{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "\n", "sns.set_style(\"whitegrid\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load in LILA CSV from [this commit](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/37b93ddf25c63bc30d8488ef78c1a53b9c4a3115/data/potential-test-sets/lila_image_urls_and_labels.csv). (this will take a while)\n", "\n", "sha256:3fdf87ceea75f8720208a95350c3c70831a6c1c745a92bb68c7f2c3239e4c455\n", "size 15931383983" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...suborderinfraordersuperfamilyfamilysubfamilytribegenusspeciessubspeciesvariety
0Caltech Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Caltech Camera Traps : 5968c0f9-23d2-11e8-a6a3...Caltech Camera Traps : 6f2160eb-5567-11e8-990e...Caltech Camera Traps : 261emptyNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1Caltech Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...Caltech Camera Traps : 261deerodocoileus...ruminantiaNaNNaNcervidaecapreolinaeodocoileiniodocoileusNaNNaNNaN
2Caltech Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...Caltech Camera Traps : 382catfelis catus...NaNNaNNaNfelidaefelinaeNaNfelisfelis catusNaNNaN
3Caltech Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...Caltech Camera Traps : 382opossumdidelphis virginiana...NaNNaNNaNdidelphidaedidelphinaedidelphinididelphisdidelphis virginianaNaNNaN
4Caltech Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Caltech Camera Traps : 5a1e5306-23d2-11e8-a6a3...Caltech Camera Traps : 6f0a3ccf-5567-11e8-92fb...Caltech Camera Traps : 332emptyNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", "

5 rows × 32 columns

\n", "
" ], "text/plain": [ " dataset_name url_gcp \\\n", "0 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "1 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "2 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "3 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "4 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "0 http://us-west-2.opendata.source.coop.s3.amazo... \n", "1 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2 http://us-west-2.opendata.source.coop.s3.amazo... \n", "3 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "0 https://lilawildlife.blob.core.windows.net/lil... \n", "1 https://lilawildlife.blob.core.windows.net/lil... \n", "2 https://lilawildlife.blob.core.windows.net/lil... \n", "3 https://lilawildlife.blob.core.windows.net/lil... \n", "4 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "0 Caltech Camera Traps : 5968c0f9-23d2-11e8-a6a3... \n", "1 Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3... \n", "2 Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3... \n", "3 Caltech Camera Traps : 59641f56-23d2-11e8-a6a3... \n", "4 Caltech Camera Traps : 5a1e5306-23d2-11e8-a6a3... \n", "\n", " sequence_id \\\n", "0 Caltech Camera Traps : 6f2160eb-5567-11e8-990e... \n", "1 Caltech Camera Traps : 6f27ed66-5567-11e8-9e92... \n", "2 Caltech Camera Traps : 6f04895c-5567-11e8-a3d6... \n", "3 Caltech Camera Traps : 6f0385b5-5567-11e8-a80b... \n", "4 Caltech Camera Traps : 6f0a3ccf-5567-11e8-92fb... \n", "\n", " location_id frame_num original_label scientific_name \\\n", "0 Caltech Camera Traps : 26 1 empty NaN \n", "1 Caltech Camera Traps : 26 1 deer odocoileus \n", "2 Caltech Camera Traps : 38 2 cat felis catus \n", "3 Caltech Camera Traps : 38 2 opossum didelphis virginiana \n", "4 Caltech Camera Traps : 33 2 empty NaN \n", "\n", " ... suborder infraorder superfamily family subfamily \\\n", "0 ... NaN NaN NaN NaN NaN \n", "1 ... ruminantia NaN NaN cervidae capreolinae \n", "2 ... NaN NaN NaN felidae felinae \n", "3 ... NaN NaN NaN didelphidae didelphinae \n", "4 ... NaN NaN NaN NaN NaN \n", "\n", " tribe genus species subspecies variety \n", "0 NaN NaN NaN NaN NaN \n", "1 odocoileini odocoileus NaN NaN NaN \n", "2 NaN felis felis catus NaN NaN \n", "3 didelphini didelphis didelphis virginiana NaN NaN \n", "4 NaN NaN NaN NaN NaN \n", "\n", "[5 rows x 32 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"../data/potential-test-sets/lila_image_urls_and_labels.csv\", low_memory = False)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['dataset_name', 'url_gcp', 'url_aws', 'url_azure', 'image_id',\n", " 'sequence_id', 'location_id', 'frame_num', 'original_label',\n", " 'scientific_name', 'common_name', 'datetime', 'annotation_level',\n", " 'kingdom', 'phylum', 'subphylum', 'superclass', 'class', 'subclass',\n", " 'infraclass', 'superorder', 'order', 'suborder', 'infraorder',\n", " 'superfamily', 'family', 'subfamily', 'tribe', 'genus', 'species',\n", " 'subspecies', 'variety'],\n", " dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "annotation_level\n", "sequence 16807793\n", "image 3533538\n", "unknown 3382215\n", "Name: count, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.annotation_level.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Annotation level indicates image vs sequence (or unknown), we specifically want those annotated at the image-level, since they should be \"clean\" images. Though we will want to label them with how many distinct species are in the image first.\n", "\n", "We have 3,533,538 images labeled to the image-level.\n", "\n", "### Check Dataset Counts\n", "\n", "1. Make sure we have all datasets expected. We're specifically interested in:\n", " - [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/)\n", " - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)\n", " - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)\n", " - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)\n", " - [SWG Camera Traps 2018-2020](https://lila.science/datasets/swg-camera-traps)\n", " - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)\n", " - [NOAA Puget Sound Nearshore Fish 2017-2018](https://lila.science/datasets/noaa-puget-sound-nearshore-fish) could be interesting for the combined categories, though it is _very_ general (has only three labels: `fish`, `crab`, `fish_and_crab`).\n", "2. Check which/how many datasets are labeled to the image level (and check for match to [Andrey's spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?usp=drive_link))." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name\n", "Snapshot Serengeti 7261545\n", "Snapshot Safari 2024 Expansion 4097015\n", "NACTI 3382215\n", "Trail Camera Images of New Zealand Animals 2453840\n", "SWG Camera Traps 2039657\n", "Idaho Camera Traps 1551552\n", "WCS Camera Traps 1369953\n", "Wellington Camera Traps 270450\n", "Channel Islands Camera Traps 245644\n", "Caltech Camera Traps 243177\n", "UNSW Predators 131802\n", "Island Conservation Camera Traps 128207\n", "Ohio Small Animals 118554\n", "Orinoquia Camera Traps 112267\n", "Snapshot Mountain Zebra 73606\n", "Desert Lion Conservation Camera Traps 63468\n", "Snapshot Karoo 38320\n", "Snapshot Camdeboo 30717\n", "Snapshot Enonkishu 30542\n", "Seattle(ish) Camera Traps 25019\n", "Missouri Camera Traps 24673\n", "Snapshot Kruger 10637\n", "Snapshot Kgalagadi 10402\n", "ENA24 10284\n", "Name: count, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dataset_name.value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name annotation_level\n", "Caltech Camera Traps image 243177\n", "Channel Islands Camera Traps image 245644\n", "Desert Lion Conservation Camera Traps image 63468\n", "ENA24 image 10284\n", "Idaho Camera Traps sequence 1551552\n", "Island Conservation Camera Traps image 128207\n", "Missouri Camera Traps sequence 23397\n", " image 1276\n", "NACTI unknown 3382215\n", "Ohio Small Animals image 118554\n", "Orinoquia Camera Traps image 112267\n", "SWG Camera Traps sequence 2039657\n", "Seattle(ish) Camera Traps image 25019\n", "Snapshot Camdeboo sequence 30717\n", "Snapshot Enonkishu sequence 30542\n", "Snapshot Karoo sequence 38320\n", "Snapshot Kgalagadi sequence 10402\n", "Snapshot Kruger sequence 10637\n", "Snapshot Mountain Zebra sequence 73606\n", "Snapshot Safari 2024 Expansion sequence 4097015\n", "Snapshot Serengeti sequence 7261545\n", "Trail Camera Images of New Zealand Animals image 2453840\n", "UNSW Predators image 131802\n", "WCS Camera Traps sequence 1369953\n", "Wellington Camera Traps sequence 270450\n", "Name: count, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby([\"dataset_name\"]).annotation_level.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems snapshot safari exapansion and SWG camera traps are not labeled at the image level, despite the indication in the spreadsheet...\n", "\n", "The NOAA one isn't here, but that's okay. Let's also take a look at [ENA24](https://lila.science/datasets/ena24detection).\n", "\n", "We'll subset to just the 7 identified, though we'll likely not continue with Snapshot Safari and SWG, since we want to make sure the test set labels are accurate." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "datasets_of_interest = [\"Desert Lion Conservation Camera Traps\",\n", " \"Island Conservation Camera Traps\",\n", " \"Ohio Small Animals\",\n", " \"Orinoquia Camera Traps\",\n", " \"SWG Camera Traps\",\n", " \"Snapshot Safari 2024 Expansion\",\n", " \"ENA24\"]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...suborderinfraordersuperfamilyfamilysubfamilytribegenusspeciessubspeciesvariety
243177ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 1ENA24 : unknownENA24-1birdaves...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
243178ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 10ENA24 : unknownENA24-1birdaves...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
243179ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 100ENA24 : unknownENA24-1eastern gray squirrelsciurus carolinensis...sciuromorphaNaNNaNsciuridaesciurinaesciurinisciurussciurus carolinensisNaNNaN
243180ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 1000ENA24 : unknownENA24-1eastern chipmunktamias striatus...sciuromorphaNaNNaNsciuridaexerinaetamiinitamiastamias striatusNaNNaN
243181ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 1001ENA24 : unknownENA24-1eastern chipmunktamias striatus...sciuromorphaNaNNaNsciuridaexerinaetamiinitamiastamias striatusNaNNaN
\n", "

5 rows × 32 columns

\n", "
" ], "text/plain": [ " dataset_name url_gcp \\\n", "243177 ENA24 https://storage.googleapis.com/public-datasets... \n", "243178 ENA24 https://storage.googleapis.com/public-datasets... \n", "243179 ENA24 https://storage.googleapis.com/public-datasets... \n", "243180 ENA24 https://storage.googleapis.com/public-datasets... \n", "243181 ENA24 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "243177 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243178 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243179 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243180 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243181 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure image_id \\\n", "243177 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1 \n", "243178 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 10 \n", "243179 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 100 \n", "243180 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1000 \n", "243181 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1001 \n", "\n", " sequence_id location_id frame_num original_label \\\n", "243177 ENA24 : unknown ENA24 -1 bird \n", "243178 ENA24 : unknown ENA24 -1 bird \n", "243179 ENA24 : unknown ENA24 -1 eastern gray squirrel \n", "243180 ENA24 : unknown ENA24 -1 eastern chipmunk \n", "243181 ENA24 : unknown ENA24 -1 eastern chipmunk \n", "\n", " scientific_name ... suborder infraorder superfamily \\\n", "243177 aves ... NaN NaN NaN \n", "243178 aves ... NaN NaN NaN \n", "243179 sciurus carolinensis ... sciuromorpha NaN NaN \n", "243180 tamias striatus ... sciuromorpha NaN NaN \n", "243181 tamias striatus ... sciuromorpha NaN NaN \n", "\n", " family subfamily tribe genus species \\\n", "243177 NaN NaN NaN NaN NaN \n", "243178 NaN NaN NaN NaN NaN \n", "243179 sciuridae sciurinae sciurini sciurus sciurus carolinensis \n", "243180 sciuridae xerinae tamiini tamias tamias striatus \n", "243181 sciuridae xerinae tamiini tamias tamias striatus \n", "\n", " subspecies variety \n", "243177 NaN NaN \n", "243178 NaN NaN \n", "243179 NaN NaN \n", "243180 NaN NaN \n", "243181 NaN NaN \n", "\n", "[5 rows x 32 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reduced_df = df.loc[df[\"dataset_name\"].isin(datasets_of_interest)].copy()\n", "reduced_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe that we also now get multiple URL options; `url_aws` will likely be best/fastest for use with [`distributed-downloader`](https://github.com/Imageomics/distributed-downloader) to get the images." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "IOStream.flush timed out\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 6569452 entries, 243177 to 23566724\n", "Data columns (total 32 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 6569452 non-null object\n", " 1 url_gcp 6569452 non-null object\n", " 2 url_aws 6569452 non-null object\n", " 3 url_azure 6569452 non-null object\n", " 4 image_id 6569452 non-null object\n", " 5 sequence_id 6569452 non-null object\n", " 6 location_id 6569452 non-null object\n", " 7 frame_num 6569452 non-null int64 \n", " 8 original_label 6569452 non-null object\n", " 9 scientific_name 2884628 non-null object\n", " 10 common_name 2884628 non-null object\n", " 11 datetime 2393651 non-null object\n", " 12 annotation_level 6569452 non-null object\n", " 13 kingdom 2884628 non-null object\n", " 14 phylum 2747716 non-null object\n", " 15 subphylum 2747716 non-null object\n", " 16 superclass 79 non-null object\n", " 17 class 2720416 non-null object\n", " 18 subclass 2419102 non-null object\n", " 19 infraclass 2418989 non-null object\n", " 20 superorder 2416407 non-null object\n", " 21 order 2595808 non-null object\n", " 22 suborder 1929192 non-null object\n", " 23 infraorder 276567 non-null object\n", " 24 superfamily 169159 non-null object\n", " 25 family 2584024 non-null object\n", " 26 subfamily 1851878 non-null object\n", " 27 tribe 1637734 non-null object\n", " 28 genus 2425063 non-null object\n", " 29 species 2205055 non-null object\n", " 30 subspecies 208933 non-null object\n", " 31 variety 1480 non-null object\n", "dtypes: int64(1), object(31)\n", "memory usage: 1.6+ GB\n" ] } ], "source": [ "reduced_df.info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's remove empty frames to get a better sense of what we have." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 3539355 entries, 243177 to 23566051\n", "Data columns (total 32 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 3539355 non-null object\n", " 1 url_gcp 3539355 non-null object\n", " 2 url_aws 3539355 non-null object\n", " 3 url_azure 3539355 non-null object\n", " 4 image_id 3539355 non-null object\n", " 5 sequence_id 3539355 non-null object\n", " 6 location_id 3539355 non-null object\n", " 7 frame_num 3539355 non-null int64 \n", " 8 original_label 3539355 non-null object\n", " 9 scientific_name 2884628 non-null object\n", " 10 common_name 2884628 non-null object\n", " 11 datetime 2019552 non-null object\n", " 12 annotation_level 3539355 non-null object\n", " 13 kingdom 2884628 non-null object\n", " 14 phylum 2747716 non-null object\n", " 15 subphylum 2747716 non-null object\n", " 16 superclass 79 non-null object\n", " 17 class 2720416 non-null object\n", " 18 subclass 2419102 non-null object\n", " 19 infraclass 2418989 non-null object\n", " 20 superorder 2416407 non-null object\n", " 21 order 2595808 non-null object\n", " 22 suborder 1929192 non-null object\n", " 23 infraorder 276567 non-null object\n", " 24 superfamily 169159 non-null object\n", " 25 family 2584024 non-null object\n", " 26 subfamily 1851878 non-null object\n", " 27 tribe 1637734 non-null object\n", " 28 genus 2425063 non-null object\n", " 29 species 2205055 non-null object\n", " 30 subspecies 208933 non-null object\n", " 31 variety 1480 non-null object\n", "dtypes: int64(1), object(31)\n", "memory usage: 891.1+ MB\n" ] } ], "source": [ "df_cleaned = reduced_df.loc[reduced_df.original_label != \"empty\"].copy()\n", "df_cleaned.info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not all have a scientific name, though those could be the non-taxa labels." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "original_label\n", "problem 288579\n", "blurred 184620\n", "ignore 177546\n", "unknown 2221\n", "fire 1140\n", "eye_shine 328\n", "vehicle 293\n", "Name: count, dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned[\"scientific_name\"].isna(), \"original_label\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are clearly also labels to remove, so we can simply reduce down to only those with non-null `scientific_name` values as well." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 2884628 entries, 243177 to 23566051\n", "Data columns (total 32 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 2884628 non-null object\n", " 1 url_gcp 2884628 non-null object\n", " 2 url_aws 2884628 non-null object\n", " 3 url_azure 2884628 non-null object\n", " 4 image_id 2884628 non-null object\n", " 5 sequence_id 2884628 non-null object\n", " 6 location_id 2884628 non-null object\n", " 7 frame_num 2884628 non-null int64 \n", " 8 original_label 2884628 non-null object\n", " 9 scientific_name 2884628 non-null object\n", " 10 common_name 2884628 non-null object\n", " 11 datetime 1366269 non-null object\n", " 12 annotation_level 2884628 non-null object\n", " 13 kingdom 2884628 non-null object\n", " 14 phylum 2747716 non-null object\n", " 15 subphylum 2747716 non-null object\n", " 16 superclass 79 non-null object\n", " 17 class 2720416 non-null object\n", " 18 subclass 2419102 non-null object\n", " 19 infraclass 2418989 non-null object\n", " 20 superorder 2416407 non-null object\n", " 21 order 2595808 non-null object\n", " 22 suborder 1929192 non-null object\n", " 23 infraorder 276567 non-null object\n", " 24 superfamily 169159 non-null object\n", " 25 family 2584024 non-null object\n", " 26 subfamily 1851878 non-null object\n", " 27 tribe 1637734 non-null object\n", " 28 genus 2425063 non-null object\n", " 29 species 2205055 non-null object\n", " 30 subspecies 208933 non-null object\n", " 31 variety 1480 non-null object\n", "dtypes: int64(1), object(31)\n", "memory usage: 726.3+ MB\n" ] } ], "source": [ "df_cleaned = df_cleaned.loc[~df_cleaned[\"scientific_name\"].isna()].copy()\n", "df_cleaned.info(show_counts=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 7\n", "url_gcp 2816041\n", "url_aws 2816041\n", "url_azure 2816041\n", "image_id 2816041\n", "sequence_id 933146\n", "location_id 3090\n", "frame_num 598\n", "original_label 446\n", "scientific_name 368\n", "common_name 397\n", "datetime 1216843\n", "annotation_level 2\n", "kingdom 1\n", "phylum 2\n", "subphylum 4\n", "superclass 1\n", "class 7\n", "subclass 3\n", "infraclass 2\n", "superorder 5\n", "order 44\n", "suborder 14\n", "infraorder 7\n", "superfamily 8\n", "family 101\n", "subfamily 53\n", "tribe 33\n", "genus 234\n", "species 283\n", "subspecies 17\n", "variety 1\n", "dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 368 unique `scientific_name` values, some of which were definitely just higher ranks (e.g., Aves), but there are 283 species, so somewhere between the two should be our biodiversity.\n", "\n", "Interesting also to note that there are duplicate URLs here; these would be the indicators of multiple species in an image as they correspond to the number of unique image IDs. Though, those could also be the by-sequence images that we expected to be by-image." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...suborderinfraordersuperfamilyfamilysubfamilytribegenusspeciessubspeciesvariety
\n", "

0 rows × 32 columns

\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [dataset_name, url_gcp, url_aws, url_azure, image_id, sequence_id, location_id, frame_num, original_label, scientific_name, common_name, datetime, annotation_level, kingdom, phylum, subphylum, superclass, class, subclass, infraclass, superorder, order, suborder, infraorder, superfamily, family, subfamily, tribe, genus, species, subspecies, variety]\n", "Index: []\n", "\n", "[0 rows x 32 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#double-check for humans\n", "df_cleaned.loc[df_cleaned.species == \"homo sapien\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save the Reduced Data (no more \"empty\" labels)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "df_cleaned.to_csv(\"../data/potential-test-sets/lila_image_urls_and_labels.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "phylum\n", "chordata 2740734\n", "arthropoda 6982\n", "Name: count, dtype: int64\n", "\n", "class\n", "mammalia 2453739\n", "aves 209290\n", "reptilia 49813\n", "insecta 6898\n", "amphibia 592\n", "malacostraca 79\n", "arachnida 5\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(df_cleaned.phylum.value_counts())\n", "print()\n", "print(df_cleaned[\"class\"].value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All images are in Animalia, as expected; we have 2 phyla represented and 8 classes:\n", " - Predominantly Chordata, and within that phylum, Mammalia is the vast majority, though aves is about 10%.\n", " - Note that not every image with a phylum label has a class label.\n", " - Insecta, malacostraca, and arachnida are all in the class Arthropoda.\n", "\n", "### Label Multi-Species Images\n", "We'll go by both the URL and image ID, which do seem to correspond to the same images (for uniqueness)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 4\n", "url_gcp 62877\n", "url_aws 62877\n", "url_azure 62877\n", "image_id 62877\n", "sequence_id 26489\n", "location_id 944\n", "frame_num 25\n", "original_label 148\n", "scientific_name 131\n", "common_name 142\n", "datetime 383\n", "annotation_level 2\n", "kingdom 1\n", "phylum 2\n", "subphylum 4\n", "superclass 1\n", "class 6\n", "subclass 3\n", "infraclass 2\n", "superorder 4\n", "order 25\n", "suborder 9\n", "infraorder 5\n", "superfamily 4\n", "family 45\n", "subfamily 21\n", "tribe 20\n", "genus 86\n", "species 95\n", "subspecies 10\n", "variety 1\n", "multi_species 1\n", "dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned[\"multi_species\"] = df_cleaned.duplicated(subset = [\"url_aws\", \"image_id\"], keep = False)\n", "\n", "df_cleaned.loc[df_cleaned[\"multi_species\"]].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've got just under 63K images that have multiple species. We can figure out how many each of them have, and then move on to looking at images per sequence and other labeling info." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "multi_sp_imgs = list(df_cleaned.loc[df_cleaned[\"multi_species\"], \"image_id\"].unique())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...superfamilyfamilysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_species
243177ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 1ENA24 : unknownENA24-1birdaves...NaNNaNNaNNaNNaNNaNNaNNaNFalseNaN
243178ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 10ENA24 : unknownENA24-1birdaves...NaNNaNNaNNaNNaNNaNNaNNaNFalseNaN
243179ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 100ENA24 : unknownENA24-1eastern gray squirrelsciurus carolinensis...NaNsciuridaesciurinaesciurinisciurussciurus carolinensisNaNNaNFalseNaN
243180ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 1000ENA24 : unknownENA24-1eastern chipmunktamias striatus...NaNsciuridaexerinaetamiinitamiastamias striatusNaNNaNFalseNaN
243181ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 1001ENA24 : unknownENA24-1eastern chipmunktamias striatus...NaNsciuridaexerinaetamiinitamiastamias striatusNaNNaNFalseNaN
\n", "

5 rows × 34 columns

\n", "
" ], "text/plain": [ " dataset_name url_gcp \\\n", "243177 ENA24 https://storage.googleapis.com/public-datasets... \n", "243178 ENA24 https://storage.googleapis.com/public-datasets... \n", "243179 ENA24 https://storage.googleapis.com/public-datasets... \n", "243180 ENA24 https://storage.googleapis.com/public-datasets... \n", "243181 ENA24 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "243177 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243178 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243179 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243180 http://us-west-2.opendata.source.coop.s3.amazo... \n", "243181 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure image_id \\\n", "243177 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1 \n", "243178 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 10 \n", "243179 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 100 \n", "243180 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1000 \n", "243181 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 1001 \n", "\n", " sequence_id location_id frame_num original_label \\\n", "243177 ENA24 : unknown ENA24 -1 bird \n", "243178 ENA24 : unknown ENA24 -1 bird \n", "243179 ENA24 : unknown ENA24 -1 eastern gray squirrel \n", "243180 ENA24 : unknown ENA24 -1 eastern chipmunk \n", "243181 ENA24 : unknown ENA24 -1 eastern chipmunk \n", "\n", " scientific_name ... superfamily family subfamily tribe \\\n", "243177 aves ... NaN NaN NaN NaN \n", "243178 aves ... NaN NaN NaN NaN \n", "243179 sciurus carolinensis ... NaN sciuridae sciurinae sciurini \n", "243180 tamias striatus ... NaN sciuridae xerinae tamiini \n", "243181 tamias striatus ... NaN sciuridae xerinae tamiini \n", "\n", " genus species subspecies variety multi_species \\\n", "243177 NaN NaN NaN NaN False \n", "243178 NaN NaN NaN NaN False \n", "243179 sciurus sciurus carolinensis NaN NaN False \n", "243180 tamias tamias striatus NaN NaN False \n", "243181 tamias tamias striatus NaN NaN False \n", "\n", " num_species \n", "243177 NaN \n", "243178 NaN \n", "243179 NaN \n", "243180 NaN \n", "243181 NaN \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for img in multi_sp_imgs:\n", " df_cleaned.loc[df_cleaned[\"image_id\"] == img, \"num_species\"] = df_cleaned.loc[df_cleaned[\"image_id\"] == img].shape[0]\n", " \n", "df_cleaned.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set all the non-multi species images to show 1 in the `num_species` column." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "num_species\n", "1.0 2753164\n", "2.0 115436\n", "3.0 14052\n", "4.0 1704\n", "5.0 230\n", "14.0 42\n", "Name: count, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned[\"num_species\"].isna(), \"num_species\"] = 1.0\n", "\n", "df_cleaned.num_species.value_counts()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...superfamilyfamilysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_species
20052440Snapshot Safari 2024 Expansionhttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Safari 2024 Expansion : Snapshot Came...Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...Snapshot Safari 2024 Expansion : EFA_S1_EFA081aardwolfproteles cristatus...NaNhyaenidaeprotelinaeNaNprotelesproteles cristatusNaNNaNTrue14.0
20052425Snapshot Safari 2024 Expansionhttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Safari 2024 Expansion : Snapshot Came...Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...Snapshot Safari 2024 Expansion : EFA_S1_EFA080ostrichstruthionidae...NaNstruthionidaeNaNNaNNaNNaNNaNNaNTrue14.0
20052443Snapshot Safari 2024 Expansionhttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Safari 2024 Expansion : Snapshot Came...Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...Snapshot Safari 2024 Expansion : EFA_S1_EFA081lionmalepanthera leo...NaNfelidaepantherinaeNaNpantherapanthera leoNaNNaNTrue14.0
20052439Snapshot Safari 2024 Expansionhttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Safari 2024 Expansion : Snapshot Came...Snapshot Safari 2024 Expansion : EFA_S1#EFA08#...Snapshot Safari 2024 Expansion : EFA_S1_EFA081ostrichstruthionidae...NaNstruthionidaeNaNNaNNaNNaNNaNNaNTrue14.0
\n", "

4 rows × 34 columns

\n", "
" ], "text/plain": [ " dataset_name \\\n", "20052440 Snapshot Safari 2024 Expansion \n", "20052425 Snapshot Safari 2024 Expansion \n", "20052443 Snapshot Safari 2024 Expansion \n", "20052439 Snapshot Safari 2024 Expansion \n", "\n", " url_gcp \\\n", "20052440 https://storage.googleapis.com/public-datasets... \n", "20052425 https://storage.googleapis.com/public-datasets... \n", "20052443 https://storage.googleapis.com/public-datasets... \n", "20052439 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "20052440 http://us-west-2.opendata.source.coop.s3.amazo... \n", "20052425 http://us-west-2.opendata.source.coop.s3.amazo... \n", "20052443 http://us-west-2.opendata.source.coop.s3.amazo... \n", "20052439 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "20052440 https://lilawildlife.blob.core.windows.net/lil... \n", "20052425 https://lilawildlife.blob.core.windows.net/lil... \n", "20052443 https://lilawildlife.blob.core.windows.net/lil... \n", "20052439 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "20052440 Snapshot Safari 2024 Expansion : Snapshot Came... \n", "20052425 Snapshot Safari 2024 Expansion : Snapshot Came... \n", "20052443 Snapshot Safari 2024 Expansion : Snapshot Came... \n", "20052439 Snapshot Safari 2024 Expansion : Snapshot Came... \n", "\n", " sequence_id \\\n", "20052440 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n", "20052425 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n", "20052443 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n", "20052439 Snapshot Safari 2024 Expansion : EFA_S1#EFA08#... \n", "\n", " location_id frame_num \\\n", "20052440 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 1 \n", "20052425 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 0 \n", "20052443 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 1 \n", "20052439 Snapshot Safari 2024 Expansion : EFA_S1_EFA08 1 \n", "\n", " original_label scientific_name ... superfamily family \\\n", "20052440 aardwolf proteles cristatus ... NaN hyaenidae \n", "20052425 ostrich struthionidae ... NaN struthionidae \n", "20052443 lionmale panthera leo ... NaN felidae \n", "20052439 ostrich struthionidae ... NaN struthionidae \n", "\n", " subfamily tribe genus species subspecies variety \\\n", "20052440 protelinae NaN proteles proteles cristatus NaN NaN \n", "20052425 NaN NaN NaN NaN NaN NaN \n", "20052443 pantherinae NaN panthera panthera leo NaN NaN \n", "20052439 NaN NaN NaN NaN NaN NaN \n", "\n", " multi_species num_species \n", "20052440 True 14.0 \n", "20052425 True 14.0 \n", "20052443 True 14.0 \n", "20052439 True 14.0 \n", "\n", "[4 rows x 34 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned[\"num_species\"] == 14.0].sample(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Found a typo above with the human check... seems all taxa are lowercase, but let's make sure it's enough to catch them all" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "num homo sapiens: (16944, 34)\n" ] }, { "data": { "text/plain": [ "(16944, 34)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"num homo sapiens: \", df_cleaned.loc[df_cleaned.species == \"homo sapiens\"].shape)\n", "df_cleaned.loc[df_cleaned[\"original_label\"] == \"human\"].shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Did any of these factor in to the multi-species counts?" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(353, 34)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[(df_cleaned[\"species\"] == \"homo sapiens\") & (df_cleaned[\"multi_species\"])].shape" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...superfamilyfamilysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_species
247895ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 4935ENA24 : unknownENA24-1humanhomo sapiens...hominoideahominidaehomininaeNaNhomohomo sapiensNaNNaNTrue2.0
247621ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 4804ENA24 : unknownENA24-1humanhomo sapiens...hominoideahominidaehomininaeNaNhomohomo sapiensNaNNaNTrue2.0
247631ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 4809ENA24 : unknownENA24-1humanhomo sapiens...hominoideahominidaehomininaeNaNhomohomo sapiensNaNNaNTrue2.0
250361ENA24https://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...ENA24 : 6989ENA24 : unknownENA24-1humanhomo sapiens...hominoideahominidaehomininaeNaNhomohomo sapiensNaNNaNTrue2.0
\n", "

4 rows × 34 columns

\n", "
" ], "text/plain": [ " dataset_name url_gcp \\\n", "247895 ENA24 https://storage.googleapis.com/public-datasets... \n", "247621 ENA24 https://storage.googleapis.com/public-datasets... \n", "247631 ENA24 https://storage.googleapis.com/public-datasets... \n", "250361 ENA24 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "247895 http://us-west-2.opendata.source.coop.s3.amazo... \n", "247621 http://us-west-2.opendata.source.coop.s3.amazo... \n", "247631 http://us-west-2.opendata.source.coop.s3.amazo... \n", "250361 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure image_id \\\n", "247895 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 4935 \n", "247621 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 4804 \n", "247631 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 4809 \n", "250361 https://lilawildlife.blob.core.windows.net/lil... ENA24 : 6989 \n", "\n", " sequence_id location_id frame_num original_label scientific_name \\\n", "247895 ENA24 : unknown ENA24 -1 human homo sapiens \n", "247621 ENA24 : unknown ENA24 -1 human homo sapiens \n", "247631 ENA24 : unknown ENA24 -1 human homo sapiens \n", "250361 ENA24 : unknown ENA24 -1 human homo sapiens \n", "\n", " ... superfamily family subfamily tribe genus species \\\n", "247895 ... hominoidea hominidae homininae NaN homo homo sapiens \n", "247621 ... hominoidea hominidae homininae NaN homo homo sapiens \n", "247631 ... hominoidea hominidae homininae NaN homo homo sapiens \n", "250361 ... hominoidea hominidae homininae NaN homo homo sapiens \n", "\n", " subspecies variety multi_species num_species \n", "247895 NaN NaN True 2.0 \n", "247621 NaN NaN True 2.0 \n", "247631 NaN NaN True 2.0 \n", "250361 NaN NaN True 2.0 \n", "\n", "[4 rows x 34 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[(df_cleaned[\"species\"] == \"homo sapiens\") & (df_cleaned[\"multi_species\"])].sample(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's fix those counts then." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "num_species\n", "1.0 2753832\n", "2.0 114825\n", "3.0 13995\n", "4.0 1704\n", "5.0 230\n", "14.0 42\n", "Name: count, dtype: int64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "human_multi_species = list(df_cleaned.loc[(df_cleaned[\"species\"] == \"homo sapiens\") & (df_cleaned[\"multi_species\"]), \"image_id\"].unique())\n", "\n", "for img in human_multi_species:\n", " df_cleaned.loc[df_cleaned[\"image_id\"] == img, \"num_species\"] = df_cleaned.loc[df_cleaned[\"image_id\"] == img, \"num_species\"] - 1\n", "\n", "df_cleaned.num_species.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actually remove human indicators" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "df_cleaned = df_cleaned.loc[df_cleaned[\"species\"] != \"homo sapiens\"].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Need to remove the images that have humans and other species too." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "df_cleaned = df_cleaned.loc[~df_cleaned[\"image_id\"].isin(human_multi_species)].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Save this to CSV now we got those counts" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "df_cleaned.to_csv(\"../data/potential-test-sets/lila_image_urls_and_labels.csv\", index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate individual CSVs for the datasets" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "for dataset in datasets_of_interest:\n", " df_cleaned.loc[df_cleaned[\"dataset_name\"] == dataset].to_csv(dataset+\"_image_urls_and_labels.csv\", index = False)\n", "\n", "# Manually moved these to the data/potential-test-sets/ directory and renamed to not have spaces in the filenames\n", "# (replaced spaces with underscores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get some basic stats" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "there are 2867312 images\n", "we have 367 unique scientific names\n", "when we filter for image-level labels, we have 184 scientific names\n" ] } ], "source": [ "print(f\"there are {df_cleaned.shape[0]} images\")\n", "print(f\"we have {df_cleaned['scientific_name'].nunique()} unique scientific names\")\n", "print(f\"when we filter for image-level labels, we have {df_cleaned.loc[df_cleaned['annotation_level'] == 'image', 'scientific_name'].nunique()} scientific names\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "num_species\n", "1.0 305821\n", "2.0 1154\n", "3.0 3\n", "Name: count, dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned['annotation_level'] == 'image', 'num_species'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will want to dedicate some more time to exploring some of these taxonomic counts, but we'll first look at the number of unique taxa (by Linnean 7-rank (`unique_7_tuple`)). We'll compare these to the number of unique scientific and common names, then perhaps add a count of number of creatures based on one of those labels. At that point we may save another copy of this CSV and start a new analysis notebook." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "annotation_level\n", "sequence 2560334\n", "image 306978\n", "Name: count, dtype: int64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.annotation_level.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get a sense of total number of unique taxa, then separate out the by-image ones for unique taxa count there. Then we'll separate out each dataset into its own CSV for individual analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Taxonomic String Exploration" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "lin_taxa = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### How many have all 7 Linnean ranks?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 2187756 entries, 243179 to 23565561\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 2187756 non-null object\n", " 1 phylum 2187756 non-null object\n", " 2 class 2187756 non-null object\n", " 3 order 2187756 non-null object\n", " 4 family 2187756 non-null object\n", " 5 genus 2187756 non-null object\n", " 6 species 2187756 non-null object\n", "dtypes: object(7)\n", "memory usage: 133.5+ MB\n" ] } ], "source": [ "df_all_taxa = df_cleaned.dropna(subset = lin_taxa)\n", "df_all_taxa[lin_taxa].info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 249847 entries, 243179 to 19469709\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 249847 non-null object\n", " 1 phylum 249847 non-null object\n", " 2 class 249847 non-null object\n", " 3 order 249847 non-null object\n", " 4 family 249847 non-null object\n", " 5 genus 249847 non-null object\n", " 6 species 249847 non-null object\n", "dtypes: object(7)\n", "memory usage: 15.2+ MB\n" ] } ], "source": [ "df_all_taxa_img = df_cleaned.loc[df_cleaned[\"annotation_level\"] == \"image\"].dropna(subset = lin_taxa)\n", "df_all_taxa_img[lin_taxa].info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(306978, 34)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned[\"annotation_level\"] == \"image\"].shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's not too bad, considering some are definitely just common names or classes: 2,187,756 out of 2,867,312. \n", "\n", "249,847 when we drop to just image-level annotations (out of 306,978).\n", "\n", "\n", "Now how many different 7-tuples are there?\n", "\n", "#### How many unique 7-tuples?" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "unique taxa in all: 355\n", "unique taxa in image-level labeled: 180\n" ] } ], "source": [ "#number of unique 7-tuples in full dataset\n", "df_cleaned['lin_duplicate'] = df_cleaned.duplicated(subset = lin_taxa, keep = 'first')\n", "df_unique_lin_taxa = df_cleaned.loc[~df_cleaned['lin_duplicate']].copy()\n", "print(f\"unique taxa in all: {df_unique_lin_taxa.shape[0]}\")\n", "print(f\"unique taxa in image-level labeled: {df_unique_lin_taxa.loc[df_unique_lin_taxa[\"annotation_level\"] == \"image\"].shape[0]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty much aligns with the scientific name counts." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "355" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_unique_lin_taxa.scientific_name.nunique()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...familysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_specieslin_duplicate
\n", "

0 rows × 35 columns

\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [dataset_name, url_gcp, url_aws, url_azure, image_id, sequence_id, location_id, frame_num, original_label, scientific_name, common_name, datetime, annotation_level, kingdom, phylum, subphylum, superclass, class, subclass, infraclass, superorder, order, suborder, infraorder, superfamily, family, subfamily, tribe, genus, species, subspecies, variety, multi_species, num_species, lin_duplicate]\n", "Index: []\n", "\n", "[0 rows x 35 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_unique_lin_taxa.loc[(df_unique_lin_taxa[\"scientific_name\"].isna()) | (df_unique_lin_taxa[\"common_name\"].isna())]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check out our top ten labels, scientific names, and common names. Then we'll save this cleaned metadata file." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "original_label\n", "eurasian_wild_pig 234736\n", "impala 231338\n", "zebraplains 121619\n", "wildebeestblue 121208\n", "large_antlered_muntjac 119774\n", "elephant 119579\n", "unknown 117522\n", "unidentified_murid 98858\n", "sambar 74459\n", "stump_tailed_macaque 73543\n", "Name: count, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned[\"original_label\"].value_counts()[:10]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "scientific_name\n", "sus scrofa 237022\n", "aepyceros melampus 231338\n", "animalia 136912\n", "equus quagga 121619\n", "connochaetes taurinus taurinus 121208\n", "loxodonta africana 121013\n", "muntiacus vuquangensis 119774\n", "muridae 98858\n", "aves 80435\n", "rusa unicolor 74459\n", "Name: count, dtype: int64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned[\"scientific_name\"].value_counts()[:10]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "common_name\n", "eurasian wild pig 234736\n", "impala 231338\n", "plains zebra 121619\n", "blue wildebeest 121208\n", "large-antlered muntjac 119774\n", "african bush elephant 119579\n", "unkown animal 117522\n", "old-world mice and rats 98858\n", "bird 79568\n", "sambar 74459\n", "Name: count, dtype: int64" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned[\"common_name\"].value_counts()[:10]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(df_cleaned, y = 'class')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAosAAAGwCAYAAADBvy/kAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVhO6ePH8XdK2igKg6hsIVEzWcoylOUrIruYSIPssrchKktRKPtajNCIYexZBjNGZpS1TNqQGcJYsrb9/nh+nSOKmjEk9+u6XFPPc56zfGLczjn35yjl5ubmIgiCIAiCIAgFKPOxd0AQBEEQBEEoucRgURAEQRAEQSiUGCwKgiAIgiAIhRKDRUEQBEEQBKFQYrAoCIIgCIIgFEoMFgVBEARBEIRCicGiIAiCIAiCUCiVj70DwqctJyeHrKwsypQpg5KS0sfeHUEQBEEQiiA3N5ecnBxUVFQoU+bt5w7FYFH4V7Kysrh48eLH3g1BEARBEP4BU1NTVFVV37qMGCwK/0rev0YaNWr0zt9spV12djYXL17E1NQUZWXlj707H43IQSaykIksFEQOMpGF7GNkkbfNd51VBDFYFP6lvEvPysrKn/0f9jwiCwWRg0xkIRNZKIgcZCIL2cfIoii3kIkJLoIgCIIgCEKhxGBREARBEARBKJQYLAqCIAiCIAiFEoNFQRAEQRAEoVBisCgIgiAIgiAUSgwWBUEQBEEQhEKJwaIgCIIgCIJQKDFYFARBEARBEAolBouCIAiCIAhCocRgURAEQRAEQSiUGCwKgiAIgiAIhRKDRUEQBEEQBKFQYrAoCIIgCIIgFEoMFgVBEARBEIRCqXzsHRAKdvPmTWxsbDhy5Aj6+vr/al25ubls2bKFQYMGAeDm5gbA/Pnz//V+5nmamcPLnMz3tr5PUS5Q27gRTzNzUMrMKdJnypRRQkNV/DEUBEEQSi7xt9Rn4OzZs8yZM0caLHp6er73bYzbEkNm7ntf7SclNxcyMjLQ0tJCSalon1k72OK/3SlBEARB+JfEYPEzkJubfxRXvnz5j7QngiAIgiB8asRg8T+Sdxl5/PjxbNy4ETs7O9q0acPSpUtJTExEX18fV1dXOnXqBEBmZibz58/nhx9+QENDAxcXl3zre/ToET4+Phw5cgQNDQ06d+7M1KlTUVNT48yZM7i7uzNs2DBWrFjB48eP6dixI35+fty5c4fBgwcDYGxsTFhYGDt37gQUl6Fzc3NZtWoV27dv586dO+jo6DBgwADGjh1bvAPOzSU3t4in00qrvEF5bi65FC2LXCA7O/u/26ePIO94Sttx/RMiC5nIQkHkIBNZyD5GFsXZlhgs/sfOnTvHjh07+OWXXxg3bhxTpkzh66+/5vjx40ycOJFt27bRuHFjgoODOXbsGCtWrEBFRUW6rzCPp6cnmZmZhIeH8+LFC3x9fZkzZw5z584F4M6dOxw8eJC1a9dy584dxo4dS7NmzejduzfBwcGMGzeOU6dOoa2tLQ0WAXbt2kVoaCiBgYHUrFmTkydP4u3tTfv27TExMSnycT55+oQX4s87ABlPnhR52aysLGIvX/kP9+bjuXjx4sfehRJDZCETWSiIHGQiC1lJzUIMFv9jQ4YMoVatWvj7+9O5c2ecnJwAMDIy4sKFC6xfv55FixYRERHB9OnTadasGQAeHh6MGDECgOvXrxMVFUV0dLR0CdnHxwd7e3vc3d0BxZlJLy8v6tWrh7GxMW3atOHixYv069cPbW1tACpXrvzG/lWrVo158+ZhaWkJgIODA8uWLSMhIaFYg0VNDU3KijOLZDx5gpamJkW9aVFFRQUzM7P/dr8+sOzsbC5evIipqSnKysofe3c+KpGFTGShIHKQiSxkHyOLvG0WhRgs/sdq1KgBQGJiIgMGDMj3nrm5OTt27ODvv//m/v37NGzYUHrP1NRU+joxMZGcnBzatm2b7/M5OTmkpqZK3xsYGEhfa2lpkZWV9c79a9myJefPn2fRokUkJiYSFxdHeno6OTlFm80rUVIq4oXX0ku69KykVOQJLkpQav8nqaysXGqPrbhEFjKRhYLIQSaykJXULMRg8T9Wrly5fP99VU5OTr5B2asTUcqWLSt9nZ2dTfny5dmxY8cb66hatSrnz58HQFVVNd97r09sKUhERARz586lb9++dOrUienTp0v3OAqCIAiCIIhS7vfIzc1NutcwNDRUej0yMpKUlBRpULdt2zZatmyJv78/lStXpmLFiujp6eU7HXzlinwfm5GREY8fP0ZJSQkDAwMMDAx4/vw5/v7+vHz58p37pfSW01zh4eGMGTMGDw8P7O3tqVixIvfu3SvSQFMQBEEQhNJPnFn8AGxtbalcuTIjR44kNDSUJUuWYGZmxunTp/n2229RUlJi0KBBLF26lBo1alC+fHnmzZsnfb5OnTq0adOGKVOm4OXlhbKyMjNmzEBbW5sKFSq8c/vq6uoAXLp0iXr16uV7r2LFipw+fRobGxuePHlCUFAQmZmZRRqEvmrpQHNU/sWp89JwCTsXxYQVFRWVIh9PmTKl4cgFQRCE0kwMFj8ANTU12rRpg7+/P8HBwTx58oTU1FSWLFlCq1atABg5ciTPnj1j4sSJKCsrM2bMGObMmSOtw9/fH19fX5ycnFBRUaFNmzZ4eXkVafvGxsa0atWKAQMGEBgYmO89Dw8PPDw86NGjB7q6unTp0gV1dXXi4uKKdYzj/2Up99rBFmiplX33giVYdnY2sZevYGZmViLvOREEQRCEf0IMFgtw48YNZsyYQUxMDLVq1cLe3p7vvvuOo0ePEhERwbp167h58yaamprY2tpKZ/teVaFCBZo3b46+vj6RkZGEhIRw9OhRJk2aBCh6GDdt2kSnTp1ITExk7ty5xMTEoKmpSf/+/XFwcGDQoEEEBwcTFxfHw4cPSUhIYOXKlbi5uWFpacmwYcNISEjAwsKCo0ePMm7cOE6cOIGhoSELFy6UziJeuHCBBw8eUKZMGYKDgxk7diydO3cGFGcdNTU1UVZW5vnz57x48YKgoKB890wKgiAIgvD5EoPF12RlZeHi4kLdunXZsWMHcXFxzJw5k4oVKxIdHY2vry8BAQE0atSIS5cuMXXqVCwtLaVy7Xc5deoUrVu3Jjg4mObNm3P//n0GDhyItbU1ERERJCcn4+XlhZaWllSzc+TIEby9vTEzM8PIyAiAxYsX4+/vT4UKFRg2bBg9e/Zk4sSJjB8/nhkzZhAYGMiKFStIT0/HxcWFiRMn0qZNG2JjY3Fzc0NXVxcLCwt8fHzQ0NBg165d3Lt3j/Hjx1O7dm3p0YBF9i9LuUtDObUomFUQOchEFjKRhYLIQSaykIlS7k/Mr7/+yp9//sn27dvR0tKibt26/PHHH+zduxcNDQ38/PykgaG+vj4bNmwgISGhyIPFvK5DbW1tdHR0CAsLQ11dHR8fH1RUVKhTpw7p6eksW7ZMGizq6enh4OCQbz29evXCysoKUNTfpKenS8t0795dmmDz3XffYWVlxTfffAMo6nXi4uIIDQ3FwsKCtLQ0TExMqF69OgYGBqxevbpI90G+7t+WcpemcuqSWqr6oYkcZCILmchCQeQgE1nISmoWYrD4mqtXr2JkZISWlpb0mpmZGXv37qVx48aoqamxdOlSrl27xtWrV0lNTaV169b/eHuJiYmYmJigoiL/KMzNzUlPT+fRo0eA3NX4qpo1a0pfq6mp5VtGTU2NzMxMAJKSkjh27Bjm5ubS+5mZmdIZymHDhuHh4cHhw4dp27Yttra2NGrUqNjH8W9LuUtDObUomFUQOchEFjKRhYLIQSaykIlS7k+MsrLyG7Uxed+fPHmSMWPGYG9vT5s2bRgzZgyzZ8/+V9srrH8R5FPEBS3z+m+mMmUKbkHKysrCzs6OkSNH5ns9b3DavXt3LC0tiYqK4vjx44wfP57hw4czceLE4h3IvyzlLk3l1CW1VPVDEznIRBYykYWCyEEmspCV1CxEz+Jr6tWrR0pKChkZGdJrly9fBhQF1r1792bOnDn07duXOnXqcP369X/VSWhkZMTly5elM4EAMTExVKpUCR0dnX+83lfXn5qaKvUzGhgYcOTIEfbs2QNAUFAQ9+7dw8HBgVWrVuHq6sqhQ4f+9XYFQRAEQSgdxJnF11haWlKtWjVmzJjB2LFjSUhIICwsTLrHMCYmhpiYGM6cOcO1a9dIT08nNDQUfX39f7S93Nxcbt++zcyZMxk2bBjJyckEBwczcODAt5Zpvy4nJ4ft27fTr1+/fK8PHDiQTZs2ERQURM+ePbl48SL+/v506NABUFymnjNnDjNnzkRZWZmffvrpH12G/rc9i7lAxvPMdy5XkuUCtY0b8TQzB6XMYj4usRQROchEFjKRhYLIQSaykBWWRZkySmiofvyh2sffgxImr15mxowZ9OjRg9q1a9OrVy9OnDjB2LFjcXd355tvvkFJSYkePXrg4OBAUlIStra2REdHF3t7ampq6Orqcv36dezt7alUqRJDhgzBxcWlWOu5ceMGv/322xuDxRo1arBy5UoWLlzIunXrqFq1KrVq1cLY2BgAb29vZs+ejaOjI1lZWbRr1w5PT89iH8e/7VksDXJzISMjAy0trSI/G7o0EjnIRBYykYWCyEEmspAVlsXawRYfb6deoZQrnuuWz71797hy5Qpt2rSRXlu7di0//fQTmzZtAiA4OJjo6Gjp+3/j1Q7GD7UeR0dHmjdvzrhx4/7VNuH/i6hjY1l87qUYLIr/8QEih1eJLGQiCwWRg0xkIXvbYPG/emBF3t/fRXmQhLhn8f/9/vvvODg40L59e4YNG0bXrl25cOEC/v7+LF68mIcPH/LVV1+xYsUKQkJCiI6Ols7OWVtbExkZCSguB69duxYbGxuaNGmCo6MjV69elbZz+/Zthg0bhpmZGT179uT69ev59uPIkSPY29tjamqKhYUFkyZN4smTJ9L7P/zwA//73/9o2rQpAwYM4MqVK5w5cwZ3d3fS0tIwNjbm999/p1GjRty/f1/63KVLl2jatGm+ezEBXr58ybx582jTpg0mJiZYW1uzbdu24geYm0tuLp/1L/L+3fWZZyFyEFmILEQOIov3lAWKQd1/9auoxGVo4PHjx7i4uODk5IS/vz/79u0jODiY/v37U6FCBTIzM2nfvj3du3dHQ0ODR48eERMTQ3Bw8BvrWrZsGeHh4fj4+GBoaMiaNWsYNmwYBw8eRENDgwkTJqChoUFERAQJCQl4enpSsWJFAK5fv86ECROYOXMmVlZWpKSkMGXKFLZv387QoUM5efIknp6eeHp6YmVlxaZNm3BxceHIkSN4eHiwfv16vv/+eypVqkTVqlU5fPgw/fv3B2D//v18/fXX+SqBAFavXs3x48cJDg5GV1eXnTt34uPjg42NDXp6ekXO8N/2LJYmGa8M7j9nIgeZyEImslAQOchEFrLXsygpHcRisAg8f/6c0aNHM3ToUJSUlHBxcSEjI4MLFy7Qo0cPPDw8GDVqFGpqagBoaGhQtmxZqWA7T25uLps3b2bSpEnY2NgA4OPjQ8eOHdm9ezdfffUVMTExHDt2jOrVq1OvXj0uXbrEgQMHAMVZSS8vL+m+Q319faysrEhISABg27ZtdOvWTSrfnjZtGmXLluXhw4eUL18eZWVlaZ9sbW05cOCANFg8cOAA06ZNe+PYGzRoQMuWLaWOw5EjR7Js2TJSUlKKNVj8tz2LpUJuLhlPnqClqclnfU1F5CATWchEFgoiB5nIQlZIFv9lB7HoWSymypUrY29vz8aNG4mLi5MKt7/88ksAdHV1pYHi29y7d48HDx7QtGlT6bWyZcvSuHFjEhMTpRnV1atXl943NTWVBouGhoaoqqqyYsUKEhISSEhI4Nq1a/To0QOA5ORkBgwYIH1WVVWV6dOnF7gv3bp1Y+PGjfz999/cuHGDv//+m3bt2r2xXIcOHfj555+ZP38+SUlJXLmi+BdMsR859C97FkuD3LwElJQ+6//viRxkIguZyEJB5CATWcgKy6KkdBCLexZR3EfYvXt3fv31V0xMTPDw8GDo0KHS+wWVYheksOWys7Olou3X5xOVLSvfuBofH0/Xrl25du0aFhYW+Pn5YWtrK73/6lNe3qVhw4bUqlWLqKgoDh48iI2NTYH7FxQUxNSpU1FRUcHe3v6f3a8oCIIgCEKp9UmdWcybhdyzZ8+3zvx1c3MDYP78+e9cp6OjI2pqamhra7Nq1Srp9U2bNr0xsDM2NiYsLKzA/sOkpCQsLCzQ09MjNjaWBg0aAIpH612+fJlWrVpRv359Hj58KJVkA8TFxUnr+OGHH2jWrBmLFi2SXktNTaVOnTqA4rnO8fHx0nvZ2dl07NiRgICAfPuUd/zdunXj2LFjXL9+nSlTpkjvx8bGUr58eQC2bt2Kt7c3Xbp0AeDatWvAm4NaQRAEQRA+T5/UYDGPra1tgZdU/yk1NTVu3brF6dOn0dfXZ//+/Rw6dAhTU9MCl1dXV+fOnTvcvHlTKuOuVasWp06dYteuXSxdupQqVapgYGDAmjVrePHiBba2tlSqVAlLS0s8PDyYMWMGN2/eZPPmzWhqagKgo6PD1atXuXDhAuXLl2fbtm1cvHhReg60o6Mjzs7OWFhY8OWXX0oDWhMTE9LT03n48CEpKSnk5ORQpkwZunXrxqpVq1BXV6dVq1bS/puYmFCvXj1pm8eOHaNx48bcvn2buXPnAopZ0sUR/C9LuUuDXBQ3I6uoqHzWl+RFDjKRhUxkoSBykIksZIVlUaZMyUjmkxwsqqmpFekewqKqW7culSpVYvz48SgpKWFqasr06dMJDg4ucNDUsWNHtm7dSteuXaWzmyoqKlSuXBlnZ2cyMjKYMWMGGRkZmJubs2nTJipVqgQoLvvOmDGDAQMGUL16dRwdHaXaHUdHR65cuYKTkxPlypWjWbNmjBkzhr179wLQrFkzZs2axbJly0hPT6dx48asXLkSNTU1WrZsiYGBAXZ2dlhZWVGxYkUMDAyoW7cujRo1yne5u2zZstIl7blz5+Lt7U3Xrl2pWrUqffv2RVlZmbi4ONq2bVusHP+rLqhPRXZ2NrGXrxSps6o0EznIRBYykYWCyEEmspCV9CxK9D2L165dw8HBgaZNmzJ48GD+/vtvQFFAbW1tLS3322+/YW9vT5MmTZgwYQLPnj3Lt57Dhw9ja2tL06ZN6dOnzxtPWilTpgyzZ8/GxsaG7t27U7ZsWQIDA6lZsyb169fPd7n7t99+Y8yYMfz111+Ympry/Plzjh49So0aNTA2NkZZWZm+ffty9+5dlixZwvXr1+nfvz++vr788ccffPvtt5w6dYoWLVqwfft2xo4dy9GjR4mMjKR3794cO3YMQ0NDli5dytKlSxk/fjyZmZkEBATQunVrNm/ejK+vL9ra2nTu3JnBgwdjZWVFeHg4kZGRXLx4kYoVK5KRkYGrqyuXL1/O9yxogLS0NGrUqAEoLq03btwYTU1N/vzzTyIjI3FxcSn2E2QEQRAEQSidSuyZxZcvXzJixAgsLCzw9fXl119/Ze7cudIM5Tz379/HxcWF/v37ExgYyN69ewkJCaFnz56AYtLI9OnTmT17Nk2aNOGnn35i+PDh7N69W7pv8FVbt27FycmJqVOnsnXrVkaMGMGhQ4ekM4MREREsWLAAHR0dpkyZwsKFCwkKCirwGFavXs3y5cu5du0akydP5sSJE8yaNQs1NTVGjx7N999/j5OTE5GRkfj4+DBr1iyaNGlCZGQkI0aM4MCBA1StWhWAPXv2sG7dOnJzc3n48CH37t1j165drF+/nj///JPp06ejq6sr1e4cPnyYL7/8kho1atC5c2c8PDxo166ddK9iHj8/P5KTk1m/fj3q6uqsXbsWT09P2rZti6qqarF+ZsWeQV3K5B2/yEHkkEdkIRNZKIgcZCIL2cfIolSUcv/yyy88ePAAb29vNDQ0qFOnDtHR0fmeSgKKsulKlSoxdepUlJSUGDduHD/99JP0/rp16+jXrx92dnYADB48mLNnzxIeHi5NBHlV3bp1pckg7u7uHD16lH379vHNN98AMGrUKFq0aAFAnz592Lp1a6HHMHr0aBo0aECDBg2YO3cuXbt2le4dtLS0JCkpCVBMpnF0dMTe3h6AKVOmcPbsWTZv3szkyZMB6N69u/TEmDNnzpCVlcXcuXNp0KABJiYmDBkyhK1bt0qDRS0tLW7cuMHixYsxNjZm/fr1JCUl5av1AcWl7aFDh1K/fn0AnJ2diYiI4N69e1SrVu2tP6NXZWdlERt7ucjLl2ZF7a0q7UQOMpGFTGShIHKQiSxkJTWLEjtYvHbtGoaGhmhoaEivmZqa5hsI5i3XoEGDfLOBTU1NpUvRiYmJ7N+/P18lTGZmJq1bty5wu6+euSxTpgyNGjUiMTFReq1WrVrS1+XLl+fFixeFHkPexBRQ3GeZd+k37/u8+yETExMZM2ZMvs+amZnl2+6rnwVFMXjejGuAxo0bs379eul7a2trAgIC8n2moH21t7cnKiqK7du3k5SUxOXLigFfcf91o/wfFod+KvIKTk1NTUvkPScfishBJrKQiSwURA4ykYXsY2RRakq539ZJ+K7l8gaL2dnZDB8+XDprl6ewCTKvdxlmZ2dTpox8a+erX7/L6z/wwj5bUP/hq92MBS3z+n7m5OTkGzAX9JutoDqcadOmERMTQ48ePXBwcKBy5crSU1+K63P/w55HWVlZZIHI4VUiC5nIQkHkIBNZyEpqFiV2gku9evVISUnh8ePH0muvdhK+utyVK1fynQl7dTkjIyNu3ryJgYGB9Gvbtm2cOHGiwO2++tns7Gzi4+Oly7//FSMjI86fP5/vtfPnz2NkZFToZx49esTNmzel7y9evFjs/czIyODHH38kKCiI8ePH07FjRx4+fAiInkVBEARBEBRK7GDRysqKatWq4enpSWJiIpGRkezbt++N5bp27cqzZ8/w8/MjKSmJtWvX8vvvv0vvOzk5sW/fPsLCwrh+/TobN25k48aNGBoacvPmTaKjo3n06JG0fHR0tHR/n5+fH8+ePeN///vff3qsTk5ObN68mV27dpGcnMzChQuJj4+nT58+b/3cjBkz+OOPPzh48CCbNm1i0KBBbyxz5syZQgeRqqqqqKurc+jQIW7evMnJkyeZM2cOUPyeRYCM55lv/Hr6MqvY6xEEQRAEoeQosZehy5Yty6pVq/Dy8qJnz54YGxszaNAgLl26lG85bW1t1q5di7e3Nz169KBZs2b06NFDOjNmZmaGv78/wcHB+Pv7U6tWLRYtWkSzZs3ynZnLY21tza+//srixYtp1KgRGzZsoEKFCv/psdra2nL37l2WLl1Keno6DRs2ZP369dKTWwrTtm1bBg4ciIaGBpMmTZIm8RSVqqoqAQEBLFiwgE2bNqGvr8+oUaNYvHgxcXFx79z+q8ZtiSGzgJORawdbFGufBEEQBEEoWZRyP+PrjTdv3sTGxoYjR46gr69frMcEfkxnzpxh8ODBXL169b0u+09kZ2cTGxvL4nMvCx0sfi5l3XlZlNRS1Q9F5CATWchEFgoiB5nIQvYxsijONkvsZegP7dq1a/z888/s2bMHU1NTBg4cmG828qVLl+jXrx9NmjRhwIABLFmyBEdHR+n9Y8eO0bNnT5o0aYKtrS2HDh2S3nN0dGTdunUMHTqUJk2a0KdPH1JTU5kxYwbm5uZ06tQpX1F4YmIi3377LV9++SVt2rQhJCREmuwSHBzM4sWLAWjevDnR0dFYW1uzceNG7OzsMDMzY8SIEaSnp+c7vvDwcNq0aYO5uTnu7u7SZebc3FxWrlyJtbU1jRs3pnXr1oSEhLz3fAVBEARB+DSV2MvQH1Jubi4jR45EQ0ODpk2bMmrUKObMmUNAQAArV67k8ePHDBs2jC5dujB//nx++eUX5s2bJ9XsnD59mnHjxjFlyhS+/vprjh8/zsSJE9m2bRuNGzcGYNmyZfj5+eHp6cmYMWPo06cP33zzDd9//z2BgYH4+vqye/du7t+/z8CBA7G2tiYiIoLk5GS8vLzQ0tLCyckJgHPnzgEQGhoqTYIJDg5m5syZNGjQAF9fX8aNG5evA/LgwYOsW7eO9PR0xo4dS5MmTXBwcGDXrl2EhoZKT6w5efIk3t7etG/fHhMTk+KESG7um8+wzOXzKVwVBbMKIgeZyEImslAQOchEFjJRyv0JeP78OQMGDJDu/wPo2bMna9euBWDfvn1oaGjg5eWFsrIytWvX5ty5c9LZu++++47OnTtLgzkjIyMuXLjA+vXrCQwMBKB9+/Z06dIFgA4dOrBv3z7pWdT9+vWTehZ//PFH1NXV8fHxQUVFhTp16pCens6yZcuk9evp6fHzzz/nO4bevXvTo0cPQPG85w4dOvDHH39I78+aNQsjIyPq16+PlZUV8fHxAFSrVo158+ZhaWkJgIODA8uWLSMhIaFYg8UnT5/wooDfd1lZWcRevlLk9ZQGJbVU9UMTOchEFjKRhYLIQSaykJXULMRgEVBXV5fOsl26dImkpCSuXLmCnp4eAFevXsXExCTfNX0zMzMOHz4MKC4bDxgwIN86zc3N2bFjh/S9vr6+9LWamhrVq1eXehHV1NTIzMyU1mViYpKvR9Hc3Jz09HRp1vbrBd2Qv0y8Zs2a6OjokJiYKD2m8PUy8bzL0C1btuT8+fMsWrSIxMRE4uLiSE9Pz9fxWBSaGpqULeDMospnVNYtCmYVRA4ykYVMZKEgcpCJLGSilPsT8PTpU4YPH07FihWxtramW7duJCUlSU9EUVZWfqN38NXvCyrVzsnJyTfger1EuzgF3XnryTtlXNAy7yoTf/03X97+R0REMHfuXPr27UunTp2YPn06gwcPLnDf3kpJiTeHiqBUwLZLu5JaqvqhiRxkIguZyEJB5CATWchKahZisIiiW/HOnTvs2bNHGnSdOnVKGlDVq1ePo0ePkpOTIw3A8h6LBwWXasfExLy1VLswRkZGHDp0iMzMTOmJNTExMVSqVAkdHZ1CPxcfH0+HDh0ASE1N5fHjxxgbG3P79u23bi88PJwxY8YwbNgwQFH2fe/ePVHKLQiCIAgCIAaLAJiYmPD06VOioqJo3Lgxp0+f5rvvvkNLSwtQFH8vWrSIefPmMXDgQM6ePcu+ffswNzcHFKXaAwcOJDQ0VJrgcvjwYdatW1fsfbGzs5MmqwwbNozk5GQWL17Mo0ePSEtLK/RzYWFhNGzYkBo1auDj40OrVq0wNDR852CxYsWKnD59GhsbG548eUJQUBCZmZnFLuVeOtAclYIeMYiirLu0KFNGCQ1V8cdGEARB+HyIv/WAypUrM2bMGGbPns2LFy8wNjZm5syZeHp6cvv2bapWrcrKlSuZPXs24eHhmJqaYmdnx507dwBo2rSpVPwdEBCAkZERixcvliaNFIeWlhZr167Fz88Pe3t7KlWqhJOTE3369JHuoSxIz549CQwM5NatW3z99dfMnj27SNvz8PDAw8ODHj16oKurS5cuXVBXVy/w0YpvM76QUu7SRpSMC4IgCJ+bz7qUu6hu3LjB7du3sbCQBwqzZ8/m2bNnJaLA29ramrFjx9KrV68Pvu13lXKXNm8rGRcFswoiB5nIQiayUBA5yEQWspJeyi3OLBZBRkYGQ4cOJSAgAFNTUy5fvswPP/xAYGAgqampzJkzh3PnzqGtrY2zszODBw/myJEjBAcHk5iYSLly5Wjbti0+Pj5oamoSHBxMXFwcDx8+JCEhgZCQENzc3Pj222/54YcfiIuLo3bt2vj5+dG4ceN8T5rZunUrsbGxbN68Wdq/hw8fsnbtWnr16sXDhw9ZuHAhR44c4cWLF1hbW+Pl5YW2tjZnzpzB3d2dNm3a8OOPP+Li4oKTkxOLFi1i37593L9/n6pVq+Li4kL//v2LF1IhPYulzdt6I0VnmILIQSaykIksFEQOMpGFTPQslgINGzZk5syZBAYG8ueff1K9enXc3d2xtLTE1tYWExMTtm/fzo0bN5g8eTK5ubkEBAQwc+ZMrKysSElJYcqUKWzfvp2hQ4cCcOTIEby9vTEzM8tXrO3r60udOnWYMWMGvr6++Yq1QXH/5Lp167h37x66urqAoieyadOmAIwdO5Znz56xcuVKALy9vXFzc2PFihUApKWl8fLlSyIjIylbtiyrV6/m+PHjBAcHo6ury86dO/Hx8cHGxuatl71fV1jPYmlTlN7IktqT9aGJHGQiC5nIQkHkIBNZyEpqFmKwWER9+/alb9+++V47cuQI9+/fZ+7cuWhpaVGvXj28vLx4+vQpXl5e9OvXD1B0LFpZWZGQkCB9Vk9PDwcHh3zr69mzpzSjeejQoUyYMOGN/WjYsCGGhoZERUXRv39/rl69ipKSEtOnTyc+Pp7o6GgOHDggDUADAgKwtbUlKSlJWsewYcMwMDAAoEGDBrRs2VLqQhw5ciTLli0jJSWlWIPFwnoWS5u39UaKzjAFkYNMZCETWSiIHGQiC5noWSzFkpOTMTIykmZNg+JJKgC3bt1ixYoVJCQkkJCQwLVr16QnrEDBxdqGhobS11paWlJR9+vynj3dv39/Dh06hJWVFTo6Ovzyyy9UqFAhX2VPnTp10NbWJikpifLlywP5C8I7dOjAzz//zPz586UycvgHp8IL6VksbYrSG1lSe7I+NJGDTGQhE1koiBxkIgtZSc2i4GZooUheL8LOEx8fT9euXbl27RoWFhb4+flha2ubb5mCirXzehXfxdbWljNnzvDo0SMOHTokrVtVVbXA5bOzs/MN/l7ddlBQEFOnTkVFRQV7e3u2bdtWpH0QBEEQBOHzIM4s/r9XJ5G8eubtbQwNDUlNTeXZs2eoq6sDsGDBAh48eECzZs1YtGiRtGxqaip16tR5Yx15M5mLo06dOtSpU4etW7eSkpJChw4diIuL49mzZzx69IikpCRq164NwLVr18jIyMDIyIi///4bAGNjY65evQrA1q1b8fb2lp5bfe3aNQBRyi0IgiAIAiAGi5Jq1apx6tQp6VnKRdG6dWv09PSYOXMmI0eOJCUlha1btzJixAh++eUXLly4QPny5dm2bRsXL16kZs2a721/u3btyooVK2jbti1aWlqMGTOGsWPH0rZtW6ZPn86MGTMARcVPs2bNqF+/PmfOnHljPTo6Ohw7dozGjRtz+/Zt5s6dC/DeSrlLm4JKxkVRtyAIglCaib/h/p+ysjKVK1cu1mdUVFRYvnw5c+bMoWfPnujp6TFt2jR69OhBfHw8Tk5OlCtXjmbNmjFmzBj27t373vbX1taWRYsW0bVr13yvL1iwAF9fX5ycnFBWVsbGxgZ3d/dC1zN37ly8vb3p2rUrVatWpW/fvigrKxMXF0fbtm2LvD+fSyl3QURRtyAIglCafdKDxbxLxwsXLsTf359nz55hb2+Pm5sbKioqHD58mKCgINLS0qhXrx7Tpk2jefPmADg6OlK/fn2OHz9OdnY2q1atonv37tJl6H379rFkyRJu3bpFzZo1mTRpkjRTOSwsjA0bNnD37l3q1auHh4eHVNidmJjIuHHjiImJQVNTk/79+zN69GjKlCnD+PHj2bp1Kzt27ODRo0csX75cOpajR4+SkZGBu7s7x48f5/HjxxgZGREVFUWHDh24evUq9+7dw9XVlRMnTqCuro6LiwtdunTB0dGRtLQ03N3d6dmzJ3///Te9evXCy8tLWv/IkSNp0KABYWFhDB48OF+OWlpaKCkpkZ6eztmzZ1m2bBlVqlT5r398giAIgiB8Aj7pwWKekJAQgoKCyMrKYtq0aWhqatKlSxemT5/O7NmzadKkCT/99BPDhw9n9+7dUm1MZGQk69atQ1VVFU1NTWl99+7dY9q0acyZM4cWLVpw4MABJk2axIkTJ7h16xb+/v6EhIRQt25dwsLCpAHcgwcPGDhwINbW1kRERJCcnIyXlxdaWlo4OTlx8uRJ/Pz88PHxwcTEhMDAwHzPe/bz8yM5OZn169ejrq7O2rVr8fT0pG3btqiqqjJmzBiUlZXZvHkzT548YeLEiVSpUoXg4GB69OiBs7MzvXr14uDBgyxduhRPT0+UlJR4/Pgxp06dYvLkydy/f1/a3uPHj6Vibn9/f+7cuYOHhwerV6/ON9Asks+klLsgeUXdomBWQeQgE1nIRBYKIgeZyEImSrk/gKlTp0pn9iZMmMDChQu5desW/fr1w87ODoDBgwdz9uxZwsPDcXNzA6Bdu3Z8+eWXgOIsZZ7bt2+TmZnJF198QY0aNXB2dsbY2Jhy5cqRlpaGkpIS1atXR19fH1dXV9q3b09OTg4//vgj6urq+Pj4oKKiQp06dUhPT2fZsmU4OTkRERGBnZ0d9vb2gOIS8Ndffy1tt1mzZgwdOpT69esD4OzsTEREBPfu3ePhw4fExMQQFRUl3fvo7e3N06dP0dHRQVlZmfLly1O+fHk6deqEt7c3586d46uvviIqKgojIyPq1auX777F58+fM3r0aIYOHYqSkhI1a9akU6dOXLhwodg/g8+llLsgrxd1l9RS1Q9N5CATWchEFgoiB5nIQlZSsygVg8W8AR9A48aNuX//PjExMfz111/5qmAyMzNp3bq19H1BXYegKL5u164dQ4cOxcjICBsbG/r27Yu6ujqtW7emfv362NnZ0ahRI+k9FRUVEhMTMTExyVepY25uTnp6Oo8ePSIxMZEBAwZI71WsWDHfpBd7e3uioqLYvn07SUlJXL58GVCM/pOTk9HR0cm3fN5l8ddVqFCBtm3bcuDAAb766iv279//RnUPQOXKlbG3t2fjxo3ExcVx7do1rl69mi/PovpcSrkLklfULQpmFUQOMpGFTGShIHKQiSxkopT7A3i1nzAnJwcAdXV1hg8fLp3Fy6OmpiZ9XVDXIYCSkhKrVq3iwoULHDlyhMOHD7Nlyxa2bNlCw4YNiYiIIDo6mmPHjhEZGUl4eDiRkZEFri9vf/JO975eSfPqvk+bNo2YmBh69OiBg4MDlStXlp7RXNQOxjzdunVjwYIFjBs3jl9++aXAy8q3b9+md+/emJiYYGVlRb9+/Th+/Djnz58v1raAz6aUuyCvF3WX1FLVD03kIBNZyEQWCiIHmchCVlKzKBWl3HFxcdLXly5dokqVKtSpU4ebN29iYGAg/dq2bRsnTpx45/oSExNZsGABTZo0YeLEiezdu5dq1apx8uRJYmJiWLVqFS1btsTd3Z0DBw7w4sULfv/9d4yMjLh8+XK+J6/ExMRQqVIldHR0qFevXr5RfEZGBqmpqdLXP/74I0FBQYwfP56OHTvy8OFDQDHANDAw4MGDB/z555/S58PCwhg9enSBx2Btbc2jR49Yt24dxsbG1KpV641lDh8+jLa2NqtWrWLIkCFYWFhw48YN0bEoCIIgCIKkVJxZ9PPzw9fXl8ePH7NkyRK++eYbWrZsyaBBgzA1NaVdu3YcPXqUjRs3Ehoa+s71VahQgfDwcMqXL4+dnR3Xrl0jLS2NRo0aoaamxrJly9DT08PS0pKzZ8/y9OlTjI2NqVq1KsHBwcycOZNhw4aRnJxMcHAwAwcOxN3dnYyMDH799VcsLCz45ZdfOHz4sHTGUVVVFXV1dQ4dOkSlSpVITk5mzpw5gKLzsF69erRs2RJPT0+mT5/OgwcPWL16NaNGjQJAQ0ODpKQkHjx4gI6ODmpqatjY2LBhwwZcXV0LPE4dHR1u3brF6dOn0dfXZ//+/Rw6dAhTU9Ni/wyK0rNYWs88lilTWo9MEARBEErJYNHW1hYXFxdycnJwcHBgxIgRlClTBn9/f4KDg/H396dWrVosWrSIZs2avXN9lStXJjg4mIULF7Jy5Up0dXWZNGmSdL+jn5+f1K9YvXp1AgICpKezrF27Fj8/P+zt7alUqRJDhgzBxcWFJ0+eAHD8+HFp5rG1tbU0sUZVVZWAgAAWLFjApk2b0NfXZ9SoUSxevJi4uDjq1KlDQEAAs2fPpn///mhpadG/f38GDhwIgIODAwsXLiQlJYWQkBAplx9//LHA+xUBunTpwtmzZxk/fjxKSkqYmpoyffp0goODefnyZaGPDyxIUXoW1w62QEuteJfTBUEQBEH4uJRyP+Frjv/kEX0lQXR0NI6OjsTHx6Ok9N+dldq+fTu7d+9m8+bN/9k2srOziY2NZfG5l5/9YDEvCzMzsxJ5z8mHInKQiSxkIgsFkYNMZCH7GFkUZ5ul4p7Fv/76C2NjY/bs2UObNm2wsLDA19eXrKwscnNzWblyJdbW1jRu3JjWrVtLZ94A4uPjGTBgAE2bNqVNmzb53jt9+jQ9evTA1NQUGxsbtm7dKr336NEjpk6dypdffknr1q3x8fHh+fPnAJw5cwZra2tmzZrFV199xerVq3Fzc8PNzY0zZ87g6OgIQIMGDQgODgbg2LFj9OzZkyZNmmBra8uhQ4ekbTk6OuLj44ONjQ3t2rXj6tWrGBsbc/z4caytrTE3N8fX15c//viDXr160aRJE3r27MmyZcvo27cvoHgGdN6yjo6O0rOh33WcgiAIgiB83krFZeg8BZVzGxoaEhoaSmBgIDVr1uTkyZN4e3vTvn17TExMmDZtGl999RUBAQEkJyczfvx4TE1Nad26Na6urjg5OWFnZ8e5c+eYPn06FhYW1K1bF09PTzIzMwkPD+fFixf4+voyZ84c6dnKaWlpvHz5ksjISMqWLcvSpUsBRZVOcHAw48aN49SpU2hoaHD69GnGjRvHlClT+Prrrzl+/DgTJ05k27ZtNG7cGCi4QHz16tUsX76ca9euMXnyZE6cOMGsWbNISEhg3rx5NGrUCDs7O44ePUpISAg+Pj4YGRmxa9cuBg8ezKFDh9DS0nrrcRZZEUq588qrSytRMKsgcpCJLGQiCwWRg0xkIROl3P8hfX19rl69Kt33V1A5d1BQEPPmzcPS0hJQ3Nu3bNkyEhISMDExIS0tDRsbG2rUqEHNmjXZsGED+vr6PH78mAcPHqCnp4e+vj76+vpUqVKFypUrc/36daKiooiOjqZ8+fIA+Pj4YG9vn+85zMOGDZOeFpNHVVUVbW1tAOlZ1N999x2dO3fGyckJACMjIy5cuMD69esJDAwECi4QHz16NA0aNKBBgwbMnTuXrl270qpVK1q1akVsbCwVKlSgTJkyrF27FhcXF9q3bw8gPXFm9+7d2NnZFXqcxVGUUu7Xy6tLq5JaqvqhiRxkIguZyEJB5CATWchKahaf9GDxdQWVc9evX58bN26waNEiEhMTiYuLIz09Xeo/dHFxITAwkG3bttGuXTt69OghDZQcHBzw8vJi+fLltG/fnt69e6Otrc25c+fIycmhbdu2+bafk5MjVeEARb6P8vWyblCcgdyxY4f0fUEF4q8WdKupqeVbRk1NjZcvX0rrDwgIkAaeAC9evCAlJQUdHZ1Cj7M4ilLKnVdeXVqJglkFkYNMZCETWSiIHGQiC5ko5f6ACirn/v7771mxYgV9+/alU6dOTJ8+ncGDB0vLjRgxgi5duhAVFcXRo0cZMmQIPj4+9O3bF29vbwYNGkRUVBRRUVFs27aN5cuXk52dTfny5fMN5vJUrVpVKrUurPT7dYWVeecdQ2HLvP4bqkyZgm9Bzc7OxsPDQzq7mkdLSwug0ON89VGE71SEUu7Xy6tLq5JaqvqhiRxkIguZyEJB5CATWchKahalYoJLnoLKuQ8cOMCYMWPw8PDA3t6eihUrcu/ePXJzc6V7DVVVVRk6dCibNm2iX79+HDx4kPT0dGbPno2BgQGjRo1ix44dtGzZkqNHj2JkZMTjx49RUlKSCr+fP3+Ov7+/dDavOIyMjN54akpMTAxGRkb/OpO89f/111/5CspXrlxJbGzsW49TEARBEAShVJ1ZLKic++zZs5w+fRobGxuePHlCUFAQmZmZvHz5knLlynHu3Dl8fHyYNGkST5484bfffqNDhw5oa2tz+PBhcnNzcXZ25vbt28THx9OpUyfq1KlDmzZtmDJlCl5eXigrKzNjxgy0tbWpUKFCsffbycmJgQMHEhoaKk1wOXz4MOvWrXsvuQwdOhRPT08MDQ1p0KABgYGBREdH4+Li8tbjLI6ilHLnAhnPM994vUwZJTRUS9VvRUEQBEEoNUrV39AFlXN37NgRDw8PevToga6uLl26dEFdXV06CxkUFMScOXPo06cPKioq/O9//2P06NGoqqqyfPly5s6dS/fu3dHU1KRPnz5SFY2/vz++vr44OTmhoqJCmzZtCnz+clE0bdpUKhAPCAjAyMiIxYsXv3HZ+N/kcvfuXZYuXcpff/1FuXLlWLFiBYaGhgBvPc6iKkopd2HWDrb4Zx8UBEEQBOE/90mXcuf5VMu5P4bg4GCio6PZtGnTe1lfcUq5C1NayrpFwayCyEEmspCJLBREDjKRhaykl3KXqjOLH0JYWBgbNmzg7t271KtXDw8PDywsLPjtt9/w9fUlOTmZVq1aUbNmTR4+fMj8+fMLHKBZW1szduxYevXqRUZGBn5+fhw/fpzHjx+jr6/PlClT6NChAwDGxsYsXryYpUuXcuvWLWxsbJg4cSKenp6cP38eExMTgoKCqFq1Krm5uaxatYrt27dz584ddHR0GDBgAGPHjiUyMlIqHTc2Nubq1avcvn0bPz8/Tp8+zbNnz6hXrx5eXl589dVXxQumCD2LhX6U0tGzJTrDFEQOMpGFTGShIHKQiSxkomexFLly5Qr+/v6EhIRQt25dwsLCcHV1ZdeuXYwYMYIBAwawaNEifvjhB9avX0+3bt2KtF4/Pz+Sk5NZv3496urqrF27Fk9PT9q2bSs9n3np0qXMnz+fZ8+eMWzYMKKjo/H09MTd3Z3x48ezZs0avLy82LVrV6El5La2tiQkJBATEyM9OWbKlClUqFCBrVu3kpuby8KFC/H29mbPnj3FyqYoPYuFKW39iyW1J+tDEznIRBYykYWCyEEmspCV1CxKxWAxr5z7v5aWloaSkhLVq1dHX18fV1dX2rdvz759+9DV1WXq1KkoKSkxadIkfv755yKvt1mzZgwdOpT69esD4OzsTEREBPfu3aNatWqAYhJM06ZNAWjYsCFGRkZ06dIFgE6dOhEfHw9AtWrV3lpCrqGhQdmyZalcuTK5ubl06NCBzp0788UXXwAwaNAgRowYUexsitKzWJjS0r8oOsMURA4ykYVMZKEgcpCJLGSiZ7EUad26NfXr18fOzo5GjRphY2ND3759OXjwIA0aNEBJSR4smZubk5GRUaT12tvbExUVxfbt20lKSuLy5ctA/lPERS3gbtmyJefPny+0hPxVSkpKODg4sG/fPs6dO0dycjKXLl0qcNl3KkLPYqEfpXT1L5bUnqwPTeQgE1nIRBYKIgeZyEJWUrMoVT2L/zV1dXUiIiIIDQ2lefPmREZG0qtXL168eMHr84ReLQh/dRCZJysrS/p62rRpLFiwgAoVKuDg4MCqVaveWL6oBdwRERE4OTnx4sULOnXqxMaNG6Wzhq/LycnB2dmZ9evXU716db799lv8/f0LD0AQBEEQhM/OZ3Vm8d/OBI6JieHXX39l1KhRtGzZksmTJ2NlZUWlSpWIjIzk+vXr1KpVC1AUhOcN0v744w9iYmKk9YSFhXH79m1mzZpF3bp1+fHHH9m+fTtNmjQB4KeffgJ4YwBaFOHh4YwZM4Zhw4YB8OjRI6mEHPIPXK9duyb1UFaqVAlQPKc6b9sFDXILE1yEnsXClCnzT89JCoIgCILwXxNnFotBTU2NZcuWERERwc2bN9m7dy9Pnz6lVatWgGIwmpSUxIYNGzh9+rT0uV69epGdnc3+/ftJTk5mwYIFKCsr4+rqSr169VBXV+fQoUPcvHmTkydPMmfOHIB/9DSYihUrcvr0aemS8sSJE6USclCcHb1z5w43b96kQoUKlClThr1795KWlsaBAwekiS/F3bZG2TJoqZX9R79EIbcgCIIglFxisFgMDRs2xM/Pj7Vr19KlSxdWrlxJQECAdDYxISGBHj168Ouvv0q1NwBff/01Q4cOZebMmQwYMICsrCzq1atHxYoVUVdXJyAggIMHD9K1a1fmz5/PqFGjqFy5cr7HFxaVh4cHGRkZ9OjRg3HjxmFsbEzHjh2ldXXs2JGcnBy6du1K2bJl8fb2Zs2aNXTr1o3Vq1fj5eWFiooKV66UntnJgiAIgiD8c5/sYDE1NZVvv/0Wc3Nz2rVrR1hYGACJiYl8++23fPnll7Rp04aQkJACJ2xERkZibW2d7zVHR0fpzJqbmxsBAQG4urrStGlTbG1tuXLlCklJSdy7d4+KFSvi6upK165dpc9369aNihUrcvbsWa5duyZNUNm5cycHDhzg7NmzPHjwAID4+Hh27twJKJ7dXKtWLZSVlXn06BF//fUXJ06coFu3bgQHB2NjY8PSpUtp3rw50dHRpKWlUa1aNXr37k2TJk2IiYlh/vz5jBs3jl69evH8+XN27NjBsWPHmDZtGk5OTly6dIkmTZowZswYpkyZwvnz59HV1aVNmzbUrVsXgD///JOYmBhiY2MxNzd/vz8wQRAEQRA+SZ/k9b8XL17g7OyMiYkJ27dv58aNG0yePBltbW3mzp2LtbU1ERERJCcn4+XlhZaWFk5OTsXeTmhoKB4eHkycOBF3d3eGDBlC586d2bZtG5s2bWLWrFl07txZWn779u0EBQWRnZ3NiBEj+OOPP95Y56lTp2jdujXBwcE0b96c+/fvM3DgwLfu85EjR/D29sbMzAwjIyMAFi9ejL+/PxUqVGDYsGH07NmTiRMnMn78eGbMmEFgYCArVqwgPT0dFxcXJk6cSJs2bYiNjcXNzQ1dXV0sLCzw8fFBQ0ODXbt2ce/ePcaPH0/t2rUZNGhQsbLKzs7+7ItVRcGsgshBJrKQiSwURA4ykYVMlHL/B06dOsX9+/eZO3cuWlpa0lNHHjx4gLq6Oj4+PqioqFCnTh3S09NZtmzZPxosNm7cmIEDBwKKs4Zz587Fy8sLNTU1HB0dCQ8P5+7du9LyHh4e0pNPGjZsKFXgvKpy5coAaGtro6OjQ1hY2Dv3WU9PDwcHh3zr6dWrF1ZWVoCiLic9PV1apnv37oSGhgKKCStWVlZ88803ABgYGBAXF0doaCgWFhakpaVhYmJC9erVMTAwYPXq1VSoUKHYWYnL1rKSWqr6oYkcZCILmchCQeQgE1nISmoWn+RgMTk5GSMjI7S0tKTXevfuzaxZszAxMUFFRT4sc3Nz0tPTefToUbG38+pzptXU1NDT00NNTQ2AcuXKAfknguTNZgbw9vame/fuPHz48K3bSExMfOc+v9qpmOddvYuZmZkAJCUlcezYsXyXlTMzM6UzlMOGDcPDw4PDhw/Ttm1bbG1tadSo0Vv3uSCNGjWSnjbzuRIFswoiB5nIQiayUBA5yEQWMlHK/R94dWD1qrwB3Kvy7ld8/XTru7oPC9pOYd2GBb2fV1Xzat9iQYqyzwUtU9TexaysLOzs7Bg5cmS+1/OOrXv37lhaWhIVFcXx48cZP348w4cPZ+LEiW/d74L253P/w55HZKEgcpCJLGQiCwWRg0xkISupWXySE1wMDQ1JTU3l2bNn0msLFixgy5YtXL58WTqrBopuxEqVKqGjo5NvHWXLluXJkyfS97m5udy8efNf7der9yheuHCBL774Ag0Njbd+xsjIqMj7/E8YGRmRmpqKgYGB9OvIkSPSs5+DgoK4d++eVAbu6urKoUOH/vV2BUEQBEEoHT7JwWLr1q3R09Nj5syZJCYmcuTIEbZu3crixYt5+fKl9HpUVBTBwcE4ODi8cSaxcePGPHjwgE2bNnHjxg3mzZv3xiXj9PR0EhMT37ovefcGAvj4+HD+/Hl+/vlnli5dmu8+yb/++ovIyEjpez8/P0xNTTl79myR9/mfGDhwIJcuXSIoKIiUlBT27NlDYGAg1atXBxSXqefMmUN8fDwJCQn89NNP/+gytCAIgiAIpdMnexl6+fLlzJkzh549e6Knp8e0adPo0KED1atXx8/PD3t7eypVqsSQIUNwcXF5Yx2GhoZMnz6dFStWsHjxYnr16pVvZjMoJtLcvXuXOnXqFLov/fr1k752cHBg1KhRZGZm0q9fP4YMGSK9V7lyZWxtbaXvb9++za5du6hYsSJ//fVXkfb5n6hRowYrV65k4cKFrFu3jqpVq+Lm5kb37t0Bxb2Vs2fPxtHRkaysLNq1a4enp+d72bYgCIIgCJ8+pdx/8ky5z4SxsTFhYWG0aNHiva43JCSEX3/9lc2bN7/X9X4M2dnZxMbGYmpqKia4/H8WZmZmJfKekw9F5CATWchEFgoiB5nIQvYxsijONj/JM4vvW1hYGBs2bODu3bvUq1cPDw8Ppk2bBsDgwYMZO3YsNWrUYPv27ejq6vLrr78ya9YsIiIiaN68OePGjQNg48aNrFu3jidPntCrVy+uXr1Kz5496dWrF9bW1owdO5a0tDRCQkIAeTDarFkz1q9fT3h4OOnp6TRt2hQvLy+MjY2l5UaPHs2WLVswNzenU6dO7Ny5EysrK9avX4+qqipTp05FTU2NBQsW8PjxY/r378/UqVMBxYxtf39/6T7FNm3a4OXlJd0TWdDxW1hYFCtD0bMoOsPyiBxkIguZyEJB5CATWchEz2IJd+XKFfz9/QkJCaFu3bqEhYXh6urKrl27aNWqFcHBwbRq1YqDBw8SExPDyJEjmTRpEhUrViQiIkJaz+7du1m6dCl+fn7UrVuXRYsWcfbsWXr27Jlve87Ozjx9+pSYmBiCg4PR1tZm2bJlhIeH4+Pjg6GhIWvWrGHYsGEcPHhQmiBz7NgxwsPDycnJ4cKFC8TExFCzZk2+//57vvvuO7y9vWnUqBErVqzg0qVLeHp60rVrVxo1akRgYCCXLl1izZo1lCtXjqCgICZMmEBoaGihx3/ixIl3zv5+PUdBoaT2ZH1oIgeZyEImslAQOchEFrKSmsVnP1hMS0tDSUmJ6tWro6+vj6urK+3bt5fOumlra6OpqQko6nZGjRoldS2+asuWLQwZMoQuXboAitnZX3/99RvLaWpqoqGhQdmyZalcuTK5ubls3ryZSZMmYWNjAygmynTs2JHdu3czYMAAAPr370/t2rUBxUzr3NxcvLy80NDQoH///oSGhjJu3DgaNGhAgwYNCAwMJCkpCSMjIzZv3syOHTukM5X+/v60aNGCq1evFnr8OTk5xRosip5F0RmWR+QgE1nIRBYKIgeZyEImehZLuNatW1O/fn3s7Oxo1KgRNjY29O3bt8AuR11d3QIHigBXr15lxIgR0vfa2tpS8fXb3Lt3jwcPHtC0aVPptbJly9K4ceN8M7FfL+bW1dWVzjrm9TC+XiL+8uVLbty4QWZmpjTozJOTk0NKSgpt27Yt8vG/TUnthvoYRBYKIgeZyEImslAQOchEFrKSmsVnP1hUV1cnIiKC6Ohojh07RmRkJOHh4flqbvIUVI6dR1lZmdfnChVl7lBh68zOzpbKuQtarqDBXEFVO3n3JGzZsuWNzkddXd23Hn/VqlXfuf+CIAiCIJRun2TPYnE5OjoSHBxc4HsxMTGsWrWKli1b4u7uzoEDB3jx4gW///57sbZRt27dfM+CzsjIIDU1Vfr+0aNHrF69Wvr+5s2bfPXVV7Rv355KlSoRGxsrvZeZmcnly5eLdGbyXWrWrImysjIPHjyQSrm1tLSYN28e9+7de2/HLwiCIAhC6fTZn1lUU1Nj2bJl6OnpYWlpydmzZ3n69CnGxsZoaGiQkJBQpJJqR0dHZs2aRYMGDahTpw5Llizh6dOn0tk+LS0tvvnmGwCeP3/OrVu38PHxoVWrVuzbt4+lS5dSpUoVDAwMWLNmDS9evMjXy/hPaWlp0bdvX7y9vZkzZw66urrMmzePW7duoa+vz9OnTws9fkEQBEEQhM9+sNiwYUP8/Pykku/q1asTEBBAnTp1cHR0xN/fn+vXr9OgQYO3rqdr166kpqYya9YsXrx4Qf/+/alRo4b0bOgyZcpIl4FfvnwJgKWlJTVq1MDZ2ZmMjAxmzJhBRkYG5ubmbNq0iUqVKr2XY3Rzc2PBggWMHz+ezMxMmjVrxurVq1FWVn7r8QuCIAiCIHwyl6Fv3ryJsbExe/bsoU2bNlhYWODr60tWVhYAhw8fxtbWlqZNm9KnTx+io6MLXdfWrVuxtrbG3NwcR0dHGjRowMGDB7l48SI7d+7kzJkztGjRgm3bttGjRw8mT55Mr169WL16Nd9++y3m5uaYmpqSnZ0tnf0LDQ1l69atjBs3Dk1NTcLDw7l9+7Y0q7pnz57s3LmTmzdvEhYWBkCHDh1wc3NDWVmZdu3aUatWLZSUlLh+/Xq+y8B5n+3evTuWlpZ8+eWXpKWlsX//frp06ULXrl3p2rUrubm5DB48mKZNm/LFF1/QqlUrQHFfZqtWrdDV1SU7O5t79+7x559/Sus3NjamYsWKlClThqdPn5KcnPxef3aCIAiCIHy6PrkziyEhIQQFBZGVlcW0adPQ1NSkS5cuTJ8+ndmzZ9OkSRN++uknhg8fzu7duzEwMMj3+aNHjxISEoKPjw9GRkbs2rWLwYMHc+jQIbS1tfHy8uLq1assX74cNTU1pk6dyuLFi5k6dSojR47EysqKWbNm8fjxY+bMmUNAQAArV67k999/586dO0RGRuLj48O2bds4evQoqamptG7dWtp+tWrViIiIoG/fvkRERGBkZERiYiJDhgzByckJPz8/zp8/z+zZs9HT06Njx44A/PDDD9LlYkNDQwCWLl3K/PnzefbsGcOGDSM6OhpPT0/c3d0ZP348a9aswcvLi/j4+LfmM23aNL766isCAgJITk5m/PjxmJqaFlj9UxhRyi0KZvOIHGQiC5nIQkHkIBNZyEQp93s2depU6ekiEyZMYOHChdy6dYt+/fphZ2cHKJ66cvbsWcLDw3Fzc8v3+bVr1+Li4kL79u0BpALq3bt30717dw4cOMCGDRv46quvAJgzZw5xcXE8f/6cAQMGMHDgQOlycs+ePVm7di0AvXr14uDBg6SkpODq6oq5uTmWlpbEx8fn276ysrJ0eblSpUqUL1+ekJAQGjVqxKRJkwCoXbs2iYmJrF27VhosmpqaYm1tnW9dTk5OUuVOw4YNMTIyknoeO3XqJG173bp1b80nLS0NGxsbatSoQc2aNdmwYUO+Gp6iEKXcspJaqvqhiRxkIguZyEJB5CATWchKahaf3GDxyy+/lL5u3Lgx9+/fJyYmhr/++ott27ZJ72VmZuY7o5cnMTGRgIAAAgMDpddevHhBSkoKqampZGdnY2JiIr1nYWEhDU4dHBzYtWsXly5dIikpiStXrqCnpwcoLvUC/PLLL1I5tZubm3SZ/G0SExNp0qRJvtfMzc3ZunWr9P3rPYugmOmcR01NLd8yeT2Leevfv39/ofm4uLgQGBjItm3baNeuHT169KBy5crv3O9XiVJuUTCbR+QgE1nIRBYKIgeZyEImSrnfs7wJI4DUQ6iurs7w4cOxt7fPt2xBBdrZ2dl4eHhgaWmZ73UtLS3u3LlT6HafPHlCnz59qFixItbW1nTr1o2kpCTWr1+fb7nXB0z/tGsxJycn3ynigpZ5/TdUYU9cyc7Ofms+I0aMoEuXLkRFRXH06FGGDBmCj48Pffv2fee+v7ovn/sf9jwiCwWRg0xkIRNZKIgcZCILWUnN4pOZ4JInLi5O+vrSpUtUqVKFOnXqcPPmTalH0MDAgG3btnHixIk3Pm9kZMRff/2Vb9mVK1cSGxsrdRK+euk4KiqKnj17Eh0dzZ07dwgLC2PYsGFYWVlx69atIg0G38XIyIjz58/ney0mJua99Czmrb+wfF68eIGvry+qqqoMHTqUTZs20a9fPw4ePPheti0IgiAIwqftkxss+vn5cfHiRX755ReWLFnCoEGDcHJyYt++fYSFhXH9+nU2btzIhg0bWLRoEU2bNuXBgwfS54cOHUpoaCi7du3i+vXrDB8+nJ07d1KnTh20tLSwt7fHz8+PCxcucPHiRYKCgmjZsiU6Ojo8ffqUqKgobt68SUREBN999510qfddQkNDSU9PB+Dhw4cA/O9//2P69OkMHDiQuLg4AgMDSU5OZufOnWzZsoVBgwa9l8wKymfjxo0YGhpSrlw5zp07h4+PD0lJSVy8eJHffvutSN2SgiAIgiCUfp/cZWhbW1tcXFzIycnBwcGBESNGUKZMGfz9/QkODsbf359atWrxxRdfYGlpyZgxY5g2bVq+z9+9e5elS5dy9+5ddHV1qVSpkjTD2MPDAz8/P4YOHUrZsmWxtbVl4sSJqKqqMmbMGGbPns2LFy8wNjZm5syZeHp6cvv27Xfud79+/aSzh1FRUQCsXr2aBg0aUKlSJVatWoW/vz/r16+nevXquLm50bt37/eSmZmZ2Rv5LFq0iGbNmgEQFBTEnDlz6NOnDyoqKvzvf/9j9OjR72XbgiAIgiB82pRy38d11A/g5s2b2NjYcOTIkSLN1LW2tmbMmDHvHHBFRkYSEhLC0aNH39euvlNISAi//vormzdv/mDb/K9kZ2cTGxuLqampmODy/1mYmZmVyHtOPhSRg0xkIRNZKIgcZCIL2cfIojjb/OQuQxeFtbU1aWlpeHh4YG1tze+//46DgwNNmzbFzMyM4cOHFzqZ5cKFC9KynTt3Zu/evdJ7MTExODg4YGZmhrW1NeHh4dJ7bm5uzJs3D1dXV5o2bcrXX3/Nrl278u1TZGQkwcHBBAcHc/bsWYyNjTlz5gw5OTmsXbsWGxsbmjRpgqOjI1evXpU+a2xszJIlS2jRogUjR44kMjISR0dHVqxYQbNmzWjVqhW7du3iwIEDtG/fHgsLCwICAqTPv3z5El9fX1q0aEGLFi2YMmVKvkvzYWFhtG/fHlNTU3r16sVvv/32Hn4KgiAIgiCUBp/cZeii+P777+nZsyfOzs506NCBHj164OTkhL+/P3fu3MHDw4PVq1fj5eWV73P37t3D2dmZ7t274+fnR2xsLNOnT6dOnTqULVv2ncXZ3333HRMmTGDy5MmEhYUxa9YsbGxsKF++vLQNZ2dnnj59SkxMDMHBwWhra7Ns2TLCw8Px8fHB0NCQNWvWMGzYMA4ePCh1Oh47dozw8HBycnK4cOECMTEx1KxZk++//57vvvsOb29vGjVqxIoVK7h06RKenp507dqVRo0aERgYyKVLl1izZg3lypUjKCiICRMmEBoaypUrV/D39yckJIS6desSFhYmdU8WNru6IKKUWxTM5hE5yEQWMpGFgshBJrKQiVLu90RfXz/f2ba3qVSpEsrKypQvXx5VVVVGjx7N0KFDUVJSombNmnTq1IkLFy688bm9e/dKT3EpU6YMtWvX5uHDhzx//pydO3e+szjb2NiY4cOHA4rC8LCwMBISEvJ1Q2pqaqKhoUHZsmWpXLkyubm5bN68mUmTJmFjYwOAj48PHTt2ZPfu3QwYMACA/v37U7t2bUBx9jM3NxcvLy80NDTo378/oaGhjBs3jgYNGtCgQQMCAwNJSkrCyMiIzZs3s2PHDoyNjQHw9/enRYsWXL16lbS0NJSUlKhevTr6+vq4urrSvn17cnJyijVYFKXcspJaqvqhiRxkIguZyEJB5CATWchKahafzGDxn6pcuTL29vZs3LiRuLg4rl27xtWrV/MN4PIkJyfTqFGjfIOkoUOHAor7DN9VnJ03SQYUvY3AO0u57927x4MHD6QnsYCiS7Jx48YkJiZKr71eyq2rqyuddczrYHz1Xs68Uu4bN26QmZkpDTrz5OTkkJKSQtu2balfvz52dnY0atQIGxsb+vbti4pK8X5riFJuUTCbR+QgE1nIRBYKIgeZyEImSrk/stu3b9O7d29MTEywsrKiX79+HD9+/I1eQ+CtA6SiFGe/Whie513zhwpaLyh+iHml4wUtV9C+KikpFbgegC1btkiDyzy6urqoq6sTERFBdHQ0x44dIzIykvDwcCIjI6latepb9/1VJbVI9GMQWSiIHGQiC5nIQkHkIBNZyEpqFqVygsurDh8+jLa2NqtWrWLIkCFYWFhw48aNAgdxhoaGXL16Nd97rq6urF279j8rzi5fvjx6enrExsZKr2VmZnL58uX3UsqdVzT+4MEDqZBbS0uLefPmce/ePWJiYli1ahUtW7bE3d2dAwcO8OLFC37//fd/vW1BEARBED59pX6wqKOjw61btzh9+jQ3btxg9erVHDp0qMAybTs7O/766y/atWtHSkoKnp6e7N+/n1atWv3j4uyTJ08CkJ6eLvUrZmVl8ccff0gzn52cnFi6dClHjx4lMTGRGTNm8OLFC2xtbf/18WtpadG3b1+8vb05c+YM165dY9q0aaSmpqKvr4+amhrLli0jIiKCmzdvsnfvXp4+fSrd31hUTzNzyHieydOX734WtiAIgiAIn45Sfxm6S5cunD17lvHjx6OkpISpqSnTp08nODj4jQFjhQoV6NatGwcOHKBbt27o6OhQqVIlGjZsCPCPirNbtGgBKC75tmnTBoDr16/z8OFDvv/+e6pUqYKuri4ZGRnMmDGDjIwMzM3N2bRpE5UqVXovGbi5ubFgwQLGjx9PZmYmzZo1Y/Xq1SgrK9OwYUP8/PxYvnw5c+bMoXr16gQEBFCnTp1ibWPclhgyc2HtYIv3ss+CIAiCIJQMn0wp94cSHBxMdHQ0mzZt+s8Ku3fu3MnSpUs5duzYe13vx5BX6rn43EtpsKil9ua9m58DUTCrIHKQiSxkIgsFkYNMZCEr6aXcJebMYt4TWhYuXIi/vz/Pnj3D3t4eNzc3VqxYQVxcHA8fPiQhIYGQkBCaNm3K0qVL+fHHH3n48CEtW7Zk1qxZVKtWDYDU1FTmzJnDuXPn0NbWxtnZmcGDBwPwxx9/4OPjw/nz56lWrRqDBw8u0nOYjxw5QnBwMImJiZQrV462bdvi4+ODpqYmwcHBb+yjm5sbY8eOpVevXjg6OtK8eXNq1KiBu7s7oKjamTdvHr169SIyMpI1a9aQlpZG3bp1cXd3lx7HZ21tTZcuXfjhhx/Q09PDzc0NDw8PRo8eTVBQEC9fvmTEiBGYmZkxY8YMbt++TceOHZk/fz5lypQhNzeX5cuXEx4ezvPnz7GwsGDmzJlUr14dgH379rFkyRJu3bpFzZo1mTRpEh06dCjeDzA3l9xcJXL5fDuzRGeYgshBJrKQiSwURA4ykYVM9CwWU0hICEFBQWRlZTFt2jQ0NTVRUVHhyJEjeHt7Y2ZmhpGREbNmzeLcuXMsWLAAHR0dFi5cyOjRo9mxYweZmZk4OztjYmLC9u3buXHjBpMnT6ZmzZpYWloyfPhwevbsiY+PD0lJScyYMQNNTU3s7e0L3a/r168zYcIEZs6ciZWVFSkpKUyZMoXt27dL9Tqv72NBbG1tefz4MevXr+f777+nfPnyREZG4uPjw6xZs2jSpAmRkZGMGDGCAwcOSDOS9+zZw7p168jNzeXhw4fcuXOHqKgoNm3axLFjx1i4cCENGjRg/vz5/P3334wbN46OHTvSsWNHNm/ezJ49e1i0aBF6enqsX78eZ2dn9uzZw6NHj5g2bRpz5syhRYsWHDhwgEmTJnHixAl0dHSK/HN78vQJL7IV92PGXv68OxdLak/WhyZykIksZCILBZGDTGQhK6lZlLjB4tSpU7GwUNz3NmHCBBYuXIiDgwN6eno4ODgA8PDhQ3744QfWrFlDy5YtAVi4cCHt2rXj559/5uXLl9y/f5+5c+eipaVFvXr1pKLtPXv2oKuri6urK6CYAZ2WlkZYWNhbB4s5OTl4eXnRr18/QNFpaGVlRUJCgrTMq/tYGDU1NcqXL4+ysjKVK1cGYNOmTTg6OkrbnzJlCmfPnmXz5s1MnjwZgO7du0uTTs6cOUNmZibTp0/HyMiI6tWr4+/vz6BBgzAzMwOgYcOGJCUlAbB27VpmzZol3T85Z84cWrduzcmTJ/niiy/IzMzkiy++oEaNGjg7O2NsbFxopU9hNDU0KZurhIqKirQPnxvRGaYgcpCJLGQiCwWRg0xkIRM9i8X0all248aNuX//Pn///Xe+UuqUlBRycnLyFVnr6OhgZGREYmIiWVlZGBkZScXYgDQRZcGCBcTHx2Nubi69l52d/c4fjqGhIaqqqqxYsYKEhAQSEhK4du0aPXr0kJZ5vTi7qBITExkzZky+18zMzN5ayg2KWhxQDEBfXyavlPvJkyf89ddfTJw4MV/Z+PPnz0lJSaF9+/a0a9eOoUOHYmRkJJVyq6urF+8glJRQApTgs/9DX1J7sj40kYNMZCETWSiIHGQiC1lJzaLEDRZfLbbOK6UuU6ZMvjNd7yqyflu5dlZWFpaWlsycObNY+xUfH4+DgwPW1tZYWFjg5OREaGhovmWKezbubZ97Vyk3vFnMXdDj+fLuSViyZMkbl8a1tbVRUlJi1apVXLhwgSNHjnD48GG2bNnCli1bpFnggiAIgiB8vkpcz2JcXJz09aVLl6hSpcob987VrFkTFRWVfEXWf//9N6mpqRgZGWFoaEhqairPnj2T3l+wYAG+vr4YGRmRnJyMvr6+VFIdGxvLpk2b3rpPq1evplmzZixatIiBAwfSpEkTUlNT3/mEljw5OTlcvny5wPcKKvw+f/58vsHd77//jrW1tbQ/RVWhQgV0dXVJT0+XjrdatWoEBASQnJxMYmIiCxYsoEmTJkycOJG9e/dSrVo1qR9SEARBEITPW4kbLPr5+XHx4kV++eUXlixZUuAsZU1NTfr27YuPjw9nzpwhPj6eqVOn8sUXX9CqVStat26Nnp4eM2fOJDExkSNHjrB161Zat25N9+7def78ufTeTz/9hJ+fH7q6uoXu05gxY8jKyuLq1atcuHCB5ORk5s+fz8WLFwss9y7IvXv3+O233wp8z8nJic2bN7Nr1y6Sk5NZuHAh8fHx9OnTp8Dl69WrV6Rtvrr+xYsXc/ToUVJSUvDy8uLcuXPUrl2bChUqEB4ezvLly7lx4wbHjx8nLS2NRo0aFWsbwQPNWTvYgjJl3nzkoCAIgiAIn64Sdxna1tYWFxcXcnJycHBwYMSIESxbtuyN5aZPny4VTb98+RIrKys2btyIqqoqgFQy3bNnT/T09Jg2bRrt2rUDYM2aNcydOxd7e3t0dHQYNGgQLi4ub90vKysrlJSUcHJyoly5cjRr1owxY8awd+/e93LMd+/eZenSpaSnp9OwYUPWr19faDH22y6zF+Tbb7/lyZMnzJw5k4yMDBo3bsy6devQ1tYGFN2SCxcuZOXKlejq6jJp0iRat25d7OP4XPsVBUEQBKE0K3GDxa5du74xcBs3btwby6mrq+Pt7Y23t3eB66lTp84b9xTmMTEx4bvvvivwvVe31atXL3bu3El0dDSzZs2iZ8+ebN26FR8fH44fP87Vq1el7sZx48bx6NEjxo0bx+nTp1FSUuLrr7+mU6dOnDlzhuTkZEDRrXjkyBFq1KhBcHCwtD1ra2v8/Pw4cuQI+vr63L59m2HDhvHbb79hZGQkzZx+XV4/ZXBwMJ6enty+fRsrKyuCg4Oly/cxMTGcPHmSR48eYWBggKOjo3Q/4q1bt9iwYQPXr19HU1MTa2trBg4cWOC2BEEQBEH4/JS4wWJJExwcTI8ePXB2dqZXr15069at0I7GvDOD4eHhZGVlMXXqVJYvX46rqyseHh5St2JRHuM3YcIENDQ0iIiIICEhAU9PTypWrFjo8itXriQwMJDc3FxGjRrFhg0bmDhxIunp6bi4uDBx4kTatGlDbGwsbm5u6OrqYmFhgY+PDxoaGuzatYt79+4xfvx4ateuXaSS8td97sWqomBWQeQgE1nIRBYKIgeZyEImSrk/cTo6OigrK1O+fHkOHDjw1o7GtLQ0NDU10dfXR11dnSVLlgCgqqr6Rrfi2yQkJBATE8OxY8eoXr069erV49KlSxw4cKDQz4wfP54mTZoAYGdnJ3Unfffdd1hZWfHNN98AYGBgQFxcHKGhoVhYWJCWloaJiQnVq1fHwMCA1atXU6FChWLnlJ2VRWxswRN4PjcltVT1QxM5yEQWMpGFgshBJrKQldQsSsxgUV9fn6tXr37s3XirpKSkt3Y0Dh48mNGjR2NpaYmlpSWdO3fGzs6u2Nu5du0aOjo60uP4AExNTd86WDQwMJC+1tLSIjMzU9rnY8eO5dvnzMxMaab1sGHD8PDw4PDhw7Rt2xZbW9tiT24BUP6My7jziIJZBZGDTGQhE1koiBxkIguZKOUuRd7V0WhpaclPP/3EkSNHOH78ODNnzuTUqVMsXLjwnet+/XTw65U8r/ZPFqSw97OysrCzs2PkyJH5Xs+bJNO9e3csLS2Jiori+PHjjB8/nuHDhzNx4sR37vPrPvc/7HlKaqnqhyZykIksZCILBZGDTGQhK6lZlLjqnJLsXR2NGzdu5PLly/Ts2ZMlS5Ywb948Dh06BICSUv5KGVVVVZ48eSJ9f+PGDenr+vXr8/DhQ1JTU6XXitOt+Po+p6amSvtrYGDAkSNH2LNnDwBBQUHcu3cPBwcHVq1ahaurq7TPgiAIgiAIn+xg8cyZM9Kzkv9rGhoaJCUl8fXXX+fraLSzs2PGjBlSR+Nff/3FnDlziI2NJSUlhUmTJkmP5PP39+fu3bukpKSQnp5OYmIiGzZsYMSIEVy4cIGlS5dK26tTpw6WlpZ4eHgQHx9PVFQUmzdv/kf7PnDgQC5dukRQUBApKSns2bOHwMBA6RJ3UlISc+bMIT4+noSEBH766ad/dBm6jJLoVxQEQRCE0uiTHSx+SA4ODnz33XfMmzePNWvWkJKSgr29PcnJyZiamkpVPxMmTODLL79k1KhR9OjRgxYtWhAcHAzAli1bqF27NnZ2dqxdu5bMzExatGjB6dOnmTx5MqNHj863zaCgICpWrMiAAQMIDAzE0dHxH+17jRo1WLlyJSdPnqRbt24sXrwYNzc3unfvDoC3tzd6eno4OjrSr18/qlSpgqenZ7G3o6YiBouCIAiCUBop5Rb1eXUlzJkzZxg8ePBHnRTj6OhI8+bNC+yBfJuQkBB+/fXXf3y2sCTJzs4mNjYWU1NTqRD9c5WXhZmZWYm85+RDETnIRBYykYWCyEEmspB9jCyKs81PYoJLamoqc+bM4dy5c2hra+Ps7Cxdgs57VF1GRgb/+9//mD17NqqqquTm5rJq1Sq2b9/OnTt30NHRYcCAAYwdOxZQDPTq16/P8ePHyc7O5scff+Tq1assXLiQK1euoKSkRLNmzfDz86NKlSoAHD58mIULF3L79m169eqVb1KKm5sbAFeuXJG6Fjt37kxYWBgtWrTA2tqasWPHkpaWRkhICKAo6A4LC6NZs2asX7+e8PBw0tPTadq0KV5eXtIxGhsbM3r0aLZs2YK5uTmdOnVi586dWFlZsX79elRVVZk6dSpqamosWLCAx48f079/f6ZOnQrAy5cv8ff3l+5TbNOmDV5eXlJpd1hYGBs2bODu3bvUq1cPDw8PLCwsivUzys7O/uy7skRnmILIQSaykIksFEQOMpGFTPQs/ksvXrzA2dkZExMTtm/fzo0bN5g8eTKBgYEAHDx4kHXr1pGens7YsWNp0qQJDg4O7Nq1i9DQUAIDA6lZsyYnT57E29ub9u3bY2JiAkBkZCTr1q2TBpcuLi44OTnh7+/PnTt38PDwYPXq1Xh5eXHt2jVcXV2ZOnUqbdq0ITQ0lN9//x1LS0tpX3/44QeWLVuGnp4ehoaGBR6Ps7MzT58+JSYmhuDgYLS1tVm2bBnh4eH4+PhgaGjImjVrGDZsGAcPHkRDQwOAY8eOER4eTk5ODhcuXCAmJoaaNWvy/fff89133+Ht7U2jRo1YsWIFly5dwtPTk65du9KoUSMCAwO5dOkSa9asoVy5cgQFBTFhwgRCQ0O5cuUK/v7+hISEULduXcLCwnB1deXEiROUKVP0uxSuXLnyD3/CpU9J7cn60EQOMpGFTGShIHKQiSxkJTWLEj9YPHXqFPfv32fu3LloaWlRr149vLy8pIHMrFmzMDIyon79+lhZWREfHw9AtWrVmDdvnjSYc3BwYNmyZSQkJEiDxXbt2vHll18CkJ6ezujRoxk6dChKSkrUrFmTTp06ceHCBQB27NiBhYUFTk5OAMyYMYNjx47l21dTU1Osra3fejyamppoaGhQtmxZKleuTG5uLps3b2bSpEnY2NgA4OPjQ8eOHdm9ezcDBgwAoH///tSuXRuACxcukJubi5eXFxoaGvTv35/Q0FDGjRtHgwYNaNCgAYGBgSQlJWFkZMTmzZvZsWOHdKbS39+fFi1acPXqVdLS0lBSUqJ69ero6+vj6upK+/btycnJKdZgsVGjRuIytOgMA0QOrxJZyEQWCiIHmchCJnoW/6Xk5GSMjIzQ0tKSXuvduzdnzpwBoFatWtLr5cuX5+XLlwC0bNmS8+fPs2jRIhITE4mLiyM9PZ2cnBxp+Ro1akhfV65cGXt7ezZu3EhcXBzXrl3j6tWr0mAyMTFRep4yKHoNX/3+9fUV1b1793jw4AFNmzbNt+7GjRuTmJhY6Lp1dXWls47lypUDFMXmedTU1Hj58iU3btwgMzNTGnTmycnJISUlhbZt21K/fn3s7Oxo1KgRNjY29O3bV+phLKqS2g31MYgsFEQOMpGFTGShIHKQiSxkJTWLEj9YfNeg5fVQ8+brREREMHfuXPr27UunTp2YPn06gwcPzrds3iAL4Pbt2/Tu3RsTExOsrKzo168fx48f5/z582+sO8/rRdivrq+oCvtMdnZ2voHt68sVlMvrXY556wHFbOy8wWUeXV1d1NXViYiIIDo6mmPHjhEZGUl4eDiRkZFUrVq12McjCIIgCELpUuKrcwwNDUlNTeXZs2fSawsWLMDX1/etnwsPD2fMmDF4eHhgb29PxYoVuXfv3hsDvjyHDx9GW1ubVatWMWTIECwsLLhx44a0fL169fKdrs3JyZEueRdFWloaSUlJADx48IBLly5hamrKunXr0NPTIzY2Vlo2MzOTy5cvS4/k+zdq1qyJsrIyDx48kEq5tbS0mDdvHvfu3SMmJoZVq1bRsmVL3N3dOXDgAC9evOD333//19sWBEEQBOHTV+LPLLZu3Ro9PT1mzpzJyJEjSUlJYevWrQQFBUn9hgWpWLEip0+fxsbGhidPnhAUFERmZqZ0mfp1Ojo63Lp1i9OnT6Ovr8/+/fs5dOgQpqamAPTr149NmzaxYsUKOnfuzLZt27h161aRj+OLL76QLpnnDTr37t2LtrY2mpqaLF26lCpVqmBgYMCaNWt48eIFtra2RV5/YbS0tOjbty/e3t7MmTMHXV1d5s2bx61bt9DX1+fp06fSpBxLS0vOnj3L06dPP1jhuSAIgiAIJVuJHyyqqKiwfPly5syZQ8+ePdHT02PatGmoq6u/9XMeHh54eHjQo0cPdHV16dKlC+rq6oU+Nq9Lly6cPXuW8ePHo6SkhKmpKdOnTyc4OJiXL19iYGDAihUrmDdvHitWrKBDhw58/fXXRT4OZWVl6dLxixcvUFdXlwaPzs7OZGRkMGPGDDIyMjA3N2fTpk1UqlSpyOt/Gzc3NxYsWMD48ePJzMykWbNmrF69GmVlZRo2bIifn5+UcfXq1QkICKBOnTrvZduCIAiCIHzaSvxgERSPvwsNDX3j9dcLuefPn5/vM9u2bSt0nXnPc86jrKzM7NmzmT17tvTajRs3OH78OM2aNaNWrVrY29vz9OlTvL292b59O7q6umzcuBEDAwPS0tJo3ry59NmbN28C8sSUtLQ0atSogZubm3T52tjYmCNHjlC5cmWysrJQUVFBSUkJNTU1ypcvn289v/32G2PHjsXOzo6KFStiYWGBj48PkZGRVKxYkXXr1nH8+HGWL19OTk4Oo0ePplevXoDisvaTJ0/IzMxEQ0MDfX19KleuLO1rYmIiT548QUlJiSpVqlC/fv1CcxMEQRAE4fPySQwWP4asrCxcXFyoW7cuO3bsIC4ujpkzZ1KxYkUAYmJiGDlyJJMmTaJixYpEREQUab2enp48f/5c+rpSpUp4enpy7tw5FixYgI6ODgsXLmT06NHs2LFD+ty5c+fYsWMHOTk57Nmzh3379jFs2DB++OEHAgMDcXV1xcLCgk2bNnHgwAEWLFhAt27dpPVnZmYSHh7Oixcv8PX1Zc6cOcydO5fDhw+zbds2li1bRpUqVQgKCsLd3Z3vv/++WHmJUm5RMJtH5CATWchEFgoiB5nIQiZKuT9Rv/76K3/++Sfbt29HS0uLunXr8scff7B3715AMfN41KhRqKmpFWu95cuXlz5TuXJlHj58yA8//MCaNWto2bIlAAsXLqRdu3b8/PPP0iSXIUOG5KsJqlixIhMmTEBJSYmePXuyf/9+PD09qVmzJt9++y1Lly4lNTWVjIwMoqKiiI6Ols5W+vj4YG9vj7u7O2lpaZQtW5bq1atTvXp1ZsyYIU3EKQ5Ryi0rqaWqH5rIQSaykIksFEQOMpGFrKRmIQaLhbh69eob/Y5mZmbSYFFXV7fYA8WCpKSkkJOTk69nUUdHByMjIxITE6XB4us9i/r6+lJVTt5+5C2T9/3Lly9JTEwkJyeHtm3b5vt8Tk4OqampdO3alc2bN2NjY4OZmRkdOnSgT58+xT4OUcotCmbziBxkIguZyEJB5CATWchEKfcnSllZ+Y2anVe/f1enYlFP777PnsWCnriSnZ1N+fLl813SzlO1alXU1NTYv38/P//8M8eOHWPdunVs376dXbt2vXMS0atKapHoxyCyUBA5yEQWMpGFgshBJrKQldQsSnzP4sdSr149UlJSyMjIkF67fPlyocurqqry5MkT6fsbN24UaTs1a9ZERUUlX8/i33//TWpq6nvpWTQyMuLx48coKSlJPYvPnz/H39+fly9fcvz4cSIiImjXrh2zZ8/mhx9+ICUlhT/++ONfb1sQBEEQhE+fOLNYCEtLS6pVq8aMGTMYO3YsCQkJhIWFoa2tXeDyjRs3ZteuXVI34tKlS9+5DWtra8aOHUvfvn3x8fHBx8cHbW1tFi5cyBdffEGrVq24c+dOsff91TOgderUoU2bNkyZMgUvLy+UlZWZMWMG2traVKhQgZycHPz9/alcuTINGzZk7969qKurY2hoWKxtPs3M4WVOZrH3tTTJBWobN+JpZg5KmYqzwmXKKKGhKv6YCYIgCJ8u8bdYIcqUKUNwcDAzZsygR48e1K5dm169enHixIkClx86dCh//PEH33zzDVWrVsXT0/OtpeEA33//PRoaGnTp0kXqQXz58iVWVlZs3LjxH98DePbs2Xzf+/v74+vri5OTEyoqKrRp0wYvLy9AMWAdP3488+bNIz09ndq1a7N8+fJCB8WFGbclhsyCH47z2cjNhYyMDLS0tMh78uLawRYfd6cEQRAE4V9Syi3s+XefuXv37nHlyhXatGkjvbZ27Vp++umnNzoaS5ozZ84wePDgN3oo/wvZ2dnExsay+NxLMVgsZLCopVb27R8sZfJ+T5iZmZXIe28+JJGFTGShIHKQiSxkHyOL4mxT3LP4FqNGjWLLli2kpaXxyy+/EBoayv/+979Cl7958ybGxsbs2bOHNm3aYGFhga+vL1lZWQQHBzN69GgGDRpE8+bNiY6OxtramsjISAAcHR1Zt24dQ4cOpUmTJvTp04fU1FRmzJiBubk5nTp1Ijo6WtrWkSNHsLe3x9TUFAsLCyZNmsSTJ0+4efMmgwcPBhSl32fOnAFg69atWFtbY25ujqOjY76B5OnTp+nRowempqbY2NiwdevW/yJOQRAEQRA+QeIydCF0dXVZvHgxS5YsYd68eejp6fHNN98wcODAd342JCSEoKAgsrKymDZtGpqamqioqHDkyBG8vb0xMzMrcPLKsmXL8PPzw9PTkzFjxtCnTx+++eYbvv/+ewIDA/H19WX37t1cv36dCRMmMHPmTKysrEhJSWHKlCls376dwYMHExwczLhx4zh16hTa2tocPXqUkJAQfHx8MDIyYteuXQwePJhDhw6hpaWFq6srTk5O2NnZce7cOaZPn46FhQV169YtemC5ueTmKhUn4tIn7yR9bi65KLLI5fMrnBVFuzKRhUxkoSBykIksZKKU+xPWoUMHOnToUOzPTZ06FQsLxb1qEyZMYOHChTg4OKCnp4eDg0Ohn2vfvj1dunSRtr1v3z7pWdX9+vVjzJgxgKIj0cvLi379+gGKzkUrKysSEhJQVlaW7jfMe6Tf2rVrcXFxoX379gC4urpy4sQJdu/ejZ2dHQ8ePEBPTw99fX309fWpUqVKvscBFsWTp094If68A5Dxyqz4rKwsYi9/noXlJbVc9mMQWchEFgoiB5nIQlZSsxCDxf/Al19+KX3duHFj7t+/z99///1Gsfbr9PX1pa/V1NSoXr16vuLtzEzFbGNDQ0NUVVVZsWIFCQkJJCQkcO3aNXr06FHgehMTEwkICCAwMFB67cWLF6SkpKCjo4ODgwNeXl4sX76c9u3b07t372JPcNHU0KSsOLNIxpMnaGlqknfTooqKCmZmZh93vz4wUbQrE1nIRBYKIgeZyEImSrk/Q2XLyhMa8oq1y5Qp884i79eLtgsq2QaIj4/HwcEBa2trLCwscHJyIjQ0tND1Zmdn4+HhgaWlZb7X855O4+3tzaBBg4iKiiIqKopt27axfPlyvv7667fubz5KSnzmQ0Xp0jNKStIEFyX4bP8nWFLLZT8GkYVMZKEgcpCJLGQlNYtiT3BZu3Ytf/3113+xL6VGXFyc9PWlS5eoUqUKOjo67239P/zwA82aNWPRokUMHDiQJk2akJqaKvUr5p2NzGNkZMRff/0llXIbGBiwcuVKYmNjSU9PZ/bs2RgYGDBq1Ch27NhBy5YtOXr06HvbX0EQBEEQPl3FPrO4cuVKOnfu/F/syycpODiY6OjofHU6fn5++Pr68vjxY5YsWcI333zDqVOniI+Pz/e5W7duMXv2bDp16lSsbero6HD16lUuXLhA+fLl2bZtGxcvXqRmzZoA0mP6Ll26RL169Rg6dCienp4YGhry5Zdfsm3bNvbv34+Liwva2tocPnyY3NxcnJ2duX37NvHx8cXep+CB5qiUwH8NfUi5KO5RVFFRkc6ylinzuZ9vFQRBED51xR4sduvWjRUrVjBixAiqV6/+j4ujSwtnZ2ccHR3zvWZra4uLiws5OTk4ODgwYsQInjx5Is08evjwISEhIejo6DBixAjpcnBROTo6cuXKFZycnChXrhzNmjVjzJgx7N27F1BU5rRq1YoBAwYQGBiIra0td+/eZenSpdy9e5e6deuyYsUK6Skty5cvZ+7cuXTv3h1NTU369OlD3759i7VPGmXLoKr6efUJvi47O5vYy1dEZ5ggCIJQqhS7lNva2ppbt269cakzz6uXYD83N2/exMbGhiNHjuSbrPK6tLQ0rK2tiYqKks4GfqrySj1NTU0/+384iIJZBZGDTGQhE1koiBxkIgtZSS/lLvaZxfnz5//jHftU5Q0CFy5ciL+/P8+ePcPe3h43NzdWrFjxxmVoFxcXbt68iYGBAe7u7lhaWkqXq+fNm4eNjQ2gqMfp2bMn8+fPJyYmBn9/f+Li4qhUqRLDhw+Xanbc3NwAuHLlCunp6YSHh9O5c2cWL17M0qVLuXXrFjY2NkycOBFPT0/Onz+PiYkJQUFBVK1aFYDDhw8TFBREWloa9erVY9q0aTRv3hxQTJjx9vYmLi6OChUq0L9/f8aOHVusjLKzsz/7rizRGaYgcpCJLGQiCwWRg0xkISt1PYt5A4yMjAyuX79O3bp1efnyZbEvpX6KCivbzpOSkgJAmzZtGDBgAIcOHWL06NEcOnRIWqZatWpERETQt29fIiIiMDIyIjExkSFDhuDk5ISfnx/nz59n9uzZ6Onp0bFjR0AxqWXZsmXo6elJl4+XLl3K/PnzefbsGcOGDSM6OhpPT0/c3d0ZP348a9aswcvLi/j4eKZPn87s2bNp0qQJP/30E8OHD2f37t0YGBgwbdo0vvrqKwICAkhOTmb8+PGYmpoWazb0lSufZ5dgQUpqT9aHJnKQiSxkIgsFkYNMZCErqVkUe7D48uVL5syZIz2m7uDBgyxYsIBnz54RGBhY7H6+T0lhZdt5Tp48SfPmzaUzgSNGjODp06c8evRIWkZZWZlKlSoBUKlSJcqXL09ISAiNGjVi0qRJANSuXZvExETWrl0rDRZNTU2xtrbOtz9OTk40bdoUgIYNG2JkZCSVenfq1EmaULNu3Tr69euHnZ0dAIMHD+bs2bOEh4fj5uZGWloaNjY21KhRg5o1a7Jhw4a3XkYvSKNGjcRlaNEZBogcXiWykIksFEQOMpGFrNT1LPr7+3Pt2jV27tzJgAEDABg3bhzu7u74+voSEBBQ3FV+Mgor286TnJyMiYlJvs+4urq+c72JiYk0adIk32vm5ub5ntFcUKH3q/c7qqmp5VtGTU2Nly9fSuvfv38/27Ztk97PzMykdevWgOKyeWBgINu2baNdu3b06NGj2E9wKandUB+DyEJB5CATWchEFgoiB5nIQlZSsyj2YPHQoUMsW7YMY2Nj6TVjY2N8fHxwdnZ+rztX0hRWtp3n9VLtoiqorDsnJyff/QQFLfP6b6jCSryzs7MZPnw49vb2+V5XU1MDFGdAu3TpQlRUFEePHmXIkCH4+PgUe0a0IAiCIAilT7FLuZ88eSL1+L3q9cHNxxAXF8e5c+f+0/XnKahs28DAIF+XIsCAAQP45ptviIqKAiA3N1d67N4333wDKEqzz58/n+9zMTExGBkZvZf9NjIykibc5P3atm0bJ06c4MWLF/j6+qKqqsrQoUPZtGkT/fr14+DBg+9l24IgCIIgfNqKPVi0trYmKCiIjIwM6bUbN27g6+tbvMfD/QfGjBkjTTL5L/j5+XHx4kV++eUXlixZwqBBg/K97+DgwG+//caGDRtITU1l1apVJCQkMHv2bNq0aQMoZh7n9SEGBQUBMHDgQOLi4ggMDCQ5OZmdO3eyZcuWN9b/Tzk5ObFv3z7CwsK4fv06GzduZOPGjRgaGlKuXDnOnTuHj48PSUlJXLx4kd9++41GjRq9l20LgiAIgvBpK/ZgcebMmZQpU4bmzZvz7NkzevfuTadOnahQoQIzZsz4L/axxMgr2540aRJ9+/ZlxIgR+d6vVasWwcHB7Nixg27dunHw4EFWrlxJnTp1pMvIjx8/lpbPuy+wevXqrFq1ipMnT2JnZ8eKFStwc3Ojd+/e72W/zczM8Pf3Z8uWLdja2rJ9+3YWLVpEs2bNAMWg9dmzZ/Tp04dvv/0WCwsLRo8e/V62LQiCIAjCp63YN9mVL1+e4OBgbty4QWJiIllZWRgZGVGnTp3/Yv+KzNHRkbS0NNzd3QkJCQEUFTY//vgjLi4ujBgxgsjISNasWUNaWhp169bF3d1dGjA9f/4cHx8fDhw4gIaGBuPHj2fWrFn5am9+//13srKyUFJS4vHjx2RlZTFu3DgiIyNxdHSkWbNmfPfdd2RnZzNw4EDc3NxQUlKSZkePHTs239Nedu7cybhx4zh27BhLly4lMTGRWrVq4erqKj1uz9HRkfr163P8+HHatWvHqlWr6N69O6tWrcLd3Z2///6b3r17M2PGDNzc3Fi3bh0tWrRg0aJFjBs3DoCtW7eyevVq/v77b8zNzfHy8pLuOT19+jTz588nKSmJKlWqMHz4cGnikiAIgiAIQpEGi7du3XrjNWVlZerXr//GMtWrV39Pu1Y8wcHB9OjRA2dnZ2rUqMGYMWN4+fIlkZGRlC1blsjISHx8fJg1axZNmjQhMjKSESNGcODAAapWrYqvry8xMTGsW7eOrKwsPD09pXswMzMzAcWActOmTdy/f186i+rl5QUo7jHU09MjPDycixcv4ubmRtu2bWnVqpW0j+bm5gQHBzNu3DhOnTqFhoYGp0+fZty4cUyZMoWvv/6a48ePM3HiRLZt20bjxo0BiIyMZN26daiqqqKpqQnA6tWrWb58OdeuXWPy5MmcOHGCWbNmoaamxujRo/n+++9xcnLi6NGjhISE4OPjg5GREbt27WLw4MEcOnQILS0tXF1dcXJyws7OjnPnzjF9+nQsLCyoW7dusfIXpdyiYDaPyEEmspCJLBREDjKRhaxUlHJbW1sX+ni/132sx/3p6OigrKxM+fLlKV++PADDhg3DwMAAgE2bNuHo6CjNCJ4yZQpnz55l8+bNjBw5kl27drFmzRrMzMwAxSBw2LBhAJw9exYAd3d36YzczJkzGTVqFBMnTgQUofv4+KClpUXt2rXZuHEjFy9ezDdYVFVVlXoo8y5Bf/fdd3Tu3BknJydAMRnlwoULrF+/XpoI065dO6m25+bNmwCMHj2aBg0a0KBBA+bOnUvXrl2lbVlaWpKUlATA2rVrcXFxoX379oCiyufEiRPs3r0bOzs7Hjx4gJ6eHvr6+ujr61OlSpVi1+aAKOV+VUktVf3QRA4ykYVMZKEgcpCJLGQlNYsiDRaPHDkifX38+HE2bdqEu7u79Dzgy5cvM3/+fPr16/ef7eg/8WqxdGJiImPGjMn3vpmZGYmJiSQlJZGZmYmpqan0nrm5ufT1gwcPaNiwIQ0bNpRe+/LLL8nKyuL69esA6Orq5nuKjZaWFllZWe/cx8TExDcu+5qbm7Njxw7p+3/bsRgQECANPAFevHhBSkoKOjo6ODg44OXlxfLly2nfvj29e/f+R8XqopRbFMzmETnIRBYykYWCyEEmspCVilLuVwcia9asYcmSJdKTQwBatGjBnDlzGDVqVL4nmnxsr3YTFtRTmJ2dTU5OToH9iLm5ue/87Kv/LWig9Oo6irKPeXJycqQex8KWKU7HooeHB5aWlvlezxvYent7M2jQIKKiooiKimLbtm0sX7682DPbS2qR6McgslAQOchEFjKRhYLIQSaykJXULP5Rz2JBZ8wyMjKke/tKooK6DM+fP4+RkRG1atWibNmyXLp0SXrv1a+NjIxISUnhwYMH0muxsbGoqKhQq1at975f77tj8a+//srXsbhy5UpiY2NJT09n9uzZGBgYMGrUKHbs2EHLli05evToe9m2IAiCIAifvmIPFrt37860adPYs2cPCQkJ/PHHH+zYsQM3N7ePPotWQ0ODpKQkHj58+MZ7Tk5ObN68mV27dpGcnMzChQuJj4+nT58+aGpq0qtXL/z8/Dh//jyxsbH4+fkBoKSkRKtWrahZsybTpk3j6tWr/Prrr/j4+NCtWzfi4uJwd3cvdJ+Cg4M5efJkoe87OTlx8OBBQkNDSUlJYePGjRw+fLjIZ2j379//1ptUhw4dSmhoKLt27eL69esEBASwf/9+6tSpg7a2NocPH2bu3Llcv36ds2fPEh8fLzoWBUEQBEGQFLs6x93dHU1NTebNm8f9+/cB0NPTY9CgQYwcOfK972BxODg4sHDhQrZv3/7Ge7a2tty9e5elS5eSnp5Ow4YNWb9+vVT5M336dGbNmoWTkxNaWloMGjSIoKAgypYti7KyMsuXL8fHx4d+/fqhqamJnZ0dkyZNIjY29q375OzsTHJycqHvN23aFH9/f4KDgwkICMDIyIjFixe/cdm4IGlpabi6ulK1atVCl3n1uO/evUvdunVZsWIFhoaGACxfvpy5c+fSvXt3NDU16dOnj3jMnyAIgiAIEqXcotxY94off/yR1q1bo6OjIw0WK1Wq9J/s3IcUFRWFpaWlVE1z4cIFBg4cSExMTL5nQr/uzJkzDB48mKtXr36oXZXcvHkTGxsbjhw5km8yz4eUnZ1NbGysNNnpc5aXhZmZWYm85+RDETnIRBYykYWCyEEmspB9jCyKs81iX4aePXt2vkFiaRgoAoSEhDB37lxSU1O5cuUKAQEBWFtb5xsopqam8u2332Jubk67du0ICwuT3gsPD6dNmzaYm5vj7u4uzUYODg6WirgjIyMZMGAAY8aM4auvvmL37t3k5OSwdu1abGxsaNKkCY6OjvkGnsbGxkRERNChQwfMzc2ZPHkyT548AcDGxkb6b2RkJACHDx/G1taWpk2b0qdPH6Kjo6V1OTo64uPjg42NDe3atSMjI4Pff/8dBwcHmjZtipmZGcOHD+fOnTv/UcqCIAiCIHxqin0ZukWLFvz444+MHDmyVJ1JWrhwIT4+Ptjb26Oqqoq1tTUeHh7S+y9evMDZ2RkTExO2b9/OjRs3mDx5slRJc/DgQdatW0d6ejpjx46lSZMmBd53GBMTw8iRI5k0aRIVK1Zk2bJlhIeH4+Pjg6GhIWvWrGHYsGEcPHgQDQ0NAJYsWYKvry+6urp4eHgwc+ZMFi1aREREBH379iUiIoL69esTHx/P9OnTmT17Nk2aNOGnn35i+PDh7N69W+qbfLXgOzc3FxcXF5ycnPD39+fOnTt4eHiwevVqqWy8qEQptyiYzSNykIksZCILBZGDTGQhKxWl3K+6d+8ey5cvZ+XKlVSqVOmNWpdXOxk/JXXr1iU0NLTQ90+dOsX9+/eZO3cuWlpa1KtXDy8vL6myZtasWRgZGVG/fn2srKyIj48vcD1KSkqMGjUKNTU1cnNz2bx5M5MmTZLOEvr4+NCxY0d2794tTRgaPnw47dq1A8DT0xNnZ2e8vb2ls7qVKlVCTU2NdevW0a9fP+zs7AAYPHgwZ8+eJTw8XHrk4KsF3+np6YwePZqhQ4eipKREzZo16dSpExcuXCh2fqKUW1ZSS1U/NJGDTGQhE1koiBxkIgtZSc2i2IPFfv360a9fP54+fUpWVha5ubmUKVNGempKaZWcnIyRkVG+4u3evXtz5swZgHwVOuXLl5cuQ79OV1cXNTU1QDHwfvDgQb7OyrJly9K4cWMSExOl1/IGdwCNGzcmOzub5OTkN24BSExMZP/+/Wzbtk16LTMzk9atW0vfv9qZWblyZezt7dm4cSNxcXFcu3aNq1ev5tteUYlSblEwm0fkIBNZyEQWCiIHmchCVipKuV/VtWtXAgICCA8Pl/oWVVRUsLOzY/bs2cVd3SejoOLuV73+wy1s3tC7isJBLgvP8+p9k3mvF1TCnZ2dzfDhw6VHGubJG5y+vs3bt2/Tu3dvTExMsLKyol+/fhw/fvyN3seiKKlFoh+DyEJB5CATWchEFgoiB5nIQlZSsyj2BBd/f39++uknVqxYwW+//UZ0dDTLli3jt99+Iygo6L/YxxLB0NCQ1NRUnj17Jr22YMECfH19//E6y5cvj56eXr76nczMTC5fvpyvlPvV521funSJsmXLYmRk9Mbzuo2MjLh582a+Au5t/9fencfVmPd/HH+lJEtKsmcII2uKsjMmDLKFscREsmTLroWEEiZhlH0b+9YIY4x9mWFuI4bsNbQQBtllKdX5/XF+XadDUWaQfJ6Ph8dd51znur7XW7q/cy3va9Mmfv/99wy3v2/fPoyMjFi8eDF9+vTBxsaGuLi4LD15RgghhBCfh2xPFn/55Rf8/Pxo0qQJhQoVonDhwnz11Vf4+fmxY8eO9zHGHKFx48aYmpri4+NDVFQUBw4cYOPGjYwZMybTzwQHByt3KWfG2dmZoKAgDh48SFRUFBMnTiQxMRF7e3ssLCwACAoKolGjRsqNLp06daJgwYLkz58fgD/++AMLCwvs7e359ddfWb16NdeuXWPlypWsXLlS6VRMc+zYMaKiojA2NubmzZscO3aMuLg4lixZwt69ezM9hS6EEEKIz0+2J4sqlYqiRYu+9rqJiYlS6ZIb6enpsWDBAu7cuUOnTp3w9/fH3d1dmbC9KxcXF7p27crEiRPp3Lkzt27dYs2aNVrXIzo4OKCnp8fq1auxtbVl4sSJgDrzDh064OvrC6ivGwwICGD9+vXY29uzefNmZs2aha2trdY2nZ2duXv3Lm3atKFDhw4MHz5cuf7Sw8ODqKgomTAKIYQQAniHUu4RI0aQmJhIYGCgcrPH48ePcXd3B2DRokX//Sg/UcHBwYSFhbFmzZp3+nzakcXVq1dTr169TJfLbjm3hYXFW9eZVVLKrSEFs2qSg4ZkoSFZqEkOGpKFRk4v5c72DS7jx4+nd+/eNGnSRLmuLiYmhrJly7Jw4cJ3G/EnLG2iFhgYSEBAAM+fP8fBwUGpqnn58iVTpkxh+/btGBgYMGDAAPr27Quob1ZZsWIFGzZsID4+nlq1auHt7a1MEtOzs7Nj2LBhdO7cmZcvXzJjxgy2b99OgQIFcHV11Vr20aNHBAYGcuDAARITE7Gzs8Pb2xsjIyPs7OwAda3OsGHDcHNzIyQkhOXLl3P9+nUKFiyIvb093t7e2fqBlZ5F6QxLIzloSBYakoWa5KAhWWjkup7FEiVK8Msvv/D7778THR1Nvnz5MDc3p1GjRhneofu5mDdvHnPmzCE5ORl3d3cKFiyInp4ep0+fxtLSkm3btnHw4EGmT59O06ZNqVixYpYKuTMSHBzMoUOHWLhwIXp6esrENM2wYcN4/vy5cpR38uTJeHp6snDhQn766ScaNGhAcHAwjRo1IiwsjKlTpzJz5kyqVavG+fPnGTduHA0aNOCbb77J8v5Lz6JGTu3J+tAkBw3JQkOyUJMcNCQLjZyaRbYni6CucmnevLlSJC1g3Lhx2NjYAOpT9YGBgTg6OlKiRAm8vLzQ0dHB2dmZ+fPnExkZSYUKFbJUyP3q6WKVSkVISAgeHh7KtYjjx49n4MCBAERERBAWFsbu3buVI78zZ87E3t6e6OhoKlSoAICRkREFCxakQIEC+Pv7KxNDMzMzfvzxRy5fvpytyaL0LEpnWBrJQUOy0JAs1CQHDclCI9f1LIqMvVqcff/+fR48eICZmZlWxY2hoSGJiYlZLuR+1YMHD7h//z5Vq1ZVXqtZs6bydXR0NIULF9aq3qlYsSJGRkZak8X0YzUwMCAoKEgp5b569apWkXdW5NRuqI9BslCTHDQkCw3JQk1y0JAsNHJqFp/veeP/WGbF2Rn9patUqiwXcmcm/X1J6bed2dG9zK4pPHLkCJ07d+bu3bs0adKEoKCgd3qCixBCCCFyJ5kspuPp6fna9X+ZsbOz0+pQXLp0qfL1+fPnKV68OMbGxgAcP35c66aVixcv0rZtWwB++ukn5fWMCrlfVaRIEUxNTbUOHae/XtDc3JzHjx8THR2tvHblyhUSEhIyXG9ISAhdunTB19eXrl27UrFiRa5du5btYu4XyVLkLYQQQuRGcho6nQkTJmR52Z9++okCBQpw9+5dQH1tYcuWLXny5Alz587lu+++4+XLlwBYW1tz9OhR5bP79++ncePGmJiYsGXLFho2bEi5cuVYunSpUsidGR0dHXr16kVQUBBlypTB0NCQ6dOnK+9XrFiRpk2b4uHhofQxTpkyBVtbWypXrgxAgQIFuHz5MtWqVcPY2JjTp08TGRlJnjx5WLx4MfHx8dnuWUyVp74IIYQQuZJMFtMxNDTM8rLpS7MBihYtiqurK6mpqTg6OjJw4EDmz58PqE8NFytWTFn2xYsX1KlTBwcHB3R1dZk4cSIJCQlYW1u/VsidkUGDBvH8+XNGjRqFrq4uQ4cOVYq5QfMYQmdnZ3R1dWnevDleXl7K+05OTgQEBHDt2jWGDRuGl5cX3bt3p1ChQnz11Vc4OjpqPWJQCCGEEJ+vT/o09F9//YWjoyO1atXCysqKAQMGcOfOHQB+//13OnXqRK1atejQoQPHjh1TPrd9+3Zat25NrVq16NGjh3IaN/1p6ODgYEaNGoWXlxe1atWiVatWHDhwQFlH2mno3bt3A+q+xXv37vHnn3+yfft2Zs2axaZNm3jy5Al//vmn1mnoBw8eMH78eFq2bMmoUaPYsmULzZo14+LFizg5OTF16lSSkpKIjIzkxo0b9OjRg6pVq+Lv78/PP/9Mnz59MDY2plq1aiQkJLB161b27t3L4sWLsba2pkePHvTo0YNTp05x4sQJXFxcGDZsGJaWlrRq1YoSJUpw9uxZxo8fj4GBAQUKFEBPT4/ExEQSExMZO3Ysy5cvf+9/f0IIIYTI+T7ZI4tPnjzB1dUVZ2dnAgICuHPnDuPHj2fJkiV0796dwYMHM3ToUOzt7dm7dy9Dhgxh7969REREMGHCBCZMmEDDhg1Zs2YNrq6uWhPBNPv27aNdu3aEhoZy4MABhg8fzvbt26lUqZKyzNdff610FC5ZskR5fceOHSxfvhyVSsWjR4+U13/66Sc6deqEi4sL7du3JykpiT59+lCuXDnWrFnD/fv3ldPH3t7eAJw+fZpBgwYxevRoihQpQkhICPPnz8ff358JEyYwdOhQvv32W7777jt++uknZs+ezdSpU/n555958eIFAwYMoFOnTvj5+REdHc3EiRMpWLAgDg4OBAUFER8fz4YNG0hOTmbcuHEsWLBAeSJPdnzuxapSMKsmOWhIFhqShZrkoCFZaOS6Uu6c4sWLFwwZMoS+ffuio6ND2bJl+eabbzh79iw//fQTtWvXZsiQIQAMHDiQZ8+e8fjxYzZt2kS7du1wdHQEwN3dnbx582pN6NIYGRnh6+uLvr4+FStW5Pfff2fLli14eHgoy6Td1Zw3b16tU80dOnRQjiYeP35ced3ExARdXV0MDQ0xMTHhwIED3L59m82bN2NkZASAj48PgwcPZtSoUYD6OsXBgwdjYGCgrOfrr7+mTZs2ALRo0YJff/2V4cOHo6OjQ7du3Rg6dCignrQWLVqUkSNHAlC+fHlu3LjB6tWrcXBw4MaNGxQsWBAzMzPy58/P3Llz3+nvIyU5mfDwC+/02dwmp5aqfmiSg4ZkoSFZqEkOGpKFRk7N4pOdLBYrVgwHBwdWrlzJpUuXlI7A2rVrExMTQ/Xq1bWWT5ssxcTEKIXXoL6eMP3kL70aNWpoVdFk1IFoZmbGsGHDCAsL03q9TJkyWdqPqKgoypcvr0wUQd3ZmJyczLVr1wD19ZDpJ4pp201jYGBA6dKllT5HAwMD5eaa6OhoIiIisLa2VpZPSUlRKn169+7NkCFDaNCgAQ0aNKBVq1a0b98+S2NPT1dPDysrq2x/LjeRglk1yUFDstCQLNQkBw3JQkNKud+T27dv06VLF6pXr07Dhg3p1q0bhw8f5syZM+jpZb5bb3rvbcumpKRk+ZGGmfUoZmW5Vw9HZ7TMq2PLbFzJyck0aNAAHx+fDN9v0KABv/32GwcOHODw4cP4+Phw9OhRAgMDszT+9D73f+xpcmqp6ocmOWhIFhqShZrkoCFZaOTULD7ZG1z27duHkZERixcvpk+fPtjY2BAXF4dKpaJcuXJERERoLd+jRw927tz52nspKSnY2dnx119/vbaNyMhIrYLs8+fPa92okib9E1qyy9zcnNjYWB4+fKi8Fh4ejp6eHl988cU7rzf9+mNiYjAzM6NcuXKUK1eO8PBw1qxZA8DKlSu5cOECnTp1Yu7cuUyfPp29e/f+6+0KIYQQInf4ZCeLxsbG3Lx5k2PHjhEXF8eSJUvYu3cvSUlJODo6cvLkSX788UeuXr3K4sWLuXz5MjY2Njg5OfHzzz+zdetWrl69yvTp01GpVK+dtgaIi4tj5syZREdHs3DhQi5cuMC333772nL58+fnzp07XL9+Pdv70ahRI8qWLYu7uzuRkZH8+eef+Pn5Ua9ePeXZz/9Ghw4deP78Od999x1RUVH89ttveHl58ffffwNw69YtfH19CQ8PJzY2lj179lCtWrV32tazpOR/PV4hhBBC5Cyf7GSxTZs2dOjQgeHDh9OlSxeOHz+Oh4cHUVFRlCxZkuDgYLZs2UK7du3Ys2cPixYtokSJEtja2jJp0iTmz59Phw4duHTpEosWLXrtmkCAWrVqcf/+fRwcHNi1axdLliyhbNmyry3XsmVLUlNTadu2Lffu3cvWfujq6rJgwQIAunXrxujRo2nevDkuLi7vFswrChUqRK9evTh//jwODg54e3vTp08fpQNyxIgR1K5dm8GDB9OxY0eePXvGzJkzs70dt/WnSU2VYm4hhBAit9FRZfe5bp+J4OBgwsLClNO1H9rx48fp3bs3kZGR/3pdoaGhzJs3j4MHD/4HI9OWkpJCeHg4P5xKYqGTDYUM8r79Q7lUWhZWVlY58pqTD0Vy0JAsNCQLNclBQ7LQ+BhZZGebn+wNLu/bmTNnCA8Pp2bNmnz55ZeMHz8eGxsbzp49y/Tp07l48SIlS5Zk+PDhynOeQ0JCWL58OdevX6dgwYLY29vj7e2Nrq4unp6eGBkZcfv2bQ4dOoSxsTGjRo3CwcEBgISEBHx8fDh06BDFixena9euWuP5559/mDJlCseOHaNo0aJ07tyZwYMHo6urS2hoKFu3bsXW1pZ169aRkpJCly5d8PT0JCwsTHl6i4WFBQcOHMDLy4u6devi5uZGUlISs2bN4tdff+X+/fuUKFECV1dXunfvnr3AVCpUfN59WdIZpiY5aEgWGpKFmuSgIVloSM/iJ+jixYv88ccfVKpUiYULF7J69WpGjhzJ9u3bcXFxoUOHDvj7+xMeHo6HhwcVK1bk8ePHTJ06VSnoPn/+POPGjaNBgwZ88803AKxbt44RI0YwZswYVq9ezaRJk2jevDmGhoZMmjSJ6Oho1q5dy/3795UnyQCoVCqGDRtGlSpV2Lp1K/Hx8fj4+KCjo6P0KZ4+fRpTU1M2bNjAuXPn8PT0pGnTptja2jJ+/HhWrFjBTz/99NqjBJcsWcLhw4cJDg6maNGibN26FT8/P5o3b46pqWmWM3v67CnJycmEX7j4H/wNfNpyak/WhyY5aEgWGpKFmuSgIVlo5NQsZLKYgRs3bqCnp8esWbMwMzNj5MiRfP311+zcuRMjIyO8vb3JkycPFSpU4NGjR7x48YICBQrg7++vTAzNzMz48ccfuXz5svKahYUFAwYMANTXCq5evZrLly/z5ZdfsmvXLlavXq3caDNkyBDlec9//vknN2/eJCQkRNmuh4cHXl5eymQxJSUFPz8/ChUqRIUKFVi5ciXnzp2jUaNGGBoaoqurq1UanqZKlSrUr19f6UgcNGgQ8+fPJzY2NluTxYIFCqL3mXctSmeYmuSgIVloSBZqkoOGZKEhPYufoMaNG1O5cmXat29PtWrVaN68OV27dmXv3r1Uq1ZNq9Owb9++ytcGBgYEBQUpBeFXr16lcePGyvvly5dXvi5UqBCg7kGMiYkhJSWFKlWqKO/XrFlT+ToqKoqHDx9Sp04d5bXU1FRevHjBgwcPAHVxd9o609afnPz2u5NbtGjBH3/8wYwZM4iOjlaek53tQ+E6OuggXYuQc3uyPjTJQUOy0JAs1CQHDclCI6dm8cneDf0+5c+fn5CQEFatWkXdunUJDQ2lc+fOylNRMnLkyBE6d+7M3bt3adKkCUFBQdSuXVtrmbx5X7/5I7P7i9I/OSY5OZkKFSqwbds25c/PP//M3r17MTQ0fG35t607vTlz5jBu3Dj09PRwcHBg06ZNb/2MEEIIIT4fn9xk8fr161hYWLxTp2FWnT59msWLF1O/fn28vLzYvXs3iYmJlCxZksjISK1J2MiRI1m2bBkhISF06dIFX19funbtStmyZbWWPXLkCJcuXQIgMTFReW71tGnTqFChAnnz5tU6HJx2hA/Uxdo3b97ExMREKda+fv06QUFBWSoEf9MyGzduZOLEiYwdOxZ7e3ueP38OZG2iKYQQQojc75ObLH4IBgYGzJ8/n5CQEK5fv87OnTt59uwZjRo14uHDhwQEBBAbG0toaCgHDhygUaNGGBsbc/r0aSIjI7l8+TJ9+/YlISGBpKQkAOrVq8eXX34JqCeOR44cAdTXJhYqVIiOHTvi5+fHmTNnOH78OPPmzVPG07hxY8qUKcO4ceOIjIzk5MmTTJw4kfz582fpcHX+/Pl59OgRsbGxr52aNjY25tChQ8TFxXHy5Enc3d0BlHFnVXBPa/Lkefcn2QghhBAiZ5LJYgaqVq2Kv78/y5Yto02bNixatIiZM2dibW3N4sWLOXnyJO3atWPp0qXMmjWLqlWrMmzYMIoWLUr37t3p27cvenp6FCxYUDmaqK+vrzzP+cmTJ8rNI8bGxgBMnDgRa2tr+vbti6enJ999950yHl1dXRYuXEhqairdunXDzc2Nr776Cm9v7yztT/369SlXrhzt27dXxpNm2rRpXLp0ibZt2+Ll5UXr1q2xtLR8bbm3KZA3DwX05RJYIYQQIrf5ZCeLu3fvpmnTptSuXRsfHx+SkpIIDQ3Fzs5OazknJyeCg4OV73/88Ufs7OywtramX79+xMXFAerTrvPnz6dx48bY2Niwa9cufvzxR86dO8eePXsYPXo0c+fOZdCgQRQtWpTz588zbdo0fvzxR6ysrOjRowctWrQgPDycWbNmcfLkSZ4+fcrRo0e5fv06N27coEyZMoSGhuLp6cnNmzcB9Z3XAL/++iunTp0iOTmZIkWKULNmTaWQ287Ojo0bN3Lx4kXMzc2ZM2cOv/32G7/88guNGjVi+vTpODo6cuLECVq3bo21tTWlSpVS7pQ2MjKiefPmGBkZ0bdvXwoWLEiXLl0AqFOnDoMHD6ZUqVLcunWLbdu24erqiqur6/v9CxRCCCHEJ+GTPRS0efNm5syZQ0pKCu7u7ixevJgyZcq88TMbN25k3rx5+Pn5Ua1aNWbPns2IESMIDQ1l7dq17Nixg1mzZmFqasqKFStwcXFhx44dyo0phw4dYsOGDaSmphIVFUWfPn1wdnbG39+fM2fOMGXKFExNTfnqq68y7Ta0t7fnyZMnynuGhoaEhobi5+fHpEmTsLS0JDQ0lIEDB7J7925KlCgBwI4dO1i+fDkqlYpHjx5x584d9u/fz5o1azh06BCBgYFUqVKFGTNm8ODBA9zc3GjZsiUtW7Z84749fvwYd3d3fH19qVevHrt372b06NH8/vvvylHPrEhJSfnsi1WlYFZNctCQLDQkCzXJQUOy0JBS7vdk/PjxSpXMiBEjCAwMZMyYMW/8zKZNm3B2dsbe3h4AHx8fli9fzosXL1i2bBmTJk2iXr16APj6+tK4cWOOHDmiHK3s3r07FSpUAGD69OlUq1aN0aNHA1ChQgWioqJYtmwZLVu2zLTb0MDA4LX31qxZg5OTk/I0l7Fjx3LixAnWrl2r7FOHDh2wsLAA1I8CfPnyJR4eHpibm1O6dGkCAgLo1auX0nNYtWpVoqOjAd64byVLluTly5eULFmSMmXK4OLigoWFBfny5cvW30f6G3I+dzm1VPVDkxw0JAsNyUJNctCQLDRyahaf7GTR0tJS+bpatWrcvXuXx48fv/EzMTExSuk1gKmpKR4eHjx9+pRbt24xatQorQ7FFy9eEBsbq3yf/shlVFSU1hgArK2t2bhxY7b3JSoqSjllnMbKyoqoqKgMt52mbNmygHoC+uoyBgYGJCUlvXXfvv76a5o1a0bfvn0xNzdXOiXz58+frX2oVq1ahvU9nxMpmFWTHDQkCw3JQk1y0JAsNKSU+z1JP/FJq3kxMjJ6bbn0d/+m3WDyqrRDsXPnzsXc3FzrvfTrTH+0LaMjb6mpqe90CDmjdaWkpJCamvrGZV7dn/SZpF8PZL5vOjo6LF68mLNnz3LgwAH27dvH+vXrWb9+PVWrVs3yPuTUItGPQbJQkxw0JAsNyUJNctCQLDRyahaf7A0uf//9t/L12bNnKVmyJHnz5uXp06fK6yqVSquPsVy5ckRERCjfP3jwgPr16/P48WOKFi1KfHy80mNYqlQpZs6cSUxMTIbbNzc358yZM1qvnT59WpmQZaX/8E3rOnPmzGuTu3dRuHDhN+5bVFQU33//PZaWlowaNYqdO3dSqlQppdpHCCGEEJ+3T3aymNZJ+McffxAUFISzszM1atTg4cOHrFmzhri4OKZPn86jR4+Uzzg5ObFq1Sr2799PTEwMkyZNwszMDDMzM5ydnfnhhx84ePAgsbGxeHt7c+rUKeUaxVf17NmTS5cuMXv2bGJiYti6dSvr16+nV69ewJu7DV/l7OzM2rVr2bZtGzExMQQGBhIREcG3336rtVxwcDBOTk7K96/e+f2m9We2b4ULF2bDhg0sWLCAuLg4Dh8+zI0bN6hWrVqW1p3mRbKUeAshhBC50Sd7GtrR0ZHBgwfz8uVLunXrRp8+fciTJw8eHh4sXLiQH374gc6dO9OqVSvlMx07duT27dtMmTKFhIQE6tatS1BQEAD9+vXj6dOn+Pj4kJCQQI0aNVi+fHmGp7YBSpcuzeLFiwkICGDFihWULl0aT09PpZImfbfh+vXr37gv9vb23L17l6CgIOLj46latSorVqygYsWKWsu5uLjg5OSkVOpk1dv2LTg4mMDAQBYtWkTRokUZPXq01jOtsyJVnvgihBBC5Eo6Knmu2ycpNDSUefPmcfDgwY86jpSUFMLDw6lYpTrGBQ0+6lg+trQsrKyscuQ1Jx+K5KAhWWhIFmqSg4ZkofExssjONj/Z09C5Vdqzr3fs2EGTJk2wsbFh6tSpJCcnv3YaOr2oqCj69etH7dq1adKkCfPmzVNukHn8+DFubm7Y2Nhga2vL2LFjSUhIANSP9Zs+fTpNmjShevXq2NnZsWnTpg+2v0IIIYTI2T7Z09C53bx585gzZw7Jycm4u7tTsGDBTO/mvn//Pj179sTOzo6QkBBiYmLw9vamUKFCODs7K6e3N2zYQHJyMuPGjWPBggW4u7uzZMkSDh8+THBwMEWLFmXr1q34+fnRvHlz5ZGEWfW5F6tKwaya5KAhWWhIFmqSg4ZkoSGl3OKdjBs3DhsbG0BTOu7o6Jjhsr/88gv58+fHz88PPT09KlasSHx8PPPnz8fZ2ZkbN25QsGBBzMzMyJ8/P3PnzlU+W6VKFerXr6+UeQ8aNIj58+cTGxubrcliSnIy4eEX3n2Hc5GcWqr6oUkOGpKFhmShJjloSBYaOTULmSzmULVr11a+rlGjBvfv3+fBgwcZLhsVFUX16tW1jjxaW1sTHx/P48eP6d27N0OGDKFBgwY0aNCAVq1a0b59ewBatGjBH3/8wYwZM4iOjlaexJLd/7rR1dNTJpyfKymYVZMcNCQLDclCTXLQkCw0pJRbvJO051EDyrWHGZVuQ+YF4aD+YWjQoAG//fYbBw4c4PDhw/j4+HD06FECAwOZM2cOISEhdO7cGQcHByZNmpTlSp5Xfe7/2NPk1FLVD01y0JAsNCQLNclBQ7LQyKlZyGQxh7p06RJ169YF4Pz58xQvXhxjY+MMlzU3N2fv3r28fPlSmWSePn0aExMTjI2NWblyJRYWFnTq1IlOnTqxc+dOvLy8ANi4cSOTJ0+mTZs2AFy5cgXQPBVHCCGEEJ83uRv6PTl+/DgWFhbv/Hl/f3/OnTvH//73P+bOnauUfWdk8eLFPH78GB8fH6Kioti/fz/BwcE4Ojqio6PDrVu38PX1JTw8nGPHjrF27VqldNvY2JhDhw4RFxfHyZMncXd3B9R3SWfXs6Q3l48LIYQQ4tMjRxbfE2tra44ePfrOn7e3t8fV1ZXU1FQcHR0ZOHAg8+fPz3DZPHny4OLiwrFjx3BwcMDExIQ+ffrg6uoKqG+QefLkCYMHD+bhw4eYmZmxYsUKAKZNm8bkyZNp27YtJUqUoGvXrujq6nLp0iWaNm2a5fG6rT/NQiebd95fIYQQQuRMMll8T/T19SlWrNg7f75t27bKZC+Nm5ub8nXnzp3p3Lmz8n3p0qVZt25dhuvKnz8//v7+gPqRh3Xr1qVs2bIA1KlThx07dmgtP3DgwHcetxBCCCFyF5ks/geuXr2Kr68vp06dwsjICBcXFywsLOjduzeRkZFcv36d5s2bExwcTEBAALdv36Zhw4Z8//33ynWIR48e5fvvvyc2NhZQP4JPR0eHGTNm4OnpCcCMGTOUbVpYWLB69Wrq1aunNZaEhAT8/f05fPgwT548wczMjLFjx9KiRQs8PT0JCwtT/qxZs4a//vqLwMBALl68iI6ODra2tvj7+1O8ePHshaBSoeLz7suSzjA1yUFDstCQLNQkBw3JQkN6FnO5xMREXFxcqF69Ops3byYuLo4xY8Ywe/bs15ZdtGgRs2fPRqVSMXjwYH788UdGjRpFXFwcgwcPZvDgwVhbW+Ps7Mz27dtxcHDI9nj8/f2JiYlhxYoV5M+fn2XLljFhwgSaNm3KhAkTiI2NxdraGldXV548eYKrqyvOzs4EBARw584dxo8fz5IlS/D29s7Wdp8+e0pycjLhFy5me8y5TU7tyfrQJAcNyUJDslCTHDQkC42cmoVMFv+lo0ePcv/+faZNm0ahQoX48ssv8fb2zrDmZvjw4VhaWgLQvn175YciJCQES0tLhgwZAkBkZCTdu3d/p/HY2trSt29fKleuDICLiwshISHcu3ePUqVKkTdvXgoUKICxsTHx8fEMGTKEvn37oqOjQ9myZfnmm284e/ZstrdbsID6CTOfc9eidIapSQ4akoWGZKEmOWhIFhrSs5jLxcTEYG5uTqFChZTXunTpwvHjx19btly5csrXhQoV4uXLl4B6clizZk2tZa2srHj06FG2x+Pg4MD+/fvZvHkz0dHRXLigfqpKRoebixUrhoODAytXruTSpUtcuXKFyMhIrULwLNPRQQfpWoSc25P1oUkOGpKFhmShJjloSBYaOTULmSz+S5k9rzkj6Yu209PV1X2t1zD99zo6OlrfJydnXlHj7u7O6dOn6dixI46OjhQrVizTo5S3b9+mS5cuVK9enYYNG9KtWzcOHz7MmTNnsrxPQgghhMjdZLL4L5UvX56rV6/y/Plz8ufPD8D333/P3r17lWVu3bql/K+ZmRnh4eGsWbOGx48fExISwpdffslff/2ltd4LFy4odyznzZtX61F/cXFxGY4lISGBX375hc2bNyunu3/77Tcg45Ltffv2YWRkxOLFi5XX1qxZI4XcQgghhFBIKfe/1LhxY0xNTZVC7AMHDrBx40blCSmAUqGT9r9LlizByMgIS0tL2rRpQ7du3QgPD2fJkiXExMSwaNEiTp48iY6ODgA1a9bkjz/+4NixY/z999/4+vpmeJRSX1+f/Pnzs3fvXq5fv86RI0fw9fUFNCXbBQoUIDY2lnv37mFsbMzNmzc5duwYcXFxLFmyhL17975TIXdwT2vy5NHJ9ueEEEIIkbPJZPFf0tPTY8GCBdy5c4dOnTrh7++Pu7s7hoaGyjJp1x+k/e+TJ08oUaIE+fLlo1ChQpQpU4agoCC2bNlC+/btOX36NM2bN1cmhB07dqRVq1YMGTKE/v37065duwyrbfT19Zk5cyZ79uyhbdu2zJgxg8GDB1OsWDEuXboEQNeuXTly5Aj9+/enTZs2dOjQgeHDhyvXWXp4eBAVFZXtCWOBvHkooC8HqoUQQojcJtdOFq9evUq/fv2wtramWbNmrF69GoCoqCj69etH7dq1adKkCfPmzSM1NRVQdxsOGTKEXr16UbduXcLCwrh9+zbDhw/H1taWGjVq0KlTJ61TxlevXmXatGmcPXsWExMTnJ2dcXR0zHRcTk5OhIWFceLECcLCwgA4deoUoaGhPHnyhHz58lG4cGGeP39OsWLFOH78OK1bt0ZfX588efLw3XffceLECdq0acOGDRuoVasWBgYGVKlShTlz5uDp6UliYiIzZsxg586dfPvtt4SEhPDLL79Qq1Ytpk2bRu/evfnpp5/Q1dXF29ubVq1aoaOjw6lTp/jzzz/59ddf0dfXf49/O0IIIYT4VOTKQ0GZdR8aGRkxbdo07OzsCAkJISYmBm9vbwoVKoSzszMABw4cYPLkyVhZWWFubs6AAQMoXLgwGzduRKVSERgYyOTJk9mxY0em2ylbtiwFChTIcGzBwcEMGjQIa2trXFxcAPDx8SE6Oprx48dTsmRJ/P39+eeff/Dw8ODRo0fcuHGDpKQkQkNDyZs3L0FBQaxatYrx48czatQovLy86NOnD61atWLTpk2sWbOGSZMmKZPAYcOGUaVKFbZu3Up8fDw+Pj7o6OgwdOhQ1q1bx4kTJ1ixYgUGBgZMnjyZadOmMXfu3GxlnpKS8tkXq0rBrJrkoCFZaEgWapKDhmShIaXcH0Fm3YcPHz4kf/78+Pn5oaenR8WKFYmPj2f+/PnKZNHU1FQ5MqhSqWjRogWtWrWiZMmSAPTq1Ut5HF52OhbTGBsbK12HxYoVIyIigsuXL9OnTx+WLVvGvXv3KF26NCqVSuvoXv/+/bWqd2rUqEHPnj0BaNeuHdOmTcPb2xsDAwOcnJzYsGEDd+/eJSoqips3bxISEkKePHmoUKECHh4eeHl5MXToUK5fv06+fPkoU6YMxsbGzJgxg4cPH2Y784sXpYw7TU4tVf3QJAcNyUJDslCTHDQkC42cmkWunCxm1n04adIkqlevrlV3Y21tTXx8PI8fPwagTJkyyns6Ojo4Ojry66+/curUKWJiYjh//rxy2jqz7QAZ9ixmJDo6msKFCzN+/HjGjx+vvF63bl2io6OVax/NzMy0Ppf+ewMDA0xNTTEwMAAgX758gPqmlqioKB4+fEidOnWU5VNTU3nx4gUPHjyge/fu7Ny5k8aNG1O3bl1atGih9czprKpWrdpnf+paCmbVJAcNyUJDslCTHDQkCw0p5f4IMus+TJtEpZc28Us7HJt+mdTUVFxcXHj8+DH29vbY2dnx8uVLhg0b9sbtZEdmE6xXT+u+OvZXt53Z0czk5GQqVKjAggULXnvP0NCQIkWKcPDgQQ4fPszhw4eZPXs2v/zyC+vWrVPuxs6KnFok+jFIFmqSg4ZkoSFZqEkOGpKFRk7NIldOFjPrPly/fj2mpqa8fPlSudP49OnTmJiYYGxs/Np6rly5wokTJzh27BgmJiYArFu3DlCfos5sOy9fvqRly5ZZGqu5uTmPHz8mOjqaChUqKNtNSEjA3Nxcq1/xXZibm3Pz5k1MTEyUo5R//PEHoaGhBAQEsG3bNvT19bG3t6dNmzaEh4fTvXt37t27h6mp6b/athBCCCE+fbnybuj03Ydbt27FwsKCjRs38sMPP5CUlKR0Iu7fv5/g4GAcHR0zPIpWuHBh8uTJw86dO7lx4wa7d+8mODgYUJ/inTJlCvny5XutY7Fx48YZjis8PByAFy9esHHjRmrWrMmOHTto2rQpHh4enD17lrNnz+Lh4YGtra3yfOd/m0WZMmUYN24ckZGRnDx5kokTJ5I/f350dXV58uQJ/v7+Stfijh07KFmyJEWKFPnX2xZCCCHEpy9XHllM6z709fXFx8eHkiVLMmjQIFq0aEHp0qXx9/fHwcEBExMT+vTpg6ura4brKVmyJJMnT2b+/PnMnj0bc3NzvL298fDw4OLFi+jo6NC7d2+OHTtGp06dMDU1xd3dnWbNmmV4zWL16tUB9WP2AHbu3ImRkREpKSlMnToVZ2dndHV1ad68uVap97+hq6vLwoUL8fPzo1u3bhQoUIDWrVvj4eEBqG/YuXXrFuPGjePRo0fUqFGDhQsX5sjD4EIIIYT48HRU8my3d2ZnZ8ewYcOyfUOIl5cXqampfP/99+9pZB9OSkoK4eHh1KxZU25w+f8srKysPuvJtuSgIVloSBZqkoOGZKHxMbLIzjY/6pHFq1ev4uvry6lTpzAyMsLFxYXevXtz4MABgoODiYqKIl++fDRt2hQ/Pz8KFixIcHAw0dHRGBgY8Ouvv1KyZEnc3d1p3rw5oJ7AtWnThu3bt2Nqaoqnpyd9+vQhMjKS69ev07x5cxYvXoyvry8PHjygS5cudOvWDU9PT6Kjo6lXrx6zZs2iUKFCeHp6AjBjxgxlzBYWFqxevZp69epp7UtqaiqzZ88mJCQEgD59+rB161amTp1KvXr1lM9t3bqVrVu3ArBt2zYOHDhAsWLFCAoK4pdffuHRo0fUr1+fSZMmUapUKWXMw4cPZ+XKlbRv354iRYoQFxeHoaEhoaGhFClSBF9fX2JjY1mwYAGpqakMGTKE3r17A/D48WP8/Pw4cOAABQoUoFWrVowbN065e3r27NmEhoby+PFjatWqhY+PD19++WW2/i6lZ1E6w9JIDhqShYZkoSY5aEgWGtKzmInMCq1VKhUzZ87Ex8eHhg0bEhsby9ixY9m8eTN9+/YFYN++fbRr147Q0FAOHDjA8OHD2b59O5UqVQJgx44dLF++HJVKxaNHj17b9pIlS1iwYAFXrlxhzJgx/P7770yaNAkDAwOGDBnCTz/9pPQuZtXixYvZtm0bs2bNwsTEhMmTJxMXF/fachMmTODFixfK1yYmJkyYMIFTp07x/fffY2xsTGBgIEOGDGHLli3K506dOsWWLVtITU1lx44d/Prrr/Tv35/t27cze/ZsRo4ciY2NDWvWrGH37t18//33tGvXTln/y5cv2bBhA4mJiUydOhVfX1+mTZvGvn372LRpE/Pnz6d48eLMmTMHLy8vfvrpp2ztv/QsauTUnqwPTXLQkCw0JAs1yUFDstDIqVl8tMliZoXWz549w9vbm27dugHqPsGGDRty+fJl5bNGRkb4+vqir69PxYoV+f3339myZYtyHV6HDh2wsLAAMu47HDJkCFWqVKFKlSpMmzaNtm3b0qhRIwAaNGhAdHR0tvdn/fr1jBw5Urm5ZcaMGbRp0+a15QwNDZUjesWKFePRo0ds376dpUuXUr9+fQACAwNp1qwZf/zxB+bm5oD6SOUXX3yhrKdIkSKMGDECHR0dOnXqxK5du5gwYQJly5alX79+BAUFcfXqVRISEti/fz9hYWHK3dB+fn44ODjg5eXFjRs3yJs3L6VLl6Z06dJMnDjxnfZfehalMyyN5KAhWWhIFmqSg4ZkoSE9i5l4U6H1zZs3WbhwIZcvX+by5ctcuXKFjh07KsvVqFFDa2JSo0YNoqKilO/TF2tnpGzZssrXBgYGWssbGBiQlJSUrX25f/8+d+7coWbNmsprFSpUwMjI6K2fjY2NJTU1lVq1aimvGRsbY25uTlRUlDJZfHWfzMzMlDu40yafacukfZ9Wyp2amkrTpk21Pp+amsrVq1dp27Yta9eupXnz5lhZWdGiRQu+/fbbbO0/5NxuqI9BslCTHDQkCw3JQk1y0JAsNHJqFh9tsphZoXVERASOjo7Y2dlhY2ODs7Mzq1ateuNnU1JStEqpMyrfTu/Vv4jMCq11dHRIf/9PcnJyhsuljefVe4Wycu9QZmNNSUlRCsMzWi6j/DLaj5SUFAwNDbVOaacpUaIEBgYG7Nq1iz/++INDhw6xfPlyNm/ezLZt25TuSCGEEEJ8vj7aZDGzQuuHDx9ia2vLrFmzlGWvXr1KxYoVle8jIyNJTU1VJkfnz5+nbt262dq+SqVi/fr1yvdpN7OklzdvXq1S7IyuQQR1H2Px4sW5cOECVapUAeDatWvKIwTTLFmyhHr16qFSqZQ7kExMTNDT0yM8PJwmTZoA8ODBA65evaocVfw3zM3NefLkCTo6Ospp7MjISIKCgpg+fTp//vknN2/epGfPnjRr1oxhw4bRuHFj/v77b62jnUIIIYT4PH20Uu70xdnpC62/+OILIiMjOXv2LDExMcyYMYNz585pnRqOi4tj5syZREdHs3DhQi5cuJDtU6cnTpzA19f3jcvUrFmTP/74g2PHjvH333/j6+urPPnlVU5OTgQFBXHs2DEiIiKURwKmL/v+7rvvAHj06BGxsbHMnTuX9evX07VrV/z8/Dh+/DgRERGMGzeOkiVLKtdR/hsVK1akSZMmjB07lrNnz3LhwgW8vLx49uwZhQsXJjU1lYCAAPbt28f169cJDQ0lf/78lC9f/l9vWwghhBCfvo96GjqtODt9oXXHjh2JiIjA2dmZfPnyYWtry9ChQ9m5c6fy2Vq1anH//n0cHBwoX748S5Ys0boOMSuycoq4Y8eOnDp1iiFDhmBoaMiIESO4evVqhsu6uLhw584d3Nzc0NXVpXXr1kRGRmpNLgsUKABoTmc3bdoUHR0dPDw8+P777xk+fDhJSUk0bNiQlStX/mc3jAQEBCil33p6ejRp0gRvb29AXTU0fPhwpk+fTnx8vPIc6axcbymEEEKI3O+j9ixWrFjxtesRAebOnfvaa8OHD1e+zpcvH99//71San316lX69eun9DUmJCQAKH2N+vr62NjY0LRpU06dOsWDBw+UDsIbN24oN4YkJCSQN29eDh48yJ9//smYMWOYPn0606dP5/bt2/j7+/PkyRP69evHl19+ycyZM6lTp47ShThgwAB0dHSwsrJi48aNAPTo0YPVq1fTqVMnpV/xyJEjAFSpUoVhw4bh5ubGV199xZkzZ4iKiiImJoZz585RqlQpzMzMqFu3Lj/++COHDx8mJSWFxYsXM2/ePA4fPqz0RTo5OfH3338rfZHNmjVTnhhjYmJC3bp1CQ8P58GDB9y5c4c7d+4oj/SrWrUqhoaGxMfHk5CQwLVr12jYsOG//wsWQgghxCfvk3/c37v0Nfbu3Zvg4GDc3Nw4evQoRkZGbN26lX379jFu3DhGjx7N+vXrGT9+PM2aNcPQ0JCxY8dSuHBhNm7ciEqlIjAwkMmTJ7Njxw5lLFu2bGH27Nmkpqby8OFDwsPDtdYPYG1trbXtAgUKcOzYMdzc3Bg7dixfffUVhw8fZtSoUWzatIkaNWoAEBoayvLly9HX16dgwYJA1vsiDx48yLx58/Dz88Pc3Jxt27bRu3dv9u7dS6FChRg5ciTOzs60b9+eU6dO4eHhgY2NjdJbmRVSyi0Fs2kkBw3JQkOyUJMcNCQLDSnlfs/epa9RV1dXOc1arFgxZV3W1tb0798fUHcxrlixgujoaCwtLWnRogWtWrWiZMmSgPqZygMHDtQayxdffMGoUaNQqVRKz2P69QPo6+u/tu1169bRqlUrpQjc3Nycs2fPsmLFCmbPng1As2bNqF27NgDXr19XxpiVvshly5bh6urK119/DcDIkSP5/fff+fnnn2nfvj0PHz7E1NQUMzMzzMzMKF68+Gvjfhsp5dbIqaWqH5rkoCFZaEgWapKDhmShkVOz+OQmi25ublrf/5u+xlelv+4xrcA6MTERHR0dHB0d+fXXXzl16hQxMTGcP39eq9oGYNq0acpd28ePH1dOdb9NVFQUPXr00HrN2tpaq+4mo+7IrPZFRkVFMXPmTGXimbZfsbGxGBsb4+joiLe3NwsWLODrr7+mS5cu2b5mUUq5pWA2jeSgIVloSBZqkoOGZKEhpdzv2b/pa3xVRn9BKpWK1NRUXFxcePz4Mfb29tjZ2fHy5Uvljuc0b+t3zExGn0tNTX1jz2JG482sLzIlJYXx48fToEEDrdfTJtiTJ0+mV69e7N+/n/3797Np0yYWLFjAV199leV9yKlFoh+DZKEmOWhIFhqShZrkoCFZaOTULD75yeK79jWmr7R5mytXrnDixAmOHTuGiYkJoD51DJnfVZ2d9Zubm3PmzBmt106fPv2f9Cymrf/WrVuUK1dOec3Ly4sWLVpgaWnJggUL8PLyYvDgwQwePJh+/fpx8ODBbE0WhRBCCJE7fbSexf/Ku/Y1pk0sz58/T2Ji4hu3UbhwYfLkycPOnTu5ceMGu3fvJjg4GCDTRwOmX//mzZvZs2eP8t6hQ4cA9anmK1eu4OzszJ49e1i1ahWxsbGsXLmSffv24ejo+O/C+X99+/Zl1apVbNu2jWvXrjFz5kx27dpFxYoVMTIyYt++fUybNo1r165x4sQJIiIiqFat2n+ybSGEEEJ82j75I4vv2tdoYWFBo0aN6NGjh9a1fBkpWbIkkydPZv78+cyePRtzc3O8vb3x8PDg4sWLGd4Mkn79M2bMoFmzZsp7mzZtAuCXX36hZMmS6OrqEhAQQHBwMDNnzsTc3JwffvjhtdPG78re3p67d+8SFBTE3bt3qVSpEgsXLlSKtxcsWMC0adPo0KEDBQsW5Ntvv6Vr167/ybaFEEII8WnTUWWlnVr8pywsLFi1ahX169f/2EP511JSUggPD6dmzZpyg8v/Z2FlZZUjrzn5UCQHDclCQ7JQkxw0JAuNj5FFdrb5yZ+Gfpu//voLR0dHatWqhZWVFQMGDODOnTuEhoYqj+irV68eNjY2TJ8+XbkG8ebNm7i4uGBtbU2DBg3w8/Pj5cuXgPo6xfnz59O4cWNsbGwYNGgQN2/eVLZpYWHB3LlzqVevHoMGDSI0NBQ7OzvlPYA+ffrg5OQEqO9W7tevH7Vr16ZJkybMmzdPubklODiYIUOG0KtXL+rWrUtYWBh2dnb89NNPdOnSBUtLS1xcXLhx4wZubm7UqlWLjh07cvnyZWU8J0+epHPnzlhaWtK+fXutU+Jv2k8hhBBCiE/+NPSbPHnyBFdXV5ydnQkICODOnTuMHz+eJUuWUK1aNU6fPo2pqSkbNmzg3LlzeHp60rRpUxo1aoSfnx8FChRg27Zt3Lt3j+HDh1OhQgV69erF2rVr2bFjB7NmzcLU1JQVK1bg4uLCjh07lMf7HTp0iA0bNpCamsrZs2eVMR09epTGjRsTHBxM3bp1uX//Pj179sTOzo6QkBBiYmLw9vamUKFCSu/igQMHmDx5MlZWVspNLz/88AMBAQEULlyY/v3706lTJ0aNGsXw4cOZOHEis2fPZuHChcTHx+Pq6sqoUaNo0qQJ4eHheHp6UrRoUWxsbN64n9khpdxSMJtGctCQLDQkCzXJQUOy0JBS7o/oxYsXDBkyhL59+6Kjo0PZsmX55ptvOHv2LNWqVSMlJQU/Pz8KFSpEhQoVWLlyJefOnaNRo0bcuHGD6tWrU7p0acqVK8eSJUsoXLgwoC65njRpEvXq1QPA19eXxo0bc+TIEeUIYvfu3alQoQKA1mQx7fpGIyMjjI2NWb16Nfnz58fPzw89PT0qVqxIfHw88+fPVyaLpqamr93s0rlzZ+WRfPXr1yc+Pl5ZpkOHDkpN0Lp162jYsCHfffcdAOXKlePSpUusWrUKGxubN+5ndkgpt0ZOLVX90CQHDclCQ7JQkxw0JAuNnJpFrp4sFitWDAcHB1auXMmlS5e4cuUKkZGRypNQihYtqlXmXahQIZKTkwHo378/48ePZ9++fTRt2hR7e3uqVavG06dPuXXrFqNGjdLqNXzx4gWxsbHK9xmVaGckKiqK6tWra/VFWltbEx8fz+PHjzNd19sKudNOJUdHR3Po0CGsra2V91++fKkcocxsP7NLSrmlYDaN5KAhWWhIFmqSg4ZkoSGl3B/R7du36dKlC9WrV6dhw4Z069aNw4cPK52GGU1u0q5Z7NChAw0aNGD//v0cPnyY4cOHM2DAAPr16wfA3LlzX+tBTP/Uk6wWdGdWyA2aQ8T/ppA7OTmZ9u3bM2jQIK3X0yanme3nqFGjsjT+9OP53P+xp5Es1CQHDclCQ7JQkxw0JAuNnJpFrr7BZd++fRgZGbF48WL69OmDjY0NcXFxmRZppzdnzhzu3buHo6MjixcvZuTIkezdu5fChQtTtGhR4uPjKVeuHOXKlaNUqVLMnDmTmJiYbI/R3NycCxcuaN1Ucvr0aUxMTDA2Ns72+jJa/9WrV5WxlitXjgMHDrBjx4437qcQQgghBOTyyaKxsTE3b97k2LFjxMXFsWTJEvbu3ZtpkXZ60dHR+Pr6EhERweXLl/ntt9+oVq0au3btolu3bvzwww+MHj2arl274u3tzalTp5RrFNM4OTkp5d0PHjzA09NTeW/FihVYWVmxdOlSkpKSlFLx/fv3ExwcjKOjY7aeApOZnj17cv78eebMmUNsbCw7duxg9uzZlC5d+o37mV3PXqbyLCn5X49XCCGEEDlLrj4N3aZNG06cOMHw4cPR0dGhZs2aeHh4EBwc/NYJ4+TJk5kyZQpOTk4kJyfTrFkzXFxc6Ny5M/v27UOlUvHTTz+RkJBAvnz5WL58udZp6FcZGRkxYcIE5fvDhw+zZMkSLCwsuH//Pv7+/jg4OGBiYkKfPn1wdXX9TzIoU6YMixYtIjAwkOXLl1OiRAk8PT3p0KFDpvuZfpxZ5bb+NAudbP6TMQshhBAi55BS7my4fv06zZs358CBA5iZmb11eScnJ+rWrYubm5vW62FhYTg5OREREfGfHD38mNJKPX84lcRCJxsKGeT92EP6aKRgVk1y0JAsNCQLNclBQ7LQyOml3Ln6yOK7unXrFtOnT+fYsWPo6OjQvn173N3dad68OQDNmzdn+vTp3Lhxg7CwMNasWQOor5EMDAzk9u3bdO7cWavDKO0UdKdOnejduzcAVapUYdiwYbi5uXHo0CGCgoKIiorCzMyMkSNH8s033wDqSWflypU5fPgwKSkpLF68mA4dOrB48WJ8fX158OABXbp0oVu3bnh6ehIdHU29evWYNWuWcrf3xo0bWbJkCQ8ePKBGjRp4e3srBeHHjh1jxowZREdHU7x4cQYMGECPHj2yF5pKhYrPuy9LOsPUJAcNyUJDslCTHDQkCw3pWfzEJCUl0adPH8qVK8eaNWu4f/8+EydOBCAkJISuXbsSEhJC5cqVWbp0qfK5K1euMHLkSMaNG0eTJk1YtWoVf/3112vPd7a2tiY4OBg3NzeOHj1KgQIFOHbsGG5ubowdO5avvvqKw4cPM2rUKDZt2kSNGjUACA0NZfny5ejr61OwYEEAlixZwoIFC7hy5Qpjxozh999/Z9KkSRgYGDBkyBB++uknnJ2dOXjwIPPmzcPPzw9zc3O2bdtG79692bt3L4UKFWLkyJE4OzvTvn17Tp06hYeHBzY2NlSqVCnLuT199pTk5GTCL0jfYk7tyfrQJAcNyUJDslCTHDQkC42cmoVMFl9x5MgRbt++zebNm5VrEH18fBg8eDDdunUDwMTEBAMDA63PbdmyBRsbG6VIe+LEiRw6dOi19evr6yvrTSvoXrduHa1atVI+a25uztmzZ1mxYgWzZ88GoFmzZko/5PXr1wEYMmQIVapUoUqVKkybNo22bdvSqFEjABo0aEB0dDSgLhF3dXXl66+/BmDkyJH8/vvv/Pzzz7Rv356HDx9iamqKmZkZZmZmFC9eXBlbVhUsUBA9PT2srKyy9bncRDrD1CQHDclCQ7JQkxw0JAsN6Vn8xERFRVG+fHmtm1Vq165NcnLyGw/ZRkVFUbVqVeX7vHnzan3/tm2+etrX2tqaLVu2KN+/SzF32k08UVFRzJw5U5l4AiQmJhIbG4uxsTGOjo54e3uzYMECvv76a7p06fLGm3UypKODDq/3P36OcmpP1ocmOWhIFhqShZrkoCFZaOTULGSy+IqMCrCzei3Bq/cKpT0n+l22mZqaqpRzZ7ZMVou5U1JSGD9+/GunxNOuZ5w8eTK9evVi//797N+/n02bNrFgwQK++uqrLI1fCCGEELlXru5ZfBfm5ubExsby8OFD5bXw8HD09PQoUqTIa8snJiZiYWFByZIltQ7npqamEhERkeVtpj1VJs3p06dfe0LMq3bv3p3h69evX2fr1q08ffpUWf+tW7e0irkXLVpEeHg48fHxTJkyhXLlyjF48GC2bNlC/fr1OXjwYJbGLoQQQojcTY4svqJRo0aULVsWd3d3xowZw4MHD/Dz86Ndu3bkz58fgIiIiNcmjm3btmXAgAEsXLiQVq1asWnTJm7evJmlbTo7O9OzZ09WrVql3OCyb98+li9f/sbPpV2D+DZ9+/ZlwoQJlC9fntq1a7Np0yZ27dqFq6srRkZGSm+ki4sLt2/fJiIiQrkTO6uCelqjAhJevHzrsh9anjw6FNCXH3UhhBDiXcj/g75CV1eXBQsW4OfnR7du3ShYsCDt27dn9OjR5MuXjw4dOjBy5EjGjh2r9bkyZcqwcOFCpk+fzsKFC2nRokWWT+PWqlWLgIAAgoODmTlzJubm5vzwww+vnTZ+VVafP21vb8/du3cJCgri7t27VKpUiYULF1K+fHkAFixYwLRp0+jQoQMFCxbk22+/pWvXrllad5rh60/zMoc2di7rLWXhQgghxLv6LCeLaeXagYGBBAQE8Pz5cxwcHPD09GThwoVcunSJp0+fki9fPn744Qdq1apFUFAQv/zyC48ePaJx48a0atWKUqVK0alTJ5o3b87u3btZvXo1CQkJODg44O3tjb6+PqA+pXz16lUuXbpEWFgYAwYMIDIyEoCbN2/i7e3N6dOnMTAwoHv37nh6epI3b16Sk5MJCgoiJiaGixcvcvnyZaZMmYKZmRllypQhLCwMMzMzXr58yddff820adP44YcflKe/eHh4APD48WPOnTvHw4cPKVy4MNbW1lhbWyt5xMfH8+LFC1JSUkhMTCQuLo7nz58rFT1CCCGE+Hx9lpPFNPPmzWPOnDkkJyfj7u5OwYLq+pcDBw4wefJkrKysMDc3Z9KkSZw6dYrvv/8eY2NjAgMDGTJkiNbdyps3b2bOnDmkpKTg7u7O4sWLcXNzIyoqij59+uDs7Iy/vz9nzpxhypQpmJqa0rJlS/z8/ChQoADbtm3j3r17DB8+nAoVKtCrVy/mzp3Ltm3bmDZtGqVLl2bSpElMmjSJoKAgrf0IDg7m0KFDLFy4ED09Pa1nUANMmDCBly9fsmHDBhITE5k6dSq+vr5MmzaNa9euMWLECHx8fGjYsCGxsbGMHTuWzZs307dv36yHqVKhUuXMp9F8qLJwKZhVkxw0JAsNyUJNctCQLDSklDsHGzduHDY26lOUI0aMIDAwEEdHR0xNTXF0dATg0aNHbN++naVLl1K/fn0AAgMDadasGX/88YdyE8r48eOpU6eO1rrc3NzYvHkz1apVY/To0QBUqFCBqKgoli1bRsuWLblx4wbVq1endOnSlCtXjiVLllC4cGFUKhWbN2/Gw8ODpk2bAjBlyhR27dqltQ8qlYqQkBA8PDywtbVVxjJw4EAArl27xv79+wkLC8PQ0BAAPz8/HBwc8PLyIjU1FW9vb6VD0szMjIYNG3L58uVsZfn02VMSc+i/9w9dFp5TS1U/NMlBQ7LQkCzUJAcNyUIjp2bxWU8W00quAWrUqMH9+/d58OCBVl9hbGwsqamp1KpVS3nN2NgYc3NzoqKilMmipaWl8n61atW4e/cujx49IioqSus9UHcobty4EYD+/fszfvx49u3bR9OmTbG3t6datWrcv3+fhw8fUr16deVzlSpVeu050w8ePOD+/ftanY41a9ZUvo6KiiI1NVWZcKZJTU3l6tWr1KhRA319fRYuXMjly5e5fPkyV65coWPHjlkPEnUpd94cemTxQ5WFS8GsmuSgIVloSBZqkoOGZKEhpdw5WPoexLROwzx58mjdOJLZTSQpKSlaPYjpOw7T+hbz5s2baYdi2uHfDh060KBBA/bv38/hw4cZPnw4AwYMoF+/ftnal/Qdj+n3KyUlBUNDQ61T5mlKlChBREQEjo6O2NnZKU+gWbVqVba2DSil3DnRhy4Lz6mlqh+a5KAhWWhIFmqSg4ZkoZFTs/isexYvXbqkfH3+/HmKFy+OsbGx1jJly5ZFT0+P8PBw5bUHDx5w9epVrR7Ev//+W/n67NmzlCxZkgIFCry1Q3HOnDncu3cPR0dHFi9ezMiRI9m7dy+FCxemSJEiWl2Nly5domnTprx48UJ5rUiRIpiammr918HFi5pTrubm5jx58gQdHR2lY/HFixcEBASQlJTE9u3bsbW1ZdasWfTs2RNLS0uuXr36WsG4EEIIIT5Pn/WRRX9/f6ZOncqTJ0+YO3cu3333Hffu3SMsLIzr169jZmZGwYIF6dq1K35+fvj5+WFkZERgYCAlS5akUaNG3LlzB1BfBzh16lQSEhIICgpSjgz27NmT1atXM3v2bDp16kR4eDjr169n4sSJAERHR+Pr64uPjw+6urr89ttvVKtWDQAnJyfmzp1LiRIlKFq0KP7+/lhZWfHrr79y69YtAHR0dOjVqxdBQUGUKVMGQ0NDrVPVFStWpEmTJowdOxZvb290dXWZOHEiRkZGFC5cGGNjYyIjIzl79iyGhoZs2rSJc+fOaT1KMCuCe1qjlwP/awjUPYtCCCGEeDef9WTR3t4eV1dXUlNTcXR0ZODAgUyfPv215Tw8PPj+++8ZPnw4SUlJNGzYkJUrVyrVOACOjo4MHjyYly9f0q1bN/r06QNA6dKlWbx4MQEBAaxYsYLSpUvj6elJly5dAPWj9qZMmYKTkxPJyck0a9aMCRMmADBw4ECePHnCyJEjlfcmTpxIvnz5mDt3rrLtQYMG8fz5c0aNGoWuri7ly5fXOpoZEBDA1KlTcXZ2Rk9PjyZNmuDt7Q2oJ6QXL17E2dmZfPnyYWtry9ChQ9m5c2e2siyQNw/6+ll7vKEQQgghPh06qs/wfGNaz+KBAwcwMzPL8nufirTqnBkzZrz3baWkpBAeHk7NmjW1Js+fo7QsrKyscuQ1Jx+K5KAhWWhIFmqSg4ZkofExssjONj/raxbfZP/+/bRo0YJatWoxaNAgHj16xDfffMOPP/6otVz79u0JCQkhNDSUHj16MHToUOrUqcPPP/9MQkICXl5eNGjQgBo1atC6dWv279+vfPbevXuMHDmS2rVr06hRI2bPnq1cK3jr1i1GjBhB3bp1qVevHlOnTiUpKQmA0NBQ7OzslPWcPHkSBwcHLC0tGTFiBM+fP9ca4759+7C3t6dWrVp8++23hIWFKe+9bYxCCCGE+Lx91qeh32Tr1q3K5G3YsGEsXbqUtm3bsmfPHqWsOioqipiYGL755hsOHDjA6dOnGTRoEKNHj6ZIkSL4+/sTExPDihUryJ8/P8uWLWPChAk0bdoUfX19hg4diq6uLmvXruXp06eMGjWK4sWLK6exy5Urx5o1a7h//75yjWPa6eM09+/fx9XVle7duzN79mx27tzJvHnz6NSpE6B+jrWHhwdTpkzB0tKS3377jQEDBvDzzz9Trly5t44xq1JSUj77YlUpmFWTHDQkCw3JQk1y0JAsNKSUOwcyMzNTHreXmXHjxin9iG3atCEiIgIvLy8WLlzIrVu3KFmyJLt27aJx48YYGRkB6ptNBg8ejIGBAQC2trb07duXypUrA+Di4kJISAj37t3j0aNHnD59mv379ys3k0yePJlnz55x5MgRbt++zebNm5V1+/j4MHjwYEaNGqU1zl27dmFiYsK4cePQ0dHBzc2N3377TXl/+fLldOvWjfbt2wPQu3dvTpw4wYYNG/D09HzjGEuVKpXlTNPfgf25y6mlqh+a5KAhWWhIFmqSg4ZkoZFTs/gsJ4tZ8cUXXyhfGxoakpiYSMWKFbGwsGD37t04Ozuza9cu5TnMAEWLFlUmigAODg7s37+fzZs3Ex0dzYULFwD1bD4mJgZjY2Otu45btGgBwJIlSyhfvrwyUQR1gXhycjLXrl3TGueVK1eoUqUKOjqaO35r1qypnIqOiopi165dbNq0SXn/5cuXNG7c+K1jzI5q1arJNYtSMAtIDulJFhqShZrkoCFZaEgp9ycqfcl2em3btmXv3r00adJEuRkmzasF3O7u7pw+fZqOHTvi6OhIsWLF6N69O6BdnP2qjIq833SI+tV7lPLmzatMFlNSUhgwYAAODg5ay6RNat80xuzIqUWiH4NkoSY5aEgWGpKFmuSgIVlo5NQsZLKYTe3atWPu3Lls27aNr776ioIFC2a4XEJCAr/88gubN29WTmennR5WqVSUK1eOhw8f8s8//yine1evXs2ff/5Jjx49iI2N5eHDh0pJeHh4OHp6enzxxRdaBeBffvklv/32GykpKcoP2KVLl5RHFpqbm3P9+nXKlSunfCYgIABzc3PatGnzxjEKIYQQQuT6u6FVKhXr1q37z9ZXunRpLC0tWbVqFW3bts10OX19ffLkycP48eO5fv06Y8eOZdiwYQAkJSWxe/dudHV1ad68OadPn+b48eMsWbKERo0a0ahRI8qWLYu7uzuRkZH8+eef+Pn50a5dOwoXLqy1nbZt2/L8+XP8/f2Jjo5m2bJl/PXXX8r7zs7O/Prrr6xevZpr166xcuVKVq5cSfny5dHX1yd//vzs3buX69evc+TIEXx9fZUxZseLZJlcCiGEELlRrp8snjhxQpkA/Vfs7e3R09OjWbNmmS6jr6+PjY0NN2/epG3btly4cIFx48ZRrFgx/vrrL+bNm8fo0aOpX78+ffv2ZcyYMXTv3p2ePXuiq6vLggULAOjWrRujR4+mefPmGe6HkZERy5Yt49y5c3Ts2JH//e9/dOzYUXnfysqKgIAA1q9fj729PZs3b2bWrFnY2tqir6/PzJkz2bNnD23btmXGjBkMHjyYYsWKaT0KMStS5UikEEIIkSvl+tPQ2T2dmtGd0ukfnwdw9+5dWrZsqXVtYefOnencubPWcqVLl6Z06dJa5di9e/fmxo0bALRq1Yr+/ftnOI6yZcuyZMmSDN97dVvVq1cnJCQk031q27ZtpkdBW7RoodxYk+bbb7/NdF1CCCGE+Lzkisli2o0mgYGBBAQE8Pz5cxwcHHBycqJ3794AWFhYsHr1aurVq8fGjRtZsmQJDx48oEaNGnh7e2NhYQGAnZ0dbdq0Yfv27ZiamrJ161aio6OZNm0af/31F3nz5iUxMZFly5Yp29++fTsLFy7kn3/+oWrVqvj4+CjPd04THBxMWFgY06dPV26KadGiBZ06dWLGjBmcPn2agIAALl26hImJCQMGDMDR0RHQPJHl4sWLxMfHs2HDBlq1asUPP/xAUFAQN2/epHnz5owaNYoJEyZw5swZqlevzpw5cyhRogSgLuaeM2cON27c4Msvv8Td3Z26desC6i7GyZMnc+nSJQoXLkz37t2VU+bZ8bl3ZUlnmJrkoCFZaEgWapKDhmShIT2LH9C8efOYM2cOycnJuLu7kz9/foKDg3Fzc+Po0aMYGRlx8OBB5s2bh5+fH+bm5mzbto3evXuzd+9epapmx44dLF++HJVKxYMHD+jZsyd2dnYMHDiQhQsXkidPHi5evEjdunU5cuQIEyZMYMKECTRs2JA1a9bg6urKgQMHMhxjqVKlCAkJoWvXroSEhGBubk5UVBR9+vTB2dkZf39/zpw5w5QpUzA1NaVly5aAekI6f/58TE1NKV++PABBQUHMmDGD58+f079/f8LCwpgwYQJeXl4MHz6cpUuX4u3t/dZibnd3d+rUqcPMmTOJiYlh+PDh1KxZk6+++irL2ackJxMefuHf/QXmEjm1J+tDkxw0JAsNyUJNctCQLDRyaha5arI4btw4bGxsABgxYgSBgYE0atQIgGLFigGwbNkyXF1d+frrrwEYOXIkv//+Oz///DNOTk4AdOjQQTnSuHr1avLnz4+fnx96enoMGTKEDRs2MH/+fJydndm0aRPt2rVTjgK6u7uTN29eHj16lOEYdXV1MTExAcDExARDQ0PmzZtHtWrVGD16NAAVKlQgKiqKZcuWKZPFmjVraj3iD9Q3r9SqVQuAqlWrKnc4A3zzzTdEREQAby/mvnHjBs2bN6dMmTKULVuWH3/8MdvPxdbV08PKyipbn8ltpDNMTXLQkCw0JAs1yUFDstCQnsUPqHbt2srXNWrU4P79+zx48EBrmaioKGbOnMns2bOV1xITE4mNjVW+T6udSVu+evXq6OlporK2tiY+Pp7Hjx8TExNDjx49lPf09fXx8PDI1rijoqKU6pr029i4cWOGY0qTvtDbwMBAaxkDAwPljua3FXO7uroye/ZsNm3aRLNmzejYsaMyuc6Oz/0fe5qc2pP1oUkOGpKFhmShJjloSBYaOTWLXDVZTF90nZqaCrxerp2SksL48eNp0KCB1uuFChVSvk5/40pGBdlp605JSdGaRL6rzLaR/nqCjJZ59QcqsyLxtxVzDxw4kDZt2rB//34OHjxInz598PPzo2vXrtndFSGEEELkMp9cdc6xY8eIiorK8L30dS/nz5+nePHiFClSRGuZ0qVLc/z4ccqVK4euri7ffPMNs2bNIjw8/LX1hYaGsm3bNi5cuMDLly+V10+fPo2JiQnGxsaUK1dOOd0L6omZnZ2dVtdh2rquX78OaDoM27Rpg5OTE+bm5pw5c0Zr+dOnT2Nubp6FRN4ufTF32p9Nmzbx+++/k5iYyNSpU9HX16dv376sWbOGbt26sWfPnmxvJ+HFy0z/PEtK/k/2RQghhBAf1id3ZNHZ2ZnVq1dTsWLF197z9/dn6tSpPHnyhLlz5/Ldd9+RP39+QD15/PLLL0lJSWHv3r1s27aN0qVLA3D48GHlesH07O3tsbW1pXv37vj4+NC/f39iYmIIDg6mZ8+e6Ojo4OTkhIuLCzY2NtSuXZs1a9agUqleq7Oxt7fn9OnTAISFhQEwd+5catSoQXJyMqtXr2b27Nl06tSJ8PBw1q9fz8SJE/+zzHr16kXNmjVp1qwZBw8eZOXKlaxatYp8+fJx6tQp/Pz8GD16NE+fPuXkyZOv1em8jdv607x8Q0vRst42/3IvhBBCCPExfHKTxText7fH1dWV1NRUHB0dGThwIMnJyTRq1IgePXowe/ZsTExMKFWqFEFBQcTHxwMwdepU5Q7j9AwMDChbtizLli3D398fBwcHTExM6NOnD66urgDY2toyadIk5s+fT3x8PDVq1GDRokXKKd7060o7bfz06VMAKleuTPHixQFYvHgxAQEBrFixgtKlS+Pp6UmXLl3+k1zSirmDg4MJCAjgiy++UIq5AebMmYOvry/ffvstenp6tG7dmiFDhvwn2xZCCCHEpy3HnoZevXo1X3/9NTVr1qRz586cPHlSuRu4d+/eBAcHExoaSo8ePfDx8QHU1/VVrFiRXr16MWLECPLkycOdO3f4448/2L17NwcPHiQsLIwjR45QpkwZdu3aBcD9+/dp0aIFtWrVonLlykoPYmhoKHZ2dlSrVo1169axbds2KlWqxNKlS/nqq6+YN28eqampfPvtt3Ts2JEWLVpQqVIlHB0dadCgARUrVlQKucPCwqhbty6hoaEEBAQA0Lx5c0JDQwH4559/ePHiBXny5KFQoUJaR07DwsIoWrQojRs3xsHBgT///JMyZcoQFxdHo0aNsLW1pWnTptSvX5/WrVtjbW1NXFwcq1atAtTF5LGxsSQkJGBgYMAXX3yh3EUNcOHCBa5fv87Lly8xNTWladOmr012hRBCCPF5ypFHFi9evEhAQADz5s2jUqVKrF69mpEjR7Jt2zYaNWpEcHAwjRo1Ys+ePZw+fZpevXrxxx9/YGtrm2m/IcCECROIjY3F2toaV1dXEhISANi6dSuzZ89GpVIxbNgwli5dytixY7U+e//+faVvMSQkhJiYGLy9vSlUqBDOzs4A7Nmzh549e7J161b27dvHzJkzadGihda1h/b29jx58oQVK1bw008/YWhoSGhoKH5+fkyaNAlLS0tCQ0MZOHAgu3fvVkq103c/Pnr0iDt37rB//37WrFnDoUOHCAwMpEqVKsyYMYMHDx7g5uZGy5YtadmyJWvXrmXHjh3MmjULU1NTVqxYgYuLCzt27ODx48e4u7vj6+tLvXr12L17N6NHj+b333/H2Ng4639pKhUqlU7mb5P7i1elYFZNctCQLDQkCzXJQUOy0JBS7ndw48YNdHR0KF26NGZmZowcOZKvv/5ambwYGRlRsGBBAHR0dOjVqxfr1q1TSrUzY2hoSN68eSlQoADGxsbKZHHcuHFKdU2bNm20blhJ88svv2j1LVasWJH4+HilbxHA2NgYDw8PdHV16d+/P0uXLuX8+fNak0UDAwMMDQ3R1dVV6mnWrFmDk5OTcrfy2LFjOXHiBGvXrmXMmDGAdvfj8ePHefnyJR4eHpibm1O6dGkCAgLo1auX0nVYtWpVoqOjAXW35KRJk6hXrx4Avr6+NG7cmCNHjlCyZElevnxJyZIlKVOmDC4uLlhYWGR49/WbPH32lMQ3/NwlJycTfuFittb5qcqppaofmuSgIVloSBZqkoOGZKGRU7PIkZPFxo0bU7lyZdq3b0+1atVo3rw5Xbt2zbCmpmjRolSsWPG15zlnxxdffKF8bWhoSGJi4mvLvK1vEdTPlU5fZ1OwYEGSk99+F3BUVBRDhw7Ves3Kykrrru839SymnTLOqGfx6dOn3Lp1i1GjRmlV67x48YLY2Fi+/vprmjVrRt++fTE3N1eyTrsxKKsKFihI3jccWdT7DEq7pWBWTXLQkCw0JAs1yUFDstCQUu53kD9/fkJCQggLC+PQoUOEhoayYcMG5fq+9N52BCwrh1kz6yd823bS9y2Cds9jGpXqDbcIv2HdKSkpyvozW+bVyXNG+5E2trlz575WxWNkZISOjg6LFy/m7NmzHDhwgH379rF+/XrWr19P1apV3zp2hY4OmU8VQYfPp7Q7p5aqfmiSg4ZkoSFZqEkOGpKFRk7NIkfe4HL69GkWL15M/fr18fLyYvfu3SQmJr7WXZgRfX195W5jgLi4uP9kTObm5m/sW/y36361Z/HMmTP/Sc9i4cKFKVq0KPHx8UrHYqlSpZTnQEdFRfH9999jaWnJqFGj2LlzJ6VKleLIkSP/ettCCCGE+PTlyCOLBgYGzJ8/H1NTUxo0aMCJEyd49uwZFhYWFChQgMuXL1OtWrUMP1ujRg22bduGvb09AEFBQVrvFyhQgNjYWO7du5etMbVv357g4GB8fHx4/PgxT5484e+//6ZSpUr07t2bunXrvtvOou5BnDBhAsbGxhgaGnLt2jUiIiKUO6mzKzg4mEuXLiljcnZ25ocffqBo0aJUqFCBnj178uzZM/z9/UlKSmLDhg0YGhrSvn17rly5wo0bNzLNNzNBPa3J+4b/GsqT503HHYUQQgiRU+XIyWLVqlXx9/dnwYIF+Pr6Urp0aWbOnEnFihVxcnIiICCAa9euUaVKldc+27dvX/7++2++++47SpQowYQJE5RORICuXbsyfvx4+vfvT3BwcJbHVKhQIaVv8dSpU+jr6zNo0CC+++47UlJSWLNmzTvvr729PXfv3mXmzJkkJydTs2ZNVqxYkWHx+Lvo168fT58+xcfHh4SEBPT09OjQoYNyQ1BwcDCBgYEsWrSIokWLMnr0aOW50Vk1fP1pFjrZUMjg9VPxQgghhPh06aiyclGd0OLp6Qnwzkf+MuPk5ETdunVxc3P7V+sJDg4mLCws0wnsf7UdUF8TGR4ezg+nkj77yWJaFlZWVjnympMPRXLQkCw0JAs1yUFDstD4GFlkZ5s58sjih/LPP/8wZcoUjh07RtGiRencuTODBw9m+/btzJs3j4MHDyrLZjbBSj8xCw0NJSQkhDp16ihVPkOHDqVr166A+oaYFStWsGHDBuLj46lVqxbe3t5YWFjg6elJWFiY8qdcuXLcvXuXRYsWKdvy8/Pj8ePHjBgxgubNmxMYGEhAQADPnz/HwcEBT09P5aaXly9fMmXKFLZv346BgQEDBgygb9++r2WQlJTErFmz+PXXX7l//z4lSpTA1dWV7t27Zy9Mleqz6FJ8E+kMU5McNCQLDclCTXLQkCw0pGcxh0or4K5SpQpbt24lPj4eHx8fdHR0KFWq1Duv99y5cxQoUIBNmzZx9uxZJk+eTKlSpWjcuDHz589nw4YN+Pn5Ub58eZYuXUr//v3Zs2fPa4Xhly5dYuDAgSQkJFCoUCFSU1PZs2cPU6dOVbY1b9485syZQ3JyMu7u7hQsWJBRo0YB6ptvLC0t2bZtGwcPHmT69Ok0bdr0tVPbS5Ys4fDhwwQHB1O0aFG2bt2Kn58fzZs3x9TUNMv7/fTZ08+qS/FNcmpP1ocmOWhIFhqShZrkoCFZaOTULD7byeKff/7JzZs3CQkJIU+ePFSoUAEPDw+8vLzw8PB45/Xq6OgQEBBA0aJFqVy5MidOnGDz5s00atSItWvXMnr0aOVxgn5+frRs2ZKff/6ZHj16aBWG16tXDyMjIw4ePEiHDh04efIkL1++pFGjRty+fRtQl4nb2NgAMGLECAIDAxk5ciQAJUqUwMvLCx0dHZydnZk/fz6RkZGvTRarVKlC/fr1lQ7EQYMGMX/+fGJjY7M1WSxYoOBn0aX4JtIZpiY5aEgWGpKFmuSgIVloSM9iDhUVFcXDhw+pU6eO8lpqaiovXrzg4cOH77zecuXKUbRoUeX7GjVqsHHjRu7du8fDhw+1nsmcN29eatSooVW+nSZPnjy0adOG3bt306FDB3bt2kXLli21uhxr166ttZ379+/z4MEDQF0QrqOjuQM5s7LxFi1a8McffzBjxgyio6O5eFF9ZDDbh8L/v2fxc/8HDzm3J+tDkxw0JAsNyUJNctCQLDRyahaf7WQxOTmZChUqsGDBgtfeO3XqVIbLZ8WrRdkpKSnkyZMn0/LwV8u302vXrh1OTk4kJCQoz5pOL/3EMW0daRPEjH7YMrqXac6cOYSEhNC5c2ccHByYNGkSdnZ2b9hDIYQQQnxOcmQp93/t0qVLr00Azc3NuXnzJiYmJkpZ9bx58+jRowe6urpaxd4qlYrr16+/dTteXl5ER0fz9OlT7OzsCA0N5fz581SuXBlDQ0NMTU0JDw/n+vXrWFhYEBsby4ULFzIt365VqxYlSpRg6dKlqFSq17ocL126hEqlYt26dZw/f57ixYtTpEiRN47x3r173L9/X/l+48aNTJw4kbFjx2Jvb8/z58+VfRZCCCGE+Cwmi0OHDiU2NlbrtcaNG1OmTBnGjRtHZGQkJ0+e5NChQ+jq6mJpacnDhw9Zs2YNcXFxTJ8+nUePHmVpW4mJiUyaNInAwECeP3/O7t276dmzJ6Auxw4KCuLYsWMAzJo1i8TERKVAPKPCcHt7e3788Udat2792tFCf39/Nm3ahK+vL3PnzqVXr15vHV9gYKDWaXZjY2MOHTpEXFwcJ0+exN3dHVDfJZ0dwT2tpXhbCCGEyIU+i8liRnR1dVm4cCGpqal069YNNzc35Qhj+fLl8fDwYOHChTg4OKBSqWjVqlWW1mtiYkKxYsXo168fq1atYubMmcp1kS4uLnTt2pVZs2YBEB8fz5o1azAxMQHUheFHjhyhf//+yvrs7e21JpTp2dvbK+vq2rUrAwcOfOv4Xj1iOG3aNC5dukTbtm3x8vKidevWWFpacunSpSztb5oCefNQQP+zvapBCCGEyLVy/WTRycmJGzdu4OXlhZ2dHRYWFsp7ZcuWxcTEhDZt2nDs2DGaNWtGSkoKEyZMYO7cuRQoUAA/Pz8mTJjAzJkzCQsLIzg4mBkzZjBjxgyuX7/OvHnzmD59urLOPHny4OHhQZEiRRg0aBBt2rTh5cuX+Pn5Ua9ePbZu3ap0NQYGBlK5cmUeP37MuHHjcHd3R19fn9q1a/PixQsAjh49iq6uLpGRkTRp0gQrKytle3Xq1OHx48cALFiwgBMnTpCUlERCQgKxsbFUr14dOzs7Nm3axMGDB7lx4wZbt27l7t27bN26FQAjIyOKFy+Orq4ut27d4vDhw8yYMUPrqTdCCCGE+Hzl+kNBwcHBdOzYERcXF8qUKcPQoUPfuPzp06epWLEioaGhHD58mLFjx1K9enXKlSv3r8Zw6NAhFi5ciJ6envIEmDQTJkzg5cuXbNiwgcTERKZOncr48eNp2bIlGzduRKVSsWfPHpYtW8adO3eUfShWrBjBwcG4ublx9OhRjIyM3tib6OLiotx57ePjQ2pqKoMGDaJhw4ZMmjSJJ0+e4Ovry8yZM7XKwLMiJSXlsy9WlYJZNclBQ7LQkCzUJAcNyUJDSrk/MmNjY3R1dTE0NMTQ0PCtyxcvXpzJkyeTN29eKlasyOHDhwkJCWHs2LHvtH2VSkVISAgeHh7Y2toCMH78eOWU8bVr19i/fz9hYWHK+Pz8/HBwcODgwYNUqFCB1NRUvL29+fLLL7GwsMDW1lY54pj2fOdixYoBb+5NtLGxwcDAAFCfLn/27Bk9evSgZ8+eFChQAIBOnTqxbNmybO9nWuWOyLmlqh+a5KAhWWhIFmqSg4ZkoZFTs8j1k8Xsqlq1qlYlTfXq1TPsQczM7Nmztb5/8OAB9+/fp2rVqsprNWvWVL6OiooiNTWVpk2ban0uNTWVtWvX8vTpU3r37q11ZLNYsWJ07twZMzMzbty4ofW57PQmFihQAEdHR7Zt28b58+eV5bNTxp2mWrVq6OvrZ/tzuYkUzKpJDhqShYZkoSY5aEgWGlLKnYOkL6lOk5ycrNWNmCeP9mWcqampWpPH9LJzCDf9jSXp15eSkoKhoSFbtmx57TMlSpTgzJkzAK9NxDKrtslOb+LTp0/59ttvKVKkCHZ2drRr147o6GhWrFiR5f1Kk1OLRD8GyUJNctCQLDQkCzXJQUOy0MipWXxWk8W0SVra85YBrl+/Tvny5ZVlLl++rPWZs2fPUr9+fUA9YUvfvxgXF/fWbRYpUgRTU1POnTtHlSpVAO1Ttubm5jx58gQdHR2++OILACIjIwkKCtK6cSYzr06AN27cyOTJk2nTpg0AV65cATSTSx0dHeXrsLAw7ty5w44dO5QJ89GjR6VjUQghhBCKXH83NKhPt0ZHR1OiRAkMDAxYtGgRcXFxLFu27LVr7W7evImfnx9RUVHMnz+fixcv4ujoCKgfqbdr1y7Onj3L2bNnCQoKeuu2dXR06NWrF0FBQfzvf//j3LlzWpPAihUr0qRJE8aOHcvZs2e5cOECXl5ePHv2jMKFC79x3Z6enixfvhyA8+fPk5iYmGlvor+/v7J/O3fu5Pbt2xgbG/Ps2TP279/P9evXCQkJYd26ddnuWBRCCCFE7vVZTBYdHR1Zt24d06ZNw8/Pj507d9KuXTsiIiJeK7L+6quvePjwIZ06deKXX35h4cKFlChRAoC+fftSrVo1vvvuO8aMGcOQIUOytP1Bgwbh4ODAqFGjcHV1pWvXrlrvBwQEYGZmhrOzM3379sXc3Py1ax8zU7hwYRo1akSPHj347bffMuxNLF68OM+ePQNg2LBhGBoa0qFDB6ysrBg6dChTpkyhQ4cOhIaG4uPjw71797h9+3aWti+EEEKI3E1HJeccP1lpFTwzZsx443LBwcGEhYWxZs2a/3wMKSkphIeHU7NmTbnB5f+zsLKyypHXnHwokoOGZKEhWahJDhqShcbHyCI72/wsjiz+F65evUq/fv2wtramWbNmrF69muPHj2NnZ8ekSZOoU6cOS5YsASA0NJQ2bdpgaWlJ586dOXHihLKeZ8+e4ePjQ7169ahXrx4TJ04kMTERgEePHjFx4kQaNmxInTp1GDdunNZjBk+ePImDgwOWlpaMGDFCeY7zixcvqF27Nnv37lWWffnyJfXq1VMeLZgmNDRU64aXAwcO4ODgQM2aNbGxsWH06NFa12UKIYQQ4vP2Wd3g8q4SExNxcXGhevXqbN68mbi4OMaMGcPs2bO5ceMGSUlJhIaGkjdvXkJDQ/Hz82PSpElYWloSGhrKwIED2b17NyVKlMDb25vIyEgWLFiAgYEB48aN44cffsDDw4Nhw4bx/PlzpRB78uTJeHp6snDhQu7fv4+rqyvdu3dn9uzZ7Ny5k3nz5tGpUycMDAxo0aIFe/bs4ZtvvgHgf//7H3p6etStW5eTJ09muF/Xrl1jxIgR+Pj40LBhQ2JjYxk7diybN2+mb9++2cpISrmlYDaN5KAhWWhIFmqSg4ZkoSGl3LnA0aNHuX//PtOmTaNQoUJ8+eWXeHt7KzU7/fv3V3oQ16xZg5OTEw4ODgCMHTuWEydOsHbtWvr378/u3bv58ccfledF+/r6cunSJSIiIggLC2P37t2Ym5sDMHPmTOzt7YmOjubYsWOYmJgwbtw4dHR0cHNz47ffflPG2LZtW0aNGkViYiL58uVj9+7dtG7d+o2HltPKvrt16waAmZkZDRs2fO2O8KyQUm6NnFqq+qFJDhqShYZkoSY5aEgWGjk1C5ksZkFMTAzm5uZK3Q5Aly5dOH78OKCeZKWJiop67ZGCVlZWREVFcfXqVVJSUqhevbryno2NDTY2Nvz6668ULlxYmSiC+k5pIyMjoqOjuXLlClWqVNGqyqlZs6ZyKrpRo0bo6+tz5MgRvvrqK/bv3//WR/aVL18efX19Fi5cyOXLl7l8+TJXrlyhY8eO2c5ISrmlYDaN5KAhWWhIFmqSg4ZkoSGl3LlA+tLujOTLly/Dr9OkpKS8sdwbXi/dTv/ZtEPFr96LlDdvXmWyqKenR6tWrdizZw958+alUKFC1K5d+43jjoiIwNHRETs7O2xsbHB2dmbVqlVv/ExmcmqR6McgWahJDhqShYZkoSY5aEgWGjk1C7nBJQvKly/P1atXlYkZwPfff8/UqVNfW9bc3Fx56kqaM2fOYG5uTtmyZdHV1SUiIkJ5b//+/XTq1Alzc3MeP35MdHS08t6VK1dISEjA3NycL7/8kosXL2pdY3Dp0iWt7bRv357ff/+dgwcP0rp16wyfWJPe9u3bsbW1ZdasWfTs2RNLS0uuXr0qpdxCCCGEUMhkMQsaN26MqakpPj4+REVFceDAATZu3MiYMWNeW9bZ2Zm1a9eybds2YmJiCAwMJCIigm+//ZZChQrh4OCAv78/Z8+e5dy5c8yZM4f69etTsWJFmjZtioeHh1L67eHhga2tLZUrV6Zt27Y8f/4cf39/oqOjWbZsGX/99ZfWtuvUqUP+/PnZunUrbdu2fet+GRsbExkZydmzZ4mJiWHGjBmcO3dOSrmFEEIIoZDT0Fmgp6fHggUL8PX1pVOnTpiamuLu7k7+/PlfW9be3p67d+8SFBREfHw8VatWZcWKFVSsWBGA8ePH4+/vT9++fcmbNy/29vaMGjUK0BytdHZ2RldXl+bNm+Pl5QWAkZERy5YtY/LkyXTs2BFbW1s6duyodRRQR0eH1q1bc/DgQWrUqPHW/XJycuLixYs4OzuTL18+bG1tGTp0KDt37vwvYhNCCCFELiCl3LnMmDFjKFeuHMOHD/8g25NSbg0pmFWTHDQkCw3JQk1y0JAsNHJ6KbccWcwlwsPDuXDhAgcOHOCXX3752MMRQgghRC4hk8Vc4siRI6xYsYJRo0ZpVfkIIYQQQvwbMlnMJdzc3HBzc/vYwxBCCCFELiN3QwshhBBCiEzJZFEIIYQQQmRKJotCCCGEECJTMlkUQgghhBCZksmiEEIIIYTIlEwWhRBCCCFEpmSyKIQQQgghMiWTRSGEEEIIkSmZLAohhBBCiEzJZFEIIYQQQmRKHvcn/hWVSgVASkoKKSkpH3k0H1fa/ksOkkMayUJDslCTHDQkC42PkUXattL+f/xNdFRZWUqITCQlJXHu3LmPPQwhhBBCvIOaNWuir6//xmVksij+ldTUVJKTk8mTJw86OjofezhCCCGEyAKVSkVqaip6enrkyfPmqxJlsiiEEEIIITIlN7gIIYQQQohMyWRRCCGEEEJkSiaLQgghhBAiUzJZFEIIIYQQmZLJohBCCCGEyJRMFoUQQgghRKZksiiEEEIIITIlk0XxVomJiYwfPx4bGxsaN27MihUrMl324sWLdO3alVq1atGlSxfOnz//AUf6/mUni8OHD9OxY0esra1p3749Bw4c+IAjfb+yk0Oa69evY21tzfHjxz/ACD+c7GQRGRmJo6MjlpaWtG/fnj///PMDjvT9y04W+/bto02bNlhbW+Po6MiFCxc+4Eg/jKSkJNq1a/fGn/nc/jszTVayyM2/M9PLShZpcsrvTZksircKCAjg/PnzrFq1ikmTJjFv3jx279792nLPnj1j4MCB2NjYEBoairW1Na6urjx79uwjjPr9yGoWERERDBs2jC5durBt2zZ69OjBiBEjiIiI+Aij/u9lNYf0Jk+enKt+FtJkNYsnT57g4uJCpUqV2LFjBy1btmTYsGHcu3fvI4z6/chqFpcvX2bMmDG4urqyfft2qlatiqurK8+fP/8Io34/EhMTGT16NJcvX850mc/hdyZkLYvc/jszTVaySC/H/N5UCfEGT58+VdWsWVP1559/Kq/Nnz9f9d133722bEhIiMrOzk6VmpqqUqlUqtTUVFXLli1VW7Zs+WDjfZ+yk8XMmTNV/fr103rNxcVFNXv27Pc+zvctOzmk2b59u6pHjx6qypUra33uU5edLFatWqVq0aKFKjk5WXmtc+fOqsOHD3+Qsb5v2cnixx9/VHXq1En5/smTJ6rKlSurzp49+0HG+r5dvnxZ1aFDB1X79u3f+DOf239nqlRZzyI3/85Mk9Us0uSk35tyZFG8UUREBMnJyVhbWyuv1alThzNnzpCamqq17JkzZ6hTp47yjGgdHR1q165NeHj4hxzye5OdLDp16sTYsWNfW8eTJ0/e+zjft+zkAPDgwQNmzpyJr6/vhxzmB5GdLMLCwmjevDm6urrKa1u2bOGrr776YON9n7KThbGxMVeuXOGvv/4iNTWV0NBQChUqxBdffPGhh/1ehIWFUa9ePTZt2vTG5XL770zIeha5+XdmmqxmATnv96bexx6AyNni4+MpUqQI+vr6ymumpqYkJiby8OFDTExMtJatVKmS1ueLFi2a5cPtOV12sqhYsaLWZy9fvsyxY8fo0aPHBxvv+5KdHABmzJhBp06d+PLLLz/0UN+77GQRFxeHpaUlEydO5ODBg5QpUwYPDw/q1KnzMYb+n8tOFvb29hw8eJCePXuiq6tLnjx5WLx4MUZGRh9j6P+5nj17Zmm53P47E7KeRW7+nZkmq1lAzvu9KUcWxRs9f/5c65c/oHyflJSUpWVfXe5TlZ0s0rt//z5ubm7Url2b5s2bv9cxfgjZyeF///sff/31F0OGDPlg4/uQspPFs2fPWLJkCcWKFWPp0qXY2trSr18//vnnnw823vcpO1k8ePCA+Ph4fHx82Lx5Mx07dsTLyytXXb+ZFbn9d+a7ym2/M7MrJ/7elMmieKN8+fK99osr7XsDA4MsLfvqcp+q7GSR5u7du/Tp0weVSkVQUBB58nz6/+SymsOLFy/w8fFh0qRJueZn4FXZ+ZnQ1dWlatWqDB8+nGrVqjFu3DjKly/P9u3bP9h436fsZBEYGEjlypXp1asXNWrUwM/Pj/z587Nly5YPNt6cILf/znwXufF3Znbk1N+bn9ffgsi2EiVK8ODBA5KTk5XX4uPjMTAwoHDhwq8te/fuXa3X7t69S/HixT/IWN+37GQBcPv2bXr16kVSUhKrV69+7fTspyqrOZw9e5a4uDiGDx+OtbW1ci3bgAED8PHx+eDjfh+y8zNRrFgxKlSooPVa+fLlc82RxexkceHCBapUqaJ8nydPHqpUqcLNmzc/2Hhzgtz+OzO7cuvvzOzIqb83ZbIo3qhq1aro6elpXXD9119/UbNmzdf+i69WrVqcPn0alUoFgEql4tSpU9SqVetDDvm9yU4Wz549o3///uTJk4e1a9dSokSJDzza9yerOVhaWrJ37162bdum/AGYOnUqI0aM+MCjfj+y8zNhZWVFZGSk1mvR0dGUKVPmQwz1vctOFsWLFycqKkrrtZiYGMzMzD7EUHOM3P47Mzty8+/M7MipvzdlsijeKH/+/Dg4ODB58mTOnj3L/v37WbFiBb179wbURw5evHgBQOvWrXn8+DH+/v5cuXIFf39/nj9/Tps2bT7mLvxnspPF4sWLuXbtGt9//73yXnx8fK64sy+rORgYGFCuXDmtP6A+mlK0aNGPuQv/mez8TPTo0YPIyEiCg4O5evUqc+fOJS4ujo4dO37MXfjPZCeLbt26sXnzZrZt28bVq1cJDAzk5s2bdOrU6WPuwgfxOf3OfJvP5XdmVuT435sfs7dHfBqePXumcnd3V1lZWakaN26s+vHHH5X3KleurNUJdubMGZWDg4OqZs2aqm+//VZ14cKFjzDi9yerWbRq1UpVuXLl1/54eHh8pJH/t7LzM5FeTugL+69lJ4uTJ0+qOnXqpKpRo4aqY8eOqrCwsI8w4vcnO1ls3rxZ1bp1a5WVlZXK0dFRdf78+Y8w4vfv1Z/5z+13ZnpvyiK3/8581dt+Lt607Mego1L9//FvIYQQQgghXiGnoYUQQgghRKZksiiEEEIIITIlk0UhhBBCCJEpmSwKIYQQQohMyWRRCCGEEEJkSiaLQgghhBAiUzJZFEIIIYT4BCQlJdGuXTuOHz+e5c+EhYXRsWNHatWqRbdu3YiIiMj2dmWyKIQQQgiRwyUmJjJ69GguX76c5c/ExcUxYMAAWrZsyfbt27GwsGDIkCEkJSVla9syWRRCiFzs0aNHzJgxAzs7O2rVqkWbNm1YuXIlqamp733bCQkJyrNthRDv7sqVK3Tr1o1r165l63Nr167F0tKSYcOGUb58ecaPH0+ePHmIjo7O1npksiiEELnUgwcP6Nq1K+fPn8ff359ffvkFNzc3Fi9ejL+//3vf/sqVK9myZct7344QuV1YWBj16tVj06ZNr7138uRJOnfujKWlJe3bt2fPnj1an/vmm2+U7/Pnz8/+/fupUqVKtrav9+5DF0IIkZPNmjULfX19li9fTr58+QAoW7YsBgYGDBkyhO+++w5zc/P3tn15mqwQ/42ePXtm+Hp8fDyurq6MGjWKJk2aEB4ejqenJ0WLFsXGxoa4uDgMDAwYPnw4J0+epFKlSvj4+FCpUqVsbV+OLAohRC6UlJTEzp076dWrlzJRTPP111+zcuVKypQpw6NHj5g4cSINGzakTp06jBs3jkePHgFw/PhxLCwstD7r6emJp6cnAMHBwYwZM4ZJkyZRu3ZtGjRowNKlSwEIDQ1l3rx5hIWFvbYOIcR/Y926dTRs2JDvvvuOcuXK0bFjR7p3786qVasAePbsGYGBgdja2rJ06VJKlSqFs7MzT58+zdZ2ZLIohBC50LVr13j27Bk1a9Z87T0dHR3q16+Pvr4+w4YN49KlSyxatIgff/yRqKgoZTKYFXv27CFfvnxs3bqVfv36ERgYSExMDPb29ri4uGBtbc3Ro0f/y10TQvy/6OhoDh06hLW1tfJn7dq1xMbGAqCrq4udnR1OTk5Ur14dPz8/UlNTOXjwYLa2I6ehhRAiF3r8+DEAhoaGmS4TERFBWFgYu3fvVk5Hz5w5E3t7+yxfAG9sbIyHhwe6urr079+fpUuXcv78eczNzSlQoAB58+alWLFi/36HhBCvSU5Opn379gwaNEjrdT099fSuWLFiWpea6OvrU6ZMGf75559sbUeOLAohRC5kbGwMoJxSzkh0dDSFCxfW+j+TihUrYmRklOXJopmZGbq6usr3BQsWJDk5+d0GLYTIFnNzc65evUq5cuWUPwcOHGDHjh0AWFlZERkZqSyflJREXFwcZmZm2dqOTBaFECIX+uKLLzA0NOTChQsZvj948GD09fUzfC8lJYWUlBR0dHRee+/ViWDevHlfW0ZubBHiw+jZsyfnz59nzpw5xMbGsmPHDmbPnk3p0qUB6NOnD3v27GH9+vXExsbi6+tLvnz5aNasWba2I5NFIYTIhfT09LC3t2fdunWvFfAePHiQgwcPUr58eR4/fqx1FPHKlSskJCRgbm6uTAQTEhKU969fv57lMWQ02RRC/HfKlCnDokWLOHLkCO3ateOHH37A09OTDh06AFCrVi1++OEHVq9eTfv27YmKimLZsmUUKFAgW9uRaxaF7qv0eAAAAVxJREFUECKXcnNzo2vXrvTr1w83NzdKlizJ8ePHmTlzJr1796ZSpUo0bdoUDw8PJk6cCMCUKVOwtbWlcuXKJCQkYGBgwKJFi+jevTt79uzh4sWLlC9fPkvbz58/P3fu3OH69evZPu0lhMhY+tPKAA0bNiQ0NDTT5Vu0aEGLFi3+1TblyKIQQuRSxYoVY8OGDZQtW5axY8fSrl07Vq1axfDhw5U7nr///nvKli2Ls7Mz/fr148svv2T+/PkAFCpUCD8/P3bu3Em7du2IiIigV69eWd5+y5YtSU1NpW3btty7d++97KMQ4v3TUcnFJUIIIYQQIhNyZFEIIYQQQmRKJotCCCGEECJTMlkUQgghhBCZksmiEEIIIYTIlEwWhRBCCCFEpmSyKIQQQgghMiWTRSGEEEIIkSmZLAohhBBCiEzJZFEIIYQQQmRKJotCCCGEECJTMlkUQgghhBCZ+j+VZHXq1HrDeQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(df_cleaned.loc[df_cleaned[\"class\"].isin([\"aves\", \"mammalia\", \"reptilia\"])], y = 'order')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "data-dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }