8D3gLcKD+uWkc+A/gT1GeZGWep3YTvL0AZvY/zrkZYJkGZkqnfQoAZvY9Mxs2s01mtgl4BLir3vgBfAP4BIBzbgu11a5/b0qxsio4566jdg3pB8zs4FnTypOcEzM7ATwD3FQfuona6uj0a79K5JWcc/dQu8vwb5lZGZQtWRkz+6yZrTvjc9ML1N7vvo7yJCtgZjPUFsKvgZfvcD0M/C8NzJR2/uSN+hzwFefcL4EI+LiZLTS5JmltDwEV4JH6qijUdgBnUZ5kZW4FHnbO3QmconZhvMgb4py7BPgLah+kvl8/Lh0wsxtQtqSxlCdZqVuBLzvnPg+EwO+b2WnnXMMy5SVJ0phSRUREREREpGXptE8REREREZE2oOZPRERERESkDaj5ExERERERaQNq/kRERERERNqAmj8REREREZE2oOZPRERERESkDaj5ExERERERaQP/B900BkVgPQSrAAAAAElFTkSuQmCC\n", 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" + "Index(['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts',\n", + " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place',\n", + " 'end_place', 'cleaned_trip', 'user_input', 'start_local_dt_year',\n", + " 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour',\n", + " 'start_local_dt_minute', 'start_local_dt_second',\n", + " 'start_local_dt_weekday', 'start_local_dt_timezone',\n", + " 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day',\n", + " 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second',\n", + " 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id',\n", + " 'metadata_write_ts', 'mode_confirm', 'purpose_confirm', 'replaced_mode',\n", + " 'Trip_purpose', 'Mode_confirm', 'Replaced_mode', 'Mode_confirm_fuel',\n", + " 'Replaced_mode_fuel', 'distance_miles', 'dura__trip_Replaced_mode',\n", + " 'dura__trip_Mode_confirm'],\n", + " dtype='object')" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expanded_ct.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "69f6ae3d", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Energy_Impact(kWH)'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Energy_Impact(kWH)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Energy_Impact(kWH)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mlegend\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplot_title\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Sketch of Energy Impact (kWH) by Travel Mode Selected\\n%s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mquality_text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36msort_values\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[1;32m 5285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5286\u001b[0m \u001b[0mby\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1559\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1560\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1562\u001b[0m \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Energy_Impact(kWH)'" + ] } ], "source": [ @@ -1398,6 +1402,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99ab7ad9", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 8e88ed2..c073357 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -182,7 +182,7 @@ def cost(data, cost, dist, repm, mode): return data -def cost(data, dura, dist, repm, mode): +def time(data, dura, dist, repm, mode): """ Calculates the cost of the CanBikeCO E-bike pilot program From 636f0de55c71dbe0868aecc526357901f9d1565f Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 12:08:42 -0700 Subject: [PATCH 09/35] debugging --- viz_scripts/cost_and_time_impact_estimates.ipynb | 4 ++-- viz_scripts/scaffolding.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index c02492f..ccd3f63 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -1334,7 +1334,7 @@ { "cell_type": "code", "execution_count": 63, - "id": "ecaff572", + "id": "0a9ce7f7", "metadata": {}, "outputs": [ { @@ -1406,7 +1406,7 @@ { "cell_type": "code", "execution_count": null, - "id": "99ab7ad9", + "id": "c3fcd17e", "metadata": {}, "outputs": [], "source": [] diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index c073357..8100067 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -178,7 +178,7 @@ def cost(data, cost, dist, repm, mode): cost[mode] = cost[repm] dic_cost__trip = dict(zip(cost[mode],cost['C($/PMT)'])) data['cost__trip_'+mode] = data[mode].map(dic_cost__trip) - + assert False return data @@ -211,7 +211,7 @@ def time(data, dura, dist, repm, mode): # Create new features in data for confirmed mode dura[mode] = dura[repm] - dic_dura__trip = dict(zip(dura[mode],dura['C($/PMT)'])) + dic_dura__trip = dict(zip(dura[mode],dura['D(hours/PMT)'])) data['dura__trip_'+mode] = data[mode].map(dic_dura__trip) return data From 6ecfa26b19f0efb692ec75e7608a8f7d305284c5 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 12:09:34 -0700 Subject: [PATCH 10/35] ? --- .../cost_and_time_impact_estimates.ipynb | 65 +++++++++++++------ 1 file changed, 44 insertions(+), 21 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index ccd3f63..5dd4818 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 64, "id": "b9624fe3", "metadata": {}, "outputs": [], @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 65, "id": "29424542", "metadata": {}, "outputs": [], @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 66, "id": "e55a1a1f", "metadata": {}, "outputs": [], @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 67, "id": "ef7dd45c", "metadata": {}, "outputs": [], @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 68, "id": "9beff67f", "metadata": {}, "outputs": [], @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 69, "id": "6a7cdcde", "metadata": {}, "outputs": [ @@ -639,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 70, "id": "c50baf4a", "metadata": {}, "outputs": [ @@ -898,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 71, "id": "c2dd6e2a", "metadata": {}, "outputs": [ @@ -1230,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 72, "id": "f948dc57", "metadata": {}, "outputs": [ @@ -1240,7 +1240,7 @@ "(2374, 36)" ] }, - "execution_count": 57, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -1253,7 +1253,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 73, "id": "9495e947", "metadata": {}, "outputs": [ @@ -1263,7 +1263,7 @@ "(2374, 41)" ] }, - "execution_count": 58, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -1282,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 74, "id": "9e27d0e9", "metadata": {}, "outputs": [], @@ -1293,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 75, "id": "e5330285", "metadata": {}, "outputs": [ @@ -1322,19 +1322,42 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 78, "id": "9448046e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'cost__trip_mode'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2888\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2889\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_CT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost_impact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/src/app/saved-notebooks/scaffolding.py\u001b[0m in \u001b[0;36mcost_impact\u001b[0;34m(data, dist, repm, mode)\u001b[0m\n\u001b[1;32m 313\u001b[0m \"\"\"\n\u001b[1;32m 314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost__trip_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 316\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrepm\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost__trip_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mrepm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Cost_Impact($)'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrepm\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2897\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2898\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2900\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2901\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2889\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2893\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'" + ] + } + ], "source": [ "expanded_ct = scaffolding.cost(expanded_ct, df_CT, 'distance','Replaced_mode', 'Mode_confirm')\n", - "# expanded_ct = scaffolding.cost_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" + "expanded_ct = scaffolding.cost_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" ] }, { "cell_type": "code", - "execution_count": 63, - "id": "0a9ce7f7", + "execution_count": 77, + "id": "8ba323bc", "metadata": {}, "outputs": [ { @@ -1356,7 +1379,7 @@ " dtype='object')" ] }, - "execution_count": 63, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -1406,7 +1429,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c3fcd17e", + "id": "5bcc1fd1", "metadata": {}, "outputs": [], "source": [] From fc96fed72de1ae8921d8bbee95952b41c3fefa54 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 12:09:49 -0700 Subject: [PATCH 11/35] blah --- viz_scripts/scaffolding.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 8100067..28462d1 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -178,7 +178,7 @@ def cost(data, cost, dist, repm, mode): cost[mode] = cost[repm] dic_cost__trip = dict(zip(cost[mode],cost['C($/PMT)'])) data['cost__trip_'+mode] = data[mode].map(dic_cost__trip) - assert False + return data From 406fbe586c72db96c577a47cf6ead761ed29d81a Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 12:18:17 -0700 Subject: [PATCH 12/35] Mimicking Energy Calculations Basically doing all the same stuff for cost and time as for energy and CO2. --- .../cost_and_time_impact_estimates.ipynb | 133 +- viz_scripts/energy_calculations.ipynb | 1461 ++++++++++++++++- viz_scripts/mapping_dictionaries.ipynb | 2 +- 3 files changed, 1500 insertions(+), 96 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 5dd4818..17cf503 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -36,10 +36,18 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 1, "id": "b9624fe3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting to database URL db\n" + ] + } + ], "source": [ "# user defined modules\n", "import scaffolding\n", @@ -54,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 2, "id": "29424542", "metadata": {}, "outputs": [], @@ -68,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 3, "id": "e55a1a1f", "metadata": {}, "outputs": [], @@ -88,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 4, "id": "ef7dd45c", "metadata": {}, "outputs": [], @@ -101,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 5, "id": "9beff67f", "metadata": {}, "outputs": [], @@ -112,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 6, "id": "6a7cdcde", "metadata": {}, "outputs": [ @@ -639,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 7, "id": "c50baf4a", "metadata": {}, "outputs": [ @@ -898,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 8, "id": "c2dd6e2a", "metadata": {}, "outputs": [ @@ -1230,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 9, "id": "f948dc57", "metadata": {}, "outputs": [ @@ -1240,7 +1248,7 @@ "(2374, 36)" ] }, - "execution_count": 72, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1253,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 10, "id": "9495e947", "metadata": {}, "outputs": [ @@ -1263,7 +1271,7 @@ "(2374, 41)" ] }, - "execution_count": 73, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1282,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 11, "id": "9e27d0e9", "metadata": {}, "outputs": [], @@ -1293,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 12, "id": "e5330285", "metadata": {}, "outputs": [ @@ -1322,33 +1330,10 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 13, "id": "9448046e", "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'cost__trip_mode'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2888\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2889\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_CT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexpanded_ct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaffolding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost_impact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_ct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'distance_miles'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Replaced_mode'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Mode_confirm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/src/app/saved-notebooks/scaffolding.py\u001b[0m in \u001b[0;36mcost_impact\u001b[0;34m(data, dist, repm, mode)\u001b[0m\n\u001b[1;32m 313\u001b[0m \"\"\"\n\u001b[1;32m 314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost__trip_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 316\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrepm\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost__trip_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mrepm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Cost_Impact($)'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/miniconda-4.8.3/envs/emission/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2889\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2890\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2893\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'cost__trip_mode'" - ] - } - ], + "outputs": [], "source": [ "expanded_ct = scaffolding.cost(expanded_ct, df_CT, 'distance','Replaced_mode', 'Mode_confirm')\n", "expanded_ct = scaffolding.cost_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" @@ -1356,8 +1341,8 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "8ba323bc", + "execution_count": 14, + "id": "41460ce2", "metadata": {}, "outputs": [ { @@ -1374,12 +1359,13 @@ " 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id',\n", " 'metadata_write_ts', 'mode_confirm', 'purpose_confirm', 'replaced_mode',\n", " 'Trip_purpose', 'Mode_confirm', 'Replaced_mode', 'Mode_confirm_fuel',\n", - " 'Replaced_mode_fuel', 'distance_miles', 'dura__trip_Replaced_mode',\n", - " 'dura__trip_Mode_confirm'],\n", + " 'Replaced_mode_fuel', 'distance_miles', 'cost__trip_Replaced_mode',\n", + " 'cost__trip_Mode_confirm', 'Mode_confirm_cost', 'Replaced_mode_cost',\n", + " 'Cost_Impact($)'],\n", " dtype='object')" ] }, - "execution_count": 77, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1390,31 +1376,28 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 16, "id": "69f6ae3d", "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'Energy_Impact(kWH)'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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\u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1559\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1560\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1562\u001b[0m \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Energy_Impact(kWH)'" - ] + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "data=expanded_ct.loc[(expanded_ct['distance_miles'] <= 40)].sort_values(by=['Energy_Impact(kWH)'], ascending=False) \n", - "x='Energy_Impact(kWH)'\n", + "data=expanded_ct.loc[(expanded_ct['distance_miles'] <= 40)].sort_values(by=['Cost_Impact($)'], ascending=False) \n", + "x='Cost_Impact($)'\n", "y='distance_miles'\n", "legend ='Mode_confirm'\n", - "plot_title=\"Sketch of Energy Impact (kWH) by Travel Mode Selected\\n%s\" % quality_text\n", - "file_name ='sketch_distance_energy_impact%s.png' % file_suffix\n", + "plot_title=\"Sketch of Cost_Impact($) by Travel Mode Selected\\n%s\" % quality_text\n", + "file_name ='sketch_distance_cost_impact%s.png' % file_suffix\n", "distancevsenergy(data,x,y,legend,plot_title,file_name)" ] }, @@ -1424,12 +1407,36 @@ "id": "84f69f84", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#eirp : energy impact replaced_mode\n", + "eirc=expanded_ct.groupby('Replaced_mode').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", + "eirc.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "eirc = eirc.reset_index()\n", + "eirc = eirc.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", + "eirc['boolean'] = eirc['Sketch of Total Cost_Impact($)'] > 0\n", + "\n", + "#eimc : energy impact mode_confirm\n", + "eimc=expanded_ct.groupby('Mode_confirm').agg({'Energy_Impact(kWH)': ['sum', 'mean']},)\n", + "eimc.columns = ['Sketch of Total Energy_Impact(kWH)', 'Sketch of Average Energy_Impact(kWH)']\n", + "eimc = eimc.reset_index()\n", + "eimc = eimc.sort_values(by=['Sketch of Total Energy_Impact(kWH)'], ascending=False)\n", + "\n", + "\n", + "subset1 = eirc [['Replaced_mode', 'Sketch of Total Energy_Impact(kWH)']].copy()\n", + "subset1.rename(columns = {'Replaced_mode':'Transport Mode','Sketch of Total Energy_Impact(kWH)':'Replaced_Mode' }, inplace=True)\n", + "\n", + "subset2 = eimc [['Mode_confirm', 'Sketch of Total Energy_Impact(kWH)']].copy()\n", + "subset2.rename(columns = {'Mode_confirm':'Transport Mode','Sketch of Total Energy_Impact(kWH)':'Mode_Confirm' }, inplace=True)\n", + "\n", + "df_plot = pd.merge(subset1, subset2, on=\"Transport Mode\")\n", + "df = pd.melt(df_plot , id_vars=['Transport Mode'], value_vars=['Replaced_Mode','Mode_Confirm'], var_name='selection')\n", + "df.rename(columns = {'value':'Energy Impact (kWH)'}, inplace = True)" + ] }, { "cell_type": "code", "execution_count": null, - "id": "5bcc1fd1", + "id": "64de99f6", "metadata": {}, "outputs": [], "source": [] diff --git a/viz_scripts/energy_calculations.ipynb b/viz_scripts/energy_calculations.ipynb index 22868ca..103b77f 100644 --- a/viz_scripts/energy_calculations.ipynb +++ b/viz_scripts/energy_calculations.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "determined-matrix", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "pharmaceutical-survival", "metadata": {}, "outputs": [], @@ -50,10 +50,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "inner-desktop", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting to database URL db\n" + ] + } + ], "source": [ "import scaffolding \n", "from plots import *" @@ -61,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "terminal-machinery", "metadata": {}, "outputs": [], @@ -80,17 +88,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "official-beatles", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(()>,\n", + " {'work_travel': 'Work',\n", + " 'work': 'Work',\n", + " 'home': 'Home',\n", + " 'meal': 'Meal',\n", + " 'shopping': 'Shopping',\n", + " 'personal_med': 'Personal/Medical',\n", + " 'exercise': 'Recreation/Exercise',\n", + " 'transit_transfer': 'Transit transfer',\n", + " 'pick_drop': 'Pick-up/Drop off',\n", + " 'entertainment': 'Entertainment/Social',\n", + " 'car_mechanic': 'Other',\n", + " 'school': 'School',\n", + " 'revisado_bike': 'Other',\n", + " 'placas_de carro': 'Other',\n", + " 'community_walk': 'Entertainment/Social',\n", + " 'gardening': 'Entertainment/Social',\n", + " 'visiting': 'Entertainment/Social',\n", + " 'church': 'Religious',\n", + " 'community_garden': 'Entertainment/Social',\n", + " 'community_meeting': 'Entertainment/Social',\n", + " 'visit_a friend': 'Entertainment/Social',\n", + " 'aseguranza': 'Other',\n", + " 'meeting_bike': 'Entertainment/Social',\n", + " 'gas_station': 'Other',\n", + " 'iglesia': 'Religious',\n", + " 'curso': 'School',\n", + " 'mi_hija recién aliviada': 'Entertainment/Social',\n", + " 'servicio_comunitario': 'Entertainment/Social',\n", + " 'pago_de aseguranza': 'Other',\n", + " 'grupo_comunitario': 'Entertainment/Social',\n", + " 'caminata_comunitaria': 'Entertainment/Social',\n", + " 'bank': 'Other',\n", + " 'religious': 'Religious',\n", + " 'no_travel': 'No travel',\n", + " 'work_break - short walk': 'Entertainment/Social',\n", + " 'work_- lunch break': 'Meal',\n", + " 'friend_was running errands before dropping me off after work': 'Other',\n", + " 'multiple_errands, etc.': 'Other',\n", + " 'lunch_break': 'Meal',\n", + " 'break': 'Entertainment/Social',\n", + " 'pet': 'Entertainment/Social',\n", + " 'recording_performance at park': 'Entertainment/Social',\n", + " 'not_a trip': 'not_a_trip',\n", + " 'on_the way home': 'Home',\n", + " 'other': 'Other',\n", + " nan: nan})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dic_pur" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "special-davis", "metadata": {}, "outputs": [], @@ -100,40 +165,1136 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "above-network", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[UUID('576e37c7-ab7e-4c03-add7-02486bc3f42e'),\n", + " UUID('8b563348-52b3-4e3e-b046-a0aaf4fcea15'),\n", + " UUID('5079bb93-c9cf-46d7-a643-dfc86bb05605'),\n", + " UUID('feabfccd-dd6c-4e8e-8517-9d7177042483'),\n", + " UUID('113aef67-400e-4e21-a29f-d04e50fc42ea'),\n", + " UUID('c8b9fe22-86f8-449a-b64f-c18a8d20eefc'),\n", + " UUID('e7b24d99-324d-4d6d-b247-9edc87d3c848'),\n", + " UUID('1044195f-af9e-43d4-9407-60594e5e9938'),\n", + " UUID('898b1a5e-cdd4-4a0c-90e4-942fa298e456'),\n", + " UUID('1d292b85-c549-409a-a10d-746e957582a0'),\n", + " UUID('cb3222a7-1e72-4a92-8b7b-2c4795402497'),\n", + " UUID('efdbea3b-eef6-48fc-9558-7585f4ad6f24'),\n", + " UUID('960835ac-9d8a-421d-8b8a-bf816f8a4b92')]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded all confirmed trips of length 953\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " source end_ts \\\n", + "0 DwellSegmentationTimeFilter 1.604364e+09 \n", + "1 DwellSegmentationTimeFilter 1.604604e+09 \n", + "2 DwellSegmentationTimeFilter 1.604604e+09 \n", + "3 DwellSegmentationTimeFilter 1.604606e+09 \n", + "4 DwellSegmentationTimeFilter 1.604610e+09 \n", + "\n", + " end_fmt_time \\\n", + "0 2020-11-02T17:45:22.115000-07:00 \n", + "1 2020-11-05T12:12:12-07:00 \n", + "2 2020-11-05T12:27:22-07:00 \n", + "3 2020-11-05T12:47:29.017000-07:00 \n", + "4 2020-11-05T13:54:28.880000-07:00 \n", + "\n", + " end_loc \\\n", + "0 {'type': 'Point', 'coordinates': [-104.9409405... \n", + "1 {'type': 'Point', 'coordinates': [-105.0670666... \n", + "2 {'type': 'Point', 'coordinates': [-105.080878,... \n", + "3 {'type': 'Point', 'coordinates': [-105.0827029... \n", + "4 {'type': 'Point', 'coordinates': [-105.0824703... \n", + "\n", + " raw_trip start_ts start_fmt_time \\\n", + "0 5fa139609ae96f3a5fcdef31 1.604364e+09 2020-11-02T17:39:08.049000-07:00 \n", + "1 5fa4690763a5e0e8d90c7fa4 1.604601e+09 2020-11-05T11:30:56.952000-07:00 \n", + "2 5fa4690763a5e0e8d90c7fa8 1.604604e+09 2020-11-05T12:22:21.130739-07:00 \n", + "3 5fa4690763a5e0e8d90c7faa 1.604605e+09 2020-11-05T12:42:19.793043-07:00 \n", + "4 5fa4771a533f6ebf89c7c5e3 1.604610e+09 2020-11-05T13:52:57.667396-07:00 \n", + "\n", + " start_loc duration \\\n", + "0 {'type': 'Point', 'coordinates': [-104.9398732... 374.066000 \n", + "1 {'type': 'Point', 'coordinates': [-104.9479963... 2475.048000 \n", + "2 {'type': 'Point', 'coordinates': [-105.0670666... 300.869261 \n", + "3 {'type': 'Point', 'coordinates': [-105.080878,... 309.223957 \n", + "4 {'type': 'Point', 'coordinates': [-105.0827029... 91.212605 \n", + "\n", + " distance ... end_local_dt_minute end_local_dt_second \\\n", + "0 384.730231 ... 45 22 \n", + "1 13765.915676 ... 12 12 \n", + "2 1508.223413 ... 27 22 \n", + "3 434.038504 ... 47 29 \n", + "4 333.230154 ... 54 28 \n", + "\n", + " end_local_dt_weekday end_local_dt_timezone _id \\\n", + "0 0 America/Denver 600533265e173ffb99e07625 \n", + "1 3 America/Denver 600533265e173ffb99e07626 \n", + "2 3 America/Denver 600533265e173ffb99e07627 \n", + "3 3 America/Denver 600533265e173ffb99e07628 \n", + "4 3 America/Denver 600533265e173ffb99e07629 \n", + "\n", + " user_id metadata_write_ts mode_confirm \\\n", + "0 1d292b85-c549-409a-a10d-746e957582a0 1.604402e+09 walk \n", + "1 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 train \n", + "2 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 skateboard \n", + "3 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 not_a_trip \n", + "4 1d292b85-c549-409a-a10d-746e957582a0 1.604614e+09 not_a_trip \n", + "\n", + " purpose_confirm replaced_mode \n", + "0 meal same_mode \n", + "1 personal_med same_mode \n", + "2 transit_transfer bus \n", + "3 transit_transfer same_mode \n", + "4 transit_transfer same_mode \n", + "\n", + "[5 rows x 36 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "expanded_ct = scaffolding.expand_userinputs(labeled_ct)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "cultural-salad", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(519, 36)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "expanded_ct = scaffolding.data_quality_check(expanded_ct)\n", "expanded_ct.shape" @@ -141,7 +1302,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "improved-venture", "metadata": {}, "outputs": [], @@ -158,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "micro-wound", "metadata": {}, "outputs": [], @@ -168,10 +1329,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "efficient-marking", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_2020_11_prepilot\n", + "Based on 519 confirmed trips from 12 users\n", + "of 953 total trips from 12 users (54.46%)\n" + ] + } + ], "source": [ "file_suffix = scaffolding.get_file_suffix(year, month, program)\n", "quality_text = scaffolding.get_quality_text(participant_ct_df, expanded_ct)" @@ -187,7 +1358,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "dimensional-bronze", "metadata": {}, "outputs": [], @@ -206,12 +1377,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "protecting-falls", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Transport ModeselectionEnergy Impact (kWH)
0Car, drove aloneReplaced_Mode576.807
1Taxi/Uber/LyftReplaced_Mode221.614
2BusReplaced_Mode99.372
3Car, with othersReplaced_Mode35.104
4Not a TripReplaced_Mode0.000
5Skate boardReplaced_Mode0.000
6TrainReplaced_Mode-3.628
7Regular BikeReplaced_Mode-4.133
8WalkReplaced_Mode-90.614
9Pilot ebikeReplaced_Mode-674.651
10Car, drove aloneMode_Confirm-339.488
11Taxi/Uber/LyftMode_Confirm-164.893
12BusMode_Confirm-243.929
13Car, with othersMode_Confirm122.079
14Not a TripMode_Confirm0.000
15Skate boardMode_Confirm1.847
16TrainMode_Confirm0.000
17Regular BikeMode_Confirm0.000
18WalkMode_Confirm35.644
19Pilot ebikeMode_Confirm724.044
\n", + "
" + ], + "text/plain": [ + " Transport Mode selection Energy Impact (kWH)\n", + "0 Car, drove alone Replaced_Mode 576.807\n", + "1 Taxi/Uber/Lyft Replaced_Mode 221.614\n", + "2 Bus Replaced_Mode 99.372\n", + "3 Car, with others Replaced_Mode 35.104\n", + "4 Not a Trip Replaced_Mode 0.000\n", + "5 Skate board Replaced_Mode 0.000\n", + "6 Train Replaced_Mode -3.628\n", + "7 Regular Bike Replaced_Mode -4.133\n", + "8 Walk Replaced_Mode -90.614\n", + "9 Pilot ebike Replaced_Mode -674.651\n", + "10 Car, drove alone Mode_Confirm -339.488\n", + "11 Taxi/Uber/Lyft Mode_Confirm -164.893\n", + "12 Bus Mode_Confirm -243.929\n", + "13 Car, with others Mode_Confirm 122.079\n", + "14 Not a Trip Mode_Confirm 0.000\n", + "15 Skate board Mode_Confirm 1.847\n", + "16 Train Mode_Confirm 0.000\n", + "17 Regular Bike Mode_Confirm 0.000\n", + "18 Walk Mode_Confirm 35.644\n", + "19 Pilot ebike Mode_Confirm 724.044" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "unknown-venice", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df= df.sort_values(by=['Energy Impact (kWH)'], ascending=False)\n", "x= 'Energy Impact (kWH)'\n", @@ -300,10 +1675,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "emotional-universal", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "net_energy_saved = round(sum(eirc['Sketch of Total Energy_Impact(kWH)']), 2)\n", "\n", @@ -326,12 +1712,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "dense-programmer", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "data_eb = expanded_ct.query(\"Mode_confirm == 'Pilot ebike'\")\n", "# ebei : ebike energy impact\n", diff --git a/viz_scripts/mapping_dictionaries.ipynb b/viz_scripts/mapping_dictionaries.ipynb index 0733074..71a2d03 100644 --- a/viz_scripts/mapping_dictionaries.ipynb +++ b/viz_scripts/mapping_dictionaries.ipynb @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": null, - "id": "53b56bce", + "id": "be0e873c", "metadata": {}, "outputs": [], "source": [] From 5d8fdc19a5d01d34188fe1df3bd53b172cc9997f Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 13 Jan 2022 14:27:14 -0700 Subject: [PATCH 13/35] Added energy_intensity() unit_test --- viz_scripts/auxiliary_files/cost_time.xlsx | Bin 0 -> 10844 bytes viz_scripts/auxiliary_files/~$cost_time.xlsx | Bin 0 -> 165 bytes viz_scripts/tests.py | 20 ----- viz_scripts/unit_tests.py | 83 +++++++++++++++++++ 4 files changed, 83 insertions(+), 20 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file mode 100644 index 0000000000000000000000000000000000000000..5a932052db2a5d1e1d32a453f59be330b8becc3b GIT binary patch literal 165 zcmWgj%}g%JFV0UZQSeVo%S=vH2rW)6QXm9G8GIQs8Il=_81fm4fjEt!gh7G9A4sQx R#Z!U2P@qgIP=x};5CA3W7%cz* literal 0 HcmV?d00001 diff --git a/viz_scripts/tests.py b/viz_scripts/tests.py deleted file mode 100644 index f3043c2..0000000 --- a/viz_scripts/tests.py +++ /dev/null @@ -1,20 +0,0 @@ -import pandas as pd -import numpy as np -import scaffolding - -def test_energy_intensity(): - - # Inputs - dummy_data = pd.DataFrame({ - - }) - - - - - - - -def test_energy_impact_kWH(): - # - None \ No newline at end of file diff --git a/viz_scripts/unit_tests.py b/viz_scripts/unit_tests.py new file mode 100644 index 0000000..45d75ce --- /dev/null +++ b/viz_scripts/unit_tests.py @@ -0,0 +1,83 @@ +""" +Author: Stanley Y +Purpose: + To test functions in scaffolding + +Credit to: +https://docs.python.org/3.10/library/unittest.html +""" + + +import unittest +import pandas as pd +import numpy as np +import scaffolding + +class TestEnergyIntensity(unittest.TestCase): + + def setUp(self): + self.constants = pd.DataFrame({ + 'mode': ['car', 'bus', 'train'], + 'vals': [12,5,2], + 'test': [0,0,0], + 'energy_intensity_factor': [0, 1, 2], + 'CO2_factor': [1, 2, 3], + '(kWH)/trip': [0.5, 0.2, 0.3] + }) + # Inputs + self.data = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'vals': [1,2,3, 4], + 'test': [0.5,3,0,8] + }) + + + def test_process(self): + expect = [('car', 12), ('bus', 5), ('train', 2)] + zipped = zip(self.constants['mode'], self.constants['vals']) + listed = list(zipped) + self.assertEqual(expect, listed, + 'Zip malfunction') + + expect = { + 'car': 12, + 'bus': 5, + 'train': 2 + } + zipped = zip(self.constants['mode'], self.constants['vals']) + a_dict = dict(zipped) + self.assertEqual(expect, a_dict, + 'Dict malfunction') + + expect = pd.Series( + [12, 12, 5, 2] + ) + a_dict = dict(zip(self.constants['mode'], self.constants['vals'])) + output = self.data['repm'].map(a_dict) + self.assertTrue(expect.equals(output), + 'Map malfunction') + + + def test_function(self): + expect = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'vals': [1,2,3, 4], + 'test': [0.5,3,0,8], + 'ei_mode': [0, 1, 2, 0], + 'CO2_mode': [1, 2, 3, 1], + 'ei_trip_mode': [0.5, 0.2, 0.3, 0.5], + 'ei_repm': [0, 0, 1, 2], + 'CO2_repm': [1, 1, 2, 3], + 'ei_trip_repm': [0.5, 0.5, 0.2, 0.3], + }) + output = scaffolding.energy_intensity(self.data, self.constants, '', 'mode', 'repm') + self.assertTrue(expect.equals(output), + f'{output}') + + + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file From 55cd7cd815c0358c2bd9a2cad7fe28df1dce936b Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 18 Jan 2022 09:24:41 -0700 Subject: [PATCH 14/35] Unit Test Energy Impact --- viz_scripts/auxiliary_files/cost_time.csv | 8 +- viz_scripts/auxiliary_files/cost_time.xlsx | Bin 10844 -> 10877 bytes viz_scripts/auxiliary_files/~$cost_time.xlsx | Bin 165 -> 0 bytes viz_scripts/unit_tests.py | 74 ++++++++++++++++++- 4 files changed, 77 insertions(+), 5 deletions(-) delete mode 100644 viz_scripts/auxiliary_files/~$cost_time.xlsx diff --git a/viz_scripts/auxiliary_files/cost_time.csv b/viz_scripts/auxiliary_files/cost_time.csv index 66b0696..f14649b 100644 --- a/viz_scripts/auxiliary_files/cost_time.csv +++ b/viz_scripts/auxiliary_files/cost_time.csv @@ -1,11 +1,11 @@ mode,C($/PMT),($)/trip,D(hours/PMT),(hours)/trip -"Car, drove alone",0,0,0,0 -"Car, with others",0,0,0,0 -Taxi/Uber/Lyft,0,0,0,0 +"Car, drove alone",0.136,0,0,0 +"Car, with others",0.068,0,0,0 +Taxi/Uber/Lyft,0.129,0,0,0 Bus,0.855,0,0,0 Free Shuttle,0,0,0,0 Train,0.855,0,0,0 -Scooter share,0.0041,0,0,0 +Scooter share,0.15,1,0,0 Pilot ebike,0,0,0,0 Bikeshare,0.09,0,0,0 Walk,0,0,0,0 diff --git a/viz_scripts/auxiliary_files/cost_time.xlsx 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5a932052db2a5d1e1d32a453f59be330b8becc3b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 165 zcmWgj%}g%JFV0UZQSeVo%S=vH2rW)6QXm9G8GIQs8Il=_81fm4fjEt!gh7G9A4sQx R#Z!U2P@qgIP=x};5CA3W7%cz* diff --git a/viz_scripts/unit_tests.py b/viz_scripts/unit_tests.py index 45d75ce..6d4bb36 100644 --- a/viz_scripts/unit_tests.py +++ b/viz_scripts/unit_tests.py @@ -14,6 +14,10 @@ import scaffolding class TestEnergyIntensity(unittest.TestCase): + """ + A unit test for energy_intensity function in + the scaffolding.py file + """ def setUp(self): self.constants = pd.DataFrame({ @@ -24,7 +28,7 @@ def setUp(self): 'CO2_factor': [1, 2, 3], '(kWH)/trip': [0.5, 0.2, 0.3] }) - # Inputs + self.data = pd.DataFrame({ 'mode': ['car', 'bus', 'train', 'car'], 'repm': ['car', 'car', 'bus', 'train'], @@ -77,7 +81,75 @@ def test_function(self): f'{output}') +class TestEnergyImpact(unittest.TestCase): + """ + A unit test for energy_impact_kWH function in + the scaffolding.py file + """ + + def setUp(self): + self.conditions = np.array([ + [True, False, False], + [False,True,False], + [False,False,True] + ]) + self.values = np.array([ + [8, 0, 3], + [3,5,7], + [4,2,9] + ]) + self.data = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'dist': [1.5,2.5,3.5,4.5], + 'ei_mode': [1,2,3,1], + 'ei_repm': [1,1,2,3], + 'ei_trip_mode': [7,8,9,7], + 'ei_trip_repm': [7,7,8,9], + 'Mode_confirm_fuel': ['gasoline','diesel','electric','gasoline'], + 'Replaced_mode_fuel': ['gasoline','gasoline','diesel','electric'] + }) + + def test_process(self): + expect = np.array([8, 5, 9]) + output = np.select(self.conditions, self.values) + if(len(expect) != len(output)): + self.assertTrue(False, + f'Select Malfunction (out: {output})') + else: + for i in range(len(expect)): + self.assertEqual(expect[i], output[i], + f'Select Malfunction (out: {output})') + + def test_function(self): + expect = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'dist': [1.5,2.5,3.5,4.5], + 'ei_mode': [1,2,3,1], + 'ei_repm': [1,1,2,3], + 'ei_trip_mode': [7,8,9,7], + 'ei_trip_repm': [7,7,8,9], + 'Mode_confirm_fuel': ['gasoline','diesel','electric','gasoline'], + 'Replaced_mode_fuel': ['gasoline','gasoline','diesel','electric'], + 'repm_EI(kWH)':[1.5*1*0.000293071, + 2.5*1*0.000293071, + 3.5*2*0.000293071, + 4.5*3+9], + 'mode_EI(kWH)':[1.5*1*0.000293071, + 2.5*2*0.000293071, + 3.5*3+9, + 4.5*1*0.000293071], + 'Energy_Impact(kWH)':[round(1.5*1*0.000293071-1.5*1*0.000293071,3), + round(2.5*1*0.000293071-2.5*2*0.000293071,3), + round(3.5*2*0.000293071-(3.5*3+9),3), + round(4.5*3+9-4.5*1*0.000293071,3)] + }) + output = scaffolding.energy_impact_kWH(self.data,'dist','repm', 'mode') + self.assertTrue(np.isclose(expect['Energy_Impact(kWH)'], + output['Energy_Impact(kWH)']).all(), + f'Error in function') if __name__ == '__main__': unittest.main() \ No newline at end of file From ae12f9dbe14e9a3076da1d6bfc02707ad3b87cb5 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 18 Jan 2022 10:47:19 -0700 Subject: [PATCH 15/35] .DS_Store in aux_files Just adding .DS_Store to .gitignore from the auxiliary files as well. .DS_Store in other directories already added. --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 7270470..4de7467 100644 --- a/.gitignore +++ b/.gitignore @@ -133,6 +133,10 @@ dmypy.json # Pyre type checker .pyre/ +<<<<<<< Updated upstream .DS_Store .DS_Store viz_scripts/.DS_Store +======= +viz_scripts/auxiliary_files/.DS_Store +>>>>>>> Stashed changes From 753e8d1b1714d5918bebb2c94d8210f0266e087c Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Fri, 21 Jan 2022 14:45:04 -0700 Subject: [PATCH 16/35] Squashed commit of the following: commit 19869478dcf058a490773fbcf52e50d139ff93cb Author: Young, Stanley A Date: Fri Jan 21 14:42:35 2022 -0700 Update .gitignore *.DS_store Ignore any .DS_store file that is created by MacOS. commit d8445f7e3a07c6e2dad5acc190dc253d3142ca50 Author: Young, Stanley A Date: Fri Jan 21 14:35:58 2022 -0700 Reset Notebook Output (again) I missed one the first time. I also think I removed the .DS_stores, but I don't see that in the commits here. commit b1fe3fa45e46ec4800278b1f97b00d1473c7aa57 Author: Young, Stanley A Date: Fri Jan 21 14:24:26 2022 -0700 Create unit_tests.py Unit testing of energy_intensity and energy_impact functions in scaffolding with manually calculated expectations. commit 93fcc0b01f7049ea669a34eaa7f167ef874c82f6 Author: Young, Stanley A Date: Tue Jan 18 13:06:47 2022 -0700 Reset Notebook Outputs commit a295529937049225b7943367c05ce74282500532 Author: Young, Stanley A Date: Tue Jan 18 11:10:23 2022 -0700 Add .DS_Store to .gitignore --- .gitignore | 3 + .../cost_and_time_impact_estimates.ipynb | 1221 +---------------- viz_scripts/mapping_dictionaries.ipynb | 24 +- 3 files changed, 42 insertions(+), 1206 deletions(-) diff --git a/.gitignore b/.gitignore index 4de7467..b6271e6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# MacOS file system +*.DS_Store + # Custom excludes viz_scripts/conf plots diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 17cf503..9ebdbbe 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -36,18 +36,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b9624fe3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting to database URL db\n" - ] - } - ], + "outputs": [], "source": [ "# user defined modules\n", "import scaffolding\n", @@ -62,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "29424542", "metadata": {}, "outputs": [], @@ -76,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e55a1a1f", "metadata": {}, "outputs": [], @@ -96,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "ef7dd45c", "metadata": {}, "outputs": [], @@ -109,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "9beff67f", "metadata": {}, "outputs": [], @@ -120,526 +112,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6a7cdcde", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[UUID('576e37c7-ab7e-4c03-add7-02486bc3f42e'),\n", - " UUID('8b563348-52b3-4e3e-b046-a0aaf4fcea15'),\n", - " UUID('5079bb93-c9cf-46d7-a643-dfc86bb05605'),\n", - " UUID('feabfccd-dd6c-4e8e-8517-9d7177042483'),\n", - " UUID('113aef67-400e-4e21-a29f-d04e50fc42ea'),\n", - " UUID('c8b9fe22-86f8-449a-b64f-c18a8d20eefc'),\n", - " UUID('e7b24d99-324d-4d6d-b247-9edc87d3c848'),\n", - " UUID('1044195f-af9e-43d4-9407-60594e5e9938'),\n", - " UUID('898b1a5e-cdd4-4a0c-90e4-942fa298e456'),\n", - " UUID('1d292b85-c549-409a-a10d-746e957582a0'),\n", - " UUID('cb3222a7-1e72-4a92-8b7b-2c4795402497'),\n", - " UUID('efdbea3b-eef6-48fc-9558-7585f4ad6f24'),\n", - " UUID('960835ac-9d8a-421d-8b8a-bf816f8a4b92')]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded all confirmed trips of length 3492\n" - ] - }, - { - "data": { - "text/html": [ - "
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sourceend_tsend_fmt_timeend_locraw_tripstart_tsstart_fmt_timestart_locdurationdistance...end_local_dt_minuteend_local_dt_secondend_local_dt_weekdayend_local_dt_timezone_iduser_idmetadata_write_tsmode_confirmpurpose_confirmreplaced_mode
0DwellSegmentationTimeFilter1.604364e+092020-11-02T17:45:22.115000-07:00{'type': 'Point', 'coordinates': [-104.9409405...5fa139609ae96f3a5fcdef311.604364e+092020-11-02T17:39:08.049000-07:00{'type': 'Point', 'coordinates': [-104.9398732...374.066000384.730231...45220America/Denver600533265e173ffb99e076251d292b85-c549-409a-a10d-746e957582a01.604402e+09walkmealsame_mode
1DwellSegmentationTimeFilter1.604604e+092020-11-05T12:12:12-07:00{'type': 'Point', 'coordinates': [-105.0670666...5fa4690763a5e0e8d90c7fa41.604601e+092020-11-05T11:30:56.952000-07:00{'type': 'Point', 'coordinates': [-104.9479963...2475.04800013765.915676...12123America/Denver600533265e173ffb99e076261d292b85-c549-409a-a10d-746e957582a01.604610e+09trainpersonal_medsame_mode
2DwellSegmentationTimeFilter1.604604e+092020-11-05T12:27:22-07:00{'type': 'Point', 'coordinates': [-105.080878,...5fa4690763a5e0e8d90c7fa81.604604e+092020-11-05T12:22:21.130739-07:00{'type': 'Point', 'coordinates': [-105.0670666...300.8692611508.223413...27223America/Denver600533265e173ffb99e076271d292b85-c549-409a-a10d-746e957582a01.604610e+09skateboardtransit_transferbus
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5 rows × 36 columns

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" - ], - "text/plain": [ - " source end_ts \\\n", - "0 DwellSegmentationTimeFilter 1.604364e+09 \n", - "1 DwellSegmentationTimeFilter 1.604604e+09 \n", - "2 DwellSegmentationTimeFilter 1.604604e+09 \n", - "3 DwellSegmentationTimeFilter 1.604606e+09 \n", - "4 DwellSegmentationTimeFilter 1.604610e+09 \n", - "\n", - " end_fmt_time \\\n", - "0 2020-11-02T17:45:22.115000-07:00 \n", - "1 2020-11-05T12:12:12-07:00 \n", - "2 2020-11-05T12:27:22-07:00 \n", - "3 2020-11-05T12:47:29.017000-07:00 \n", - "4 2020-11-05T13:54:28.880000-07:00 \n", - "\n", - " end_loc \\\n", - "0 {'type': 'Point', 'coordinates': [-104.9409405... \n", - "1 {'type': 'Point', 'coordinates': [-105.0670666... \n", - "2 {'type': 'Point', 'coordinates': [-105.080878,... \n", - "3 {'type': 'Point', 'coordinates': [-105.0827029... \n", - "4 {'type': 'Point', 'coordinates': [-105.0824703... \n", - "\n", - " raw_trip start_ts start_fmt_time \\\n", - "0 5fa139609ae96f3a5fcdef31 1.604364e+09 2020-11-02T17:39:08.049000-07:00 \n", - "1 5fa4690763a5e0e8d90c7fa4 1.604601e+09 2020-11-05T11:30:56.952000-07:00 \n", - "2 5fa4690763a5e0e8d90c7fa8 1.604604e+09 2020-11-05T12:22:21.130739-07:00 \n", - "3 5fa4690763a5e0e8d90c7faa 1.604605e+09 2020-11-05T12:42:19.793043-07:00 \n", - "4 5fa4771a533f6ebf89c7c5e3 1.604610e+09 2020-11-05T13:52:57.667396-07:00 \n", - "\n", - " start_loc duration \\\n", - "0 {'type': 'Point', 'coordinates': [-104.9398732... 374.066000 \n", - "1 {'type': 'Point', 'coordinates': [-104.9479963... 2475.048000 \n", - "2 {'type': 'Point', 'coordinates': [-105.0670666... 300.869261 \n", - "3 {'type': 'Point', 'coordinates': [-105.080878,... 309.223957 \n", - "4 {'type': 'Point', 'coordinates': [-105.0827029... 91.212605 \n", - "\n", - " distance ... end_local_dt_minute end_local_dt_second \\\n", - "0 384.730231 ... 45 22 \n", - "1 13765.915676 ... 12 12 \n", - "2 1508.223413 ... 27 22 \n", - "3 434.038504 ... 47 29 \n", - "4 333.230154 ... 54 28 \n", - "\n", - " end_local_dt_weekday end_local_dt_timezone _id \\\n", - "0 0 America/Denver 600533265e173ffb99e07625 \n", - "1 3 America/Denver 600533265e173ffb99e07626 \n", - "2 3 America/Denver 600533265e173ffb99e07627 \n", - "3 3 America/Denver 600533265e173ffb99e07628 \n", - "4 3 America/Denver 600533265e173ffb99e07629 \n", - "\n", - " user_id metadata_write_ts mode_confirm \\\n", - "0 1d292b85-c549-409a-a10d-746e957582a0 1.604402e+09 walk \n", - "1 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 train \n", - "2 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 skateboard \n", - "3 1d292b85-c549-409a-a10d-746e957582a0 1.604610e+09 not_a_trip \n", - "4 1d292b85-c549-409a-a10d-746e957582a0 1.604614e+09 not_a_trip \n", - "\n", - " purpose_confirm replaced_mode \n", - "0 meal same_mode \n", - "1 personal_med same_mode \n", - "2 transit_transfer bus \n", - "3 transit_transfer same_mode \n", - "4 transit_transfer same_mode \n", - "\n", - "[5 rows x 36 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Just expand the user_input feature to multiple features for each entry\n", "expanded_ct = scaffolding.expand_userinputs(labeled_ct)" @@ -1238,21 +145,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "f948dc57", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2374, 36)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Removes some rows that don't show a change from another mode to pilot e-bike + name same_mode as confirmed_mode\n", "expanded_ct = scaffolding.data_quality_check(expanded_ct)\n", @@ -1261,21 +157,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "9495e947", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2374, 41)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "## Mapping new labels with dictionaries\n", "expanded_ct['Trip_purpose']= expanded_ct['purpose_confirm'].map(dic_pur)\n", @@ -1290,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "9e27d0e9", "metadata": {}, "outputs": [], @@ -1301,20 +186,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "e5330285", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Based on 2374 confirmed trips from 12 users\n", - "of 3492 total trips from 13 users (67.98%)\n" - ] - } - ], + "outputs": [], "source": [ "file_suffix = scaffolding.get_file_suffix(year, month, program)\n", "quality_text = scaffolding.get_quality_text(participant_ct_df, expanded_ct)" @@ -1325,12 +200,12 @@ "id": "e0420cf9", "metadata": {}, "source": [ - "### This is where I need to make changes to include the cost and time impact..." + "### Analysis of Time Impact" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "9448046e", "metadata": {}, "outputs": [], @@ -1341,56 +216,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "41460ce2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts',\n", - " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place',\n", - " 'end_place', 'cleaned_trip', 'user_input', 'start_local_dt_year',\n", - " 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour',\n", - " 'start_local_dt_minute', 'start_local_dt_second',\n", - " 'start_local_dt_weekday', 'start_local_dt_timezone',\n", - " 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day',\n", - " 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second',\n", - " 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id',\n", - " 'metadata_write_ts', 'mode_confirm', 'purpose_confirm', 'replaced_mode',\n", - " 'Trip_purpose', 'Mode_confirm', 'Replaced_mode', 'Mode_confirm_fuel',\n", - " 'Replaced_mode_fuel', 'distance_miles', 'cost__trip_Replaced_mode',\n", - " 'cost__trip_Mode_confirm', 'Mode_confirm_cost', 'Replaced_mode_cost',\n", - " 'Cost_Impact($)'],\n", - " dtype='object')" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "expanded_ct.columns" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "69f6ae3d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "data=expanded_ct.loc[(expanded_ct['distance_miles'] <= 40)].sort_values(by=['Cost_Impact($)'], ascending=False) \n", "x='Cost_Impact($)'\n", @@ -1433,10 +272,18 @@ "df.rename(columns = {'value':'Energy Impact (kWH)'}, inplace = True)" ] }, + { + "cell_type": "markdown", + "id": "e12ee241", + "metadata": {}, + "source": [ + "### Analysis of Time Impact" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "64de99f6", + "id": "3065d606", "metadata": {}, "outputs": [], "source": [] diff --git a/viz_scripts/mapping_dictionaries.ipynb b/viz_scripts/mapping_dictionaries.ipynb index 71a2d03..b87b6d4 100644 --- a/viz_scripts/mapping_dictionaries.ipynb +++ b/viz_scripts/mapping_dictionaries.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "available-fusion", "metadata": {}, "outputs": [], @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "obvious-chapter", "metadata": {}, "outputs": [], @@ -20,7 +20,6 @@ "df_pur= pd.read_csv(r'auxiliary_files/purpose_labels.csv')\n", "df_re = pd.read_csv(r'auxiliary_files/mode_labels.csv')\n", "df_EI = pd.read_csv(r'auxiliary_files/energy_intensity.csv')\n", - "df_CT = pd.read_csv(r'auxiliary_files/cost_time.csv')\n", "\n", "#dictionaries:\n", "dic_pur = dict(zip(df_pur['purpose_confirm'],df_pur['bin_purpose'])) # bin purpose\n", @@ -30,25 +29,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "younger-indication", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stored 'df_EI' (DataFrame)\n", - "Stored 'df_CT' (DataFrame)\n", - "Stored 'dic_re' (dict)\n", - "Stored 'dic_pur' (dict)\n", - "Stored 'dic_fuel' (dict)\n" - ] - } - ], + "outputs": [], "source": [ "%store df_EI \n", - "%store df_CT\n", "%store dic_re \n", "%store dic_pur \n", "%store dic_fuel " @@ -57,7 +43,7 @@ { "cell_type": "code", "execution_count": null, - "id": "be0e873c", + "id": "8a25d7e2", "metadata": {}, "outputs": [], "source": [] From e3c264eb6b6bbc0527e34ec782316c022577ed54 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Fri, 21 Jan 2022 14:49:42 -0700 Subject: [PATCH 17/35] Add Unit Tests (again?) I thought I already did this. I guess I have to do it again though. --- .gitignore | 3 + viz_scripts/.DS_Store | Bin 6148 -> 8196 bytes viz_scripts/unit_tests.py | 155 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 158 insertions(+) create mode 100644 viz_scripts/unit_tests.py diff --git a/.gitignore b/.gitignore index 6a0eab5..aed09a5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# MacOS +*.DS_Store + # Custom excludes viz_scripts/conf plots diff --git a/viz_scripts/.DS_Store b/viz_scripts/.DS_Store index 147fc0ae208e4357c20fe72befadf1956a5d71e7..3784b986d64f2401a3b5cac930a34e3e4fb600ff 100644 GIT binary patch delta 582 zcmbVJyGjF55S=9(b0zB|pfO^^jVK7lm{l>6AgEvw8a-M-OA|rW_M6FX{M7boLucdeX468NT(TC$pPG2IP zcW@$>1}4wDo{)VjC#2aRLK%P$oumqS%!9T_`>-q$1zDBK;)SGfuJ*`U=ch+V8AaTL zm8pQ~=+1l$+%+FbaJ*}yniMXYx*I|qj#Gf+1W2yg=lSCFY2 a3%@f@=9lpV*}=dBu@B?~hRyLjbC>~Ed>N1c diff --git a/viz_scripts/unit_tests.py b/viz_scripts/unit_tests.py new file mode 100644 index 0000000..6d4bb36 --- /dev/null +++ b/viz_scripts/unit_tests.py @@ -0,0 +1,155 @@ +""" +Author: Stanley Y +Purpose: + To test functions in scaffolding + +Credit to: +https://docs.python.org/3.10/library/unittest.html +""" + + +import unittest +import pandas as pd +import numpy as np +import scaffolding + +class TestEnergyIntensity(unittest.TestCase): + """ + A unit test for energy_intensity function in + the scaffolding.py file + """ + + def setUp(self): + self.constants = pd.DataFrame({ + 'mode': ['car', 'bus', 'train'], + 'vals': [12,5,2], + 'test': [0,0,0], + 'energy_intensity_factor': [0, 1, 2], + 'CO2_factor': [1, 2, 3], + '(kWH)/trip': [0.5, 0.2, 0.3] + }) + + self.data = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'vals': [1,2,3, 4], + 'test': [0.5,3,0,8] + }) + + + def test_process(self): + expect = [('car', 12), ('bus', 5), ('train', 2)] + zipped = zip(self.constants['mode'], self.constants['vals']) + listed = list(zipped) + self.assertEqual(expect, listed, + 'Zip malfunction') + + expect = { + 'car': 12, + 'bus': 5, + 'train': 2 + } + zipped = zip(self.constants['mode'], self.constants['vals']) + a_dict = dict(zipped) + self.assertEqual(expect, a_dict, + 'Dict malfunction') + + expect = pd.Series( + [12, 12, 5, 2] + ) + a_dict = dict(zip(self.constants['mode'], self.constants['vals'])) + output = self.data['repm'].map(a_dict) + self.assertTrue(expect.equals(output), + 'Map malfunction') + + + def test_function(self): + expect = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'vals': [1,2,3, 4], + 'test': [0.5,3,0,8], + 'ei_mode': [0, 1, 2, 0], + 'CO2_mode': [1, 2, 3, 1], + 'ei_trip_mode': [0.5, 0.2, 0.3, 0.5], + 'ei_repm': [0, 0, 1, 2], + 'CO2_repm': [1, 1, 2, 3], + 'ei_trip_repm': [0.5, 0.5, 0.2, 0.3], + }) + output = scaffolding.energy_intensity(self.data, self.constants, '', 'mode', 'repm') + self.assertTrue(expect.equals(output), + f'{output}') + + +class TestEnergyImpact(unittest.TestCase): + """ + A unit test for energy_impact_kWH function in + the scaffolding.py file + """ + + def setUp(self): + self.conditions = np.array([ + [True, False, False], + [False,True,False], + [False,False,True] + ]) + self.values = np.array([ + [8, 0, 3], + [3,5,7], + [4,2,9] + ]) + self.data = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'dist': [1.5,2.5,3.5,4.5], + 'ei_mode': [1,2,3,1], + 'ei_repm': [1,1,2,3], + 'ei_trip_mode': [7,8,9,7], + 'ei_trip_repm': [7,7,8,9], + 'Mode_confirm_fuel': ['gasoline','diesel','electric','gasoline'], + 'Replaced_mode_fuel': ['gasoline','gasoline','diesel','electric'] + }) + + + def test_process(self): + expect = np.array([8, 5, 9]) + output = np.select(self.conditions, self.values) + if(len(expect) != len(output)): + self.assertTrue(False, + f'Select Malfunction (out: {output})') + else: + for i in range(len(expect)): + self.assertEqual(expect[i], output[i], + f'Select Malfunction (out: {output})') + + def test_function(self): + expect = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'dist': [1.5,2.5,3.5,4.5], + 'ei_mode': [1,2,3,1], + 'ei_repm': [1,1,2,3], + 'ei_trip_mode': [7,8,9,7], + 'ei_trip_repm': [7,7,8,9], + 'Mode_confirm_fuel': ['gasoline','diesel','electric','gasoline'], + 'Replaced_mode_fuel': ['gasoline','gasoline','diesel','electric'], + 'repm_EI(kWH)':[1.5*1*0.000293071, + 2.5*1*0.000293071, + 3.5*2*0.000293071, + 4.5*3+9], + 'mode_EI(kWH)':[1.5*1*0.000293071, + 2.5*2*0.000293071, + 3.5*3+9, + 4.5*1*0.000293071], + 'Energy_Impact(kWH)':[round(1.5*1*0.000293071-1.5*1*0.000293071,3), + round(2.5*1*0.000293071-2.5*2*0.000293071,3), + round(3.5*2*0.000293071-(3.5*3+9),3), + round(4.5*3+9-4.5*1*0.000293071,3)] + }) + output = scaffolding.energy_impact_kWH(self.data,'dist','repm', 'mode') + self.assertTrue(np.isclose(expect['Energy_Impact(kWH)'], + output['Energy_Impact(kWH)']).all(), + f'Error in function') + +if __name__ == '__main__': + unittest.main() \ No newline at end of file From a269ee2f75e5d05f15d60acbb251c3fb9ea7449f Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Fri, 21 Jan 2022 14:53:16 -0700 Subject: [PATCH 18/35] Remove .DS_Store (again) Why does this keep getting committed>? I added it to the .gitignore! --- .DS_Store | Bin 6148 -> 0 bytes viz_scripts/.DS_Store | Bin 6148 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .DS_Store delete mode 100644 viz_scripts/.DS_Store diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index cf3a8894f99304d498ec589f7d349758bba2edcc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK%Wl&^6upzC)TTmY0Ti+F28l(Al86EoLNaNxh*TcJ2o``+J2uq9@kFuHs0~5c z@D=a_d7!U-C8sh5^IC|HgpWyH_a0j_uJq`g8vdFqUEDCT1f#03Kpj zC!K`%f`kW%6Nkn<=#8IJV2Oj`4bA{gDdmVtDJ=MZDfq9^E_G;!+VqG#YA<@Tje_@O zdQ1bE*F%jFD)rFYrC4Gr*PCIb{bF7Av5;vMc@pHvDJ#_cQSh|sDOUG@TJ#WjOzx6j zh*$?(F&$AC>*-;QF={QB8G~Zu?K@w+qt@fRjR?4gA)-2n$>G zJT)N;bt~5&J!c-_wg);rbU@9Erj`njk&J?O}V*2sHbY-SrC`?vI zen+|!a}}D_Fkl#1WMD;gE8_ldpZxw`3^F~#fMMWFF~G_#uhqmS>AQ95V`+%1_$pKi<{V)FU5zt^7=f4{0ZD^t3?n2`N(jn@ zcR+#{;8{3uszAp|?o?l-%hpY^xv-7FEQ#-O!9R3oAa3S;>as^17d z=W;?yre^`DBt}dTdDN#Pa!**>4$FXL;BRAqzuiTAvjcjKZ+H6pZA&|(Y&T8=KTdsE z@-}fF<>n6&Xhd`5!!jvYeJ)s)DWH@RYR}rKrGni&wO|*}D+;M!uzi1)tv}3qW|Uw2 zUL0ll`uaCjnk!$JzgVeOE7gVS%VsAXHghi@WDT$XoLi5*yyF}FNt(vTy5~QQ!l7Hc z`Y=s$FG|9`I7h7zCNG{vNh=*T(m~S7Bu-O5kVI~6Wi-0Aap&IVs@Ky1l`gc7j36&~^0J%uyn+k?#@EI#MNZ~|Sy{sfidnxGe6P`= zVaq#a&qYrlW5^KMm1rFE5T%7NddP7Gvn8*P1I_b^S#*`l=QFofmI2Gaf5ZUq4-N`r z-QYx{Y#pfN3IHslTLe1)NnjtxVBO$EBc4E%b_HrzWv&=XyQAMRc6EajjoO`*xqK*- zS(zJ(lI)1@h&idcMq673ECaI)%;{#0&;LiK-~VTwY|AoW8Th9dQ01NGP6MxG&ejtz w$7ijBvWLRLb`y=_g327nqVQ3?j3R<(4lBUA!HGunK Date: Fri, 21 Jan 2022 15:21:58 -0700 Subject: [PATCH 19/35] Function + constants + load Defined speed function stub and started test of speed function. Loaded available constants for cost into cost_time.csv. Added code to mapping_dictionaries.ipynb for loading cost_time constants into dataframe. --- viz_scripts/auxiliary_files/cost_time.csv | 6 +++--- viz_scripts/mapping_dictionaries.ipynb | 2 ++ viz_scripts/scaffolding.py | 24 +++++++++++++++++++++- viz_scripts/unit_tests.py | 25 +++++++++++++++++++++++ 4 files changed, 53 insertions(+), 4 deletions(-) diff --git a/viz_scripts/auxiliary_files/cost_time.csv b/viz_scripts/auxiliary_files/cost_time.csv index f14649b..822ffce 100644 --- a/viz_scripts/auxiliary_files/cost_time.csv +++ b/viz_scripts/auxiliary_files/cost_time.csv @@ -1,7 +1,7 @@ mode,C($/PMT),($)/trip,D(hours/PMT),(hours)/trip -"Car, drove alone",0.136,0,0,0 -"Car, with others",0.068,0,0,0 -Taxi/Uber/Lyft,0.129,0,0,0 +"Car, drove alone",0.55,0,0,0 +"Car, with others",0.275,0,0,0 +Taxi/Uber/Lyft,2.5,0,0,0 Bus,0.855,0,0,0 Free Shuttle,0,0,0,0 Train,0.855,0,0,0 diff --git a/viz_scripts/mapping_dictionaries.ipynb b/viz_scripts/mapping_dictionaries.ipynb index b87b6d4..baa1b45 100644 --- a/viz_scripts/mapping_dictionaries.ipynb +++ b/viz_scripts/mapping_dictionaries.ipynb @@ -20,6 +20,7 @@ "df_pur= pd.read_csv(r'auxiliary_files/purpose_labels.csv')\n", "df_re = pd.read_csv(r'auxiliary_files/mode_labels.csv')\n", "df_EI = pd.read_csv(r'auxiliary_files/energy_intensity.csv')\n", + "df_CT = pd.read_csv(r'auxiliary_files/cost_time.csv')\n", "\n", "#dictionaries:\n", "dic_pur = dict(zip(df_pur['purpose_confirm'],df_pur['bin_purpose'])) # bin purpose\n", @@ -35,6 +36,7 @@ "outputs": [], "source": [ "%store df_EI \n", + "%store df_CT\n", "%store dic_re \n", "%store dic_pur \n", "%store dic_fuel " diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 28462d1..387e655 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -337,4 +337,26 @@ def time_impact(data, dist, repm, mode): data[repm+'_dura'] = data[dist] * data['dura__trip_repm'] data['Cost_Impact($)'] = round((data[mode+'_dura'] - data[repm+'_dura']),3) - return data \ No newline at end of file + return data + + +def calc_avg_speed(data, dist, time, mode): + """ + Purpose: + To determine average speed of modes in CanBikeCO data + + Parameters: + data - CanBikeCO data input + dist - feature name in df of feature with distance in miles + time - feature name in df of feature with time information + mode - feature name in df of feature with confirmed mode + + Process: + Calculate and append speeds of each trip + Aggregate speeds by mode + Find average each mode + Save averages in auxiallary files + """ + + + None \ No newline at end of file diff --git a/viz_scripts/unit_tests.py b/viz_scripts/unit_tests.py index 6d4bb36..027e1f8 100644 --- a/viz_scripts/unit_tests.py +++ b/viz_scripts/unit_tests.py @@ -151,5 +151,30 @@ def test_function(self): output['Energy_Impact(kWH)']).all(), f'Error in function') + + +class TestCalcAvgSpeed(unittest.TestCase): + """ + A unit test for calc_avg_speed function in + the scaffolding.py file + """ + + def setUp(self): + pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'dist': [1,2,3,4], + 'time': [] + }) + + + def test_process(self): + None + + + def test_function(self): + None + + + if __name__ == '__main__': unittest.main() \ No newline at end of file From 01f583d94be151d0aaea268df5d263a260a4c78f Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Fri, 21 Jan 2022 15:26:24 -0700 Subject: [PATCH 20/35] unit_conversions + duration Calculated duration in the unit_conversions function in scaffolding for input into the calc_avg_speed function. --- viz_scripts/cost_and_time_impact_estimates.ipynb | 10 ++++++++++ viz_scripts/scaffolding.py | 1 + 2 files changed, 11 insertions(+) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 9ebdbbe..4ef6eca 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -195,6 +195,16 @@ "quality_text = scaffolding.get_quality_text(participant_ct_df, expanded_ct)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "7937a492", + "metadata": {}, + "outputs": [], + "source": [ + "expanded_ct.columns" + ] + }, { "cell_type": "markdown", "id": "e0420cf9", diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 387e655..53539b2 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -105,6 +105,7 @@ def data_quality_check(expanded_ct): def unit_conversions(df): df['distance_miles']= df["distance"]*0.00062 #meters to miles + df['duration'] = df['end_ts'] - df['start_ts'] def energy_intensity(df,df1,distance,col1,col2): """Inputs: From c37fd3db1dcf79a0ec1551c6dad404af86906f49 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Fri, 21 Jan 2022 15:30:17 -0700 Subject: [PATCH 21/35] Convert duration to hours Anticipate speeds in MPH, changing the duration feature to hours. --- viz_scripts/cost_and_time_impact_estimates.ipynb | 4 ++-- viz_scripts/scaffolding.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 4ef6eca..5dffc6a 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -198,11 +198,11 @@ { "cell_type": "code", "execution_count": null, - "id": "7937a492", + "id": "9e21a643", "metadata": {}, "outputs": [], "source": [ - "expanded_ct.columns" + "expanded_ct['duration']" ] }, { diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 53539b2..6939b72 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -105,7 +105,7 @@ def data_quality_check(expanded_ct): def unit_conversions(df): df['distance_miles']= df["distance"]*0.00062 #meters to miles - df['duration'] = df['end_ts'] - df['start_ts'] + df['duration_h'] = (df['end_ts'] - df['start_ts']) / 60 / 60 #seconds to hours def energy_intensity(df,df1,distance,col1,col2): """Inputs: From afe129ccdd3ff205b8a3759b22cf3ae2eca38c39 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Fri, 21 Jan 2022 15:32:59 -0700 Subject: [PATCH 22/35] Discovered pre-existing duration feature Using pre-existing duration feature now. Assuming it is in seconds. --- viz_scripts/cost_and_time_impact_estimates.ipynb | 4 ++-- viz_scripts/scaffolding.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 5dffc6a..e576d6d 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -198,11 +198,11 @@ { "cell_type": "code", "execution_count": null, - "id": "9e21a643", + "id": "bec31447", "metadata": {}, "outputs": [], "source": [ - "expanded_ct['duration']" + "expanded_ct['duration_h']" ] }, { diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 6939b72..c8a0422 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -105,7 +105,7 @@ def data_quality_check(expanded_ct): def unit_conversions(df): df['distance_miles']= df["distance"]*0.00062 #meters to miles - df['duration_h'] = (df['end_ts'] - df['start_ts']) / 60 / 60 #seconds to hours + df['duration_h'] = df['duration'] / 60 / 60 #seconds to hours def energy_intensity(df,df1,distance,col1,col2): """Inputs: From 45f568b5d04a74732d1ed1e16d22fdceed035720 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Fri, 21 Jan 2022 16:23:17 -0700 Subject: [PATCH 23/35] Calculate Average Speed Calculate average speed defined and tested. --- .../{cost_time.csv => cost.csv} | 0 viz_scripts/auxiliary_files/cost_time.xlsx | Bin 10877 -> 0 bytes viz_scripts/auxiliary_files/time.csv | 4 ++ viz_scripts/mapping_dictionaries.ipynb | 2 +- viz_scripts/scaffolding.py | 31 ++++++++-- viz_scripts/unit_tests.py | 58 +++++++++++++++++- 6 files changed, 86 insertions(+), 9 deletions(-) rename viz_scripts/auxiliary_files/{cost_time.csv => cost.csv} (100%) delete mode 100644 viz_scripts/auxiliary_files/cost_time.xlsx create mode 100644 viz_scripts/auxiliary_files/time.csv diff --git a/viz_scripts/auxiliary_files/cost_time.csv b/viz_scripts/auxiliary_files/cost.csv similarity index 100% rename from viz_scripts/auxiliary_files/cost_time.csv rename to viz_scripts/auxiliary_files/cost.csv diff --git a/viz_scripts/auxiliary_files/cost_time.xlsx b/viz_scripts/auxiliary_files/cost_time.xlsx deleted file mode 100644 index 79d7936b1025ced26532aabf99d3a7009f7e5962..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10877 zcmeHtg;$)(@-{930)xA|I|K>t?(S~E9RdvQK?6ZU2=49#ch^CK1qcqo?VIH8{q1h{ zet*Heea`#NneKjSx_h4LuC6L&IcOM62sj7?2nYx=h-^lCM>|Le2sBs-2n+}WC_OR9 zH*OYh+>F${oh)1pn7thAN%LT!XmcT;!2SPM{)<^x(4!!Tob*lLvLJfmsa`%upVR<%fyLg_EF0Vqd){Dk2wv!ZDj5?q_nW!HC#pE<=BZs+6C{qt-SAvh$K*I)c zct}AEi*^zo?S)<7Vj6k51V!EBx7S-az=T2z0aj_>4$74tzZLQ9homza5gW zq8S@3m zm4*4|`+vImU#yUSnR;2Gg3`Zq^C5ijdT#X{ny8$oh;%EN`rAO671a9JPm~0!oiv1K z>iEIXQUUF6ABLA#1!BJsl3i_bmPMmu@>4W;mWRE+b9RTPr*=t|bS~TI1$fL|&RwNT z$$Hazw8ztzHWhu88(OE3oH-M(Mw?*P!bimTOc;hOm>#6tub{JLdQ$;8C#rT-9#+-J 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Did not separate the unit_test classes into separate files. --- viz_scripts/{unit_tests.py => run_unit_tests.py} | 4 +--- viz_scripts/scaffolding.py | 12 ++++++------ 2 files changed, 7 insertions(+), 9 deletions(-) rename viz_scripts/{unit_tests.py => run_unit_tests.py} (99%) diff --git a/viz_scripts/unit_tests.py b/viz_scripts/run_unit_tests.py similarity index 99% rename from viz_scripts/unit_tests.py rename to viz_scripts/run_unit_tests.py index 17d2baf..6fa9976 100644 --- a/viz_scripts/unit_tests.py +++ b/viz_scripts/run_unit_tests.py @@ -7,12 +7,12 @@ https://docs.python.org/3.10/library/unittest.html """ - import unittest import pandas as pd import numpy as np import scaffolding + class TestEnergyIntensity(unittest.TestCase): """ A unit test for energy_intensity function in @@ -152,7 +152,6 @@ def test_function(self): f'Error in function') - class TestCalcAvgSpeed(unittest.TestCase): """ A unit test for calc_avg_speed function in @@ -227,6 +226,5 @@ def test_function(self): f'calc_avg_speed with incorrect method failed.[2]') - if __name__ == '__main__': unittest.main() \ No newline at end of file diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 0f20119..de37297 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -109,7 +109,7 @@ def unit_conversions(df): def energy_intensity(df,df1,distance,col1,col2): """Inputs: - df = dataframe with data from CanBikeCO + df = dataframe with trip data from OpenPATH df1 = dataframe with energy factors distance = distance in meters col1 = Replaced_mode @@ -150,10 +150,10 @@ def energy_intensity(df,df1,distance,col1,col2): def cost(data, cost, dist, repm, mode): """ - Calculates the cost of the CanBikeCO E-bike pilot program + Calculates the cost of each trip by mode Parameters: - data - CanBikeCO data input + data - trip data from OpenPATH cost - dataframe defining cost ($/PMT) for each mode dist - feature name in data of feature with distance in miles repm - feature name in data of feature with replaced mode @@ -185,10 +185,10 @@ def cost(data, cost, dist, repm, mode): def time(data, dura, dist, repm, mode): """ - Calculates the cost of the CanBikeCO E-bike pilot program + Calculates the time of each participant trip in OpenPATH Parameters: - data - CanBikeCO data input + data - participant trip data from OpenPATH dura - dataframe defining duration ((1/speed)/PMT) for each mode dist - feature name in data of feature with distance in miles repm - feature name in data of feature with replaced mode @@ -205,7 +205,7 @@ def time(data, dura, dist, repm, mode): dura[repm] = dura['mode'] # Pair dura with mode - dic_dura__trip = dict(zip(dura[repm],dura['C($/PMT)'])) + dic_dura__trip = dict(zip(dura[repm],dura['D(hours/PMT)'])) # Create new features in data for replaced mode data['dura__trip_'+repm] = data[repm].map(dic_dura__trip) From c825124c9605a665631809fbb7fb8f405dc0c759 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Mon, 24 Jan 2022 10:54:51 -0700 Subject: [PATCH 25/35] Documentation Minor Updates Forgot some documentation updates last time. Fixing this time around. --- viz_scripts/scaffolding.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index de37297..4cc2349 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -301,10 +301,10 @@ def CO2_impact_lb(df,distance,col1,col2): def cost_impact(data, dist, repm, mode): """ - Calculates the cost impact of the CanBikeCO E-bike program + Calculates the cost impact for participants in OpenPATH Parameters: - data - CanBikeCO data input + data - participant trip data from OpenPATH dist - feature name in df of feature with distance in miles repm - feature name in df of feature with replaced mode mode - feature name in df of feature with confirmed mode @@ -322,10 +322,10 @@ def cost_impact(data, dist, repm, mode): def time_impact(data, dist, repm, mode): """ - Calculates the cost impact of the CanBikeCO E-bike program + Calculates the time impact of participant trips in OpenPATH Parameters: - data - CanBikeCO data input + data - participant trips OpenPATH data dist - feature name in df of feature with distance in miles repm - feature name in df of feature with replaced mode mode - feature name in df of feature with confirmed mode @@ -336,7 +336,7 @@ def time_impact(data, dist, repm, mode): data[mode+'_dura'] = data[dist] * data['dura__trip_mode'] data[repm+'_dura'] = data[dist] * data['dura__trip_repm'] - data['Cost_Impact($)'] = round((data[mode+'_dura'] - data[repm+'_dura']),3) + data['Time_Impact(hours)'] = round((data[mode+'_dura'] - data[repm+'_dura']),3) return data @@ -344,10 +344,10 @@ def time_impact(data, dist, repm, mode): def calc_avg_speed(data, dist, time, mode, meth='average'): """ Purpose: - To determine average speed of modes in CanBikeCO data + To determine average speed of modes for participant trips in OpenPath Parameters: - data - CanBikeCO data input + data - participant trip data from OpenPAth dist - feature name in df of feature with distance in miles time - feature name in df of feature with time information mode - feature name in df of feature with confirmed mode From b42502eedb77b4dc8c220cf7611681f3ff681fd8 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 25 Jan 2022 13:37:41 -0700 Subject: [PATCH 26/35] Fixed .gitignore Conflict I also did some other rummaging around. Nothing that is finalized though. --- .gitignore | 7 - viz_scripts/auxiliary_files/cost.csv | 30 +- viz_scripts/auxiliary_files/time.csv | 17 +- .../cost_and_time_impact_estimates.ipynb | 214 ++- viz_scripts/energy_calculations.ipynb | 1461 +---------------- 5 files changed, 263 insertions(+), 1466 deletions(-) diff --git a/.gitignore b/.gitignore index b6271e6..67fcf04 100644 --- a/.gitignore +++ b/.gitignore @@ -136,10 +136,3 @@ dmypy.json # Pyre type checker .pyre/ -<<<<<<< Updated upstream -.DS_Store -.DS_Store -viz_scripts/.DS_Store -======= -viz_scripts/auxiliary_files/.DS_Store ->>>>>>> Stashed changes diff --git a/viz_scripts/auxiliary_files/cost.csv b/viz_scripts/auxiliary_files/cost.csv index 822ffce..2c6ac8a 100644 --- a/viz_scripts/auxiliary_files/cost.csv +++ b/viz_scripts/auxiliary_files/cost.csv @@ -1,15 +1,15 @@ -mode,C($/PMT),($)/trip,D(hours/PMT),(hours)/trip -"Car, drove alone",0.55,0,0,0 -"Car, with others",0.275,0,0,0 -Taxi/Uber/Lyft,2.5,0,0,0 -Bus,0.855,0,0,0 -Free Shuttle,0,0,0,0 -Train,0.855,0,0,0 -Scooter share,0.15,1,0,0 -Pilot ebike,0,0,0,0 -Bikeshare,0.09,0,0,0 -Walk,0,0,0,0 -Skate board,0,0,0,0 -Regular Bike,0,0,0,0 -Not a Trip,0,0,0,0 -No Travel,0,0,0,0 \ No newline at end of file +mode,C($/PMT),($)/trip,D(hours/PMT) +"Car, drove alone",0.55,0,0 +"Car, with others",0.275,0,0 +Taxi/Uber/Lyft,2.5,0,0 +Bus,0.855,0,0 +Free Shuttle,0,0,0 +Train,0.855,0,0 +Scooter share,0.15,1,0 +Pilot ebike,0,0,0 +Bikeshare,0.09,0,0 +Walk,0,0,0 +Skate board,0,0,0 +Regular Bike,0,0,0 +Not a Trip,0,0,0 +No Travel,0,0,0 \ No newline at end of file diff --git a/viz_scripts/auxiliary_files/time.csv b/viz_scripts/auxiliary_files/time.csv index 5abe4f1..dc54a91 100644 --- a/viz_scripts/auxiliary_files/time.csv +++ b/viz_scripts/auxiliary_files/time.csv @@ -1,4 +1,13 @@ -mode,speed -bus,1.0 -car,1.0 -train,1.0 +Mode_confirm,speed +Bikeshare,15.528673804428736 +Bus,8.658398163532109 +"Car, drove alone",15.717732517579352 +"Car, with others",17.809838666428867 +Not a Trip,2.5721570250884667 +Pilot ebike,9.745192742581557 +Regular Bike,8.34062410808631 +Scooter share,23.731360584008794 +Skate board,4.425242235684407 +Taxi/Uber/Lyft,9.597996120242797 +Train,13.504787430136716 +Walk,6.045029911646732 diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index e576d6d..24f33b8 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -210,7 +210,7 @@ "id": "e0420cf9", "metadata": {}, "source": [ - "### Analysis of Time Impact" + "### Analysis of Cost Impact" ] }, { @@ -228,7 +228,9 @@ "cell_type": "code", "execution_count": null, "id": "41460ce2", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "expanded_ct.columns" @@ -265,21 +267,90 @@ "eirc['boolean'] = eirc['Sketch of Total Cost_Impact($)'] > 0\n", "\n", "#eimc : energy impact mode_confirm\n", - "eimc=expanded_ct.groupby('Mode_confirm').agg({'Energy_Impact(kWH)': ['sum', 'mean']},)\n", - "eimc.columns = ['Sketch of Total Energy_Impact(kWH)', 'Sketch of Average Energy_Impact(kWH)']\n", + "eimc=expanded_ct.groupby('Mode_confirm').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", + "eimc.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", "eimc = eimc.reset_index()\n", - "eimc = eimc.sort_values(by=['Sketch of Total Energy_Impact(kWH)'], ascending=False)\n", + "eimc = eimc.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", "\n", "\n", - "subset1 = eirc [['Replaced_mode', 'Sketch of Total Energy_Impact(kWH)']].copy()\n", - "subset1.rename(columns = {'Replaced_mode':'Transport Mode','Sketch of Total Energy_Impact(kWH)':'Replaced_Mode' }, inplace=True)\n", + "subset1 = eirc [['Replaced_mode', 'Sketch of Total Cost_Impact($)']].copy()\n", + "subset1.rename(columns = {'Replaced_mode':'Transport Mode','Sketch of Total Cost_Impact($)':'Replaced_Mode' }, inplace=True)\n", "\n", - "subset2 = eimc [['Mode_confirm', 'Sketch of Total Energy_Impact(kWH)']].copy()\n", - "subset2.rename(columns = {'Mode_confirm':'Transport Mode','Sketch of Total Energy_Impact(kWH)':'Mode_Confirm' }, inplace=True)\n", + "subset2 = eimc [['Mode_confirm', 'Sketch of Total Cost_Impact($)']].copy()\n", + "subset2.rename(columns = {'Mode_confirm':'Transport Mode','Sketch of Total Cost_Impact($)':'Mode_Confirm' }, inplace=True)\n", "\n", "df_plot = pd.merge(subset1, subset2, on=\"Transport Mode\")\n", "df = pd.melt(df_plot , id_vars=['Transport Mode'], value_vars=['Replaced_Mode','Mode_Confirm'], var_name='selection')\n", - "df.rename(columns = {'value':'Energy Impact (kWH)'}, inplace = True)" + "df.rename(columns = {'value':'Cost Impact ($)'}, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e5aba5a", + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df78180e", + "metadata": {}, + "outputs": [], + "source": [ + "df= df.sort_values(by=['Cost Impact ($)'], ascending=False)\n", + "x= 'Cost Impact ($)'\n", + "y= 'Transport Mode'\n", + "color = 'selection'\n", + "plot_title=\"Sketch of Cost Impact ($) by Transport Mode\\n%s\" % quality_text\n", + "file_name ='sketch_all_cost_impact%s.png' % file_suffix\n", + "overeall_energy_impact(x,y,color,df,plot_title,file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5da0d49b", + "metadata": {}, + "outputs": [], + "source": [ + "net_cost_saved = round(sum(eirc['Sketch of Total Cost_Impact($)']), 2)\n", + "\n", + "x = eirc['Sketch of Total Cost_Impact($)']\n", + "y = eirc['Replaced_mode']\n", + "color =eirc['boolean']\n", + "\n", + "plot_title=\"Sketch of Cost Impact for all confirmed trips \\n Contribution by mode towards a total of %s ($) \\n%s\" % (net_cost_saved, quality_text)\n", + "file_name ='sketch_all_mode_cost_impact%s.png' % file_suffix\n", + "energy_impact(x,y,color,plot_title,file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcff73a3", + "metadata": {}, + "outputs": [], + "source": [ + "data_eb = expanded_ct.query(\"Mode_confirm == 'Pilot ebike'\")\n", + "# ebei : ebike energy impact\n", + "ebei=data_eb.groupby('Replaced_mode').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", + "ebei.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "ebei= ebei.reset_index()\n", + "ebei = ebei.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", + "ebei['boolean'] = ebei['Sketch of Total Cost_Impact($)'] > 0\n", + "net_energy_saved = round(sum(ebei['Sketch of Total Cost_Impact($)']), 2)\n", + "\n", + "x = ebei['Sketch of Total Cost_Impact($)']\n", + "y = ebei['Replaced_mode']\n", + "color =ebei['boolean']\n", + "\n", + "plot_title=\"Sketch of Cost Impact of E-Bike trips\\n Contribution by replaced mode towards a total of %s ($)\\n %s\" % (net_energy_saved, quality_text)\n", + "file_name ='sketch_cost_impact_ebike%s.png' % file_suffix\n", + "energy_impact(x,y,color,plot_title,file_name)" ] }, { @@ -296,7 +367,128 @@ "id": "3065d606", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "trash, df_T = scaffolding.calc_avg_speed(expanded_ct, 'distance_miles','duration_h', 'Mode_confirm')\n", + "expanded_ct = scaffolding.time(expanded_ct, df_T, 'distance','Replaced_mode', 'Mode_confirm')\n", + "expanded_ct = scaffolding.time_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74e5546d", + "metadata": {}, + "outputs": [], + "source": [ + "data=expanded_ct.loc[(expanded_ct['distance_miles'] <= 40)].sort_values(by=['Cost_Impact($)'], ascending=False) \n", + "x='Cost_Impact($)'\n", + "y='distance_miles'\n", + "legend ='Mode_confirm'\n", + "plot_title=\"Sketch of Cost_Impact($) by Travel Mode Selected\\n%s\" % quality_text\n", + "file_name ='sketch_distance_cost_impact%s.png' % file_suffix\n", + "distancevsenergy(data,x,y,legend,plot_title,file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3125b5d9", + "metadata": {}, + "outputs": [], + "source": [ + "#eirp : energy impact replaced_mode\n", + "eirc=expanded_ct.groupby('Replaced_mode').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", + "eirc.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "eirc = eirc.reset_index()\n", + "eirc = eirc.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", + "eirc['boolean'] = eirc['Sketch of Total Cost_Impact($)'] > 0\n", + "\n", + "#eimc : energy impact mode_confirm\n", + "eimc=expanded_ct.groupby('Mode_confirm').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", + "eimc.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "eimc = eimc.reset_index()\n", + "eimc = eimc.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", + "\n", + "\n", + "subset1 = eirc [['Replaced_mode', 'Sketch of Total Cost_Impact($)']].copy()\n", + "subset1.rename(columns = {'Replaced_mode':'Transport Mode','Sketch of Total Cost_Impact($)':'Replaced_Mode' }, inplace=True)\n", + "\n", + "subset2 = eimc [['Mode_confirm', 'Sketch of Total Cost_Impact($)']].copy()\n", + "subset2.rename(columns = {'Mode_confirm':'Transport Mode','Sketch of Total Cost_Impact($)':'Mode_Confirm' }, inplace=True)\n", + "\n", + "df_plot = pd.merge(subset1, subset2, on=\"Transport Mode\")\n", + "df = pd.melt(df_plot , id_vars=['Transport Mode'], value_vars=['Replaced_Mode','Mode_Confirm'], var_name='selection')\n", + "df.rename(columns = {'value':'Cost Impact ($)'}, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2769f502", + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db91fd02", + "metadata": {}, + "outputs": [], + "source": [ + "df= df.sort_values(by=['Cost Impact ($)'], ascending=False)\n", + "x= 'Cost Impact ($)'\n", + "y= 'Transport Mode'\n", + "color = 'selection'\n", + "plot_title=\"Sketch of Cost Impact ($) by Transport Mode\\n%s\" % quality_text\n", + "file_name ='sketch_all_cost_impact%s.png' % file_suffix\n", + "overeall_energy_impact(x,y,color,df,plot_title,file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54e1b96c", + "metadata": {}, + "outputs": [], + "source": [ + "net_cost_saved = round(sum(eirc['Sketch of Total Cost_Impact($)']), 2)\n", + "\n", + "x = eirc['Sketch of Total Cost_Impact($)']\n", + "y = eirc['Replaced_mode']\n", + "color =eirc['boolean']\n", + "\n", + "plot_title=\"Sketch of Cost Impact for all confirmed trips \\n Contribution by mode towards a total of %s ($) \\n%s\" % (net_cost_saved, quality_text)\n", + "file_name ='sketch_all_mode_cost_impact%s.png' % file_suffix\n", + "energy_impact(x,y,color,plot_title,file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a900d383", + "metadata": {}, + "outputs": [], + "source": [ + "data_eb = expanded_ct.query(\"Mode_confirm == 'Pilot ebike'\")\n", + "# ebei : ebike energy impact\n", + "ebei=data_eb.groupby('Replaced_mode').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", + "ebei.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "ebei= ebei.reset_index()\n", + "ebei = ebei.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", + "ebei['boolean'] = ebei['Sketch of Total Cost_Impact($)'] > 0\n", + "net_energy_saved = round(sum(ebei['Sketch of Total Cost_Impact($)']), 2)\n", + "\n", + "x = ebei['Sketch of Total Cost_Impact($)']\n", + "y = ebei['Replaced_mode']\n", + "color =ebei['boolean']\n", + "\n", + "plot_title=\"Sketch of Cost Impact of E-Bike trips\\n Contribution by replaced mode towards a total of %s ($)\\n %s\" % (net_energy_saved, quality_text)\n", + "file_name ='sketch_cost_impact_ebike%s.png' % file_suffix\n", + "energy_impact(x,y,color,plot_title,file_name)" + ] } ], "metadata": { diff --git a/viz_scripts/energy_calculations.ipynb b/viz_scripts/energy_calculations.ipynb index 103b77f..22868ca 100644 --- a/viz_scripts/energy_calculations.ipynb +++ b/viz_scripts/energy_calculations.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "determined-matrix", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "pharmaceutical-survival", "metadata": {}, "outputs": [], @@ -50,18 +50,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "inner-desktop", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting to database URL db\n" - ] - } - ], + "outputs": [], "source": [ "import scaffolding \n", "from plots import *" @@ -69,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "terminal-machinery", "metadata": {}, "outputs": [], @@ -88,74 +80,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "official-beatles", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(()>,\n", - " {'work_travel': 'Work',\n", - " 'work': 'Work',\n", - " 'home': 'Home',\n", - " 'meal': 'Meal',\n", - " 'shopping': 'Shopping',\n", - " 'personal_med': 'Personal/Medical',\n", - " 'exercise': 'Recreation/Exercise',\n", - " 'transit_transfer': 'Transit transfer',\n", - " 'pick_drop': 'Pick-up/Drop off',\n", - " 'entertainment': 'Entertainment/Social',\n", - " 'car_mechanic': 'Other',\n", - " 'school': 'School',\n", - " 'revisado_bike': 'Other',\n", - " 'placas_de carro': 'Other',\n", - " 'community_walk': 'Entertainment/Social',\n", - " 'gardening': 'Entertainment/Social',\n", - " 'visiting': 'Entertainment/Social',\n", - " 'church': 'Religious',\n", - " 'community_garden': 'Entertainment/Social',\n", - " 'community_meeting': 'Entertainment/Social',\n", - " 'visit_a friend': 'Entertainment/Social',\n", - " 'aseguranza': 'Other',\n", - " 'meeting_bike': 'Entertainment/Social',\n", - " 'gas_station': 'Other',\n", - " 'iglesia': 'Religious',\n", - " 'curso': 'School',\n", - " 'mi_hija recién aliviada': 'Entertainment/Social',\n", - " 'servicio_comunitario': 'Entertainment/Social',\n", - " 'pago_de aseguranza': 'Other',\n", - " 'grupo_comunitario': 'Entertainment/Social',\n", - " 'caminata_comunitaria': 'Entertainment/Social',\n", - " 'bank': 'Other',\n", - " 'religious': 'Religious',\n", - " 'no_travel': 'No travel',\n", - " 'work_break - short walk': 'Entertainment/Social',\n", - " 'work_- lunch break': 'Meal',\n", - " 'friend_was running errands before dropping me off after work': 'Other',\n", - " 'multiple_errands, etc.': 'Other',\n", - " 'lunch_break': 'Meal',\n", - " 'break': 'Entertainment/Social',\n", - " 'pet': 'Entertainment/Social',\n", - " 'recording_performance at park': 'Entertainment/Social',\n", - " 'not_a trip': 'not_a_trip',\n", - " 'on_the way home': 'Home',\n", - " 'other': 'Other',\n", - " nan: nan})" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dic_pur" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "special-davis", "metadata": {}, "outputs": [], @@ -165,1136 +100,40 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "above-network", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[UUID('576e37c7-ab7e-4c03-add7-02486bc3f42e'),\n", - " UUID('8b563348-52b3-4e3e-b046-a0aaf4fcea15'),\n", - " UUID('5079bb93-c9cf-46d7-a643-dfc86bb05605'),\n", - " UUID('feabfccd-dd6c-4e8e-8517-9d7177042483'),\n", - " UUID('113aef67-400e-4e21-a29f-d04e50fc42ea'),\n", - " 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5 rows × 36 columns

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"1 5fa4690763a5e0e8d90c7fa4 1.604601e+09 2020-11-05T11:30:56.952000-07:00 \n", - "2 5fa4690763a5e0e8d90c7fa8 1.604604e+09 2020-11-05T12:22:21.130739-07:00 \n", - "3 5fa4690763a5e0e8d90c7faa 1.604605e+09 2020-11-05T12:42:19.793043-07:00 \n", - "4 5fa4771a533f6ebf89c7c5e3 1.604610e+09 2020-11-05T13:52:57.667396-07:00 \n", - "\n", - " start_loc duration \\\n", - "0 {'type': 'Point', 'coordinates': [-104.9398732... 374.066000 \n", - "1 {'type': 'Point', 'coordinates': [-104.9479963... 2475.048000 \n", - "2 {'type': 'Point', 'coordinates': [-105.0670666... 300.869261 \n", - "3 {'type': 'Point', 'coordinates': [-105.080878,... 309.223957 \n", - "4 {'type': 'Point', 'coordinates': [-105.0827029... 91.212605 \n", - "\n", - " distance ... end_local_dt_minute end_local_dt_second \\\n", - "0 384.730231 ... 45 22 \n", - "1 13765.915676 ... 12 12 \n", - "2 1508.223413 ... 27 22 \n", - "3 434.038504 ... 47 29 \n", - "4 333.230154 ... 54 28 \n", - "\n", - " end_local_dt_weekday end_local_dt_timezone 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[], "source": [ "expanded_ct = scaffolding.expand_userinputs(labeled_ct)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "cultural-salad", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(519, 36)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "expanded_ct = scaffolding.data_quality_check(expanded_ct)\n", "expanded_ct.shape" @@ -1302,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "improved-venture", "metadata": {}, "outputs": [], @@ -1319,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "micro-wound", "metadata": {}, "outputs": [], @@ -1329,20 +168,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "efficient-marking", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "_2020_11_prepilot\n", - "Based on 519 confirmed trips from 12 users\n", - "of 953 total trips from 12 users (54.46%)\n" - ] - } - ], + "outputs": [], "source": [ "file_suffix = scaffolding.get_file_suffix(year, month, program)\n", "quality_text = scaffolding.get_quality_text(participant_ct_df, expanded_ct)" @@ -1358,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "dimensional-bronze", "metadata": {}, "outputs": [], @@ -1377,25 +206,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "protecting-falls", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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Transport ModeselectionEnergy Impact (kWH)
0Car, drove aloneReplaced_Mode576.807
1Taxi/Uber/LyftReplaced_Mode221.614
2BusReplaced_Mode99.372
3Car, with othersReplaced_Mode35.104
4Not a TripReplaced_Mode0.000
5Skate boardReplaced_Mode0.000
6TrainReplaced_Mode-3.628
7Regular BikeReplaced_Mode-4.133
8WalkReplaced_Mode-90.614
9Pilot ebikeReplaced_Mode-674.651
10Car, drove aloneMode_Confirm-339.488
11Taxi/Uber/LyftMode_Confirm-164.893
12BusMode_Confirm-243.929
13Car, with othersMode_Confirm122.079
14Not a TripMode_Confirm0.000
15Skate boardMode_Confirm1.847
16TrainMode_Confirm0.000
17Regular BikeMode_Confirm0.000
18WalkMode_Confirm35.644
19Pilot ebikeMode_Confirm724.044
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" - ], - "text/plain": [ - " Transport Mode selection Energy Impact (kWH)\n", - "0 Car, drove alone Replaced_Mode 576.807\n", - "1 Taxi/Uber/Lyft Replaced_Mode 221.614\n", - "2 Bus Replaced_Mode 99.372\n", - "3 Car, with others Replaced_Mode 35.104\n", - "4 Not a Trip Replaced_Mode 0.000\n", - "5 Skate board Replaced_Mode 0.000\n", - "6 Train Replaced_Mode -3.628\n", - "7 Regular Bike Replaced_Mode -4.133\n", - "8 Walk Replaced_Mode -90.614\n", - "9 Pilot ebike Replaced_Mode -674.651\n", - "10 Car, drove alone Mode_Confirm -339.488\n", - "11 Taxi/Uber/Lyft Mode_Confirm -164.893\n", - "12 Bus Mode_Confirm -243.929\n", - "13 Car, with others Mode_Confirm 122.079\n", - "14 Not a Trip Mode_Confirm 0.000\n", - "15 Skate board Mode_Confirm 1.847\n", - "16 Train Mode_Confirm 0.000\n", - "17 Regular Bike Mode_Confirm 0.000\n", - "18 Walk Mode_Confirm 35.644\n", - "19 Pilot ebike Mode_Confirm 724.044" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "unknown-venice", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df= df.sort_values(by=['Energy Impact (kWH)'], ascending=False)\n", "x= 'Energy Impact (kWH)'\n", @@ -1675,21 +300,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "emotional-universal", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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hxGkTFEW1HzrknqIo6h+Ax+Nhw4YNAF6t0H3u3DnExMTg/PnzmDdvntiw4xMnTqC6uhpOTk5YvXq1WHkFBQWorq7GyJEjmzzmkydPWhzKee/ePcjIyGDIkCFSnYekVZu7du2Kp0+ftrhvUVERZGVlMWjQILFtn376KVJSUlBUVAR1dXWpYpFETU2t2TpprF+/fmJpoukMovMpKyvD06dPkZaWhhEjRkgsR9RIakxSvRcVFaFHjx5ii33Jy8ujb9++qKio4KQ7OTkhISEBhw4dwsyZM0EIQXx8PLS1taGrqyvVOTZF0nVUVVWVOHRcNB3g6dOnnNi7dOnCDvVtbNCgQTh58iRqamrYhuyxY8cQHh6O/Px8sbm8z549Y/+/tfdjWxBdF0mL5Q0ePBj5+fkoLy/nNDhbO0Q+Ly8P27ZtQ3Z2Nmpqajjb2vLVZqIv1oYOHdpsPtHaAZ9++qnYNoZhALwafq6np8emS/O8vKnm6lPScVVVVaGhoSG2iJzoy0ZRPHfu3IFQKERsbCxiY2ObLf/JkyeoqamROE1g8ODBUp2HtFr6XH6dtM+an58f5s+fDzc3N/To0QMmJiYwNzeHjY0NXXCPotoRbdBTFEX9w2hqakJTUxN2dnb4/PPPkZOTg9zcXM470vX19VFcXIykpCQ4OTlx/rAGXvX0qaurIygoqMnjSPpj/XWEkFb1esrJyUmdV9KxPiTNnYsoVtF/R44cidmzZ0tdduPRFK+XKa1hw4aBYRjExcVh5syZyMjIQHFxMWbNmtWqciRp6tylqRORpu6b1/OdOHECS5Ysgb6+PlasWIHevXtDUVERL1++hI+PDyd/a+/HtvAm92VreldLSkrg5uYGFRUVzJ07FwMHDoSSkhJkZGSwbt06sQb+2xCdS0t1+Cbn3Jp7o7Waq8+3uVdF//3ss89gb28vMa9oJIi0ddcWJH0+NEfaZ43P5yM5ORlpaWnIyspCVlYWjhw5gp07dyIqKor9AoaiqPeLNugpiqL+oWRkZGBgYICcnBw8evSIs61Xr14IDAyEp6cnvLy8sGfPHs5w1/79++Pu3bswMDBo8fVOzf2BOmDAAPz555+4ceMG9PX13+6EWqClpYW0tDQUFBSI9cAWFBQAaNveyragrq6OLl26oKqqSuqe/6b07dsXGRkZqK6u5lyz+vp6FBUVcaYyiDg6OmLt2rXIzc1FbGwsFBUVpVp88H149uwZSktLxXoO//rrL3Tr1o3tnf/999+hqKiIX375hdNwE13zxqS9H9uy0aWlpYU///wTFRUVYtegoKAAKioqYgsqtkZycjJqamqwc+dOzlBu4FVPclv2nIp6l/Py8mBmZtZkPtEibbdu3RJbsO327dsAJPeMf2y0tLQgIyOD+vr6Fp9f0T0r6b4U1Yk03sUXAtI+a8CrBU9tbGxgY2MD4NUCmKtXr0ZsbGybTx2gKEo6dA49RVHURy49PR0NDQ1i6c+fP2fnb0oaht6zZ09ERESgR48e8Pb2xoULF9htU6dOhVAoxJYtWyQeU/QuaOB/83cbD20WETUOt2zZIvEVdW3Zq25tbQ3g1doAjcu9efMmTp06BSMjo7cabv8uyMrKwtbWFrm5uTh+/LjEPNK+0srS0hIvX77EL7/8wkmPjo5GZWWlxH3s7OygqKiIPXv2IDk5GePHj5fY8G8vYWFhnN+Tk5Nx584d9loDr3pSZWRkOPPQCSHYuXOnWHnS3o/N3dOtZW1tDaFQKHYuf/zxB/Ly8mBpaSlxWoW0RD3Jrz9L0dHREl+R+DbMzMygpqaG8PBwsS8JG8dgaWkJGRkZ7N27lzMF4tGjR4iPj4empmaLw/Y/Bmpqahg7diySk5Nx6dIlse2EEHaNCjk5OVhYWODq1aucV38SQrBnzx6pj9mW92Zj0jxrktYg0dHReSfxUBQlPdpDT1EU9ZFbv349nj59CktLSzAMg44dO+LBgwc4fPgw7t69i6lTpzb5SjYNDQ3s378fM2fOhI+PD3bt2gUTExNMmDABDg4OiIiIwLVr12BhYQE1NTU8ePAAly5dwr1799jFktTU1NC/f38cPXoU/fr1Q/fu3aGkpARLS0vo6+tj9uzZ2L17NxwcHDBx4kRoaGigqKgISUlJiImJabMGpJmZGSZOnIijR4/i2bNnsLCwYF//pqioiG+++eatj/Hw4UP8/vvvErfx+Xyx3khpLFmyBDk5Ofjyyy8xceJEGBgYQF5eHiUlJThz5gx0dHTY9RGa4+joiIMHD+KHH37A/fv32dfWHT9+HP3795f4pY+qqipsbGzYxeIcHR1bHf+7oqamhuTkZDx69AgmJibsq7S6d++OBQsWsPlsbGyQlJSEGTNmYOrUqWhoaMDJkydRW1srVqa09+PgwYOhrKyMqKgodOzYEV26dIG6unqT6xw0x97eHgkJCdi9ezeKi4sxfPhw3L9/nz0XPz+/t6qnMWPGQElJCUuXLoW7uzu6dOmCnJwcnDlzBlpaWnj58uVbld+YkpIS1q5di8WLF8PW1pZ9bV1ZWRnS0tIwc+ZMWFtbY+DAgZg1axb27NkDd3d3TJw4EdXV1YiOjkZNTQ02b978VtNrPiTfffcdPv/8c7i7u8POzg5Dhw6FUChEYWEhUlJSMHXqVCxcuBAA8OWXX+LMmTPw9fWFu7s7evXqhdTU1FYt1qmnpwdZWVmEhobi2bNn6NSpE/r27QsDA4M3Pgdpn7VJkybB0NAQ+vr66NGjB0pLSxEdHQ15eXlMnjz5jY9PUdTboQ16iqKoj1xAQABSUlJw4cIFJCUlobKyEp07dwbDMJg9ezYcHBya3b9bt2745Zdf4OXlhTlz5mDnzp0YMWIE1q9fD4FAgOjoaOzatQv19fXQ0NDA0KFD8dVXX3HK2Lx5M9atW4etW7eitrYWmpqasLS0BPDq3fBDhgxBREQE9uzZA0IIevXqhTFjxrR6rmdLNm/ejKFDhyIhIQEbNmxAp06dYGxsjMWLF0v1nvmW5OfnY+nSpRK3rVmz5o0a9J07d8aBAwewb98+HD9+HCkpKZCTk0OvXr1gZGQkdSNbQUEBP//8MzZu3IiUlBQkJiZCX18fP/30E/7zn//g+fPnEvdzdnbGoUOH0L9/f5iYmLQ6/nelU6dO+Pnnn7Fu3ToEBQWBEILRo0cjICAAPXr0YPNNnjwZ1dXV+OmnnxAYGAhVVVVYWFjgq6++gkAgECtXmvuxY8eO2Lp1K3744QesW7cOdXV1MDExeaMGvby8PPbu3YudO3fi2LFjSE5OZt8y8OWXX6J3795vXkl4Nex79+7d2LJlC0JDQyEnJ4dhw4Zh//79+P777996hfjXWVlZISoqCqGhoYiNjUV1dTW6d+8OIyMjzjP29ddfo3///oiKikJQUBDk5eVhYGCAoKAgznoeH7vevXsjLi4Ou3fvxqlTp3Do0CEoKiqid+/esLCwwMSJE9m8WlpaiIyMRGBgICIiIqCgoIDRo0dj48aNUk+56dOnD9atW4fdu3dj1apVqK+vh729/Vs16KV91ry9vfHHH39g//79qKysRLdu3WBgYIAvvvjivS40SVEUlwz50FYRoiiKoiiqzbx8+RKmpqbQ19fH3r17xbbn5ubC0dERfn5+Yq9Way8eHh4oLi7GqVOn2jsUivpHo88aRX386Bx6iqIoivqHkNQLf/DgQVRUVDS5iFlERATk5eVbHMlBURRFUdSHhw65pyiKoqh/iG+++QZ1dXXg8/lQUFDAxYsXceTIEfTv3x9OTk5svpqaGqSmpuLWrVs4dOgQnJycJL6HmqIoiqKoDxtt0FMURVHUP8SoUaMQGRmJjIwM1NTUoFu3bnB0dMTixYuhoqLC5isrK4Ofnx86deoEGxubJtcFoCiKoijqw0bn0FMURVEURVEURVHUR4jOoacoiqIoiqIoiqKojxBt0FMURVEURVEURVHUR4g26CmKoigUFRWBx+MhODi42bT2iqU98Hg8BAQEtGsM7eFDqf+PlaWlJTw8PNo7jA+ah4cHLC0t27xcoVCI4OBgWFlZYejQoeDxeG1+jH8DQgicnZ3x1VdfcdI/hs/EkydPQldXF3fv3m3vUCjqvaENeoqiqDby8OFDbNy4Eba2tuDz+dDV1YWlpSX8/f2RkZHxXmKIj4/HTz/99F6O9TaKiooQHByM/Pz89g6Fek/y8/MRHByMoqKi9g7lXyc4OBgnT578YMp5VxISEhASEgKBQIC1a9di48aNzeZPT0/Hf//7X0ybNg16enrg8XjIysqSmPfSpUtYtGgRxo0bBz6fDz6fjylTpiAkJASVlZVi+QMCAsDj8ST+HD9+XKrziY+Pb7KM1atXi+W3tLRsMn9ZWZlUxwSAI0eO4MqVK1i4cKHU+4hs3rwZPB5P4r95u3btAo/Hw+effy62raGhAXw+H7a2tmyah4cH+Hx+k8cKDg4Gj8fDlStX2DRra2swDIPNmze3OnaK+ljRVe4piqLawOnTp+Hn54e6ujpMmDABzs7OUFRURHFxMVJSUjBz5kyEhYVh7Nix7zSOhIQEFBcXY+bMma3aT1NTE7m5uZCTk3s3gb2muLgYISEh0NTUhLa2drvGQr0f+fn5CAkJgYmJCfr27dve4fyrhISEwN7eHtbW1h9EOe/K2bNn0blzZ6xduxYyMjIt5j98+DCOHDmCTz/9FIMGDWr2C8a7d++itrYWtra26NGjB4RCIa5cuYLQ0FAkJSUhJiYGHTt2FNtP0pcK+vr6rTovX19fDBw4kJM2YMAAiXkHDhwIX19fsfTGb7loyY4dO2BhYYFPPvmkVXECgEAgwO7du5GZmYkRI0ZwtmVnZ6NDhw7Izc1FbW0tlJSU2G1XrlxBTU0NBAJBq4/5Ok9PTyxbtgy3bt3Cp59++tblUdSHjjboKYqi3tKtW7ewePFiqKqqIjw8HIMGDeJsX7x4MQ4dOgQFBYV2irBpVVVVUFFRgYyMDBQVFds7HAD4oGKhqMbq6+shFArp/fmBKi0tRZcuXaRqzAPAkiVLsHr1aigoKGDv3r3NNuinTp2KqVOniqUPGjQImzZtwqlTpzBp0iSx7XZ2dtKfQBNGjhwpdUO3e/fub3XMjIwM3LlzR2y4vbSMjIwgLy+P7OxsTnpDQwNycnLw2WefIT4+HhcvXsTIkSPZ7aL8JiYmbxy7yLhx4/Ddd9/h4MGDWLly5VuXR1EfOjrknqIo6i1t374dz58/x5o1a8Qa88CrBqqdnR2nt6KhoQFhYWGYNGkS9PT0IBAIMH/+fNy4cYOzb+P5zKmpqezQ0FGjRiEwMBANDQ1sXktLS2RnZ6O4uJgz1FI0hFQ0b7WwsBCLFi2CiYkJjIyMxI4jyZEjR2Braws9PT2Ym5sjODiYc+zG5b/u9bLj4+Ph6ekJAFi+fDkbp2jecVOxvIs6k8bZs2fh5OQEAwMDmJmZYc2aNaipqWG3h4eHg8fj4ezZs2L71tXVwcTEBDNmzGj2GI1jPnbsGOzs7KCvr49x48YhLi4OAFBSUsJeNz6fD39/f1RVVYmVdf36dcyfPx8CgQB6enqYNGkSdu/ejZcvX4rlPX/+PFxcXKCvr4+RI0di9erVnHNrjBCCqKgoODg4wMDAAHw+Hx4eHsjMzGz23IBXQ2OXL18O4FXvmeiaN56PW1ZWhlWrVmHs2LHQ1dXF2LFjsWrVKpSXl7N5RPf26/eGt7c3eDye2HQTR0dHTiOroKAA3333HSZPngw+nw8DAwM4ODggOjpaYsw8Hg+3bt3C+vXrMWbMGOjr6+PSpUsAgL///huLFy+GkZERhg0bBl9fX9y/f1/i+Z8+fRru7u4QCATQ19eHubk5FixYgDt37rRYd2lpafjyyy9hZWUFfX19DB8+HN7e3mINJklE9xXwavRO48+FxmJiYmBvbw99fX0YGRnB29sb58+fb1U5x44dg6+vL8zNzaGrqwuBQIB58+bh+vXrLcbZkpbiy8rKYj/rGn/+tTTfu2fPnm/9RWufPn0AABUVFRK3E0JQVVUFoVD4VsepqqpCXV2dVHkbGhokfjZIIzExEXJycjAzM5Mq/7Vr12BmZoZJkyahpKQEnTp1gq6uLq5cuYLa2lo2n6gH3sXFBRoaGmJTG2hP78AAACAASURBVLKzsyEjIwNjY+M3irsxZWVlGBkZST21gaI+drSHnqIo6i28ePECp0+fRu/evTFmzBip9/P390diYiLMzMzg6uqKx48fIzIyEi4uLoiMjMTQoUM5+f/44w9ERUXBxcUF06ZNQ0pKCvbt2wdVVVV2eOWKFSsQFBSE8vJytvEEgPMlQ3V1Ndzd3TFs2DB8+eWXUs2rTE1Nxc8//ww3Nzd0794dp06dQkhICEpKSrB+/Xqpz1nE2NgYvr6+CA0NhbOzM/ulQvfu3Zvd713UWUuuXbuGpKQkODo6ws7ODllZWdi/fz9u3bqF8PBwyMrKYurUqdiyZQtiY2M5PU4AkJycjGfPnmH69OlSHS81NRUHDx6Eq6srunbtitjYWKxYsQLy8vLYunUrTE1NsWTJEly5cgVxcXFQVFTE2rVr2f2vXLkCDw8PdOjQgb1eqamp2Lx5M65fv46goCA27+XLl+Hl5QVlZWXMnj0bnTt3xrFjx7Bs2TKJsX399dc4evQobGxs4ODggLq6Ohw+fBje3t7sQmRNGTduHEpLS/Hrr79yhg9raWkBACorK+Hq6op79+5h2rRpGDp0KPLz83HgwAFkZmYiJiYGKioq0NTURN++fZGRkcHO762rq0NOTg5kZWWRmZnJTjepqqrCtWvX4OLiwsaRnZ2N8+fPw9zcHH379kVtbS2OHz+OlStXory8HF988YVY7P7+/ujYsSO8vb0BABoaGqioqICbmxsePHgAFxcXDBo0COfOnYOnpyeeP3/O2T87Oxtz584FwzD44osv0LlzZzx69AgZGRm4f/9+k0OnRRISEvDs2TNMnToVvXr1wsOHDxETE4OZM2fil19+wfDhw5vcV11dHRs3bsTSpUsxfPhwODk5ieXZtGkT9uzZA319ffj5+aGqqgrR0dGYMWMGfvzxR4wdO1aqciIiIqCqqgonJydoaGjg/v37iI6OhqurKxISEt5o+La08Q0aNAgbN25EaGgo5/NPdH+1pdraWtTW1uL58+e4du0aNm/eDHl5ebFnX8TIyAjV1dWQl5eHsbExvvzySxgYGLTqmHPnzkV1dTVkZGTAMAxmzZrVZC/85cuXYWhoiPr6enTu3BlWVlbw8/NDz549pTrWuXPnMHjwYHTq1KnFvGlpaVi4cCF4PB5CQ0PRtWtXAK+G3V+8eBE5OTnsFwPZ2dno1KkTdHR0MHz4cE6DXtR7z+PxoKamJnacpv6davyFwev4fD7S0tJQUFAg8Yt2ivpHIRRFUdQbu379OmEYhnzxxRdS75OWlkYYhiGLFy8mQqGQTc/Pzyfa2trE1dWVTSssLCQMwxADAwNSWFjIpguFQjJ58mRiZmbGKdvd3Z1YWFhIPK67uzthGIZs2bJFbJvoONu3bxdLGzJkCLl69Srn2PPmzSMMw5CLFy+2eGxJZWdmZhKGYUhcXJxU+d9lnTWFYRjCMAxJTk7mpH///feEYRhy5MgRNs3Pz4/o6uqS8vJyTt6ZM2cSY2Nj8vz582aP1TjmoqIiNv3JkydEV1eX8Hg8sm/fPs4+8+fPJzo6OqSqqopNc3Z2Jtra2iQ/P59z3osWLSIMw5CzZ89y8uro6JC//vqLTXvx4gWZNm2aWP2fOHGCMAxDDh48yImhvr6e2NvbEwsLC851kSQuLo4wDEMyMzPFtm3ZsoUwDEMiIiI46REREYRhGLJ161Y2bcWKFURHR4dUV1cTQgjJzs4mDMMQf39/wufzSX19PSGEkJSUFMIwDElKSmL3Fe3T2MuXL4m7uzsZNmwYqaurY9O3b99OGIYh7u7ubJkiQUFBhGEYEhsby0lfs2YNu4/IunXrCMMw5PHjx83WT1MkxVxaWkpMTEyIj4+PVGUwDEOWLVsmll5QUEB4PB5xcXEhL168YNMfPHhAjIyMiIWFBWloaGixnKbivH37NtHR0SHffvstJ725z6m3iU/aciXZs2dPk/dnYxs2bGA/GxiGIZMnTyZ//vmnWL5NmzaRdevWkd9//50kJyeT4OBgMnz4cKKjo0PS09Oliuno0aPEz8+PREdHk5SUFPLzzz+T8ePHE4ZhSHBwsFj+2bNnkx07dpDExERy5MgRsnLlSqKtrU1Gjx5NHjx40OLxGhoayJAhQ8j8+fMlbm98/RMSEoiOjg6ZO3cuqa2t5eQ7e/YsYRiGBAUFsWne3t7E29ubEEJIZGQk5xnOyckhDMOQNWvWcMoR/ZvV0k9ubq5YrL/99hthGIYcP368xfOmqI8dHXJPURT1FkTDGluz4FBycjKAVwsdNZ7rOWTIEJibm+PChQtiPRJWVlachcRkZGQgEAhQWlqK6urqVsU8a9asVuUfOXIkdHR0OMf28fHhnMu71l51NmDAALEFwObMmcOJCQCcnJzYHmuRoqIiZGRkwNbWVuo511ZWVtDU1GR/V1dXx4ABAyArKws3NzdO3uHDh6O+vh7FxcUAgCdPnuDixYuwtLTEkCFDOOctGpEgirlx3sY9xAoKChIXVDx06BCUlZVhbW2NsrIy9qeiogKWlpYoLi5+q9dEJScnQ11dHc7Ozpx0Z2dnqKmpcVZWNzU1RX19PS5cuAAAyMzMRLdu3eDp6Ynq6mp2xeusrCzIyspy5uQ27nV88eIFysvL8fTpU5iZmaGqqgp//fWXWGwzZsxAhw7cAY0nT55E9+7dxeZUz549W2z/zp07AwCSkpJaPd3j9Zirq6tRXl4OWVlZGBgYIDc3t9XlNZaSkgJCCHx8fDhDz3v27Al7e3sUFxcjLy+vVXGS/x9iXlZWBjU1NQwYMOCN42zL+NqKs7MzwsPDsW3bNnh5eUFBQYEzLUTE398fy5cvx2effQZra2ssWLAAMTEx6NChA7777jupjjVp0iQEBQXB0dERlpaW8PT0xOHDh8EwDHbu3Cn2xoiwsDDMmzcPEyZMwOTJk7F69WoEBgbi4cOHUr2G8unTpxAKhVBVVW02X1hYGAICAuDg4IDg4GCxxQD5fD4UFBTYaSGiHnjRs2hiYoL6+nrk5OQA+N/8eUnrBCgqKiI8PFziT3NrBYhGCzx58qTF86aojx0dck9RFPUWRA351jSqi4qKICsrK3EY4KeffoqUlBQUFRVBXV2dTe/Xr59YXtEfLE+fPoWysrJUx1ZXV0eXLl2kjhWAxDgHDx4MACgsLGxVWW+qvepM0vF69OiBLl26cM5dIBDgk08+QWxsLLsWQHx8PAghcHR0bPkEm4lZVVUVGhoaYnN9Rdfx6dOnAMD+cS+6Nq+fh6ysLBuz6L+vr5zd1P4FBQWorq5uclgx8OoP55aGjzelqKgIurq6Yg3nDh06YMCAAZxGm6mpKYBXDfnRo0cjMzMTAoEAOjo6UFVVRWZmJvh8PjIzMzFkyBD2mgOvntOQkBAkJibi77//FotD0jxoSUPFCwsLoaenJ/YmBtG90ZibmxtSUlKwatUqbN68GUZGRhg9ejSmTJnCuV+bcv/+fWzduhVpaWli8Um7+FtTRPeMpJXAGYYB8L9zbUleXh62bduG7OxssXUY3vStBm0ZX1v55JNP2HtiwoQJ+PPPP+Hj4wMZGRlMmTKlxX0nTpyI+Ph43Llz542eFwUFBXh7eyMgIADp6eliX4K9ztbWFlu3bsXp06dbLFt0PxFCmsxz4sQJVFdXw8nJSeKr8wCgY8eOMDAwwMWLF1FTU4MbN26gpqaGnR8/ePBgqKurIysrC6NGjUJ2djZkZWUlzp+Xk5Nr8nNH9KWeNOdEUf9ktEFPURT1Fj755BMoKCi0auGn5v5Yakpzr3BrTXmNXxMkrbf9g0jSYmyt1V511tS5S9rfyckJGzduxNWrVzF06FAkJCRAV1eX01vekqZiluZcWlNHorySzk9SOYQQqKurc+bgv+59vR5KQ0MDgwYNQmZmJmpra3H58mWsXLmSbRBkZGTAxcUFN27cgJeXF2ffr776CqdPn4aTkxOMjY2hqqqKDh064I8//sBPP/0kceEySa8iA6S/N9TU1BAbG4vz58/j7NmzOHfuHNavX4/g4GCEhYU1+57t6upquLm5oba2FjNmzADDMFBWVoasrCx27dol1YKEzXmT50qSkpISuLm5QUVFBXPnzsXAgQOhpKQEGRkZrFu3rsmFFt9XfO/S6NGj0b17d0RFRbXYoAfAjsApLy9/4y/AGpchbf6LFy+2mK9r166QlZXFs2fPmsyjr6+P4uJiJCUlwcnJqckvUwQCAc6dO4cLFy4gLy8PSkpKnLzGxsbIzs5me++HDBnS4siA1hB90SnNl2YU9bGjQ+4piqLegqKiIsaOHYuSkhKkpaVJtY+WlhaEQiEKCgrEtonSPqT3dN++fbvJtMY9yl27dmX/iGpMUi9+a78kaK86k3Tujx49QmVlpVhvur29PeTl5REbG4v09HSUlJRIvRheWxDFIynmv/76C0KhkM0jWiysufpsrH///nj69CkMDAwwcuRIiT8t/THe3DXv168f7ty5IzYkvaGhAXfv3hWra1NTU+Tl5SE1NRX19fXsGyRGjBiBixcv4syZMyCEsL35wKve99OnT8POzg6rV6+Gra0txowZg5EjR0JeXr7Z2CXFe/fuXbEvq0T3xuvk5OQgEAiwZMkSREVFISEhATU1Ndi5c2ezx8nIyMCjR4+wfPlyLFy4EDY2Nhg1ahRGjhzZ7IJg0hLdB7du3RLbJukZb0pycjJqamqwadMmzJkzB9bW1jAzM8PIkSMlfia87/jetRcvXjTbCG5MNDWlpUVAm3Pv3j0AQLdu3aTKf//+fanyikZBicqXpFevXoiIiICamhq8vLzYtz68TjR8PisrC9nZ2TA0NOQ8Z8bGxrh69SqysrLa7P3zjYneOEHfQ0/9G9AGPUVR1FtatGgROnbsiG+++UbiHFwAOHz4MDIyMgCAnZMdFhbG6YG6efMmTp06BSMjozfuVVBWVsazZ8/atGfr7NmzuHbtGvs7IQR79uwBAM788k8++QTV1dWc+bJCoVDsVWLA/+bbSvtH8Luss+bcuXOHM38bAHbv3s2JSURdXR3W1tY4cuQIIiMjoaSkBFtb2zaPqSndunUDn89Hamoqbt68yaYTQhAWFgbg1WrzoryGhoY4deoU59VpdXV1Eq/X1KlTIRQKsWXLFonHfvz4cYvxNXfNRXPzY2JiOOnR0dEoKysTq2tTU1MIhUKEhISgT58+bMPP1NQUdXV1CAsLQ4cOHdg3KACvGiuAeK/vo0ePxI7bEisrKzx+/Bi//fYbJ110bzQmaYXugQMHQlFRscX7XzQy4/WY09LScPnyZanj7dSpk8SGtaWlJWRkZLB3717U19ez6Y8ePUJ8fDw0NTU5b49oqpym4oyOjkZpaanUcb5tfO9SU+eRkJCAyspKzsr1NTU1ePHihVjevLw8HD9+HIMGDeKswF9bW4uCggI8evSIk19SD3xlZSV2794NeXl5jB49mk1v6ouTyMhIPHjwABYWFs2f4P8zMTFBQUFBs6+969mzJyIiItCjRw94e3tLHPpuaGgIRUVFnD17Fjk5OWLD6Y2NjdHQ0MB+qdUW759v7NKlS+jevbvEaUUU9U9Dh9xTFEW9JYZhsG3bNvj5+cHOzg4TJ06EgYEBFBUVUVJSgpSUFFy/fp39Y9/MzAwTJ07E0aNH8ezZM1hYWKC0tBRRUVFQVFTEN99888axGBgYIDU1FatXrwafz4ecnBxMTU2l7smRZMiQIZgxYwbc3NygoaGBlJQUnD17FnZ2dpzhwk5OTggPD8f8+fPh6ekJeXl5JCUlSRxyP3jwYCgrKyMqKgodO3ZEly5doK6uzva0vu5d1llzGIbB119/DUdHR/Tv3x9ZWVlISkqCiYkJ5/3mIs7OzkhMTERqairs7e1btVhiW/jPf/4DDw8PuLm54fPPP4eGhgZSU1ORlpaGKVOmcOo3ICAAHh4ecHV1hZubG/vaOknXa8KECXBwcEBERASuXbsGCwsLqKmp4cGDB7h06RLu3buHlJSUZmPT09ODrKwsQkND8ezZM3Tq1Al9+/aFgYEBfHx8cPz4caxevRp5eXnQ1tZGfn4+YmNjMWDAAHYRRhGBQABZWVkUFBTAwcGBTR88eDA0NDRw+/Zt8Pl8Tv2rqKjAzMwMhw4dQseOHaGnp4fi4mL8+uuv6Nu3b6t6kn18fHDkyBGsXLkS165dw+DBg5GdnY1Lly6JvXZr5cqVePDgAUaNGoU+ffrg+fPnSExMRHV1dbOLegGvXnmmoaGBwMBAFBcXo1evXsjPz8fvv/8OhmE4X9w0x9DQEBkZGQgLC0OfPn0gIyODyZMnY+DAgZg1axb27NkDd3d3TJw4EdXV1YiOjkZNTQ02b97Mme7RVDljxoyBkpISli5dCnd3d3Tp0gU5OTk4c+YMtLS03njaTWvja63r16/j1KlTAMAu0Pb777+zDVQPDw92UcM5c+aga9euMDQ0RJ8+fVBZWYmcnBykpKSgV69e7GsUgVc96LNnz4aVlRU++eQTKCkp4fr164iLi4OcnJzY3PPc3Fx4enrC3t4eGzZsYNNtbW1hYmIChmHQrVs3FBUVIS4uDqWlpQgICECvXr3YvL/99hvi4uIwatQo9O3bFw0NDcjOzsbJkyehpaWFRYsWSVUnEyZMQGRkJM6cOSPxM05EQ0MD+/fvx8yZM+Hj44Ndu3ZxGuUKCgrsWhaAeIOdx+Oha9euOHfuXJPz599UdXU1Lly4gGnTprVZmRT1IaMNeoqiqDZgbm6OxMRE/PTTT0hLS0NycjLq6+vRo0cPGBkZYcWKFZwhhZs3b2bnWW/YsAGdOnWCsbExFi9eDB6P98ZxzJgxA4WFhUhKSsLBgwchFArxyy+/vFWDXrQS+q5du3Dnzh1069YN8+bNw7x58zj5+vXrhx07dmDLli3Ytm0bunbtCjs7O0ybNg0TJ07k5O3YsSO2bt2KH374AevWrUNdXR1MTEyabNAD767OmqOjo4Ply5dj69atOHjwIFRUVODu7o4lS5awPb6NmZqaon///rh37957HW4voqenh4MHD2L79u04cOAAampq0K9fP/j7+7PvURfh8/kIDw9HUFAQwsLCoKKiggkTJsDV1VXiyIL169dDIBAgOjoau3btQn19PTQ0NDB06FB89dVXLcbWp08frFu3Drt378aqVatQX18Pe3t7GBgYoHPnzjhw4AC2b9+OU6dOIT4+Ht26dYOLiwsWLlwo9sWIqqoqtLW1ce3aNbGhugKBAEeOHJE4hHfTpk0ICgrCqVOn2HejL1myBB06dGDfXS4NVVVVREZGYsOGDfjtt99ACIFAIMAvv/wi9pYAOzs7xMfHIyEhAWVlZVBRUcHgwYOxfft22NjYNHucLl26YM+ePdi0aRMiIiLQ0NAAXV1d7N69G7GxsVI36L/99lusXr0aoaGh7AKekydPBgB8/fXX6N+/P6KiohAUFAR5eXkYGBggKChI7B33TZWjpaWF3bt3Y8uWLQgNDYWcnByGDRuG/fv34/vvv2ffxPAmWhNfa4kW8mssLi6O/f/PPvuMbdBPnz4dJ06cQExMDJ4+fYoOHTqgX79+8PHxgbe3N+eLnO7du2PEiBHIysrC4cOH8eLFC2hoaGDSpEmYM2eO1O9FnzJlCrKzs5Geno6qqiqoqKhAX18f69ev5/TOA6+e/czMTCQmJqKsrAyEEPTt2xezZ8/GnDlzpF4M1cTEBIMHD8ahQ4eabdADr0b6/PLLL/Dy8sKcOXOwc+dOzme4QCBAZmYmFBUVOSMYgFdTcIyMjJCSkgJtbW22ntvCiRMnUFtb2+KCgRT1TyFDPoYVRyiKoijqIzF58mS8fPkSx48fb+9QKIqiWu3o0aP4+uuvceTIkY9yyLqDgwP69OmDkJCQ9g6Fot4LOoeeoiiKotpIRkYGbt++TXuGKIr6aE2ePBl6enrYsWNHe4fSaidPnsTNmzfh7+/f3qFQ1HtDe+gpiqIo6i1lZGSgsLAQu3btQk1NDZKTk9/7/HmKoiiKov596Bx6iqIoinpLP/74Iy5cuIBBgwYhMDCQNuYpiqIoinovaA89RVEURVEURVEURX2E6Bx6iqIoiqIoiqIoivoI0QY9RVEURVEURVEURX2EaIOeoiiK+ujxeDwEBAS0dxjUGzp+/Dg+++wz6Ovrg8fjISsrC/Hx8ez/f2yKiorA4/EQHBz8To9jaWkJDw+Pd3qM5pSVlWHp0qUYNWoUeDxeu8ZCURT1b0UXxaMoivqXysrKgqenJydNQUEBPXr0gImJCXx8fDBo0KB2iu6fw9LSEsXFxRK3ZWRkQF1dnf39r7/+QkxMDK5du4a8vDxUVlZiwYIFWLhwocT9q6ursWPHDpw4cQIPHjyAqqoqxowZgy+//BI9e/Z8J+fT1u7cuYOvvvoKhoaGWLlyJRQUFDBo0KAm6+yfJisrC9nZ2ZgxYwa6dOnS3uG0SmBgII4dOwZfX1/069cP3bt3b++QpPbo0SNERkbi6tWruHbtGsrLy2Fvb48NGzaI5X327Bl+++03/PHHHygoKEB5eTl69+4NExMTzJs3D717926HM6AoinqFNugpiqL+5aZMmYIxY8YAAF68eIEbN24gJiYGSUlJOHz4MDQ1Nds5wo/fwIED4evrK5b++mr4ly5dQnh4OLS0tKCjo4PMzMwmy3z+/Dk8PDyQl5eHqVOnwtDQEEVFRYiMjERGRgZiYmKgoaHR5ufS1rKzs9HQ0IAVK1ZAR0eHTbezs8PkyZMhLy/fjtG9e9nZ2QgJCYG9vX2rG/THjx9/R1FJJz09HaNGjcKCBQvaNY43cefOHYSGhqJ3797Q09PDmTNnmsx7+fJlBAYGYsSIEXBzc4Oamhpu3bqFX3/9FYmJiTh48CAGDx78HqOnKIr6H9qgpyiK+pcbOnQo7OzsOGn9+/fH2rVrkZycjJkzZ7ZPYP8g3bt3F6tjSSwtLZGdnY0uXbrgypUrmD59epN5Dx48iGvXrsHPzw9ffPEFp4zPP/8cP/zwA9auXdsm8b9LpaWlAABVVVVOupycHOTk5Frc/+XLl6irq4OSktI7ie9DU19fD6FQCEVFRSgoKLRrLI8fP0bXrl2lyltVVfVBvc5RR0eHHSFTVlaGESNGNJl34MCBOH78OLS0tDjp5ubm8PLywvbt27F9+/Z3HfI78aFdF4qiWo/OoacoiqLE9OjRAwDEekcjIyPh7e2N0aNHQ1dXF6NGjYK/vz+KiorEyjh9+jTc3d0hEAigr68Pc3NzLFiwAHfu3OHke/ToEb799luYm5uzZa5cuRJPnjwRK/PWrVuYNWsWDA0NYWJiAn9/f4n5mlNWVoZVq1Zh7Nix0NXVxdixY7Fq1SqUl5dz8onmcGdkZGDv3r2wtraGrq4ubGxskJCQ0KpjAkBDQwOqqqqazdO1a1epe2lFc8sdHBw46cOGDUP//v1x7NgxPH/+XKqyMjMzMWfOHAgEAujp6cHKygorVqxAWVkZJ/6wsDBMmjQJenp6EAgEmD9/Pm7cuMEpq/H88dTUVEybNg16enoYNWoUAgMD0dDQwOZtPM/cysoKPB4PlpaWACBxDr0o7ezZs9ixYwesra2hr6+PxMREZGVlgcfjIT4+HpGRkbCxsYGenh5sbW1x+vRpAMCNGzcwa9YsDBs2DAKBAGvWrEF9fb1Yfdy9exdff/01Ro0aBV1dXVhaWiIwMBA1NTViec+fPw8XFxfo6+tj5MiRWL16tcR8kgQEBCAkJIRz/o3rJDg4GDweD7du3cL69esxZswY6Ovr49KlSwAkz6EXpV27dg2enp7g8/kwMTHBsmXLxJ6VFy9eIDg4GDY2NjAwMMDw4cNha2uLwMDAZuMWxUUIQUJCAht3fHw8gP+taZGRkQFXV1fw+XzMnTuX3f/kyZNwcXEBn88Hn8+Hi4sLTp48KXYc0blcv34dM2fOBJ/Px4gRI9j76MWLFwgMDMTo0aOhp6cHNzc3FBQUSFX3KioqnOkuzenbt69YYx4ARo4cia5du+LmzZtSldPUWh+S7vWnT59i3bp1sLa2Zp83BwcH7NmzR2z/Y8eOsfVsYGAAR0dHiaM3mrsurTkeRVEfFtpDT1EU9S9XW1vLNtxevHiBmzdvYuvWrVBTU8P48eM5efft2wdDQ0N4eHiwf8jGxsYiMzMThw8fhpqaGoBXw4jnzp0LhmHwxRdfoHPnznj06BEyMjJw//59DBgwAABQUlICZ2dn1NfXY/r06dDS0sK9e/dw4MABZGVlIS4uDp07dwYAFBYWws3NDXV1dXBzc0Pv3r2RmpoKHx8fqc+1srISrq6uuHfvHqZNm4ahQ4ciPz8fBw4cQGZmJmJiYsR6q7Zu3Yrnz5/D2dkZCgoKOHDgAAICAqClpQUjIyOpjnv58mUYGhqivr4enTt3hpWVFfz8/N5qnntdXR0ASOyZVlJSQk1NDW7evAl9ff1myzl48CC+++479OzZEy4uLtDU1ERJSQlSU1Px8OFDttHj7++PxMREmJmZwdXVFY8fP0ZkZCRcXFwQGRmJoUOHcsr9448/EBUVBRcXF0ybNg0pKSnYt28fVFVV2ekHGzduRHJyMpKTk7F8+XKoqalBWVm5xXMXNeicnJygrKyMAQMGsPURGRmJiooKODo6QkFBAfv378f8+fOxbds2fPPNN5gyZQqsra2Rnp6O/fv3Q11dHfPmzWPLvnr1Kjuf3dnZGT179sT169exf/9+XLx4Efv372e/6Lp8+TK8vLygrKyM2bNno3Pnzjh27BiWLVvW4jkAgLOzM6qqqjjnD7xqeDXm7++Pjh07wtvbGwBanErx4MEDzJw5E+PHj4eNjQ3y8vIQFxeHq1evIjY2lr1nVq1ahbi4OHbKhlAoxN27d1tciHDcuHHQ0tLC0qVLMXz4cDg5OQF49WVS43pMSkqCk5MT7O3t2fTIyEisXr0aAwcOTv554gAAIABJREFUZBuTCQkJmD9/PlavXg1nZ2exc/Hy8sKkSZNgY2OD9PR07Nu3D7Kysrh9+zaeP3+OOXPmoLy8HPv27cO8efOQmJgIWdl332dVWVmJ6upqfPrpp21e9uLFi3H+/Hk4OztjyJAhqK2txV9//YXs7GzOZ97WrVsRGhqK0aNHY/HixZCVlUVycjIWL16M//73v3Bzc+OU29R1kfZ4FEV9gAhFURT1r5SZmUkYhpH4M2nSJHL79m2xfaqrq8XSzp49SxiGIWFhYWzaunXrCMMw5PHjx83G4OvrS0xNTcnff//NSc/NzSXa2tpk+/btbJqfnx9hGIZkZGSwaUKhkMybN48wDEOWLVvW4jlv2bKFMAxDIiIiOOkRERGEYRiydetWNi0uLo4wDEPs7OzIixcv2PQHDx4QHR0dsmTJkhaPRwghs2fPJjt27CCJiYnkyJEjZOXKlURbW5uMHj2aPHjwoMn9cnNzCcMwnDpobP369YRhGJKcnMxJf/jwIdHT0yMMw5CkpKRmY/v777+Jjo4OmThxInn27JnY9pcvXxJCCElLSyMMw5DFixcToVDIbs/Pzyfa2trE1dWVTSssLCQMwxADAwNSWFjIpguFQjJ58mRiZmbGOcb27dsJwzCcvIT8r/4zMzPF0saPH09qamo4+UX386hRo0hFRQUnRoZhCI/HE6sPe3t7sXhsbW2JjY0Nqays5KSfOHGCMAxD4uLi2DRnZ2eio6ND/vrrLzbtxYsXZNq0ac1eO2nOv/E2d3d3Ul9fL7bdwsKCuLu7i6UxDEPCw8M56eHh4YRhGLJr1y42zdjYmPj4+LQYY1Oaeu5EnyPp6emc9KdPnxJDQ0NibW3Nqd/KykpiZWVFDA0NOfeh6FyOHTvGKcfe3p7weDzi6+vLuR9//vlnwjAMOXPmTKvO48mTJ1J/hjS2YcMGwjAMiYmJkSp/U8d4/V6vqKggDMOQb7/9ttnyrl69ShiGIUFBQWLb5s6dS/h8Pqeem7ou0h6PoqgPEx1yT1EU9S/n7OyM8PBwhIeHIzQ0FP7+/igvL8ecOXPEVhrv1KkTAEAoFKKyshJlZWXg8Xjo3LkzcnNz2XyiXvWkpCTOEOvGKisrcfr0aVhaWkJBQQFlZWXsj6amJrS0tJCens4e79SpU9DV1YWpqSlbhoyMTKt6j5KTk6Guri7WC+js7Aw1NTWJw34///xzzlzlnj17YsCAAbh7965UxwwLC8O8efMwYcIETJ48GatXr0ZgYCAePnz4Vq81c3V1hZKSEr777jscO3YMxcXFOHfuHObPnw+hUAjg1eiL5hw/fhz19fVYsGCBxKH+ol7O5ORkAICvry9kZGTY7UOGDIG5uTkuXLjAGZ4PvBpC3rdvX/Z3GRkZCAQClJaWorq6+s1O+v+Jzl0SBwcH9v4Txfh/7N15XE35/wfwV3tatKloI8u9aVOKpNAmSxKlITQYZMuSfYy174w1mrpiKoYZImQbSwY1Ysg6GUvImhpSSvte5/eHxz2/rnurG6Xi/Xw87uPhfu7nfD6fc84tvc9nU1JSgpaWltCIk549ewq059GjR3j06BGGDRuG8vJyge+klZUVFBQU2O9kdnY2kpKS4OTkxI44Ad7vFNHY605MmDAB0tLiD6pUUlLC2LFjBdLGjh0LJSUl9l7y8z158kTsIeMNYWRkhL59+wqkXb58GcXFxfD19RUYCaOkpITx48ejuLgYV65cEThGW1sbQ4YMEUjr2bMnGIaBr6+vwPfR2toaAJCamtrYpyPkzJkz2LVrF+zt7eHl5dWoZfPXR7hz547I6Ux8J06cgISEBEaMGCHwXc3JyYGTkxOKiorY6Rl8ou6LuPURQlomGnJPCCFfuY4dOwr8gefo6IjevXvjm2++QVBQEIKDg9nPEhMTsW3bNvz7778oKysTKCcvL4/997hx4xAXF4c1a9YgKCgIVlZW6NevH4YNG8YO4X7+/Dmqq6sRExODmJgYkW3T19cH8D54Ki4uRufOnYXyNGR16fT0dJiamgoFR9LS0jA0NERycnKtbahJVVX1k7ZVc3d3R3BwMDu3+2N07NgR4eHhWL58OQICAth0V1dXmJiYYP/+/fUudsV/KNG9e/c686Wnp0NSUlLkNobdunVDXFwc0tPTBeYk13bdgPfzdcUZWl+bmgH0h2o+ROBTUVFB+/btRabXbA9//jWPx6v1Ycvbt28BvJ8CAuCTv5Pi6NSpU4Py6+vrCy2YJysrC319fbbdALBs2TIsXrwY7u7u0NfXh42NDRwdHeHk5PTJQ9ZFtZkfLIoaos7hcABAoH1A7fdT1Gf8h1K5ubkNb3ADJCQkYOHChTAxMUFISIjAQ4XGICsri2XLluGnn36Cs7Mzunbtij59+sDFxUVg8b6nT5+CYRihBx418b+vfKLui7j1EUJaJgroCSGECOnRoweUlZUFtk27c+cOJk+eDAMDAyxYsAB6enqQl5eHhIQEAgICwDAMm1dNTQ0xMTG4efMmrly5ghs3bmDdunXg8XiIiIiApaUlm3/48OECczlrkpOTAwA2b2P/4SyOppqLq6uri6SkpE8qw8bGBmfPnmX3xtbT00OHDh0wd+5cAKKDzZrEva4176246lqh/mPKq0leXr7B9TakPfyFH0XhB411XbtPPb8P1XW+otR2Pz9sl4uLC+Lj45GQkIAbN27gypUriImJgbW1NXbt2vVJq+g31q4Ddd232n42G/v613Tx4kX4+/ujW7du+PXXXxtlhfiqqiqhNB8fHzg7OyMhIQHXr1/Hn3/+ib1792Lo0KHsQ1aGYSAhIYHIyMhar9OHD5dquy/i1EcIaZkooCeEECISfzswvpMnT6KqqgqRkZECva/FxcXIz88XOl5KSgo2NjawsbEBADx8+BBeXl7Yvn07IiIiYGBgAAkJCVRUVAgNAf2QhoYGFBQURK5g/eTJE7HPSV9fH8+fP0dlZaVAL31lZSVevHghsle5qbx8+RIaGhqfXI6EhITAH+3l5eW4evUqOnbsWGdPNvD/Pd3Jycl19gIbGBjg77//xtOnT2FkZCTwGf+eiOpJbW06duwI4H2gWN93kr/quajvpLgrrQNN85Dq5cuXKC8vFwjIy8vLkZ6eLvSQR1VVFR4eHvDw8ADDMAgKCsKOHTsQFxdXZ8/vx+D/fD1+/Fio55f/c/w5fwYb6tKlS/D390fnzp2xa9cuoa0W66Oqqipy9MCHoxL4tLS04O3tDW9vb1RVVWHx4sU4efIkJk2aBHNzc3Tq1AmXLl2Cjo6OyNEzDVVffYSQlonm0BNCCBHCn+tqYmLCptXWAxQeHs7O2eb7cD418L63WE5Ojh2ar6amhgEDBuDcuXNC8zyB971P/HKkpKTg6OiIe/fuCYwaYBimQdsqubi4ICcnB4cOHRJIP3jwIHJycuDi4iJ2WeKobehvVFQUMjIy4Ojo2Kj1AcCWLVuQm5vLriRfl8GDB0NGRgZhYWEit9Tj93Tyr0tERIRA72dKSgri4+NhZWUl9hZgLZmxsTE4HA6io6NFBlmVlZXsPdXQ0ICFhQXi4+MFtmIsLy/H7t27xa6Tvy5FzSkrn6qwsBD79u0TSNu3bx8KCwvZe1lVVSX0IE5CQoLdraAx28NnZ2cHBQUF7N27V+D7VlhYiL1790JBQQF2dnaNXm9j+PvvvzFr1ix06tQJu3fvZqePNESnTp1w+/ZtgbUt8vLy2O3++EpKSoTWv5CSkmJ3P+Dfm+HDhwN4/zMvqpdf3C09xa2PENIyUQ89IYR85ZKTk3H8+HEA74ORJ0+e4ODBg5CRkcG8efPYfC4uLti9ezemTp2K0aNHQ0ZGBpcvX8ajR4/Y7bb4VqxYgYyMDNjb20NHRwelpaWIjY1FUVERPDw82HyrV6/G2LFjMX78eHh4eMDY2BjV1dVIS0tDXFwcRowYgdmzZwMA5s2bh4sXL2L69OkYP3482rdvj7/++kvkw4PaTJkyBWfOnEFgYCCSk5PRvXt3PHjwADExMTA0NGz07ZmOHTuGw4cPw97eHnp6eqisrMT169dx/vx5GBgYYM6cOQL5CwoKsGfPHgBAZmYmAODGjRvYtm0bgPf7ctfsIff09ISNjQ06duyI8vJynD9/HteuXcPo0aOF9qcXpX379li2bBkCAwPh7u4ODw8P6Orq4s2bN4iLi8PatWvRvXt32NnZYciQITh16hTy8vLg6OiIrKws7Nu3D3Jycli+fHljXbJmJSEhgY0bN2LChAkYPnw4vLy80LVrV5SWliI1NRXnzp3D/Pnz2Wu7dOlS+Pr6wsfHB+PGjWO3rRMVXNWmR48eAICgoCC4u7tDTk4O3bp1Y+eUfwwDAwOEhYXh8ePHMDExwf3793H48GF07tyZ3be+qKgI9vb2cHJygrGxMdTV1ZGeno79+/dDRUWlSR42tW3bFgsXLkRgYKDAtmlHjx5FamoqAgMDBRY0bGr8n6vS0lIA7xdF5Kf16tULvXr1AgDcvXsXM2fOBMMw8PT0xMWLF4XKqvl7rTbjxo3DokWLMGHCBHh4eCA/Px+HDh2Cjo4OsrKy2HwvXrzA+PHjMXDgQHTr1g1t27bFs2fPsH//fujp6bGL/5mbm2P27Nng8XgYMWIEBg0aBG1tbWRmZuL+/fu4ePEi7t27V2+7xK2PENIyUUBPCCFfuZMnT+LkyZMA3g81VlVVhZ2dHfz8/ASGWVpZWYHH42Hbtm0ICQmBnJwc+vbti71792L8+PECZXp4eODIkSM4evQocnJyoKSkhK5duyI0NBSDBg1i83Xo0AGHDx9GZGQk4uPj8ccff0BOTg4dOnSAo6OjwJBfAwMDREVFYcOGDdi7dy9kZWXRr18/bNy4sd7h0XzKysrYv38/QkNDER8fjyNHjkBDQwNjxozB7NmzG2U+bE1mZma4evUqYmNjkZOTA4ZhoKenh6lTp8LPz09oZfm8vDyEhIQIpF27do3dF7x9+/YCAX2PHj0QHx+PjIwMSElJoXv37ti8eTOGDRsmdhvHjh0LAwMD7Ny5E3v27EF5eTm0tLRga2srsJBcUFAQjI2NcfToUaxfvx4KCgro1asX5s6dK7RvemvWvXt3HD16FOHh4YiPj0d0dDQUFRWhq6uLkSNHCgwVt7S0xK5du7B582ZERERASUkJgwcPho+PD9zd3cWqz8rKCgsXLkR0dDRWrFiByspK+Pv7f1JA3759e/z888/YsGEDTp06BRkZGbi7u2PJkiXsiAB5eXlMmDABiYmJSExMRFFREbS0tODk5IRp06ZBW1v7o+uvy7hx46ClpYWdO3ciLCwMwPuV18PCwhp9hEx9PvxZS05OZhfG9Pf3ZwP6x48fs4uArlu3TmRZ4gT0w4cPR2ZmJqKiorBu3Tro6+tj5syZkJSUxL///svma9++Pby8vHDt2jWcP38e5eXl0NbWhre3N6ZOnSowD97f3x+mpqbYs2cPfv/9dxQXF0NDQwPdunXDsmXLxLoODamPENLySDBNuXIIIYQQQgj5bJycnKCrq8uO9CCEEPJlozn0hBBCCCGEEEJIK0QBPSGEEEIIIYQQ0gpRQE8IIYQQQgghhLRCNIeeEEIIIYQQQghphaiHnhBCCCGEEEIIaYUooCeEEEIIIYQQQlohCugJIYQ0qbS0NMycORN9+vQBl8vF0qVLm7tJXxQnJyf4+vo2aR08Hg9cLhfp6elNWk9doqKiMHjwYJiamjZ7W0jzYhgGo0ePxoIFC5q7KS1aZmYmevTogaNHjzZ3UwghTUi6uRtACCHky/b999/j0aNHmD59Otq1awcDA4M68799+xahoaFISEhAdnY22rVrBxcXF8yZMwdt27YVyMvj8bB161aR5SxevBiTJ09m3z979gxhYWFITk5GZmYmKisr0aFDBwwYMACTJ0+GlpZWveeSn5+P3377Db1794aNjY0YZ9+05bT0OhvL1atXERgYCGdnZ0ydOhXS0tJQV1dv7maJLTo6Gjdu3MD9+/eRmpqK6upqPHr0SGTeuLg4nD9/HklJScjIyICSkhK6du2K7777Dv379//MLW+ZTp48ibt372LDhg0C6UuXLq01eA0JCcHgwYNrLTMzMxNDhw5FQUGB0O8OcVVXV8PHxwe3b9+Gg4MDwsPDReZLSkpCREQE/vnnHxQXF0NTUxMWFhZYv349ZGVlAQCVlZUIDQ3FsWPHUFpaCjs7O6xYsULoe3/37l34+Phg7969sLCwEPhMS0sLY8aMQXBwMAYPHow2bdo0+JwIIS0fBfSEEEKaTHl5OW7evInx48eL9QdydnY2vvnmG2RmZmL06NHo1q0bHj9+jOjoaNy8eRP79+8X+Ufp999/DzU1NYE0U1NTgfdv3rxBVlYWBg4cCG1tbUhLSyMlJQUHDx7EqVOncPz4cWhoaNTZvvz8fGzduhX+/v6fHNA3Rjmfq84ZM2bAz8+PDTY+tytXrgAA1q5dC1VV1WZpw6eIiIjAu3fvYGxsjJKSEmRkZNSad+XKlVBSUoKTkxM6d+6M3NxcHDlyBFOnTsW8efMwY8aMz9jyliksLAyOjo7o1KmTyM83btwolGZubl5nmT/++COqqqo+qV379u1DSkpKnXkOHz6M5cuXo0ePHpg2bRqUlZWRmZmJW7duCdS/e/du7Ny5E5MnT4a6ujoiIyOxbNky/PLLL2yeyspKLF++HGPGjBEK5vl8fX3x22+/4ciRIxg3btwnnR8hpGWigJ4QQkiTefv2LRiGgYqKilj5f/nlF/z333/YvHkzhg0bxqZbWlpiwYIF2LVrF2bOnCl0nIuLC/T09Oos29bWFra2tkLp1tbWmDdvHhs0kf9XWFgIJSUlSEtLQ1q6+f5kyMrKAgCxgvnS0tJmb++Hfv/9d+jo6EBSUhLTpk2rM6APCgoS+p6OHz8eI0aMQFhYGMaOHSv2z1NL0lj3JTExEc+fP69zuL2Hh0eDyoyLi8O5c+ewYMECbNq06aPalZGRgS1btmDOnDlYv369yDxPnjzBqlWr4OnpiR9//BESEhK1lnfu3Dm4u7tj/vz5AABlZWUsX74cZWVlkJOTAwD8+uuvyMvLw7x582otR09PD9bW1oiOjqaAnpAvFM2hJ4QQ0mA5OTlYs2YNBgwYAFNTUwwYMABr1qzBu3fv2DxLly6Fo6MjAGDr1q3gcrngcrm4du1areVeu3YN8vLycHNzE0gfOnQo5OTkcOTIkVqPLSwsRGVlZYPPRVdXF8D7Huy6XLt2Dc7OzgAEz8fJyYnNU1lZiYiICAwdOhRmZmawsbHBrFmzBIZXi1NOVFQUvvvuO/Tr1w+mpqawt7fHwoULP3reeH11pqeng8vlgsfj4fTp0/D09IS5uTl+/PFHAKLn0PPTHj9+jB9//BF2dnYwNzeHt7c3EhMThdpw4cIFjB8/HjY2NjA3N4eDgwP8/f3x/PnzWtvNbxf/vvPbzV8zYOnSpeByucjJycH333+Pvn37wsLCgg2Y09PTsWjRIvTt2xempqZwcXHBli1bUFJSIlAP/1yePHmCn376Cfb29rCwsMCECRPw7NkzAMDZs2cxcuRImJubw8nJCQcOHBD7+uvp6UFSUrw/uUQ9dGrTpg0cHR1RUVFR5/Xi418XUUStY3Hs2DGMGjUK1tbWsLCwgLOzMxYsWICcnByBfC9evMCiRYtgb28PU1NTODk5YcOGDSguLhZZf233Rdz6RImNjYWUlBTs7OxqzcMwDAoLC1FdXV1veYWFhQgMDISPjw/MzMzqzV+bwMBA6Ovr49tvv601z86dOwEAixYtgoSEBIqLi2v9nVVaWirw4EZFRQXV1dUoKysDAKSmpiIsLIwd0VGX/v37IyUlBU+fPm3oaRFCWoGW8/iaEEJIq1BQUAAfHx+kpqbCy8sLxsbGePDgAfbv34+rV6/i0KFDUFJSwujRo2FkZIR169Zh4MCBGDhwIACgS5cutZZdXl4OOTk5oZ4rSUlJyMvLIy0tDTk5OULzSIcPH46ioiJISUnB3NwcM2bMwIABA0TWUVZWhqKiIpSXl+PJkycICgoCgFrz83Xp0gXff/+90PkoKiqyeRYuXIjY2FjY2dnBx8cHb9++RVRUFMaMGYOoqCgYGxuLVc6vv/4KCwsL+Pr6QlVVFSkpKYiJicHVq1dx4sQJoekF9RGnTgA4f/489uzZAx8fH4wZM6beQAEAlixZAklJSUydOhWFhYU4cOAApkyZgsjISPTt2xcAcP36dcyYMQMcDkdgmHFiYiJevnwJQ0NDkWWrq6tj48aNOHjwIG7evMkOpW7Xrp1AvkmTJqFdu3aYOXMmiouLoaCggP/++w/e3t7s97VTp064fv06wsPD8c8//2D37t1CvcVLliyBgoICpk2bhpycHOzatQtTpkzBnDlzEBQUhDFjxsDLywsxMTFYuXIlunTpAmtra/FuwifiB8P1TQtpqOPHj2PJkiWwtrbGnDlzIC8vj1evXuHixYvIzs5mf9bu3buHCRMmoG3bthg9ejS0tbXx8OFD7NmzB0lJSdizZw9kZGQEyhZ1X8StrzY3btxA165doaCgUGseKysrFBUVQUZGBr169cK8efPQo0cPkXm3bNmCqqoqBAQEIDk5uYFX770zZ84gPj4e0dHRkJKSqjXfpUuXYGhoiBs3bmDjxo14+fIlZGRkYGtrix9++EFgCoGFhQVOnToFV1dXqKmpYefOnejSpQu7jsjKlSvh6Ogo8CCwNvzh+NevX6/z9y8hpJViCCGEkAbYsmULw+FwmL179wqk7927l+FwOExwcDCblpaWxnA4HCY0NFSssv39/RkOh8MkJycLpCcnJzMcDofhcDjMvXv32PRdu3YxK1asYI4cOcKcP3+eiYyMZOzt7Rkul8scPnxYZB179uxhy+JwOIyjoyNz/PhxsdpX1/n8/fffDIfDYebOnctUV1ez6Q8ePGC6d+/O+Pj4iFUOwzBMUVGRUNqVK1cYDofDRERECKQ7Ojoy48eP/6S28z8zNjZmnjx5IvR5aGgow+FwmLS0NKG0UaNGMWVlZWz669evGQsLC2bw4MFs2tq1axkOh8O8ffu23naKsmTJEobD4dSavmDBAqHP5s+fz3A4HObChQsC6evXr2c4HA5z8OBBoXOZNm2awL377bffGA6Hw1hYWDD//fcfm56dnc2YmpoyAQEBDT4XPz8/kedSlwcPHjDGxsbM2LFjxcpf2/ViGIbhcDjMkiVL2PezZs1iLC0tmYqKijrLdHd3ZwYNGsQUFBQIpJ89e5bhcDgCP2913Rdx6xOlsrKSMTIyYmbNmiXy802bNjFr165ljh8/zpw7d47h8XiMtbU1Y2Jiwly+fFkof1JSEmNkZMScOnWKYRiGuXr1KsPhcJgdO3aI3ab8/HzGzs6OWblyJZvG4XAYPz8/oXwcDofp3bs3Y2xszPz000/M2bNnGR6Px5iamjK2trZMZmYmm//t27eMl5cX+3vKzs6OuXXrFsMwDBMTE8NYW1sL5K/L69evGQ6HwwQGBop9XoSQ1oOG3BNCCGmQc+fOQV1dHaNHjxZIHz16NNTU1HD+/PmPLnvChAmQlJTEvHnzkJCQgFevXiEhIQHz5s1je/9qDpeeOHEiAgMDMXLkSDg7O2PKlCn4448/0K5dO6xbtw5FRUVCdbi4uGDXrl0ICwvDrFmz0LZtW7GG+tbn3LlzAIDp06cLjDAwMjKCg4MDbt26JXY9/N7H6upqFBQUICcnB1wuF8rKyrhz584nt7U2AwYMaHAP3sSJEwUWy2vfvj3c3d3x7NkzdoivsrIyAODPP//8qGkR9flwwcXq6mrEx8fD2NhYaOTFtGnTICkpKfJ76uvrK3Dv+L3vzs7O0NHRYdPV1dVhaGiIFy9eNOJZiJaTkwN/f3/IycmxUyAak7KyMkpLS3HhwgUwDCMyz6NHj/Do0SMMGzYM5eXlyMnJYV9WVlZQUFDA5cuXhY4TtRCmOPXVJjc3F9XV1bWuIbBw4UJ8//33GD58OFxcXODv749Dhw5BWloaq1evFshbUVGBFStWoG/fvhg6dGiD2lHTpk2bwDBMvVvo8X8X5ebmYsqUKVi2bBkGDhwIf39/rFmzBtnZ2di9ezebX0NDAwcPHsTp06dx+PBhxMXFoWfPnsjOzsbGjRuxePFiaGpq4s8//8TIkSPRv39/LFy4ELm5uUJ189eeyM7O/ujzJIS0XBTQE0IIaZD09HQYGhoKDVeWlpaGoaEh0tLSPrpsa2trbNmyBUVFRfDz84OjoyNmzJgBGxsbODg4AEC9w8DV1NQwZswY5OfnIykpSejz9u3bo2/fvuxWeOvXr0dQUFCtW0yJKz09HZKSkiID4m7durF5xJGYmAhfX19YWFjA2tqaXdCvoKAAeXl5n9TOutS2anhdRJ0vP43/XRg3bhyMjY2xZs0a9O7dG1OnTsXvv//eKA9SAOF25+TkoLi4GF27dhXKq6qqCk1NTZHfU319fYH3/OHNohZcVFFRERk8Nabc3FxMmjQJmZmZCAsLq3VqwqeYNm0adHR0MGvWLPTp0wezZ8/GoUOHUFhYyObhP5jh8Xjsd7Hmq7i4GG/fvhUqW9T3SZz6asN/2NKQBwGdOnXCkCFDkJqaKrD+QGRkJFJTU7Fq1Sqxy/rQzZs3cfDgQSxZskRoS80P8ReyAwBPT0+Bz4YPHw4pKSlcv35dIJ3/+8TU1JQ9/qeffgKHw8GoUaPw77//Yu7cufDy8sLWrVvZNQ4+xL9edS3CRwhpvWgOPSGEkBZlyJAhcHV1RUpKCoqKimBoaAgNDQ2MGjUK0tLS6NixY71l8Be6q7lIX22MjIxgbGyMffv2Ydq0aR/d7ob2Ntbmzp07mDx5MgwMDLBgwQLo6elBXl4eEhISCAgIaLR6RGmsfao/bKOamhpiYmJw8+ZNXLlyBTdu3MC6devA4/EQEREBS0t42QZHAAAgAElEQVTLT6rvw3Z/7DWqbeG6uuZFNxV+MP/s2TOEhYWJXCyvNrUFbqJGR3Tq1AmnT59GYmIiEhMTcf36dSxfvhyhoaGIioqCgYEBm5e/UKMoogJaUd+nhtT3IVVVVUhKSjb4oVbN3weGhobIzMzEL7/8ghEjRoBhGKSmpgJ4v7Ul8P7ap6amQlNTs865+oGBgTAyMkKPHj3YMvhKSkqQmpoKZWVlqKurQ1VVFW3atEFJSYnQGhDS0tJQU1Ord2HOhIQEnD9/Hn/88QckJCQQExMDS0tLjB8/HgAwf/589gGQlpYWexz/etW3PgEhpHWigJ4QQkiD6Ovr4/nz56isrBTopa+srMSLFy+Eejk/hpSUFLp3786+z8rKwoMHD9CrVy+xgk7+UOgP/3CuTWlpqVhBQl09XAYGBvj777/x9OlTGBkZCXzG7+Hk9/TWVc7JkydRVVWFyMhIgWtZXFxc7x/8H9v2TyHqfPkrw9dsv5SUFGxsbGBjYwMAePjwIby8vLB9+3ZEREQ0aps0NDSgqKiIJ0+eCH2Wl5eHrKwsge9XS5OXl4fvvvsOjx8/RlhYGPr379+g4/lD0nNzcwW2+qtt9IysrCwGDBjATk9ISEiAn58fdu3ahVWrVrEP0SQlJdmFDj9FffXVht9j/WHwXJ8Pfx9kZ2ejrKwMBw4cELlbQUREBCIiIhASEoLBgwfXWu6rV69QUFAAV1dXoc+uXbsGV1dXjBs3DitXroSEhARMTU1x48YNZGRkCIxsKS8vx7t37+p8mFFUVITVq1dj5syZ7MiHjIwMdOjQgc3Tvn17Nr1mQM+/XvyRQoSQLwsNuSeEENIgLi4uyMnJwaFDhwTSDx48iJycHLi4uDRqfdXV1fjxxx9RVVWF6dOns+mVlZUoKCgQyv/69WtER0dDVVVVoOeXv5f5h65evYrHjx/Xugp2TfzeOlHBP/+8IyIiBHqIU1JSEB8fDysrK7aHrK5yausNDg8PF2sbro9p+6fYvXs3ysvL2fcZGRk4ceIEDA0N2aBF1ND6zp07Q05OrkmmEEhKSsLR0RHJycm4ePGiwGcRERGorq5u9O9pY8nLy8OkSZOQkpICHo9X7+4LovADvitXrgik79q1SyivqHtjbGzMtoX/nsPhIDo6WuRDgcrKSrGnH4hTX1169+6Np0+fCg3RLy4uZrd0qyk5ORlnzpxBly5d2IBZT08PISEhQq/Zs2cDAEaMGIGQkBCB3x+vXr3C06dPUVFRwaZt2LBBZDkAYGJigpCQEHh7e7P5PTw8AAD79+8XaOOBAwdQVVVV573++eefoaSkJLAugZaWFh4/fsy+T0lJYdNr+vfffwEAvXr1qrV8QkjrRT30hBBCGmTKlCk4c+YMAgMDkZycjO7du+PBgweIiYmBoaEhpkyZ8tFlFxUVwdvbGwMHDoSenh4KCgpw8uRJ3L9/HwEBAejTpw+bt7i4GM7OznBxcUHnzp2hoqKC58+f49ChQyguLsbmzZshLy/P5l+9ejWysrLQp08f6OjooKysDPfv38fp06ehqKgotDe3KGpqaujYsSNOnToFfX19tGvXDm3atIGTkxPs7OwwZMgQnDp1Cnl5eXB0dERWVhb27dsHOTk5LF++XKxyXFxcsHv3bkydOhWjR4+GjIwMLl++jEePHjV4uzpx2/4pqqqqMG7cOLi5uaGoqAjR0dEoKysTON8VK1YgIyMD9vb20NHRQWlpKWJjY1FUVMQGOY1t/vz5uHLlCmbNmoWxY8fCwMAAN2/exOnTp9GrVy+MHDmySeoVJT4+Hg8fPgTw/72l27ZtA/B+qDp/yDTwfqu3+/fvY9iwYcjPz8fx48cFyurZs2e9o2CGDRuG4OBgrFy5Es+ePYOamhouXrwocgrK5MmToaSkhF69eqFDhw7Iz8/H0aNHISEhwd4bCQkJbNy4ERMmTMDw4cPh5eWFrl27orS0FKmpqTh37hzmz58vNDdcFHHqq8vgwYMRFRWFixcvCixml5qaiqlTp8LZ2RmdOnVCmzZt8PDhQxw+fBhSUlIIDAxk8yorK4vseb927RoAgMPhCH2+ZMkSXL9+HXFxcexIG2dn51rbqampKVSGp6cnjh07hj179uDdu3ewtrZGSkoKDhw4gG7dusHX11dkWXfu3MH+/fsRFRUlsDXg8OHDERMTg8WLF8PMzAwRERHo3bs321PPd+HCBXA4HNqyjpAvFAX0hBBCGkRZWRn79+9HaGgo4uPjceTIEWhoaGDMmDGYPXu2WHuX10ZGRgZcLhcnTpxAVlYW2rRpAzMzM+zYsUNo7q68vDxcXV1x584dnD9/HsXFxVBTU0Pfvn0xZcoUmJubC+R3c3PDsWPHcPz4ceTk5EBCQgI6OjoYPXo0Jk+eLLCKeV2CgoKwdu1aBAcHo6SkBLq6umxQHBQUBGNjYxw9ehTr16+HgoICevXqhblz54LL5YpVjpWVFXg8HrZt24aQkBDIycmhb9++2Lt3r0Dg9zHqavvH2rBhA6KjoxEZGYn8/HxwuVysX78ednZ2bB4PDw8cOXIER48eRU5ODpSUlNC1a1eEhoZi0KBBn1R/bXR1dXHw4EGEhobijz/+QEFBAbS1tTFt2jTMmDFDaFHHpnT27FkcPXpUII3fk6urqytwX+/fvw/g/dSLkydPCpW1bt26egN6JSUlREREYN26dQgPD4eCggJcXV2xadMmoV5aHx8fxMbG4sCBA8jLy4Oqqiq6d++O5cuXCzxA6969O44ePYrw8HB2z3VFRUXo6upi5MiRYs/xF7e+2vTu3Rtdu3bFH3/8IRDQt2vXDra2trh27RpOnDiBsrIyaGpqYujQofDz82sRwayUlBQiIyOxbds2nD59Gn/++Se7iOfcuXOhqKgodExlZSWWL1+OMWPGCI0isrGxwU8//YSIiAjExcWhd+/eWLNmjUCe9PR0/PPPP1ixYkWTnhshpPlIME25ug4hhBBCvkg8Hg9bt24V6LEk5HM4deoUFi1ahJMnT6Jz587N3ZwWbe3atThz5gz+/PPPRlv0khDSstAcekIIIYQQ0mq4ubnBzMwMYWFhzd2UFi0rKwsHDhxAQEAABfOEfMFoyD0hhBBCCGlVRK1OTwRpamqyC+IRQr5c1ENPCCGEEEIIIYS0QjSHnrQI1dXVKCoqgoyMTJPtlUwIIYQQQgghLRXDMKioqICioiIkJcXre6ch96RFKCoqYvdPJYQQQgghpKlIS0uDYRjqRCItBv/7WFlZCeD99pnKyspiHUsBPWkR+PuqcjgcyMrKNnNrvgz37t2DqalpczeDNCG6x18Hus9fB7rPXz66x82voKAA2dnZ0NPTg7y8fJME9CUlJbQI4Vegse8zwzAoLS1Feno6CgsL2dhIHBTQkxaB/wtVVlYWcnJyzdyaLwddyy8f3eOvA93nrwPd5y8f3ePmlZaWBn19fSgoKDRpPeIOlSatW2PfZ0VFRejp6eHJkycNethE3zZCCCGEEELIF6+iooJ6z0mLJi8vj4YucUcBPSGEEEIIIeSrQPPmSUsmISHR4O8oBfSEEEIIIYQQQkgrRAE9IYQQQgghhBDSClFATwghhBBCCCGtAI/HA5fLFfk6fvx4czev0eTk5CAwMBDOzs4wMzODvb09Jk+ejPPnzzd6XUeOHAGXy0VRUVGjl/050Cr3hNSntBSQl2/uVjSYlZVVczeBNDG6x18Hus9fB3MOp7mbQMhXq31Qe7wpevPZ69VW1EbGwowGH6esrIwdO3YIpRsYGDRGs5pdRUUFJkyYgJKSEkyfPh0GBgbIyMjA5cuXkZiYCBcXl0atz8HBAQcOHGi1CyZSQE9IfeTlAVpAhRBCSBOSaeCqxoSQxtMcwfyn1CslJQULC4tGbo34SktLId+EnV3Xr19HSkoKDh06BHNzczbdw8OjwSvAi0NdXR3q6uqNXu7nQkPuCSGEEEIIIeQLkZ6eDi6Xi9OnT2PlypWwsrJC//79ERoaiurqaoG8KSkp8PPzg6WlJSwtLTFnzhxkZWWxn1+7dg1cLheXLl3C9OnTYWlpicDAQADAw4cPMWbMGJiZmcHNzQ0JCQnw9PTE0qVLAQAXLlyAkZER0tLSBOpMS0uDkZER4uLiRLY/Pz8fAKCpqSn0Wc0V4J8+fYqAgAAMGDAAPXr0gJubG3bv3s2eY3FxMSwsLBAVFSVUjqenJxYtWgRAeMh9Q65fbGwsXF1dYW5uDl9fXyQnJ4PL5eLIkSNsnri4OHh6esLCwgK9evWCt7c3rl+/LvLcPwYF9IQQQgghhBDSilRWVgq9PhQUFAQFBQWEhoZi+PDhCAsLw5kzZ9jPU1NT4ePjg7KyMmzatAnr16/HkydPMH36dKGe8B9++AFGRkbYtm0bRo0ahZKSEkyZMgWlpaXYsmULZsyYgbVr1+L169fsMf369YOWlhaOHTsmUNbRo0ehrq6OAQMGiDy37t27Q1JSEsuWLcPNmzdFnhsAZGZmwtDQEKtWrUJERAS8vb3B4/EQGRkJAFBQUICDgwNiY2MFjktLS8P9+/cxdOjQOq5w/dfv7t27mD9/PoyNjbF161Y4OzsjICBAoIyXL19i7ty5sLGxwfbt2xEUFAQHBwfk5eXVWXdD0JB7QgghhBBCCGklcnNzYWJiIpQeFxcHPT099r21tTXbW25nZ4dLly7h3LlzbCC7detWtGvXDpGRkZCVlQUAcLlcDBkyBAkJCXBwcGDLGjx4MObNm8e+j4qKQm5uLg4fPgxtbW0A7+fwe3t7s3mkpKQwcuRIHD16FP7+/pCQkADDMDh27Bg8PDwgLS06FO3UqRMWL16MzZs3Y9y4cZCTk0OvXr0watQoDBkyhM1na2sLW1tbAADDMLCyskJpaSkOHjyIadOmAQDc3NwwZ84cvHnzhm3n6dOnoaKiAjs7uzqvc33XLzIyEl26dEFwcDAkJCTQv39/VFRUICgoiC0jOTkZioqKWLJkCZtW24OMj0UBPSGEEEIIIYS0EsrKyti1a5dQupaWlsD7DwPWrl274tWrV+z7xMREjBgxApKSkmwvuJ6eHnR1dXHv3j2BgL7mv4H3vdMmJiZskAwA5ubmaNeunUC+UaNGITw8HNeuXUOfPn1w9epV/Pfff/D09KzzHCdNmoShQ4fi/PnzuH79Oq5cuYK///4bycnJWLBgAQCgrKwM4eHhOHHiBF6/fo2Kigr2+MrKSkhLS6N///5QUFDAmTNnMGHCBADvA3oXFxf2IUZt6rt+9+7dg5ubm8A0ACcnJ4GAnsPhoKCgAEuWLIG7uzt69uwJBQWFOuttKBpyTwghhBBCCCGthJSUFMzMzIReHwaobdu2FXgvIyODsrIy9v27d+8QGRkJExMTgVdaWprA0HkA0NDQEHiflZUFNTU1obZ9uLicvr4+evfuzc4pP3LkCMzNzdGtW7d6z1NbWxvjxo1DSEgIEhIS0K9fP+zcuRPv3r0DAGzatAm//vorvvnmG0RERCAmJgYzZswAAPY85eTk4OzszA67f/bsGR4+fAg3N7d666/v+mVlZQmd74fvO3fujG3btiEtLQ1+fn7o06cPFixYgJycnHrrFxf10BNCCCGEEELIV0ZFRQUuLi4Cw+T5PgzWJT7Y8UlTUxPPnz8XOk5UoOrt7Y0VK1ZgwYIFOHfunMDwc3EpKChg7NixuHTpEl6+fAk1NTWcOXMG48ePx9SpU9l8CQkJQscOHToU06dPx6tXr3D69Gmoq6ujT58+DW7DhzQ1NYXOV9T5Ozg4wMHBAQUFBbhw4QLWrl2L//3vfwgODv7kNgDUQ/9ReDweuFwu+7K3t8fs2bPx8uVLNg+Xy8XevXvZ976+vpgzZ06D6nn+/Dl4PB670uOn+nAFx9p82FYejwcbG5tGacOHIiMjYWpqirZt28LJyUngaZmEhITI18SJE4XKGTx4MPv57du3a61v4sSJtZZLCCGEEELI18LW1haPHz+GqampUG9/zbn4opiZmeHevXt48+b/t967c+cO3r59K5TX1dUVMjIyCAgIQHV1db2947m5uSIXwktNTQXw/73gZWVlAqMSqqqqcOrUKaHj7OzsoKKigtjYWMTGxmLQoEGQkpKqsw3iMDU1xV9//SWwgGB8fHyt+ZWVleHu7o6BAwfiyZMnn1w/H/XQfyRlZWXs2LEDwPuVEkNCQjBx4kScPHkSCgoKOHDgQL0/CPV58eIFtm7dipEjRwoN+WhKq1atqnWRisa0fv16fP/992jbti1GjRqFgoICPHr0iP187ty5Avl3796NvLw8dO3aVSB969attW578SFXV1eoqqqy769fv47ExER06dLlE86EEEIIIYSQz6OqqkpkB1aHDh0E5rTXx9/fH97e3vDz84OXlxfU1NTw5s0bXLlyBSNHjqyzQ8/T0xPbt2/HtGnT4O/vj9LSUvB4PKirqwt1lMnJycHd3R1RUVEYNmxYvXHN1atXsWXLFnh6esLMzAySkpL4559/EBkZCUdHR+jr6wMA+vbti6ioKBgYGEBVVRVRUVEoLy8XKk9GRgYuLi7YtWsXsrKysGrVKrGvUV2mTp2Kb775BgEBAfD09MSzZ89w6NAhAICk5Pt+8+joaNy+fZtd8f/Fixc4c+YMPDw8GqUNAAX0H01KSgoWFhYAAAsLC3To0AHjxo1DQkIChgwZwn7WGn0YMDeF/Px8/O9//4OsrCyuX7+OTp064d69ezA1NWXz/Pzzz+y/7969i5CQEMjJycHPz49Nf/jwIRYvXowVK1aI9cM5duxYjB07ln1vbW0NAA0ePUEIIYQQQr4M2oraeFP0pv6MTVDvxygoKMDo0aOF0ufOnYuZM2eKXY6hoSEOHDiAkJAQrFy5EqWlpdDW1oatrS06duxY57Ft2rTBjh07sHr1asybNw+6urpYtGgRNm3aBCUlJaH8Li4uiIqKgpeXV73t6tGjBzvvfceOHaiqqoKenh5mzJiBb7/9ls3H//s/MDAQ8vLyGDFiBAYOHIgVK1YIlenm5oaYmBhoaWmxf/9/KjMzM2zevBnBwcGIi4uDqakpVq9ejUmTJrHXgMvlIj4+HuvWrUNeXh40NTXh7e0t1HH5SRjSYKGhoUzv3r0F0kpKShgOh8NERkYyDMMwHA6H2bNnD/v5+PHjmdmzZwscc+XKFWbUqFGMqakpY2try6xatYopLCxkGIZhrl69ynA4HIGXo6Njne26ceMGM27cOMbc3Jzp3bs388MPPzAFBQXs54cPH2Y4HA7z77//Mj4+PoyZmRnj6urKnD17VqCcD9v64flWV1czgYGBjLW1NXP79m2GYRjm3bt3zIoVKxhbW1vG1NSUGT16NPuZKH/++ScDgNHR0WHs7e0ZBQUFpnPnzsy+fftE5v/uu+8YAMx3333HppWXlzPW1tZMnz59mMrKSgYAA4BJSkqq8zrxXbx4kQHAqKioMPn5+XVnBuhFL3rRi170aroX+eLdvHmzuZvw1UtOTm7yOvh/y3+NXr58yRgbGzMxMTFCn23YsIFxcHBgqqqqmqFlja+2+3zs2DGGw+EwL1++/Khyq6qqmNu3bzOlpaViH0M99I3kv//+AwChrRpq8+TJE0ydOhV9+/YFj8fD69evsXnzZqSlpWHnzp0wMTHBkiVLsGHDBmzduhWampp1bq1w69YtTJw4ES4uLggNDcW7d++wefNm5OfnIzQ0VCBvQEAAxo4di2nTpiEmJgZz587FkSNHYGRkVG+7q6ursXLlSpw/fx6//fYbjI2NUV5ejkmTJiE/Px+LFy+Guro69u/fj4kTJ+Ls2bPQ1NTE9evXsW/fPrYc/tyXV69eQUdHB+7u7jh06BB8fX3B4XBgZWXF5s3KymKPrbn/5Zo1a/Dw4UPcvn37o+bB8EcATJ48GcrKyg0+nhBCCCGEkK9VeHg4tLS0oKOjg9evXyM8PBxqamoYNGgQm+fZs2d4+vQp9u/fD39/f3Yo+pdi1apVsLOzQ9u2bZGcnIzt27fDwcGBnRbwOVBA/wn4izWkpaVh9erVUFRURN++fcU6NiwsDDo6Oti+fTsbjKqoqCAgIABJSUmwtLSEoaEhAKB79+71zsffvHkzLC0tBYapa2trY+LEiUhJSQGHw2HTvb29MXnyZABAv379MHToUISHh9e70mJVVRWWLl2KK1euYM+ePex2E8ePH8fjx49x8uRJdOrUCcD7OS2DBw/Gr7/+iiVLliA5ORkhISFsWWfPnmX/ffr0abRt2xbp6em4fPkyIiMjBerdsWMHSktL0atXL5SXl+PWrVsAgF27dkFBQUFokbzvvvsOU6dORe/evWs9l1evXuH48eOQkpKCg4MDW6YoNR8uEEIIIU2lrv+LyJeB7nHzkpaWrndx6MbwOepoCSoqKsDj8ZCVlQVZWVlYWFggICAAEhIS7DVYvnw57t27h/79+8PLy+uLujZFRUXIzs7G6tWrkZeXBxUVFQwcOBBz5879rOdJAf1Hys3NhYmJCfteR0cHwcHB0NLSEuv4O3fuCK2wOGjQIEhLS+PWrVuwtLQUuy0lJSW4ffs2li9fLrAipJWVFWRkZHD//n2BgH7gwIHsvyUlJeHs7IwzZ87UWUd1dTUCAgLw77//Yu/evezDBgBITEyEiYkJ9PT0BOrv1asX7t27B+D96vI1A+9Xr15BSkoKVVVVbBrDMAAg0ENfUVGB48ePAwBWrlwpEFzLyMjg1atXyMzMFGhrUlIS2rRpAysrK7x8+RLFxcXQ1tYW2H5j3759qKqqgpeXF9zd3es8d0IIIeRzoAfIX7Zbt27RPW5mDx48gKKiYpPWUVRU1OR1tBT+/v7w9/evM0/NEbpfEv593rp1a6OWW11d3eBjKKD/SMrKyti1axckJCSgqakJLS2tBm19lpWVJTQ8X0pKCqqqqsjLy2tQW/Lz81FVVYU1a9ZgzZo1Qp+/fv1a4D1/uDufhoYGsrKy6qyjpKQEly5dgqurq0AwDwDv3r3D7du3BR5w8BkYGIgsT0dHB+PGjcPvv/+OoUOHokuXLrh69SqUlJQwcuRINl90dDRev36Nbt26CW1x8eLFC4H3/OuflJTELkr47bffIiEhAcHBwexw/cLCQuzcuROA4BB+QgghhBBCCGlNKKD/SFJSUjAzM/vo4zU1NZGdnS2QVlVVhdzcXKioqDSoLGVlZUhISMDf3x8DBgwQ+vzDUQM5OTkCvdXZ2dnQ1NSssw5FRUX8/PPP8PPzg6amJhYuXMh+pqKiwq7q+KG65v1v27YN8vLyOHz4MB49egQLCwsEBwcLPDDgD9OfM2dOo+0Vz9/+zsrKCvb29o1SJiGEEEIIIYR8bhTQN5MePXrg/PnzmD9/Pjvs/uzZs6isrGSHY8nIyAAAysrK6ixLQUEBFhYWeP78eb3DXgDg3Llz7L7r1dXViIuLg7m5eb3H2draIiQkBLNnz4aioiJmzJjBpl++fBk6OjrQ0NCotxw+RUVFhIeHIzw8HGVlZULb1gHAzZs3xS6PP2S/pgsXLgiliTM8iBBCCCGEEEJaOgrom8mMGTMwcuRIzJo1Cz4+PsjIyEBQUBDs7e3Z+fP8nuoDBw7Azc0N8vLy4HK5IstbuHAhJk6cCElJSQwaNAiKiop4/fo1Lly4gICAAIFe70OHDkFGRgbdunXDoUOH8PLlS2zZskWsdjs5OWHjxo1YuHAhlJSU4OvrixEjRiA6Ohq+vr747rvvoK+vj9zcXNy5cweamppCi9YRQgghhBBCCPl0FNA3k27duiEyMhJbtmyBv78/lJSU4ObmhkWLFrF5dHV1sWTJEuzZswd79+5F+/btER8fL7I8a2trREVFITQ0FIsXL0Z1dTV0dHTQr18/obn6wcHBWLt2LX7++We0b98ewcHBMDY2Frvtbm5uKC4uxsqVK6GoqAhPT0/8/vvvCAkJAY/HQ3Z2NtTV1WFubg4nJ6ePu0CEEEIIIYQQQuokwYgap0zIZ1ZzyL2cnFxzN0dYI83fJ4QQQkSiP8e+eLTKffN78OABunfv3qR1fE2r3H/Nmuo+V1dX4+7duzAyMhI7JpJs9FYQQgghhBBCCGl0PB4PXC5X5Iu/1fOXwMnJCRs2bGjuZrQKNOSeEEIIIYQQ8vVq3x5486ZRimpQn622NpCR0eA6lJWVsWPHDqH02raLJl82CugJIYQQQgghX69GCuY/V71SUlKwsLBo5MaIr7S0FPLy8s1WPxFEQ+4JIYQQQggh5AuRnp4OLpeL06dPY+XKlbCyskL//v0RGhqK6upqgbwpKSnw8/ODpaUlLC0tMWfOHGRlZbGfX7t2DVwuF5cuXcL06dNhaWmJwMBAAMDDhw8xZswYmJmZwc3NDQkJCfD09MTSpUsBvN8+2sjICGlpaQJ1pqWlwcjICHFxcR99jlVVVeDxeHBwcICpqSnc3Nxw4sQJgTyPHz/G5MmT0bt3b1hYWGDIkCGIiopiP7958ybGjh2Lnj17omfPnvDw8EBsbOxHt6m5UA89IYQQQgghhLQilZWVQmnS0oKhXVBQEFxdXREaGorExESEhYWha9euGDp0KAAgNTUVPj4+MDU1xaZNm1BVVYWQkBBMnz4dMTExkKixKPQPP/wAT09PTJgwAXJycigpKcGUKVPQrl07bNmyBWVlZVi7di3y8/PB4XAAAP369YOWlhaOHTuG2bNns2UdPXoU6urqGDBgwEeff2hoKHbs2IFZs2bBzMwMZ8+excKFCyEhIYFhw4YBeL9NeOfOnbFp0ybIysri2bNnKCoqAgAUFhZi+vTpcHZ2xqxZs8AwDFJSUlBQUPDRbWouFNATUp/SUlp9mBBCSJOqKCiAjLJyczeDENIK5ObmwsTERCg9Li4Oenp67Htra2u2t9zOzg6XLl3CuXPn2IB+69ataNeuHSIjIyErKwsA4HK5GDJkCBISEuDg4MCWNXjwYMybN499H8UwRdUAACAASURBVBUVhdzcXBw+fBja2toA3s/h9/b2ZvNISUlh5MiROHr0KPz9/SEhIQGGYXDs2DF4eHgIPYBoyPn/9ttvmDFjBmbOnAng/cODjIwM8Hg8DBs2DDk5OUhLS0NYWBi4XC4AwNbWli3j+fPnKCgowIoVK6CkpAQAsLe3/6j2NDcack9IfVrpHKFbt241dxNIE6N7/HWg+/x1uJOS0txNIIS0EsrKyoiJiRF6aWlpCeSzs7MTeN+1a1dk1FiELzExEQMHDoSkpCQqKytRWVkJPT096Orq4t69ewLH1gzuAeDu3bswMTFhg3kAMDc3R7t27QTyjRo1Cq9evcK1a9cAAFevXsV///0HT0/Pjz7/x48fo6SkBIMHDxZIHzp0KF68eIHs7GyoqqqiQ4cOWLVqFU6fPo3s7GyBvAYGBlBQUMDChQtx/vx55Ofnf3R7mhsF9IQQQgghhBDSSkhJScHMzEzoxe9l52vbtq3AexkZGZSVlbHv3717h8jISJiYmAi80tLS8Pr1a4FjNTQ0BN5nZWVBTU1NqG3q6uoC7/X19dG7d28cOXIEAHDkyBGYm5ujW7duDT/xGnWLahP/fV5eHiQlJbFz505oampi2bJlsLOzw9ixY5GcnAwAUFFRwa+//oqKigrMmzcPtra28PPzE5rv3xrQkHtCCCGEEEII+cqoqKjAxcVFYJg834fBes359ACgqamJ58+fCx2Xk5MjlObt7Y0VK1ZgwYIFOHfuHJYsWfJJ7dbU1GTrqtlOfi+8qqoqAKBLly7g8XioqKjAzZs3ERQUBD8/P1y8eBGSkpKwtLTEzp07UVpaiitXrmD9+vVYsGABDh48+Ent+9yoh54QQgghhBBCvjK2trZ4/PgxTE1NhXr7a87FF8XMzAz37t3Dmxpb7925cwdv374Vyuvq6goZGRkEBASguroabm5un9Tubt26oU2bNkIr0sfGxqJTp05CowRkZGRga2uLSZMmISsrS2h4vby8PJycnODl5YUnT558UtuaA/XQE0IIIYQQQkgrUVVVhdu3bwuld+jQQWBOe338/f3h7e0NPz8/eHl5QU1NDW/evMGVK1cwcuRI2NjY1Hqsp6cntm/fjmnTpsHf3x+lpaXg8XhQV1cX6s2Xk5ODu7s7oqKiMGzYMKGpALV5/vw5zpw5I5DWpk0bDBgwABMmTMAvv/wCaWlpmJqa4uzZs0hISMCWLVsAvN9Sb+PGjRgyZAj09fWRn5+PyMhIGBkZQVVVFRcuXMDhw4fh7OwMHR0dvHnzBgcOHECfPn3Evn4tBQX0hBBCCCGEENJKFBQUYPTo0ULpc+fOZVd9F4ehoSEOHDiAkJAQrFy5EqWlpdDW1oatrS06duxY57Ft2rTBjh07sHr1asybNw+6urpYtGgRNm3axK4aX5OLiwuioqLg5eUldvv++usv/PXXXwJpurq6iI+Px5w5cyAlJYX9+/cjOzsbBgYG2LRpE9v7r6mpCQ0NDfzyyy/IzMxE27ZtYWNjg4ULFwJ4vyiehIQEgoODkZ2dDXV1dTg4OGD+/Plit6+lkGAY2o+LNL+ysjLcu3cPpqamkJOTa+7mfBFu3boFKyur5m4GaUJ0j78OdJ9rKC1ttTuP1Ie2rfvy0c9y83vw4AG6d+8u/EH79kCNoeOfjbY2UGPV+dYuLS0NgwcPRmBgoFDgvnHjRsTGxiIuLg6Skq1/1ndRUREUFRUbvdzq6mrcvXsXRkZGYsdE1ENPCCGEkNZBXh74YCjnl0KG+lcIaT6NGFQ3VaDXEoWHh0NLSws6Ojp4/fo1wsPDoaamhkGDBrF5nj17hqdPn2L//v3w9/f/IoL5loYCekIIIYQQQgghDSIhIYGtW7ciMzMTsrKysLa2xuLFiwWG3K9atQr//vsvnJyc4Ovr24yt/XJRQE8IIYQQQgghpEH8/Pzg5+dXZ549e/Z8ptZ8vWjMAyGEEEIIIYQQ0gpRQE8IIYQQQgghhLRCFNATQgghhBBCvgq0wRdpyRiGafB3lAJ6QgghhBBCyBdPRkYGJSUlzd0MQmpVWloKiQbu5kIB/Rfq0KFD4HK5yPhgG45NmzaBy+Xi+PHjAumXL18Gl8vFP//8U2/ZPB4PNjY27Ptr166By+UiJSWlcRpPCCGkRUlMTISjoyNUVFSgrq6Ob7/9Fjk5OQJ5wsLC0KVLF8jJyYHL5eK3336rt9y7d+/Czc0Nbdu2hYKCAkxNTfH333831WkQQr5yWlpa+O+//1BcXEw99aRFYRgGxcXFSE9PR2VlZYOOpVXuv1A9e/YEAPzzzz8YOnQom56UlIQ2bdogKSkJHh4eAumysrIwNTX97G0lhBDScj179gyurq4oKirC6NGj8fbtW+zZswdZWVmIjY0FAERHR8Pf3x+amprw8fHBH3/8gYkTJ6J9+/YC+xHXlJKSAjs7OxQUFMDFxQVdunTBo0ePkJ6e/jlPjxDyFWnbti0A4NWrV6ioqGiSOsrLyyErK9skZZOWoynus4yMDDQ0NFBYWNig4yig/0J17twZqqqqSEpKYgP6iooK3L9/HyNGjEBSUpJA/qSkJJiamtIvIEIIIQJiY2NRWFgIBwcH7N+/H9XV1WjXrh3OnDmDW7duwcrKCuvXrwcAbN++Hf/H3p3HVVH2/x9/HUSWREVEMLdwRRMExR3LlTJtMfeN3HND0szUsjTN28zKBTAVRS3TNEXLFPNOy3K5NUkj01xv/eWOiOZ2QPH8/vDLuTsCehAQD+f9fDx4xMx1zcxn/ECcz1wz13To0IEFCxbQv39/pkyZkmVBP2nSJK5cucL48eOZMGHCQzwjEbFnxYoVMxf2eSE+Pp6AgIA82788GvIqzykpKdneRrfcF1AGg4HAwECLwv3AgQOYTCa6d+/OoUOHzFd/bt++zW+//Ubt2rX58ccf6dOnD40aNaJOnTp07tz5gW5/XLduHX5+fixbtizXzklERB4+FxcXAE6cOMH58+fZv38/165dA+C3337j1q1b7Nu3D4C6deta/Hfv3r1Z7nfTpk0A/PLLL5QsWZLSpUszbNgwrl+/nmfnIiIiUtBohL4Aq127NpGRkRiNRlxcXNizZw81a9akWrVqFCtWjISEBBo3bszhw4e5cuUKderU4eTJkzRv3py+ffvi4ODATz/9xIABA1iyZAlBQUFWHTc2NpZ3332XiRMn0r59+zw+SxERyU27du1i6dKl5uW+fftStWpVDh8+jLe3t0Xfs2fPcuHCBdLS0gBwc3MDoEiRIgBcvnzZ/DfobhcuXABg+/btdOzYkbi4OCIjIylUqBAzZszIk3MTEREpaFTQF2C1a9fm5s2b/P7779SrV489e/YQGBiIwWAgICCAX3/9lcaNG5tH8evUqYOHh4d5+9u3b9OgQQOOHDnCypUrrSroly1bxuTJk5k6dSpt27bNdszpozySO+Lj4/M7BMljyrF9eJh5Xr9+PTNnzjQv16hRg4ULFxIXF8fp06fx8fHhm2++IT4+nuvXr3PixAkKFSpEWloaO3bs4PHHH+fgwYPAnQL/jz/+yPQ47u7uJCYm8sorr/DKK69QpUoVxowZw8qVKwkNDc10G2svLNsq/T4XfMqxfVCe7cOjkmcV9AVYrVq1cHR0ZM+ePeaCPv15+vSCHu48P+/j44OHhwdnz55l+vTpbN++ncTERPMMoOmT7N3L559/zpo1a5g+fTohISEPFLOfnx/Ozs4PtK1YSn+2VQou5dg+POw8BwUFZXimPTU1leDgYAD+/PNPJk+ejMFgoE+fPlSuXJmaNWuSkJDAjRs3CAoKMv99qVOnDkFBQdy8eZOjR48CUK1aNRwcHAgKCmLDhg2UK1eOoKAgc7uHh4fd/lzb63nbC/0/2z4oz/Yhr/KckpKS7QFOFfQFmKurK9WrV+fXX3/l7NmznD17lsDAQODO6P3ChQu5ffs2e/bsISgoiNu3bzN48GCuXbtGeHg4TzzxBK6ursyaNYukpKT7Hm/jxo088cQTNGrUKK9PTUREHiJ/f3+CgoJwcnLi66+/JjU1laFDh1K5cmUARo8eTY8ePRg6dCjr1q0zvxp1zJgxAJw6dYoaNWoAkJycjLu7O2+++SYbNmxgypQpHDp0iA0bNgDQq1evfDhDERER26RJ8Qq42rVrs3fvXn799VfKli2Ll5cXcGf0/tq1a+zatYsTJ05Qp04dTpw4wf79+xk3bhydOnWifv36+Pv7YzQarTrWtGnTuH79OoMHD7Z6GxERefT5+fnx3XffsXTpUjw9PZk6dSqzZs0yt3fv3p2ZM2fi5ubG0qVLKVWqFAsWLOC5557Lcp/Nmzfn888/x8vLi88//xxXV1emTZvGiBEjHsYpiYiIFAgaoS/g6tSpY74Vvnbt2ub1bm5uVKlShZiYGOBO4Z/+moR/vrru1KlT7Nmzh2rVqt33WKVLl2bx4sV0796d8PBwoqKiKFy4cC6fkYiIPGyrVq26b5/w8HDCw8MzbfPx8TE/wvVPPXv2pGfPnjmOT0RExF5phL6AS3+246effjLfbp+udu3a/PTTTxQvXpzKlStTqVIlSpcuzdSpU/nxxx9Zt24dffv2NY/qW6N8+fIsWrSIhIQERo0axe3bt3P1fEREREREROQOFfQFnLe3N2XKlMFkMmUo6AMDA83rDQYDTk5OREREUKhQIcLDw5k5cyYDBw6kfv362Tpm5cqViYmJYevWrYwbNy7TURkRERERERHJGYNJ1ZY8AtJndNQs97lHs6wWfMqxfVCe72Iw5HcEeUMfxwo8/S7bB+XZPuT1LPfZqYk0Qi8iIiIiIiJig1TQi4iIiIiIiNggFfQiIiIiIiIiNkgFvYiIiIiIiIgNUkEvIiIiIiIiYoMc8zsAEREREasYjQV2NvibV65QuGjR/A5DRERsjEboRURExDa4uOR3BHkm4dCh/A5BRERskAp6ERERERERERukgl5ERERERETEBqmgFxEREREREbFBKuhFREREREREbJAKehEREREREREbpIJeREREJDcYjQ+8aa1q1XIxEBERsRd6D72IiIhIbnBxAYPhgTYtbDLlcjAiImIPNEIvIiIiIiIiYoNU0IuIiIiIiIjYIBX0IiIiIiIiIjZIBb2IiIiIiIiIDVJBLyIiIiIiImKDVNAXILGxsXTq1InAwEDq1KlDz5492bRpk0Wf5cuX8/3332fYtkWLFkydOvVhhSoiIiIiIiI5pIK+gBg/fjzjxo0jICCAqKgopk+fTtmyZRkyZAjz5s0z98uqoBcREbFFly9fJjQ0lCpVqvDYY4/h7e1Nly5d+OuvvzL0PXLkCG5ubhgMBgIDA++5X4PBkOlX79698+hMREREsk/voS8Avv/+e7788ksmTJhAt27dzOubNm2Kp6cn06dPJzg4mJo1a+ZLfEajERcXl3w5toiIFGzJycksXbqUp59+mubNm/P111+zYsUKjh49yu7du8390tLS6NmzJykpKVbt97XXXrNYXrRoEZcvX6ZKlSq5Gr+IiEhOaIS+AFi8eDFPPPEEnTt3ztA2aNAgihQpwpIlSwgNDeWPP/5g9erV+Pr64uvrS2xsrEX/RYsW8fTTT1OvXj1GjBjB33//bdF+6dIl3n33XRo3boy/vz9du3blt99+s+jj6+vLwoULmTx5Mg0bNuSFF17I/ZMWEREBPDw8+O233/jhhx+Ijo5mxYoVAMTHx3Px4kVzv/fff5+EhARGjhxp1X5nzJhh/urXrx+XL1/G2dmZV199NU/OQ0RE5EFohN7G3bp1i71799K9e3cKFSqUob1o0aI0aNCA3bt38+mnnzJs2DDKly/PkCFDAKhQoYK5b1xcHL6+vkyaNImzZ8/ywQcf8MknnzBhwgQAUlNT6dOnD3///TdvvvkmHh4eLFu2jN69e7Nx40ZKlSpl3teCBQuoW7cuH374ISaTKW//EURExG4VK1YMPz8/83JqaioAxYsXx83NDYBffvmF999/n5kzZ/LYY49l+xgzZswAoEePHnh5eeVC1CIiIrlDBb2NS05OJjU1lTJlymTZp0yZMvz8889UqVIFV1dXPDw8Mn120NHRkaioKBwd7/xYHDlyhPXr15sL+q+//prDhw/z7bff4uPjA0Djxo1p3bo1MTExjB492rwvT09P8wcgERGR3LJr1y6WLl1qXg4LCzPfBn/u3DnCwsIA+Ne//oWTkxPXr1+nZ8+ehISEMGTIEBYtWpSt4yUmJpqPN3z48Nw5CRERkVyigl7MGjRoYC7mAapUqUJSUhKpqak4OTmxY8cOatasSbly5bh165a5X7169di3b5/Fvpo2bfpAMdy9H8mZ+Pj4/A5B8phybB+U5/9Zv349M2fONC/XqFGDy5cvc/LkScLCwjh58iSDBg2iQYMGxMfHEx8fz6FDh3B2duapp57i/PnzwJ2L1k899dR9Lz7Pnz8fo9FIvXr1SE1NvWcugoKCcnRuynPBpxzbB+XZPjwqeVZBb+NKlCiBk5MTp0+fzrLP6dOn8fb2vu++ihUrZrFcuHBhTCYTN2/exMnJieTkZPbu3Zvp5Hr/vHUf7ozQPwg/Pz+cnZ0faFuxFB8fn+MPl/JoU47tg/JsKSgoyHznWLq9e/cycOBAEhMTmT17NoMHDza3XblyBYDff//dYptr166xdetWgoKCuHnzJkePHgWgWrVqODjcmWLo5s2bfP311wC8++67eZ4H5blg0++yfVCe7UNe5TklJSXbA5wq6G2co6MjgYGB/Pjjj4wePdr8ISTd1atX2bVrF61atcrxsYoXL46fn1+GD1IATk5OFssGgyHHxxMREbmfpKQkmjZtyt9//42fnx8HDx403xofFhZGs2bNLOZyWbRoEX369CEgIIC9e/cCcOrUKWrUqAHceZTN3d0dgC+//JIzZ85QtWpV2rZt+5DPTERE5P5U0BcAvXr1YujQoXz11Vd06dLFom3evHlcvXqVnj17AncKb2tf2XO3Ro0asW3bNsqUKUPJkiVzHLeIiEhOXblyxfxGln379lmMbLRr1y5Hr5lLv7U/PDxcF6pFROSRpIK+AGjVqhVdu3Zl4sSJHDlyhObNm3Pr1i3i4uKIjY1l5MiR5tvkK1asyNatW/n5559xd3enXLlylChRwqrjtGvXji+//JLQ0FD69u1L+fLluXTpEgkJCZQqVYrevXvn4VmKiIhk5OPjk623qfTu3TvD36us9vHP99iLiIg8ilTQFxATJkwgICCAZcuW8dVXX2EwGKhZsyazZ8+mZcuW5n5DhgzhzJkzDB8+nKtXrzJlyhTat29v1TGcnZ357LPPmDlzJhERESQlJeHh4UGtWrVo0aJFXp2aiIiIiIiIZMJg0kvC5RGQPgGEJsXLPZqUpeBTju2D8mxjHvTWfH0cK/D0u2wflGf7kNeT4mWnJnK4fxcRERERERERedSooBcRERERERGxQSroRURERERERGyQCnoRERERERERG6SCXkRERERERMQGqaAXERERERERsUEq6EVERERyg9F45/VzD/B188qV/I5eRERskAp6ERERkdzg4vLAmyYcOpSLgYiIiL1QQS8iIiIiIiJig1TQi4iIiIiIiNggFfQiIiIiIiIiNkgFvYiIiIiIiIgNUkEvIiIikh+MRvO3tapVy8dARETEVjnmdwAiIiIidsnFBQwGAAqbTPkcjIiI2CKN0IuIiIiIiIjYIBX0IiIiIiIiIjZIBb2IiIiIiIiIDVJBLyIiIiIiImKDVNCLiIiIiIiI2CAV9CIiIiIiIiI2yO4K+oiICHx9fc1fwcHBDBw4kD///DPf42rQoEGeHqNFixYW596wYUMGDBiQ4dzHjBlD+/btzcuxsbH4+vpy7dq1PI1PRETkUfOvf/0Lg8GAwWBgxowZ9+w7duxYfH19zf0XLVr0cIIUERG7ZZfvoS9atCjz588H4NSpU8yaNYu+ffuyfv163N3d8zm6vPX8888TGhoKwPnz55k7dy79+vVj/fr1FC9eHIAhQ4ZgNBrzM0wREZF8t2fPHiZMmICjoyO3bt26b/+dO3fyxBNPkJSURFJS0kOIUERE7J1dFvSFChUiMDAQgMDAQMqWLUuXLl34+eefeeGFF/I5upwxGo24uLhk2e7l5WU+d4BKlSrRtm1b9u7dS9OmTQGoUKFCnscpIiLyKDMajfTo0YOnn36amzdv8tNPP913m82bNwN3PluooBcRkYfB7m65z0z16tUBOHPmjMX6r776irZt2+Ln50fz5s2Jjo7OsO2SJUto2rQpgYGBDBkyhB07duDr68vOnTsBOHnyJL6+vvzwww8W2919W/vdrl+/zsSJE3n22WcJCAigRYsWvPfee1y9etWin6+vLwsXLmTy5Mk0bNgw2xckihQpAsDNmzetjg1g/vz5+Pv7s2nTJgBSUlL48MMPadq0KX5+frz44ots2bIlW7GIiIg8KkaPHs3Zs2dZtGgRBoMhv8MRERHJlF2O0N/t9OnTAJQrV868bv78+UyfPp3+/ftTv359/vjjD2bOnImrqys9e/YE4N///jeTJk2ie/futGzZkvj4eN5+++1cicloNJKWlsaIESPw8PDgzJkzzJkzh9dee40FCxZY9F2wYAF169blww8/xGQy3XO/JpPJfNtgYmIi06ZNw93dnfr161sdW1RUFPPmzWP27Nk89dRTAISHh5OQkMCwYcOoUKECcXFxDB48mFWrVlGjRo1snr2IiMjDs2vXLpYuXWperlmzJhERESxbtszis4GIiMijxm4L+vSi9vTp00yaNIkaNWrQqlUrAK5evUpUVBSDBw8mLCwMgODgYG7cuMGnn35Kt27dKFSoEHPmzKFp06aMHz8egCZNmpCcnMyyZctyHJ+HhwfvvfeeRbzlypWje/funD59mjJlypjbPD097ztRT7qFCxeycOFC83KxYsWIiIigWLFiVm3/ySef8PnnnzNv3jzzJH47duzgxx9/5PPPPzdfGGjSpAnHjx/n008/ZdasWVbtW0REJD/s37+fmTNnmpd9fHxwdnbm888/5/PPP+f3338HMN+pN3z48HyJU0RE5G52WdBfunSJmjVrmpfd3d1ZuXIlTk5OwJ1JcK5fv07r1q0tJsFp2LAhs2fP5uzZs5QuXZo///yTd955x2LfLVq0yJWCHmDNmjUsWrSIEydOcP36dfP648ePWxT06c++W+PFF1/klVdeAeDy5ct8++23hIWFsWTJEvOjB1n54IMPiIuLY8GCBdSpU8e8fvv27ZQqVYo6depY/Hs1atSI2NhYq2MD2LdvX7b6y73Fx8fndwiSx5Rj+6A85y1/f392795tXp4wYQLHjx9n3bp1Fv3279/Ppk2beOqpp7h06RKXLl3isccew8vLy6LfjRs3gDt/r++Vu6CgIItl5bngU47tg/JsHx6VPNtlQV+0aFEWLlzI7du3+fPPP5k6dSpvvPEGy5Ytw8HBgeTkZADatm2b6fZnzpzBycmJW7du4eHhYdF29/KD+ve//83o0aPp1q0bI0aMwN3dncTERIYOHUpKSopFX09PT6v36+npib+/v3k5ODiY/fv3M3v27PuOpG/cuJGaNWtSq1Yti/XJyckkJiZaXCRJV6hQIatjA/Dz88PZ2Tlb20jm4uPjM3xYlIJFObYPyvPDt3btWovlZs2asWXLFqZPn24enZ8wYQLvvfceL730EmvWrAHuXPj+888/SUxMBOD777/n+PHj9O/fnyZNmtz3uMpzwabfZfugPNuHvMpzSkpKtgc47bKgL1SokLmoDQgIwNnZmdGjR7NhwwbatGljfn3b3LlzKVmyZIbtK1asiKurK46Ojly8eNGi7e7l9OL0n5POwZ27BO5lw4YNBAQEMGHCBPO6Xbt2Zdo3J5P1GAwGKlWqxOHDh+/bd86cOQwaNIjRo0czbdo0HBzuzKlYvHhxvL29iYqKeuA4REREbN2GDRssJoTdtm0b27Zto1mzZlYV9CIiItlllwX93V566SXmz59PdHQ0bdq0oXbt2ri4uHD+/HmaNWuW5XbVq1dn06ZNdO3a1bwu/ZU16UqWLEnhwoU5evSoed21a9fYu3evxW3zdzMajeZHANLdPWKQG0wmE0ePHqV06dL37VutWjWio6Pp3bs348ePZ9KkScCdW+sXLlzIY489RuXKlXM9RhERkfz0448/Zlg3YcIEi4vuWfUTERHJSyrouTNKPXDgQN544w127NhBo0aNCAsLY/LkyZw6dYp69epx+/Ztjh8/zs6dO80j0YMGDSIsLIyJEyfSokULfv31V/OV+fTRawcHB1q0aMGiRYsoU6YMxYoVIyYm5p7vigdo3LgxEydO5NNPPyUgIIAtW7awY8eOHJ/r+fPn2bt3L/C/Z+gPHTpEeHi4VdvXqlWLuXPn0r9/f9zc3Bg9ejTBwcE0adKEvn37MmDAAKpUqcLVq1f5888/SUlJYeTIkTmOW0RERERERCypoP8/bdq0ITIykvnz59OoUSMGDBiAl5cXixcvZuHChTg7O+Pj40ObNm3M24SEhDBu3Diio6NZtWoV9evX580332T48OG4ubmZ+7377ru88847vPfeexQvXpxBgwaxZ88eDh06lGU8Xbt25eTJk3z22WekpKQQHBzMxx9/TOfOnXN0nt9++y3ffvstcGcugUqVKjFr1ixCQkKs3ke9evWIiIhgyJAhFClShLCwMCIjI5kzZw6LFy/mzJkzFC9enOrVqxMaGpqjeEVERERERCRzBtP9Xlwu2TJ79mzmzJnDrl277jsKL/+TPgGEJsXLPZqUpeBTju2D8lzApc+Do49jBZ5+l+2D8mwf8npSvOzURBqhz4GLFy8yd+5cGjRogKurK7t37yY6OpqOHTuqmBcREREREZE8pYI+BwoXLsyxY8dYs2YNV69epVSpUrzyyiu89tpr+R2aiIiIiIiIFHAq6HOg9uoD7AAAIABJREFUaNGiREdH53cYIiIiIiIiYocc8jsAEREREREREck+FfQiIiIiIiIiNkgFvYiIiIiIiIgNUkEvIiIikh+MxjuvqzOZuHnlSn5HIyIiNkgFvYiIiEh++McrbhMOHcrHQERExFapoBcRERERERGxQSroRURERERERGyQCnoRERERERERG6SCXkRERERERMQGqaAXERERyQ9Go/nbWtWq5WMgIiJiqxzzOwARERERu+TiAgYDAIVNpnwORkREbJFG6EVERERERERskAp6ERERERERERukgl5ERERERETEBqmgFxEREREREbFBKuhFREREREREbJAKehEREREREREbpIK+gPL19b3v186dOx9o3ydPnsTX15cffvghl6MWERF5NISEhODl5YWTkxPe3t506NCB//73v/fdLjo6Gj8/P5ydnfHw8OCZZ555CNGKiIi90nvoC6jly5ebvzcajfTq1YvBgwfTrFkz8/oqVao80L69vLxYvnw5lSpVymmYIiIij6STJ0/SunVrXF1d+fbbb4mNjeXChQts2bIly20++OADxo4dS7FixejSpQuFChVix44dDzFqERGxNyroC6jAwEDz99euXQOgQoUKFuv/KS0tjbS0NJycnO67bycnpyz3IyIiUhAcOHDA/H1sbOx9R+j//vtvJk2ahJOTE7t27cLX1/dhhCkiInZOt9zbqTFjxtC+fXu+//572rZtS61atUhISOD8+fOMHTuWli1bUqtWLZ599lmmT59OamqqedvMbrlv0aIFU6dOZdGiRTz99NPUq1ePESNG8Pfff+fH6YmIiOTYxIkTGThwIOHh4RQqVIhRo0Zl2fc///kP169fx9PTk/79+1OkSBFq1qzJypUrH2LEIiJib7I1Qp+WlsbatWvZunUrSUlJjBo1iieffJLLly/zww8/0KhRI7y9vfMqVsllp06dYtq0aQwZMgRPT0/KlStHcnIy7u7u5lsGjx8/TkREBMnJyUycOPGe+4uLi8PX15dJkyZx9uxZPvjgAz755BMmTJjwcE5IRETkAezatYulS5eal8PCwqhSpQoxMTGcOHECgBo1alCnTp0s93HhwgUATp8+TZkyZWjXrh1ffvklXbt2ZefOnQQFBeXtSYiIiF2yuqC/ceMGffv2Zc+ePbi6umI0Grl8+TIAbm5ufPTRR3To0IERI0bkWbCSuy5dusSiRYuoUaOGeV3p0qUZPXq0eblOnTq4urry1ltvMW7cuHveku/o6EhUVBSOjnd+rI4cOcL69euzVdDv27cv+yciWYqPj8/vECSPKcf2QXnOW+vXr2fmzJnm5Ro1anD58mVWrVrFjRs3WLduHR988AHPPfcc69evx9XVNcM+kpOTzd9PmTKFEiVKcOLECbZt20Z0dHSmx727yFeeCz7l2D4oz/bhUcmz1QV9REQE+/btIzIykjp16tC4cWNzW6FChXjmmWfYunWrCnob4u3tbVHMA5hMJhYvXsyKFSs4efIkKSkp5rYzZ87wxBNPZLm/Bg0amIt5uDPpXlJSEqmpqVY9mw+YZwaWnIuPj9eIUAGnHNsH5TnvBQUFWVx8vnr1KkWKFMFgMABQuXJlPvjgA65cuULZsmWpWLEiFy5c4MKFCxQtWpSyZcvy+OOP89prr5GWlkZAQAClSpWiWLFiAFSrVs2qHCrPBZt+l+2D8mwf8irPKSkp2R7gtPoZ+g0bNtClSxdatWpl/gP3TxUqVODUqVPZOrjkL09PzwzrFi9ezNSpUwkJCWH27Nl89dVXvPvuuwAWxX1m0j+4pCtcuDAmk4mbN2/mXtAiIiJ5bOXKlVSpUoWePXvy6quvmgcxfH198fHxASAyMpIaNWowdOhQAMqUKUOPHj0AaNOmDT169OC7777Dzc2Nl19+OV/OQ0RECj6rR+jPnz9/zxlbXV1dzbOpi+3asGEDrVu3trjT4ujRo/kYkYiIyMNVrVo1vLy8WLduHTdu3MDb25u+ffsyfvz4TAc10s2ePRsXFxdWrVrFwYMHadKkCVOnTqVixYoPMXoREbEnVhf07u7unDt3Lsv2w4cP4+XllStBSf4xGo0Zbo9fu3ZtPkUjIiLy8DVu3Pi+74+fMGFChjliihQpwty5c5k7d24eRiciIvI/Vt9y36hRI2JjY7lx40aGtr/++otVq1bx1FNP5Wpw8vA1btyY9evX88UXX/Dzzz/z5ptvmmf4FRERERERkUeH1SP0YWFhdOjQgY4dO9K2bVsMBgM///wz27dv58svv8TJyYmBAwfmZazyEAwdOpTk5GTzbL8hISGMGzeOQYMG5XNkIiIiIiIi8k8Gk8lksrbzvn37eOuttzh06JDF+qpVqzJt2jSqV6+e6wGKfUif0VGz3OcezbJa8CnH9kF5LuDSn8m3/uOY2Cj9LtsH5dk+5PUs99mpiaweoYc7rxT75ptvOHToEEePHsVkMuHj48OTTz75QAGLiIiIiIiIyIPJVkGfrlq1alSrVi23YxERERERERERK1k9KZ6IiIiIiIiIPDqyHKGvXr36Pd+1mhmDwcD+/ftzHJSIiIiIiIiI3FuWBX27du0yFPT79u3j8OHDVKxYkcqVK2MymTh27Bj//e9/qVq1Kn5+fnkesIiIiIiIiIjco6D/4IMPLJa3bdvGhg0biIqKomXLlhZt33//PaNGjWLMmDF5E6WIiIhIQWM0mme3v3nlCoWLFs3ngERExNZY/Qz9zJkz6dq1a4ZiHqBVq1Z06dKFGTNm5GpwIiIiIgWWi4v524S7XgksIiJiDasL+oMHD1K+fPks2ytUqMDhw4dzJSgRERERERERuTerC/pixYqxbdu2LNt//vln3NzcciUoEREREREREbk3qwv6559/nk2bNvHWW29x9OhR0tLSSEtL4+jRo4wdO5Yff/yRF154IS9jFREREREREZH/k+WkeHcbMWIE/+///T9iY2NZvXo1Dg53rgXcvn0bk8lE8+bNGTFiRJ4FKiIiIiIiIiL/Y3VB7+TkRFRUFFu3buX777/n5MmTmEwmKlSoQMuWLWnSpElexikiIiIiIiIi/2B1QZ+uSZMmKt5FREREctHjjz+e3yGIiIgNynZBD5CcnMzJkycBKFeuHCVKlMjVoERERETsSZkyZfI7BBERsUHZKuj//PNP3n//feLj4y3W161bl7fffpvq1avnanAiIiIiIiIikjmrC/pDhw7RrVs3UlNTadGiBVWrVgXgyJEj/PDDD/To0YMvv/zSvF5ERERERERE8o7VBf2sWbMoXLgwX375Jb6+vhZthw4domfPnsyaNYuIiIhcD1JERERERERELFn9HvpffvmF7t27ZyjmAapVq0a3bt3YtWtXrgYnIiIiIiIiIpmzuqC/ceMGpUqVyrLdy8uLGzdu5EpQIiIiIiIiInJvVhf05cuX54cffsiy/YcffqB8+fK5EtTDFhsbS/v27alduzb16tWjXbt2TJkyxdx+8uRJfH1973n+1khISMjVRxIiIiJo0KBBru0vtzVo0ECPYIiIiIiIiOQRqwv6l156ia1btzJy5EgOHz5MWloaaWlpHDp0iJEjR7Jt2zZefvnlvIw1T8ydO5dx48bRpEkTIiMjmTp1Ki1btmTz5s25fqyEhAQiIyNzfb8iIiJSMBmNRoYNG4aXlxeurq4EBwezc+fOXOsvIiK2zepJ8fr168f+/ftZt24d69evx8HhzrWA27dvYzKZeO655+jbt2+eBZpXlixZQpcuXXj99dfN61q0aEFYWFg+RmU7UlJScHZ2zu8wRERECqThw4czd+5c/Pz8aNmyJcuXLyckJIRjx47h6emZ4/4iImLbrB6hL1SoEDNmzGDBggV07dqVxo0b06hRI7p160ZMTAzTp083F/m25MqVK5n+gTMYDPfcbufOndSuXZtPPvkEgD179jBo0CCaNGlCYGAgL730Et988425f2xsLJMmTQLA19cXX19fQkNDze2HDh3i1VdfpXbt2tSuXZvw8HASExOtOof4+Hhefvll/P39eemll9i9e7dFe1paGhERETRr1gw/Pz/atm3L2rVrLfrcL/70c/D19SUhIYHQ0FBq1arF/PnzgTuTJr744ov4+/vTvn17fv31V6tiFxERkcydP3+emJgYHBwc2LRpE8uWLaNHjx5cuXIl0zv+sttfRERsn9Uj9OmCg4MJDg7Oi1jyxZNPPsmSJUsoU6YMzZo1o0SJEvfd5ueffyYsLIwBAwaYR/JPnz5NnTp16NatG05OTvz666+89dZbODg48Pzzz9OsWTP69u1LTEwMy5cvB8DNzQ2AEydO0K1bN/z8/Jg2bRppaWnMnDmTQYMGsXLlynteXDAajYwaNYqBAwdSqlQpFi5cyIABA9i4caN5EsNZs2Yxf/58hg4dir+/Pxs3buSNN97AYDDw/PPPWxX/P73++ut069aNoUOHUqxYMc6dO8eAAQPw9/dn1qxZnD9/njfeeAOj0Zj9hIiIiAgAf/zxBzdv3sTHxwcvLy8A6taty5IlS9i7d2+O+4uIiO3LdkFf0Lz77rsMHTqUMWPGYDAYqFy5Ms888wz9+vUzF9z/tGnTJoYPH87w4cPp16+feX3btm3N35tMJurVq8e5c+dYsWIFzz//PB4eHpQtWxaAwMBAi31GRkbi6elJdHQ0Tk5OwJ1R/Oeee44tW7bQrFmzLOM3Go2MGDGCF154AbgzEV3z5s1ZvHgxb7zxBpcuXWLx4sUMHjyYIUOGAPDUU09x9uxZIiIizMX6/eL/p9DQUHr16mVe/vDDD3F2dmbevHm4uroC4OrqyqhRo7KMW0RERO7t3LlzABafR4oUKQLA2bNnc9xfRERs3z0L+uzenmUwGBg6dGiOAnrYqlevTlxcHFu3bmXr1q385z//Yfbs2axfv57Y2FjzH0KAjRs3snbtWsaOHUuPHj0s9nP58mUiIiLYtGkT586dIy0tDQBvb+/7xrBjxw7atWuHg4MDt27dAqBcuXKULVuWffv23bOgBwgJCTF/X6RIERo3bkxCQgIAhw8f5saNG7Ru3dpimzZt2jBmzBiSkpIoWbJktuK/O57ff/+dxo0bm4t5gGeeeeaBCvp9+/ZlexvJWnx8fH6HIHlMObYPynPBFxQUlCHPly9fBuDixYvmtgMHDgDg4uKS4/7y8CkH9kF5tg+PSp7vW9AbDAZMJpNVO7PFgh7AycmJFi1a0KJFCwC++uorxo0bx8qVKy1Gojdv3oy7uzutWrXKsI8xY8bw22+/MWTIECpXroybmxvLli1j06ZN9z1+cnIy0dHRREdHZ2g7c+bMPbd97LHHcHFxsVhXsmRJDh48CGB+Dr9kyZIZ+sCdP/4lS5bMVvx37ysxMRFfX1+LdS4uLjz22GP3jD0zfn5+mmQvl8THxxMUFJTfYUgeUo7tg/JsP+7Oc7ly5Rg2bBhnz56lXLlyeHt7m+fuadq0KVWqVOHMmTO4uLjg4+Nz3/76Ocpf+l22D8qzfcirPKekpGR7gPO+t9w7OzsTEhLCiy++aNXz5QVBp06d+Oijjzh27JjF+nHjxrFw4UL69u3LkiVLzP8eKSkpbNmyhXfeeYdu3bqZ+y9dutSq4xUvXpxWrVrRqVOnDG33+ze/fv06RqPRoqhPSkoyPz+f/t+LFy9a7CspKQkAd3f3bMd/9zP9pUqVMu8vndFo5Pr16/eMXURERLLm7e1N7969iY6OpmXLlvj5+bFixQrc3NwICwtj9erV9OnTh4CAAPbu3Xvf/iIiUvDcs6CPiIhg1apVxMXFsWHDBpo3b06HDh14+umn7zsLvK1Iv+X8ny5evJjp7Pdubm4sWLCA0NBQ+vXrx2effYabmxupqamkpaWZn38HuHr1aoZ32RcuXBjI+Kq3Ro0acfjwYfz8/B7o3/Xf//63+Rn6a9eusX37djp37gxA1apVcXV1JS4uzuKPeVxcHD4+Pnh4eHDlyhWr4s+Kn58fsbGx3Lhxw3zb/caNG7N9HiIiImJp5syZFC5cmBUrVnDkyBEaNmzIxx9/bL5gn9P+IiJi2+5Z0IeEhBASEsKFCxdYvXo1q1evZuDAgXh5edGuXTvat2+Pj4/PQwo1b7zwwgu0bNmS4OBgSpYsyalTp4iJicHFxYV27dpl6F+iRAliYmLo0aMHAwcOZP78+RQtWhR/f3+ioqJwc3PDwcGBefPm4ebmxtWrV83bVqpUCYDFixfTsGFD3NzcqFSpEmFhYXTq1IlXX32VDh06UKJECc6dO8f27dt5+eWXadCgQZbxu7i4MH36dK5fv46XlxcxMTHcvHmTV155BbgzAt+rVy/mzJmDo6Mjfn5+bNy4kS1btphvw7M2/qz07t2bpUuXMnDgQPr06cP58+eZO3duhkcBREREJHtcXV2JiooiKioqQ1vv3r3p3bu31f1FRKTgserF8Z6engwYMID169ezdOlSnn76ab744guee+45evTokeG957Zk6NChnDp1ivfff5++ffsya9YsqlatyldffUX58uUz3cbLy4tFixZx6tQpwsLCSE1N5eOPP6ZcuXKMHj2ayZMn88wzz2S4IFC3bl3zyH7nzp0ZP348ABUrVmT58uW4urry7rvvMmDAACIiInBycuKJJ564Z/wuLi58+OGHLF26lGHDhnH58mXmzZtnfl0NQHh4OK+++irLli1j0KBB7N69m2nTplnMbG9N/Fnx9vZm3rx5JCcnM2zYMJYuXcq0adNU0IuIiIiIiOQhg8naGe/ucvHiRUaNGsX27dsZOnSons2SHEmfAEKT4uUeTcpS8CnH9kF5FikY9LtsH5Rn+5DXk+JlpybK9nvo9+7dS2xsLOvXr+fq1asEBgbSsGHDbAcrIiIiIiIiIg/OqoI+MTGRr7/+mtjYWI4dO4anpyddunShQ4cO5ufCRUREREREROThuWdBv3HjRmJjY9m6dSsAzZo1Y9SoUTRt2hQHB6sevxcRERERERGRPHDPgj48PBwXFxdat27Niy++aH6924EDB7LcpmbNmrkboYiIiIiIiIhkcN9b7o1GI+vWrWPdunVW7fBexb6IiIiIiIiI5I57FvSauV5ERERERETk0aSCXkRERCSfnT59mjJlyuR3GCIiYmM0s52IiIhIPjtz5kx+hyAiIjZIBb2IiIiIiIiIDVJBLyIiIiIiImKDVNCLiIiIiIiI2CAV9CIiIiIiIiI2SAW9iIiIiIiIiA1SQS8iIiKSzx5//PH8DkFERGxQlu+hX7NmzQPtsF27dg8cjIiIiIg90jvoRUTkQWRZ0I8ZMwaDwYDJZDKvMxgM5u/T1/9zHaigFxEREREREXkYsizoP/vsM4vlW7du8dFHH3Hp0iW6du1K5cqVMZlMHD16lOXLl+Pu7s6oUaPyPGARERERERERuUdBX79+fYvlWbNmkZKSwjfffIObm5t5fatWrejRowedO3dm9+7dNGrUKO+iFREREREREREgG5PixcbG0r59e4tiPp2bmxvt27cnNjY2V4MTERERERERkcxZXdBfvHiRtLS0LNtv375NUlJSrgQlIiIiIiIiIvdmdUFfqVIlvvrqKy5fvpyh7dKlS6xYsYLKlSvnanC2JiIiAl9fX/r165ehLTw8nNDQ0GztLykpiYiICE6ePJlbIVpo0aIFvr6+9/zK7K6LnTt34uvry6FDh/IkLhEREREREbm/LJ+hv1tYWBjDhg2jdevWdOjQgYoVK2IwGDh69CixsbFcvnyZWbNm5WWsNmPr1q0kJCRQq1atHO0nKSmJyMhI6tevT7ly5XIpuv+JjIwkNTXVvNy/f3+effZZOnXqZF5XoUKFDNvVrFmT5cuXZ9omIiIiucdoNDJq1CiWL1/OlStXqFOnDp988gkNGjTIlf4iImLbrC7oW7VqxaxZs5g8eTLz58+3aCtdujTTp0+nVatWuR6grXF3d8fb25s5c+Ywe/bs/A7nnp588kmL5UKFClG6dGkCAwMz7W8ymUhNTcXNzS3LPiIiIpJ7hg8fzty5c/Hz86Nly5YsX76ckJAQjh07hqenZ477i4iIbbP6lnuAkJAQNm/ezIoVK/jkk0/4+OOPWbFiBZs3b+bZZ5/NqxhtzqBBg9i8eTMHDx68Z78DBw7Qq1cvAgICqFevHiNHjuTChQsAnDx5khdeeAGAV155xXwLfFaOHj3KiBEjaNq0KQEBAbRt25ZFixZx+/btBz6PiIgIGjRowO7du+nQoQP+/v7ExcVlesu9r68vCxcu5P3336d+/frUrVuXSZMmWdwBICIiItY7f/48MTExODg4sGnTJpYtW0aPHj24cuUKkZGROe4vIiK2L1sFPYCDgwO1atWiTZs2tG3bllq1auHgkO3dFGitW7fGx8eHOXPmZNnn4sWLhIaGYjQa+fjjjxk3bhy//PILffr0ITU1FS8vLz766CMA3n33XZYvX87y5cuz3N/58+epWLEi48ePZ968eXTq1ImIiAiio6NzdC5Go5ExY8bQqVMn5s+ff8/HCGJiYjh37hzTpk1j8ODBLF++nOnTp+fo+CIiIvbqjz/+4ObNm1SoUAEvLy8A6tatC8DevXtz3F9ERGyf1bfcp/vll1/YunUrSUlJ9OnTh8qVK3Pt2jX279+Pr68vxYoVy4s4bYqDgwOvvvoqb7/9NuHh4VSsWDFDn5iYGAAWLFhgfhVgxYoV6dSpExs3buT55583j8hXqVLlvre4N2rUiEaNGgF3bo0PCgrCaDSyYsUKBg4c+MDnkl7Q//NxisTExEz7FilShJkzZ+Lg4EDTpk1JTU1lzpw5DBw4EHd39weOQURExB6dO3cOwOKVwUWKFAHg7NmzOe4vIiK2z+qCPi0tjZEjR/Ldd99hMpkwGAy0bduWypUr4+joyNChQ+nbty+DBg3Ky3htxosvvkhkZCTz5s1jypQpGdoTEhIIDg62+KNbq1YtypYtS3x8PM8//3y2jpeSksLcuXNZu3YtZ86c4ebNm+a2W7du4eiY7Ws3ABgMBp5++mmr+rZs2dLibo1nnnmGGTNmcPjwYerVq2fVPvbt2/dAcUrm4uPj8zsEyWPKsX1Qngu+oKCgDHlOf7PQxYsXzW0HDhwAwMXFJcf95eFTDuyD8mwfHpU8W13lRUdHs3HjRsaMGcNTTz1FmzZtzG3Ozs60atWKLVu2qKD/P46OjvTv35/JkycTFhaWoT0xMZGqVatmWO/p6ZnpqwHvZ9q0aaxcuZKhQ4dSs2ZNihYtyqZNm/j0009JSUl54IK+ePHiODk5WdW3ZMmSFsseHh5A1iP6mfHz88PZ2dn6ACVL8fHxBAUF5XcYkoeUY/ugPNuPu/Ncrlw5hg0bxtmzZylXrhze3t588sknADRt2pQqVapw5swZXFxc8PHxuW9//RzlL/0u2wfl2T7kVZ5TUlKyPcBp9cPva9as4aWXXqJXr16UKFEiQ3vlypX566+/snXwgq5jx454eHhk+hx7qVKlSEpKyrD+woULFC9ePNvH2rBhAz179mTAgAE0btwYf3//By7iH9Td53Px4kXgzrmKiIhI9nh7e9O7d29u375Ny5Yt6dq1K8uWLcPNzY2wsDBWr15NjRo1aNeunVX9RUSk4LG6oD916hS1a9fOsr1YsWIPNLJckDk5OdGvXz9WrVrF+fPnLdoCAgLYunUrV69eNa9LSEjg1KlT5qs9hQsXBu5cqbmflJQUi5H0tLQ01q1blxunYbVNmzZZzKq/ceNGXFxcMr0TQURERO5v5syZDBkyhHPnzrFmzRoaNmzIxo0bs7xYnt3+IiJi26wu6IsUKcKlS5eybD9x4oT5Fmv5ny5dulCkSBH27Nljsb5Pnz4A9O/fn++//55vvvmGYcOGUa1aNZ555hkAypQpg4uLC2vWrGHPnj38/vvvWR6ncePGfPHFF6xZs4Yff/yRQYMGPfRXxl27do3XXnuNn376iZiYGKKioujWrZsmxBMREXlArq6uREVFkZiYiNFoZPv27eZJcHv37o3JZLKYwf5e/UVEpOCxuqAPCgpi7dq1mEymDG2XL19m1apVNGjQIFeDKwhcXV3p3bt3hvUeHh589tlnODk5MXLkSCZOnEjdunVZuHCheaTd2dmZSZMm8ccffxAaGkrHjh2zPM4777xD3bp1mThxIm+99RZVq1bN0ez2D6Jv376UKlWKkSNHEhUVRadOnXj99dcfagwiIiIiIiL2wmDKrELPxO+//0737t0JDAykffv2jB07ljFjxuDi4sK8efO4ePEiK1eupEqVKnkdszyCfH19eeedd+jZs+cDbZ8+AYQmxcs9mpSl4FOO7YPyLFIw6HfZPijP9iGvJ8XLTk1k9axp/v7+REZG8vbbbzN27FgApk6dislkomTJkkRGRqqYFxEREREREXlIsjUNetOmTdm8eTPbtm3j6NGjmEwmfHx8aNKkCa6urnkVo4iIiIiIiIjcJdvvNXNycqJ58+Y0b948L+IRG3Xw4MH8DkFERERERMSuWD0p3v79+/niiy+ybP/iiy84cOBArgQlIiIiIiIiIvdmdUEfGRnJjz/+mGX7Tz/9RFRUVG7EJCIiIiIiIiL3YXVB//vvv1OvXr0s2+vVq0dCQkKuBCUiIiIiIiIi92Z1QZ+cnIy7u3uW7cWKFSM5OTlXghIRERGxJ6dPn87vEERExAZZXdCXLFmSw4cPZ9l+6NAhihcvnitBiYiIiNiTM2fO5HcIIiJig6wu6Bs3bszKlSszLeqPHDnCqlWraNy4ca4GJyIiIiIiIiKZs/q1dYMHD2bjxo107NiRDh06UKNGDQAOHDjAqlWrKFy4MEOGDMmzQEVERERERETkf6wu6CtUqMCiRYsYO3YsS5cutWirWrUq//rXv/Dx8cnt+EREREREREQkE1YX9AD+/v58++23HDhwgOPHj2MymahUqRLVq1fPq/hEREREREREJBPZKugznXUpAAAgAElEQVTT1ahRw3zLvYiIiIjkzOOPP57fIYiIiA2yelI8EREREckbZcqUye8QRETEBmVrhD4+Pp558+bx22+/8ffff2MymSzaDQYD+/fvz9UARURERERERCQjq0fof/nlF3r16sVvv/1GQEAAt2/fpkGDBvj7+2MymahatSovvfRSXsYqIiIiIiIiIv/H6oJ+zpw5lCpVivXr1zNlyhQABg4cyIoVK5g/fz4nT56kY8eOeRaoiIiIiIiIiPyP1QV9QkICHTt2xMPDAweHO5ul33LfpEkTXnrpJWbOnJk3UYqIiIiIiIiIBasL+tTUVLy9vQFwcnIC4Nq1a+b2GjVq8Mcff+RyeCIiIiIiIiKSGasL+lKlSnH27FkAHnvsMYoVK8ahQ4fM7WfPnsXR8YHegiciIiIiIiIi2WR1Qe/v78+ePXvMy8HBwSxevJg1a9YQGxvLF198Qa1atfIkSFsTERGBr68v/fr1y9AWHh5OaGhojvYfGhqKr6/vPb8iIiJydIzcEBoaSnh4eH6HISIiYrOMRiPDhg3Dy8sLV1dXgoOD2blzZ671FxER22b1kHrHjh2JjY3FaDTi4uLC66+/zu7duxkzZgwAnp6ejBo1Ks8CtUVbt24lISEh1y90jB8/nqtXr5qXx44dS/ny5RkyZIh5XenSpXP1mCIiIvLwDR8+nLlz5+Ln50fLli1Zvnw5ISEhHDt2DE9Pzxz3FxER22Z1QR8cHExwcLB5uXz58nz33Xfs2LGDQoUKERQURNGiRfMkSFvk7u6Ot7c3c+bMYfbs2bm67ypVqlgsu7q64uHhQWBgYJbbpKSk4OzsnKtxiIiISN45f/48MTExODg4sGnTJry8vHB0dGTJkiVERkYyYcKEHPUXERHbZ/Ut95l57LHHaNmyJc2aNVMxn4lBgwaxefNmDh48eM9+Bw4coFevXgQEBFCvXj1GjhzJhQsXHvi4sbGx+Pr6kpCQQGhoKLVq1WL+/PkAfPTRR7zwwgvUrl2bp59+mpEjR5KYmGjedvTo0Zm+fnDJkiXUqlXLPBHi7du3mTdvHiEhIfj5+fHss8+yevXqB45ZRERELP3xxx/cvHmTChUq4OXlBUDdunUB2Lt3b477i4iI7ctRQS/31rp1a3x8fJgzZ06WfS5evEhoaChGo5GPP/6YcePG8csvv9CnTx9SU1NzdPzXX3+dZs2aMW/ePJo3bw5AUlISAwcOZO7cubz11lucPHmSXr16kZaWBkCbNm34/fff+euvvyz2FRcXR7NmzShSpAgAkyZN4tNPP6Vz587MmzePVq1a8dZbb/HDDz/kKGYRERG549y5cwC4ubmZ16X/HU6fqDgn/UVExPZlecv9K6+8ku2dGQwGFi9enKOAChIHBwdeffVV3n77bcLDw6lYsWKGPjExMQAsWLDA/Ae4YsWKdOrUiY0bN/L8888/8PFDQ0Pp1auXxbopU6aYv09LSzOP1P/666/Uq1eP4OBg3N3diYuL49VXXwXufECIj49nxowZAJw4cYJly5YxZcoUXn75ZQAaN25MYmIikZGR5osHD2Lfvn0PvK1kFB8fn98hSB5Tju2D8lzwBQUFZcjz5cuXgTsX/9PbDhw4AICLi0uO+8vDpxzYB+XZPjwqec6yoD958uTDjKPAevHFF4mMjGTevHkWxXS6hIQEgoODLa6m16pVi7JlyxIfH5+jgr5Zs2YZ1m3ZsoVPP/2Uw4cPW0ysd/z4cerVq4ejoyPPPPMM69evNxf0cXFxuLq6mve3Y8cOHBwcCAkJ4datW+Z9NGrUiHXr1pGWlkahQoUeKGY/Pz89659L4uPjCQoKyu8wJA8px/ZBebYfd+e5XLlyDBs2jLNnz1KuXDm8vb355JNPAGjatClVqlThzJkzuLi44OPjc9/++jnKX/pdtg/Ks33IqzynpKRke4Azy4J+8+bNOQ5IwNHRkf79+zN58mTCwsIytCcmJlK1atUM6z09Pc1X2h9UyZIlLZYTEhIYMmQIrVq1YsCAAZQsWRKDwUDnzp1JSUkx92vTpg0rVqzgv//9LxUrViQuLo4WLVrg4uICQHJyMmlpaVn+ECcmJmqWfRERkRzy9vamd+/eREdH07JlS/z8/FixYgVubm6EhYWxevVq+vTpQ0BAAHv37r1vfxERKXisnuVeHlzHjh359NNPiY6OztBWqlQpkpKSMqy/cOECNWvWzNFxDQaDxfL3339PiRIlmDFjhrnt1KlTGbZr0KABpUqVYv369bRr147ffvvNPFoPULx4cRwdHVm2bFmGYwB4eHjkKG4RERG5Y+bMmRQuXJgVK1Zw5MgRGjZsyMcff0ypUqVypb+IiNi2Byrojx07Zp40rXz58lSqVClXgyponJyc6NevHx9//DE1a9akcOHC5raAgACWLVvG1atXzbfdJyQkcOrUqVy/jcNoNFK4cGGLInzt2rUZ+jk4OPDss88SFxeHs7Mzbm5uPPXUU+b2hg0bkpaWxpUrVyxeZSgiIiK5y9XVlaioKKKiojK09e7dm969e1vdX0RECp5sFfQ7/j97dx5XVbX/f/wFAqJCKCA4khpKySSD84ChOKXftDRzBKebmUOpt2xAszK8paWIF6cUbTAc0NIyzUxLI0s0cUzTtBRURHGKWX5/+ONcTwyCAUfk/Xw8eDw4a6+99mezDnA+e629dmwsb731FidPnjQqb9SoEa+99hqtW7cu0eDuJ/3792fBggXs27ePFi1aGMqHDRvGypUrGTlyJCNHjuSvv/5i9uzZNGnShC5dupRoDG3btmX58uXMmDGDwMBA9u7dy+eff55v3e7du/PRRx8RFRVFUFAQVlZWhm2NGjXi6aefZuLEiYwYMQJPT0/S09M5fvw4p06dYsaMGSUat4iIiIiIiORV5IQ+NjaWUaNGYWlpSb9+/XB1dSUnJ4cTJ06wceNGRo0axeLFi5XUF6BKlSqEhITw/vvvG5Xb29uzYsUKZs6cyaRJk7C0tCQgIICXX37ZKIkuCQEBAUyePJmPPvqI1atX06xZMxYuXEjXrl3z1PXz86N27dokJibSo0ePPNunTZtGgwYNWL16NeHh4djY2ODq6prvM+xFRERERESk5Jnl5OTkFKXiU089xfnz51m1ahXOzs5G286dO8dTTz1F7dq1iY6OLpVA5f6Wu6KjVrkvOVpl9f6nPq4Y1M8i9wf9LlcM6ueKobRXuS9OTmRe1MZ//fVX+vfvnyeZB6hVqxb9+/fn6NGjRY9WRERERERERO5akRN6W1tbqlWrVuB2GxsbbG1tSyQoERERERERESlckRP6bt268cUXX5CVlZVnW2ZmJl988QXdunUr0eBEREREREREJH9FXhTv6aefZu/evQwePJjg4GAaNWqEmZkZv/32G8uXLyc7O5sBAwaQkJBgtF+dOnVKPGgRERERERGRiq7ICX3Pnj0xMzMjJyeH/fv3G23LXVevZ8+eefY7cuTIPwxRRERERERERP6uyAn9c889h5mZWWnGIiIiIlIhJSQkaFajiIgUW5ET+nHjxpVmHCIiIiIVVmJiohJ6EREptiIviiciIiIiIiIi945iJfTXr18nIiKCAQMG0KVLF/bt2wfApUuXiIiI4MSJE6USpIiIiIiIiIgYK/KU+0uXLjFgwADOnDmDi4sLf/75J2lpaQDY29uzfv16rl27xssvv1xqwYqIiIiIiIjILUVO6OfMmcPFixdZtWoVtWvXpk2bNkbbO3XqRGxsbIkHKCIiIiIiIiJ5FXnK/bfffsvAgQNxd3fPd7X7+vXrc+7cuRINTkRERERERETyV+SE/vLly7i4uBS43czMjPT09BIJSkRERKQiqV27tqlDEBGRcqjICX3NmjX5888/C9x+5MgR/TMSERERuQt6ZJ2IiNyNIif0HTp0YM2aNVy4cCHPtv3797N+/Xo6depUosGJiIiIiIiISP6KvCje2LFj2bZtG3369CEwMBAzMzPWr1/P6tWr2bJlC05OTowaNao0YxURERERERGR/69YU+5XrVqFl5cXa9euJScnh88++4xNmzbRrl07PvnkE6pXr16asYqIiIiIiIjI/1fkEXq4tWBLZGQk169f5+TJkwC4uLgokRcREREREREpY0Ueob+djY0NXl5eeHl5GZL5uLg4goODSzQ4EREREREREclfkRL6y5cvEx8fz+nTp/Ns++WXXxg+fDiDBw9mz549JR5gaZs3bx5ubm55vkJCQkwW06VLl3jjjTfo1KkTnp6etGvXjhEjRrB161ajuFu2bFkix4uPj2fevHl5ynfu3ElUVFSe8ilTpvDEE0+UyLFFRERERETk7hQ65T47O5vp06ezZs0acnJyAPDy8uK///0vlStXZtq0aXz55ZeYm5vTs2dPRo8eXSZBlzRbW1uWLFmSp8wUMjMzCQ4OJjU1ldGjR+Pi4sK5c+fYtWsXsbGxdO7cucSPGR8fT0REBOPGjTMq37VrF5s3bzbpxQ0REZGKLi0tjX//+99ER0dz7do1fH19ee+99+54YX/lypUMHDgQgAkTJjBnzpyyCFdERMpQoQn9hx9+yKpVq6hVqxbe3t788ccf7N+/n+nTp3P+/Hni4+N5/PHHGTNmDC4uLmUVc4mrVKkSzZo1K1LdtLQ0rK2tSy2Wn376iWPHjrF69Wq8vLwM5Y8//rjhooqIiIhUHM8//zwLFy7Ew8ODTp06ER0dTVBQECdPnsTR0THffc6cOcOYMWOwsLAgKyurjCMWEZGyUuiU+88//5wmTZqwadMm5s6dy7p16xgwYABbtmzh9OnTfPLJJ8ycObNcJ/OFOXPmDG5ubnz++ee8+OKL+Pv7G2YhpKSkMHXqVNq0aYOnpydPP/00+/fvN9r/5s2bLFq0iKCgIDw8POjatSvr1q0r9JhXr14Fbj1V4O/MzMzylB0+fJinnnoKb29vevfunee2Bzc3Nz766COjstun68fExPDmm28a6rq5uTFkyBDmzZvH0qVLOXv2rKF8ypQpBcadkJDACy+8QIsWLfD29mbEiBGGhRNFRETk7ly4cIGlS5dibm7ON998w8qVKxk0aBDXrl0jIiIi331ycnIIDg6mTp06PPnkk2UcsYiIlKVCE/rff/+d3r17U6VKFUPZgAEDABg1ahQ+Pj6lG10ZysrKMvq6fTT8nXfeoVq1asydO5dnnnmGjIwMhg0bxq5du3jxxReZP38+NWrUICQkhKSkJMN+b775JpGRkTz11FMsWrSIzp0788orr/Dtt98WGMcjjzyCubk5r7zyCnv27Cn0qnpaWhovvfQS/fv3Jzw8HCsrK5577jlSU1OLfN4dO3Zk+PDhAERHRxMdHc20adPo168fPXv2pGbNmobyMWPG5NtGSkoKAwcO5Pfff+f1119nzpw5/PXXXwwbNoy0tLQixyIiIiLGDh06RGZmJi4uLjg5OQHg7+8P3FrHKD9z5sxh586dfPzxx6U6q1BEREyv0Cn3qampeaZy5b5u0qRJ6UVVxlJSUnB3dzcqW7ZsmWHmgbe3N9OmTTNsW716NcePH2fjxo00aNAAgDZt2tCtWzeWLl3KSy+9xOnTp1m5ciVhYWH06dPHUCcpKYmIiAgeffTRfGNp0KABL774IrNnz2bQoEFUrlyZ5s2b07dvX7p3725UNy0tjVdeeYXWrVsD4OTkRO/evfn555/p0KFDkc7d3t6eunXrAuS57cDJyQkrK6s73o4QFRVFamoq69evNzz1wNfXl8DAQNauXcugQYOKFIuIiIgYO3/+PHDrCUO5qlWrBsC5c+fy1D948CAvv/wyb7zxRpFvJxQRkfLrjs+h//s079zXFhbFeoT9Pc3W1pZly5YZlTVs2JCUlBTg1ij27WJjY3F3d6devXpGI+jNmzfn4MGDhjrm5uYEBQUZ1WndujVffPEF2dnZVKpUKd94hg0bRo8ePdi6dSs//fQTP/zwAzt37uTw4cNMmjTJUM/S0tJoQZyHHnoI+N8//7ISGxtLmzZtsLGxMZxrtWrVcHd3N/w8iqq49aVwcXFxpg5BSpn6uGJQP9///Pz88u3nK1euALeegJO7/ciRIwBYW1vn2WfRokVkZGTw+eefs3HjRo4fPw7AmjVruHLlCmPHji3N05A70O9yxaB+rhjulX6+Y1a+Y8cOLl68aHidmpqKmZkZX331FUePHjWqa2ZmVi5XRK9UqRKenp55ynMTegcHB6Pyy5cv88svv+QZ1QcMo/qXL18mOzsbPz+/fI+ZlJRErVq1CozJ2dmZQYMGMWjQIP766y/Gjx/PBx98wPDhw6lRowZwK2k2N//fXRNWVlYApKenF3a6JS735/Hll1/m2ZY7e6CoPDw8qFy5ckmFVqHFxcUV+P6T+4P6uGJQP1cc+fVzvXr1GDduHOfOnaNevXo4Ozvz3nvvARAQEICrqyuJiYlYW1vToEEDateuTU5ODj/88INRO2fPnuX333/Xe8mE9LtcMaifK4bS6uf09PRiD3DeMaHfuHEjGzduzFMeHR2dp6y8JvR38vdZCnZ2dnh4ePD666/nqZubVNvZ2WFhYcHKlSvzXczO3t6+yMevWrUqAwcO5Pvvv+ePP/4wJPRFYWVlRWZmplFZ7tX+kmJnZ0dgYGC+99jnTgsUERGR4nN2diYkJITFixfTqVMnPDw8WLVqFTY2NowdO5Z169YxbNgwvL29+eWXX3j99deNPp+EhISwfPlyPbZOROQ+VWhCv2LFirKKo1xp3bo1u3btok6dOnlG73O1atWK7Oxsrl27Rtu2bYvcdkpKCjY2NnluaTh9+jRQvAsBALVq1eLEiROG1zdv3uTHH380qmNpaQncuiJ0++i4paVlkUb7W7duzaZNm2jcuLEW3xERESlhc+fOxdLSklWrVvHbb7/RqlUrZs+ene8TcUREpGIpNKFv0aJFWcVRrvTu3ZtPP/2UIUOGMHz4cOrXr09KSgrx8fHUrFmTkJAQGjVqxNNPP83EiRMZMWIEnp6epKenc/z4cU6dOsWMGTPybfvHH3/kvffe44knnsDT0xNzc3P27t3L4sWLefTRR6lfv36xYu3cuTOffPIJjzzyCPXr12fNmjVcv37dqE6jRo0AWL58Oa1atcLGxoZGjRrRqFEjLl68SExMDI0bN6ZGjRrUq1cvzzFCQkL4/PPPCQ4OZvDgwTg7O3Px4kV+/vln/Pz86NmzZ7FiFhERkf+pUqUK8+fPZ/78+Xm2hYSEFDo7MioqiqioqNILTkRETOr+WdmuDFWuXJkVK1Ywd+5c5s2bR3JyMvb29nh5eREYGGioN23aNBo0aMDq1asJDw/HxsYGV1dX+vbtW2Db3t7edOrUiU2bNrFkyRKys7OpV68ezz77LEOHDi12rGPHjuXSpUuGq/uDBg2icePGRs+m9/f3Z8SIEaxYsYL33nuP5s2b8+GHH9K9e3d2797Nu+++y6VLl+jTpw8zZ87Mcwx7e3uio6OZM2cOYWFhXL16FScnJ3x9fXFzcyt2zCIiIiIiInJnZjm3P3BdxERyF4DQonglR4uy3P/UxxWD+lnk/qDf5YpB/VwxlPaieMXJiczvXEVERERERERE7jVK6EVERERERETKISX0IiIiIiIiIuWQEnoRERERERGRckgJvYiIiIiIiEg5pIReREREREREpBxSQi8iIiJiYgkJCaYOQUREyiEl9CIiIiImlpiYaOoQRESkHFJCLyIiIiIiIlIOKaEXERERERERKYeU0IuIiIiIiIiUQ0roRURERERERMohJfQiIiIiIiIi5ZASehERERETa9K0SYm3mZaVVuJtiojIvcXC1AGIiIiIVHS2VWwxm25Wom3mTMsp0fZEROTeoxF6ERERERERkXJICb2IiIiIiIhIOaSEXkRERERERKQcUkIvIiIiIiIiUg4poRcREREREREph+7JhH7Lli0MHToUf39/PDw86Nq1K++//z6XLl0ydWiFCgwM5D//+Y/h9ZdffklMTEyeekOGDGH8+PGlEkNycjLz5s3jzJkzRuW7d+/Gzc2NY8eOlcpxRUREREREpGzdcwn9zJkzmTBhAvXr1+edd95h6dKlBAcH8+233xIaGmrq8AoVERHBkCFDDK+/+uor1q1bV6YxJCcnExERwdmzZ8v0uCIiIlJ+BAcHU7duXSpXroyjoyPdunVj3759AERFRWFmZpbna8+ePQW2t3//fjp16oStrS1mZmY0aNCgjM5ERKRiu6eeQ79t2zaWLVvGjBkz6Nu3r6G8RYsW9O/fn507d/6j9tPS0rC2tv6nYRaoadOmpdb2vS49PZ3KlSubOgwREREpgtOnTxMQEICdnR3btm1j8+bNHDlyhNOnTxvqBAUFGX22cXZ2LrC9P/74g3PnzuHj48P3339fqrGLiMj/3FMj9FFRUbi7uxsl87kqVapEQECA4fWsWbPo1asXPj4+dOjQgUmTJpGUlGS0T2BgIDNnzmT+/Pl06NABPz+/IscyaNAgoxkB33//PW5uboSFhRnKNm/ejIeHB6mpqYbj5U65nzJlCps3b+ann37Czc0NNzc35s2bZ3SMDRs2EBQUhK+vLyNHjuTcuXN3jOvIkSMEBwfj7e1N8+bNmTRpEhcvXgTgzJkz9OrVC4ChQ4cajnu7y5cvM378eHx8fOjUqRMff/xxnmPs2bOHwYMH4+3tTcuWLXnttde4fv26YXtMTAxubm7Ex8czZMgQvLy8WLJkCQALFy4kKCgIT09P2rRpw4gRI/L0i4iIiJjW9u3b+eSTT4iMjGTlypXArc8RmZmZhjoDBw5kzpw5hq/69esX2F6vXr04dOgQEydOLPXYRUTkf+6ZEfrMzEz27dvH8OHDi1Q/OTmZZ555BicnJy5dusSyZcsIDg5mw4YNVKpUyVBv48aNuLq6Mm3aNLKzs4scj7+/P1u2bDG83rNnD5UrVzaabvbzzz/TtGlTqlSpkmf/MWPGkJCQwLVr15g2bRoAtWrVMmzfv38/Fy5c4KWXXiI9PZ0ZM2YQGhrK4sWLC4zp0qVLDBkyhIceeojZs2dz48YNZs+ezbBhw1i7di1OTk7MmjWLyZMnM3XqVNzd3fO0ERoaSu/evenfvz8bN27kjTfewNPTEy8vLwDi4uIICQmhc+fOhIeHc/nyZWbPns3Vq1cJDw83amvixIkMGDCA5557jgceeID169ezYMECJk+eTOPGjUlJSeHHH380XPAQERGRe0dERASHDx/mm2++AWDSpElYWloatk+YMIFnn32WBx98kGeffZYJEyaYKlQRESnAPZPQp6SkkJGRQe3atYtU//aR8uzsbMNI/d69e2nevLlR3YULFxZ7Ori/vz8LFizg0qVL2Nvbs2fPHvr27cunn37KjRs3qFatGnFxcbRq1Srf/V1cXKhevTo5OTk0a9Ysz/br16+zcOFC7OzsAEhKSiIsLKzQ2wKWLl0KwAcffICNjQ0ADRs2pF+/fmzZsoWePXsaRuRdXV3zPe5jjz3GmDFjgFu3Mnz77bds2bLFkNDPnj0bHx8f5syZY9jH2dmZkJAQjh07RpMmTQzlQ4YMITg42PB6zZo1tGvXjkGDBhnKunTpku+5iIiIiGmtWbOGHTt2AFCvXj3atm0LgLm5Oc2bN8fb25vk5GQ+//xznn/+eapUqcK//vUvU4YsIiJ/c88k9LnMzMyKVG/Hjh1ERkZy/Phxo+ngp06dMkroW7VqdVf3dvv4+FCpUiXi4uIICAggPj6e1157jc2bN/PLL7/g7e3Nr7/+ytixY4vdNoCnp6chmYdbCTjA+fPnefDBB/PdJz4+nrZt2xqSeQAvLy/q1q1LXFwcPXv2vONxc/9ZA1haWtKgQQPDVP/U1FR++eUXXnvtNbKysgz1/Pz8sLS05NChQ0YJfceOHY3afuSRR1izZg3h4eF07NgRd3d3o9kSRXHw4MFi1ZfCxcXFmToEKWXq44pB/Xz/K85tgcVR2Htn9uzZpKenExsby4svvsiTTz7JunXraNq0KZGRkYZ6dnZ2REVFsWzZsjvGeeLECQAyMjL0vs2HfiYVg/q5YrhX+vmeSeirV6+OlZUVCQkJd6wbHx/PmDFj6Ny5M6NGjcLBwQEzMzOeeuop0tPTjeo6OjreVTw2NjY8/PDD7Nmzhxo1amBtbY2bmxt+fn7s2bOHrKwsbt68ia+v7121/8ADDxi9zp3i9vf4b5eUlETjxo3zlDs6OnLlypW7Pm5GRgYAV69eJTs7m+nTpzN9+vQ8+yYmJhq9dnBwMHr95JNPcuPGDaKjo5k/fz7Vq1dnwIABjBs3rsiJvYeHhxbXKyFxcXGl9gFR7g3q44pB/Sz/RH7vndTUVKysrAz/m/38/HjjjTe4evUqNjY2VK9e3TDQAP+7ZbBGjRqG9o4ePQrcmil4+//tP//8EwArKyu9b/9Gv8sVg/q5Yiitfk5PTy/2AOc9k9BbWlri6+vLzp07eeGFFwqtu3XrVmrUqMGcOXMMI/oFPaatqCP++fH392fPnj1Ur14dX19fzM3N8fPzY+vWrWRlZeHq6kqNGjXuuv3iqlmzJsnJyXnKL168mO/98sWV+6iZsWPHGi1AmMvJycno9d9/tubm5oSEhBASEkJiYiIbNmzg/fffx9nZmQEDBvzj+EREROSf2717NwMHDqRDhw7UqFGD77//nqtXr1KzZk18fX15/PHHuXTpEs2bN+fy5ct8/vnnAEb/yx955BEA9u3bR7NmzTh69CgzZ87kjz/+AG59NgkJCcHR0ZFZs2aV/UmKiFQQ99Qq98HBwRw8eDDfZ7ffvHmT7777Drj1+DlLS0ujhHLDhg0lHo+/vz9Hjhxhx44d+Pv7A9C8eXPi4+OJjY2941UZS0vLQkfci8vb25udO3ca3WIQHx/P2bNnDbEUZaS/IFWrVqVZs2b8/vvveHp65vkq7HE1f1e7dm3+9a9/4eLiYkByDTIAACAASURBVJh+JyIiIqZXp04dmjRpwtdff80HH3zA5cuX6devH9u2bcPOzo7BgwdjbW3N2rVr+frrr/Hy8iIqKoohQ4YU2Oa5c+dYvnw53377LQA3btxg+fLlrFmzpqxOS0SkQrpnRujh1mPfhg0bxquvvsrevXvp1KkTVatW5eTJk3z66afUrVuXDh060LZtW5YvX86MGTMIDAxk7969hqvHRbF7926GDh3KihUraNmyZYH1/P39yc7OZt++fUyZMgWAhx9+GAsLCw4cOGC0IFx+GjZsyDfffMPWrVtxdnbGycmpWEnx3w0bNoyVK1cycuRIRo4cyV9//cXs2bNp0qSJYfG5OnXqYG1tzfr167G1tcXCwgJPT88iH2Py5MmEhIRgbm5O165dqVatGomJiWzfvp0XXniBhg0bFrjv1KlTsbOzw9vbG1tbW3bv3s3p06f597//fdfnLCIiIiWrSZMmbN++vcDtuZ8zCpOTk2P0umPHjnnKRESk9N1TCT3cen67j48PH330EZMmTSI9PZ26desSGBhoeKRdQEAAkydP5qOPPmL16tU0a9aMhQsX0rVr1yIdIy0tDch7D/jf2dvb06hRIxITEw1T2s3NzfHx8eH777+/4wj9wIEDOXLkCK+88gpXrlxh7NixjBs3rkgxFhTPihUrmDlzpuHRMgEBAbz88stYWVkBULlyZd58803mz5/PkCFDyMzM5Ndffy3yMfz9/fn4448JDw/nxRdf5ObNm9SpU4f27dvfcT2CZs2asWrVKqKjo0lPT8fFxYU333yTzp073/U5i4iIiIiISP7Mcirg5dTw8HB+/vlnPvzwQ1OHIv9f7gIQWhSv5GhRlvuf+rhiUD9XHGbT737dn/zkTKtwH/HuafpdrhjUzxVDaS+KV5yc6J66h76s7Nu3j2HDhpk6DBEREREREZG7ds9NuS8Ly5YtM3UIIiIiIiIiIv9IhRyhFxERERERESnvlNCLiIiIiIiIlENK6EVERERERETKISX0IiIiIiIiIuVQhVwUT0REROReci31Wok/Zi4tKw1rC+sSbVNERO4tGqEXERERMbFjh4+VeJtK5kVE7n9K6EVERERERETKISX0IiIiIiIiIuWQEnoRERERERGRckgJvYiIiIiIiEg5pIReRERExMSaNG1i6hDKVFpWmqlDEBG5L+ixdSIiIiImZlvFFrPpZqYOo8yU9CP6REQqKo3Qi4iIiIiIiJRDSuhFREREREREyiEl9CIiIiIiIiLlkBJ6ERERERERkXJICb2IiIiIiIhIOaSEXkRERERERKQcUkJfwc2bNw83NzfDl7e3N7169SI6OtrUoYmIiEgFdPjwYXr16oWjoyO2trb07t2b06dPA5CWlsarr75Kw4YNsba2xsPDg7Vr1xap3eTkZOrUqYOZmRnVq1cvzVMQESkzeg69YGtry5IlSwBITU1l27ZtTJ06lapVq9KrVy8TRyciIiIVRUpKCkFBQSQkJNCzZ0+srKyIiYnht99+Iz4+nokTJxIZGUnjxo0ZOnQoa9eupV+/fuzatYvWrVsX2vbo0aNJSkoqozMRESkbGqEXKlWqRLNmzWjWrBmtW7fm1VdfxdPTk61bt5o6NBEREalAdu3aRUJCAg0aNGDDhg2sXbsWb29vDh06xLp161i9ejUAixcvZtGiRYSGhpKTk8Pbb79daLvLly8nJiaGV199tSxOQ0SkzCihl3xVq1aNrKwsAGJiYnBzc+PGjRtGdQIDA/nPf/5jeL1nzx4GDhyIr68vvr6+PP7442zatKlM4xYREZHyy9raGrg1Pf7kyZOcOXOGhIQEAPbv32/YvnfvXlJTU9m/f79hW0FOnz7N+PHjmTRpEh07dizdExARKWNK6AWArKwssrKyuH79Op999hk///wzQUFBRd7/+vXrjB49mvr16zNv3jzCw8N5/PHHuXbtWilGLSIiIveTgIAA2rZty7Vr13jooYeoX7++YZr8uXPneOWVVwCYOHEiVatWJSoqyrAtPzdv3mTo0KE0bNiQt956q0zOQUSkLOkeeiElJQV3d3ejsiFDhtC7d+8it/H7779z7do1QkNDsbGxAaBdu3bFjuXgwYPF3kcKFhcXZ+oQpJSpjysG9fP9z8/Pz9QhlLmC3tezZ8/m66+/5uTJk9SqVYt9+/bx1VdfkZWVRYsWLYiKiuLHH38EwNHRkbfeeovq1avn215iYiLfffcdjRs3plOnTly5cgW4NRDRvn17pk6dir29femd5N/od7liUD9XDPdKPyuhF2xtbVm2bBkAGRkZHDp0iPDwcKpXr87YsWOL1IaLiwtVq1Zl8uTJ9O3blxYtWvDAAw8UOxYPDw8qV65c7P0kr7i4uAr5AbEiUR9XDOpnuV8V9L7OyMigZcuWACQlJdG0aVPg1mCDp6cnfn5+BAcHAxASEgJA9+7dDe0dPXoUgIYNG+Lg4ADA8ePHOX78uOEY2dnZ7Ny5k8aNG9OgQYMSP7f86He5YlA/Vwyl1c/p6enFHuBUQi9UqlQJT09Pw2s/Pz+ysrJ47733GDx4cJHasLOzY+nSpURERPD888+Tk5ND27ZtCQ0NpX79+qUVuoiIiNxnunTpgqOjI3Z2dmzatImLFy/y2GOP8eijjzJ//nw+/vhjPD09OXjwID/88AN2dnaEhoYa9n/kkUcA2LdvH82aNSMnJ8ewbfv27Tz66KPY2dmRkpJS5ucmIlLSdA+95Ouhhx4iMzOTP/74wzBinpmZaVQnd9paLh8fHz744AP27NnDvHnzOHXqFJMmTSqzmEVERKT88/Ly4vvvv2fFihVYWFjw0ksvsWbNGgBcXV25dOkSy5cv58CBA/Ts2ZNdu3bh6upq4qhFRExDI/SSr9xpabVr1yYjIwOAEydOGKaW7N+/n+vXr+e7r7W1NYGBgRw/fpyFCxeWTcAiIiJyXwgPDyc8PDzfbV27djVMqS/I7SPyf9exY8dCt4uIlDdK6IXs7Gx++eUX4NYo/KFDh4iMjKRTp07UrFkTOzs7nJ2dmTFjBhMmTCAlJYUlS5YYFr+DW1PY1q5dS6dOnahTpw7nz58nOjqaVq1ameq0RERERERE7mtK6IVr167Rv39/ACwtLalTpw5PP/00zz77LABWVlZEREQwffp0xo8fT8OGDXn99df597//bWjDxcUFMzMz3n//fZKTk7G3t6djx45MnDjRJOckIiIiIiJyv1NCX8GNGzeOcePG3bGel5cXa9euNSrbtm2b4ftGjRoVOD1ORERERERESp4WxRMREREREREph5TQi4iIiIiIiJRDSuhFREREREREyiEl9CIiIiIiIiLlkBJ6ERERERERkXJIq9yLiIiImNi11GvkTMsxdRhlJi0rDWsLa1OHISJS7mmEXkRERMTEjh0+ZuoQypSSeRGRkqGEXkRERERERKQcUkIvIiIiIiIiUg4poRcREREREREph5TQi4iIiIiIiJRDSuhFREREREREyiEl9CIiIiIm1qRpE1OHIKXMz8+v2PukZaWVQiQicj/Rc+hFRERETMy2ii1m081MHYbcY3Km5Zg6BBG5x2mEXkRERERERKQcUkIvIiIiIiIiUg4poRcREREREREph5TQi4iIiIiIiJRDSuhFREREREREyiEl9CIiIiIiIiLl0H2X0Lu5ud3xa/fu3f/4OEOGDGH8+PF5yuPj4/Hw8ODatWtMmTKFJ554It/9x48fz5AhQwyvC6tb0m6PMSYmBjc3N27cuHHX7UVERNC+fXsefvhhpkyZws6dO4mKiiq5gEVERETEYNSoUTRt2hQbGxscHBzo0aMHhw4dMmxfuXIl7du3p2bNmlStWhUPDw+WLl1q1MawYcNo0KABZmZmmJmZsX379jseNzs7m7CwMFxdXbGysqJmzZoMHz68pE9PRIrhvnsOfXR0tOH7tLQ0goODefbZZ+nYsaOh3NXV9R8fZ9q0aVhY5P3xbd++HV9fX2xtbf/xMUpLScZ44MAB5s2bx8SJE2nRogUODg6sXLmSzZs3ExIS8s+DFREREREjS5YsoWXLlgwYMICtW7eyadMm4uPj+e2337C2tmbz5s2cPHmSrl27cuHCBb7++mtGjBhBzZo16dWrFwCxsbF4enqSmJhIRkZGkY773HPPsXDhQmrVqsXgwYPJyMjg5MmTpXmqInIH911C36xZM8P3uaPOLi4uRuUloaCLAtu3b6dnz54leqx/KjMzE3NzcypVqgSUbIy5f8QHDRqEjY1NibQpIiIiIgXbtWsXbdq0AeDUqVM0bNiQs2fPcvjwYXx9fRk7diyLFi3CysoKgI4dO7Jjxw6+/vprQ0J/9OhRAKpXr16khP63335j0aJFODo6Eh8fT82aNUvp7ESkOO67Kfd3sn79egYMGECLFi1o3rw5Q4YM4cCBA4btV69epUOHDrz44otG+40ePZquXbuSmpoK5D/lPikpicOHDxMQEHDX8W3dupVu3brh6enJgAED+O2334y237x5k0WLFhEUFISHhwddu3Zl3bp1RnVyY4uOjqZz5854eXlx4cKFYsV4/Phx3Nzc+Omnn4zKb9y4gY+PDytWrGDKlCmGn5Ofnx9ubm4MGTKEpUuXcvbsWcMtDlOmTLnrn4eIiIiIGMtN5gFDMm5ubk7t2rUB8Pf3NyTzt9epV6/eXR9z27Zt5OTk4OjoSJcuXahWrRr+/v5FmqovIqXnvhuhv5MzZ87Qu3dvXFxcyMjIYOPGjQwePJiNGzdSv359HnjgAWbMmMHIkSPp0qULnTt3Zu3atezYsYOPP/6YKlWqFNj2jh07qFevHg899NBdxZaQkEBYWBgTJkzA2tqaefPmMWLECLZs2ULlypUBePPNN1m/fj1jxozB3d2dXbt28corr1C9enUeffRRQ1t79+7ljz/+YPLkyVSpUsUwvb6oMTZu3JhmzZoRExNDixYtDOVfffUVmZmZ9OzZk44dO1KrVi0iIyNZvnw51tbWODk5MXv2bHbv3k1ERAQA9vb2d/XzEBEREZGCXb9+3XCL46RJkwwJ/e3ee+89YmNjcXV1ZfTo0Xd9rIsXLwK3RvYfffRRgoKC+Oyzz+jVqxdHjhz5RxcLROTuVbiEfuzYsYbvb968Sdu2bTlw4ACfffaZYVv79u3p378/U6dOpU6dOoSFhTF8+HB8fX0LbXvHjh1G9+oX1+XLl/nvf/9rOI67uztBQUHExMQwYMAATp8+zcqVKwkLC6NPnz7ArSu0SUlJREREGCX0V69eZd26dXmmQxUnxr59+/L2228TGhpKtWrVAIiJiSEwMBB7e3vs7e1xcXEBwNPT01DHyckJKyuru7rN4eDBg8XeRwoWFxdn6hCklKmPKwb18/3Pz8/P1CHIPaqg3/+UlBTGjx/P4cOH6dOnD0899VSeuosWLWLRokXUrVuXOXPmcPz48TztZGdnA/Drr78Wur7SX3/9BUC1atUICwvDwsKCAwcOcPLkST744IN77pZTU9Lf7IrhXunnCpfQnzhxgvfee499+/aRnJxsKD916pRRvZdeeoldu3bRv39/HnzwQSZMmFBou5mZmezatYu5c+fedWwODg5GFw3q1q2Lu7s78fHxDBgwgNjYWMzNzQkKCiIrK8tQr3Xr1nzxxRdkZ2cb7pN3d3fPk8wXN8YePXrw9ttv89VXX/Hkk0/yxx9/EBcXx4IFC+76HO/Ew8PDMBtB/pm4uDh9QLzPqY8rBvWzSMWW3+//6dOnGThwIMeOHWPKlCmEhYUZbb9586bhPnofHx++/PJLatWqlW/7uZ8d3dzcjI6VmJjIlStXsLe3x8nJiaysLGbMmIGFhQV+fn5YWFgYPrN5enrq79T/p7/ZFUNp9XN6enqxBzgrVEJ//fp1hg8fjoODA1OmTKFOnTpUrlyZ1157Lc9iINWqVePRRx/lww8/pG/fvkb3IeVnz5495OTk0LJlS0NZpUqVuHnzZr71b0++czk4OOSp5+DgQFJSEnBrBD87O7vAN09SUpLhj7Wjo2ORYixMtWrV6NatGzExMTz55JPExMTg6OhI+/bti7S/iIiIiJS8Nm3akJCQgIuLC6mpqTz//PMADBw4kBYtWhAaGkpkZCTm5ub4+Pgwc+ZM4NaizrkzUidPnszFixcNI+8zZ84kKiqKKVOm8PDDD/Pyyy+zfPlyJkyYwJw5c2jZsiUBAQHs2LGDrl27Ymtry5EjR6hbty6BgYGm+UGISMVK6H/55RfOnTvH0qVLje4hv3btWp66Bw4cYOXKlTRt2pTIyEgee+yxQlfz3L59O61atTJK/O3t7Q33G/1dUlIS9evXNyq7fcbA7WW5K+rb2dlhYWHBypUrMTMzy1P39nvV89ueX4x30q9fPwYMGMCpU6f47LPP6N27d54LESIiIiJSdhISEgD4448/jGZeNmvWjBYtWnD27Fng1kj97c+fDwgIMCT0a9as4fTp04ZtmzdvBiAkJISHH3443+NGR0czYcIENm3aRKVKlejRowezZ8+mevXqJXuCIlJkFWqV+7S0NACjhHbv3r2GP3q50tPTeemll2jXrh2ffPIJdnZ2hIaGFtr29u3b89yb7u/vT1JSEvHx8Ubl586d49ChQ3lG2pOTk9m7d6/hdUJCAocPH8bLywuAVq1akZ2dzbVr1/D09MzzdadEPb8Y78TX15dGjRrxyiuvkJCQYLh3vzCWlpakp6cX6zgiIiIiUjQ5OTn5fuUukBcVFZXv9ttXpD916lS+dXI/K+a2MWfOHMM+zs7OfPrpp1y5coVLly7xxRdfFJj8i0jZqFAj9M2aNaNq1aqEhoYycuRIzp07R0REBM7Ozkb15syZw8WLF4mKiqJKlSrMnDmTQYMGERMTwxNPPJGn3dOnT3Pq1Kk8j4Jr3749Pj4+PPPMM4wdO5ZGjRqRkJBAZGQkderU4fHHHzeqX6NGDV588UXDKvfh4eHY29sbjtmoUSOefvppJk6cyIgRI/D09CQ9PZ3jx49z6tQpZsyYUeC5FxRjrq1bt+a5d93T05O6devSt29f3nnnHXx8fIq0gn+jRo24ePEiMTExNG7cmBo1amjlUxERERERkRJWoRJ6R0dH5s6dyzvvvMOYMWN48MEHmT59OkuWLDHUiYuLIyoqinfeeQcnJyfg1ih1SEgIb7/9Nm3atMmzqMj27dt5+OGH85Sbm5uzePFi5s6dy6JFi7h48SJ2dna0b9+eiRMnGlaFz1WnTh1Gjx7N7NmzOXv2LB4eHsyePdso0Z42bRoNGjRg9erVhIeHY2Njg6urK3379i303AuKMVfu8+RvFxYWxhNPPEHnzp155513ePLJJws9Rq7u3buze/du3n33XS5dukSfPn0M926JiIiIiIhIyTDLycnJMXUQ5d3w4cPx9PTkhRdeMHUoBfonMX788cfMmjWL77//Hhsbm1KI7n8rOmqV+5KjVVbvf+rjikH9XHGYTc+7/o1UbDnT9DG9vNHf7IqhtFe5L05OVKFG6EvL7YuN3KvuJsYzZ85w6tQpFi5cSJ8+fUotmRcREREREZHiU0IvBYqIiGDjxo00b96cCRMmmDocERERERERuY0SeinQzJkzde+7iIiIiIjIPapCPbZORERERERE5H6hhF5ERERERESkHNKUexERERETu5Z6TSuaSx5pWWlYW1ibOgwRuYdphF5ERETExI4dPmbqEKSUxcXFFXsfJfMicidK6EVERERERETKISX0IiIiIiIiIuWQEnoRERERERGRckgJvYiIiIiIiEg5pIReREREREREpBxSQi8iIiJiYk2aNjF1CFLK/Pz8TB2ClAH1c/mRlpVm6hBKhJ5DLyIiImJitlVsMZtuZuowREQqjJxpOaYOoURohF5ERERERESkHFJCLyIiIiIiIlIOKaEXERERERERKYeU0IuIiIiIiIiUQ0roRURERERERMohkyf0W7ZsYejQofj7++Ph4UHXrl15//33uXTpkqlDY/z48QwZMsTUYZSI3bt34+bmxrFjx0wdioiIiIiIiJQAkyb0M2fOZMKECdSvX5933nmHpUuXEhwczLfffktoaKgpQxMREREREZEKqGPHjpiZmRl9eXh4ANCgQQP8/f3zbO/YsaNh/+zsbMLCwnB1dcXKyoqaNWsyfPjwQo+5evVqWrVqRfv27XF2dqZdu3Zs27btjrGa7Dn027ZtY9myZcyYMYO+ffsaylu0aEH//v3ZuXPnP2o/LS0Na2vrfxpmoXJycsjIyKBy5cqlehwREREREREpWxMmTDB8X7t2bQCGDx/O0aNHcXJyAiAmJoY///wTV1dXQ93nnnuOhQsXUqtWLQYPHkxGRgYnT54s8DgnTpzg6aef5ubNm7Rp04acnBx27dpFz549SUpKolq1agXua7KEPioqCnd3d6NkPlelSpUICAgwvJ41axY7duzgzJkz2Nra0rx5c6ZMmULNmjUNdQIDA+nSpQu2trZER0eTnJzMoUOHihxPYmIiU6dOZffu3Tg6OvLss8/mqTNv3jw++ugj5s+fT1hYGL/++itvvfUWvXv35ssvvyQyMpLff/8dBwcHevfuzbhx47CwsODPP/+kc+fOLFq0yOi8srOz6dChA/369eP5558H4NixY8yaNYuff/4ZgPbt2xMaGmp0rn934sQJIiIi2Lt3LykpKdSrV49+/foxdOhQzM0LnoSRmprK7Nmz2bRpE1evXqVJkya88MILtGvXzlBnyJAh1KhRg6CgIMLDw0lOTsbX15e33nqLWrVqGeqlp6czd+5cvvjiC5KTk2nUqBGTJk0yOl8REREREZHyYs6cOXnKpk6dSlxcHH5+fiQlJbFw4ULg1u3aAL/99huLFi3C0dGR+Pj4QvO4XKdOneLmzZs4ODgQHh5OgwYNcHR0JDU1lQsXLtCwYcMC9zVJQp+Zmcm+ffvuOO0gV3JyMs888wxOTk5cunSJZcuWERwczIYNG6hUqZKh3saNG3F1dWXatGlkZ2cXOZ6cnBzGjBnD5cuXmTFjBpUrV2bevHmkpKTQoEEDo7ppaWlMmTKFkSNH0qBBA5ycnNi5cycvvPACvXv35t///je//vorc+fO5fLly7zxxhvUr18fLy8vvvzyS6ME96effuLixYv06NEDgNOnTzNgwAA8PDx49913yc7OZu7cuYwePZo1a9ZgZmaWb/y5ndyrVy+qVavGkSNHmDdvHunp6TzzzDMFnvdrr73Gtm3bmDhxIi4uLqxevZpnnnmG5cuX4+/vb6i3f/9+Lly4wEsvvUR6ejozZswgNDSUxYsXG+qMHz+e+Ph4xo0bh4uLC5s2beLZZ59l7dq1PPLII0XuCxERERERkXtBjRo1APD19WXmzJk0b97caPuCBQtIS0sjMDAQLy8v4NZM9JycHBwdHenSpQvHjh3jkUceYdasWUbT8m/Xvn17WrVqxY8//sj48ePJyckBYOjQoYUm82CihD4lJYWMjAzDtIU7CQsLM3yfnZ2Nj48PHTp0YO/evXl+qAsXLiz2FPjvvvuOw4cPs2rVKry9vQFwd3cnKCiowIS+c+fOhrIpU6bQokUL/vOf/wDQoUMHAN577z3GjBlDrVq1eOyxx5g3bx4ZGRlYWVkBsGnTJlxdXWnSpAkAERERODo6snjxYkMdNzc3unfvzo4dOwp8A7Ru3ZrWrVsDty5O+Pn5kZaWxqpVqwpM6E+cOMEXX3xBWFgYffr0AW69kf7v//6PyMhIPvjgA0Pd69evs3DhQuzs7ABISkoiLCzMcFtDbGws27dv58MPP6RFixYAtGvXjlOnThEZGUl4eHgRekFERERERMT0bG1t6dmzJ3Xr1iU2NpZt27bRtWtXDh8+bJilnJmZSWRkJIBhtjXAxYsXATh69CiPPvooQUFBfPbZZ/Tq1YsjR45Qr169PMezsrIiJCSE/fv388MPPwDg7OxMz5497xiryabcAwWOOP/djh07iIyM5Pjx41y/ft1QfurUKaOEvlWrVnd1P3t8fDyOjo6GZB6gbt26uLu75xtzbsIOty4wHD58mJdfftmoXo8ePZg1axb79u2je/fudO/enZkzZ/Ldd9/RuXNnsrKy2LJli9Eq+rGxsfTu3Rtzc3OysrIAqFevHnXr1uXgwYMFJvTp6eksXLiQDRs2kJiYSGZmpmFbVlYWFhZ5u/nAgQPk5OTQrVs3Q5m5uTndunVjyZIlRnU9PT0NyTxguD/k/PnzPPjgg/zwww/UrFkTX19fQ9xw60JDTExMvjEX5ODBg8WqL4WLi4szdQhSytTHFYP6+f7n5+dn6hBERCqcgv6/Tps2zZCrhoSE8MQTT5CYmMgHH3xgyJ/CwsJITEykfv361KpVy9DWX3/9BUC1atUICwvDwsKCAwcOcPLkST744IN8k/Rdu3YxYcIEatWqRUxMDHXr1qVNmzb079+fpk2b5puX5jJJQl+9enWsrKxISEi4Y934+HjGjBlD586dGTVqFA4ODpiZmfHUU0+Rnp5uVNfR0fGu4klKSsLe3j5PuYODAzdu3DAqs7OzM4yeA1y+fJnMzMw8x859feXKFeDWFRY/Pz82bdpE586diY2N5fLlyzz22GNGbS1evNhoKnuuxMTEAuN/9913WbNmDc899xzu7u7Y2tryzTffEBkZSXp6er4J/YULF6hatSpVqlTJc86pqalGMwkeeOABozqWlpYAhp//5cuXSUpKyveNdvstEUXh4eGhRQZLSO69PXL/Uh9XDOpnERGR0pHf/9e//vqLlJQU6tSpA2CUFzVu3Bg/Pz/i4uL4/PPPAXjxxReNBpmzsrKYMWMGFhYW+Pn5YWFhYchvPD098fPzIzExkStXrmBvb4+TkxPffvstcGvgtGbNmjRp0gQHBweuX7/O0aNH772E3tLSEl9fX8O954XZunUrNWrUYM6cOYarJGfPns23blFH/P+uZs2a+T73z/Jp3wAAH+tJREFUPjk5+Y4r5deoUQNLS0uSk5ONynOnWtw+sp07ap+WlsaXX35J06ZNjab029nZ0blzZ/r165fvcQry1VdfMXjwYEaNGmUo27FjR6FxOzk58ddff5GammqU1CcnJ1OlShWjixZ3Ymdnh7OzM/Pnzy/yPiIiIiIiIveaCxcu4ObmRmBgIA8++CCxsbGcPn0aZ2dnAgMDAdi3bx9xcXHY2dkREhJitH/Lli0JCAhgx44ddO3aFVtbW44cOULdunUN+7/88sssX76cCRMmMGfOHNq0aYOZmRk7d+5kypQpVKpUidOnT2NtbW20tll+TPYc+uDgYA4ePMi6devybLt58ybfffcdcOuedUtLS6NkfcOGDSUai6enJxcvXmT//v2GsoSEBA4fPnzHfStVqoS7uztfffWVUfmmTZswNzfHx8fHUNatWzfS09P5+uuv2bp1q2ExvFytW7fm+PHjeHh44OnpafSV370WudLT040S8OzsbL744os7nrOZmRmbN282lOXk5LB58+ZijwS1bt2aixcvUrVq1Txxe3p6FqstERERERERU3FwcGDo0KEcO3aM5cuXc/78eXr37s0333xjmIW9cuVKAEaOHImNjU2eNqKjo+nfvz979uzhu+++o0ePHmzdupXq1avne8w2bdqwYsUKvL29+eGHH4iNjaV169asX7+eBx98sNB4TXYPfWBgIMOGDePVV19l7969dOrUiapVq3Ly5Ek+/fRT6tatS4cOHWjbti3Lly9nxowZBAYGsnfvXsP0hqLYvXs3Q4cOZcWKFbRs2TLfOgEBATz88MNMmDCByZMnU7lyZcLDw/Odhp+fcePGMWLECF5++WV69OjBsWPHmDt3Lv369TN6tJuDg4Nh8byrV6/SvXt3o3bGjh1Lv379+Ne//sWTTz5JjRo1OH/+PD/88AN9+vQpMP42bdrw8ccf4+LiQvXq1fn444/JyMgoNOaHHnqIxx57jDfeeIPr168bVrk/efIk06ZNK9J552rbti3t2rVj+PDhjBo1CldXV8P0kPT0dCZNmlSs9kREREREREzB1tY231ugb/fOO+8UOgjq7OzMp59+WuD2qKgooqKijMoGDx5Mv379OHjwYLFuQzbponhTpkzBx8eHjz76iEmTJpGenm6YipD7SLuAgAAmT57MRx99xOrVq2nWrBkLFy6ka9euRTpGWloacCuZLoiZmRmRkZGEhobyyiuv4ODgwDPPPMMPP/zA5cuX73iMdu3a8f777xMZGcmGDRuwt7dn+PDhjBs3Lk/dxx57jNdee41mzZrlGXVv2LAh0dHRzJ07l6lTp5KWloazszOtW7cu9MpMaGgo06ZN44033sDa2prevXsTFBREaGhooXG/9dZbzJo1i//+97+G59AvWLDgjtM6/s7MzIyIiAgWLFjA8uXLSUxMxM7Ojocfftho0T8REREREREpOWY5uQ+5u0+Fh4fz888/8+GHH5o6FClEenp6sa9GSeG0kNb9T31cMaifKw6z6Xe3FpCIiBRfzrS7T4NL63/z3eREJruHvqzs27ePYcOGmToMERERERERkRJl0in3ZWHZsmWmDkFERERERESkxN33I/QiIiIiIiIi9yMl9CIiIiIiIiLlkBJ6ERERERERkXJICb2IiIiIiIhIOXTfL4onIiIicq+7lnrtHz1CSUREiictKw1rC2tTh/GPaYReRERExMSOHT5m6hCklMXFxZk6BCkD6ufy435I5kEJvYiIiIiIiEi5pIReREREREREpBxSQi8iIiIiIiJSDimhFxERERERESmHlNCLiIiIiIiIlEN6bJ3cE3Jybj2qJyMjw8SR3F/S09NNHYKUMvVxxaB+rhjUz/c/9XHFoH6uGEqjn3NzodzcqCjMcopTW6SUXLt2jWPH9MgeERERERGp2Jo0aYKtrW2R6iqhl3vCzZs3uXHjBpaWlpiZmZk6HBERERERkTKVk5NDZmYm1apVw9y8aHfHK6EXERERERERKYe0KJ6IiIiIiIhIOaSEXkRERERERKQcUkIvIiIiIiIiUg4poRcREREREREph5TQi4iIiIiIiJRDSuhFREREREREyiEl9CIiIiIiIiLlkBJ6kXLu7NmzTJw4kRYtWuDt7c3//d//8d133xnVOX/+PM899xw+Pj60bNmSN954g9TU1DxtrVq1ii5duuDp6ckTTzxBbGxsWZ2GFFFUVBRubm6MHz8+zzb1c/l0/fp1wsPD6du3L35+frRt25bnnnuO33//PU9d9fH95bfffiM4OBhvb2/atWvH3Llzyc7ONnVYUgSbNm1i9OjRtG/fHh8fH5544gk2btxoVCcnJ4cFCxYQEBCAl5cXgwYN4siRI3na0vugfDh//jw+Pj64ublx48YNQ7n6ufzLyspi0aJFdOnSBQ8PDzp06MDbb79tVOde7mcl9CLlWGJiIv379+fq1au8/fbbREZG8vjjj5Oenm6ok5WVxYgRI/5fe/ceFVW5/gH8CwjIJW5KKBcVEVBCROWqaApyjqEdTqLiBYVQOi7DvIAXzLxlGR2TkxQL8JqiYQvwCGKkC1GzdKSjRmiSxkUUMQR1AEFu8/uDxf6xnUGhPIcZ+n7W4o9532fveWeeGeDZ7373RllZGWJiYvDuu+8iKysL7733nmhfmZmZ2LBhA/z9/bFz504MGTIE//jHP/DLL7/8r18WdaCyshJxcXEwMTGR62OeVVdZWRm++uoreHl5YceOHdi0aRMqKiowc+ZM3L17V4hjjnuWR48eISQkBGpqaoiLi8Pbb7+NvXv3YseOHd09NOqEffv2QU9PD1FRUYiLi4O7uzsiIiJw4MABISYxMRFxcXEICwtDfHw8dHV1ERISgoqKCiGGnwPV8fHHH0NXV1eunXlWfVFRUdi/fz9CQ0OxZ88eREREoHfv3qIYpc6zjIhU1rJly2SzZ8+WNTc3dxiTkZEhGzp0qOzWrVtCW2Zmpsze3l5WVFQktP3lL3+RrVmzRnjc3Nwsmzp1qiwiIuK/MnbquqioKFlkZKQsKChItmTJElEf86y6amtrZXV1daK2Bw8eyJydnWWxsbFCG3Pcs8THx8tcXFxk1dXVQltiYqLMyclJ1EbKqbKyUq5txYoVsokTJ8pkMpmsvr5eNmrUKNF3uLa2Vubu7i7bvn270MbPgWrIzc2Vubq6ynbt2iWzs7OT1dTUyGQy5rknOHPmjMzBwUF248aNDmOUPc+coSdSUdXV1Th58iTmzJkDdfWOv8pnz57F8OHDYWVlJbRNmjQJmpqa+PbbbwEApaWlKC4uxmuvvSbEqKur469//asQQ90rLy8PX3/9NSIjIxX2M8+qS1dXV24mwMjICObm5qisrBTamOOe5ezZs/Dy8oK+vr7QNmXKFNTX1+PixYvdODLqDEVnSg0bNgxVVVUAgEuXLqGmpkb0XdTV1cXEiRNF30V+DpRfc3Mz3n//fSxevBjGxsaiPuZZ9aWmpsLDwwNDhgzpMEbZ88yCnkhFXb16FY2NjVBTU8OsWbPwyiuvYPz48UhISIBMJhPiCgsLMXjwYNG2WlpaGDBgAAoLC4UYAHJxNjY2ePjwofAPCnUPmUyG999/HwsXLoSZmZnCGOa5Z6mqqkJJSYnoHwzmuGdRlE9zc3Po6OgIeSTVcvnyZdjY2ABoza+GhgYGDRokirGxsRHll58D5ZecnIwnT55g7ty5cn3Ms+rLy8vDoEGDsHnzZowaNQojRoxAeHg47t27J8Qoe55Z0BOpqPv37wMA1q9fDxcXF+zevRsBAQH417/+hUOHDglxUqkUL730ktz2BgYGkEqlAFrX/LS1tWdoaCjqp+6RmpqK+/fvY8GCBR3GMM89y0cffQQ9PT34+fkJbcxxz9KZfJLqOH/+PLKzs4WiTyqVQldXFxoaGqI4Q0ND1NXVoaGhQYjj50B5PXjwAJ9++imioqKgqakp1888q76KigqkpaXh559/RkxMDLZu3YqrV68iPDxcmCBT9jz3+q/tmYi6rLq6Gr/99ttz42xsbNDS0gIAGD9+vHAatoeHB8rLy5GYmCg6kqympia3j/az+B3FtcUo2p5+v67kubq6WrgA2tOnZT+NeVYeXcnx0w4dOoT09HTExsbKnd7JHPcsHeWTeVItt2/fRkREBHx8fDBt2jSh/Vnf1/Z9/Bwor5iYGDg5OeHVV1/tMIZ57hni4uKEv7mmpqYICgrChQsX4OnpCUC588yCnkiJZGVlYd26dc+NKygoEGbc3N3dRX0eHh5IS0tDTU0N9PX1YWBggOrqarl9VFdXC7N4bft6+shi29HEp2f76I/pSp7j4+PRr18/eHl5CfloampCY2MjpFIp9PT0oKGhwTwrma7kuL3s7Gxs2bIFkZGR8PX1FfUxxz1LR/msqalROMNDyunhw4cICwtD//798c9//lNoNzAwQG1tLZqbm0WzelKpFDo6OsJsLz8HyuvGjRtIS0tDUlKS8Du07TahNTU1wt9e5lm1GRgYwMrKSnQAffTo0dDU1MTNmzfh6emp9HlmQU+kRGbMmIEZM2Z0KlbRzB4gf7Rw8ODBcut2GhoaUFpailmzZgkxQOvaHwsLCyGusLAQRkZGCi/+Q79fV/JcVFSE/Px8uLq6yvW5urri4MGDcHFxYZ6VTFdy3ObSpUtYsWIFZs2ahYULF8r1M8c9i6J83r17F48fP5Zbg0nKqa6uDosWLUJjYyMSExNFtzQbPHgwmpubUVJSIsrn02ts+TlQXiUlJWhsbERgYKBc3/jx4zF9+nRMnTqVeVZxNjY2winzT2u76LSyf5+5hp5IRVlaWsLW1hbnz58XtV+4cAEDBgyAnp4egNY/Oj/99BPu3LkjxJw6dQoNDQ0YN24cAMDKygqDBg1CVlaWENPS0oKsrCwhhrrHsmXLsH//ftHP0KFD4erqiv3798Pe3h4A86zqbty4gUWLFmHcuHEdzuwzxz3L+PHjce7cOdTU1Ahtx48fR+/eveHm5taNI6POaGpqwtKlS1FcXIydO3eiT58+ov5Ro0ZBX19f9F2sq6tDTk6O6LvIz4HyGjVqlNzf37CwMACt9yRfsGAB89wDTJgwAQUFBaKLxubm5qKxsVH4H0vZ86yxcePGjf+1vRPRf5WpqSliY2NRW1sLNTU1pKam4tChQ3j33XeFX0LW1tY4ceIETpw4gX79+iE/Px8ffPABfHx8MHPmTGFfxsbG2LFjB9TV1dHc3IzPP/8cP/zwA6Kjo+X+UaH/nT59+sDS0lL0c/z4cZiYmCAsLAza2toAmGdVVllZiblz50JLSwsRERGorKxEeXk5ysvLUVNTI8yqM8c9i62tLQ4fPgyJRIKXX34Z33//PbZv347g4OBnrtcl5bBhwwYcP34cERERMDIyEr6z5eXlMDExEX43x8fHC6frbt26FeXl5YiOjhZm8/k5UF46Ojpyf3/Ly8uRnZ2NTZs2wczMDL16tZ7szDyrLjs7O6SlpeH06dPo06cP8vPzsWnTJjg5OWHx4sUAoPR5VpMpupoOEamMo0ePIj4+HqWlpejfvz9CQ0Mxe/ZsUUx5eTk2b96M8+fPQ0tLC35+fli1ahV0dHREcV999RV27tyJu3fvwtbWFqtWrRIuBkLKY968eULR1h7zrJokEgnmz5+vsM/NzQ0HDhwQHjPHPcvNmzexefNmXLlyBQYGBpg+fTqWLFkidyVlUj7e3t6is2Xay87OhqWlJWQyGeLj4/Hll1/i4cOHcHR0xLp16+Dg4CCK5+dAdaSlpSEqKgqXLl0SzoRknlVfSUkJtmzZgtzcXGhqasLHxwdRUVHCdWkA5c4zC3oiIiIiIiIiFcQ19EREREREREQqiAU9ERERERERkQpiQU9ERERERESkgljQExEREREREakgFvREREREREREKogFPREREREREZEKYkFPREREpGKePHkCb29vxMTECG23b9+Gvb09YmNju3Fkz7dv3z64u7vj0aNH3T0UIiKVx4KeiIhISUkkEtjb23f44+Dg0N1D7DZt783u3bu7eygvnFQqRWxsLCQSSYcxe/fuhVQqRWhoaJf3v3z5ctjb2+PWrVtyfevXr4e9vT1Wrlwp11dRUQF7e3ssWrRIaPP29sbUqVM7fK41a9bA3t4eVVVVQtvs2bOhpaWFuLi4Lo+diIjEenX3AIiIiOjZpk6divHjx8u1q6vzuHxPJJVK8dlnnyE8PBzu7u5y/fX19di9ezemTZsGQ0PDLu/f3d0dx48fh0QiwYABA0R9EokEvXr1Ungwoa1N0Zi6QltbG4GBgUhISMCiRYtgbGz8h/ZHRPRnxoKeiIhIyTk4OMDf37+7hyGor69Hr1690KsX/43oDhkZGZBKpfj73//+u7ZvK8glEglmzJghtFdUVKC4uBjTpk1DWloaSkpKMHDgQKH/4sWLAAA3N7c/MPpW/v7+iI2NxZEjR37XWQZERNSKh/aJiIhUXPu10zk5OQgICMDw4cPh5eWF6OhoNDU1yW1TXFyMlStXwsvLC46OjvD29kZ0dDQeP34simt/ynRUVBTGjBkDZ2dnlJeXAwCuX7+O0NBQODs7w93dHatXr0ZVVRXs7e2xZs0aAMD9+/fh6OiIyMhIhePfuHEjhg4dijt37ryw9+H48ePw9/eHk5MTfH19kZqaCgAoKyvDO++8Azc3N4wcORKRkZGoqanp8DWvWrUK7u7ucHZ2RnBwMK5duyb3vAcPHkRoaCjGjRsHR0dHeHl5ITIyErdv31Y4zgsXLuCtt96Cu7s7hg8fDh8fH6xduxZVVVWQSCTw8fEBAHz22WfC8gpvb29h+6ysLJiamnZ6ycW3336LkSNHYs6cOXj06BGsra1hZmYmNwvf9njRokUKZ+klEgkMDAwwbNiwTj3vs1hZWcHa2hpZWVl/eF9ERH9mPLRORESk5Orq6kRrkNtoaWlBX19feHzmzBkcOnQIs2bNQkBAALKzs7Fnzx4YGhqK1j3n5+cjODgYBgYGCAwMhJmZGa5fv44DBw7g8uXLOHDgADQ1NUXP9eabb6Jv375YvHgxHj9+DF1dXRQXF2Pu3LloaWnBvHnzYGZmhjNnziAsLEy0bd++feHt7Y0TJ05AKpXCwMBA6Hvy5AkyMzMxZswYWFhYvJD3KycnB8nJyZg9ezaMjIyQkpKCtWvXQlNTEzExMfDw8MDy5cvx008/ITU1Fdra2vjggw/k9rNw4UIYGhoiPDwc9+/fR1JSEubOnYvDhw/Dzs5OiNuzZw+cnZ0xb948GBkZ4ZdffkFKSgouXLiAjIwM0SnlycnJ2LhxI8zMzDBr1ixYWFigrKwMOTk5uHfvHmxsbBAVFYWtW7fC19cXvr6+AAA9PT0AQHNzMy5fvtzp096PHDmCdevWYeLEifjkk0+gra0NoHWWPSMjA8XFxRg0aBCA1hl4a2trDBw4EA4ODpBIJJg5cyYA4LfffkNxcTF8fHzklno0Nzcr/HwCQENDQ4djGzlyJNLT01FbWyu8PiIi6hoW9EREREouNjZW4ZXLJ0yYgISEBOHxzZs3cezYMVhaWgJovfjY66+/jqSkJFFBv3btWpiamiIlJUV0QMDT0xPh4eHIyMjAtGnTRM9la2uLbdu2ido2bdqEmpoaHDp0CKNHjwYABAUFYdmyZcjPzxfFzpw5E9988w0yMjIwd+5cof2bb76BVCrF9OnTu/q2dKiwsBCZmZnCAQI/Pz+8+uqrWLVqFVavXo0333wTQOv7I5VKcfToUaxdu1auqDQ3N0dsbCzU1NQAAL6+vpg+fTqio6NFF+PLyMiArq6uaFsfHx+EhIQgJSVFOMBRXl6OLVu2YPDgwUhOThYd2Fi2bBlaWlqgrq6OSZMmYevWrbC3t5dbalFWVoba2lq5te+KJCYm4pNPPsHs2bOxfv16USHu7u6OjIwMSCQSoaCXSCTCgQI3Nzekp6cL8c863b6wsBCenp7PHc/TrKys0NTUhKKiIjg6OnZ5eyIiYkFPRESk9AIDAzF58mS5dhMTE9FjHx8foZgHADU1Nbi7uyMpKUmYBS0oKEBBQQGWLFmChoYG0czq6NGjoauri++++06uoF+wYIHocXNzM86ePQsnJyehmG8TGhqKr7/+WtQ2duxYWFpaIjU1VVTQp6SkwMjICJMmTerku/F8Pj4+otl+ExMTWFtb4+bNm6LnBgAXFxecPHkSd+7cEc26A60z9G3FPAA4Ojpi7NixOH/+vGhWua2Yb2lpQW1tLRobG2Fvb4+XXnoJeXl5wvZZWVlobGxEeHi4qJhv05mLHD548AAAnnkxvJaWFmzevBkHDx7E0qVLsXjxYrkYDw8PAK2FemBgoDADv2TJEgCthfuuXbtQVFQEa2troaBv2649CwsLbNmyReFYdu/ejXPnzinsMzIyAgBUVlZ2+FqIiOjZWNATEREpuYEDB2LMmDHPjbOyspJrayuaHj58CD09Pfz6668AOp71B1rXvD+tbRa3TVVVFR4/fgxra2u5WEVtampqmDFjBmJiYvDzzz9j2LBhKC0txcWLFzF//nxoaWk99/V1lqL3wdDQEKampnLP01ZYP3z4UG4bGxsbhW3nzp1DWVkZbG1tAQDnz59HXFwcfvzxRzx58kQU3/5e68XFxQDwQtagy2SyDvu++OIL1NbWYvny5aIzM9qzsrKCubm5sE6+rWB3dXUF0HpwR0NDAxKJBNbW1pBIJDAyMoK9vb3cvnR1dTv8fLaf5e/oNbQ/aEJERF3Dgp6IiKiH0NDQ6LDv6QKw7SJuiiiaPdbR0Xnm/trrqEALCAhAbGwsUlJS8N577yElJQUymUx0pfUXoaP3oSvvT2fj8vLysGDBAgwYMAARERGwtLRE7969oaamhuXLl4viX0QB23ZWRvsDBU8bO3YscnNzcfjwYUyZMkXhAQ6g9bT7I0eOoLCwEBcvXsTAgQNhZmYGANDX18fQoUNx8eJFeHt7o7i4GL6+vi+0+G57DU+faUJERJ3Hgp6IiOhPpO02ZOrq6p2a9e9Inz59oKuri6KiIrm+wsJChduYmppi4sSJyMjIQEREBP79739jxIgRwky3svn111/h7OwsaissLISGhgbMzc0BAMeOHUNzczN27twpKpwfP34MqVQq2rbtzIVr167JnfHQ3rOK5v79+0NfXx8lJSUdxtjZ2eGdd95BSEgIgoKC8MUXXyh8vraCXiKRQCKRCLPzbdzc3HDs2DFh9v6P3n/+abdu3UKvXr0UntFBRESdw9vWERER/Yk4ODjAzs4OycnJKC0tletvampSePr50zQ0NDBu3Djk5eXhP//5j6hvz549HW43Y8YMPHr0CBs2bEB5efkLn51/kXbt2iWaYb969Sq+//57eHp6CuvnO5r1T0hIQEtLi6ht8uTJ0NTUxOeffy53qzzg/2fw29bkK5qF19DQgIuLC3788cdnjt3W1hb79+9Hc3MzgoKChKUW7bUV6JmZmSguLpYr6F1dXVFRUYHk5GQAL+b+8+1duXIFr7zyCq9wT0T0B3CGnoiISMldu3YNR48eVdjX1YvJqamp4eOPP0ZwcDD+9re/ISAgAEOGDEF9fT1KSkpw8uRJrFixQu6ieIosW7YM586dw8KFCxEUFIR+/frh9OnTwoX2FM00jxs3DhYWFkhPT4euri78/Py6NP7/pbKyMixYsADe3t6oqKhAUlISevfujZUrVwoxkyZNwr59+xAWFobAwEBoamriu+++Q0FBgeh2dQDQr18/rF27Fps3b8brr78Of39/WFhY4N69e8jOzsaHH36IYcOGwdjYGAMHDkRmZiasrKzQt29f6OjoCPeinzx5Mk6fPo28vDw4OTl1OH4bGxskJSUhODgY8+fPx759+0RnQ5ibm8PKygq5ubkA5At2FxcXqKurIzc3F8bGxnIXDfwjbt26haKiIqxevfqF7ZOI6M+IBT0REZGSO3bsGI4dO6aw78SJE89cG67IsGHDcOTIESQkJODUqVNITk6Gnp4eLCws8MYbb3T6FmSDBw/GwYMHER0djf3790NbWxsTJkzA+vXrMWnSJOGe5+2pq6sjICAAO3bswGuvvabUs7O7du3C1q1bERsbi/r6eowYMQKrVq3C0KFDhZjRo0cjNjYWcXFx+PTTT6GtrY0xY8YgKSkJQUFBcvucM2cOBgwYgN27d+PAgQNoaGjAyy+/DE9PT/Tr10+I27ZtGz788EPExMSgrq4OFhYWQkHv5+eHjz76CEePHn1mQQ+0XsywfVG/d+9e0fjd3d1RWloKCwsLYRlBG0NDQ9jZ2eH69etwc3N7oevn09PToaWlhTfeeOOF7ZOI6M9ITdbZq8AQERERdUJ+fj4CAgIQERGBt956S65/586d2LZtG5KTkzFy5MhuGOGzrVmzBkeOHEFBQUF3D6VDiYmJSEhIQHZ2tnAnA1Xx5MkT+Pj4YMqUKYiKiuru4RARqTSuoSciIqLfrb6+XvRYJpNh165dAKDwontNTU04fPgw7OzslLKYVxXBwcEwNDR85vUKlNWXX36JhoYGLF68uLuHQkSk8njKPREREf1u/v7+8PDwgJ2dHerq6pCTk4MffvgBfn5+cHR0FOJKS0tx5coVZGdno7S0FNu3b5fbV0NDwzNvx9bGxMSky8sMehptbW2cOnWqu4fxu4SEhCAkJKS7h0FE1COwoCciIqLfzcfHBzk5OUhPT0dTUxMsLS2xdOlShIWFieJyc3MRFRUFY2NjvP3225gyZYrcvi5fvoz58+c/9zmzs7NhaWn5wl4DERGRquIaeiIiIlIKjx49wtWrV58bN3r0aIUX3CMiIvqzYUFPREREREREpIJ4UTwiIiIiIiIiFcSCnoiIiIiIiEgFsaAnIiIiIiIiUkEs6ImIiIiIiIhUEAt6IiIiIiIiIhX0fyIeZzrY0xZqAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "net_energy_saved = round(sum(eirc['Sketch of Total Energy_Impact(kWH)']), 2)\n", "\n", @@ -1712,23 +326,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "dense-programmer", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "data_eb = expanded_ct.query(\"Mode_confirm == 'Pilot ebike'\")\n", "# ebei : ebike energy impact\n", From 4efd1307bcc41a7f8bf439729d5f8461c2dd4119 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 25 Jan 2022 13:44:57 -0700 Subject: [PATCH 27/35] Trashed time.csv Don't need time.csv, or the code that saves the mean/median duration to csv. Just return the average durations from the function. --- viz_scripts/auxiliary_files/time.csv | 13 ------------- viz_scripts/scaffolding.py | 25 +++++++++++++------------ 2 files changed, 13 insertions(+), 25 deletions(-) delete mode 100644 viz_scripts/auxiliary_files/time.csv diff --git a/viz_scripts/auxiliary_files/time.csv b/viz_scripts/auxiliary_files/time.csv deleted file mode 100644 index dc54a91..0000000 --- a/viz_scripts/auxiliary_files/time.csv +++ /dev/null @@ -1,13 +0,0 @@ -Mode_confirm,speed -Bikeshare,15.528673804428736 -Bus,8.658398163532109 -"Car, drove alone",15.717732517579352 -"Car, with others",17.809838666428867 -Not a Trip,2.5721570250884667 -Pilot ebike,9.745192742581557 -Regular Bike,8.34062410808631 -Scooter share,23.731360584008794 -Skate board,4.425242235684407 -Taxi/Uber/Lyft,9.597996120242797 -Train,13.504787430136716 -Walk,6.045029911646732 diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 4cc2349..67e2636 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -341,7 +341,7 @@ def time_impact(data, dist, repm, mode): return data -def calc_avg_speed(data, dist, time, mode, meth='average'): +def calc_avg_dura(data, dist, time, mode, meth='average'): """ Purpose: To determine average speed of modes for participant trips in OpenPath @@ -354,31 +354,32 @@ def calc_avg_speed(data, dist, time, mode, meth='average'): meth - string representing method for aggregation by group ['average', 'median'] Process: - Calculate and append speeds of each trip - Aggregate average speed for each mode + Calculate and append durations of each trip + Aggregate average duration for each mode Save averages in auxiallary files Returns: - data - data with speed feature for each trip - df_T - a dataframe representing average speed by mode + data - data with duration feature for each trip + mdur - average duration by mode (dataframe?) """ data = data.copy() - data['speed'] = data[dist] / data[time] + data['D(time/PMT)'] = data[time] / data[dist] grup = data.groupby(mode) - mspd = None + mdur = None if(meth == 'average'): - mspd = grup['speed'].mean() + mdur = grup['D(time/PMT)'].mean() elif(meth == 'median'): - mspd = grup['speed'].median() + mdur = grup['D(time/PMT)'].median() else: print(f'Method invalid: {meth}.') return data, None - mspd.to_csv('auxiliary_files/time.csv') - df_T = pd.read_csv('auxiliary_files/time.csv') + # Shankari says not necessary + # mspd.to_csv('auxiliary_files/time.csv') + # df_T = pd.read_csv('auxiliary_files/time.csv') - return data, df_T \ No newline at end of file + return data, mdur \ No newline at end of file From 53450cd8fdada410502f4a784629537f1345a3d1 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 25 Jan 2022 14:35:18 -0700 Subject: [PATCH 28/35] Reformat calc_avg_dura Reformatted calc_avg_dura to return a pandas series with average duration for each mode instead of saving as a csv file. --- viz_scripts/run_unit_tests.py | 40 +++++++++++++++++------------------ viz_scripts/scaffolding.py | 4 ---- 2 files changed, 20 insertions(+), 24 deletions(-) diff --git a/viz_scripts/run_unit_tests.py b/viz_scripts/run_unit_tests.py index 6fa9976..0a1f575 100644 --- a/viz_scripts/run_unit_tests.py +++ b/viz_scripts/run_unit_tests.py @@ -10,8 +10,7 @@ import unittest import pandas as pd import numpy as np -import scaffolding - +from viz_scripts import scaffolding class TestEnergyIntensity(unittest.TestCase): """ @@ -152,9 +151,9 @@ def test_function(self): f'Error in function') -class TestCalcAvgSpeed(unittest.TestCase): +class TestCalcAvgDura(unittest.TestCase): """ - A unit test for calc_avg_speed function in + A unit test for calc_avg_dura function in the scaffolding.py file """ @@ -189,8 +188,7 @@ def test_process(self): speedm = groupd['sped'].median() self.assertTrue(expect.equals(speedm), f'Agg by median failed.\n{expect}\n{speedm}') - - # Save to file (TODO:?) + None @@ -200,30 +198,32 @@ def test_function(self): 'mode': ['car', 'bus', 'train', 'car'], 'dist': [1,2,3,4], 'time': [1,2,3,4], - 'speed': [1.0, 1.0, 1.0, 1.0] + 'D(time/PMT)': [1.0, 1.0, 1.0, 1.0] }) - expect2 = pd.DataFrame({ - 'mode': ['bus', 'car', 'train'], - 'speed': [1.0, 1.0, 1.0] - }) - result1, result2 = scaffolding.calc_avg_speed(self.data,'dist','time','mode','average') + expect2 = pd.Series( + data = [1.0, 1.0, 1.0], + index = ['bus', 'car', 'train'], + name = 'D(time/PMT)', + dtype=np.float64 + ) + result1, result2 = scaffolding.calc_avg_dura(self.data,'dist','time','mode','average') self.assertTrue(expect1.equals(result1), - f'calc_avg_speed with average failed.[1]') + f'calc_avg_dura with average failed.[1]\n{result1}') self.assertTrue(expect2.equals(result2), - f'calc_avg_speed with average failed.[2]\n{expect2}\n{result2}') + f'calc_avg_dura with average failed.[2]\n{expect2}\n{result2}') - result1, result2 = scaffolding.calc_avg_speed(self.data,'dist','time','mode','median') + result1, result2 = scaffolding.calc_avg_dura(self.data,'dist','time','mode','median') self.assertTrue(expect1.equals(result1), - f'calc_avg_speed with median failed.[1]') + f'calc_avg_dura with median failed.[1]') self.assertTrue(expect2.equals(result2), - f'calc_avg_speed with median failed.[2]') + f'calc_avg_dura with median failed.[2]') expect2 = None - result1, result2 = scaffolding.calc_avg_speed(self.data,'dist','time','mode','break') + result1, result2 = scaffolding.calc_avg_dura(self.data,'dist','time','mode','break') self.assertTrue(expect1.equals(result1), - f'calc_avg_speed with incorrect method failed.[1]') + f'calc_avg_dura with incorrect method failed.[1]') self.assertEqual(expect2, result2, - f'calc_avg_speed with incorrect method failed.[2]') + f'calc_avg_dura with incorrect method failed.[2]') if __name__ == '__main__': diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 67e2636..c23c87d 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -377,9 +377,5 @@ def calc_avg_dura(data, dist, time, mode, meth='average'): else: print(f'Method invalid: {meth}.') return data, None - - # Shankari says not necessary - # mspd.to_csv('auxiliary_files/time.csv') - # df_T = pd.read_csv('auxiliary_files/time.csv') return data, mdur \ No newline at end of file From 4c7b3b4518ef977b96af3b39c5c811a6cdf8e128 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Tue, 25 Jan 2022 15:56:16 -0700 Subject: [PATCH 29/35] Generalizing function (maybe?) Some work towards generalizing the energy, cost, and time calculations on per trip basis functions. Also delete row in cost.csv. --- viz_scripts/auxiliary_files/cost.csv | 30 ++++++++++++------------- viz_scripts/scaffolding.py | 33 ++++++++++++++++++++++++++-- 2 files changed, 46 insertions(+), 17 deletions(-) diff --git a/viz_scripts/auxiliary_files/cost.csv b/viz_scripts/auxiliary_files/cost.csv index 2c6ac8a..d451f80 100644 --- a/viz_scripts/auxiliary_files/cost.csv +++ b/viz_scripts/auxiliary_files/cost.csv @@ -1,15 +1,15 @@ -mode,C($/PMT),($)/trip,D(hours/PMT) -"Car, drove alone",0.55,0,0 -"Car, with others",0.275,0,0 -Taxi/Uber/Lyft,2.5,0,0 -Bus,0.855,0,0 -Free Shuttle,0,0,0 -Train,0.855,0,0 -Scooter share,0.15,1,0 -Pilot ebike,0,0,0 -Bikeshare,0.09,0,0 -Walk,0,0,0 -Skate board,0,0,0 -Regular Bike,0,0,0 -Not a Trip,0,0,0 -No Travel,0,0,0 \ No newline at end of file +mode,C($/PMT),($)/trip +"Car, drove alone",0.55,0 +"Car, with others",0.275,0 +Taxi/Uber/Lyft,2.5,0 +Bus,0.855,0 +Free Shuttle,0,0 +Train,0.855,0 +Scooter share,0.15,1 +Pilot ebike,0,0 +Bikeshare,0.09,0 +Walk,0,0 +Skate board,0,0 +Regular Bike,0,0 +Not a Trip,0,0 +No Travel,0,0 \ No newline at end of file diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index c23c87d..7705590 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -107,6 +107,35 @@ def unit_conversions(df): df['distance_miles']= df["distance"]*0.00062 #meters to miles df['duration_h'] = df['duration'] / 60 / 60 #seconds to hours + +def synthesize(data, const, mode, repm, feats): + """ + Calculate trip aggregate results from constants and append to data + + Parameters: + data - trip data from OpenPATH (presumably) + const - Pandas DataFrame with constant values for each mode + mode - feature name in data of feature with confirmed mode (probably Mode_confirm) + repm - feature name in data of feature with replaced mode (probably Replaced_mode) + feats - python list of feature names in const DataFrame that are of interest + + Returns: + data with appended features for each trip for both mode and replaced mode + """ + + const = const.copy() + const[repm] = const['mode'] + dic_cost__trip = dict(zip(const[repm],const['C($/PMT)'])) + + # Create new features in data for replaced mode + data['cost__trip_'+repm] = data[repm].map(dic_cost__trip) + + # Create new features in data for confirmed mode + cost[mode] = cost[repm] + dic_cost__trip = dict(zip(cost[mode],cost['C($/PMT)'])) + data['cost__trip_'+mode] = data[mode].map(dic_cost__trip) + + def energy_intensity(df,df1,distance,col1,col2): """Inputs: df = dataframe with trip data from OpenPATH @@ -359,8 +388,8 @@ def calc_avg_dura(data, dist, time, mode, meth='average'): Save averages in auxiallary files Returns: - data - data with duration feature for each trip - mdur - average duration by mode (dataframe?) + data - data with duration feature for each trip (pandas DataFrame) + mdur - Pandas series with average duration by mode """ data = data.copy() From 0a76b5147926f2dc34b1513aa12cd4a02d36c1cd Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Wed, 26 Jan 2022 15:26:49 -0700 Subject: [PATCH 30/35] Generalized first functions energy_intensity, cost, and time generalized with function eng_feat. Testing yet to be done! --- viz_scripts/scaffolding.py | 42 ++++++++++++++++++++++++++++++-------- 1 file changed, 33 insertions(+), 9 deletions(-) diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 7705590..8e227eb 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -108,7 +108,7 @@ def unit_conversions(df): df['duration_h'] = df['duration'] / 60 / 60 #seconds to hours -def synthesize(data, const, mode, repm, feats): +def eng_feat(data, const, mode, repm, feats, prefs): """ Calculate trip aggregate results from constants and append to data @@ -118,22 +118,46 @@ def synthesize(data, const, mode, repm, feats): mode - feature name in data of feature with confirmed mode (probably Mode_confirm) repm - feature name in data of feature with replaced mode (probably Replaced_mode) feats - python list of feature names in const DataFrame that are of interest + prefs - prefixes to append to current feature names for new feature names Returns: data with appended features for each trip for both mode and replaced mode + + Note: + Assumes 'mode' is a feature in const """ + # Check features list and prefix list same length + if(len(feats) != len(prefs)): + print("Prefix list and feature list not the same length.") + return None + + # Check all feature names in constants dataframe + for feat in feats: + if(feat not in const.columns): + print(feat + ' not in constants dataframe.') + return None + + # Use copies, don't change original + data = data.copy() const = const.copy() - const[repm] = const['mode'] - dic_cost__trip = dict(zip(const[repm],const['C($/PMT)'])) + + # Duplicate mode feature in constant dataframe + for m in [mode, repm]: + const[m] = const['mode'] + + # Feature engineering! + for i in range(len(feats)): + for m in [mode, repm]: + dic = dict(zip(const[m],const[feat[i]])) + + # Create new feature in data + fn = prefs[i]+m + data[fn] = data[m].map(dic) + print('Created ' + fn + ' feature in data.') - # Create new features in data for replaced mode - data['cost__trip_'+repm] = data[repm].map(dic_cost__trip) + return data - # Create new features in data for confirmed mode - cost[mode] = cost[repm] - dic_cost__trip = dict(zip(cost[mode],cost['C($/PMT)'])) - data['cost__trip_'+mode] = data[mode].map(dic_cost__trip) def energy_intensity(df,df1,distance,col1,col2): From ca63e31ee0155fb8e5f2987e8f62e262c32896ad Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 27 Jan 2022 08:23:59 -0700 Subject: [PATCH 31/35] Change eng_feat to feat_eng Feature engine sounded better to me. I also made some changes to error checking and to the order of parameters. --- viz_scripts/scaffolding.py | 32 +++++++++++++++++--------------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 8e227eb..fddff19 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -108,37 +108,39 @@ def unit_conversions(df): df['duration_h'] = df['duration'] / 60 / 60 #seconds to hours -def eng_feat(data, const, mode, repm, feats, prefs): +def feat_eng(data, const, feats, prefs, mode='Mode_confirm', repm='Replaced_mode'): """ - Calculate trip aggregate results from constants and append to data + Calculate trip aggregate results from constants and append to data (Feature Engine) Parameters: - data - trip data from OpenPATH (presumably) - const - Pandas DataFrame with constant values for each mode - mode - feature name in data of feature with confirmed mode (probably Mode_confirm) - repm - feature name in data of feature with replaced mode (probably Replaced_mode) + data - trip data from OpenPATH + const - Pandas DataFrame with constant values for each mode (requires 'mode' feature) feats - python list of feature names in const DataFrame that are of interest prefs - prefixes to append to current feature names for new feature names + mode - feature name in data of feature with confirmed mode + repm - feature name in data of feature with replaced mode Returns: data with appended features for each trip for both mode and replaced mode - - Note: - Assumes 'mode' is a feature in const """ + # Check that const has a mode feature + if('mode' not in const.columns): + print('Error: mode not in constants dataframe.') + return data + # Check features list and prefix list same length if(len(feats) != len(prefs)): - print("Prefix list and feature list not the same length.") - return None + print("Error: prefix list and feature list not the same length.") + return data # Check all feature names in constants dataframe for feat in feats: if(feat not in const.columns): - print(feat + ' not in constants dataframe.') - return None + print('Error: ' + feat + ' not in constants dataframe.') + return data - # Use copies, don't change original + # Use copies, don't change originals data = data.copy() const = const.copy() @@ -146,7 +148,7 @@ def eng_feat(data, const, mode, repm, feats, prefs): for m in [mode, repm]: const[m] = const['mode'] - # Feature engineering! + # Feature engine! for i in range(len(feats)): for m in [mode, repm]: dic = dict(zip(const[m],const[feat[i]])) From df70624b1407f9ff85c78e91b37924f42cfb24da Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 27 Jan 2022 08:43:44 -0700 Subject: [PATCH 32/35] Tested General Function Tested general function feat_eng. Did basic debugging to ensure passed basic functionality testing. --- viz_scripts/run_unit_tests.py | 10 +++--- viz_scripts/scaffolding.py | 60 ++++++++++++++++++++--------------- 2 files changed, 39 insertions(+), 31 deletions(-) diff --git a/viz_scripts/run_unit_tests.py b/viz_scripts/run_unit_tests.py index 0a1f575..5f8b2b9 100644 --- a/viz_scripts/run_unit_tests.py +++ b/viz_scripts/run_unit_tests.py @@ -10,7 +10,7 @@ import unittest import pandas as pd import numpy as np -from viz_scripts import scaffolding +import scaffolding class TestEnergyIntensity(unittest.TestCase): """ @@ -69,15 +69,15 @@ def test_function(self): 'vals': [1,2,3, 4], 'test': [0.5,3,0,8], 'ei_mode': [0, 1, 2, 0], - 'CO2_mode': [1, 2, 3, 1], - 'ei_trip_mode': [0.5, 0.2, 0.3, 0.5], 'ei_repm': [0, 0, 1, 2], + 'CO2_mode': [1, 2, 3, 1], 'CO2_repm': [1, 1, 2, 3], + 'ei_trip_mode': [0.5, 0.2, 0.3, 0.5], 'ei_trip_repm': [0.5, 0.5, 0.2, 0.3], }) - output = scaffolding.energy_intensity(self.data, self.constants, '', 'mode', 'repm') + output = scaffolding.energy_intensity(self.data, self.constants, '', 'repm', 'mode') self.assertTrue(expect.equals(output), - f'{output}') + f"{output[['ei_mode','ei_repm','CO2_mode','CO2_repm','ei_trip_mode','ei_trip_repm']]}") class TestEnergyImpact(unittest.TestCase): diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index fddff19..08e623a 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -151,7 +151,7 @@ def feat_eng(data, const, feats, prefs, mode='Mode_confirm', repm='Replaced_mode # Feature engine! for i in range(len(feats)): for m in [mode, repm]: - dic = dict(zip(const[m],const[feat[i]])) + dic = dict(zip(const[m],const[feats[i]])) # Create new feature in data fn = prefs[i]+m @@ -170,35 +170,43 @@ def energy_intensity(df,df1,distance,col1,col2): col1 = Replaced_mode col2= Mode_confirm """ - - # Create a copy of the energy_factors dataframe - df1 = df1.copy() - - # Create a replaced mode column in df1 same as mode - df1[col1] = df1['mode'] - - # Pair energy intensity with mode - dic_ei_factor = dict(zip(df1[col1],df1['energy_intensity_factor'])) + return feat_eng( + df, + df1, + ['energy_intensity_factor', 'CO2_factor', '(kWH)/trip'], + ['ei_', 'CO2_', 'ei_trip_'], + col2, + col1 + ) + + # # Create a copy of the energy_factors dataframe + # df1 = df1.copy() + + # # Create a replaced mode column in df1 same as mode + # df1[col1] = df1['mode'] + + # # Pair energy intensity with mode + # dic_ei_factor = dict(zip(df1[col1],df1['energy_intensity_factor'])) - # Pair CO2_factor with mode - dic_CO2_factor = dict(zip(df1[col1],df1['CO2_factor'])) + # # Pair CO2_factor with mode + # dic_CO2_factor = dict(zip(df1[col1],df1['CO2_factor'])) - # Pair (KWH)/trip with mode - dic_ei_trip = dict(zip(df1[col1],df1['(kWH)/trip'])) + # # Pair (KWH)/trip with mode + # dic_ei_trip = dict(zip(df1[col1],df1['(kWH)/trip'])) - # Create new features in data for replaced mode - df['ei_'+col1] = df[col1].map(dic_ei_factor) - df['CO2_'+col1] = df[col1].map(dic_CO2_factor) - df['ei_trip_'+col1] = df[col1].map(dic_ei_trip) + # # Create new features in data for replaced mode + # df['ei_'+col1] = df[col1].map(dic_ei_factor) + # df['CO2_'+col1] = df[col1].map(dic_CO2_factor) + # df['ei_trip_'+col1] = df[col1].map(dic_ei_trip) - # Create new features in data for confirmed mode - df1[col2] = df1[col1] - dic_ei_factor = dict(zip(df1[col2],df1['energy_intensity_factor'])) - dic_ei_trip = dict(zip(df1[col2],df1['(kWH)/trip'])) - dic_CO2_factor = dict(zip(df1[col2],df1['CO2_factor'])) - df['ei_'+col2] = df[col2].map(dic_ei_factor) - df['CO2_'+col2] = df[col2].map(dic_CO2_factor) - df['ei_trip_'+col2] = df[col2].map(dic_ei_trip) + # # Create new features in data for confirmed mode + # df1[col2] = df1[col1] + # dic_ei_factor = dict(zip(df1[col2],df1['energy_intensity_factor'])) + # dic_ei_trip = dict(zip(df1[col2],df1['(kWH)/trip'])) + # dic_CO2_factor = dict(zip(df1[col2],df1['CO2_factor'])) + # df['ei_'+col2] = df[col2].map(dic_ei_factor) + # df['CO2_'+col2] = df[col2].map(dic_CO2_factor) + # df['ei_trip_'+col2] = df[col2].map(dic_ei_trip) return df From 1d0b58b9468d36ac2f9a70d36e4f7323102d23c2 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 27 Jan 2022 08:55:49 -0700 Subject: [PATCH 33/35] Add tests cost and time Erroneous file in the directory, deleted. Added testing for cost and time in TestEnergyIntensity. --- viz_scripts/run_unit_tests.py | 27 +++++- viz_scripts/unit_tests.py | 155 ---------------------------------- 2 files changed, 25 insertions(+), 157 deletions(-) delete mode 100644 viz_scripts/unit_tests.py diff --git a/viz_scripts/run_unit_tests.py b/viz_scripts/run_unit_tests.py index 5f8b2b9..62108fa 100644 --- a/viz_scripts/run_unit_tests.py +++ b/viz_scripts/run_unit_tests.py @@ -25,7 +25,9 @@ def setUp(self): 'test': [0,0,0], 'energy_intensity_factor': [0, 1, 2], 'CO2_factor': [1, 2, 3], - '(kWH)/trip': [0.5, 0.2, 0.3] + '(kWH)/trip': [0.5, 0.2, 0.3], + 'C($/PMT)': [1,2,3], + 'D(hours/PMT)': [3,2,1] }) self.data = pd.DataFrame({ @@ -68,6 +70,8 @@ def test_function(self): 'repm': ['car', 'car', 'bus', 'train'], 'vals': [1,2,3, 4], 'test': [0.5,3,0,8], + 'C($/PMT)': [1,2,3], + 'D(hours/PMT)': [3,2,1], 'ei_mode': [0, 1, 2, 0], 'ei_repm': [0, 0, 1, 2], 'CO2_mode': [1, 2, 3, 1], @@ -77,7 +81,26 @@ def test_function(self): }) output = scaffolding.energy_intensity(self.data, self.constants, '', 'repm', 'mode') self.assertTrue(expect.equals(output), - f"{output[['ei_mode','ei_repm','CO2_mode','CO2_repm','ei_trip_mode','ei_trip_repm']]}") + f"energy_intensity failed:\n{output[['ei_mode','ei_repm','CO2_mode','CO2_repm','ei_trip_mode','ei_trip_repm']]}") + + # expect = pd.DataFrame({ + # 'mode': ['car', 'bus', 'train', 'car'], + # 'repm': ['car', 'car', 'bus', 'train'], + # 'vals': [1,2,3, 4], + # 'test': [0.5,3,0,8], + # 'C($/PMT)': [1,2,3], + # 'D(hours/PMT)': [3,2,1], + # 'ei_mode': [0, 1, 2, 0], + # 'ei_repm': [0, 0, 1, 2], + # 'CO2_mode': [1, 2, 3, 1], + # 'CO2_repm': [1, 1, 2, 3], + # 'ei_trip_mode': [0.5, 0.2, 0.3, 0.5], + # 'ei_trip_repm': [0.5, 0.5, 0.2, 0.3], + # 'cost__trip_mode': [], + # }) + # output = scaffolding.cost(self.data, self.constants, 'repm', 'mode') + # self.assertTrue(expect.equals(output), + # f"energy_intensity failed:\n{output[['ei_mode','ei_repm','CO2_mode','CO2_repm','ei_trip_mode','ei_trip_repm']]}") class TestEnergyImpact(unittest.TestCase): diff --git a/viz_scripts/unit_tests.py b/viz_scripts/unit_tests.py deleted file mode 100644 index 6d4bb36..0000000 --- a/viz_scripts/unit_tests.py +++ /dev/null @@ -1,155 +0,0 @@ -""" -Author: Stanley Y -Purpose: - To test functions in scaffolding - -Credit to: -https://docs.python.org/3.10/library/unittest.html -""" - - -import unittest -import pandas as pd -import numpy as np -import scaffolding - -class TestEnergyIntensity(unittest.TestCase): - """ - A unit test for energy_intensity function in - the scaffolding.py file - """ - - def setUp(self): - self.constants = pd.DataFrame({ - 'mode': ['car', 'bus', 'train'], - 'vals': [12,5,2], - 'test': [0,0,0], - 'energy_intensity_factor': [0, 1, 2], - 'CO2_factor': [1, 2, 3], - '(kWH)/trip': [0.5, 0.2, 0.3] - }) - - self.data = pd.DataFrame({ - 'mode': ['car', 'bus', 'train', 'car'], - 'repm': ['car', 'car', 'bus', 'train'], - 'vals': [1,2,3, 4], - 'test': [0.5,3,0,8] - }) - - - def test_process(self): - expect = [('car', 12), ('bus', 5), ('train', 2)] - zipped = zip(self.constants['mode'], self.constants['vals']) - listed = list(zipped) - self.assertEqual(expect, listed, - 'Zip malfunction') - - expect = { - 'car': 12, - 'bus': 5, - 'train': 2 - } - zipped = zip(self.constants['mode'], self.constants['vals']) - a_dict = dict(zipped) - self.assertEqual(expect, a_dict, - 'Dict malfunction') - - expect = pd.Series( - [12, 12, 5, 2] - ) - a_dict = dict(zip(self.constants['mode'], self.constants['vals'])) - output = self.data['repm'].map(a_dict) - self.assertTrue(expect.equals(output), - 'Map malfunction') - - - def test_function(self): - expect = pd.DataFrame({ - 'mode': ['car', 'bus', 'train', 'car'], - 'repm': ['car', 'car', 'bus', 'train'], - 'vals': [1,2,3, 4], - 'test': [0.5,3,0,8], - 'ei_mode': [0, 1, 2, 0], - 'CO2_mode': [1, 2, 3, 1], - 'ei_trip_mode': [0.5, 0.2, 0.3, 0.5], - 'ei_repm': [0, 0, 1, 2], - 'CO2_repm': [1, 1, 2, 3], - 'ei_trip_repm': [0.5, 0.5, 0.2, 0.3], - }) - output = scaffolding.energy_intensity(self.data, self.constants, '', 'mode', 'repm') - self.assertTrue(expect.equals(output), - f'{output}') - - -class TestEnergyImpact(unittest.TestCase): - """ - A unit test for energy_impact_kWH function in - the scaffolding.py file - """ - - def setUp(self): - self.conditions = np.array([ - [True, False, False], - [False,True,False], - [False,False,True] - ]) - self.values = np.array([ - [8, 0, 3], - [3,5,7], - [4,2,9] - ]) - self.data = pd.DataFrame({ - 'mode': ['car', 'bus', 'train', 'car'], - 'repm': ['car', 'car', 'bus', 'train'], - 'dist': [1.5,2.5,3.5,4.5], - 'ei_mode': [1,2,3,1], - 'ei_repm': [1,1,2,3], - 'ei_trip_mode': [7,8,9,7], - 'ei_trip_repm': [7,7,8,9], - 'Mode_confirm_fuel': ['gasoline','diesel','electric','gasoline'], - 'Replaced_mode_fuel': ['gasoline','gasoline','diesel','electric'] - }) - - - def test_process(self): - expect = np.array([8, 5, 9]) - output = np.select(self.conditions, self.values) - if(len(expect) != len(output)): - self.assertTrue(False, - f'Select Malfunction (out: {output})') - else: - for i in range(len(expect)): - self.assertEqual(expect[i], output[i], - f'Select Malfunction (out: {output})') - - def test_function(self): - expect = pd.DataFrame({ - 'mode': ['car', 'bus', 'train', 'car'], - 'repm': ['car', 'car', 'bus', 'train'], - 'dist': [1.5,2.5,3.5,4.5], - 'ei_mode': [1,2,3,1], - 'ei_repm': [1,1,2,3], - 'ei_trip_mode': [7,8,9,7], - 'ei_trip_repm': [7,7,8,9], - 'Mode_confirm_fuel': ['gasoline','diesel','electric','gasoline'], - 'Replaced_mode_fuel': ['gasoline','gasoline','diesel','electric'], - 'repm_EI(kWH)':[1.5*1*0.000293071, - 2.5*1*0.000293071, - 3.5*2*0.000293071, - 4.5*3+9], - 'mode_EI(kWH)':[1.5*1*0.000293071, - 2.5*2*0.000293071, - 3.5*3+9, - 4.5*1*0.000293071], - 'Energy_Impact(kWH)':[round(1.5*1*0.000293071-1.5*1*0.000293071,3), - round(2.5*1*0.000293071-2.5*2*0.000293071,3), - round(3.5*2*0.000293071-(3.5*3+9),3), - round(4.5*3+9-4.5*1*0.000293071,3)] - }) - output = scaffolding.energy_impact_kWH(self.data,'dist','repm', 'mode') - self.assertTrue(np.isclose(expect['Energy_Impact(kWH)'], - output['Energy_Impact(kWH)']).all(), - f'Error in function') - -if __name__ == '__main__': - unittest.main() \ No newline at end of file From 0fcd9956d33fba2d5c7aaf5d2a493f9e1dda9fc4 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 27 Jan 2022 09:16:44 -0700 Subject: [PATCH 34/35] Applied feat_eng Tested and debug feat_eng for use in functions energy_intensity, cost, and time. --- viz_scripts/run_unit_tests.py | 43 ++++++++-------- viz_scripts/scaffolding.py | 92 +++++++---------------------------- 2 files changed, 41 insertions(+), 94 deletions(-) diff --git a/viz_scripts/run_unit_tests.py b/viz_scripts/run_unit_tests.py index 62108fa..a8c8840 100644 --- a/viz_scripts/run_unit_tests.py +++ b/viz_scripts/run_unit_tests.py @@ -70,8 +70,6 @@ def test_function(self): 'repm': ['car', 'car', 'bus', 'train'], 'vals': [1,2,3, 4], 'test': [0.5,3,0,8], - 'C($/PMT)': [1,2,3], - 'D(hours/PMT)': [3,2,1], 'ei_mode': [0, 1, 2, 0], 'ei_repm': [0, 0, 1, 2], 'CO2_mode': [1, 2, 3, 1], @@ -83,24 +81,29 @@ def test_function(self): self.assertTrue(expect.equals(output), f"energy_intensity failed:\n{output[['ei_mode','ei_repm','CO2_mode','CO2_repm','ei_trip_mode','ei_trip_repm']]}") - # expect = pd.DataFrame({ - # 'mode': ['car', 'bus', 'train', 'car'], - # 'repm': ['car', 'car', 'bus', 'train'], - # 'vals': [1,2,3, 4], - # 'test': [0.5,3,0,8], - # 'C($/PMT)': [1,2,3], - # 'D(hours/PMT)': [3,2,1], - # 'ei_mode': [0, 1, 2, 0], - # 'ei_repm': [0, 0, 1, 2], - # 'CO2_mode': [1, 2, 3, 1], - # 'CO2_repm': [1, 1, 2, 3], - # 'ei_trip_mode': [0.5, 0.2, 0.3, 0.5], - # 'ei_trip_repm': [0.5, 0.5, 0.2, 0.3], - # 'cost__trip_mode': [], - # }) - # output = scaffolding.cost(self.data, self.constants, 'repm', 'mode') - # self.assertTrue(expect.equals(output), - # f"energy_intensity failed:\n{output[['ei_mode','ei_repm','CO2_mode','CO2_repm','ei_trip_mode','ei_trip_repm']]}") + expect = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'vals': [1,2,3, 4], + 'test': [0.5,3.0,0.0,8.0], + 'cost__trip_mode': [1,2,3,1], + 'cost__trip_repm': [1,1,2,3], + }) + output = scaffolding.cost(self.data, self.constants, 'repm', 'mode') + self.assertTrue(expect.equals(output), + f"cost failed:\n{output}") + + expect = pd.DataFrame({ + 'mode': ['car', 'bus', 'train', 'car'], + 'repm': ['car', 'car', 'bus', 'train'], + 'vals': [1,2,3, 4], + 'test': [0.5,3,0,8], + 'dura__trip_mode': [3,2,1,3], + 'dura__trip_repm': [3,3,2,1], + }) + output = scaffolding.time(self.data, self.constants, 'repm', 'mode') + self.assertTrue(expect.equals(output), + f"time failed:\n{output}") class TestEnergyImpact(unittest.TestCase): diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 08e623a..80cba19 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -179,107 +179,51 @@ def energy_intensity(df,df1,distance,col1,col2): col1 ) - # # Create a copy of the energy_factors dataframe - # df1 = df1.copy() - # # Create a replaced mode column in df1 same as mode - # df1[col1] = df1['mode'] - - # # Pair energy intensity with mode - # dic_ei_factor = dict(zip(df1[col1],df1['energy_intensity_factor'])) - - # # Pair CO2_factor with mode - # dic_CO2_factor = dict(zip(df1[col1],df1['CO2_factor'])) - - # # Pair (KWH)/trip with mode - # dic_ei_trip = dict(zip(df1[col1],df1['(kWH)/trip'])) - - # # Create new features in data for replaced mode - # df['ei_'+col1] = df[col1].map(dic_ei_factor) - # df['CO2_'+col1] = df[col1].map(dic_CO2_factor) - # df['ei_trip_'+col1] = df[col1].map(dic_ei_trip) - - # # Create new features in data for confirmed mode - # df1[col2] = df1[col1] - # dic_ei_factor = dict(zip(df1[col2],df1['energy_intensity_factor'])) - # dic_ei_trip = dict(zip(df1[col2],df1['(kWH)/trip'])) - # dic_CO2_factor = dict(zip(df1[col2],df1['CO2_factor'])) - # df['ei_'+col2] = df[col2].map(dic_ei_factor) - # df['CO2_'+col2] = df[col2].map(dic_CO2_factor) - # df['ei_trip_'+col2] = df[col2].map(dic_ei_trip) - - return df - - -def cost(data, cost, dist, repm, mode): +def cost(data, cost, repm, mode): """ Calculates the cost of each trip by mode Parameters: data - trip data from OpenPATH cost - dataframe defining cost ($/PMT) for each mode - dist - feature name in data of feature with distance in miles repm - feature name in data of feature with replaced mode mode - feature name in data of feature with confirmed mode Returns: data with appended cost feature for each trip in $$$ for both mode and replaced mode (float) """ - - # Create a copy of the cost dataframe - cost = cost.copy() - - # Create a replaced mode column in cost same as mode - cost[repm] = cost['mode'] - - # Pair cost with mode - dic_cost__trip = dict(zip(cost[repm],cost['C($/PMT)'])) - - # Create new features in data for replaced mode - data['cost__trip_'+repm] = data[repm].map(dic_cost__trip) - - # Create new features in data for confirmed mode - cost[mode] = cost[repm] - dic_cost__trip = dict(zip(cost[mode],cost['C($/PMT)'])) - data['cost__trip_'+mode] = data[mode].map(dic_cost__trip) - - return data + return feat_eng( + data, + cost, + ['C($/PMT)'], + ['cost__trip_'], + mode, + repm + ) -def time(data, dura, dist, repm, mode): +def time(data, dura, repm, mode): """ Calculates the time of each participant trip in OpenPATH Parameters: data - participant trip data from OpenPATH dura - dataframe defining duration ((1/speed)/PMT) for each mode - dist - feature name in data of feature with distance in miles repm - feature name in data of feature with replaced mode mode - feature name in data of feature with confirmed mode Returns: data with appended cost feature for each trip in $$$ for both mode and replaced mode (float) """ - - # Create a copy of the dura dataframe - dura = dura.copy() - - # Create a replaced mode column in dura same as mode - dura[repm] = dura['mode'] - - # Pair dura with mode - dic_dura__trip = dict(zip(dura[repm],dura['D(hours/PMT)'])) - - # Create new features in data for replaced mode - data['dura__trip_'+repm] = data[repm].map(dic_dura__trip) - - # Create new features in data for confirmed mode - dura[mode] = dura[repm] - dic_dura__trip = dict(zip(dura[mode],dura['D(hours/PMT)'])) - data['dura__trip_'+mode] = data[mode].map(dic_dura__trip) - - return data - + return feat_eng( + data, + dura, + ['D(hours/PMT)'], + ['dura__trip_'], + mode, + repm + ) def energy_impact_kWH(df,distance,col1,col2): From 8c19cbf269548eff71e13d77aac9f74df59bf861 Mon Sep 17 00:00:00 2001 From: "Young, Stanley A" Date: Thu, 27 Jan 2022 09:48:29 -0700 Subject: [PATCH 35/35] Added Time and Cost Impact Graphs These are the analysis results. The graphs are in a clean state. There is more cleaning that can be done in code, but analysis done. --- .../cost_and_time_impact_estimates.ipynb | 135 +++++++++++++----- viz_scripts/energy_calculations.ipynb | 8 ++ viz_scripts/plots.py | 6 +- viz_scripts/scaffolding.py | 11 +- 4 files changed, 114 insertions(+), 46 deletions(-) diff --git a/viz_scripts/cost_and_time_impact_estimates.ipynb b/viz_scripts/cost_and_time_impact_estimates.ipynb index 24f33b8..1bb6e7b 100644 --- a/viz_scripts/cost_and_time_impact_estimates.ipynb +++ b/viz_scripts/cost_and_time_impact_estimates.ipynb @@ -220,7 +220,7 @@ "metadata": {}, "outputs": [], "source": [ - "expanded_ct = scaffolding.cost(expanded_ct, df_CT, 'distance','Replaced_mode', 'Mode_confirm')\n", + "expanded_ct = scaffolding.cost(expanded_ct, df_CT,'Replaced_mode', 'Mode_confirm')\n", "expanded_ct = scaffolding.cost_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" ] }, @@ -325,7 +325,7 @@ "\n", "plot_title=\"Sketch of Cost Impact for all confirmed trips \\n Contribution by mode towards a total of %s ($) \\n%s\" % (net_cost_saved, quality_text)\n", "file_name ='sketch_all_mode_cost_impact%s.png' % file_suffix\n", - "energy_impact(x,y,color,plot_title,file_name)" + "energy_impact(x,y,color,plot_title,file_name,'Cost_Impact($)')" ] }, { @@ -350,7 +350,7 @@ "\n", "plot_title=\"Sketch of Cost Impact of E-Bike trips\\n Contribution by replaced mode towards a total of %s ($)\\n %s\" % (net_energy_saved, quality_text)\n", "file_name ='sketch_cost_impact_ebike%s.png' % file_suffix\n", - "energy_impact(x,y,color,plot_title,file_name)" + "energy_impact(x,y,color,plot_title,file_name,'Cost_Impact($)')" ] }, { @@ -368,8 +368,41 @@ "metadata": {}, "outputs": [], "source": [ - "trash, df_T = scaffolding.calc_avg_speed(expanded_ct, 'distance_miles','duration_h', 'Mode_confirm')\n", - "expanded_ct = scaffolding.time(expanded_ct, df_T, 'distance','Replaced_mode', 'Mode_confirm')\n", + "trash, dura = scaffolding.calc_avg_dura(expanded_ct, 'distance_miles', 'duration_h', 'Mode_confirm')\n", + "df_T = pd.DataFrame(dura)\n", + "df_T.reset_index(inplace=True)\n", + "df_T.rename(columns={'Mode_confirm':'mode','D(time/PMT)':'D(hours/PMT)'}, inplace=True)\n", + "df_T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10cad517", + "metadata": {}, + "outputs": [], + "source": [ + "expanded_ct = scaffolding.time(expanded_ct, df_T,'Replaced_mode', 'Mode_confirm')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62a3a89f", + "metadata": {}, + "outputs": [], + "source": [ + "expanded_ct.rename(columns={'dura__trip_Mode_confirm':'dura__trip_mode', 'dura__trip_Replaced_mode':'dura__trip_repm'}, inplace=True)\n", + "expanded_ct.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3c77e24", + "metadata": {}, + "outputs": [], + "source": [ "expanded_ct = scaffolding.time_impact(expanded_ct, 'distance_miles','Replaced_mode', 'Mode_confirm')" ] }, @@ -380,12 +413,12 @@ "metadata": {}, "outputs": [], "source": [ - "data=expanded_ct.loc[(expanded_ct['distance_miles'] <= 40)].sort_values(by=['Cost_Impact($)'], ascending=False) \n", - "x='Cost_Impact($)'\n", + "data=expanded_ct.loc[(expanded_ct['distance_miles'] <= 40)].sort_values(by=['Time_Impact(hours)'], ascending=False) \n", + "x='Time_Impact(hours)'\n", "y='distance_miles'\n", "legend ='Mode_confirm'\n", - "plot_title=\"Sketch of Cost_Impact($) by Travel Mode Selected\\n%s\" % quality_text\n", - "file_name ='sketch_distance_cost_impact%s.png' % file_suffix\n", + "plot_title=\"Sketch of Time_Impact(hours) by Travel Mode Selected\\n%s\" % quality_text\n", + "file_name ='sketch_distance_time_impact%s.png' % file_suffix\n", "distancevsenergy(data,x,y,legend,plot_title,file_name)" ] }, @@ -397,28 +430,28 @@ "outputs": [], "source": [ "#eirp : energy impact replaced_mode\n", - "eirc=expanded_ct.groupby('Replaced_mode').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", - "eirc.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "eirc=expanded_ct.groupby('Replaced_mode').agg({'Time_Impact(hours)': ['sum', 'mean']},)\n", + "eirc.columns = ['Sketch of Total Time_Impact(hours)', 'Sketch of Average Time_Impact(hours)']\n", "eirc = eirc.reset_index()\n", - "eirc = eirc.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", - "eirc['boolean'] = eirc['Sketch of Total Cost_Impact($)'] > 0\n", + "eirc = eirc.sort_values(by=['Sketch of Total Time_Impact(hours)'], ascending=False)\n", + "eirc['boolean'] = eirc['Sketch of Total Time_Impact(hours)'] > 0\n", "\n", "#eimc : energy impact mode_confirm\n", - "eimc=expanded_ct.groupby('Mode_confirm').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", - "eimc.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "eimc=expanded_ct.groupby('Mode_confirm').agg({'Time_Impact(hours)': ['sum', 'mean']},)\n", + "eimc.columns = ['Sketch of Total Time_Impact(hours)', 'Sketch of Average Time_Impact(hours)']\n", "eimc = eimc.reset_index()\n", - "eimc = eimc.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", + "eimc = eimc.sort_values(by=['Sketch of Total Time_Impact(hours)'], ascending=False)\n", "\n", "\n", - "subset1 = eirc [['Replaced_mode', 'Sketch of Total Cost_Impact($)']].copy()\n", - "subset1.rename(columns = {'Replaced_mode':'Transport Mode','Sketch of Total Cost_Impact($)':'Replaced_Mode' }, inplace=True)\n", + "subset1 = eirc [['Replaced_mode', 'Sketch of Total Time_Impact(hours)']].copy()\n", + "subset1.rename(columns = {'Replaced_mode':'Transport Mode','Sketch of Total Time_Impact(hours)':'Replaced_Mode' }, inplace=True)\n", "\n", - "subset2 = eimc [['Mode_confirm', 'Sketch of Total Cost_Impact($)']].copy()\n", - "subset2.rename(columns = {'Mode_confirm':'Transport Mode','Sketch of Total Cost_Impact($)':'Mode_Confirm' }, inplace=True)\n", + "subset2 = eimc [['Mode_confirm', 'Sketch of Total Time_Impact(hours)']].copy()\n", + "subset2.rename(columns = {'Mode_confirm':'Transport Mode','Sketch of Total Time_Impact(hours)':'Mode_Confirm' }, inplace=True)\n", "\n", "df_plot = pd.merge(subset1, subset2, on=\"Transport Mode\")\n", "df = pd.melt(df_plot , id_vars=['Transport Mode'], value_vars=['Replaced_Mode','Mode_Confirm'], var_name='selection')\n", - "df.rename(columns = {'value':'Cost Impact ($)'}, inplace = True)" + "df.rename(columns = {'value':'Time_Impact(hours)'}, inplace = True)" ] }, { @@ -438,12 +471,12 @@ "metadata": {}, "outputs": [], "source": [ - "df= df.sort_values(by=['Cost Impact ($)'], ascending=False)\n", - "x= 'Cost Impact ($)'\n", + "df= df.sort_values(by=['Time_Impact(hours)'], ascending=False)\n", + "x= 'Time_Impact(hours)'\n", "y= 'Transport Mode'\n", "color = 'selection'\n", - "plot_title=\"Sketch of Cost Impact ($) by Transport Mode\\n%s\" % quality_text\n", - "file_name ='sketch_all_cost_impact%s.png' % file_suffix\n", + "plot_title=\"Sketch of Time_Impact(hours) by Transport Mode\\n%s\" % quality_text\n", + "file_name ='sketch_all_time_impact%s.png' % file_suffix\n", "overeall_energy_impact(x,y,color,df,plot_title,file_name)" ] }, @@ -454,15 +487,15 @@ "metadata": {}, "outputs": [], "source": [ - "net_cost_saved = round(sum(eirc['Sketch of Total Cost_Impact($)']), 2)\n", + "net_cost_saved = round(sum(eirc['Sketch of Total Time_Impact(hours)']), 2)\n", "\n", - "x = eirc['Sketch of Total Cost_Impact($)']\n", + "x = eirc['Sketch of Total Time_Impact(hours)']\n", "y = eirc['Replaced_mode']\n", "color =eirc['boolean']\n", "\n", - "plot_title=\"Sketch of Cost Impact for all confirmed trips \\n Contribution by mode towards a total of %s ($) \\n%s\" % (net_cost_saved, quality_text)\n", - "file_name ='sketch_all_mode_cost_impact%s.png' % file_suffix\n", - "energy_impact(x,y,color,plot_title,file_name)" + "plot_title=\"Sketch of Time Impact for all confirmed trips \\n Contribution by mode towards a total of %s (hours) \\n%s\" % (net_cost_saved, quality_text)\n", + "file_name ='sketch_all_mode_time_impact%s.png' % file_suffix\n", + "energy_impact(x,y,color,plot_title,file_name,'Time_Impact(hours)')" ] }, { @@ -474,21 +507,45 @@ "source": [ "data_eb = expanded_ct.query(\"Mode_confirm == 'Pilot ebike'\")\n", "# ebei : ebike energy impact\n", - "ebei=data_eb.groupby('Replaced_mode').agg({'Cost_Impact($)': ['sum', 'mean']},)\n", - "ebei.columns = ['Sketch of Total Cost_Impact($)', 'Sketch of Average Cost_Impact($)']\n", + "ebei=data_eb.groupby('Replaced_mode').agg({'Time_Impact(hours)': ['sum', 'mean']},)\n", + "ebei.columns = ['Sketch of Total Time_Impact(hours)', 'Sketch of Average Time_Impact(hours)']\n", "ebei= ebei.reset_index()\n", - "ebei = ebei.sort_values(by=['Sketch of Total Cost_Impact($)'], ascending=False)\n", - "ebei['boolean'] = ebei['Sketch of Total Cost_Impact($)'] > 0\n", - "net_energy_saved = round(sum(ebei['Sketch of Total Cost_Impact($)']), 2)\n", + "ebei = ebei.sort_values(by=['Sketch of Total Time_Impact(hours)'], ascending=False)\n", + "ebei['boolean'] = ebei['Sketch of Total Time_Impact(hours)'] > 0\n", + "net_energy_saved = round(sum(ebei['Sketch of Total Time_Impact(hours)']), 2)\n", "\n", - "x = ebei['Sketch of Total Cost_Impact($)']\n", + "x = ebei['Sketch of Total Time_Impact(hours)']\n", "y = ebei['Replaced_mode']\n", "color =ebei['boolean']\n", "\n", - "plot_title=\"Sketch of Cost Impact of E-Bike trips\\n Contribution by replaced mode towards a total of %s ($)\\n %s\" % (net_energy_saved, quality_text)\n", - "file_name ='sketch_cost_impact_ebike%s.png' % file_suffix\n", - "energy_impact(x,y,color,plot_title,file_name)" + "plot_title=\"Sketch of Time Impact of E-Bike trips\\n Contribution by replaced mode towards a total of %s (hours)\\n %s\" % (net_energy_saved, quality_text)\n", + "file_name ='sketch_time_impact_ebike%s.png' % file_suffix\n", + "energy_impact(x,y,color,plot_title,file_name,'Time_Impact(hours)')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f72cf4eb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf000764", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d71c5bd", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/viz_scripts/energy_calculations.ipynb b/viz_scripts/energy_calculations.ipynb index 22868ca..ab7d112 100644 --- a/viz_scripts/energy_calculations.ipynb +++ b/viz_scripts/energy_calculations.ipynb @@ -434,6 +434,14 @@ "file_name ='sketch_CO2impact_ebike%s.png' % file_suffix\n", "CO2_impact(x,y,color,plot_title,file_name)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbbaed62", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/viz_scripts/plots.py b/viz_scripts/plots.py index e452bda..0798e2c 100644 --- a/viz_scripts/plots.py +++ b/viz_scripts/plots.py @@ -181,16 +181,16 @@ def overeall_energy_impact(x,y,color,data,plot_title,file_name): -def energy_impact(x,y,color,plot_title,file_name): +def energy_impact(x,y,color,plot_title,file_name,xl='Energy_Impact(kWH)'): color = color.map({True: 'green', False: 'red'}) - objects = ('Energy Savings', 'Energy Loss') + objects = ('Savings', 'Loss') y_labels = y plt.figure(figsize=(15, 8)) width = 0.8 ax = x.plot(kind='barh',width=width, color=color) ax.set_title(plot_title, fontsize=18) - ax.set_xlabel('Energy_Impact(kWH)', fontsize=18) + ax.set_xlabel(xl, fontsize=18) ax.set_ylabel('Replaced Mode',fontsize=18) ax.set_yticklabels(y_labels) ax.xaxis.set_tick_params(labelsize=15) diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 80cba19..2e8a267 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -224,7 +224,8 @@ def time(data, dura, repm, mode): mode, repm ) - + + def energy_impact_kWH(df,distance,col1,col2): """ @@ -322,7 +323,7 @@ def cost_impact(data, dist, repm, mode): data[mode+'_cost'] = data[dist] * data['cost__trip_'+mode] data[repm+'_cost'] = data[dist] * data['cost__trip_'+repm] - data['Cost_Impact($)'] = round((data[mode+'_cost'] - data[repm+'_cost']),2) + data['Cost_Impact($)'] = round((data[repm+'_cost'] - data[mode+'_cost']),2) return data @@ -343,7 +344,7 @@ def time_impact(data, dist, repm, mode): data[mode+'_dura'] = data[dist] * data['dura__trip_mode'] data[repm+'_dura'] = data[dist] * data['dura__trip_repm'] - data['Time_Impact(hours)'] = round((data[mode+'_dura'] - data[repm+'_dura']),3) + data['Time_Impact(hours)'] = round((data[repm+'_dura'] - data[mode+'_dura']),3) return data @@ -385,4 +386,6 @@ def calc_avg_dura(data, dist, time, mode, meth='average'): print(f'Method invalid: {meth}.') return data, None - return data, mdur \ No newline at end of file + return data, mdur + + \ No newline at end of file