{ "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": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "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/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 12710778\n", "unknown 3382215\n", "image 3258163\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 iimage vs sequence (or unknown), not analogous to `taxonomy_level` from lila-taxonomy-mapping_release.csv. It seems `original_label` may be the analogous column.\n", "\n", "We'll likely want to pull out the image-level before doing any sequence checks and such since those should be \"clean\" images. Though we will want to label them with how many distinct species are in the image first.\n", "\n", "We now have 66 less sequence-level annotations and 2,517,374 more image-level! That's quite the update! The unknown count has not changed.\n", "\n", "### Check Dataset Counts\n", "\n", "1. Make sure we have all datasets expected.\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", "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", "Island Conservation Camera Traps 128207\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", "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", "Orinoquia Camera Traps image 112267\n", "SWG Camera Traps sequence 2039657\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 Serengeti sequence 7261545\n", "Trail Camera Images of New Zealand Animals image 2453840\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 all the unknown annotation level images are in NACTI (North American Camera Trap Images). At first glance I don't see annotation level information on HF or on [their LILA page](https://lila.science/datasets/nacti)--will require more looking.\n", "\n", "Desert Lion Conservation Camera Traps & Trail Camera Images of New Zealand Animals are _not_ included in the [Hugging Face dataset](https://huggingface.co/datasets/society-ethics/lila_camera_traps).\n", "\n", "There are definitely more in [Andrey's spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?usp=drive_link) that aren't included here. We'll have him go through those too." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...suborderinfraordersuperfamilyfamilysubfamilytribegenusspeciessubspeciesvariety
12061234Snapshot Serengetihttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Serengeti : S8/N06/N06_R2/S8_N06_R2_I...Snapshot Serengeti : SER_S8#N06#2#628Snapshot Serengeti : N063hartebeestalcelaphus buselaphus...ruminantiaNaNNaNbovidaeantilopinaealcelaphinialcelaphusalcelaphus buselaphusNaNNaN
6586492Idaho Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Idaho Camera Traps : loc_0067_im_006951Idaho Camera Traps : loc_0067_seq_006947Idaho Camera Traps : 670emptyNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2786883NACTIhttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...NACTI : FL-28_09_01_2016_FL-28_0059198.JPGNACTI : unknownNACTI : archbold_FL-28-1unidentified birdaves...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
13855386Snapshot Serengetihttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Serengeti : S10/O12/O12_R1/S10_O12_R1...Snapshot Serengeti : SER_S10#O12#1#104Snapshot Serengeti : O121emptyNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9992570Snapshot Serengetihttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Serengeti : S5/U12/U12_R1/S5_U12_R1_I...Snapshot Serengeti : SER_S5#U12#1#63Snapshot Serengeti : U121zebraequus quagga...NaNNaNNaNequidaeNaNNaNequusequus quaggaNaNNaN
2900006NACTIhttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...NACTI : FL-31_01_21_2016_FL-31_0036063.jpgNACTI : unknownNACTI : archbold_FL-31-1bos taurusbos taurus...ruminantiaNaNNaNbovidaebovinaebovinibosbos taurusNaNNaN
16472694SWG Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...SWG Camera Traps : d98b4691-8c29-11eb-85d7-000...SWG Camera Traps : fad4a034-8c29-11eb-8cda-000...SWG Camera Traps : loc_08900unidentified_muridmuridae...myomorphaNaNmuroideamuridaeNaNNaNNaNNaNNaNNaN
18408513Trail Camera Images of New Zealand Animalshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Trail Camera Images of New Zealand Animals : E...Trail Camera Images of New Zealand Animals : u...Trail Camera Images of New Zealand Animals : E...-1robinpetroica australis...NaNNaNNaNpetroicidaeNaNNaNpetroicapetroica australisNaNNaN
17944687Trail Camera Images of New Zealand Animalshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Trail Camera Images of New Zealand Animals : E...Trail Camera Images of New Zealand Animals : u...Trail Camera Images of New Zealand Animals : E...-1mousemus...myomorphaNaNmuroideamuridaemurinaemurinimusNaNNaNNaN
674082NACTIhttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...NACTI : CA-06_08_10_2016_CA-06_0015357.JPGNACTI : unknownNACTI : lebec_CA-06-1bos taurusbos taurus...ruminantiaNaNNaNbovidaebovinaebovinibosbos taurusNaNNaN
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10 rows × 32 columns

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" ], "text/plain": [ " dataset_name \\\n", "12061234 Snapshot Serengeti \n", "6586492 Idaho Camera Traps \n", "2786883 NACTI \n", "13855386 Snapshot Serengeti \n", "9992570 Snapshot Serengeti \n", "2900006 NACTI \n", "16472694 SWG Camera Traps \n", "18408513 Trail Camera Images of New Zealand Animals \n", "17944687 Trail Camera Images of New Zealand Animals \n", "674082 NACTI \n", "\n", " url_gcp \\\n", "12061234 https://storage.googleapis.com/public-datasets... \n", "6586492 https://storage.googleapis.com/public-datasets... \n", "2786883 https://storage.googleapis.com/public-datasets... \n", "13855386 https://storage.googleapis.com/public-datasets... \n", "9992570 https://storage.googleapis.com/public-datasets... \n", "2900006 https://storage.googleapis.com/public-datasets... \n", "16472694 https://storage.googleapis.com/public-datasets... \n", "18408513 https://storage.googleapis.com/public-datasets... \n", "17944687 https://storage.googleapis.com/public-datasets... \n", "674082 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "12061234 http://us-west-2.opendata.source.coop.s3.amazo... \n", "6586492 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2786883 http://us-west-2.opendata.source.coop.s3.amazo... \n", "13855386 http://us-west-2.opendata.source.coop.s3.amazo... \n", "9992570 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2900006 http://us-west-2.opendata.source.coop.s3.amazo... \n", "16472694 http://us-west-2.opendata.source.coop.s3.amazo... \n", "18408513 http://us-west-2.opendata.source.coop.s3.amazo... \n", "17944687 http://us-west-2.opendata.source.coop.s3.amazo... \n", "674082 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "12061234 https://lilawildlife.blob.core.windows.net/lil... \n", "6586492 https://lilawildlife.blob.core.windows.net/lil... \n", "2786883 https://lilawildlife.blob.core.windows.net/lil... \n", "13855386 https://lilawildlife.blob.core.windows.net/lil... \n", "9992570 https://lilawildlife.blob.core.windows.net/lil... \n", "2900006 https://lilawildlife.blob.core.windows.net/lil... \n", "16472694 https://lilawildlife.blob.core.windows.net/lil... \n", "18408513 https://lilawildlife.blob.core.windows.net/lil... \n", "17944687 https://lilawildlife.blob.core.windows.net/lil... \n", "674082 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "12061234 Snapshot Serengeti : S8/N06/N06_R2/S8_N06_R2_I... \n", "6586492 Idaho Camera Traps : loc_0067_im_006951 \n", "2786883 NACTI : FL-28_09_01_2016_FL-28_0059198.JPG \n", "13855386 Snapshot Serengeti : S10/O12/O12_R1/S10_O12_R1... \n", "9992570 Snapshot Serengeti : S5/U12/U12_R1/S5_U12_R1_I... \n", "2900006 NACTI : FL-31_01_21_2016_FL-31_0036063.jpg \n", "16472694 SWG Camera Traps : d98b4691-8c29-11eb-85d7-000... \n", "18408513 Trail Camera Images of New Zealand Animals : E... \n", "17944687 Trail Camera Images of New Zealand Animals : E... \n", "674082 NACTI : CA-06_08_10_2016_CA-06_0015357.JPG \n", "\n", " sequence_id \\\n", "12061234 Snapshot Serengeti : SER_S8#N06#2#628 \n", "6586492 Idaho Camera Traps : loc_0067_seq_006947 \n", "2786883 NACTI : unknown \n", "13855386 Snapshot Serengeti : SER_S10#O12#1#104 \n", "9992570 Snapshot Serengeti : SER_S5#U12#1#63 \n", "2900006 NACTI : unknown \n", "16472694 SWG Camera Traps : fad4a034-8c29-11eb-8cda-000... \n", "18408513 Trail Camera Images of New Zealand Animals : u... \n", "17944687 Trail Camera Images of New Zealand Animals : u... \n", "674082 NACTI : unknown \n", "\n", " location_id frame_num \\\n", "12061234 Snapshot Serengeti : N06 3 \n", "6586492 Idaho Camera Traps : 67 0 \n", "2786883 NACTI : archbold_FL-28 -1 \n", "13855386 Snapshot Serengeti : O12 1 \n", "9992570 Snapshot Serengeti : U12 1 \n", "2900006 NACTI : archbold_FL-31 -1 \n", "16472694 SWG Camera Traps : loc_0890 0 \n", "18408513 Trail Camera Images of New Zealand Animals : E... -1 \n", "17944687 Trail Camera Images of New Zealand Animals : E... -1 \n", "674082 NACTI : lebec_CA-06 -1 \n", "\n", " original_label scientific_name ... suborder \\\n", "12061234 hartebeest alcelaphus buselaphus ... ruminantia \n", "6586492 empty NaN ... NaN \n", "2786883 unidentified bird aves ... NaN \n", "13855386 empty NaN ... NaN \n", "9992570 zebra equus quagga ... NaN \n", "2900006 bos taurus bos taurus ... ruminantia \n", "16472694 unidentified_murid muridae ... myomorpha \n", "18408513 robin petroica australis ... NaN \n", "17944687 mouse mus ... myomorpha \n", "674082 bos taurus bos taurus ... ruminantia \n", "\n", " infraorder superfamily family subfamily tribe \\\n", "12061234 NaN NaN bovidae antilopinae alcelaphini \n", "6586492 NaN NaN NaN NaN NaN \n", "2786883 NaN NaN NaN NaN NaN \n", "13855386 NaN NaN NaN NaN NaN \n", "9992570 NaN NaN equidae NaN NaN \n", "2900006 NaN NaN bovidae bovinae bovini \n", "16472694 NaN muroidea muridae NaN NaN \n", "18408513 NaN NaN petroicidae NaN NaN \n", "17944687 NaN muroidea muridae murinae murini \n", "674082 NaN NaN bovidae bovinae bovini \n", "\n", " genus species subspecies variety \n", "12061234 alcelaphus alcelaphus buselaphus NaN NaN \n", "6586492 NaN NaN NaN NaN \n", "2786883 NaN NaN NaN NaN \n", "13855386 NaN NaN NaN NaN \n", "9992570 equus equus quagga NaN NaN \n", "2900006 bos bos taurus NaN NaN \n", "16472694 NaN NaN NaN NaN \n", "18408513 petroica petroica australis NaN NaN \n", "17944687 mus NaN NaN NaN \n", "674082 bos bos taurus NaN NaN \n", "\n", "[10 rows x 32 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sample(10)" ] }, { "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": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 19351156 entries, 0 to 19351155\n", "Data columns (total 32 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 19351156 non-null object\n", " 1 url_gcp 19351156 non-null object\n", " 2 url_aws 19351156 non-null object\n", " 3 url_azure 19351156 non-null object\n", " 4 image_id 19351156 non-null object\n", " 5 sequence_id 19351156 non-null object\n", " 6 location_id 19351156 non-null object\n", " 7 frame_num 19351156 non-null int64 \n", " 8 original_label 19351156 non-null object\n", " 9 scientific_name 10192703 non-null object\n", " 10 common_name 10192703 non-null object\n", " 11 datetime 15404050 non-null object\n", " 12 annotation_level 19351156 non-null object\n", " 13 kingdom 10192703 non-null object\n", " 14 phylum 10177119 non-null object\n", " 15 subphylum 10141160 non-null object\n", " 16 superclass 79 non-null object\n", " 17 class 10174780 non-null object\n", " 18 subclass 9022382 non-null object\n", " 19 infraclass 9021471 non-null object\n", " 20 superorder 8812501 non-null object\n", " 21 order 9796763 non-null object\n", " 22 suborder 7289300 non-null object\n", " 23 infraorder 510351 non-null object\n", " 24 superfamily 1805446 non-null object\n", " 25 family 9693066 non-null object\n", " 26 subfamily 7662399 non-null object\n", " 27 tribe 6614011 non-null object\n", " 28 genus 9445408 non-null object\n", " 29 species 7521712 non-null object\n", " 30 subspecies 74052 non-null object\n", " 31 variety 2050 non-null object\n", "dtypes: int64(1), object(31)\n", "memory usage: 4.6+ GB\n" ] } ], "source": [ "df.info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The overall dataset has grown by about 3 million images, we'll see how much of this is non-empty. I'm encouraged by the number of non-null `scientific_name` values seeming to also grow by about 3 million; most of these also seem to have genus now.\n", "\n", "We'll definitely want to check on the scientifc name choices where genus and species aren't available, similarly for other ranks, as it is guarunteed as much as kingdom (which is hopefully aligned with all non-empty images).\n", "\n", "No licensing info, we'll get that from HF or the datasets themselves (Andrey can check this; most seem to be [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/))." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 20\n", "url_gcp 19249146\n", "url_aws 19249146\n", "url_azure 19249146\n", "image_id 19249146\n", "sequence_id 4772758\n", "location_id 9931\n", "frame_num 11005\n", "original_label 1227\n", "scientific_name 908\n", "common_name 999\n", "datetime 8559012\n", "annotation_level 3\n", "kingdom 1\n", "phylum 2\n", "subphylum 5\n", "superclass 1\n", "class 8\n", "subclass 3\n", "infraclass 2\n", "superorder 5\n", "order 58\n", "suborder 17\n", "infraorder 9\n", "superfamily 12\n", "family 187\n", "subfamily 71\n", "tribe 46\n", "genus 538\n", "species 739\n", "subspecies 12\n", "variety 1\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 739 unique species indicated, though the 908 unique `scientific_name` values is likely more indicative of the diversity.\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. We'll check this out once we remove the images labeled as \"empty\"." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...suborderinfraordersuperfamilyfamilysubfamilytribegenusspeciessubspeciesvariety
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0 rows × 32 columns

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" ], "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check for humans\n", "df.loc[df.species == \"homo sapien\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by removing entries with `original_label`: `empty`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df_cleaned = df.loc[df.original_label != \"empty\"].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save the Reduced Data (no more \"empty\" labels)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "df_cleaned.to_csv(\"../data/lila_image_urls_and_labels.csv\", index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check where we are with annotations now that we've removed all the images labeled as empty." ] }, { "cell_type": "code", "execution_count": 14, "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", "Orinoquia Camera Traps image 112267\n", "SWG Camera Traps sequence 2039657\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 Serengeti sequence 7261545\n", "Trail Camera Images of New Zealand Animals image 2453840\n", "WCS Camera Traps sequence 1369953\n", "Wellington Camera Traps sequence 270450\n", "Name: count, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby([\"dataset_name\"]).annotation_level.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We started with 19,351,156 entries, and are left with 10,965,902 after removing all labeled as `empty`, so more than half the images now; it's an increase of about 2.5M from the last version. \n", "\n", "Note that there are still about 3.4 million that don't have the species label, 1.5 million that are missing genus designation. 10,192,703 of them have scientific and common name, though! That's nearly all of them." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 10965902 entries, 1 to 19351155\n", "Data columns (total 32 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 10965902 non-null object\n", " 1 url_gcp 10965902 non-null object\n", " 2 url_aws 10965902 non-null object\n", " 3 url_azure 10965902 non-null object\n", " 4 image_id 10965902 non-null object\n", " 5 sequence_id 10965902 non-null object\n", " 6 location_id 10965902 non-null object\n", " 7 frame_num 10965902 non-null int64 \n", " 8 original_label 10965902 non-null object\n", " 9 scientific_name 10192703 non-null object\n", " 10 common_name 10192703 non-null object\n", " 11 datetime 7620269 non-null object\n", " 12 annotation_level 10965902 non-null object\n", " 13 kingdom 10192703 non-null object\n", " 14 phylum 10177119 non-null object\n", " 15 subphylum 10141160 non-null object\n", " 16 superclass 79 non-null object\n", " 17 class 10174780 non-null object\n", " 18 subclass 9022382 non-null object\n", " 19 infraclass 9021471 non-null object\n", " 20 superorder 8812501 non-null object\n", " 21 order 9796763 non-null object\n", " 22 suborder 7289300 non-null object\n", " 23 infraorder 510351 non-null object\n", " 24 superfamily 1805446 non-null object\n", " 25 family 9693066 non-null object\n", " 26 subfamily 7662399 non-null object\n", " 27 tribe 6614011 non-null object\n", " 28 genus 9445408 non-null object\n", " 29 species 7521712 non-null object\n", " 30 subspecies 74052 non-null object\n", " 31 variety 2050 non-null object\n", "dtypes: int64(1), object(31)\n", "memory usage: 2.7+ GB\n" ] } ], "source": [ "df_cleaned.info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 20\n", "url_gcp 10864013\n", "url_aws 10864013\n", "url_azure 10864013\n", "image_id 10864013\n", "sequence_id 1412018\n", "location_id 9848\n", "frame_num 11005\n", "original_label 1226\n", "scientific_name 908\n", "common_name 999\n", "datetime 5479009\n", "annotation_level 3\n", "kingdom 1\n", "phylum 2\n", "subphylum 5\n", "superclass 1\n", "class 8\n", "subclass 3\n", "infraclass 2\n", "superorder 5\n", "order 58\n", "suborder 17\n", "infraorder 9\n", "superfamily 12\n", "family 187\n", "subfamily 71\n", "tribe 46\n", "genus 538\n", "species 739\n", "subspecies 12\n", "variety 1\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.nunique()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "phylum\n", "chordata 10166819\n", "arthropoda 10300\n", "Name: count, dtype: int64\n", "\n", "class\n", "mammalia 9089968\n", "aves 1068301\n", "reptilia 8416\n", "insecta 7856\n", "amphibia 134\n", "malacostraca 79\n", "arachnida 20\n", "diplopoda 6\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": [ "We have 10,965,902 total - 10,864,013 unique URLs, suggesting at most 101,889 images have more than one species in them. That's only 1% of our images here and even smaller at the scale we're looking for the next ToL dataset. It is interesting to note though and we should explore this more.\n", "\n", "I'm curious about the single \"variety\", since I thought that was more of a plant label and these are all animals.\n", "\n", "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, arachnida, and diplopoda 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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 13\n", "url_gcp 99682\n", "url_aws 99682\n", "url_azure 99682\n", "image_id 99682\n", "sequence_id 30668\n", "location_id 514\n", "frame_num 488\n", "original_label 167\n", "scientific_name 133\n", "common_name 143\n", "datetime 42962\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 27\n", "suborder 9\n", "infraorder 5\n", "superfamily 5\n", "family 54\n", "subfamily 23\n", "tribe 21\n", "genus 90\n", "species 92\n", "subspecies 6\n", "variety 1\n", "multi_species 1\n", "dtype: int64" ] }, "execution_count": 17, "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 100K 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": 20, "metadata": {}, "outputs": [], "source": [ "multi_sp_imgs = list(df_cleaned.loc[df_cleaned[\"multi_species\"], \"image_id\"].unique())" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...superfamilyfamilysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_species
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...NaNcervidaecapreolinaeodocoileiniodocoileusNaNNaNNaNFalseNaN
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...NaNfelidaefelinaeNaNfelisfelis catusNaNNaNFalseNaN
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...NaNdidelphidaedidelphinaedidelphinididelphisdidelphis virginianaNaNNaNFalseNaN
5Caltech 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 : 5a096955-23d2-11e8-a6a3...Caltech Camera Traps : 70096335-5567-11e8-a99a...Caltech Camera Traps : 361carNaN...NaNNaNNaNNaNNaNNaNNaNNaNFalseNaN
6Caltech 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 : 59b93a3b-23d2-11e8-a6a3...Caltech Camera Traps : 7013c982-5567-11e8-be89...Caltech Camera Traps : 612rabbitleporidae...NaNleporidaeNaNNaNNaNNaNNaNNaNFalseNaN
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5 rows × 34 columns

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" ], "text/plain": [ " dataset_name url_gcp \\\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", "5 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "6 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\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", "5 http://us-west-2.opendata.source.coop.s3.amazo... \n", "6 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\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", "5 https://lilawildlife.blob.core.windows.net/lil... \n", "6 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\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", "5 Caltech Camera Traps : 5a096955-23d2-11e8-a6a3... \n", "6 Caltech Camera Traps : 59b93a3b-23d2-11e8-a6a3... \n", "\n", " sequence_id \\\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", "5 Caltech Camera Traps : 70096335-5567-11e8-a99a... \n", "6 Caltech Camera Traps : 7013c982-5567-11e8-be89... \n", "\n", " location_id frame_num original_label scientific_name \\\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", "5 Caltech Camera Traps : 36 1 car NaN \n", "6 Caltech Camera Traps : 61 2 rabbit leporidae \n", "\n", " ... superfamily family subfamily tribe genus \\\n", "1 ... NaN cervidae capreolinae odocoileini odocoileus \n", "2 ... NaN felidae felinae NaN felis \n", "3 ... NaN didelphidae didelphinae didelphini didelphis \n", "5 ... NaN NaN NaN NaN NaN \n", "6 ... NaN leporidae NaN NaN NaN \n", "\n", " species subspecies variety multi_species num_species \n", "1 NaN NaN NaN False NaN \n", "2 felis catus NaN NaN False NaN \n", "3 didelphis virginiana NaN NaN False NaN \n", "5 NaN NaN NaN False NaN \n", "6 NaN NaN NaN False NaN \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 22, "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": [ "#### Save this to CSV now we got those counts" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df_cleaned.to_csv(\"../data/lila_image_urls_and_labels.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...superfamilyfamilysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_species
495Caltech 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 : 5a1cafe6-23d2-11e8-a6a3...Caltech Camera Traps : 70139a99-5567-11e8-bb56...Caltech Camera Traps : 613badgertaxidea taxus...NaNmustelidaetaxidiinaeNaNtaxideataxidea taxusNaNNaNTrue2.0
496Caltech 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 : 5a1cafe6-23d2-11e8-a6a3...Caltech Camera Traps : 70139a99-5567-11e8-bb56...Caltech Camera Traps : 613coyotecanis latrans...NaNcanidaeNaNNaNcaniscanis latransNaNNaNTrue2.0
2645Caltech 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 : 5968bf89-23d2-11e8-a6a3...Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3...Caltech Camera Traps : 431squirrelsciurus...NaNsciuridaesciurinaesciurinisciurusNaNNaNNaNTrue2.0
2646Caltech 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 : 5968bf89-23d2-11e8-a6a3...Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3...Caltech Camera Traps : 431bobcatlynx rufus...NaNfelidaefelinaeNaNlynxlynx rufusNaNNaNTrue2.0
14220Caltech 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 : 59a49aa6-23d2-11e8-a6a3...Caltech Camera Traps : 70139a99-5567-11e8-bb56...Caltech Camera Traps : 611badgertaxidea taxus...NaNmustelidaetaxidiinaeNaNtaxideataxidea taxusNaNNaNTrue2.0
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5 rows × 34 columns

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" ], "text/plain": [ " dataset_name \\\n", "495 Caltech Camera Traps \n", "496 Caltech Camera Traps \n", "2645 Caltech Camera Traps \n", "2646 Caltech Camera Traps \n", "14220 Caltech Camera Traps \n", "\n", " url_gcp \\\n", "495 https://storage.googleapis.com/public-datasets... \n", "496 https://storage.googleapis.com/public-datasets... \n", "2645 https://storage.googleapis.com/public-datasets... \n", "2646 https://storage.googleapis.com/public-datasets... \n", "14220 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "495 http://us-west-2.opendata.source.coop.s3.amazo... \n", "496 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2645 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2646 http://us-west-2.opendata.source.coop.s3.amazo... \n", "14220 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "495 https://lilawildlife.blob.core.windows.net/lil... \n", "496 https://lilawildlife.blob.core.windows.net/lil... \n", "2645 https://lilawildlife.blob.core.windows.net/lil... \n", "2646 https://lilawildlife.blob.core.windows.net/lil... \n", "14220 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "495 Caltech Camera Traps : 5a1cafe6-23d2-11e8-a6a3... \n", "496 Caltech Camera Traps : 5a1cafe6-23d2-11e8-a6a3... \n", "2645 Caltech Camera Traps : 5968bf89-23d2-11e8-a6a3... \n", "2646 Caltech Camera Traps : 5968bf89-23d2-11e8-a6a3... \n", "14220 Caltech Camera Traps : 59a49aa6-23d2-11e8-a6a3... \n", "\n", " sequence_id \\\n", "495 Caltech Camera Traps : 70139a99-5567-11e8-bb56... \n", "496 Caltech Camera Traps : 70139a99-5567-11e8-bb56... \n", "2645 Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3... \n", "2646 Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3... \n", "14220 Caltech Camera Traps : 70139a99-5567-11e8-bb56... \n", "\n", " location_id frame_num original_label scientific_name \\\n", "495 Caltech Camera Traps : 61 3 badger taxidea taxus \n", "496 Caltech Camera Traps : 61 3 coyote canis latrans \n", "2645 Caltech Camera Traps : 43 1 squirrel sciurus \n", "2646 Caltech Camera Traps : 43 1 bobcat lynx rufus \n", "14220 Caltech Camera Traps : 61 1 badger taxidea taxus \n", "\n", " ... superfamily family subfamily tribe genus \\\n", "495 ... NaN mustelidae taxidiinae NaN taxidea \n", "496 ... NaN canidae NaN NaN canis \n", "2645 ... NaN sciuridae sciurinae sciurini sciurus \n", "2646 ... NaN felidae felinae NaN lynx \n", "14220 ... NaN mustelidae taxidiinae NaN taxidea \n", "\n", " species subspecies variety multi_species num_species \n", "495 taxidea taxus NaN NaN True 2.0 \n", "496 canis latrans NaN NaN True 2.0 \n", "2645 NaN NaN NaN True 2.0 \n", "2646 lynx rufus NaN NaN True 2.0 \n", "14220 taxidea taxus NaN NaN True 2.0 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned[\"multi_species\"]].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many different species do we generally have when we have multiple species in an image?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "num_species\n", "2.0 195134\n", "3.0 6069\n", "4.0 368\n", "Name: count, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.num_species.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 97,567 images with 2 different species (most multi-species instances), 2,023 with 3 different species, and 92 with 4.\n", "\n", "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`) and then by all taxonomic labels (`unique_taxa`) available). 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": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "annotation_level\n", "sequence 5133907\n", "image 2919136\n", "unknown 2912859\n", "Name: count, dtype: int64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.annotation_level.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've got ~3M labeled to the image and another 3M unknonwn labeling (all from NACTI, which Andrey will check on), leaving ~5M labeled only to at the sequence level. This _should_ give Jianyang something to work with to start exploring near-duplicate de-duplication. \n", "\n", "Let's update the non-multi species images to show 1 in the `num_species` column, then move on to checking the taxonomy strings." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "num_species\n", "1.0 10764331\n", "2.0 195134\n", "3.0 6069\n", "4.0 368\n", "Name: count, dtype: int64" ] }, "execution_count": 29, "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": "markdown", "metadata": {}, "source": [ "### Taxonomic String Exploration" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "lin_taxa = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']\n", "all_taxa = ['kingdom',\n", " 'phylum',\n", " 'subphylum',\n", " 'superclass',\n", " 'class',\n", " 'subclass',\n", " 'infraclass',\n", " 'superorder',\n", " 'order',\n", " 'suborder',\n", " 'infraorder',\n", " 'superfamily',\n", " 'family',\n", " 'subfamily',\n", " 'tribe',\n", " 'genus',\n", " 'species',\n", " 'subspecies',\n", " 'variety']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### How many have all 7 Linnean ranks?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 7521712 entries, 2 to 19351155\n", "Data columns (total 19 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 7521712 non-null object\n", " 1 phylum 7521712 non-null object\n", " 2 subphylum 7521548 non-null object\n", " 3 superclass 59 non-null object\n", " 4 class 7521712 non-null object\n", " 5 subclass 6917435 non-null object\n", " 6 infraclass 6917376 non-null object\n", " 7 superorder 6717750 non-null object\n", " 8 order 7521712 non-null object\n", " 9 suborder 5374157 non-null object\n", " 10 infraorder 481338 non-null object\n", " 11 superfamily 324711 non-null object\n", " 12 family 7521712 non-null object\n", " 13 subfamily 5857614 non-null object\n", " 14 tribe 4854254 non-null object\n", " 15 genus 7521712 non-null object\n", " 16 species 7521712 non-null object\n", " 17 subspecies 74052 non-null object\n", " 18 variety 2050 non-null object\n", "dtypes: object(19)\n", "memory usage: 1.1+ GB\n" ] } ], "source": [ "df_all_taxa = df_cleaned.dropna(subset = lin_taxa)\n", "df_all_taxa[all_taxa].info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's pretty good coverage: 7,521,712 out of 10,965,902. It looks like many of them also have the other taxonomic ranks too. Now how many different 7-tuples are there?\n", "\n", "#### How many unique 7-tuples?" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 891 entries, 1 to 19350355\n", "Data columns (total 35 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 891 non-null object \n", " 1 url_gcp 891 non-null object \n", " 2 url_aws 891 non-null object \n", " 3 url_azure 891 non-null object \n", " 4 image_id 891 non-null object \n", " 5 sequence_id 891 non-null object \n", " 6 location_id 891 non-null object \n", " 7 frame_num 891 non-null int64 \n", " 8 original_label 891 non-null object \n", " 9 scientific_name 890 non-null object \n", " 10 common_name 890 non-null object \n", " 11 datetime 812 non-null object \n", " 12 annotation_level 891 non-null object \n", " 13 kingdom 890 non-null object \n", " 14 phylum 889 non-null object \n", " 15 subphylum 886 non-null object \n", " 16 superclass 2 non-null object \n", " 17 class 888 non-null object \n", " 18 subclass 445 non-null object \n", " 19 infraclass 441 non-null object \n", " 20 superorder 432 non-null object \n", " 21 order 883 non-null object \n", " 22 suborder 249 non-null object \n", " 23 infraorder 73 non-null object \n", " 24 superfamily 56 non-null object \n", " 25 family 870 non-null object \n", " 26 subfamily 338 non-null object \n", " 27 tribe 168 non-null object \n", " 28 genus 829 non-null object \n", " 29 species 739 non-null object \n", " 30 subspecies 5 non-null object \n", " 31 variety 1 non-null object \n", " 32 multi_species 891 non-null bool \n", " 33 num_species 891 non-null float64\n", " 34 lin_duplicate 891 non-null bool \n", "dtypes: bool(2), float64(1), int64(1), object(31)\n", "memory usage: 238.4+ KB\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", "df_unique_lin_taxa.info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interesting, we have 891 unique 7-tuple taxonomic strings, but 1 scientific and common name seem to be missing.\n", "What's the uniqueness count here?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 20\n", "url_gcp 891\n", "url_aws 891\n", "url_azure 891\n", "image_id 891\n", "sequence_id 661\n", "location_id 620\n", "frame_num 12\n", "original_label 885\n", "scientific_name 890\n", "common_name 886\n", "datetime 806\n", "annotation_level 3\n", "kingdom 1\n", "phylum 2\n", "subphylum 5\n", "superclass 1\n", "class 8\n", "subclass 3\n", "infraclass 2\n", "superorder 5\n", "order 58\n", "suborder 16\n", "infraorder 9\n", "superfamily 11\n", "family 187\n", "subfamily 71\n", "tribe 46\n", "genus 538\n", "species 739\n", "subspecies 5\n", "variety 1\n", "multi_species 2\n", "num_species 3\n", "lin_duplicate 1\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_unique_lin_taxa.nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "They're across all datasets. We have 890 unique scientific names and 886 unique common names (from 885 original labels)." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...familysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_specieslin_duplicate
5Caltech 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 : 5a096955-23d2-11e8-a6a3...Caltech Camera Traps : 70096335-5567-11e8-a99a...Caltech Camera Traps : 361carNaN...NaNNaNNaNNaNNaNNaNNaNFalse1.0False
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1 rows × 35 columns

\n", "
" ], "text/plain": [ " dataset_name url_gcp \\\n", "5 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "5 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "5 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "5 Caltech Camera Traps : 5a096955-23d2-11e8-a6a3... \n", "\n", " sequence_id \\\n", "5 Caltech Camera Traps : 70096335-5567-11e8-a99a... \n", "\n", " location_id frame_num original_label scientific_name ... \\\n", "5 Caltech Camera Traps : 36 1 car NaN ... \n", "\n", " family subfamily tribe genus species subspecies variety multi_species \\\n", "5 NaN NaN NaN NaN NaN NaN NaN False \n", "\n", " num_species lin_duplicate \n", "5 1.0 False \n", "\n", "[1 rows x 35 columns]" ] }, "execution_count": 38, "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": [ "It's a car...We need to remove cars..." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4717, 36)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned[\"original_label\"] == \"car\"].shape" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name\n", "Caltech Camera Traps 4717\n", "Name: count, dtype: int64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cleaned.loc[df_cleaned[\"original_label\"] == \"car\", \"dataset_name\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### How many unique full taxa (sub ranks included)?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 909 entries, 1 to 19350355\n", "Data columns (total 36 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 909 non-null object \n", " 1 url_gcp 909 non-null object \n", " 2 url_aws 909 non-null object \n", " 3 url_azure 909 non-null object \n", " 4 image_id 909 non-null object \n", " 5 sequence_id 909 non-null object \n", " 6 location_id 909 non-null object \n", " 7 frame_num 909 non-null int64 \n", " 8 original_label 909 non-null object \n", " 9 scientific_name 908 non-null object \n", " 10 common_name 908 non-null object \n", " 11 datetime 827 non-null object \n", " 12 annotation_level 909 non-null object \n", " 13 kingdom 908 non-null object \n", " 14 phylum 907 non-null object \n", " 15 subphylum 904 non-null object \n", " 16 superclass 2 non-null object \n", " 17 class 906 non-null object \n", " 18 subclass 458 non-null object \n", " 19 infraclass 453 non-null object \n", " 20 superorder 444 non-null object \n", " 21 order 899 non-null object \n", " 22 suborder 256 non-null object \n", " 23 infraorder 74 non-null object \n", " 24 superfamily 58 non-null object \n", " 25 family 882 non-null object \n", " 26 subfamily 346 non-null object \n", " 27 tribe 172 non-null object \n", " 28 genus 837 non-null object \n", " 29 species 747 non-null object \n", " 30 subspecies 12 non-null object \n", " 31 variety 1 non-null object \n", " 32 multi_species 909 non-null bool \n", " 33 num_species 909 non-null float64\n", " 34 lin_duplicate 909 non-null bool \n", " 35 full_duplicate 909 non-null bool \n", "dtypes: bool(3), float64(1), int64(1), object(31)\n", "memory usage: 244.1+ KB\n" ] } ], "source": [ "#number of unique 7-tuples in full dataset\n", "df_cleaned['full_duplicate'] = df_cleaned.duplicated(subset = all_taxa, keep = 'first')\n", "df_unique_all_taxa = df_cleaned.loc[~df_cleaned['full_duplicate']].copy()\n", "df_unique_all_taxa.info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we consider the sub-ranks as well we wind up with 909 unique taxa (still with one scientific and common name missing--the car!)." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 20\n", "url_gcp 909\n", "url_aws 909\n", "url_azure 909\n", "image_id 909\n", "sequence_id 673\n", "location_id 629\n", "frame_num 12\n", "original_label 901\n", "scientific_name 908\n", "common_name 901\n", "datetime 821\n", "annotation_level 3\n", "kingdom 1\n", "phylum 2\n", "subphylum 5\n", "superclass 1\n", "class 8\n", "subclass 3\n", "infraclass 2\n", "superorder 5\n", "order 58\n", "suborder 17\n", "infraorder 9\n", "superfamily 12\n", "family 187\n", "subfamily 71\n", "tribe 46\n", "genus 538\n", "species 739\n", "subspecies 12\n", "variety 1\n", "multi_species 2\n", "num_species 3\n", "lin_duplicate 2\n", "full_duplicate 1\n", "dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_unique_all_taxa.nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now captured all 908 unique scientific names, but only 901 of the 999 unique common names." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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5Caltech 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 : 5a096955-23d2-11e8-a6a3...Caltech Camera Traps : 70096335-5567-11e8-a99a...Caltech Camera Traps : 361carNaN...NaNNaNNaNNaNNaNNaNFalse1.0FalseFalse
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" ], "text/plain": [ " dataset_name url_gcp \\\n", "5 Caltech Camera Traps https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "5 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "5 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "5 Caltech Camera Traps : 5a096955-23d2-11e8-a6a3... \n", "\n", " sequence_id \\\n", "5 Caltech Camera Traps : 70096335-5567-11e8-a99a... \n", "\n", " location_id frame_num original_label scientific_name ... \\\n", "5 Caltech Camera Traps : 36 1 car NaN ... \n", "\n", " subfamily tribe genus species subspecies variety multi_species num_species \\\n", "5 NaN NaN NaN NaN NaN NaN False 1.0 \n", "\n", " lin_duplicate full_duplicate \n", "5 False False \n", "\n", "[1 rows x 36 columns]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_unique_all_taxa.loc[(df_unique_all_taxa[\"scientific_name\"].isna()) | (df_unique_all_taxa[\"common_name\"].isna())]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Let's remove those cars" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 10961185 entries, 1 to 19351155\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 original_label 10961185 non-null object\n", " 1 scientific_name 10192703 non-null object\n", " 2 common_name 10192703 non-null object\n", " 3 kingdom 10192703 non-null object\n", "dtypes: object(4)\n", "memory usage: 418.1+ MB\n" ] } ], "source": [ "df_cleaned = df_cleaned[df_cleaned[\"original_label\"] != \"car\"].copy()\n", "df_cleaned[[\"original_label\", \"scientific_name\", \"common_name\", \"kingdom\"]].info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have 10,961,185 instead of 10,965,902 images; they all have `original_label`, but only 10,192,703 of them have `scientific_name`, `common_name`, and `kingdom`. What are the `original_label`s for those ~800K images?" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dataset_name 12\n", "original_label 24\n", "dtype: int64\n", "\n", "Index: 768482 entries, 245042 to 16833833\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 768482 non-null object\n", " 1 original_label 768482 non-null object\n", "dtypes: object(2)\n", "memory usage: 17.6+ MB\n" ] } ], "source": [ "no_taxa = df_cleaned.loc[(df_cleaned[\"scientific_name\"].isna()) & (df_cleaned[\"common_name\"].isna()) & (df_cleaned[\"kingdom\"].isna())].copy()\n", "\n", "print(no_taxa[[\"dataset_name\", \"original_label\"]].nunique())\n", "no_taxa[[\"dataset_name\", \"original_label\"]].info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are these 24 other labels and how are the 768,482 images with them distributed across these 12 datasets?" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "original_label\n", "problem 288579\n", "blurred 184620\n", "ignore 177546\n", "vehicle 26445\n", "unknown 26170\n", "snow on lens 17552\n", "foggy lens 15832\n", "vegetation obstruction 6994\n", "malfunction 5640\n", "unclassifiable 3484\n", "motorcycle 3423\n", "misdirected 2832\n", "other 2474\n", "unidentifiable 1472\n", "foggy weather 1380\n", "lens obscured 866\n", "sun 835\n", "end 616\n", "fire 578\n", "misfire 400\n", "eye_shine 328\n", "start 321\n", "tilted 56\n", "unidentified 39\n", "Name: count, dtype: int64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no_taxa[\"original_label\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name\n", "SWG Camera Traps 650745\n", "Idaho Camera Traps 66339\n", "NACTI 26015\n", "WCS Camera Traps 18320\n", "Wellington Camera Traps 3484\n", "Orinoquia Camera Traps 1280\n", "Island Conservation Camera Traps 1269\n", "Snapshot Serengeti 568\n", "ENA24 293\n", "Channel Islands Camera Traps 159\n", "Snapshot Mountain Zebra 7\n", "Snapshot Camdeboo 3\n", "Name: count, dtype: int64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no_taxa[\"dataset_name\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name original_label \n", "Channel Islands Camera Traps other 159\n", "ENA24 vehicle 293\n", "Idaho Camera Traps snow on lens 17552\n", " foggy lens 15832\n", " unknown 11900\n", " vegetation obstruction 6994\n", " malfunction 5640\n", " misdirected 2832\n", " other 2315\n", " foggy weather 1380\n", " lens obscured 866\n", " sun 835\n", " vehicle 137\n", " tilted 56\n", "Island Conservation Camera Traps unknown 941\n", " eye_shine 328\n", "NACTI vehicle 26015\n", "Orinoquia Camera Traps unknown 1280\n", "SWG Camera Traps problem 288579\n", " blurred 184620\n", " ignore 177546\n", "Snapshot Camdeboo fire 3\n", "Snapshot Mountain Zebra fire 7\n", "Snapshot Serengeti fire 568\n", "WCS Camera Traps unknown 12049\n", " motorcycle 3423\n", " unidentifiable 1472\n", " end 616\n", " misfire 400\n", " start 321\n", " unidentified 39\n", "Wellington Camera Traps unclassifiable 3484\n", "Name: count, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no_taxa.groupby([\"dataset_name\"])[\"original_label\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interesting. It seems like all of these should also be removed. Vegetation obstruction could of course be labeled in Plantae, but we're not going to be labeling 7K images for this project. \n", "\n", "Let's remove them, then we should have 10,192,703 images." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "non_taxa_labels = list(no_taxa[\"original_label\"].unique())" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 10192703 entries, 1 to 19351155\n", "Data columns (total 36 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 10192703 non-null object \n", " 1 url_gcp 10192703 non-null object \n", " 2 url_aws 10192703 non-null object \n", " 3 url_azure 10192703 non-null object \n", " 4 image_id 10192703 non-null object \n", " 5 sequence_id 10192703 non-null object \n", " 6 location_id 10192703 non-null object \n", " 7 frame_num 10192703 non-null int64 \n", " 8 original_label 10192703 non-null object \n", " 9 scientific_name 10192703 non-null object \n", " 10 common_name 10192703 non-null object \n", " 11 datetime 6873684 non-null object \n", " 12 annotation_level 10192703 non-null object \n", " 13 kingdom 10192703 non-null object \n", " 14 phylum 10177119 non-null object \n", " 15 subphylum 10141160 non-null object \n", " 16 superclass 79 non-null object \n", " 17 class 10174780 non-null object \n", " 18 subclass 9022382 non-null object \n", " 19 infraclass 9021471 non-null object \n", " 20 superorder 8812501 non-null object \n", " 21 order 9796763 non-null object \n", " 22 suborder 7289300 non-null object \n", " 23 infraorder 510351 non-null object \n", " 24 superfamily 1805446 non-null object \n", " 25 family 9693066 non-null object \n", " 26 subfamily 7662399 non-null object \n", " 27 tribe 6614011 non-null object \n", " 28 genus 9445408 non-null object \n", " 29 species 7521712 non-null object \n", " 30 subspecies 74052 non-null object \n", " 31 variety 2050 non-null object \n", " 32 multi_species 10192703 non-null bool \n", " 33 num_species 10192703 non-null float64\n", " 34 lin_duplicate 10192703 non-null bool \n", " 35 full_duplicate 10192703 non-null bool \n", "dtypes: bool(3), float64(1), int64(1), object(31)\n", "memory usage: 2.6+ GB\n" ] } ], "source": [ "df_clean = df_cleaned.loc[~df_cleaned[\"original_label\"].isin(non_taxa_labels)].copy()\n", "df_clean.info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 20\n", "url_gcp 10104328\n", "url_aws 10104328\n", "url_azure 10104328\n", "image_id 10104328\n", "sequence_id 1236468\n", "location_id 9789\n", "frame_num 6071\n", "original_label 1201\n", "scientific_name 908\n", "common_name 999\n", "datetime 4850339\n", "annotation_level 3\n", "kingdom 1\n", "phylum 2\n", "subphylum 5\n", "superclass 1\n", "class 8\n", "subclass 3\n", "infraclass 2\n", "superorder 5\n", "order 58\n", "suborder 17\n", "infraorder 9\n", "superfamily 12\n", "family 187\n", "subfamily 71\n", "tribe 46\n", "genus 538\n", "species 739\n", "subspecies 12\n", "variety 1\n", "multi_species 2\n", "num_species 4\n", "lin_duplicate 2\n", "full_duplicate 2\n", "dtype: int64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean.nunique()" ] }, { "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": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "original_label\n", "bos taurus 2041202\n", "mouse 1229642\n", "wildebeest 534713\n", "zebra 354233\n", "gazellethomsons 323932\n", "deer 263368\n", "human 257159\n", "eurasian_wild_pig 234736\n", "cervus elaphus 183794\n", "bird 172834\n", "Name: count, dtype: int64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean[\"original_label\"].value_counts()[:10]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "scientific_name\n", "bos taurus 2088019\n", "mus 1229642\n", "connochaetes taurinus 534813\n", "sus scrofa 389702\n", "equus quagga 374570\n", "aves 332757\n", "eudorcas thomsonii 324105\n", "odocoileus 311993\n", "homo sapiens 257159\n", "cervus elaphus 186592\n", "Name: count, dtype: int64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean[\"scientific_name\"].value_counts()[:10]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "common_name\n", "domestic cow 2044000\n", "mouse 1229654\n", "blue wildebeest 533564\n", "plains zebra 374570\n", "deer 360489\n", "thomson's gazelle 324105\n", "bird 264967\n", "human 257159\n", "eurasian wild pig 234736\n", "red deer 186592\n", "Name: count, dtype: int64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean[\"common_name\"].value_counts()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also 257,159 humans in here! Glad the number agrees across labels. We'll probably need to remove the humans, though I may save a copy with them still on the HF repo (it is just our dev repo). Which datasets have them? I thought humans were filtered out previously (though I could be mistaken as they seem to be in 15 of the 20 datasets)." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name\n", "WCS Camera Traps 162501\n", "Snapshot Serengeti 47015\n", "Idaho Camera Traps 22195\n", "Orinoquia Camera Traps 7441\n", "Channel Islands Camera Traps 5071\n", "Island Conservation Camera Traps 4808\n", "SWG Camera Traps 3808\n", "ENA24 887\n", "Snapshot Enonkishu 870\n", "Trail Camera Images of New Zealand Animals 817\n", "Snapshot Mountain Zebra 536\n", "Snapshot Kruger 532\n", "Snapshot Camdeboo 324\n", "Snapshot Karoo 219\n", "Snapshot Kgalagadi 135\n", "Name: count, dtype: int64" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean.loc[df_clean[\"original_label\"] == \"human\", \"dataset_name\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do human labels look like (as in do they have the full taxa structure)?" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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4451993WCS Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...WCS Camera Traps : 5768343a-92d5-11e9-8890-000...WCS Camera Traps : unknownWCS Camera Traps : 2812-1humanhomo sapiens...homininaeNaNhomohomo sapiensNaNNaNFalse1.0TrueTrue
4995616WCS Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...WCS Camera Traps : bc7c7b89-92d5-11e9-b600-000...WCS Camera Traps : unknownWCS Camera Traps : 4607-1humanhomo sapiens...homininaeNaNhomohomo sapiensNaNNaNFalse1.0TrueTrue
4916766WCS Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...WCS Camera Traps : adc8775e-92d5-11e9-afee-000...WCS Camera Traps : unknownWCS Camera Traps : 4446-1humanhomo sapiens...homininaeNaNhomohomo sapiensNaNNaNFalse1.0TrueTrue
5729969Idaho Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Idaho Camera Traps : loc_0004_im_000009Idaho Camera Traps : loc_0004_seq_000000Idaho Camera Traps : 49humanhomo sapiens...homininaeNaNhomohomo sapiensNaNNaNFalse1.0TrueTrue
10798486Snapshot Serengetihttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Snapshot Serengeti : S7/H07/H07_R1/S7_H07_R1_I...Snapshot Serengeti : SER_S7#H07#1#1523Snapshot Serengeti : H071humanhomo sapiens...homininaeNaNhomohomo sapiensNaNNaNFalse1.0TrueTrue
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5 rows × 36 columns

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" ], "text/plain": [ " dataset_name \\\n", "4451993 WCS Camera Traps \n", "4995616 WCS Camera Traps \n", "4916766 WCS Camera Traps \n", "5729969 Idaho Camera Traps \n", "10798486 Snapshot Serengeti \n", "\n", " url_gcp \\\n", "4451993 https://storage.googleapis.com/public-datasets... \n", "4995616 https://storage.googleapis.com/public-datasets... \n", "4916766 https://storage.googleapis.com/public-datasets... \n", "5729969 https://storage.googleapis.com/public-datasets... \n", "10798486 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "4451993 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4995616 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4916766 http://us-west-2.opendata.source.coop.s3.amazo... \n", "5729969 http://us-west-2.opendata.source.coop.s3.amazo... \n", "10798486 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "4451993 https://lilawildlife.blob.core.windows.net/lil... \n", "4995616 https://lilawildlife.blob.core.windows.net/lil... \n", "4916766 https://lilawildlife.blob.core.windows.net/lil... \n", "5729969 https://lilawildlife.blob.core.windows.net/lil... \n", "10798486 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "4451993 WCS Camera Traps : 5768343a-92d5-11e9-8890-000... \n", "4995616 WCS Camera Traps : bc7c7b89-92d5-11e9-b600-000... \n", "4916766 WCS Camera Traps : adc8775e-92d5-11e9-afee-000... \n", "5729969 Idaho Camera Traps : loc_0004_im_000009 \n", "10798486 Snapshot Serengeti : S7/H07/H07_R1/S7_H07_R1_I... \n", "\n", " sequence_id location_id \\\n", "4451993 WCS Camera Traps : unknown WCS Camera Traps : 2812 \n", "4995616 WCS Camera Traps : unknown WCS Camera Traps : 4607 \n", "4916766 WCS Camera Traps : unknown WCS Camera Traps : 4446 \n", "5729969 Idaho Camera Traps : loc_0004_seq_000000 Idaho Camera Traps : 4 \n", "10798486 Snapshot Serengeti : SER_S7#H07#1#1523 Snapshot Serengeti : H07 \n", "\n", " frame_num original_label scientific_name ... subfamily tribe \\\n", "4451993 -1 human homo sapiens ... homininae NaN \n", "4995616 -1 human homo sapiens ... homininae NaN \n", "4916766 -1 human homo sapiens ... homininae NaN \n", "5729969 9 human homo sapiens ... homininae NaN \n", "10798486 1 human homo sapiens ... homininae NaN \n", "\n", " genus species subspecies variety multi_species num_species \\\n", "4451993 homo homo sapiens NaN NaN False 1.0 \n", "4995616 homo homo sapiens NaN NaN False 1.0 \n", "4916766 homo homo sapiens NaN NaN False 1.0 \n", "5729969 homo homo sapiens NaN NaN False 1.0 \n", "10798486 homo homo sapiens NaN NaN False 1.0 \n", "\n", " lin_duplicate full_duplicate \n", "4451993 True True \n", "4995616 True True \n", "4916766 True True \n", "5729969 True True \n", "10798486 True True \n", "\n", "[5 rows x 36 columns]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean.loc[df_clean[\"original_label\"] == \"human\"].sample(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It does seem to have full taxa...interesting." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "df_clean.to_csv(\"../data/lila_image_urls_and_labels_wHumans.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "df_clean.loc[df_clean[\"original_label\"] != \"human\"].to_csv(\"../data/lila_image_urls_and_labels.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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original_labelkingdomphylumsubphylumsuperclassclasssubclassinfraclasssuperorderordersuborderinfraordersuperfamilyfamilysubfamilytribegenusspeciessubspeciesvariety
4913880humananimaliachordatavertebrataNaNmammaliatheriaplacentaliaeuarchontogliresprimateshaplorhinisimiiformeshominoideahominidaehomininaeNaNhomohomo sapiensNaNNaN
4799380humananimaliachordatavertebrataNaNmammaliatheriaplacentaliaeuarchontogliresprimateshaplorhinisimiiformeshominoideahominidaehomininaeNaNhomohomo sapiensNaNNaN
4994742humananimaliachordatavertebrataNaNmammaliatheriaplacentaliaeuarchontogliresprimateshaplorhinisimiiformeshominoideahominidaehomininaeNaNhomohomo sapiensNaNNaN
4879871humananimaliachordatavertebrataNaNmammaliatheriaplacentaliaeuarchontogliresprimateshaplorhinisimiiformeshominoideahominidaehomininaeNaNhomohomo sapiensNaNNaN
7201111humananimaliachordatavertebrataNaNmammaliatheriaplacentaliaeuarchontogliresprimateshaplorhinisimiiformeshominoideahominidaehomininaeNaNhomohomo sapiensNaNNaN
4419687humananimaliachordatavertebrataNaNmammaliatheriaplacentaliaeuarchontogliresprimateshaplorhinisimiiformeshominoideahominidaehomininaeNaNhomohomo sapiensNaNNaN
3869956humananimaliachordatavertebrataNaNmammaliatheriaplacentaliaeuarchontogliresprimateshaplorhinisimiiformeshominoideahominidaehomininaeNaNhomohomo sapiensNaNNaN
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" ], "text/plain": [ " original_label kingdom phylum subphylum superclass class \\\n", "4913880 human animalia chordata vertebrata NaN mammalia \n", "4799380 human animalia chordata vertebrata NaN mammalia \n", "4994742 human animalia chordata vertebrata NaN mammalia \n", "4879871 human animalia chordata vertebrata NaN mammalia \n", "7201111 human animalia chordata vertebrata NaN mammalia \n", "4419687 human animalia chordata vertebrata NaN mammalia \n", "3869956 human animalia chordata vertebrata NaN mammalia \n", "\n", " subclass infraclass superorder order suborder \\\n", "4913880 theria placentalia euarchontoglires primates haplorhini \n", "4799380 theria placentalia euarchontoglires primates haplorhini \n", "4994742 theria placentalia euarchontoglires primates haplorhini \n", "4879871 theria placentalia euarchontoglires primates haplorhini \n", "7201111 theria placentalia euarchontoglires primates haplorhini \n", "4419687 theria placentalia euarchontoglires primates haplorhini \n", "3869956 theria placentalia euarchontoglires primates haplorhini \n", "\n", " infraorder superfamily family subfamily tribe genus \\\n", "4913880 simiiformes hominoidea hominidae homininae NaN homo \n", "4799380 simiiformes hominoidea hominidae homininae NaN homo \n", "4994742 simiiformes hominoidea hominidae homininae NaN homo \n", "4879871 simiiformes hominoidea hominidae homininae NaN homo \n", "7201111 simiiformes hominoidea hominidae homininae NaN homo \n", "4419687 simiiformes hominoidea hominidae homininae NaN homo \n", "3869956 simiiformes hominoidea hominidae homininae NaN homo \n", "\n", " species subspecies variety \n", "4913880 homo sapiens NaN NaN \n", "4799380 homo sapiens NaN NaN \n", "4994742 homo sapiens NaN NaN \n", "4879871 homo sapiens NaN NaN \n", "7201111 homo sapiens NaN NaN \n", "4419687 homo sapiens NaN NaN \n", "3869956 homo sapiens NaN NaN " ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa = [col for col in list(df_clean.columns) if col in all_taxa or col ==\"original_label\"]\n", "\n", "df_taxa = df_clean[taxa].copy()\n", "df_taxa.loc[df_taxa[\"original_label\"] == \"human\"].sample(7)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 9935544 entries, 1 to 19351155\n", "Data columns (total 36 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 9935544 non-null object \n", " 1 url_gcp 9935544 non-null object \n", " 2 url_aws 9935544 non-null object \n", " 3 url_azure 9935544 non-null object \n", " 4 image_id 9935544 non-null object \n", " 5 sequence_id 9935544 non-null object \n", " 6 location_id 9935544 non-null object \n", " 7 frame_num 9935544 non-null int64 \n", " 8 original_label 9935544 non-null object \n", " 9 scientific_name 9935544 non-null object \n", " 10 common_name 9935544 non-null object \n", " 11 datetime 6633995 non-null object \n", " 12 annotation_level 9935544 non-null object \n", " 13 kingdom 9935544 non-null object \n", " 14 phylum 9919960 non-null object \n", " 15 subphylum 9884001 non-null object \n", " 16 superclass 79 non-null object \n", " 17 class 9917621 non-null object \n", " 18 subclass 8765223 non-null object \n", " 19 infraclass 8764312 non-null object \n", " 20 superorder 8555342 non-null object \n", " 21 order 9539604 non-null object \n", " 22 suborder 7032141 non-null object \n", " 23 infraorder 253192 non-null object \n", " 24 superfamily 1548287 non-null object \n", " 25 family 9435907 non-null object \n", " 26 subfamily 7405240 non-null object \n", " 27 tribe 6614011 non-null object \n", " 28 genus 9188249 non-null object \n", " 29 species 7264553 non-null object \n", " 30 subspecies 74052 non-null object \n", " 31 variety 2050 non-null object \n", " 32 multi_species 9935544 non-null bool \n", " 33 num_species 9935544 non-null float64\n", " 34 lin_duplicate 9935544 non-null bool \n", " 35 full_duplicate 9935544 non-null bool \n", "dtypes: bool(3), float64(1), int64(1), object(31)\n", "memory usage: 2.5+ GB\n" ] } ], "source": [ "df_clean.loc[df_clean[\"original_label\"] != \"human\"].info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dataset_name 20\n", "url_gcp 9849119\n", "url_aws 9849119\n", "url_azure 9849119\n", "image_id 9849119\n", "sequence_id 1198696\n", "location_id 9524\n", "frame_num 1023\n", "original_label 1200\n", "scientific_name 907\n", "common_name 998\n", "datetime 4688143\n", "annotation_level 3\n", "kingdom 1\n", "phylum 2\n", "subphylum 5\n", "superclass 1\n", "class 8\n", "subclass 3\n", "infraclass 2\n", "superorder 5\n", "order 58\n", "suborder 17\n", "infraorder 9\n", "superfamily 12\n", "family 187\n", "subfamily 71\n", "tribe 46\n", "genus 537\n", "species 738\n", "subspecies 12\n", "variety 1\n", "multi_species 2\n", "num_species 4\n", "lin_duplicate 2\n", "full_duplicate 2\n", "dtype: int64" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean.loc[df_clean[\"original_label\"] != \"human\"].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 1,198,696 distinct sequence IDs for the 9,849,119 unique image IDs, suggesting an average of 8 images per sequence?" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "annotation_level\n", "sequence 4156306\n", "image 2892394\n", "unknown 2886844\n", "Name: count, dtype: int64" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean.loc[df_clean[\"original_label\"] != \"human\", \"annotation_level\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check Number of Images per Scientific Name?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(df_clean.loc[df_clean[\"original_label\"] != \"human\"], y = 'class')" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6ulKpUiVWrVqFnp4e3bt3Z+vWrYwcOZKAgAAKFSrEsmXLsLOz48iRI0ycOJGdO3cC2fmNVapUkXpQ3759m4SEBFq1aoW3tzfPnz9n8ODBGBsbM3r0aBwcHAgJCWHVqlXUr1+fffv2MWnSJE6fPs3du3dxdHSUnvfAgQM4OTkxc+ZM9PX12bhxI35+fu85xj+Ht40w+aHtUUFGW5OptRWht2YRemsWobfm0Fat833QeOrUKYyMjBg8eDAymYxvv/2WTp06cfv2bbXzDh48SPXq1aUgsXnz5jRr1kw6vnnzZnr06CG10GvWrBmOjo4EBgbyyy+/vHffpk2b0rp1awBcXV0JDAzk4MGDUs5j//79pTzF5s2bs2bNmg8+w4ABAzAyMuKbb76hePHidOjQgZIlSwJQu3ZtoqKiANi9ezdubm5Uq1YNgNatW3P48GF27dpFzZo1AXB2dsbU1FS6tkwmY/r06ZiYmGBiYsKgQYNYuXIlo0ePBqBBgwb88MMPALRp0wZPT08iIiKk++fg7e2NsbExenp6REdHY2xsTGpqKklJSVLXms/lbSPM12x79F8mvyZTF1SE3ppF6K1ZhN6aI79qXWCMMLGxse85k21sbN4LGmNjY9/rx2xjY0NiYiIAUVFRhIeHs23bNum4Uqnk+++/z/W+ZcuWVXtvZWVFfHy89N7MzEx6raen99Gl3bfP1dHRUQv65HK5ZEx5/vw5ZcqUURtrbW3NnTt3pPfvuq2LFCmitgVvZWVFXFxcrvfW18/Oq8ttrhEREcybN4/Hjx9TtmxZKQXgS9zTbxth8kPbo4KMtiZTaytCb80i9NYsQm/Noa1a5/ug0dLSkqioKLKyspD/f3PF232a3z7v1KlTap+9XW/Q0tISV1dX3NzcpOPR0dEYGhoSGRlJeHg4lStXlo69XUsRIDIyUm07Ny98rkO6dOnSREREqH0WERGhFii+e63k5GTevHmDkZGRNM93g2dA2jbPjYyMDIYMGcKYMWPo2bMnMpmMGzducODAgc+a97u8bYTJUKq4H5esdtyskD7FTAy+6NoCgUAgEAj+N+T7BsGOjo6oVCr8/f1JT0/nxo0bUo7f27i4uHDv3j127NhBZmYmf/zxB0ePHpWOd+3alY0bN3Lt2jUgO5+gY8eOBAcH53rfo0ePcu7cOTIzM9m1axf37t2jbdu2X+ch/z9dunRh1apV3Lx5E6VSyaFDhzhx4gQdOnT44BilUsncuXNJS0vj4cOHrF27ls6dO+fpvhkZGaSmpmJoaIhMJiM6Opr58+dLx/LKgsN3mX3wNrMP3sYr6CZT991Q+3n5Oj3P1xQIBAKBQPC/Jd+vNJqamrJ27VpmzJjB+vXrsbW1xdnZmUePHqmdV6ZMGVasWIGPjw+//vor1apV48cff5SOt2zZktevXzN58mSio6MxMzOjX79+9O7dW8ophGyndmhoKCqVigEDBiCTybC1tWXVqlWUKVOGc+fOAeDm5kaVKlX49ttvOX78uDQ+Z7Wzbt26720l9+7dm1evXrFixQpmzpyJjY0NlpaWPHjwgLp161KkSBHq16/Pzz//THx8PMWLF8fGxobhw4djamrKq1evJBe2v7+/dK89e/Zw8OBBkpOTqVevHrt372b58uUYGRlRu3bt9zQdN24cycnJqFQqUlNTKVSoEDNnzmT27NlMmTIFAENDQwwNDbl79y7lypXL25f2iTaC/3ZLvf8i+dWBV1ARemsWobdmEXprjvyqdYFxTwNUrlyZwMDAXI917NhRel2/fn3279//wet07NhR7fx36d69Oz179sTW1paGDRsyffp0Jk+eTFxcHA4ODkRGRjJ06FBmzpxJp06duHr1KkOHDqVKlSps2rSJPXv2sGPHDn777Tfs7e0JDw9n5MiR3L59G2trawAMDAxYtmwZNjY2DBgwgHPnzuHn50eTJk3w9fXlyJEjHD9+nIcPH9K+fXvGjRtHjx49ePbsGaNGjeLevXvSfG/evAnAn3/+KfWtfvz4MatWraJcuXLMnj2bc+fOqa0W9urVi4kTJ/Ly5Uu6dOlCcnL21vGLFy+wsrJi3bp1lChRgitXrvDTTz9RpEiRz/iG1PlUG0HhqP730FYHnrYi9NYsQm/NIvTWHNqqtVYEjZpCT0+P7du3s3z5crKysoiKisLMzEz6coOCgqhSpYpUhqZevXp07dpVOr579266desm9ZKuX78+3bp1IzAwUFr1dHZ2pmLFitL4V69eSfUfGzduzPr166V72dnZ0bdvXwBsbW0ZO3Ys7u7uTJ48Gcg2xcTGxqq5mwcOHEiVKlUAmDRpEvXq1ePy5cvS8fbt22NgYEDJkiX57rvvePr0KZC9fd+hQweKFi1KXFwcqampGBsbv5fb+Tl8qo2gcFT/c/KrA6+gIvTWLEJvzSL01hz5VesC457WJHK5nD///JOjR4+SmZlJXFycWu/nZ8+eUbp0abUxZcqUkYT+kPs5p/g4vO+kfnsl720ndUJCAmXKlCEyMhInJyeOHz+OtbU1qampJCQkSNd6N6jLcT0DGBkZYWZmJm11Q/ZK440bNwCk/wK8efOGmTNncuHCBSwtLalatSoqleofu6dzPywc1f8W2urA01aE3ppF6K1ZhN6aQ1u1FkHjWyQkJDBr1iwCAwOpXr06AOvWrZPyJ0uXLs3JkyfVxkRHR0uvra2tpZW7HCIiIqSADfLmpD5y5IjaZ0+fPkVfX18KNIsUKcLdu3fVznk7iExJSSExMRErKyspF/Ltreo6depIr6dOnUqRIkX4448/MDAwICsrS6ppmVfedk/nRm6OakHeUKlUyMysePg85R/1Ly/oCKe+QCAQ/HuIoPEt7ty5g1wux9DQEICrV6+yceNGKeBq3749y5YtY9++fbRr144bN26wY8cOvvnmGyC7vd+UKVNo3Lgx9vb2XLhwgW3btvHmzRt8fHy4dOkSb968wdLSkt9++40nT56gq6vLgQMH1HpoT5w4kaNHj5KSkoKXlxeQHZz6+vry448/MnnyZI4fP05GRgbTpk1jwoQJmJiYkJaWxsyZM3n06BEhISG8fPkSQ0NDKlWqxOnTp6Xr16lTh3Xr1kk9qAFevXpFTEwM7du3JzY2lqysLFJTU0lPz7vTecHhu2SIdtNfFZUKFAoFJiYmiJjxw3i7VhdBo0AgEPxLiKDxLX744Qd69uxJr169yMrKwtramt69e7Nw4UKeP3+OpaUlS5YsYd68eXh5eVGlShUaNmwoFRBv1aoVCoUCb29voqOjKVmyJEOGDMHPz4+UlBTq1KmDoaEhPj4+LF26lPDwcE6cOIGXlxdGRkZS0e8nT55w9OhRrl27xvjx4wHw8PDAxcWFS5cuUb58eX766ScuXLhAXFwc06ZNw9fXF8jOS9i7dy8ZGRnUqVOHBw8eEBgYSK1ataTnvHLlynvPXrNmTXbs2IFcLsfExIQaNWpw8uRJzpw5Q/fu3fMm5Cfc04J/gf+fxoBKheqjyQD/bf4tp35+dTwWVITemkXorTnyq9afOx+ZKieJTvBJnj17RmJiolrfax8fH+Lj41m4cGGuY3JyErdu3cq3335Lr169qFWrFhMmTJDOmTdvHnfv3mX58uV8++23rFixggYNGgDZq5/t27fn+PHjvHjxgh49ehAeHi6ZXx49ekTLli0JCwujbdu2PH/+nD/++EPaEh87diz6+vrMmTNHKvCds6Xt6ekpPUNSUhIZGRkULVqU+Ph4Hjx4gIeHB5MnT8bV1fWz9FEqlVy9epVfQ1981D0tEGiKWa7VUL189r+ehkAgEGgFtWvX/miupVhpzAOJiYn07NmTzZs3U716de7cucOBAwckN/PHyKnZ+DGzTGJiIunp6VhZWUnH3j43MjISpVJJkyZN1Mbr6+urdZJ5O4dST0+PD/27IGd7E/5dI8yn3NOCfwGVCkVKCibGxoj96Q/zbzn186vjsaAi9NYsQm/NkV+1Fu7pr0DVqlWZMmUKY8aMIT4+nmLFiuHm5vZZnWJyzAofM8uYm5tjYGBAREQE5cuXB9RbJlpaWmJoaMj58+elX7b09HQiIiLUXNOfw5YtW7hy5QqNGjUC/l0jzKfc04J/jrQlLZOJmPEj/NtOfW11PGorQm/NIvTWHNqqtQga80iXLl3o0qXLF4/PzSyzfft2pk2bhr6+Pq6urixevBg7OzuMjY2ldn6QnXdoa2uLj48Po0ePRkdHBx8fH44fP86xY8fw9fX9YH9pQOrDnZyczIsXL9SOKRQKSpQogVwuR6FQsHTpUhQKxRe1EfylbWXkOuJX62uiUqlIVigobGIi3NMfwayQ/v96CgKBQFBgEP9n/0L8/f3ZtWsXb968oUyZMgwfPlyqp+jr60tUVBR169bFwsJCbUx4eDiTJk2SzDLp6em0bdsWV1dXYmNjefnyJY8fP6ZJkybI5XKcnJyk8dWqVWPs2LEsX76cjRs3oqOjQ/Xq1SlatCjff/89JUuWlM5NT09n0aJFHD16lNTUVE6fPs2PP/5I3bp1qV+/PllZWSiVSoKDg/Hx8aFfv35MmTKFPXv2oFKpKFy4MFWrVlXrQPO5lC1qjL6++J/110SpVHI18m/Kl/14/olAIBAIBP8WImj8AsLCwti+fTt79uyhePHibN++nSlTpmBtbY2HhwezZ8+mdevWHDt2jLFjx9KhQwepjSCor1Y6Ojri4OAAZG8Rm5mZcfbsWfT19dm4cSN+fn5cvnxZMr6cOXOGkydPkp6eTps2bYiJiWH9+vVYWVkxcOBAqfbihg0bCA0N5dChQ2ptAdesWUP9+vWlAHbTpk0ALFmyhJ49e+Lu7o5KpWLBggUcPnyYX375Jc/6KJXKfOcMK2jkVwdeQUXorVmE3ppF6K058qvWBar3dH7DwMCApKQkduzYQbNmzejSpQvdunXD39+f6tWrSzUXW7ZsSVBQ0Gdf19vbG2NjY/T09IiOjsbY2JjU1FSSkpKkoPGnn36SuspUqlSJqlWrUqFCBQC+//57qfZiXtsCrly5kpIlS6JSqYiKisLU1PSLWggC3Lp164vGCfKOtvYv1VaE3ppF6K1ZhN6aQ1u1FkHjF1CnTh38/f3ZtGkTa9aswdDQkN69e5OQkECpUqXUzi1XrhzPnz//rOtGREQwb948Hj9+TNmyZSVzy9sO5o+1IXzz5o1UiDyvbug7d+4wfPhw4uPjqVChAhYWFh90XX+KqlWriu3pr0x+deAVVITemkXorVmE3pojv2ot3NNfkejoaIoWLcratWtJT0/nzz//ZOTIkbRp04aQkBCp0DZku591dbNlvnTpErdv35aOZWVl8fLlSyC7vd+QIUMYM2YMPXv2RCaTcePGDQ4cOKB27w+ZHp4/f87mzZulGpJ5cUPHxsbi4eHB0qVLcXR0BODw4cPvtTH8XLTVFaaNCK01i9Bbswi9NYvQW3Noq9YfbhAs+CDXr19n0KBB3LlzB319fYoWLQpAlSpVANi+fTuZmZmcO3eOw4cPS+O6d++OUqnk77//JjMzkzVr1vD69WsgO2hMTU3F0NAQmUxGdHS05Jz+HAdzamqq2nkKhQIDAwPJDT1v3jw1N7SBgQEKhQKVSkVKSgpKpRIjIyMA7t+/T0BAAMAXtREUCAQCgUBQ8BArjV+As7Mz58+fp0uXLqSnpyOXy2nYsCF2dnYArFixgunTpyOTyTA3N5cCtbt372JsbEy/fv148+YNhoaGFC5cGC8vL/T09PDy8mL27NlMmTIFgNKlS1OkSBHu3btHuXLlAAgJCWHixIkkJSWhp6dH1apVUSqVUq3I69evc/DgQaZOnYqHhwc1atQAoEiRIlSvXl1yQx8+fJhHjx5RpUoVSpQogYeHB+7u7igUCmQymfRz6tQpWrRooVF9BQKBQCAQ5D9E0PgFvHz5kpCQEIYMGYKbmxsxMTH07t2bx48fA9CsWTMmTpzIy5cvadGiBQ8fPgRALpdTrlw5Nm3axJ49e5g0aRI+Pj60adOGrKwsZsyYQenSpfH398fKyopt27axePFiKfADuHz5Mps2bcLMzIwJEyZw/fp1dHR0CA4OxsnJiaNHj2Jtbc3p06dJSkpi06ZN1K1blzNnzjBmzBh8fHwAKFSoEKamphw+fBgjIyNev37N8uXLCQgIwNHRkcTEREaOHMnRo0fzHDQK9/TXJ7868AoqQm/NIvTWLEJvzZFftRbu6a/IyZMnMTAwYMSIEchkMmxsbFi/fr2URDpq1CgMDAwoWbIkxYoVIyUlJdfr6Onp0b59e+RyOWlpaQQHBxMQECAZYPr27UtQUBDBwcG4ubkB4OHhIbUWnDBhAi1btiQyMvK9a2/evJkePXpIeYzNmjXD0dGRwMBAqYxO48aNpdqOhoaGhISEYGNjg0KhICYmBnNz8y9yUAv3tObQVgeetiL01ixCb80i9NYc2qq1CBq/gPj4eKysrNRMKeXLlyc+Ph4Ac3Nz6XO5XP5BF3Lx4sWRy7PTSpOSksjIyFCr5wjZbQffDgrfbheY49SOj49X6zcNEBUVRXh4ONu2bZM+UyqVfP/999L7nH7YkB3ABgcHExgYiEwm45tvvkGhUEgmnrwg3NNfn/zqwCuoCL01i9Bbswi9NUd+1Vq4p78ilpaWPHv2DJVKJQWOx44dQ6FQSOd4enoC8O2333L79m169+6Nvb09z58/p02bNgwcOBCZTMZvv/0mmU709PSIiIiQ6i4CPH36VHI0Q7bT+ZtvvgGQgslSpUq9Z5axtLTE1dVVWqGEbNe3oaGh9P7toPfQoUNs3ryZbdu2SYHprFmzvqgjTMTLVOQ6mXkeZ1ZIn2ImBnke919GWx142orQW7MIvTWL0FtzaKvWImj8Apo2bYqPjw8rVqxg4MCBxMTEMHv2bIYMGfLJscWKFZNyGgG2bt3K8OHD6d+/P15eXvj6+lKuXDkpp/H+/fssXLhQGh8QEEDlypUxMDBg7ty5NGrUiJIlS0qrnDmBa9euXfH29ub777+nZs2aXL9+ncGDBzN8+PBc+1MnJycjl8sxNDREpVIRGhrKvn37qFSpUp71mRV8h4wvKPHo7VpdBI0CgUAgEORTRND4CW7dusWsWbO4c+cONjY2tGzZkp07d7JmzRrc3d1ZsmQJKpUKfX19Dh069MnrxcfH4+joyMiRI6XVSl9fX06ePMnKlSuZMmUKbdu2JT09HX19fVq1akXp0qWl8TExMTg6OpKens4PP/zAo0ePWL9+PXv37kUul+Pq6srgwYO5c+cOSUlJ9OrVC7lcTtGiRenXrx8VKlSgc+fO3Lp1i/v372Nra4uLiwsdOnTg5MmTNGvWjKysLPT09ChXrhwPHz6U5vLZqFSoVLnXk/z4MFW+Sw7Or+TXZOqCitBbswi9NYvQW3PkV62FEeZfQKFQMGjQILp168aGDRt49OgRQ4cORSaT8fjxY1JTUzl06BBly5blwYMH9OzZE29vb7Vr+Pj4SH2eR40aRenSpVm6dCkdO3akY8eOUgDZsWNHHj58yNGjRxk3bhw9evTg2bNnjBo1innz5jF16lQAqdd0zZo1MTAwoEWLFuzcuZN169ZhYmJCp06d2LJlC2vXriUgIIAJEybw4sULNm7cyJ07d+jWrRvz58/HycmJv/76i+HDh2Nubk6jRo1QKBQMHz6cESNGkJiYSN++fRk2bFie8xNTXqeQ9gV/HpIVCq5G/p33gf9htDWZWlsRemsWobdmEXprDm3VWgSNH+HEiRPo6OgwatQo5HI5dnZ2DBo0iLVr19K4cWPq1q2LpaUlL168IDExETMzsy/u1wwQFBSEnZ0dffv2BbJNL2PHjsXd3Z3JkycD2eaV+vXrq43r1KkTlpaWANSsWROFQkGdOnUAaNiwIcuXLwcgMDAQJycnqYRO3bp16dq1K1u2bKFRo0YYGBgQGhpKhQoVqF+/Pvv375eMOnnBuJAxel+w0ljYxITyZWvnedx/kfyaTF1QEXprFqG3ZhF6a478qrUwwvwLxMTEUKpUKbXAKafcjUqlYtGiRZw8eRILCwuqVKlCRkbGF/drBkhISJCun4O1tTWpqakkJCQA6r2nc3i3H7WJiQkxMTFYWlqqubejoqIICwujXr160vlKpRIbGxsA/Pz88Pf3Z9GiRYwZM4a6desyY8aMvOc1ymTkPWTMNubkpz9E2oC2JlNrK0JvzSL01ixCb82hrVqLoDEXoqOjadOmDWPGjCE6OlrNJR0dHQ3AggULiI6O5sSJE5iYmADQrl27f3TfQ4cOUbhwYbXPnj59ir6+PkWKFGHkyJGEh4e/N+7dftRnzpyhfv36dOzYUe1zS0tLOnTowMyZMwE4f/48ffr0YdWqVWRlZXHr1i1GjRrF5MmTefbsGXPmzMHT05Pdu3fn6TnGOdsh+4IVygylivtxyXke9zkIZ7ZAIBAIBP8METTmQqlSpbhy5QqvX79m5cqVkks6MjKStWvXAv/X21lHR4e0tDS2bNnCvXv3aNas2Rff18jIiLi4ODZs2ECPHj2IiYnB19eXdu3a5Smv8EP9ojt37kz//v1p0aIFP/zwAzExMQCsW7eOSZMm4e3tjb29PRMmTMDCwgIDAwO1mpOfy4LDd7/IPf01Ec5sgUAgEAj+GQUmaIyMjMTJyYmpU6eyYsUKUlNTcXR0ZPr06ZiYmBASEsKKFSuIjo7G1taWMWPG0LBhQwB69+5N6dKlOX/+PCqVipUrV+Li4sLx48dZtmwZHh4eLF68GJlMRuHChdHV1WX06NFMmjSJevXqkZWVhVwux8zMjHPnzjFmzBgAnj9/Tq9evbh27RqQvf2bU5A75z7Pnj3Dy8uLv//+G5lMRv/+/Tl8+DCLFy8mMzMTPT09nj59yvnz56X6jAARERH8+uuvREdH4+3tzbVr15g8eTJnz57l9evXTJ8+nRs3bhAeHk5iYiIAtWrVwtfXFw8PD9LS0ihUqBAAY8eOBaBHjx4sXLiQLVu2oFKpKFKkiFRDMk98oXv6a1LQnNn51YFXUBF6axaht2YRemuO/Kr1f9Y9feTIEYKCglAqlYwYMQIvLy/atm3L9OnTWb58udSHedSoUezYsUPK1zt37hw7d+7EyMiIV69eAdldWiIjI3n+/DkHDx6kfPnyzJgxg8DAQAwMDBg5ciTjxo1jz549FC9enO3bt+Pn50dGRgZubm60b9+ecePGsX79eskJbW9vz4kTJ9i1axcbNmxg165dVKpUiaVLlxITE0O5cuUYO3YsgwcPxszMjJkzZ6Kvr8/GjRvx8/Pj3LlzZGZmMnDgQBwcHLh06RKpqakMHDgQf39/QkND1RzZBw8eZPr06VLZnOrVq5OWlsahQ4eIjo6mT58+6OvrExMTw5w5c1i8eLFa7+kdO3ZIrQg/ly91T39NCqozW1sdeNqK0FuzCL01i9Bbc2ir1gUuaJw0aRIWFhYAuLu7M2zYMBITE/PUhzknaMzKymL8+PFAtvO4bt26hIWFMXr0aEqUKEFUVBRJSUns2LGDZs2a0aVLF7p164ZMJvukE3r//v107dqVatWqAdk9pXfs2CE9h7e3N8bGxujp6REdHY2xsTGpqalSIBsVFcXkyZMxMjLC2NiYpUuXkpWV9Z4ezZs3x8vLixMnTtCyZUuCgoKoU6cOZcqUkfIzASwsLP613tNf6p7+mhQ0Z3Z+deAVVITemkXorVmE3pojv2r9n3VPv92b2crKivT0dB4+fMiFCxc+uw9zDubm5ixevBgfHx82b97Mxo0b0dXVJT4+nrS0NOrUqYO/vz+bNm1izZo1GBoa0rt3b4YNG/ZRJ7Snpye3bt2iTZs2qFQqxo0bx/Hjx8nMzJSczhEREcybN4/Hjx9TtmxZ6bmysrKIj4/H3NwcIyMjtWvnhr6+Pm3btmX//v20bNmSvXv3MmDAgPfO+zd7T3+pe/prUlCd2drqwNNWhN6aReitWYTemkNbtS5wQWNsbCzly5cHsvMcjYyMKF26NN27d//sPsxvU6dOHXx8fKTcxcuXL+Pu7k758uVp1qwZRYsWZe3ataSnp/Pnn38ycuRIqlWrRunSpTly5IjatXKc0N7e3sTGxhIREUFcXBzBwcHs3r1bKhyekZHBkCFDGDNmDD179kQmk3Hjxg0OHDgAZLugExMTefPmjRQ4Xrx4kRs3btCvX7/3nqFTp0507dqVK1euEBkZibOz83vn/Ju9p7/UPf1PMDHQJesj5Y7MCuWtQLlAIBAIBAJ1ClzQ6O3tzdmzZ/Hw8CAgIACZTEZWVha//fYb3377LceOHePw4cNERUVRqFAhOnbsKK3uXbhwgTlz5vD48WMgu89zt27dGDBgAIMGDeLw4cNER0eTlpbGhQsXKFasGNOmTWPx4sXs2rWL0NBQ0tPT2blzJx4eHixfvpyZM2dy7NgxqlatyunTp6latSrTpk0jMzOTbdu2sWnTJgC6detGZmYmANu3b0ehUDB37lwCAwPp3LkzJ06cAGD06NFSUPzDDz+wYcMGunTpQunSpUlKSsLPzw8dHR1u3brFb7/9RkREBDVr1qRs2bLMnDmTVq1asXPnTrZs2UJcXBwAN27ckHpPh4WFMXz4cKKiokhNTZWKhueF/4V72tu1OhVLFP70iQKBQCAQCL6IAhc05riTc1r1DRs2jJ9//pkyZcowatQoXrx4QcmSJfn555+xt7end+/eVKhQAYAJEybg7u7Od999h5OTE6dPn6Zp06ZMmjQJLy8v9PX1MTMzw8XFhQMHDjBgwAD69+/PoEGDyMrKomTJkri5uXHhwgVWrFjBmjVr8PLyIjY2luTkZHr37s2QIUOYP38+ZcqUoWXLlqxcuZL4+HicnJx4/Pgxly5d4vfff8fNzY3g4GCePHnCnDlzaNeuHcWKFePNmzeEhYWxefNmAgICpK1mfX19Tp06xbNnz3BxcWHz5s04OTnx22+/0b17d2xtbTlz5gwODg6sX7+e5cuXk5CQID3D/v37CQ8PZ9q0aRgbG1OlShUqVqzIjh07uHz5MnXr1v38L+F/4J4uaO7oT5FfHXgFFaG3ZhF6axaht+bIr1p/7nxkqn/SwiQfkVNyZ8uWLfTq1Yu9e/dStWpVAP744w+GDRvGyZMnAShatCjx8fE8ePAADw8PJk+ejKurKy1btsTGxkYyzRQqVAi5XE5qaiqNGzemSZMmdOzYkbp166Knp4dcLufatWv06NGD8PBwjI2NAXj06BEtW7YkLCyMe/fu0adPH86cOSMZbTw9PYHsvtQ58z5+/DjW1tb06tWLWrVqMWHCBOnZ5s2bx927d1m7di29e/fG1tZW6nGdM37Xrl3UqFEDgCZNmtC/f39pq3rChAnExcURGxuLTCajT58+dO/eXbp+9+7dadGiBT179vzgc34OSqWSq1ev8mvoC427p2e5VkP18plmbyoQCAQCQQGidu3aH821LHArjTnkZojJyMhgxowZXLx4kVKlSlG1alVUKpXkOt6wYQP+/v54eXkRHx9Po0aNmDFjBpaWlmzbto1ly5YxduxYFAoFzs7OTJ06lcjISJRKJU2aNFG7v76+PhEREdL73Iw2ufH8+fNcDTQ529Mfuta7rQRNTU0BSExM5NWrV9y+fZsRI0awcOFC5s6dy4IFC6TzMzMzqV69OoaGhh98ziJFinzW/OF/454uaO7oT5FfHXgFFaG3ZhF6axaht+bIr1r/Z93TOeRmiPnll1+4du0aEydOpHz58gwePFhaRUtLS+P+/fvMmDEDXV1dHj16xNSpU5k9ezazZ88mLi6OhQsXAnD79m3GjBnDihUr+PHHHzE0NOT8+fPSL0B6ejoRERHY2tpy6dIlALU2hAcOHKB58+YAUuHvtm3bMmHCBKytrXn69Knas0RERFC8eHHpfW6mnQ8ZeW7cuMHp06cpUaIE3bt3Z8uWLbi7u9OmTRvpnKdPn2JmZoZCofjgc06cOPHzxf8fuKcLqjv6U2irA09bEXprFqG3ZhF6aw5t1brABI3W1tbcvXuXyMhIAGlFLSUlhSVLltC+fXvu3r1LRkYGMpmMypUr061bN9avXy99NmbMGAYOHMiAAQMoXrw4urq6mJubk5KSwuDBg5kzZw5t27alRIkSyOVyzM3NqVmzJra2tvj4+DB69Gh0dHTw8fHh+PHjHDt27L15lipVChcXF+n90aNHAQgODsba2hpzc3OmTJlC48aNsbe358KFC2zfvp1p06Z9kS6NGjWiffv2QPbqZ9euXVm+fDmVK1emQoUKhIaGMnz4cPz8/KhevfoHnzMv/NK2MnIdzf5qCXe0QCAQCARflwKT05hDTo7fgAEDOHToEG/evKFdu3aMHz+e3r1789dffwHZUb5SqaRRo0YULVqUffv24ebmJgWRurq6NG3aFB8fH0xMTPD09OTgwYOkp6cjk8mwtbVl586dmJqa4uHhwe3bt4mOjiYjIwNDQ0OmTZvGrVu32LNnD69fv8bb25suXbpI82vRogUymYxjx46hVCoxNDTkwoULxMfH4+7uzu3bt1EqlRgbGzNs2DAGDx7M+fPnGTRoELa2tsTExODm5sb169c5cuQIDg4OXL9+nWLFivH69WsqVarEzZs30dfXp0yZMpQtWxYfHx8ePXrEiBEjePToESqVClNTU37++Wd69OiBQqHAzc2Nq1evolQq0dXVpVWrVsyePfuzel/n5DTWqFEjT72yBXknR+tP5Z8I/h2E3ppF6K1ZhN6aI79q/bnzKjArje/Sq1ev97ZUd+zYIbXYK126NH369GHNmjVERkayb98+Hj58yLlz50hPT6d///4ULVqUwoULc/DgQc6ePUtQUBBly5blwYMH9OzZk8OHD9OlSxeMjIyIjY3lt99+o27duowfP55ffvmFSZMmceHCBbZu3crMmTOlFT+AiRMnYm1trWaKycjIYMCAAVSvXp3ffvsNlUqFl5cXhw4don///kD21nerVq1wc3MjLS0Nb29vDA0NcXd3l+596NAhnJycWLNmDVu3bmX+/Pls3LiR169fM3DgQNq0acPIkSN58eIF7u7uUmeYdevWYWxszOXLl5HL5UybNo2kpKQ8B4BKpTLfOcMKGvnVgVdQEXprFqG3ZhF6a478qvV/tvf0P2Hw4MGSgcTR0ZErV64A2S0G69ati6WlJS9evCAxMREzMzO1Fnvff/899erVk16fPn2a3r17A9ltC3/99VeeP3/+0ftfvHiRiIgIdu/ejYmJCQBeXl7Y29tz48YN6bzOnTujp6eHnp5enu599epV0tPTGTNmDDKZDCsrKzw8PHB3d2fs2LEYGhpy584d9u3bR4MGDZg9e/ZnO6ff5tatW3keI/gytLV/qbYi9NYsQm/NIvTWHNqqtQga36JYsWLSa11dXanot0qlYtGiRZw8eRILCwuqVKlCRkYGb+/sv+1eTkxMlIJP+D+TSm69od8mISEBc3NzKWAEMDExwczMjKioKGl+77qnP+ScfvfeUVFRvHjxQurBnfNsGRkZJCQkMHjwYPT19dm1axczZ86kTJkyjB07lhYtWnx03u9StWpVsT39lcmvDryCitBbswi9NYvQW3PkV63/s+7pHEPMP+XkyZNSx5QFCxYQHR3NiRMnpICuXbt20rnHjh3Dzs4OgC1btrB3794PXjfHLZ1DZGQkf/31F0ePHmXYsGEkJiaiUCik+yQnJ5OYmEjx4sWlIPVdp/SHnNPvYmlpiY2NDb///rv0mUKhICEhAQsLC+7evYujoyP9+vUjOTmZrVu38vPPPxMWFkbhwp/fbUVbXWHaiNBaswi9NYvQW7MIvTWHtmqt2QbB+QB9fX2Sk5M/eV6zZs0oV64ckB1YGRgYoKOjQ1paGuvWrePevXtkZGQA0Lx5c6m24osXL/iYt6hmzZpq7x8/foy1tTWXLl2iX79+VKxYkenTp5OcnExycjIzZszAxsYmbx1ZPvJMKSkprFmzhvT0dF69esXEiRP5+eefkclk7Ny5kwkTJpCQkICJiQkmJiYUKlQoz6uGiSkZ/3iuAoFAIBAI8hcFZqXxxIkTrFq1iidPnvD69Wtq1KiBt7c3ZcuWJSgoiJUrVxIVFYWhoSHz58/HwsICyC7o/dtvvwEwdOhQpkyZQv369Tl27JiUg2hubs4ff/zBt99+i1KpxMjIiFq1anHv3j0ADh8+TOXKldm7dy8rV66UekhDdm5knTp1ABgyZAhDhw6VjnXu3Jn4+HieP39O8+bNOXbsGBMnTmTChAnSFrK1tTXLli1DV1eX0NBQAAYMGMC1a9eYPn06oaGhFCtWjB49enD79m2KFCmCUqlk7NixnDx58r2t7qlTp/LLL7+wYMECZDIZlSpVYtWqVQD07t2b3r1706BBAwAKFy7Mr7/+ioGBQZ6+i6TUdEpinKcxAoFAIBAI8jcFImiMiYnBw8ODxYsX4+joSGJiIiNHjiQgIIDOnTszefJkli5dSqNGjfjjjz8YPnw469at49q1a8ydO5cVK1ZQq1Ytdu/ezbBhwzh16hRVqlSRrl+kSBFevnyJp6cnP/30E+fPn2fo0KGsXr0ayA4qu3TpQocOHYiMjCQ8PJxNmzZJ4x88eMCFCxeA7ILZkB0M7tq1i969e2Nvb8+oUaNITExkzJgxdOzYEQ8PD5KTkxk3bhyLFi1i+fLlUrHydu3asWLFCrKystixYwcPHz5kw4YN2NjYMGDAAK5evUqrVq3w8fHB19eXI0eOYG1tTWxsLFOmTOHnn3+ma9euPHnyhOHDh3Pq1Cm6d+/O8uXLadKkCdOnTyctLQ13d3fCw8PznNOoUuU/Z1hBI7868AoqQm/NIvTWLEJvzZFftf5PuactLCwICQnBxsYGhUJBTEwM5ubmxMbGsm/fPlq0aCG1+WvcuDFbt26lZMmS7N27l27dukkrgV26dKFChQoYGhq+dw87Ozup7E3Dhg1xdnZm//791K9f/5Pzc3Z2VjOnfIjjx4+jp6fHuHHj0NHRwdDQkF9++YU2bdoQHx8PgJ6eHu3bt1dzNTs7O1OxYkUA6tWrx6tXr6SOM40bN2b9+vUAHDhwgAoVKtCrVy8AKlasyMCBA9m8eTPdu3fHwMCACxcuEBISQv369VmzZs0XuacVr1O4evVhnscJ8o62OvC0FaG3ZhF6axaht+bQVq0LRNCop6dHcHAwgYGByGQyvvnmGxQKBbq6usTFxVG1alW183PyCuPj4ylVqpTasQ/lDpYtW1btvZWVlbRq+Ck+t+90QkICpUqVUkuOtba2BiAqKgqA4sWLvxfIveuefrtPtFwul3Iso6KiuHnzplSeR6VSoVQqpdI9U6dOZeXKlaxduxZPT08qV67M1KlTpfM/F5NCxlQsVztPYwR5I7868AoqQm/NIvTWLEJvzZFftf5PuacPHTrE5s2b2bZtG7a2tgDMmjWLe/fuYWVlJRWvzmHdunXUrl0bKysrnj17pnZs0aJFam3+cni7JiNku56trKw+a36PHz+mTp06Ut3HD1G6dGmio6NRKpXSL1NOH+rixYvz8OHDT/ad3rBhg1rpoLextLTEwcGBtWvXAuDu7o61tTU9e/YEsusrduvWjVGjRvHixQsCAgIYOXIkYWFhn/WcOWRmQeKbTIqZ5C0XUpB3tNWBp60IvTWL0FuzCL01h7ZqXSDc08nJycjlcgwNDVGpVJw5c4Z9+/aRkZFBhw4dOHr0KH/88QdZWVmEhobi7+9P4cKF6dixI9u3b+fatWtkZWWxe/dutmzZkmuv5atXr7J//36USiWnT5/m+PHjdOrU6b3zDAwMUCgUag7qcuXKfTJgBKQt9AULFpCamkp8fDy//vor33//PaVLl/4HCmXTrl07rl69yoEDB8jMzCQ2Npbg4GB8fHwAWLFiBbNmzUKhUGBqaoqRkVGe+04D+By6w8vX6f94vgKBQCAQCPIPBWKlsUOHDly6dIk2bdqgo6ND+fLl6du3L1u2bKFGjRrMnTuXuXPnEhUVRenSpfH19aVSpUpUqlSJV69eMX78eOLj46lYsSKrV6+WnNVvU6VKFY4fP463tzfFihVj/vz5Ui7k25QpU4b79+9TuXJlihUrRlpaGg8ePMDOzo67d+9KuYk7d+5k+fLlxMTE8OzZM7p06YKlpSXr169n7Nix/Pbbb2RlZVGyZEm+//57PD09sbe3JzExEU9PTynQA1i6dCn29vY4ODiozSU2NpalS5cCUKtWLYoVK0avXr3Yvn07kydPJiMjA5lMRmpqKpCdGzlnzhzq1auHSqWicOHCLFiwIO9fiEolbX0Lvg75NZm6oCL01ixCb80i9NYc+VXr/5QRRl9fn3nz5r33ubu7OwCtWrWiVatWuY7t1auXZAx5m7eDMsguP7NkyZJcr3HixAkAXr58yYwZMxgyZAhubm7ExMTQu3dvqYYjQO3atQE4deoU+/btk/pcL1u2jJkzZ6JQKHj48CEBAQE0btyYkydPMnr0aNq1a0fHjh0JDw9Xu/emTZukwuIApqamuLm5Adk5ipaWlly9ehV9fX02btyIn58f586dw9jYWM25HRMTw7Rp095zoIeEhNC0adNcn/tDpLxOIVmh4Grk33kaJ8g72ppMra0IvTWL0FuzCL01h7ZqXSCCxvzCyZMnMTAwYMSIEchkMmxsbFi/fn2uvxwf6nO9e/duWrRogaOjIwA//vij5ITOK97e3hgbG6Onp0d0dDTGxsakpqaSlJSEsbF6HcWPOdDzinEhYwqbmFC+bO0vmrfg0+TXZOqCitBbswi9NYvQW3PkV63/U0aY/EJ8fDxWVlZqxpTy5ctLW9Jv86E+18+ePXvP7V2mTBmp0HheiIiIYN68eTx+/JiyZctKJqHcemB/zIGeZ2QyZDJZvvoDUVDR1mRqbUXorVmE3ppF6K05tFVrETR+BqNGjfqs8ywtLXn27BkqlUoKHI8dO4ZCoXjv3GvXrmFtbc39+/fZvHkzr169YuHChZKD+m2io6OlVn5yuZy0tDTp2IsXL3KdS0ZGBkOGDGHMmDH07NkTmUzGjRs3OHDgQK7nf8yBnlfGOduRoVTxXJEmHNQCgUAgEBQQCoR7Or/QtGlTMjMzWbFiBenp6Tx9+pTZs2erBXk55NSKPHr0KLq6unz77beMHTuWLl26cPToUUJDQyWn9pEjR6RxFSpU4OLFi8TGxpKamkpAQECuZXgyMjJITU3F0NAQmUxGdHQ08+fPl46Beh/ujznQ88qCw3fxCropHNQCgUAgEBQgxErjZ3Lr1i1mzZrFnTt3sLGxoWXLluzcuZORI0eyefNmzMzMuHbtGoMGDWLNmjUsX74cc3NzevXqJeUPRkZGSte7evUqmzdvZvPmzWRmZvLixQsePXqEtbU1tWrVws3NjaysLCwsLKhduzZ6enpERkYyb948ypYtS5MmTTA0NKROnToYGhqycuVKhg0bxps3b7h+/TrPnj3D0NCQKVOmMH36dCwtLenatSv37t1j6tSpPH78mPT0dMLCwvjrr7/YtGkTFy5cwMnJiczMTORyOSVKlODvv/8mPT1dWun8LFQqVCqZcFB/RfKrA6+gIvTWLEJvzSL01hz5Vev/lHv6a6NQKBg0aBDdunVjw4YNPHr0iKFDh0orfDdv3sTHx0fqB3327FnJlQzqwWJOh5fixYvj6emJsbGxWq/qzp07I5fLOXPmDIULF2bx4sVs3bqVGjVqSNewt7cnKCiI1NRUNmzYwJ9//knXrl1Zu3YtCxcuZN26dfTt25ewsDBOnz7NiBEj2LBhA1ZWVhw7dozSpUuzcuVKMjIymDRpEoUKFUJfX5/69evz999/s2nTJoyNjVmyZAknT57MW8BItns6TYlwUGsAbXXgaStCb80i9NYsQm/Noa1ai6DxMzhx4gQ6OjqMGjUKuVyOnZ0dgwYNkjqr5NYP+kuIiIjg+vXrmJub8+bNG4oXL07jxo1Zt26dmtvZ1dUVfX19KZirWLEiLVu2BKBBgwasXr2aoUOHoqenJ7mwo6OjSUhI4ObNm6xfv1663sSJE2nZsiW//PILhoaGREZGsmvXLho3boyHhwc///xznp/DuJAxeiqZcFB/RfKrA6+gIvTWLEJvzSL01hz5VWvhnv4E/v7+ait8HyMmJoZSpUqpBYVv1158ux90ZGQk4eHhVK5cOddrvWtEuXr1KpcuXeK7775jypQpQHbtyD59+pCUlETp0qUpU6aM2mrfu72s3+49nTOPnP7TOe+zsrKIjIxEqVRKnWdy0NfXJyIigjZt2pCRkcHOnTvx9fWlaNGiDB06lB49enxSIzVkMmQgHNQaQFsdeNqK0FuzCL01i9Bbc2ir1v/ZoDEvlCpViujoaDVX9NsO59yMKJmZmdLrxMRE6bWLiwvjx4+X3l+/fp1SpUpx7Ngx4uLiAGjZsqW0ta1UKmncuDHFixf/4P1yu39uWFpaYmhoyPnz56Vf1vT0dCIiIrC1teXRo0dUq1YNV1dXUlNT+f3335k4cSL16tWjUqVKn3UPgBkuVVHJ5JgVytu2tkAgEAgEgvxLgQgac3IKb9y4gbGxMV26dMHd3Z1Lly6xaNEi7t69i6mpKS4uLgwfPvy9HL09e/awdOlSqbMLoNYt5dSpU7x69YpWrVoRExODmZkZSqWSlJQUvLy8SE9PZ+fOnXTp0kUaf/ToUU6fPs2bN28wMjJSu1cODRo0ICkpiVevXtGnTx82btxIrVq16NGjByqVClNTU4oXL05mZibNmjUjICAAgOHDhxMTE0NAQAAbNmzAzMwMFxcXnjx5Iq2ADh48mIsXL2JlZSXdr2bNmhQrVoxGjRqRlpZGiRIlKFmyJA8ePODYsWPs2LGDLVu2oKenh6mpKTY2Nujo6FC4cOE8fR/KrCy+sSqSpzECgUAgEAjyN1ofNL58+ZIBAwbQu3dv1q5dK7XuK1myJL/++ivjxo1j/fr1PHv2jFGjRqFQKJg6dWqe7qGrq0tWVhYyWbYjOCUlheTkZMzNzRkzZgzz589n5syZtG/fXhqTkZHBmzdvMDEx+WCu49mzZ6lXrx7FihVj48aNPHz4kNu3b1O1alUiIyNJTEzk5cuXODk5qW1BDx48GEdHRwwMsmsgxsfHc/jwYUxMTGjdujWQHVhWq1aNCRMm8ODBAwDu379PXFwc1apV48mTJ8TExPD06VOmTZuGgYEB165do0qVKkRGRvLixQueP3+Oi4sLlpaWedJLpcp/zrCCRn514BVUhN6aReitWYTemiO/av2fcU9/qHXf6tWrsbOzo2/fvgDY2toyduxY3N3dmTx5cp7ukZaWRrVq1QgMDARg586dzJo1C1tbW7p06cL3339P8+bN1bq2bNiwQcpr/OOPPxg2bBilSpUCoHTp0jg4OADqvaKDgoKws7Nj+/bt0nVOnz6Nu7s7WVlZmJqaYmtrS7t27aTjpqam9OrVSwrsHBwcUCgU1KlTB4CGDRty/fp1HBwcmDFjBs2bN8fX11ca7+vry8mTJ+nWrRuGhoZSUF2/fn1MTU2/yNyjeJ3C1asP8zxOkHe01YGnrQi9NYvQW7MIvTWHtmqt9UHjh1r36enpqZlVILvcTWpqKgkJCXm6h0ql4urVq5w+fZomTZqQlJSEUqmkWbNmwP/lFL7dni+ntA6AlZUV6enpvHz58qP3SUhI+OSc3zXBgLoRRkdHRzLBQLYRJqdAd1RUFGFhYdSrV086rlQqsbGxAcDPzw9/f38WLVrEmDFjqFu3LjNmzMhTPiOASSFjKparnacxgryRXx14BRWht2YRemsWobfmyK9a/2fc0x9q3VeyZElu3rypdu7Tp0/R19dXC6ogO7BKT1fvXvK2ecXQ0BB7e3sWLFjA6NGj0dXVxcjIiIEDB35wXrGxsZiYmADZjupChQphYWHxwfPbtGlDpUqViIiI+OicczO9fMwIEx0dTWxsLJCtVYcOHZg5c6Z0PC4uDpVKRVZWFrdu3WLUqFFMnjyZZ8+eMWfOHDw9Pdm9e/cHr58bMhn56g9DQUZbHXjaitBbswi9NYvQW3Noq9Za30bwQ637ihUrxoMHD9iwYYP0ua+vL+3atXvPCFOhQgWeP39OWFgYKpWK/fv3S3mAOZQqVYqgoCCuXLnCpEmTMDU1RVf3wzH3/PnzSUpKIiYmhsWLF9OtW7ePPkdISAjjxo377Dl/LqmpqdLrzp07ExwczB9//EFWVhaPHz/mp59+Yt26dcjlcry9vfHz8yMtLQ0LCwsMDAwwNzfP8z0zlHA/LjnPP88V77dbFAgEAoFAkD/Q+pVGU1NT1q5dy5w5c1i/fj1GRkb06tWLbt26UaFCBXx9ffH398fQ0JC2bdsyevTo965Ro0YNhg0bhqenJykpKTRv3hxnZ+d/NK/y5ctjb2+PsbExSqWSJ0+ekJCQQJ06dXj16hUDBgwgKSmJ6OhoHj16hKOjIyNHjmTNmjUMGTKEBQsWqG13X7t2jSNHjnD//n0cHR2ZNm0aTZs2BeDixYts3ryZqKgoFAoFRYsW5cWLF6SkpLB+/XoA6tSpw7p161i4cCFTpkyRVh9LlCghFQb38/Nj9OjR1K5dm6ysLAoVKkT37t3z/Ow+h+6Qocq7Zt6u1SlmYpD3gQKBQCAQCL46MpVK9QX/exd8isjISJycnLC3t2fx4sUolUpGjBiBra0tNjY2LFu2jHXr1lGzZk0MDAxo0aIFI0eOpGPHjvTu3ZuHDx+yYcMGbGxsGDBgAFevXsXPz48mTZrg6+vLkSNHOH78ONeuXZPK9dSsWZOYmBj69u1Lq1atGD16NOfPn6dPnz7cvXsXgC1btki9sStUqMD+/fuZO3cuhw4dwsTEBHt7e1avXo2DgwO3bt2iV69ebNiwgZo1a37ymZVKZfY8L6WRrvq82pFv4+1ajfLFjD99oiDf5sUUVITemkXorVmE3pojv2qdM6/atWt/dF5av9KY35k0aZKUy+ju7s6wYcPo168fZcqUoX79+h8c5+zsTMWKFQGoV68er169onnz5gA0btxYWkH85ptvCA4OxtramqSkJOLi4rCwsJBWEt9ly5YtDBkyRHJ2d+7cmV27dnHgwAF69uyJoaEhu3btIisri7p163Lp0qU8O6hzek/nFdGrOu9oqwNPWxF6axaht2YRemsObdVaBI1fGVtbW+l1jos6KSkpVxf023zKEZ2zQCyXy9m4cSNBQUEUKlQIOzs7FAoFH1pAjoqKYu7cuSxYsED6LDMzk+rVq2NoaMi2bdtYtmwZY8eORaFQ4OzszNSpU98zD32MnN7TeUX0qv588uu/VgsqQm/NIvTWLEJvzZFftf7PuKfzO7GxsZQvXx7I3rI2MjLC3Nz8k63/Pqc14KBBg7C3t+fQoUPo6+tz/PhxAIYOHfrBMZaWlri7u9OmTRvps6dPn2JmZoZCoSAuLo6FCxcCcPv2bcaMGcOKFSuYOHHiJ+fz1uTJe8goelV/CdrqwNNWhN6aReitWYTemkNbtRZB41dm4cKFzJ07l5SUFJYsWUL79u0/6rrOC2vWrMHX1xe5XI5MJiMzM5OQkBBCQ0Mlc0tO15jk5GQKFy5M165dWb58OZUrV6ZChQqEhoYyfPhw/Pz8qF69OoMHD2bOnDm0bduWEiVKIJfL8+yg/qVtZeQ6eX9G0ataIBAIBIL8iwgavzI2Nja0bduWN2/e0K5dO8aPH8+qVas+a2yOmaZRo0ZcuXKFb7/9FkdHR2mVsHfv3vj7+3P69Gnu3r1Lo0aNqFq1Kk5OThw/fpx69epRuHBhLC0tadiwIUuWLKF169bs3LmTtm3bolKpMDIy4ueff8bJyQmFQoG9vT2TJk1i3LhxyGQyatWqRb9+/fL0zGWLGn9xiSCBQCAQCAT5ExE0fmV69er13tbuqFGj3jvvxIkT0utNmzYB2UEjZLcxPHPmjOTADgkJYeTIkYSHh2NmZkbfvn1ZunQpJ06c4OHDh7Rv355x48bRo0cPqef2jz/+SJMmTZg4cSL16tUjKCiItLQ03N3diY6OBpDK/Jw7d47ChQtz5MgR3N3defbsmVpu5qdQKpX5rq9mQSO/9i8tqAi9NYvQW7MIvTVHftX6P9N7+r/AhxzYuZHTv/pDPbcNDAy4cOECISEh1K9fnzVr1kju6FGjRqGjo4OJiQkxMTHS1nZcXFyegsZbt279g6cV5AVtdeBpK0JvzSL01ixCb82hrVqLoFEL+JADOzc+1b966tSprFy5krVr1+Lp6UnlypWZOnUq9erVIyEhgV9//ZVbt25hbW1N9erVAfWe2p9D1apVxfb0Vya/OvAKKkJvzSL01ixCb82RX7UW7ukP0Lt3b+zt7XPdIv43sba2lgpqv8uKFSu4ePEia9asAWDu3LkEBgair6/P4cOH1crtwIcd2I8ePXrv2qVLl+bIkSNqn73dv/rWrVt069aNUaNG8eLFCwICAhg5ciRhYWF4eHjg6OjI2rVr0dXVJTExkR07duT52bXVFaaNCK01i9Bbswi9NYvQW3Noq9Za33taGxk6dKgUMAJs3LiRuXPncv78+fcCRsh2YCsUCmJjYz/pwG7Tps1H+1evWLGCWbNmoVAoMDU1lQJQyHZYGxoaoqOjw4sXL/D29gYgIyPj3xdBIBAIBAKBVqGVK405ruKpU6eyYsUKUlNTcXR0ZPr06ZiYmBASEsKKFSuIjo7G1taWMWPG0LBhw/euo1Kp2LRpE1u2bCEhIYFvvvmGyZMnS9uyL168YPbs2Zw+fRq5XM4PP/zAjBkzKFKkCJcvX8bPz4+HDx+SlJREpUqVmDZtGrVr1+b8+fNMmjSJLl26sHXrVtLS0nBwcGDOnDmYmJjg7+9PeHg4S5YswdHRkczMTMaNG0fr1q3x8fHh2LFjLFu2TFpJTE5Opk2bNqSmpmJhYUFCQgKHDx8mOTmZp0+fMmnSJMzMzHB2diYmJoYqVaqwZ88efHx8yMrKolixYlIO5MyZMxk2bBjfffed1F86x6gzZ84cfvnlF5YtW4ZKpcLExAQLCwvu3LmTq34fQhhhvj75NZm6oCL01ixCb80i9NYc+VXr/4QR5siRIwQFBUmuYi8vL9q2bcv06dNZvnw5devW5cyZM4waNYodO3ZQqVIltfFbt25l/fr1an2Y+/fvz6FDhyhWrBgeHh4YGxtz5MgR9PT08PDwwMvLi9mzZzNs2DDc3d3p0aMHqampTJ48mXnz5rF161Ygu/NKbGwsR48eJTY2ll69erF161bc3Nyk+5ubm3PlyhXs7Oykfs9hYWGMHj2aefPmUa1aNVq0aMHDhw8ZNGgQ/fr1w9PTk8OHD7N9+3YsLS0xNTUFoFKlSixdupT09HTatGmDkZERwcHBWFlZMXDgQNavX8+cOXO4ffs2T548YePGjZI+Y8aM4dtvv6VRo0bIZDJmz55Nhw4diIyMpEePHu/lSH4KYYTRHNqaTK2tCL01i9Bbswi9NYe2aq3VQWNuruLExER69OjBd999B0CzZs1wdHQkMDCQX375RW38x/owOzs7Ex4ezu+//y5t3/r4+PDy5Uv09PTYvn07tra2pKWlERUVhZmZ2Xu/BCNGjMDQ0BBbW1scHBxyzUF8lz179uDk5ETr1q2lkjs9evQgMDBQWi2sXbs233zzjdq4n376SdrarlSpElWrVqVChQoAfP/991y6dAmAzZs3f1QfAwMDDh06hJmZGd999520ypoXhBHm65Nfk6kLKkJvzSL01ixCb82RX7X+TxhhcnMVP3z4kAsXLrBt2zbpmFKp5Pvvv39v/Mf6MMfHxwPZxpIcihcvTvHixQE4f/48gwcP5vXr11SsWBFdXd33+j3nnAugp6eHSqVi2rRp3LhxA2NjY1QqFePGjQNgzJgx/PHHHyQkJFClShW161haWhIVFSW9z61v9ef2qo6KiiI8PPyD+mzYsAF/f3+8vLyIj4+nUaNGzJgxA0tLy/fu+SG0NcFXGxFaaxaht2YRemsWobfm0FattTpozM1VXLp0abp37662DRwdHY2hoeF74z/Wh/nNmzfS2LJlywJw//59goODadasGbNmzSIwMFDKf1y3bt1nrSTOnDlTymmMi4sjODgYAF9fX2QyGaVLl+bp06fA/zmwN2/erBaA5taX+nN6Vec8s6ura676pKWlcf/+fWbMmIGuri6PHj1i6tSpzJ49myVLlnzW9QUCgUAgEBRMtC5ofPr0qbTN3KFDB3r27EmvXr3w8vJCJpPRq1cvvL29+f7775k/fz62trYcO3aMMmXKYGVlxbVr17h27Rq6urokJSWp9WGeOnUqu3btIiAggOHDh2Nra4uLiws6OjrUrVsXpVJJ0aJF8fDwICsrSwpEr169ysKFC6X3YWFhANSrV49atWpRqlQpLl26xLfffounpye3b99GR0cHZ2dn6bn27NmDg4MDZmZmbN++nZo1a2JjY0ObNm0IDAyka9eu9O7dm5iYGOLj4wkLC2PlypW4uLgA8PPPP/P69WtatWpFcnIy27ZtY926ddSsWZNq1aoB2cafkiVL4ufnx/Lly6lSpQpdu3bFx8eH4cOHU7RoUcaNG4euri7W1tY4Ozujq6ub597TAoFAIBAICh5aFTS+fv2afv360bhxY8LCwnBxcZHcz99++y0qlYqWLVvy+vVrJk+ezIMHD7h9+zaDBg3i0aNHHDlyhIoVK1K/fn06deqEv78/rVu3Zvjw4cTFxZGRkYGrqytOTk5A9vJxo0aNuHz5MmFhYRQpUoQjR46wbds2Vq9eTa9evcjKyqJo0aLIZDJev37NqVOnpN7SYWFhnDx5ktGjR1OqVCm1ZzE2NiY4OFi6V8eOHdmzZw9btmzBw8ODo0eP8uDBAxYvXkzbtm0ZOnQoYWFhxMXF0bRpU2bNmsWrV6+k6/n4+EiriAYGBnTt2pUhQ4bQvXt3qTj31q1bCQsLY8SIERw8eJDr169z+fJlBg8eTJcuXXBwcGDKlCkEBQVx9+5dli9fTqNGjaQt9M9FuKe/PvnVgVdQEXprFqG3ZhF6a478qnWBdE+fOnWK9PR0Bg4cyLZt2xgyZAjOzs64u7vTrl07li5dCmQHYB07dpQKeQ8dOhRPT09q167N+vXrpes1atQIgMOHD3Pz5k1++uknNbPMmDFj+PHHHwF49OgRLVu25OXLl7i6uuLn50dgYCA1atTA29ublJQU5syZw9SpU3F2dsbX1xeAH3/8kebNm2NkZISPjw+enp5UqVIFHx8fyehy/PhxrK2t6dWrF926dWP48OEMHz4cgHnz5nH37l0p96Fdu3ZS/cScoHHXrl3UqFEDyM6j7N+/v5ppRkdHhzlz5tC6dWspkBwxYgQA3bt3x9zcHJlMhqGhIX/99RejR4+mbt266Onp5dkEA8I9rUm01YGnrQi9NYvQW7MIvTWHtmqtVUFjVFQUL168oH379gC4uLggk8nIyMggISHhk+PfNZB07NiR6dOnM2nSJPbu3UvLli0xNjaWjr9ttMlZKYyPj6dOnTo0atSI/fv3U7lyZYKDg/H39wfg2bNnVK1aVe0+ZcqU4fnz55+c3/Pnz3NtAXjixIkPPgO8b4LJKcMD75tgPmT8MTQ0ZNu2bSxbtoyxY8eiUChwdnZm6tSpaqaaz0G4p78++dWBV1ARemsWobdmEXprjvyqdYF0T1taWmJjY8OaNWtwcnLiwIEDmJmZkZCQwJUrV0hPT1c7PzExUe39u2aRnILgZ8+e5dChQyxevFjteGxsrFTaJmdVMCd47NSpE15eXjRo0IDChQtLJWxKly5NdHS02nU2b94sOaLPnTtH0aJFASSzTevWralRowbW1taSCSaHiIiIf9UE8yHjj0KhIC4ujoULFwJw+/ZtxowZw4oVK6Ti35+LtrrCtBGhtWYRemsWobdmEXprDm3VWqvaCDZr1oyUlBR+//13rl+/jqmpKRMnTuTnn3+mQoUKPH/+nLCwMFQqFfv37+fBgwcfvZ6enh4uLi4sXrwYExMT6tWrp3Y8ICCA+Ph4Xr16xdy5c2nUqBElS5YEoGnTpiiVSpYsWULHjh2lMV26dOHo0aOEhoaiVCo5ffq0FBwC/PDDD9jZ2QFI8wsMDGTLli107tyZ7du38+eff6JUKgkLC2P79u106tTpX9Gva9euLF++XLpvaGgobdq04cKFC6SkpDB48GCCgoJQqVSUKFECuVwuTDACgUAgEAgALVtpNDEx4bfffsPHx4c1a9aQlZWFg4MDy5cvp2TJkgwbNgxPT09SUlJo3ry5mjv5bW7evImPjw83btzAwMCAxMREOnfujJ2dHXfv3pXOS0pKwtnZGblcTtOmTalYsSI//vgjz58/p1y5cnz33XccPXoUGxsbPD098fHxoUaNGnh5eTFo0CAMDQ1xcHDAwMBA+hdFaGgoxYoV49ixY4wfPx7ILt49depUOnXqREhICIMGDSIzMxN9fX26deuGq6srkO3STklJITAwkOLFizNw4EAAtm/fzp49e0hNTQWycwpXrlxJXFwcZmZmUjDcs2dPQkNDcXFxITMzEyMjI0aMGCGZcTp16sSkSZMYN24cOjo61KtXT8qNFAgEAoFA8N9Gq4JGgAoVKrB69epcj3l4eODh4ZHrMR8fHwBevnzJgAED6N27N2vXruXevXt06tRJLX8xh1KlSlGrVi18fHzYs2cPc+fOZcWKFdSqVYvdu3fj5eWFvb09RkZG0phHjx5Rs2ZNAFatWoWDgwO1atWSciXLly+Pvb09zZs3Z82aNfTp04e//voLAH9/f65fv86OHTuws7PjyJEjTJw4ERcXF2rWrEnx4sXJysri0KFDQPYWMkB6ejqnTp3i3LlzuLm58fDhQ3bs2MGrV6/o1KkTDRo0AGDJkiUkJSXx+++/U6JECVavXs3OnTvp378/cXFx7NmzhwMHDlC+fHlCQ0MZMWIEL1++zDWP8mMI9/TXJ7868AoqQm/NIvTWLEJvzZFftS6Q7ul/g5MnT2JgYMDgwYN58uQJ27dv54cffpC2nT/E3r176datG3Xq1CE+Ph47OzvMzMzo3r07p0+fls67f/8+Xl5e0vvz58+TlpYmtSr8GLt378bNzU2qq9i6dWsOHz7Mrl27pEDU2dlZzegCMGTIEPT09GjYsCGQvXJZpEgRihQpQqVKlYiMjESlUhEYGMiSJUsks82IESPYsWMHp06dokaNGtI5zs7O1K9fn6tXrwr3dD5HWx142orQW7MIvTWL0FtzaKvW/7mgMT4+HisrKzIyMujevTtWVlasWLFCrU3fh8blmGBOnTqFt7c37du3p1WrVmpB448//sj9+/fx8/Nj8ODB2NjYYGZmlutK5rt8yD19584d6X1uq345eYc5W+C5uadfvHjB69ev8fDwUAsEMzIyiIqKwtnZmU2bNrFmzRqGDh2KUqmkY8eOjB8/HgMDg0/O/W2Ee/rrk18deAUVobdmEXprFqG35sivWhdI9/Q/5eLFi/j7+2Nubo6xsTGXLl0C4NixY1IuY3p6Ovr6+ty9e5chQ4ZIY62srHj27BmQbXbp0qULixYt4sGDB8jlcg4ePIi9vT0dO3akW7du+Pn5sXr1ahwcHHB0dFSbx8qVK6lfv770vmXLlsTGxlKoUCEiIiLUzo2IiFALFL/UPW1ubo6BgQHr1q2jdu3a0ucPHz6kZMmSJCQkoFQqCQgIICsri8uXL+Pu7k65cuXo1avXJ6//NtrqCtNGhNaaReitWYTemkXorTm0VWutck//U+rVq8fZs2fJzMxkxYoVpKen8/TpU2bPno1KpUJXV5eQkBAguzROTjtAyK7puH37dq5du0ZWVha7d+9my5YtmJubU6FCBdLS0nj16hWpqakEBAR8NJAbMmSImlPb0NCQixcv4ubmxqpVq7h58yZKpZJDhw5x4sQJOnTo8I+fXS6X07lzZxYuXEhMTAxZWVns3buXtm3b8uTJE6KjoxkwYAB//vkncrlc2q4X7mmBQCAQCASQj1Ya33Y0Gxsb06VLF9zd3dm9ezdbt24lKiqK9PR07O3tmTNnDhYWFvj7+0t9nENDQ7GwsGDIkCF069YNyK7D2LBhQ44fP07x4sWZOHEi/fr1Y9++fcyYMQM/Pz+KFClCVlYWa9asoVq1avj6+jJ58mRkMhnFihUjLS0NgLNnz2JjY8P48eOJj4+nYsWKJCcn8/fff9OtWzf8/f3x8/Nj7dq19OzZk0KFCjFs2DAKFy5MSkoK8+bNo3Tp0gAsXboUe3t7tm7dCsDdu3epV68eu3btIiUlhT59+qBQKNDR0aFy5crSSmNmZiaTJk3i3r177N69W6oN6eHhgaGhIUePHgUgPDycixcvsmXLFl6+fCkFsBMnTmTu3Lm0bNmS1NRUdHR0qF+/PjY2NpiYmDBlyhRGjRpFcnIyMpkMS0vLT+Z6CgQCgUAg+G+QL4LGdx3NMTEx9O7dGyMjI5YtW8bGjRupWbMmMTEx9O3bl40bNzJ69GgguwWfp6cnvr6+nD9/nqFDh2JjYyNt/167du09t3GVKlVYuHAhTk5ONGjQgNmzZxMREYGrqyuVKlVi165d6Onp0b17dypUqCDNs3z58pILG5DqLZqYmGBhYcHIkSPp2LEj06ZNo2zZsgQEBGBqaoqXlxf79+8HYNOmTdK4xYsX4+/vT3h4OJs2bQKyt9CrV6/OggULKFy4MIsXL6Zfv34EBwezdetWnJycSElJ4ezZs6SmprJhwwYCAgLw8/PDx8eHhQsXsnz5cvr27cvp06c5ffo0I0aMICoqCisrK27cuEGLFi2YNm0aGRkZTJo0iWnTpuHr64uuri6lS5dm06ZNGBsbs2TJEry8vDhw4ECevk/hnv765FcHXkFF6K1ZhN6aReitOfKr1lrlns5xNI8YMQKZTIaNjQ3r16/HyMiI1q1bY21tTVJSEnFxcVhYWBAbGyuNtbOzo3///gA0bNgQZ2dn9u/fLwWNubmN32bAgAEYGRnxzTffULx4cTp06CCtrtWuXfuTBpl3ycjI4MCBA/j7+2NlZQXAtGnTCA4O/uTYiIgIwsPDCQkJkbrAjBs3jqCgIE6fPk2tWrUAcHV1RV9fXzKbVKxYkZYtWwLQoEEDVq9ezdChQ9HT05PyKaOjo0lISODmzZusX79eKgE0ceJEWrZsyS+//IKhoSGRkZHs2rWLxo0b4+Hhwc8//5yn5wfhntYk2urA01aE3ppF6K1ZhN6aQ1u1zhdBY46j+e08wPLly5Oens6CBQsICgqiUKFC2NnZoVAopF7KAGXLllW7lpWVlbSiCLm7jd/mc/s2fy4vX77kzZs30lY0ZK9Efk5uYE5/6rcd1Do6OlhZWREVFSUFje8+09vPkOOMzukXnfM+KyuLyMhIlEolTZo0URuvr69PREQEbdq0ISMjg507d+Lr60vRokUZOnQoPXr0+NzHB4R7WhPkVwdeQUXorVmE3ppF6K058qvWWuWetrS05NmzZ6hUKilwPHbsGHfu3OHs2bMEBQVRrFgxAIYOHao29u1VR8juEZ2zwgefdhZ/bt9muVwu5TcCvHjxItfzihYtiqGhIdHR0ZQvXx6A169fS32w393qvXr1KpcuXeK7776Tel8/ffqUSpUqAdlfZHR09Ef7T+el97ShoSHnz5+XflnT09OJiIjA1taWR48eUa1aNVxdXUlNTeX3339n4sSJ1KtXT5rP56CtrjBtRGitWYTemkXorVmE3ppDW7XOF+7ppk2b5upoDgwMRFdXFz09PTIzM9m/fz+hoaFkZGRIY69evcr+/fulPs/Hjx//13o1v02FChW4ePEisbGxH3VI57iU/f39iY2N5c2bN8yZM0fKF3BxcVE7//r165QqVYoLFy7www8/0KRJE7y9vYmPjyc1NZUFCxagVCpp1qzZP36GmjVrYmtri4+PDykpKaSmpjJ79mz69euHUqnk5MmTjBw5ksjISAwNDTEzM0NXV5fChQv/43sLBAKBQCDQbvLFSqOpqSlr165lzpw5Ui5jr1696Nq1K56enjRr1gwDAwOqVq1Kz5491UrhVKlShePHj+Pt7U2xYsWYP38+derUyfU+V65cAaB+/fqkpKQA2SuT4eHh7Ny5k5cvX+Lt7c38+fNxdHQkKysLuVzO33//zdGjR3n+/DlNmjTBwMCAIUOGUKpUKTIzM1m8eDExMTHMnDmTQ4cOMXbsWF6/fk3r1q1RKBTStrKfnx9dunSR5tOgQQOSkpJ49eoVffr0YePGjVIf6kaNGgFQunRp1qxZg5mZGQEBAQAMHz6cmJgYAgIC2LBhA2ZmZri4uPDkyRNpa3vw4MFcvHhRbdVVV1eXwYMHM3PmTDZt2oRMJqNChQqsX78eAwMDfvjhB3777TeaN28OZG+rz5kzB0tLyzx9n8II8/XJr8nUBRWht2YRemsWobfmyK9aa5URBrKDv40bN773+YoVKz46rnDhwixZsiTXYydOnJBe5wRZy5cvx9HRkcTEREaOHMnu3bupX78+ly9fZvTo0QwaNIjbt2/Tt29fvL29adOmDT/99BMNGzZk27ZtJCYm0rdvX4yMjDhx4gRz584lLCyMw4cPS/2cBw0axN69e5kzZw52dnaYmJgAMGXKFO7du0fp0qVxcHDg7NmzODo6Sq7rhw8f4u7uzrhx4+jRowfPnj1j1KhR7Nu3j+rVq2NqaopcLmfSpEnUrFkTAwMDTE1N0dfXZ9WqVZiYmNCpUyeMjY0ZPnw41apVY8KECRQrVgwHBwfu3LnDlClTmD9/Pk5OTvz1118MHz6cZ8+eUbFiRby9venatSsjRoyQnjMhISHP36UwwmgObU2m1laE3ppF6K1ZhN6aQ1u1zjdB49fGwsKCkJAQbGxsUCgUxMTEYG5uLuVEGhoaMnToUGQyGTVr1sTOzo5Hjx4BYGBgQGhoKBUqVKB+/frs379fMsnk1s951apV/Pzzz6xZswbI3rIuV64c1atX5969ex+cY1BQEHZ2dvTt2xcAW1tbxo4di7u7O5MnTwayTTJvd5MB6NSpk7QaWLNmTRQKhbTa2rBhQ5YvXw5AYGAgTk5OtGjRAoC6devStWtXtmzZQqNGjT74nHlFGGG+Pvk1mbqgIvTWLEJvzSL01hz5VWutMsJoAj09PYKDgwkMDEQmk/HNN9+gUCjQ1dXl+fPnFC1aVC1HUU9PT3JO+/n54e/vz6JFixgzZgx169ZlxowZWFhY5NrPGSAxMVFyKcfHx7Nq1apPzjEhISHX3tOpqanSil9ubvB3HeA5zmlQd4BHRUURFham1o1GqVRiY2Pz0efMiwkmZw756Q9DQUZorVmE3ppF6K1ZhN6aQ1u11uqgcdSoUZ997qFDh9i8eTPbtm3D1tYWgFmzZnHlyhX8/PyklTpPT0+1cVlZWdy6dYtRo0YxefJknj17xpw5c/D09GTnzp0f7edsbGyMnZ0df//9txSY7d27V3JSq1QqXrx4wbRp01i6dCldu3aVurrk8PTpU/T19aVA8Et7T0O2e7pDhw7MnDlT+iwuLg6VSvXR59y9e/dnXV8gEAgEAkHBJV+4pzVBcnIycrkcQ0NDVCoVZ86cYd++faSlpX00AVQul+Pt7Y2fnx9paWlYWFhgYGCAubn5J/s55/D29Tt06CDVbIyLi+PNmzcMGTKEEydO0LZtWx48eMCGDRskF7mvry/t2rX7V7Z7O3fuTHBwMH/88QdZWVk8fvyYn376iXXr1n30OQUCgUAgEAi0eqXxXT7Wv3rbtm28fPmSxo0bo6urS5UqVXB1dWXz5s1AdseUHHd1QkICf//9N9euXWP79u107tyZq1ev0rBhQzIzMzEyMkJHR4eaNWtSunRpKlasSM+ePXnx4gVv3rzB0dGRvn37Sq5pgDp16rBu3Tp27txJYmIit27domfPngCsXLkSlUqFu7s7vXr1wtfXl9mzZ6Ojo0ODBg2YNm0aAHv27CE9PZ1mzZqhUqlYuXIlUVFRXL58GX9/fxITEylevDi2tra4uLgQERGBlZWVFLTWrFmTtm3bMmzYMNLT09HV1aVVq1aMHTsWyA4qFy1axObNm5HJZJQtW5b169fn+XsQ7umvT3514BVUhN6aReitWYTemiO/aq117ul/Sl77Vzds2JDRo0fTokUL+vTpw507dwDYvn07YWFhrFy5kvr167Nv3z4mTZrE6dOnKVmyJK1bt8bJyQl3d3dUKhULFizg8OHDnD59msjISJycnChWrJjUG/r27dv06dNHCkhtbW2xtbWlatWqBAcH4+TkxO+//461tTV79uwhMDCQFStWYG9vT3h4OCNHjiQ0NJQff/wRa2trnjx5QmBgIEZGRrx69QqAlJQUDh48KPXPLlSoEKtXr5b6Z+eU+dm6dSuhoaHs3LmTChUqsH//fubOncurV68wMTFh4cKFrF69GgcHB27dukWvXr2kbj15QbinNYe2OvC0FaG3ZhF6axaht+bQVq0LTND4T/pXv0uDBg344YcfAGjTpg2enp5ERERQsmRJVq5cScmSJVGpVERFRWFqavretd7tDf257N69m27duknu6Pr169OtWzcCAwP58ccfAWjcuLHUGzsnaPzc/tlbtmxhyJAhVK5cGcheWdy1axcHDhygZ8+eGBoasmvXLrKysqhbty6XLl0S7ul8Sn514BVUhN6aReitWYTemiO/av2fck+fP38eT09Pateu/UX9q9/lbTdyTvCTs3R7584dhg8fTnx8PBUqVMDCwgKVSkXv3r2lYCw3h3OdOnVYvXo1kN0i8cCBA9StWxfILtb99OlT5HI5bdu2VRtnbW2tVm/yc9zTH+qfHRUVxdy5c1mwYIF0PDMzk+rVq2NoaMi2bdtYtmwZY8eORaFQ4OzszNSpU9Xc2J+DtrrCtBGhtWYRemsWobdmEXprDm3VukAEjTl8af/qzyU2NhYPDw+WLl2Ko6MjAIcPH+bIkSNq5+XmZs7Znt61axfNmzfHxcWFyMhI6brh4eEMGzaMp0+fqo2LiIj4aN/pD32WG5aWlri7u9OmTRvps6dPn2JmZoZCoSAuLo6FCxcCcPv2bcaMGcOKFSuYOHHiZ11fIBAIBAJBwUXrgsbczC4ODg5AtkPawcGB9PR06tWrx/3798nMzMTCwoLly5dz7tw5IiMjSU9Pp2zZsqhUKgwMDAAYO3Ysly9fJjExEScnJ06cOMGqVaskF/Svv/7K+PHjUSqVGBkZsXPnTvz9/YmLiwPg9evX0hznzp2LTCbj77//Jj4+HgA7Ozup483hw4ext7eXgsakpCQcHBzo27cvGzZs4NmzZ1y5coVXr16RmprK8OHDpWsvXbqUpKQkgoKCpJVNDw8P7O3t2b17N8nJyZw7d47MzEyWL19OXFwc1tbWAHTt2pWAgABOnDjBxYsXSUlJISUlhdmzZ/PDDz8wePBgmjZtyvXr10lJSSE9Pf2LOsIIBAKBQCAoeGhV0Pghs0tO/l7Tpk2Jj4+XVhd/+OEHFi5cSI8ePdiyZQtFihTB3t6ewoULc+jQIcLCwqhVqxYmJiYEBwczd+5czpw5Q2ZmJh4eHixevBhHR0fs7OwAOHDgABMmTMDDw4OkpCRKlSrFxIkTmTdvHjdu3JC2mwFCQ0PZvn07RYoU4eeff+bSpUv89ddfas/TsWNHAgICKFGiBGfOnAEgLCyM48ePI5fLsbS0pHLlyqxfv55OnTpJ454+fcqpU6d48uQJ7du358aNG7Rr146wsDC+//57QkJCUKlUHDx4kNGjR3P69GkuXLhAv3792LdvH8eOHUMmk1GiRAns7OzYvn07rq6ujBgxAn9/f/T19TE0NKRKlSqcOXOGjIwM9PT0Pvt7Eu7pr09+deAVVITemkXorVmE3pojv2pdIN3THzK75CRvTps2TaorOHbsWAwNDTEzMyMwMJCMjAyKFi1KfHw8Dx484Ny5c8TGxlKoUCGqVq2Kra0trq6uuLq6kp6eTkxMjNRycN++fQQEBBAbG8v8+fO5f/8+6enp0lZu//796dChA6ampty9e1fKr/zmm2+AbNeynZ0dtWrVws3NjfDwcCA7X3HOnDksXboUgLS0NG7cuEFAQIDUTQb+r77ipk2bsLOzo23bthgZGVG5cmXmzJnDrFmz6Nu3LzKZjF27dvHjjz8ycOBAjIyMWLlyJU2bNiUqKory5ctz7949Dh06RPny5QF48+YN9erV4+bNm3z//fcsW7aMQYMG0axZMypXroxcLv/s7e8chHtac2irA09bEXprFqG3ZhF6aw5t1Vqrgsac8i/vml1ytoDfLkStp6cnRc5v3rxh5syZXLhwAUtLS6pWrSp1QcnhbYPJx1oOQnYOYrVq1dTm9m77v9wMK58iKSmJjIwMaTs5B2tra2krO7drm5mZSZrkuJ3fNcNkZWVJLuquXbuqjdfR0SEyMpKWLVvi7+/Ppk2bWLNmDYaGhvTu3Zthw4blyUUt3NNfn/zqwCuoCL01i9Bbswi9NUd+1bpAuqctLS1zNbsoFIoPjjl48CATJkxApVKxaNEiWrRoQVZWFt99953aeXfu3MHR0ZETJ058sOXgvXv3pHlERESojY+JiVHr0fyh1bk2bdpI+Y9xcXEEBAQQHR3NuHHjmDdvHgYGBkRERFChQgVpzNOnTyXjTW7X/tyVwJxt/EOHDqmZa+7fv0+ZMmWIjo6maNGirF27lvT0dP78809GjhxJtWrVaNq06WfdA7TXFaaNCK01i9Bbswi9NYvQW3Noq9Za1UawadOmZGZmsmLFCqnN3uzZs0lLS/vgmJ07d2JmZkb79u1p1qwZCoWCefPmoVAoyMjIyHXMh1oO5pzfqVMnjh07xsmTJ8nMzGTv3r3v5St+iJCQEAoVKgRk5y8mJiZiZWXFggULkMvldOrUCV9fX548eUJ6ejobNmzg/v37ao7nL6VkyZI0bdqUX3/9lcTERDIyMli+fDmdO3fm1atXXL9+nUGDBnHnzh309fUpWrQogGglKBAIBAKBQLtWGk1NTVm7di1z5syRCnf36tWLsmXL5nr+qVOnSEpKQi6Xc+DAAYKCgsjKykJHRwe5XM7SpUsl5/XbdOjQgUOHDtG0aVOysrLQ19fHwcGB69evk56eTmJiIiVLlmTYsGEAVKhQQSoG7unpyZUrV0hNTaVOnTpYWFjQt29f6dqOjo6kpKRw9uxZjhw5QkZGBq9fv+bcuXPUrVsXPT09IiMjcXZ2RiaTUbFiRdauXUu5cuUkQ87w4cP57rvvaNGiBStXriQpKQl7e3vkcjm9evUCoEePHrx+/Vot2FQoFBQvXpzg4GAOHz6MXC6ncuXKrFmzhuLFi3Pv3j0yMzNxdXUFwMLCgsmTJ6u1QxQIBAKBQPDfRKuCRoAqVapIpWve5u7du2rvfXx8AOjduzf29vZ06dKFH3/8EX9/fxwdHUlMTGTkyJEEBASwadMm9uzZI7USjIqK4tKlS8yaNQtXV1fu3LlDnz59WLx4MZcvX2b06NHMmzePFi1acPfuXYYPH06XLl3o168fnp6eREVFsXLlShwcHNi1axczZ87kzJkz0vbwxIkT6dixI3v27GHp0qVS8W5PT09u3brF3r17sbKyYtu2bSxevJjSpUtLz9WkSRMWL15MRkYGx44d4/Hjx0yaNIk+ffoQGBiIt7c3bdu2xdvbm/v379OtWzc2bNjAd999h7u7OykpKRw/fhxDQ0N8fHz4+++/+fbbbwkLC2P79u3S1vX27dvx8/Ojc+fOef6OhHv665NfHXgFFaG3ZhF6axaht+bIr1oXSPf0P8HCwoKQkBDJER0TE4O5uXmu7QRDQkKoVq2aFDBVr16drVu3UqJECXx8fHBycqJ169YAVKtWDTc3NzZt2kS/fv0AcHBwoEGDBkD2Vvb06dN5+vSpFDTmRlpaGsHBwQQEBEh5lH379iUoKIjg4GDc3NwAJOe0kZERAIUKFZKc0w0bNkSpVErO6Ro1alCiRAnJOX348GEOHTokbTtPnjxZck4bGBiQlJTEjh07aNasGV26dKFbt255dk6DcE9rEm114GkrQm/NIvTWLEJvzaGtWv9ngsZPOaLfJi4ujlKlSql9llNIOyEhgSpVqqgds7a2lpzJgJrJJKe+4dtO7dwoKM5pEO5pTZBfHXgFFaG3ZhF6axaht+bIr1oXSPf0P+FTjui3sbKy4vTp02qf7d69m6JFi1K6dOlcW/2Zm5tjZ2dHixYtMDY2zvP8ihUr9kXO6bi4OA4cOICLi8t714yMjCQqKkrKwYSv75wG7XWFaSNCa80i9NYsQm/NIvTWHNqqtVa5p/8Jn3JEv02bNm24desW+/btQ6lUcuPGDXx8fNDV1aVTp05SWR6lUsmtW7dYvXo1rVq1+kfz+1LndMmSJXMNGHM7T1PO6ccJKdyPS1b7ea74sMNdIBAIBAJB/uc/s9LYoUMHLl26RJs2bdDR0aF8+fJ06NCBTZs2sX79epYuXUpKSgrjx49n+vTptGrVilmzZjFp0iQABg4cSLVq1fD19cXIyIixY8eiUqkoXrw4PXv2pHXr1vz2229ERETw9OlTzpw5g6OjI56entIcjh07RlxcHDNmzGDlypVUq1YNlUoFwN9//82dO3d49OiRFIBWq1aNtWvXUrJkSWbOnAnA0KFDqVevHjNmzACy60Pu2bOHjh07SvUfXVxcMDY2pnv37moaTJw4kaFDh/LDDz+gUqmwsLDA39+f4sWL4+zsTHBwMJ07dyYjI0Nybr9btPxzmBV8hwyV+mfertUpZmKQ52sJBAKBQCDIHxT4oHHTpk3S63nz5qkdi4yMZNOmTZw4cYKjR4+iVCoZMWIEXl5e2NjY8Pr1a9atW0fNmjUxMDCgf//+yOVyDh48SOHChVm8eDGHDh3ip59+4uXLl0B2juG6devIyspi+PDhzJ49m7t37xIWFpar67p///4AeHl50aBBA7Zu3UpiYiJ9+/aldevWfPfdd3h6evLgwQNOnTpF0aJFmT59OmPGjGH79u1SC0KANWvWUKdOHQICApDL5YwdOxYAZ2dnsrKypPaGe/bsISMjg0mTJrF3714aNWrEtWvXCA0NZevWrdSsWZOYmBj69u3Lxo0bGT16dN5EV6lQqWTvfKTKd24xbSa/OvAKKkJvzSL01ixCb82RX7UW7uk8MGnSJCwsLABwd3dn2LBh9OvXjzJlylC/fn0gO28xPDyckJAQKSdw3LhxBAUFcfr0aamWoaen53vX8vb2Zs+ePR91XRsYGBAaGkqFChWoX78++/fvRy6Xk56eTkhICMuXL8fKykqa75MnT9SeIT09nUOHDrFixQppa3nChAm0b98egBs3bnDz5k3Wr18v5VxOnDiRli1b8ssvv/DNN98QHByMtbU1SUlJxMXFYWFhkau7/FOkvE4h7Z3fv2SFgquRf+f5WoKPo60OPG1F6K1ZhN6aReitObRVa60NGpOTk8nIyJACtLyiVCp59uwZgGSMgWwTTHp6OklJSWpO5efPnwPqPaZ1dHSwsrIiKipKChrfdj/nXOvly5efdF37+fnh7+/PokWLGDNmDHXr1mXGjBmYmZmRnp6u5uY2NTWlRo0aatdKTEwkPT1dCizfnWtkZCRKpZImTZqojdPX1yciIoLKlSuzceNGgoKCKFSoEHZ2digUCmn7PC8YFzJG752VxsImJpQvWzvP1xLkTn514BVUhN6aReitWYTemiO/al3g3dM//vgjixcvzrWjy+fw888/S47ia9eu0a9fP44fP05kZCRGRkaYm5tLTuUDBw4QEBAAZLuZc3pMK5VKoqOj1dzIsbGxmJiYANmBWqFChbCwsJBc14MGDaJevXoMHTqUBw8eIJPJqFWrFra2tmzZsoXJkyfz7Nkz5syZg6enJzt37kRfX59nz55Rvnx5ILvsz+rVq9W2jc3NzSX3dc55MTEx0nFLS0sMDQ05f/689Iuanp5OREQEtra2rFu3jrNnzxIUFESxYsWA7PzJL0Im493qjjKZLF/9ASkoaKsDT1sRemsWobdmEXprDm3VWmvd04mJif/a+NWrVwPZq4lLliyhffv2avUbXVxcOHz4ME2aNMHb25v4+HhSU1NZsGABSqWSZs2aSefOnz+fpKQkYmJiWLx4Md26dQOQXNedOnVi8ODBkus6LS2N06dPo1Kp8PPzIy0tDQsLCwwMDDA3N0cul+Pq6oq/vz+xsbGkpaXh5+fH1atXMTQ0lO6rr6+Pq6srixcvJiYmhuTkZObPny8dr1mzJra2tvj4+JCSkkJqaiqzZ8+mX79+KJVKqWalnp4emZmZ7N+/n9DQ0A/25/4YM1yq4u1aXe3HrJCo2ygQCAQCgTaT71ca/f392bVrF2/evKFMmTIMHz5cMrQMHjyYUaNGUbRoUTZv3oyZmRnXrl1j+vTp7NixA3t7e0aNGgVkr/o5OTlx/Phxli9fzsWLF7l8+TKAtPXbt29f9PX1uX//PsWKFZNW3HLa/e3Zs4cFCxbQpk0bkpOTkclkFC1alN9++00qe/PmzRsaNGggFdWOj49HT0+PsWPHsnjxYsaNG8e4ceMwNTUlKSkJgGbNmjF48GDOnz/Pd999R3p6OnK5nEqVKhEaGoqnpyeLFi2iadOmUjBra2vLn3/+SUxMDGfOnGHhwoWkpqZiY2NDy5YtSUtLU1tR1NXVZenSpQwdOpTNmzeTlZWFubk58+bNw8DAgAEDBnDy5Elp5dbIyIjvvvsu1zqWnyJTmUXFkibvfZ7fEn+1mfyaTF1QEXprFqG3ZhF6a478qnWBMMLk9EPes2eP1A95ypQphIaGUr16dVavXo2DgwN79uzh5s2b+Pj4sGLFCrKystixY8cHr/vrr7/y9OlTKY/P1dWVnTt30qhRI+bMmUNWVhb9+vXDzs5ObZyZmRkDBgxg//79eHp60qNHD549e8aoUaNQKBTcvXsXf39/zp8/j4+PD+3ateOPP/5gyJAhODk54eTkRPXq1aVg9vz58/Tp04crV64A2V/a48ePWbZsGXZ2dhw5coThw4ezZcsWpk6dyokTJzA1NZV6b9++fRulUknJkiU5deoU586dw83NjQYNGrBo0SJevXpFp06duHbtGuXLl2fr1q3o6+tz5MgRSpQowerVq/Hy8uLgwYMkJyfz+PFjDh48SPny5QkNDWXEiBEcO3Ysz9+b4nUKV68+zPM4Qd7R1mRqbUXorVmE3ppF6K05tFXrPAWNa9asoWfPnhQqVOhrzUeNvPRD1tPTo3379nluefc2Q4cOpXDhwgBSGZp3CQoKws7Ojr59+wLZK35jx47F3d2dyZMnA1C2bFlcXV0BaNKkCcWLF+fx48fUrl37o/ffvXs3bm5uVKtWDYDWrVtz+PBhdu3aRc2aNYHs8jlvtwkEGDJkCHp6ejRs2BCAHj16UKRIEYoUKUKlSpWIjIxEpVIRGBjIkiVLJIPMiBEj2LFjB6dOnaJGjRrSOc7OztSvX5+rV69+kZ4mhYypWO7jzyr4Z+TXZOqCitBbswi9NYvQW3PkV62/ihFm1apVUl1BTVCnTp0P9kN+l+LFi39WgBMfH/9ef+cczMzMpNd6enq5LtcmJCS8V/Da2tqa1NRUEhISpLm8jZ6e3id7T0N2TmVu175z5470/t3e0/B/XVsiIiKA93tPq1QqXrx4wevXr/Hw8JB0ysrKQqlUEhUVhbOzs6Tz0KFDUSqVdOzYkfHjx2NgkLei3DIZ+eoPQ0FGW5OptRWht2YRemsWobfm0Fat87SM1KhRI1avXk1cXNzXmo8ab/dDDg8PZ+7cuaxYsYIzZ868d+67q49yuVzNxJFjfLl06ZL0mampKXfv3sXS0lJtrL+/P3v27Ml1Trn1nn769Cn6+voUKVIEgPDwcM6fPw9ktyTM6dTyLvHx8UB2vmXOtXMCvxwiIiLUAsXcVlllMhm3bt2ibdu2ud4H/s9dvW7dOi5evEiXLl1o0aIFe/fupVu3biQkJKBUKgkICOD8+fOsWrWKgwcPsmvXrg9e80MUMRSmF4FAIBAIChp5ChovXbqEn58fTZo0oUqVKmo/X4OP9UPW19cnOTn5g2MrVKhAaGgor169Ijk5WXJI5/Cp8R+iTZs2PHjwgA0bNpCens7Tp0/x9fWlXbt26Ou/HyyFhIR89nZ+ly5dWLVqFTdv3kSpVHLo0CFOnDhBhw4dPjk2p27lh5DL5XTu3JmFCxcSExPDixcvePLkCW3btuXJkydER0czYMD/Y++8o6K81rd9zcDQFRQRUOwFjSVojGjsEMUINlCx9y7F3is27AWxgsQWRREL9ihqNFHRxBJ7jEaaICJShYFhvj84vJ8TKznH+QHZ11pnnSlv2e89uNaTvff93IO5dOkScrlcakf0T7KnSxkrCnyOQCAQCASCwk2Blqf/HsP3uXFycuKvv/5i1KhRJCUlYW5uzvTp0/nyyy9xd3dnwoQJDBw4UKM5dz4jRoxgxowZODo6UqJECalwW716NQcPHuSPP/7gxo0b3L59W3quJUuW4OfnB0BOTg6PHz/G3t5eakMDecvFlStXJigoCD8/PwwMDGjVqhUhISGMHj36rXE4ODiQnp4OQFpaGn/++Sc3btxg79690h7EfDp27CjlP6vVakxNTVm0aBGNGzfmypUrxMXFcfbsWVatWkVWVpbULzIqKophw4ZJ1/njjz+oU6cOvr6+3Lx5k99++429e/fi7u7Ol19+iYuLi1Qwly9fni+++ILffvuN8uXLM2TIEFQqFbq6unTt2lXKwS4IKpWq0DnDihuF1YFXXBF6axeht3YRemuPwqr1Z3FPN27cGIDk5GSioqL44osvyMnJeecM2/+KESNGMGLEiLc+nzlzJjNnzpTeu7q6anxvaWlJQECAxmcODg54eHhQvnx5+vfvr7FU/ffZvOfPnzNr1izc3d2JiIhgxIgRXLt2jUaNGlGiRAkcHR012vnkL+N6enpq5EFDXlyfq6srkydPpmLFim9lQ0Pe/kJPT09q1qzJzp07pWzoc+fOSe18VCoV5ubm/Pjjj8THx9OnTx8mTJhAhQoV2LJlC/379+fBgwcAzJ07l+joaC5evEiJEiU4deoUXl5enDp1iokTJzJ16lQAfH19yczMZNSoUXh5edGrVy8yMzOZPn06jx8/fudy+Me4e/dugc8R/DOKqgOvqCL01i5Cb+0i9NYeRVXrAhWN6enpzJ49m6NHj2JgYEBoaCiDBg0iKChISiEpLtSuXZu+ffsC0KxZM5ycnDh06BCNGjUC8rTIyMj45KXn/zYbOp8xY8ZgYGBApUqVsLe358mTJ++8n6enJzo6OpiYmBAXFyeZWZ4/f/7WzKxCoSA4OJhKlSqRlZVFTEwMZmZm//iP+osvvvis/yEhKLwOvOKK0Fu7CL21i9BbexRWrT+Le3rp0qVkZGRw/PhxevToQYUKFWjTpg0LFy4kMDDwHw+2MPJ3h7W1tbXU6Do7O5udO3fSt29fjIyM2LVr1wevtXnzZh48eIBSqSQkJISTJ0/i4+Mj3cPZ2RkjIyNycnLemw2dz5vObIVC8d5s6MTERBYuXMjdu3exsbGhbt26AO90cevo6HDlyhWGDRtGRkYG1atXR1dX9x/lTudfrzD9YyjOCK21i9Bbuwi9tYvQW3sUVa0LVDSePXuWsLAwTE1NkclkKBQKpk6dSsuWLT/X+D4Lbyal5M+IJSUlaZg+/u4Qj4qKonz58gCo1WoN00mHDh3YunXrB+9paGiIvr4+Xbp0kQrDO3fuALBq1Sqpcfj7sqHfXEr/FLy9vXFwcCAwMBBdXV2SkpLe2/D85s2bzJ8/nz179kjF5datW987iykQCAQCgeDfR4GKxtzcXKnIyp+FevOzwk6+Y7pixYro6upy9OhRunbtyi+//MLly5c1TB+3bt3C39+f69ev8+uvv5KRkUH79u1JTU2VpnCdnZ2ZPXv2O2dZJ0yYQHp6Omq1GnNzc3R0dOjSpQuTJ0+mSZMm9O3bl4EDBwIwbtw4Bg0aRKVKlRgyZAjx8fHEx8dLs30XL16Urjt16lSuXLmCWq2mdu3ahIeH06RJEylneuLEifTs2ZOnT5+ybds2bt++zZw5c/D39wfg+PHjzJ49m5iYGIyNjbl9+zapqanI5XJ+/fVXpkyZQkxMDEqlssD9GQUCgUAgEBRfClQ0NmnSBB8fH2bPni0ZJFavXi0ZZAo73bp1Y9WqVfz+++9Mnz6d9evXM3/+fJo0aYKrqyuvX7+Wjv36669Zv349kJfwMnToUA4ePMjkyZP54YcfcHd3R1dXF39/f2rXrs2ff/4JwKVLlwAYPHgw/fv355tvvpH6MM6YMYOIiAjOnDlDREQEAwYMICgoiMDAQBo1aoSpqSnLly/HyMgImUyGjY0NT5484aeffpIadv/yyy/s27cPQ0NDZsyYAcD58+c5fPgwQ4cOJSwsjOvXr+Pr68u6deu4cuWKlKRjZWXF0aNH2bFjBy9evMDLy4tu3bpx4cIFevTowaJFizAyMqJKlSp8/fXXbNu2jQsXLtCiRYsC6Szc05+fwurAK64IvbWL0Fu7CL21R2HV+lPHI1MXYONaYmIio0aN4u7du6hUKgwMDKhcuTIbN26U+voVF0JCQli9ejXnz5+XlosfPXqEs7MzwcHBuLu7c+bMGWxsbPDz8yMiIoIdO3Ywbdo0lEolK1askK7VtWtXHBwc8PT01HAtR0dH4+joKF2nT58+fPnll0yePFk6d+nSpTx48IDAwED69etHpUqVWLBgAYB0fkhICPXq1QPyYgsHDRokzWJOnjwZHR0dFi9eTIcOHejfvz89e/aUrt+zZ0/atWtH7969admyJa1atcLV1ZWGDRuiUCgKFCOoUqm4cePGP9JbIBAIBALB/y12dnYf3GtZoJlGc3NzgoOD+f3334mJicHKyor69esXyc2cHyMxMZFy5cppPFu+cSU/LvBdxMfHS9nR+fw9GhDgr7/+QldXU/73xQiGh4dL798VI/hm/KGOjs47YwQBYmJiWLJkCcuXL5e+z8nJoW7duhgYGLB7927Wr1/PhAkTSEtLw8nJiZkzZ0pJN5+KcE9/fgqrA6+4IvTWLkJv7SL01h6FVev/qXs6NjZW432ZMmUoU6YMkFckAZQrV66gYyzUlC9fntjYWFQqlfTD5scHXr9+/b3nWVlZvRUFGBcXJzXiBnj27BlDhgxh27ZtGsfZ2Ni8FVEYFRWl4ZiWyWSEhoaybt06tm/fDoCLiws3b9786DNZWVnh5eWFs7Oz9FlkZCRmZmakpaXx/PlzaYb03r17jB8/no0bNzJlypSPXvtNiqorrCgitNYuQm/tIvTWLkJv7VFUtf6ktUcHBwccHR0/+L/iRr7Defny5WRmZpKQkMDChQvR09OTiri0tLS3znNzc+P06dOcPXuWnJwcDhw48FZBp1Qq39nOplu3bgQHB3Pp0iVUKhWXL18mODgYNze3D4716NGjn/RMPXr0YMOGDdL+ywsXLuDs7MzVq1dJT09n2LBhhIWFoVarKVu2LHK5/B/FCAoEAoFAICh+fNJM45kzZwA4dOgQv/76K5MmTaJixYo8e/aM5cuXY2dn9znH+H9CiRIlCAoKwtfXVyog9fT0yM7OZvny5VSoUIHu3btTrlw5nj17BsD333/PgAEDWLp0KQsWLCA2Nha5XI6enh7Hjh3D3d2dhIQEbty4gVqtlmb8xo8fT4sWLfD09CQtLY05c+bw9OlTbGxsmDx5MvXq1aNfv35cu3ZN2lf5Jo6Ojjx48IDo6GhiYmK4evUq69atIzk5GUNDQ7766isABg4cyJMnT+jSpQtKpRKFQkHnzp2lon/q1KnMmjWLiRMnIpfLqVy5Mt27d9eW5AKBQCAQCAoxn1Q05vcnDA4O5vDhw9Iet2rVqrF06VLat2//zqi/ok6NGjXeaqeTH0XYrFkznJ2d6d+/Pz169ODp06eMHj0aAwMDevbsyfnz51EoFMyZM4esrCy8vLzYvHkzgYGB0vJy/l7Ffv36Sdfv3r07TZs2xdHRkW3btmFpacl3331Hy5YtCQgIIDIykmHDhiGXy7GxsWH79u30799fY4wpKSkcPHgQpVLJoEGDpN/rjz/+ICwsjBUrVuDo6MjNmzcZPXo07du3p0WLFpw4cYKhQ4cyZswYkpKSGDBgAAcPHmTQoEEF0k24pz8/hdWBV1wRemsXobd2EXprj8Kq9WfJnk5PT38rUSQjI0Oj0fW/hcOHD1OtWjX69OkDQPXq1RkyZAg7d+6kZ8+e6Ovrc/XqVY4ePUrTpk0JCAgokBM5n+vXr/Ps2TMmT56Mvr4+NWrUYNCgQW/th3yTYcOGSWYYBwcHaQ/mnj17cHR0pF27dgA0bNiQHj16sGvXLlq0aIG+vj4XLlygWrVqNG3alEOHDv2jMYvsae1RVPNLiypCb+0i9NYuQm/tUVS1LlDR6OjoyOjRo/Hy8sLa2pqoqCjWrFmDi4vL5xpfoSUmJoY7d+5IWdSQ1+g8f2PrzJkz2bRpE4GBgUydOpVatWoxc+ZMjeM/hfj4eEqVKoWBgYH0WcWKFT94Tr5JCdCIA4yJieHSpUsaY1CpVNL1Vq9ejZ+fH6tWrWL8+PE0bNiQuXPnaph4PgXhnv78FFYHXnFF6K1dhN7aReitPQqr1p8le3r27NnMmzePESNGSIkhnTt3lnoP/puwsrLC3t5eY/k6KSmJ9PR0IG+2zd3dHU9PT16+fIm/vz8eHh5cvnz5rWvJ5XKN2dqkpCSN1wkJCaSnp2NsbAzkubH/CfnpPdeuXQNg6NCh1KpVi379+pGbm8vdu3fx9PRk+vTpPHv2jMWLFzN16lT2799foPsUVVdYUURorV2E3tpF6K1dhN7ao6hqXaC1x3PnzjF//nyuXbvGTz/9xLVr1/Dx8flXzSrlRxF27NiRGzducPjwYXJycnj+/DkjR47E19cXgI0bNzJ//nzS0tIoWbIkhoaGkhNZX1+f169fk5OTA+TtDb1w4QIpKSmkpqayZcsW6X75s3wLFizg9evXPH369KM51++jfv36ZGRkcPHiRXJzc5k5cyanTp1i69atyOVyFixYwOrVq8nKyqJ06dLo6+sL97RAIBAIBAKggDON8+bNo127digUinc2mS6O+Pn5ERISwuvXr6lQoQJ2dnasWrWK8PBwLC0tmTJlCpMnT8bQ0JD27dszffp0Nm/ezNOnT4mMjKRRo0bI5XIaNGjAmjVrmDp1Krm5uWRlZVGnTh0sLS1xd3fH3NwcR0dHjIyMpEKtb9++dOrUCYDnz5/zzTffYGpqiq6uLs+ePaNNmzaSMxrgxIkTAKxbt45z584hk8mwsrLCyMiIK1euEBAQAOTteTQwMCA7O5s6depIzbyrVKlCSEgIO3fuRCaTUblyZYKCgrSsuEAgEAgEgsJIgYrGevXqcezYMTp37vy5xlOoyO+TGBoaioWFBcHBwaxevZrQ0FA6d+7MnDlz6NKlC/fv36d///506NCBCxcusH37dnbu3EnlypX5888/6d27N126dKFmzZoAHDt2jE2bNmFvb09ISAg+Pj6cPXsWS0tL+vTpQ6lSpdi5cyepqamMGjUKgMDAQDIyMnBxceG7777Dw8ODly9f4uXlxfDhwwEoXbo0AJUqVeLChQvcu3ePAQMGsGDBAuzt7Zk3b95bru3GjRujp6fHokWLSElJ4cKFC5QoUYJTp07h5eWFUqkssG7CPf35KawOvOKK0Fu7CL21i9BbexRWrT+Le/rVq1dMmTKFWbNmUaZMGWQymfRdfi/H4oS+vj7Jycns3buXNm3a0L17d9zd3fH396dOnTp069YNgLp16/LDDz9QtmxZ9PT0aNiwIVZWVrx8+ZKkpCTMzMyk5BwAe3t7mjVrBuQ1A58zZw6RkZHk5ORw7do1Tp48iYmJCSYmJnh4eDBmzBggb3uAUqlk/PjxyGQyrK2t8fb2xsvLiwkTJgBgYGDAyJEjkclk1K9fH1tbW548efLRZ/X09ERHRwcTExPi4uLQ19cH8mY4K1WqVCDdhHtaexRVB15RReitXYTe2kXorT2KqtYFKhr79u0LQEJCAjKZDKVSWeziA9+kQYMG+Pn5sWPHDgICAjAwMKBfv37Ex8e/9dy1atUCIDU1lVWrVnH27FlKly5N7dq1yc7O1kiAeTMWUKFQAHkmlXdFMr7plI6JieHly5d8/fXX0mdqtZrs7GwpD9vc3FyjmFcoFO9Mn/k7iYmJLFy4kLt372JjY0PdunWlcRUU4Z7+/BRWB15xReitXYTe2kXorT0Kq9afxT3dtm1bJk+eLC1vAjRt2pQ1a9YUfIRFgN9++40RI0Zw5swZypYty6VLl/Dw8GD06NGcP39e49j9+/djbm7O2bNniY2NJTw8HBMTEwA6duz4SfezsrIC8vKmq1WrBmg6pa2srKhYsaK0dxFg7969BAQESEvTf+fx48ekpKTg6en5wXt7e3vj4OBAYGAgurq6JCUlsXfv3k8a998pqq6woojQWrsIvbWL0Fu7CL21R1HVukDu6ZUrV5KRkcHRo0e5efMmhw8fRq1Ws2zZss81vv9T7t+/D8Cff/6Jnp4e5ubmAHzzzTfcvXuXgwcPolKpuH37Nr6+vujq6pKWloa+vj46OjpkZWWxdetWHj58+EkN0MuVK0fz5s1ZvHgxycnJJCQksG7dOun7Nm3akJ6eTkBAAEqlkpSUFM6fP4+JiYnG7OL7+Ltr+01SU1MxMDBAR0eHly9fsmDBAoB/ZeN2gUAgEAgEb1Ogmcbw8HBpRg2gZs2aLFu2jE6dOjF//vzPMsD/S1q2bAkguYsBKleujJWVFaVLl2bVqlXMnz8fc3Nzpk6dypIlS+jYsSOhoaE0bNgQHR0d1Go1X331Fb///jtjx47l/PnzZGVlcfXqVUaNGiXti8xv2XP9+nWys7P55ptvKF26NG5ubty8eZNhw4Zx8+ZNdHV12blzJ1u2bEGtVmNjYyMtTQO8fv0aZ2dnYmJisLe31yj6vv76a/T09Khbty6GhobA/5/dXLx4MT4+PmzYsEE6XqFQEBISQvPmzQukmzDCfH4K62bq4orQW7sIvbWL0Ft7FFatP4sR5vXr15QoUULjs5IlS/6jfW9FiSZNmrB48WJyc3MZOHAg/v7+9OnTh9DQUGmZ+vbt20RHR9OnTx/KlCnDtGnTWLRoEc7OzuTm5uLp6YlCoeCXX35BT0+P7du3M3/+fL777jvu3LlDhw4dsLe358KFC2RmZjJkyBBatGjBgAED2L17N9WrV8fPz4/U1FQmTpyIkZERGzZskHKsAezs7EhNTWXatGl06NCBc+fOceHCBWrXrg3AvXv3SE9PZ8eOHTRs2JCffvqJ8ePH88cff9CiRQsqV66MnZ2d1Htz+/btrF69WqOx+KcgjDDao6hupi6qCL21i9Bbuwi9tUdR1bpAReOXX37JmjVrmDhxIjKZDLVazZo1a6hXr97nGl+hYOTIkVKx3KJFC27duoWnpyerV6+WNrQePHiQ9u3bS8WVQqGgc+fOUnbzggULMDY2RqFQEBsbi7GxMZmZmSQnJxMdHU1MTAzTp0/H0NAQY2Nj1q1bR25uLmfOnEGhUDBx4kR0dHQwMDBg1qxZODs7k5CQoDHOY8eOUbduXam347fffkubNm2k73fu3EmvXr0kI02bNm1wcHBgz549zJo164NjLEjRKIwwn5/Cupm6uCL01i5Cb+0i9NYehVXrz2KEmThxIv369ePw4cOUL1+emJgYZDJZsW8AbWZmJr1WKBSoVCosLCxo0aIFhw4dolatWhw5cgQ/Pz/pOAsLC6lgBLh69Srbt2/nr7/+onLlylIbm9zcXBISEihVqpS0ZAxgY2MDwPHjxylXrpzGH1f+dzExMRrjfJeru2LFilIsYUxMDBEREezevVv6XqVS0aRJEyDPgLN06dJ3jrEgFNUNvkURobV2EXprF6G3dhF6a4+iqnWBisaaNWty8uRJzpw5Q2JiIuXLl6dVq1aSS1jbREdH4+joyJkzZ6RCSpu4ubkxb948mjVrRokSJTRa4bxpTPnxxx+ZOHEis2bNonfv3gwbNkzS7PXr1wQFBZGQkECvXr2kgu7atWvcvn2b8uXLExsbi0qlkv7AIiMjgbzC9PHjx9J9rKysOHfunMYY3+y5aGVlRZcuXaRm4ACxsbFSOsyIESMYP348vXv3RiaTcfv2bQ4fPvw/VEwgEAgEAkFRpUDuacibdXNzc2P48OE4Ozv/nxWMhYHWrVujUqlYu3Ytrq6u7z0uMTERtVqNgYEBMpkMHx8fqdh78OABv//+O1WqVMHW1pbXr1/z4sULFi9ezMuXL2nVqhUAy5cvJzMzk4SEBBYuXEiTJk0oX768xn06derEw4cP2bt3Lzk5OVy8eJEff/xR+r5Hjx5s376dW7duAXl7KlxdXTly5AjZ2dlkZmZKY4yNjZVc8cJBLRAIBAKBoEAzjYWVgwcPcvDgQTIyMnBwcGDq1KmcOnVKIzIP/n9snqenJzk5Ofj7+xMaGkpqaiq1a9dm1qxZ1KpVC6VSyYYNGzhw4AAA06ZNY8GCBdJybUREBAsWLCAsLAwTExPu379P7969cXV15a+//sLQ0JDs7Gxyc3O5evUqCxcuRCaTMWPGDHx8fFCr1TRs2JASJUowdepUAJ49e8b169dp1aoV2dnZ6Ojo8Ndff/HLL7/g4eHBjz/+SKtWrUhOTqZy5cokJyfTpUsXmjVrRlxcHAEBAQQFBSGTyVi9ejU+Pj7I5XJyc3O5cuUKSqWS9u3bk5KSwogRI6Ql60qVKtGyZUuMjIxYtGgRCxcuZMaMGUBeo3ATExMePnxIlSpVPvn3EO7pz09hdeAVV4Te2kXorV2E3tqjsGr9WdzThZVr166xd+9ecnNzGT16NIsWLaJRo0YfPGfDhg0cOXKEwMBAqlSpwrp16xgxYgTh4eGsWrWKy5cvs2PHDsqWLcuWLVsYPHgwx44dw9PTk3Xr1hEZGcm5c+fYsWMHp06dYt68eSxdupR27drx4MEDRo8ezfbt2xk4cOA7M5+/+uorvv/+e65cuUL//v25efMmAH5+foSEhLB+/XpsbW05deoUU6ZMYdeuXdSvXx8HBwcMDAykxtv37t0jICCAhIQEzp07xy+//MLw4cNp1qwZq1atIiUlBTc3N44dO0aXLl148uQJVlZW7N27V3q2IUOGcOzYMRo2bMjr1685duwYVatW5cKFC4wZM4YGDRoU6PcQ7mntUVQdeEUVobd2EXprF6G39iiqWheLonHq1KlSIoqXlxejRo2iYcOGHzznwIEDjBgxgurVqwMwatQoWrVqRW5uLnv27GHt2rVUqFABgDFjxrB3717OnTuHk5MTAK1ateKPP/4gODiYcuXK4ejoSIcOHQCoU6cOw4cPZ8eOHQwcOLBAz7J//36GDx9OnTp1AOjQoQMnT54kJCSE+vXrA+Dk5ETJkiU1zhsxYgQKhULqqdirVy9MTU0xNTWlRo0aREdHo1arP/hs9erVk45xcnKiadOm3LhxQ8PQ8ykI9/Tnp7A68IorQm/tIvTWLkJv7VFYtf4s7unCypsmGGtra5RKJa9evfrgOQkJCRpOYz09Pezs7EhMTCQjIwNvb2+NYik7O1vDrRwdHc3SpUvp3LkzMTExUhH25pj+7m7+FF68ePHOa+Wn0wCULVv2rfNKlSoFIP0RvllUyuVy1Go1L1++/OCzOTk5STnbI0eORKVS4erqyqRJkyQzzadQVF1hRRGhtXYRemsXobd2EXprj6KqdbEoGuPj4yVDTnR0NEZGRpQpUwalUqlxXP4+PsgrLp89eya9z87OZtmyZQwZMgR9fX22bt2KnZ2d9P3jx4+xtLSU3rdu3ZopU6YAMHv2bMnRnE9UVBQWFhYFfpby5csTFRX11rXeLBTfFRn4KTGCpUqV+uCzJSYmolKp8Pf3Jzc3l99++w0vLy+qVKlCnz59CvwsAoFAIBAIig8Fdk8XRpYtW0ZycjJxcXGsWbMGd3d3qlWrxosXL7h8+TJqtZpDhw7x559/Sue4uroSGBjIkydPyMnJYdOmTZw+fZrSpUvTrVs3VqxYQVxcHLm5uRw4cAAXFxeePn36zvu7ubkRHh7O8ePHUalU3L17ly1btkifb9q0idjYWL788kv69u1LZmYm9+7dw9XVlaVLlwIQFhZGfHw8+vr6LFy4kHr16uHo6MicOXMIDw+na9eu2NrakpaWxooVK2jQoAE9e/bUKDDv3LlDv379AJg0aRLff/89arUayJt6XrZsGfr6+vTq1Qt7e3t8fHwIDQ3FxcWFR48esXLlSvr06UPDhg0ZMWKElFGdP4spEAgEAoHg30uxmGls0KAB7du3Ry6X4+Liwrhx49DX12fUqFFMnTqV9PR0vv32W2k/IsDQoUPJyclhyJAhJCcnU69ePbZs2YJCoWDKlCn4+fnRu3dvXr16RYUKFVi7di1ffPHFO++fn5Tj7+/P9OnTKVWqFL169aJjx460b98eHx8fAgMDiY6OJj09nfj4eCwsLLhz5w7Dhg3j9u3btG3bFk9PT2rWrImTkxMHDx7k2bNnBAcHs2zZMho3bgzk9XUcM2YMrq6ueHl5sWfPHiBvtnXAgAGMGzeOiIgIxo4dy8aNGzEwMADg5s2bJCQkcODAAfbs2cOBAwfYtWuX9GwnTpzg7t27TJgwgX379nHp0iV+/vlnhg8fznfffVeg30O4pz8/hdWBV1wRemsXobd2EXprj8Kq9aeOR6bOn4oS/M9RKpXExcVRsWJF0tLSiIqKwt/fn5SUFLp06cLs2bO5deuWtL8wPj4eY2NjDAwMePbsGZcuXWLWrFmcPXuWcuXKYWtry8qVK3F2dgZg7969bNq0iTNnzrBlyxZOnz5NcHCwdP/g4GB27txJWFgYycnJZGdnY25uTkJCAn/++Sfe3t5Mnz6dzp0707BhQ9auXUuLFi0AUKvVtGrVihkzZmgU2x9CpVJx48aN/62IAoFAIBAItIKdnd0H91oWi5nGwopCoeDIkSPs2bMHmUxGzZo1SUtLQ1c3T/a/Rw1+SoxfmTJlpNe6urrS8nNMTAx37tzRaDWUm5sr/fivX7/Gx8eHq1evYmVlxRdffIFarSY3N/ejBpmCItzTn5/C6sArrgi9tYvQW7sIvbVHYdX6X+WeLixs3LiRa9euERAQAOTlRu/cuZPdu3dLBeD8+fM5d+6c1IgbYMmSJezZs4eMjAwmTZrEkCFDChzjZ2Vlhb29PYGBgdJnSUlJpKenAzBz5kxMTU25ePEi+vr65ObmSrGHHzPIFJSi6gorigittYvQW7sIvbWL0Ft7FFWti4URprAwcuRIqWAESE1NRS6XY2BggFqt5qeffuLgwYNYWFgwaNAg6bjt27fj4+ODrq4upUqV+kcxfh07duTGjRscPnyYnJwcnj9/zsiRI/H19QUgLS0NfX195HI5aWlpLF26lLS0NLKzs5HL5QU2/wgEAoFAIPh3IWYaC0h0dDSOjo7MnDmTjRs3kpmZiYODA3PmzCEoKIiIiAh27NgB5PV+zMzMpGXLlsjlcipVqsSAAQMICAhg06ZNqFQqGjRoQE5ODjNmzODLL7/E39+fuXPnolarkclk6OjosGnTJhYtWgTA5s2b2bVrF3/88YfUasjW1hYfHx9MTEyYMmUK06ZNw8jICF1dXR48eICbmxsjR45kw4YNNG7cGB0dHeRyOTo6OqxevZry5ctL5h83NzdevnwJQOnSpTlw4AA1a9aUltQFAoFAIBD8OxGVwD/k1KlThIWFoVKpGDNmDPPmzaNixYrS91euXGH27NmsW7eOFi1acPHiRUaPHk2HDh2QyWQaxaWtrS1btmzB3t6ey5cvM3To0HdGEj548ICpU6dy8uRJgoODsbKyomTJktja2hIWFsahQ4dQKpU4OztjaGhIUFAQ1tbWDBkyhPDwcEJDQzl//jwTJkzA39+fhg0b8tNPP+Hp6cnevXuZOHEiR48eZdGiRXTt2pXo6Gh69epFo0aNPtkMA8I9rQ0KqwOvuCL01i5Cb+0i9NYehVXrf1X29P8F06ZNeyu68M3IwIMHD9KuXTtatWoFQMuWLfnhhx8+ukcwNDT0o5GEdnZ21KxZU+O8vn37YmZmBkCNGjX44osvqFatGgBNmjTh119/BWDnzp306tVL2s/Ypk0bHBwc2LNnD7NmzUJfX5/jx49jZmbG119/zfnz5wscIyiyp7VHUc0vLaoIvbWL0Fu7CL21R1HVWhSN/5B8Ywv8/+jC5ORk6bPnz5+/1dcxPzv6QyQmJlK7dm2Nz/4eSfiuGEEzMzOeP3+OiYkJOjo6mJqaSt/lxwhCnss6IiKC3bt3S9+rVCqaNGkCwLZt2/Dz82PevHkkJCTQokUL5s6di5WV1UfHno9wT39+CqsDr7gi9NYuQm/tIvTWHoVVa+Ge/i/J37t45swZjWzrfOLj46latap0rKGhIaVKleLJkydAXiEZGxvL7NmzAfDx8XnLnQzg4OCg8b58+fIfjSR8V2RgSkoKTk5OhIWFffC5rKys6NKlC8OHD5c+i42NxcDAgKysLB49esTcuXPR1dXlyZMnzJw5k0WLFrF27doPXvdNiqorrCgitNYuQm/tIvTWLkJv7VFUtRbu6X/IihUrSEtLIz4+nrVr19K5c2cNs0jXrl358ccfadeuHXPnzuXChQv4+flRokSJD173Q5GEH0KpVJKRkfHRcffo0YPt27dz69YtIG+K3NXVlSNHjiCTyRg/fjxbt24lJycHCwsLydEtEAgEAoHg342YafwEXrx4ga+vL5cuXZKWea2trXFxcSElJQW1Wk2zZs3YsmULOTk5LFy4kEmTJrFkyRLGjRtHRkYGVatWZcWKFZw5c4atW7eSlZXFkiVLNDafZmZmcvLkSYyNjRk/fjwA5ubm9O3bl2HDhmFra0udOnV4/PgxJ0+epHHjxvj4+AB5eywBXFxcKFeuHABHjx5l48aN/PXXX+jq6nLx4kXat29PRkYG/fv3Jzs7G7VajZGREV27duXixYtYWFiwZs0aVqxYgY6ODi1btmTixInalFsgEAgEAkEhRBSNH0GtVjN69GgqV67MyZMnefr0Ka6urkRHR3Pu3DmuXLlC//79iYyM5JdffuHFixcMGzYMY2Njxo4dy/nz5wHw9fUlJCSEbdu2sXPnTmrUqMG6deuIi4tj8eLF2NvbM3XqVO7evcvevXuxtrZm9+7drFmzhk6dOknT2EqlkrCwMMzMzJg8eTLjxo3jwYMH0nL6kSNHsLGxkVzSGzZseMsl7erqyoEDB3j69Cn79u3D0NCQ9PR0vL29WbNmDQ4ODiQlJeHh4UGJEiU+Ojv6d4R7+vNTWB14xRWht3YRemsXobf2KKxaC/f0/4ikpCTu3LlDUFAQxsbGksHk7NmzJCUlAXl7DOfMmYOJiQkmJiYMHTqUTZs2MXbsWI1rHTp0iB49elCnTh0AvL292bt3LwBZWVkcOXIEf39/yWQzYMAAwsLCOHLkiLQH0dvbmwoVKgAwefJk2rdvT3R09Fvj/phLGvIc3flubgMDA44ePSrlZMfFxVGqVCni4+MLrJlwT2uPourAK6oIvbWL0Fu7CL21R1HVWhSNH+HKlSuoVCqpdU5+DrRCoSAqKgoAU1NTjX1/1tbWPH/+/K1rPX/+HGtra+m9jo6OtJScnJxMdnb2W6YbGxsbjaLwTdd2/rkJCQkaRhn4uEsaNF3YH8vJLgjCPf35KawOvOKK0Fu7CL21i9BbexRWrYV7+n/EV199hYGBAVeuXNFYIo6KiqJSpUr8+uuvpKam8vr1awwNDYE8N3V+QfcmVlZWUqEJeUvf+cVlmTJl0NfXJyoqSuqvCBAZGanhsI6Pj5d6NOYXk+XKlXsravBDLul83nRhvy8n++HDhwWRCyi6rrCiiNBauwi9tYvQW7sIvbVHUdVauKc/grm5OZUqVcLX15f09HQyMzNZtGgRAwcO1NibsGTJErKysnj8+DGBgYF069btrWt1796dvXv3cv36dbKzs9mwYQMJCQlAXi9FNzc3Vq5cydOnT1EqlWzbto1Hjx7h7OwsXcPf35+EhARSUlJYsmQJLVq0wNLSEn19fSAvYxo+7JJ+F+/Lyf6U3GuBQCAQCATFHzHT+BHys5+XLFlCu3btyMrKon79+gQFBUmFGuQtUTs6OgLQs2dPhg4d+ta1XFxcSEpKYty4cSQnJ9O+fXtsbW2l7ydPnoyfnx8DBw7k1atX2NraMmbMGKZNm8bTp0+BvGXn7t27k5SUhKGhIW3btqVJkybIZDLKlStHjx49mDZtGjdv3qRChQpMnz6d2NhYzMzMSEpKkmYpb9y4QXp6Onv27MHCwoI9e/YQGhpK69atpdzrChUq8Oeff6JUKgu03CyMMJ+fwrqZurgi9NYuQm/tIvTWHoVV608dj0yd30NG8I/Id08/ePDgf37tuLg42rZtKzmabW1tpf81bdqUadOmMXbsWIYOHcq9e/cYMGAACxYswNnZmalTpwJ5ru18bG1t2b59O/b29jg4OFCyZEm2b98OwF9//UX//v3Zvn079evXJy4ujgEDBvDdd9+9Zeh5HyqVihs3bvyvZRAIBAKBQKAF7OzsPrhsLmYaCzGlS5fWcDQDmJiYSI5mAwMDRo4ciUwmo379+tja2kqJNJ+Ck5MTJUuWBKBmzZpSu57k5GSeP39O6dKl/5F7WhhhPj+FdTN1cUXorV2E3tpF6K09CqvWwghTDPi7oxny4gLzndrm5uYaZhaFQkFBJo7fdE/L5XK2b99OWFgYRkZG2NrakpaWVqDr5VNUN/gWRYTW2kXorV2E3tpF6K09iqrWwgjzX2Jvb//Rpelr167RoEEDIM/B3KBBA2JjYwG4evUqrVu3pkGDBvzwww8a5+U7mnfs2MH58+d58OAB9vb2nzQuuVyuYWJ5+fLlW8e8WXB+//33/Pzzz4SFhXHmzBnWr19P+fLlP+leAoFAIBAIij+iaNQCjRo14vr160Bee5zr169LLXkOHTpE7dq1uX79Or1799Y4779xNFerVo1r164RHx9PZmYm/v7+GkXi38nvyahQKMjJyeHQoUNcuHBBuKcFAoFAIBAA/5Ll6Tt37uDr68vt27cxNjame/fueHl58euvv7Jq1SoePHhAyZIl6dSpE6NHj0ZPTw8/Pz+uX79OcnIyUVFR+Pv7U7p0aZYuXcqDBw94+fIlNjY2TJo0iTZt2nzwPhEREZJZJj/u78yZMyxdupQzZ84A0KBBA65cuUJCQgLLli3jypUryGQyDA0N+e6771AoFFID79u3b3Pv3j309PSYOnUqRkZGxMTEcO3aNR48eECjRo14+vQpL168oFWrVpiYmDBy5EjKlStHfHw8I0eO5NmzZyxZsoSnT58yZswYBg8ezN27d2natCm5ubno6upiYWHxj7rWC/f056ewOvCKK0Jv7SL01i5Cb+1RWLUWMYL/4dWrVwwePJh+/foRGBhIXFwc/fr1w9LSkoULFzJx4kSCgoJ49uwZnp6epKWlMXPmTAAuXbrE1q1bqV+/Pvr6+nTq1AlHR0fWrVuHWq1m+fLlzJ07lzZt2nzwPlWqVHnn2NauXavhcs7Ozmbw4MHUrVuXU6dOoVarmTdvHk+ePGHv3r38+uuv9O/fHy8vL4YPH05WVhYLFixg//79BAYGsmHDBiZNmsSQIUOktjs//PADy5Yto3///vTu3RsXFxecnZ1Zs2YNL1++xMvLi9zcXCZMmED9+vWRyWT4+fkhl8uZPXs2ycnJBdZcxAhqj6IaRVVUEXprF6G3dhF6a4+iqnWxLxrPnj2Lvr4+Y8aMQSaTUbFiRYKCgtiyZQu2trYMGDAAyIvnmzBhAl5eXkyfPh2AChUq0LRpU+lamzZtwtLSErVaTUxMDCVLlpTcxe+7j5GRkdRj8WNcu3aNqKgo9u/fj4mJCQDz5s2jcePG3L59WzquW7duKBQKFAoFAE2aNKFRo0bS6/Pnz9OvXz8gL3N64cKFvHjxghs3bqBUKhk/fjwymQxra2u8vb3x8vJiwoQJGBgYcP/+fQ4ePEizZs1YtGgRcnnBdzAI9/Tnp7A68IorQm/tIvTWLkJv7VFYtRbu6f+QkJCAtbW1xn6+qlWrolAoqFChgsaxNjY2ZGZmkpiYCCC1o8nnp59+Ys+ePSQkJFCtWjVKly4tuYvfdx/gk4vGxMRESpUqJRWMkNdix8zMjJiYGMqUKQNoup4BzMzMpNc6Ojoa484fT25uLjExMbx8+ZKvv/5a+l6tVpOdnU1iYiLDhg1DT0+PkJAQfHx8qFChAhMmTKBdu3afNP43x1CY/jEUZ4TW2kXorV2E3tpF6K09iqrWxb5otLKy4tmzZ1LKCcDp06extLTkzp07GscuXrwYuVyOqakpCQkJGrN7oaGh+Pj4sGHDBikL+uTJk5w6deqD90lLS8Pa2lq6Tv7MZHx8PDY2Nrx48YJr165x8uRJevbsSVJSEmlpaVLhmJqaSlJSEhYWFlKB+ndDy4cMLn/XomLFipw4cUL6LC0tjcTEREqXLs2DBw9wcHBg4MCBpKam8sMPPzBu3DguX75MiRIlPukeAH8lpiPXydL4zMxIjzIm+u85QyAQCAQCQWGn2LunW7duTU5ODhs3bkSpVBIZGcmiRYsoU6YMf/75J9u2bZM+j4+Pp2vXrujp6ZGVlaXRozA/I9rQ0BCAR48e4e/vD4BSqXzvfbKyNIsnS0tLjf+PjIzE1NSU69evM2HCBKpXr86cOXNITU0lNTWVuXPnUrFiRRo2bPhfa9GmTRvS09MJCAhAqVSSkpLClClTGDduHDKZjH379jF58mQSExMxMTHBxMQEIyOjAi81zz9yn5kHb2v871WG8r8ev0AgEAgEgv87iuVMY75DeebMmWzcuJHXr1+zY8cOtm7dir6+PsbGxqxYsQIDAwPWr1/PmjVrMDIywtTUlOzsbKKioggLCwPyXM0jR45k3bp1AAwcOBAjIyPKli0rFZBfffUVZcuWpVevXly6dImgoCAUCgUlS5Zk6dKl0kygUqkkLi4OyIsIXLp0KZGRkdJ9rly5wrx58xg7dqy0hGxlZYWfnx+6urrcu3cPgAkTJnD+/HmGDx/Or7/+iq6uLiNGjCAiIgIDAwPkcjnz58/n8OHDGtPfJiYmLF68mEmTJrF8+XIAKleuTEBAAAAjRoygZ8+eNGvWDLVajYmJCbNmzdLI2P4k1GrUatnfPlIXOrdYUaawOvCKK0Jv7SL01i5Cb+1RWLUW7mng1KlThIWFoVKpGDNmDJUqVUIul6NQKJgzZw5ZWVl4eXlRtWpVZs6cKTmZK1SoQFBQEP3795f6K1pYWLBu3TrCw8MBGDZsGGZmZuzevRs9PT22b9/O6tWr+eWXX9DX16dDhw40atSIqVOnkpmZyZAhQ/Dz88Pd3R3IKwbf5Z6eOnUqjRo1Yu7cuZJ7es6cOezdu5fatWsDeXslfX19ycrK4vHjxxw/fpz58+dL7unjx49TuXJlLl26JLmny5YtS0ZGBrNnz6Zbt254eHhI7ung4GAmTJjAnj17qFatGseOHZPc0ydPnqRLly4F0j09I52sv/39paalcSP6j3/6UwreQ1F14BVVhN7aReitXYTe2qOoal2si8Zp06ZRunRpALy8vBg1ahQuLi78+uuvHD16lKZNmxIQEPCPHMILFizA2NgYhUJBbGwsxsbGZGZmkpycTHR0NDExMUyfPh1DQ0OMjY1Zt24dubm5H7xmcXFPGxsZo/jbTGMJExOqVrYr8LUE76awOvCKK0Jv7SL01i5Cb+1RWLUW7mny2ujkY21tjVKpxMPDg9DQUAIDA5k6dSq1atVi5syZUtH1d6ZOnYqvry+nT5+W9jUCREVFsXTpUv766y8qV64s3Ss3N5eEhARKlSolLV8DODo6sn37dima7/Tp0wwcOJDMzEwuX76MnZ0dtWvXLhbuaWQy/m7NkclkheofSHGhqDrwiipCb+0i9NYuQm/tUVS1LtZFY3x8vNT2Jjo6GkNDQ549e4a7uzuenp68fPkSf39/PDw8uHz58idfNzs7mxEjRjB+/Hh69+6NTCbj9u3bHD58GMhbek5KSuL169caheOJEycYMmQIAN9++y2QZ7BJT0/n119/5c6dO/Tp06fIu6dnudRCrqP5p2VmJPo2CgQCgUBQlCnWReOKFStYsmQJ169fZ+zYsVKxZ2VlRXp6Oj/++CMPHjwgLS2NRo0akZmZiaWlJZmZmZL5Iz97WUdHB5VKxeTJkzl//jxpaWncvXsXmUxGbGwsAwcOlI6vX78+5cuXx87OjqNHj0ozgampqdLYTpw4gZ6eHr/99hu5ubnY29uzdu1aqlatSvfu3UlPTycjIwOFQoGlpSUNGzbk119/BfKWxsPCwmjQoAHJyck8ffqUJUuWsH//fnJyctDV1WXv3r1s2LBBI9GlTZs2+Pr60rNnT2JjY1Eqlejp6WFmZsbhw4fZt28fp0+fJjc3l6ysLGn5vaDu6crmxqK5t0AgEAgExYxiXTRWrFiRDh068Pz5c+rWrUtQUBC3b99m5MiRZGVl0bBhQ9RqNevWraN169aMGTOG8+fPExYWhrOzM+bm5hw/fpxOnTpRvnx5VCoVx44dIzg4mGPHjhEQEEBYWBhly5bF1NSUnJwcHj58SJUqVVi4cCF9+/alZ8+eUgHq6uqqMb7+/ftz/Phx7t+/Lxlu9u3bxy+//IKOjg5qtZqyZcsSGRnJ8+fPpfMiIyM5d+6cVAQnJiZiaWnJ5cuXmTp1KocOHeLKlSscO3aMX375hdGjR3Pr1i06dOhArVq1uHPnDrm5uajVanR1ddHV1UWtVtOiRQtCQkLQ19dHqVRibGyMrq5ugfc1iuzpz09hdeAVV4Te2kXorV2E3tqjsGot3NNAnz59qF27NsuWLWPv3r3I5XKaNm3K5MmTCQwM5PDhw6SlpWFlZcXLly8ZNGgQjx49Ij4+HiMjI1q2bAlAq1atuHXrFrVr1+bgwYMA1KlTh7i4OIyMjJg/fz79+vWjS5cuODk5AUgNvQ8ePIiNjQ22trbo6OhgY2ND+fLlJYNO9+7dpXY+WVlZhIeH4+/vT6tWraTn6NatG0eOHGH48OEAuLi4YGhoiKGhId27d+f+/fsMGDAAmUyGh4cHhw4dYsiQIRgaGuLo6Cjt50xMTOTixYscP35cWrZ//fo1jRo14s6dO5iampKbm0v//v1p06YNtWrVQi6Xf/Lydz4ie1p7FFUHXlFF6K1dhN7aReitPYqq1sW6aIS8fojlypXTmC3Ljw9Uq9WsWrWKs2fPUrp0aWrXrk12drZGU+83sbGx0XhvbW3Nw4cP/2djTU5OJjs7+6372NjYEB0dLb1/lxEmv7DLf843zTByuVwywgD06NFD43wdHR2io6Np3749fn5+7Nixg4CAAAwMDOjXrx+jRo0q0GyjyJ7+/BRWB15xReitXYTe2kXorT0Kq9bCPf0fypUrR2xsrEa8X2xsLACTJ0/m3LlzXL16VTKedOzY8b3XenOJGPIc1PluaLlcLu1/BEhKSpJeDx06VHo9e/Zs6Tu1Ws2ePXuIjY3FwcGBH3/8EX19faKioqhWrZp0TmRkpBRdCP/cCJOfQnP8+HEsLCykzx89ekSFChWIjY3F3NycwMBAlEolly5dwsPDgzp16tC6detPugcUXVdYUURorV2E3tpF6K1dhN7ao6hqXSxjBG1sbHjw4AE2NjY4ODigVquleL/Hjx8TGBgIgJGREc2aNUNHR4esrCy2bt3Kw4cPNYq/N7l16xb79+8nOzubs2fPEh4eTvfu3QGoVq0aFy5cICUlhdTUVLZs2SKdl5+4AuDj40OpUqWAvCL05s2blClThvDwcHR0dHBzc2PlypU8ffoUpVLJtm3bePToEc7Ozv+1LpaWlrRu3ZqFCxeSlJREdnY2GzZsoFu3bqSkpPD7778zdOhQ7t+/j56eHubm5gDSeAUCgUAgEPx7KfYzjUZGRqxfv55p06axdu1aAPT19dHT06N169ZMnDiRb775BiMjI2rUqIG5uTkBAQGEhoZKS9aQNztZokQJZs+ezYwZMyhVqhTLly/niy++wM/Pj+joaBISEvj666+Ry+XSfkhAarQNeX0fk5KSiI2NlfY/vnjxgrVr1+Ll5UX16tU5duyY9F3FihUJDAykSpUq0nXGjRuHnp4emzZtYtq0aZQqVYo2bdqQlJQk3XfYsGHExcVRv359aYOrWq3mq6++YvPmzTRt2hSZTEaNGjUICAiQZh51dHSkBJgSJUowffp0vvzyy8/18wgEAoFAICgiFPuiMSkpiaSkJF68eMGYMWMYPnw4GzZsYMuWLaSnpwNw/fp1Xr16hZOTE/369WP48OHExcXRr18/GjRowOPHjzly5AgTJ06kV69ePHv2DE9PT65evSoVdz/99BO+vr507NiRixcvMmLECIKDg6X9iR4eHtjb23PgwAGcnJzw8PCgS5cuODo6cvr0aWxsbAgNDWXlypWsW7eOxo0bExERgYeHB69evZKex9LSkn379mFoaEhKSgoATZs2ZdGiRURFRdGlSxdq1arF5s2bUSgU9OzZk+7du+Pq6squXbvYvXs3O3fupFq1ahw6dIglS5ZQuXJlMjMzmTRpElu2bMHe3p67d+/Sp08f6tSpU2DNhXv681NYHXjFFaG3dhF6axeht/YorFoL9/R/UKlUjBo1ihIlSjBmzBhiYmI4ceIEffr00Wi8ffbsWfT19RkzZgwymYyKFSsSFBSEkZERwcHB2NraMmDAACAvaWbChAl4eXkxffp0ACpXrizN0LVq1QoLCwv++usv7OzsPnms+/fvx93dnaZNmwJ5xaC7uzt79uyhbdu2ALRs2VLam5hfNA4ePBhDQ0Nq1qyJhYUFXbt2lY6xs7OTDDC7du1ixIgR1KpVC8hzZYeEhHD48GF69+6NgYEBISEh5ObmSn0h/0mMoHBPa4+i6sArqgi9tYvQW7sIvbVHUdW62BeNZcqUwcXFhZMnT9KwYUNKlixJ165d8fDwkJplQ14yi7W1tYapJL8tTWJiouS4zsfGxobMzEwSExMBNIwlAAqF4qNZ03/nxYsX77xPeHi49P7vzmn4cIygXC6X3OAxMTEsWbKE5cuXS9/n5ORQt25dDAwM2L17N+vXr2fChAmkpaXh5OTEzJkzMTU1LdBzCPf056ewOvCKK0Jv7SL01i5Cb+1RWLUW7uk3aN68OZcvX+b8+fNSUXj69GnS0tKkY6ysrHj27JmGyzr/mPLly3Pq1CmNa0ZGRqKnp1fggupD2NjYEBkZqfFZVFSURkH6Lqe0TCZj6tSpH72+lZUVNWvW5NWrV+zYsYPDhw+zbt06vLy8SEtL4/nz56xYsQKAe/fuMX78eDZu3MiUKVMK9BxF1RVWFBFaaxeht3YRemsXobf2KKpaF0v39N9p3bo1OTk5koM6MjKSRYsWkZWV9UnHODs78+eff7Jt2zbpu5UrV9KxY8f/6Yxat27dCA4O5tKlS6hUKi5fvkxwcDBubm7/k+v36NGDa9eu8fr1ayDPFf3s2TOuXr1Keno6w4YNIywsTEqikcvlwjktEAgEAoEA+JfMNJYsWZLAwEAWL15MUFAQhoaG9OnTh8qVK3/0GHd3dyCvbc7KlSvx8/NDR0cHAwMDjh8/zk8//UTlypVJTU2lVatWNGrUSJrRzM3NZfPmzdy8eZMbN25w/vx5SpQoIe03zDe4dO7cGV1dXb755hvGjh3LggULiI6Olv4rZNmyZVy4cAGlUimN98yZMyxZsgSA6dOnY2hoSKlSpVCr1cybNw+FQiH1nMzPtl65ciW1atXi119/pUGDBhgZGWFkZISjoyMAPXv2ZMaMGUycOBGA8uXL06lTpwLrLYwwn5/Cupm6uCL01i5Cb+0i9NYehVXrTx2PTP2++BPBO3mfy3rUqFHMmTMHLy8vhg8fTlZWFkFBQYSEhLB+/XpsbW05deoUU6ZMYdeuXdSvX59+/fphbGzM4sWLUSgUeHt7Y2pqypIlS3BxcaFu3brMnTtXKgSfPHnC3r17iYyMpFOnTixatIgOHTpw7tw5vLy86NSpE76+vsydO5fo6GipP+TJkydZsmQJZ86cYd26dURERLBjxw5CQ0NZt24d4eHh3Lp1i/79+7N9+3bq169PXFwcAwYM4LvvvmPs2LGfpI1KpeLGjRufT3yBQCAQCASfDTs7uw8um/8rZhr/l7zPZZ2/gbRbt24oFAoUCgX79+9n+PDhUtuaDh06cPLkSUJCQjA3NyciIoITJ05IS8C+vr68evWKa9euERUVxf79+6Wkmnnz5tG4cWNu377NxYsXqVu3rjQL+O2339KmTRtpjG5ubri7u5OQkICFhQUHDhzA1dX1g8kxNWvW5MiRI9jY2JCcnMzz588pXbo08fHxBdZIGGE+P4V1M3VxReitXYTe2kXorT0Kq9bCCPOZeJ/LOiEhAdB0N7/PDX3//n3p+PwYQshzYFtYWPDgwQNKlSolFYwAJiYmmJmZERMTQ3x8POXKldO4bsWKFaV4wnr16lGtWjWOHj0q9Y2cOXPmB59LLpezfft2wsLCMDIywtbWlrS0tPfmcH+IorrBtygitNYuQm/tIvTWLkJv7VFUtRZF4zvIdyL7+vq+9d2HXNag6W4uX748UVFRGudfuXKFv/76C2trawDWrVvH7t27AVi4cCF3796lVatWJCUlkZaWJhWOqampJCUlYWFhgZWVFefOndO4blxcHPr6+tJ7Nzc3jh49ikKhoFGjRlKT8ffx/fff8/PPPxMWFkaZMmUAGDly5IeFEggEAoFA8K/hX+Ge/l/yKU7sfLp3787mzZu5c+cOKpWK48ePc//+fdavX4+lpSXNmjVj+/btDB48mLNnz7J//36ioqKoV68e1atXZ86cOaSmppKamsrcuXOpWLEiDRs2pFOnTjx8+JC9e/eSk5PDxYsX+fHHHzXu3alTJ+7fv8++fftwdXX96HOlpaWhq6uLQqEgJyeHQ4cOceHChffmcAsEAoFAIPh3ofWZxujoaBwdHVmyZAlr1qwhKSmJ7777Djc3N3x8fIiKiqJ+/fqsWrWKpUuXApozfra2tmzfvh17e3tOnjzJ2rVriYuLo2zZsnTs2JHRo0cDeY2sly1bxpUrV5DL5TRp0oQpU6ZQtmxZrly5wqRJk3Bzc+OHH34A8oqsSZMmSXvxEhMT8fLy4sqVKygUCkaOHEnfvn0pWbIkc+fOZdasWaxZswa1Wk3p0qWlpWHIS0SZP38+9+/fx9jYmMGDB5OcnEytWrXo0aMHU6dOJTw8nHv37vH69WtWr17NunXr6NSpEx06dGDAgAFER0cTGRnJmTNn0NHRoVmzZrRq1Yrhw4eTnJyMjo4OW7ZsYdasWdjY2GBgYMDhw4d5+vQpM2fOZPXq1ahUKh48eCDNakJe38c7d+7QqFEjDA0NpaJw8ODBXL9+nSZNmqBWq9HT06NixYrcv3+/wL+xcE9/fgqrA6+4IvTWLkJv7SL01h6FVetCHyN4/vx5jh07JuUl3717ly1btkh5yfnF3Pt4X1Zy8+bNqV27NoMHD6Zu3bqcOnVKch+PHDmSvXv3AhAfH8+TJ084c+YML168YNiwYRgbG0tO4cuXL7Np0ybWrFnDwYMHmTZtGm3btsXS0pLVq1fTo0cPvLy8UKvVLF++nN27d/PgwQPS0tIYOnQo7u7ubNu2jSdPnjBy5EiMjY05ePAgoaGh0tLypUuXcHBwwMPDA1dXVx4/fkznzp2ZOHEiQUFBUsZ148aNmTlzJn5+fly6dImtW7dSv3599PX1adeuHfr6+hw5cgQTExPc3Nzo168fgYGBVKpUiXPnzuHn58f27du5f/8+J0+eZNmyZTg6OnLz5k1Gjx7NhQsXaNGiBWq1mjFjxjBmzBiSkpIYMGDAJ81S/h0RI6g9imoUVVFF6K1dhN7aReitPYqq1v9nReOn5iV/iPdlJV+6dOmD7mPI23s4Z84cTExMMDExYejQoWzatEkqGps1a8Y333wDgLOzM1OnTiUqKgpLS0s2bdqEpaUlarWamJgYSpYsKbmMw8PD0dHRwdPTE7lcjq2tLUOHDiUwMPCjzxMWFvbRjOsKFSpI2dT5uLm5YWVlBUD9+vVJTEwkMzOTsLAw+vbty8GDBwHYs2cPjo6OtGvXDoCGDRvSo0cPdu3aRYsWLdDX1+fChQtUq1aNpk2bcujQoX+UPS3c05+fwurAK64IvbWL0Fu7CL21R2HVutC7pz81L/l9fCgrOTEx8YPu4zJlymBqaqqRdmJtbc3z58/fOb78Aih/+vb+/fuMHj2ahIQEqlWrRunSpaXxxsXFUa5cOY1i6+8O6veRn3GtUqmIjY2lQoUKb2Vcf0r29IsXLxg9ejRDhw7F2tpaI3v68uXLNGrUSDpepVJRsWJFAFavXo2fnx+rVq1i/PjxNGzYkLlz51KjRo1PGv+bYyhM/xiKM0Jr7SL01i5Cb+0i9NYeRVXr/zMjzId6BuYjl8s1jBgvX76UXr+ZlfzLL78QHBzM7du32bhxI+XLl5fcx/m86T7Of58fpwd5ey3/3sbmXcTHx+Pt7c24ceNo3bo1FSpUwMXFBYChQ4dSrlw5YmNjNYre2NjYj14X8tzWkZGRjBs3Tpod/HvG9fuyp9/kiy++4Pr161IiTT5WVlZ07dqVa9euce3aNWrXro27uzubN28mNzeXu3fv4unpyalTpwgPD8fc3PyTMq3/zl+J6Tx6nsqj56m8SHvbICQQCAQCgaDoUajd09WqVePatWvEx8eTmZmJv7+/VCB9KCv5Y+5jyJthW7JkCVlZWTx+/JjAwEC6dev20TGlp6ejUqkwNDQEICUlBX9/fwDWr1+Pg4MDarVaclfnX/tTyM+4vnfvHiqV6n+ecd2tWzeOHDnCxYsXyc3NJTMzk9DQULZu3YpcLmfBggWsXr2arKwsSpcujb6+/j/Knp5/5D4zD95m5sHbvMpQfvwEgUAgEAgEhZ5C3afR3d2d33//nU6dOqGnp8eAAQOk2UBLS0vWrl3L6tWrmT17NgYGBnTo0IGBAweiq6vLpk2b8PX1xcnJCaVSyTfffENQUBC6uv//kU1NTXF0dESlUknZ07t378bExITatWtrxOzlM2fOHL755hsmTZpEUlIShoaGeHp64uvrS8+ePQkNDaV79+5s2rQJPz8/yYlsYGAgzT6q1WqWLl3K0aNHiY+PZ/PmzVSvXp369etjb2/PuXPn2LBhA4GBgVSpUuWt/Z0+Pj6kpaXh5eVFTEwMv/zyCytWrCAzM5NSpUrx5ZdfSscqlUqSkpJo3rw52dnZtGzZkpUrV+Lt7U1WVhZ16tSRlverVKlCSEgIO3fuRCaTUblyZYKCggr+w6nVqNUy6VkLm0usOFBYHXjFFaG3dhF6axeht/YorFoXWve0jY0NDx480PjszaIMNFvsrF69WuO7oUOHSq8dHBxwcHB4532sra1Zs2bNB8cybtw4RowYgYuLC+3atcPDw4OXL1/i5eUlmXLyyR/zunXraNiwIUFBQdLS7YABA0hJSSEiIoKkpCRUKhWvX79m4MCBjB8/ntWrV7Nt2zZCQkLo3r07ERERXLx4ke3bt2Ntbc3u3bsZOHAgR44cYdOmTfTr14/GjRvj6enJrVu3cHd3Jz4+Hk9PT5RKJS1atNB4tvj4eMLCwlCpVIwZM0ZjP2VsbCxLliyhU6dOXLlyhYEDB7J7924aNGgg3UdPT49FixaRkpLChQsXKFGiBKdOncLLywulsuAzhekZ6WT95+8vNS2NG9F/FPgagk+jqDrwiipCb+0i9NYuQm/tUVS1LtQzjdrg3LlzKJVKxo8fj0wmw9raGm9vb7y8vD4avfcuVCoVAQEBGBsbM3HiROLj4zl37hxfffUVhw8fplOnThw5cgR/f38qVaoE5BWdYWFhHDlyhOHDh2tcr379+lSrVo0jR44wZMgQzp07h4mJCfb29tIM5LRp0yhdujQAXl5ejBo1ioULFwJQo0YNOnfuDECTJk0oU6YMkZGRNGjQgJycHGlfp6enJzo6OpiYmGikyzx//lwa56dibGSM4j8zjSVMTKha2a7AOgo+TGF14BVXhN7aReitXYTe2qOwal3o3dOFhZiYGF6+fMnXX38tfaZWq8nOzpYcywWhTJky9OrVi3379tG4cWNKliyJjY0N8fHxyGQykpOTyc7OfivWz8bGhujo6Hde848//mD37t0MGTKEKVOm0Lx5cw3zS35RFx0dzZAhQwB49eoVoOmshjwneP409K1btyTndGJiohRjaGNjQ926dQHIzc0tsAbIZMikl7JC9Q+juFFUHXhFFaG3dhF6axeht/Yoqlr/K4tGe3t7abnZysqKihUrcuLECen7tLQ0EhMTuX79+lvLs28mv7yPOnXqcPLkSS5cuIBMJsPPz4+QkBCqVq1KmTJl0NfXJyoqimrVqknnREZGvnepHfKWma9fv05OTg7Tpk3T+C4+Pp6qVatK7z/VwJKTkyO99vb2xsHBgcDAQHR1dUlKSpIaoReUWS61kOvk/WmZGYl+jQKBQCAQFAcKtXtaG7Rp04b09HQCAgJQKpWkpKQwZcoUxo0bR7Vq1Xjx4gWXL19GrVZz6NAh/vzzz3deJ39P444dO1i4cCEJCQl07dqVpKQk4uPjSUhIIDc3l5YtWwJ5S8pPnz5FqVQSEBDA/fv32b17N19//TX379/nyZMnGtf/8ssv8fHxQSaTcfnyZQAyMjIAcHFxoXnz5lKKTtu2bVEoFKSkpPDo0SOaNm1Ks2bNmDBhgjTL6OTkBOQ1FN+yZQspKSncuXOHLl268NVXX9GiRQsgzy1eUCqbG1O9bAmqly1BGRP9Ap8vEAgEAoGg8PGvnGl8ExMTE77//nt8fX0JCAggNzcXe3t7NmzYgKWlJaNGjWLq1Kmkp6fz7bffSsXW+zh16hTe3t5s3LiR+Ph4WrRogYGBASqVCldXVzp16sSFCxcYNmwYvXv3JiMjAwMDA8qXL09AQADW1tbMnDmTQ4cOERkZSUhICAAtW7Zk9erVGjOIa9euBaBHjx6Eh4ezbds2AMaMGUN2djaHDh1CR0dHI0oxMTERlUrFyZMnsbW1pWPHjgwbNozU1FQCAgJQKBSUKlUKJycnDh48yKFDhz44A/ouRPb056ewOvCKK0Jv7SL01i5Cb+1RWLX+1PHI1B+LXhF8EtHR0Tg6OnLgwAHu37/PunXr8PHxYdSoUQwcOJBz584RFhYmHd+8eXMmTZpE+/bt+eqrr/D396dVq1bS9926daNdu3YMHz4cW1tbFixYwNKlSzExMcHT0xMXFxcaNmxIdnY2Z86cwcbGhvv379O5c2fOnDlDVFQUQ4YMISIiQkrGSUtLo3Hjxvzwww/Y2dlha2vL9u3bsbe3Jy0tjbS0NKysrHj58iWPHz9mxowZdOzYEQ8Pj0/SQKVScePGjf+prgKBQCAQCLSDnZ3dB/da/utnGj+F1NRUsrOzJYfyh6hUqRL3798H8tr+KJVKkpOT32tI+ZgxJj/VJiQkBFdXV3788Ucgb2/lm2k5oBlX+LEoRTs7O41z1Wo1q1at4uzZs5QuXZratWuTnZ390TjHdyGypz8/hdWBV1wRemsXobd2EXprj8KqtXBP/w9p27Yta9aswd7e/p3fDx06VMpnjo+Plz6Pjo7G0NCQUqVKSXsUnZ2dGTFiBJC3D3L06NEAzJ07lx07dkjn5htj4uLigLy9haNHj5aKxlKlSqGnp6dh1Mk/FtCIUswvHP8epfgmy5cvJzY2lvDwcOn4jh07FkQmiaLqCiuKCK21i9Bbuwi9tYvQW3sUVa3/9UaYT+FjjumAgAD69OkDwIoVK2jXrh27d+9m7dq1dO7cWSOF5ujRo3Tq1AmAP//8k5iYGNzd3UlOTpaMMdu2bePRo0c4OztTvXp1AGbNmiXlT0PeTGXXrl2pU6cOurq6pKamsmzZMun7T4lS1NPTIzU1FchbutbX10dHR4esrCy2bt3Kw4cP35rNFAgEAoFA8O9EzDS+QX5rnNevX1OhQgVGjx7N0qVLARg2bBienp6Ym5uzc+dOzMzMuHXrFnPmzGHv3r3UqlULyFtWbtWqFenp6ejr61O7dm1CQkKkrGoHBwc8PDxIS0sjNDQUlUrF4cOHadWqFQMGDCAhIQGZTIZCoWD27NlMmjRJGp+trS3GxsbMnz+fU6dO4eDgQHh4OG3btiU7OxsDAwMgrzDds2cPycnJZGRkSFGKjRs3xs7ODkdHR5RKJdbW1owfP55BgwYxduxY+vfvT4MGDQAoUaIEjRs35uHDh9r8CQQCgUAgEBRSRNH4Hy5fvkxwcDChoaFYWFgQHBzMjBkzuHDhAnXr1mXLli3Y29sTGhrKnTt38PX1ZePGjeTm5mr0M9TV1cXU1JTg4GAqVqyIn58fcXFxLF68WON+ERERb2VbT506lbt37+Ln56cRL3j27Fkpc7tRo0asWbOG7OxsTp8+TUJCAtOmTaN///7s2bOHBQsW8PDhQ44dO8ajR49wd3dn27ZtfP3113h5efHixQtCQ0MxMDDA19eXP/74g7Fjx3LlyhWys7P56aefpOdfvXo1Fy5cKLCWwj39+SmsDrziitBbuwi9tYvQW3sUVq0LbfZ0YUVfX5/k5GT27t1LmzZt6N69O+7u7hrJK/koFAo6d+6skfGcz/Hjxxk+fLi0rDx27FgOHDjw0ftnZWV9Urygi4sLhoaG0sylkZERAwYMQCaT0bx5c1QqFUOGDMHQ0JB69epRtmxZYmJiqFq1KidPnuT48eOYm5sDMH36dBo1asSdO3cK9Pwf4+7duwU+R/DPKKr5pUUVobd2EXprF6G39iiqWoui8T80aNAAPz8/duzYQUBAAAYGBvTr149Ro0a9dayFhcU7C0bIy2ouX7689F5HR0eaJfwQnxovWLZsWY3vzczMpMIuf0wlS5aUvpfL5eTm5ko51T169NA4X0dHh+joaNq3b//e53/fs74P4Z7+/BRWB15xReitXYTe2kXorT0Kq9bCPV1AYmNjMTc3JzAwEKVSyaVLl/Dw8KBOnTpvHZuamkq/fv003M4lS5bkwYMHODk5ERsbK32uVqt59uyZxvlz5syRMp+zs7Np37498fHx6OjofDRe8O8zf586E2hpaQnkzYS+6Z5+9OgRFSpU+ODzt27d+pPukU9RdYUVRYTW2kXorV2E3tpF6K09iqrWwj39H37//XeGDh3K/fv30dPTk5Zw81vb5LuMP4a7uztbt27lyZMnKJVK/P39ef78ucYx8+bNo1GjRgBkZmZiYGDAtWvXcHd3Z+XKle90Uf+3WFpa0rp1axYuXCj1eNywYQPdunUjJSXlg88vEAgEAoFAIGYa/4OTkxN//fUXo0aNIikpCXNzc6ZPn86XX36Js7MzY8aMoX79+jx8+BAdHR3S09OZOXMm586dIykpCZVKhaenJwMGDCAuLo7OnTuTnZ2Nrq4uenp6JCcnS/eaNm0a5cuX5+jRo6SkpJCWlkajRo3YvXs3MpmMLl26kJGRgY6ODvXq1dOYTezfvz+DBg1i//79fPHFF6SkpODt7Y2BgQGnTp0C4Pz587x69Ypdu3bx8uVLwsPDcXV1ZenSpSxcuJDWrVuTlZWFjo4OLVu2xNDQECcnJx4/fkyvXr3IyMhALpdjY2NDTk5OgbUURpjPT2HdTF1cEXprF6G3dhF6a4/CqrWIEfwfkh8R2KNHD2bNmsWyZcvYsWMHS5YsoVOnTly5coWBAweye/du5HI5Bw4c4ObNm6xduxYLCwvs7e0xMTEhPDwcfX19jfg+Pz8/IiIipKXufv36IZfLWb58OSVKlGDNmjUcP36cI0eO8OrVK41xZGZmsm3bNvz9/Vm9ejVOTk6sWLGCrVu3MmDAAMaPH8/58+cZM2YM4eHhWFtb07NnTypXrszs2bPJzs5m2rRpGBkZsXLlSvbv38/27dvZsWMHxsbGrF27lrNnz3L48OFP0knECAoEAoFAUHQRMYL/Q7p06YKenh4lS5akRo0adO7cGYAmTZpQpkwZIiMjuXXrFsHBwaxZswZra2u2b9+Ovr4+crmcc+fO4eTk9N7rR0VFERERwdGjR6V9hxMnTiQsLIzz58/z5Zdfaowj32xSvXp12rdvD0CzZs3YsmULI0eORKFQSPshY2NjSUxM5M6dOwQFBWFsbAzAlClTaN++PbNmzcLAwIDo6GhCQkJo2bIl3t7ejBs3rsA6CSPM56ewbqYurgi9tYvQW7sIvbVHYdVaGGE+A286l9+XJT1gwAB27tyJp6cnkLfZVV9fn6ysLMnB/D5evHgBaGZI6+joYG1tTUxMjFQ09u7dmzNnzmBjY8OmTZs0jDP5TmdTU1OuXLlC//79AcjNzSU6OhqVSkWrVq3eGntUVBTOzs5kZ2ezb98+Vq5cibm5OSNHjqRXr14FkanIbvAtigittYvQW7sIvbWL0Ft7FFWtRdFYAD7FqWxjY4O+vj7bt2/Hzs5O+vzx48eSg/l95LfqiYyMlLKsVSoVsbGx78yLBhgxYgQRERGfNH4rKysMDAy4cuWK9MeqVCqJioqiUqVKPHnyhDp16tClSxcyMzM5ceIEU6ZMoVGjRtJ4BAKBQCAQ/DspFkVj/p7DJUuWsGbNGpKSkvjuu+9wc3PDx8eHqKgo6tevz6pVq9DT08PX15eIiAieP39OiRIl6NOnDyNHjgTg5cuXLFq0iPPnzyOXy/nmm2+kxtqOjo7069ePffv2SZF9p0+fZv369cTGxrJixQpSUlJwc3NjxYoVjB49Gj8/P+7cuUNmZiatW7dmxYoVQF4zbx8fH0JDQ1EqlQwbNoy5c+fSqlUr5s2bR4UKFfj5559JTk5GpVJRv379dz77unXrpAjD58+fs2rVKmmsf3ddm5mZIZfLadCgAaVKlcLFxYWUlBTOnTvHjz/+yOLFi/nll1/Q09NDR0eHWrVqoaOjQ4kSJf73P5pAIBAIBIIiRbEoGvM5f/48x44dIyoqii5dunD37l22bNmCQqGgZ8+e/PDDD7x48ULat1eiRAlOnTqFl5cX3333HZUqVcLb2xtjY2NOnTqFQqHA29ubNWvWSPeIjIxkwIABXL16lcuXLzN27FiWLl3KsmXLcHV1JSgoiH79+mFsbMzw4cNRq9VUrVqVwYMHExgYyL59+wDYtm0bKSkp9OjRgzt37lCmTBnGjx/Ppk2bcHNz48aNGygUCuzs7ChZsiQzZsxg2bJlH3z+cePGScvTO3fu1GhMnpWVxdChQ+nSpQsvXrwgIiKCrVu3Ym1tTVBQEOHh4dy5c4eOHTty4cIFUlJSuHbtGj179sTKyqpAv4NwT39+CqsDr7gi9NYuQm/tIvTWHoVV639ljODgwYMxNDSkZs2aWFhY0LVrV2lJ2M7OjpiYGCZOnIiOjg4mJibExcWhr68P5M3S6erqEhERwYkTJ6T+hL6+vrx69YqNGzdia2uLi4sLnTp1AmDy5Mk4OjrSoUMHOnToAEDp0qXZsWMHJ06c4N69e6SlpTFw4ECaNm1Kp06dkMvl9OnTh6+++ooNGzbQvHlzAFJSUnj69CkqlYro6GiOHz9O1apVAXj9+jWNGjUiOTmZM2fO4OjoqPHc06dPJyYmhmvXrnHy5EkqV64MgIeHB2PGjOHBgwccO3YMpVLJrFmzpGX2ixcv4uXlRfXq1bGysqJhw4ZYWVnx8uVLHj9+zIwZM6R+jQVBxAhqj6IaRVVUEXprF6G3dhF6a4+iqnWxKhrfNKfo6Oi8FaenVqtJTExk4cKF3L17FxsbG+rWrQvkGUUSEhIANGIALSwsNPYTvmmGSUxMpHbt2hpjsLGxkQwvq1evxs/Pj1WrVjF+/HgaNmzI3LlzMTMzQ6lUasQLlixZknr16nHr1i3g/XF/fzfg5BMfHw+gcc381BmAmJgYXr58yddffy19plaryc7OJjExET09PVatWsXZs2cpXbo0tWvXJjs7m3/SkUm4pz8/hdWBV1wRemsXobd2EXprj8Kq9b/SPf0pRhVvb28cHBwIDAxEV1eXpKQk9u7dC4C1tTWQ154mf7bu0aNHHDlyhKysLABGjx5NeHg4ZmZmlC9fnsjISI3rR0VFYWFhQW5uLnfv3sXT05Pp06fz7NkzFi9ezNSpU9m3bx96eno8e/ZMmk1MTExky5YtDBo0CHh/3F9+Ybtr1y6mTJkifZ+/hPxmDOG0adM0vq9YsSInTpyQPrO1tWXZsmWULl2auXPnEhsbS3h4OCYmJgB07Njxo3q+i6LqCiuKCK21i9Bbuwi9tYvQW3sUVa3/dTGCqampGBgYoKOjw8uXL1mwYAGQlwFtaWlJs2bNWLp0qZTUsmzZMqKioti+fTsAGzZskGb73NzcCA8P5/jx46hUKmkPpZubG3K5nAULFrB69WqysrIoXbo0+vr6lCpVCrlcTpcuXfDz8yM+Pp6srCxWr17NjRs3Phr3l0+fPn00nqtcuXI0b96cxYsXk5ycTEJCgsYfZJs2bUhPTycgIAClUildy9/fH5lMRlpaGvr6+ujo6JCVlcXWrVt5+PAh2dnZn/PnEAgEAoFAUEQoVjONn8LixYtZtGgRW7duxdTUlA4dOvDFF1/w8OFDlEolycnJXL16la+//hodHR1atGjBmTNnpDi9zZs3ExMTw86dOzEzM0NXV5dly5Yxbdo0qUj7/vvvuXz5MqNHjyY4OJjmzZuTlZWFTCZDR0eHjh074u7ujr6+Pt27dyc9PR19fX2USiUtW7akefPm5Obm0qVLF5KSkpDL5VSrVo3vvvtO6v+4bds2ZsyYAcD+/fuZPHkyKSkp6Ovr07JlS0xNTTWKRn19fb755htWrVrF8uXLpb2c+c27u3btyvjx42nQoAFqtRpjY2Ps7e15+PCh1n4bgUAgEAgEhZdiUTTa2Njw4MEDjc/Cw8M13vv6+kqvjx8//tY14uLiaNu2LWvWrMHBwYGkpCQ8PDwoWbIkN27c0Ij+Cw0N5c6dO/j6+rJx40Zyc3OZO3cud+/exc/PD2tra3bv3s2MGTM4cuQIKpWKDh06cPDgQapWrcqFCxcYM2YMp0+fZsyYMbRv357OnTvj7e1NamoqEydOxMjIiPPnzxMaGsq0adPo27cvzs7O5Obmcvr0aWm/poeHB4cPHyYwMJAqVaqwbt06QkNDCQ8PZ+DAgbi6ugKwfv16bty4wYkTJzA3N2fu3LkcOnRIMvwsWrQId3d3vLy8UKvVLF++nJMnT3L+/PkC/x7CPf35KawOvOKK0Fu7CL21i9BbexRWrf+V7un/htKlS3P06FEqVqxIWloacXFxlCpVSjKY/B2FQkHnzp2Ry+VkZWVx5MgR/P39qVSpEgADBgwgLCyMI0eO4OLiglqtZs+ePTg5OdG0aVNu3LiBXC4nJCQEhUIhuboNDAyYNWsWzs7O0v7FN+/1dw4cOMCIESOoXr06AKNGjaJVq1ZvGVgOHTrEyJEjpbSZmTNnamRKb9q0CUtLS9RqNTExMZQsWfK9z/4xhHtaexRVB15RReitXYTe2kXorT2KqtaiaPwPCoWCI0eOsGfPHmQyGTVr1iQtLQ1d3XdLZGFhIRVxycnJZGdnY2Njo3GMjY0N0dHRlCtXjh07dhAQEMDIkSNRqVS4uroyadIkEhMTKVeunMZScv518l3Yb97r7yQkJGg4pvX09DSSaPJ5/vy5ZPSBPLe2qamp9P7+/fuMHj2ahIQEqlWrRunSpf+RcxqEe1obFFYHXnFF6K1dhN7aReitPQqr1v9K9/R/w/Hjx9m5cye7d++WZgu7devGH3/8IR0zfvx4fv75Z0DTqV2mTBn09fU1nMuQ1wjcwcGBxMREJkyYwNixY/Hz88Pd3Z2QkBCqVKlCmTJluHv3Ll9++SV169Zl165dkiPbwsKCx48ff9AVbm1tzbNnz6T32dnZLFu2jKFDh2ocZ2VlRVRUlPQ+IyOD1NRUIK9dj7e3N+vWrcPBwQGAkydPcurUqYKJ+B+KqiusKCK01i5Cb+0i9NYuQm/tUVS1/te5p99HamoqcrkcAwMD1Go1P/30E/fv3/+k2Ta5XI6bmxsrV67k6dOnKJVKtm3bxqNHj3B2diY2NpYXL15IM4YrV67EyMiIUqVKUbZsWbKzs3FzcyMwMJCEhAQWLlxIkyZNNPpFvg9XV1cCAwN58uQJOTk5bNq0idOnT0t7FfPp3r07AQEB/Pnnn2RlZeHr6yvtYUhPT0elUmFoaAjktffx9/cH8rKpC0pSunBcCwQCgUBQ3CiUM435WdIzZ85k48aNZGZm4uDgwJw5cwgKCuL69eskJycTFRWFv78/1atXZ+XKlZw9e5bs7Gzs7OyYNm2a1Gsx37Ry+/ZtjI2N6d69O15eXshkMum7e/fukZubS9u2bTEwMKBatWrY2dnx22+/vbNwSk9Px9XVlZiYGJRKJV999RVfffUVAwcO5Pnz5xgaGlKuXDnc3d3x9/fH0NCQCRMm8Pr1a7Kzs2nQoAEKhYIxY8YAsHv3bkJDQ9HT06NChQokJSXx1VdfYWZmRmZmpnRfBwcHlEolt2/f5syZM0ycOJGAgAB69OghFb5ubm789NNP3Lx5k+vXr/Pw4UOWLVvG69ev6dOnj+TWlslkLF++nOXLlzN58mQmTZpESkoKKpVK+i+g9evXM3bs2AL9fsmZSiwx/mc/vkAgEAgEgkJJoSwa8zl16hRhYWGoVCrGjBnDvHnzqFixIpcuXWLr1q3Ur18ffX19Bg0ahFwu58CBA5QoUYI1a9YwcOBAjhw5Qk5ODoMHD6Zfv34EBgYSFxdHv379sLS0pE2bNgwYMIBx48axdetWnj59yujRoxk8eDA9e/bEz88PmUyGnp4eixcvZt26dQBUr16drKws5s6dS/369YmLi2PAgAHUrVuXs2fP4ufnx/r16/Hz85PGaGxsjIeHB66urvTr14/GjRvTtm1btmzZQv/+/bl37x4Afn5+hISEsH79emxtbTl16hRTpkzh1q1b1K9fH8hbDj927BgA9+7dIzU1lYEDBzJx4kR++eUXhg8fTkxMjJQh7ebmxokTJxg7dixZWVlcvnyZtWvXUrZsWbZs2cLgwYM5duwY7dq1o0OHDoSFhWm4vHv37q2RhPMx1OrC5wwrbhRWB15xReitXYTe2kXorT0Kq9bFwj09bdo0SpcuDYCXlxejRo1i4MCBVKhQgaZNmwJ5CSgREREcPXpUSlCZOHEiYWFhnD9/HqVSib6+PmPGjEEmk1GxYkWCgoIwMjLi8OHDVKtWTWqUXb16dYYMGcLOnTvp2bPne8dVs2ZNjhw5go2NDcnJyTx//pzSpUtruI3fHGNB2L9/P8OHD6dOnToAdOjQgZMnTxISEiIVjU5OThoRiQAjRoxAoVBIWda9evXC1NQUU1NTatSoQXR0tOTgXrt2reSiHjNmDHv37uXcuXPUq1fvvS7vgpCWkc6NG48L/OyCglNUHXhFFaG3dhF6axeht/YoqloX6qIx35ACeYaP/Obbb856vXjxAkAqgiBvg6m1tbXkPra2ttYwk+RH98XExHDnzh0aNWokfZebm/vRzalyuZzt27cTFhaGkZERtra2pKWlaex/LMjM3Ju8ePFC41kgz019//79D147fw9j/tjflbv98uVLMjIy8Pb21igEs7OziYmJwcnJ6b0u7/xm4J+CiZEx1avYffLxgoJTWB14xRWht3YRemsXobf2KKxaFwv3dHx8vFTgRUdHY2hoSKlSpTQKwHyzSGRkJDVq1ADyHj42NhYLCwt0dHR49uwZarVaOu/06dOkpaVhZWWFvb09gYGB0vWSkpJIT0//4Li+//57fv75Z8LCwihTpgyQN/t34cIF6ZhPycF+F+XLl9dwOUPebOqbheK7rv0p9ytVqhT6+vps3boVOzs7qWG5hYUFlpaWJCYmolKp8Pf3Jzc3l99++w0vLy+qVKnyVmzhh5DJKFT/GIozRdWBV1QRemsXobd2EXprj6KqdaF2T69YsYK0tDTi4+NZu3YtnTt3fqtvYtmyZWnVqhULFiwgISGBzMxMli9fjkqlok2bNrRu3ZqcnBw2btyIUqkkMjKSRYsWkZWVRceOHblx4waHDx8mJyeH58+fM3LkSI30mHeR379RoVCQk5PDoUOHePr0Kbm5uf/1M3fv3p3Nmzdz584dVCoVx48fJzw8nK5du/7X15bL5XTr1o0VK1YQFxcHwIULF3BxceHp06fExsYyePBgLl26hFwux9LSEuAtJ/bHMDUQPRoFAoFAIChuFOqZxooVK+Li4sLr16/p2LEjkyZNYvPmzW8dt3TpUpYvX07Xrl3JyMjAzs6Obdu2YWZmBkBgYCCLFy8mKCgIQ0ND+vTpg7u7OwABAQEsX76cBQsWoKOjQ+vWraVM5/cxePBgrl69SpMmTcjNzaVEiRJUqVJF6pcYFRUlLXtbWloyYsQI6dycnByio6O5e/cuQUFBGg3B4+Pj+f3338nIyMDNzQ3I66+4cuVKGjdujK2tLSVLlsTX15f58+fTuHFjjWLy9OnTrF+/HoDJkyczaNAg+vfvD+TNvi5evJijR4+SmZlJx44dAThx4gRr165FX1+fpUuXYmBgwKBBgwAwNTWlX79+fPfdd5/+owElDeSFbpNvcaOwbqYurgi9tYvQW7sIvbVHYdX6U8cjU//T2I/PSH7LnTNnzryVslIYSEpKol27dgwbNoxBgwZx69Ythg8fzhdffMGMGTNwd3dn2bJlODo6cvPmTUaPHs2yZcto0aIFfn5+HD58mA0bNryVFT1y5EjMzMzw8fFBT0+P7du3s3r1an755ReMjY2xtbWlRo0abNiwATMzMyZPnkxycjI//PADly9fZujQoSxdupR27drx4MEDRo8ezaBBgxg4cCBr1qzh2LFjBAQEaGRP5+dpd+jQAUdHx/8qe1qlUnHjxo3PJ7xAIBAIBILPhp2d3QeXzQv1TGNh5dy5cxgaGjJs2DBkMhlfffUVbm5u3Lt3jz179uDo6Ei7du0AaNiwIT169GDXrl20aNHig1nRCxYswNjYGIVCQWxsLMbGxmRmZpKcnIyxcV7fQ29vb8koM3nyZNq3b090dDShoaE4OjrSoUMHAOrUqcPw4cPZsWMHAwcO1Gr2tIgR/PwU1s3UxRWht3YRemsXobf2KKxaFwsjzH9DVlYWSUlJWFlZ/c+vHR8f/5Yju2LFity7d4+YmBguX76s4chWqVRUrFgR+HBWdFRUFEuXLuWvv/6icuXKknv8zb2SbzrK86+TkJBAYmIitWvX1hinjY2N5CDXZvZ0Ud3gWxQRWmsXobd2EXprF6G39iiqWhfKotHGxoYHDx78V9fo3bs3ffr0wdXV9X80qv+PlZUVMTEx5ObmSq1r8o0lVlZWdO3aFR8fH+n458+fSwWYnp4emzZtokWLFqjVasaPH8+pU6coU6YMqampTJgwgd69eyOTybh9+7bGbCDkFaw1a9YE8pbxIa94LF++vJRZnU9UVJTUu1Kb2dMCgUAgEAiKH4XaPf3fkJSU9Nmu7eDggFqtxs/PT4r027dvHwDdunXjyJEjXLx4kdzcXP766y/69u3L1q1bARg2bBgJCQk8efKE2NhYjh07RqlSpThy5AhZWVkYGBggk8mIjY1l2bJlQF4fxXz8/f1JSEggJSWFJUuW0KJFCywtLXFzcyM8PJzjx4+jUqm4e/cuW7ZskQw12syeFggEAoFAUPwolDON/y2DBw8mNjaWOXPmsHXrVlJTU2nUqBHnz59n+PDhDB06lICAAPbu3UtSUhJVqlTB29ubFi1aAHkFp4+PDz/99BNmZmYMHDiQxYsXc+rUKWxsbHj27BmVKlVi06ZNrF+/HjMzM9q0aUNMTAx//vknVlZWTJo0iZcvXyKTyahevTpeXl4APH78GAMDA/r160dCQgIAKSkpBAUFsWjRIhYtWiS5t83NzTExMeHhw4dUqVIFgOTkZFq1akVubi4tW7bkwoULHDx4kDVr1iCTyZg7dy5TpkxBpVIhk8m4cuUKvXr1YtiwYWRkZNCtWzcyMzPR1dVFR0eHJ0+eYG9vz+TJk/Hy8iItLQ3Ia7Mjl8t5+PAhdevWLZD+KpWq0DnDihuF1YFXXBF6axeht3YRemuPwqp1sYgR/Kds3boVBwcHPDw8KF++PP3796dq1ar4+vqSlZWFv7//W/nOo0ePZteuXdSvX5+JEycik8k4c+YMubm5TJw4URI0KSmJ/v374+rqKhWkEydOJDk5mR07dhAaGsqff/7J2LFjGTp0KPfu3WPAgAGcO3cOZ2dn5HI5tWvXxtfXV3KJHzt2DBsbG0JDQ8nJySEoKIjGjRsTERGBh4eHRnpLUlISZ8+exdDQkJSUFBwdHTl//jzHjh0jKiqKLl26UKNGDTZv3oxCoaBnz5788MMPeHh4ULZsWczMzNiwYQPVqlXj0KFDLFmyhG+//ZY+ffqwZs0avv/+e+zt7bl79y59+vT5R70n7969+z/7LQUfpqhGURVVhN7aReitXYTe2qOoal0si8Z30a1bNxQKBQqF4oP5zpaWlly8eJHjx49LfR6nT5+Os7MzAGfOnEGhUDBx4kR0dHQwMDBg1qxZODs7SzOHBgYGjBw5EplMRv369bG1teXJkycfHeP+/ftxd3eXMqubNm2Ku7s7e/bsoW3btgDUr19farqdkpIC5M2sGhoaUrNmTSwsLOjatat0jJ2dnWSG2bVrFyNGjKBWrVqSJiEhIRw+fJjevXtjYGBASEgIubm5NGzYkF9//bXAudMg3NPaoLA68IorQm/tIvTWLkJv7VFYtf7Xu6f/zt/zqt+X75zfoPvN/pBvHpuYmEi5cuU0fuz8Y/OLM3Nzcw1ntUKh+CQn8vvGFR4eLr1/VzpLfnGrUqlQq9XvzJ3OH9+SJUtYvny59H1OTg5169bFwMCA3bt3s379eiZMmEBaWhpOTk7MnDlTw2X9KRRVV1hRRGitXYTe2kXorV2E3tqjqGr9rykaY2JipIbhH8p3zm9jExMTI+0jzC8GIS8bOjY2FpVKJf3g+a5lCwsLHj9+/I/HaGNj80EHdOPGjd/Z7Dy/QB03bhwZGRnvvb6VlRVeXl7SrGn+2M3MzEhLS+P58+esWLECgHv37jF+/Hg2btzIlClT/vEzCQQCgUAgKB4UW/e0np6e1FLm73wo37ls2bK0adOGZcuWkZycTHJyMkuXLpXObdWqFQDLly8nMzOThIQEFi5cSJMmTShfvvx/NeZu3boRHBzMpUuXUKlUXL58meDgYMkB/TE+5hjv0aMHGzZs4M8//wTycqednZ25evUq6enpDBs2jLCwMNRqNWXLlkUulxc4d1ogEAgEAkHxpNjONHbr1o1Vq1ZpLNUChIWFcfjwYV68eEHv3r2BvObZTk5ONG7cGICFCxfStm1bmjdvjpGRkdSKpkuXLujq6tKjRw/OnDlDUFAQarWaKlWqsGfPHiCvqfirV69o1aoVSqWSJk2aSC1zoqOjOXDgAA0aNMDBwYFXr14Bec25bWxs+O6774iIiGDEiBFkZWWhUChwdHSkU6dOQJ7zOjU1latXr3Lr1i1pBhJgxowZXLt2jdzcXLZt24arqyuRkZFcunSJly9fcuXKFVxcXOjYsSOjR48mNjYWyFt6nzJlCnPmzMHHx4d58+YxadIk1Go1JiYmBV6aBuGe1gaF1YFXXBF6axeht3YRemuPwqr1v9o9DTB06FCGDh0qvc9vhH3nzh327t1Lbm4uAwcOpF69elSoUIHQ0FDp2BMnTpCbm8vly5c5efIk06ZNQyaTceHCBQ4cOMCCBQv47rvvCAkJ4dGjR7i7u/PHH3/w9ddfc+nSJRo0aMDSpUsxMDDA19cXAA8PD409j5s3b0apVDJo0CCpkLx8+TL79u17Kz96+/btDBw4kBYtWnDo0CFWrFjB1q1bOXLkCDNnziQrK4uFCxcSGRlJ48aN8fT0JCMjg4EDB9KpUyc8PDx4+fIlXl5eNGnShJMnTxIaGsq0adMYNmwYzs7O5Obm4unpSZs2bTSyrxctWoSLi4sUY/gpCPe09iiqDryiitBbuwi9tYvQW3sUVa2LbdH4PkaOHEmJEiX466+/aNGiBbdu3cLT05PVq1dLjqa1a9dSsWJF9PX1yczMREdHhyZNmmBoaEjz5s1RqVQMGTIEQ0ND6tWrR9myZYmJiaFq1aqcPHmS48ePY25uDuQ5rxs1asSdO3ckw8qwYcOkGVAHBweuX78O8NH8aIDWrVtL33fp0oU9e/Zw7NgxPD09NZ7z3LlzKJVKxo8fj0wmw9raGm9vb7y8vJgwYQKQZ9Dp3Lmz5JD+lOzrT0G4pz8/hdWBV1wRemsXobd2EXprj8KqtXBPvwczMzN27drFiRMnaNy4MSqVCgsLC2kWr1atWuTm5kqFYk5ODnp6elI6S36B9XeHcm5urjST2KNHD4176ujoEB0dLRWNJ0+exM7OjoyMDI4ePUpMTAx9+vTBwMDgg/nRAJUrV9b43traWmr18yYxMTG8fPmSr7/+WvpMrVaTnZ1NYmIikGfcebOlzqdkX38KRdUVVhQRWmsXobd2EXprF6G39iiqWv/rikaAly9fvvWZm5sb8+bNo1mzZpiZmXHgwAEgb/Zv3bp10szhh8jvjXj8+HGN/YaPHj2iQoUKUnHXp08fIM+hHBUVRcOGDdm1axezZ8/+oHsa8nKi3yQ6OlrKi34TKysrKlasyIkTJ6TP0tLSSExMpHTp0gAabYGys7MZMWIE48eP/2D29afwV2I6cp0sAMyM9Chjol/gawgEAoFAIChcFOmiMTw8nM2bN/P06VMyMjKoV68eCxYs4LfffmPPnj3UqlWLI0eOYGRkhIuLC5A3y7dp0yZUKhU3btzAzs6OtLQ0fv75ZxITExkzZgzVqlXjxYsXlClThqSkJGJiYvD19WX//v188803QF6T7wEDBgB5M3g+Pj5s2LCB1q1bM2zYMJKSknj9+jWWlpY8evSIAQMG0LdvXwC2bduGvb0948ePB+DGjRvs27ePrl270rt3b7755huysrKwsrIiMTGRfv36SWPPzMzkwoULlCtXjm+++YZbt25hb29Ps2bNpHt+8cUXrF27lqioKFxcXNizZw+5ublMmTKF33//HX19fRITE8nNzeXp06dUqlSJ7OxsMjIyWLduHatWrdIoVN/Mvv4U5h+5T/Z/2lIu6FJXFI0CgUAgEBQDimzRGBcXh7e3N2vWrMHBwYGkpCQ8PDzw9/enadOm3Lx5kwYNGnDp0iUePnzI4MGDAXByciIjI4OIiAgpqm/69Omkp6fTvXt39u7dS82aNfHw8GD37t3S/dLT0/n55595/PgxJ06c4Mcff5SKxszMTAwMDGjSpAnR0dH4+PhgYmJCTk4OGRkZ5ObmvrUn8Ntvv2XLli3079+fRo0a0b17d/z8/ChZsiSmpqY8e/aMxMREUlJSaNasmXSeoaEhtWrV4vbt22RmZgJ5Rd25c+dYtWoVgYGBTJkyhTNnznD79m2GDx9OixYtUCgUmJmZSTGCv/zyC8uWLWPw4MEcO3aMxMRE5HI5urq6ZGZmkpKSwqtXryhdurRG9vUnoVajVsv+81Jd6FxixYHC6sArrgi9tYvQW7sIvbVHYdW62LunS5cuzdGjR6lYsSJpaWnExcVRqlQpafnWzMyMiRMnolAoqFevHr169eL69esazbE9PT1JTEzkm2++4fjx41y8eJHmzZuzYMECybzi5OTE0qVL6dKlC3p6etSqVYuQkBDc3d1JSEjAwsKCL7/8krp16yKTyTh27BgDBw6UzCaQ1/4H8vYnNm7c+K02QDt27ADyYgS9vLyk5WsAb29vQkNDsbOzA6BSpUrS8VeuXKF///6MGDEChULBhAkTCAwMZPHixZiamtKsWTPs7Oxo2rQpY8aMoWHDhqxdu5YKFSrg7u5Ojx49aNWqFefOnaNevXrIZDK+++47nJyc+PLLL5HL5f8oRjA9I52s//z9paalcSP6jwJfQ/BpFFUHXlFF6K1dhN7aReitPYqq1kW2aFQoFBw5coQ9e/Ygk8moWbMmaWlp6OrmPVL58uVRKBTS8dbW1pw8efKt6+SbTNzc3Hj9+jUGBga0aNHiLfPKmzGE9erVo1q1ahw9epSOHTty8eJFZs6cCcCzZ89wcnLSuMffowHfx4fiDfMxMDB467z8Btz5m2rfFSP48uVLMjIy8Pb21igEs7OziYmJwcnJiR07dhAQEMDIkSNRqVS4uroyadIk9PULtrxsbGSM4j8zjSVMTKha2a5A5ws+TmF14BVXhN7aReitXYTe2qOwal3s3dPHjx9n586d7N69W3L5zp8/nxMnTrBv3z6eP3+OWq2WzB7R0dFSROCb5JtXVCoVPXr0wMfHB3jbvCKTyTh8+DCbNm3i6NGjuLm5cfToURQKBY0aNZJmMPNjBt8kNjaWqlWrSq8PHz6s0SKnQYMG6OnpYU+2iAAAIPVJREFUYWVl9d54ww/xpqHlfZQqVQp9fX22bt0qzVpCXsNwS0tLEhMTUalU+Pv7k5uby2+//YaXlxdVqlTRmPn8JGQyZNJLWaH6h1HcKKoOvKKK0Fu7CL21i9BbexRVrYtsjGBqaipyuRwDAwPUajU//fQTBw8elNrDJCQksHnzZrKzs7l16xb79u2je/fuAOjr65OWloZarcbS0pLWrVvj4ODAuHHjyM7OZsOGDXTr1o2UlBSNe3bq1ImjR49Kr+/fv8++fftwdXWVjunRowd79+7l1q1b5OTksH//fm7cuCF9X65cOSnhJZ8lS5Zw5coVevbs+d54Q8gr/PKfoaDI5XK6devGihUriIuLIzc3lwMHDuDi4sLTp0+JjY1l8ODBXLp0CblcLhXT/yRGcJZLLRZ0qcuCLnUxMxL9GgUCgUAgKA4U6plGPz8/QkJCeP36NRUqVGD06NGYmJgwadIkunTpwqtXr2jZsiV6enp88cUXDBgwgM2bN5Oamoqenp7kBC5ZsiTe3t506NCBtLQ07t+/z/3796lVqxZt27ZlypQpfP/997i4uPDixQsqVqyIsbEx7du3p2bNmtJ48tvvhIeHU7JkScqWLcv9+/dZuHAhe/bsYcKECTg5OREZGcngwYNJT09HJpOhr6/P48ePgbwZz+joaPr27cuwYcMAmDhxIh06dGDhwoU8ePCAnj17olQq0dPTo2PHjjRq1AjIy5betWsXW7ZsISkpienTpwMQHBzM1q1biYuLA+DJkyesWrWKBw8eSEv3AFOmTGHs2LE4OjqSk5ODvr4+o0aN4osvvgCgX79+DB8+HKVSiY6ODnXr1qVt27YF/t0qmxuL5t4CgUAgEBQzCm3RePnyZYKDgwkNDcXCwoLg4GBmzJjBihUriI+PJzIyksuXL/PixQuGDRtG06ZN8fLyIjY2lrCwMExNTfn55585ePAg06ZN49tvvwWQnNIXL16UYv6mT5/O7t27GT58OI6OjtSsWZPFixdLMX/u7u7Y2NgQEREhje/QoUOkp6fTtWtXFixYwNq1a5k3bx5Lly6lXLlyZGVlsXHjRlq0aIGTkxMnT57k0aNHuLq6EhERQalSpbh16xa2trZs2bIFe3t7Ll++zPHjx98bI5h/neDgYKysrChZsiSTJk0iLCyM4OBglEolzs7OrF+/nqCgIKytrRkyZAgZGRmSplevXuX777+nYcOG/PTTT4wfP5727dtTo0YNjh07ho+PD127diU6OppevXoRHh7+1h7NjyGypz8/hdWBV1wRemsXobd2EXprj8KqdZF3T+vr65OcnMzevXtp06YN3bt3x93dnYiICGQyGXPmzMHExAQTExOGDh3Kpk2bGDt2LADVq1cnNTUVmUyGs7MzU6dOJSoqCl1d3f8q5i+fZ8+eERkZSVJSEiVLluTJkyd4e3szbtw4vv/+e/z9/WnZsiUtW7bk+PHjxMfHs2bNGmnJ9318SoygnZ2dxuwnQN++faWx16hRgy+++IJq1aoB0KRJE3799VcAdu7cSa9evaSUmDZt2uDg4MCePXuYNWsW+vr6HD9+HDMzM77++mvOnz//j9zTIntaexRVB15RReitXYTe2kXorT2KqtaFtmhs0KABfn5+kqPXwMCAfv360bBhQ0xNTTX22llbW/P8+XPpvZGREampqQDSMqlKpfrkmL8yZcpI3+nq6qJWqzWO37t3L9u3b6dt27bcvn2bXbt2YW5uzsiRI+nbty/btm3j559/pmHDhlStWpUVK1bw1VdfUaJEiQ8+c2Ji4kdjBN9liskfd/6zmJqaSu/z3dOQ5xS/cuUKP/zwg2SeUalUNGnSBMhrOu7n58e8efNISEigRYsWzJ07Fysrqw+O+++I7OnPT2F14BVXhN7aReitXYTe2qOwal3k3dOxsbGYm5sTGBiIUqnk0qVLeHh44OfnR2pqKq9fv8bQ0BB42xldqVIljcbc+fw95s/Pz4+IiAhatmzJ2rVr2bx58yeNzdvbm06dOpGTk0ONGjXIzMzkxIkTTJkyhUaNGtGsWTNev37NihUrgLzZvvbt279lgPk75cuXf2eMoFKp5MqVK8C7ndKf4p6GvN6WkZGRHDp0CBsbGzZu3MjPP//MwoULycrK4tGjR8ydOxddXV2ePHnCzJkzWbRoEWvXrv2k6+dTVF1hRRGhtXYRemsXobd2+X/t3XlU1PX+x/EniywJBSiFB1NvZnhdUhG3cEm9aSmmgMv1llZmmhtXDPoZqeGtTG4qpTfNWDSTsnLJRE3MpTQXIis1M70qouBRA1woZQTm9wfH75HrMiA6A+Prcc6cI9/vzHzf846j7z7fz3veyrf1VNdcV9nu6T179jB8+HD279+Pi4uLcTv5wIEDFBcXExcXR2FhIYcPHyYpKcn4Au0budwp/eabb5Kfn2+sPr733nt8+OGHFYpv06ZNjB07luPHj+Pm5oaXlxfOzs54enoSGhrK+vXr2bp1KyUlJeTn57N8+XKLK43h4eFs3LiRtWvXUlxczL59+0hISKhQXDfyxBNPcOnSJeN7H4ODgzl48CCpqak4ODgwYcIEkpOTKSoqwtfXF2dn55vqns7M/YP/njpfqcfvBYW37HOLiIhI5VXZlcaePXuSmZnJqFGjyM/Pp1atWsTExBjfd3jPPffQvXt3AP7+978zfPhwi++5a9cuTp48yYEDB/j6668xm83Gbe9BgwaxaNEiAGNayscff8yFCxfw8vLCZDIBpR3MERERHDx4kBMnTjBgwAD++OMPatSogZubG+Hh4bRv356YmBji4uI4eLB0GkpJSQmbN2823iMsLIzMzEwAvvrqK9q0aUOLFi2YNWsWsbGxjB8/HkdHR4KDg42uaIDCwkImT57Mpk2bjJnQl88fP36c9PR0PDw86NatG2fPnsXb25tatWpRXFzM22+/DcDYsWNxcXHBxcWFe+65x5ht/cQTT/Dee+8ZK6T+/v7MmDGjwv/tXl/1KyZz+VY/r+eNfk3xdq+yv542V1U3U9sr5du6lG/rUr6tp6rmuto3wgCMHDmSkSNHljl2+TZtZGQkkZGRV71m+vTpVx377bffyM/Pp0ePHrzwwgt8/vnn7N69mxEjRtCkSRNjrnLdunVZtGgRQ4cO5ciRI2zYsMHozp47dy7jx48nPT29TAezu7s7ISEhNGvWjNjYWMxmM1OnTuWzzz5jxYoVODs7ExAQQFJSktEhPX/+/Ks6pOvXr8+zzz7Lr7/+iqenJ59++im1atUiNja2zGcbMmQIeXl5rFixAk9PT959913i4+ONAhpK9zF+8cUXRvd3QEAATk5OpKam0r17d77++mvq1q1r3J6/PP7w66+/ZuXKlTRo0IBDhw7xj3/8g82bN1f4uyGvHCN4szR+sHyq62bq6kr5ti7l27qUb+uprrmu0kXjrbR582bc3d154YUXcHBwoHXr1oSHh/Prr79e9VxL3dlXdjBv376dY8eOsWzZMjw8PACYOnUqbdu2Ze/evWWmr4DlDumVK1fy4osvGuMEJ02axJdffgmU7m9MT09n9erV+Pr6AqXf8bhq1Sq++eYbWrRoAVju/r6Wzp07ExgYiJ+fH3l5eeTn5+Pl5WXM8q6IK8cI3iyNH7yxqrqZ2l4p39alfFuX8m09VTXX1b4R5lY7efIkderUKdM0Uq9evWsWjZa6s6/sYM7NzcXb29soGAE8PDzw8vIiOzv7qqLRUof0qVOnqFOnjnHu7rvvNrqhf//9d6DsLGsnJyfq1KlDdna2UTRa6v6+FrPZTHx8PJs2bcLHx4e//vWvXLp0qVyvvcoVYwRvlsYPlk913UxdXSnf1qV8W5fybT3VNdfVrmhs164dv/32W4Vf5+fnR3Z2NiUlJcZ3D165V/BKlrqzryw8/f39yc/Pp6CgwCgcz58/T35+vrEaCDBt2jRWrlzJpUuX+Pjjj4mKigJg4cKFxMfHGw0q/zt/+s8//zS+Psjf3x+ArKwsGjVqBJT+30FOTk6Za92MGTNmkJOTw8aNG43P0adPn5t6r8khjXF0qtyvlsYPioiIVC1Vtnv6VuvWrRtms5k5c+ZgMpnYu3cvn3/++TWfW5Hu7ObNm/Pggw/y2muvcf78ec6fP09sbCz16tUjMDAQKN1jeHmT6UsvvYTJZDI6pBcuXEiNGjUYN24cjRs3ZsCAASQmJnLo0CEKCwuZPn268dp7772XLl268MYbb3D69GkuXrzIjBkzKC4upmvXrhZz4OrqCkBBQcFV5woKCnB1dcXJyYnCwkKSk5M5cOCA0WxTEQ1q1eTBez0r9ajt4Vrh64qIiMjtU+1WGm/W3XffTVJSErGxsSxYsID69evTs2dPjhw5cs3nX687+9dff+Xo0aNs3LjRmIf94osvEhkZyfbt2zGZTDzyyCMEBAQwadIkpk+fTrNmzdizZw/x8fHUr18fT09PEhISjD2STk5O7Ny5kzFjxtCqVSs++eQTevfuDUCLFi2M29Nz5szhwoULHDx4kE6dOuHq6orZbOapp55i6NChRjf2gQMHmDp1KhkZGbi6uhq3u2vXrk1gYCD9+vXDxcWFmjVrGl8MPn78eMaPH09gYCBmsxlXV1fuv//+m5ruojGCt19V7cCzV8q3dSnf1qV8W09VzbVddE/fao0bN2bJkiXXPBcWFlbm52t1Z+/YsYPc3FzS0tKumocNsG3bNuO5EydONP7cuXNn3NzciIyMZPny5bi7u7N8+XKgdAV07NixhIWFcfjwYZ5//nmioqIYPHgwJ06cYNy4cTz55JO0a9eO9PR0MjIySE5O5uGHH8bV1ZUePXrwzTffkJycjIeHB+Hh4URFRZGUlETTpk15+eWXycvLA0q7yPft28fs2bPp3r07P//8M6NHj2bLli106tQJd3d3xowZw5gxY8jPz+eZZ54hODi4wnnWGEHrqa4deNWV8m1dyrd1Kd/WU11zfUcVjZV1o3nYt8KqVasICAjgmWeeAUon27z00ktEREQQExMDlDbBdOjQoczrwsPDjVF/Dz/8MAUFBbRq1QqAjh07Mm/ePACWLFlC9+7d6dGjBwCBgYEMHDiQlJQUY+Vyy5YtNGzYkA4dOrBy5cqbmj2tMYK3X1XtwLNXyrd1Kd/WpXxbT1XNtbqnryMzM5MGDRrc1GtvNA/7VsjNzS3TGQ2lndUXL14kNzcXqPzs6R07dhAUFGScLy4upl69egC88847zJkzh/j4eCZMmEBgYCCxsbFG0015VdeusOpIubYu5du6lG/rUr6tp7rm+o5phIHS26YhISE3fM6NurOvnIednp5OXFwc77//Pj/88AOAMTUGSqe+XOnw4cPGLevz58+XmWCzZs0aWrVqxcqVKzl8+HCZ12VlZRmTW6Bys6f9/PwIDQ0lIyPDeKxbt44PPviAkpIS9u3bx7hx40hLS2Pjxo3UqlWrzG12ERERuXPdUUXj+fPnb6ob+LLrzcNu27Ytzs7OrF69Gijd27hjx47rvo+npyeJiYnGz9999x1xcXGsXr2ao0eP8uGHH2IymcjKymLWrFn06dPnltzu7d+/P6mpqcZM7MzMTJ5++mmSk5NxdHTkjTfe4J133qGwsBAfHx9cXV1vava0iIiI2B+7uz19/PhxunfvzqRJk3j//fe5ePEi3bp14/nnn+eFF14ASm8zJycn07JlSz766CNSUlLIzc3loYceIiYmhmbNmgGlTSodO3Zkw4YN+Pr6snz5crZu3cqAAQMwmUw4OTkRHBxMy5YtiYmJYe7cuUyZMgUnJyeKiorYtGkT27dvvyrGc+fOMWTIEGbPnk1OTg5ms5moqCh69epFYmIiU6ZMYfr06ZjNZjw9Penbty8lJSVA6Yrl5dnX+fn5uLi48Morr3Dp0iWSk5M5duwYvr6+7N27lzfeeINffvkFs9nM0aNHjdnWr732mnFdHx8fY4/kO++8Q3R0NC1btqSkpAR3d3d69epFUVERzs7l/1VR9/TtV1U78OyV8m1dyrd1Kd/WU1Vzfcd3T6elpbFq1SqKi4sZM2YMSUlJJCQkMHToUGOsXkpKCgsWLGDevHk0bNiQlStX8txzz7F27Vpjqsru3btZu3YtULof8osvvriqu/nf//43kyZNwt3d3bhl3aJFC5YtW8aoUaPYvHmzMfElLCyM7Oxs0tPT8fb2Zv/+/QQEBJCQkGDMps7KymLmzJllZlM7Ozszbtw4srOzy8y+vvvuuwkICGDVqlV8+umnmEwmevfuzejRo1mwYAF16tTh+eef5/333+ett97CwcGBs2fPsmjRIgIDA/n222+JjIzks88+o1GjRuTn5zNt2jRCQ0M5fvw4gwcPZsOGDfTs2bPcuVf3tPVU1w686kr5ti7l27qUb+uprrm226LxlVdewcfHB4CIiAhGjRrFk08+WeY5KSkpjBw5ksaNGwOlt2+XLl3Kl19+ybBhwwDo2bOnMcd5wYIFN+xuXrFiBYMGDTI6lwcMGEDDhg1xc3Mrd9yWZlND2dnXlz399NNGQ0yjRo1o0qQJDRs2BKB9+/bGvsvFixczePBg2rRpA0DXrl3p1q0bS5YsYfLkybi6urJ27Vq8vLxo06YN33zzTYU7qNU9fftV1Q48e6V8W5fybV3Kt/VU1Vzf8d3T9evXN/5cp04dTCYTZ8+eLfOc7Oxs4uLimDFjhnGsqKjIuD0NV8+ZvlF38+nTp8uMGwQq3FltaTb1/8Z0WUU6qNPT0/nkk0+M88XFxbRv3x6ADz/8kDlz5jB16lROnz5Np06diI2NNb7Spzyqa1dYdaRcW5fybV3Kt3Up39ZTXXNtt0XjyZMneeCBB4DSfY7u7u5XNXX4+fkRERFhTF+B0m7lKwuw/50znZaWVuY9ruxurlOnDidOnChzPj4+/qoVzhvx9/cnKyurzLHL+xSvFdONjl2Ln58f/fr1Y8SIEcaxnJwc3NzcKCws5L///S+xsbE4Oztz5MgRJk2axLRp05g9e3a5P4OIiIjYH7vtnp45cyYFBQWcPHmS2bNn07dvX2rWrAmUdlEDDBw4kHnz5nHo0CEAtmzZQu/evfn++++v+Z69e/fm0KFD1+1uDgsL49NPP2X37t2UlJSwbNkyUlJSKtSBHB4ezsaNG43Z1Pv27SMhIYHw8PBKZgTjMy9atIjdu3cDpfsqwsLCSE1NxcHBgQkTJpCcnExRURG+vr44Ozurg1pERETsd6WxXr16hISEcOHCBfr06UN0dDTFxcW0bt2aTp068e677/Lss89iNpsZPXo0p06d4r777mPKlCnGzOn/VbduXRITE5k1axZz5szBzc2NkJAQY4Z0nz59OHfuHNHR0Zw+fZoHH3yQhIQEY29lebRo0YJ3332X9957j5iYGLy9vRk8eLDR+V1Zjz/+OH/++ScxMTHk5OTg5eXFs88+y5AhQ3BwcGDevHnExcUxf/58nJyc6Ny5M1FRUbfk2iIiIlJ9OZgvb3azE5e/cmfDhg3UrVvX1uHcUYqLi/npp59o3ry5GmFus8u5btmyZbXcF1PdKN/WpXxbl/JtPVU11+WNy25vT4uIiIjIraOiUUREREQssrs9jXXr1r3u7GgRERERuTlaaRQRERERi1Q0ioiIiIhFKhpFRERExCIVjSIiIiJikYpGEREREbFIRaOIiIiIWKSiUUREREQsUtEoIiIiIhapaBQRERERi+xuIozYjtlsBkoHnxcXF9s4Gvt2Ob/Ks3Uo39alfFuX8m09VTXXl+O5/O/49TiYLT1DpJxMJhN79uyxdRgiIiJyE5o3b46Li8t1z6tolFumpKSEoqIiHB0dcXBwsHU4IiIiUg5ms5mSkhKcnZ1xdLz+zkUVjSIiIiJikRphRERERMQiFY0iIiIiYpGKRhERERGxSEWjiIiIiFikolFERERELFLRKCIiIiIWqWgUEREREYtUNEql5ebmMnr0aIKCgmjXrh1vvvkmRUVFtg7L7uXl5fHYY4+xc+dOW4di1/bv389zzz1H27ZtCQ4O5uWXXyYvL8/WYdmt7du3M2DAAAIDAwkODub111/n4sWLtg7LrhUXFzNkyBAmTpxo61Ds2po1a2jSpAmtWrUyHtHR0bYOq0JUNEqljR8/nrvuuostW7awdOlStm/fzsKFC20dll374YcfGDRoEFlZWbYOxa5dvHiR4cOH06pVK7Zu3UpqaipnzpwhJibG1qHZpby8PEaOHMngwYPJyMhgxYoVpKen88EHH9g6NLv2n//8h4yMDFuHYff27NlD3759+fHHH43H22+/beuwKkRFo1TK0aNHSU9PJzo6Gnd3d+6//35Gjx5NSkqKrUOzWytWrCAqKorIyEhbh2L3cnJyaNy4MWPGjMHFxQVvb28GDRrE999/b+vQ7JKPjw/btm0jLCwMBwcHzpw5Q2FhIT4+PrYOzW5t376dtLQ0evToYetQ7N6ePXto1qyZrcOoFBWNUikHDx7Ey8uL++67zzjWsGFDcnJyOHfunA0js18dO3Zk/fr19OrVy9ah2L0HHniAxMREnJycjGPr1q2jadOmNozKvnl4eADQpUsX+vTpg6+vL2FhYTaOyj7l5uby6quvMnPmTNzd3W0djl0rKSnhl19+YfPmzXTt2pXOnTszefJkzp49a+vQKkRFo1TKH3/8cdVfNpd//vPPP20Rkt3z9fXF2dnZ1mHcccxmM/Hx8WzatIlXX33V1uHYvbS0NL799lscHR2JiIiwdTh2p6SkhOjoaJ577jkaN25s63DsXl5eHk2aNKFnz56sWbOGJUuWkJmZWe32NOpfHqmUu+66iwsXLpQ5dvnnmjVr2iIkkVuuoKCAV155hV9++YXFixcTEBBg65DsnpubG25ubkRHRzNgwADOnj3LPffcY+uw7Mb8+fNxcXFhyJAhtg7ljlC7du0y27bc3d2Jjo5m4MCBFBQUGCvsVZ1WGqVSGjVqxJkzZ/j999+NY4cOHcLPzw9PT08bRiZya2RlZREeHk5BQQFLly5VwXgb7dq1i8cffxyTyWQcM5lM1KhRQ7dPb7GVK1eSnp5OUFAQQUFBpKamkpqaSlBQkK1Ds0v79+9nxowZmM1m45jJZMLR0REXFxcbRlYxKhqlUho0aEDr1q2ZNm0aBQUFHDt2jLlz59K/f39bhyZSaWfPnuWZZ54hMDCQpKQkNWTcZgEBAVy8eJGZM2diMpnIzs4mLi6O/v37V6t/WKuDr776il27dpGRkUFGRgYhISGEhISoi/o28fLyIiUlhcTERIqKisjJyeHtt98mNDS0Wv1uq2iUSps9ezZFRUV0796dgQMH0qlTJ0aPHm3rsEQqbfny5eTk5LB27Vpat25d5vvV5NarWbMmiYmJHDx4kODgYIYMGcIjjzyirziSas/Pz4/58+ezYcMG2rZtS3h4OM2bN2fKlCm2Dq1CHMxXrpWKiIiIiFyDVhpFRERExCIVjSIiIiJikYpGEREREbFIRaOIiIiIWKSiUUREREQsUtEoIiIiIhapaBQRERGpZvLy8njsscfYuXNnuV+zbt06QkJCaNmyJY899hhLly6t0DVVNIqIiFVkZmbaOgQRu/DDDz8waNAgsrKyyv2aHTt2MHHiRKKjo/nxxx95/fXXmTp1Krt37y73e6hoFBG5Qxw5coT/+7//o3PnzrRq1Yq//e1vzJgxgz/++OO2XzslJYXJkyff9uuI2LsVK1YQFRVFZGTkVee2bdtG//79CQoKonfv3nz55ZfGuYULFzJ06FC6dOmCg4MD7du3Z9myZdSrV6/c11bRKCJyB9i1axehoaH4+/vzxRdf8OOPP5KQkMDPP//MsGHDKC4uvq3Xz8vLu63vL3Kn6NixI+vXr6dXr15lju/fv59Ro0YxYsQIdu7cyeuvv860adPYsmULALt378bLy4sRI0bQrl07+vbtS1ZWFl5eXuW+topGEZE7wJQpU+jXrx8RERH4+PgA8Je//IX4+Hhq1arFsWPHyM7OZvz48XTo0IHg4GBeeuklTp06BcDOnTsJCAgo854TJ05k4sSJAMyZM4eIiAiioqIICgqic+fOzJw5EyhdGZk/fz4ZGRkEBQVZ8VOL2B9fX1+cnZ2vOr5kyRK6d+9Ojx49cHJyIjAwkIEDB5KSkgLA2bNnSUpKYtSoUXz33XeMGTOGyMhIfv7553JfW0WjiIidy8rK4uDBg4SEhFx1rnbt2sydOxd/f3+GDRuGk5MTaWlprF27FoAXX3yRoqKicl0nLS2Njh07GqscCQkJ/PTTT4SGhjJy5EiCgoLIyMi4pZ9NREplZ2ezfv16goKCjMdHH33EiRMnAHBxcSE8PJxWrVrh7OxMjx496NChA+vWrSv3Na4uVUVExK5cvjVcu3bt6z4nIyODY8eOsWzZMjw8PACYOnUqbdu2Ze/eveW6ToMGDejXrx8AXbp0wdfXl8zMTFq2bFmp+EXEMj8/P0JDQ/nXv/5lHDt16hRmsxmAhg0bYjKZyrymuLjYOF8eWmkUEbFzvr6+AJw+ffqa53///Xdyc3Px9vY2CkYADw8PvLy8yM7OrtB1LqtRowYlJSU3GbWIVET//v1JTU1l69atlJSUkJmZydNPP01ycjIAgwcP5pNPPmHbtm2UlJSwbt06du7cec07ENejlUYRETvn7+/PQw89xJo1a2jTpk2Zc7m5uXTt2pWxY8eSn59PQUGBUTieP3+e/Px8fH19cXQsXWMwmUy4uLgAkJ+fj7e3t3U/jIhcU4sWLZg1axazZs3in//8J+7u7oSEhDBhwgQAwsPDcXR05K233uL48eP4+/sTHx9P06ZNy30NFY0iIneAyZMnM3z4cGrVqsVTTz2Fl5cX+/fvZ8qUKTRt2pRhw4axevVqXnvtNWJjYwGIjY2lXr16BAYGkpeXh7OzM6tXryY0NJRt27axY8cOnnjiiXJd39XVlYKCAsxmMw4ODrfxk4rcOX777bcyPz/66KM8+uij131+aGgooaGhN3093Z4WEbkDtG3blsWLF7Nv3z569+5NYGAgERERtG/fnsTERGrUqMH8+fMpKiqiZ8+edO3alUuXLrFgwQKcnZ259957iYmJYe7cuQQGBrJ48WLCwsLKff2uXbty5swZWrduzblz527jJxWR28XBXJEdkCIiIiJyR9JKo4iIiIhYpKJRRERERCxS0SgiIiIiFqloFBERERGLVDSKiIiIiEUqGkVERETEIhWNIiIiImKRikYRERERsUhFo4iIiIhYpKJRRERERCxS0SgiIiIiFv0/pMmXXdNiEi4AAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(df_clean.loc[df_clean[\"original_label\"] != \"human\"], y = 'order')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "std", "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": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 2 }