diff --git a/Cohort_Selection_Using_MIDRC_Temporal_COVID_Test_Data.ipynb b/Cohort_Selection_Using_MIDRC_Temporal_COVID_Test_Data.ipynb index 9c8b4f9..c9e57d7 100644 --- a/Cohort_Selection_Using_MIDRC_Temporal_COVID_Test_Data.ipynb +++ b/Cohort_Selection_Using_MIDRC_Temporal_COVID_Test_Data.ipynb @@ -20,38 +20,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Install some Python packages:" + "# Python packages:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting gdcm\n", - " Using cached gdcm-1.1-py3-none-manylinux1_x86_64.whl (2.7 MB)\n", - "Installing collected packages: gdcm\n", - "Successfully installed gdcm-1.1\n" - ] - } - ], + "outputs": [], "source": [ - "## May need to install these Python packages. \n", - "## We should add them to the base workspace image so this is unnecessary..\n", + "# The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n", "\n", - "# !pip install --upgrade --ignore-installed PyYAML\n", - "# !pip install --upgrade pip\n", - "# !pip install --upgrade gen3\n", - "# !pip install cdiserrors\n", - "# !pip install pydicom\n", - "# !conda install gdcm -c conda-forge -y\n", - "# !pip install pylibjpeg" + "#!pip install --upgrade pandas\n", + "#!pip install --upgrade --ignore-installed PyYAML\n", + "#!pip install --upgrade pip\n", + "#!pip install --upgrade gen3 --user --upgrade\n", + "#!pip install cdiserrors\n", + "#!pip install --upgrade pydicom" ] }, { @@ -63,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,115 +60,87 @@ "import gen3\n", "import pydicom\n", "import matplotlib.pyplot as plt\n", - "import pylibjpeg\n", - "#import gdcm\n", "\n", "from gen3.submission import Gen3Submission\n", "from gen3.auth import Gen3Auth\n", "from gen3.index import Gen3Index\n", - "from gen3.query import Gen3Query\n" + "from gen3.query import Gen3Query" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import some custom Python scripts from personal GitHub repo\n", - "home_dir = \"/home/jovyan/pd\"\n", - "gen3_dir = \"{}/gen3\".format(home_dir)\n", - "demo_dir = \"{}/demos/Find_COVID_Positive_Imaging_Studies\".format(home_dir)\n", - "os.system('mkdir -p {}'.format(gen3_dir))\n", + "# change these directory paths to reflect your local working directory\n", "\n", - "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O /home/jovyan/pd/gen3/expansion.py\")\n", - "%run /home/jovyan/pd/gen3/expansion.py\n", + "home_dir = \"/Users/christopher\" \n", + "demo_dir = \"{}/Documents/Notes/MIDRC/tutorials\".format(home_dir)\n", "\n", - "os.chdir(demo_dir)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# # Initiate instances of the Gen3 SDK Classes using credentials file uploaded. This is unnecessary with functioning WTS\n", - "# api = 'https://data.midrc.org'\n", - "# cred = '/home/jovyan/pd/gen3/midrc-credentials.json'\n", - "# auth = Gen3Auth(api, refresh_file=cred) # authentication class\n", - "# sub = Gen3Submission(api, auth) # submission class\n", - "# query = Gen3Query(auth) # query class\n", - "# exp = Gen3Expansion(api,auth,sub) # class with some custom scripts\n", - "# exp.get_project_ids()" + "os.chdir(demo_dir)\n", + "\n", + "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O {}/expansion.py\".format(demo_dir))\n", + "%run expansion.py\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2022-05-13 16:03:15,148][WARNING] Unable to find refresh_file\n", "Getting all project_ids you have access to in the data commons.\n", - "['NCU-A2', 'NCU-R2', 'Open-A1', 'Open-A1_PETAL_BLUECORAL', 'Open-A1_PETAL_FIRECORAL', 'Open-A1_PETAL_REDCORAL', 'Open-R1', 'TCIA-COVID-19-AR', 'TCIA-COVID-19-NY-SBU', 'TCIA-COVID-19_CT_Images', 'TCIA-RICORD-1a', 'TCIA-RICORD-1b', 'TCIA-RICORD-1c']\n" + "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1', 'TCIA-RICORD']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christopher/Documents/Notes/MIDRC/tutorials/expansion.py:213: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead.\n", + " df = json_normalize(res[\"data\"][\"project\"])\n" ] }, { "data": { "text/plain": [ - "['NCU-A2',\n", - " 'NCU-R2',\n", - " 'Open-A1',\n", - " 'Open-A1_PETAL_BLUECORAL',\n", - " 'Open-A1_PETAL_FIRECORAL',\n", - " 'Open-A1_PETAL_REDCORAL',\n", - " 'Open-R1',\n", - " 'TCIA-COVID-19-AR',\n", - " 'TCIA-COVID-19-NY-SBU',\n", - " 'TCIA-COVID-19_CT_Images',\n", - " 'TCIA-RICORD-1a',\n", - " 'TCIA-RICORD-1b',\n", - " 'TCIA-RICORD-1c']" + "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1', 'TCIA-RICORD']" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "## Initiate instances of the Gen3 SDK Classes using the WTS (no need for uploading credentials.json)\n", + "# Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n", + "# Change the directory path in \"cred\" to reflect the location of your credentials file.\n", "\n", - "api = 'https://data.midrc.org'\n", - "auth = Gen3Auth(api) # authentication class\n", + "api = \"https://data.midrc.org\"\n", + "cred = \"{}/Downloads/midrc-credentials.json\".format(home_dir)\n", + "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n", "sub = Gen3Submission(api, auth) # submission class\n", "query = Gen3Query(auth) # query class\n", "exp = Gen3Expansion(api,auth,sub) # class with some custom scripts\n", - "exp.get_project_ids()" + "exp.get_project_ids()\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", - "## 1. Will the date of the imaging exam and the date of the COVID test be provided in the metadata?\n", + "## How can one associate the date of an imaging exam with the date of COVID-19 test results for a patient?\n", "---\n", "\n", - "Specific dates are not allowed, but given a single \"index_event\" for a case, \"days to X from index event\" properties are provided.\n", + "Specific dates are not allowed in the MIDRC data commons, but given a single \"index_event\" for a case, \"days to X from index event\" properties are provided.\n", "\n", - "For example, one can query or export the imaging_study node, which has \"days_to_study\", and the measurement node, which has \"test_days_from_index\", and merge into a single table on \"case_ids\" to create a temporal timeline of imaging studies and COVID-19 tests for a cohort of patients.\n" + "For example, one can query or export the imaging_study node, which has \"days_to_study\", and the measurement node, which has \"test_days_from_index\", and merge into a single table on \"case_ids\" (the unique, de-identified patient identifiers) to create a temporal timeline of imaging studies and COVID-19 tests for a cohort of patients.\n" ] }, { @@ -195,64 +154,56 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christopher/Documents/Notes/MIDRC/tutorials/expansion.py:248: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead.\n", + " json_normalize(\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/TCIA-RICORD-1c_imaging_study.tsv has 998 records.\n", - "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/TCIA-COVID-19-NY-SBU_imaging_study.tsv has 7363 records.\n", - "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/TCIA-COVID-19_CT_Images_imaging_study.tsv has 650 records.\n", - "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/Open-A1_imaging_study.tsv has 14528 records.\n", - "\n", - "Output written to file: node_tsvs/imaging_study_tsvs/NCU-A2_imaging_study.tsv\n", - "node_tsvs/imaging_study_tsvs/NCU-A2_imaging_study.tsv has 0 records.\n", - "Removing empty file: node_tsvs/imaging_study_tsvs/NCU-A2_imaging_study.tsv\n", - "\n", - "Output written to file: node_tsvs/imaging_study_tsvs/NCU-R2_imaging_study.tsv\n", - "node_tsvs/imaging_study_tsvs/NCU-R2_imaging_study.tsv has 0 records.\n", - "Removing empty file: node_tsvs/imaging_study_tsvs/NCU-R2_imaging_study.tsv\n", - "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/Open-A1_PETAL_REDCORAL_imaging_study.tsv has 3169 records.\n", + "node_tsvs/imaging_study_tsvs/Open-A1_imaging_study.tsv has 46083 records.\n", "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/TCIA-RICORD-1b_imaging_study.tsv has 120 records.\n", + "node_tsvs/imaging_study_tsvs/Open-A1_PETAL_REDCORAL_imaging_study.tsv has 3170 records.\n", "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/TCIA-COVID-19-AR_imaging_study.tsv has 256 records.\n", + "node_tsvs/imaging_study_tsvs/TCIA-RICORD_imaging_study.tsv has 1238 records.\n", "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/Open-R1_imaging_study.tsv has 2426 records.\n", + "node_tsvs/imaging_study_tsvs/Open-R1_imaging_study.tsv has 25231 records.\n", + "length of all dfs: 75722\n", + "Master node TSV with 75722 total records written to master_imaging_study.tsv.\n", "\n", - "Output written to file: node_tsvs/imaging_study_tsvs/Open-A1_PETAL_BLUECORAL_imaging_study.tsv\n", - "node_tsvs/imaging_study_tsvs/Open-A1_PETAL_BLUECORAL_imaging_study.tsv has 0 records.\n", - "Removing empty file: node_tsvs/imaging_study_tsvs/Open-A1_PETAL_BLUECORAL_imaging_study.tsv\n", - "\n", - "Output written to file: node_tsvs/imaging_study_tsvs/Open-A1_PETAL_FIRECORAL_imaging_study.tsv\n", - "node_tsvs/imaging_study_tsvs/Open-A1_PETAL_FIRECORAL_imaging_study.tsv has 0 records.\n", - "Removing empty file: node_tsvs/imaging_study_tsvs/Open-A1_PETAL_FIRECORAL_imaging_study.tsv\n", - "File previously downloaded.\n", - "node_tsvs/imaging_study_tsvs/TCIA-RICORD-1a_imaging_study.tsv has 120 records.\n", - "length of all dfs: 29630\n", - "Master node TSV with 29630 total records written to master_imaging_study.tsv.\n" + "rows:75722, columns:24\n" ] } ], "source": [ "# Export all the records in the imaging_study node\n", - "st = exp.get_node_tsvs(node='imaging_study')" + "st = exp.get_node_tsvs(node='imaging_study')\n", + "print('\\nrows:{}, columns:{}'.format(st.shape[0],st.shape[1]))" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rows:21962, columns:24\n" + ] + }, { "data": { "text/html": [ @@ -279,10 +230,14 @@ " project_id\n", " submitter_id\n", " age_at_imaging\n", + " age_at_imaging_gt89\n", " body_part_examined\n", " case_ids\n", - " days_to_study\n", - " series_count\n", + " days_from_study_to_neg_covid_test\n", + " days_from_study_to_pos_covid_test\n", + " ...\n", + " loinc_method\n", + " loinc_system\n", " study_description\n", " study_location\n", " study_modality\n", @@ -295,16 +250,20 @@ " \n", " \n", " \n", - " 9011\n", + " 2\n", " imaging_study\n", " 00043f69-d5a6-479f-9309-71c253b6c09b\n", " Open-A1\n", " 2.16.840.1.114274.1818.53927660811344067041430...\n", " NaN\n", " NaN\n", - " 10000364-1375261\n", - " -339.0\n", " NaN\n", + " 10000364-1375261\n", + " 829,799\n", + " 642\n", + " ...\n", + " XR\n", + " Chest\n", " XR CHEST PA AND LATERAL\n", " NaN\n", " DX\n", @@ -315,5432 +274,511 @@ " 10000364-1375261\n", " \n", " \n", - " 9012\n", - " imaging_study\n", - " 00066b23-4297-460a-a7e1-2e483fd27a47\n", - " Open-A1\n", - " 10000364-1519515_2.16.840.1.114274.1818.572394...\n", - " NaN\n", - " NaN\n", - " 10000364-1519515\n", - " -1.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " DX\n", - " 2.16.840.1.114274.1818.57239462775962082858120...\n", - " NaN\n", - " True\n", - " c6e03e4a-e0ce-4103-8c00-81fd06d555e2\n", - " 10000364-1519515\n", - " \n", - " \n", - " 9031\n", - " imaging_study\n", - " 005618eb-8df1-40de-ba6a-a27184c62f34\n", - " Open-A1\n", - " 10000364-1607825_2.16.840.1.114274.1818.550767...\n", - " NaN\n", - " NaN\n", - " 10000364-1607825\n", - " -40.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " DX\n", - " 2.16.840.1.114274.1818.55076773370404755863752...\n", - " NaN\n", - " True\n", - " 35f564eb-c9b4-4870-a1fb-626a8f79b3d6\n", - " 10000364-1607825\n", - " \n", - " \n", - " 9039\n", - " imaging_study\n", - " 0092e28f-5d61-4760-9b75-937e16a04d78\n", - " Open-A1\n", - " 10000364-1465830_2.16.840.1.114274.1818.567734...\n", - " NaN\n", - " NaN\n", - " 10000364-1465830\n", - " 35.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " DX\n", - " 2.16.840.1.114274.1818.56773440092156751147307...\n", - " NaN\n", - " True\n", - " df555ac4-3f1a-4272-a240-6f4d7cc296a7\n", - " 10000364-1465830\n", - " \n", - " \n", - " 9042\n", + " 5\n", " imaging_study\n", - " 009adbf3-abf5-40e4-9f3a-4074076cfca8\n", + " 000cafbb-3d80-401c-9de8-42b937f81cab\n", " Open-A1\n", - " 10000364-1418152_2.16.840.1.114274.1818.486873...\n", - " NaN\n", - " NaN\n", - " 10000364-1418152\n", - " -324.0\n", + " 2.16.840.1.114274.1818.54486547105285539711177...\n", + " 57.0\n", + " No\n", + " CHEST\n", + " 10000364-1204441\n", " NaN\n", " NaN\n", + " ...\n", + " XR\n", + " Chest\n", + " XR CHEST PA AND LATERAL\n", " NaN\n", " DX\n", - " 2.16.840.1.114274.1818.48687302357140701881833...\n", + " 2.16.840.1.114274.1818.54486547105285539711177...\n", " NaN\n", " True\n", - " ad573d81-2ff2-42db-91c9-ccda6f1626f2\n", - " 10000364-1418152\n", + " 302d1b1b-b902-47d7-b17c-92b68a398c7d\n", + " 10000364-1204441\n", " \n", " \n", - " 9043\n", + " 11\n", " imaging_study\n", - " 009ca121-c289-4249-ab5b-94fc7530f2e7\n", + " 001192b4-3d01-4a3e-a863-5d7eb06ff208\n", " Open-A1\n", - " 10000364-1070656_2.16.840.1.114274.1818.548398...\n", - " NaN\n", - " NaN\n", - " 10000364-1070656\n", - " 45.0\n", - " NaN\n", - " NaN\n", + " 2.16.840.1.114274.1818.46473000059984716974550...\n", " NaN\n", - " DX\n", - " 2.16.840.1.114274.1818.54839864627065410741641...\n", " NaN\n", - " True\n", - " 3036d286-a022-4008-8c7e-b7440c95def3\n", - " 10000364-1070656\n", - " \n", - " \n", - " 9067\n", - " imaging_study\n", - " 00f57c3b-d7c5-4e53-8e0e-d02f9cea8206\n", - " Open-A1\n", - " 10000364-5636388_2.16.840.1.114274.1818.544657...\n", " NaN\n", + " 10000364-2615190\n", " NaN\n", - " 10000364-5636388\n", - " 124.0\n", " NaN\n", + " ...\n", + " XR\n", + " XXX\n", " NaN\n", " NaN\n", " DX\n", - " 2.16.840.1.114274.1818.54465758664852955678989...\n", + " 2.16.840.1.114274.1818.46473000059984716974550...\n", " NaN\n", " True\n", - " f212791d-300a-42ba-afb0-8b3e73dc5c04\n", - " 10000364-5636388\n", + " 66a32b7a-a78c-492c-a084-e6c482bb8711\n", + " 10000364-2615190\n", " \n", - " \n", - " 9070\n", - " imaging_study\n", - " 0104b55a-6288-4754-8a37-494df95a95ad\n", - " Open-A1\n", - " 2.16.840.1.114274.1818.51607810898232549052164...\n", - " NaN\n", - " NaN\n", - " 10000364-2470802\n", - " -114.0\n", - " NaN\n", - " XR CHEST PA AND LATERAL\n", - " NaN\n", - " DX\n", - " 2.16.840.1.114274.1818.51607810898232549052164...\n", - " NaN\n", - " True\n", - " e313882b-bed5-4ba2-9d40-5c6491538332\n", - " 10000364-2470802\n", + " \n", + "\n", + "

3 rows × 24 columns

\n", + "" + ], + "text/plain": [ + " type id project_id \\\n", + "2 imaging_study 00043f69-d5a6-479f-9309-71c253b6c09b Open-A1 \n", + "5 imaging_study 000cafbb-3d80-401c-9de8-42b937f81cab Open-A1 \n", + "11 imaging_study 001192b4-3d01-4a3e-a863-5d7eb06ff208 Open-A1 \n", + "\n", + " submitter_id age_at_imaging \\\n", + "2 2.16.840.1.114274.1818.53927660811344067041430... NaN \n", + "5 2.16.840.1.114274.1818.54486547105285539711177... 57.0 \n", + "11 2.16.840.1.114274.1818.46473000059984716974550... NaN \n", + "\n", + " age_at_imaging_gt89 body_part_examined case_ids \\\n", + "2 NaN NaN 10000364-1375261 \n", + "5 No CHEST 10000364-1204441 \n", + "11 NaN NaN 10000364-2615190 \n", + "\n", + " days_from_study_to_neg_covid_test days_from_study_to_pos_covid_test ... \\\n", + "2 829,799 642 ... \n", + "5 NaN NaN ... \n", + "11 NaN NaN ... \n", + "\n", + " loinc_method loinc_system study_description study_location \\\n", + "2 XR Chest XR CHEST PA AND LATERAL NaN \n", + "5 XR Chest XR CHEST PA AND LATERAL NaN \n", + "11 XR XXX NaN NaN \n", + "\n", + " study_modality study_uid \\\n", + "2 DX 2.16.840.1.114274.1818.53927660811344067041430... \n", + "5 DX 2.16.840.1.114274.1818.54486547105285539711177... \n", + "11 DX 2.16.840.1.114274.1818.46473000059984716974550... \n", + "\n", + " study_year study_year_shifted cases.id \\\n", + "2 NaN True 420af82e-4a6f-4a4e-8f82-051a3adfc901 \n", + "5 NaN True 302d1b1b-b902-47d7-b17c-92b68a398c7d \n", + "11 NaN True 66a32b7a-a78c-492c-a084-e6c482bb8711 \n", + "\n", + " cases.submitter_id \n", + "2 10000364-1375261 \n", + "5 10000364-1204441 \n", + "11 10000364-2615190 \n", + "\n", + "[3 rows x 24 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Filter the imaging_study data for only studies that have a non-null \"days_to_study\" and \"DX\" study_modality\n", + "s = st.loc[(~st['days_to_study'].isna()) & (st['study_modality']=='DX')]\n", + "print('rows:{}, columns:{}'.format(s.shape[0],s.shape[1]))\n", + "s.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christopher/Documents/Notes/MIDRC/tutorials/expansion.py:248: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead.\n", + " json_normalize(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File previously downloaded.\n", + "node_tsvs/measurement_tsvs/Open-A1_measurement.tsv has 43284 records.\n", + "\n", + "Output written to file: node_tsvs/measurement_tsvs/Open-A1_PETAL_REDCORAL_measurement.tsv\n", + "node_tsvs/measurement_tsvs/Open-A1_PETAL_REDCORAL_measurement.tsv has 0 records.\n", + "Removing empty file: node_tsvs/measurement_tsvs/Open-A1_PETAL_REDCORAL_measurement.tsv\n", + "\n", + "Output written to file: node_tsvs/measurement_tsvs/TCIA-RICORD_measurement.tsv\n", + "node_tsvs/measurement_tsvs/TCIA-RICORD_measurement.tsv has 0 records.\n", + "Removing empty file: node_tsvs/measurement_tsvs/TCIA-RICORD_measurement.tsv\n", + "File previously downloaded.\n", + "node_tsvs/measurement_tsvs/Open-R1_measurement.tsv has 45619 records.\n", + "length of all dfs: 88903\n", + "Master node TSV with 88903 total records written to master_measurement.tsv.\n", + "\n", + "rows:88903, columns:15\n" + ] + }, + { + "data": { + "text/html": [ + "
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typeidproject_idsubmitter_idcase_idstest_days_from_indextest_methodtest_nametest_result_textcases.idcases.submitter_idconditions.idconditions.submitter_idprocedures.idprocedures.submitter_id
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9086imaging_study014819db-b6a4-4771-a70f-92f095192d5bOpen-A12.16.840.1.114274.1818.55908318442052087227659...NaNNaN10003752-e46VS8SPBU2GwExoQZnQw140.0NaNXR PORT CHEST 1VNaNDX2.16.840.1.114274.1818.55908318442052087227659...NaNTrue911fd6a8-ecbd-4798-aeb8-5d8ca8ab95b610003752-e46VS8SPBU2GwExoQZnQw
9104imaging_study019efd72-1d0d-4756-9603-4b91331a1888Open-A12.16.840.1.114274.1818.53637607404012607865191...NaNNaN10000364-1199747-284.0NaNNaNNaNDX2.16.840.1.114274.1818.53637607404012607865191...NaNTruec6d55c8b-124a-4b01-a7c9-33f2fa70cf9c10000364-1199747
9108imaging_study01ae7064-27a3-45fd-ac65-b53303adf8daOpen-A12.16.840.1.114274.1818.48804153751524269081294...NaNNaN10000364-148641012.0NaNXR CHEST PA AND LATERALNaNDX2.16.840.1.114274.1818.48804153751524269081294...NaNTrueea8249ea-0250-4919-ad84-cda3dd44ea1f10000364-1486410
9111imaging_study01ba6f56-f1cc-46d3-a8ee-29aa87bd4b89Open-A12.16.840.1.114274.1818.55597468530447216761290...NaNNaN10000364-1485538-137.0NaNNaNNaNDX2.16.840.1.114274.1818.55597468530447216761290...NaNTrue70d220ba-344f-4bfa-ba33-625905adede710000364-1485538
9122imaging_study02069d10-c2d5-402f-80d8-8f255d10deafOpen-A12.16.840.1.114274.1818.47774226683317353367804...NaNNaN10000364-1246784-21841.0NaNXR CHEST PA AND LATERALNaNDX2.16.840.1.114274.1818.47774226683317353367804...NaNTrue1aed6381-f9dd-46f6-b705-415d28d97c4c10000364-1246784
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9142imaging_study025e018b-c497-4910-b6e5-18db2a8b804cOpen-A12.16.840.1.114274.1818.57634025747018793522352...NaNNaN10000364-6590138171.0NaNXR CHEST PA AND LATERALNaNDX2.16.840.1.114274.1818.57634025747018793522352...NaNTrue4465593d-3e93-44f0-a084-6514ad832da810000364-6590138
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9176imaging_study02e93e00-c291-4ba0-8c24-b734cad266d1Open-A12.16.840.1.114274.1818.52895319710719735562850...NaNNaN10000364-1562926-39.0NaNXR CHEST PA AND LATERALNaNDX2.16.840.1.114274.1818.52895319710719735562850...NaNTruec1fd1142-87c0-4d7a-a6e4-36bddc68d7e910000364-1562926
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" + ], + "text/plain": [ + " type id project_id submitter_id \\\n", + "0 measurement 000066a4-1355-4828-893b-bcfa3f88d4f3 Open-A1 20385772 \n", + "1 measurement 000255bf-4f4c-4462-8380-2045ea9808d9 Open-A1 1871007 \n", + "2 measurement 00028e86-e680-4058-9f87-8e633dbff9d4 Open-A1 20058937 \n", + "\n", + " case_ids test_days_from_index test_method \\\n", + "0 10022779-roAQeA6Bf0izu75zfNb2ng 308 RT-PCR \n", + "1 10000364-799838 132 RT-PCR \n", + "2 10008204-aUL88To8KkerKLbM9POnCg 0 RT-PCR \n", + "\n", + " test_name test_result_text cases.id \\\n", + "0 COVID-19 Negative c667e8aa-c19c-485e-bd25-886e523f59bc \n", + "1 COVID-19 Negative e05b5b00-b14e-4fca-a5bf-dea75dd280d6 \n", + "2 COVID-19 Negative 9b0be752-ea88-40f5-9b57-73ca8516bb0b \n", + "\n", + " cases.submitter_id conditions.id conditions.submitter_id \\\n", + "0 10022779-roAQeA6Bf0izu75zfNb2ng NaN NaN \n", + "1 10000364-799838 NaN NaN \n", + "2 10008204-aUL88To8KkerKLbM9POnCg NaN NaN \n", + "\n", + " procedures.id procedures.submitter_id \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Export all the data in the measurement node, which is used to store the COVID test data\n", + "meas = exp.get_node_tsvs(node='measurement')\n", + "print('\\nrows:{}, columns:{}'.format(meas.shape[0],meas.shape[1]))\n", + "meas.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "rows:88903, columns:15\n" + ] + }, + { + "data": { + "text/html": [ + "
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9209imaging_study0396e229-5908-4cfc-af0f-077b032287ebOpen-A12.16.840.1.114274.1818.52146711140973334311295...NaNNaN10000364-6555707-86.0NaNChest childrenNaNDX2.16.840.1.114274.1818.52146711140973334311295...NaNTrue1be5e0f3-e583-419c-bbc2-96b4489dfbbe10000364-6555707
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"9176 NaN 10000364-1562926 -39.0 \n", - "9185 NaN 10000364-1358401 65.0 \n", - "9187 NaN 10000364-5728851 -144.0 \n", - "9196 NaN 10003752-eVUhQjRkU6S0whXSKe2lg 262.0 \n", - "9197 NaN 10000364-1560684 37.0 \n", - "9201 NaN 10000364-689112 15.0 \n", - "9209 NaN 10000364-6555707 -86.0 \n", - "... ... ... ... \n", - "29468 CHEST 419639-009930 468.0 \n", - "29469 CHEST 419639-009961 7.0 \n", - "29470 CHEST 419639-007018 153.0 \n", - "29471 CHEST 419639-008054 93.0 \n", - "29474 CHEST 419639-009568 5.0 \n", - "29475 CHEST 419639-003230 350.0 \n", - "29477 CHEST 419639-009611 379.0 \n", - "29479 CHEST 419639-005610 350.0 \n", - "29480 CHEST 419639-009542 0.0 \n", - "29482 CHEST 419639-009171 60.0 \n", - "29483 CHEST 419639-009825 16.0 \n", - "29484 CHEST 419639-009772 -1.0 \n", - "29485 CHEST 419639-004276 288.0 \n", - "29486 CHEST 419639-008881 58.0 \n", - "29488 CHEST 419639-000682 430.0 \n", - "29489 CHEST 419639-009541 5.0 \n", - "29490 CHEST 419639-006977 166.0 \n", - "29491 CHEST 419639-004597 301.0 \n", - "29492 CHEST 419639-004557 296.0 \n", - "29493 CHEST 419639-006501 220.0 \n", - "29495 CHEST 419639-009236 43.0 \n", - "29496 CHEST 419639-004397 278.0 \n", - "29497 CHEST 419639-009744 2.0 \n", - "29498 CHEST 419639-009446 241.0 \n", - "29499 CHEST 419639-005279 240.0 \n", - "29501 CHEST 419639-009825 18.0 \n", - "29502 CHEST 419639-009061 103.0 \n", - "29503 CHEST 419639-008025 119.0 \n", - "29505 CHEST 419639-008020 155.0 \n", - "29506 CHEST 419639-009395 24.0 \n", - "\n", - " series_count study_description study_location \\\n", - "9011 NaN XR CHEST PA AND LATERAL NaN \n", - "9012 NaN NaN NaN \n", - "9031 NaN NaN NaN \n", - "9039 NaN NaN NaN \n", - "9042 NaN NaN NaN \n", - "9043 NaN NaN NaN \n", - "9067 NaN NaN NaN \n", - "9070 NaN XR CHEST PA AND LATERAL NaN \n", - "9072 NaN NaN NaN \n", - "9073 NaN NaN NaN \n", - "9079 NaN XR CHEST PA AND LATERAL NaN \n", - "9083 NaN NaN NaN \n", - "9086 NaN XR PORT CHEST 1V NaN \n", - "9104 NaN NaN NaN \n", - "9108 NaN XR CHEST PA AND LATERAL NaN \n", - "9111 NaN NaN NaN \n", - "9122 NaN XR CHEST PA AND LATERAL NaN \n", - "9139 NaN XR PORT CHEST 1V NaN \n", - "9142 NaN XR CHEST PA AND LATERAL NaN \n", - "9143 NaN XR CHEST PA AND LATERAL NaN \n", - "9146 NaN XR CHEST PA AND LATERAL NaN \n", - "9147 NaN NaN NaN \n", - "9154 NaN XR CHEST PA AND LATERAL NaN \n", - "9176 NaN XR CHEST PA AND LATERAL NaN \n", - "9185 NaN NaN NaN \n", - "9187 NaN NaN NaN \n", - "9196 NaN XR PORT CHEST 1V NaN \n", - "9197 NaN NaN NaN \n", - "9201 NaN XR CHEST PA AND LATERAL NaN \n", - "9209 NaN Chest children NaN \n", - "... ... ... ... \n", - "29468 1.0 XR CHEST 2 VIEWS PA AND LATERAL NaN \n", - "29469 5.0 XR CHEST 1 VIEW AP NaN \n", - "29470 1.0 XR CHEST 1 VIEW AP NaN \n", - "29471 NaN XR CHEST 1 VIEW AP NaN \n", - "29474 NaN XR CHEST 1 VIEW AP NaN \n", - "29475 3.0 XR CHEST 1 VIEW AP NaN \n", - "29477 2.0 XR CHEST 1 VIEW AP NaN \n", - "29479 NaN XR CHEST 2 VIEWS PA AND LATERAL NaN \n", - "29480 NaN XR CHEST 1 VIEW AP NaN \n", - "29482 1.0 XR CHEST 1 VIEW AP NaN \n", - "29483 1.0 XR CHEST 1 VIEW AP NaN \n", - "29484 1.0 XR CHEST 1 VIEW AP NaN \n", - "29485 1.0 XR CHEST 1 VIEW AP NaN \n", - "29486 NaN XR CHEST 1 VIEW AP NaN \n", - "29488 NaN XR CHEST 1 VIEW AP NaN \n", - "29489 1.0 XR CHEST 1 VIEW AP NaN \n", - "29490 NaN XR CHEST 1 VIEW AP NaN \n", - "29491 1.0 XR CHEST 2 VIEWS PA AND LATERAL NaN \n", - "29492 1.0 XR CHEST 1 VIEW AP NaN \n", - "29493 1.0 XR CHEST 1 VIEW AP NaN \n", - "29495 1.0 XR CHEST 1 VIEW AP NaN \n", - "29496 NaN XR CHEST 1 VIEW AP NaN \n", - "29497 1.0 XR CHEST 1 VIEW AP NaN \n", - "29498 1.0 XR CHEST 1 VIEW AP NaN \n", - "29499 NaN XR CHEST 2 VIEWS PA AND LATERAL NaN \n", - "29501 2.0 XR CHEST 1 VIEW AP NaN \n", - "29502 2.0 XR CHEST 1 VIEW AP NaN \n", - "29503 2.0 XR CHEST 1 VIEW AP NaN \n", - "29505 1.0 XR CHEST 1 VIEW AP NaN \n", - "29506 1.0 XR CHEST 1 VIEW AP NaN \n", - "\n", - " study_modality study_uid \\\n", - "9011 DX 2.16.840.1.114274.1818.53927660811344067041430... \n", - "9012 DX 2.16.840.1.114274.1818.57239462775962082858120... \n", - "9031 DX 2.16.840.1.114274.1818.55076773370404755863752... \n", - "9039 DX 2.16.840.1.114274.1818.56773440092156751147307... \n", - "9042 DX 2.16.840.1.114274.1818.48687302357140701881833... \n", - "9043 DX 2.16.840.1.114274.1818.54839864627065410741641... \n", - "9067 DX 2.16.840.1.114274.1818.54465758664852955678989... \n", - "9070 DX 2.16.840.1.114274.1818.51607810898232549052164... \n", - "9072 DX 2.16.840.1.114274.1818.47645127050117245123626... \n", - "9073 DX 2.16.840.1.114274.1818.50586810227525701468386... \n", - "9079 DX 2.16.840.1.114274.1818.54928928112017018811692... \n", - "9083 DX 2.16.840.1.114274.1818.53152655753620975495873... \n", - "9086 DX 2.16.840.1.114274.1818.55908318442052087227659... \n", - "9104 DX 2.16.840.1.114274.1818.53637607404012607865191... \n", - "9108 DX 2.16.840.1.114274.1818.48804153751524269081294... \n", - "9111 DX 2.16.840.1.114274.1818.55597468530447216761290... \n", - "9122 DX 2.16.840.1.114274.1818.47774226683317353367804... \n", - "9139 DX 2.16.840.1.114274.1818.53480975917321290286248... \n", - "9142 DX 2.16.840.1.114274.1818.57634025747018793522352... \n", - "9143 DX 2.16.840.1.114274.1818.53718125731382949371524... \n", - "9146 DX 2.16.840.1.114274.1818.56291327224908245331279... \n", - "9147 DX 2.16.840.1.114274.1818.48505210608740034266635... \n", - "9154 DX 2.16.840.1.114274.1818.51007332026062543531797... \n", - "9176 DX 2.16.840.1.114274.1818.52895319710719735562850... \n", - "9185 DX 2.16.840.1.114274.1818.56281060874214565451166... \n", - "9187 DX 2.16.840.1.114274.1818.53127040887925579643064... \n", - "9196 DX 2.16.840.1.114274.1818.56110935053786483716375... \n", - "9197 DX 2.16.840.1.114274.1818.48876454785890143861628... \n", - "9201 DX 2.16.840.1.114274.1818.52973680514595972265563... \n", - "9209 DX 2.16.840.1.114274.1818.52146711140973334311295... \n", - "... ... ... \n", - "29468 DX 1.2.826.0.1.3680043.10.474.419639.803155758390... \n", - "29469 DX 1.2.826.0.1.3680043.10.474.419639.255424158986... \n", - "29470 DX 1.2.826.0.1.3680043.10.474.419639.331046402853... \n", - "29471 DX 1.2.826.0.1.3680043.10.474.419639.240747359640... \n", - "29474 DX 1.2.826.0.1.3680043.10.474.419639.185765067409... \n", - "29475 DX 1.2.826.0.1.3680043.10.474.419639.309550899099... \n", - "29477 DX 1.2.826.0.1.3680043.10.474.419639.267876043150... \n", - "29479 DX 1.2.826.0.1.3680043.10.474.419639.211715727299... \n", - "29480 DX 1.2.826.0.1.3680043.10.474.419639.281688764291... \n", - "29482 DX 1.2.826.0.1.3680043.10.474.419639.242782770069... \n", - "29483 DX 1.2.826.0.1.3680043.10.474.419639.942087707286... \n", - "29484 DX 1.2.826.0.1.3680043.10.474.419639.567723223994... \n", - "29485 DX 1.2.826.0.1.3680043.10.474.419639.333213236853... \n", - "29486 DX 1.2.826.0.1.3680043.10.474.419639.303592988249... \n", - "29488 DX 1.2.826.0.1.3680043.10.474.419639.329676287123... \n", - "29489 DX 1.2.826.0.1.3680043.10.474.419639.109005461692... \n", - "29490 DX 1.2.826.0.1.3680043.10.474.419639.681206477026... \n", - "29491 DX 1.2.826.0.1.3680043.10.474.419639.201950769006... \n", - "29492 DX 1.2.826.0.1.3680043.10.474.419639.128378711121... \n", - "29493 DX 1.2.826.0.1.3680043.10.474.419639.499186005486... \n", - "29495 DX 1.2.826.0.1.3680043.10.474.419639.223052558257... \n", - "29496 DX 1.2.826.0.1.3680043.10.474.419639.186083638840... \n", - "29497 DX 1.2.826.0.1.3680043.10.474.419639.843884617567... \n", - "29498 DX 1.2.826.0.1.3680043.10.474.419639.121582552211... \n", - "29499 DX 1.2.826.0.1.3680043.10.474.419639.186432802886... \n", - "29501 DX 1.2.826.0.1.3680043.10.474.419639.331827081560... \n", - "29502 DX 1.2.826.0.1.3680043.10.474.419639.213195235621... \n", - "29503 DX 1.2.826.0.1.3680043.10.474.419639.187905213221... \n", - "29505 DX 1.2.826.0.1.3680043.10.474.419639.128474464346... \n", - "29506 DX 1.2.826.0.1.3680043.10.474.419639.426271337693... \n", - "\n", - " study_year study_year_shifted cases.id \\\n", - "9011 NaN True 420af82e-4a6f-4a4e-8f82-051a3adfc901 \n", - "9012 NaN True c6e03e4a-e0ce-4103-8c00-81fd06d555e2 \n", - "9031 NaN True 35f564eb-c9b4-4870-a1fb-626a8f79b3d6 \n", - "9039 NaN True df555ac4-3f1a-4272-a240-6f4d7cc296a7 \n", - "9042 NaN True ad573d81-2ff2-42db-91c9-ccda6f1626f2 \n", - "9043 NaN True 3036d286-a022-4008-8c7e-b7440c95def3 \n", - "9067 NaN True f212791d-300a-42ba-afb0-8b3e73dc5c04 \n", - "9070 NaN True e313882b-bed5-4ba2-9d40-5c6491538332 \n", - "9072 NaN True 8abfd896-5fb1-4c6a-a44c-fa7d8a5586b8 \n", - "9073 NaN True fa5c0caf-6cb5-43a1-aedb-739b90ae759a \n", - "9079 NaN True ac9071f4-6b8e-4e66-b76b-06ad788b78fb \n", - "9083 NaN True 5cf9c33c-b3c0-4a4b-8df2-8a78a5fc3226 \n", - "9086 NaN True 911fd6a8-ecbd-4798-aeb8-5d8ca8ab95b6 \n", - "9104 NaN True c6d55c8b-124a-4b01-a7c9-33f2fa70cf9c \n", - "9108 NaN True ea8249ea-0250-4919-ad84-cda3dd44ea1f \n", - "9111 NaN True 70d220ba-344f-4bfa-ba33-625905adede7 \n", - "9122 NaN True 1aed6381-f9dd-46f6-b705-415d28d97c4c \n", - "9139 NaN True c5f5b98e-fc02-4934-a058-4ed9042dea7e \n", - "9142 NaN True 4465593d-3e93-44f0-a084-6514ad832da8 \n", - "9143 NaN True 04db0a57-3281-4c18-996b-bcea12a2085c \n", - "9146 NaN True d3b3dd7e-87cb-49a0-a1f1-a135953703bc \n", - "9147 NaN True ad56e8bc-86c7-4440-8b04-00c7debfe263 \n", - "9154 NaN True ab2f737d-2c6a-4bc5-836d-f7d023897e0e \n", - "9176 NaN True c1fd1142-87c0-4d7a-a6e4-36bddc68d7e9 \n", - "9185 NaN True 48d59e79-814c-4542-8a40-25e9924a9679 \n", - "9187 NaN True d3145810-b98e-4005-88c8-212c841525d1 \n", - "9196 NaN True 7323ff8c-5ae5-4ccc-aa45-deaa92c355e4 \n", - "9197 NaN True a5f11a51-173b-4aa1-a745-674fc280f39e \n", - "9201 NaN True 3d8a8fca-7d64-4ae7-80f0-a2a12d64a952 \n", - "9209 NaN True 1be5e0f3-e583-419c-bbc2-96b4489dfbbe \n", - "... ... ... ... \n", - "29468 NaN True 6c3c062b-eb41-4c6c-8942-89d938f9e231 \n", - "29469 NaN True 5081ff31-ec7f-4c4a-ae45-803b06119d3a \n", - "29470 NaN True 140cc187-d6b4-4466-9c33-22dd3d23245e \n", - "29471 NaN True 934c8b71-a717-4e7f-a9b4-28bf1c79be6c \n", - "29474 NaN True b9bc5b2c-cab3-46b9-8252-700d24770d1f \n", - "29475 NaN True 5aea0547-c671-4904-af7d-47368e50edd9 \n", - "29477 NaN True 703512cc-afb2-4c9d-b382-1b887a48cbe6 \n", - "29479 NaN True 16e21d8d-82bb-4706-96a4-35fe2338cdea \n", - "29480 NaN True 4c07f3ac-ccff-4e10-941d-90349ea8d879 \n", - "29482 NaN True 9d6cae02-4a87-4b13-85c2-c9f04bd8f42c \n", - "29483 NaN True b4dd32de-0cfd-4c6a-80d2-dab10fe55404 \n", - "29484 NaN True 134e35fb-02b3-41b0-8416-3a3045560a5e \n", - "29485 NaN True 2d2f2069-2c47-4d5f-a115-5c1ef06ae3b1 \n", - "29486 NaN True b68875b4-130e-41db-9736-f54abdaba697 \n", - "29488 NaN True d31c17de-17f5-4ac9-8f8e-ac3a6a899b29 \n", - "29489 NaN True 67e6283f-1eb8-49eb-97f5-70d599ffd3d0 \n", - "29490 NaN True 2ea1f2fc-7b88-4e99-9706-b0c905018803 \n", - "29491 NaN True a3c2ea94-3477-4b3a-8a58-ed069e3d29f0 \n", - "29492 NaN True 50f307b5-f198-4ec6-8c4b-2280d927086b \n", - "29493 NaN True af7f2d8d-8e32-4e35-911c-6f4bcf9858e9 \n", - "29495 NaN True f79dfd67-8919-473a-a672-6ecb63860d62 \n", - "29496 NaN True c2e126c0-ad9a-45c6-9172-6406f225ace3 \n", - "29497 NaN True 573d6f90-08e9-4313-8753-36b00d717d6a \n", - "29498 NaN True ab4cfd48-fe80-4237-800c-3e0309cd9fe2 \n", - "29499 NaN True e056130b-fb02-4c8a-8452-8c8e4375aa60 \n", - "29501 NaN True b4dd32de-0cfd-4c6a-80d2-dab10fe55404 \n", - "29502 NaN True c2e1e05e-6ffb-4f1a-9731-9b16f04fbd70 \n", - "29503 NaN True 14263d36-f13a-42c5-aa41-718ccad773af \n", - "29505 NaN True 70241468-91d0-4454-a897-aa06624bfb0a \n", - "29506 NaN True 0c53940e-f0c2-4a3b-92ab-8794bbd57a93 \n", - "\n", - " cases.submitter_id \n", - "9011 10000364-1375261 \n", - "9012 10000364-1519515 \n", - "9031 10000364-1607825 \n", - "9039 10000364-1465830 \n", - "9042 10000364-1418152 \n", - "9043 10000364-1070656 \n", - "9067 10000364-5636388 \n", - "9070 10000364-2470802 \n", - "9072 10000364-6604535 \n", - "9073 10000364-989371 \n", - "9079 10000364-6202975 \n", - "9083 10000364-6083812 \n", - "9086 10003752-e46VS8SPBU2GwExoQZnQw \n", - "9104 10000364-1199747 \n", - "9108 10000364-1486410 \n", - "9111 10000364-1485538 \n", - "9122 10000364-1246784 \n", - "9139 10003752-7B8dHnJXUS1n4upcgzkQ \n", - "9142 10000364-6590138 \n", - "9143 10000364-1244858 \n", - "9146 10000364-2598844 \n", - "9147 10000364-1576681 \n", - "9154 10000364-5453082 \n", - "9176 10000364-1562926 \n", - "9185 10000364-1358401 \n", - "9187 10000364-5728851 \n", - "9196 10003752-eVUhQjRkU6S0whXSKe2lg \n", - "9197 10000364-1560684 \n", - "9201 10000364-689112 \n", - "9209 10000364-6555707 \n", - "... ... \n", - "29468 419639-009930 \n", - "29469 419639-009961 \n", - "29470 419639-007018 \n", - "29471 419639-008054 \n", - "29474 419639-009568 \n", - "29475 419639-003230 \n", - "29477 419639-009611 \n", - "29479 419639-005610 \n", - "29480 419639-009542 \n", - "29482 419639-009171 \n", - "29483 419639-009825 \n", - "29484 419639-009772 \n", - "29485 419639-004276 \n", - "29486 419639-008881 \n", - "29488 419639-000682 \n", - "29489 419639-009541 \n", - "29490 419639-006977 \n", - "29491 419639-004597 \n", - "29492 419639-004557 \n", - "29493 419639-006501 \n", - "29495 419639-009236 \n", - "29496 419639-004397 \n", - "29497 419639-009744 \n", - "29498 419639-009446 \n", - "29499 419639-005279 \n", - "29501 419639-009825 \n", - "29502 419639-009061 \n", - "29503 419639-008025 \n", - "29505 419639-008020 \n", - "29506 419639-009395 \n", - "\n", - "[4755 rows x 17 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Filter the imaging_study data for only studies that have a non-null \"days_to_study\" and \"DX\" study_modality\n", - "s = st.loc[(~st['days_to_study'].isna()) & (st['study_modality']=='DX')]\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Output written to file: node_tsvs/measurement_tsvs/NCU-A2_measurement.tsv\n", - "node_tsvs/measurement_tsvs/NCU-A2_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/NCU-A2_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/TCIA-COVID-19-AR_measurement.tsv\n", - "node_tsvs/measurement_tsvs/TCIA-COVID-19-AR_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/TCIA-COVID-19-AR_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/Open-A1_PETAL_REDCORAL_measurement.tsv\n", - "node_tsvs/measurement_tsvs/Open-A1_PETAL_REDCORAL_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/Open-A1_PETAL_REDCORAL_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/TCIA-RICORD-1c_measurement.tsv\n", - "node_tsvs/measurement_tsvs/TCIA-RICORD-1c_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/TCIA-RICORD-1c_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/TCIA-RICORD-1a_measurement.tsv\n", - "node_tsvs/measurement_tsvs/TCIA-RICORD-1a_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/TCIA-RICORD-1a_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/Open-A1_PETAL_BLUECORAL_measurement.tsv\n", - "node_tsvs/measurement_tsvs/Open-A1_PETAL_BLUECORAL_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/Open-A1_PETAL_BLUECORAL_measurement.tsv\n", - "File previously downloaded.\n", - "node_tsvs/measurement_tsvs/Open-A1_measurement.tsv has 11282 records.\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/NCU-R2_measurement.tsv\n", - "node_tsvs/measurement_tsvs/NCU-R2_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/NCU-R2_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/Open-A1_PETAL_FIRECORAL_measurement.tsv\n", - "node_tsvs/measurement_tsvs/Open-A1_PETAL_FIRECORAL_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/Open-A1_PETAL_FIRECORAL_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/TCIA-COVID-19_CT_Images_measurement.tsv\n", - "node_tsvs/measurement_tsvs/TCIA-COVID-19_CT_Images_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/TCIA-COVID-19_CT_Images_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/TCIA-RICORD-1b_measurement.tsv\n", - "node_tsvs/measurement_tsvs/TCIA-RICORD-1b_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/TCIA-RICORD-1b_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/TCIA-COVID-19-NY-SBU_measurement.tsv\n", - "node_tsvs/measurement_tsvs/TCIA-COVID-19-NY-SBU_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/TCIA-COVID-19-NY-SBU_measurement.tsv\n", - "\n", - "Output written to file: node_tsvs/measurement_tsvs/Open-R1_measurement.tsv\n", - "node_tsvs/measurement_tsvs/Open-R1_measurement.tsv has 0 records.\n", - "Removing empty file: node_tsvs/measurement_tsvs/Open-R1_measurement.tsv\n", - "length of all dfs: 11282\n", - "Master node TSV with 11282 total records written to master_measurement.tsv.\n" - ] - }, - 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typeidproject_idsubmitter_idcase_idstest_days_from_indextest_methodtest_nametest_result_textcases.idcases.submitter_idconditions.idconditions.submitter_idprocedures.idprocedures.submitter_id
0measurement000f0255-3060-4725-8438-6bd8cf361454Open-A1368526210000364-1482780258RT-PCRCOVID-19Negative5fbc12f0-d746-4988-8cee-2e9657d6f3b310000364-1482780NaNNaNNaNNaN
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4measurement003e1f6b-73d8-4740-8e8e-0ff014b14df9Open-A11926402810000364-248952116Rapid antigen testCOVID-19Negative1c4cb131-00dd-433b-8861-b65bfb53387510000364-248952NaNNaNNaNNaN
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" - ], - "text/plain": [ - " type id project_id submitter_id \\\n", - "0 measurement 000f0255-3060-4725-8438-6bd8cf361454 Open-A1 3685262 \n", - "1 measurement 0026f750-8752-4ea2-b0f9-8a39be6ac3fa Open-A1 828397 \n", - "2 measurement 00296991-9b0d-4a51-8161-2bca885a8ff3 Open-A1 20041645 \n", - "3 measurement 0029b125-a25a-40a1-a2dc-7fb3350aea09 Open-A1 4903183 \n", - "4 measurement 003e1f6b-73d8-4740-8e8e-0ff014b14df9 Open-A1 19264028 \n", - "\n", - " case_ids test_days_from_index test_method \\\n", - "0 10000364-1482780 258 RT-PCR \n", - "1 10000364-1373511 95 RT-PCR \n", - "2 10003752-HFzMVp7y60qpWBAtXZcw 89 RT-PCR \n", - "3 10000364-5526101 53 RT-PCR \n", - "4 10000364-248952 116 Rapid antigen test \n", - "\n", - " test_name test_result_text cases.id \\\n", - "0 COVID-19 Negative 5fbc12f0-d746-4988-8cee-2e9657d6f3b3 \n", - "1 COVID-19 Negative 615e08a5-b712-4859-a7ba-3f85ba7c149c \n", - "2 COVID-19 Negative f27e0e17-acb3-457b-8813-5b9014635693 \n", - "3 COVID-19 Negative 866ef74a-dadb-4b30-b8c1-619acc13c4d2 \n", - "4 COVID-19 Negative 1c4cb131-00dd-433b-8861-b65bfb533875 \n", - "\n", - " cases.submitter_id conditions.id conditions.submitter_id \\\n", - "0 10000364-1482780 NaN NaN \n", - "1 10000364-1373511 NaN NaN \n", - "2 10003752-HFzMVp7y60qpWBAtXZcw NaN NaN \n", - "3 10000364-5526101 NaN NaN \n", - "4 10000364-248952 NaN NaN \n", - "\n", - " procedures.id procedures.submitter_id \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now export all the data in the measurement node, which is used to store the COVID test data\n", - "meas = exp.get_node_tsvs(node='measurement')\n", - "meas.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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"11281 measurement fffd27e6-2fff-4ba5-ad9c-8014470e11e0 Open-A1 \n", - "\n", - " submitter_id case_ids test_days_from_index \\\n", - "0 3685262 10000364-1482780 258 \n", - "1 828397 10000364-1373511 95 \n", - "2 20041645 10003752-HFzMVp7y60qpWBAtXZcw 89 \n", - "3 4903183 10000364-5526101 53 \n", - "4 19264028 10000364-248952 116 \n", - "5 2835337 10000364-1432680 154 \n", - "6 3414070 10000364-6597094 18 \n", - "7 20034544 10008204-iLz3dR5ZX0a3B8rV9bojw 0 \n", - "8 2474933 10000364-2080371 1 \n", - "9 1367895 10000364-504715 62 \n", - "10 2519203 10000364-1243754 0 \n", - "11 2544236 10000364-1593464 45 \n", - "12 3559799 10000364-1771245 100 \n", - "13 3024376 10000364-2598844 239 \n", - "14 20046693 10003752-ztYe3cRoo0ORQQNRQw5Wsw 13 \n", - "15 5245109 10000364-5580391 26 \n", - "16 4119387 10000364-585086 0 \n", - "17 1056002 10000364-5381170 8 \n", - "18 943194 10000364-5935726 0 \n", - "19 5706309 10000364-1355704 338 \n", - "20 1271088 10000364-2042431 2 \n", - "21 5752528 10000364-2616385 100 \n", - "22 5279228 10000364-1295788 43 \n", - "23 2352643 10000364-5830998 3 \n", - "24 967322 10000364-5871151 43 \n", - "25 3429746 10000364-1770875 1 \n", - "26 20038900 10003752-mC4VvgxF0ytiVHbjvVuAA 0 \n", - "27 20039647 10003752-SJc0fQ89ECzBq8tRCn6HA 0 \n", - "28 5608714 10000364-1501204 1 \n", - "29 4882722 10000364-974578 30 \n", - "... ... ... ... \n", - "11252 20039207 10003752-j7KpLxPerECPC3YTXtR5g 28 \n", - "11253 20039445 10003752-4XTm7tLCnU1gzIYioyPXA 174 \n", - "11254 20039706 10003752-6Ed2xh1hRE4DbIw7e7Gg 33 \n", - "11255 3462725 10000364-5484331 120 \n", - "11256 4907496 10000364-6499541 213 \n", - "11257 20039112 10003752-ehZ6c3wwAEWNoW5pYdiYg 106 \n", - "11258 4820693 10000364-1834582 14 \n", - "11259 2308991 10000364-2382604 1 \n", - "11260 20041543 10003752-6comtwTANEeWjGbxAJ2kbg 84 \n", - "11261 20046881 10003752-YmG8BJHhjky58JPQfjrGyg 0 \n", - "11262 2888190 10000364-1393714 1 \n", - "11263 4985706 10000364-1581518 100 \n", - "11264 2907505 10000364-1635504 247 \n", - "11265 2410024 10000364-1763507 254 \n", - "11266 4921697 10000364-912067 62 \n", - "11267 5271611 10000364-6329321 141 \n", - "11268 2072799 10000364-1219563 304 \n", - "11269 2684188 10000364-6594947 0 \n", - "11270 20046854 10003752-0R4aMDUqMkOKlxP1XhQKvQ 167 \n", - "11271 20047214 10003752-yZWGOW9WfUWRKTWo1OZEQ 3 \n", - "11272 2402410 10000364-5952494 0 \n", - "11273 3146873 10000364-1576956 0 \n", - "11274 20047120 10003752-s2WVhd9lkEKKTwxDAB3Z8g 79 \n", - "11275 5818321 10000364-2716759 205 \n", - "11276 2488199 10000364-5602252 169 \n", - "11277 3275863 10000364-1468545 138 \n", - "11278 4750753 10000364-1102543 -224 \n", - "11279 3140765 10000364-867146 72 \n", - "11280 3316302 10000364-1413791 250 \n", - "11281 2024563 10000364-6604907 0 \n", - "\n", - " test_method test_name test_result_text \\\n", - "0 RT-PCR COVID-19 Negative \n", - "1 RT-PCR COVID-19 Negative \n", - "2 RT-PCR COVID-19 Negative \n", - "3 RT-PCR COVID-19 Negative \n", - "4 Rapid antigen test COVID-19 Negative \n", - "5 Rapid antigen test COVID-19 Negative \n", - "6 RT-PCR COVID-19 Negative \n", - "7 RT-PCR COVID-19 Negative \n", - "8 RT-PCR COVID-19 Negative \n", - "9 RT-PCR COVID-19 Negative \n", - "10 RT-PCR COVID-19 Positive \n", - "11 RT-PCR COVID-19 Negative \n", - "12 RT-PCR COVID-19 Negative \n", - "13 RT-PCR COVID-19 Negative \n", - "14 RT-PCR COVID-19 Positive \n", - "15 RT-PCR COVID-19 Negative \n", - "16 RT-PCR COVID-19 Negative \n", - "17 RT-PCR COVID-19 Positive \n", - "18 RT-PCR COVID-19 Positive \n", - "19 RT-PCR COVID-19 Negative \n", - "20 RT-PCR COVID-19 Negative \n", - "21 RT-PCR COVID-19 Negative \n", - "22 RT-PCR COVID-19 Negative \n", - "23 RT-PCR COVID-19 Negative \n", - "24 Rapid antigen test COVID-19 Negative \n", - "25 RT-PCR COVID-19 Negative \n", - "26 RT-PCR COVID-19 Negative \n", - "27 RT-PCR COVID-19 Negative \n", - "28 RT-PCR COVID-19 Negative \n", - "29 RT-PCR COVID-19 Negative \n", - "... ... ... ... \n", - "11252 RT-PCR COVID-19 Negative \n", - "11253 RT-PCR COVID-19 Negative \n", - "11254 RT-PCR COVID-19 Negative \n", - "11255 Rapid antigen test COVID-19 Negative \n", - "11256 Rapid antigen test COVID-19 Negative \n", - "11257 RT-PCR COVID-19 Negative \n", - "11258 RT-PCR COVID-19 Negative \n", - "11259 RT-PCR COVID-19 Positive \n", - "11260 RT-PCR COVID-19 Negative \n", - "11261 RT-PCR COVID-19 Positive \n", - "11262 RT-PCR COVID-19 Negative \n", - "11263 RT-PCR COVID-19 Negative \n", - "11264 RT-PCR COVID-19 Negative \n", - "11265 RT-PCR COVID-19 Negative \n", - "11266 RT-PCR COVID-19 Negative \n", - "11267 RT-PCR COVID-19 Negative \n", - "11268 RT-PCR COVID-19 Negative \n", - "11269 RT-PCR COVID-19 Positive \n", - "11270 RT-PCR COVID-19 Positive \n", - "11271 RT-PCR COVID-19 Not Reported \n", - "11272 RT-PCR COVID-19 Negative \n", - "11273 Rapid antigen test COVID-19 Negative \n", - "11274 RT-PCR COVID-19 Positive \n", - "11275 RT-PCR COVID-19 Positive \n", - "11276 RT-PCR COVID-19 Negative \n", - "11277 RT-PCR COVID-19 Negative \n", - "11278 RT-PCR COVID-19 Negative \n", - "11279 RT-PCR COVID-19 Negative \n", - "11280 RT-PCR COVID-19 Negative \n", - "11281 RT-PCR COVID-19 Positive \n", - "\n", - " cases.id cases.submitter_id \\\n", - "0 5fbc12f0-d746-4988-8cee-2e9657d6f3b3 10000364-1482780 \n", - "1 615e08a5-b712-4859-a7ba-3f85ba7c149c 10000364-1373511 \n", - "2 f27e0e17-acb3-457b-8813-5b9014635693 10003752-HFzMVp7y60qpWBAtXZcw \n", - "3 866ef74a-dadb-4b30-b8c1-619acc13c4d2 10000364-5526101 \n", - "4 1c4cb131-00dd-433b-8861-b65bfb533875 10000364-248952 \n", - "5 b28265eb-46c0-408b-a5af-3dcc26ce843b 10000364-1432680 \n", - "6 acf5ee90-89e2-4352-9043-893d1d4c3b22 10000364-6597094 \n", - "7 524f9bc6-1ac8-4b6b-988f-3ed7ebc5400b 10008204-iLz3dR5ZX0a3B8rV9bojw \n", - "8 533ee767-e790-4604-a83f-eabd8a52b7a2 10000364-2080371 \n", - "9 d5c1b8db-7006-49d2-af98-a00038baa6f1 10000364-504715 \n", - "10 c3253c1b-743e-481f-918b-86684b3a4b8b 10000364-1243754 \n", - "11 9580a7b0-982a-4ca3-8b2a-b25eef885ce9 10000364-1593464 \n", - "12 d3802fe4-38bf-497d-9334-37c1c3c86105 10000364-1771245 \n", - "13 d3b3dd7e-87cb-49a0-a1f1-a135953703bc 10000364-2598844 \n", - "14 e44a5aff-be05-400f-9c3f-0f16dc1b25bc 10003752-ztYe3cRoo0ORQQNRQw5Wsw \n", - "15 1ae26090-5e23-4d37-9f58-eebd4aadff02 10000364-5580391 \n", - "16 a9962257-3206-43b6-bc39-4f13bbe6877f 10000364-585086 \n", - "17 ec057c3b-7523-49fb-a57c-4254d0d8923c 10000364-5381170 \n", - "18 7d54018a-909d-4f4b-837b-be989142f739 10000364-5935726 \n", - "19 704a5a10-43a0-4a0e-a22d-4444974754ae 10000364-1355704 \n", - "20 4bd10e04-07e0-4425-8829-79ad8a778144 10000364-2042431 \n", - "21 f2ea65ba-7142-4a6b-8b39-e7278aa3d03d 10000364-2616385 \n", - "22 00d68288-b45a-4024-9009-493f941fc8a1 10000364-1295788 \n", - "23 3b0cb409-6011-49b2-8f5c-7ccb344c0d91 10000364-5830998 \n", - "24 0c7805fa-181c-4c60-b411-c2551c005dca 10000364-5871151 \n", - "25 1bce4755-d5d8-4f8d-8b6c-10978ec6c3ff 10000364-1770875 \n", - "26 44ae4097-6273-4a39-9bfb-43336a8125c7 10003752-mC4VvgxF0ytiVHbjvVuAA \n", - "27 961489d6-5714-4c77-adaa-fea5f1ad3bca 10003752-SJc0fQ89ECzBq8tRCn6HA \n", - "28 5efe5a97-0d98-4491-abc7-6ddaf945b6f9 10000364-1501204 \n", - "29 c2e2c14b-ea91-432a-a834-4b1912ebff2e 10000364-974578 \n", - "... ... ... \n", - "11252 5c498c3b-2dc9-4826-a787-6c6f68db1ea5 10003752-j7KpLxPerECPC3YTXtR5g \n", - "11253 9c637a45-82f7-42ec-a029-fc4ef103473f 10003752-4XTm7tLCnU1gzIYioyPXA \n", - "11254 0e0b7e82-a48f-4aff-8a63-d91aaf0e902e 10003752-6Ed2xh1hRE4DbIw7e7Gg \n", - "11255 b1951135-2ec5-4133-944a-83e081cff366 10000364-5484331 \n", - "11256 d642ebec-f4dc-495a-8e38-2dcae2ad239c 10000364-6499541 \n", - "11257 675eab07-2ffd-4865-ad8c-c11580b6b1a3 10003752-ehZ6c3wwAEWNoW5pYdiYg \n", - "11258 c4d15197-0893-4159-a89d-20118392b2c5 10000364-1834582 \n", - "11259 102ea215-3724-4388-b1b8-828f44ddb6cf 10000364-2382604 \n", - "11260 fdbb9bcc-cae3-445b-8997-ddc1dba3db62 10003752-6comtwTANEeWjGbxAJ2kbg \n", - "11261 8ad5d0be-1a22-48a3-876f-4ecfb4429940 10003752-YmG8BJHhjky58JPQfjrGyg \n", - "11262 d269e0ee-ae0e-441d-a9da-f5331e56f2ac 10000364-1393714 \n", - "11263 14ef7491-24ff-430c-9f05-2ac2c6f68643 10000364-1581518 \n", - "11264 5b15d79a-6a52-4370-a82f-54be3406174e 10000364-1635504 \n", - "11265 a2a39469-3d68-4e78-a23f-051ef656920e 10000364-1763507 \n", - "11266 93f2013b-04be-4e85-816a-0b0dccb31da7 10000364-912067 \n", - "11267 a3a62767-5399-4b5d-923c-848fce744ab1 10000364-6329321 \n", - "11268 b2662ecd-880e-45e9-991c-fd313eb8bb29 10000364-1219563 \n", - "11269 6dabd9a2-5d24-42de-941d-bb1f4abd9f66 10000364-6594947 \n", - "11270 1c386bbf-deee-4dd3-8ca3-138d692025db 10003752-0R4aMDUqMkOKlxP1XhQKvQ \n", - "11271 01cc6f54-911c-487e-9455-5a40ca7d79bd 10003752-yZWGOW9WfUWRKTWo1OZEQ \n", - "11272 874b12fd-f690-4028-8cfe-8e942dfac255 10000364-5952494 \n", - "11273 7e0207e6-e347-4099-8fab-fc9abed82d6b 10000364-1576956 \n", - "11274 0a771673-7eee-4594-93f9-ed93fe5ff4d4 10003752-s2WVhd9lkEKKTwxDAB3Z8g \n", - "11275 105982f5-769e-4d39-98b7-847a323f6cbd 10000364-2716759 \n", - "11276 5859edea-d80a-448a-8235-72d2186cacf1 10000364-5602252 \n", - "11277 3daaba70-827d-439e-a1df-06c9a3aebb36 10000364-1468545 \n", - "11278 afa8e9e1-15ec-4161-94f0-772a2bb4a778 10000364-1102543 \n", - "11279 bafa901e-7f09-4f3d-b9d6-3f10dbb42e08 10000364-867146 \n", - "11280 a503a98e-e965-442d-af9b-868bf7cff361 10000364-1413791 \n", - "11281 af182c6c-0bde-41b0-865a-491b4c37104d 10000364-6604907 \n", - "\n", - " conditions.id conditions.submitter_id procedures.id \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "17 NaN NaN NaN \n", - "18 NaN NaN NaN \n", - "19 NaN NaN NaN \n", - "20 NaN NaN NaN \n", - "21 NaN NaN NaN \n", - "22 NaN NaN NaN \n", - "23 NaN NaN NaN \n", - "24 NaN NaN NaN \n", - "25 NaN NaN NaN \n", - "26 NaN NaN NaN \n", - "27 NaN NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "... ... ... ... \n", - "11252 NaN NaN NaN \n", - "11253 NaN NaN NaN \n", - "11254 NaN NaN NaN \n", - "11255 NaN NaN NaN \n", - 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52.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11913938COVID-19Positive101
62.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11689955COVID-19Negative0
72.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11691260COVID-19Positive56
82.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11916704COVID-19Positive79
92.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11791797COVID-19Positive49
102.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11749585COVID-19Negative33
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122.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11913938COVID-19Positive101
132.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11689955COVID-19Negative0
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152.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11916704COVID-19Positive79
162.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11791797COVID-19Positive49
172.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11749585COVID-19Negative33
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202.16.840.1.114274.1818.47827819242494077718021...231.010000364-1465830Open-A11819952COVID-19Negative0
212.16.840.1.114274.1818.47827819242494077718021...231.010000364-1465830Open-A11720667COVID-19Positive122
222.16.840.1.114274.1818.51607810898232549052164...-114.010000364-2470802Open-A15659580COVID-19Negative0
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252.16.840.1.114274.1818.54928928112017018811692...-290.010000364-6202975Open-A13170727COVID-19Negative0
262.16.840.1.114274.1818.54928928112017018811692...-290.010000364-6202975Open-A12965619COVID-19Negative0
272.16.840.1.114274.1818.54928928112017018811692...-290.010000364-6202975Open-A13169371COVID-19Negative-16
282.16.840.1.114274.1818.55908318442052087227659...140.010003752-e46VS8SPBU2GwExoQZnQwOpen-A120041630COVID-19Negative4
292.16.840.1.114274.1818.55908318442052087227659...140.010003752-e46VS8SPBU2GwExoQZnQwOpen-A120045963COVID-19Positive10
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66832.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13406840COVID-19Positive272
66842.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13411550COVID-19Negative255
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6702 rows × 8 columns

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"\n", - " case_ids project_id submitter_id test_name \\\n", - "0 10000364-1375261 Open-A1 1757168 COVID-19 \n", - "1 10000364-1375261 Open-A1 1970385 COVID-19 \n", - "2 10000364-1375261 Open-A1 1852379 COVID-19 \n", - "3 10000364-1375261 Open-A1 1757167 COVID-19 \n", - "4 10000364-1519515 Open-A1 1749586 COVID-19 \n", - "5 10000364-1519515 Open-A1 1913938 COVID-19 \n", - "6 10000364-1519515 Open-A1 1689955 COVID-19 \n", - "7 10000364-1519515 Open-A1 1691260 COVID-19 \n", - "8 10000364-1519515 Open-A1 1916704 COVID-19 \n", - "9 10000364-1519515 Open-A1 1791797 COVID-19 \n", - "10 10000364-1519515 Open-A1 1749585 COVID-19 \n", - "11 10000364-1519515 Open-A1 1749586 COVID-19 \n", - "12 10000364-1519515 Open-A1 1913938 COVID-19 \n", - "13 10000364-1519515 Open-A1 1689955 COVID-19 \n", - "14 10000364-1519515 Open-A1 1691260 COVID-19 \n", - "15 10000364-1519515 Open-A1 1916704 COVID-19 \n", - "16 10000364-1519515 Open-A1 1791797 COVID-19 \n", - "17 10000364-1519515 Open-A1 1749585 COVID-19 \n", - "18 10000364-1465830 Open-A1 1819952 COVID-19 \n", - "19 10000364-1465830 Open-A1 1720667 COVID-19 \n", - "20 10000364-1465830 Open-A1 1819952 COVID-19 \n", - "21 10000364-1465830 Open-A1 1720667 COVID-19 \n", - "22 10000364-2470802 Open-A1 5659580 COVID-19 \n", - "23 10000364-2470802 Open-A1 5685081 COVID-19 \n", - "24 10000364-6604535 Open-A1 1680073 COVID-19 \n", - "25 10000364-6202975 Open-A1 3170727 COVID-19 \n", - "26 10000364-6202975 Open-A1 2965619 COVID-19 \n", - "27 10000364-6202975 Open-A1 3169371 COVID-19 \n", - "28 10003752-e46VS8SPBU2GwExoQZnQw Open-A1 20041630 COVID-19 \n", - "29 10003752-e46VS8SPBU2GwExoQZnQw Open-A1 20045963 COVID-19 \n", - "... ... ... ... ... \n", - "6672 10000364-1047271 Open-A1 5738834 COVID-19 \n", - "6673 10000364-1047271 Open-A1 5351225 COVID-19 \n", - "6674 10000364-1047271 Open-A1 5743455 COVID-19 \n", - "6675 10000364-1047271 Open-A1 5565232 COVID-19 \n", - "6676 10000364-1047271 Open-A1 5738833 COVID-19 \n", - "6677 10000364-1047271 Open-A1 5322590 COVID-19 \n", - "6678 10000364-1047271 Open-A1 5427471 COVID-19 \n", - "6679 10000364-1047271 Open-A1 5703021 COVID-19 \n", - "6680 10000364-1047271 Open-A1 5743027 COVID-19 \n", - "6681 10000364-6011283 Open-A1 2583877 COVID-19 \n", - "6682 10000364-6556476 Open-A1 3007218 COVID-19 \n", - "6683 10000364-1548264 Open-A1 3406840 COVID-19 \n", - "6684 10000364-1548264 Open-A1 3411550 COVID-19 \n", - "6685 10000364-1548264 Open-A1 3278414 COVID-19 \n", - "6686 10000364-1548264 Open-A1 3357320 COVID-19 \n", - "6687 10000364-1548264 Open-A1 3586187 COVID-19 \n", - "6688 10000364-1548264 Open-A1 3314608 COVID-19 \n", - "6689 10000364-1548264 Open-A1 3532382 COVID-19 \n", - "6690 10000364-1558041 Open-A1 4773729 COVID-19 \n", - "6691 10000364-1008341 Open-A1 19174788 COVID-19 \n", - "6692 10000364-1008341 Open-A1 19452157 COVID-19 \n", - "6693 10000364-6477239 Open-A1 5413266 COVID-19 \n", - "6694 10000364-6477239 Open-A1 5699634 COVID-19 \n", - "6695 10000364-6477239 Open-A1 5478856 COVID-19 \n", - "6696 10000364-6477239 Open-A1 5724075 COVID-19 \n", - "6697 10000364-6477239 Open-A1 5684302 COVID-19 \n", - "6698 10000364-6477239 Open-A1 5371492 COVID-19 \n", - "6699 10000364-6477239 Open-A1 5724383 COVID-19 \n", - "6700 10000364-1005952 Open-A1 5743776 COVID-19 \n", - "6701 10000364-1163342 Open-A1 5311668 COVID-19 \n", - "\n", - " test_result_text test_days_from_index \n", - "0 Positive 303 \n", - "1 Negative 490 \n", - "2 Positive 303 \n", - "3 Negative 460 \n", - "4 Negative 47 \n", - "5 Positive 101 \n", - "6 Negative 0 \n", - "7 Positive 56 \n", - "8 Positive 79 \n", - "9 Positive 49 \n", - "10 Negative 33 \n", - "11 Negative 47 \n", - "12 Positive 101 \n", - "13 Negative 0 \n", - "14 Positive 56 \n", - "15 Positive 79 \n", - "16 Positive 49 \n", - "17 Negative 33 \n", - "18 Negative 0 \n", - "19 Positive 122 \n", - "20 Negative 0 \n", - "21 Positive 122 \n", - "22 Negative 0 \n", - "23 Negative 0 \n", - "24 Negative 0 \n", - "25 Negative 0 \n", - "26 Negative 0 \n", - "27 Negative -16 \n", - "28 Negative 4 \n", - "29 Positive 10 \n", - "... ... ... \n", - "6672 Negative 146 \n", - "6673 Negative 0 \n", - "6674 Negative 169 \n", - "6675 Negative 146 \n", - "6676 Negative 120 \n", - "6677 Negative 142 \n", - "6678 Negative 169 \n", - "6679 Negative 95 \n", - "6680 Negative 120 \n", - "6681 Negative 0 \n", - "6682 Negative 2 \n", - "6683 Positive 272 \n", - "6684 Negative 255 \n", - "6685 Negative 222 \n", - "6686 Negative 0 \n", - "6687 Negative 188 \n", - "6688 Negative 183 \n", - "6689 Negative 129 \n", - "6690 Negative 1 \n", - "6691 Positive 0 \n", - "6692 Positive 0 \n", - "6693 Negative 97 \n", - "6694 Positive 172 \n", - "6695 Negative 56 \n", - "6696 Negative 267 \n", - "6697 Negative 1 \n", - "6698 Negative 162 \n", - "6699 Negative 277 \n", - "6700 Negative 2 \n", - "6701 Positive 0 \n", - "\n", - "[6702 rows x 8 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Merge the imaging_study and measurement data using \"case_ids\" as a foreign key\n", - "temp = pd.merge(s[['study_uid','days_to_study','case_ids']],meas[['project_id','submitter_id','test_name','test_result_text','case_ids','test_days_from_index']],on='case_ids')\n", - "display(temp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate the days from COVID-19 test to an imaging_study\n", - "---\n", - "Now that we have the temporal data for imaging studies and COVID-19 tests in a single DataFrame for all cases in MIDRC for which this data is provided, we can calculate the number of days between each COVID-19 test and each imaging study, which we'll call `days_from_test_to_study`.\n", - "\n", - "* Note: In MIDRC, a negative \"days to XYZ\" indicates that the event XYZ took place that many days prior to the index event, while a positive \"days to\" indicates the number of days since the index event. For example, a \"days_to_study\" of \"-10\" indicates that the imaging study was performed 10 days *before* the index event. A value of \"365\" indicates the imaging study took place one year *after* the index event. \n", - "\n", - "We expect a positive value if the test was performed before the study.\n", - "- So, if `test_days_from_index` is `1` and `days_to_study` is `4`, the `days_from_test_to_study` should be `3`, which means the study took place 3 days after the test. \n", - "- If the test is on day 4 and the study is on day 1, then the `days_from_test_to_study` is `-3`, meaning the study took place 3 days before the test.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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study_uiddays_to_studycase_idsproject_idsubmitter_idtest_nametest_result_texttest_days_from_indexdays_from_test_to_study
02.16.840.1.114274.1818.53927660811344067041430...-339.010000364-1375261Open-A11757168COVID-19Positive303-642.0
12.16.840.1.114274.1818.53927660811344067041430...-339.010000364-1375261Open-A11970385COVID-19Negative490-829.0
22.16.840.1.114274.1818.53927660811344067041430...-339.010000364-1375261Open-A11852379COVID-19Positive303-642.0
32.16.840.1.114274.1818.53927660811344067041430...-339.010000364-1375261Open-A11757167COVID-19Negative460-799.0
42.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11749586COVID-19Negative47-48.0
52.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11913938COVID-19Positive101-102.0
62.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11689955COVID-19Negative0-1.0
72.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11691260COVID-19Positive56-57.0
82.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11916704COVID-19Positive79-80.0
92.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11791797COVID-19Positive49-50.0
102.16.840.1.114274.1818.57239462775962082858120...-1.010000364-1519515Open-A11749585COVID-19Negative33-34.0
112.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11749586COVID-19Negative47-295.0
122.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11913938COVID-19Positive101-349.0
132.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11689955COVID-19Negative0-248.0
142.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11691260COVID-19Positive56-304.0
152.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11916704COVID-19Positive79-327.0
162.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11791797COVID-19Positive49-297.0
172.16.840.1.114274.1818.47382997383489073254791...-248.010000364-1519515Open-A11749585COVID-19Negative33-281.0
182.16.840.1.114274.1818.56773440092156751147307...35.010000364-1465830Open-A11819952COVID-19Negative035.0
192.16.840.1.114274.1818.56773440092156751147307...35.010000364-1465830Open-A11720667COVID-19Positive122-87.0
202.16.840.1.114274.1818.47827819242494077718021...231.010000364-1465830Open-A11819952COVID-19Negative0231.0
212.16.840.1.114274.1818.47827819242494077718021...231.010000364-1465830Open-A11720667COVID-19Positive122109.0
222.16.840.1.114274.1818.51607810898232549052164...-114.010000364-2470802Open-A15659580COVID-19Negative0-114.0
232.16.840.1.114274.1818.51607810898232549052164...-114.010000364-2470802Open-A15685081COVID-19Negative0-114.0
242.16.840.1.114274.1818.47645127050117245123626...-21.010000364-6604535Open-A11680073COVID-19Negative0-21.0
252.16.840.1.114274.1818.54928928112017018811692...-290.010000364-6202975Open-A13170727COVID-19Negative0-290.0
262.16.840.1.114274.1818.54928928112017018811692...-290.010000364-6202975Open-A12965619COVID-19Negative0-290.0
272.16.840.1.114274.1818.54928928112017018811692...-290.010000364-6202975Open-A13169371COVID-19Negative-16-274.0
282.16.840.1.114274.1818.55908318442052087227659...140.010003752-e46VS8SPBU2GwExoQZnQwOpen-A120041630COVID-19Negative4136.0
292.16.840.1.114274.1818.55908318442052087227659...140.010003752-e46VS8SPBU2GwExoQZnQwOpen-A120045963COVID-19Positive10130.0
..............................
66722.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15738834COVID-19Negative146-110.0
66732.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15351225COVID-19Negative036.0
66742.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15743455COVID-19Negative169-133.0
66752.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15565232COVID-19Negative146-110.0
66762.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15738833COVID-19Negative120-84.0
66772.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15322590COVID-19Negative142-106.0
66782.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15427471COVID-19Negative169-133.0
66792.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15703021COVID-19Negative95-59.0
66802.16.840.1.114274.1818.49194983278729057281820...36.010000364-1047271Open-A15743027COVID-19Negative120-84.0
66812.16.840.1.114274.1818.52358446283564579374480...176.010000364-6011283Open-A12583877COVID-19Negative0176.0
66822.16.840.1.114274.1818.48065642175238177321392...143.010000364-6556476Open-A13007218COVID-19Negative2141.0
66832.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13406840COVID-19Positive272-539.0
66842.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13411550COVID-19Negative255-522.0
66852.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13278414COVID-19Negative222-489.0
66862.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13357320COVID-19Negative0-267.0
66872.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13586187COVID-19Negative188-455.0
66882.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13314608COVID-19Negative183-450.0
66892.16.840.1.114274.1818.50679961778563398083977...-267.010000364-1548264Open-A13532382COVID-19Negative129-396.0
66902.16.840.1.114274.1818.50224474340696597131759...0.010000364-1558041Open-A14773729COVID-19Negative1-1.0
66912.16.840.1.114274.1818.55896316209634308081473...-199.010000364-1008341Open-A119174788COVID-19Positive0-199.0
66922.16.840.1.114274.1818.55896316209634308081473...-199.010000364-1008341Open-A119452157COVID-19Positive0-199.0
66932.16.840.1.114274.1818.49251887420692145151582...254.010000364-6477239Open-A15413266COVID-19Negative97157.0
66942.16.840.1.114274.1818.49251887420692145151582...254.010000364-6477239Open-A15699634COVID-19Positive17282.0
66952.16.840.1.114274.1818.49251887420692145151582...254.010000364-6477239Open-A15478856COVID-19Negative56198.0
66962.16.840.1.114274.1818.49251887420692145151582...254.010000364-6477239Open-A15724075COVID-19Negative267-13.0
66972.16.840.1.114274.1818.49251887420692145151582...254.010000364-6477239Open-A15684302COVID-19Negative1253.0
66982.16.840.1.114274.1818.49251887420692145151582...254.010000364-6477239Open-A15371492COVID-19Negative16292.0
66992.16.840.1.114274.1818.49251887420692145151582...254.010000364-6477239Open-A15724383COVID-19Negative277-23.0
67002.16.840.1.114274.1818.54227836952985538421827...-70.010000364-1005952Open-A15743776COVID-19Negative2-72.0
67012.16.840.1.114274.1818.46462464228338035604085...55.010000364-1163342Open-A15311668COVID-19Positive055.0
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6702 rows × 9 columns

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" ], "text/plain": [ - " study_uid days_to_study \\\n", - "0 2.16.840.1.114274.1818.53927660811344067041430... -339.0 \n", - "1 2.16.840.1.114274.1818.53927660811344067041430... -339.0 \n", - "2 2.16.840.1.114274.1818.53927660811344067041430... -339.0 \n", - "3 2.16.840.1.114274.1818.53927660811344067041430... -339.0 \n", - "4 2.16.840.1.114274.1818.57239462775962082858120... -1.0 \n", - "5 2.16.840.1.114274.1818.57239462775962082858120... -1.0 \n", - "6 2.16.840.1.114274.1818.57239462775962082858120... -1.0 \n", - "7 2.16.840.1.114274.1818.57239462775962082858120... -1.0 \n", - "8 2.16.840.1.114274.1818.57239462775962082858120... -1.0 \n", - "9 2.16.840.1.114274.1818.57239462775962082858120... -1.0 \n", - "10 2.16.840.1.114274.1818.57239462775962082858120... -1.0 \n", - "11 2.16.840.1.114274.1818.47382997383489073254791... -248.0 \n", - "12 2.16.840.1.114274.1818.47382997383489073254791... -248.0 \n", - "13 2.16.840.1.114274.1818.47382997383489073254791... -248.0 \n", - "14 2.16.840.1.114274.1818.47382997383489073254791... -248.0 \n", - 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"\n", - " case_ids project_id submitter_id test_name \\\n", - "0 10000364-1375261 Open-A1 1757168 COVID-19 \n", - "1 10000364-1375261 Open-A1 1970385 COVID-19 \n", - "2 10000364-1375261 Open-A1 1852379 COVID-19 \n", - "3 10000364-1375261 Open-A1 1757167 COVID-19 \n", - "4 10000364-1519515 Open-A1 1749586 COVID-19 \n", - "5 10000364-1519515 Open-A1 1913938 COVID-19 \n", - "6 10000364-1519515 Open-A1 1689955 COVID-19 \n", - "7 10000364-1519515 Open-A1 1691260 COVID-19 \n", - "8 10000364-1519515 Open-A1 1916704 COVID-19 \n", - "9 10000364-1519515 Open-A1 1791797 COVID-19 \n", - "10 10000364-1519515 Open-A1 1749585 COVID-19 \n", - "11 10000364-1519515 Open-A1 1749586 COVID-19 \n", - "12 10000364-1519515 Open-A1 1913938 COVID-19 \n", - "13 10000364-1519515 Open-A1 1689955 COVID-19 \n", - "14 10000364-1519515 Open-A1 1691260 COVID-19 \n", - "15 10000364-1519515 Open-A1 1916704 COVID-19 \n", - "16 10000364-1519515 Open-A1 1791797 COVID-19 \n", - "17 10000364-1519515 Open-A1 1749585 COVID-19 \n", - "18 10000364-1465830 Open-A1 1819952 COVID-19 \n", - "19 10000364-1465830 Open-A1 1720667 COVID-19 \n", - "20 10000364-1465830 Open-A1 1819952 COVID-19 \n", - "21 10000364-1465830 Open-A1 1720667 COVID-19 \n", - "22 10000364-2470802 Open-A1 5659580 COVID-19 \n", - "23 10000364-2470802 Open-A1 5685081 COVID-19 \n", - "24 10000364-6604535 Open-A1 1680073 COVID-19 \n", - "25 10000364-6202975 Open-A1 3170727 COVID-19 \n", - "26 10000364-6202975 Open-A1 2965619 COVID-19 \n", - "27 10000364-6202975 Open-A1 3169371 COVID-19 \n", - "28 10003752-e46VS8SPBU2GwExoQZnQw Open-A1 20041630 COVID-19 \n", - "29 10003752-e46VS8SPBU2GwExoQZnQw Open-A1 20045963 COVID-19 \n", - "... ... ... ... ... \n", - "6672 10000364-1047271 Open-A1 5738834 COVID-19 \n", - "6673 10000364-1047271 Open-A1 5351225 COVID-19 \n", - "6674 10000364-1047271 Open-A1 5743455 COVID-19 \n", - "6675 10000364-1047271 Open-A1 5565232 COVID-19 \n", - "6676 10000364-1047271 Open-A1 5738833 COVID-19 \n", - "6677 10000364-1047271 Open-A1 5322590 COVID-19 \n", - "6678 10000364-1047271 Open-A1 5427471 COVID-19 \n", - "6679 10000364-1047271 Open-A1 5703021 COVID-19 \n", - "6680 10000364-1047271 Open-A1 5743027 COVID-19 \n", - "6681 10000364-6011283 Open-A1 2583877 COVID-19 \n", - "6682 10000364-6556476 Open-A1 3007218 COVID-19 \n", - "6683 10000364-1548264 Open-A1 3406840 COVID-19 \n", - "6684 10000364-1548264 Open-A1 3411550 COVID-19 \n", - "6685 10000364-1548264 Open-A1 3278414 COVID-19 \n", - "6686 10000364-1548264 Open-A1 3357320 COVID-19 \n", - "6687 10000364-1548264 Open-A1 3586187 COVID-19 \n", - "6688 10000364-1548264 Open-A1 3314608 COVID-19 \n", - "6689 10000364-1548264 Open-A1 3532382 COVID-19 \n", - "6690 10000364-1558041 Open-A1 4773729 COVID-19 \n", - "6691 10000364-1008341 Open-A1 19174788 COVID-19 \n", - "6692 10000364-1008341 Open-A1 19452157 COVID-19 \n", - "6693 10000364-6477239 Open-A1 5413266 COVID-19 \n", - "6694 10000364-6477239 Open-A1 5699634 COVID-19 \n", - "6695 10000364-6477239 Open-A1 5478856 COVID-19 \n", - "6696 10000364-6477239 Open-A1 5724075 COVID-19 \n", - "6697 10000364-6477239 Open-A1 5684302 COVID-19 \n", - "6698 10000364-6477239 Open-A1 5371492 COVID-19 \n", - "6699 10000364-6477239 Open-A1 5724383 COVID-19 \n", - "6700 10000364-1005952 Open-A1 5743776 COVID-19 \n", - "6701 10000364-1163342 Open-A1 5311668 COVID-19 \n", - "\n", - " test_result_text test_days_from_index days_from_test_to_study \n", - "0 Positive 303 -642.0 \n", - "1 Negative 490 -829.0 \n", - "2 Positive 303 -642.0 \n", - "3 Negative 460 -799.0 \n", - "4 Negative 47 -48.0 \n", - "5 Positive 101 -102.0 \n", - "6 Negative 0 -1.0 \n", - "7 Positive 56 -57.0 \n", - "8 Positive 79 -80.0 \n", - "9 Positive 49 -50.0 \n", - "10 Negative 33 -34.0 \n", - "11 Negative 47 -295.0 \n", - "12 Positive 101 -349.0 \n", - "13 Negative 0 -248.0 \n", - "14 Positive 56 -304.0 \n", - "15 Positive 79 -327.0 \n", - "16 Positive 49 -297.0 \n", - "17 Negative 33 -281.0 \n", - "18 Negative 0 35.0 \n", - "19 Positive 122 -87.0 \n", - "20 Negative 0 231.0 \n", - "21 Positive 122 109.0 \n", - "22 Negative 0 -114.0 \n", - "23 Negative 0 -114.0 \n", - "24 Negative 0 -21.0 \n", - "25 Negative 0 -290.0 \n", - "26 Negative 0 -290.0 \n", - "27 Negative -16 -274.0 \n", - "28 Negative 4 136.0 \n", - "29 Positive 10 130.0 \n", - "... ... ... ... \n", - "6672 Negative 146 -110.0 \n", - "6673 Negative 0 36.0 \n", - "6674 Negative 169 -133.0 \n", - "6675 Negative 146 -110.0 \n", - "6676 Negative 120 -84.0 \n", - "6677 Negative 142 -106.0 \n", - "6678 Negative 169 -133.0 \n", - "6679 Negative 95 -59.0 \n", - "6680 Negative 120 -84.0 \n", - "6681 Negative 0 176.0 \n", - "6682 Negative 2 141.0 \n", - "6683 Positive 272 -539.0 \n", - "6684 Negative 255 -522.0 \n", - "6685 Negative 222 -489.0 \n", - "6686 Negative 0 -267.0 \n", - "6687 Negative 188 -455.0 \n", - "6688 Negative 183 -450.0 \n", - "6689 Negative 129 -396.0 \n", - "6690 Negative 1 -1.0 \n", - "6691 Positive 0 -199.0 \n", - "6692 Positive 0 -199.0 \n", - "6693 Negative 97 157.0 \n", - "6694 Positive 172 82.0 \n", - "6695 Negative 56 198.0 \n", - "6696 Negative 267 -13.0 \n", - "6697 Negative 1 253.0 \n", - "6698 Negative 162 92.0 \n", - "6699 Negative 277 -23.0 \n", - "6700 Negative 2 -72.0 \n", - "6701 Positive 0 55.0 \n", - "\n", - "[6702 rows x 9 columns]" + " type id project_id submitter_id \\\n", + "0 measurement 000066a4-1355-4828-893b-bcfa3f88d4f3 Open-A1 20385772 \n", + "1 measurement 000255bf-4f4c-4462-8380-2045ea9808d9 Open-A1 1871007 \n", + "2 measurement 00028e86-e680-4058-9f87-8e633dbff9d4 Open-A1 20058937 \n", + "\n", + " case_ids test_days_from_index test_method \\\n", + "0 10022779-roAQeA6Bf0izu75zfNb2ng 308 RT-PCR \n", + "1 10000364-799838 132 RT-PCR \n", + "2 10008204-aUL88To8KkerKLbM9POnCg 0 RT-PCR \n", + "\n", + " test_name test_result_text cases.id \\\n", + "0 COVID-19 Negative c667e8aa-c19c-485e-bd25-886e523f59bc \n", + "1 COVID-19 Negative e05b5b00-b14e-4fca-a5bf-dea75dd280d6 \n", + "2 COVID-19 Negative 9b0be752-ea88-40f5-9b57-73ca8516bb0b \n", + "\n", + " cases.submitter_id conditions.id conditions.submitter_id \\\n", + "0 10022779-roAQeA6Bf0izu75zfNb2ng NaN NaN \n", + "1 10000364-799838 NaN NaN \n", + "2 10008204-aUL88To8KkerKLbM9POnCg NaN NaN \n", + "\n", + " procedures.id procedures.submitter_id \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Filter the measurements for only COVID-19 tests with a non-null \"test_days_from_index\" property\n", + "m = meas.loc[(~meas['test_days_from_index'].isna()) & (meas['test_name']=='COVID-19')]\n", + "print('\\nrows:{}, columns:{}'.format(m.shape[0],m.shape[1]))\n", + "m.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['type',\n", + " 'id',\n", + " 'project_id',\n", + " 'submitter_id',\n", + " 'age_at_imaging',\n", + " 'age_at_imaging_gt89',\n", + " 'body_part_examined',\n", + " 'case_ids',\n", + " 'days_from_study_to_neg_covid_test',\n", + " 'days_from_study_to_pos_covid_test',\n", + " 'days_to_study',\n", + " 'loinc_code',\n", + " 'loinc_contrast',\n", + " 'loinc_long_common_name',\n", + " 'loinc_method',\n", + " 'loinc_system',\n", + " 'study_description',\n", + " 'study_location',\n", + " 'study_modality',\n", + " 'study_uid',\n", + " 'study_year',\n", + " 'study_year_shifted',\n", + " 'cases.id',\n", + " 'cases.submitter_id']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "21962" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "['type',\n", + " 'id',\n", + " 'project_id',\n", + " 'submitter_id',\n", + " 'case_ids',\n", + " 'test_days_from_index',\n", + " 'test_method',\n", + " 'test_name',\n", + " 'test_result_text',\n", + " 'cases.id',\n", + " 'cases.submitter_id',\n", + " 'conditions.id',\n", + " 'conditions.submitter_id',\n", + " 'procedures.id',\n", + " 'procedures.submitter_id']" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "88903" ] }, "metadata": {}, @@ -5748,29 +786,26 @@ } ], "source": [ - "## Calculate the days from COVID-19 test to an imaging_study\n", - "temp['days_from_test_to_study'] = temp['days_to_study'] - temp['test_days_from_index']\n", - "display(temp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Identify \"COVID-19 positive\" imaging studies\n", - "---\n", - "Now that we've calculated `days_from_test_to_study`, we can define a cut-off value and filter the imaging studies using that value to determine which imaging studies were performed within a certain time-frame of receiving a positive COVID-19 test.\n", - "\n", - "Our new property `days_from_test_to_study` has a positive value if the COVID test was performed before the imaging study (i.e., there were +3 days from the test date to the study date) and a negative value if the test was performed after the imaging study (i.e., go back in time 3 days from the test date to the study date). \n", - "\n", - "For this demo, let's assume that an imaging study was performed when a person was \"COVID-positive\" if the imaging study was performed within a 10-day range of a positive test result. So, we'll filter the DataFrame of studies for a `days_from_test_to_study` in the range of -5 to 5 and also require the `test_result_text` to be `Positive`.\n" + "## Check out the properties in each DataFrame to help make a list of properties to merge into a single table\n", + "display(list(s))\n", + "display(len(s))\n", + "display(list(m))\n", + "display(len(m))" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "rows:171857, columns:8\n" + ] + }, { "data": { "text/html": [ @@ -5800,800 +835,703 @@ " test_name\n", " test_result_text\n", " test_days_from_index\n", - " days_from_test_to_study\n", " \n", " \n", " \n", " \n", - " 519\n", - " 2.16.840.1.114274.1818.49730838145991625671237...\n", - " 0.0\n", - " 10000364-1441843\n", - " Open-A1\n", - " 19375484\n", - " COVID-19\n", - " Positive\n", - " 0\n", - " 0.0\n", - " \n", - " \n", - " 804\n", - " 2.16.840.1.114274.1818.54429352050740460131192...\n", - 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" Positive\n", - " 0\n", - " -1.0\n", + " Negative\n", + " 40\n", + " \n", + " \n", + "\n", + "

171857 rows × 8 columns

\n", + "" + ], + "text/plain": [ + " study_uid days_to_study \\\n", + "0 2.16.840.1.114274.1818.54486547105285539711177... -219.0 \n", + "1 2.16.840.1.114274.1818.46473000059984716974550... -66.0 \n", + "2 2.16.840.1.114274.1818.54212141679844111981241... 309.0 \n", + "3 2.16.840.1.114274.1818.46916719691230524734515... -2.0 \n", + "4 2.16.840.1.114274.1818.46916719691230524734515... -2.0 \n", + "... ... ... \n", + "171852 1.2.826.0.1.3680043.10.474.419639.898313542871... 0.0 \n", + "171853 1.2.826.0.1.3680043.10.474.419639.898313542871... 0.0 \n", + "171854 1.2.826.0.1.3680043.10.474.419639.898313542871... 0.0 \n", + "171855 1.2.826.0.1.3680043.10.474.419639.115092168736... 0.0 \n", + "171856 1.2.826.0.1.3680043.10.474.419639.115092168736... 0.0 \n", + "\n", + " case_ids project_id \\\n", + "0 10000364-1204441 Open-A1 \n", + "1 10000364-2615190 Open-A1 \n", + "2 10008204-2MhSAK2fUyQMB7Y5csg Open-A1 \n", + "3 10000364-1510925 Open-A1 \n", + "4 10000364-1510925 Open-A1 \n", + "... ... ... \n", + "171852 419639-010649 Open-R1 \n", + "171853 419639-010649 Open-R1 \n", + "171854 419639-010649 Open-R1 \n", + "171855 419639-001129 Open-R1 \n", + "171856 419639-001129 Open-R1 \n", + "\n", + " submitter_id test_name test_result_text \\\n", + "0 5049241 COVID-19 Positive \n", + "1 55709574 COVID-19 Positive \n", + "2 20056262 COVID-19 Negative \n", + "3 55266707 COVID-19 Positive \n", + "4 55300802 COVID-19 Negative \n", + "... ... ... ... \n", + "171852 Measurement_419639-010649_0001 COVID-19 Negative \n", + "171853 Measurement_419639-010649_0002 COVID-19 Negative \n", + "171854 Measurement_419639-010649_0008 COVID-19 Negative \n", + "171855 Measurement_419639-001129_0001 COVID-19 Negative \n", + "171856 Measurement_419639-001129_0002 COVID-19 Negative \n", + "\n", + " test_days_from_index \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 18 \n", + "4 0 \n", + "... ... \n", + "171852 -78 \n", + "171853 -384 \n", + "171854 -131 \n", + "171855 0 \n", + "171856 40 \n", + "\n", + "[171857 rows x 8 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Merge the imaging_study and measurement data using \"case_ids\" as a foreign key\n", + "temp = pd.merge(s[['study_uid','days_to_study','case_ids']],m[['project_id','submitter_id','test_name','test_result_text','case_ids','test_days_from_index']],on='case_ids')\n", + "print('\\nrows:{}, columns:{}'.format(temp.shape[0],temp.shape[1]))\n", + "display(temp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate the days from COVID-19 test to an imaging_study\n", + "---\n", + "Now that we have the temporal data for imaging studies and COVID-19 tests in a single DataFrame for all cases in MIDRC for which this data is provided, we can calculate the number of days between each imaging study and each COVID-19 test, which we'll call `days_from_study_to_test`.\n", + "\n", + "* Note: In MIDRC, a negative \"days to XYZ\" indicates that the event XYZ took place that many days prior to the index event, while a positive \"days to\" indicates the number of days since the index event. For example, a \"days_to_study\" of \"-10\" indicates that the imaging study was performed 10 days *before* the index event. A value of \"365\" indicates the imaging study took place one year *after* the index event. \n", + "\n", + "In the case of a derived property like `days_from_study_to_test`, the date of the study can be thought of as the 0 point, and the test takes place in time either before the study, moving backwards on the timeline (negative value) or the test takes place after the study (moving forward in time).\n", + "\n", + "So, we expect a positive value for `days_from_study_to_test` if the test was performed after the study.\n", + "- For example, if `test_days_from_index` is `1` and `days_to_study` is `4`, the `days_from_study_to_test` should be `-3`, which means the test took place 3 days before the study.\n", + "- If the COVID test is on day 4 and the imaging study is on day 1, then the `days_from_study_to_test` is `3`, meaning the COVID-19 test took place 3 days after the imaging study.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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47432.16.840.1.114274.1818.47990836239286471671305...-2.010000364-46603212.16.840.1.114274.1818.46473000059984716974550...-66.010000364-2615190Open-A1387954255709574COVID-19Positive0-2.066.0
47442.16.840.1.114274.1818.47990836239286471671305...-2.010000364-46603222.16.840.1.114274.1818.54212141679844111981241...309.010008204-2MhSAK2fUyQMB7Y5csgOpen-A1382099220056262COVID-19PositiveNegative0-2.0-309.0
47452.16.840.1.114274.1818.47990836239286471671305...32.16.840.1.114274.1818.46916719691230524734515...-2.010000364-46603210000364-1510925Open-A1388934555266707COVID-19Positive0-2.01820.0
47462.16.840.1.114274.1818.47990836239286471671305...42.16.840.1.114274.1818.46916719691230524734515...-2.010000364-46603210000364-1510925Open-A1355087855300802COVID-19PositiveNegative0-2.02.0
48052.16.840.1.114274.1818.56022837896301096993434...3.010000364-6113118Open-A15216988COVID-19Positive03.0..............................
52402.16.840.1.114274.1818.48121086708486269392100...3.010000364-1924274Open-A144554691718521.2.826.0.1.3680043.10.474.419639.898313542871...0.0419639-010649Open-R1Measurement_419639-010649_0001COVID-19Positive03.0Negative-78-78.0
52872.16.840.1.114274.1818.52779345069819425519366...1.010000364-1583101Open-A145266431718531.2.826.0.1.3680043.10.474.419639.898313542871...0.0419639-010649Open-R1Measurement_419639-010649_0002COVID-19Positive01.0Negative-384-384.0
55082.16.840.1.114274.1818.46952685839100937222972...-1.010000364-2372675Open-A146076571718541.2.826.0.1.3680043.10.474.419639.898313542871...0.0419639-010649Open-R1Measurement_419639-010649_0008COVID-19Positive0-1.0Negative-131-131.0
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55412.16.840.1.114274.1818.56675936572140486211641...1718561.2.826.0.1.3680043.10.474.419639.115092168736...0.010000364-6505935419639-001129Open-R1Measurement_419639-001129_0002COVID-19Negative4040.0
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171857 rows × 9 columns

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" + ], + "text/plain": [ + " study_uid days_to_study \\\n", + "0 2.16.840.1.114274.1818.54486547105285539711177... -219.0 \n", + "1 2.16.840.1.114274.1818.46473000059984716974550... -66.0 \n", + "2 2.16.840.1.114274.1818.54212141679844111981241... 309.0 \n", + "3 2.16.840.1.114274.1818.46916719691230524734515... -2.0 \n", + "4 2.16.840.1.114274.1818.46916719691230524734515... -2.0 \n", + "... ... ... \n", + "171852 1.2.826.0.1.3680043.10.474.419639.898313542871... 0.0 \n", + "171853 1.2.826.0.1.3680043.10.474.419639.898313542871... 0.0 \n", + "171854 1.2.826.0.1.3680043.10.474.419639.898313542871... 0.0 \n", + "171855 1.2.826.0.1.3680043.10.474.419639.115092168736... 0.0 \n", + "171856 1.2.826.0.1.3680043.10.474.419639.115092168736... 0.0 \n", + "\n", + " case_ids project_id \\\n", + "0 10000364-1204441 Open-A1 \n", + "1 10000364-2615190 Open-A1 \n", + "2 10008204-2MhSAK2fUyQMB7Y5csg Open-A1 \n", + "3 10000364-1510925 Open-A1 \n", + "4 10000364-1510925 Open-A1 \n", + "... ... ... \n", + "171852 419639-010649 Open-R1 \n", + "171853 419639-010649 Open-R1 \n", + "171854 419639-010649 Open-R1 \n", + "171855 419639-001129 Open-R1 \n", + "171856 419639-001129 Open-R1 \n", + "\n", + " submitter_id test_name test_result_text \\\n", + "0 5049241 COVID-19 Positive \n", + "1 55709574 COVID-19 Positive \n", + "2 20056262 COVID-19 Negative \n", + "3 55266707 COVID-19 Positive \n", + "4 55300802 COVID-19 Negative \n", + "... ... ... ... \n", + "171852 Measurement_419639-010649_0001 COVID-19 Negative \n", + "171853 Measurement_419639-010649_0002 COVID-19 Negative \n", + "171854 Measurement_419639-010649_0008 COVID-19 Negative \n", + "171855 Measurement_419639-001129_0001 COVID-19 Negative \n", + "171856 Measurement_419639-001129_0002 COVID-19 Negative \n", + "\n", + " test_days_from_index days_from_study_to_test \n", + "0 0 219.0 \n", + "1 0 66.0 \n", + "2 0 -309.0 \n", + "3 18 20.0 \n", + "4 0 2.0 \n", + "... ... ... \n", + "171852 -78 -78.0 \n", + "171853 -384 -384.0 \n", + "171854 -131 -131.0 \n", + "171855 0 0.0 \n", + "171856 40 40.0 \n", + "\n", + "[171857 rows x 9 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Calculate the days from COVID-19 test to an imaging_study\n", + "temp['days_from_study_to_test'] = temp['test_days_from_index'] - temp['days_to_study']\n", + "display(temp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identify \"COVID-19 positive\" imaging studies\n", + "---\n", + "Now that we've calculated `days_from_study_to_test`, we can define a cut-off value and filter the imaging studies using that value to determine which imaging studies were performed within a certain time-frame of receiving a positive COVID-19 test.\n", + "\n", + "Again, our new derived attribute `days_from_study_to_test` has a positive value if the COVID test was performed after the imaging study (i.e., from the study date to test date is moving forward in time) and a negative value if the COVID test was performed before the imaging study (i.e., go back in time from the imaging date to the COVID test date). \n", + "\n", + "For this demo, let's assume that an imaging study was performed when a person was \"COVID-positive\" if the imaging study was performed within a 7 day window after a positive test result. So, we'll filter the DataFrame of studies for a `days_from_study_to_test` in the range of -7 to 0 and also require the `test_result_text` to be `Positive`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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study_uiddays_to_studycase_idsproject_idsubmitter_idtest_nametest_result_texttest_days_from_indexdays_from_study_to_test
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1035 rows × 9 columns

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" ], "text/plain": [ - " study_uid days_to_study \\\n", - "519 2.16.840.1.114274.1818.49730838145991625671237... 0.0 \n", - "804 2.16.840.1.114274.1818.54429352050740460131192... 0.0 \n", - "805 2.16.840.1.114274.1818.54429352050740460131192... 0.0 \n", - "890 2.16.840.1.114274.1818.48021771159095447451766... 0.0 \n", - "891 2.16.840.1.114274.1818.48021771159095447451766... 0.0 \n", - "1287 2.16.840.1.114274.1818.48157555954506242031292... -2.0 \n", - "1349 2.16.840.1.114274.1818.52277190787710549031199... 231.0 \n", - "1373 2.16.840.1.114274.1818.53840514870842921501028... 172.0 \n", - "1793 2.16.840.1.114274.1818.53254162557788114198494... 3.0 \n", - "1923 2.16.840.1.114274.1818.49623065213006336731706... -1.0 \n", - "1935 2.16.840.1.114274.1818.54732464788503339771643... 0.0 \n", - "1970 2.16.840.1.114274.1818.52270037051786749239628... -3.0 \n", - "2130 2.16.840.1.114274.1818.54360925607417617277874... 0.0 \n", - "2190 2.16.840.1.114274.1818.56410020409655965101569... 2.0 \n", - "2620 2.16.840.1.114274.1818.51896756758058544441365... 86.0 \n", - "2891 2.16.840.1.114274.1818.46728737024781783052904... -2.0 \n", - "2983 2.16.840.1.114274.1818.50789209828925772371353... 9.0 \n", - "3206 2.16.840.1.114274.1818.48922965582546147864511... -2.0 \n", - "3374 2.16.840.1.114274.1818.48693005854382886101591... 0.0 \n", - "3375 2.16.840.1.114274.1818.46255839168561943616688... 0.0 \n", - "3489 2.16.840.1.114274.1818.56957313098786761497942... 19.0 \n", - "3763 2.16.840.1.114274.1818.52513335950400868962638... -1.0 \n", - "3791 2.16.840.1.114274.1818.47264046370082065051643... 85.0 \n", - "3888 2.16.840.1.114274.1818.57465404281606135501004... -1.0 \n", - "3938 2.16.840.1.114274.1818.55394178898960577691784... 56.0 \n", - "4036 2.16.840.1.114274.1818.56954099544056097696952... -4.0 \n", - "4037 2.16.840.1.114274.1818.52672479988862611843187... 4.0 \n", - "4044 2.16.840.1.114274.1818.49938722868158878271423... -2.0 \n", - "4065 2.16.840.1.114274.1818.49916930337862494302491... -2.0 \n", - "4383 2.16.840.1.114274.1818.55556814984927382924042... 1.0 \n", - "4645 2.16.840.1.114274.1818.48948218660006007801571... -1.0 \n", - "4646 2.16.840.1.114274.1818.48948218660006007801571... -1.0 \n", - "4703 2.16.840.1.114274.1818.53473230394172531113668... -2.0 \n", - "4743 2.16.840.1.114274.1818.47990836239286471671305... -2.0 \n", - "4744 2.16.840.1.114274.1818.47990836239286471671305... -2.0 \n", - "4745 2.16.840.1.114274.1818.47990836239286471671305... -2.0 \n", - "4746 2.16.840.1.114274.1818.47990836239286471671305... -2.0 \n", - "4805 2.16.840.1.114274.1818.56022837896301096993434... 3.0 \n", - "5240 2.16.840.1.114274.1818.48121086708486269392100... 3.0 \n", - "5287 2.16.840.1.114274.1818.52779345069819425519366... 1.0 \n", - "5508 2.16.840.1.114274.1818.46952685839100937222972... -1.0 \n", - "5532 2.16.840.1.114274.1818.50216899689345619598946... -1.0 \n", - "5541 2.16.840.1.114274.1818.56675936572140486211641... 0.0 \n", - "5696 2.16.840.1.114274.1818.53256507062586789144869... -4.0 \n", - "5840 2.16.840.1.114274.1818.47992548380110931489204... -1.0 \n", - "5906 2.16.840.1.114274.1818.49737935697536517401293... 4.0 \n", - "5937 2.16.840.1.114274.1818.55325790822657183492326... -2.0 \n", - "6001 2.16.840.1.114274.1818.49155496286746304941350... 2.0 \n", - "6040 2.16.840.1.114274.1818.55590644766723628335535... -2.0 \n", - "6215 2.16.840.1.114274.1818.48930555774261197064687... -2.0 \n", - "6325 2.16.840.1.114274.1818.56700013406773114451534... 12.0 \n", - "6393 2.16.840.1.114274.1818.49960126038261448015780... 4.0 \n", + " study_uid days_to_study \\\n", + "619 2.16.840.1.114274.1818.52637752272179376241686... 4.0 \n", + "654 2.16.840.1.114274.1818.50336413771141670171123... 6.0 \n", + "896 2.16.840.1.114274.1818.52429628221501850121575... 2.0 \n", + "1228 2.16.840.1.114274.1818.56932985904003450841697... 4.0 \n", + "1507 2.16.840.1.114274.1818.56160437523063747754866... 4.0 \n", + "... ... ... \n", + "171276 1.2.826.0.1.3680043.10.474.419639.115847197653... 6.0 \n", + "171345 1.2.826.0.1.3680043.10.474.419639.135281667774... 63.0 \n", + "171427 1.2.826.0.1.3680043.10.474.419639.299205477090... 6.0 \n", + "171496 1.2.826.0.1.3680043.10.474.419639.240842622217... 24.0 \n", + "171772 1.2.826.0.1.3680043.10.474.419639.185647165292... 3.0 \n", + "\n", + " case_ids project_id \\\n", + "619 10000364-6269042 Open-A1 \n", + "654 10000364-1257874 Open-A1 \n", + "896 10000364-1592202 Open-A1 \n", + "1228 10000364-1525651 Open-A1 \n", + "1507 10003752-8tJIePzC70GHh7e97RJjw Open-A1 \n", + "... ... ... \n", + "171276 419639-004459 Open-R1 \n", + "171345 419639-007806 Open-R1 \n", + "171427 419639-010628 Open-R1 \n", + "171496 419639-007069 Open-R1 \n", + "171772 419639-010135 Open-R1 \n", "\n", - " case_ids project_id submitter_id test_name test_result_text \\\n", - "519 10000364-1441843 Open-A1 19375484 COVID-19 Positive \n", - "804 10000364-6481317 Open-A1 4916547 COVID-19 Positive \n", - "805 10000364-6481317 Open-A1 4447830 COVID-19 Positive \n", - "890 10000364-1430082 Open-A1 4559135 COVID-19 Positive \n", - "891 10000364-1430082 Open-A1 4976581 COVID-19 Positive \n", - "1287 10000364-1629557 Open-A1 4029236 COVID-19 Positive \n", - "1349 10000364-1841406 Open-A1 5068111 COVID-19 Positive \n", - "1373 10000364-2579994 Open-A1 2610336 COVID-19 Positive \n", - "1793 10000364-5623150 Open-A1 5260278 COVID-19 Positive \n", - "1923 10000364-582005 Open-A1 3360799 COVID-19 Positive \n", - "1935 10000364-1398174 Open-A1 1695287 COVID-19 Positive \n", - "1970 10000364-737164 Open-A1 1680085 COVID-19 Positive \n", - "2130 10000364-5414336 Open-A1 953441 COVID-19 Positive \n", - "2190 10000364-1525021 Open-A1 19393421 COVID-19 Positive \n", - "2620 10000364-6319802 Open-A1 5600018 COVID-19 Positive \n", - "2891 10000364-1712991 Open-A1 1354128 COVID-19 Positive \n", - "2983 10000364-1373610 Open-A1 924092 COVID-19 Positive \n", - "3206 10000364-1292850 Open-A1 4698564 COVID-19 Positive \n", - "3374 10000364-6567146 Open-A1 2837873 COVID-19 Positive \n", - "3375 10000364-6567146 Open-A1 2837873 COVID-19 Positive \n", - "3489 10000364-311109 Open-A1 19354379 COVID-19 Positive \n", - "3763 10000364-1594555 Open-A1 5310776 COVID-19 Positive \n", - "3791 10000364-1585294 Open-A1 3945483 COVID-19 Positive \n", - "3888 10000364-1529476 Open-A1 2955808 COVID-19 Positive \n", - "3938 10000364-6599632 Open-A1 5798715 COVID-19 Positive \n", - "4036 10000364-1451946 Open-A1 4524325 COVID-19 Positive \n", - "4037 10000364-1451946 Open-A1 4524325 COVID-19 Positive \n", - "4044 10000364-6563400 Open-A1 2461557 COVID-19 Positive \n", - "4065 10000364-1565887 Open-A1 4170035 COVID-19 Positive \n", - "4383 10000364-1509832 Open-A1 878724 COVID-19 Positive \n", - "4645 10000364-2463023 Open-A1 5003423 COVID-19 Positive \n", - "4646 10000364-2463023 Open-A1 5236865 COVID-19 Positive \n", - "4703 10000364-6452978 Open-A1 19242136 COVID-19 Positive \n", - "4743 10000364-466032 Open-A1 3879542 COVID-19 Positive \n", - "4744 10000364-466032 Open-A1 3820992 COVID-19 Positive \n", - "4745 10000364-466032 Open-A1 3889345 COVID-19 Positive \n", - "4746 10000364-466032 Open-A1 3550878 COVID-19 Positive \n", - "4805 10000364-6113118 Open-A1 5216988 COVID-19 Positive \n", - "5240 10000364-1924274 Open-A1 4455469 COVID-19 Positive \n", - "5287 10000364-1583101 Open-A1 4526643 COVID-19 Positive \n", - "5508 10000364-2372675 Open-A1 4607657 COVID-19 Positive \n", - "5532 10000364-5694324 Open-A1 5585441 COVID-19 Positive \n", - "5541 10000364-6505935 Open-A1 19178959 COVID-19 Positive \n", - "5696 10000364-1563819 Open-A1 3597511 COVID-19 Positive \n", - "5840 10000364-1532266 Open-A1 3705604 COVID-19 Positive \n", - "5906 10000364-900710 Open-A1 3885970 COVID-19 Positive \n", - "5937 10000364-1384763 Open-A1 3427199 COVID-19 Positive \n", - "6001 10000364-288530 Open-A1 3438533 COVID-19 Positive \n", - "6040 10000364-1717700 Open-A1 4805685 COVID-19 Positive \n", - "6215 10000364-1470254 Open-A1 2010633 COVID-19 Positive \n", - "6325 10000364-12388 Open-A1 2092906 COVID-19 Positive \n", - "6393 10000364-1626259 Open-A1 5108041 COVID-19 Positive \n", + " submitter_id test_name test_result_text \\\n", + "619 55977426 COVID-19 Positive \n", + "654 3869225 COVID-19 Positive \n", + "896 55431089 COVID-19 Positive \n", + "1228 55301335 COVID-19 Positive \n", + "1507 20045346 COVID-19 Positive \n", + "... ... ... ... \n", + "171276 Measurement_419639-004459_0002 COVID-19 Positive \n", + "171345 Measurement_419639-007806_0002 COVID-19 Positive \n", + "171427 Measurement_419639-010628_0005 COVID-19 Positive \n", + "171496 Measurement_419639-007069_0001 COVID-19 Positive \n", + "171772 Measurement_419639-010135_0002 COVID-19 Positive \n", "\n", - " test_days_from_index days_from_test_to_study \n", - "519 0 0.0 \n", - "804 1 -1.0 \n", - "805 0 0.0 \n", - "890 4 -4.0 \n", - "891 0 0.0 \n", - "1287 0 -2.0 \n", - "1349 231 0.0 \n", - "1373 170 2.0 \n", - "1793 0 3.0 \n", - "1923 0 -1.0 \n", - "1935 0 0.0 \n", - "1970 0 -3.0 \n", - "2130 1 -1.0 \n", - "2190 0 2.0 \n", - "2620 83 3.0 \n", - "2891 0 -2.0 \n", - "2983 13 -4.0 \n", - "3206 0 -2.0 \n", - "3374 0 0.0 \n", - "3375 0 0.0 \n", - "3489 18 1.0 \n", - "3763 0 -1.0 \n", - "3791 87 -2.0 \n", - "3888 0 -1.0 \n", - "3938 57 -1.0 \n", - "4036 0 -4.0 \n", - "4037 0 4.0 \n", - "4044 0 -2.0 \n", - "4065 0 -2.0 \n", - "4383 0 1.0 \n", - "4645 0 -1.0 \n", - "4646 0 -1.0 \n", - "4703 0 -2.0 \n", - "4743 0 -2.0 \n", - "4744 0 -2.0 \n", - "4745 0 -2.0 \n", - "4746 0 -2.0 \n", - "4805 0 3.0 \n", - "5240 0 3.0 \n", - "5287 0 1.0 \n", - "5508 0 -1.0 \n", - "5532 0 -1.0 \n", - "5541 0 0.0 \n", - "5696 0 -4.0 \n", - "5840 0 -1.0 \n", - "5906 0 4.0 \n", - "5937 0 -2.0 \n", - "6001 0 2.0 \n", - "6040 0 -2.0 \n", - "6215 0 -2.0 \n", - "6325 13 -1.0 \n", - "6393 0 4.0 " + " test_days_from_index days_from_study_to_test \n", + "619 0 -4.0 \n", + "654 0 -6.0 \n", + "896 0 -2.0 \n", + "1228 0 -4.0 \n", + "1507 0 -4.0 \n", + "... ... ... \n", + "171276 0 -6.0 \n", + "171345 59 -4.0 \n", + "171427 0 -6.0 \n", + "171496 23 -1.0 \n", + "171772 0 -3.0 \n", + "\n", + "[1035 rows x 9 columns]" ] }, "metadata": {}, @@ -6601,41 +1539,20 @@ } ], "source": [ - "ps = temp.loc[(temp['days_from_test_to_study'] < 5) & (temp['days_from_test_to_study'] > -5) & (temp['test_result_text']=='Positive')]\n", + "ps = temp.loc[(temp['days_from_study_to_test'] < 0) & (temp['days_from_study_to_test'] > -7) & (temp['test_result_text']=='Positive')]\n", "display(ps)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ + "#Saving the data frame to a csv\n", "os.chdir(demo_dir)\n", "filename = 'DX_imaging_studies_plus_covid_tests.tsv' \n", - "ps.to_csv(filename,sep='\\t',index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 360\r\n", - "-rw-rw-r-- 1 jovyan 7076 May 13 15:32 covid_positive_DX_imaging_studies_10d_window_with_object_ids.tsv\r\n", - "-rw-rw-r-- 1 jovyan 6614 May 13 16:03 DX_imaging_studies_plus_covid_tests.tsv\r\n", - "-rw-rw-r-- 1 jovyan 5418 May 13 15:50 MIDRC_DX_imaging_studies_covid_positive_manifest.json\r\n", - "drwxrwsr-x 4 jovyan 4096 May 12 22:55 \u001b[0m\u001b[01;34mnode_tsvs\u001b[0m/\r\n", - "-rw-rw-r-- 1 jovyan 339581 May 13 16:01 Xray_Temporal_Data_and_Files.ipynb\r\n" - ] - } - ], - "source": [ - "ll" + "ps.to_csv(filename,sep='\\t',index=False)\n" ] }, { @@ -6644,18 +1561,20 @@ "source": [ "## Get the imaging files for the identified studies or cases.\n", "---\n", - "Now that we have a list of imaging studies that were deemed to take place when a patient was infected with COVID-19, we can use the study_uid, which is a unique identifier for imaging studies, to collect the associated files. If we want *all* the imaging studies for the cohort of identified cases, e.g., to have a \"healthy\" or \"baseline\" images for comparison, we can instead use the case_ids to pull all imaging files for the cases, keeping in mind that this will pull any additional imaging studies that may fall outside our defined temporal range." + "Now that we have a list of imaging studies that were deemed to take place soon after a patient was infected with COVID-19, we can use the study_uid, which is a unique identifier for imaging studies, to collect the associated files. \n", + "\n", + "Note: If we want *all* the imaging studies for the cohort of identified cases, e.g., to have a \"healthy\" or \"baseline\" images for comparison, we can instead use the case_ids to pull all imaging files for the cases, keeping in mind that this will pull any additional imaging studies that may fall outside our defined temporal range." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "44" + "458" ] }, "metadata": {}, @@ -6664,7 +1583,7 @@ { "data": { "text/plain": [ - "46" + "814" ] }, "metadata": {}, @@ -6686,522 +1605,242 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "## This query retrieves ALL imaging_study records, we will next filter these results based on the COVID test data\n", - "res = query.query(\n", - " data_type=\"imaging_study\",\n", - " first=30000,\n", - " fields=[\n", - " \"study_uid\",\n", - " \"case_ids\",\n", - " \"object_id\",\n", - " \"project_id\"\n", - " #\"file_name\"\n", - " ]\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "29630" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[{'case_ids': ['A342018'],\n", - " 'study_uid': '1.3.6.1.4.1.14519.5.2.1.99.1071.27284350109903016803354882740889',\n", - " 'project_id': 'TCIA-COVID-19-NY-SBU',\n", - " 'object_id': ['dg.MD1R/ddc683ce-621c-4b64-a146-253c0d1f9ca7',\n", - " 'dg.MD1R/7d309ba2-0692-4b7e-9a55-b99e28f249f0']}]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Take a glance at the returned data\n", - "st = res['data']['imaging_study']\n", - "display(len(st))\n", - "st[1:2]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "18415" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert the query data to a DataFrame and remove any records that lack a study_uid or object_id\n", - "oids = pd.DataFrame(st)\n", - "oids = oids.loc[(~oids['object_id'].isna())&(~oids['study_uid'].isna())]\n", - "len(oids)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "# convert lists to strings; necessary because the properties case_ids and object_id are arrays in the dictionary, and thus are returned as lists.\n", - "\n", - "oids['case_ids'] = [','.join(map(str, l)) for l in oids['case_ids']]\n", - "#oids['object_id'] = [','.join(map(str, l)) for l in oids['object_id']]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "33" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now filter the imaging studies based on our temporal results\n", - "toids = oids.loc[oids['study_uid'].isin(sids)]\n", - "len(toids)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"13456 10000364-1841406 [dg.MD1R/85d06659-aa26-41f5-bf44-7a7f69d0403a,... \n", - "13740 10000364-1525021 [dg.MD1R/4fa1d1f9-d24d-48a2-8070-955fe17aea9e,... \n", - "13743 10000364-1565887 [dg.MD1R/90753c6d-92eb-4bbc-8670-675d9f136cd2,... \n", - "15290 10000364-1398174 [dg.MD1R/2b1ab5fa-6da6-4045-b226-6c614bb720db,... \n", - "15896 10000364-5694324 [dg.MD1R/0bdb7aca-2e6c-41b0-9290-183ddbffbb56,... \n", - "16724 10000364-12388 [dg.MD1R/99e65e3a-22ab-46ea-bc33-e3d02db59780,... \n", - "16913 10000364-2372675 [dg.MD1R/c1198c08-2141-412c-968b-83ca0f1a2553,... \n", - "20554 10000364-6567146 [dg.MD1R/e21088c2-f006-446e-b43e-f680eddaca83,... \n", - "20735 10000364-737164 [dg.MD1R/182d2d43-4cb2-4bc3-bcc6-8dde99cbde53,... \n", - "22599 10000364-1470254 [dg.MD1R/c8a897d8-6bd0-4594-93f5-a1114bf67f1e,... \n", - "22926 10000364-6567146 [dg.MD1R/93def475-73e3-4732-acb2-ad8cc637c179,... \n", - "22930 10000364-311109 [dg.MD1R/38dcf5ff-c25e-4e39-9088-e3d139c7de49,... \n", - "23200 10000364-1292850 [dg.MD1R/408210ae-f1fc-4d65-9eeb-f83497ed4745,... \n", - "24229 10000364-466032 [dg.MD1R/5dfb91a1-6be7-4906-b3cf-7ce929cfbd15,... \n", - "24459 10000364-6452978 [dg.MD1R/45b73162-8618-4868-b76d-d28dd278bfe9,... \n", - "24773 10000364-5623150 [dg.MD1R/152191a0-0129-4983-9953-05da92f539de,... \n", - "25001 10000364-1451946 [dg.MD1R/1e286e46-f59c-43bc-b55d-12991cfbd08f,... \n", - "25607 10000364-6481317 [dg.MD1R/6957b34a-ad52-4070-9351-5dfb28dde895] \n", - "25814 10000364-582005 [dg.MD1R/95aa961d-09a0-4993-9b41-10104d2b0ea3,... \n", - "26860 10000364-1629557 [dg.MD1R/2c62d2eb-f570-489c-bf35-3d0ba06487fb,... \n", - "28391 10000364-1594555 [dg.MD1R/a3c1a1c1-848b-4c12-bc55-8d2fcb75e406,... \n", + " case_ids \\\n", + "0 [10000364-2380481] \n", + "1 [10008204-ar93QgsQkuD7lflFCqCA] \n", + "2 [419639-006430] \n", + "3 [10008204-fzTFLpvuESmd49MBc1Lw] \n", + "4 [10008204-4Oo0B9ubBUSfJpmDhkfg] \n", "\n", - " project_id study_uid \n", - "850 Open-A1 2.16.840.1.114274.1818.49938722868158878271423... \n", - "1360 Open-A1 2.16.840.1.114274.1818.52779345069819425519366... \n", - "1782 Open-A1 2.16.840.1.114274.1818.57465404281606135501004... \n", - "2134 Open-A1 2.16.840.1.114274.1818.49737935697536517401293... \n", - "3533 Open-A1 2.16.840.1.114274.1818.55556814984927382924042... \n", - "4545 Open-A1 2.16.840.1.114274.1818.49960126038261448015780... \n", - "7303 Open-A1 2.16.840.1.114274.1818.53256507062586789144869... \n", - "8921 Open-A1 2.16.840.1.114274.1818.52672479988862611843187... \n", - "9822 Open-A1 2.16.840.1.114274.1818.47992548380110931489204... \n", - "10347 Open-A1 2.16.840.1.114274.1818.46728737024781783052904... \n", - "10507 Open-A1 2.16.840.1.114274.1818.54360925607417617277874... \n", - "13234 Open-A1 2.16.840.1.114274.1818.53840514870842921501028... \n", - "13456 Open-A1 2.16.840.1.114274.1818.52277190787710549031199... \n", - "13740 Open-A1 2.16.840.1.114274.1818.56410020409655965101569... \n", - "13743 Open-A1 2.16.840.1.114274.1818.49916930337862494302491... \n", - "15290 Open-A1 2.16.840.1.114274.1818.54732464788503339771643... \n", - "15896 Open-A1 2.16.840.1.114274.1818.50216899689345619598946... \n", - "16724 Open-A1 2.16.840.1.114274.1818.56700013406773114451534... \n", - "16913 Open-A1 2.16.840.1.114274.1818.46952685839100937222972... \n", - "20554 Open-A1 2.16.840.1.114274.1818.46255839168561943616688... \n", - "20735 Open-A1 2.16.840.1.114274.1818.52270037051786749239628... \n", - "22599 Open-A1 2.16.840.1.114274.1818.48930555774261197064687... \n", - "22926 Open-A1 2.16.840.1.114274.1818.48693005854382886101591... \n", - "22930 Open-A1 2.16.840.1.114274.1818.56957313098786761497942... \n", - "23200 Open-A1 2.16.840.1.114274.1818.48922965582546147864511... \n", - "24229 Open-A1 2.16.840.1.114274.1818.47990836239286471671305... \n", - "24459 Open-A1 2.16.840.1.114274.1818.53473230394172531113668... \n", - "24773 Open-A1 2.16.840.1.114274.1818.53254162557788114198494... \n", - "25001 Open-A1 2.16.840.1.114274.1818.56954099544056097696952... \n", - "25607 Open-A1 2.16.840.1.114274.1818.54429352050740460131192... \n", - "25814 Open-A1 2.16.840.1.114274.1818.49623065213006336731706... \n", - "26860 Open-A1 2.16.840.1.114274.1818.48157555954506242031292... \n", - "28391 Open-A1 2.16.840.1.114274.1818.52513335950400868962638... " + " study_uid project_id \\\n", + "0 2.16.840.1.114274.1818.49373240802761910141558... Open-A1 \n", + "1 2.16.840.1.114274.1818.54462533841901021771524... Open-A1 \n", + "2 1.2.826.0.1.3680043.10.474.419639.180864919270... Open-R1 \n", + "3 2.16.840.1.114274.1818.54981496388317779801044... Open-A1 \n", + "4 2.16.840.1.114274.1818.50963453839748958445029... Open-A1 \n", + "\n", + " object_id \n", + "0 [dg.MD1R/b0ea9b28-825e-4a09-ae82-11647b3f986a,... \n", + "1 [dg.MD1R/be77c8b3-74c1-42bd-87e9-16d6c5f96ce4,... \n", + "2 [dg.MD1R/4b45d7b2-d6e1-4e64-9a91-567854c864f2] \n", + "3 [dg.MD1R/ec1d315c-e1e6-4150-96a7-80303d01b757] \n", + "4 [dg.MD1R/4bdddaae-d184-4e9d-b7e6-c40a318675e2] " ] }, + "execution_count": 17, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "display(toids)" + "# convert the query data to a DataFrame and remove any records that lack a study_uid or object_id\n", + "studies = pd.DataFrame(data)\n", + "studies = studies.loc[(~studies['object_id'].isna())&(~studies['study_uid'].isna())]\n", + "display(len(studies))\n", + "studies.head()" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "# save our result to a DataFrame\n", - "results_name = \"covid_positive_DX_imaging_studies_10d_window_with_object_ids.tsv\"\n", - "toids.to_csv(results_name,sep='\\t',index=False)\n", - "\n" + "# Convert lists to strings; necessary because the properties case_ids and object_id are arrays in the dictionary, and thus are returned as lists.\n", + "studies['case_ids'] = [','.join(map(str, l)) for l in studies['case_ids']]" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 19, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 360\r\n", - "-rw-rw-r-- 1 jovyan users 7076 May 13 16:03 covid_positive_DX_imaging_studies_10d_window_with_object_ids.tsv\r\n", - "-rw-rw-r-- 1 jovyan users 6614 May 13 16:03 DX_imaging_studies_plus_covid_tests.tsv\r\n", - "-rw-rw-r-- 1 jovyan users 5418 May 13 15:50 MIDRC_DX_imaging_studies_covid_positive_manifest.json\r\n", - "drwxrwsr-x 4 jovyan users 4096 May 12 22:55 node_tsvs\r\n", - "-rw-rw-r-- 1 jovyan users 339581 May 13 16:01 Xray_Temporal_Data_and_Files.ipynb\r\n" - ] + "data": { + "text/plain": [ + "780" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "!ls -l" + "# Now filter the imaging studies based on our temporal results\n", + "covid_studies = studies.loc[studies['study_uid'].isin(sids)]\n", + "len(covid_studies)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "object_ids = list(set([a for b in toids.object_id.tolist() for a in b]))" + "# save our result to a csv\n", + "filename = \"covid_positive_DX_imaging_studies_7d_window_with_object_ids.tsv\"\n", + "covid_studies.to_csv(filename, sep='\\t', index=False)\n" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "object_ids = list(set([a for b in covid_studies.object_id.tolist() for a in b]))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "86" + "1026" ] }, - "execution_count": 29, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -7210,15 +1849,1127 @@ "len(object_ids)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now that we have a list of file object_ids for the desired imaging studies, we can use the Gen3 SDK \"drs-pull\" commands to access the files themselves.\n", + "---\n", + "First, we'll create a manifest.json file using a [simple script](https://github.com/cgmeyer/gen3sdk-python/blob/389e3945482439ace6e4536e6d0e35c6e48de9c9/expansion/expansion.py#L2575). Then we'll use the `gen3 drs-pull manifest` command to download the files.\n", + "\n", + "See the detailed documentation to learn more about the Gen3 SDK drs-pull command: https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md\n" + ] + }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\tdg.MD1R/02ab7ba6-f0de-4518-a2d0-a8700ac82060 (1/1026)\n", + "\tdg.MD1R/6af92c37-7651-44f4-a7b0-917f3d6552b7 (2/1026)\n", + "\tdg.MD1R/0dad503b-1677-4abb-a8f9-357efc98b984 (3/1026)\n", + "\tdg.MD1R/760c1d8c-eff9-4810-8837-c6e2c80aa7c8 (4/1026)\n", + "\tdg.MD1R/6fd94614-7082-46fb-ab1f-c375db9e5568 (5/1026)\n", + "\tdg.MD1R/0c7e5ba8-89b3-4cac-a1ee-e493c90cd491 (6/1026)\n", + "\tdg.MD1R/3f5c34f6-5365-4f6b-b384-01cd7b19b30e (7/1026)\n", + "\tdg.MD1R/90947c6c-cdcd-433d-9226-cbdc320af26a (8/1026)\n", + "\tdg.MD1R/de4b7e4f-cce2-4f32-b404-9fb746fc45bf 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"\tdg.MD1R/7d50a0d4-de17-41ed-958d-3553c201fe30 (1015/1026)\n", + "\tdg.MD1R/802359ca-6df7-42b4-829d-a9e2a936d368 (1016/1026)\n", + "\tdg.MD1R/466cf8d8-6a2f-4571-9567-84064a3907b7 (1017/1026)\n", + "\tdg.MD1R/43dc0a44-4595-48dd-9976-53c6925fa242 (1018/1026)\n", + "\tdg.MD1R/c422843a-6ae6-4119-9b89-01c80268e104 (1019/1026)\n", + "\tdg.MD1R/9373298d-fcbd-4448-9742-5551464cf7f7 (1020/1026)\n", + "\tdg.MD1R/eba8df1e-db94-4684-8ed6-1fa0ac9bf432 (1021/1026)\n", + "\tdg.MD1R/6d0864f2-fc02-4f3d-91ab-a7ffa8c991bc (1022/1026)\n", + "\tdg.MD1R/bbfd9809-4c70-45bb-a3ac-d1c0ef339f45 (1023/1026)\n", + "\tdg.MD1R/363b79ae-e7bf-46af-8cb1-de7684a6310e (1024/1026)\n", + "\tdg.MD1R/1e812d47-23b5-456d-a91b-48ffc6e1ed17 (1025/1026)\n", + "\tdg.MD1R/374c3974-f0dc-4da7-bbf8-9ca215818b6a (1026/1026)\n", + "\tDone (1026/1026).\n", + "\tManifest written to file: MIDRC_DX_imaging_studies_covid_positive_manifest.json\n" + ] + } + ], + "source": [ + "# Save the manifest of file object_ids to a JSON file\n", + "mani_name = 'MIDRC_DX_imaging_studies_covid_positive_manifest.json'\n", + "exp.write_manifest_for_guids(guids=object_ids, filename=mani_name)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull manifest MIDRC_DX_imaging_studies_covid_positive_manifest.json /Users/christopher/Documents/Notes/MIDRC/tutorials/images\n" + ] + } + ], + "source": [ + "# To download all files in the manifest, use the \"gen3 drs-pull manifest\" command\n", + "download_dir = \"{}/images\".format(demo_dir)\n", + "\n", + "if not os.path.exists(download_dir):\n", + " os.makedirs(download_dir)\n", + " \n", + "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull manifest {} {}\".format(cred, mani_name, download_dir)\n", + "print(cmd)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "## Run the manifest download command. \n", + "## Note that this will take some time if the manifest is very large. It makes more sense to copy the above command and run in your terminal instead of from a Jupyter Notebook to monitor the progress in real-time.\n", + "# subprocess.run(cmd, shell=True, capture_output=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now download a single DX image and display it in the notebook\n", + "---\n", + "Now we'll download a single x-ray file using the `gen3 drs-pull object` command and display the image and it's embedded metadata on the screen.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "86" + "'419639-010774'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'1.2.826.0.1.3680043.10.474.419639.998625199684256323608132905330'" ] }, "metadata": {}, @@ -7227,92 +2978,7 @@ { "data": { "text/plain": [ - "[{'object_id': 'dg.MD1R/5226b41f-6b8e-4f39-be19-6196d3914576'},\n", - " {'object_id': 'dg.MD1R/6e65395a-0d7a-4bd7-b460-7e1e99b08604'},\n", - 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" {'object_id': 'dg.MD1R/b64d9d16-aa52-4d08-88e2-0c6aaa14027c'},\n", - " {'object_id': 'dg.MD1R/e41724d1-f5f2-4d2c-80e8-8dcaaa27a6d9'},\n", - " {'object_id': 'dg.MD1R/62e06fcc-66de-4aca-8209-917609d2a5ac'},\n", - " {'object_id': 'dg.MD1R/e2d2d976-bbaa-4d20-ad78-300e05139d3f'},\n", - " {'object_id': 'dg.MD1R/1e286e46-f59c-43bc-b55d-12991cfbd08f'},\n", - " {'object_id': 'dg.MD1R/3eec8a20-fd01-495b-bed0-62acc06535d1'},\n", - " {'object_id': 'dg.MD1R/25132d84-a3c2-4a46-8a5b-59391c3149a5'},\n", - " {'object_id': 'dg.MD1R/408210ae-f1fc-4d65-9eeb-f83497ed4745'},\n", - " {'object_id': 'dg.MD1R/72892da0-b253-4f67-9f55-ad427f7cb341'},\n", - " {'object_id': 'dg.MD1R/6a3fe453-c8f0-4525-9a93-178ba1a82cfe'},\n", - " {'object_id': 'dg.MD1R/e449c864-d85f-4f4d-8732-5ff965633dc9'},\n", - " {'object_id': 'dg.MD1R/aff4e7f6-566c-447f-9b59-465c79793dcf'},\n", - " {'object_id': 'dg.MD1R/ec9a7328-edac-4645-9803-b7e1b1e60045'},\n", - " {'object_id': 'dg.MD1R/c1198c08-2141-412c-968b-83ca0f1a2553'},\n", - " {'object_id': 'dg.MD1R/94e6bc16-07ed-47d7-b02b-656a2c4fb093'},\n", - " {'object_id': 'dg.MD1R/95aa961d-09a0-4993-9b41-10104d2b0ea3'},\n", - " {'object_id': 'dg.MD1R/4b84fd74-0615-4129-b281-35a3f1ccfdc6'},\n", - " {'object_id': 'dg.MD1R/981457dc-db83-4ad1-addc-e29350d30324'},\n", - " {'object_id': 'dg.MD1R/1ce5b6e0-c53f-4006-8da2-cd385615cb55'},\n", - " {'object_id': 'dg.MD1R/b2821687-ef09-46a8-961e-f98bba21c344'},\n", - " {'object_id': 'dg.MD1R/c5fb8d7c-8d96-4727-91fa-f7e56860dd60'},\n", - " {'object_id': 'dg.MD1R/fe45a299-f1fc-4f1a-b66c-7c5f86f2d964'},\n", - " {'object_id': 'dg.MD1R/54dc92fc-912d-436d-ace2-c22cf385a9be'},\n", - " {'object_id': 'dg.MD1R/4fa1d1f9-d24d-48a2-8070-955fe17aea9e'},\n", - " {'object_id': 'dg.MD1R/50d75731-9e12-4699-ac25-54cfe3624c27'},\n", - " {'object_id': 'dg.MD1R/90753c6d-92eb-4bbc-8670-675d9f136cd2'},\n", - " {'object_id': 'dg.MD1R/70c2e10b-14c9-41f1-b3a6-45cf26bf350c'},\n", - " {'object_id': 'dg.MD1R/5f8784cb-ca10-44d7-ad88-84f53118b681'},\n", - " {'object_id': 'dg.MD1R/f8c1527a-7e38-429d-901e-fc77abc47985'},\n", - " {'object_id': 'dg.MD1R/77d633e9-6dc5-4a9e-b1d8-2e05056a4f47'},\n", - " {'object_id': 'dg.MD1R/5a5bc1d7-4c42-4232-afbb-37286657498a'},\n", - " {'object_id': 'dg.MD1R/4646b756-f585-4eb0-854d-3d2a9f532e48'},\n", - " {'object_id': 'dg.MD1R/6957b34a-ad52-4070-9351-5dfb28dde895'},\n", - " {'object_id': 'dg.MD1R/62ccf37d-a3f8-4b90-9f3d-7c36bd67bcd7'},\n", - " {'object_id': 'dg.MD1R/0bdb7aca-2e6c-41b0-9290-183ddbffbb56'},\n", - " {'object_id': 'dg.MD1R/e38becb2-fc45-4397-ac7b-d8240a554452'},\n", - " {'object_id': 'dg.MD1R/5746000a-b513-442f-a2f4-05acbe847f0a'},\n", - " {'object_id': 'dg.MD1R/93def475-73e3-4732-acb2-ad8cc637c179'},\n", - " {'object_id': 'dg.MD1R/de4b0400-7853-4eaf-83b7-58192a9793b8'},\n", - " {'object_id': 'dg.MD1R/5dfb91a1-6be7-4906-b3cf-7ce929cfbd15'},\n", - " {'object_id': 'dg.MD1R/b26e2d8d-3750-43b7-95c6-298d1ba90cbe'},\n", - " {'object_id': 'dg.MD1R/9a1016f3-1d5e-4d3f-875a-5715e5432b90'},\n", - " {'object_id': 'dg.MD1R/7f181e8c-cce1-47a3-8b4b-84028df7887b'},\n", - " {'object_id': 'dg.MD1R/c3bda6a7-4488-4152-8574-f5e3985c1a2f'},\n", - " {'object_id': 'dg.MD1R/85d06659-aa26-41f5-bf44-7a7f69d0403a'},\n", - " {'object_id': 'dg.MD1R/182d2d43-4cb2-4bc3-bcc6-8dde99cbde53'},\n", - " {'object_id': 'dg.MD1R/a1d7256e-1803-4f90-af98-1e31672107bd'},\n", - " {'object_id': 'dg.MD1R/c58c317a-3654-4429-bb77-ec8f17b264a7'},\n", - " {'object_id': 'dg.MD1R/5291ed45-f8d4-4879-9c9e-4948bbc75d56'},\n", - " {'object_id': 'dg.MD1R/536d1848-2e6a-4991-b111-eb64d1389d8e'},\n", - " {'object_id': 'dg.MD1R/2c62d2eb-f570-489c-bf35-3d0ba06487fb'},\n", - " {'object_id': 'dg.MD1R/a337d438-bf01-4476-93ff-91ccdfa7075e'},\n", - " {'object_id': 'dg.MD1R/2b1ab5fa-6da6-4045-b226-6c614bb720db'},\n", - " {'object_id': 'dg.MD1R/9f93df7f-6e56-4912-b263-760a9ffe2405'}]" + "'dg.MD1R/dfe90c75-a733-4d1c-a2c7-e7c00f6a16c1'" ] }, "metadata": {}, @@ -7320,125 +2986,181 @@ } ], "source": [ - "manifest = [{\"object_id\":i} for i in object_ids]\n", - "display(len(manifest))\n", - "display(manifest)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "mani_name = 'MIDRC_DX_imaging_studies_covid_positive_manifest.json'\n", - "with open(mani_name,'w') as mani: \n", - " mani.write(str(manifest))" + "# Prepare to download a single image file via it's object_id using the gen3 SDK; save object_id to a variable\n", + "case_ids = covid_studies.iloc[0]['case_ids']\n", + "study_uid = covid_studies.iloc[0]['study_uid']\n", + "object_id = covid_studies.iloc[0]['object_id'][0]\n", + "\n", + "display(case_ids)\n", + "display(study_uid)\n", + "display(object_id)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 27, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 360\r\n", - "-rw-rw-r-- 1 jovyan 7076 May 13 16:03 covid_positive_DX_imaging_studies_10d_window_with_object_ids.tsv\r\n", - "-rw-rw-r-- 1 jovyan 6614 May 13 16:03 DX_imaging_studies_plus_covid_tests.tsv\r\n", - "-rw-rw-r-- 1 jovyan 5418 May 13 16:03 MIDRC_DX_imaging_studies_covid_positive_manifest.json\r\n", - "drwxrwsr-x 4 jovyan 4096 May 12 22:55 \u001b[0m\u001b[01;34mnode_tsvs\u001b[0m/\r\n", - "-rw-rw-r-- 1 jovyan 339581 May 13 16:01 Xray_Temporal_Data_and_Files.ipynb\r\n" - ] + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/dfe90c75-a733-4d1c-a2c7-e7c00f6a16c1'" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "ll" + "# Build the SDK command to send to the shell.\n", + "# Note: \"gen3\" refers to a Gen3 SDK function that runs at the users command line\n", + "# Users may experience errors or warnings but may have still downloaded the file. Check this in your working directory.\n", + "\n", + "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id)\n", + "display(cmd)\n" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'dg.MD1R/7c34fa51-8e61-464d-9793-aa1d52a8977d'" + "CompletedProcess(args='gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/dfe90c75-a733-4d1c-a2c7-e7c00f6a16c1', returncode=0, stdout=b'', stderr=b'')" ] }, - "execution_count": 98, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "object_id = object_ids[3]\n", - "object_id" + "# Run the download command.\n", + "subprocess.run(cmd, shell=True, capture_output=True)" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2022-05-13 16:39:29,570][WARNING] Unable to find refresh_file\r\n" + "CompletedProcess(args='ls -l 419639-010774/1.2.826.0.1.3680043.10.474.419639.998625199684256323608132905330', returncode=0, stdout=b'total 24592\\ndrwxr-xr-x 3 christopher staff 96 Oct 11 19:01 1.2.826.0.1.3680043.10.474.419639.225598082635508243339677818993\\n-rw-r--r-- 1 christopher staff 12541742 Oct 17 12:28 1.2.826.0.1.3680043.10.474.419639.225598082635508243339677818993.zip\\n', stderr=b'')\n" ] } ], "source": [ - "!gen3 drs-pull object dg.MD1R/7c34fa51-8e61-464d-9793-aa1d52a8977d\n" + "# The above command should have successfully downloaded a new directory with a zipped file. \n", + "cmd = \"ls -l {}/{}\".format(case_ids,study_uid)\n", + "stout = subprocess.run(cmd, shell=True, capture_output=True)\n", + "print(stout)" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total 3600\r\n", - "-rw-r--r-- 1 jovyan users 3682944 May 13 16:39 2.16.840.1.114274.1818.493290470347749632917396619355176140189.dcm\r\n" + "1.2.826.0.1.3680043.10.474.419639.225598082635508243339677818993.zip\n", + "1.2.826.0.1.3680043.10.474.419639.225598082635508243339677818993\n" ] } ], "source": [ - "#ll 2.16.840.1.114274.1818.5347323039417253111366886901564145793\n", - "!ls -l 2.16.840.1.114274.1818.52513335950400868962638133225506790845" + "## Grab the filename and series UID of the downloaded file using RegEx\n", + "import re\n", + "\n", + "m = re.search(' ([0-9\\.]+.zip)', str(stout))\n", + "\n", + "if m:\n", + " zip_file = m.group(1)\n", + " print(zip_file)\n", + "else:\n", + " print(\"No zip found.\")\n", + "\n", + "series_uid = re.sub(\"(\\.zip)\", \"\", zip_file)\n", + "print(series_uid)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "#study_uid = \"2.16.840.1.114274.1818.5347323039417253111366886901564145793\"\n", - "study_uid = \"2.16.840.1.114274.1818.52513335950400868962638133225506790845\"" + "# Unzip the imaging series package\n", + "from zipfile import ZipFile\n", + "\n", + "with ZipFile('{}/{}/{}/{}'.format(demo_dir,case_ids,study_uid,zip_file), 'r') as zipObj:\n", + " zipObj.extractall()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CompletedProcess(args='ls -l 419639-010774/1.2.826.0.1.3680043.10.474.419639.998625199684256323608132905330/1.2.826.0.1.3680043.10.474.419639.225598082635508243339677818993', returncode=0, stdout=b'total 24704\\n-rw-r--r-- 1 christopher staff 12541378 Oct 17 12:28 1.2.826.0.1.3680043.10.474.419639.409694369128140176158536266909.dcm\\n', stderr=b'')\n" + ] + } + ], + "source": [ + "#Input the name of the newly create .dcm file\n", + "cmd = \"ls -l {}/{}/{}\".format(case_ids,study_uid,series_uid)\n", + "stout = subprocess.run(cmd, shell=True, capture_output=True)\n", + "print(stout)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.2.826.0.1.3680043.10.474.419639.409694369128140176158536266909.dcm\n" + ] + } + ], + "source": [ + "# Get the name of the first DICOM file in the extracted imaging series\n", + "m = re.search(' ([0-9\\.]+.dcm)', str(stout))\n", + "\n", + "if m:\n", + " dcm_file = m.group(1)\n", + " print(dcm_file)\n", + "else:\n", + " print(\"No DCM files found.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "#dfile = \"{}/2.16.840.1.114274.1818.522083088410440752714944649785971821491.dcm\".format(study_uid)\n", - "dfile = \"{}/2.16.840.1.114274.1818.493290470347749632917396619355176140189.dcm\".format(study_uid)\n", - "dimg = pydicom.dcmread(dfile,force=True)" + "# Read in the DCM file using the python DICOM package pydicom\n", + "dimg = pydicom.dcmread(\"{}/{}/{}/{}\".format(case_ids,study_uid,series_uid,dcm_file),force=True)\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": { "scrolled": true }, @@ -7447,66 +3169,37 @@ "data": { "text/plain": [ "Dataset.file_meta -------------------------------\n", - "(0002, 0000) File Meta Information Group Length UL: 222\n", + "(0002, 0000) File Meta Information Group Length UL: 240\n", "(0002, 0001) File Meta Information Version OB: b'\\x00\\x01'\n", "(0002, 0002) Media Storage SOP Class UID UI: Digital X-Ray Image Storage - For Presentation\n", - "(0002, 0003) Media Storage SOP Instance UID UI: 2.16.840.1.114274.1818.493290470347749632917396619355176140189\n", - "(0002, 0010) Transfer Syntax UID UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", - "(0002, 0012) Implementation Class UID UI: 1.3.6.1.4.1.25403.1.1.1\n", - "(0002, 0013) Implementation Version Name SH: 'Dicom 0.1'\n", - "(0002, 0016) Source Application Entity Title AE: 'CPV-ACUOPRIAPP'\n", + "(0002, 0003) Media Storage SOP Instance UID UI: 1.2.826.0.1.3680043.10.474.419639.409694369128140176158536266909\n", + "(0002, 0010) Transfer Syntax UID UI: Explicit VR Little Endian\n", + "(0002, 0012) Implementation Class UID UI: 1.2.826.0.1.3680043.2.1143.107.104.103.115.3.1.0\n", + "(0002, 0013) Implementation Version Name SH: 'GDCM 3.1.0'\n", + "(0002, 0016) Source Application Entity Title AE: 'gdcmconv'\n", "-------------------------------------------------\n", - "(0008, 0005) Specific Character Set CS: ''\n", - "(0008, 0008) Image Type CS: ['ORIGINAL', 'PRIMARY', '']\n", - "(0008, 0012) Instance Creation Date DA: ''\n", - "(0008, 0013) Instance Creation Time TM: '000000'\n", + "(0008, 0005) Specific Character Set CS: 'ISO_IR 100'\n", + "(0008, 0008) Image Type CS: ['DERIVED', 'PRIMARY']\n", "(0008, 0016) SOP Class UID UI: Digital X-Ray Image Storage - For Presentation\n", - "(0008, 0018) SOP Instance UID UI: 2.16.840.1.114274.1818.493290470347749632917396619355176140189\n", - "(0008, 0020) Study Date DA: '19591231'\n", - "(0008, 0021) Series Date DA: '19591231'\n", - "(0008, 0022) Acquisition Date DA: '19591231'\n", - "(0008, 0023) Content Date DA: '19591231'\n", - "(0008, 0030) Study Time TM: '095159.000000'\n", - "(0008, 0031) Series Time TM: '095822.000000'\n", - "(0008, 0032) Acquisition Time TM: '100130'\n", - "(0008, 0033) Content Time TM: '100130.000000'\n", - "(0008, 0050) Accession Number SH: ''\n", + "(0008, 0018) SOP Instance UID UI: 1.2.826.0.1.3680043.10.474.419639.409694369128140176158536266909\n", + "(0008, 0020) Study Date DA: '20140813'\n", + "(0008, 0021) Series Date DA: '20140813'\n", + "(0008, 0022) Acquisition Date DA: '20140813'\n", + "(0008, 0023) Content Date DA: '20140813'\n", + "(0008, 0030) Study Time TM: '045513.341114'\n", + "(0008, 0031) Series Time TM: '045518.909002'\n", + "(0008, 0032) Acquisition Time TM: '045518.909002'\n", + "(0008, 0033) Content Time TM: '045518.909002'\n", + "(0008, 0050) Accession Number SH: '48289'\n", "(0008, 0060) Modality CS: 'DX'\n", - "(0008, 0061) Modalities in Study CS: ['', 'DX', '']\n", - "(0008, 0064) Conversion Type CS: 'WSD'\n", - "(0008, 0068) Presentation Intent Type CS: 'FOR PRESENTATION'\n", - "(0008, 0070) Manufacturer LO: 'GE MEDICAL SYSTEMS'\n", - "(0008, 0090) Referring Physician's Name PN: ''\n", - "(0008, 1010) Station Name SH: '[Statio.143]'\n", - "(0008, 1030) Study Description LO: 'XR CHEST AP PORTABLE'\n", - "(0008, 103e) Series Description LO: 'XR CHEST AP PORTABLE'\n", - "(0008, 1090) Manufacturer's Model Name LO: 'Revolution XRd ADS_28.2'\n", - "(0008, 2112) Source Image Sequence 1 item(s) ---- \n", - " (0008, 1150) Referenced SOP Class UID UI: Digital X-Ray Image Storage - For Processing\n", - " (0008, 1155) Referenced SOP Instance UID UI: 2.16.840.1.114274.1818.53256368815083570053581521914758631809\n", - " ---------\n", - "(0008, 2218) Anatomic Region Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: 'T-D3000'\n", - " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", - " (0008, 0104) Code Meaning LO: 'Chest'\n", - " ---------\n", - "(0010, 0010) Patient's Name PN: '1594555^10000364^^^'\n", - "(0010, 0020) Patient ID LO: '10000364-1594555'\n", - "(0010, 0030) Patient's Birth Date DA: ''\n", - "(0010, 1010) Patient's Age AS: '039Y'\n", - "(0010, 1020) Patient's Size DS: None\n", - "(0010, 1030) Patient's Weight DS: None\n", - "(0012, 0010) Clinical Trial Sponsor Name LO: '[Clinical Trial Sponsor Na]'\n", - "(0012, 0020) Clinical Trial Protocol ID LO: '[Clinical Trial Protocol ]'\n", - "(0012, 0021) Clinical Trial Protocol Name LO: 'CIRR'\n", - "(0012, 0030) Clinical Trial Site ID LO: '[Clinical Trial Site ]'\n", - "(0012, 0031) Clinical Trial Site Name LO: '[Clinical Trial Site Na]'\n", - "(0012, 0040) Clinical Trial Subject ID LO: '10000364-1594555'\n", - "(0012, 0050) Clinical Trial Time Point ID LO: '[Clinical Trial Time Point ]'\n", - "(0012, 0060) Clinical Trial Coordinating Center LO: '[Clinical Trial Coordinating Center Na]'\n", - "(0012, 0062) Patient Identity Removed CS: 'YES'\n", - "(0012, 0063) De-identification Method LO: ['Basic Application Confidentiality Profile', 'Retain Longitudinal Temporal Information Modified Dates', 'TRIAD Anonymization; Date=20210308; Time=112954']\n", - "(0012, 0064) De-identification Method Code Sequence 3 item(s) ---- \n", + "(0008, 1030) Study Description LO: 'XR CHEST 1 VIEW AP'\n", + "(0008, 103e) Series Description LO: 'AP'\n", + "(0010, 0010) Patient's Name PN: '419639-010774'\n", + "(0010, 0020) Patient ID LO: '419639-010774'\n", + "(0010, 0040) Patient's Sex CS: 'M'\n", + "(0010, 1010) Patient's Age AS: '065Y'\n", + "(0012, 0063) De-identification Method LO: 'RSNA Covid-19 Dataset Default'\n", + "(0012, 0064) De-identification Method Code Sequence 4 item(s) ---- \n", " (0008, 0100) Code Value SH: '113100'\n", " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", " (0008, 0104) Code Meaning LO: 'Basic Application Confidentiality Profile'\n", @@ -7515,153 +3208,97 @@ " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", " (0008, 0104) Code Meaning LO: 'Retain Longitudinal Temporal Information Modified Dates Option'\n", " ---------\n", - " (0008, 0100) Code Value SH: '900901'\n", - " (0008, 0102) Coding Scheme Designator SH: 'WIF'\n", - " (0008, 0104) Code Meaning LO: 'BASIC PROFILE OVERRIDES POTENTIALLY VIOLATE SECURITY STANDARD'\n", - " (0008, 0108) Extended Code Meaning LT: Array of 229 elements\n", + " (0008, 0100) Code Value SH: '113108'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'Retain Patient Characteristics Option'\n", + " ---------\n", + " (0008, 0100) Code Value SH: '113109'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'Retain Device Identity Option'\n", " ---------\n", + "(0013, 0010) Private Creator LO: 'CTP'\n", + "(0013, 1010) Private tag data LO: 'MIDRC-RICORD'\n", + "(0013, 1011) Private tag data LO: 'MIDRC-RICORD'\n", + "(0013, 1012) Private tag data LO: '419639'\n", + "(0013, 1013) Private tag data LO: '419639'\n", "(0018, 0010) Contrast/Bolus Agent LO: ''\n", "(0018, 0015) Body Part Examined CS: 'CHEST'\n", - "(0018, 0060) KVP DS: '120.0'\n", - "(0018, 1010) Secondary Capture Device ID LO: ''\n", - "(0018, 1020) Software Versions LO: 'Ads Application Package VERSION ADS_28.2'\n", - "(0018, 1110) Distance Source to Detector DS: '1850.0'\n", - "(0018, 1111) Distance Source to Patient DS: '1801.0'\n", + "(0018, 0060) KVP DS: '90.0'\n", + "(0018, 1020) Software Versions LO: '1.4'\n", + "(0018, 1110) Distance Source to Detector DS: '0.0'\n", "(0018, 1147) Field of View Shape CS: 'RECTANGLE'\n", - "(0018, 1149) Field of View Dimension(s) IS: [404, 354]\n", - "(0018, 1150) Exposure Time IS: '7'\n", - "(0018, 1151) X-Ray Tube Current IS: '200'\n", - "(0018, 1152) Exposure IS: '2'\n", - "(0018, 1153) Exposure in uAs IS: '2400'\n", - "(0018, 115e) Image and Fluoroscopy Area Dose Pro DS: '1.11571'\n", - "(0018, 1160) Filter Type SH: 'NONE'\n", - "(0018, 1164) Imager Pixel Spacing DS: [0.1947027, 0.1947027]\n", - "(0018, 1166) Grid CS: ['FIXED', 'FOCUSED']\n", - "(0018, 1190) Focal Spot(s) DS: '1.25'\n", - "(0018, 1401) Acquisition Device Processing Code LO: '8#2001871501001009590100#508000#1040#20350#35220'\n", - "(0018, 1405) Relative X-Ray Exposure IS: '109'\n", + "(0018, 1149) Field of View Dimension(s) IS: [127741, 145116]\n", + "(0018, 1150) Exposure Time IS: '14'\n", + "(0018, 1151) X-Ray Tube Current IS: '307'\n", + "(0018, 1152) Exposure IS: '4'\n", + "(0018, 1153) Exposure in uAs IS: '4300'\n", + "(0018, 1156) Rectification Type CS: 'CONST POTENTIAL'\n", + "(0018, 115e) Image and Fluoroscopy Area Dose Pro DS: '0.0'\n", + "(0018, 1164) Imager Pixel Spacing DS: [0.139, 0.139]\n", + "(0018, 1190) Focal Spot(s) DS: '1.2'\n", + "(0018, 1191) Anode Target Material CS: 'TUNGSTEN'\n", + "(0018, 1405) Relative X-Ray Exposure IS: '421'\n", + "(0018, 1411) Exposure Index DS: '421.19'\n", + "(0018, 1412) Target Exposure Index DS: '200.0'\n", + "(0018, 1413) Deviation Index DS: '3.23'\n", "(0018, 1508) Positioner Type CS: ''\n", - "(0018, 1511) Positioner Secondary Angle DS: '90.0'\n", - "(0018, 1700) Collimator Shape CS: 'POLYGONAL'\n", - "(0018, 1720) Vertices of the Polygonal Collimato IS: [2032, 1771, -17, 1771, -17, 1, 2032, 1]\n", - "(0018, 5101) View Position CS: 'PA'\n", - "(0018, 6000) Sensitivity DS: '0.18445267'\n", - "(0018, 7001) Detector Temperature DS: '31.299999'\n", - "(0018, 7004) Detector Type CS: 'SCINTILLATOR'\n", + "(0018, 1700) Collimator Shape CS: 'RECTANGULAR'\n", + "(0018, 1702) Collimator Left Vertical Edge IS: '0'\n", + "(0018, 1704) Collimator Right Vertical Edge IS: '1044'\n", + "(0018, 1706) Collimator Upper Horizontal Edge IS: '0'\n", + "(0018, 1708) Collimator Lower Horizontal Edge IS: '919'\n", + "(0018, 5101) View Position CS: 'AP'\n", + "(0018, 7001) Detector Temperature DS: '29.0'\n", + "(0018, 7004) Detector Type CS: 'DIRECT'\n", "(0018, 7005) Detector Configuration CS: 'AREA'\n", - "(0018, 701a) Detector Binning DS: [1, 1]\n", - "(0018, 7020) Detector Element Physical Size DS: [0.2, 0.2]\n", - "(0018, 7022) Detector Element Spacing DS: [0.2, 0.2]\n", - "(0018, 7024) Detector Active Shape CS: 'RECTANGLE'\n", - "(0018, 7026) Detector Active Dimension(s) DS: [409.6, 409.6]\n", - "(0018, 7030) Field of View Origin DS: [13, 139]\n", - "(0018, 7032) Field of View Rotation DS: '0.0'\n", - "(0018, 7034) Field of View Horizontal Flip CS: 'YES'\n", - "(0018, 7050) Filter Material CS: 'COPPER'\n", - "(0018, 7052) Filter Thickness Minimum DS: '0.0'\n", - "(0018, 7054) Filter Thickness Maximum DS: '0.0'\n", - "(0018, 7060) Exposure Control Mode CS: 'AUTOMATIC'\n", - "(0018, 7062) Exposure Control Mode Description LT: 'AEC_all_cells'\n", - "(0018, a001) Contributing Equipment Sequence 1 item(s) ---- \n", - " (0008, 0070) Manufacturer LO: 'Claron'\n", - " (0008, 1010) Station Name SH: 'CPV-ACUOPRIAPP'\n", - " (0008, 1090) Manufacturer's Model Name LO: 'Deidentification'\n", - " (0018, 1020) Software Versions LO: ['WIF 2.1.17', '1.0.0.0']\n", - " (0018, a002) Contribution DateTime DT: '20210820051543.042877'\n", - " (0018, a003) Contribution Description ST: 'Cleaned'\n", - " (0040, a170) Purpose of Reference Code Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: '109104'\n", - " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", - " (0008, 0104) Code Meaning LO: 'De-identifying Equipment'\n", - " ---------\n", - " ---------\n", - "(0020, 000d) Study Instance UID UI: 2.16.840.1.114274.1818.52513335950400868962638133225506790845\n", - "(0020, 000e) Series Instance UID UI: 2.16.840.1.114274.1818.4961016135722817071126780105079634588\n", + "(0018, 7006) Detector Description LT: Array of 23 elements\n", + "(0018, 7008) Detector Mode LT: 'PatientMode'\n", + "(0018, 7010) Exposures on Detector Since Last Ca IS: '217'\n", + "(0018, 7011) Exposures on Detector Since Manufac IS: '0'\n", + "(0018, 7012) Detector Time Since Last Exposure DS: '0.0'\n", + "(0018, 7014) Detector Active Time DS: '200.0'\n", + "(0018, 7020) Detector Element Physical Size DS: [0.139, 0.139]\n", + "(0018, 7022) Detector Element Spacing DS: [0.139, 0.139]\n", + "(0018, 702a) Detector Manufacturer Name LO: 'CARESTREAM HEALTH'\n", + "(0018, 702b) Detector Manufacturer's Model Name LO: 'DRXPlus3543C'\n", + "(0018, 7060) Exposure Control Mode CS: 'MANUAL'\n", + "(0018, 8150) Exposure Time in uS DS: '14000.0'\n", + "(0018, 8151) X-Ray Tube Current in uA DS: '307142.8'\n", + "(0018, 9332) Exposure in mAs FD: 4.3\n", + "(0020, 000d) Study Instance UID UI: 1.2.826.0.1.3680043.10.474.419639.998625199684256323608132905330\n", + "(0020, 000e) Series Instance UID UI: 1.2.826.0.1.3680043.10.474.419639.225598082635508243339677818993\n", "(0020, 0010) Study ID SH: ''\n", - "(0020, 0011) Series Number IS: '37662'\n", - "(0020, 0013) Instance Number IS: '3'\n", + "(0020, 0011) Series Number IS: '1'\n", + "(0020, 0012) Acquisition Number IS: '1'\n", + "(0020, 0013) Instance Number IS: '1'\n", "(0020, 0020) Patient Orientation CS: ['L', 'F']\n", - "(0020, 0022) Overlay Number IS: None\n", - "(0020, 0024) Curve Number IS: None\n", - "(0020, 0026) LUT Number IS: None\n", "(0020, 0062) Image Laterality CS: 'U'\n", + "(0020, 1002) Images in Acquisition IS: '1'\n", "(0028, 0002) Samples per Pixel US: 1\n", "(0028, 0004) Photometric Interpretation CS: 'MONOCHROME2'\n", - "(0028, 0008) Number of Frames IS: '1'\n", - "(0028, 0010) Rows US: 2022\n", - "(0028, 0011) Columns US: 1771\n", + "(0028, 0010) Rows US: 2349\n", + "(0028, 0011) Columns US: 2669\n", + "(0028, 0030) Pixel Spacing DS: [0.139, 0.139]\n", + "(0028, 0034) Pixel Aspect Ratio IS: [1, 1]\n", "(0028, 0100) Bits Allocated US: 16\n", - "(0028, 0101) Bits Stored US: 14\n", - "(0028, 0102) High Bit US: 13\n", + "(0028, 0101) Bits Stored US: 12\n", + "(0028, 0102) High Bit US: 11\n", "(0028, 0103) Pixel Representation US: 0\n", - "(0028, 0300) Quality Control Image CS: 'NO'\n", + "(0028, 0106) Smallest Image Pixel Value US: 0\n", + "(0028, 0107) Largest Image Pixel Value US: 4095\n", "(0028, 0301) Burned In Annotation CS: 'NO'\n", - "(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'\n", "(0028, 1040) Pixel Intensity Relationship CS: 'LOG'\n", "(0028, 1041) Pixel Intensity Relationship Sign SS: -1\n", - "(0028, 1050) Window Center DS: [4299, 4299, 4299]\n", - "(0028, 1051) Window Width DS: [3205, 2404, 4808]\n", + "(0028, 1050) Window Center DS: '2048.0'\n", + "(0028, 1051) Window Width DS: '4096.0'\n", "(0028, 1052) Rescale Intercept DS: '0.0'\n", "(0028, 1053) Rescale Slope DS: '1.0'\n", "(0028, 1054) Rescale Type LO: 'US'\n", - "(0028, 1055) Window Center & Width Explanation LO: ['NORMAL', 'HARDER', 'SOFTER']\n", "(0028, 2110) Lossy Image Compression CS: '00'\n", - "(0028, 3010) VOI LUT Sequence 3 item(s) ---- \n", - " (0028, 3002) LUT Descriptor US: [16384, 0, 16]\n", - " (0028, 3003) LUT Explanation LO: 'NORMAL'\n", - " (0028, 3006) LUT Data US: Array of 16384 elements\n", - " ---------\n", - " (0028, 3002) LUT Descriptor US: [16384, 0, 16]\n", - " (0028, 3003) LUT Explanation LO: 'HARDER'\n", - " (0028, 3006) LUT Data US: Array of 16384 elements\n", - " ---------\n", - " (0028, 3002) LUT Descriptor US: [16384, 0, 16]\n", - " (0028, 3003) LUT Explanation LO: 'SOFTER'\n", - " (0028, 3006) LUT Data US: Array of 16384 elements\n", - " ---------\n", - "(0032, 000a) Study Status ID CS: 'COMPLETED'\n", - "(0040, 0009) Scheduled Procedure Step ID SH: ''\n", - "(0040, 0244) Performed Procedure Step Start Date DA: ''\n", - "(0040, 0245) Performed Procedure Step Start Time TM: '000000'\n", - "(0040, 0302) Entrance Dose US: 0\n", - "(0040, 0310) Comments on Radiation Dose ST: '100 %'\n", - "(0040, a032) Observation DateTime DT: '18390727000000.000000'\n", - "(0040, a491) Completion Flag CS: ''\n", - "(0040, a493) Verification Flag CS: ''\n", - "(0045, 0010) Private Creator LO: 'GEMS_SENO_02'\n", - "(0045, 101b) [Clinical View] CS: 'postero-anterior'\n", - "(0054, 0220) View Code Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: 'R-10214'\n", - " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", - " (0008, 0104) Code Meaning LO: 'postero-anterior'\n", - " (0054, 0222) View Modifier Code Sequence 1 item(s) ---- \n", - " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", - " ---------\n", - " ---------\n", - "(0054, 0410) Patient Orientation Code Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: 'F-10450'\n", - " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", - " (0008, 0104) Code Meaning LO: 'recumbent'\n", - " ---------\n", - "(0054, 0414) Patient Gantry Relationship Code Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: 'F-10470'\n", - " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", - " (0008, 0104) Code Meaning LO: 'head-first'\n", - " ---------\n", - "(0070, 0080) Content Label CS: ''\n", - "(0070, 0081) Content Description LO: ''\n", - "(0070, 0084) Content Creator's Name PN: ''\n", - "(0400, 0500) Encrypted Attributes Sequence 1 item(s) ---- \n", - " (0033, 1215) Private tag data OB: b'9k\\xf9\\xda\\xd2\\x0c\\x93\\xe0'\n", - " (0400, 0510) Encrypted Content Transfer Syntax U UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", - " (0400, 0520) Encrypted Content OB: Array of 624 elements\n", - " ---------\n", - "(2050, 0020) Presentation LUT Shape CS: 'IDENTITY'\n", - "(4008, 0040) Results ID SH: ''\n", - "(7fe0, 0010) Pixel Data OB: Array of 3579586 elements" + "(7fe0, 0010) Pixel Data OW: Array of 12538962 elements" ] }, - "execution_count": 16, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -7672,22 +3309,22 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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fuhXo4FrveOA7+jKNZC5wUH6Wi7g91nSYdmi2DwbELLtU9ZcHGDcyggPMaDz/z0ycsySZGTE9ni/mJ8E+Y7ddtT2YlNngO363gCX/e1kyM4+0tOu2ITkm+Lq+vt5lnAEM2M9jx451/xNEsvQDzdvb27WxsdHRQ0Bi+w+fnRXf3t6utbW1qjoIDB/+8IfXNddcU3t7e/X2t7+9q1Gbn5/vskStLOaDbecNiGhFgi0jmWAAhRqPx50DzmgXoXIEjmDi0Pf29rotl1xjg0Qj6k3DiiBiABAigw+EyoJbVXXBBRfU4uJiLS0t1alTpzoUm0sD5kdmGkD/KHhmKNySP/ABcJBR0PT0dA9QACZYN7RhcvoRAGR+T3IWnl/T2frcjpfxWh5sQBmnt8wZjNjpp/HMSMYRpCMh15JkYV1LhluAgeurDoxwgio76UlAJpe2Jjk5jFs+P3lgnhlot/5ujSn1uurwckXr2TQcfWboDCAyQ2G6MoJGRjy+XBdn3k236xMA7vSZ40XOcVYJaJNX1DZZJxP8W34Nls2/DCDG43FXtAcAcYDEuFhK9bMdfCXtBqfWMTt/Ah9nUVLPMtNmW+lnZW2R+Z4ZPW/d5lkEdCzdeFcPfVBjxv8LCwu1vr7e1TRw7fT0dC0vL9fc3Fzn6Bl3Zh4Jgixn09PTtb6+XhdeeGFdeeWVvcByONyv1ZqZmelsK8HPQ2nnPIhItN5Cw/6fa5gorx05Gs31TBcjOvVadWBw5+fne857dnb2UAUvzcbd+8K9hx6jNRqNanNzsxYXF7sxAES8bnv8+PFaWFionZ2dWl9fr9OnT9fp06d7acrkjxUyIzDGaAV2RgSjVFVd5oFn+VAsO1qiGqqYUSTqUrI4k3TwJKcGTW4JNkhRppJinDBqzFOmi/ksDZTnvwUKeX6meBNQIAuOEPP5OVd+RvKDwrl0/klb9s2ctoysWwIJxp9Az9dPotdOvQUQEwh6HEmP9RoZBkB4aRJ6HPnxOYa7xTvziG2YBuPWc57lvgzSz5SJ428HOTk33vbIuL0sR3Om03TxXTp0z5VruwD9zoQAgDPTV9V38NgQ5tN6ZOCe2RXPVfImQZptRMpK8tdgBXpa2Z+0FwAH8422sbFR8/PzNT8/X4PBfsH+xRdfXLfddltvKcy203bfNSeAgZWVlTp9+nRtbGzU/fff3wGFqqpjx451tTzD4f7223vvvbez+66pS/v4YNs5DyKqJtc/+O/Wb/4m9WTHubm5WVXVpQkBEBgOo38mdmpqqhYXFzulmJ+f7xTYe3stSKBIlMm7PVC8ubm5jgbu85KLi0CrDnZpLC8v1/Hjx+vkyZN18uTJbpyMH+W0k686iIZtCGlGzPCnqnqRA1GXC8TgjyN9wMTi4mI3DgAQGQruy+a0uo0g48mIyOnU1ronP64yT8CZzUYrI0HTbePVklUX79ngTAIJaVCh1yC4BRR8X97L/2fKCmRzxJTA0nPhOWA8dvrpBGxMs3LfPM1Mm+fb6/0pc1zjnQjQihyQCuY5gA6i0AT7LYfueTdPeTa0EcgYlHE935kfgAiuby1/4YgM3j3vps99YAMZa1X1ggvGhs3BJmF7vKTH2G3LAG7IiZdoPBfmW/LOepG2Kpvljpox7k25bGWzmFuA0uzs7KG6Ea5bW1vrMq5VVQsLC3XJJZfUPffc082F77OdwhZiT3d2drqizfX19ZqZmenOoJidna277767Lr744lpeXq7BYFCPecxjamdnpz7xiU90yx7r6+u1vLw8kTcPtJ3zIGISgGhFO/48ozsKKlFawMJ4PO62a+3u7tbS0lIPNYL46At0yN88H0XZ3NzsGVvus7CSCbFS+XhrOylfYyOEgs7Pz9dFF11Uy8vLdd9999WpU6d60X46Qt9r44ERsxGpqi4SwfFVHURfpslGgEgMWuE3fOBkzo2Nja64yP27L6chM72ZvM+MAgYlwU5em4bFfMOQZZTbosNRa0Y5LRnN53nO8hrmhzlqAWk7Ej83jQwydiYgYQfAda4P8FhNb0Z8psFLDsnfBBue0+SL1/CTnxhsR/I8L4EYxY3o8tLSUuckWnU92I90FHaYGbUnQDZo5VrXIFgP7ZRsj1rAHf5kEJBzYx7wDGdTLN/oXStb6QxHK1gx2IRO/m5l/Ryt8zzLaeqH7YV5SmbHW2tzHnyv+WZA6TnhemyaZZ/dc/fcc0+PBurR2Nlm+5fygI/xAV+j0ajuvffeOn78eFXtZ0KWl5frmmuuqVtuuaVb8jhTEPRA2zkPItwmgYk0uJ58/42z297e7hzb7OxsL2IhQtne3u7SV+PxuJaWljoFJ0oADDiiYM+5nT8KYsWiyttFiQiwsxapWAgYysrv6enpuvTSS2txcbFOnz7dGQwUAME3ADBgscFP442hAew4Y+IDcmg8zwgf5R4Oh7WwsFBLS0u1t7fXHRXMCZlef7WxzegvIxs7OhsoGwyPzXMDnTY4k9Z7nVZ2SwBU1Y/w0nlybRr+SQDCPE7wMglYJW3c67ltXdNy3AkIaK3lCBvlnI+cM3jvkx4zKjUNPoKZZxqkzM/P18LCQk/+s0+DQvSfw90MHsggYeC9awfn6sxIypF3VaQTzKWGVnYnAwGctA+VOlMWyvriui+Ww1heNBBIeTSAd62HZSNtW2ZOvOuD/1PuMgNhfniekzfp1JED29LkCfbVfEvAwtiTv9g/f3b8+PEajUZ1//33dz4G+807kxJc2S7ZP5AlH41Gdeedd3b6s7q62tVbXHrppZ1PyuzI2bRzHkQ4E3EmAJGCNym1zA8on4IaljAwKlXVOfatra1etfbs7Gx3WBSO1Hu3MWDsNa7aVxxX9GI0vWXMAsv9RFWgYxt4CqocDS0sLNTCwkLt7u52hT9WcEd85jHbkVBmDI4jakem8Jlx2IBa4Z1NwPjhBNjSyoFgBhQ22lWHD7mhj1aq3gbbxsKOywYmwYJp5PqsGdnd3T30QrV0zC6qzEr9dMwJBJyWbTmd7MP3JSAw0Mw+bcigJT9L5+hIlu/Nd753VO/ntpplraXnXOM198Fg0Okr31nWvTQCCHcdBdcwdxsbG53MATa8rS4BZ2YqLGMZ3UOrC5OdCUH30nExXpYCAWae+zy/wnJskDU/P99bnqDoz0EK82NwwXcuGDd/eZ4zSp5n/+8+W4DcWYaWnFveDYiRO8upWxbL2laZB6nPDgCq9oPMhYWFHpCdnj44mG91dbWbD7Ldtk2MG9tJsT+ZCmwk8nbzzTd3vGfMx44d6+5nWfuhtPMKRPC/P8/PbJBb6N73Gc2TEeA3mQoKClluoLiQfjhL346n6kBZiBwoLByPD5ZPLMjcz/X0gTB5+cVFlzMzMx04sQEdDod17NixWlpa6nZz5LnzOEPzGqFPA2yjAO2g7laU6WsdbaQyYVxZM1xeXq6NjY3a2tqqtbW17jAXG3top087MfpuRdI2XjbUBh02KO6He5z2dMrd686mw7zO1HYrtWlHZcOba72tZrpazenWpK91jx2QHZJptUGuql5tUVU/cptEG3LtcbToMnhw7RIG2MDRQLGVeYJ+dB0A4SCj6qB4OkF3yxka6LfsVFW/EJeMiOsfLDs8h+xIOmL+dmTsYMF2yu9egD8EIc7WuDgUmcY2EVF7zjPrAB0G+wl+U3fhR2Y44FFmZFJv/BzGkdkIxuysC31lViCDjMwEbW5u9l68ht0kCM0dLn4u99gWw2/qaJwN2tzc7NnjqampuuSSS2phYaGzj5OA+QNt5zyIyCgtAUIaVDO0lQ6lGf37OXYqKysr9axnPatTIq+13XrrrXXXXXf1iqbm5uZqdna2HvvYx9bVV19dc3Nztbm5WTfddFPdfPPNVVXdEsbKyko95SlP6cZiJ3Hy5Mn6zGc+U/fff38Nh/uVuddee21PGKuqZ8BuueWWuummm3qK9/SnP70uuuiieu9731v33Xdfra6u1sbGRvf86667rifI4/H++wA+8YlP1F133dUZlMc97nF15ZVX1l/91V91R7g6IhmPx/WkJz2pLrvssp7TsWLefvvt9dGPfrRn7O2wuQfwxlGwgDeAkNekEzyYJ/4fWTCAQHG5xjy1EYROGz4+Ix2c19iJWN74zACK6x0JOpr0OC2nuQ7useZz7aSRNRvVVuTobY3WkVYzbzich2jXAMMRozN0BnjmjyNBPue0Vut2BgvmKY7UkZz5x/OdUeMetnS7nmpSxsSApTUn5qfv99Zo2x5HvjnPthW5W8MgAh4BtOAHuzC8Fk+GhDS8HRm8coTN5wbQrp/I2hDzyTLjuQXY2MYz3tQXF9W2gke+s+wztmwJ8jwfBmN8t7m52VtqJuubAZ7ps+wBHF1YPj8/XydOnOhez4AsGMw4I+QXwD3Uds6DiKr2soUNRisTwf9GwhZCvku06Am89tpr6+d//ud72YGqgy18f/zHf1z/9b/+104Zr7322nrZy15Wj33sY2ttba1OnTpVS0tL9Q3f8A1100031Wte85q68847azwe1zXXXFM/+IM/eKjvqv3o56677qpf/uVfrltvvbUe//jH17d+67f2vh8Oh90Ok6qq1772tXXTTTd1SnrxxRfXD//wD9exY8fqla98ZSf48/Pztba2VldeeWX95E/+ZFVVb9lgenq61tbW6n/8j/9Rb37zm2s4HNZznvOc+qZv+qZ6z3ve04wWxuNxvehFL6pnPetZvS2wbtdff319+MMf7g7nwTANBoPa2Njo7c0mysTgjcf763+kYIkavY5uWaE5sqvqG/RWoZMNtY2ojQL9ZnN0Q7Phc1SYdFb1X/aWEX9ea/lnjF5G4JoWUDHYsX5MOh6Z/1v92VHBV5yH09926JYfnuOxc41PNXS07Xkwb1s1ONBkZ+ulQeteZjxw7jhxnsUylsEQADgdhufLzpVm/TFPcJquD3JLx9lq8IwardFo1CswTpDseeX8Cn/HPXNzc50sG6R5bJaH1nxlZG5dM0hHVsxXGvw2L7kPcMM8+/tJwUIrE5GZSIIzbBK1dNg8eGzg7mLJDEao3WF8i4uLvaU/Z44As/wmoF1eXj5Un/Vg2zkPIlpKYiOXAMKp11Q0/kaAvE/afU1NTdWXfumX1stf/vIaDAb1pje9qVPKnZ2duueee+oZz3hGvfCFL6zrr7++3ve+99XVV19dP/zDP1wrKyv1qle9qsbjcV1++eV1zz331OLiYr3whS+sf/JP/kn95//8n+uKK66oF7/4xTUcDusNb3hDD1lvbGzUiRMn6vnPf3593dd9Xb3nPe+pL//yL68//dM/7QTsKU95Ss3OztbrX//6uuKKK+o973lP3XDDDbWystJFS9/xHd9R09PT9a53vaue85zn1I033tgV+lxzzTX1bd/2bTU1NVV/+Id/2L0Jbnt7u06fPl3PfOYz6+Uvf3m9853vrK/6qq+qpzzlKVVVvfViA68Xv/jF9djHPrZOnz5dv/mbv1mPeMQjevP1gQ98oN7xjnfU0tJSz5naMZEytiMcDoddfQrInReQuSgzK+YtO6m4GFYiCEeATk/bsOOUJi1XpFzRMiOSgAY+pLx+ofSki7BcX2JZxpglYKBlxi6bdSwzM/xvQ1dVvSjPDhAeO2Xfip5pjuwZ22BwsITBfAM6nY0wn53SznqQ8Xjci/J5/sLCQneYD1k7nu0sQo7Fsgu4g+fppHEAeb6DnaZbZlqcbag6/L4Jxmya/f4gf+bn2gYm4ON7L9skQPY4DYTtoFO+PL4EuCkj+b3nwaDD9xhgQj92wDruvs0Lg0PzxK9bh3ZOmqRvLw9Bq+WcQklsDvV5e3t7tbW1VadPn+6dbePsIY16oIfSzgsQ0QIL/q7qwEEkiGgZJ4Qj0WHVgTF+2MMeVl/zNV9Tg8Gg1tfX65GPfGT3IpVrr722Njc3a2pqqq677rq64YYb6iUveUk97GEPq3//7/9996rWxcXFes5znlOnTp2qz3zmM/XkJz+5rr322pqamqonPvGJVbW/1/eqq67qDOS1117b1Vk87nGPq4985CO1s7NTF110UQ0Gg3rc4x5Xx48fr1tuuaVmZ2frsssuq7W1tbrgggs6A3v11VfXs571rHr3u99d73nPe+p7v/d7uzeCVu0fZPL0pz+9BoNBra2tdWtsV1xxRV1++eV1+vTpOn78eF122WX1+Mc/vq6++ureefAYwqp9x/EVX/EVdemll3b9X3bZZb053Nzc7LbPJeJPQ1hVh5y707IAi729vVpYWOjO7djd3e2dQ89c2sBxrwvEuK6VsUCu7BxYm06QwbW5fovc0a/Xbnm2n5XGk/sMXBwhtUB2i8eZLXBmwgAj+7O+JCAxwICHAC6Pwc5x0tjt7DPSzaXEVhSeus7309PTPaPOd65dQLYAl6dOneqWzlpgynUCtkvpxG2nzDe2iTPnni+cCBkE+JYOztmoBNCAnjzKuiUfrnuwXmAXXfBHgAKAI7ORzs0gtwUELOMGwik7lrcEmi3ZbWUTcg4SBMJfB5r5/NZukknZMDI1zljYNxk45S6b4XDYvRfD7yRKuijy97tZHko7b0BE1eEMhCcFY2GDzz3+zd9Gh44iWob805/+dP3sz/5sVe0jv3/7b/9tffM3f3PX15Oe9KR60YteVB//+MfrN3/zN2tpaakuvvjiWlhYqCc96Un1r/7Vv6qFhYXa2tqqZz7zmfUXf/EXXd+f/exn61WvelX3/xOe8IT6+Z//+VpZWanPfvaz9Za3vKWXCfmVX/mVOn78eI3H43r1q1/dReoXX3xxVe079ec85zk1Oztb73vf++q6666rU6dO1XOf+9x67Wtfewi13nXXXfVf/st/6VK4P/3TP13PeMYz6tSpU/XpT3+66aSycttG7E1velO98Y1v7DniqupAEHNqJ2wlzvSmU+HMMcrD/xgikDxnTwB8eKYBhF8F7fVVF5Q5nQ6tPMfyaCNmo9iK5C2/Kde0BDH+LPtx89IGLQ2qx2LeZI3FJFABTabD4N2gMMdmcE8zaKA/+uE75sjOE2dMvzg7+sh0uOnOpQWq7be3t7tj5Z3dGA6HtbGxUQsLCx39lo2Munl+zpWzMT7u3UWovh8+mK8GDpY/z7mP4OZZZKZIybuWx04658vjGo8PXh0ALXNzcx3dPt0yZYw+WrLUylg48s9MDr+RJy+rtHjO+FwIbplIPbQO2DYlfdhBZAnbMRwODwUbnifAF+Mmw80BfD5EzNtjGQc1GAaZD6WdNyAiwYN/bMgSNPh3Oi8EyOmuFuiYn5+vyy+/vBOud73rXfXCF76wxuNxvfOd76zv/u7vruXl5Xr/+99fF110UbeeOj09XZ/85CfrrW99a730pS+t7e3tuvrqq3sg4qlPfWp9yZd8SVXtCydHX29vb9dv//Zv1+zsbF1wwQW9HQy0iy++uNtahJJccMEF9dSnPrVuv/32uv322+uJT3xivfe9763nPve5Tef+tV/7tfWkJz2pE/BHP/rRtbq6Wq985Su7vcluRvZWkqp9Y/yKV7yilpaWuvHQ3vSmN9XnPve5Q8ru6zAoueUVA4YxpIaCOXeREdtbvV3UW9+8XGFHkk7AkVICUhxEGhfLHzyiD8sVxibTv244Dm9NS/CR6VFHTo4EfY8dHH8bEDrLAL02zNa7zKpkQZ7H7nvd9yQe8Dm7oSw3ptf1FIBB+s3lp3SQ6BJb8zDgnj+DOF7VzHc4ZTsrr2l7zuzYmSM7QooduTcdcfLN4zCIhQ9V1XtNtZ28nbSfx+feApsg33QNh8NDRbTONqTc5DxbHgwGUm5NQ4LczFa0Mh+ptwBTZxmtD5ZNmrMWzt6wzRugy3kj1G7ZwbPEB4gw7wB5lg/shMcJrTs7O93OjCMQ8QVaIkobMAu6nYGFJo0ofRpA0GyQfP3LXvay+s7v/M7uf1Lor3nNa+oTn/hEPeEJT6iq/VPFfFz22tpa7e7u1p/8yZ/U13/919fx48e7bTz0/8xnPrM2Njaqan+ZYWpqqt75znfWa1/72nrf+95Xj3jEI3pRlpudIQj4KU95Sl144YX1O7/zO3XHHXfU1tZWLS0t1YkTJ+pJT3pSve997+s58Msvv7zHA/ZBP+95z6t77723y3RUVe88jHS2fP/IRz7ykFDv7u7WPffc0zOIzJ8rwlFyUDzLH84cVB2kPZmnhYWFzjm77+np6e5QK876AHBZ8aHL7w9oRSWtKN5gwnIE3ZY/px3tBBLYZbTpZ7SieehIEGMD6ZYAxksRzK379bhwiNmvAcHc3FyXQnfkTx88I7NZzJ3Hi8H3PQAECsyyeA7++B0jjrihd25urtbX12t1dbU31nRumWlyUW6CU/fDNc6auPG9j+Xn/ox6+Zu5SsBgx1hVnaw7m+f+WPr0Zwkgkuf8oI8uWqVZ7u34c34sX9ZbdNsRuPmfwJf5sq4ZXJqfXA/fckmK/rNY2UW4/GaHhF9jwGnH4/G4A42nT5/uXuwFgMC+OTPmrJEBV1X15Ns6xxlAWZj/YNs5DyJsfBJAOOWajt+pKBsR92PwcSYj/alPfapuu+227rO1tbX6yEc+Uv/zf/7Pevazn90d9nHRRRf1BGA02j/A5rbbbqvPfe5zdfz48W69i/bmN7+5VlZWamZmpi677LJ67GMfW8ePH69PfvKTdeWVV/YOscpmB4jgsm3zec97Xn3d131drays1Jd92ZfV1NRU/eN//I/rhhtu6PXx9re/vS6//PJaWFioqqpbb721PvGJT9T3fd/31bvf/e7ezo2pqf2KYi81OBo9ffp0/eVf/mU99alP7e7Z29urX/qlX6qdnZ1aWVnpGWh+++AuLyN4/nmGDZ9fi+4iNae3oZU1b4MJjElGkDZ6jjYzKjBdXJ/OmTH4Hmh0ejVTn3ZI+YwEEBhIX0dzhJk0mH6eg1E3EHTUyVo9Z6PY6TBO5gWDaQO+s7PTAWlnRDLydCobgDgYDLqXuTEuy4B57uxiCwAyht3d3U4ucIjWT5bPoD1lNOeC55keXtrEHBg0e7sl13sXhT83gLLswiv+b72RNuuAUoYzyOJ7HCjP5l4yGykfBvqtflvPy+jfWRPz2VkOeJwg29faSVtOud+2i2u5j+YdPPYRrjmx3nqeyZYCInwtWQyPx+9zcp/QYV7Tn09YPdt2zoMIOxyDh0zppVGcFPnRjw1XAhD3U7UPIp71rGd1xubjH/94/e7v/m6dOHGii3bG4/2zEnCyNsJen//whz9cVQeod21trWZmZurxj398XXLJJfW//tf/qpe85CX1Az/wA/X617++oz2jSZqj3kc96lF1zTXX1K233lo33nhjDyAtLy/XU57ylEM7J06dOlUf/OAHa3Z2ti6//PJ6/vOfX1/5lV9ZVVWPf/zje9tI9/b2eoekEEVZQT7wgQ/UXXfd1XPAJ0+erCuvvLJ3nVE/QCkzGAkK4XMqJEa1VdXOdjwMEnv/cUZV/T3qVQeKaaOQdNmo+TMbplYWLOetZfy8HJDAIJ3sJAOamYLMKEziL8/LNf+FhYXOEPPqY7a4mV7rEhkJZ5GYc0fQ6KSzW/zGaQ6Hw+40VvTJETtRIg4IoGIbYMC9sbHRARycL31yqJIdxWAw6Nb/08GbT27IG9fCK5Y1kD94MRwOD+00glcGYo5c4TkBBdk0AKr1xjLCfZYzzz9y6Ps8XxnlO6pnjicBYc+Hs27Qlj+eRxdx5rjoL5cAvdTtOaNQlGtbztnPsw/yu1mYQ+sv31ELx1kjjNHXI+ODwaCWlpY6sMyJos40M07P47Fjxw7x9sG0cx5EOHOQdQ/p/NNot5TakQp92PBb8Gg333xzvepVr6qpqak6ffp0t23z+PHjdfr06W79/fLLL6/v//7vr9/4jd+otbW17vkXX3xxXXbZZbW9vV2///u/3x0cVVV199131+tf//r6oz/6o3rlK19Zz3ve8+rkyZP1dV/3dfWud72rPvvZzx6K1miZTr7uuutqenq6rr/++nr2s59dF110UceHO+64o9bX1+sf/aN/VB/4wAe6+1ZXV+vP/uzPOoN9yy231E/+5E/WcDjs3iRH29vbq7W1tUNRr+n40Ic+VB/+8Ie7Q4emp6fr4osv7o3BCmveZ8vIGBlwapEtTkb46Vy8I4Ef9mQzd60sgB2q0+o2VDQbUM9TRhQ2gM4+wMNclkgQkgCCZ7XWTvnczzZtdgRuLFdgcL1VEMDG/U6nOgrF+JHyBUykY7GTTkfCswAG29vbNTc3V8vLy131ei4jQJsjfI+F68mkOIp3dsK2wo7Mz7LTTVtD1iaXJiiigz4vf9KPt+c6gEig6rS+HbfvzZqN3IlggGKQAghx//DT2ZBcJmFOkc9WASf9JA+hyYGCsw6+NmskEkjzzAQOKb+eGy858Dm6bx45mIWGLPy1HAAksGEGD4C+quplnQeDQU9fcqeZ9W2S/Xyg7ZwHEQikhSEdvlsr6jMIqTq8NGJkbkdAG41GXcX2YDCoSy65pBPe++67r2699dY6ceJEfeADH6hv+ZZvqS/90i+tP/zDP6y77rqrHv7wh9eLXvSiuvDCC+tVr3pVfeQjH6nHPOYxPbBy8cUX13g8rte//vX1vd/7vXX33XfX9vZ2vfSlL61f/dVf7bb0tCID6LvgggvqyU9+ct1///31hje8of70T/+0G9d1111X3//931+33nprPetZz6qPfvSj3b3XXnttfeu3fms3nquuuqrG43GdPHmy3vKWt9T3fM/3dDz82q/92h6vxuNx3XXXXV1f09PT9fVf//XdkbDm++7ubr3zne/s0nwZadsYeI2eHxy3+yRadMSCDNipYjjyqFq2ic7Pz3evJ896AdNm5w2tBhOtCL8ljxhk+JJA4guBKl/rKC+XJNyIaFsAx/PnF1wluHZEiAG0ITffmBNoAuz5pU84k8zKuDKdvnAkfl0yDhmHwmFqVdWLIlvzYMdiJ+QtwwZXdrzYCRytAUduL+ZezzF0OSgyIIC/nmMDgNStquq2AxrUZH98niDU8mPg0nKw9GlA7ijZNhrQZF1MkJMAib+dAfD8GHhnc780y5m/c6YlAzMHOM5UmUbkwADLvIEeMmi8vruqejUS2G6yenfffXedOnWqO0KbTDAyB7+yDuihtHMeRCTiS2fhlsIzacINTFzAw7U2HjgXC7gVZWtrq9785jfXj/zIj9TJkyfr93//9+uJT3xi/ciP/Ei3LnzLLbfUT//0T9frXve6euQjH9ktbdA39L7vfe+r97znPfW0pz2tPvKRj9RVV11Vj3/84+u9731vp6go7erqaoead3Z2uh0Wf/EXf9EzgOPxuD7+8Y937wiYm5ura6+9tkajUW1tbdVXf/VX13XXXdeNd3t7u97znvfUf/yP/7HuvPPOTpE2Nzfru77ruw7x/B3veEenFOPxuL7v+76vKdj33HNPveENb+giU/hPxsLzlWlWrwczB8iDnZBTqHb2BiVev8fQ4+S2t7drbW3tkKOmPxyUU8lOb9tATYpSU/kNnjLr4O8ZYzoJrkkwhqxnxsd65Mxd6pflnHVxA3m/vKmqOjDHHCCrNnwGIOZvaxtdK0PlbAAOE1ly1InM8L2jRJ+Z0Nq9Yafhl255vliC87ICz4YPyKwdVfLCn6VzNV00roNHPjTNZ6sYWNrZW87MU4/LfDFPTAPyZ7n3XLvo0mOYtFyZmbnkX8qq5SB1J+kxzciI//c4na1ijlKXkHH7pdGo/7LE7Ht6ev9FgxyMh7zv7OzUsWPHanFxsaN/eXm51tbWuiyr62Os814ee6jtnAcROYHpSLjGzRPo9UI7HVfV2rlgRBDSn/mZn6m3v/3t3XYwPxdBfdvb3laPeMQj6pu/+Zvroosuqne961310Y9+tNvmc/LkyfrzP//zuuSSS7o15fF4XP/hP/yHeu9731uXXXZZl/r7gz/4g3rsYx9bF110Ub3iFa+o2267rTuoCR7cdNNN9RM/8RO1srJSVfvbGr/6q7+63vSmN9Wv/dqv1VVXXdWLOO688876yEc+Uo9+9KPrh37oh2phYaFe9KIX1Yc//OG6/fbbe7zb3d2tm266qW644Ya6+uqrq2rfYbzzne/sFVlW7S9dXH/99fVzP/dztbq6Wu94xzuaQn3y5Mn6rd/6rVpfX+/25A+HB69Yxpk7W+DlB0fXdvCOtj0nNjjIB0DF26uIujk/woWqvJqcPnLLFc8EKHrfvwHnpKiJ5ijQICoNhseDTJuG1AFk3Wvjfp5l347G9DpydMqdDIqXL3Jp0RX20EHmCJqgo+U87YTS2eM8GCMFap5L5pzroXM8Hndy7KyMnRDOeHZ29tCbZF1k66Uz5ApQbEcE79lNkoGLZdzONEFYZm/saE2jfxuQt8AKzyI75B++S4CcDs1AiLmnf7+3g+9MqwMEA4jhcNjbKZUyiUxBRwtQuGUWwiCZMbR2kziD4fucjSKA2dzc7GX7LOOWJRdqevdbVXW1R846GeiZ3i9kWx5oO+dBhFNJaXg82RaaNEbezuO/HYHwG8XZ2Niot7/97XXzzTd3zpq+HSUjQL/zO79Tb3nLW+q5z31ufeVXfmUtLCzUnXfeWX/5l39Zr3vd62p6errbxbG5uVlvf/vb6zOf+UxdcMEFPUNwxx131Jve9Ka65ppr6sSJE7W2ttZD3R/96Efr5ptvrpmZmVpaWqrd3d16+MMfXp/+9Kfr3e9+d7dl0wh6b2+v/uzP/qxOnz5d8/PztbS0VO9+97u7t5LSTp48Wf/7f//v+su//Mt65CMfWdPT0/W5z32u3vnOd/aMIY1syC233FJra2sTd5KMx/vbXZeXl3uGhgI5gwTS06wVux7BadGcu5ZhOFMKv5VSrtqPqFdWVjqlZ909I8aMBKzolj8btlx68LxmBOn0sCvLvY2P56bj9zMcnZtW5Jb/E/wZuKRuDQb7O2pYWoCfXwhIwD/uTZ45vW6QaBsADckfQIPBtrc4uqbDvEh6bQO8RGFQa/lJXuP8+Bu6nXHM4MdgMWXJDszZUwM5fw8tnlOALtdZLvjOMuQsQ2a+0nlZjvx8nuc5SnDssWZ2KefG9ixBTMpmjsOy5ayQl2EmOeQMThin7QH37u7u1traWgdkTQtBiw+h8pHVBm4nTpyoO+64o5s/78LxDhjz96G0wd8XGvlia4PB4FhV3f8lX/IlPeH8/Hc9Ba46fLgUf8/Pz/eAhI1qKl+ms06fPl3j8fhQ9asFx4i+an/vLsaV6t/l5eUe6h8M9o/WnZqaqiuuuOKQ0E1NTdU999xTm5ubdeLEiQ6tI0inT5+uEydOdGMZDAZ199131+7ubl144YW9yMKpsJMnT3YptFOnTnXfIdgI8MrKSo3H467I7N577+0ZEDscrl1dXe05QuYI45tpZcutjZqNzfz8fPfj1HpV9YqO4JnBnaNdv2qZazOqwBjieFwsyMEuHB6TzrKqekV++V1LZtwcdVCX4CNtDVbhv083nNSsCy0A4+ebj84CpGO30d3b2+u9JttA0xFjvswJfrtiPbM8zkYAnlrRoOsRFhcXu90QZD5MPzT7O4MDZwIy+oVP3tGBzBlY2a4wl4BRZ1WtIwlwoZVn+hlTU/0XN3kXUdpJAyj6SrBtubKN4l50qWWDaYzXOugshTOPtoG2I96x0wIP9GH6MquBHLk5i+JmOrjPy+Y5977PIBQgZ9DNWF00y7tYNjc3u+XfhYWF7nUFlr319fX63Oc+1+3qcLDhAmXouOGGG+od73jHoTGqHR+Px6cmfXnOZyI8aY5Eco24ZTATPLSU159V9YXGGQg302HFJ53JIUkt5M5zjx071gmX3wpI3xdeeOGhVDPGlHdTOFo4ceJE1z9LBN4/DBhC+QBGGYEOBoPuZL7jx493p6IZ/bec4IkTJw45HjtbFCArzx0VYxRR8M3Nze7Vz2zvY95JA9poeV4z0qiqQ+8ssLNJR8LW3dFo1HvzaL7x1HvlTY+zQC2gT6QNuGplyHgm82zj6fQx11q2eYaBi0GADTC8cyp+UnRsPldVtx0y+3ME23I81DIYhEC/U7iO2pEp/odOHNDU1MHuihyDI9EsZk3ZgVc8y3LtMdkR+dRUOxWq7y2HtgnOMLSctmVrOBx2W1cpULWuWReQE+ua7Qt0ptNPYGr747GnvWz9byAEaARUJj9Mp/mCDrn+IIM/xslPylsuiUyyY+7TY0lgzHwmCHLGD6CAzclzaODH1tZWD7QDxubn5zuQPgn4IROm+WzaOQ8iJiHedM60XIfKmgjARVW7mt7Cy/+ta5yCnkSzhT4FF4HZ2trqCqSgyWnVdN4YwQRNThfzbEc0k6L2HOd4PO7qAbx7gbPdQcE2omlgHdkbqMF7r79m9Gngg6Gcnp6uzc3NWltbq/n5+Zqbm+syTKbdBaU2mgZWps90WtENOuEVy0ej0aj3chw7y3SATjsaCBJNzs3N9c4Z4T4Kbw20zG94lZmBjLhaRjLb9PR0pycY7CyAc8u0vvXRsm7ZAgQ5s+C6A8aRSwStuUinCC/IZFg+oSGjRoOWvMZg0HbAGRqDAubT25C9DAc9/JCVoW94Q2OO4S8yToBioAT/rUf0mQDTQRPPNNBIR265y7lNR+vraf7fNJnP8CnnIOXL2RjzPMdN/84wue/UD8scc5wBjn2ODxvzkotrtaznBvv+bdnExhkgEFRwhD9AYxJ9RyDiATRHC8nIqsM7KobDg6rtXMLIjEQLOTvCsHC7OTXJ/elMfZ2dbYKKra2tHupOA2VBB/H6exo1BU6PWfFaDikjGEdogImFhYUuPYdQe0eGHVor9UmNgwvgiOjhr52X5xYlo/qZ92bMzc3VsWPHukIkeI3RduEbkdDOzk7vTAkbIcbeysw40rWS+0U50Gwjlo1lCsAD9zgyzggpDXtGk4zb13p5BFpawGBubq6LbG3knD7NluDUNPh6g9/UE8uNj1G3g7XOJ2C2g3dBLDJkffMWSHhjcGk9sQ0wWLKT8Ri4h+wN48f2cDCbwWgCzcyMpg3iebyTYRIQynd2eL4sLwkwzF941go80ub6Xtu8tG3p4NBFeGIQ6oxM1mnkyadp/13rknrtuXbgQ8tlbD5LYIItcb/0Zb5Z9y0nPN/HprM84SzW5uZmJ1dkJDJIycDjobTzAkRUHc4E+LOW4WcfuesfbAS4FyGwkmSEZ4Piz5OeB0J/pilJfXnbI4LnwjKWAdJBp7C3QA+O0E6o5Ww8DgMAajvIArBbhK1IWazWcj4+wAcFqaouw5FRURow+OSTCQE6S0tL3VIHrwnPxmf5Xcso0L8LPX1+As6J79nLbUdvcEUKl6UZAE0WdtkwpuO1kYXHmVGywXKE57Sv6wgwvi6Cgxa31i4Y/22An0V2dsDmMUYZEOF3BmSGib9b6WOPPYEQY3QGLXXf/2emx/OR+u/MCOO2rclzJuzc0ml7zv1snu8tyrkUAyDP6JTm4Mm0GMilzbPcpKPKIIi+DCASiPgZfO46DMuIAVbSa2cM6Ex7lwAoQZS/M18stxl4ZXauBUD43LSmbhpkDIfD7lUDa2trXZaDejpsDLbD/HYGZTAYdAX7Z9vOGxBRdXhdt+owGuYz1pudCp0UxWXElJmP1vONQvmu6vB7EiZlMfiOv/MNgb43o4Xs2yjWgu/npRG2E0pjnTx1JL+xsdE5w2PHjnWv3caIptHJtXJnVaqqlxlIfiXgog/6QfHIjiwtLXUG147PRY/eNuZ5dIQLzzkLwQ3ZSvlwEZ/b0tJSV/cwGo267EU6pRZ4ymyGaXY06roLX2N+wRO/zM1yni3l3NekPNnpZw0I9+E8mX9nBLIgk9+OvgaDQe/gnRbPzH/GSqTnLEDqkh1gginu89KVI2iOUU9b4IK4BIg8s+WkrXd2JD4+PJftTG9rnlKGvQTg+0ynHSmfj0b9bdUGOb63JVP5vfU4QUnKBBnQnC/3w/xb3g2sE6B7/glw0Cm/Yt5HtmdLPUu5yawyc857fBxI5THsVftF+gSYDgIZC9knwMjZtvMCRCSKTgG10g0Gg956uff+T0L9VX3jlY4gaUlEbMVL0DBJ0BL44BS91ub0m9F6pjIzovFyRgKvBA08K8eQY8nMBGk5p9x8Zj99tlLAOQ4MZW6pzJaG3tEdz6fWwKARcGbw4EgoDbLXtydFwBhPHOLi4mJXRDc1dXDcMw7ASx8Z6bgOxvzO1K2Ba74UykDTc2yDnFkrfw9tzmCkU7Ucpwxy7fT0dHc4m4FN1jGZntSFjCa9HObCVuYngarfNkt9iQGZXw0Pv1KukJ90PgbZAGCyHmR0UoYNxni+gZcDHdPg48bRDWfUEijmHPFsnDz3tQCN+7DeQl8r62Ce28YmMMi6BcuQM6/WAXTIINJ2zv22sg+2YdYPxpZ1FtDsXXKZpbTtd7Yl58HLHgZe9M/hUrSFhYVegTEywu5A6pZyjMn7s23nPIhAKBG2TJNyDcICw51ydkEdUaEzBnaQNByTU/QtQGCFssLRrDhJtw0JUbIRNAbE16FQ9OPdDI6O4Rtr9/DHimFDZAOZz7WycR/RP8gaMME4uMbRh8dqXmSK2AplY0hfXlqg8T6Cra2tbqsf1w0Gg26ZwxHleDzupfLpO+Uq17jT8Rk0ODLb29vrCjBtMD0mxuxmB25gwD0+6ttg1vx0lOhlmHyGI7f8jr9bTt6Ohsb/mY0z6Mhtmy05tNPjMxct2imkMXexXi4pwEensO0Y0I1WSh067eTyUKnB4CB75b39nkfodBGv7ZIBI31sbm42tzQnwGU+M1PmYMe88txnFiFrhar6+uD7bHuwmfAjZTPlIp8Ln22bDer44bsWSDFgsi11vQzN8+KAJ31D8tjAh7E6W5Agz3LqF39hc5aXl+vUqVPdcu3q6mpnbxijD+7Cv7Uy3Q+2nfMgomryiZRG7VyH83AVf2Yi0vD4c/eLQDKxKEhGrghzGgp+W/A8plRoqnRRfveVRpXP8joUHeNhWj1W74IwT426GVvLCNrIsJ+ZCBAQhwN1361+TaMddm5RNUDKZsOxvr7evXKaM+kpZiRi9JzxG4OOoTQwdaGm55zId3FxsVegtbm52T0nZSx5bLnjOwwFqVyDWPOA334WnzuaNHjNiLflaHmO5dZRYz7fzm9SIacdgc938LOS9pz/1ANn68hAGbz5rbMJPEyPI2w7RtdyJG9wdj791iA0o3gDlla/BikUvOYx3Z5H7rMMm1cGrY6SDTLo40y8bgEq8wLe+rfBQ+p49uN6BF9H9sWgbDwed7uJrDfIsQMX6LAsYcczQLKOps1E7xO0MgbPT2YrMggYjUbd0gWggH5Go/3lTo7HBkiS8XWgaVrybIwH2855EJForhUJVh1swZqdna35+fmu0M4tESn32bDn80ajUW8bHhPtM83t1CyUVhgbXj/LAknfHhuoNsdCs3FIZXfkgILl+N2P6UmH4SwMPHNqGQHf29vrtq0CKlhqaLVMi/M3ywCuRk8nbDozGvI8kgpEaUm5Y7gN3myMcXbQ6fmDDvpkqyJGzNt204i7L+Q2065kEHxAjwtK7exId3t+DBQtj8kf5jVBLnTnbxcQZmRsWYHWzJL5Hhtfp2sZj7MuXiP3WNBpflg6SrDRkrGMKt23zzPhc8uFx898wlfLArwxkLAuma/wwkDaeuN7kHvTYQBKAS+60Yqo/XzzI69JIMmYW6DX/ztKb/E7baCzOgQnfObCcs+7Ay5An20h8t6yGZ5XgwWas74tHfH/yQcvi+T1Bgu88G1tba0LxpAb3gCKjbjgggtqY2Oj7rnnntrZ2an5+fmO7py7B9POeRCRxsn/Vx043qr9IrZjx47V8vJyZ5gdUSQ6TuVr/W/HgjDhIP0aVzc7YFpG4WlgjVSpjcgINe9PlGyDyliIOkwPUaCNDPdbSeBhy8F6vbbqAOEzVo755Zht+vc+fm+78ziZdxvbL6Qk5tVwODy0RczKjnO2kbehMe9YXqFfp9PJciXvmBccILR4eQyavGPDmQ/LLrzyHPpEUoCb5yxrJRyZWY+81GfZ8vMNntxHgmTmwXKRfRgkcb0Bro0/48jlBeu/z4AxiMvonNdyZ1FrAgXLU8oD43ahG9+TOXOU2Dqp02ND9sx3ApbcEZA0tIIG6791MWU/m+kyLbaBzEHOu6Nvj836Bx8yG+FAK22idy9ZFrG3fmkfP17Gze+c1TQ/XIjdAt0GH1UHW2nz8wRUBmC2D+br3t5enTx58hDQ41UE2NiFhYVaXl7uMqtzc3N155131ubmZmfLHko750EEZxS0sg4YLH4vLi72FNfOPNNRiUyrDh+rbcDRisyp/uYV0o4wE4Vmf4mWLXSk8ZIOXwe9ODP6ghdVB4qGUmEIUE7TCtLH0NJ/onanoBl/y2gPh8Mu7Uh/ROzQurW11VXcGzwkmJiamuqMdPI1eZHODGNsYOE1aMbA2OnHWzhzDrkfg2UZs5xBhwtPc4nBPBmPx73dG54Ly2nKD7x2OhyaMpXLc3B4OG+v4VuvnLUwoPSc4fSqDoAXdPmsBAMx9CcLOTPDwXdeauIznoH8OrPEuOyUMvK10W8tnxg8OcK0XCVQhO9Zw5E055jNO5ww8mSepywwr/TnSD0zdJ63dJAJpgxqzeeW7bRTtOyZdwlmrSP0yb3MYwt0JF8d3OWPgSANEOB7U59cjJ92mb9ptleeS2QCHUj7ZDlzDYiPTh8M9s8H8WsXyNpdeumldeedd/Z4d7btnAcRGDcfK5uHy4zH4+7QIYTL69heB2eS0ximwa06cEQZ0TrF7YOHHC2mYlX1axZaxt4Cwau7XUhjQSWTYEXIZkG2YeA7xs5YU/lQOKfbx+P+oUCZMTDv/MydnZ1aX1/vIm+/G8KFY6n0OGEMd6L2lgJ5juCrt3EloPKaIhFQq7iv6iA6SNABL8wP+IbBJNXOuDyvpK4NRnkWIMHjZUzefYDz9PhSHizj0ITTYoxeYskoMvtzgyZn0Rxh2gmnfvg1ypkxdIYmMxg4HMAOdRBVB2/NJJvk4MP3p4N0sGFQzv2eI8tipsOhLR0N9sz3wRs+s/O2fTIob82DHa5/IxN2spZf09dyTOaTx5RgwfprwOpiTM+fARD3+d0QrSDKoM621IWU9EkfPhkVeXdthTMnuWWXvg2Oki+TQGhLb+AL19C3eeSglWd63glkmNeH0s4LEIFRcrTkQ4dczOQKWSsSRjUNaSqlJ84KbGebguGo2zsh+A4a3AfPOlOtA+kqAwYbYUeWNtbuw81RQKZDrRz0ad6gKBnJme82qLmFjX5wWtvb211qDl57T7azG0TsACoyP8xfAg/uz0hld3e3W4qCvyivo2Pv5PB8Gbx5/DYiNnzwwdkfHzaF02dc5jXywdz4x985Y+ODu1rGiyJTywkym9EgUTYN3jDXpoFmYJ6ZqIzUDOZwvugDgIixZxEc9sByz3y1MoLOOGStRYLrBPOeT2c2rLdsaU1+0Id3BeT9mfnJnTy0nO8EEu4zU+4JbqoOtr4yRoBBBj4OIGyHEmhYVg30ba/hgYM67qEP26XMwjjLg1ykPbIdagVGjNcRv+3oYDBoZh/9t+cmswvIXZ5rYVDgIn1sHnbQOsGYtra2eksW4/H+qwkSiJ1tOy9ARK4bMuk+rwBhBXTYMNEyRdVivrMPTre1onkjxqrq1q92d3d7aXp/n5+5pcBCI87WaTaeb0MKj2gGGGd6hpXO17SyDI5gzJdEzhnR4LgxKPCY+eL10Lwp02OwobWhdGailUmCLhwaa41OfSNPLCuwdp7ro3Z2flZG9sxZgljGj9xW9d9E6n4ceTkq9v2mPQGygY7nxvPB9Xb0mdJlrtNo2qEZcNIM8KC/lermf+71fZZ/A1jTwNh4GRvjsINy/342mb50Lhk0YFNyDjPTMikbwPXMk+XfMoM8OvVt8JN0eX7trHO5jGucPTNYtY00gE/HzVyalrSv5q95SfMutwTXaaed6UmeOgOVGUOCEehxgx7mM4OptFlJQytgawUvBiR+rrORyOlgcPDiO4Me69P6+np36irNb4V9qO2cBxFGxo5mq6ozgjgAr1VX9d+yaETuanw7TzcbboTNjsOOAifFa7Srqm6//fa66667umdXHbyqmP3AFv7BYFArKytdpe709HRdeOGF3VtBvb/eW4DG4/1irOXl5drc3Kz19fXuQJPRaNS98ttggtqRU6dO1c7OTi0vL/dqSfwbwV9dXe0pmI2VPzM4aNVWYIz43HPLFj0yDVmIaADJXKOM5iUN4EAEXnXwLg4U0oo4HA67AkUDyFTwlAta/u0MhnlatS+bvNgsnQaO17Jj8OAsSUZdVQcgyYYfmiwL3uoHL3mmo1LfY6DIeIimHKnTfPqf6SAzZJDE2AF1BiuZ2TNvBoNBN8fOdCW9lqPx+OANugZhngcaqe+qPtDhf/OiNffYCoMPL+OQDbJjccTN9Z7jrLWwnJg33OvAqMWLFghKXk9K6Sc45XpnddKGWxa99Aa/DFZpDgx9fepKK1tsGqqqt0OrBdqZX4ONBFH+ncvW2RxA0RdZUYPtVmal6sBuodcA4O3t7Tp27Nih+q0H0855EMGBRlWH90mzRsokOFqp6qcAWwjZggrgsCNEwNOoGtnv7e3VNddcUy996Uvr4Q9/eNfv1tZWve9976tXv/rVdd9991VV1Ytf/OJ65jOfWT/0Qz90SOlPnDhRP/uzP1tvfvOb64/+6I/qYQ97WP3UT/1ULS4u9vgxGo3q5MmT9e53v7v++q//unZ2duqqq66ql73sZfWrv/qrtbm5WceOHat/+S//ZW1vb9ev/Mqv1Orqam/d+AUveEE96UlPqh/90R+tubm5esELXlDPfvazJ87B2972tnr1q1/dvcXSyuP0bCpAK1NjMMZn8Ixi0qrqosvMOJhnGDQfF45D9BkRBhqOyKGR4l3XtHh5I0GmDUIaE2QMehzdWSYNtixX8DPTsrl01Yro4RfGhtS9l5aqDtZ3PVbO0PB4nO2g2ZHAaz4HFACyB4NB56ht7NPRmSdEl47GzF/00w6FOWXsZO1wEgYb/s09tgXpZKHZBcHcy1ykA7Zu8JnHwHgpLKWQOs9uMRhhicf9+pmeH9s3jyH7ymVcvnNmg/v43OPOls7TOuH5y+yNQbRruEyzASgtl2kyY0IzUCIT4UCCazxW9MZ0wC/XUFmWUq5tb2heljVYsW5k9s/0QxfySDDyUDIS5zyIsKFw+r6qeofL2FBy7aR0Ty4JMDEZJdjBMYmZMj9x4kR9z/d8T21ubtaP//iP16233lorKyv1jd/4jfXSl760Tp48Wa9+9aur6iAyXl1drZWVlZ5wTU1N1dLSUvcGt8FgvzL3He94R73hDW/onr28vFz/9J/+03rRi15UH//4x+uOO+7orr3//vu7iGVhYaEuuOCC+sZv/MZ6zWte06OdA6FWV1e7SL2q6md+5mdqbW2t4zuC/NnPfvbQnmSMDGPIojjPgRUaI+Eoylkh7t/a2uoQt9dlfYobfJuenu4Ok/I6PvTklkmWT2jUEtAfdHv5A9myI2iBBsuVI/UEjd6VQESxvb3dgWYfHuWMjefGGTHo9FKHwQgG3PINPePxwQE+fpbBFv3YqAM04G9GStCEsXM0Dv3WgUny40gQUJeOjswCfSCTgNMEegZ1bKl2VGn7YYDsCDUdnnlqOTEQpJFJI2vibcCWIWdtfJCSC4dbkXtmbggikG9n+RIcOSrP/gyM/Rkt5XwS4ICm1hb0BEEJwBPEew5yidoAHPrQN8u0wVEGSV7WbYHhqoOj8q0zzrT4vswq5fj8dwZPzvalLTnbdl6AiEwdVtUhBGaUbsGs6k+2lSMVzUqbz8/ogn4f9ahH1aWXXlr/4l/8i/rc5z5Xl1xySQ0Gg3r7299eT37yk+vLvuzLajQaHXJufgaHilgRUIZPfvKTdeutt9bx48e7M9avv/76uvbaa+tRj3pUfepTn+rRZMN8//3319Of/vT64Ac/WDfddFPNzMz01uEZDxHjhz/84brwwgt7Edl4PK7jx493SpLoOaMNohyiRXjKD3PD70TZOPnNzc3OuNIvhpdxepmB8bhehmeaN14f55wPxuX6jIx0M3qy3Ez6P+8zILUMGOh4yaKqD2Bw8GQOaIzZ40k6subCNDFP3mHUagZGjhxdMwDte3t73U6cLFxzlqV1hLmzfY6e7bgtv8iJjbOzLHNzc71CwlY0T8bEz7LjYHuu5dRONIFRRqC2RZ4/gCP8tANyPVhmDtATL6fk8yaB9XTWnlfLh7/n86TBrQWmWv1mgJFZj5ZzNYAzgMXe2JHSt7Nm9g2ASj+bcVrHoCGBRRaJmlbrN887E1g2cE2/k9kxnmcZmxQoP5h2zoOIXKJgMnySX1UdUsKqwxORxpz7bMQcLVlwuXZSe+pTn1rvf//7eyng//bf/lt94hOfqNFoVFdcccWh6HM4PKhgT0PqZ+FgoPWuu+7qsggbGxuHHARK/4d/+If1rGc9q77lW76lbrnlljp9+nRv7DzXKTuiI68T22BaoBlHpuS4xjUpLeOQkaSXkQaDQa/OxS/VItI1r8kmYDgwBHbcAAuuc6TBcxwx25kjTx6HDYbnCGdnmcpozX0bzLkWINePfTgWz9jc3OwBNe+DhzYiPhtaR9QGMNRoGJDleQ70SxTsbZ3QBb/9tsJ0WgAf+mfczgqSJbBuM1/Mdab50yYMh8Newazl2vIGLdY/y6UBzdbWVq9OKaN3O/BMS1OJn2efAFTgYysad8solecmQHKmFp30sgd95d/+P39nADYJMDgT4CxGAn/4iq1JG+Tnu38HRdjHBHcGLbmU47F6DMiW7aNBkG1gy/c4G5d2FLqcIfE4DZYcEOeuJIPch9LOeRCxt7d3aHuL38/gaMqgweumabhayNcOz8Kb6fj8/JOf/GTdfPPN9bKXvaw+85nP1Ic+9KH6xCc+UZ/97GdrbW2trrzyyqo6HA0S9dKnn+FUo4WUivJHPepRtbKyUjfeeOMhkGWenDx5st7ylrfUy1/+8vqGb/iGet3rXndojd/O65nPfGYdP368ZwAGg0H93d/9Xd155529e21QM+JxwV5WwJu/Hh9KB9o2P6DDDp0jYl14lNtrrZhOfbqxFMJ1e3t7XeWz5xxaoSejffryGCkQZH5JSSMPptF71jF0vGLdMmeggYNrFat57HznTJIjU8YNiPA+fRvKVtQDrbyZknmEFngA/5KvBndeYjJA5Rr0guU3wKPnJrMoDiIMPgxu04HaEZFJsS2BDlf50z9jbkWKrvWAds/vpLNJfPy1bUi+jTQdGS157lqA5FcrogYY2oFlQGZQ3loOy+Zof9JnmUlOu+X+E/hwje08B8tZFxxwms5WIJJOfzQ6WF61bUCPvQst7ab10+ChBcYsY2lrAcgPJRtxXoAIo9fBYFALCwu9qLGqv07M5BK9WugxJBi9RI+OIhEc7nPUwPPW1tbqv//3/14XXXRRPfvZz66v+ZqvqRe84AW1trZWH/nIR+pP/uRP6tZbbz2UAZgEIHguyvTc5z63Hve4x3XG4sSJE/WoRz2qPvShD9Uf//Ef19Oe9rRD93rp5IMf/GB94AMfqK/+6q+uD33oQ/XhD3/4kMCPx+NaWlqqH/iBH2galZ/6qZ/qvbzKRpNr+MzRH/NjYEfL1Kr5TZ/0w3z6bAXOeGAcTkUy13lCpB00KeVMg+basQ1jpso9FkeAvsfLKoAcoi/6QgYsz8Phfl0LoIa+TJedckZ48AOgkXNgQ+X1eMCO+wZUeG7hJXqIU/QSBeM2z0wfhpbm2gT6wNF6fMhGAjl/l87EDshBhXV6kgHnb+aIN3VCOwcZpdOzI4YGF/WZP0lvVXVzwbzwubNCdnSm0zJrmra2tjq6udeBlWXcjhp6kKkEJraLHr+j9pwv29383OPJeXUQiB6Zbp7t+w3mbWN8TQaMSXcGoDkOB3CZzbMPSvmkj6yDcgDJZ+7HS1kPpZ3zICKjHyI2DK6dURpQr5UlynR61xORBiCdXNXho5lPnTpVJ0+erBtuuKGqqi699NJ6xjOeUd/4jd9YV1xxRf3CL/xCT4GyOKwV3dF49zxKdMstt9Tv/d7v1Z/+6Z/WYx7zmC4jY35ZkXd3d+v666+vq666ql7ykpfUpz/96d7ziB5PnTpV3/3d3927l363trbqMY95zKHo1fzA6PhEwLzOykVE1DIkdsBE4xhuZyWYH4ANz25t+bSR8OmIfOYDjVJ28swEf0bffA9QwPG5P+jK1zpXVXf4loEE1/glVIzPQNNAAqdgB2IwYXot3/A3jeBwOOwtcVgWebaja+/QSMPcipbm5+cPRYcpAwa7PA9w4T6dmUknkoEIvHB62+M2GPdn/O/5cVEu99kBmVcGa55nPwMnbzkGsJkG02EAb7Bn/rhGwHIDLfRHoJWRvufQgCOdfoIOzwPP5bsWWOdz+mHuMovovg16ElBwDToIfx1seazWgVxyNE0GL2Q6PL4WUMhgKr/L7JV5Z9rMg1ZW9MG0cx5EuJF6dnGdDblTalUH621GiGmA7WwTAWb/TtNz73XXXVcnTpyot771rXXxxRd3juSP/uiPanNzs77zO7+zjh07Vvfcc09VHZyWNjs720XTFvzc5vXmN7+5Xvva13avBh4Oh3XixIl68pOffOhsByupBe3uu++ut7zlLfVt3/Zt9YIXvKD73BEajvSqq67qaPKpfhkFpRE0jzPV6MjR67GmE4VHyXmrHXQ5YjQ9VdWL6ljq4jPGiYMxeMRg+T0Ydu52ahhWGw1+A3I2Nze7Q8Y8JqfPW9E5/MGR28ExXuTP0YczDDzHRhgHacPH38gyUTG7QsgsbGxsdM+00WMcgCh45CwPvAfgerxcBy+TFjeniq2zLv7MiDlTvv4cXmaamhNKvaRjOaEv5HBSRGowZf1z1s5bjb1saUfdanbYzGMrI8C1+VnSYxuWWRoDAc+LdZz/rS925naIyGM6ddMA0DZgNMgwn2zToc+BjJcqzPsWfabFc+BxeUdZCwgD8LJfPzMBv1srk5EBCn3nb+vF2bZzHkR4QtiOmGvzFmqExujMkZcdp5XFQpT3V/Urtk3XNddcU0996lPrL/7iL3rnHCwvL9f6+npVVVf8ds8999TS0lJdffXVderUqV6R09VXX12zs7P1sY997NA+5Msvv7y3Xszugdx/bzppfP/BD36wvvzLv7yuu+66uvvuu7u+zUuMexYJWvkcKdN/Ko0NHcbBvKblzgOv1fp+qqmzQK/lTEmJ85ZOABGnUWamxsVKlgVH2Gl0vSOBZRbvbMhUZsqhn2G+GQDAA6f3nXHw0obn0jUCzOEkYw9/7NgyS2c9ML3cBw8tf46suS/BWS4jQTPNR0mbJi8PZb9peC0nzLtBmK8HpAMmDG7NQ0fFXOMlAa5x3yzNJIg2iGBevDXWdi0zA0l/C7ynLbD9S5lOEGPQm87NWQJnKNIB+ns3O2OPxRleaLTt4TmZPRyPx119iMdqeiyPflYCV+uGQXwCcdNomU/7knreAiEtnuV9VQdyZvCdc3k27ZwHETCJMwC8zu6og6iJ1mI4SpqKZpTsZZKqvgBVHV43veGGG+qrvuqr6hWveEW95S1vqY997GO1u7tbl156ab3whS+sO+64o2666aZ67GMfW5/61Kdqa2urfuAHfqDe/OY3180331ybm5t16aWX1otf/OK67bbb6qabbqqrrrrqkEKYnqrq+OFxp3GkUYj31re+ta6++uq6/PLL69577+2h4+FwWJdddlldeOGFvTVwlO3UqVNVdVBBziuL7fDYaucsQBq34XDYZRsyWuGZVtSqftbD/xuwMA7u50AWok4fPMWP084tAGFnZMAzNXXwjhR2eliRM9qk33Q2lq18DtezYwA+kxkYDofd+ja8w/BBd77UzDRA28bGRjcvzvBRj+HUuzM7ngvG6GczL156bAEJA1PoYg59mp+LNR3x2ZHaeFvHM4LOzIgdKAXP0MTnudzmvnh+/gB8kTtnzHLJhWZ5tI1LJz8p25Dy7DHQn5finJm1XvoZptPzmLz2s7k2Zadln0x3fm7dz1R/ZggYi/XLPsJ2xQAidRL5bAFG8wPfYV4kKPT1Dl5bICo/z/k2UDGvyb46C/pg2jkPIhCk5eXl3mFCLdQKGMjCuFSUqoMteVV9J4Xh9bayqsOpQgT3U5/6VL3xjW+sZzzjGfUd3/Edtba2Vnt7e7W0tFS33HJL/ciP/EidOHGipqam6s4776w/+IM/qOuuu65e/vKX19raWo3H+0WNt956a/3Yj/1YLS4u9iLMSaAAJ54ClYbJFeH33XdfvfWtb61v+ZZv6Yw8hm1lZaV+/ud/voloT548Wf/6X//ruvzyy3tvscQ47u7u9g7MMSDINCSRGKlqrmUeHaVlNEQ/jNnRsOekqrpsARkI5MAp8qx1MN/pz2CHBmjz8gU8wbAgR+aF5ScjIGc2DH75znI+Go162wR53whgxvPDer3pxjhm2p0xZ6bAemadIRr0kgbfZX8GpY6QHU15ntFlL5ukPGSxpbNnfM5vZ4VaEfhodPAWRc8T/QF+fR88bEXLjIG5BnTaGRgA2v7Qp5dTMvBJ+fA8mr/cyzzRoIMMiXlpIGcnmc7b8nEm52lQkvrA9a0iUIOW/N5A0n3AD8+dt3SaLvMrsyP+bfDK51mjwrNyPpEdA1z6zHszQPWcm37bVvjzUA6cGiR6O1faYDA4VlX3P+1pT6ulpaVaWVnpojADBJyBDZ8NAZPDfUyGBcCOiZapJNLMLlxzGn19fb1mZ2friiuuqKqqT3/60/WRj3ykrrzyynrYwx7WE/a1tbVaXl6uSy+9tEajUf3d3/1dffSjH61LLrmkLr744hoM9k+gnJubq/e///112WWXdcdfD4fD7n0a/H/8+PG6/fbb62//9m/riU98Ynco1Y033tgVX5oH8/Pz9alPfaruvvvuesxjHlOXXHJJ3XzzzYciIhqv8X7Sk57UjRcjyCE+OIvh8OAFOJMyDHagBhD+DjBm55PbUzM9bdo5NGp+fr67DmMyHO7vfMhxVh04BRcTOnIBkHKN18ir+lteucZG1c7WGZ+q6kXkyHIexuQdRzimnZ2d7nXq0O/MFTSzs8B0t+Tdbzr17ogECc4iOJNhwMT3PueD+STb4O3auWVufn6+kwtoyHSxZc3G2Trn/3m+Wz7T2y2hjWW17Msyjvz6JNisdWGu8l0HmRZna3uOi+e6kM8ZEPPDtPJ5nk46Ho97rxcAbJtHtrkJtJIftAQbea0dLePOyD4duyN2/6ZPb/9HJm13zIsWsIQ+7p3EY2yR9TmDTvSd/5nDVlDRAmGmM+vz3M/W1lb97M/+bN17772HeP35dnw8Hp+a9OU5DyK+6qu+qi688MLeS5S8pGHUye9UGqftAREIWqJXhAJjYOXh+xYyxfBjMKoOXr2McXHxD8AjAYxT45lKxSj7TAHG7CiNSBgj7ajRvGHcfJ+pWUAb0TxAIF+QlRmQjGoSRU+KWjifYTQadVkaxu3MkMGbFdPp86WlpUMGhmtdT8LnPJfdIDm/gArGTfTvJQrzzkY8ax0Yk/82/wB7rYwUYCoNFrxYX1+vzc3NHlBzCntra6s2Nja62pGUSWSC+cbwAhZ9XVX1+Mg4OBwMfvj8CJojXmp8HM0x1unp6Z7z9v2tlsY6azRynhKAMnbkAltD9swpboMU/80JtFUHL05iPD7PxKCIZzNvgC7oSHBlB5VFe9gUZzISkBNYQHvW0uRZKc78mg5HyzkvyA9yAe9Mh/lJSxtlPfWL4jLDAZ04f7/czDJQ1X5jqQEi/HXwZp+DPrUyCCmblr2cQ8tP9pf9ZKaGwHlnZ6de+cpXnjWIOOeXM9j2VtWvVrfzdXonJyINraOUSSlYGujVGQhXELsP+vFR3Ik4M/JbXl7ufUYBJmPNFC/30ayA8CMVK8EBzTtN7GTsuO0IMESsw2MUcI5OiXrsGAsyF3xHy9Q048rxpGOHHj6zg11eXq6qfhW/AWjr8KO9vb3O+VpJDUAY49bWVrdMkPUQGSVktsTy6OyYU9/c4yxEOjzPK0YNIDs3N9fRaGNJc2Go+eNnUagGf3hBmZdAUqb5jveYWP5xgpmKR46YC37TrzNdyIN12IDDAM4Zo4z63B800OjHQNgy2XIC6ZCwVwAG5oij3LNwk+8duHhu0q7Bn7RZHqtra+gToE39EuNnKdKA2LxuOUqe55Y60MqEWP6t42caCzbGtWvw2UELc+FlKQMOz7PtiMFM0pnynkugCVDNgxbA8vgzQPDfCWzMR9u8v492zoMI6iAs0JNAgh2FdwQwyT7S2RNl5Fl1cOhNSwkcHaQRcjTSUjwLsgXAipsGluuI5uw8s3mt233bUdIXimCauBaHYpBgJ5rPzm2brfE6KkT56IstdRgUKvPdD46WuWwZ0bm5ue4V6C54TACBs6VGJt85wdzxvwvitre3a2Njo6MDUMmYKMozGMv6m9Fo1DlZAwh4yC6f0WjUpfM9b2S7HNFZTpBNDkHa3d3tTvjc3t7uwBbPtNPGgfK5T4tFJmzoMgJjHl0t72g+afU4POfoXzpzyxfPRtcnnd1h+loG383G2fOSToaxG6gDvIkOXQexvr7eK3S1I0AmkSOfd2J6M8MCvS07VVXdXEOvsxPwjWU+5h2Q14rUE5CnDhr8ODuVc2AgbL4nX92gI+W19QzvrHD2wU4/AxTLofv3Z/CGuckMtelOwEtf7s/9+z7rovtNgILup919sO2cBxFOo1b1C1X43pNbVT3nYcbjELk/19MRDlo6dIQgt/9YKKoO702vOnzsdYKivb29LoI0ynVkQ982xplWdV2I1+sd8Sb4gjdEtPAhUTjXO3tggc7Iz9vV4JUNMzUGGSVNinBoCWSGw/0ah7m5uS597CyNix49DqL1NNQ2oN62yTo2WQjzIYGb0/c825GmDYOdkZ00zsg8MPjy2HwNc+iXX01PT9fa2lpXCIrjcKrZ2Shocjqa32Qy4C/jZT26BUjQB3QzC/ashxxPbBmlP19nnXatk+XE97WyMtYhgCXj4TvrAfd7zvjOp7q6PsWFldb7Fu12frYPlpvUe2cs0/laLtNm2QbSD/dYJ2jOGrQK+ZyVawEFz1u2VlDEPNjZe6wGVs4sGDg4iGzdR0uaPMdeQnbg0HqOgWrOi0GuZTD5kn7HvPdnBmwPJStxzoOIqv72qmR4KndVP3VNs6PlGgwiRZYW5JZQ83kWVyJYVvgWMsxnu/GZ05o2FhhBKz9rrEasOAMbbZ7taNBKQDQE8GINEaWtOkgT2ki25sJjsIGycXSBkx2Dt4dmn1YYz8309HQtLi52B0w5ik7wwI4WwIABS0aVVQfp6ASQbjbSBgK53JXGwGCl1R/8Yi6cIjcgZTwJ4uz4KZKjGDffO+IzGLjPvDNgyTnjGmoAqvpFyx4Tc5hZvIymvFUYvjJ/meo3OOBZrmNq2Qf+tpNy/5Y/02snYUc7Ho87AGGw6aUpxgJoNmAjM+XsEH2bNwbg5qHlz3+3bFArPW/eGHynbLulHmQzkKC/BF05Py368ncGTTzLGT0DCUf/dvqWfWdycu4Bx5a5zDj4GQY0tl0GgNmXA+Dsy5+1xp5/n007L0BE1eEDV9I42LCmsLkPJisLE6sOFKUVDTg7wITaeWdayvf6GabFRsSfQ4OFCGdAM0I3Grey5lhYV6RPvvdbFCkY5P7cFjg9vf8KdjshaMERuZKesTu1y2emGcdB8/dG9m7T09O1sLDQFd8BCuzs+A2wou4hDS5jNsjxboyq/la9lswYiABS7DA8p3YKCV68LNXKRNjx7O3tF3kChjPtakcJjURW3vJqWYK3Bgo2VLk27WI37vWY7Uxt8A1QM0Pm6NEg08+ZlMLFUft9CWmcPX8GTqYBufLz0ngjoyz75C6Z8XjcWw5jPC62ZDeIx52Biemi5VKPm21B6oMdUjqxzNK2wFi2DAgySs9roKFly20j0jYyDi9HO8vlII0gKJ9h++2sXwJdxpE+J39zT2a3/BzzNulBl8yfDKJs+1IOfO/ZtvMCRNjgVh1Obxow+HQ4X2/j5DRzOl5PelUfqToa2d3d7SrYLUROYfEM05EoN5VrOOzvRKg6fHqd+eLoCIOd17g5RYcRY4zUBphnRr5Oxy8uLvbOUGB5wny1Y07lIV3NdSwr0H+2RPjD4X7lNKc0tpZWGCfzbeBj0Ef2grE7o8H1BketKM8ZEEfURJdE5waJPM+Ay8aQ57GFOY2LD5fi2O3hcH9ph1oKeEwfbP/1WyEBAo6ac3cSsmJZJcNhw2c5mZ2drYWFhW5baa6/W3cSsPs5dvzW9XyLpeXWvHEU6LlPkIksevcX82IQkGCVDA/nhtiwe2mDNhwOuyUhy1Hq02Bw8BI527SkP1tL/5154X/z1te6LqIV9bvvzCy0gEOr2dHafjJXvob+U7+SnnTGBvV8n7Y9s4EGht4RaGCRwK5Fr2XOumH73wKk8BUdAWCnP6PZ7p5tO+dBhKNnIl0bND53NFN1eCtmGhoUFodtVFp1kGb1xLofr29Z0RxhtbIB9OXxOYXIdYwDY+KxMSY/twUy3BeG0grPNihH3SnYVkDT6qjQDs6ONwXfDgIeE6nxf6u1sjK8f8QZDEfdg8Gg94rcVsbH/IQGHKwNg+ljLhJ82uEQlbowzmO2YTHPTKufwd84TTsCrxMzpyyBMH7GSjYGneJ8CWiZmZmppaWl2tvbO7RN1PQ4dex9+KYLmZqbm+sO+/L4AHTQ4ogQHrt4jd+W51ZU68wZ8oXcW3/TEeVZKvDbW6fT8SGHU1NTtb6+3tu6yTWMk8+wN2yDZRxsLTYgBtz7YDnrUv6dsp1LYy394/p0wPAhM4St7IHBqvuxXFr/HVlzv+XeAUxem5mJBAup03bytgH0mVtGDSgdnJlfyX/6s16Yx7bTptP84FrTmmNPwJ1Bwtm2cx5EZGTvnRetScUQ5cQbQdox2iDQnJFwlbkLKhMY8CwLRDr0BBeMx/RxL5EKTt5GlpYKbSea33ntF0XBqWAo/BxHBjSMycbGRvc8G+vkn/nvBm8SbEyah+xjbm6u5ufnux0YyQPSy/AxIyLoxWhjbLwOb6Bq8Ir8GETY0biwj+WGjFoMNsg8IdM5nnwJmp1cK0Vs2SADkEDc4BYw4cK/ubm5WllZ6YEBG3b6dxGul23MZ+R4YWGhA6pu5kka0Zwv5Dudn3ngaBW6vPuHcXN/Rpcpf95pkzrl44aRG+84aW1p9pITcsQ8jUYHO5MYG8A9i7kTEPO5eZIF2bYvHpOXLtyvAZiDBvMavhnUuVnP07YkEKmqnp61HLXpnSQv9O3MqJ9tHfZ9zIsPmGrJgcGc5TbHw+etINZym8GoaTONDjaTj0tLS4d4/0DbOQ8iHP1ZKdMxeG+wnaH74V4bkzSoOGr377Vt9w8drfSaFTIVHsPgdKsFyVsw03jThx2hDz6xwYWelpFsveY4kS8CTC2A1x8dZaTx4J5UAo+VMWU60eNoHapETYYL11xIh1PGifJ/ZgTy2XbSaUxNLzK4t7d36NW/gE4+w6FBCzQga9QxAOicArdjQBbIHDkayToB98EcGORQDwFARa6RSR9S5uuYY8CGl9i8ZGPwmVEZWQkbQ+9IwJGan+l48nMb8IyEkRnus/O1s8+sVDovR5DczwFsu7sHr6sfDA623+L4naXyPVzP3/DesgYf6Q+etUCV6bcsoKuM0dkijzsBErLHb2yMAQH3Jx/TKZ7JFjtln47eY4MnjAG5Ntinb9tYwFfS5iDQz3Jf2JaWbbRddTM/nc2gYUOdicx58D3Ws0lA0M89m3begAhapvWrDoTRE2GhdOU5PzkxOK18k6SNuAXPAKI1oQkuqtoRQ6JZZxLSSbeK3fL47jMVWzkFbSWz0jI+xu4sBc8wv43Kaa3In9++Dr7ybPMB/ngc09P7J1EaABjYuZhufX29l+Yzb2zEMRKul0j+j0aj7owFzwdG3/NrgMAYmW8v9XinCxkJy6tl1VtlPUettfaky/xm+QI6Od4aEMP5KN6d436npqY6YEHfWa9jJ21gbqc3Pz/fyaAB9MbGRm95jaLGBBKWJWTFtRnJD4NnO0Jn5dwfy6O53dtOeWZmpjv9k4yYwdXe3l7HR/jmoAW9hRbvSkIXnHUynQaonmODBQMRg1IDb+/GScdGvwkOeLYDOtvUBCWev6p+1tEyZjm2DWzZl/wbnhvAVh32Dwk2TLuzNrbNBGn0l0t2mc1I2Uvwie1KH3Sm1vItWbuWwdiDaec8iHB0mMYki1f4zPcS4bWcVFU/8siaABTaEaUNS26P4lmJxKGllbUw/R5bOp9M47bQfWZSDFbglQ9cSpSLsONQbZBSmU13Rk3QYMPtiMUHS6WBZ3weP3zjCFpnOTyf3oFh2Un+uOFYARZWShth10mY99Bg4+w+nOYGpG5tbXWpcJYyDBzIOvD3+vp6R7vlJw+MspzlnPG/dwow/+wOmJub657tLFhr3FUHmQV4m8tBnp/WDhWn0V3QyHznLh/T0pKfXLu33lunfL/f20EDAKTxx+m6kNLbUKEXmwFtAK/kRdoHOzS/J6YViZofZwLw9O3frejZNst6nzYHmfFx+gYsLdBhgGL7kICjZWPOFJAk8GB+83l2/p57+vHyVj6DMfr+HJttkO1iy/m7P2fd7AMSyFkGPW6P86G0cx5EOBOAYUfpbTBttGgYIGciWlGNnbu3daHUbl4bY0JdHJaOtKpv0CzoRtwtQNASKo87hcf8MO8cwWAAUWJ+4wB8BC608zvpsEHPazPjwz1+tgufbET8PD7n7aZ5OBT9YnT91kqv0zsF6vSx35XBc10IBxBpHXqWMsrYqvqvA/e8Me9eFmi9gAqH6DSyzxDx2r8dlPlr+c23g+Kkq6pWV1erqrrlEooF4YfT8mmMbTBz6cMAPNPEm5ubPV1BP+0YOHEzgYz56mUJt9SXSY44Zc5Ay3VCPBM59BKGdc6RqeWOeXEGwnLXysz4uZavPLbdummHZDDj65NG8y4BhMeGPuEIfS3zaho8355bX+dmWtOm2JH7OtOawYrnGzvoz6w7zmRldg8eOCBp2cG83uNDnuAjepUAD/q8U8dgzc/l2lw2fLDtnAcRjg6dpiLlaAFora9zfUYjFhZPgo11ghaem9GghcUCVnW4kte0JFL15xmNG1V7bNmPaeA+lB9HC1ggLUnk5fV1K6KNicGNW16f39kIJtKuOlwt77kjUs4XMTE25o/1aAy1I33mm6wDa9nOMPC8lsLCN64ZDvvvFkkH6LkmtV/Vr8PhGu8Mge6UC+bATsDgyw7ehYT0yY+dFIWfyLnfLskLoFj2INOQc+yxA4Yw+CyVDAYHO2UAb/AE4ORIzZGiQTu8wyY4W5MyZ721nDkKZJmBz+FD7viBFp9J4p0Ylhlk2fNkIAUPvGWYcRhcWJ+Gw/62b+/SyQwmepRpd/62zmREzu90wv7cekcABR9atg/5bGVIfF1r/jweficoN22WcWcEmR/ohdet001NE80+w/1a95LPPI+xO1NpG9uyhwR0zoRZr7IGyueMnE0750HE6urqIcNbdbDOZ+NadRglOn2Ua6GOElwzkdGNDRrC4cJE7vd20VbL5Y80OkbViW65nuuc9rUAGnwwDgAE4IgTCxFUnxtAc+YkU4M2JqbNjr9liFogowUo/DfFaE5/GiQRwXkXAjzw4Vc4R4MJO1h46egtZcRpT0fRjhYNMnNLJs9wAaKdKPPn5bs0IsgXwJDMCzLEvM3NzdXGxkaP9xT1+ZRTG2X6BtgABEjH+8RIy6KzFPy2M7McDYfDDmw4U2HAng7EqV/zBr4i5wl2rb/pXFm+yWwmdOTcGkj5LbZZH5C0J7iGx66NgQa/MI5m2THv/b8dfzrxVkTfcqipk2cCEfkc5N7LqLSUL/OGZpuWAZGbawkyIEuQ7CAq5wY5ThpML33yXYIo/+Yegz+Da9dSWbYSACNLPuDMvIYO2/7k5dm0cx5E2LhWTd4+mA60qn1oka9xSs7fG9WmYzdAyEjYtKRjsjBbiFIZMX5Gq5MijUT4jjQAPFn974wDjtV8aY3D/9PSoDGeROeTgIPRvJUy54Zo2Sl1ruWsiIzMoAGDnxkWb+u0weGeVuW+HZmjRkc6Vf0tu/Apow/mA7BicMr4WHtmq6oBsOVjYWGhNw+jUf9cAYMvO7yMnuGDU640+tnb2+ve0GmZRhbNmzTm0OF7cMoG0emsAH6WIWd1LJdnAtmpizzTy3nMMc+DPwCoquplsrLg2DqdzhBe5DwC2AzUDQihGRlq8SttkOU6+WQakLEEthmcEEnbdlrP6Y/iXJqLGZMmeGDgiP617L3nhGfZBvHjACDrLtA1N/q27sEz5t+O3zbABZ0ZYPHM/Mz2OvvCViEv9k/piwzUH2o7b0CE/4eJrbQfzCZTYOFFYWhca6XHmDBJOBWMqA0lffjHhjILLVtRSTrpVFBaAhWj00TyNugYQAwyWQefIpgCCc0YDwxMa5zMBbRA28zMTO8gI/r1vnMDD88PykMVP8sUfE+EPD8/31uS4m9oN1DiXl66ZUPtMXjdkznhcyu4eeUahDSSjjxtrL3jwobMYIAMkmUjDbyNtPUBAFZVveUCgzQXD9q5QoufS2MJp+oAlDAflgNHTd5FYnBhJ5x6lHIOLS0donmbLM3zmnrr+eM+ZM1zv7Cw0MmOly8MOOjL55PYGTLX/Aa48FlGlY5u/b9BMvrNHPh5Bnl2rK3gJcGJwRT9ZUbD+mHd4/CmDNA8r9mHnbXBlb/3fFoGMojit/WOoMqHSCWfU878ucfB3wbjABbLg5f/7PzTPtAH2WFa2lrGYdps7x9KO+dBhJ2kDQZCa8dOQ4n9mVGrHTWTknURVjgLjCNTX28n4+e11h5zvc5CmkYwUbjTtDynqv3GTJzWYDDoigi3t7e74kQ7TxxLGlCitZbxMY35+YkTJ2p1dbVOnTrVGzcGynQS+VspAAnOlNgQ4FxT8WzcMY42Kt4tYZ46osJI5LhwyhjNNM5eJ87sUyvNi9N30aYjjgRFyB20I3POkiDf0O/MFvSye4XP01nk87e2tg7JM2DQ21O3t7e775gj66cDAIMlaHE0bBkEUFkXbIDTePt+g214yfx7jM4M0e94PO4V9HrZz/PH/COfeS5HZqJYUuJ+aM4lxQS0liWa331i/fE2X9/vQkLvCEqnm4DeMtHKAHlO+d7yS3/mmXkCvzzHBsR2wAah6Zh5rs/OyTo2g087YcunaWCuXdDqZ9mPeF4Mjty/M2Kj0aj3Ph/ohcYMQtOGHIGIB9gykncUaYPN5Hut1VF1ToYRoh0lzRFdpv3ps6pf/OX+MKg2NFYiC0COsYXCaTbIRsLwwgcJobjsZ2d9zsaa55rfLcSLUaw6ODPAUaiLUaljWF1d7c1bVX9t03x2epSjkquqNxb+BxiZdwZbOErPGcbF5yJUVc+ge/cG91YdHIdt0ONCT0csacBtsDHErsfwLgRHgRgvR5cYMmQc52cQxrjgW0b+zuKZbssTUb1lFfC3ubnZ7eBw9iGXQPwWVMbg7AjX2fFktGp5tJNPfYVeOyHTgly4X5+RMhr1lxUGg0FXM+EzQpx2h04DduaW5zpAGI/HvW2hHnOeE2FHxzy7sDVlNG2h5dIgkT7ML28nRe4Yk+nnWakbmR1D9vjO/Xo+Pb/wJ2XZc+xnm57MMPG/l9lSjhI0t7IEzuyhlwlYsBkeD7LE95PkEXmwHGYgwXUJFO1jqqqOHTtWZ9vOCxAB81IYzPT829saLWSZJrOyZeTo73xAkw0N3zuicX82xHxOM2DJPvneApiOFz44CjDSxcFsb2/X5uZmd6RuAhXz0wpu5+UxLS0t9SrvbcgxAHNzcx2ASLBgBWfszrA4uuXsAoOYFg/4jeOyfEAX/GH9kVMvnc5uOc6qg9eCO8NQ1X91N7y2g/Gplhhs0t5+6ZULRO0oeaYr+G3c+YxlG2cfvK7L/FCI6YiZZgfmz/if5/Ic5qnqIAMGQGM5C576TBIDJvpszRPjyyWD5L8BfUtfMtr2mPy/62RcV8SynJeb4A1zD8/J8tle0ZC5tEc+XyLngmZH5cAh6UVWrJct24fs5/Jaggnfb7vg7/MaroN+g7vM+iYQ8ec5lwkgmL+Wf0C+DXAyKGpleqAlmx16y+7Ab8Cd58oNmScIsb4ngDCYMpBws78723bOgwgbRaNHC7iZmA6m6mAi6cdCk4KUCsL1To/RZyvSaGU50hCnYzVYcUvj6fE6ArTB4MU+jmjX19drc3Pz0LgzGrDAmteOzJaWlppLQ17bpQYDh2VD1+KJ54D7Wyk9K2AaMWcRqg6cGZ+vra3V+vp6zxmkctoIOXvB32QTqJNJxXXamn68rACdPN+7RbzdzI4Vw4RcmjY+I5PhJRjG5joB1uuphQC0IF/O6LkPnmPjCxBwVsVvPeS6TPk7/W560+FZBv2SJNPluU/A4f4y7Zv1L9DpN5+yJRUAAY8MyjL9zLWACkfjZCByGdS0GWADBOCxx27+QQunjTLngHzLs2XLvCMT52LFtIPp4BNYWE/9uZduM3jws7JlBJ81OhnoMC4DOj5nTjymBCn0nZmJBAMpf54fzym2w/Ll+8lWpJ/gWfTvrE/6DNvrFvB5oO2cBxGJhs24jGZcLZsOOR2GBcTPshHkMzs/FNMpSwuRAYsFg88sfChVIuwENP47izv5caTrNytubGx0L8zyuPx3Rp4WWGhcWVnpjIyVaG9vrzv+dzAY1IkTJ7qzGch8QD/XJ0+YExwiB2LZwKZjd0GijbuXDVi+wYDDS2czTBNz6Xnj79nZ2drY2OjkgT6cZk5FdnQHbYPBwWl/jDHXop065To7aloCqvyb8dhhswRlcER0zLziwFiisgO2cd7a2updCy9d6OelPmpSoDMLj13H5IxQrsOnXpkfZ2qOTDOCtZGfn58/9FwclIshrXteojCQHo1Gtba21ls7pybCcoKsOLtnfc/MgMEetMNbxpYOyHbDwRn8dk2Lx2LgbUDcss38NsDJA6r8k06YlnpivUAOoMFZGAd7ztA6cOA+14dYRhPU+v+USzfrsmWR/6Ef4J08sT2G7waYjLsFpB9KO+dBRKvBSCYmkbWjpqp2vUELNVcdRrzZlxXJygOAyYjWYMXgp+pgGxG/DRo8joy6W2OpOjhxEOEjAnd6zRmHjAIMvqB9evrgfRVra2tdoRnj2t7e7k48XFxcrIWFhaqquv/++3vr3uYhLR0TWZScEzspxmW0bmNK8WjuqmEsGWXYuFYdnJJYdeDUAKtO1SfQgD4XeqYc4ERtkIickLvZ2dnOAAP+GIPrOTCC/p4+/Wx/ZtlDhgFsGHPLNGNNB8kccJ+Nt52CsyepiykPKQtuRG3OHFj+cYytLAS/0Sc7Fvqmf7IRgAp2Y9AcSDhTgJ76tM7B4GDZzDQBaB0AOBPngs8EyAYeaVMs06PRqNsu6OyG7VY6foMyb3P2PEKDwTrz60xk2pqqg+UznCj92alnwJSykSDI2YdW0OjXqqf8PZAMS4LV9BkGElknkrJtx+9dSSnLafcdDFhmeMZDyUDQznkQ0XJynqjRaNTbVuTPLTDpHI2mrVAtJOoJz9S7FS2NmgXHEY2jzBQEoh3apNSbeYLB5+QyeEHRTmY0EinT0ujOzMzU0tJSTU9P1+nTp7t3Pjgbc/r06Y7G48eP1/T0dK2trdX999/fmwvT33LkXoaxkTEPcz6N2Le2tjoa+Y7n5rU5dhyyQQk8ciEa7+5A8XPnBwcYuT7Cz3B06x0MZAoMJpEBTnfE8Kyvr1dVf6slDsLnfmBELWc2fOYnwIlozvqVdQoJ6BKgmP6sD4LWNOSWCZp1C/rhnf83D5gXrvPSQTpEZxepVfDccay15w/b4Od4DIAAxrG9vd3bpssul7RT6DDOGFBjsNkCoL7fwY0zCOg/8unI1xkvO7Cqg7R8FtiSZWSs8MuZKM+Fgyvz0rLSagZJ7j/teAZu8MGgIW109mugm3Y7gUTaItPK8yxjeZ/HYBlojYnPbDtagR96erbtnAcROKOq/gS5tUBA1cEEZdqUezwxCE6mTR2JIawWMj7PLWNW7Bad/izXSVFM023j636npvZ3SSwsLPScF1X0aTwngZHk62AwqMXFxa6afmFhoSsGpE8vESwtLXVbMu+7774u/Z70mg8GQK0jjB09ch+GlHlYW1urzc3NLsJzNOGxITtePkknBY2OAm1cAHh2Yk6Lc50jSDsMz6Odswv2ssAOwFF18CKw1dXVGo32088ucLUBMigxsHVWys1Ay8+2/kCbHRB8cMbGOkTdRepuRoAG1hkVZ4RoOiwfnkfPn2UtHRh/80K08Xh8KAOBTCR4ACgxJnRlPB53GTEaUXECW/jCMhPAlJoMAwh4btpZPkIWnTWiWNfRP+DVc4rd9FxwbavOhmuQvwR/CfYMXLi3VedhgGSgaUeL7AFQ/BwDigwsDRoNRLCzDvw8ltSVDP4MKjyulG2uzaVUX+NmO4VM+Te0GJyfbft7BxGDweDfVtW/iY8/Oh6Pv/Tz389X1S9V1T+pqrmq+uOq+hfj8fgO9fHIqvqvVfWPqmq1qn6zqn5iPB7v1oNsubxAMwKzEU5hdCqs9b8nws+wcLplhIyg2VnTl2m1Ak3KDJg+K5cdnPvFUJAdAED43HUUJceczUI8HA470MD4MkrFiPD/sWPHajwe18mTJ3vFaC1gRsOZGkXnXBrc4YSnpqZqY2Oj1tfXu8ic/tKZWka8e8FGOKP1TI+moXO06Gug37y30QcQeIuq19gNUqsOjmZGFr3Uwps9mX/vlEhAgFHH2WRWgM8z9ez+XKRK314esjNvgcYEsCnrpt/GnmfYWbuhVzn/btZPL/3g7Pf29mppaakHwK13jMtbgVtymiCDbcRbW1sdoLdsQLvP07BTsk3JF+fxPaDDcmddSr5w3WAwOPRSOeuns0neKux5g1+Z9clne6nNICCf7flJm4q8JaBMEG6+Iete0kz58BJZ6nlmSUxT/m9bYl7ahqX+eL6SPmctWjUjLfrOtv1DZSI+VFXP1/92/r9SVd9YVd9aVfdX1X+qqtdX1TOrqgaDwVRV/WFVfa6qnlFVl1fVb1XVTlX9q4dKWCsV5ijJqLWqXWxlA5GRop1eVX/rmvtGcTCe6TA8ya3Iz7Tn+JIeO0VHs8PhsDuGGAHl+X6xVAvp0pyGxJgNBoNubdgGz0YGB0jURBaCbZ1uqXzQhIPc2dnpHGTOL3wm7U/WgXMv3H9GIFkAlwVhAKPNzc0eyGOMrfNBTGM64ZxHO0L6drGp6eZ71z0w//yPATLAtbOfn5+vhYWFXjSKgfVbPOH5eHxQRJl6grzbwZHK9nKb5QLacE7pCE1vgowWDelMM6VtG8Az7GTsaA0A8xksDa2vr3cyZeeys7PTFdUmj8igmTbGz+moFNExHmcYveQD+E999bKUZTgPWrO+VPUDMOjlO2SqlXmyncsdNJZdg6bMJuTcQgMyjizZTmWmwLQaoHpufRqqn0lzzULKBv1h+xxceOwOLpy9MjDzWD0WA4WWX7Jeu3npzP35Oxcwt3zMA23/UCBidzwefy4/HAwGx6vqe6vq28fj8Z99/rOXVdXfDgaDrx6Px39dVf+fqvqyqnr+57MTNw4Gg5+uql8YDAb/djweb2e/Z2pmbqYnnSmYxEh/lo6bScK4ICBpbPy/EStKTBoTIXWq1dFzixYbYUeiXtaw4hiNOwWLcbfA2jg7wrATNd8MgtbW1npveeQESejk5U6DwcEZGvfdd9+h9Nuk6JTI09G855fIg77H43Gtra114CENeiqhwR9jyGiKewBAKT/uA6DqjAa8xBDZ4NI3YAAHYICUqU36dD0B8+pjpW3gmXvS33Nzcx0wcNTbWtIzLalLHgNvr8ShIvcYMYNmnuXnO2Xuvl0InJEsNDFn9Md3gCs7POj17qRMdbtBF4bcxbi+j8OmfCiQ5xY9qaruWnYFVfWP3LZTsnPMiJ15oKXtc/YyAw5nJZjf8XjcO3mUXVALCws9Hjq7ZN4DIk0PdEOPx2l9g/cJCumHJQX6M3893/5JO5fBlvlEM4DwfdhZt6SZezMwaemM7anpQX+sM4wvAxHrjwOYDHast2fb/qFAxGMGg8FtVbVZVX9V+0sRn6mqp1bVTFX9P1w4Ho8/MhgMPlNV11XVX3/+9wfHWt6o/SWP/1pVX15V72s9cDAYzNX+8ght5fP9830vcuP/VorU6U1+e3ItdFX98+ltvHIZwNcYFLjgJVN1rQjcDowxorQoFGM1uuc3QkgERWEYKD+dhXng6M1ritABDU7dgvgxPkQQdsLj8bhbWkh6k5alpaWqOji10JG6oyacoY1yppPhm9OCpI69pdLRYmvJgefj1DJLNRwOu0I53obpqNxLEjRvA4W3PCMdAPxyhoexra2t1crKSs9ge703DSynSVZVV9PDUgpyAC0+tdNO2zzzXFhmPWaDbANtdMgAF5nOt5y6b6eGkSNnyjIazCDCESCy4CWbqn2HDwDwq72tC8i+d1RAE7QDILe3t7st1WQ0zG/otA2A1/7cvDXwQHYdDfPbtSrWDcuvl3IYK+dLsNyVWVj64z7TavuK3cnMIfdY7+Af9xGJm78pDwajtsWtAIw+/CzPbS5XJvg339JnZHOmA3lJG588dHaXvqHJ/UED4+acFz43jeyKO5v2DwEi3lVV311VH639pYh/U1V/ORgMHl9VD6uq7fF4fDLuuePz39Xnf9/R+L50Tav9RB2uxWg65HSu/I+x9gTaqGMYLPx21hntWBhaqVRosICYrqqDSJg+oSORq50GzQaE57IEgEOgoBBHYQdLHzamqaTOEBiZJ8oejUZ16tSpQ2ccOJKjmNLzYzqGw2GXzXAKz0VP4/G4F3WTDrbTtoHNbE0CSz/D8mC+UJmfQIY5S2DAPV6PpqUhASDZINnwZWSThxtNTx+8gtupS2+XswHa29vfmgiQgO+eK+jII6h5Jvx3LQC8gOcYNWdgnHmDNmf0WsB5kmHP66DN8uTrU+edOXIE7cb4DcI9N36G5Z57vbxDLdLa2lqXqYFWZDTtC5kPZzodxDBO14P4O+stf9sOpb3z4VcODlhiQzfpG6dP32w5TmedDtky4IDGtBlwZMCWjtJyRXOmhaUZwKJ1zwGeQUIGpClb2ZzRSUBhgOeGfMNn/oa3APXUBQfC5j+ykM07x86m/b2DiPF4fL3+/cBgMHhXVX26ql5aVRvtu/5e2s9V1S/r/5WqutXK0YquMxWVzisdFNe4tQxTRk20TItnQRROhfRyKhtClDSB+hmTD2sZDA5eCY1yVFV3lHUCJUcoaWjpL1O70DTJ4fFMO033R1TpKDpBCwWg3hrp6DizBeyz55kYbHiOM4N/o9Gotx7v67yO6IxVRsKOnL1+7/vo24a/qjqH76jchhin5jMW/EZRO3g7DQyleTMYDLp7XZzqLJKXg+zoKcaE/xh0ZMY6hzPY2trqnsO18N9ziNw7guJvaDT/LR+ZfchougVMW7qMfDlzhy54y67lzBkeyzWG36l1AzPAA3UTgMB0WvAt6xi8jFpVXUbEcuntpnaCbrYx0GZ5cn/OiDEfu7u7tb6+3i2deR6tF5ubmzU3N9dbfjD4hv9sTc6sHs0BleXNW5Qd2FkOnOavOrCd/O2zciwzzIlBPuM30EFXfb1tpufAACXng+dY31PemY+q/hk19OHMtsGjZb+VIXkw7R98i+d4PD45GAw+VlWPrqo/qarZwWBwIrIRl9V+IWV9/vdXRTeX6btJz9mqqi3+T+fnFB0C4u2GnnC+z8g/hcLXuo4hQYONAcJnVMkznLIdj8ddcaKL5KyULYPI+Oy0DRxwCBg/npfpUkc+k8bjqMeOxtERkcXMzEzvICk70DRqBl4oAZGblxKsDKaxqrq3jQJOXECV1zqqshM2iLDj4294ihwhA1nzkFkpZI/r7GDSaE5NTXVAL0EHz7T8eA9+RvHONDEnuTRDNsipaMsHyxIJkmiAC2ch4CHP9gubyEhAX0bEXvrynDNHllvrCHKBXH6hZt0yz9gCi3Pe3d3t6CUKdwAA7XZENF4Kl/QZHNpueYkBQOcjvFkj97Phn+tauDZ55Ll3cAPf3YbD/ou2AKGO3MliAYwo2uZ6p9qdGchMoOfES3meF3jrABB77OsSVHtJhHtMG9cAlEyPCxENfAwgTLefY3tmG8r1pt2BBjxNEAg92A9skME313rZMIOtzBqfTfsHBxGDwWC5qh5VVf+zqm6o/V0Wz6uq3//894+tqkfWfu1Eff73Tw4Gg0vH4/Gdn//s66rqVFV9+GxosDCksvF3Cim/E83xuYFFK1XIpDt6ctrYKNjPoi+iaAxIIndf7+jfRtPOy8riPec5phRW0+Tnm3fwC6N37NixnhIk6LAT5DqnX5Pns7OzXR1Bpi29dRRj6Jdo5XzZSWfUamDAfGG43b8jtVx7hCZ+ZwTFHM3Pz9f8/HznOJxetWzYMJwperDMWgZsbA12cH7IKM9i3MPhsDY2Nmo4HNbi4uKhrAuGH0PmaM5V+4Aqy+NodLDd1LtJiGyhI2UwgacjK+tj8id5Zked2TZfx2/zxjtdAKnOLDL+lAm/IM0OwzoHn2nOQgFm0JXULesLfLD+AJLN3yzGS/54DkajUQfmDJYGg0FvKY92+vTprv6H82Loi+Uyn1GSz0/blTR6/pCBfPOn5x/d4lqDuZYcJPiwnW09w/fDV2fYEmzYbhpEOSjgGc9//vPrtttuq/e+972Hiqk9h9jCSUDQY/DytYPps2n/EOdE/GJVvbn2lzCuqKp/V1V7VfWa8Xh8/2Aw+P9V1S8PBoN7ax8Y/Meq+qvx/s6Mqqq31j5Y+J+DweAVtV8H8R+q6j9/PtvwoJpBgyfSLR2YhToFIBXNEZQBgtfQQYsGD1ZQP8d0W9lpFuQ0pvRjwIGRwrBkUWO2NAYtxJw8yMjefLEjNZjw/dvb27WysnKobgTjyx731pyhAAYQXp+u6r/UJp2TFYnnGZyZ53Yq5ncaY88x16DcXGtnj4Hb2NjodkoYoNhBMJfpsAyCUz4SMMIDGxAcnPtAdlZXVzujD48s01xvEJXLFqS/7SAAbfCb5RVoNP+4ziDSekm/qau5zsznrVqY1jUzMzMd4BoMBl1k7betOsKDZmeE4HM6bJYA2KmUjtMAhUjfoNC6jmw44wBfshFEQI8Bje9JGeMZ5q13o1jmoJtDs3ykPXPL8h1zy7j57T79fMZueq3XBl9cl04fmaS17JRlwjT4e/dBjQo6zrwzZmf2nCFyvwYtGxsbNT8/X895znPqNa95TZ06dapOnDhxyH7u7e0X9hIoeC6dgbcM5Vgn+YMH0v4hMhGPqKrXVNVFVXVXVb2jqr56PB7f9fnvf6SqRrWfiegOm+Lm8Xi8NxgMvqn2d2P8VVWt1f5hU//6bIjxgUZp6O3AEUw70XTwjlSrDpwEIIK/7Si9tGAhaYEbvrdzwiCn83W0ZlRr0MCYMSx5rLEVw8Jrh6l56dGQUT6f4cxz/bqq/zY6GzpSxLOzs93bQlFAOy/WhBk34+K5fNeiyzLgJQrz3wrvqDkVL40H/GZ8PonTBpV7HRViaIl2ne5EPoiyPKc5LwYDHODjiMdRvOtlnCXwnFF1j9He29t/L8Te3n69DkCHTIN3pVgmcbzQlstmg8HB+QbIuqM6zyM0cm8ub6TMeqnF88H/yHo6nNR5/mf7K2v/lhXT7L8NIKCbDAZLbknDpKyUT1RtjdlOH3CagZBtGHNHkW/qpUFzFmemE7QNM9/5bm1trba2tmp5ebm3o4K+kAuPzc+zHKQuugEOnPFJGbB8WCacZbSeOJjIPtxGo1G3Lddjt1/xkh28tZ2CZubFwAsaPD8en+0GIJVrPK98j29EP8+2/UMUVv6TL/D9ZlX94Od/Jl3z6ar6hr8Penh9c1W/iKcVmXkC7CzTgfpeF+TkMgPPdMso34phI+JIIZckuNd9ew3ZAIP+JmUzGIMV19FzRqe+N8EPY1hfX+94tba2VsPhsJaXlw9FyAAenD/LIDicxcXFzuBhyHiZl8fp+g5HgqYb2ng/iLMLPqTHNHqOMjLBSVNDwP9U2nuHAnTBK9bSp6amuqUaeEG0UFU9h+mMiaMLxukIyGPe3d2t+fn53rz6CGTmN3cgmA/w2LsG6Be58Ror8ri9vd05XnjpyKt1tDBtUjbMc+OTEBOAt0CsgZnrgSyPpsNnHrDcYtkzf9zQV4/X8rW3t9fbbgzdBvk83/xsLXXksulgMOiiUuYJOpDRjPDNdy+hpUNyQ54z+4GMWm8c9JBt8mm2DlzsJPkcWi1j1inrJbINmM6gyjKVYN208Hx4fCag6J1OllWDD2fhclcFPGZeAYyj0ajb/s7YnK1zRoY5NV89DviQtVLIWCtj90DbOf/ujKrDqSgLqic9jVUL8We2AoPk1G3V4fXEjPTpq6qP5hPA2DHm9/SRac6qPqpOBUpUmkKXIMQGogWCHHmAcNfX1zsF8H08I4/qXV9fr2PHjvWi1uFweKhincwEaeWNjY1eQaRBiOc+jb15CyIn0rUzbsmGeVt1kB7mmdBmecGp2kDz/HwpEc4amWI92dElhpJrDBTdL7S6xgaDTPSP3IxGo95bUFvLXjgoeAQow3nQtwGxt8DyHVGwi8Pgrd81Y0PoCNqyy+9WlMa9eX3aAfhtMGyQhhOgyC2jOoPAll4bdAIgvGMnHZYzNlmnxPO8k4m5soxY1hxMoLPMObJn506fFNAaKHAtNMA3ZzzRA8bnOhFqbaCBfrN+xHPpZ1mnMrDydRQvp+2BF75vEtBwc1Bnp9tazrRNNY8ta9YF6IPftASo8CiLrLOfDEot3/QLjebR2bTzAkQ4GqHZ0VT1nYQ/9/3ZB4qB43Hk4mI/BCgdk9FnKpoNBul+O3MjdBsx6MUgZbrRfzsCyygtnXECDDuvzGwQaU1NTdXKykpntAwgvGVwamqqVldXa2FhoTY3N7vIleOpPQ+884HdGjZSpscKCrJ3w4gnoHK9goGT+U1zlEiqeTweH4oKs5gOQ83rls1LQIN3k9hAcY2BD/yam5s75BhIQ+PM7RwtH44ss2E0vdPCUfPi4mIv25B1QciyQYaddX6Wz2WOvGPEUbp10xFeK9tAc5Ygga6XXZBXQA8GF/n1UhdyaHqcbdjZ2emOXfczrTvQ5Wg+dcs8TUfnZ1peGJvrbNL5kf306aXIVFX1jj6Hb8yrd2HAb+9QyftWV1e7LBVn19AMrhgDfW5vb3fA1aDI9tL32HG2girb4KqDA96S7858mJcuKM7spefY8gef+SwzL5bhdO4OFvi/BTxNg/WI/rxs6Kz32bTzAkSg3Gm4zNx0qOlcaBY2R+tZiVt1oAytfuw4qtrHwGJQuC6fWdUvaLMRttOHB5nFoGW0YgF0ms7XuW+DChtGHD7RLX3Mzc112xYzesTAujgpswmgZ6Ji+gZoWQFRkDQe0Ex2IA9hccYJB+K5cxYFAMM7PBx12snYIeIo6A+Di2N3RgSjYT5CG31ijBkXUaQBkWkHWHmN1tmVPDsii3WJVFdXV3v6NDs72y3xuJYH2WXMu7u7vSUU646dhyNfz7UBUDpT/s+x2TFYl6zzOcfwAZmkL/OL4kIfsZ5Zr52dnVpfX+/k2+O1UwIgMw5aOtRs6SD528V+ACt0zktfDkC81AavoNnv8gAw5Uu0oMFnv7gfvl9bW+tkenFxsWfDcv6gDV1EJiwnbgZL09PTzWVZZyLcDGAy02GA5qUhz43BRH5Hn7bVrbnNwKuqf2qrx+wsJ3LtHVKtAIE5sTycbTvnQURmGJziSYfM9a3MhQ2skacNXzpj+kujYuNXdZAVyUia72g2sImWszAqFcTKkBmYpI/PjWztnG3YE3QxdgzT6upqF60YcC0uLnap4eFw2It+UE5fz9ihHQOVjRSqHbDnAlqJUBwFOCLGGfIdjd0TRKfr6+udA8GBE7VnNJXRvBtgyzUiOGY7GZwpyx5e+66qXn0FGQ/z3vx0BOI5repvTbbxyQgdpzMYDGp5efmQPjFvBtn59sfBYNC9xMxLBYwti5kzgrMeeE5thP0982RZ4Bp/5+xgq17BOgYQhK/IAXO+tbXVvYTLO1IMCL3fH3os54wdQGBw5egUO5fRZS53obP0z7xAH892PQZyT9qd73lmbrm2LCSAGY1G3VIlOzWgL2XH8+8MCADJyzTWV2i3reJvgy7bwbR1dtyuvXLGyfRlsw33s9L2mGYHcJYHF7naPiQwoR8/K3nlloeDPZh2zoOIqv6kZUTeikbyt9diW07UAmcjaLDC/aYnoykLtH9XHTgvmp1sRlBOY7WalT6vg36DgUl8SRozowPdp0+frqWlpd5rqfnfhgLjnVGEIxP6xJjZIBFdExk68vcYDYxwsjgTF13Ozs52lfTMn40dn2FUHdE5SvEylf82/2zs0lBBG+MlFW1ZqDooKiPFagOMAffSGstKme0x/7y2nGv83ra4vb1da2trnbGiLyJaeAvwMQgwz2wwUzZz6cWG2Pe19CIzfl6jtgMxSEImnWa2LDIHODQ7MIw1YNbZmQTxdghpxN1nXgutlhX+NxByzQP9s4vAjpB7cjsqmQZ4RHbGsgUNODkyEyzLGSxZJ0ej/WUebAS6hcO083dGxCl8g3dnRZ3JRNadlUyQQPNc04dBdy4DWkZbwHWSrcwA0b4krzFI8HJP7vyDNgMFy4blPWskzradFyCi6jDS5DN/h6BkKssZCAOI1v38735oKeA8g+aMRKsZOFiITWsicY+f1koXu7Ucrq+xgTatLcMO/3gnwMLCQue0PQ4E2iljGwboRIFAzii5ox9HjHYIjq54ZlX/wC+MF7RgUKywVk7u59kuxEOp7VD8MiFoI8qFL472GB81EFZ8R3YsX3i7ZK75cz2G2iALfjBWsiH04Vet24nbkbgozC/cYp6d5YAeg51WdikBpI0ntCaQSLlm/NaLVrbCMmswxHUsl5F98vOtU8w7P5lZ8tkmjtatUw4u7PRyqcvBQCvSZi7sWLyO77V5zwUZBme/6N+7e5xV85KI64BaTtov4iKbRZ+Mz6CauTBfvPRn0GM7D//ch7O2lgXLBnNoIDHpfB3zOuWAzzIr07KTCXwdUOU9/Lbtgne2LdyXz8tM1VFNxANsiej5OyfOBggBcETIxDklbpBSdfgsARs7FNOpVRsCrreB41o74AQuifSzWVh9v8fnPea+x7xK1Jx9Qj/CyjM4D8GRinnjwi0AhJ24jZYjMCuAlZ7rHIFwjfnPXNpQWumdZjXP/EIheOU0+XA47Go3XMXuuhoyJ454Pf9+bTZvjYR+lj9wylUHUWMCFu6hSJdtn6ylYlRcLOwx00eutTqD4XM2MLgcJpQRHXRZx/I6y3+mZlt6ZXpSPi0jmcFyY3x2jCk/WT8Djxwl8rmBlZdAcNbca5vifnH8jjJdj2NgxxJW1eEDkwzGeF4rUrYselzWE4NYeExmAhljqTIzBAYaWbMCaGEe+Z9xGDTYiVr2uD9lAZnE7mRRte245cvpf/dpm9myx2kHW4HXJDtuv8DnX/EVX1GPeMQjuuAJGt74xjfWPffc05tv+zcDNfhnPUQWfMLvg23nBYiwMaEhDDDcaJx7nOamOQJxRJagxI6XtxW2HK2blczG1kJAVGGjZqGyECFAjpTo28LUykQkMrXyeJz83TLsacitzE7HU2PgKNqgwUY7QYyNpJsNvo2DjRiRu3njfkiBOrIkLV11cHaFl0Cq9mWEZRDmwBECz+MkznSCyCHzSOrZIMPzZsdlA8s9ZDlcG0GmxLx1tT4083pq+s7KefpjycJgFrr5zHKczt5padLv1i/3C20GoWmo/bk/Yz4sf77eIBXnYT2y3mX2CV44je/595i5n+fQl+twADC5JJAOELqhzVuGU/es1/Rn0OCI3QXQGeXaThGlOzCwI4dfziq2fjvz54JI5ALZBpgmmHOtj3XEuo/sG0h53uwPsrDZQP8LRfoJfi3H0JJBom0s49je3q477rijHv7wh9fDH/7w3ni3t7frU5/6VB07dqwHDKAdubDM+AVuVQdAMHevPZh2XoAInG5LeDMlSnM0acEhWrOTtPO3IBhIOFpMY2ihQch8v4U6HTf3eM0yjSbNRjWRrhWAa23sUxnzvgQZ8N2I2DRZoShiBClDFwpv422QhIGxwTK4yHXhqoMo0uvVNgBOYTqNy/5+b5PEwLDd1AaTJYp0TDyfSI1TIEejg/dJQAt8WF9f7/HOspw8Yy5sEG1sMb6OVphH3iDqNW6K/TLb4+zZaHTwlk47dvjs954YkPO9ZcXvmMjo18stlskMEjyXjiBbRp7fuTXTcmQn6PoY66uzSwaeVdU7SyP5QP+cXulnuZlvLuDkOf7N1mF0ILMbPBfdyECIjBc1WJYhZ1EzciXbho3MJbDMaHgpJR0goB3gwq4QLwenbsP/DK48bo+nlZ3KuTbQSN61InfbRfsEyyH9ZH/Z53g8rtOnT9eP//iP94qRLccGdV6etT1GHt0/96YNPJtsxDkPItbW1jp0bVBgAJGMs5LYUFcdPhEsMw8WiIyEUBIXbzmqyagpgQ19+HMvqeTz8/80ni0h9rMNpCyY3OO/EyygdOZNC6jxplIbFK4jes4DYzwGR+EorZ+HElkGnDbNe1GswWDQLUNQXY8swJOq6mjza7VB9ltb+696wUElqMTAJ9iyQ6rajwhziylz4jF4zPkWSWTEURDXe84MRp0VwoD7OVUHBovvyHqQfXP2xid50r+zEDa21p9W5oMxtaJ75hHZM+j3c9PJGATBb8uF5yZlkXlxnwmqDCrhiwGEAZWjY4Naz7W3btoxOSihTwN/+vJSk+2hlzxco+QsSWYY6McZEPoCFKYjtC5OT0/3tr9mEGHQjNwBwh0YpM1wkJNzn4XFtmPmWcvWTbL/lsHMNMO7tPG21QZADjbn5+d7AA1+LCws9ABCLhfZh6FrBEXYKevR2bRzHkSkkWUSW9kHvq+qQ8bJSmYDntdlpiAdrIV4UhbAn7mvquqclSPN7MsKPqlvC7oVZRKwsANuKU0rE+HnJx0Ydw5WSuNb1T/TIB1iggEXXJlnNiSkhbe3t3uZj8Fg0EsZw2dAhCNSZwlYXrIzX1hY6IBm1UHGA0VmWxrRObx0JTo0cVAV2QAMtLMyLvb0mrGLLj3nPMtbF5lDDJSjND7zq4b9SmgcJUWHXlslg9F6Xr4oiOZ0OOP1vBv0Wz4NQOGhm0Gq5YRxMAbmDNrshKGB++yUaZmJM9jySZwp1waggAfzwfLrMbqOxEDLoMd08mz0IGtgvC3Yy3HQ4KJM8xX5n5qa6mUP0Rv4m9lDnJrPfrAeeEt0LvnAj4WFhY5+sijQbxmzjbHumFfw17se0nYZjNqWWrbSnnKfwQf2qQVgWkEefTqga2XOuN463MqmjUYHB9C1lvceaDvnQYQFAOba4bkQLoEAjEexHOXkhNM8uV5qINLx5CZtKCxC1XKCOC+nkzOrYcTpCMUgiOcnWGmNycjb15m2BA+OaDKNSfNWSfp1ZI4BbRnwXK82H2xwiIDYj+6xecssjSIjH2TlTELOCYVO1A3kdlDLlh2Kd0pgwIg2GA88JLOQWRloo1+yOsnnrFXg5MTcHkYqmCjNxp0iTnjnaLkVTfEcrz8PBgfnQcBPZ2EcEecSoZ0kPGqBYXTTRpcxejmQz7iXY7mRt8xGMoaMPg3WMvpnTPDCINVAkPG3Uuz8dlBgO+biRGcITGf2yVxXHZwNYdoBjsgQINKZCIObzGpl1tJyg4w5G2bbwBicicHRedcIespY0R2ygq6z8nzbjnkpkKJvPrO+WudbvLUsQ5+bbXD2a5rcr+0af9sOp923vsBjg5PxeNxlUy1zzo6fbTvnQQRt0qS0ImQbRaIugwJapi/dT6ZkHQW5cDCf3aK5qr+Vx5FHKmIqip9rQckIsOWQ85oETklviy+M04rJj50iQM2OzVG8n4tDqjqIaO2gibJY08yCKZwGRsAgzW8Cpd9Uep4DrzC4djR+B4GzBnYwjqpI5+LMElD5NEyMuOXIPOfZfpYNB3z1VsOUeztq77QAXJFVccGbaycyRbq1tdVb5uAZlhEbR8ZnuQdMGcBYl6v6RwpbFrN/R/yeP+skkXhmHDIgmZR9I+pztiHnwjrmpQCu8ZICvLJDxmF4Xlp2yXJge2OAbztn8D41NdVl2OxwLcfQDe8d0WOv0EWDSDJXdsoG3wZ0Bi2Mm7GPx+MutQ890OuUvTM5/A8tlp1WQOU597w5G2oAhC2zHpg+f5cyljJr25nPruoHL54bZ238Sns/20uvZ9POeRBh54ig2Cm4Gf3aOVT1DUgrAuM7C0xVX8EynWrH7+uNDFGeNMoW9klOz3Ql7fk8C6fpTIFv0eztgPxvPieAIAXplCaGwUY/Eb15TLaF/kajgy2kPMeRp4/wtcPBURmRG/jxGyXFONjQZ/oX57G4uNgzvDzf1fjw11EUNBjA2uA7KoMPWak+NTXV1SIABLy/H73wmrvBmZ0wNJJud+RoJ4xRJqVteWQcU1NTvRqVqgMDaIBk+XLE2NJZrscJIIvOwNg4J03wFz2DRjtXR+ypJ8i3f/zuE4MkDDz/W5fsOFtBAvOV43Af8AtaGSd9+Fr6txxzHzqF/dna2qr5+fmam5vrgVTLpMcAqLTDy0wD+sAzuT+ddNUBwBqPxx3QhtdeYsX2oG9Ja8v+Gpw4Q9T6YZy2TQkaq/rBZ9LmpdT0F24JeM1HX8PcunjfOk8Qgt7xQj/3YZv2YNs5DyKq+o6VtE86fAsuRgEhsNHMNfh8jpvvzygEQ2sBdfYCwXfhT7ZWNuFMPGjRmEbWtEOn03DmB8bAzqzq8FbE5K8Pv/FuASNtG30rk5UER8muCa/X+llZI0DE7PRfa3zsoHC1OM81v1wJD22sgRuY5vIIBhoHaaDrZYsEj1XVMwT0D++rqnecMCDBToQokOUYg0Z2adjBQuPU1FS3o8TLRp4z10wY8NjRWA4dRRkUcK8Bc26npR/L/qSo0/NsOUcWvV4PIMxrMc7JcwCCd/1kAGHw6oJUG2/TYKfgXVzOongM+X4I042MZv2G//fSUgIPaMPhordTU1O9KBa6kXeD09RNZJ3nmm98b7BtO+ljmrHn1A8x97wt1fSYJwZJDt4SRNnmG0w4m5Qg1vVAyD7LQoPBoE6ePNm9pHCSbjiQ8WeZmXXQxDwCVqihM23Q6kAhsy8Ppp3zIMKRI/9XtR1uTigtQYAVwut97sMT75QhhgXFMm3uE+E1gEhH6gxLVT9T0ELzfr6/S8SLoGYaDdqsgAissxlO6ZnX9Ad4SvDAGD12p0Whnah/c3Oz1tbWenPgvmwIUWhHNAY4/vFcWfm4nvXY1n0Gqevr613xmhUUUOMCPmQqZcEv0qLhFMhyDIcHp1BiOFmWsVE2T5hLR/mWEX4AYTay/PAMZMXz6QgVvq2trfXu9304ccCRQXXqVAtwmmbGYDn1vNJMB7xqZQMsW44qHVRQR2M+G2DBb/px8TDPc/aO52ZdjYsFvWPGa/kJ6niO09nQ7owA8uftuvCG7ZrIPw57fn6+d3BZ6nwCVNtY21W/jM1LbQmMsJ2j0ajbmcFcQQMglvsNhrFVgD5njABGju7927bPtjHtpcGC+QAfjx07VlVVd911V/dadC8LZpbAeklz9te6wLiwdwQ3Bgyp862M74Np5zyIqGpXumYWIq+307QTQ0joE0Vw5MVE2nk6leZ7fGKghdfOxM4507VcmyDFwtXih4XOvKnqZytsiGn+25kI6HVf2UDkGTmnstgRwQPuIfvAi5/8vVPINi5V1UVMjnAS8Njp+rx95h0HXnVw2BSRu50PzzNPXQnvyJXvbYzsqHgWzVEu/MEAzs/P18bGxqFlGcZMhOQtjM682TnmQT98D5Cbnp6u9fX1puO2vjjdDD8NJJl/okcKRKHZupBjsq5YJvPvVvSGDsJPaDeYSKCT9sMRNHLHc62rlm9nVPw8O/ZWRApwNRixXPBMZxj8vWtMcC52fMzZ1NRUV7uS2Sg79a2tra6I0WDCwMeg0nzzso7lD9oAKS07xX2ANs+3afZ1XgJxZjFl00FBq6XtM1jDcWMTBoNBF+wgOzMzM3XBBRfU4uJirays1L333tvNq2tGkKWURb7L4CCDkgT0ptktAdPZtPMCRLhlliA/AxU6pUa6s6r/lkQ7lqoDhO70Vkb99IkBSbDS+o57km4cAN9X9Su8rYAGHL4+DRb9Gmkn/2zc0vlbAVvZDl7ClSjdfZtOO9Ktra0u0ua7jNxd42BnSd8uiITXmZ3w2rh/e5eGjWkqMI6lqnovXoJejDHrtQZ8GD3Gg1HB2XKegCMRaHZE4i2UXsrxXPE7IxPLfjo/zyeRVWZviCQTqAGecBKe5+QfR3Pb0QNmTF/LwLq1dlTw2+838fgs9zg3g107iCzEtWPCTqQjd4ZrNOovnaQuEilbz9nR42f6Wsux629c6GfHl/zic2Qq34zrzBzAZ3V1tWZmZmpxcbEHQgx4p6cPXsltAGPgjew6C+Jsgxt8y/fo0LzclvrGG1XTDnpnB/Rka813LvXMz8/X7u5unTp1qvb29mpxcbHTbQqop6amulNB0XkHRZZnB3KWeVoLGHO/570VDD0UAFF1noAIT4YNhNeCnPbJ9CzfYeSdnag62BHgKNLPtRFKQ5XLJFWHT7vEQKJ0NoZVB07fztHZD2cK/JxJRpPvJoEY05RjNc8dLUMfKfg0HBn5wXPa5uZmF/Xm+rDXhjGkdvIUFOI0PBZHQew+MNCgEclgkJxxcWbCUSuNyBvnyjotjozPDSCzUJI99lV9YGU+4mwAGQmYyZjYUDE/7K1H/sbj/W1zXEvmwYDZYM5RtVPCLRDiiNnb9xy5Z0rd6XbLqqN8dMxRaIJxrnXBneXBcmWa+dxLOXa4dt6mJbMQ6E1rCc40eGnCDsR2wIDdvEtg4PudrXNGh0yH19WZ20ljSXrYEbWystI5YgNTFzd6WSGzOHt7e12wlrtTPCfwnSXDBOS7u7sdEHXxpPnsZl1B3nInSs6ta1hsH2dmZrqA55JLLqmVlZVuPKdPn+7GDDA2H3xujm2jbbz5gKzAEwejrUwD32UwlAHjA23nPIgA+S0tLVVVHTIqdsZ2hBkpI/hMkhXAlfstUMDkc40BRcs4pEPPpZK8x0plw5B9WcgdYaXRdISRwMER3yTAwWc4ba718b+ORHBUBlsYSCqM80Cqqv56Mgrhk//oIwsnbTgxZj550tkHpzcNIJ0areovUfC/5x6gkGldAyB4TFozj/x2VEnBFA4WA020t7u72xlyj98GxnKRDtiOmLlxEWiLp35zp0GMnUTKPjQnMGZueH6C0Yw87XRTDhkn80m62fKcjsFjMM2OsF2MaeMNvdYfMpzoqeUr61Fcq2AAkEsd1l/rY2YnfaCQx2x+8106UsuB55rmfpnH1dXVqqo6fvx4xw9H6r7XAZm/p7aB01pbtgq6NjY2amZmpjsrwmMwcOcgKmQ1l7HMb9e35PsmLGcJIKxX09PTtbS0VEtLS3XnnXfW3t5eXXTRRbW4uFj3339/T9YzsEr5te1zphy5YL7QN2eeHegx57ZjR5mIL9C8doQBynV7T74ZimLY4HGfFZI+7FxaSt7KKvga/22aEvTkDzQlQMBBui/346jcLZXK/bu/pJN7TRMCPjc3d+gNdPlc+sPAY6i9lzwzFkbUPnzGRqQlE9xrZ59V5mlgmHe2aHn+4SlFVc5StQyVs1xei3UakkiqqnpvAkWGDayYTxxg7q6AZ65vaC3JkUkwn5AT0uiOgFzwCsCAPgANBtzGLKOkBHjQTNYn7zH4tJP1/OX1/J0ZF2SB9Dg0ppwaFHneAcX0m9ka+Mj/rkfxMlSCKDt7OxmWxqAPubdMGbjZfjhjwRxZ3pEl2xEDAMuxC0udBQBc3XPPPTU9PV0LCwvd8hbjJ/ORY6V5KcVni7T0Gfqpo0CeCT743kvSLLl4ztO+jEajXnYy/YXl3royHA67eqjFxcXefGxtbR16K7BBlOcc2TJ/z/Q77bHBqHXCwSvz+FCAxDkPItyYcAsDqM2RIYrA5zYETgVm33zutFNG6fn3mbINFugEDInI7Zz5nUDD11qQjKx9rZ+Thq51vY0Y9+NQiACsbPDePDC/cUBEO66kZq7cUPyMjj1/s7Oz3dbNnAOvHXJeg+XEFfh+hqugHbmbTsbpd3Ck4aDZ+XkuqvoHEvF8OzD4AO3et478zs3NdcsHGHTuM8CwTNoppZNijNACOBsMBodqM5h715243xbvJhl5f861dn7WIQNU02SgbueYusbnOCbXBlBYaCDnDI+DjpbdsM3wvNoZICMJXq1j8Mq7J8iUeEdQ6ie/XT8CXZy3kFmeBCc5l+g2OrO4uNgBTh9KZiDpDBOONunJ7CP359IavHUNAnbdILequsJGnm3HXVVdUNMKtkx7ZrOQB4opR6NRl4VIO4qtyp1YlmXrhOeglR0y2PfSieXIGbMjEHGG1lJQR+hGlU6x29A4+rBxrTqcyvfk+ZqMZpwNsSHx/dkPRjHvo9+MzlsgwP/biGQfGdH7+8xEtBqGjqjctNK3HY5Tcd4RQaTlojGez/1EYh5L0mjEzzza6GNceLYdJ05v0iExaVz5PyPGVFYX5VnRbRidys111CzEYizUYDAGO0icCsdoO4L1ch3j5DobX8ufnYELPquq6380GnVnS/j0wFa2x2dMGMQxBqK+BIEtoJ5zj6G2s7BNSD2xoXdxH/cw1iyQ9bIVPPc47AQSyLugkDEbsNt5851rDQx4uMcnk6aeQQdzS6YjayKgwfc4ijdffAorgObUqVMdkMhdF9Ylnp+ROOPwWP1c5Ijgj3E6i+CjzZ3hcTE2Y+M3ejA9Pd3LpmUgZvnb3t7uHUF/33331cLCQk1NTXUvhLQMe7nQ48ralgQLzoLkPDm7YH3iGteqMK8Jkh5oO+dBBIy2oTWgYNIQGO/vddTk9dmq6imlBauF8q0sOYEWxnTsGYHRrESZgTAQgg7fn0aSsdNagMKCbRqSf/CE72wwuCfHZGfmtX9He3agvn88Hh+KCqv6hYced0bNnicbDht7nKMds50p74dAFhyFZLORsPHHQJGx8XsKqqr3UqtcMkiDaiCAQfUe/6qDsyQYix2pt8GaV4yd51teAHpOO9s5OkMDjXye8+p1f+bSOmkQbwM+KTNmmc0Dinx9gi2anT7/Ox1Nsy47jUwh8dTUwSmYNvZE+vSNDDlrk6AeOlPPTYv5Cv3QjWxQP8QY4Su1NMy5q/i513KHbYXfuRMJWsgIuHbGoMS8w3mbXp5lO2LQ6iymswLINXLMc02vswLmqZeQ0tGaB7TxeNztIvMOJc+Dx8P/9OfPMyDiM9vvVubEtsL6ZFn2mFsZxwfTznkQUdVPGdrppXHA+Rg8uDrY6dKctBSSqgPDZMeeQpcO3X0kkOBZCSqSHvfpQqMWsEgjhJFOFFt1eCuQ6W6lw1ydzfUGHjY8REY44TTQVf2TIdOw8hyi5xwfjt6A0gaUtU+cOrzgmY6Y+N/G0Ag/eba3t3/+Ac/EMOPkPc+Z6XDE5DnHIGZ2zLzIFCjAhHGR5XCqGdrgAXx0lgd+YnAyKwA9gCkApc858JgZr5/FM1xfYqBgcJ5ywjyYX/4/o3YbYlrqmB2lj8B2ipixMycpm5YdrmUXAvLjaDfpQjbtcMnu2DFlVsVAhLEQXbcyErwi3DbLy4vOmrhfv2MF8F3VPxhpZ2enTp8+3RVBskTkXTKeT9PaKlq2rPvHgBe6ARIGRoyB69KuWI7RIcuFnS6yjW5Zdiwrm5ubHXhM+nPpIoM/Z7zMfz5j3Abh2Zd9AvOMjpruB9rOGxBhAc2UIEzHEGDgicps4IyAPXlVhw/uyAyAr3E0ZafDd601vzTk3G/nnJG6/85npxGuOlwsxTMyqki+JhAi6jN/zQsbQmcfzKekLZdbeJadDspqusxfMgqO9hzBm6+M2dG8AZvXocfjcXfUNIbHzgMjiBHz0gl8gk4K0Ryx29DTj3cK8AynkJ1hIFPgpR9HmMvLy4e2WVqGbJgGg/6r0607Tv8yfkeG0GtZ5Fpnr4j+8rnw2obespeAh/nnja02utCThtN6DJ+RvVYhnvUmswPOGFh+W5lNO1769k6RnAfLdAJ/ZwozOrcsUc9hEOosGWN2ut80Q9vU1MHx1wmgEmSgH+zWYqwJRrnHS2uMi10bXu5jzlxA6bljSZJzPpztGI/HPf1wZgmeuFA4nT0N2jOTmMDjsY99bH3P93xPT+b29vbqnnvuqRtvvLH++I//uO69996OXz/2Yz9Wj370o3vPGAwG9dnPfrbe9ra31d/8zd/U3t5eXXnllfWjP/qj9Wd/9md1/fXXd/0zv9/5nd9Zj370o+sXfuEXamNj4xB4Ppt2XoAIG35PZkbbCEuu3TpVnxFKGqzMFGQ2IcFLthagoB/+N2DI7IYRtIFHK7OQQCPHlfT7eclL0+glDP5vpckwmpnGM812OIPBoLfDA+PFNTzDp93xP0bCqfXZ2dnO8ftQKuYe+swbnLsjrjxbIjMBuc5N1IOzRe4MphYWFnpzyKu4M8vh+Xc6NnkE/7ybwgYaJ+Moj/EbPDr6STDI83jjo4GadQlaWMt1VAWQoC+emXOTzteZRT8vrzWASB21jjDnZNPghesbXIibS2+eb2TToAcACkDkGi8vmU+ZRWDup6b67zJhTtmBMB6Pa2Nj49BuoZRPxsp16+vrvRfaWScYV/LLIMl1EbR0pKdPn66ZmZlaWlo6lNXILBr3ZOMIfBeNWscsc8gjgDWBfgZKmRUh+2dgDV1pSzlN15kDg/aVlZV62tOeVu9///vrU5/6VHffIx/5yHr+859fL3zhC+sVr3hFfe5zn6vhcFhPeMIT6tGPfnS97W1v64HAJz/5yfVN3/RN9cu//Mv1e7/3ezU/P19Pf/rT62/+5m+6pVYDnkc/+tH1hCc8odbW1np8PKqJ+ALNkXqmhXzOOs6EArpJDh2BSfBhY+h2Joffas5uWAizv1wa4Pr8zGOGVoOd/My02fDRf+s6GpX/GFY7efMTo4mxyZSwDTsGO/ftJ48duXE/Bs1ZheRP0mRD5jEQSbkCPDM8GXUbvAI6DJAw0i7CZIxsE6NOwrKahtWZhbm5uV4hpEEn8+PzDQzWGC9RYoIcO3jzkucsLi7W3NxcJ08pI/l3SxbpL/lsWUij7Wu4Hx4YGMGrSfLCveiLs5PUpsB/aHC2IeWxJWe+Bl7yG+dGVG+ZcQTqZQ3zAhlxtg25ZOw0O3iPg+tY1gBoQzN/G6AanBgw0RfPdUaj6uAld/Pz811mzQGB+WTb4wzHeDzuZQhs0wgofL8BfNpk26u0u/RvG5RzbiBFXYRBcAazv/Vbv1XXX399N8czMzP1z//5P68f+qEfqn/2z/5Z/eIv/mLHu9tvv71e8YpXVNUByHrUox5Vv/3bv10vfvGL641vfGPPJ1k+W8uh9HOUiXgADQPnKBrGIjQYUwCEr6MPhMNGueqwYUygkEaglYGY1FrRpcFAAgGaMyQWKP/tKM5Rk51hGkCn3Q0M7OzMN9OVWY08xCWNANGR6cr0sDMVLT47PWwnw2ek/x2tEsXgNNNJ8bmfCx8clTpCJmUM/VtbWz3ASl/T09Od44B+R0Z23jgL11F4vFzr7ImdowvnAEim3RmF0WjUc2g2juPxuHt3wszMTLeN1TLA2LPQy2DKNSbcY2fNs5AV92u5MDjw2Q2WG3ibGUGDTa4lbT4pg9GSr8zQ2Ilm5F514LyYF/pPx2Sgl/aAZ3G/T1NEHgzSDS6cDfTSEjJJsaDHa9230/fceOypo4yLTMLS0lIHXL1MB18TjOQSi3W/dZ4E12TwkkdcW+Y9N7ahjNMtbaWDBDJDnksayyvc/7u/+7t13XXX1Qte8IL6jd/4jW5LaFW/OLiq6o477qgbb7yxnvzkJzdf8T0p2Es72Fome6DtnAcRCI0nqap6UR3GjKp1p6OzwMjOm/4TGNhY0TINPakZITq1lhEaz8msRBpZ6LURzcyC+08jblqdQrVRHwwGXSTBddzn9KEj/FwuYmyAtcHg4GVNTp9mIVgWvbaWTnB+PMu1CPPz892xufRJGtRb4/iOcbTOd/BhTPRPxOh7ccJVB0s/jAs5gmcuYMRwOttDYZ6/zyjOfKqqnlO00zJ4RB+cAQGUOEPA8gV6gtG0c8dptQ7+SVmyXOLsE0Dn9f4+MyNOk+e1zhpxjxv88FwnkHB0a567Tz/TssT/CerNQ28tdd/Is0FwOo2M1mnOQHguPK/wC3to50WRsMGYbZXrWuyc0g47ezEYDGp1dbXm5uZqZWXl0EmrHjsgwPfiQL0DinEggx57Zl49PwZA8N+1T9bJSc1yNR7vLynZHlNY6awE96yurtaf//mf11Of+tR65CMfWR/84Ad7fTkQXFhYqEsuuaROnjxZ6+vrHV2XXXZZPeEJTzhUj8YbROG/s21HIGJCY+3tggsu6CFjr3MilDCVZgebCmqQkN9PmoycqEyLoQTpWNMptIwiCprX5TO9xk8/KE0aQ/fD37mlys7bfbpfAx2iDmdRqg4bMObF0Zd5YvBmZ5BZAx/ChDPl+UTT8N3XOTrB6eJkNjY2uh0M+b4BOz8i/KmpqW4Lp42554aXF5GhoA8XnlleXWTm2hBoJL2Lk2HeGEvKjaMR85AMBTs47Jzm5+e7wjxoM1A0aJqUmXB2xb9t7G1o+cxO11khg3zPPTR4ycC6B68yqvfz3AzK3UwL0XSCCuumASb0O/Dxszxu+OLiYMaMTGZNlPWHcdHcDw0ABY8ZG/U69EHmh+s8Bzk+1wXw2zaFHRQsp/l0UcuuAxDshXdV0Rx82E5Bi8GQszMJ/nmOg7IMBi3jvh46kLVJjbkaDod1880313A4rEsuuaT7fnl5uZ73vOd1tM7Pz9fzn//8+tIv/dL6hV/4hV5m91u/9VvrJS95yaFnzMzM1H333dc9z2M723bOgwiaAYT3yI7H4+5UtXTwrfv524bFkb5bRiAJLjKlZMVs9cd1LZBiZ8+z+e3sgYXZ4CcRvCNwPyPXgqsOsjpOhaMMLupz8RPG2tmHquoBOqdaDSJs6HHc5lsez5xAys4E2nC2Ntw8ww6N/+0A4MXW1lZn9GyoMHAGA4wNnpmv3nqK8bNjxNH6VeU44N3d3a4inWZwwNgNfDzfPBOaWVPGQI5GB28ZNY3wh3luGSWvN7d0zQDCvDXAcyreICD10TLJNc5KOJt1pt0ZVdX73g7YOuD5dh/Owhk4M9cGxM66wd8EyM4U4bwdeOBoAX8JIMwby475y3UJsqr6p9I6+PD24QxmfA90mD/wgeeyFZiaDHbKJS9sW53hyzHTNjc3u+ydaU2wadrdDAwm2WBkrmXP+Tvvbz3HfIKmyy67rP7dv/t3PVpvv/32+sVf/MX67d/+7Tp+/Hg3l6973evq1a9+dc+PDAaD+sEf/MF6ylOe0j0HPjyUdl6AiMxAVB0IHpGflZvvLTSeCPebQMGOid82GpPoS6VNwbLS+j4/xwJnRc8oxp97+cDPatGAcbcBcBSchtLnNfgcexf70VLp7WTTqHMvvxmLnT08wPFTxc24XENAP67udlSQ67PQ7f3tdp52gPDY9FI0ZxCwt7dXGxsbPVC1s7PTpZGt8NDNEhLX8krhNBzwDPqh0bUS/KaewW/tpKjQ8+VlGINCy52dtVPKmV0znQaFrfl3ajmNoGXaS34JyOnHc20e+3qAVc59tpa+Ym88BuhNfXT2zXIKrbYNBrAAPh8TneCKa9HJjD4N4qDXuzladsBBSlX1HLGX5jxPyEdmCnwfoGM0GtX6+nqvMJRnpS64AX6Tv1wH0Let8NHvlhf40LLf1E1ls79woMNc8rl9hhvXfsmXfEnt7e3VzTff3H33mc98pr7ru76rl3E4efJkDYfDOnHiRK/fO+64o+6+++7OPjDnACgD6kky/UDbeQEi0kChmNvb270T+ixsrqh1PxbkTAf6WguTo/EEJ1yb9NrRG8VmytGRcI6j9ZkVrxUN2rjaEU2qiHdhKjRijBxZZ4RlI5RRHcWALSVNsGPaHW0zfvowfX4WBocM1cbGRgc6aAYQjrJtQEmP+wwHZyVybd60cI1T0FmLw3UuwoJOHw7kjEEaNGd8EjRX9Q/zsjwwpzZEyDSFodBh48m9dozpfJxSbTlyO+4EzcPhwRHLOU5oN9Bx3YZlKYFWAvKULzt3L4tyrWWNTJTlx6DBdUSmy6l2v1PB51QQAPE8y7x5kQDGOsEYnInjngQczkjYrmWWs6p6AYOfhXxT05DAb2pqqhYWFmpra6tWV1drOBx2Z5i0tiFmNtfLepn1QqY2NzdrcXGxN3dk+HJ8AC/zDbozI50Nu5mf0b9ly38vLCzU8573vLr99tvrYx/7WPcSvp2dnY4ntOXl5Q6sWn+sBwm0zLv0Y2fTzgsQkY7YkZuNuJmdCJrPjFRTcTLi4d6WILvfbExs/ua7SdmHjKA8FjvO/DyvT6NqQ2Il80ud6AdDYOdnh01/XtYgcvH4UvCd9k0DiZOzU8S4OsPDOPwaYH/nebJDxhj4lDmKMdmjD3Dg80xTOyXvOeX5NtLQ6dS+6cU45Do29LivdAyZ8cr1cZ7rKIr59b5zwBcZigTEfJffV/WBiY0sfLPjgU6aU72WO56RW1JbywmtqD4zco7SElyYV3kNOoLD96FcjDudcm5BNH+rDoAj/xugMQbmBtr9Rlr6XlhY6AFQg3TXoTgCt32ABhciJxAzYON/8z91znNnMMx22uFwWOvr6z3ZTjvWAqAGR9APSN/d3a2NjY3ufRZVB69Ld8YAO+Xs4KT5Ny2WrwQN2ZA97NeFF15Y3/7t315f9mVfVr/2a7/Wy3TmM+jfWUA3Bw5Jh0ESejI7O1vr6+tNOs/UzgsQYQFD4TY3N3vbwGwEYawZP8mQJDr3c3LiWtFSCiQNwbAxcYRIP9Dpv9PZ+lpappTtvKALAed/R1guemJsmQmgMYZW1sJRmbegGVRAv6MRj9VGGONoA+n+oSGjCFeoZ8TI9d7RMx6PuyjS/KEegT4wqmQoMILuj3stJ2kA+T+XD/z+A+bFTpblCdM9HB5eO+cHGWPMGGPPM3xiN5PXmJFNO8OMkLMmaXp6unMgyKBT+JZJ6LQO2pGbRzhL62DW2ljf3KcdvsFWZlNS//NoaI+DZv4kYEdWW880kGxFwLZR6By82t3drbW1te4zMkeO7qmpSZvh+hvoAzTbbiYgtm7k77QzBn/eXk3WAlBBUWfW9CTwhG+ZMYAu7MPS0lJPTuyM4ZtrjFoAIZuzUa3xmzff//3fX9/+7d/e6d4VV1xRCwsL9Tu/8zv167/+63X8+PHe9X6m9ZV+fW1mIlpy6D6s5w+mnRcgIqNCinT8eStTUNU/Y6HVMAZ2dhlltowwv51uZLL9Gf2lQLSMWQpYZh/8vRXdBgEepVKmwQZ45X5sOxn6w1jm2nXyyYY+jxxP4ObI06l/DHimJml26l6jNx3Q7iUqOyr3j3OnT+h3NMP9BlAGLZYPxuYTB4kMR6P+a5QBTK5VAZjYAVgGskgS2qanp3vLFDYsOBzuNy+tH2w/pQGUiMrNxzTWmc3AuWQ9gYFqyo51I4ETz/DSWjYDmwQsrWsc0ZsO949scI31PgGaMxFcw/ezs7M9YGyQYWDsrJNBiPnqWh522OTLuXLMzLd3QZjH0J/208DdnyXddrwAdPgLP9jpxJZi10ukbeAeF52mnSGYXFhY6MbnLCr6Zr2zDaPw2M81AEve+//777+/Xvva1x6a09e//vX113/91/Wxj32sVlZWus/f9a531f3339/xgDFl4Le5uVlvfOMb68YbbzwUbI3H47rpppvqb//2b7slnfQjZ9POCxABo2Ay24hojnjSGVvxjaAtLC3UTX9nSsu3QEYaIT8L4bGByIyJFT7p5Jqkxc/y/amQfM96f64t23i1Mh1u0J1pfteP8JsxEMmmoXL0l/zz1l0bPzu2rBOBDpYtnIUxqLMT4J7RaNQrnLTSIz8eu4GUl0FsoFlXd+TqzA3X8HyPhXuoHbDj8WfQwLidFbLsEZVCr5+fvPOcGXAYjOH0zCPLjPlueYb3BqRkWOAF8mqeJG8dBVt+DH4TuBuEZuRbVYdApb+3Lnmuq+oQLQYrw+GwS7+TMUAu4aGzKAYTzlwA7HDC3gnFsfC2FYBO5IDtvHyH7Ge0jSwbzBpsAVoBJg5qPMfMpeV4ODzYAgptaZfdnO3gjAsahZb5+nbbRWTNtglZZS7O5IAzA1BVddNNN9V73vOeQxlgeHPixIkeAPn1X//12tra6gol02dA12c/+9n6pV/6paqqXk0L1/3u7/5ubW1t1fz8/CHfcLbtvAERpOGyEjejonT6tHSmbonGW99npGJ0bEGycTUoyEjNdNv4Gd37PitmvuXS31VNBhBV1a2dORLM6MeZlKr+trd8TqY2Pb5J80Abj8ddcWyOZ2/v4EAajCdruUbnfsGRnTzzxvsDSPvzKmM7Pgzd4uJiN1YXvME36HBxpp2jHQEFi6T52VbpjIiBB58bDGS2COfC3AAG7azow3JNPwZqPhoex53Aw1kXAzHPPfx0FO11YACbZd7ywWcGQgbC1i2/fCwdOv8ngLBTwYbwHC9b+Hrzk98s/bnGpOXsEkRbL+CFaeF789aZv3SG0EAGz9kzb/lcWlrqySiRd/bpZybooV++B8B4qYzvADN21qbVYGh1dbV2d3drcXHx0LNzCdCO2LbL97EriuusI+mw5+bmevUmlpFW9iFtiu9bWFjo2Xffa33kewosE9zmXJANTJ/DWJaWlprPONt2XoCIqoNiIxeaVbUj3paCpEO3YLSu55lVfSTqdGnV4bfyZdbAIMCOqAU0qg6/0c9jwwnyWWusfsGQo0M7nqmpqd51FmgvP7TQuZUUQzIJyTsKcWRno2wekx1xLUICFzvU3HaK0yEaZLzcQx0NxtTFaFXVpf3py5kTjKQdjukyv5hDjCfzZgdisEafBg2j0ahLUfMdPMTxG4BYDv0GVi+N4MTgBXPhazKzRORontJIoyfoNJAxuE/HY3lKfvId1zri5TNAr/UtdQL+Om2d40weGUjYFvBuCIBkPs+Rt58PjXbIyKDH5WyXHS864b5ytwP9IB+87dJv+OR+HJJ5gB54a2jWJbk+Bh1lXNZt2xIHSG6A+6WlpV5WIDMy1mcHV5nxIsq3g0ae6Ne2zoAJ3lhePZYWgMj/betTBp1xO1PGw/3ZryX/0IdWYHo27bwAEffdd19dfPHFXTR5pmxDNgtyRv3+nvsnOWd/1vouI5fsz9EF36XBhh6DJDusVnRjGmy4M4XLZ35FdY6DCJ2KfDs4R9BG2y1+2OliuLiWyKTqoEjOSwIt55zGg74c2bW2/RHZEAEwbrJZdkpV+28+hEcACEAQET+FjgaGreJH16QMhwfbKre3t2t+fr7HE+hKuabwEV6QwqQPA0o7IEdiAA2c0ubmZi+a9vKI09VV1dvPbhDM3z4q3fUfBn8ZFbZSwwke/BxHYTacjJNxGZgDAgaDQS9zyZzzNzRZllpZSdPnMxZozkx6fd/zQbPD9HtlctkogxPmgD7RLYM/gxgDBXTABdIABOue+W+AYttiPSaw89z5gCkCvsyM8hm1U9BWdaALpseZUPrI7DC1EewA8TwCRJjf3ELr+Wo5eeQgl3SZE/82nWmH7Qssk/ksz4kz085EWjccZJ5NOy9ABMcU5xqkEX/V4QOP/HnL8Vf1z4eYBBKYQF9no+/IKzMcGc2kEZ0EiPy8vC7BUKJn/8aQ4AhRIq5B0IkynBL1cw0kzD+E3KDIyxBpgFESOxz6tRL7JUS+LtOLLqLE8FrhnCr2s1pRWNY4wDuiJtaBrbi5C8XRI7zzsgMAIt9amBkjgyPLu50P13gpx+Ng/j1/0IbjIqNBxoathskj+iJ7wz3MVY4jZdLAIo2gDSt/O2DA6Vjf7WjN98waGoz7e4P1lrNPJ+63arplup9nWgb5nuwDGR4vs7kwEtrhB/x2lsKONTM0yDT0uM6EQMFz1HLYjAebgI5At4FCLudmdoVxwW9no3Z397drzs3N9eyCeZHLqQZ8tlds++QeAz7ugw/YFOjgmeZFK4hKkGHQntk3/40+GfiZL+4LncjlHPPAMsLfDyTL0WrnBYjwGm0iOE9I1eStO1YaO8KM7s8EQhLhttAi/eezMIStdBzjyYilhWLpxwbK1yRgoR8MSKbxcMKMLyOOFq3+nLQ/a+wLCwuHeJ2G3oVYjjKrDt7A53nAgCIDdlqZpq7qRzMYWaJxAEEa5DQSjtboY2trqzPOFLeNRqOuYI5Ct9wp4egM+swbyzSpWe/YgB7zkOyG0848DwOaANDzjszCf7JPng87fMsfvLT8G9h5LAY7NqLwNYGwI0P6AewYZLjorwUgDG7tCJyxsHzxvwFvVXXLOb4Wuqhz8bkluesgnYb1ir+dFcgsgOcaHjBmy25mlZh/7AXP2d3dP5jJS16eS+aJ5vn1ZyyHATgN2CyjyHnaaHQPkDMYHNQMTMqApFx45xJ2pXUGBvJsfUggZdmyQ065djNoTLBBS6A2KZhNHrWWyenHsmy6jzIRD6CZsZOAQH7WQny01iTl31x3JmGyAtth5jNMT/6fxUj0i5DYWLbG4ejYY8j+STFWHT4GG0OS4zU9vo6iSKJaftN3Oi7TawNYdaCQLhTjc4zMpPnwkoiVHwOKkyCliQFM5+WICiOdBZbQgrEy2MM4Wx78vXlI9GkAR/M2S2ohEugBpGZnZ2tubq7W19d7YMyAiLmC5zg+G82cNwyjdYHn44gsG5P0KyNL66UdP9f4pFPu9VKVHYzHl7piftlR5LznvDijNBgc1O607IidGH0D8JkL0+G1+ZRpnDdzahBHFsZ1D8wD3xlgGDhZ12hkExYXFztwMh6PO13GRkBPZsWcdamq7pA2nzvjzKZl28DK40cnc8cIYMFz4O+9dEM/nALJnKLXrrlwNsI6mX+fyeYbKGXwaafufpj/rOlr9Q/teV3yoaV7D6adFyAioxf/zqi/lRpNgJDok+v43tGKjV4r2nLxnGnz/63shj+3ETRQIiJxlXory2E+GJm72amPx+NDaU1HLE6r0aALWtbW1jqjkylWO3B4kMYEI5PbxBJhJ912bi78IlMArc5WcC/0cUolp556qxlvUcw+mVcbdBsRxuPIFIcAEHI/0FdV3fWm30aqZdAMkLiHz3EuVdW9vZNo3qeUWu4YV0bOzIPrKuwUybpMAtlOc+eSJE4ahwg/bOjRAcu+HbajcgNB63TKsCNvZwyg0fdlNsgOycAX+YHvHK7kLEfaHvqpOoiWDchxNkTYtmM8F5p8cFrLaQEMGD/yzRxalhiPsz3ojj/3mObn53unrWZmKAGcx8icbm5u9uojWqCBviwz7H5Cx7e2tmphYaFbqvP93pnGuFtOuBX8VLWDwlZ22TJknntO3Hy962rymgzMnKnxMx5MOy9ARNXhdA+fVfWjCDM318S4pxUxJQptCZAVs1VA5efQ/DnPsCGs6i8pVB2saY/H415xm5XIhtw0Z9qx6qBgkq2UNriZObHzcroOQ0SxnyMbDDBr/3bYjuoYk2sUMuLltx074+NaHDJpe0f9GBOup1gQY+mIm3VU10IALuwwiNacbcF44XicfcjoAFmxvHK9naMzHx4v19o5tJb2vN7M/xhbyzeH9PBMnPne3l63rAFfE8ywpbMFZDOKs3HNrIOds2XXn0G7MyaZyfBzMmrL4MC8MjjL7ILniGZQm1kLZMG65Peu2LF6acA8sW1DHpEzrncGIosN+Z7PDFKRBc/V9PTBibVe/vLSB9ezVGl5NQhiZ4Szi5nF8nwwj4AmH0xFXYOBiDNOHgN08wNd1Fhwj7O8tm1pm53JshyZfj7LZTfun5S5MN8za5qBqoEmfHHfaefd99m08wZEuLWi/myeDKf3sg9H8JMyBq3nu+il6jAKRelMK/dmXwYFCJfXOk2fnVsCH//2934BkI0YY7ehSx4yhs3Nze7lVqbNCphr5RnZoRB2jjaiNiw4dFKQKN/29nav2DKvIQrHONkw2GEzf5mBYg08gVRVPxWbc+555DvG4LkBjLlPLyvxA1hy2tXgEn4zr0RYPkiM7ZfMJX2ur6939PqV215KIoNhOQZAwK+UaxvWNNx5jw2i+3FWgP8dMcM/y4zl1Tpj2UzAkbxu6TPPS0ef47Z+DgYHB4OljTG4mZo6OPOA3T3OODpQga+APNeWZKYAPhiw2/Hbjlgv/Xr4XKpKAJagw2A1o3I78bQv9JnFqmxPpaVjt575KHbbb050NLDkOQYcllPLUssfnAkoWNZaMmnQknbdepM2xTL7hZ7d4u8DaecFiMhoOSONZKIV1tHNpOv48fcocabuEiw4O2B6LaCOGv29abcQWYAT4DgqzegtMwpVB8cfe33TjtVRjA28HdvW1lZtbGwcMtrZMFqOPBM12/B4Dlv1Bxim+fn5LjKamto/FIoMg0EHhpkCMtZZ7UTskE2rz5OgYWzozwpto0UkVLW/VOKK9ar93UVkNAweNjc3u4h/NBp1ACnnEwOYyw0GKF6KIip0pAmPOe2VbAO8y2I6lqrgG9ksy1byNvXEIDQdv6/1Grxlw3zn3uRLRvamzTULLVnlJ7N5dgZ21q3xm1bLIXS7+NK2wlmszc3NXvAALcibayx4vmt9rOu0zGa1mgFsLm1m4Szzk5kd+Gw9rNrXA5bnXGvkZ7QicniCTraCvLSVNGfINjc3u+Jnywv8d9bONq1lr/yMBJytpYkErwlGkhbPnflqvqTO2Semf3yw7bwAEVWHzyTIiffnFjaj8IwIE5jYeCPsLZCQwpsthdMRsP93NF9VvQjDzQqIEKcgWbCtmN4axn1JX243xGFQ+Z8OJg2rnYX5awRto2ZjTfTFvYAJz53PYTAIYtzeOoexg7/Qy3OhxdG8sxJZW4Bhd8SfztFr7VUHWzEdfRDtufaA7Z4GM5ZbzzsGnPnhehd3eqxuHDpkp+ilLK9P4+BSn3Ltvqp6WQm+8312GPSfNS5cmw4/6eIzG++cM8t1K3qzQ7UMWUb9fDd0oBUYZPTotXY7QusQcm67xBzbqTubwDXpaCzH5rUDDPSZ5zvTwv3w3DrgvmZnZztgwHcc0EbGyABybm7u0CvDGV8rcrbTRe/ysCzXIhlUGqi4LgXa3KAnM5imw9clkJiUdYAv9M14EzSfqU/LU2bHzCM/r6r/RtwH284LENFyrDSnufKzqgO0nddMykpMmrQWEm6ll1LAeRbP8Jorvy0AKUiOPO2Q/J2V3WOx8DpqSQBWdXjnCwAi32vhdVccTvIT9O/ouOpgZwh9oWAYGm+BxFm52Cuf5esxunNzc52BZvcIBp16DfjrdX8aGQ/Ak+cxiwj5TQGXq+sNQinkpC/Ga2PNuLJqPx2gs1cYTaeWcVoAFpozQNSK+FneBcL2Pz4H7Fi26NOAG3DCd5lts6xbBtjR4vnkf/jkyNt0GBw6U5ORm+Xda8y+vqUXDiQy2jP4HQ6HHfjFuTqrlMCViJt6G7YeJ7jMQ6Esi4zX9ReeHwqF4aXtjLN/yR/rosEY+msgkGv8tgnorGUxAYFlm2aATpEk/TAu88SgEfvDvbOzs723leaShkFE2vKWfXdrBXt8nn7F9xho8ZmDuGwOPJypq6rO/pwtgKg6T0DE5ubmIeU1w1tpcwQaBXJlOd/nZLcmIlNZk1rrOgwfk+6iLByV6cy0V+sZKdhWaI+d5znyt/FOA+z+XG2eUY+diSMKaPJpkPRnwwVf+BxnzxxiIMxDnocSOeVLP9C6sbHR0Tk1NdU7Kx+H5HePTE9PdzUCrnmgVsDHPvtsCEfmjMtRXAIEjKyzDKPRqFfg6KxGGhPuhXfUO3AexWg0OvROEB9tbX4xFopKl5eXu+fYIbM8ZHppPh48Mx8eK33aYXONs0subjVo4LedlMfhmg/rcIJzt9RD654BA83OKo1/yqn57RR1Zi/H4/1TFjlBFPBrIFVVh4pYebZBhPUyASbNL2vz2Lx7gYwbtEKv+cX1yCB64qUO+GXQj5wbgNsmG2RjmwACBrYO6px9MV8tU5zuarnIZZnWclPaItNtYOzxWnaTvylTlhNoTrvu7Ap0ZIDhJeCjTMQZ2sbGRs9AWADcbAxsJByhcL9T/lWHMxqZGWi1llNPehyZO11tobaBMs2+1p+1aDMoQgBxKq1IMLc+oYwGD04NprI7TQcY8Dp89k3/XDcej7siMc+p+VB1sA3NDpSoj//9Xgq/2ZP+cJgYF2emvL+esdlZ5ZjMuywk47Ac0qiO/tn9QJ/URPiwHjspGyOMluU7oy+e0UqBGphQLwGvvBRDMRtFmfDAIMHG1DJsOXMq1zri66GLhmxZ/4iyDVjpn3m0o0TeuLdV1Gq94jmZCXT6nc+cXfO9DmCInu2ALIvQZnn2UpazCvlsshbIvLNP0OGlLTtXL2OkvbK+OWNoEAEfWVowP7xTKgEc45yenq6FhYXerhKAYdpXL+PQ3+bmZu+5CfQMWGy7mI+dnZ0uG+jMkGUngbDpSnBEy8AvAybrceqMdTezYwaKngPrgJeaH0oWouo8ARFVfSdpRuf3NE98GhI7GEezmXZqAYQ0jIkecZr0ibEwnRmRtlJfqehuFroURu7FyFrRWs8xyEHJU8FsPNMREMEYoPkZCLlTisyj15kTRCVosQGqqp5DBihg1LiOVGZVdXvYHXFAr2sqGFOCCL4zoKDOgPMXXMhlhwU/fWIo4AWw57nb2dnpnHgr80GUxbOcmTCIIBIjEqzaPzcCfi8tLfWWpxgfe/XtJOGVo1x/l47XIMIA0PObYNZr+inf8DN/8yxkBN5nhsB9Ww+QI+jPLJCNf6bknYXhuX5GyyaNRqPemQ9E8ZNqTjKzhYM339JWZdbVz7Y9NHjY2trq7Ja/T7CIYwTI55ZO67j1zMseHoezKeiSeU9bX1/vsjW2V16ic0O2RqNRd/6ExwSgALQY6Dt4SjCZfsetZbPPFIym8/f9toVZw4aNMIA4U7D7hdp5ASJaaZ4EEhbeqn4Ftx071/Bj1OtI0JkDt0TDCTS8P3uSA88MSKZiW+M3IOC5mbL0uJ12NNjxEga/Uah8ARNK6hoFAwJ/biH2Gp4jdejxTgfzxev1Xif1tY7gmNOtra3OCPpZnHnAnnEbnL29g+1y9J1ry55bxsG9BgoABEfG3gZL7QGOjvFxLwDAYIyqfu5njZ1CNqJCv3kUOgEopIBtfEgPJ72MLesO2FECLaPRqIuI7ZDtQBwx2SlBI3OFEU/AD7/ch/ll2UW2J0WIyKnpZBxpV9AF66J1MyNBj9u2A31tLb0hE9Q7GEBwnR0qIIG5WVxc7Gh3JiyfYTk20HR63UAE/eB5LJcYhPEWZb/My7TQZwIrg635+fluFxKBQSs7koANx4m8ZwbIQN82FWAAMKdfdNAvJGQXmHnZyiKYt36eQRzjhz8ZqLZ8C/da33K50HarRcvZtPMCRFQd3uZpZG3lznu41vdbudxybSnRPJ8lCOAZBg9cg1A6guX6jOozmk8HxjUZAaWhYy3bBpPxtQy210KTz47w+HH05giEwj4bcPPN9DpSsHGlH2/BtEHY2NioqsNbq7jHWSBXu3sbJjzhmv8/e/8W6uv69ndh1zPmbowx11r/95+AidCTYHpg0SCohBwoYonNYY4N1Naj9ECtIhHBNpDWih5IDyKFSGI9iOQk2iKYRJIqWpCAgWoRkdoGqrzZ8PbNP/81d2Nuxq8Hc32e8fl9xv2MNefKm1IycsNgjPFs7s11X5vvdd3XfT/lHWi4Ekye77q6wWv/t3fTZFBoidH3YVn0rx6/gaKNLMbGY3/58uW+hdB9wIDX27XRYmzwrpfIGjEwT9vgWxEyBh+5zZgc3bAseSnGYIRnuxSx8pofAuie8xqygokj4NToR71XfjvUzvvQFf70B9C63m2A5zwf080yyBgKWq0z/fVQR7Ic6YMnvEup8t8oETJGnfx4O6/npHxikEIBSHjZxg5T6Uk/OYWWb9zghEAv+gVQM/CzDui4PP/WX434VLcU8K70ZKPXjmLhYLhf1P1TyqMAEUZxjkCsFIyVeD2i1YR1oosUXbcVRdun8LczZlfMRPsPTbwZzoDCCt7jgDbeiWHDzzv0G4OCoSrapm4DEQTWJ2mipPD4iTZ0i6TD7wiiS42Ar3n7JILvQ31ssOypfPr0+ThpfwPBiaP2sKGdDakNa/kORWvPiecbykVx8TyKy7SHHs3ZgMYoPidwzsyeOPbp06c9SkC0iP74vAKUJ14dbXsMzIHX3G18HAb2uDuX5r/yliMhllvLlHnZtC+PUMzDBl/lrwL1LutYZi1bq5C167XyN126VZp7/k4INPBSko2pdzdxz9EYjCc5Oaulu0YmvY3WwJbnGbf5lGhal4ocofNcNOJFzs0qmlsw2bkjikUk7uLiYl+qQD68LAPAmZn9iHufMQO9eZbzYtonO2mMdbVsZN5iXs0frrNLKjzfKAX1AyBcr2WhjuqXlkcBImbuPDcm0AChSKyRB6PGFnvGVRy8b8ZuqZKy5+779VpsTDFQFnALgoXT94zGGSfhRPfVSsGGwPW2bw7BV8FbCRmZo+RseGfuf27dmeKmM3/b+zLoIcLiJQgbKZ67vb3dkxaZh5ubm93A0jaKxMlt/rw11xuedb+oz0odWqP0rOChh4EKipnCgVndHmfPikgThghwYEPkkDcAyQDL9KYfKFR73fUavdXPfFyer0GFTp5zg3Xeoe5GEbzc4vaYG+pplIM2LWvVB3YQes99NuAzn2IUSiPo6zl3/o1lZNUn6oV/rB9s1OxB02fApunhKKLr9bbIGjwDGOaatnAOvIxhXrXOqexdX1+fLW3WwWsxXyOr8Cz9mLnLCalzRXIqMlPH6uPHj3sUqLxb+1CHy/etJ2rsy1NctzPjAk0NII76tLJvX1IeBYg48pK9Jc5rwitiHk0w1/yb56vsbDwMAGbuwulWRhWKXocZ21+DpTKXBdVMdHt792VHhLRM6THVgNao8Az1uJ/22uwx2FCaXs4T6RcaZ+aeccWQU//RB574eqANhemDIfZee8aJ4mtY2GFfGwrqgmZG/Qa1GPZtuwsjewmEvng9FlqhxMh/YE5tVG0QiEoAFFCO0IBdTTOzr2OXBlVC/rEhNti2craRr1Ktofe8A/Ac+q3XZr71+OmTjcoKpHtctG0+X7VR3vW4aM+6g+etC7jnuWXOkS30hSMxAHeDY95tBIE6LHM+mIl+wZs2vvxN5Aq+pl5kotGa5nsxtsqDnS8DWWgI30OT6hPT0oAHoHJxcbEb1G+//fZMB/n9VQSBvph/XffMnDkqtTvmV4/X/LPirz57ZNMMyKkHcNdozW9UeRQgokoGJW1l6rICDFy3IbdBbT010LRnxWsvgXcaQl31a+Y8/FtlQT/tCazqsMfYhKYqIYwjBeFeeZD8vRIElgdqNJ0xzXOOgDAmLxH4vZm7Lx96np3oRF0cLQ3AsBfnhFKffMlOCmixbduekMjXPFfhwKurq72v9lwAeCQedm3e81awQKKXDSJ05H948cWLF2eRCYept23bz7H4/vvv52c/+9k8efJkzxvxWM3vzIGjF36+nvvM3QebakBtpFHS5h3zruXIeRaO9tAHeKaK2bJhQ7EC11W6ljf/9ljKawanlat6rI3a0cZRu4wDPbLy5B0Fgk6crWKep55VPoSBD147f/NMI5rILwYY/eIQvj9uR2kuQ7d1NloAADGoIUrgHCL4gGfhe9Nt5Th5Hg2W7Jw5gno6nebFixd7HlGdSfPjqg3LT8F2ecvzDNgrgP306dNZNNI6ftWPn1IeBYhwMfJeTWpBAs95Un3NxcqhHhqKrQiRNo12i1xnzrc4+ZkV0vVvnrEiKhJGuN3fAggj8pnZkW2BlkP7fp9woL8RUG/UBov8CEcKDNLqTXt+u23s3bt3Z2v3jKMhYq8BOxrjD3EZgHoN2FnfngPoQ1Y5fbYBcxgZUEPY2LkQKLJt2/avoVqpWpFZkZPMeH19vXtJ/KBMKQ5r96RJK1nm1HwHXc2T/O7WVSvY8uiqmMe7FGXvtksNVrAeg5WsC3xWxc0PxsrzWNlYLcO4npXjYd1hetB/ogyMr0sf/my1QYN5hHvMAXTx0o9p7BwbTm41jdBJyFh3JhhA+YdlNtrzUdhu30CCeTNYpm886xNUna/FM0QobVA5TGrl9FhPoS8AMh5jnT8Atp2QFW9bDxcMlAaeu75Lse2ieOnL76zs108tjwJEWPBn1oa0RLUhPWKwowgGbcJoTebyMkDr9HMVGNqwl9S2jxiQaw5lMQaj0+Yj+Fn3033BK2m4lPrdD4f58IJQSPZUUU4gfWiKUELT1fHMjizwPx+rYoxWpj422x6B6yi96CcGnuSsmbtESBs0RzR8vkWXJfw+9+s1sEQBCPDc4hURgfD3NgAMHz9+3Jcq6P/pdHdGBuN04paXmaDB7e3tGXCiPt5HkXq+qcPG1OC5UQqDTdO3gMDtVq7Nx75ex8HPVJbtHFgmC04Yk4FCIwkFmY66ebzUDX+67+XD0oy/qd9yTnvMkT3tAlw7O45YmHbedWFde7R0A924z/HyBh5+Fzki38IgyHSBB25ubnZA4Z0YM3dOCTlByAS5Du4j4+ky2MycLamsaOyj6htFWBXoamDqOYVm5jvati0y8OJ3+aH1t86vLY8CRHz//fdnXuTM/fyFlgKIKrpGMKw0LITcgyEbXi9o6DXX7TofQqnugz1rrjusDrI2s5cubpO+VDisPF0aXbm+vt7raVjUytKIH7oxfw0f8owFporbit8GepXM5rA/oVEnFVpgUUgdZz1i+M8Kl+soVN4nC9w5O/asUICdW/pERAUFatDy6tWr/X+ff+G5tMLlk97wkg0P76JooaFpZMVoZUybpoPzPbjmuWVuADCOZvA/4zY9yru+7tIohMEJ/1deDRgMyCiNGvhdh/yP+NbX+uynT5/2Q8CgvXcRWVbrjDA/5l0bQTsc0P90Oj/0zTtGAMeVwdvb27PzRpA5AwX6Xt6BlkTPDJAMuuyUWQ90LMwrsg19ADDerszzq5wQ083z4giHj7ivzqA0CuHr1iMru1I9W5qXpo40r8oqsvGl5VGACBs8G2ZPHkxnhGdP24p25jg/gom20p1Zb9l0MWPUA16NZXW/QKdhLIMRA6EyrPvpkCftU79phMDBxLRlb9r9sZBbCZcmq0jMzLnxghYYIo/T7/EdDHs7eNz2GhqxIXeBNmbuvGFCnPSDKILBpxWShdrG0Yc3kUfBIViAhtevX59tL6XvAAUnlM7MHoVY0QclCjgyiK1yhA+sxEwnv8eBPCuPFFr6HebYZ0+YR2xQytvlhSO56M4Trx9bdsqf9nrhfRvyGn/G1vXpGhCDE8ubwf3MnTdJn2wUWPJCN9HHbt/FaDviBo2cB+VcLQp0pf9EC+mXPxUP3wFEeNegj2UZtw3vux3GeHl5uQP3LkFbvjwH1p0ABssgzkmf81LFxcX5x9Ac9aF/5EMV/MEH0GoVcWZe6vgxzys7YZ1k3Uub5n/oZGBdfUv9BuoFJl9aHgWI6ERZWawQmL3+VdSh3kgZsp6hmdsT5/7V24EpfgxQ8D7vVBm6fvpM2yg9ez8rTxpFZWXnZ63QbXzrLRSIrDw7jJWNHHS1t4cR5gRFFNPM3UmZ9B2B7frwzN1x1v7sr4Xs+vr6bB5RyNR7e3t7tl7Ne/TZisTLA6a5lQwG3Qoc5WvjRP0AIAOifpXPxtX0hmZEXLhOsXF3bgWhX/OwDeKKz+2poZShgxV+5cIeJH0yuDE/ruTEER4DW3tszC9tMkfmhebZWJ4YU5Wz6chcmV4GNbzXOXA/bTTgcZ731zShF3zhXQk23AaEjsrxu0mN9ZhtoHnHH4SzvmBuuqMEAw4NLWcYfe8wYuzwLsAIfnaU0Y5eo3oGLT7y3nS3PuoSFlEfJ5V6LpwXUX1ZHlkZb98vkK1+9/Put5812PypYOGoPAoQUc+cYlTHc1ZqTF697pUn7vBtPWt7TkWSbrth3CrhFRAwyp65H6Z1n61kiq5XQGbmPAu6nirFiZc944KEL+4Tpm+UxHkJVmjeg+72GIfHW1BjsNHn6gVaoVgpGUSxC+P58+f7rgpHmnwQ06dPdyf/kXRmY2Bgxf+MnxwORzAcKmUMFxcX+xY7ABCfxWZ83m3y/v37M4NCmNl5Cz4h1eOHd61MkY+Ozcrc79hgOuJg+cHIo4yZC66tPLXyfCMAnnPzspfHbPCY1xWAcH2WPfO/2+34zMfUYzm0grcucRvmV4wUnrvHQ39ZtkIWDKKYHwM7+uEcGfjGYBP5cpSr0Rq3YwAPXXrNO6yYD47bBuzDx/zvHCiDYxtTg4rqa6KB9NsfMWt0BECD02EgAx3QFU4mrb7zHJqebrfv1IZZ/7j/vu926Bt1N8r7U8ujABEz98ONM3Mo8DPrvIQacoSr7aBMXK/foX4b+JZ6eTU4NphmwK7zty3e9xcbjbZdl8e08toQENPUjG46eywrQaB92rJndXFxsScJUq+jEqYvSuji4m6N0wmIzUfgJEboVkOL8nC9NoZ46Bh6K2IAwWorG8VLaDakpo/n3kYYulihE/6lPxgHzo+op09x5MRLRIASQriWl4aFzfuAQX+NtDzpMaOAAXHla8ZnZcmc2cOy4sTAMdc11I6OlS/txRbkcO3Is6z82Hi5//Xs7UW6X/AwfbbRRC58yJFpbdoDfE1f6izooh3PqwGi9YkjYRcXd9+isdzXKTCtu8RgfQOgJnHZO4uglYvlqc6Lox7OETBfEGF4+vTpGUBjDAVadXCYR2jiHV8+7MkAwP2wjub+KonUdDVwLI+vAEhtlufhp5RHAyIoDo0aLTcD12itxt5eOoLjHQQNzzVcaQ9qVT996/Nc9++GBitUMJYjIBaEeldWbAVO9MXeLXWYyevpex286BrDXTAAmqc9e4ONljAXRAPevHlzBowAFA7Pztz36ld8YSMH4KiRcLKsacv42lf6Sw6Dn/Nc2INzeLKgEeX36dOn/WTNbdvm6upqTqfTXF1d7YqGeXCEx3xIRMMJev4egxWf56LG2F6ql6DMSwaBq2WKGvtGIQwkUdo2aKWljbP5rHLH2BwlsCw6ctH3eNfXV3JiQ9k59S6Ihsr7PF9NJepknobfGwUxwJ45j2ZYFsghgn6WQRtPA1b49c2bN2cy5HHYy3a/DAoYH3189erV/p7p1mgZ9ZufDdzssFjf0QcMvc+FoY/wss8occ5X+Yr76EuAGXzkcfpdj+MImPp509S83lJbUR42Lb+mPBoQgWFjMor2KSjAeo41pA1DrybNAmxFTX0wJULGcw6Rzqz3kjPZHpOVOsxkZE5xuK6I1X2ATihTt8lzbd/Aw88VsduTd7+bE2Av22DNW0JXXqsVFXW+efPmLFLTyEK9W4/f65rmEQTdXhrrwvTFwu15tveFAbSX6TwE8wMGHQVV5UsoFYXu7HYAgXnaCWUYJg7tcTjbWfY24DP3T0mFp7w84DHwt5W7l5LoH3Q1f1JsjG1IMCIGrQUvGEXLMsXv+X0/w7x7/v28573ygIE3+GuUyrIPf/jrpMy36UZfmG8/U4fGO5AaKbJD4N1JHh/0Q1a6nZfTLzkDwmeteBeGoxfug5cA+r0H3rFzVmeJe4AIAxqD+hUw97ZP7pN4WX5GBgsszZvwI2OqXuRv85eX0a2jkTmDC8v+qhg0uA0DudqIrymPBkSU8DABE+znKBDdRqNZzEyyz5n35FRYeKftHaHqjqHFwuNiRe42DSqqKPnbnr6VZfvuPtQwN6x65O0ZFNRjwiPjACaUZbctFchxnQRA5nr10R57cChFG31+Ey2x8XbS1MXF3Se0Ha6Fb/x3IzSOYjFHNrCNUlAX9wxGnezJerJ5C/qjKJ2EhkF5+fLlmZHwuwbfBdPlwVUUxmuyBnAYIhuZ5uzQdxuL0tbbgstP1O2+me7l04Jm/veyW8foiN9DdRVUMaYaNN6tx7syRH0W/ufZd+/e7XWTfIvechTDO4AMuOg39DPtqLfLuMwp/SLyiJxQl/92G2xhNQjna5p2FJABZKJLb4AcRyFWy0qABZY1DVasEzxXM3MPINjeOCJhHiwoLR9aBioHfs5j51r70XHWFh5FOr6kPAoQ0Qnpmp4ZceZ8DdMC3WONbdBmzk+qs6J0PzqRq//7nhWln3f/VkCiigiPxop75v6JlGWohsHahpUNDFsvwcaohqXgyd4G9+ljvw/gcDbv1cP3/SZaYnALLO0p4olDK+bakRCunU6nsy2FNlYGDNu27cCEvrJtrNEZ8jEYV4GQPTMbMT5fbPCD4eAa+RpOvkOJ8g40taJ1KHxmznZWYNgaQq7RttKkLgyRP+BmkGWeba7Jqh1HkFZyxrwxdnu7fc/Xac+eoXnIhqZ9d53MXZcITR/mg7+td8wP3mXjqNLpdNoBhCNg8ALzb+BK+yyJAOQpzYfCUNajhi8ApBhm98N1vnv3bo9KACCs/+ANRwCgqZeGS0fPnfW0n6V/yOC7d+/m22+/PWsDGlYPP3v27OzkTQOqLuchd/TNfS0weqiYn6jXet1yYL71/BxFL76mPAoQQTHKX4V46j1wbcWU/plZ7zNflXpyK+Ra5ln1gQLDGoVamMykGOOjdWDqs6H8MW9q5s6QuKDcHfor4jdYsAFEGN6/f78n9uH5N4xsGtSzs9ff9VwDH+pl3E7+cu4ItDWAIWzrpCmUSpWVlwzev39/diyuT+u012JPz5EKGz6+LmqDZi+IOj0WxkNYmx/6TdY7bTBuG7yCWYeQaZvxNmReGfGYCkbpvyM/1L/KUalH5nl0n/m7+UEFy47cFVS0/UZGuGdaFGj7a7HOaTjyPuEXaA3/GZi4cB2etj6pge+4AbbermhZsaFytID2GFMdnUb/Zj4vI5BL0Sim9Zo/VQ6QIIIArfnfDpCjMnXuGD9toTM4+M180SgMdPCSE21AF3jVS4m2IejBRg2hW3mzQIB+2aldOYa1eR3HTymPAkSAbFlLXgGImfOtYfUUKDAXz/tdF4fLZ+4fLmKP3F6RmaieOaV9XzGYGY0xWaBs3M10DZ+Zsc34HbeNeqMOpp2Fgj55fAZE1P/hw4e5vLyc0+m0JwvaMJrG/C4aL9pH+bht98n0uLj4vE2zUQmeZQwGX9ThqATK156NvSDGhfLetm0/wvd0+rzWzVLDKupjpe1tnyg4IggoyydPnszl5eX+3Q8DHBQeBtjKD2VuY9atejNztnPE82Ol6Hds8E+n09kZIMyPjfKHDx/2CArXmltAu5UNK3d7tAW19Lf99NKl55BSAGC5QqbaV3uUGHQDgEZEyAsCDANkLQ/Oa3FkY2VE2VYMnzr5d+buwDwDc2gFb3m5dOX5UxzlILrgd7btLsmXPlo/ffz48ezLsl6K4VlHsQxeoP2TJ092MI8R9tzc3t7O69ev55tvvrkX9eiyXPWNec/LSMwZ9KRv6D3rEPOc+aOO7sq5s65dPWOa/o2WRwEiHPqbue9BmekwQCh/C5tRXpVgDV+Nlj0T/7RPVVxlAuovUHBbBSu85yS8KigrNIdpV6gbAWsehMexYnYMTdG6FZD7wG8bq227C8G77ZXxsOEg3IgCwgPzkgLryEQqZmY3sN714ZwF2rFhhV6n0+lsW6XbNjhkPg3I8KQw4ERlrNRnPq9zs8ZMQiTKvUabhDt+X15ezvX19b198Yytc7KaH+ZktSRg3uO+AbqLw7sAR/MojgBGgyUe87LrrsI1fRtJsLy6z9YNfo+59hbIgnEX2nEuD+/SL8sZOsi89PTp0/0kU4/b4MCOgWXPx4l7TvnSJMaW8w54Bt54+vTp/mVX06OGDp1p/QDAcOIhQMPfffHns8073kVC35hrllmIACBbTRy2sbccuH2e9/IjtLu5uZnr6+t7jmBLk5wNFKsrXZd1ax2+Om1cc4EHGvls1KIy0HFUX39peRQgYuZucuoNW9HaqHqPMRNkRqyxmLnvRc+cM6iVRj1e2ll5zlVqTuxqiHQ1JlA94MBJQDC0Ga3LGFyfuTvF0P1eGU8zqO9hFK1UutPC651WSvztE+asOPBeoY2Ny83NzW5weQejTluAE+7ZQDx58mRfH7UXCjB6+vTpHvp02BRAglfYyAHziRI2mKWOJok5UmDPkTmnL4BhP4dSBjDZQ/aZENRJX/3tEvprIMd47NEVSMMztG/vjbqaqwPtmj8CnxlA+d4KlDcKYb52JM3XDZAth7TpnQau27IGffGCDR6tkwpgDJAYn7cFQy+357FYpxisejcBCYQsKwDSP336tLflqAF95nkfSmYepD+OXNiowwPwGnJDPxkvY+I5L7nB5y9fvpxnz57N69evzww4NOrSBrzp5El0fJf8ZubsK8D0qbzg6A/3qzMdtWD8yMsKSHzpMoP5ubp3BWhXNqrA82vKowERM8cErvJwONMhsZlzZfiQsa130DasbF13+2TlskLA9kRW4VErSStoj7VI1V7cSrmt2vB6nIUfGrhehzzdho2r6YewcR0l4uUIP884rQSZB3+8CuWMEsPbMihzxIK+49nhzbMVjLP+6wUzLz7l0tt/+6VLRwPwqjxG5tLHW19eXt4DQYydU0M5lAgaokCd04IxoB/0Dd5wPgk0MGDoB+bKX5YPaOtlKfjEXwe1d2jPvfVZzlbXC3gLYiwvR9GIVTTDybpeXqsjsJLho77wjj1Z5sLhcIM657AYhFhemLObm5vdE4dvSAykjrdv3+5tcVKkedc8ydKDgaTpTR9qPMnnMV0rz/SnZz5458bV1dW9KKyjhvCBkzmbZN3kVkea3rx5Mz/72c/2vqJDDFbslDXyVkDjqBr9saxZz9ZJXdHnaDnFTjP9sGw+FEH70vJoQMQKkdVjtUeOojYaXIU8/ffKO/OkrUBLIxBVNI022NOqwuwYeN8K2cazIIL2246BxEoA8V4rVDPn5+87GcseCoqJ+vmqnufKxgZF5VMtPQcoj+YVoPCJYjRkT7iYPj9//nyPLqA0LcREQujvKi8ARe5Q6ocPH/aQao10v5BJyNbz7UiSeZl7ThKbmbOjx3nPiZuMq9n3VrrmY5R4w9kFqCuFVRBPO5Y387ujEdQHeLKR4p4jcbRhwFGgcCTLyA7XPYbOsZ/pWG0kPH7mwSd0GmA6Wmbgal1lXqd4eyx1mK5EH4jINdpmvmOOAW0Yv4uLu3MgmAfmkK3YPrvCSw7wOO3MzB7RMdCxk2WZr8P06dOnPXeGY6gtc9Bq5g4kwL+WIydPQkfrO+Scg714x8DfwIG+c8+8A90BFESCSF6FbuY/O4uWfetg8+HKYV5FN+Bh6/qvjUh89QkT27b9w9u2/fvbtv3qtm2nbdt+b+5v27b9oW3b/tK2bW+3bfuz27b9D/PMb9q27Y9v2/bLbdt+sW3bH9227Zs88zu2bftPt217t23bf7dt2x/42r4u+r4kso0vRHRC0sw5aluF6GZmqURpa+Y8cZM6Xb+f9/WHxsH/q+d4394RhrRtGVnT16P+r5Rthc/vtT/2eiwcFjj6Tf02NnxR8/nz53N5eXnmbaDACpIaHqV8+vRpT67i7zdv3uyeDgmV8AhGFUVqpfzhw4f55S9/Oe/evZs3b97sORTcY4y3t3dJcKaBvTSWG1C4/WIidKFexkXeByDKyz8GQZ1jgyAiDs1uL9AAcHm91wbRINyGwPPCPEMXnnFYmLYMXjwOA4iOz/9TV8PNjSiaD3vNBtsRmfKs6bGKQhg4Qh8vYfmZ1TWWAJmnZ8+e7UnH23a3LXhm9r+JBpALQX12ZOijlxd4BsACX7x48WKur6/3fAHo6K230AT+6s4oeKi85Oe9JGRQ4GgY+RxXV1c7mOAbN05mdDsAgeZd8PVbL42gD+iPdR7P8Ns8XV63g0BfiNS4LzX21G2bZZ42bx2V2olVtOdL6mn5KZGIlzPzX8zMH5uZf3dx/w/MzD89M//EzPzFmfnfzMyf2bbtf3Q6nd798Mwfn5m/c2Z+98w8m5l/a2b+yMz84zMz27Z9NzP/4cz82Zn5/TPz987MH9u27Ren0+mP/IQ+nxlmhLee0MydYuD6CgVaeVFW0YRVCKmTP7Nej60B7voq/bN32nqpy202/8F9NI3KnGVqC3DXPh2VQPlbSFGCDTfSnkP9pa3D8VX+CLgNir1u14GipX+AG3t+jSoR2aAeL3EQ7fn48eMeZmU5xTslXJyVToQCBQS93JfLy8t9H72329nrgX+fPn26f2+AcaG4yIWAFgBmxst9e8ebIgDwisdjQ9Qwvnmx4JjfzpKnGFgfOQCObhlMuO32xxEMy4R1AHRraQj8oWjEyjhjXAva6yl67ICrmfNPfQPokAWANf3Hs8fAA/CdYwQtiAYAQilNPnQfunPDz5lXrF89Zsbk79IwFwAD5xx567D5lTwJ/n/58uXZc7RrnnX0xNEf6xTmw47Yu3fv5uXLl0tHyaChPGg+5p4jRB67jyG3/Wm0z84fdbe9AmqPi+uu76eUrwYRp9PpT83Mn3JHKdvnC//Lmfnfnk6n//MP1/6nM/NXZub3zsyf2Lbt756Z3zMz/+DpdPrPf3jmn5qZ/2Dbtn/+dDr96sz8vpl5PjP/5Ol0ej8z/9W2bX/fzPxz8xlsfHWxAjKA0LjOhG7mvhJpzoGLDXpBgIXJ/bGSqFfVPq9AicGQvSr6g8Ew0OicWakiWGZKj6+MiOL3WqsZG6NmWnjtDsHx1inTz4YC5eMthYwbBWIh9tjILeA+Atp1XJSWPVXef/v27R5+JAIx81lZdquWw5aEKuk/gMZLH55/h5jxSpz7AN+aRy4uLs6Os/bcOppAf1C6BnNOWMPo+ONkjLXt2mA5fwNDYABn+tiA2Xs1cPcuGaIqLB018gZ9vPvFuTduz7xio14FbGBuWbTiLhCBNxs1MSj03NjIm1/rbOCtek3ePAQfXV9f70m1rhsewsOvJ12+ZIzkQcAf1MH82vA6bG8Ayrg8Fz7XwjkW5iePy3rZES7oYmBlkNxoVHNPLHsFfQZAPPPu3bt9yyf1u0CbRtK4x2/65WhEoxz078i4G5C5GECYl6nHYzbYqW340vIbnRPx22bmt87nCMLMzJxOp7++bdufn5nfNTN/4offvwBA/FD+7MzczszvnJl/74dn/pMfAATlz8zMv7Bt289Pp9Nfa8Pbtr2YmRe69K3vMxlFxkwoin4VOeA6xC96q9KxEvBE1jNyn2hL4zn7u56JoxH2xjCo9f6tUC1Y9shW0RSDEyuEKg+/D3M7WuJxIsgo0YKG0s1z5DEY9SPUbt9CfDqdzjyn0+l0ljjWnAuUKl4/UQOWT/DyCQ+/fft2v+c2zCuADSeOXl5e7tEDgz1Cm47yvHnzZlc2bPuEPozDxszLMAYmJH9CR96z0X/79u0OLmowoTP8ZvBiZe0wuN9Z0cXGw8+ton7mD7dj2nPfgN4JbZY9vD+36Xse92o5rP2D31eg2KDZfIYBrdx3ayTGwNEnGxlHtm5vb88iarwDb1AnbfsDc5Y3DiLzToZGHuh7o5CM+ebm5kxPeZsl43GEwjkd9GOlI+i7j0x//fr12QmqzKP1h8Em+mNllFeA9ebmZn72s5+d2QvnM/Be9Sy8YZAxM/do5WgM91f2ivsP2aRGShpls2ysnMwvKb/RIOK3/vD7r+T6X9G93zozf9U3T6fTx23bfj3P/MVFHdy7ByJm5l+cmT/4UOdqjC0QFfiHJsXGy0S3QvZkefvZanL9f5UR1zDKHouZH2FCyWBsGKe9rQIfxuzr9RTtLRpEUAfKwPRshKWo1+G6zo0TA62o2GHgqEmjJFasKCg8dSsRFJ8NKv0+nU67YvDntdlCalr5o2Gmn70+e/RuwwqEMVp5QCd2jjiSwJkQKOVGocwv7gNK18stnheDHcblXA3zKJEu07t1VnHzPIbCoA+6rdqC1+w1u1/mWYNK6GramE/oiz3mgiXPVx0M6q+B824Z6G+QBH3oM4DM0Zubm5uzraT0k7qaX0Ahega9DLpXEQcMLLQ0YKBP1O9Imw2pnQPGv23b/kE6L6dwn9MnG7GA3qfTaT9QzLrBhf+RTZJGSUh2ffQT3cm4nOtDWUUAcCwAo5SCD+vkGuxVnaYfesPA1UuuXsopuKg9si6ojEJTJ3f+lPK30u6Mf2Vm/nX9/+3M/Pczd4JiIzVzt21q5WlB+Ia7jEqtkOwVGIFSx1HOgssKBVKnwQH1VJi63ML/RaqlAUrABt0MWQVlRgZQeCnDYc3V+Iy2rZgLXLyFs56nP4jFGHmetqyYEEQnWM3MEu2bBxz58FwTFfBYeA4Fz99eo7Xyb1QFQ+K8BgwJkRLoiPEkpQABAABJREFUTDSCPj55cnfCZOevXrWT8swHM7MnlV1dXe30AVQQzVmdWVAv0e21WAbsVWN8UGrmD0c1+BseW4E3J7YhP6azaePogncBdDweq/tAPe4nxtjza952++2X1/edE+CtwD2cy2OgHhItMajQx+Xq6mpevXp1ttxC37v90jJgsAvgNlh0JMDOBv+Tj2TaWjbhL89do4UetyM2GOCPHz/uu0gADStedCSw9dZYG9R/++23y6Uhip3JI+fR16CvI5HQFrqhzwroCiZsb6xn3BfLmmXma8tvNIj4yz/8/i0z85d0/bfMzP9Nz/wdfmnbtqcz85v0/l/+4R2X36J798rpdLqZmX2js4nhdesaFQu2gYNDb1bCNTBMmg2O2zfKrLH2c508e3egeKNTK8KZ+wCI8KO9LSsvK3k/Y6BFVIG/7TlbkVGgpz+/i+D7IKW2Y2PEOz49D7RsD8xRCL9rIaUNFDCKxqif52bODzfyM46UMDZ76byLx2gPBN5rNIV3jubePMmzBlM2ljZETrIzj3IP47DKevcpkc6kt7EGPF5fX+9j8kFh9nboH8VzvxqH57rzWbABX5WXqM80POJ/6nUom/YMQFdgqP1bRcXoV0G459u6xWOAXualOhKWK6/BcwYE9OKz7mxPRMaePn26JwE3Isnyw9OnT88SiJmfd+/enR32VtDU5Eu3Y55yW54/55JYV/ccEnSjI30z55FGjCTjs4zTf3viBQadr9PptNPEetQGHXm0Q0Fd1e0readOJ5LPzL3E1xZHPWxr+o7BA0tLHM/9teU3GkT8xfls5P/H8wNo2D7vtPidM/N/+OGZ/2xmfmXbtr//dDr9hR+u/aPzebvpn9cz//K2bc9OpxOW6nfPzH9zWuRDfEkxIyBgNcQz59vt+l49mKMQ1lH79cCq7NpOFbL/tsA4VDVzt3xhQXWOBM+smN/9sqGtQvXWwnqGVroYrq6n2vtYAYCZ83B6Q7XNJah3hwfNNbx4J3HVAM3MHgp1voaBU9dfi/T9P89zZDV98zzAh0QGOierKI3pzzzDL/YOnTODQr++vj6bHxsfgyPyFJ49ezbX19c7DVg3t+fCOM1DyIdBlvnUiWk827ngvus1r5g2Vb6VTfMxdZh/KY0e+B3o47F6Scy85nwh6quseU3bxWDCBs71YQChJbRlh4V3R5jHSBB2n7w8xDxzMJrlwYl/po91EyDTZ9OYrvClaerEbOdDwOd21Jor4fbt+fP71atX89133y15AX1Af6BDnRWigh4nuVBOsnY50i826Jab6jfGczp9Xs7xd2SODL15o9Ef18/yEb+hw//PljO2z+c5/HZd+m3b550Tv346nf7f27b972fmX9q27f8xd1s8f3Vm/k8zM6fT6b/etu1Pz8y/uW3b75/PWzz/8Mz8idPnnRkzM//OfM5v+KPbtv2rM/P3zMw/MzP/7FePUAUDZoKuJsQo18qB56vo2obrKCApOnQbKzDR3yuvxQABxrVCsNGwEjXittcEjZpENnMXbeB+x+tnjNI9Jitb94tr7psFgndZ/8fQ0Q8LJOuWCAn9bP4LgoNR5swHMry9Zm/ghMdf2tproP/uW5W1FSDz6XV/lIZ3JaAAyNmg39AKRUydKEAvdxQEMxceL207CsJHj+oVm7dME94zaCgYtwLnHYNYR0K6lm+D5Ou0ZSW+kq0q+ebJFOR7To+Av2XKwM+eKTxbh6CerQ2cAaLp68KzDuNzvgF5NQU2FEeI4H34h+cr9/AUAALjz04n3iGXx2CKaAF/Q3/km/doB9oaRMK7lh94mLHd3NzsZ8vwvIGW59TLYCuny/PsZFzzKfNoHqiud/F8WN+1X6ZdgbJ5pc6qgSu6gMRwzgzxV3u/tvyUSMQ/MDP/kf4nD+Hfnpn/2cz8a/P5LIk/MjO/MjP/15n5Pae7MyJmPm/h/MMz8+fm866MPzmfz5aYmX1Hxz82M//GzPyFmfm1mflDp594RsTMnBEKRrCirNE3qnMdRwDCjMD/nWR76o1wmNm4ZrDhZ9snio1YgZCft9C5Pdoi3M7/poWVp6Mc/G+a8o63MGHMbFRtNIq2LUQIuD0cz2vXhR0JsAK3wrK3Vk/fnhXvQROf6YBRdVTG4WH6jBLlPu9gQK00oBXt2hCRLQ/9oK8jA6ypUuBpRzyggYHPtm1ngMWeJV6xvawaM4MC9wfet/G3MaUOxsjWQvfDSzduy32Bj9ove+QFBoBwy6znAz5bgR/Lnue6cmKv01EKxruKnvAO/GegDQ+ZdjbIzqvx0e7Mp8PZvE/UkHoKwqDjzJ0Xb8NXPeWoH/P34sWLXV6rl+gLS5n02YDV8tvIgK+ZhpY/5MUFGlouAC/eWmoZpC8kbB/pevOweWIFTG2TGk3gOhFN84R59qg4ksSZNJ7Dh979sfJTzon4j2fm0BU/fe7N//qHn6Nnfn1+OFjqgWf+y5n5h762fwd1zfv37/fz1WfOIwUVFK67FIX2XouF2+3wt41MGb99px5HDKx0rFzxUv2uAYINtIELhvGhLGUbgSr7I6GYOV/yMABYJTM1YoLxR8mjRBAGP2fF7uiM57dRGATcAIYQJUbzzZs3y7kxDxEa9NqvvUd7vhgAG+T2G8VocIRSJaTuNWp7Wd2Vw/iJIjQSgZI3yLOxxCCZjva47LUZkPu3D0lqvhH08PsNefMePOh+2IBjvCxvjow4wlAQYJlaRTAMLLyV0s/wbiNN1id1XPy++0x7XcrgviNR8Jp5hSU9xgqAdG7QzOw5Mlwnj8ey7hNfvcumQKLGkDl78uTJfvy6I3weE330Vkx74eiIOhvImHOrqMc8QoIp0Q7GSqlThI7x6bKNGnjXV0HRzN0ZGNW5nkfLgXkCvfTu3buzJUS+k9Ooi/Wt+4HO4KwVb1m3fkLPfm1E4m+l3RlfXMy8NcBWyDNzb1J97Qh4rLwK7iNgVrwGFdRrz8WMW0bzOjOJVgidlaABhz2gmTtj/hDz1LPyNSNhj2FlLOqNPX36dN99YEBTAEIdVopWLNDIXjsCaEHB+FqRYcScwOboAHXVyFoh2AjTL4dl/a2O0p33abd8haLxSZsGQY0QASKcC2HPk3mw4baH56UNA9eZu48E0S/T2/NeA2kQxTX+Z3zQzlEjG5CV/BVorJbg7G2vvFDzV2XXQNzg1/XVwVgZExd7lp5jyxLvenwGc/TB24vpr/kA2lnvsFTnKNAvfvGLve5G+gwI6Mfbt2/PnjOocT6D69y2bf8WzfX19f7VTc9PI0K057bM3wYBK/3F3KJjzA8AJ0cNOn+A9Oo+3ndUqbxn3lrZCgPDLqPxDPWTi2RQ5zqPdDfgAfDmCIR5Bvl7yKE9Ko8ORNRbnjn3pmrMG32oEedahb9ltQ/XdbbUm6+ig7kr5BaERiAoNoBdC7NA2bOw4UaZeRxOvlp5WfY2bfytHCgOL5s+1Ol1exIF3Y8KJ0bcIUEbqCoVZ7YbpM3cATXTY2bm+++/3/f5O8mNMXs9G/AEzxkA0XciGygPL2/wXr0kR15sSPBATGd4vWvtVlw8c3t7nudhsOwchQJHR586j6Zfc0AYp/mIZ/jmSgFEAbi9eK75twHiCoxQF/SmGIwjA6aBl+5433MC8Kqe4RnLfYEBtDfP8L5BKACZubPh8Jx5LjnLwUbU/GKZczTCEaFt2/YlOwNf3iM3Y+Zz9OPq6mpfunASNREJ6rWegBc5I8W7JKCpo1hOsD6dTvPq1auzL+8CfOxMui7nVVEc6Zv5nHPR74f4Pjy0AqfWP+Z3xup2feqoaWTetGwSmWEJhDwxH+5m/lvZoS8pjwZE1MM9AhOUotIKuEu9rAKKRhXch/69usc7KGgnmHEfg+elgUZZ6KuNnD+W4/7yLIiZKIC/N2EAY8BRIEN9TkykXowkY3Z0g7HMfFZiCA3KhnvPnj3bIw70yeHzhtIx1gACK/htO/9K5MznvfRc82FAzq0oraCl16QbeuQ9xuXttBiBI2CAsWj4188BNOmbIw3UQ3+trOz9YbDY6udipWsFZFoa9NbL99828IA458CYNx09ajGA4FkfGMZ4LV819p4r19Ox1WM2bc37BSylIe2aDtDNSziUhvM935xkikHjI3L0BwBMJIE+wEvQ38sUM3NmiP03z0A308RRT/KScDY4RAvZYr7pHwWg4HlgS6iBBTT0XHo+LHuAGWRiFXlocR4Xcs1crKJpLQXxPGfgYf1fR42+We+hj+ATwB3giKUP8sKcA9F6/ftry6MBEUXZM/fXMRtK8j0r9XrGq3dm7i+VWOHWe3qo1OuYuQufw7he6+adro3Vo3WiaT0grqHA/FEYK04reUdyHJ3oWraFx+DFyYoGQtT/4cOHM4U4cxcB8DkQgCxHQLbtc04AgoSQObfC2xcdZnVmOX29vb09OxGvXoS9+BUNTqfT/pVB/md+UCDQxt+kAJR494q9Yvro7Vs2UDbWvub1dY8JXvDZFuaXRp3Mr/RrBUppw/ky9cgMqI/W0WfuFK49xPat/a3XVe+8Xrbr6DKjI06+Xto48lL6Wy4sT7S7iha6bsAvoW7C9wXMXvO2saRPyIbBlI04hsr9aeTP151cSTvU9erVq/nw4cO8fPlyv++dVwZP8CJ1sCxiB6aRFvfn48eP+3kIt7efj5AnzO+6C8Id3TC9LX/ox0b1HDUwwDL/GwBRrMsKVO3Q2cFYJYXiYOD8rQDTb0R5NCBi5nz/9sx9IlppWTBmzk+ye8ijsOddI97nVlEN98VMV8VJ/WUgXzcDdk28STUrUEPbHCdrAYGeKFW8GATSYWArbnvv9I92VrkNNo4GGqYNRzjjpfi9hiHx1Ajx0xcUl42yDYPBQD9ERR8YA+ARhYvidL0NK8/cecEXF3dHBnOs9KdPn0+R9IfEatBQog3F2vBSP+OkX1UuNpw+tp3SCE752ks+BtLmY0cp/CzFxtLRK3uhljcn/Pl6+9Vo28qx4HloU4MAyLXnaKfB9VmWOxd+/6gvBqSuHwPqdXlklcgRoKJAs2vqXK/hdl1tyxEdR7n6PrRygV5v376dbdvm5cuXZ4Clzgr9t1w2Z8Zj4H/nQbAUxrteDi7PmQfQmwD8mTnTpa7Pc136eg5cv9tGZ5ReKzDBc4CXy8vL3eF4/vz5/sXf7sBY2ZyjCMqXlEcBIgjf/exnPzsT1CrGhgf5Xe+h77aeladjL7wItF6PQ6Cuk/v10NovSgEEdcKAbmuFjmfuULFBkI0YgmXGtvJ2WM7t0y7o2ELvsH7Dy9RZb805BgCNKmgUImuE1IOCsVG4vLw8+/SwwaON5yoyRVuM9fXr13NxcXH2dUGebV/Nf0RA8HLoi7+2aVoXaOKd3t7e7qAJkOHlHehigw3NW695xXPgnAZ4gfqgRXfy1HCafo2EdEdBedy8YJDE/DQaY2NovvLptjb6Bdb2gM0Hq8iDx+xxrgyNn2H8BUWWhxpt5MUAuHTh8CKP3XPtJGfu846XawwgHamw3DYCufra7IcPH+b169fzzTffnLWJ3iDy9u7du3uRDuYYT/vi4mLPk4AuBZm8+/r16z03gmikeQI68j9OhOu0w9hIkqMv9LU2xvOPo+Z5beStzotphDPz4sWLPceFeg1AGmkzn6yc1R8rjwZEmIEq6CuPxcy/qq9e2Ep5zKxDj7RTT4rnDV68TEHfXKcF1YCi3q1BgJ+vsjAI8fKFETf99zqz2zPzW0mZphgd5x8ggM6TAAARQbABsMdPNMLe2czcU6bQ7chLskfVJQ3ofQTcmK+GxhmflTNescO7Vlq0B8hg2YN6bVgYTyMbNiqrqISfNYCyJ0T4m3bMC6and6fQ94JsflZAnX6iCN03ytH4qxRt9J14RylwgBfsBTIPzo3gXc+tr0Hn0tZy7MTjlZPAPJr+BpzVAeUx33fbXsK4uLjYZZcoQ0EEfwPKT6e74+xZ2rT+YxnkdDrtRzOXD61vqrvev3+/nwB5e/v52xS8h1zDW45o2cDf3t6enXHinCpH7KDZ69ev9zkxfZzLAf27bdr6yUDCoMPjt9OxAsTQ3ZHTFX9Q6qS6Hn9m3WOu/avDaR3zNeVRgAiXI8Vm4z1zHn3wFp8VgoOJfsxTqvI4eq+eh/tqBbpav/d77auN3Wo8pk2Fz/Tjx4c4EbaHYVeJQTN3OxDc59aPsqrQGSn72GeMWT8mhMG0UUJYHZbEUDhJyp4HihIe4RrrkldXV2fj53kUmgET/QAU+UNYKEz+Z3/4ixcvzj5jTrsr/jFNnaNiA955RsnVsK/mvN5VjRpzDG392xEO12mv76gfVtQrGfRYfM3/F/xxrbk+HouNKnPHdctzAZb7QL9pb9Uf64kaD/Md9HUEz5G5mbskQANGRwlou0sBnrfu2qKt5n7YaDG/jgZ2qbXXAKDbtu1nrLDrCKNM3av5Md9fXV2dySWgxgnBBsszn2WMY7Hp+6dPn84+fW4aAP7RD37Pf5uufh/9YHkkH8lffaUuj9F9MD1xRi4uLvblIfjNAKzJsq7LH0z72vJoQISV+4oJPak83/dteFfKosq0pZNkBbIKWTV0ZuQP81iZ+RneNyAoOrZXXjBFaNdr0fbECU1ygJFBh5USSoN7TlSqgjat6asViOsxXbxWC2BAIZkWBhdeT8UD9Rx42x7/r7xeK0vnAaBseM/JUs4xcOSBQ3/MQ9RrI2Uv00dZO2JQ/oIG0Km8XO/Vx3pzrUbPBoo6/HVVG1B7neUTFD5zXa/cxtIK1p74aneJS5eRLFMrWfbPau3cythjdN/NC27b7x0p9c4NvOK5chTF3wOyt942WRpj/F3WhG6uewVS+00c7+gwb3VHFXrLkUrkwiD7m2++2Y2/dxUQ9TAPEf0jP4CoicfHXHg5hvvv37+f6+vre7uckKm+w9gsA9AKOekHH0u/Rgesj93GEcjkHo4Oy54+m4MfJ8pCb+t+5rGA70vLowERq/CPFXInb+Z+6NJGdgUIHgIQ1NcoBO9aWdA3e79VBo5CmAGNQMt4FtyVwjJdHEnwu1aoKAhn9puufN8BAe/+5AIa6IL38OnT5w9t2QDYWLDGznLG7e3tftrk9fX12fcz3E49LxT0zJ3y9P8Iq7c5Qm/AhucH2uIF2HuzUkDh0C8r/ouLzydnXl5e7nXSXz9L5rXn059l9rsoFMZEW/ZwZ2Y/V9+Gkef6Hm0678Uggn46/8G8DX1WIJr5NrCnHoAf4IL79MW5AjYi0M79qTdP/720YSBu/vB8VEd4nK6jdFjxj8fsCICdGcYKL61ky/NqfYL82kgiz07YY0wAUPjIsk0979692+UDXWEw7h1T9NmgGdmjjcvLy/3bDozZkQnaqR4D1DRa1CgXwJUtn3xvxOC+eteyRft2UJurZFkzLzgax5jtJK54xPI1c7f0wgmWgC1HvZzzxM4X8zZAr6Dza8qjAREz54bSoVOjcAMIBIf/jwTfjEopYEBoimaP/jeg8P8It/dTr8CNmdQgwMquwIWxWCnZ+y2qdsKQlyAsAGbqArnVfNSoGRTB/Fb+9p6urq52ASZCYMWKAWU8Dh9WyXcLrcPo23a3LkzCopW+0T3KquP0ATcIMu3WsALoMOpWxF6qseKzsZ85Tzy158kzeLOOZnhHj5VpecHzZhkwL1iu6Cvz7PkuEPf75kfGDF+5fzXwzp2hj3YgbFgsB5SjpTfLlPtLm6u+NpJlurgfK5nDqPeMAwMtjAkfkvOOBNPfMvnixYv9aPfqJAzPCtiYpugA+NTbhR05oDDXza9gjl+9erW3390K7qNBi+eOfnQJyxE+6+13796dfd6+8zEze94VgKjtHs2d6yhvoEschXjIkBdAcOiWl7gopoF1UqPBAD5Owv3a8qhAhEsnygxn78phoRrf1nUUiahS9PUqPIoNUz07748206Lg7eW2H1YAM+fRGNfF/yicKtyiVx/G4tMhoakBVYGLk7dsWK04Pn36tO9ucB6DQ50GHF2u4G8iBBhxktVQzhj41RxDcw7w4bqTrHiuhpf5Y3spp1saaFg5On/AfGA+dSIjhWte6z+dTnubjM95Ph8+fNijNqfT51wOrwkbsBqs1rDYI6txpW9WcuZZ6Oj6HMGCf+FJ88Vqnd6RMbdFnzHENtKdb0e9KtsFxDYqBlrURRs1dr7P367nITrzu4abiIHzj2xMLy4u9uRH0w7aGnC6XeaAJMomQDuSMXMHaJAVJ2yuolxdNuJ0SV/3nDhSSh/qPJlOpTFjhqY3Nzfz7bffLiNQ9K9trsA8uq9LbDzv5Y7b2/tfloZGBaH8DWhjZ4mXSv2u6QQfML/sULNz+lAU/aHyaECEJ7HG2aFCC3oVD9eOJsuhzv591Cd7M663CBpmtQdXxeJ+1+PgOv30+N13Z1nbIMycZ3b7q49mUCNse6gY75k70EAY3h6VFQ39Z3sXAkAdLNngjQBkMNAImGllpfDp06f55ptv9nkwjU0zRyBubm72vey854NmjPoroN4O++zZs32pBlo9efLk7Mht+mll2JwBJ8D5We6bx6EX9OZ/e402jo5sGOC5bvjD4wUUeRwrOYI27i908uE4Bsw86xC9ASptHPW18mR+dUEu+dt8SV3mJYr53TLt+y7tmyM61S2NeFhf+ewSGxtO6+zOHcZU+iM75sPSDoDltq0LHVb3t2vslNAm4Bnd4q3cRCRevny5n3sAzzPP5g/vEDIYpxjYl46fPn3+HsiLFy/OznwpHzBftOXlTgM6HxB3xGN2rsoP1tHU47ac80GbK/1FsUPBc1dXVztAO4oWf0l5NCDCBtbXKpj2TGbuhzp9b8Vg/M2zK6NKW26DSXT4uB4S3pmXYmyoaQ8hcp88Prdvj79rpw53+drM/a9Nzqw/LGTgw9opjE8fV2fXm4Zcf/v27VxcXOzLFu4rdOPAFScj2hgBVqAN66EFafQPWllYGaeTxmZmT2yykjGg5B0fVetkJoOkzhvFSwCeXyvjRnFevHixey1OHoMW5VWKva5GGaARtDdvrZQ11xzN84/XhD9+/HgWDUIRlw/tYfvv8vgqQohStaEw7xXwNPG24B8Aaa/YfGSjxhxarl2v9YL7brDEujpyRWQB3nDyZGlgXehD10xnjBZySY6BPV6+QVNPFpmmT8yp5Qu+ubq6usfrdkw+fvy4AwnG7GJjfzqdf4IeRwJHBHqVr83/r169ml/5lV/Z+2yd7vwG3mFOaYNnzW8rsOBIYZ9tob/IN0mtRA+tqyie78o0ILtLtl5C/ZryaECEBQhmrpc5c54gRll5DwYPDVFy35EGG3yHhFfAxfXwtyfaXhD3nbvhcdiLtfdlZWvvjfHaSFuQrKgaAqP/vtd+UQ9eN8aiz6GQbWS9VYtwnPvpvmBULy4+H6/LuzYS0MlbwmzEjeq9zky7zB8eie/BXz67ArpaSbs9G1P6h6Ab6Pjz03iP9kw9H97+5cRML/UYqEFze3Q2QgY8lhvPP30z4DGvM08YCcZtBe06CgosXzzn++WHVbTPRqp1WzYKKODRtkV9R/Pg/7sE03G6jZUH2/qJMnFaK/2+uro6awMQCdC1ATFPcFAR73uZhLq8M8zgDTlA/uHBmbszOwD7BexeiiDaR52vX7/eE405Q8JGHjoxRnvs/YIuBtS6oDtW0C+O6nbu7YQWQBsA1nnkHgCA9wo0Wq+XL3usuUt1aes1iPTXTR2x+pryaECE10gthEVeRu/8X2/A4MD3Vihy5jysTB01fmZIe5LuKwaB9ygNp9I/fhB2BNtelb3Mes1eS/M4jeZ5FyElEQrvBQWLwtq2uxPzrMAqCKYpfbDX6SQgh1RpD2AAOHAEwv2mbxgUhJQEqtvb27MlAwqRCQz6agwzdydMMoc+oGtm9nCzAYm9E3sZBg/MVaMZ8BpjZ+nJkS7qd84PNGH8pn2LFad5x8bc9fEs/e6JpDYE8Cfv27M32KwBb7je9y0zlo3+b+DP8/7tJZUaCy8Bmg8tv3i3GJAjINS2rWeoo/PlpaqZu2iEzyDAc6UevHPoSp/evXu3J+whc0QdVv30bo4CYeYGHi/49JH6yEqBAbxOjgQRTRvnGnPzhulfMMxzAMBt2/bxk1vluni3SwbQbiUHBs9+70hnrApzzXw6wroCmm7DTgwOgh0fnvnbkYgfKYTCZ+ZMUbocGTKjwZlzr+Uh9FhGNcO3rTL/KjTrKIPbdl+swCoALW6D961Qug7c/tlbszHB6wUorEL0rdNJS84hcH8cNse40yZnLNjj9/HWVpYos4Z9nYfC+Gqs6HtDw9TFWAjpEi52CJexML4CRRQqvzFyjmrQpn/7uyIobmiG0sGDrHcOT/PjqFTpY/53+51b1w2fQJ8ajUYhHDEzuHKIneIdOy4GugbNBpSWkxqXFagAxNL3gmDobNBsehWw2dC47cp2HR/Liz8c577DE2y9rN7AwMOrvg4vGVivDJXnFIME7RkT0Uaes+7Fm/Yz1kl2FE6nz0dVf/fdd/cAkPsGv3DPdTv/x8/b0Zr5bC++/fbbe4DGxfNhO2Ag5/Gs5rX6vRFmaOAdeRj/I+C5miOecZSRfJWPHz+eHWb3teXRgAgrfRvxMtOR52UUauamWBH3ur0h94W6G7Zf9QtDRJ1lxpnzr/6t1snMSCuFUzBR42Kv22OpkDg8BpN6rR7hp78IvL8quhJyRyK8Q8URGvpAJIF3CPd6Xpp05agKioexcooenr356Pb2dg+1opDt5fI+NCAvgn7QpoEYHif3adN7wuEJr4+j8AFVzA3101+3ZyNej65AeuZ8Zw51mn4Fv9DXStP8aeNaY2saQQeMuOs2uCzAKwA2n7uYJ1yn6zA9Cn4Apsx7gXLHtwISlbn2n3bgEwMMb4M2MPI2SvdxZvYlOtONSCP5QjYwBeaMwfKHbvTWTsBfwT18YfDMmAAz3rkEkPj5z38+19fX+9KLDe4qagM9Czi6tAR9kDHkvvzIu9AL+lEX9QLEViDkS6IQACJkzruoVs/aQSh/s7vN0QxsD/Nonfil5dGAiCoUM+pKSK0QG4L7UmYwGncdVT6NOtAHX0Mwi9g7nnrONQ5G3UaotOWM7W7JM1gpcLBifvLkyf6xnHph9tqcN+FQoA+v4l0U0JHHieA7oY22oYvP9ndI20tUjBPF4HCflTvC6B0mnHrnbHWAC1/gZAx4UvTffIXyAWgUYHrZiX5DQwDE6hnzoHM9+H8VoXC+hRW86erEPBtUrnXOWNNt6JR5M8gvYLaBcH8LlmwQDWAYB8rdxYbZCtgybH2xUtYYKi832ciZfp5v08y/TQd+W1YMSEwj+LLRFR9CZ0++SYeAByJsRDswrl7P54A3A5H23wCafiIDPkfFyyrMkbdhw4OvXr2a7777bj9yfmbOnqNUvxPBaZTKYBjw8fbt27O8Eesx87OXfa3ry7uOFHue6WeNvp1HdKp3sK3Gt4qMM+4ubxkEvnjxYrnV9EvKowERM/cTs7hW4z1zHrJfeQirSEaLFUyZz8xuI1EFRT1VNO67x4OA0F8zoe8bbFD8xU4MEki13h1jcxKlDYrHw99u24J3e3v3aWLuzdx9/MmRB9MWdE7ilftltA79UIYGUR8+fNg9I88x7aNQUIBOcAQgcB1lg6Kyh3Nzc7OP0d82MF0AJtC0a57Qy7xpQ+GPkHluPWf87SUmzw+0goY+VttgA/rUo3Z/VzzWegymLy7uDh8qGHYfnGOCTEAzgye/vwLDR7Lbuu2tuRiYmfeow07KKsJgsOq663i4z41iGkyZnwARbI10f+1xuu/ml4uL850b9IEEY4fse/AafXL+S3UfAJX6cTqct2RnwLQ+nU774Vh8mdkOgQsy410bdepm7uTBS4REVQ2uTLM6YQYm8JyjFAV11eXtN4af5FD6U34xj9E+v+FdfqjD83f0wbsvLY8KRMzczz2okFqp1ZB3os1AFdAVenU79RJdv0ORCCNMak+nEQkrsJnz70WU0Rx+W6F33zcg6bszd8sRPIfQ2svFq+d5aIqAWDjs2dpzd36Bcwrosz1R0xNvx4oIJYsx5x0nh2Gw+Nv5Fp7nJ0+e7MsnM3fLOMwBgKNr+Y0OmBcRbAAGz/rbFPamDcBq9Jx452fpA9cAbY5sWPl5bqCBQYWVJ+PwNRssQBcJsC7lbfOvgYTlrGDahtu/Tf/OR2XcOUXOgaDfHrOLZR/6GbgaBJoXVkDH/5fXW5g370RiGY0lRc+/QY+XZ70zx3k5nQ/+d/4UUQnqQ+68hg89V/3nGX/jw3rWfX779u08efJkvvnmm3tOFO24v47++V71iHXIzc3NXF9f35sLR8EK6l33Chz43Z5jQ0HnQb/Og/vL70aBZuZMXxKhgs7whuXeUaEvLY8KRNSIeoJXhtRGvd5E71dhVWnMrE/7s6KusPCcvV8Y1v1uGBkGdH9WoTSHtA0cGBP1lw4WAmgFsDFNK5yeA8Lub968OTOSBgsowZm7xEX658N1fA6Elbvbff78+b4nnbETznOUxUqA0J9P4mP85D/wHn9fX1+fgRQ/8/Hjx30Nl/89P47qmAcb3THohB9q/ByerRFrnw1kDAhal404IM7tmoddaMtgt8tT9U4dWrW8uA8G7w7xeu7NdwA6DJ+VvunLs6fTaT/giHsrr62OAuMy6LacOnJn2vm59p92asypE3o2cfDy8nJevHixf93RO3xmzgEv15jXhs5N3xobH35EXfTBeRfIFbSlPo/f43SCqoEQOufm5mbfsVHeNi2Z8y6dmv4G5QUlzTuiWL+btwFgjZrxTqMtLQZQLGN0ebj80z7Bg/x4W6idJiJJ6LevLY8KRFjQZ84VpAvXKSvPaGX4acPFCqYKoYrb9dlQOJrg923I3Y6NUBne69oepxVGPbqO0WN1n1gqsMK9ubk5S+LxXEBbh+551gld9NvChEADVOpdP3nyZN6+fbsLxqtXr+bi4nNGsoXGwvTixYtdwbBEYcXM/8yHPdOGsdki5xB7oyAz58CyhgegRDveaUG7XTapsWReq6A7Vw71oqC7rODlix/jjRXIpG/29Ax2mVPzseWkcuSQOWNxZKZhX4MDg2RH2/xuP2x2e/v5DAXL2GpJx39bb5j+Bk4U95V3LGMGfS6VQ++AIILCe/0ypvkaungXD97ryqsnStcolfvL8gcg4f3792e7DMyrjriinwCWRC3t/NjIbtt2tq5f/oM3mDuetUx7HF1uZLtneQraG7Qc6XmesS6sXYGnmMMmeHs8lKNkSHjT0V5yWzx/jiT9lPJoQASGyIazwjhzbiAtFGaOKpa+b+FfhZ2qkG2Y2me+4vhQMg3C1OdWz9sTKl3ov/vX962AEUr3BQXp6Ea9Sq7NzFk7pp8VufMAtu18+xaFKAVKxYoPxYHwOKHMRoF5eP78+dkhNTPn5z2YloQE4SuflElm99u3b/f3abfbXusBUDd/N4fB0QfTa+UFo+gdssaoO++C7H4UTD0f+NUHDdmg0t4KUJdPeLbG2NEuR+C4hrEzP2EIeK7twpPmY9fTZ1z3qi/1YP13wUT7Y15tBKm0sQ5ZgQ7TBB50fg5HxXOUsz/XbSDTby9gyD0vnmP0hXMAHCYnN4d34X/qd96LQaXpUX0EXZ1YDs3Ja0JeSNot4ETPwN92QJgT5BNeRF8gzwZI5hWAlee8Or9zbf7y8mGjrqbzyhHr9Zm7M4V8fgj62ofq2ckoiP3S8mhAhIXRYa9O3mpi7BVRVyMAFDOKn6+n1rKaRDxiK1IzJoJXUFTPZAU4TJd60fauKxQ2YA0NW7D6PBnB9pBs+O3xc+/Tp7vPh2MkfCY9wML0RTlxuM6LFy/OjCzbKx3+ZfwoKM64YH2XbVXOM3jy5Ml+f+bOm/Nc1DtrkhgFmgDoTNsa5PIPdOG+wY552MCR66a3w7ZEZJh3jJKXIKwADbhrpGfmLFnOcmM6OCLj5NJ64vVcoZ+NTo04bVG8Zk5/Li4uzs6b4Hl79Txvg1iAYJBoENs2jwBEDRM0Ooo+AHC87Idn6dyj7777bv76X//r+9kt7hu0pT3zo3Nw3AZLFNYnBiUUonLWvz0/xuDA8+r5d9SDsbltlkW7/XjmbgmKd+AX54pQT+lGHT5LoR/oo4/YEkebTNvOr3mN+pA9gJmdsfLbCqAyTmzHtm27/vI9ljdc908tjwZEYJCs7Jg4CxPFTGJvaeZ8zdVekp+vYNU7WhWUsQ0QCt8GneKEmZWiaVkhWQwX9+mrkzfteUPL1kUEBEVMKB6lUK+K+px8icAQ1oauTjKEvig7H/jDmNgW5jMnEJ7379/Py5cvd164ubk529nheYM2zCdKx0Dr6dOnc3l5eTZf3nfviAj12UPiGjkWKDEUi71solLmSejJc1WuBmc86+PDDW6537wSFNIKQPg53yswNZhZRbm6Jmv5dCkY4ZoBhQ2kwRgGdlU3c2K+XzkSM/fzCArKbVDsHNiJYU4dMXIdXONvg7JVn4hcGYACgD59+jTffffdWf5RnalGkzwHdhLwxjuvGCbqrz6wHmikxxFFn3/idtmWTVvWqdt2920OoiAGrj7O2blMyBTeuOeyS4Q+Abd5DLUVTjSHj+rAuu7+TxQQXVZe9Nw8BCKaB+HdHt6aTllFC7+kPBoQwZagCrXDZQYVvr8q9mJm7oMS18vzXmM2s1b5YqQcQqXvR+vxZigzdJl2FYmgD10ycbKkjQZeq/vFdd6rgiKislKwViqmSfvB31ZM7i9KroYGD8VeCoc2cQS36cQ5C8yhP+SzbdtcX1+f7aHHS4OGK6/DvOJQMXQ1sACAmWeYZxsG08Fe/8yd12WAaZBgj9hzTL/LpwY49WCr0K30LBPmUQNw7jl3xEra99s/07agG3BVUEP/MGAGtVb4q3A39dgYVZEjuwaOpk+dEfOw5/oIQFGP6ct4VnlNM5/1xTfffHPvw2aAD/MOfEEdjo45wY9xmVcsu/XAAeDwL9ecxzIzezKzZbn09bKXy5s3b+b6+nrPSdq2bV/OYQ6dOOoj4Wfun8kycyev6CEcHs+vlwegp/W6i5dwep32u1y0AhKrgu4in6IAgmRx109ff2pE4tGACAvlCv3x20a2Cr73GtLit+vv5PhePf16Gk1U4u8a2RXQ8XUn37luo2MrRCs6EqGqTBi/oyRG4D4LgbPoSWaiX16PN4I/irzY0JZuRDW8vc0Kwob75uZmvvnmm7m9vd1PdnSippcDaLMKlg8IeXlm5u5Lng5tQnfOo6hhMW1dl//mGRSwwYWjFcwDfbExhObMr5WY++I6oSM0pQ3zZAFIjTTtlSaNoEFvj8+l8msgU0DvZ7nf3TjMMfxsGTGIpI9+z/wAL0KnhtQdYWH8R2Nr9KIOhA2XdYuNPjzAUt3pdNp3J7169ereGAw8Z+6+zsn3aQxIvAxYPoBGnFViEGG+pA5k3u+bP8zH1Tkeu51BZPWbb77Zv1zr8y5Ke8ZI3+F9iu+xHFvd6vmhGIw6ykV/PbfMmY2/ozn0ewXOqY+ILVFDwB72wrupHHlZ0eRry6MBETP30XmvGznWcDuicORF2zvgmhndbXh9fVWsRMxsfs997Pox9zsenkEYrSSrEHiedVH3CwPBMwiRjRn1EIVAmFhLRTChi71kir1L6AY44D3qtYczc/eRrJm7UxI/fPiwfwmQMVxdXc3bt2/P6LOaO4f1bdBX4AaljuKnHwi4o0j0lXkseOQgHgMKjxmDXz70vHurGWMhmmElbP6D15orRDuODphO5jne97sOVdvDdd0NfZsezHnr9fhcN4rTeTVepqr37DHzDPSv0VvJsvu2bXe5BKviearz4XlxMrGjSjZWrcNLJozbS3Jui90H5j0Mk6Mq1kXQtZEcz4fPOTDfmt6da96HBwH+nksXz1+BoHUH8uOImgGU9Znn1XqAugCH5iv34yHn0Tzi5wEsyKVLIxGVV+dBOGKEQ4eecALsql8/BUw8GhCxMq5VPghiJ7d1WEGtIgm8Z+Gm3nrXfq/CM3MewuR91+dixOu++h4KvgCAYkRtYFKvimeauWwBo108F0CDlSC0qqfoUD2Knf3g9dL6gR+8ERQuYycK8uLFi3n//v2e0c1cWXCdle1xPHnyZE/WRLn7y4N40qab6+S3l5Bo1+DLa7zME5GWbdv2PIxGClBoTkYtD11eXu7za2XEemm9MfO5+dlgdiVPfh/amCf7N/VDP0czXG93ShTYuD6Pu7TwM1bQ0K8G3iHr1oFRMl9bPm2gGbfn2152ae3nKZZne7k1nBgVAAFnpXgJwXPnnTzOr1nlyThixjXzB6Dr4uJuyQQdYPBWMO55oC/btu1gGnogk3ZCcFL4voZ5tbRiPEd5MvQD445OsUx6SZJ5ps/8vQLCnmfztRO9CxpMf/OmQQJzRbss4/p7GZ031/tTyqMBEUz0ymsxYLAxWKGyoyUI/23hb/v2nGtkDE6Mzi0s1GUkb8awMHlcNQBek6SelWHomKir7bOMsAI23lbGeiLC3uLM6JnzcKGFlWvMqRO6MIars+C3bZu3b9/unvn19fWuDPB2oIvbAuV76cCgzmOxB3BxcZdoaXDoJQbmo9Eu6OwlE/OIQaNBDvdYWjE9AS4km/m6dyI4YdW0L/CzUuz8MO81bAbL9MdrwSsg4LwN7pdmlju3V4NHPZZl19XlqMqy27Y8MDbCxwXT7teRM9GIomkMn/vdAizAts9U4fqzZ8/m+vp6T7Zr5AD+LFixYTLNLi4u9iWDApoCMOsL6A1grW72uG30MbKAEEfgDHrtNJD7ZEBOnw0gnKdFfY02I1OrpRHrNoPDzo/5mWK5Le1aCibhNWTMSd3oKwDEKv+EUv30NeXRgAjKUbjGBomJssAUNPT3URtW9gUJfr8Gi+dmzpN9VgrI9a6MJvfwhhzWazSlNKGOegVmdp4l5OuQq6+XBoRQDa6c/GTg4egQ9LRX1G8u2KhaIA268I6+++67fb2TbU+8z/jhDRKX3r17t29Vw7vCS6lX69wJ89XM3Ul/DlcfJW1hcMmtoF57yubL1ZG6BR0YF2h2dIBWab8CPvy2twS/AhzbRytXPw+PeVw2UuUJ97dyYVkw7XmePkNvxreStToC9MOG1VEJjEMBlg1s2zHgX/XTtChY4jqGkncxHnyFlrGSE8DYMKzwYSNrBWPwYfUg9DOw8hxVV5nGyB26yt5+t7wbSMzMmVzzvRrnapkerafJs4zDfYIG1oHINH1Fr1anGpRTlxOg+zVVA2BHfqkH+ec5H6LHPZ8T4Xqto1z+Noj4kVKFY2BQpGh03tJIQr32Kp1GPxxVqHfHbxjcCukIABW1loGNhjHuPkip3qGfsXKyVzwzZ4YBQ4rSnJl9f7KZ1gLnftqbw+NAGfL+tm37QTIFYSyn0AciA7TTD0kRPSHhkxC/8yoM3hBkz7s9CProszAwnqYtc28v0aXrwwABgAMZ5XjvzE0jV/ZAq7gLav1s+c98BS/TnjP1XczPjUw5YmBD6HVgy6dBfA3VKmrGs12qqaHnPXub/um4TRPqc1k5Gh5fIzj8Tf2miYFBgcJDRtggoIbF81Ge4JPfzbcysHJbjlZUF8Lvp9P5kiJ8a1DhSAj5SgZPFN6lbRI/7WDQn8vLy3sfobO+OZ3ujttm/AU5jSoaWOFA4ExQB7zh00Eb6Tla/vHSx5ENMeCB1ugnL1VAfyKnM3c6pKDhoajY15RHBSLMoPagu1ZZBcH1/l2Gtwdho++/bfwcevYzRpf1Ct2u20M4V0rd74Cku9WrSsqKiLHZa3JioQ0ObXsJw94MgnZ1dbWHVZuwRgjOyU4zd1naGGs8f+hVheZ2raBIjnzz5s1uiL/55pv98CjvzbYHQF99HLWL53IFQk1DaGwl5vHjTdRw4YkBlmw0ue95gw60w7UaZvjC/G9DYV5z3R03/9dYM/4aMBvwgnG/43oKHjquvud1dPffOsCGgDlYKXIMpduznHqJq0DGsmnQQduNhjjqUc+xBsHzY55jrD5q3Wv8M+vvXfBsQ+CdLxwd5wK4Tu/GcmQAOYKOK0dn27Yd3Hvu0Q/dVVK+Op1O+0e60LWWbwNDOxjmCdcP2FmBF943rx3pd/MrPzb0dSbNh/yPDrPDBfjxmS5EN1Z5PJbXlc370vKoQMTMw1siKVUkVoI2AlbA/K4Q2zuauZ9973sUK/ky0Mz5B35sZOuZVEm72ECxQ2LmLovf9ZsOBgNW1NTTs+vrcddzR2l2PI5AfPjw4exwFKIOzudAcAyqUECmA2OwcoW219fX+5ZWRxDoI/NH/Sx9GPw5E59xmf4+A8KlnndpZGDjPtHP9+/f7x4YtOCkwBV/+W8r0gIOKxp7kPCYeRiFaHDtsZsXDX5WAGIFwv1/5YzykCdnoGMFT/8MrFbLJSvF622LK3rCB75fulHfUVmBsG27W+Yx+DFtSz9HtQoA3b53DxnccI+6rTe4z84rooF1qJo4yPsGrqtIUI0r47An72RoA6Q3b97Mz372s7M52bbtDEy4Le7xnB0w60xHUyy/jpLUhlCsY70zy+M74gF4s6d4ztydsOqlWc9t5bq26KeURwciZu6OVXaxgrM3sfKOVopuZg7/rgdF+1aEfh5EXwHluUYhCmp4rkbaAkQ/7PXYCNOGhbuAwnRwxMHrdgYIRvKlP8bb3rXDkF46cXTAkYmZu08IM5arq6v9uwGl4+Xl5X7eA2PgtDvyNRiP6cHYuY5A15DMzP5tjno4Xubhuj1ClBIKA9qyhs3ed3tXeCjU6+9f2DN2XkL5izluZAmadenDESzTptEF+mT+qkLjb9Ov9KBYcZcf3S/e5294yn0wfeFNaG9jbIBlr556V/PIcwXS0NNGdQUsPLY6NKZneZsxOceI3T2n0+dzIzAynQOiYMwr8lTjyLN836ZLhvTBO7KgM32zl+5xbtt2BnZcrD/8PQvmw+v/p9NpX37oSaOMk/d4xzxiHrA3b93jtrz0g3OD3FneAR/Ua4BjEE+9dmyhHbrJupH7Xt4wb3iMLX8bRPxI4ZO+JR4TaQNrpeO1cIevVp5kw+nUB/PYQ6hHQ51Pnjw5O762bRVsWDjtOczMPeF1Xe2fFS7jnjneGofA0A/TDqbHCNMe106nu6S/0tAesRMvybKu0jdN+OAQ+QJeEnAkAmEDRCDIKC5QvJd9PMdeZrEh6pycTneHblHs3a5ACv3k1D6uU4eP8DVtDTKqqFBmXDNoKjCqwbbSsVysgOSKN23suG5+9Byan2q8CxLMh6t7RxEEX/OcGhR4TMhkt7xCEwNExmWDZF5oHwwIKBiwOiHtU9/tHNJn5v7Dhw9nRpSDp1berw19Zd+8ZXqgr6pnrJtK9zpTBs2AXfgWGXEyI3Js3rNh92/OhfESrJ2R7lQiIbE0gL7IrUGMwZCjNNWtpgX8gZ2B7i68Tx9pkz5YL6EzABeruWtfCrK/tjwaEFGjVdRfr2WlsFaePMrFRnXVDn/XM2u7CEbXcSuUFWKDAxtwGwYzCHU0NE0bFIyr/2fsCKJRPYqA57xEwdJBBZNzG3ied9j90GUVBM2gjmz0Dx8+7NvbuPfs2bMdhDhBiv46c93tdN6ZY7wuivtsb63Cf3Fxt0uBObK3ALCxl2aj5jk336BEbNirnB0RMlCwl2Mj0KgBz0B7A1YrJHiRugzSy/+OyLgu19dxUXe916NiJdnlP9PVkSF/q6UA3ACCOa7OsMyb153TU2O80h2mS+nqOfQPQAFd4gghBgij6e9GlAYG1vBGvWGWLArETK8VEKsO8xkSp9Pn6CHtOFphh66A7MmT8w9K0ZYjMM+fP9+TMr0zzdEI+v/s2bNdj9hO0J75px68Iw51LttnnNver93wMsXM7Fs2CyBm5l5ko32D3xwRe8ju/Vh5NCCipUrIE71C1QUcVi4W8pn7WzRnzkPGNioUnvc3JuqtcM0eJvcs7D0b3W2XUXmfcVhxwoh4XI4kOLeh4MMHQpF17UQqgw3aarLezP2Er9Pp8xcAKe4rfWJZBDrbE6Vu7nt727Z9Pj+Co7mddwEdvWXy06dPe1Sgx8xitB3B4v0CviY8wXfQyFEG858BoUEp/aNd8wn0MJDxHHoee9/XbcAKhJ88eXIG2M2zbmMVvXKpcaQOG1N4yf+7TStJGwPT3G3zjPM+WgwODZBWtDDP1UDTb56txwgP0f96+AWRBq7dqghooD/w/NXV1dkH4qxHHOqH3z0e+oC+cuTD8uZ+UmejrzN3QOJ0Ou25PR6b63Mdlq9GOqEFfQBINOfJIAAeA5A4IsP9Aj+ACve9HF4n1fNLcZ6ZaWseYrn04uJiXz6yM+WdGEcAwnW7fyvw+zXlUYGIoveZc2Vmj8n37Onxv+uy0W09Vjoz5+GiTprX8qtwy3iuw3/b0FWx2rDBSDBwS4XGdKEQqgchF60DivC4qYtIi9dR+4EuRxwAD1Y8zZHgXS8B+NlGYrwkgDEHCDhBkTbwGHqOv/MHoBeG6v3792feqsdlBVHvEr4xTextWEl5PtkGCk2dM2EPxKHh8qTnrXxm8Mh7NfAOAVOYf8vPqm9ty3zT6A595beNMm2YztDWe/gL8rkPb0BPg/ojL7Rg1evg0Jd5s/GqI+Kx9ZoNJ32ol9z3aZccGvPi6iNXrtveutubOf9kNteePn26GzhAhSNN0Ny5RHbYyE+CNwACBqXeiUXbzJ1lkT7Ay4Co1bjNI+VjAwRoWT5jrO4Lz1jnmlaNipkHPK/Oibq4uDg7NIp2ecYRWDvBLXZ0aNd9+9ryqEAEpYrLKH8VorQHYdDhUKsRste12g5GivcMELhuw75SGAYUZlyMIEax3s3KazMSNWgqjVCE7tsqTIegYqw8bhICEVb3xcbXRg2voMKF0iCnYUUf9pfXo6JeR0xsmJ0L4bXR77///uxZjpOdmf38CitUgwYrVntBKDV//pdnUaDsVPG2rhU4deTFYMoGznwK7VHS3l/v3wXfBXv1vm0w/K7lhuet7KyAV96hlbxlAjq6mC89r8in+Xzm/OC1nvVhZcwzNaDlTa7RNrxh4+DnmnPiUDXtGnC6L7xvWvE3S4tuHxq4rwWW9Mu8yJIjuobdEVyDhyz3fE2TYvn2WN6+fTtXV1dnh2TBKz5IzuAKOQSwA0T4Dg59cOQRuj579myPaqIznfdiY+8IqunReYFm5mHLkSMp5qfOLf3heZw0ABoRM+bXfTTPU/9RnkMBkcf9NeXRgYiGb2bODwY5QvZHxF55Jka1FBsr1+nrD0UpjiYXprXX6zU/K64qWnvwMFuTumywoRECa4PhZ53UU+VphG6a0b6VEH87DOqIh+cKYSuat0fvPA3TD7r5eNiZ+3u2/Zy9Xydl2bjaiJg3WPO1MfLeeYM8+MLA4f379/v2VRSpgakNeAEDxWCXfnisBjqm2Yo3PN6VbDlfpvegMTxbQ8k9828NF9dsYNxHfjeEy3MGb5aVggL+dp2NBrgd5MV124CW/22YjorH0LGWNk3ApU8sHRDa37Ztvv/++z3aZnq6wBcUko+d7zBzF94HPHdpz4elOeLF8mKTWJ2oahnnHYNPDCv0Mb1wSD59+jTffvvtXF5enoGCLpM0rwsd00iEQcBRVAc9SJ+so/18dbZBgpdWrRN8UiXz5PY9f9XZtA9f+rtAX1oeFYjwJNvAuRREGE1C3Cb4WAnY4/e7Lr0Hc3LPzGelZ1QLg3GGgp+3x2XhWEVHqky9LkyB8S2QrC+ukkB5HwWCkXKY0mjaTG+j6/H6zAeHEKF7PUcLoseM0rSB9bzSDzKvnWPRcyecA8F4rRToq8/egM4GHeYJt0+bNlYoUYOaep8ei0Fqw/vQxUddtw8GF36mfbBXzHsAiNvbuyPOO8fmG/fVCs+G2rLncZun6ZsjDvbMVmDWPOd5sme5Wg5b8Rhj8LXVmKGbga95ohEH/nZZGYXKr3WVjSHet5MXX79+fWawmUPrCdriuouXgDhMDnmlX2zdtE6Ab375y1/O9fX1zNydOYFOXY3fQHRmznIhHO0DbNMWEUDzu3McbCdwZkprdABRVr9rgM6zBhLoCtPP9XpuemCU5w5g4fwxF+tst1HeMD99bXlUIGLm/hpmr1MgJkYSBoTR7NWvwrMW6hp/nkWZmZndNxjNXhz9wCvwgS60ZzRLffbO60l5DF6/deKOFXaFqkaLECPF65E1pPb2vIa58jy8pcs08dYm05+xgeZXwIiQZvvHzhB2jlxfX+/9ICzqkLCB3ul0l5uCgNuomH4ON6IwbCB7qh9r236H/gPW6EOVVD0d5tX9dMSpCgj+Mu+Yp1ag2n1cFa9jU4e9QcsDfbZBc7HR9f/+u4B/5r4xNDAzsLTM4zVDpxbLpOkFz9jA1It0O56vAk7TmDHa+HHPtGykEWPkg9a+//77PbLpjH+DRhwYG0X3zfxpXboK19sAnk6nefPmzX6+i3kah6nLwY5m0U6TPGkTXWsHgKVL+mFdYvACDewEEikFGK3kgrF5WZW5Zg4cYXDek5PS0QuO2DL+6nzaechZdl/+RsqjAxGUEnVmrYTszXRtqZNjZdcQKMJXhWAl5C1VK6/F3t/M3dcGrXQcQuQ9DJ6PiLYis8e1QqoOiRvE0J6BEErJIKqREHvijA+hYXxG2aYB3ouFrv8Tpnz79u09D8ph1YYteZ9i43JxcbGvAbPOzKl1BlT+LLgNidelrWAwVPWEDUgM5pxTYzrzfA12lZ775LwN5spgtCDCRsp5A5WBetAeD+MGHFlWXMojlQvfs+FscZTNwMkyQ5/Kt/Zc64UaBNOfPmdD5750zKU519x3Azvzpftk40R/nCsxM2e5AH7/2bNn8/Lly3n9+vW+3OZPbzeC47E4yc85B+QT2SHgGf/tfpxOp93z/uabb87oYmfGEQpo7uUHvgdC3aYTEVTrWEcoTBt/vbTRLC+/mY8qz9TtCFmNt3nQ8wzAtHyvlovsGLr/DwGIo/+/tjwqEGFvZHW9Xmy9MJ6tsLdeK20rTrdhRV4GqOL2+1ZiZi6Hu1q85mtjPzP7yXUcjtJIi9cMEbwqMQs4Suvy8nLPMWBnhYEKYXr3j/pJNLSX0udn7s6Ip00bHpB7E5Ec5mT8NvwIL+CAhEn3HUNe2q/C3hiMznfPyucaf9NnolGOFB15NwAsF/NSAa55kZAsER/ue5mo8uCtgzYwnicbU9o2/5su9AvZM1i1/NCfyqGfa7SiQKjhYMu8C8837OtlnHqcbpN+WDbNp36m0RfAN+NuVKJ/d85NO+qxnDradTqd9qjbhw8f9kgAnxUnSmZHyGOgLn5Op7utmnjStO8dVR6XnSmWNr799ttd5xhIMU4vm8JHBgJPnjzZQZMNPnPOUh7ghfY9/y6dDxt8J2GbH+gn13yAnfmL96A1jgrjssNE+wYQ5t+VzViVv1EAMfOIQMQqtNN1bMLo3a7Is1VMrptyFFayEvZE0xbGmutGxY5iEOKqsmuY0v2qAbI3T71t24YX0IBhb5TADM47GCVHHxCUladqg0BB0VfJAEJ4xjSdufOg3NcaL4dbDUJML+py8hanXALIZu4OfrFBtvddg9fcCdPMNLKCoc/QFHobuFU5e0mKOWS+ewSwaW4ATKkRqtE2AKBdH0TkMD7fWKkxK7CoErQRKL/0mZWBX9XZ4giDjddK3qBH66YU1FsHGQx1/k0D645GA3jf9HdbDsWbVv6Im9fm4QsibOx+evr06X66pT1ixocR9ymNNqwsfTBWeJecCejY6MzFxcW8fv16vvnmmx0cFEjxjmnt+XdeAXXy7Lt37+bly5dnkb3b27uveFoGHJEx7Q2YjwCt+WPl6FmG6V+TKKE5QGGVB1EwYXtjoPdQWenTHyuPBkTAsCj+GkrKyqAh4Eb8XqLgvUYQWlZAgr7Qj5VHBJPZM/W7q4/amJG4vlLK/DYtPH4rUBs5b8Wq0atC9x7qmdkRu8ESXi3F9LZh7RqraYeyslLE23C9KAtvg0TBoBDfv3+/Z6t7buknffJ2tpnzz7xb2dnrWnmcVUAGEAZoKPdVBKi8Y0PiKISXIQygy6Otr8bUBq9Kx0avEYWGyE2v9oFnGmExWDpSkg95WR6j573ePfzZvIIaaPOT59rOQGllA0SxfNsQFCzYuzXvQr96+Lzfrb+mFfx2fX09b9682fvJUse2bWef0XbEygbPfXQ/WEagL+Qy2bN2f23Uv/vuu72P1kOMwbrD/OSlFvSMafn27dt7YNm09FhqK7g+M/uOqRVfWj91Gc0gmm/8mB6AIC+91pkteDgCyl8CIH5KeTQgoorRyK+Kw0CCyTtCcfWkCyYoRdAXF3dJPq5nZfjrwVr5YDQtxDa+MDNCaYacmTMGd71HAIRij8J5Cz7oyMlieC4kDs3MLjAzd0sTHt/MedIkYIR6bdAJGTuBCmWA4nDUx6FWz9/p9DmsiRdtwEfd7969m2+//Xb/vgX0s4BDMydrGXSgPE0Ph71t4G0smK9ug/Nc8K4BnhOEoVeBGnNuEFrFUtBonql36HksAPd9eMCyQaEvDVE7T2Dl3ZdXPX+UVUIlv90effZ9P4Mu6UmdDu+7Dcue+2y6ILcGY66jdPfYmudSuiCjNkqWAWTj/fv3Z4e8EYUjOkedPi/FIICo08ydkUdmSUQkosccc1qsdR2h/aurq/n+++/vAaknT54cHppFn7zM4eea28TfdYSo07S3A2EQZoBv/lgZe+6xE8zL1Og25ssf1VoV2xfT4UvBwY+BjKPyaEAExYahQoyHZ0/8x8I6KLkCCNdvxvIkW1nP3Ck5T6aRu1E4fTOA4Jq9p5nzhE97k/YIrHjskdlouH7ew8PoGQu0i/LA4NbzBm0bULi/XkPF6LGcUO/MdLGR27bzaIS9DfoMALJns23brlS8Bj4z8/3338/Lly/P5qUheQMZ8xh/W4k4JAwN/FyNCPdsJGzcVmAEGqGIzOteczZ/rIyzAVeB7yoC5j57DppnUADm8RqE1MAXbK8UKWNoxM7AwKC7xr5GaQWsa+zr4XoMR/1zNO+h9k1r+g5otaxaZ3S5ztGt1ZkIV1dXO69cXl7uf19fX8/pdNrzhZxTNDNLWaMgyx4T+oooh7eAQhciIxcXnz+mhRw5smjnsEDBdCzYYFmDeny/+o+lUdPcdOPbN1zz/DhxvryH/vCWWH7cLjrwS8rKmf0pz/xYeVQgYuUhzZxPaEO2ZqhVuLQolb9rLFyvjaQVrtuzkUE5eM2Rdvw3zFjPq6CIH3uzDkPacJm5Z+5AjZMaCf/j2Vi4GJOFEKNu8HS0ftv5OPJuG26kP4yN33hHPqClOScI8cz5LgSiKcwbEQ0fvmVPAhBlhQco4hPhKHZABPXbo/N8el3akRt23jgCwnwYGJnHzJemo5V8AYT5tobenjDvMEYDaL+7kgHeZR7slVtOac+Gt6B6Vaf73Dlfjc0OAM/bUJpWBnyuu15i9cERIKR9F7/H/43MILsU6wLzHI6Td3rxN5EHcrCsf66urva5cZSB/piGTpIuGOdZf4TL8kc/Z2ZevXo1L1682A+rIgLRuutAUW+PuuY5IpheIvF81gZ4bNV15rNGvswv7l/PgbDjxBItc+P6j0oBaIv5yUt1XxqxaHlUIIJiQ2UmsYexUp5W4DP3Q7QIZZVbvbaZ8/39vm6PwcqFtq2QGuJFAJ0r4N0Y7XejCk6EgwZux2FL+mLFZw+f8RkcMAaMpZUZYVTas8GFFnyBj9Ce56Yg6fb2dt/LX2Di5+gfIVOUGX168eLFWT8AEozzdDrNy5cvdy/K4VDmzkoeD859Lj80SQwFg0LpevDz58/33SIoKLajem6gg5eTDB4pK8WDYrcxPfKq4R0iMTbSBRBtDxqt+tTohttqFMDFdO7ym6MhBRiWDbdtsEbblk/Ln3WLI1XVQe5rPWF77KZ56WqwVo96ZSAc8aAOb+uEz1++fLmDUeszL3t094nno2DGh5t5aW3m/DA4v8vvd+/ezeXl5X6cNkDdgAg+bVRmZvYvl3o3GnJl0Fva0w78YHm2rKNDHHHxHNMG73qrJvyJg4DDAz09js7nClQcgYLaG4NjLw9/aXlUIMLKz55Bw+s8a0W9QnY2xjYKVWYrQGKFWqGxgPG+FR+/Gy51sfEtwnaxIPiQJ6NoJ0Z6K9PKO6R4DzW06Xq917ZRgkb4XAcIWNARNGhhxG46da/4zc3NWZRoZvata/TbhhfPx4AAoGGvlE8H0w/zBO1wnX47scx79xu9AnR0iy2HA3HC38q4O9eh/SGC5HcNggt0TFdKvcrOoZNQoQF0aOTLbVmRV67Kx5YJ05CyUoiM14bCpR69aVfwsnq+zkFp2KUc6uBdG4uCm7ZnmTX9CmJX4zUYNwj1GAHTtO3PUDshGiCNEUJODDxph2s9twKdgHFf6d9Pnz7NN998c0argnwDb94B8DjS4Cgh+R/lvUZ4TKPqIebW+ou6KERh+WCWdVX1hSMUnv/KT4uBDWUF4F1WztaXlEcFImr06i04hFaw4Guuw/er7AxIGkVwOw3hWZk7PE4xOq8iYowNXc6cn0lBO2Y0o9ImLIL67WUbfXftDgHtLo5t287OVLBC4RkbPgwdz9hItf+8S3s+XIszGKjTChjPwoL59OnTsy8fel6qhOCH6+vr/Wx+AzMrHECAvTRO4qOPtP/kyZN9LZj1aXaMsIcfpdddIs7RMD1srDEMPGvFt1KeBTemlw2m5cUGjAiJgYS9afridw3kzbcG7w9590dG3P1fASPTEqBY58ORONO1IMOgwH3mb8uowQ2yYgfHf3uMdSpW77gfzAcAdeb+x7HqZcP/ni/4+eLiYr777rvdMBKlWP3As2whNZ+zDAlP4h3D8/DOzc3N/OxnP5u3b9+e9dVy7OWL0qMOIjrm5cuXO482iuHy5MmTHZDY8TP/0q6BAX3wLjKAh+nrCIXlzOXIMeReI1Gm06o4N+ZryqMCETNzT5hnzpGyDX2VoSMHM+sjaW0MZ+aeEeIafeiP63f433VTVozl51fezkPKdZXMQ7EB8W+Mbfvl8x26rs3/Vib0CQOFAibkjhJpbgZKhRC91zbpC2unXrutJwltLi7Od1A0V8TAzmFN6OctdLe3t7uXVdAJsLHCsMJEsXlZx/198eLFrqgNFu1t8qyTsrbtbqud58gA0crbxQbaxtegj/7TVkEEdJ2587QsD7RTw2xwWAPqubFCXhUDBwOgPmMP1QcSOUrlLydaxv1DW05c9RgMKgrwHQGx92t+tN6ANynVDwY/0K0fvIPWGHH4xnzWZVTT8Pr6ek985FnmGJ6z3j2dTmdf+gRcdssm8k4fPnz4MO/evTs7Qt5Old+3XNOPbdv2XVieQ3aH2Og3vL9t2/6NEM+Pl1QcsSoPe37Qa42K2DFY8bKvNRpmuvWZI7mABnakv7Q8OhAxczd5Rt0O4a2QX6MRRnZcLwhooU1vh2q/nNGPkbTX7Pqt/GfOt2Ya2FTonfUMkyOo/qAXgKKAi75a2OzB27i4XSfx2KDYQ6IuaMDfThSkbc8VHhX1O6ERo+LfPrDLoMCAztEXz60TJw3UDAS6jmngyRi8/AItvMvFmdoYLE4CtfdNCJf/DdhsDJhvll3sOTUKV4NmkLBt29n+/pm75SvGCv39+WiHqFdAr/KwSl7s39CX3wYgPGvlamNrg2pv0SCjRpf5N59Cf+bfhtWyXHC1ApYer4HTasmh81qQ43njGddhoGggz7M+VbXRKHjGy39E/q6urvbkYQNldBg6iDMavE3a46vu8xxt2+cvj3733XdnAIEx3NzcnDkWpqvBBJEP2iYiwbPwuCOOK2C7Aog84+fsGBo4kNO0+iKn+bq8XL05c3xGUUvr8/x/TXlUIKKoncnhXpVPBdNK0sLqunjehp2JMgq3cNvoug2eqRLxD8W5APX6/a6TkWbmnuF0sXfrH69blsl53sqFawiqdyBYQV9cXOzbxqjfe8hR1PZyDMjq0fpvjLKFETDlE/wIUZJ8RWIlCoX3UHz+0FaVKYrKPIBiJBnNY2drm71p8yiK1vxLu971Yj4BYBUI8j90ND8URHyJR+PQKt6VjZ1zLaAJ9fTLqq2/ANq8Y8Vtj73goPSyMTCvdhzWA5b7FW2gT42tgY37TT8d3YOXXOi327PuMD/4+ko+PX+lE8DQ4NJ0M9DzMgt8XO/dhy8ZeBV0G3ixTNAlPzsEl5eXczp9/uIoZ7aYF6wLTMPyDEC3+ojkZYojssgH70AXnjcA8Q80NEhDVh35sR2y42E+aVlFDwowTAfuW6+UN76mPBoQQQjMW5Ngmhp3/+ZvK7KZ89CUEaUFyYq7HvgR4rNSgKk8qe4D4/K7rgOwYCWIYXNolHEAOLymbwatxzdzp+B5t+cTeJzU5TPmQecN7Zm2NtCmJ/97RwXLG8yF+2lQRRjUx+GSb2Aw6NNATQf6iwFiZwvHBLNf3CDTkQef08B4PGYUy8zsHzOy0maJxqd/FmgCLkwPK1KDQvOCv11io1olZiXP/1Winiv37UsUnNv0PfenRqCOgj1y97t5RswP/FHvmzlxdMkA3/oDQ2z6rcCL71tv2FBTMLRsufRhaOZJz4+NdaMpyBX8UafJeqefsvdyJ/1CljCOHz9+vCfL3cEB/5pHvTzoT4HzPv1GzqA3ETxoZR4xUC7fNYpkQL4ypgWZnm94B1CB/oWejvrVWbQz4LmyHjZgWc33ytktL9gW+Brv/ZTyaEAEBqFENJCYub/l0kJqxdswYxUz73pirMTrpZuZzCQ22EayVsRdroAZEWKfDc8ztFVmdj+9Ru6lkpWSrnLlvmmOIsZTt8JweygnTsxrH/GuSUZEsFA4VXaMy+0alBiooGyIClAPCtB74XkHpY5Sfvny5dzc3OyKzvxn42fwRdskcxrQAJTsIXq+ABjwEaf6cc2h2AJKG1kr7BoSG7sq2EYWmhfhd1YesuXD/OPlxsqyn19FCCt/Bk9+xlG7guq2R58KUpnHetz29DBmVvxcc6m+oe7r6+uznIWLi8/flWBczs+g/YI8y8YRsKBdR8I8Phd4kvoYDzIMP9s48gPvGVg7adhz76Ux5oKPgn38+PlDXd98883ZEhv3mvTtyKtzQZBfZIycBwo6ybkt8Hp5fxX9MpDx+75nebH8mXdXxTLD/5bzFW9Z7o8c2i8tjwZEUGw8LeTc82+KvcmZ+0ly9or7DALDNf9w35PuhCCv21qojya/R8wSomyYFEb1j1Fy6YDypC/1tCg1FNRnj7y0w3PxVzfpaz1y2mYuHF51SNTbChsGbp8Mruhj96rTVtfRK5T0+927d/uWz2+//XafGwM8FFejLvQPBX17+3mbKREl53wUVDCOZ8+ezdu3b3cPrTRsFMLG14aFeUaJ20OvQnOdPRQNkFfD1eQ3+mEQfuQNUg/PG5TUi/NcraI2jNUACjBvIFkAZE/TkYsCXngTPiqApZg2jJOoGbwMbxPSf/PmzVnk0N6xl6g6bxgtR3NYl6c+lsFMN971aZZsmwYoeJkA4HN5eXnmFEDHmc8G+vLyck9Cdp1OHmYs3UFwe3u75/nwv3dcwefO4ajzBy8Y2HqnhAGWn3d0g/5ax8NLtjnQ/N27d/fqN0+sAID1ku8dgQY/uwILBfM/BVA8OhAxcz+0U3Rpw7TybOxpW1HwzErxlQlWgMAeQL2hrimaOe2Z2RBiQIpsC0Ds9VWZ21hzvUl8HQMeuRU3gkOiHUqwczFzl/yJkSwSp09eZ6cuxus5sOG8vb3b6uilF/7mE8jUiTGhX9t2d5a/Q+kAIRtsQrXPnz/fj+qGRuw6QXE6Ccxz3xwFG7cqHWiPN1cPeuXRGJDUo3E/GKcV58prdxuWE8+HI12dfxtcrruvpsVKsVqhuj3uFVSSTGxA01wV2nbfmyNgWjiyUX1SQFpgZuM6M3teAW3igV9cXOxbEQGjBf3uC/LH+PCqucfhZDc3N3v0kv7Qvj/nbdoDFOwEMF6iAeij29vbefXq1czMnm/EM3yZ16fNol8c0SwgAETc3t7O1dXVHgVBLikAqzpxAHWfV8FBdU5CZ4xEN6wzibDCH3yQy86U5dHLmr5eI77iI9O3faMeypEtMq/6+ur5HyuPEkQw+RXYmftZ0i5WRp6whmv9voW5CtCI2t4Mwuvli5Wypt6Zu1Ad2fsODRfZUjf9o19WcvTF/bZyMhOiLBoariLHgHcMNigYZkBd11VNc4/fbVkx4ZXY0OMV2FPYtm1ffiBk7Qxve+IGH+YJFLHPfCBp8/r6eqfP5eXlvH79et68ebOP2xEOR1DgE5Sakzat/GmT8RjcmjZ+l3n2354XgxXzwWpeC0TbLjzT0HoNrOfQoLhLb42EFCi5jRU/zpx/KdZAyvRvG45eee5ND8uUedz0LaBbATK24tL3m5ubfTszBte5K41k1Gkw6HT/fv7zn8/Hjx/n137t1+7xSh0V5pgoBf0jH4JPiXdZB/0EoEa+kBv330sB5h3oS6QCMAZAur293QGhdSlLKgXflOZqOGpmPjRd7dwAuNAVTuA2aGSufIx4+czA0ryymtMVf36pPXMbdSwqWz9WHiWIqJfhYiDg55wcZubg/26RmrkLgVrZOHrhCUbhHU0gk+7wmb1Bku9WSmLVzsz9Ly/yHILjZCcnHiHkNexWOPy/yvY3XaxQqQ+hdwjV6J0+2QujLocUHUXAg0ABOPLE2q2f4beND7+dLMV4+I2CQLGw75ydHoACgybPMeCHduy1w4d8MhhDApj49OnTfoqlPRwUlhUodRLRKe+aPtDfCq4fWaK/0NaAwgUet4H3345sWZHCR9yvkrXMFuS3PnvnKxBqOenODfiyyW1OFnb/SjvnFnkM3KPuhvL9gTrmGFD54sWLefv27Vm/eday7zah58uXL+f58+fz9u3bM/l/+vTpvHr16h7goQ5HzgACFCKRlhXnDgAaiEo6ubO8Yt6lXdO5p8cCZpz/BV0qr+ZPwFmjKdTFvDuvwfSHHwAxq4iX9XUBtfVJjTrXKJ7H0mtVHgISlhP0pXXSl5RHByLshVIKEkpwMy33LZgrD80FpWGvk3q8FkudZjD3gbrqwTib3vUxNp83QB322qqIUSQoNa+nu++rUBjP23vxOfckP7kgpDb6NtQoUYOKm5ub/Zx/gygMIz8W2m5LQ5EW+TMuFPUKSMzcHYxjRWNa8AEjjObHjx/Ptox6m6j/Z77omwGEgQr1OCrD3w63OhfBYVd/ArlgxV4mfGQAaf7y/RpQ+uJlmIJD12eDSzHtPV8FIpW/8rWfdyQOpYnxc90rMGUgYFrb03dUrk5E+2KZ8rO+j8E172HMrq6udlBtelkmPW7G9/Of/3wuLi7m13/91/eo2MxneWV5wA5Gc6Go20uI/QotYMIywBj5MujM/Q8dUgDcrm+V40Ed1A9tVvqSPjXPC+eB51lydG4Msk7yNV8w5l2P1YDOCdl2mNBRR/bj6LplwKWydwQeeLZO7yoC92Pl0YEIe0hGaF77qwAXNFA8iVV8FN4zIzLRRbxVQvWgZuZsO5bPszdIMOBw6J17FiyuG0hxDwb3mKmzzF1A4aQu2nMImLFjyGxcoYEjAgUSrN12DKsflDBzUE/d7dKWDTeKAO/r9vZu6yfLAwYrBg4GM93rXnDn+qEndQEytm3b14yJcEB/exMoUv6/vr7elT/KG8/JPAPtDWisEM3jjih5/FaMLgY4NXSWgUaofJ+xem6P2uF+edN8vnIC6qG5XoO6gilHqjCorrfgpNEIz0PzDnyPeXM04vr6el6/fn0PvBjUeXzffffd3N7ezi9+8Yv9vYuLi3vLEU54NoB1vzDsjN1tzcwZcOF53vX2bBtXivmPvhvcwcs+II/3MO4GQdxbGWCAs7+qST0AF+SKZWMML3Q2f0AjAwjyLniuOSSNYHne/dwR4KC47gItj9cRpj77peVRgYgavYfCP1U8vr5SXlYMq7oLRDppVabub8NdM+drk2YMR1Qs/EfjsmFwPRgDDPBK2Rlo2eg5sQkji5ChFAqOMLLUSzTDUQ0LHMBj5k7R+JhqBNnhVvrOew7h1ru04oX+rANzFoUFEC+fZCo83K73WnD5OiL0YozQ7fLycq6vr/e6Z2b/NsHMzJs3b+b6+no39p53R71QjhgJxtes8Co+xmalb+/bXpXfNa81okBbKxlZyZyvWx5WQMWgowBk5i70XpBQo2j5tudcUG3DVtrbObEXbzlqiHulj+jv8+fPzw4NO50+f8Dp+vp657uZzzxtkF+gRF2n02levXq1h+eJYHLKpGXPPIAMejlr5tzYm89nZu+beR+e9VbM6mc7HOgHRxCZH/rnJS+WdzwXzBU7o7wrywDYBtjLLP4eiHduOPxvR6FOqmXlyPbQxyOQU34tzaz/DXAqbwar1cVfWx4diLBA2QgUBPC8Iw1MhhGeJ84T5Xq9Bu+kt5n7R5Va6Gu46U+3IDIeBOBofBR7Y6WPmctKsiE3DB7hRtZwEXb60AQ0JzbSzsz9U/q8BlyvxUL+6dOn3VBbaOwlW2gbYfAJkgj5p0+fdsVn4+4wKArt/fv3u3KhTkcWDKAcwaB4Oy7LM6fTac/KRzn7gz3Q/8OHD/PmzZuz/fozc+9rpsw/82Klbd432LHBNLhhPhqNMo8VcJi/WgoWCsStAFdKENpZzswjNSCWGXvq/N+oUfvSZasCigIdt2eAY0BjnQHPWR9htJw7gOF8+/btPSDBx6wM8t1f6zX0CduC4UH60C/7QlvmgGfQbTN3xrZbMZkTh/0xwkRSvNzsKCpLCOYZ14+z4gRcABI83KhgZYP/0a/mE3/WHrn0XCLrLBfO3C05raIL1FvnxQDCfbZurjOykjvmuu922d4yaT3/NeVRgQiUq5XmzB3RLSwrtOjJsWFcPWskaWVlz5bCfXsltOVJ73M1nB4TxusI3SMAZcAagCb+2QjCzBg6gysLJoU+euwGAwYqrPHSPl8DNaM7J2FljBqJWBkp+u93O48zd9v+UFYzd1s47eWhpGrYmDM+HOScBbLVT6e7rxjSD8K0PmfCQJE+G+CUhwwEeo/3C7IcNvbc+Rpz3To8R54H81eNpyM1LjV4LubvLtuZFu7/Clj4eQybgavlq+C7CYwrgE59zh3ovMEr5VHTAFm3ooefiEj5q5gkGR7lxjx9+nTf5cDHqBr6px2MIeCeZEf4u8mHHrvnq+c+8AwGv0sN3hWFgV+dFcH4nLTM9moDZH9wC3r7+GknZkJnch8Ygz95boBj8FFdaJpST3nSINO8uZIreOJIf7sd11UwYge373xpeTQg4nQ67QLjyUKgrOgbnixCW3lwXJ85D/lX8fq9KnE8xRYUwUrJWlm7TQMhM5y95XolRa5VwKYBHogzkfEe/Cz3TQt7N+63kzH5TdjRyt9GCcPsiIsjASihAjpAAX3k/rt375af5YZWKCfeRXG4386st3cJiDWYZBxsAfV3APqhLK/p1liWHwAd5VHztXmVJRiHf+tJtx7LwSrUXw92pfz87AqMV4HaQzOIKIg0mKunx1gtixSMGXUDHFsw6OgO982K2gbMwN18UVnjfQOKbdv2pTIbJfIU3r59u+cVEZl7+/btvgbfiIk9a8sDPLSKiAI8COMzD9VpjSqaLjNzdn6C9RQAnbq9vRpwTb4GfXGbzFWXDUjmpl7rEoNuA/sCHeT81atXe92AJ/ph28IcesmkdOIZaOy59pyt/q/jdGQ3ajM89tqSn1oeDYigrBSLw3Q2ag8Z9Zn7mbbU62eq3Pzb9ZWxVgxnQ4ISMHrv+p8FifuE3twHBMt0MYMbtdNfBLmRjZ7zYKGwN92IgMfhDG/TrECrhzvxjgWbsdNfaItHYqWDAkaRUZ8Vmr178w40AnDwXQADK0KdzCNAhfucAuhjtJt4BU+4HzZm9ojtPdfDMZ+bz+oBMk4DEo/Zc+8CPa2MHY42YLUnu1JwBqTO1fAz0N3g0oX2PFfmP8ZIf+m/6+F508IGtsbTPMu4DUC55jbblvl05nNkhOOhTSsOQLu+vt6N8cuXL+fZs2fz5s2bfdkAzxuZcD/9U/rSb0dDHA2lHmSFE1vrgdu7J+qDo2A5Y+mmiY4zs/NLgXr1QncX8dv62qAHOaKPJDXPzFkEA94g+gA9/SVhnl3xhgEjhX6Y/pZRz0X/XgFv600DXL9fAP1Ty6MDETPro6hXXn4NmIlvlDlzHp7l2XqLRfYt7oOZkXte8/UzNcYUP1/kTZ0oZAyWvSqE0aCioAn6OYKBoXf7jMvhxipOvAVHAOxxoDw6l/TFoImIgcOrFm6yrk1nL314m1s9c2hoYNXs9PbNXh8ekw0f0RQM5bad74Enqc6Rj9vb2z183UhLj99d8WSjCOVf85iBhufTslMvvPJkuSto8Rj6ntt1vs1KJuiT+YFrBgaNHNiYmK95t8sg0MXyU1krHZlfZK33HM1i9wWhc/Ie4BvnyPi9V69ezeXl5T7/7CLAg7+9/Xw40i9/+cszYECfzbPeGcV993vmc2I3S42m7czsxtXAzruZKNDkdDrt26I/fvy4e/20Wzm2l2/dZ+Bwe3u75y0BSI5AG/MJkGHnx9u3b/f52LbPZ2tcXFzsydpESaqzqXdlF9C95cE+09IISQFE2yuA6HM/1t6XlEcFIqpAfW2F2Bz2rIJbhXV9fcVMKBx7L+1DBaUMX0XWiff7MDnjctge4baAux/e+lelyjPeR41h9PjsMVpxsP7v8a4QtZWmjSdtOUmq/beiNviBXv4qJvNcXiDnwTTjPAoUPeu5fCG2JwjakDl7vIfi4ClyrDFHGq/mvFEFJ4wZrFpRNSrg+hyedf1eL7XRxyhZXrxltnUDDh3F4H7lxXJi4FLvrP/bQ7QccM31o+y9NLD6bfq6by6M30teK5nif655TDVmHpMjjvAvfMk4TbPT6bQnSHKGwZMnT+bq6mqX2V/+8pfz7t27MwNG7sfM7N+3gAdMDxs8xu/8BEcS6RN8DR0deeA+fzNWgNLNzc2ZYwH/NcJlueZ/Eoh53xFBj4Fxo2fQBURDmAt0Bv1j3NYRFPOA+1odZx6C3wvEV8tv1mUF9v5dO7JyJqyvf0p5VCDCBteC67VfF69HW3H3WRtzfrv+lQfH345oVKiMpLnnaAEM5f75Hv833Ihyn5kzAXNfYVxHAbzV0+CCuhvONc3M9PyNgHPNX+8DyCCwDsm7j7w3cxdJcHgSA9bSZQivqXLd3jXK0oCJegxqqnCdB+Eoxfv37+fdu3f7Udvv3r07Gx8nCTpxrYbaCsKAwQqiSmTldXiJiOtNJiyIrBfDOImeUAf8WUNJOxj4Lh8xJui4it4VqBt81Cv1fBsUNvnXbZsmjNURLI+/nnzH4GKDUrBSJ+Ly8nKnidskF8LRE4/V0S1o3PC+jQpOR3XdzN23LGzgrEOgEcCEMQKUaY9juy8u7hJA6e+TJ0/Ozi/Ztm3PEwJYo4tevHixn/Vigw5gtcMC77x582bPkYI/0Hs+pA56sUvF8s5S58zdWROOBrkeA4GCSfOl7xtUVka45/cKpM1bR209ZKf42zL8JeXRgQgrByvPKgHKChFaUTeE2b/tyVWh2BA3QtC/+d/GgnXBGu+OZ5VsZBTfNW7/b2FogqFBDQxd1ExbeOzum8N5XLdBcbSEexW0Fd2d4W3a2qDVyy39TSd7r07qMj+5LRscsudnzg+TskI3SKHN169f72vabhPasBbLmFB+Nl4GAB2Hw+/Mw6rYSzHAa4b8ShnxXiMj7o/nzsr2qD7ar5d8FEWAv8xnVsb1+qy4C7jsKVqebXTLAx6nowbmVzsTvuc5NW86n4Z+wiOdE2SPObu+vp5vv/12Li4u9g9hWa9whoLzprwd2DQCoNe4WqYMWngHcM1yKe3Sl27dLCDs/Fq/eqeH6Q9IX31cj6UfxlDPnOiGdYTnwrIPn5nnCuwpjn6Wvx2NWjmt/XtVz+qd8lzbLYD8kvKoQIQRXr0NE85KimImm1mHfo5CRa17VerF8u7MnWdREOR2jYgtDG2v2bn8eGxG0ixZALi8hEB9DdV5uyF0RIhLu6PMd0cmLi4u9tAmntlqvgyOuO9QtmllDx2FQ1+91k1f8PowwCgrvBqWMgweavCtPK3Q6JcjKqfTaU/Uol4U3opezluwIeoPz61AELR0n/C8zS+msxVl1/ln7nvkpmu9ooLKVTkCjlWOVubQyH2lDwXMq+iDaUM91FEvz2Ny38qjniMDvSPdcXFxvtuCOcP75ZCoVWjaNIA+19fX+zkT9BeDSPIvvznF0lEKR/jK575nOcQQf/r0af/UN30lqgAQYmwAn9evX+/8yM/FxcW8efNmd6a4Dz08bvcZp8r5NRzN79wk3uVbNW/fvt3zNQqsoPMqErWyFY0WF3x53szrzFPrajEf1gbQp0aSzPtfUx4ViJiZMwO4Cg9xncn0uinFnkMN8soorhS0GQsmJ1eACbaQNmxZQ+HoRPtkpWqU7v7Yq6Nue6wGGhaAlZFCILwswyl5p9PpLNP5dDqdZVkjiCgc6vBX+5wDwTUj/q6Lr+ajgIM5pv8OxZ9O5wfQnE6ns+N/PecFNwCWy8vLPUu+IBVwwDhY8/Y2U5Q+itrrxAUI9ZjqTTsiYUO1UmBdyuj7jBOw47mhPkdBamRXkYPVdc+Ved88bKXIPWQF2hpYrwB5AbXlp56jgQH8tDLiBV/ut6MNTtDjGYwijoRlF15hLZ+tyTNzpkuQXw6jItLl5UnTEJn0lvjS2ON0hLGOlnkZY//x48d5/fr1HjFcOSdeunv69OluvOFJ2oO+gB+3TTIkzwIicEQcAfG3SQBQyCQAysDInnzl0JFk5L+2hXGs5NJA0wClupxxOpLmNh4CAwbRltGfUr46k2Lbtn9427Z/f9u2X9227bRt2+/N/f/jD9f986fzzG/atu2Pb9v2y23bfrFt2x/dtu2bPPM7tm37T7dte7dt23+3bdsf+EkjVHFYycsSNZxWpPV0/OzMuYLwpFMX75Tx6iVxrTsK+NvhM9roMgRMWS/IDIxA2yu2cHS5xlupeMbRC7wHG3B2N1jBvXjxYs8U95Y3R4QMhHzAC8rFme2mqaMhKJSiavexwvju3bt9DdQn9rlOQA3voqyd9Pnu3bt58+bNTlP30dnxnm+DLBRXjSkG5sWLFzvt+Ju6mCcAiZPqzP+Oxh2F9w1ky9e83zngHebJxtaGljY6fy2+Z95w6Nh1W6lW7ixblSsrXIMM/nf7/m0+83sFW478EDFypMvFxqag2stPBnH++/Lycl6+fDlXV1dzfX199tl4tlTaQTHIo31HGJ8/fz7X19e73MKbvLfaNQXQ8amVzIeXLjj8jfFipDmnpcDlyZPPyaHNX4DW5nvoav43YGc+2A7L842AAB7gQ/rG/HieeQa9YX6CZpUL3m89lIKCAjjeW/XFddvWVNZNm/btS8tPiUS8nJn/Ymb+2Mz8uwfP/OmZ+Z/r/5vc/+Mz83fOzO+emWcz82/NzB+ZmX98Zmbbtu9m5j+cmT87M79/Zv7emflj27b94nQ6/ZGf0Oe9NBmr/8+cH4BEIZRtIbeS+qHfSy+qQGTmDgk6SQtGcWIgzxnQwLD009na3LcirLK3gYThe/gM94xo62UhIH3PStmKFBoyniJ5z4frQBFZ4biOlfK2YlgZbZQnisIZ4tu2na3Xnk6fM95RBKUFz9zc3JytkdJXwrf2aA0GqYtvejj65R0dLOd4B4hBsZdubBwxNvTTS1rlPa55Xm3MCqJRtlxzpnrBNu1bKZo/oQ/jWUUWCg5X3pmL3zOI8ljNi63b/Fma9xn3AzkuP9tzb12WNfoM/5ivVgbH9QEG+kE3xkiOjguGuk6Ol7Jub2938OocDOac/lpXUeBj6HNxcbEfOtXzKlZ9IoIAMKEeHA1HduiHgb8dl5mZq6urs91PyDb1EwU04EJH0DfLTvmSebYMmGdWnr/nnzpob5UcbqfGbZvnV9EFAxveMyhe9fmh8tUg4nQ6/amZ+VN09qDcnE6nv7y6sW3b3z0zv2dm/sHT6fSf/3Dtn5qZ/2Dbtn/+dDr96sz8vpl5PjP/5Ol0ej8z/9W2bX/fzPxz8xls/KTy5s2bmVkDAEq9ogKKKpAar943s7nYINm7tfH3b3vsVvj00YzQcdhwEArvGPAmDKrstdHOSum7fSc0+Z7HT36AhcSeVQ0dShHF5hwGhNXGievuD/vDGY89aj/rMZo+FkYno7p9gy/GyT0Oj6pH+vTp56OHb25u5uXLl/eMjz/g5TV88iQAJYz7SIkbSJhnTHeuNypXvusWPhsfGwnPb+nqfpXOjIP3zO/wlmlhmj9UbKALqO2xmregrftnfjcgshKvMjavlgYrw+E+eY4qezXyTfQzMJu5+2AfwIT6Z+YsOuHcBpYFnjx5cnagEv/zLvPbudi2bY8QMLfwB/R/+vTpvY9imW68/+TJk7MDuBpdcV4F/ztqgrME8IU+nz59Ojtinr6iP0wT+mEedaTTOmUFcqGfQd+23X2PhmfMF5bRVUSi7xyBbkqBh/9+wK4vy9+snIh/ZNu2vzozf21m/i8z8y+dTqf/zw/3ftfM/AIA8UP5szNzOzO/c2b+vR+e+U9+ABCUPzMz/8K2bT8/nU5/rQ1u2/ZiZl7o0rd9xmF7I3qutaw87lXkoh5f67IiNoKuQrdhM/AoMDHTcq2Kx+2VmY7uwdRHqHfm/gdc3J4NatdJURQ+4AbaAm7qNVphmh4N3x2BFRTKhw8fzvaPY9DtaVOgIUrNodhGiAgXn06ns8iFPSgrJJQugIVkMDLinXHPuizjJpzsNvjfc8ffVljmI9PWpVEGAyKvnZYXac9hf7/nuni+OTk2ikcKzMCC0uiD/zd4sWGzAfUY3TbPNtfF0bCCXhtQj82gg/ccEakjwu8CFPpl42Yv/HQ67YcisQWy8wQoqOEwz5lmjoLQB6IHzRXj79LfSZcAAbZ+esu0+9Ut755bgLjzqgwMDI683AoY5yu423aXTEkBiBjYQB8fNsW2UNNwFXUwmED2HIU08F/Nu+d2xf+mi4v5zzS0bqV/fo55L+D4sfI3A0T86fm8zPEXZ+bvmpn/3cz8qW3bftfpdPo0M791Zv6qXzidTh+3bfv1H+7ND7//Yur9K7p3D0TMzL84M3/wSzu5UnJGpiZmw4gzc0+hWPnam6AYdXdNtIqiiob2zGzdbum++f8ak48fP+7G0cmCGDwDrBXN6uX0+ZVBQJEydowooMLI3KfkUQ9ehJ8jGuCPdBmErASBMWLI8Vj424lYzjRfCSIFA0/9jLGA5+LiYm/31atXe9KWv3cwc5fU5UOGDJLoiw2cAeUKmJq/DPZ4z/zN3B4Z9OYRYJhor0rN/OW6mVuDQJeVF+WogI2fn2HslR/mZpXvYp61IbV8Gbyt5MP0w8A6ouTnVobXtOF9A6Ej/jKN/a0JA2xvNbYhcVKdZZo6beApGP7OmQEUYzeNfeYLS2Dwk2lFnciiE45tiB154HnGOzN7fsTl5eUOFm5vb/eP3ZHkbH5zhM3zA21qzC1jjmTV4Jve1UvmOXRQ59W6hLprY1Z6byWHvmf7wLXK4ZeU33AQcTqd/oT+/b9v2/Zfzsz/c2b+kZn5c7/R7an8KzPzr+v/b2fmv+9DNuBHUYNO1CpRzAZxRXwLpENRIGMzzsycIWC8TMCCQ9xWZvQBQMD5+e2LGYX7GCv64voI3VUJty6McJdAbIAQ/IZ7Wd90Alazr+1BVWkhbFYoM3ffFPH7zDURCMaKR7QySvaGnBSJoqqitldKHXhLvAd4A8xRD2O8vb3d+08SWcGY6ep1ZnuenncDTxQx46N9+m0aFzRTX0FCDXz7C9DwXLieFdhbtWHQUkBKf63cq0ANTKvg/Y77XmMB3WxUeNaAyEbUHqfltkCKvhxFNzwn8LuNF+3iIfswJMBrdR26BTm2MwQvMhbnIXgMJCdykBTvefzkBAEkMOo3Nzf76a/oIeShO328FLht23zzzTdnh1lBA3KG0InOi3A/rO9MD3jVWzyhB/PjHVbWG83j8dkSBtMrHi0fNVrUd/vezPkS9or/V9GJVfn/h0jEWTmdTv+vbdt+bWZ++3wGEX95Zv4OP7Nt29OZ+U0/3Jsffv+WVPVbdG/Vzs0ogXPlRT00QVaaDfHYs6belbfJ30aH3hsNs7Vv9jBdj8diJT1z/xPEDZvV+2xkouH5ledpgOG23T8bEdO90RsLEwBpVTBkAIsaJdrynnjG4P56LvCmqI+6PY8FgjXWHjPj8jrwtn0Oj3pdtwaF61dXV3tf2LmCogeQOJRKP52zgtLyGBqR8ZG9Bog1gjZyK14qYF5FCeiHed31mKb13lpspNzPKl2P1eHklQcGb6zkvDrBoNc0teExLQySbNSbrGsgYp2y8lDdJvVxzbJv79mRD4Nez7O91849tIMHedbGsO/T5suXL8/OT7CD4kROQAbbLHGebKytd4keELVEdqjD0VNAt0+0BHC9e/du3r59O6fT6SyPg0iF5Rh6lSbmIw7ZKp+ZH7stHAcDfbCKwFEsfyveOJLDFTBhnhppOqp3dUzBQ+VvOojYtu1/MDO/eWb+0g+X/rOZ+ZVt2/7+0+n0F3649o/O5+2mf17P/Mvbtj07nU7Ed373zPw3p0U+xNcWG1Ibs4fWXH9ssqvwbbBt6K2g3La9D79D2/ZwzQj8b4Vrj6ZbDVceiRUf9a6YzLSgP/4ioPtgr6zrs2Vo+u9T8jCKFmCUur06rztWGZvWtIVyc0iVsdgQWMBPp9N+JDDejz1avCSPYbVmzG9Crex/py3mrX2iHpQnmfdO8HQoG5AEEDFfFyhVsbjN8glzSejYQMDj64+z2lsM9OyJlbfNT9CmRrYG3jzn3wYU1Gv+pT7TwOC2POc+2GOnzcqS+925oD9edqkesOGgj81FKk3cH8uo6/MyCuO3E4TBZpz+n7pZnqNPPOslP593wo6jlWNgg4auYXcIdQMYrHs/fvw4v/zlL3e6spW9Uajqd+4ZrNsIQ0cnNXtOGKuX/Dy/5jfofORMVQebf6r/PR7f5z3rWX4ftQsNv6Z8NYjYPp/n8Nt16bdtn3dO/PoPP39wZv7kfI4Y/F0z86/NzH87nxMj53Q6/dfb53Mj/s1t237/fN7i+Ydn5k+cPu/MmJn5d36o549u2/avzszfMzP/zMz8s1/b31WBOVcAoQpM476HKq3UDBKqxFyHmcIhMBC8BdLKg2IUSx9h7IbI7DFb4ZSJvfOB9+zt0I6NnNvwfStqGzkryYZ6W1/nw56KjXIFuNEcKzKDDtZa6TfzdRQC5F2HiK2ciazY04AG33333R66BdBhIGx0HFpmnz+AieUUL3FdXNxlmK8iKPADwItIR+llGTAv+7ejcYzLhpT7K9BUxbbyrFH8HguK2FEw5opnnIzn9fTKx5MnT/aDnDxm08N8VzBtQOF3+W2lbB1h75ZrpctquamRINfnsfkeda6WMzsf8KB5HGPtkxgdmaIdjLHnhJ1P1GdwAXhAVpBneBdvftu2efv27dkhU7z35MmTPYmYnApoZ53y4cOHvQ6WdmtQt+0uoRI6ALjdZ/q7bdu9z4CXP8rbnlOue0ncvy035fMjWaJO3m27HmsdhfItdbVvX1N+SiTiH5iZ/0j/k4fwb8/M/2JmfsfM/BMz8ysz86vz+byH/9Xp83ID5ffNZ+Dw5+bzrow/OTP/NDdPp9Nf37btH5uZf2Nm/sLM/NrM/KHT3+AZEVb+M3eGqIjRxV6in+nEtx0/ayG0IrTSNePZyOCBdjmkDAaQwLv2ej/3y1Bm7npy9qigQelWZWPa2AD5OjStIoeWDjnPnC+TWImiOOwhYOhtUAxauM9RuTY0DsHP3G13g25e/jCtaoBo0waeyAORDJQsz5FpT2iVKAUH8nz69Gk/PIo+NSqFAaE/Xu6w4rDyMCAsCG4kht+ed/Mf7VVOeI5wtBWWcw5ajow3Y27fC076PPfgFQMf08JjqBfo6400mSbID7JuPqkM2JAbZGzbdral0Dy8cjY875RuGWQeanwKijtW09mnOEIvxmv6GXgb7NFH2oHHkRMffocsexuraWhDykFQAHaDUN6DRx2h43c/PlZZMG+5fS8Rmg9M7zo7TZxdAXbXYR3uQr30qU5kHaNVNGPlyPm5Lyk/5ZyI/3hmHoIr/5MvqOPX54eDpR545r+cmX/oqzr3I8WAwMK1ImSf51muf8n7rmPmHCh0QjvheMyn0903FJz448TImfPoSMP6XDcAoS8IuhV7PZBVdIRCvxAKGxqDlCpKjErp2edqtOzNuG9VTu6nFaS9I9Oc9VQUoo0LIKM5BTZw9ANPqREbj8kGCxp4Hz00o00Ut43FKgn3dLo7UpxIBHPeubdCriGyImXsjVYZqEFv06wG06Fi08Qen+fKfG3edMKvl14MJH3NIIZ7GGcKdCo4spw2ymg+LQ8zN47mmT9MF/O0QbvBXvmZOrnud8ojHiOF9m5vb/f8AM+DaeFo1soBsQybbgBveI1oBRG0fmLbidWO+JkHV3zGTovb29v9OyDWzV3eYikQuiEvgLYCNPilQBa9/OLFizOZNv+t7EznlGcL7KoPV6WApddbqL8guoDlyJYdlUf37YyZ+4j2qJgBTGR7oWa6FbqzMvKPUSTvuKAs2VFgg0Ox8kGpu/6Z8y8H2qAbJFjYGk7jORtNK64VHR8CWx4vxqV0c90wugXMJ9y1Tis25oqxc92C6jCuw4IYTvrdj1DZoHkpqrSjWHF61wWgzR6cjYDn1+P12OijcxUcQSJKtQLEfs5ztOJzz5v5yMbcPAB9ze89nbBG/4hXoJP5xUCjy1ati3bKlzbG0KOAuBEWr98D7gwOuhRYYOJ2Oh8GXF4CcN/tAUO76hzeXxkTt40Bhw+IBrgev1e6l37mXdo37ze6QuSAz4Bz1gPj9hHY9JFEZD49Dlip8+OcJfpMVI+zWayHbVi9LMmYXAe8DMj33FuvNJpHfbVBjYxZBuykuFRGVqDTOoli5/Ao6vE15VGBCKPOGiYLTAWzguIJ93tlBIQSVO46VuiT9kDE33///T0kjQEq4rUx8rpmw7y0Uw9/5s5LQmA8jgIGRx9cGmZ222VQgxjGZLpiFB0BQMF4uaCgyWN1v8nm5jknbdEe/6MgUED0wfu4oS9Kpd+qcLTEYAW+6BKJgYSXW1BYVTB4VTaujjLwN+Ogf47YNFO83rHnw8mMfacJhTbkDbOap20UV6C6ytT1Vfn7fkGWf/c52lmBDxdH70wb+othXHnNBZkeZ+XZvEwEpYeWrfro/z02075JzeY/g1/o2WRL6yG32S3aBbz0w0AXvnWSJs9Qj+nnXRp84+b9+/dn0RSeBawAOrw8ycFwdpDev38/19fXZ3TxElwBhNsrf5hHVnxk3rDuX/3t+TbP8H71vJ9p5MR9a1+O6vmS8qhABMsCVcY1Op7AlaDy2x6ZFTPF6+dMUgXL7ThE/OrVq91Q2jC4DStORxIctSh48DiN3q1Yrfx5D2GzssfDdZ32jgwSDKLqKXhfOYrZBqqepA1aEXpBE0rYB1ahHG9vb3evxAcmMeai9EYdPL89L8OGjb/ZjWGvD0+dcfkdZ7qzVuzcDS8b0IeCYoMt+sDpfeaJRhLMM5aJesWVjxooF4MpK0eHgj2ehuMBRp5fj9fRCECWnzEoXIF4wBD1mQ+RceqzweVdQETvmT414iv9UP3UvAPfKzDif+uLOjedLwMi6FbenTk/RwFDzZjKR45wlN6MxfPrpQF2YDh61uRkto3SXy8LsvvDhtFz2YRcZApwwfVGF2jPPGSae34sLw+BAM9neWHlyLoO84FLnd0jcNA5W8n/l5RHBSJK+E7UzH2UVjRX0FHj7tBTE6pqCAssYDZQ9ul02s8dcDtmHLfd5DYY2G3ZeFi4MUqE6Kn7yFh7fFV8pY/D8h5vAVEFzh4S/1uJuY0aHZSXPRLvaqDPzsT2uj0KhxwHklbpD9niHF4zc/9LgT58x3NiEOBPigPs3r59O5eXl/teeis2H42N4iq4cRSCfpnfb25uzpQ08+Y5LJB0nVbMHhd8ZNDJdZ7vMqAV3CqqxPWVXBb42MjXMfA4TAvLpPvvKAbFIMbXC1baZxsw09f8apm2UjcYXsm9eaegxksKHcfKiakx4W9yfKzjAN2Xl5e7QTeQo89EwJz0awfHkY53797N1dXVTis+Xe4oG8sdnGjpNqCdedf8RdvItA39zOxfygWAWJc6Omka25mkruox6z36s1pqKtgzL1BWYPAIIHRZ7cgZgMYrHf8l5VGBCBtzG6cj4lmhlXH8TA3EzF24z8xjZupk0s6bN2/OQIS9aCc3VSnYG525C5tb+Rj925B5ucDGAWXXPlshug+rsRoJ28iuwEDD3/WyZu4SBrnmLHk8EHtOnPVPvRxSw9IW7drAeSwAkc65+276eNmCuukv2yyJfhBdoJ/evcBasPvEXDEWvDCAhRVjT/uzQULB8I7f69itqBzt4G8vr60AycycLQEZ9JqOpjvjI9mU+15OoC6e9dzYIBZQ1LOzsbZhb6TH82358LjchmUUeSmAsUGy11/jQv02TJbTyhttuU/UfZRb474XzEA/aMLJq43QGHxY1ptjUV1qQHw6nc7OfjBAsX51FITSKI/Bt3MxfFZM6c0SkuesbZtOlvvyXWnJvJXO5S/PcdtzH1pch6NsBn9+tmDyKIL4Y+VRgYiZ++vzKwY5UjwIVBWVFSPPWDHbkBq8WGFu27YvYVS5FtnyLvVhiGbOw9f0x31AEVSAiXb4mdKnfShDNzQ/c5cc5vCo6Wwje+RxUrfp7LYYLx6P59Ngw8jcRtfzVM90ZvYDoXzevgW0dOEZn5NBu/bQ6yV5iy6Ab3WanvnOCptn6bsjQDaUBT/2upgfQEINZcE38+blIj/viEj7b55YyWGBaD0pngUw23A2ymX6l0cbnbMRcp01Kv2Ak5+rx1ggt+IhA/z2sfVZHhsBaX/8fPnO7VX+bBhX+T28C981IoeMIXumD0tqnserq6v9vToHgG+2b0JLR94MyiiOxhAV9FwyTm+7dmR25rNO9Zb52gHzuAFX58btWoaZI0rnfmWrViDA/f+x0metY742IvHoQAQMUs/gaFJmzreArbygvoOQrZAopcz46tWrubm5uaeIKYAF/nYfEHKjf8JvFtJ6smXihrC57vCyDabHUJBlwNC6XY8VtUNqNlD0e2X8aKseBdedSe0+WaHgffAcdDNAgJ609fbt27Njbyvcq4gEHgtLIYA/Rzq8zcxJjwUNHq/5mHagGZGZRnnMN9DWOTl+BwXjHQgrPqhytfGv0qRe5pdnVwrSy24e76r0sKWVYi2YdNsG1AUkpreBqyN0rrdG3kDBp7O6TzWsNSQ2VozryBEyX1DXCugY1HLPz5r2no/SFZoYAKyiH/6CsfOyWKZATrzD4fb2dt/qXOOHTDnSSv0GIJZJf2xvlQhrAFXgRB22I+aL8oh5zNFFz2mBYwH3kR2pk0K7BjamoedhxdeM/WvKowMRFAvgzPmSQBG8lW1RGkJgAfQkVsArrDOflzBAwtRZhWCGwJDZ+7NxsBGjGFn7b/63YNqQFyAV5aJwHSEonX0aow1cwZYNJeOsgjH9TG97BFXAjsz0bApoyYmGzLXbsfdiz7NrjoRJoT/jZBwYHBSUD/qif44IAGq4X6NeHqZPNoorOqGg69F5Hn2WgvlmZfS9ddb8WN4y0DA/eP5pr8avUYDO85EB9Pzw98po1AN3H468xBoI71AxbzcvwQ5JjTNz2DXqFVB0X1YGocDEDoGdKObG0cCCRECPIyfmvRUt3DYyZBmnz+/fv9+XLZz3UGcKMMB1dCYHVrn/t7e3O8i3bnHfzevWnd1Jh95nThztNU9bjmtDKL1vEMD91VKvdbNBQKNHBXZ9v6D0N6I8OhCxmtyibjOFFePM3Pu/ChLhQ2DrDVSZeA81z1fJ3Nzc3BNq+mJk6yUIjNaKqRrKK9q1x77qf706C+lKKOzdtm0rPwsFbfpLmPZa+PG2VCsAK2T3xQoYg+s5N0hwvoAjPR67z0G4uLhLBoN2KCgbaubL37vwEhP3nCzmuaBt86SBrvnEysq7F6AH9IPnzLPeSWGPDBo5TM19t+m6jgCAoyD1iAoECvT7nPnH7SEnVcTuT8feA8dcfI+6Gk4vXzXptnJkma/TYVBWcMXvRhMsV36u0ZwaavOH6WLaWT94rBcXF/vOCkfzDF48hyQl85xP+STv4vr6euczg/HV2PgiKEae571rqTrAIN2Alzr6LRy370gadbuUvtRdfur9OrLWvf7deTa9yyN2PPxe57D88CXl0YOIGlCeWUUpXKzUeN+eZ5/1df7nrPcVc6yYz0xeIZ6Ze0rMCtXvYTSOmAYlSbTAz1WpOTP6iEbuWxX7yhtCaTj5qV7PzN0uCnvo/G0DbcVsL8YK1YqAJEfPp5WNd0jwDlsnndDIePp5cuafvpvGtEHmub1YG3JHwExH6nOUxHzlrax4cMxj+cqRt+bMmJ4AJYMl87G9VPOFFd1Dyss8V7m0Z9Y2/L75swl11OHlGvOG58487DmxZ+7+mv8N5MzTR0ul5hXer+dZz7ZGo/TxHM7MmcE1GF7R0csxpa35EFlvNAFwQUIxvw04aN+7jpBH3oEe6FB0heeU4m/kWEZoD+BR+XPfCwTNi3YKmt9l3imtDD7tDPo5+tVcmRUQKCjt/dq4VX9+anl0IGLmfC3UQn+k5H7sGn8fIUyDjZm79XHvEKjAd8LZ6okXiMEyA9g4WZER6WDtcGV8LET0y6FZC2o9E4qVCGPy+AykqmBn5iyLu0lSjmZUgXPdxoA62bFhT5G10yo7gzEiAY5AoNTon5ceADQ2KjN3x1nbaJgXHBYlsuHscRsAGwza8Dw7wmDD7uiK+2iarbwYK/UjEOs+dLnP8tDtjJSV1w2fMC82jAX/LiuwsgJTjshUgboP5sWjObRi94/5u7qjssM9AzU/gwFbzd0K9PiZI4C2kl8bQeqyTiqdqYe/ye0xL5n+PgabZMVGDis/1MOOjefPn++713imp6Ii4/z/7Nmzs2O3V0C0EYtt2/ZzhQwSbPh5znX1ectAQYV1APy+sjOepxWQaVnZKV9fgY86Bl9THhWIQAlYyC0oFf6Z+wqPeswMML3RMIbFdVgJ87U53oUhzMi82/VRr7c6V6MeuRWiw+UW7obRXT9tUB/94v8qUhsyA4R6GRZAe8Be3pg5P8RmZaBdl0OdnkMUW4HNtm37x7Aq2IAt6IRRx8jzHPWSKMnhUC70seCukYgnT+6+ZFnw1OO6jzz7lTe2omtB0JFH03teYz9SyNRhfgDIVjG6zf5eKVErQ4+TubE8Qy+MFzSCpxyeLigo0DN9H8qnaLtWygZ7fqYRwdLf4zetG/lwvQZJBS+mq3WfoyMek+ff9492kvTMDGSMd8sHlhdHACyT7IxgPD6cjud8EJZ1EDQi4lBjbVnkWe+Q8rZrj9X8bd1oR8uG3nxW/rYcu37Pf8FCEyofikqs+HQVtVmBly8pjwpE8KEWo15PVj3EVSkgmPk8OWxZsqBTrEA+ffp8mJA/n0uYzgk1MLdDfTNzth5oD3nVZxupVa7CSigsrA8x1JEnCI1qDAETVuYGO37fwuR6Wi9KgXFa+a/64nlwFKEepg/XMQBr3wAY/HQpy8bK46E+2vHnkqFNjYMNg5X5EQjw/PR/jJdLjcFqTjwPbnO11OEwrduAzsgC/avRK3ivjBZAGiQ2UrIy6vUkCwpW/zta4LZa94p+ls3V6a2NQHjcVvgFNqbnis7MSXctOL+I+laebT1jz8WqLtPXPMAZKRSDW8+/8x/sFN3c3Mz19fXOb9Cx+SnIr+XI9KA+ci5oh90b6F2f1WPAxPu0Rx+bj3R7e5dAbUel83sE2r/UoPeZ1f+tC5ofzXX1+o+VRwUiZu576TNrBAgD+lhnGAzvxkJoZdH6/D6o3CE0nm+YjHaqJGE26kFAPn78uIf9ME6MhVIFvQIMt7e395IyGXsFqsrDYMRGBcPVKIT7sqrHCrpnRRgoePnDCn/mPB/D71oRO+xqD8NGq8lxZIrXCEFDe2X2mg0kurbrscATnZvyag1yPRsfyc3zjQhZyTlyAq3s+blv5nF7dFZeNmg1gqZ3lVdpavCw8jY7t5TVsornvSeKuq32aQUm3T87AL5mGq3qKjCjlOYrI1Md49/83ShVQVf1hK8XaEAbz92K7+Bp6i1oBJBhZB0Bdd4DdaBzrUNrDHGaLEfmU0et6IvBiutxdMHRrPKIjW+XbD1mningWI2Dd+tIrHjEzuwRD5Veq+d+anl0IIJiRWOmsBCuvIhVqMin9vEMwsX7ML8T+hDq29vbe2fJg7BtcAA1Xd6gDXvEFnIvfwBcPH6Y3++YNvQXz9mGtcqyYIe+2WAgUDayVlpuo4qTfvjZKln64bnsvPp9+mjPBWEvHXz0rXMvPM9HoUSv9dtwMe/26GvUqNv9cX30z4pv5i7PxMrI87xafqmxMA1p08cYU79p17VgxllFaSBksGHF6Dr8d2XOdbreFuhUb55i3qHYUHt+DGhWHqWTOD3eleF3AVhSb4FqIzHll/bNbbkPHsuRk9A++n2Me2UDGjsCUroWCHoOO3dED7pV1MuO1hXwKQnPHz9+3E+BtSxZ1m1omTvGZL1pHjPPeh4MEF38fx0E6jUtGLvfbTTDvNyy4pfVcwZTXxIBcXl0IKICv/IsTPheM5NaYdYgeGKZQJiYtmi7KJdnVoi6/SEHwIcYVZiog/e7dc9eij2fI+VWhWYFBjMaBJjO/Zv/HRKtgLsd6utBNBjg5mCgoDwmG1PTyV5aPSvPA/PnsKuBBdcAhhX6I140fSn+u2Fj7jPGJiKu/i4o6DzQn/K7aeu56PweGe2ZOUv2rEe98uxXIN+KtHkuplnH7uhjk/mQLfOsgYCV68pgrxQ11wssVlGalTzwu8sOK6915vz4dz9bXqohMo0N8jtuj9PjMLgmqlDQaE8f/cO7PZMBww1dDHZpixMm0Wm0b31qEM0Weer48OHDDj6QWdN9Zs6cMUdIumvDP8gIQGMVbWg7Bg4upi3/44j6mUYYLMMrMFCZdZ++FjxQHh2ImLm/5rnySFbKtsJkj9fKwgqL+/4KI4xoUGDmoH3nM1C/mfjFixc7cGhCodcFV6CgiHi1nl9v4KgOGygrMv5mLO7LytM8YnroSF+t7OyNcb80bCKVlRmnRhpoFOStlF8BxKdPn87yXApCOlaDJSta+gwP2YMzPW34/GwNb4FP57IKDSXta7TVeaF4nJ1jlHTBrcdf8Mb9Psc16OMIlp2BI2+x91eAuXzf/lbZ+xk/5/GaFp5zg84j4Ej/jgySr6081c79Qx6u+avy4MiC6V4QxPOW047NtGwbM+dAwn01f3F6JbrVesLOCHV4V5XPeaA96OA2nfj+7NmzefHixT6u5rU4ggN/mra+zxgMxlysCxtxcul8r/R3/6YUuBpM+LTOLymPCkTAhFWsJfBDpYDCIMLbsGxU7ZHWy/n06e4kRhsFvtfQ8OK23R1E1GQg+kWbPhGxXrqVr4XziB718OmLvZMKjpVjw42Uer9W8Bjnepw2uBYgv8s1R0ZWhtTr+KZLaYAi6XMrL7KKwQYPMNI1aQM5h2yt/A1KPSfmNzxCK4KO3UrOysNKtQaF/piPy+uuz2C8RtH8U56w8e/f0Kn9tWzUYHbOTTfeqdGfueNXG2/Trwmz3F9F02xQmZt6mK7b/ezSZQ0MpUCJvhfgmb/9vo2djTvXfM+Akjr96e+VrGOcPGeOiLLl088zLs/n7e3nJMurq6szveflH/fdc0hfX758ec8xM0/PzD4Wvmfjs1xqoJ2AiW6mvvLoCli12OHxvDPOFTitXvW7zV3ifuWiMvW3QcSiGAWvFOzKA+j7FDw2K2NPvD1Ie649yc17p6mzCXnUBQK/uro6izzUg/O4egy1jY37uxqr8ySsuI6EqZ/Kdb1G3wiqIzYOXdrQcP6C58+KpXVAw5WidOF6w4m8w7Yyt4tCIqzo+h2+XIEpxsQ7l5eXZ+0ejbfgimLlZc+8zxi4OqLRdt1Pz5tlpnPq9zAePe6Z9qGFD8oC7EIz2uhXH6nbfFLjYMVrz9njNvijfgMNDJuNViN0BRUGvJ4rgxQbO8ZjIOP2DdRdB3PVfnYubGAKQGiz0Q+3zTX0g+lTcFzPtyDPvMG1VRQFOvb4afpcRwHHiy/hmke55x1Wds6o2w6B+2w9Yh1ruhUsep7NMwZwR3p2dY32LR8GDat6Wwy+DTz8Tusp7b+0PCoQsSoFEmb+VYKVSxWy0Rv3fCKbwQLM7uLIgkGJw2mXl5dzeXl5xuwrFOqQbsdb4/+l4MmAxDRzPVak9ZR5z8rAfexnvldzRR97jUKbVhgFVq7D91hn5YAq5qqgwONaKXuE1VvReG/btrPcFYq/tUH/bATprw1DFbyVnH/oA30qDQzu3C9HqTw+J/3WqDRS0LX6ApLSmXF4fAZQqx0EBTids8pl8wz8PM8c8bqNm+fIhr/84HkxkKPtlRFxn5kD5s40cL9X1+hbD7LyXMMbztmi3VWUiH5T3CePfSWDq2UxAx//mA978iSy6nFad5u3vYRcYGSdD+08BsAN7/K3+7vSofy2PFrn2aBXh5n25jt0vWnWYp40fb0M7nfbF7/zpeVRgYgSZhUOpVjQ/V6R6sx5uMjRAe45dAYzFP36k882cM+fPz/7oV4bg47RdZQ5KPU4a1StSLq10krfhsGgxszpcoS6G7puPyywK+OFgkZQO68VZt7zfFEntLUH1HqgA4DDfeF3DxtrohbGit+OVjSyZfpZ6XqODc4YB9Gver5VflXIpX1Dna7H/aWU75z4Zro3IkRfym9VtvYu61lVkQLeLRsYZbdf+TdfrsbniEn1Bf1thMiG0d4l4Mb0Lx8zPsaM8ad03I7S8X7536eZcq1LNTW+1SeWFecXmZYFhE2epB7mxlFcePPZs2c7L7N9lJ0XGGdHawzaqNf8Co09poLggnLAV/uGHuD6aslqVe+RfJvPHvrf/LbSuSs91vle2ZEjcLMqjwpEeMKOvPfVO1aWTIrDZX7WBpVDpMw0LXifq892X15ezvPnz3cPvV7Mirnsta089gKMKgaKDVq3PlrBmq69tlI6R3Seuf855NblPpsOft5t2PBdXFzcC5N3rkz7jsX1rOhmY9YoEHOHV+O5R+EUcKxoZqMDvf0MNDQY9Tgc2SgveA7N8+67x9P5qUe8Mpy+ZuDNXGLUzW887/yAleH0/0dJoMxjo4wrg2lgab6iviPFTj/pi+tcea82aB7vSvEb6FS2vWZPqUx7bvxjQ9OoW4ECfbAu7DwUQKwMuOnir596nNUhlmnkGb4xePKYPn36tEcxPNcG1eYXf30U+jTXonqxfMn96nTTZhWBWpXKuwHmyg601D79jQKGVXlUIIKz2rsuO3OuCMrAKBIrNntLZn570RSjWScusUwBs/E+UYcXL17cM97uF23WY5mZs5yHCo0Zu0qnnkvX0d0P6vJvykop95kaOdPOtK7BtpG0UqWP9j4srE54Ws2hlbg/zFM0736tgFrHzzwXGMzcgcjStYCo0RAUpNvnGa5bITaC1cgDclEl12epj3FUSUL/lVGxIfJ4oFU/KU5/m2tDP+sIrPJ/uvPIPGResSEumOU5j3kF1AvIShfq8vvmmeZVcCZMQZ0BS2lY/eAdYOgf+uFIRqMcjp7StmlFW0fJnx0b183f9AHZxZmysZ45Xz4zX2/bdpb/4ATQAlMf2sf4qdMOBHJvYAHdKD7Ezjyyku8V//iZtsU984rnYUXj6ijXY/tg+VvJT+v+0vKoQESjARVKeyAz52GoErafaXYEgGe9TcntrtYnbbzIjfD6eI2QQZBBEaXr9i021KuMbD+3AgR+7giQEbE5ouGqLl+fuTMoVm4FJs4ZKMCxQq9BtqItmEIpPH36dN+6ybNW4iuP1MCH8zvsjdm757fbdy6Fl1YaHrUissH2Gq6NitsywHL7BozMX4GCx95wcMF25c2A2nxfkAZP4QU2F4S2TqfT8mRWgzQDoQJiQNwRwLZOsNw5fG7D1vn32Mwn9L8eafnY82e6ug3rNTsqtF0jYjqvwP3M7LsSXDeyeDqdzpZByos1aNDfbdqwsjvNBt5zWL3mZQ2ACDJ2RMfKpuumPgMbgxiK59z08DhNT89vo3DliYeM+kpntTiaxdhWuzLajnX7Ty2PCkTMnJ8zMHOu+CtUVjZlDlCzDZA9vk+fPp0daU1b9uqs9Ph4E6E3JptQd4WdPh+FAOm/f1MsIPWsrdwxJA3fuR0rDnvUR+CFQntdO6zC71hsmFbGzWP2vHY+V8qqmeAYc3vDXPPW0yql9sf1G/jZINSTq7fg+TGNanRN3yove7Ous88cgU//D+9Cs3pf5imedTv0zXSpwu18dM4MLrscRYFW3cHR5Qzzs4GWfzsqYLBmXqBNly57FvQ2vN8x2jgZNJj3+b8Awvc7f663YM7zbRCz8ngLdAyyzSvmYfrenWhOLjavO9rKfJCUW742P9FXHLM6DM5pWMma5WzmM+BheXnFH9BnxQ/+XX3bZ10MvB8qlr/qyNW7nYuH+vBQeXQgYoVQIaJDravs7JWSWhmIGif+rsDg5fm6+9nr9fb428LidVwz1WocMLz/XykTh6jbHys0J7vV+NfQInwN79bgz5zvXTeatyEwoPFyxgpkWIE13OuIAX31nPA/J9653nqMvuY1a2jEfBl8GtSVDoybKJgBlcdZD9fG1uMprfAweb5g2gCj/GPDAa83eXjlfbkevz8zZxn4pnV5pu1UaXs+DVoNVkx7e8UFAKa5DfzK67Ns1OAcGd6V0i/N21c7RiuA4aikQZp5tUCofOh2V8WG1HPPvRUwalTAckFb5gvG2RwdaEDUs3zuJRfuO3I0c7fUeXFxcbZL6wisOEJXA29dVc+/wI9rTv5txNzvfanRt2PmOT5678ciUw+VRwciahzNwPw2c9e4zZxv2WqxIub/hvFcp6MMHLZig+0+r4TM6NtofaUY7IE6XO9kPJeG0ax8/IOwWACs/E1zBL1Ks4IPrVZGwfOBovF6bRWgx97/rVhRNDZk9BmlwrseY+faXjT0YC5stJoPYfoYsH369OmMNyge6ypRDLoUcK28z4ImgwZHoKxcV+CTUl5cGR8bHkq9Qc8R9RkcruTQSt3zal4031FvDYPnoX0oaDPQMk3ct9KWukrvjqP8b8NVPdHolevhno145818XoNVmvG857oysZp3OxymzSpKbF3ViBIyxjOOYtC3gon2ycnW9IFCpKl6sAf5wWOehyPgZx5cycUKIJSfe/9ItizXDwGI/r1yvh4qjwpEWMkeGSz+9/P+H0Y1SoboMLnDnjX6rqcfXILprfAsjCvvsCjTCs39OvIgqKtjdX9t9I6Y/qj+GvSGUeuVrebEwlpFTWkUxPVX0AourCxN5wpuDYrbc/TKwI6xVKj5AqhBBf9Xsa+AmhWrPXOPxQCl4/c8e/7tqRkwuv/uX42T6XNEdwPMbqkr35TfHSIv/3fOq8jLP/bcPQcewwqEmFcKHkwX58DwfIFAQfIR7Trn7gP9syFd0d50sbH3GDzH9vy5V+O/cqSot4mydYYs46vomWXesmJPH16gXz0QyvRxn8sz7pu3tHdphWedk9EoVPnBfOY+rADACnAcAYFVOar3qNhO2OYUUD1UHhWImLkfQqoC5JmGHWc+E/zFixczM2eJRdQ3c98DqzeAUHpP8cozMfiYOd/5sRpPi/vfKEMZbEUDrtMnK7h6CvVODGKsgKxMrVT6QTArcAv1Cmw0UlQFb0VkxWRgxJx5q9jKULgPngvTg3cc1qf/PGMFZiF2/w0YZ+7vLLG3czqdH3NtnoIGVsKrMZjPbSic4Aj9MBAGUyuwa0ADLSw3K0WHfLj/FOpt2+a5GjjPCcDDbViBOpG1QMTRl/IyRsX8YHBTIHRUCo5Nw/Kk36mnXOBd3qMv9M+g5OLi4kwOXEyryrsTTrnH7pImbheUwb+WRdPWtMD5qhxX9qxzaJO8B89DebdAeeXcUJeXT2r0/V7pWPlbvVfwDv07974OcO08r3jEc2p91Ta+pDw6EFHlaAG1wqWUgRw9qIAzibe3dwdR1fvxrou+X0+WYgDhfjq5zYqiAmChoFhJVOFYWSGQq+SxKif3zULgNmrU3Wd7bu6LBb5z2bGbZowBGrkOP9v1VhsaxlIvvzxiunm+rExqxG2gUY4U5tbGy+FYz+3RAT/12I5AQ//2nJuenUt/GnkFQgoaV7SvwTKIskIsTxQk85y3/1n2mMNVlAcl3rM66tGtQtY8Z0VO3xgDcuT3C1Sgsa87qkY/Cq7MG3ZgbIDdpo2VZdWRB5eCOMvJ0TMer+fORp/3PJZGWKAJ/5su3GvSpg2ix90v6lqnwJ/woiMbBoeAotbRuTQw8PjQoeabRjEMBExbA9KVHPGMaVIgX5Bqme08fk15VCCiRKP4Whmd92bujICZh2tVGK2X983sqyhFDbMVtyfZiNnKx+G7AoIqHo+tf5c2XSZwH49oZZCzur8qjOGIwU0P96nAxQZgNa6GZXlutf22kQyebca4FZ+jMPa0zDP0w98MqAfIXNYT5v3V9wbsTW/bdvZBL36oj4Q003flsaz6VE+3gGLl1TFGy5INGX1aJQNSRxWx2wCU2ZgYaFAcVbHMFbT0HfenBqtAlWsGCaZNgYf5nrboF1Ec09L0tZxWtp2052dt+NxHRwBn7oCsAbl1gQ39qu816CvQ7nwk81eft9F3nRjf8nyBYP83Heg/oL4A3OOiz6a1+wRv9FPn9K90OQJ+BRl9dxWZaL2r6+7n0btfWh4ViKjANJxZxGjG+Pjx41xeXu73q2Cd/FaFy7V6clVa7mMZuMBklUhI3R6P/7eXzfjbLyu0GkYr20ZTqA+Fxzvuc4GQaUCd7ruNIz/0g9/1yj2//t0IU4XFoUnXYbpa0aBk3Dd7SDY4btuKZjWWKqL2A0XTuXMpOKBvbgOgwRz7vnnQ4zIgmrnzzErTZsJ77F7SMD+t+KIGl3fMk+Z/e5AAJY+hfNjrBo81rOVxz5tBmw+OWwEe+mpASb30reNnzAZElmvow46hIyBdALyK7FCsZ8x3/F0jbmBBWTlT1mmeR/eTiACGuvxlevE3wHHFT/SlvOQ2ASEUbIAPlvJv19t+rkAaxUmrfafyzjzC1ys6+H3Pt3Wix+q+zJzv7jNI+pry6EDE+/fvz06CdBizBr1Gugf/oAycUEk9vG8w0Amq12HU6GdWhqLJQjxbZexSlNu8gH7gp+HRmbsPJj3UvxoRg44ecVsv2gDKStQGl/eqSP1sQ4luw7RZCV2VqhW3S40s1xptgkdozx5y66zBtMKHtlYWBiC0YRBn3nJfDQBWoMw08vg9PzWcNjxuE/qugNRDIKGK0PTpnHvu3TZ0sZL1ssWK//rb9Hjy5MlusBo9sCyXbu1Lo1vlP+uCeqj10Bu+rzH10gLFfO7xd4eE+YO6Ww/3CkS91OBxr+SwMssYV9cx2qZPjfLM7Gfu1Jlo9Ke8xRd26ZfnucDDc2h+Md9Upmw7LNstK3q5rECK351Zf7Cv/ff4XfeXlkcFIkCsRvcWSEqFeGZ2VOrEMCtlK+QqIpcKgw3VCmx0MmmzSslG1++1/pVnYvpUecycC3oNDu9Z+TVy0n4dAR0Mpo2Cn6lh5p1GLKw83T8XgwQrJAu+x9S94gU7Dhl3ntx2193blxq1i4u7Uxs7t040dIRkBZ6c02Kl5vHas7biJpzt3IsqsC7PUGf54OLi4l4CY6Nc7ne9IytWG93+XY/Ru3cKaPEmPQftUw29v4FylMxm/qms084q2kabXo9/6LcBko39kVeJHug8GthQDFbdT+jnvnTMls3VeP2bNqyXvXRc/Wij/mO8b1lyn+EH3odfq9NtbJsXYxlfOYD+23krjSwYTBTc+Jp1wKoOz8MKxPi5lXO6uvZj5VGBCEpDdxZyM7cFzJ+i5VkbED4Gw7v1dN12/7cwN+rB/TIW/XOmc43vQ4AC5E5f3S+vSxZ5HwEcK/wfE6iuqft+vaojhdx7q8iEx+ZnrIwQ7pVH2PF27mo4ClSqwDouH3xEv+p1WtHZwzR/Fcw1dAl4dlSku4sMBgys+V0+sQLzF2WdX2GeMH0NIFbG9aGykoPOg8fDfQMkgJHH0eVLio1M59J8ZiBSGeF+PdTVwVf0vfzsumoovYRD/6hzRbcV2ChvrYCmwVT5x7SvHJlmBgkXFxf3+l59aYfJRruHmVVmfVBUk04pBrGAbPplOahzQDSrjqDp4Heqh6t/VtGNzkn77EgYPOBnXOiLI0arqPmK/l9SHiWImLm/tmVGXhmPp0+f7p+frRf6/v37pcF+yJDSh77nw5hWz3vdl75VYAuGKDDOytuwdwINViFQ12/kzP/2dgtOVmO3cTVQoA+eA3ul/F0vpCFN+tRoSufGhoIfK1OUhZMUO0dVGnjc5jULM0tI3pXRCIlBFcrOSZGMz5EQ06PfATidTjvgtXdpsODwsA2gM9attBsuNz+sAJl5x+2z934FFMybBicFzf6/H+QqTx0p0MqWDTr9WK1RW279fAGNn7F3XM+xNCzfuq72vc/Qv5WRWIXbV/TwtZlzA1+587w/5PEWRPCtGtdRXb0ax0p3Q1eOqe5z1T116GZml5WCH8C4dY5BJD8G+fC2oyG1NauCrLv/tMdYWio/LQZAff9roxGPFkS41EMtcX1GvoXN31Fw6dcAKStDb6au17EyfPUSYOx6kkbIRtwFEBWahnLbNvXRRz9Tb6jG2MXRCOr0OGocPC8z5wlKVvamnftsgEPBIHq8Kw+qdXucrqsK3zRaXbeicX5KjQb0a9Ke63E/PH7Ps8dDn8z7Xj81rc1vBgsGK67fXvkqAtO2qZdiI+bxeyxW1NDexsy06/HxlocVgGiCbw2xebqAaOVdM77Klcft7YUel+WvOqLPHIE26LXyVqFPDV6Nv+dpZdi5BrBdAUFHPqG56Uy7fPzL9CsdDDCIDHj+HTGBJ6m3tHmIrxrxoPS8k+pGrlc2+0xtxKp4vlfA90uiB+UbxtM5OAKaD5VHBSKqdGAuXyswePr06bx48WIXDnvfRpUUKyAb7Ya9auCs/GfOJ93eNm3Qx5Vx4H3/beaw0DbaQD3Pnz+/l59QkGOF5b57jFVutFHAwfPuh2nqOhoO5O9GPzwXndPT6c4jP/KurKCtqHnOis2Gu16qgd4KPJovj4AG/xec8qx5w3kIXlf2O17S8HgLcgEuDvvDu12DNr/WsHLf/OrxUzw20+xomct8X4BcY+dlIYNx8/YRIPNuBu65f32nBpzykLPg/tar9VxX+VcmDb7cP5JC6ziUH80PnlfueY5W/NU54L4T2GuYPQ8rI7b61IB1toF+wefNzc1cXl6e6U3LsfvttleGtfxS/WZeoM5GKqjHbRmQMqdH/XGpTVmV8p/bLG9RHBH6sfKoQMTM/R0YfI+eCavyQ1n6K28kWWKIYABvCbLgFjA4zMq9esNGmmZW941S47lithryFcNV4bUtA6iCHhRDPc+VV+axr4ABbTjfw8p31Q60Zr6aZFRAdySczEeNKe36fysr08Vz06PNPU4be9Pdis00N//VEJoHOjc9CrigxWO2QWM5xR4ySzDMreujv40C1aBWqeENr7yqlZFaGVVvae2Jlu7LKjfB8+z/+0z7vBqb+2XwWiPDnBXoMl/mL99fgRD3yzQxj0ETl/bzCJyvEo9XpV51l2s7v77vMfiegQH1rHKE6L9BmI3k+/fv5/Ly8p7eLJhBhnjfstT+2wFjLksngwHPN2NZAWj4zuMz//S8kALiVSlo8fXS5GvLowMRM+de68x9r9rFmeYUG2x7aN7+uPLObDTc9soDrGC7bffH9VDqsTSBq+jano2FYuWhtL+maQ17PSEbuUZXvP5fpbkCIr6/OjbbnpoNUSNQnpvyQJcWmttgo9w5qoHw/HuODBBXc8j4Pb6VJ/nQ++67x1hlVoPm+x2TPVIDJIM17pGX4R01fs48RRvOYnd7K36H5u2DDQH5Fivw5nFSl3mshqQAwwao4JQx2uC0HRtI81I9YLfduXRbdUBaZ+tuP5pPY1p0jOWBVX2lJXNQw7YCWr5XmTcPep6aI2QQxDzWBrjfPFPaFtR5bj0283HHUnBrOkHbbsssz60AzVEpn3YMK17/2vLoQESNkUPl9VjMkF62QFG9f/9+Z7h+d2GlgKok+uzMuWJbgR3/70x47lPHShiNzO3Jbtv9j87YMPA8bbSPXLdBcJ+stPu7Y3dxO63bRsfer6NCKxo6ybJAxtd7bWVsjp6lffe5p0b6755T4rbov2mz8lDYPeSyyq+oAbRhX4GeKkUDQ4Ozo/lnfrxnv3xj75H8Iwrg0ltUXQpcrWRr+B3BWgFAA5aHlCn0gI71qCsj1gH0saCiht51e2ztuxOtbZjd7pGxOuKzGpQaNBte84uL+QJ6tW3/7/acU0NdrofrRJC9XEA/zQvcIxqBY+V5LO1dj6OiNt7lU/fddawAPnSuXB/pw4uL8w+adU79jq+tANnK5vC7OvhLyqMDETOzFE7/DZFRaAYQLFnc3t7u+/d5bqV4avittI2cKWXQKrojBN22q+AMiNxWowUW+JXSrhGsInK/qQcBXIWSjzx0t2WFV8VYw2uDZzo4cgRdCrSspGoEStvSvZ6doy6lm0vDm/5tAGVD6PGsoj+M0/+v2mZO7GXxbun4/Pnze8qxoKTGnHYL2mbOk4+dcLeaa/rqHB3qsPKmDWhfvi6Y4sfLDZ2zKlXLr58pP5ofGpVy235/ZaA9Joq9bI/NdDe/1GtdGcHyZ/9uBKLgmeedb2CaFTh6Pm2ca3Srtzon3O8Yq99vb2/3JWmD0gKJ6ibXUR151LfqxPbD9Pa1Agruu42j6MNDoGLFx77/U4CDy6MFEUcGlPsz95N5EBa2e87cbR/y5Fnga+Rox0LbjPcaPtfjBLrWtxpTlftqHZLS8KY9HCvmKrqV8qnnUY91ZXgajl15SjZKNfKep9X/rofr7kfDxf7f4KoAoWMxCKvHVwPivrmPPGteoL+ut3xgw3U6ne5FOeh/vT23exQ5cn5EoxDeemrlWqBl3msUkPcsAzUoBacz598P4f7qi6Gdy23bzrbw0dcq4vK4+a5KuLKwUuLokBqvzr3/7xy5Hw29t482qPWYKxuVj+oEz10dgtZjOXTEoMs0rt9RB94r7SoXBo4dE3VDD5a1mq9hvigvGniU7qYPdVOPeZuf5k103B7TCtis5okxrHYD+tmVrevzRyDlofLoQMTr16/nN//m33zGHDP3hRVwYGRNWLbMuAIQVlT8T1mBCt+rMfMzNbowj4VrZcjNoIy3jN/6V+3SXte3+94RMl7VbfBjoGBvm9KQe2ll5eZ5q5DWAyjQ8r2G6auwbAQ9Z6sE0Nbn96oA+b/LEtCOHJyCLitVt1vAVWXl64zTiq+gaTVO3zMvGzy2Pzxjj90AtiCkfOE6a5xOp/NPh9drdj2dL4O9jov56f0aohrMI7DguTdfmRdqiNyuZd0Jr5X76qoaIn6aa+DxlNcM/tGN1Wmtp4a09VeuV301XeAdxn863QFo6+HT6XMyPLvPnHjqeunPagvmCrC5Lx57Zax9qeH2/9UnP1ZKI8qPRS9W4GEFMo7KowMRNRxmQDN7w7cIiZN2jtaEqwTM8D4u131YTZqZ6GhSbZRW9dKu17NtsOylrBD8CvBYmdtYGQTQruuYOVfcR+OmHYc7O6YahPa9St/1cd99dHiyxpr3nDXfaMRqzgo+ChZWhtfP1ViuvCyeN3jy/BkwVelRZz0VG3j4vIbTSrbAqCFt+K+0hqZu6yi0a1rV86R+Rx481tPpbkdPjfPM+ZH1XnorLxUwrPgXvlxtw2UsvgYtCgi9dON3apzNK4zTYID7BmUPhcbNu27Hsm0dWB62XmA+DLhXiZCVVc+t66V/jaLRpufxiE4snzHHjMN8vTLsrXM13zzn/DjL3ZGuox+dD/fFv1dG/0tKbcTM/R01D+nko/LoQITRLv/PnCsKThlEQTFhjk6swrX1qClG2Ssk7r7Yy1iFg1tqxGtoETru24jXYMD0bqvGvp43ZRUCrQF1vTVYBnIGYqaRlYHb9RitXEy7KgKDRwOqFf1WoMh12DCVbu6njZPpaCNgXnBx/e7fKtoC73TcNawFVDPrXAr/+GNbpSE8vloyc3sGPJ7vlVHgfvnV7xtMmJ+gi3nBvz9+/LgvSxpglC/Nw40Y2Jtd6RW/t5rnzpFl1uPzvBXItY7yaKMQKz3CdW8fdJ+7TdWyVN5zfZ6Xflr8CJhVdxrIWh9DW4MUrlFPowDUSZJlZb1z43urOaN4TvptGL9PXw1g2pbLKrqxKqt+/1jxOz8FPFAeHYjodh8zGIwKc1mhohwvLu4+iESx0KNUVqHWXqMPrasGuwLXNltqaAxGCnoMbni3iNWKrUKxQtquz54D9/qM52J1Cuhq7Zy260GsaLWivw0fy1KrUoN9e3t+zkeVpg2plZ15wsqudTCO1bZc3y+oWG0hfAioYgDr9ZluBjr0w3MF4C5ftg81DAUoHnsBbY2M31ktJdbIFPRwngR1m8703fLS6CFlFRVazYt/m4Y8vwLKVe5+1+87XO/5O8pXaBQEGnp+3FbBc/lo5TDYgBdINR/DMt1nGolwv91u9aVlZkUD/v/48eO8f//+8HwSF96HVj7l1gCrpWDG/WvdFI/L9+wUrHj+CHz1mv8u3/v314CSNST9W7hUgXMNZdikGz/T61YiZnR7Hr7msjJ0ve92ETAzxSpLu4aWUs+s3oR/e6wIixXWSrDbXj2WlcKtknEUpxEW951nVsqlRry0qcdiWhpkHc2Lo1MrD8NjZiePv5+xqncFRKzUTbsaAnii3iqgpeDFwHTlFa/qXtGaek0zR3LqudK2wUXnrYbIdVsOPI5GBXxt5T17a9/KcNhw2llwXztX5m/3t2CJa6vxdp5dj8ew2slFGyu5h9aNqHgsjpD52dXPSj7g85WOW/1fmhWAem7q9PAbHvv48ePOp0eHNa2iFzPnB3BZllcAsXS24S5YcZ2mV43zURu8ewS8PW+WzS8t7Tf1FqB/aXl0IKIIuAzGfnYbaEciVok2nWDq67P1GFYgo0aeArgxQ662KPHuqtgzOnrONKlHYwW9Eo4+4zo7nlW/Z2bfhuUfTgU1HW1kPAcW5I7PitrKyUbJtLfi5jeeB/20AjTt6om5Pyuv3/Phax3r6jTDvmPeMMhZAY0myNrIul7LSQEn73keaKfgzO17rlaAvKWeU0EMc2T5suziRWI8PKaCDUfpVv1aAdFtu/9JeM9haVGauB7PbesDlNpQuu6ClDokHVNlxbxcw0j/auRcj/uwAv5+zjxoJ6mGuIDPtGHsPs9npRtMW967ubk5mw8/5yXSVV2r/vB89dBqTlf6qXbAtGt5KHpyVFbAZ3XtIYDT8uiWM2bOlVi38azWqm5v7z79yhc7i+bNnDU+Vkpu/yFlYUFb5V5QahjqzdTYMBYrXN+38am35/6uxmnFW4/fp7CtwBHFArcCXCvgNXP/GyCOZhx5GAaFpo8Bg5XSCiQ6T4bf5YWeb9C+u9gLNB94jL7vuarRWOUewBfuo591ncwB4fyOze/hCa7AE9eYz9VyX0FclWzHXVoyJh/I0wRoQITrMc86EmYeOOqP+0FiZ+fVc+K6+0znmv/pE38XpMJzD8mgwU1phzGt0bbO6fvus+Whu5q4X33A+/166VFkjLpXDhxzuDKojkiZpgUg0MyRrIKoDx8+nOW+GCza8TG9fIKxx2V7Y/rVUWLZ5AhA+f9VgqSjOUdRN+vlo/p/rDy6SIQLQmRPc+bcG+d+d2JQjoxchdbPUIcVVL1Tt7EyvjCGjWC9TysR/qc/MH/7WNpUAbp9GwYKzN8xOsOf56rMbZxq8PxeaW8Pq/Rbec9eB6+yLR8w9zayz549u7ebp99MsZJpNGEFGv3/SlnP3C0hrLwEZ5l7LtumacpzRDfcZiMM5imeseHqUgLvrMLfBW59ZgU2Vn13vwDG7a/pwPWCMt61scbbb5TxKMpWfq4swaMrPeDQ+KqYpw2afL99glambfu/Amn+v/zruWH+j4wT7eJ8UQruHCmC5u6v59k0KthoG77eeSzw5ENTpuuR57/a4VV6roDD6p2VXvX9h6IBpY1L6yg4aT2Wh1V9P1YeJYiwovYksq3TyBaFAvo0OEBxrCbTUYCZcwY1I68YsYqiyyAOv1UB1QiVaY7W5qtA+vcKrVohr0KY3aJJ/6zQADPQ1sqrimwVGmWMXv8+CudWibv/9pSO6qkBq2H0nBmItB9uczU/TtykrMBFad8IQ/ncz1JHQR6/yeUwwPA4TEsDKs9do2IFZOYNPDMbYerqMp75x8a5Y6n83N6eL43xbuXWRq3GqXNWo2zPuzyyUvaOHJhHqlv6d8Gl6VYZ8rUVqC5YOwJDqxA794/mqI6B+dugsxHCtuf3V+UIzJgfaHPmbunEDpV50c/wtz/C2PvUvTLA1c0rZ2lVDDAtx132KTChmA957+gd9F9165eURwkiZs6F6HQ6HX7J00lDDf1A9Ar1zJ1RcnLazDkjmRlWnoAFrAJZxWtAAfOtDJSVSw2H6ynit8HlfxtZj8Og4cOHD/eWGlxq5CzEVvb+4VqBWcPm9cqtoEuXlcdZpbj6sddaRV5+qOI4ikoY0ByFeHm+UYbSxABqVdxn19VxUmqYOx7Ly5GXs1KmR4BtZaTdD+qsl22Qg5Fw+HwVvWr0pUbDc7gCZSu61gHoD33x74JY11OwVP5wv1ZAz7lVfmcFdqwL+oyLZdZj9zyZ/+oErWRnFUUpYPFSaPWyaWmwYvBjB6XRMeush5ZRPB7LMmNb8bD1U+V/pbeP6PxjtG8xDfx7Fal4qJ6WR5cTQejK4UcUX8M527adAYgiyNvb82/ZHylkl1WyjoXWxcaUe0cHqlCP1/ItdJSVF7My4H6+z7o/PFNhXQEbexJdTnBo/EjojKwL9CyAVlIGVStDWtBTRb4yAFaMBqH1FP0egNLzdERnj6/XXYf7XSDhZ6pAGWef72/C1TZ2FAOY8m+9IY/H/GF6Mz+OUlAX9K2yt8yVdp5v8xRLUQVaNQTUj4xbXxzNlfu7osvM+Y4RA3U/byfDc2Sj5ncYa52M8vQRWGlb5He03Xrhq75zzbS1/DkCsDKgHtMqOmLa+9Co8qdlkP99b+VcOBJ69O7M7FEL81d14LZ9PlK93v1qfkpn9NwqX+uI5i2r+5bBzrl169eWRwci3r17NzP30bKZl3ve1jhz90VBSg1wDRLP0N7Kq7DSOkL7TLARNsXKtMxmIUEZui0bszLdypAXeXvcfseGw4bF/auQe3yNgri/bf/HojErj9Z9ccKWadux1rNof2y0DCILJjpnzs9Yzf3KIFlRrXjEvw0c/HyftTExvazQC2CrEBsG9TxabiozBhA1nCseqvKvsly19fz58/1ve/D89jsGraWV5dxeuvvXPh2BjNJyVU/rK/haAQu3szJUjqiapxmLQ/t+nnd8MiiloKzjqY512yvQWnr7PtcAO77ew9LKI9YvBXKcIbLSU6uE+zoGpvHKnqx0qp8zLxkcVY7dXgvjMP36fMFt217poYfKowMRNYCEL8sQrAfzjpMQuTZz7snUMHdXRSe+/1t4+IBQPQ0/V9RswbdBrqHw79VBVPX8DV5qDH8MEbvtVbsr76GhSN/zs23DIIZxrHaFdKzMBUqkyVOr8dbDpx9WQitgaRoWBHhs8OIKAJhPTAN+r3aduDTitaKNgTO0ad9XUYEVEGPcNQruTwFHFV2L+Yr/V/LJPLVN553Y8zPvOeJUwMTfNprl74KD9q0g/4hffM9yUb4/OieBawZM5nHm8wjMFjD6nRVg6LurCJVBq50A0/4hUGR97f+ZSwOd5rGVTzz3ACjPHzrU0QTrZDtYAI6V7rKMeq5XMoo8HS2l+LnKVJetvwSArPj0S8ujAxEmLpPYUCnXPAnei2yl5/W8VUKQJ9cCUEHpBLr+hpnMxCsmpE57cf298uiMvG34/cxRf422G4rEuBqZW7FUAZtGrXtFy5n7pwx6Tvy+QUCBDfW4f1ZsfrZz5zpNE9Nq9b9p5GTM1XyavuaJ0rofVaOOFeigVOnyjreVmj5VjDX2K7rVON/efk50fPbs2S5bGDn+d/SvRgY6NDHw/fv38+TJk/0LnbzPb8LMBpr19vAEPR/lPxvhzlvntjzl6M5D4LiA0/PW/pUXuVfg7mcbXSh/Vt6tW9y2+2r5r7ft4uf52wbQurkAx+27b05+X0XT/F7rYj4KWGbuZKLz1TllXlxHI2MPGevOv3+73yteWwGw0qfAwjLlNr6mPLrEyiLH58+f7wh05jMzPHv27AxEmLE6AdTpuqmnBpv7VcwGNWUce4T1vri/8hKK2rl/tI2nXsmK2as4rOCK7mfuklKhb9dZC75oo4qINtunlRGDHkcAy4muVjieg5WS9DOlGWPozpfuEnC2v5WvaVCQ4jbLu6ZZFW3Dk33Hc+WxQhfTw3vpC+76UzqXN+lvn+0coQwLhqrAPVbGQZTB2wah/ZMnT/Yv8dpDbXKf56nGZtUnR/P87EpWH8rqr7F+iM5H4AJeWM0L79VouO/uE896Tlpn33X/PDYDBepZ7ZxoPav54cc8UL6ibetS64QuAcH7ziervJl2djhKF/OkowXVLaZNeWcVZejceawU93mVvL+SxQKRrymPDkRQHAqsYTKA8PN+poLJdZ6tsbYBn7mfyNL6mnVcBnM0g1JDe8QcReP2BixkKwaukPpex+Q6ul2ufa1irlIuYDHqZkw1dK2POfdulI7JBnkFklaeAPV7rpqN7nwU08Hvcd/z5/MKrIBLY+pysSJb9ZdnOidV6qX5ChiUZxjnahlu5tyjewiw2nC4LfpU+rn9J0+enCVS1nP3vdK6oNTAsH2twW1/bWA8F0dLCjZ+K2fgSP9wr9E3fpBBQLaf8bhXeqSRjba5AnqWTz9T0Fhg1Ihh5WMFpFdgjeXgjsmyjSNhJ6dAgjr8XulTEMP9VSIt9/ldJ4o5WY2tAIBrpl+jlCu9YN6q8zBzvtz3Y+XRgoiZ9T7xbg2bOfYKrOjMtBQmi61iq3C3BcaFMK6NXo0TfSvipe4yghVKvbh6yDxfRbIyWBYyxs0YuiNh5VFY+Bxehi5VplV41OX+UFeBUxP3qMfAwuNbrafWqBjw1Mh3DDZwR+FxK33utf++5vnq3/bOj4DDCiD7Z0XnGtfed2SmkYICjnpxNVQ10kcRDtfd+syLK8PmuVnVa77xmQGN4tSYPGQwV4CsRsDF0TnL7sqgWq/0Z2Xw/bf1lMfi65UT5LtOSOnod6jfUatVxLWAyeMjcmzeti6yLuG+58r9NG+hyysLBgfMgfnXy7Yd88XFxdlYV/O70kFHYL3XSqfaheru2o4jsPJj5dGCCAMIGwoY0ETlMCRKjT73PZkWqpn7a9BHXqL7YQXjRB/acMEw2UgbIFRZPNSm3/GzVvplYnsrCIvr9zMrMGEFZ3CxipDQRj0YAwGjf6P7ClwFpwqj1z1uf8Gy3s4KALoUfNBXhx/NI/aia/C8NRJldRQRKj96jAU/HleTHc1jq5CtFWkBmsGb++Nk2/Lf6qAfSkGR2yoNDCTMF478eS6PeMTz2rEVGJjWtOvEWRsi5tJAedvuvgth2UTOjiJT/rs0KbBwOfoya4FjAQOlsrUC0S4Fh+Zxv9fonoElBty85bbraVMMbD0n0Hvm+IC+ygM/XiI1rf3OEX9Zrkqbvrea99oM19u63Z/VuL60PFoQwTqpJ8efoTbqrzfRSTTDWlHbIKyM4Mz9HIQaTLdlwaFYadmrrsfSUiE0Yn+I2cxgFvqVx7XytH9MoDwmFMeR52iQtPIaVqHBKv+idNOxxq4KkJCnDYPpQR8dWTgCUAViR/SvwXKf2//WtVI+HpsBpO+bZuYxb5Xs3Hueeh+6HAHSjplnPN+Njlh2PKYjmtAvK2sDCRskR38KHNwH6LQCfAVvgDI/D1CyITUILrCemXvLhNUdq4gEfV/lDRk0dk6P5ojnGyFaARfkxHNl8OQxm6bWewYx0K0y1M+3GwitwERpYP3hOa5Os8w0odLzYH75Maeuxe0WvP9Y6Tx2B89Kl6z+f6g8WhBxJGwWhE+fPs2HDx92ISkq5Jmixhr6Mh6/y/hmEBvdentFpivmpj8rZW4GLMhZMeaRkeNePWbXbcVtcFWjX4VqgeN/ij05969j8W/T9XS679mulN2RN2Al5r73mbZdL8l999hub+8+vrMCdC4+aZX59dhK+ypG9+PIK2n/qnxWyXErD8pz2URS073GnuetoN3nbqd8SAZXBtXPGhi1joIVrrmvtFWgUbDB39VBNaI1YB1j24V+vrfKz/J4qb99Wo3TMmt+9jy1r5XL7lZY8UppVj52X90v5x4198mlEU73w/rEkauVsTeImrn/zaQVCOXvRh6tS80HKz5sH45K9aTnxf1veajOlke3xdN7200oh/BgRhigTLbayuliRqzRLTPU6MycA5y+7zaOAMjqPSPmFermGX5bGdWgu/9l6G4Vs1HwFxTbZwvwEejynFkhuR1oavqVJn4e4LOifWlX5bhC7wWipU2jEKaVy9F2TxuKledX+tVImd4tq/l8qK8eL/WuQEQ9ZY/LBs9z6eUTy9wRSGgfGz43vUwHrtfbXxmflcGpPJsefd56w7xUupV3fc1gyvPu655faFddZZmrHrKeQGZtgEzrlhUAWH3wzn1olMwGtPNO8ZbuJ0/OD4yjvWfPnp1FBGnTfEHdnUPGu5I184u3D59Op3n//v2ZjJjuBQv01zRx6XXsUXXCkb5a1ftQ9OJrgIPLowMRnFjp8FtzF46S7Ko0VsrEwrxSMKuyerYovN5+AcqqDgt9PXorSRCxoy2us4aoimJltKCFlbKfq6J1XwqI2t+VQavxXxkZ6iT0eCTs23Z+WNAqGtI5OgJ1Labt6l3vDDoChP02RZ9v/6y0VvNQD8nFY4JHujWO5+xhNomxRs50Lm0MCD0G+lBFuAIeHp9l3aCmUS8DRdfXKEb50N6jjdCKvwwMKkc85zyHFQBoO64D/rGz5HlspMLPdA5W/fXznkv366HfHa/5qICCcbh/K7BDaQ7AzOcdOJwX4nebQM+PzwHiHUcFeXcVVaNNn7lCXZ6PjoelHH/G3s9Wblwquwadft7fjfmx+r62PLrljCrvmfPjra1kT6e7D/fACBUAKx7qKLJue2WkGhyfI+HQpie965lFrPZwqtCqNAykVkxkRjYNS1O33z5ZIPyemd7ouctER8+vjL/ba3hw1ZdGDuhHPffOv+fafOB5swdkWlKcMMd9t83/nm/TgWt4YM65sHJ2cd8aibIX437M3PGc+1LgxO9+lMy8U3Cz4q16cAUbBgWe3yp3G4SCO8s043KpwWjeT0PspYnH4+J26FfnH91jgFjDT0K3+YGxlMfpj/tq+vGMQTbz32jAil9pr0Z/BbpKN8/pKprlehwdLS+YNta/q91t1geNNLRP79+/v9cP6+WZ+7bD/zsSQ2Ec5tfKrMe5imK0FND5HV8vfVc/X1MeXSTidDqdbZ1DWLp1rsh95tw4ch3GNIotsm5YqQbs6NPP/d+JWytmWylgC44Z0h6vjUWF1H05AiL/3/beNkazLbvvWru6q6r73rl9Bw8zdxiZwZYTBohtDEaxJ1h+ZXAgDiSIxBA+2BEocuIIZAEO/hBiLJSQOEos4YlDSCIDShRZsmUJGXvMhDixkmEUbEJsEhtiTxIGj2ecMZ6XfqmqW3348NS/+vf8au2nqnv6Tt/bdZZUquc5Z5+91157vfzXOvuchwbtYOe+fE34obN2hsH5OSt3OdBVA84rbfwWRDrp7hxlTOfQ3XKwM+vKmaToTMbtKjRcVwd+6irHp1OiI88aUa6Uj9eKjtHPvEdXwrfX3E7J+kCeGHwyT4IhAjKWr60LDjAkyuGyrM7BxoCMoIXyZz/+bx4sZ8vIMmQbrkVk1z2pYdBqsNathYNj+syr+Ds/sAs4dYEr/Ppaj+vKDPu0zsaOorM5F33p5tUlKvnO9uGhC7AOwB3YiT6nPRNDr33IYN/z7vj2OqeNY5zHY3vL+jK6diDCxm4U2im7HR/7ikF343Dxq7bRP4N3F2jtoPjLcR0oMTnoMcDbubt0bEfpHdJ28OlrFohCrLBk3Mg++wXYF51a2ls+aWeg1GUrMzDnAOvgmj5Y5mbgrLpYGSI4oRPsnJT5zHnuLwkZ4IQvOq8ucBhURIYB1QYXIfI9kx3JMp3dw09brrFtj3rmdTF/5NuO1hUG6j9BJjN/6owrB5yPwQiv9xwdTEnMVr3xsCtzdz6JvogJTwfeXDFgZuwkJP8ZnF0RpFx5bca33NiXj1m29KOUgfng7TaCh729vTo4OKjj4+OtNrO1M39Vm9sBuY3oQN/5HPKZseijDAS8Fr4ddVkyQiLw6OyKa+HPXqer0LW7nVG1bTBWKC4wnUpoJnArOVFkV6YLdQ4mRAPM2/ViPOSH/62MDLI2eLa3I2BASrt89nPkHpdZEp28A1nXjrLMHDL3XY7bx3zfkv1l0yxBWo7zO+eec3ykrntkswNoDICWt4M6ZZi1pkzy0qSAk1zHQOw1NoDJZ+qFwQIz/9xycenVfVLOBsoEVw78BKfsi4Cb/KQfztfzphOmHZnn9GXwQB4oW66X50IZdDqZ9q4k5T+PVz16dNEANevG/kjRD4JvVwhntpFj9hUEE55DF4hmMuC8nU3bX3CukT9tLWSdpQ3T9jo7MF9e76r+N5G48T7jGsCFOhl4rl578mO+O1DsRJYymwEmkpOex6FrWYkIOrx161ZVbWdqNCQ6dzrx7tllv9GuanvnOT+7bxttKEZgwzOSdeDNGK4ikA/2GYWjQ+L5WeAzHx7fAMzG3xm1+WZ2MnNS3slPfrrsmgGtyxjdLv/zZrwZ8HF7z8NOjoEzACXXuYxpgBEeTk9Pa39/f2ud0m+3BgQcrIR5TSxjHp9lQpGDQYzXLf10WSlfOOV5MwDy0VbzadAQnhiwLROCQb8siBvrDCSs05Zj9yiyeUs/DMYd6OkSAAJJ8sM14zVeK59LYOSadP0wSHFcyo22y/WzLnRz45p1+sZbWh0QogyoN+TbfKY/3mIkf/mxOPtQAuzw0Pljj+k557hfiHYZAOio6zvfqV+OYbaNq9C1BRHdfWFnakTyndFXbf8ISxSVqN1KQxDAfmysM97Jn51oiM6QGa1L8FQkghrOuQvgztooHwcOOp0uYM+AG4lZSD7P9pFwbSk3lw/ZL4M61458EtB1ICKInzv6bYwcJ2QHzVsfDkS8lZF2/OlyZ9DdM+6XAYLMK+ccSNmOfFM2lHX31I/3P7DNLtDswE/d5Lytt+bX8/DtCj7hQF0jYDVQ5a7+8MrSucFaB/Rsy8yACdC4LpwTA45v/8zaO9AbjHZP4/DaDnRxLTxfz8/7IuyDza8/s28f53o4WeLTEGzPH2KkHbnSQd5mlYaq7Zc7cc6u9mUNCFy6fqnfne+3bnZ9zcAo+7sKUAldOxARioFydzYdTYTaBXYaHD/TyKnANoYs0sOH25sqcyw/jUwHwOucpXXZbcBMB1o6xzLGON+XQH7o7Aw46OT82WjXwYTAIOMnOPKFMeTZ7WkMnK8Bmp1eJ1c6DVcvnGVxbwl53BWk7SQ7tM/ARX2grENdWZd6QedU1f/mRle2tLMxeOb6+7OBBysdBNCUBz+zikA+8jnBPX0nyNnBO+Asy7JVTbBN7wpWHj/82s4pdwNojhEebbuWT1dCJ4+7Ag7l6mPkhfZMvfWakWcGGvfJ9ScQ5f9d8iYATX/WUepGZ39dW4I6g2TOz/6f/ObzycnJ+fp09sMgb3BFedOu8tetEWXkNTdo8nHqI3kjGOkAxOPStdwTUdXfK/U9ZrbN8Xy3o+PC+BgNsmo7y/WrbrlDn0pM46Tip7+06zIO8uO5hKjoDnZ0vL7XnDEyNrO2EMEDjYhgI46e8yUvDC52Rt2ck210ZUXy3t1vpmHzj2vFuXdVKDowrrerNs5oKPtcm/0YlGWnZzPinLogGl543M6HfXUAhrL3eAad3ttjgMrr/YjlzNF2upJ5cB3sxD12riOY84bkrFO3T8ZztlwJMLwm3Ofw8OH2vWmDiq7fjN+tb7d+tiMCG8uV+hteHejSnjLwOttH8nr7tfjDXRtj7Z/83/a0v79/vl75SXjrTIiJJO2WvphE+2XV1H6X/HRkvXY86JIFXtsB8U5mHMtg7HHAxLUDEWOM80w/3xnAq7bR5KyPbgFzbYhIdAZAfO8/f9xAl+sYoKou3ttlGyot+5jxHefCazMHVzGcqbGPGFfnqOIYraCee753QSVj5nXknmvKlARm3b1TXtMFOgNMZjRE+ZaPZevvdvLWMzps9uGg5GBDuXV64HHtmMlrF8jdBytJWSvO0XrCPkieK28HkR+upfsgQLC+dSDBAcb2bqCUPm7cuHEegCjXnPOTM9RjgkA68s6+wz9llH75mxAZ17bDQNLdaujsz7IgdbrCdeN8HSy9Jrv48O1Cr6V5oy1yXak7lklAec4FSHT8RG89/9lGRYMR2wXtk2tPWbEv6lmOd7dqO5rJ2QCmAxa7+u3o2t3OODo6qqqL5SJnJww8djJR3FlWGCXuFspBZ4ZKUy7jOy3SL7MZGo+VxU6JJW0HGQZkKhWdgvlkpkEZcu5d/zYuzoPOvyM6Yx8L+Irh03E4KLsiQEdjx2Hd4H/yb8fSzWFWoZldkzW3bjoocw7MhhggqmrLgfI6bqqyPjtT5VrmGq6pjzsj4noZxHGOdLiUDfWN4xwcHNTJycmFylcX8Lr+wwMrHh3g6GTN67O2PsasNt/ZL8lPI3kNvEa2U47d9T8L8A5ws2DjtzJS/7u+PH7mmNsDmcfMhnz7gD7Z8iHPrq5E5ulvjFEHBwftLzW7b+qy29KndglIN7eO7EcsN++xMNmWZsCtW/eQ/fNldO0qEUbmdgoWuBelaltRu6yIt0VcfrRj75B/nBvLpFQuZkYZJ8fNp52f59Q5Um786RxQh9x3BaBufFYauDYO9h6X/RgIUNZxCs6+Ox67QOV55ZzXwKDOfNOR2JmmijCrXrgCQOPuqmSUJ8+7GhW++USCb/3kMytiBoKUnQNZxugyYY7BTDp98Hi3PgQP+c69TQQWbk8ZcJ5cA45LgJg/vo3TGfcM+FgfrMMdCCBQsR9ydZD21IGZDuwYIBHgUE/4xAznaYqcOr+Q68gPAYTn3FF33EGRt4MzR/Nr/02/bL3Pn+MCbcLrHv/T2ZRBTRfwO9nmvNeHcw9f7N8ytY5kDlcBODN6LBAxxviOMcbfHGN8aozxsTHGD48x3qU2t8YY7x1jfHyM8ekxxg+OMV5Rm3eOMX5kjHHvrJ/vHmPcVJuvHmP89BjjaIzx98YY3/zEsxQ503VAyrH8jzLEoEj+gRo6MGa0Xqi0IVDYtaEv3x0EDUxIM0fW9R3FNwpm8JsFRKPwEI0vc2WGTwOnAc4cMZXf92Rnjo1yI29Zy5xLZuWgTidDnjvQRFl0wMuZCgMUP7uyxHnFEfMcM0HrCp03Aw75JX9eI8rcc51VwAxWu4BsIE9ZEnAzsFnOLllXbW847Urq4YOVKts/50kAFp4ss05GDtheR5JBgvU6/wn6OJcO3GU8y7ijzv/xHHmxj3MfBISUoXWUPmN2HXXCGx25LuSzC5ZO5jImAYd1zVVC80JwYeBGUDvbz9HJufNTBg6uEI0xdlYnyH9np/atHRC8jB63EvFVVfXeqvryqnpPVe1X1Y+PMV5Emz9ZVb+1qn7HWft3VNUP5eQY40ZV/UhVHVTVb6qqb6qqb66q70Kbzz9r81eq6kuq6nuq6s+OMb7+MfltyULK4jizdeDsBEuDDRHNsk2ncCQGsBgdA52dMRfcAaJzoOTZxuY5hGbOkbJ0MLUzdkDr+vUYDqwMsOzXFaFZZSXtu4oA5W2ZkC868YCurBvnw2yD46fvWcC4LPBYDwy0GEQMHtw/N7VabnZKdK6WTef4899BL99nVQ/PowsW4YHO38HTt7psf7M+O8ARUEXwSBkwOJkPV3Fsh7Psr5Op+ctYBlls68oeZWe/wT/bmAPbrmNMajpA2VVuIiP7S9u558l5UF+6eUbeXjeu/c2bN+vmzZsX9sylDSl24sdfOQ8+2tvpDOXsBJXjdZVmztO8dd87H7/rusepTDzWnohlWX4zv49NdeBjVfWlVfXXxhgvV9W/X1W/a1mW/+Wsze+uqr87xvjyZVn+16r6V6vqn6uqf2VZlo9W1d8aY/zBqvqjY4zvXJbluKq+pao+tCzLf3w21N8dY3xFVX1bVb3vcXjeMRfOo6q2H1W0gPl4EK+jsVgp6NBZCnTATzs+BulSPBXV/dpROJB0CkTl5vW8htk3AxCzXzqsqroQNCKL9Ee5RN78bHDBOVOGIcuZfSZrYgDkXDJugkX647iWVfrJfLpH8ezsZyDLc7ZOcX6cL/vgenVr7bE5n05uOc9xHASYtWXtmaF2czLwsBOkHVX195zZPp/9/oocz9z4Y1E5TnLQ7fbaMGCG1+6Wie0pr7J2BYz2wcDH8Rwc+L0DhTMiIKQcKRtXEbvA1MmCczeQpj4YcNA2unmTF8qs8w3UF4J32hF9SNrwFk03T4Ma+njv5eBTR7HX8MMKYAdM6Pt9zrL0HDqeXXUxgEkfBLf+c5y7jD7TPREvn/3/1bP/X1qb6sT702BZlp+rqn9YVe8+O/TuqvqZMwARel9V3amq34A2769teh/6+IyJzpZIlw7Xj6KFZmU3B56unBgn2xlMF6zI2yz7iiMy+u6czcy5s7/8xTFwj0Ta+hrLdjYmM+Aus2D/kYlLuCae8+0jzo8G7fI+18AvsqITixxsuBmTa8VAznXg3HLcwChjcU34i7KzwOEgTD699tR5ynh2fOZcAipYYfA13V4gZ/dcj1zLftwny8qcJ+UQuWWd/MSUb7mRn65aERulnhk0np6eno95mTPe5bRn+tF9Z3tWKAxw0r7TD/c38y2X7ZMhD+bP/mIG7GinTqY6n2p/zH7tZzmnDkh0T6mFXPmILDi22/P9JuTHcu90wHbupMj9UP6dX+b8edyA4nFAxBM/nTHG2KvNbYa/vizLz54dfntVHS/L8mtq/tGzc2nz0eZ8XaHNnTHG7WVZ7jf8HFbVIQ69NON99shS1SOURqPogqev9eJ1QWBWZjUPDDpE0zaOtMm4mZuVhFkBFZCOvHPmnFOumSF380r+eI0rOg4OHdCpuvhERfjqHFXm0vXr4E7Ha96rLmaTM3CW650FdfNi0AnNsieO4yDLcqn74me/b4Dr3PHEwGYnRjDgasWMOtmxGpI2BhLdelheGdevpuZ88lbPjOcqHwGk+fRYPNcdM4/OILsA4KBGXQ15zbmmzKrDa6f/XK8uMZrpq2VKO+xK8JwL5Zd+vPnR5OSLPJi3DujN5tj5kg5kE2D5qZCuX45tu011y5l/V7ns5ss5UqZZa9td2nS2ST3gtV7rTq676DN5xPO9VfWFVfUVn0EfT5O+o6r+0FUb83ntqu2AQmWlAThj8MLTCVRto0E7rV1O0kGD/DAAdo6/e6NZlxXnvBXIxmUwsctIZ32yrMc+rbDOJlixYcXIzrhzNA7G4YMVIhpz9/y/My0DLDprrv0sABt8dGPlf/RzdhttWbZ/0t6PLHZgwMFgRm4fIoDoyrm5lnroQGIHR/BAAEgbMah0P5GRg3BkE/6YQef9AN4bwjGo4+7bQMXr3D2+yyAcolzYX2TYARdXwmhbXsPoFEEu+TB4M68ZswMFJI7jtXB76mf6m43JtaYd0DZZoe328/A8/TkBV7c+3szZBeyqDYDN79c4PuR6Vq46fbFPyGdv5Pcx61cn18jBAMJghvPc5R9MT3Q7Y4zxvVX1DVX1NcuyfBinfrmqDsYYb9Ylr5ydS5tXmvN1hTafXJoqxBn9kdrcXsnf5+6aA5XFO3Nt1DmW/7PPXXad/kMzo3XQ68pJdLoGOLvIToYKYufCNkbpXcnT8+V9VqJmX5Prwh8zU2/IS1sHIP/v1oNypANKyZn3wTmejasDOl5LOj22dVDjOVc5ZjIiP3SUfpS1A1Ds6/T0tE5OTs5vjXQBkWBjb29v63XPDBAGZNRdrqGdeBdYLVuCRss860fZnJ6e1vHx8Tm48Rj5JdiqOn9hk51qjnXgdgYeWAHgWnJu5MXrTT7YhsDI6+ESfzeefQPHoO9z1r+rIsTz3GuStbUOct05B1dJuN6USyf3Dox4HjzH813Sw9tZBvKd/XTVjciD4JfyOj3d/EieKxzRnc6Hdz59WS7+WmcXQxgjstaWLT936/+agYixoe+tqt9eVV+7LMuH1OSnquqkqr4O17yrqt5ZVR84O/SBqvqiMcbbcN17quqTVfV30Obrapvegz4u0LIsR8uyfDJ/VfWpS+ZSVdvK76oD2/k6LkwXWOgE2Z5IUfxvKal3VvN6Krl3NqdteElfGSP/O4Oi8+A4nn/nwDw2HQHnHMNxQKGSuyJg4/BcbIQci46enxMcuQmwe7yTvGUc7taPnN3WFSNePwOIBD0dsHMA8fPruY6yieN5+PDh1o8LdfMjZazwxX0ikREDp50P5UOQyPnOgGDGdjA04GCJmP8zT+vLjRs36uDgYEtWzuppGxmTcu6AQWfvnucYY+v9EuGnkwv73BVsOmDrvScEYjzHfU6xdd/fpy54Tt4824GWLsB1oIa3N9gHQU8HMGYypz5mXvxv+XH9yAfBcNp7raILx8fHF+TGMVjxImhJP7vkzXnnfFeJ7cbld/tOfjYAfRwQ8bi3M95bVb+rqv7NqvrUGCN7GD6xLMv9ZVk+Mcb4c1X1J8YYv1obYPBfV9UHls2TGVVVP14bsPA/jDG+vTb7H/7LqnrvsixHZ23+dFX9/jHGH6uqP19VX1tVv7Oqfstj8nuBkoGyfE3Hz8dzrKAsq9EoiD5DcRIMDt215Kvq4qaeKHiHxukYiIKdDXXOOue6EpyNqivNsh/KpxuDmUz67JxF5pPrfM5j5jOfauFasATKjGoGUNIn+/BaGSRUPfphqC4YWxY85vcxuD3BHPni+nZAM/050Jq/qwBC6kDVxQ14XSnd5FsbXaC2jnGt/Dy+qweeE4FMKO0TlI+Pj8954DzTprNVl5Gto+QhmXp+X8MAiOCk8zPkubP7tOv8SHSkA+j2UQb9Hos2FJ7ZVxfgM6Z9KAPULDvOteZ51xrQhtM31yT7mihLJ1bRceomx6ev4HUEHl5f85n4Yr9rfe3Ahfu0D9j1GDn9B/m33s4AyWX0uCDi9579/wkd/91V9f1nn7+tqh5W1Q/WZqPj+6rq96XhsiynY4xvqKrvq01l4W5V/XdV9Z+jzYfGGL+lNu+c+I+q6sNV9R8sy/IZP96ZMm4UNcLuQEDV470HoWq7lJvvIQctZuQclwEsCsxSmJ0H++syAJIRPxWfjiOGSACUczQs3ofkPPwil4zDtplr9ygpg2fVo0DNsdkHr++CkcuMHeDhuTgavgzMcrMMyXd0oHOsCWS8DUFHZJm6ZN7pE/WHn2dB1ZkMgWqOU/5cP+ou9bRzphzLjpNrQ3lSD+kA+WI3Vw3JK/n3ngfK0/KjbTl4z9acwYE6xM8dyKJ9+UmH6AD59g7/br5V2789knb8z/FzPQOqbczvQaCsO1ASsm/tkhX+p48xoMln+8JsmHU1jDrroGgbMh8ca8YD5xe+U8WJXdy8efP8VuNs/cOPQWaIrxZ30Kc/oG4bgHRAIfzO5Lwsy4Un1HbR474nYnfKsWnzoKq+9exv1uYfVNW/fkk/P1FV/8Lj8HdVsoK49EqQYTDASgGNhIF/Vo6iA3Rm5woEnZnLbmlHB0FFpCNLME/w4ni878o5edOpgxDPOcvkfNmn5xbybv8ZAMrue86f1R22t9PvjvGznR+vy+trve6ep+dEOfmlM3R2WY9kS+4jn6lnfBLB/XsNOJeHDx9ugbG0ISBzOZxytdPqHFUHEhhE+J/zTz8EFeTRztg6E94d+Aw20q9L1NQtzok8zgCbbSVzpr3T7lgBoX2z/y554XGS+TKA4Fp14NX+gnIiP926M/inz/hPtrW9ddUb/u9kmvMzkDDru7tF43674Etb5eZc6jF5iK/gy6pyLr/pkj075jHUAald7WZkQDir3jm+dLZwGV27H+Cq2n6OOn92nhQkHXaoM6iULtnOzqMDGbOyVhfgvGPYC06n1wV2Z/u87rI2u/plG4Mqgp+0t2M3zYzHQKqTZQdWqrb3mdCp2Rmw7wCwHHOFgXNJG86py6QYAOl4CVb4tAWdXdUjUMGqBsf1i3QyD1d8ujXmuNbDrj2/s1/K/uTkZKvPWcWiC5BxcgYclDX7mIEL3zry2vM4K2zkkU7YlQIH3i6guTKVMcmH97l0ukXZsW+CJ4MT82+Q4s+sAnU2Q30yZS7Uh05/2Sev7fRjFlQN8mZAz/Jmv5RZx1MHqMMnfyqAwMO28/Dhw/PNvawwzPicnad80k8nk/CT86485L//6KOuSk/0dMYbnTpBRbC8/xoyUksf7It9Gwx4cxTHm43P4DUr1XUlJ7alUjDQ0BAYbGy8+UzwY6edz3ZSGZ+b+uxQGOQ6Q7AsGTDdF+fF+UceNqLw5fn4qQJuZFyWZWsebh/eunKpebQMWb2w/AMeGRgij65/65n5YbAx792TKq+++modHx9feF5+lw2QvG/CINBy8a2BGejl+ruyZ90ygKQOMhjSRma2w+tzLV8uxXGymTIJRsbknizKx0kB9ZjBzoHOmbR9kMl2ZL3zOnVyJ2A1v2zPfihXtvE8ctw+pbOtDnjO2npunR8xeLed8jwpfo7yZWWH4MIVburtLr4NEFxhSH+zzcC7KhKc/1X2Op3L8kqtnmOi8VrxSA7KPDbLvl0S9fGqi87PGRNBjUuKVI5Z1sp7huaZGV4+ezMonayNlorI+5SzIEY+Xe7NsZnRREadk+KcfI80bXiLhvOyQc1KnwzivldJfXBW5M+eG0vw3e5xZ48uP1Mu1t1lWS6AIAMB91t18XXlVdtBmUCjc7Sct9fATjJ8cYyAJoKJGRHEsS9f52zMFQjqLQG85+MAwg3Rl1X5yHNK/jNnPQPLBNOUJXnqZBDgwkDO9jmWjee81jpnQDgDCg545Mu+stMBg0y36QIh/Yh9gHWVfqoLrFW1VWkgeR9L+ojudD6Gj5Pbz3DeBvbk2UCg8xdOIimfbo4h2ujMZ3V0LW9nzIzXGRDBQY5XXdycaCdmRaVR8p6oQYidRhdkrVBdRtAh811O3kE0gXGWLfD6Dljk+i6AdLyzPz/uFcqx7B2w84nx0pkb9HVr7kBHw7S8u7UOAOiAjTNTr8fsqZeMzXmFuhKyqwfp05UwHnOGGh4ItuiEPbbHYJbF85yfs0quTdWjWzpcvw4chTcGbdou15PrE7CQ8bus0EAremxdz1yijxmnc9rRIffjJwS8fpTfDCDs7+9PgwODEa/JPCkrVzoYsP0YuTdvzgKT7TttCVa9p4oB0P2TF16fOXVgxbrDubs6yopl+jTopY/KurtykP0PBwcHW7ccGE94DauiHQC8LPhzzgYPu66jjUU2AT4nJyeXjnku0yu1ugbk+1Qdkq66uGGwe1TUi07FOT4+PjcQG3HaMgh16D/90wDpMKKQBB4OZkb2vJ6Olv859iwr4TFmTJ3sQnQcs37DYwyf57MLOmPYcaR/ZvwMLOmrq/p01R/K2AHLciKY7LINXktHygCeXxfsKidcT/fTZV3UA/Js0Gj5jLH9WyqeH4OP19ky5Xc6fgLiWV/kN8c8jgEM5XB6unnZ1sOH2z/+5nW0zlrmBpe7AFrGzbz4o2H2HawI5XvGCB+WGwFAeDHYZAXNesE/2k/k6T0MuS5zMRCwfVt+XNtOPwi6YvddcKR+WI+o62yb9e+eduPa5jsreZmj14R+jutjf5Djs70xnA/36HF+1g/yzH7Yb3eL3qCDPtZJ0WV0LSsRNnIqRj53baq2F6t7P4AdCq9J8PA1DEq+p+Yx2TcdqLP+tKfy0ahCdKb8zoBG57RrfuzLzpGGmj75fgfzNkPtNNYYSNpww6GDC2XEuXPNHj68+GM5vp2QR7cCSJiBco4dUKqqrbc/Zo143jphObPq4AoV+3XVpQtGXSCjzlj2DLrul33RYc0ADzMv63EHTkgE4bQdgl9muZ43wUzW2yC7C8IGkVyfXUA7cnGVzTxmXNr5rDKXcTrw6mPkwW342fOxr/GtV/Zp/aXMOKZ9iH1hyOAifcwqDuTZdk9eOFcnApmP+bYPoC0b/PCaqkcAg0kJEwJfy9t4Bm6cp6uEnBevNeDs5EbgvLe3V0dHR+16zuhaViLo4IOmQ64MVG3vJu4QarcoNgA6RQMSggvyQQOn07ShsF2XBTDwkkf2y+OzsR2wQnR4VHAGb8vVGU6uM6BwIHWWzjmGR2f55CttzX9Vbb3/3nJgf3ZQXbbA80T5lDM/JwByjwT7dDYxkxXn6bHovLtMPrLoPjPIk7/wk+yOa28e6UwdrCwTZ4rZnEh5BjBGbp4H589KQI4RCKQv38ogD+k7IJJBlTrAfm2T7otj0974kirKiQCIfXCuBo8OEryeNkHZdUGIa0sZG+hZ37qARDvqgPuMf9pn12cnBwM690ef1f1KrkE2zwUQuBJh/Q/FRrgp3rHCcYUxhzKZUWfPnJNtJLzv7++fV+kepxpxbUFEh9S5aFwkB+ps5rJCd8G9anunbsb3eFX9pj4bKJ2eA4GvI3WK2TkEgx4rHzMJonmWNDvwkev9pIoBCseizMgj5UC5sS9mGXwfBufTBZ58ZgbYrUnmQF4NOmj0nfNP8GDWFx4JUhlQOQ/rKGXsANPtKvf4DlRpw7X2ujE4sb112hkb+zCwNFCjjeX2ofXI+mg7tJ5Rj/i7BwwABFwhV2PIu/cTZTxvxPU6eY4Zx2CYPFsfuqDBuXZjEcyxH7+eO/fHaRvkgwCIPPjPuuUKA+cU3e18qfWIfsG6PAOsnfw6faK/y6ZT8khb5VwDFrr4kHmYZ/sbJ7K2d8+LPibXe84GSNFDbvh0m8vo2oGI09PTunfvXhskSZ2gCSKqLu7w5p+NikoSQyUP3X0rGhD79f9QpyBWTqNRKrmNcjYHo2Y7D+4CtyHNKjMGM+m7u4YAIusxA27kzxkb5WInkICWtSF4o7xzvuqR43fwZ8DpglsclDOwjrhuBgUhro/1imtB0Gqgw7UwiEw/DAKUHYO8bwtQX/Od4Pr09PQ8iPmpII7l7D9AII6+4znX5y+VB/4YWfpmBk777wI25+39RGOM8x/8cpAxn52MOr2kzXjtbc/UEQOHTv6258zJfoQyCdHW6S8IEGbAmnbDoM3v9neUC9ejq1SwD9ua7do+NMeYmISYbBB0sKpFQLgsS1vJ4thdctftOyE5TngeXVvyltu0rjZdha7dnggqJ5WcCsjzRsY5z2tsSDxGsMCgzHv5dIhWjhxzH0HAdHzh2WNx3r5tYgcTeXQlMzufXRlaDIT3eO3446TsBDpjZSnTcqZj5Lqwz5mToMztlF1C7Z7CYaBjSdMyYrvIgvMJUf7dvCnT6JAdXYgZMEEwbcCZVxwcZciAZZlz3pSt+ejWz3rHsbpMrVtLOnAGw/TBX5XluBwrYxDAmHJ9+ov+cb4dwOMadjKkPVtu9CGUkZ08bcnXkhzUKVuCfupt51e6fkKzd4lQztRD+t/IzKV08xcfbKDNOZtfBuPL9MjVJQI5tnUfBDXZcGo/Gh8cXZs9Uh55eL9Vl2hEZr7+MlBhANHJ9Cp07SoRVduKZYNPYGAm6+DXlRnTj1G3z5NsoGlX9ShQMPOn86ET5rVdJtIF7LT33KzwBj6UYfpgJua9IlR0O7kueDt7ynmXnD2unQ4zji6DcaDsgFpkcvPmzTo4ONiqsLCcbnDRARHKjFkA9YxrQh1xNcjOoHuaoXMGy7Kc3/OkUzZYy/VdRsbvnaPi8YAY3vrzeOGL+sM+vG68xnK2DXdO0XqSjDHy8CN+dsDUTeuX+fZ6mxwcusDMwETbyH+CLwdgB0wDja6iahl3VSX2YwBPX5Lx7VdscwQuM3vMWGxv3eOjqFy3WSVwF9DjepM/649BH/1KQLnXLzpHee4CJIwTHSDIcYObmb9ImwCI6M2TAIiqawoiqDBUVhuwFShEx1O1jbINTAgCTLuqEHRs7MPOoFMsO/Q48W6c8EG+w7sfraKcWLqnwnN8BgwHRvZJww7ZoJjlk8fOURvgsETn3e+zYNs5bvbZgRJm+6xipA3PcS1DBhSUF/XAjtHXMygRcBlcUR8YsNl3yu45xiodgyjHtANm1YMgxXrIgONMmNQFVgZEXkdwkATAIDVjE/z4cd/0F70niKQMMzb7o1wMVDsnb/BCUMM5WnZdNuqNou6jq0qQv5lOdnxbFxlQ2QeDZtf3bK+SAz554VwodwM88uoEimNVbf/YXGTi62cJQsYPkPbbhT33DkjQT8X+uXk5RLBI/nbFhuhvqhCzGHUVupYgws6uqjfMOJ1dv2i2C5U7KHfjOSgYRTug5Xj46zIII2JnIOSdikwed2UEDuo85nvHVmx+Z9WCbQig7Kh43MDB8uoyhQ7JJzvogrUDLQ2awcvy8qOYcegGAF2WknMZg08lcN04HuUasBQZV/XvfaB+zBw61yX88N0A6ZuVIq8V9YKAlQ5xFhS66gT1f5decB3Sr0FOjvP+9bJsKjZ8+VXmmeu798Okz4xzfHx8gbf0x7kQlFAnSOmT6zMD37GhrnKY9bC8HejZNvNiH9EnZrDkn/12NkeeeI5Age287l3Qpdw63Yj+WVYcz77OfXX+iudsV0xeKMvILvtlTJ2/57lZvOLcOqKd7+/vb+0l8vytgzO6liCi6iK6rboouDjwTrG7Jy3s9HKex5ntcMy063iwEZAfB7kOoXZVC865c7oGWQxczs6M3J0FWE7mz/OlkRHw5druPiKdMp2WNzt6TpQDeaXx0/lQLuTR47K06rE8zxCDhJ0E9aibix2yN00aQJjo/DK+NzZelq0QCDuTZ7DJMY5FJ+4glOO2K/NMsOHM0nLLH22IskqgzDPz1t8ACY5pnhmsaYcO/lwXgr/4nozhShfl4qRhjEePDfPppJxLdZJ+q6s2+FFby4vXdADCMu/8gQHEjLiunI910uP6HI/l9h7tuwO15tHjEqx2tnjjxqNf9+QYY4ytX/2kTDuf2VXoZoCBfNJvUV+yqZh62s1jF11LEJHFY0ZigQVA0Ph5nOVMAgeDCAZEZpleuC7T8Dk6CQMCZzAuP/NY+mX/Pm4jZ3BzeTd9M2B4n0Dk0gGaDgCECKAYzNmeIMzVEc+9G58/XUw5UJYcx7IxuOOTBR25D8quK5t3Ds2AoHM0dEYEo13FwOtU9WgTmAGzy+K8hkQQxf7ICwNTtwYuKbNvzittCQwsE4PufGel0VXFhw8fnr8Dw7bH6gXtwkGca+VgyqDI8+TBMqEtUW4EEl77fObaW04GNx6XvBFcmx8GOoOnDkiwumdeHACduHBtfS3Xk/OITuXRVW5A7tpzLlW19bQNYwjHpk0FqFBO3POW/Tuel3XcPHXU+bxcE+Ab3Tw+Pr5QVbzqOKRrCSK60k0oSp9yZZwDs4qZInujDM/H6GZOMdcxiFDpYiRdJjoLtOHLTo3zmGWsNA4HCvLDzIbZlLMsZxGd00z7GHpnQHQWBnfps3uTXMbyy2TIH29puAJEkDdbX8oxu7oJnLo9JHSgmYezLa51+HRGYhkyaDJYdLJ3Jhfdr7p4XzhjMgjOAo/nQN66J10MzqwTM5lxDamXlAttPUmAA5ODsIN95MB1D3AJ2Mr68KmQDnSSHwfWLpgTtBG0MlgS8Fl+1Guuq2Uz05tZcrS3t317a29vb0v3ueadLLxetitfMwO0XTXQMsh1nIdlkTYz4G6509/QRqhb3EhJmUZXWBkwcWxXKHzNDATSX44xzl8qFT31WLzmKnTtHvGs6sGDj1lhonxVF1EnDcSZEAGIy/BE3wQrIQY7olcH7bRNn+6Dbem0DGQoj4zpY+6vc3RdUOB4zjzssClTE1E7rzcvBDPdY1LsP+OyquISMeefcZlp2inaEaadN7nmOoIQvho75xwM2W/6oaxNDjhsa2cbWTlDIsBx31wbt6POEdhRpp4Px6ac0l/kmLWi0+7Gtn4RPLESZJ3LXBhYaIeUqR/FpHwZULu1nMm0k5H59CZvztXg0307KHJ+BKvkYYyxNSYBbQeyaRvUhU7vIr8OSM/WxBUMf+4qvs6+d9lmB/6yIbHq0aPUlNUY2y8Zy49yRb7Hx8fnbfb3988f9+RcrUfsm3xzzpmfZRb7zQ+28d0opMcBD6FrDSL4uUPC/Gwk2QUhfnZ2wyzGitkhyvThDDe8doGTxHIZ55ZMIceovEb6diDMLiKTzNGy6LKlEOdvYls7204WDIg0Nh/3HOzczVva8350lxU4KDAw5Xo6fTowBmYH9lCXoXT7cahXBLXMQGey6mRW9egdJ93u7bSh/JzJd7dMzLd1mc6QGSb7JLA2Edj79gL55ppmXULUa1+b/lxty7p6XwYzfwZfrkdXFbC8Pb5lxODGX2BkoE1bbzbN/F1pcXCifB3kqfe7QCqvpdw7sMdr0oeBjfvIPGYglnPh+apH7zPxccqZ/3nbz9dZB2NDWZ/8+mv4724H0oYMrnjMPsu+Nbykwnj//v0Le5RMuwDuBT6v3PI5oqOjowvBLd9tYFEg74Hgdby+ajtDtjOwE5wpX84bDYevOC2jThpb54TIc46ZD5ciO0fbzcG8MJBxjh2AsMPKteSV2YMBhflkdulgZSBnmdDAeY4BxPLogjidgANajvM3J7yOzqriCHi7IXKmXDog3GVs/t995uZB6iX5NM8Zr8vq047ghrKNw02JvHtsMWMYgJPntO14csBKPx0oTzvy9/Dho/vpBAKdv6CjPjk52WqbvqxfDOjdhj3rcD5H55jsuOoWXpgVG9R0VQDLhTpuEMo9JtQl+zC26UA57bWriNC37uLVem8/nzXpqmz2LUzAOO/w6lsl5JMghT6EyUoX2Lv+6J99jcFY9CH7IDq7ckzogMWMrmUl4u7du1vfI/AODdoIQkbbdso5ZwXsfmbb6L3rl0pBo+oyrPAXpTVPdC52ynRQ7r/LKniczizf6QgIriJvO9JdysvsL3N0MO3m5Myc6+o52cidjZA4Htvy+s7hcS26sn2ImQ8DDYOnHbiPu29n81kHyow6ybEMJh3geD7z6hw7wVSn4wzosypFxuetrc4GGRgdaBywuVGSe0FYmSClqpD21q+AUYIL6gPXgJ874ET5GEx4fWgnrgh5/ZZl+1dvs8nYAZrjey2ta3zyzCDTAYp6ze/We9vJ7Mkub5LugMEMcCQxo94xCaAe0o8a0LnfyDIVC45P/xF9SZ/dHN1nNx/6WwIIgnPPvwPPV6VrCSLsaB8+fHjhMZssBO9P24l0mUuOE3lGKbrgTCV0JuBqwCxb6oBG5hkn6N3UVkw6Jjs3O64ocMrd4deGx+BEo3Pfs/lYHgxuvPdO9E8522l1BknZMoiFfzpqOmzK0P12YIF9dPemHajCc3SoA5nO5gnU8p3z6pz4sixbb9Xjvh+DQupoJ0eua7f5M585N8ol1FWwQum/q7gQGBicdM6S+sSxHVgsD8+TQdgBPeNwfRNQqrYfS7WsPOcO9Fr3On3uQDJ5MKDpgpPHo99iewPEjhxoO5/QAWvePliWZWtfQmfvmW/no72OsbG8G4RrZlnQh4ZXg7XctggPiQep7HkfS9cHgbjBZ8CG18/yCoBI9YxPBbJ9ZytXpWsJIqq2kW4ARITJcnZeeRzKC2QSvKr6TVBc5K5UZ1DA/74mytr9dQEjSknHFr5skAQD+c6+OD8e4/WeR65h0PG9YDoiyoNz7kriXXmXJVtnCt1P7noHOx2BAUTKw3T23biXVZLSnufoHEg0Yt9j74LrLDB2DtWB17KZ6VfWp6saORszwPJYrIZ0ehad5HgGAZRd+phtTDPY2wX6OtlS52ey73SNtsSfWSaRHwYRV934iKmriO7Lcup4JVHOtGXymgBLXmZzcHXIICW80Ec5cLli5v4NmC1Tz5m6anCVsTLfJF3uK2N6g733F3gfVFehyV40+0lXXEIEHgZAJFYy4reykZPnuLaX6c1ldG1BRNWjwGuFyQKMMerw8LBeeOGF83NHR0d19+7dreBG58f/ISJdljertrN180AD61D9zIA658J5ce4MKt48SOXOvLIDnX3QMO246Shma8D5GhXPnCqfnqDDZl8kZqfkM0ZP4/V9YgeEzvCdVRlUUMZ0smybY6ywhBg8ZnPp5jsLWq4oUOYGHuaffbNk2mVL5J/9JBgk80sbB1POk33NgmP4ow5zjZzBsd+sjZ9KCVE/8p366Z+JNlhm5pzzrFamX7friEDfgZa/yOh5zDJNV0gMyllap02kLz6ZQmBIYMpx0w/Xj/1Zb2nf3X6tDlzuCoQEEtQDrgkBEfnOf9oR52m9pz1Q36Ov5LkDZwY9u5KIUCoe+/v75y9M68BJ18/jAIiqa7qxclewYVbJoJZA49eUeqH9c6pjPPpZWAeiGZJ23+ZxluVRCZjlud0sO+hKkUTr/M5xunbpi/P2PLwHJW18GyT904nY2JyZ2Zk7O47TTgC0fLo/O9Nc77UwQEv/lCE3NzFDyPdujiHPhQ6djt7Om8+Fe0Mng0X3BjvrIudv8GjZcw4O+M44aTvmrSubh1wRoyziQO3gM/7+/v4WcOjK/A7qTAy4pvxJcQfOqkcvKvJxVjFcAaDsMq51h/KivXGTpXWZgCpjEZyzXRd0mYARfBjgsOpEANLpeCczBmsmat07DjLnHKNMOuK8KGtXSSIrVitow1l/8sJ5uSLLyifnTHvxI5j2n52vXpZlK1ZFJ9OXQRP74n/yfBldSxARQdFgqi7+bPfh4WEdHBxsCTOGY4Osumj8OcfSP69jcAlydIY9C065LvOhYnQ/P07jnilQiEHNczc/MbDulobH8DUuP9PRd/ynH5737RE7PBo/+WRA4TnOkU6DQasDbVxLOu6u9Ok5uezJNbKDMRjreCLf5Iegh20JjggmTk9Pt0rwXF/aEPmJw8o5UlchYJ8GXl3Qmq0DAwD1NudYMfAtMeuhZc2xXXVj2ZrzdLmYepRfhLVcCRLCa/piUMt13i9jcOUAZaBp6oJS+u3myP+cK5/84LFlWbb2MHjfQwdAzaf1n3ZCWZrsKzm/DqR2QIDgiMRf97V+UpaxKdoofUX4NPC2DGbBPboTADHGqJOTk3Mb7uTCeXfnr0LX8nbGGOPCY0hemJs3b9bh4eGW0TqAddlOyAGLhuwgUrX9rDEV1WVFonr2OSuJ83HA8M17dl1Q6hxMru8MLC8w4fV2VnSQRP42aPbB7MOgwk468yKx7J423MzXGU3O37p161z+lpcBUdcP23PXtQEC180yJHmMzMsOh2P7OHU5MjRAYxAjoOgCO3mjM3Lw2jUnyjc8OkgYtASYGzCxT7bJsbzYxwHQbSkny7uqf5/Asmxv9Evmx0BqgMB5eWMdqw0z8B7e+b8LZl35nzpAWRjoEYxbHuTTFSlfY5vpbNg67usJPHK9b9dQHwysqRvUR/vtXEPgxv4tc9rLbC7k2baSigHjS+dPTQZ7TBT39vbq6OjovKo481ezfmcxoKNrWYkwiPC5vb29unXr1rnQ81gMFczIt9tNTINMWytl2rHszyDaoU5nSM6u0ifLkjmW/97dnnbcD7ALTFBxPV/fP6VM7KhtCDzm8iHn1smxm3+Xodop07nHqM2zA0uXbXB+4YG3EOJEdoG2TvasfnVPonBuaU9eMiYrXfnODNv7VzyO5+fv3oBK572rP8qqI+sO/7KeruD52rTJ+L6NRXA/0ymCWuqMb5XQTxwfH5+DCQf48JG2BCmWhe2bfFum5tl6weOdf6EMOt9F4lx5K9egk/6T+ucASHDS8eQ5cK0NdDq7ZRD3f7/rw7xxrM53clzrfD7TpxGouAr9OEE8xNsYeRKDt3y6PjvA/rhjX8tKxMyoIryUG+kMjWDz+E7VRQTqDITKb/CR7929dfffldwNENhXxs79XhoijaLjL+M6oOQ4g1mHoBnIOjBBGXhOHZF3Zh82ePeda13RodxmemAH5DVjduW9H2yTLDgBm+V+Z32drJlZjzG2Kj+UKQNbvjsDnGVL1JFcw/u23RjO+AweIk/uKrfeOZhWbW/G5SN3nWwobwLn8Jl5dCDV87BezMCU58g9PN2mzKOjo/N9F17v8Jnd+uQrxKydFUuvP+diWXbkqgPnyLnG7hx4HzfgODjOHmvm+mRNvYmVwdc2Sh/BfgwwIwPrZHixXfJ6v/OBY3Rztf7v7e2dv/qaPsiblLkO5Dlj5DNvhweEdvsg2Ffny7gOu/wx6VpWIrqMh4JmJtqh46BJGhT7IKqOUhjZUqnp+NgXx3GA6JQrfceQkn0ty/bPFhswOAvI+OkjO687Y+KcfH+PgKVD7gY0lmO3Rh2QypoYsJhfbnojz15fzqt7Ttuf7YDyn/dAyQf54eZGO3U6HYMGyrCrRAV08H4t14Jyz3e+K4VjRw/9EiHrJB0fbaXTG66XZeq1zto4uHVVMMuJAaZ74oJEGXYViawlz4UH8sIqR9q++uqr50HDoCTtOH9WsSh/y8p2aBCQimtXneJahU8HZF/j4EYZcd4k9kOfFmIVjNe4Hdchc9oVbG2PnY51PtR62ukgwYsBgytS3KO2LEsdHR1ttc0ax459q70L8Kz45C22uS3CfRCztfBnz3vmgzu6lpWIT3ziE1W1XSXI52QMVReRmBW0u2XAAMJ76S7TW+m92csG6ixo5ghznNUI8st2NCKjf2YfDNosgbqsy/Essy6ohMxXN7cxxoXntwkWyGM3Budo43dAZcbjIGHjshPmrZwQ+aYcuOHW8iBwI78ci46FgKgL8gTIfFSxczSWG4MONwQuy/aO9MzJwZtztL7NnC+rR+SF/yMb2hHXjrZ3cnJyIcCSR1ejGDBdlYhMmBGz8kJ+DTJTkaKcokPZU0GASH1Ku11Bj3pqmXP9utJ/eHGSlPMZn5sj2c6bbGfyzDHqgu3PczTIStuDg4Pzd/eEPz4BEYBCfenkxmNp28nF5LXJU0/+ET32aWA7xjgHmAHzkUFXSSLP2fMWf3F8fLwFIGbJx665+/NldC1BhClGXfVokyCDCx2VnfIs6EWZjVg5VtX2/VmCi5wj0UHOxuwyHYvJoZQAACMJSURBVDurrm9ea2feOQjOn/11AMfn3E+X0Rr9s72BjtE5b0WZYpxB/Sw/R7Ysn5NnOjfyyL753+XrLmMh/wYGs2DAOfI1xemTust+I7dU2qjXnm94m5WFGQi5xinzJiBGviz5d1knZcL5sP8O0HU8mifaHHWCzpyVkRkgsD11iUGnF9SdBLZUfRhg9/b26vDwsI6OjrbmR99jGRqUWZeoO/xvUE2eA2TCKzdNduszxqOfwzbA7taZPHZBjXOnzRnIcJ0MZjkmwd7MTxmA8Dxv85lsJ44RnNdMt/huktxK9yOkbBtKdTl0dHR04QkQEtcpfdk/eQ2uQtcWREQRl+XRa38TzJ0tOMOjMXdBk7cUqh4pbFfmZFDvgh+N3++F95id0yMP/Mx7ilXbmbHLhB06N9HhxVgYOELJhILWaYCUMWXEYMDxs3Y2cH52oMt6duXqqos/q+w1JJ8GMVwLAprI1+vSydHvKnD2xmA1CyAdKOj0inzQeVK3s16zShJ5MXCJw+pestTJKvrI8TMHvmLdQaeTAwEB+80cop+Us8FsvrMiR7BhYEhQwPkxcw8dHx+fgznznezawZ3yyHwMJgwiOtuIPBjQMpY34tn+A2A4X7bvHtu0bLm/wfMir5T3TM7OuH19gipth+tHcEnb7+TJfUNeG/px8+v5ZP4ELZlL9DK3NXhLy3OLb7lx40Y9ePDg3B9S9ow71jPum+jeJ3FVurYgourRwvO+4RiPNk2+8MIL7T1zggqfq7qY5UfpeT3HI+rmsart+3YuAVZtl37Jk43AVRXyEYoyOZPKHKzEVrauguB5UFZ2gj5PedBh0VH7TXndOhD4+TaPeWWQYJtc6yqNx4qMKW+2oeOwg+6qMZxDHIwDmjd5ee27deO8DACYVTmgWy4GMHbumQudINffG5QD3skDZcpj6XvGTweeMoYzsozBx1kjc8qba5zxOSd+TlA1f+Epj5x2sg6QyHEHJmfoBlSZi2WWtpfpsjcvdyDU+mUd4LVeC86HNsF1su3M5p0++BPoPG+Q1PkZAmEH7LQhrwSkIVaUbS8EfZQVeWXl4caNG3VwcFBjbG51pDqV/nkb4969e/Xqq69u2VJ8IZ/6YBIb2eQ6Vr947ip0rUFEiE40C0NnSSdCBLirr3zOf47Bdzd0xsNrq/oMnOf4+F/nqDv+qCwOvh7XzpABewYcDHY4DoN6V21gIM+1lGVVvwmOPKUtMy/PrTtmogOcVRO69WMbfieQcYbLY5EzM3ODqM65d23imKi3CTDpn9dxjTknyp+647kbPLEvZqGxBQNXy9fZpnWBwdEBkk6fwM7gLY65CwAO4JYB7dtBJf9dlk5QOjk5qf39/S1wl3MHBwdbv7rYgVsHrW5eM+DXVWIyX4JBAhP2leMpx9MHpW8HZ/ZhsEr+O3l1vqyTRdrz2px3NcFjGshz7q6sdkAg+sb+onecU2wi+xccD05OTmpvb+/CG5JZwT04ONj6US2C8IyZ2yMB7Pfv37+w2Tu6f+fOnXr11VfPN34GvFxG1xZEdMEoxnx4eNhmPaHuB6uyEAyGN27cqFu3btX+/v7577hnbJatWHLtnHeO2YlEUViSs9Nj0OoyDho4z/kRVzvLjOdATNnuAkIGWwxePOY+ovSuUNA5MbBaXgxQnjfXwPKxg88aGljR6RjMGFzZ4TmrZtWEIGyWfdM5e17s32Avspk9Ysj5+Rry4nm6muDM0fJ0ECMgdsDg3GdrnwBnoBzg0vGT6yjP2bgMllzPyIUB2vIm78fHx+dvxvW9fQYs6zV5rLr8d2q8wdfg14CUNAMRAYK+LvKNPdl3MYBzTmnvjaecM+2W16c/tuFx2oXtOWTbzzECAgOcDmB2tk9+Tk9P6/DwcMs38zw3AvOWKB8FTsCPfAMuUsVIm/v375/fqghoIegIBbTcuXOnjo6OzjesXkbXHkRQAbkT2sYaxY6iHx4enu+orXpUVg9wuHXr1tY9f2aEzgKp0OGpCxJ27l0g7jJ+G70dDZW3qwywrEW03jl8EisWnoudKvvy+nTzNpihHM1fHJP5mWVWlB+zkL29zbPdHNfy7yoKadutYdX22wvJgwEUefK68nyCU+cgCSAogxlvLtd3utQ5+sjdlQQHa2f5+blk6xb770AUnTX/yDdlPAvImbPBvMEin3LwOa4N7aWrUqR/bmKkzKgb5N1zIP/83lWyuHGPbbnWlF34I4h3wpN2maf1y+vC9g7YHI/k68l35JkMnoEy7QxmzbfHonydxHGN/L2z+44o84yRvphwZoPyCy+8cF514IukEvwDOLI59/79+3V8fLwFHsyPdero6Kju3bs3fVy3o2sJIqIQLG1V1QWHQOOLcmahbt++vYXU0t/t27fPn/A4Ojo6LzcZiTtA5JgzsC5LpUMIdZmJHZWDFctsRPcu6XUBy4ZMg+NYRNjulzKY3dcnqGGw45oxYw9a7+TN67p50lGTH/NlEGO5c77dWpgMGjtQxvPMJnntDGBwHF9jR+j2GYcVt06u+c9SaafjdppVj1413mVG5KMDOy4zu5JFZ5z2XvuM0W3+dDAjWMx/bpD0M/5cJ/POIM574RyDicmDBw+21jrzoT1Th+3XujXgcQd3ysZt+MQNZUbwk3G7PUCUC/Uyf7wdwrZdBYk+hq81t4/Lsa5KyLWxfGxflBkTz/TBtZtthqedUHbkmS+liwwPDw+rqs5/SZqv58/j2w8ePDiPO9knwiSDssmxo6OjevDgwZZcXHGZ0bUEETQ8I64s+MnJST148OD8eJBmHMbt27cvlHxyGyTlo6pHykHnwABrxGuUTL6iDC5JUsm74MG2Dt48x7mmDzoF92vHnGu7DM5Ba1dQpUHS+VBmdpzOsP1IXwyG9289x1znwFz1qFTsazMnjm2gwf7Di4HVzIFbDl37jN1l2TyfuXK9DTiof55TF0w4XwYDtvFeHVc/ArqdpVonu+8cjw7Q+poxE2S6CowfFSVANd/pk76DID088n0BucZ7JAymeVuUcsw98G4PCRMRZ8lcd6+X5Rr7Ytm/8xdcAz7Z42ycthHflUya/Ha3CDKWdTKydrWB5xnIeW0XGAk4bavUafprAw3Lx9UI+mmPm9slM+JtqJOTk/OY88ILL2yNmapDwIsBdtplzICMzNt8dL6oo2sJIqouluyqakv4fE1xFCJvAsuu2du3b58jyoODg3OAwcpDgAeDz6yMmO88zsyJyJEBxdnRzOj9mYa/t7e9YdQZm413ZhSzrHqWHdPgCLbSzo4+oI+O2gAmczKPPGan6ioDx+/mmvG6+TBw2ynxlljV9sba09PTC6+05jp7LPJOorPbFYg5h12BiBmxgZKdNKsL5GHXfotZX7M585iBrLNe23eu4YZOZsNer06mDE6ez6yU7ba0ZYKg+CButjQwpr56DPZP31V18YfDKEvrEO02fXRAzvNzlYZgvOunC/Ad+Dboocw6m78si2b/3WOpHmPXea5R+uZx+7VQV5Xr1oHn7t69e34ut9WrauuR4IDlgNXEKN/WIHBwZcRz2kXXEkTcu3fvQtaUBb5//34bXG/dunW+uzUv+bh161Y9fPiwjo6O6tatW3VwcLC1mzXXEjV3QS00xqM32uWeK4MYAymVmDui4+QYfLvgUbVdqs21Vibzx75sSDnWtTOxdBjwYsRMR5P52wExS7dx+3rKivNnHzEwAygGDTsSz5OPnfoetwO7waDPX6VK5PUgT3bcATC8XUOQzLVw8PKa2064dgno3Tz43ggH/vTJStKuSg75yty6LJYy6uSWeTIAdWvH8UKcv6sTtEfrLdeTfSzLspV9cg3jSyzbEOfO8TqdoIxMBGE3btw4L4vbzilzA0/308k9smFFoQM1licreVwv2nTmbnsywDBvtjGDNq4HrzFwpS5bDtZfV2gj91Sl9vb2zh/DzBwPDg7q3r17W7cu/KKwJMRZH4KttPG62qdfRtcSRGRDpIMCnT6NO1WGvb3NrYp79+7Viy++eL7Z5fDw8Dxw8D5WHHIMkIGwqt9Y4wzVRt4FdS82FZrOiw6Ois0nEjgGgzRlFF6dKTEgUxHtMClrO9zOqYUflk1ncvGcnblk454rLJQdHVo2LNm5+PoYrkvRacv/pGTEBDE5znGdzcyATGgWKGcVOJaa2dbE8dOW68uM3Zm3nbBlOKu4dMfp+AlIO34NPCkTZ/cM3uaVVRbPLQ6bwcCZaAcOXAXMWiQ7TNWTuu4XmOU6EoOG7cHnCRbHGFt7Q6qqlUmXABFAdBXP9GV+ySdBqcGiwVBk76djPCbXKOeoA/Rj5MOP39MvZH6uuFjG/G+/yOO+NvafJ/j4uyvZPHn//v168ODB1quuXWUI4LR+sO1VwcKMriWICFHRWQ51G+58zX3JAInDw8Pz57k//elPbz2T77Ing76NgUpNJxxFmjlIO+tlWbaqGDRotuNmSgIXZuXOLp1R2SjtqHKdgVMXXDpAFIDDPgm07DjSzjzTKZ2enp7vdubtG8qoqzakbzo3zoVj7FpL8hViwMl6d46OcjBQzDE7JsqoA5acO0GA59GtrWVuINPxFVnlf4AkM75unuxzdsuh45UgubuPzjEZDPg4IAFEVV0I+LShbq+P197z4rEE7MwxoNcBen9//7zqaX8SfrOpL9f4FenUXV7P8TKnrprSBW7PJ2MTjFHeXDPqdGw0RODCMXlLynyEBx5z4OyqAuSJ/YzxaMM1+Up766p9me0j+sOXRD18+PA8Ya16VF1JX3kU+N69e3V8fHwOILjh1ADBOm7eLLPHpWsNIqouZtsRepDgwcHBVonp8PDwfAfs8fHxOSrMLtm0CxnFhzLerOR6WbB2dkrHEMfZKREp8+S1DljkYVYizTzSjuiec6JszDfbEHgxQOWvyyg6WTIgdhvt8hdZGdwQaPCePgNG1peAZAaiDCIJoBzgu8BGvaFT59zZJjLgenie1BmSM2r3Q1kSJHWZjR1odK67b91lsfnPwDKrZnDT4ixQk5dUFSJLBnD27b68r4mAlbqXPjO2Ze/qTwdCoqPe6b+/v3/hLYVe94zvJ7AM0tivq6D0Ka5w5dwsEJsnHs84BgJMHsijwQiBJwFwR7Pj5N9AlgGXdtZVSmzXBGKUPWXANXK/+cv6xldXVX3605+u4+PjevDgQftWSdt7B/iuKp+r0LUFEQ6IVdv3sG7fvr1V9mY2wl/by0bMW7du1eHh4VapswvmJAd/GrPL4r5v7CCd/vjfFQq28+OtDAT53gUvBwoHFgIbAgaed1VjNgdebweUdnxqgvLgfJ0NRTZpQ6dJeWT9mWk6gPAaZr6WN42am0M7gEdnNgOWBnteG19HvqlzdILOIEkdgLPuzmTuAJ7A4VtqWceZbOjw6ZQZZHY5Q47PoGGAaruIjvg2gkEheeP6cOz4DL+gyfKt2gZwrobkfHhzNYFyJpiJjFiqp81E79kf7d22ZFlQz6ivHYCdgRQD3gRTAi3rZ8Y0eIq8KSPrBO2St0z537rIV5Z3YIPX2MdSdqly891BY2w2Rt69e/e8n/iiu3fvnj+h0W1E5ZqTX3/v/I6/d206urYgguQgcePGjfPFzTEay8HBQS3LsvWu9tPT07p9+/b5AseYnAF3m/mqtisi5ilKRepARPpZlkc/WGTj5Rh0ZOzHTtwO1bz6Wo/rzU4OgP7utnY+BCp0tJYH55b1c6mWPMeBpJzI+5Ih3vLhOHQerHB0oIYZl8+FX4MIr0uXsdrZ0UF3wZxO1UCaa2Kgx77ZnpkybaebO/lIUOicnPWXdhHiW/y4ls5uc55jGQBRtu7TwZZyYDWhAwKUR3RwVqHMddQ1gi4DJ78LY29v78JaODCyL6+H15+Ag1U7/jeACR+Uh3/p07bB9bYcKWOX5GfB0POfrZ95se5RP2YVF/NkQG4dstwTd/KfoCj+MO9xCCDq/ITBgvnkmB0vbHtVEHFx59Q1IiJWOpYsVFWdo+B79+6dL1wqFXki4/bt2+dGeevWrdZ5BR0yS+8CRPjKtbNqgg2e99FprF0A6AyIfzac8BeyA6KxOvDwOx1tDGPXZkE7FY4R4/S1BGj8TBl3xsw5e6MWHUA2Onlelu2sqtM5K57zmnDu3W2trn/qQrf+dnC8f2qnR6LsrLv8b5BD+VGPZjLxnNwvA2GCKOdCmvUX3TOfzNa9htZFA+xUJzlH3xronkTp9CPXZG0cOKyf5ImZrvsjLzzeyYqJVSc7yph/BA8Gr7vWpQOb9MVcJ1+XdfMcO79Ksi5f5is5F26kpoxsw64gGcTu7W32uNy8efP8EW/a1I0bN85/5juVboPfGXjo5EyaAbCrgIfQtaxE0AjHGBdQ3+npad29e7deeumlc1RPw8wCZx9EFoeP2HDDGJ0VHQOdSGeoDJQEIKEOZDiDz2c6386YHcDZp6kLdLuCWtV2OdjzdnaSz7wt4AzBWZz5DkXeXaXj4cPtt1umPe9Td/dmPWbnnP3kCeXtLKBzZB0wmbUP3waOXmfrB6kDNRnP+1e6TI6y7vieObNuTM6jazcDt/zOQOa+2Cfn0QHX2GCcu6sAlAd5sSwYOKhXfDqnqs6fyqCt85YEb3MySPn2Qhe4zRPlyqDmoJv78gZ5LP/7fj83BHqvQfrpbqnYJ9s/eKOh559j/klzVmy668yfj+W419T6E56zVnkxVJftRw7RLVbUuNZ58oL7Iyi3zh6px5f59M53zvxpR9cSRFT12TgpQCJPX2SDpZWX/eRd5TRunu8U1vfPq+qCQ+gCUgIp91x0CN3EUll46Rz0TDnZ/6wS0N0nNf+d8Zkf8mkZdA7IgYjAIP89H1Ynur5mb3DjdSzxdrywDMuA3IGbfHa5Mn0SPHWAgRkKZe823X6C8Mg19mucDfisD3TWBgHJpi1L9kX+3D/Ph28GqO4aVz4YkDs58p489dhPNnjtfD+ac8/9c68LdZxr0+laKJu5SXt7e+cBiLf3wrsrhpQhQTmDd3gg4LZuUdYM1PalrqCxb+sw+SFo87pbb+h30jf10D7PVZrO51AO3Vp4/4bJoLIj3u6inLKmefsxfxbePHe8XgYacvxxwMKMriWIoNB4/9pKenp6ev5jJLxlwX5yfX4ng0rFDZgMNkaRrkKEXJYOOUA5C7UTZuDKfg1XJmzc4WWW6VkOLgOTOvDldfAxy4ZG5mzTQJDOjpmV3wSYtnxZjR0j13lXidTyyLE4QYMgjuGsgXPIGvNYV8a2TjEw8DzHsh14LbpKUBeUOgdoZ0a9iEzskCk/8pDztoPw6gBMOeSzAannSyDAgMpbPZ6fqxwGTwSX1J/wZpu2nnXzZXvKjnxxPdie/JqHDjDwVoJ/+bTTHQb/zJ8y8bVcO+p02ngO3tBscEcAQ/4MnmZ7KhygPb5lx2Puh7dX+Lhw+syti4AF/2ImAUR+foHrkad1Zvbc8W9+nwaAqLqmIOLk5KQ+8pGPXMhguoCZxc9f3kAZ4XMPBANsAnWABRWcQZBlTf6GABF22jIw0DFaKewgeCvBZGfrANrtC/D17sMypNOwQc+UPee9i518dMDKQC/XsApAvrvPPJY16gIU5xYi8OmCvqs35LELqgZ1/G9ycOgy6u6aGRgzfzPHzLHcxgGeemW5d9kTA6PBcuf82dcseNAOKeeMwR8tclCmw4798VYH52kgwLXs9CbjE3hSpgyied1xNl8mEFG/LD/zb5l5XUkzvvjnTcsdmOY5+yruofDtCc7BlVrrXa7p+pn5mo4oi6ydH6nkGs/ABeVXVefggTGC/e3v759XHjIefUjAnMHvDBRw7a/ivx8XWFxLEPHxj3+8fuAHfuCp9MUXg8wM8Crkd02EukW/jJ83v/nNOwPNVfra29urt73tbe2vEnZt2cbG8/LLL9eLL764M3PuzhmY3bx5s9761rde4IVtOnkZlC3LUm9+85u3bg0wc4jBc9NU2mW89JcXkDmA07HF0PmqZx6P4+SvQRpwhjpH0AV4r3O3druAwcw5jnHxyRAGhPTrPQBV27e/XJ0inwYHJN8qM4/MSDuQx347HsMbK4jOlvnZT3WxsmUA4c9jbL+8KPxHfq50eJ8BK24dAOluK7EaxDkQDFh/057juUJjEJl16ICoEyUnMdbx2f4KgvToZbcpOn3zqTmvDXWje8Nj2ucH3CLb7gk8zpOViKo630AZe/c658WFBBBeQz65k7W5ir13873KucvoWoKIqn6jy5MQfyvjM6F79+49lX6eZ0rQ3nX+qvTCCy9c2GH+JH2++OKL9aY3vWnrWAfgLkP4BwcH9Y53vOPSrGF2nGO/853vbOU0y+DZD8+//PLL9Za3vKUdyxUGZzL5e/jwYR0eHtbb3va2NhgzmKY/BgEDypdeeulC1szxHMwYIOl48wNFzpz954oJ97dUbf/+DKtJ5KFz8rym0w1WQTnX8BKQS9DBvgIknH0bCHOOs7VhJa/b1MjrKH8Gu+72ioFVjrvt7LsDtTPz2R6cLuB670W3hyLnvRHfAI7rRmCTKnau4f4ZAojc2uh4tq2QX1fqnNR09Dg+c0bXFkSs9MajZVm2Sn+fCfke5JPSr/zKrzyVfl6v9DSczN7e5seCngYvL730UvuOjMelO3fu1Jve9KYLznXmdGf3l2/fvl2vvPLKFo851/U7A4JVVW9/+9vr1q1bF4BY+jWg6p4cOjk5qTt37tSdO3e2znXAkSCC55g1p4LIWwisQvh2ECscAW5VVbdv394Kcgz0rGxU1XkG3oG6TpaZi/eGcG9LPnuDIgP5DOTyv0FjgAr5J/AMYEj1IZvh439y/uDg4PzJHO8vsh6xWkrd4Nx5W6hbc/bnufr4ZTSepHzxRqAxxp2q+sSz5mOllVZa6bNNTwv8Ze9F1We2m9+3M6/SR9fmhRde2AKkHTjyZ4Ia3sJ58cUXL/RxWRWkA4cvvvjihQofwQff7cA+U03i7dEcZ8XBv8yZNgFAOZ52GZeVEYMwz7k7j6rTy8uyfPLCYpzR81yJ+JxnzcBKK6200rOgp5Ec5um0p0H3799/Kv2s9Ezopaq6liDiV8/+f25VfepZMvIc0ktV9eFaZfta0Crb145W2b52tMr2taNnKduXquqXdjV4nkFE6FO7SjErPT6hVLrK9inTKtvXjlbZvna0yva1o2cs20vHu9a/nbHSSiuttNJKKz05rSBipZVWWmmllVZ6InqeQcRRVf0XZ/9Xerq0yva1o1W2rx2tsn3taJXta0eva9k+t494rrTSSiuttNJKry09z5WIlVZaaaWVVlrpNaQVRKy00korrbTSSk9EK4hYaaWVVlpppZWeiFYQsdJKK6200korPRE9lyBijPGtY4y/P8Z4MMb44BjjNz5rnl7vNMb4zjHGor+fw/lbY4z3jjE+Psb49BjjB8cYr6iPd44xfmSMcW+M8bExxnePMa7DC822aIzxlWOM/3GM8UtncvxtOj/GGN81xvjIGOP+GOP9Y4xfrzafM8b4C2OMT44xfm2M8efGGG9Smy8eY/zkmZ7/P2OMb/8sTO+Z0hVk+/2NHv+Y2qyybWiM8R1jjL85xvjUmf3+8BjjXWrzVPzAGOOrxxg/PcY4GmP8vTHGN38WpvjM6Iqy/YlGd/+02rzuZPvcgYgxxjdW1Z+ozSMx/2JV/R9V9b4xxtueKWNvDPo/q+qfwN9X4NyfrKrfWlW/o6q+qqreUVU/lJNjjBtV9SNVdVBVv6mqvqmqvrmqvuuzwPfrjV6sjd596+T8t1fVf1hV31JVX1ZVd2ujo7fQ5i9U1W+oqvdU1TdU1VdW1Z/JybH5gbkfr6p/UFVfWlX/aVV95xjj9zzVmbz+6DLZVlX9WG3r8b+r86tse/qqqnpvVX15bWSzX1U/PsZ4EW0+Yz8wxvj8szZ/paq+pKq+p6r+7Bjj61+TWb0+6Cqyrar6b2tbd8/B6+tWtt3Prb6R/6rqg1X1vfi+V1X/b1X9Z8+at9fzX1V9Z1X9rcm5l6vquKr+bRz7Z6pqqaovP/v+r1XVaVW9gjbfUptfUj141vN7hnJdquq34fuoqo9U1X8i+T6oqn/n7Ps/e3bdv4Q2v7mqHlbVO86+/97a/D7MAdr8V1X1c896zs9KtmfHvr+qfnjHNatsry7ft57J6ivPvj8VP1BVf7SqflZj/aWq+rFnPednJduzYz9RVd+z45rXpWyfq0rEGOOgNpnD+3NsWZaHZ9/f/az4egPRrz8rE//iWbn3nWfHv7Q2yJly/bmq+of1SK7vrqqfWZblo+jvfVV1pzZZ30ob+vyqentty/ITtQG/lOWvLcvyv+G699cm0H0Z2vy1ZVmO0eZ9VfWuMcY/9hrx/kahrz4r9f78GOP7xhhvwblVtlenl8/+58cMn5YfeDf7QJvr5KMt29C/N8b4R2OMnx1j/JExxgs497qU7fN2v/ofr6obVfVRHf9obRDzSnP6YG1KYz9fmzLaH6qqnxxjfGFtgt7xsiy/pms+enauzv53ci+0WemRLDpZUZYf48llWV4dY/yq2nyo6SPn/r+nwu0bj36sNuX1D1XVF1TVH66qHx1jvHtZltNaZXslGmPs1aYU/teXZfnZs8NPyw/M2twZY9xeluW5/t3wiWyrqv5ibW6h/VJVfXFtqgrvqqp/6+z861K2zxuIWOkJaVmWH8XXvz3G+GBtFPp3VtVzbdQrPT+0LMtfwtefGWP87ar6har66qr6y8+EqTcmvbeqvrC290Wt9HSole2yLH8GX39mjPGRqvrLY4wvWJblFz6bDD4OPVe3M6rqH9XZPSMdf6Wqfvmzz84bl86yjf+rqn5dbWR3MMZ4s5pRrr9cvdyrVtmTIotdOvrLVbW1EfhsB/bn1Crvx6JlWX6xNn7h150dWmV7CY0xvrc2G06/ZlmWD+PU0/IDszafvAZViJlsO/rg2X/q7utOts8ViDi7h/lTVfV1OXZWOvq6qvrAs+LrjUhnj7x9QW02Af5UVZ3UtlzfVVXvrEdy/UBVfZGegnlPbX6P/u98Nnh+g9CHamPolOWd2tyPpyzfPMb4Ulz3tbWx1w+izVeOMfbR5j1V9fPLsjz35far0hjjc6vqLbXR46pVtlMaG/reqvrtVfW1y7L4ls7T8gMfYB9o89z66CvItqMvOftP3X39yfZZ71J9DXa9fmNtdrp/U212Yv83tbmH+cqz5u31/FdVf7w2jyF9Xm0eH/qfq+pXquqtZ+e/rza3N76mNhus/kZV/Q1cf6OqfqY2m3j++ar6+trce/7Dz3puz0CWb6qNA/iS2uzA/razz+88O/8HznTy36iqL6qqH66qX6yqW+jjR6vqp6vqN1bVv1ybqtBfxPmXawNG/vvabKr6xto8Kvp7nvX8n5Vsz859d20eo/u82jjTnzqT3eEq20tl+6eq6tfO/MDb8XcbbT5jP1CbzcV3q+qP1Wav2u+rqler6uuftQyelWxrk7D9wTOZft6Zb/iFqvqrr3fZPnPhvkYL9vvPFP2oNtnFlz1rnl7vf7V5DOiXzmT24bPvX4Dzt2pzL+9Xz5T0h6rq7erjn6qq/6mq7tUGgPzxqrr5rOf2DGT51WcBzn/ff3Z+1ObZ7l+uDeB9f1X90+rjc2qz0epTtXmE689X1ZvU5our6ifP+vhwVf2BZz33Zynbqrp95mA/VptHEf9+bd7/8Ir6WGXby7aT61JV34w2T8UPnK3j/37mb36BYzyPf5fJtqr+yar6q1X18TOd+79rAwTuvN5lu/4U+EorrbTSSiut9ET0XO2JWGmllVZaaaWVPnu0goiVVlpppZVWWumJaAURK6200korrbTSE9EKIlZaaaWVVlpppSeiFUSstNJKK6200kpPRCuIWGmllVZaaaWVnohWELHSSiuttNJKKz0RrSBipZVWWmmllVZ6IlpBxEorrbTSSiut9ES0goiVVlpppZVWWumJaAURK6200korrbTSE9EKIlZaaaWVVlpppSei/x+JEeRFSnhhPQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -7703,7 +3340,7 @@ "plt.figure(figsize = (6,6), dpi=100) \n", "\n", "## tell matplotlib to display our image, using a gray-scale lookup table.\n", - "plt.imshow(dimg.pixel_array, cmap=plt.cm.gray) " + "plt.imshow(dimg.pixel_array, cmap=plt.cm.gray)" ] }, { diff --git a/MIDRC_CT_Scan.ipynb b/MIDRC_CT_Scan.ipynb index 6992d0c..9dd6ffb 100644 --- a/MIDRC_CT_Scan.ipynb +++ b/MIDRC_CT_Scan.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "188e41c5-acfc-455a-8495-61b0685684ea", + "id": "af83418b", "metadata": {}, "source": [ "# Demo - Intereacting With MIDRC CT Scan Images" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "c4821a3a-0be2-47dd-80c0-9d387549528e", + "id": "2890d927", "metadata": {}, "source": [ "*Please note: This notebook uses open access data*\n", @@ -21,41 +21,35 @@ }, { "cell_type": "markdown", - "id": "1df9c244-247a-4476-b0dd-bf672acc6d4c", + "id": "a0198857", "metadata": {}, "source": [ "### Import Data And Packages\n", "Import the packages pydicom, pillow, and dicom_csv, as well as pandas, os and numpy. If any of these packages are not already installed to your workspace you can run one of the following:\n", - "- 'pip install < package >' in the workspace terminal\n", + "- 'pip install < package >' in your terminal\n", "- '!pip install < package >' in a notebook cell" ] }, { "cell_type": "code", - "execution_count": null, - "id": "c549ef75-5f7b-4034-b42e-7ae3c9a7ddd8", + "execution_count": 1, + "id": "9182747a", "metadata": {}, "outputs": [], "source": [ - "!pip install gen3 --user" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "937e641f-5220-459e-b55e-9b99cb976d8b", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install pydicom\n", - "!pip install pillow\n", - "!pip install dicom-csv" + "#The packages below may be necessary for users to install according to the imports necessary in the subsequent cells\n", + "\n", + "#!pip install gen3 --user\n", + "#!pip install numpy --upgrade\n", + "#!pip install pydicom --upgrade\n", + "#!pip install pillow --upgrade\n", + "#!pip install dicom-csv --upgrade" ] }, { "cell_type": "code", - "execution_count": null, - "id": "be7d4148-b13d-41a0-b972-9491880f43af", + "execution_count": 2, + "id": "eb457b4b", "metadata": {}, "outputs": [], "source": [ @@ -64,47 +58,189 @@ "from PIL import Image\n", "import pandas as pd\n", "import os\n", - "from dicom_csv import join_tree" + "from dicom_csv import join_tree\n", + "import subprocess\n", + "\n", + "\n" ] }, { "cell_type": "markdown", - "id": "767559ac-5e0d-4c21-b592-88ccc9aa2f69", + "id": "ca92d009", "metadata": {}, "source": [ - "Import data objects of CT scan images using the gen3 SDK" + "## Import data objects of CT scan images using the gen3 SDK\n", + "---\n", + "* Note: \"gen3\" commands are utilizing the Gen3 SDK \"drs-pull\" function, which runs at the users command line. See the detailed documentation to learn more about how to access data using the Gen3 SDK: https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md \n", + "\n", + "* Users may experience errors or warnings if the file's metadata is incomplete, but the file may have still downloaded. Check for the files in your current working directory.\n", + "\n", + "* Users will need to change the path to their \"--auth\" credentials file for each drs-pull command. Credentials are available at https://data.midrc.org/identity in the form of the api key file.\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "70569c88-ffb4-4cfa-9f57-a518f78a4373", + "execution_count": 3, + "id": "06ee94f0", "metadata": {}, "outputs": [], "source": [ - "!gen3 drs-pull object dg.MD1R/ea669b5e-ae51-40ba-b375-ed23a9cd1855\n", - "!gen3 drs-pull object dg.MD1R/a745ed98-0cb9-4537-826b-13b2e354e8bb\n", - "!gen3 drs-pull object dg.MD1R/e604979a-c71b-4ec6-b8a0-959837b86384\n", - "!gen3 drs-pull object dg.MD1R/b5cee98d-46ff-4438-aa00-90727a383340\n", - "!gen3 drs-pull object dg.MD1R/8a5a5579-7925-432d-a614-3ed208f1c182\n", - "!gen3 drs-pull object dg.MD1R/33034812-47f3-4c0e-b60b-fa7a2a04ecda\n", - "!gen3 drs-pull object dg.MD1R/5ca987c5-c660-4785-a67d-a3424cc8ec6e\n", - "!gen3 drs-pull object dg.MD1R/44148117-1858-49ef-b30f-d239abfaff80\n", - "!gen3 drs-pull object dg.MD1R/9ea205e8-a774-4318-a323-95eadda9bc5c\n", - "!gen3 drs-pull object dg.MD1R/09ece36f-a0fa-48e8-8fc2-62110eaae570" + "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # change this file path\n", + "\n", + "object_ids = ['dg.MD1R/ea669b5e-ae51-40ba-b375-ed23a9cd1855',\n", + " 'dg.MD1R/a745ed98-0cb9-4537-826b-13b2e354e8bb',\n", + " 'dg.MD1R/e604979a-c71b-4ec6-b8a0-959837b86384',\n", + " 'dg.MD1R/b5cee98d-46ff-4438-aa00-90727a383340',\n", + " 'dg.MD1R/8a5a5579-7925-432d-a614-3ed208f1c182',\n", + " 'dg.MD1R/33034812-47f3-4c0e-b60b-fa7a2a04ecda',\n", + " 'dg.MD1R/5ca987c5-c660-4785-a67d-a3424cc8ec6e',\n", + " 'dg.MD1R/44148117-1858-49ef-b30f-d239abfaff80',\n", + " 'dg.MD1R/9ea205e8-a774-4318-a323-95eadda9bc5c',\n", + " 'dg.MD1R/09ece36f-a0fa-48e8-8fc2-62110eaae570']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8294b02d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/ea669b5e-ae51-40ba-b375-ed23a9cd1855'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/a745ed98-0cb9-4537-826b-13b2e354e8bb'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/e604979a-c71b-4ec6-b8a0-959837b86384'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/b5cee98d-46ff-4438-aa00-90727a383340'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/8a5a5579-7925-432d-a614-3ed208f1c182'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/33034812-47f3-4c0e-b60b-fa7a2a04ecda'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/5ca987c5-c660-4785-a67d-a3424cc8ec6e'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/44148117-1858-49ef-b30f-d239abfaff80'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/9ea205e8-a774-4318-a323-95eadda9bc5c'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'gen3 --auth /Users/christopher/Downloads/midrc-credentials.json --endpoint data.midrc.org drs-pull object dg.MD1R/09ece36f-a0fa-48e8-8fc2-62110eaae570'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for object_id in object_ids:\n", + " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id)\n", + " display(cmd)\n", + " subprocess.run(cmd, shell=True, capture_output=True)\n", + "\n", + " " ] }, { "cell_type": "markdown", - "id": "5dc3a710-c717-4119-a658-1d07adb50f05", + "id": "6a1a9b24", + "metadata": {}, + "source": [ + "All 10 data objects are now stored under the folder 'COVID-19-NY-SBU'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "561dc3dc", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 0\r\n", + "drwxr-xr-x 3 christopher staff 96 Oct 17 12:09 \u001b[34mA034518\u001b[m\u001b[m\r\n", + "drwxr-xr-x 3 christopher staff 96 Oct 17 12:08 \u001b[34mA117394\u001b[m\u001b[m\r\n", + "drwxr-xr-x 3 christopher staff 96 Oct 17 12:09 \u001b[34mA546520\u001b[m\u001b[m\r\n", + "drwxr-xr-x 3 christopher staff 96 Oct 17 12:08 \u001b[34mA587516\u001b[m\u001b[m\r\n", + "drwxr-xr-x 3 christopher staff 96 Oct 17 12:08 \u001b[34mA770557\u001b[m\u001b[m\r\n", + "drwxr-xr-x 3 christopher staff 96 Oct 17 12:09 \u001b[34mA783752\u001b[m\u001b[m\r\n", + "drwxr-xr-x 3 christopher staff 96 Oct 17 12:08 \u001b[34mA840445\u001b[m\u001b[m\r\n" + ] + } + ], "source": [ - "All 5 data objects are now stored under the folder 'COVID-19-NY-SBU'" + "!ls -l COVID-19-NY-SBU" ] }, { "cell_type": "markdown", - "id": "3a99afeb-2651-4a35-b71c-8a44ce0ef103", + "id": "58aa3021", "metadata": {}, "source": [ "### View Image" @@ -112,10 +248,21 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "3a67450d-1394-4c1d-af1b-c56b82d0092a", + "execution_count": 6, + "id": "0b59aa5a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'COVID-19-NY-SBU/A034518/12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W IV CON-21869/4.000000-Lung 1.0 CE-04129/1-273.dcm'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "image_path = 'COVID-19-NY-SBU/A034518/12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W IV CON-21869/4.000000-Lung 1.0 CE-04129/1-273.dcm'\n", "image_path" @@ -123,7 +270,7 @@ }, { "cell_type": "markdown", - "id": "5e87d23c-9eba-4c72-83dc-d55d8b994b01", + "id": "f5d35a95", "metadata": {}, "source": [ "Read the dcm image using the relative file path." @@ -131,8 +278,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "80f91180-6084-42cc-97f5-5520b4676ffe", + "execution_count": 7, + "id": "3b20a8db", "metadata": {}, "outputs": [], "source": [ @@ -141,7 +288,7 @@ }, { "cell_type": "markdown", - "id": "07be2401-747b-496a-bc90-3489e04ddcba", + "id": "ca111869", "metadata": {}, "source": [ "Get the pixel arrays for the image." @@ -149,10 +296,27 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "eb2a2b59-d26b-496e-986b-abd20c97ace1", + "execution_count": 8, + "id": "d0a8ffdf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n", + " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n", + " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n", + " ...,\n", + " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n", + " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n", + " [-2048., -2048., -2048., ..., -2048., -2048., -2048.]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_image = ds.pixel_array.astype(float)\n", "new_image" @@ -160,7 +324,7 @@ }, { "cell_type": "markdown", - "id": "6fbc9cca-a6de-4859-acdc-71e61f09c821", + "id": "08663cf8", "metadata": {}, "source": [ "Scale the image's pixel array and convert to a uint8 integer." @@ -168,10 +332,27 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "65180d29-4c48-4e74-abe3-3a966106486d", + "execution_count": 9, + "id": "b8692d0a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0\n", "scaled_image = np.uint8(scaled_image)\n", @@ -180,7 +361,7 @@ }, { "cell_type": "markdown", - "id": "5178dd7b-6b37-4e52-89e2-376817b1a040", + "id": "630f91a1", "metadata": {}, "source": [ "Use the Image package to convert the image array and show the image." @@ -188,18 +369,38 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "7d98025e-f429-4106-86a5-9639d9d082a5", + "execution_count": 10, + "id": "591cc96f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "final_image = Image.fromarray(scaled_image)\n", - "final_image.show()" + "print(type(final_image))\n", + "final_image" ] }, { "cell_type": "markdown", - "id": "471d7259-a73a-471f-aa94-cc6ea2985713", + "id": "c4c076c0", "metadata": {}, "source": [ "### Convert Images\n", @@ -208,8 +409,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "302f875e-0e95-4fae-83ea-4a729e3829ec", + "execution_count": 11, + "id": "8a259ff7", "metadata": {}, "outputs": [], "source": [ @@ -223,7 +424,7 @@ " \n", " final_image = Image.fromarray(scaled_image)\n", "\n", - " final_image.show()\n", + " return final_image\n", "\n", "def dcm_to_png(image_path):\n", " \n", @@ -253,7 +454,7 @@ }, { "cell_type": "markdown", - "id": "713fa890-089f-46f3-bd20-ae15f95e2054", + "id": "80839ccb", "metadata": {}, "source": [ "Convert dicom image to png and save." @@ -261,8 +462,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "70facdf4-428a-42d2-8dfb-768c1a06ebbe", + "execution_count": 12, + "id": "67d98c20", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "c698cb4d-d07c-4832-8d53-b780376a3990", + "id": "cdfd06b6", "metadata": {}, "source": [ "Convert dicom image to jpg and save." @@ -280,8 +481,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "9b50b421-bea9-49e2-919d-aaf68ac89e20", + "execution_count": 13, + "id": "bd90fdaa", "metadata": {}, "outputs": [], "source": [ @@ -291,7 +492,7 @@ }, { "cell_type": "markdown", - "id": "753d98ed-fed8-463a-9a8a-ea73996dd6c7", + "id": "f3899246", "metadata": {}, "source": [ "Display a few dicom images." @@ -299,10 +500,22 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "f43f070e-f566-4b64-9df7-37bacc734bcb", + "execution_count": 14, + "id": "ff550791", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "image_path = 'COVID-19-NY-SBU/A546520/12-30-1900-CT CHEST PULMONARY ANGIO WITH IV CON-13804/11.000000-CTA 3.000 CE-95792/1-119.dcm'\n", "view_dicom_image(image_path)" @@ -310,10 +523,22 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "a3df1180-3cfe-49bc-bcdf-3c81f7010229", + "execution_count": 15, + "id": "61579705", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "image_path = 'COVID-19-NY-SBU/A770557/12-19-1900-CT CHEST WO IV CONT-97223/5.000000-Lung 1.0-84269/1-127.dcm'\n", "view_dicom_image(image_path)" @@ -321,10 +546,22 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "b27ab66e-f5b4-4d11-b058-9dcc72b344d9", + "execution_count": 16, + "id": "c801d0df", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "image_path = 'COVID-19-NY-SBU/A770557/12-19-1900-CT CHEST WO IV CONT-97223/7.000000-Body 3.000-78395/1-53.dcm'\n", "view_dicom_image(image_path)" @@ -332,7 +569,7 @@ }, { "cell_type": "markdown", - "id": "bd728395-6abf-4823-bbaa-7c26928b2468", + "id": "105b7f99", "metadata": {}, "source": [ "### Extract Metadata" @@ -340,7 +577,7 @@ }, { "cell_type": "markdown", - "id": "35b6f935-2a8f-4140-922e-3d010a14356d", + "id": "f912e626", "metadata": {}, "source": [ "The following function will extract the file and patient metadata from the header of each dicom (.dcm) file within a given folder and place the collected metadata into a pandas dataframe." @@ -348,8 +585,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "58bab749-3d4a-44ab-942c-33a22a7406cb", + "execution_count": 17, + "id": "be0be963", "metadata": {}, "outputs": [], "source": [ @@ -368,10 +605,406 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "ef7dde8b-2ffe-4e1f-9edc-657af27b6fbc", + "execution_count": 18, + "id": "0e318e25", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01-01-1901-CT CHEST WO IV CONT-84206/6.000000-Body 3.000-02742/1-034.dcm: : 1it [00:00, 45.38it/s] \n", + "10-08-1900-CT ABD AND PELVIS WITH IV CONT-39755/9.000000-CTA 0.5 CE-40834/1-0163.dcm: : 1it [00:00, 60.99it/s] \n", + "12-19-1900-CT CHEST WO IV CONT-97223/5.000000-Lung 1.0-84269/1-127.dcm: : 3it [00:00, 96.97it/s] \n", + "12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W IV CON-21869/4.000000-Lung 1.0 CE-04129/1-273.dcm: : 1it [00:00, 68.28it/s] \n", + "12-23-1900-CT CHEST PULMONARY ANGIO WITH IV CON-62918/5.000000-CTA 15.000 CE-36514/1-008.dcm: : 2it [00:00, 103.65it/s]\n", + "12-30-1900-CT CHEST PULMONARY ANGIO WITH IV CON-13804/11.000000-CTA 3.000 CE-95792/1-119.dcm: : 1it [00:00, 93.58it/s] \n", + "04-22-1901-CT CHEST WO IV CONT-40216/2.000000-Body 5.0-01241/1-16.dcm: : 1it [00:00, 88.81it/s] \n" + ] + }, + { + "data": { + "text/html": [ + "
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AccessionNumberAcquisitionDateAcquisitionNumberAcquisitionTimeAcquisitionTypeBitsAllocatedBitsStoredBodyPartExaminedCTDIvolClinicalTrialTimePointDescription...PathToFolderFileNameContrastBolusAgentContrastBolusStartTimeInStackPositionNumberSpecificCharacterSetStackIDTablePositionTemporalPositionIndexPatientComments
0190101013013948.750SPIRAL1616CHEST4.1Days offset from diagnosis...01-01-1901-CT CHEST WO IV CONT-84206/6.000000-...1-034.dcmNaNNaNNaNNaNNaNNaNNaNNaN
0190010085150559.700SPIRAL1616CHEST3.8Days offset from diagnosis...10-08-1900-CT ABD AND PELVIS WITH IV CONT-3975...1-0163.dcmCE150538.350163.0ISO_IR 1001_1206_0000248.61.0NaN
0190012193004429.700SPIRAL1616CHEST2.5Days offset from diagnosis...12-19-1900-CT CHEST WO IV CONT-97223/7.000000-...1-53.dcmNaNNaNNaNISO_IR 100NaNNaNNaNNaN
1190012193004429.700SPIRAL1616CHEST2.5Days offset from diagnosis...12-19-1900-CT CHEST WO IV CONT-97223/8.000000-...1-021.dcmNaNNaNNaNISO_IR 100NaNNaNNaNNaN
2190012193004429.700SPIRAL1616CHEST2.5Days offset from diagnosis...12-19-1900-CT CHEST WO IV CONT-97223/5.000000-...1-127.dcmNaNNaNNaNISO_IR 100NaN126.0NaNNaN
0190012313173550.750SPIRAL1616CHEST3.3Days offset from diagnosis...12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W ...1-273.dcmCE173430.850NaNISO_IR 100NaN272.0NaN
0190012235003129.050SPIRAL1616CHEST3.3Days offset from diagnosis...12-23-1900-CT CHEST PULMONARY ANGIO WITH IV CO...1-077.dcmOMNI 350003112.300NaNNaNNaNNaNNaN
1190012235003129.050SPIRAL1616CHEST3.3Days offset from diagnosis...12-23-1900-CT CHEST PULMONARY ANGIO WITH IV CO...1-008.dcmOMNI 350003112.300NaNNaNNaN21.0NaN
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0190104223111507.900SPIRAL1616CHEST6.9Days offset from diagnosis...04-22-1901-CT CHEST WO IV CONT-40216/2.000000-...1-16.dcmNaNNaNNaNNaNNaN75.0NaNNaN
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10 rows × 108 columns

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" + ], + "text/plain": [ + " AccessionNumber AcquisitionDate AcquisitionNumber AcquisitionTime \\\n", + "0 19010101 3 013948.750 \n", + "0 19001008 5 150559.700 \n", + "0 19001219 3 004429.700 \n", + "1 19001219 3 004429.700 \n", + "2 19001219 3 004429.700 \n", + "0 19001231 3 173550.750 \n", + "0 19001223 5 003129.050 \n", + "1 19001223 5 003129.050 \n", + "0 19001230 5 013553.850 \n", + "0 19010422 3 111507.900 \n", + "\n", + " AcquisitionType BitsAllocated BitsStored BodyPartExamined CTDIvol \\\n", + "0 SPIRAL 16 16 CHEST 4.1 \n", + "0 SPIRAL 16 16 CHEST 3.8 \n", + "0 SPIRAL 16 16 CHEST 2.5 \n", + "1 SPIRAL 16 16 CHEST 2.5 \n", + "2 SPIRAL 16 16 CHEST 2.5 \n", + "0 SPIRAL 16 16 CHEST 3.3 \n", + "0 SPIRAL 16 16 CHEST 3.3 \n", + "1 SPIRAL 16 16 CHEST 3.3 \n", + "0 SPIRAL 16 16 ABDOMEN 6.4 \n", + "0 SPIRAL 16 16 CHEST 6.9 \n", + "\n", + " ClinicalTrialTimePointDescription ... \\\n", + "0 Days offset from diagnosis ... \n", + "0 Days offset from diagnosis ... \n", + "0 Days offset from diagnosis ... \n", + "1 Days offset from diagnosis ... \n", + "2 Days offset from diagnosis ... \n", + "0 Days offset from diagnosis ... \n", + "0 Days offset from diagnosis ... \n", + "1 Days offset from diagnosis ... \n", + "0 Days offset from diagnosis ... \n", + "0 Days offset from diagnosis ... \n", + "\n", + " PathToFolder FileName \\\n", + "0 01-01-1901-CT CHEST WO IV CONT-84206/6.000000-... 1-034.dcm \n", + "0 10-08-1900-CT ABD AND PELVIS WITH IV CONT-3975... 1-0163.dcm \n", + "0 12-19-1900-CT CHEST WO IV CONT-97223/7.000000-... 1-53.dcm \n", + "1 12-19-1900-CT CHEST WO IV CONT-97223/8.000000-... 1-021.dcm \n", + "2 12-19-1900-CT CHEST WO IV CONT-97223/5.000000-... 1-127.dcm \n", + "0 12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W ... 1-273.dcm \n", + "0 12-23-1900-CT CHEST PULMONARY ANGIO WITH IV CO... 1-077.dcm \n", + "1 12-23-1900-CT CHEST PULMONARY ANGIO WITH IV CO... 1-008.dcm \n", + "0 12-30-1900-CT CHEST PULMONARY ANGIO WITH IV CO... 1-119.dcm \n", + "0 04-22-1901-CT CHEST WO IV CONT-40216/2.000000-... 1-16.dcm \n", + "\n", + " ContrastBolusAgent ContrastBolusStartTime InStackPositionNumber \\\n", + "0 NaN NaN NaN \n", + "0 CE 150538.350 163.0 \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "0 CE 173430.850 NaN \n", + "0 OMNI 350 003112.300 NaN \n", + "1 OMNI 350 003112.300 NaN \n", + "0 CE 013536.200 NaN \n", + "0 NaN NaN NaN \n", + "\n", + " SpecificCharacterSet StackID TablePosition TemporalPositionIndex \\\n", + "0 NaN NaN NaN NaN \n", + "0 ISO_IR 100 1_1206_00002 48.6 1.0 \n", + "0 ISO_IR 100 NaN NaN NaN \n", + "1 ISO_IR 100 NaN NaN NaN \n", + "2 ISO_IR 100 NaN 126.0 NaN \n", + "0 ISO_IR 100 NaN 272.0 NaN \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN 21.0 NaN \n", + "0 NaN NaN NaN NaN \n", + "0 NaN NaN 75.0 NaN \n", + "\n", + " PatientComments \n", + "0 NaN \n", + "0 NaN \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "0 \n", + "0 \n", + "1 \n", + "0 NaN \n", + "0 NaN \n", + "\n", + "[10 rows x 108 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "base_folder = 'COVID-19-NY-SBU'\n", "metadata = extract_metadata(base_folder)\n", @@ -380,7 +1013,7 @@ }, { "cell_type": "markdown", - "id": "075b4095-a104-457b-8283-052a7582602b", + "id": "3241ff77", "metadata": {}, "source": [ "Included in this metadata are import pieces of file and patient data, such as the body part examined, the patient's sex, the patient's age, etc. " @@ -388,40 +1021,121 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "19ca1110-857e-4ae9-86e8-2eb6c997e3d5", + "execution_count": 19, + "id": "fd079296", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Manufacturer', 'ManufacturerModelName', 'Modality', 'PatientAge',\n", + " 'PatientBirthDate', 'PatientID', 'PatientIdentityRemoved',\n", + " 'PatientName', 'PatientPosition', 'PatientSex',\n", + " 'PerformedProcedureStepStartDate', 'PerformedProcedureStepStartTime',\n", + " 'PhotometricInterpretation', 'PixelRepresentation', 'PixelSpacing0',\n", + " 'PixelSpacing1', 'PositionReferenceIndicator', 'ProtocolName',\n", + " 'ReconstructionDiameter', 'ReferringPhysicianName'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "metadata.columns[40:60]" ] }, { "cell_type": "code", - "execution_count": null, - "id": "6ba032f9-5a7f-4c84-b84a-4bacd87bba6b", + "execution_count": 20, + "id": "7fee9d5c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 CHEST\n", + "0 CHEST\n", + "0 CHEST\n", + "1 CHEST\n", + "2 CHEST\n", + "0 CHEST\n", + "0 CHEST\n", + "1 CHEST\n", + "0 ABDOMEN\n", + "0 CHEST\n", + "Name: BodyPartExamined, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "metadata.BodyPartExamined" ] }, { "cell_type": "code", - "execution_count": null, - "id": "db69cae5-062a-4060-a9c2-166c6e15efb9", + "execution_count": 21, + "id": "c5cedc88", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 M\n", + "0 F\n", + "0 M\n", + "1 M\n", + "2 M\n", + "0 F\n", + "0 M\n", + "1 M\n", + "0 M\n", + "0 F\n", + "Name: PatientSex, dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "metadata.PatientSex" ] }, { "cell_type": "code", - "execution_count": null, - "id": "a121d061-98ed-4274-95f0-d68dd0e45ea3", + "execution_count": 22, + "id": "d0fa4cb1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 074Y\n", + "0 084Y\n", + "0 090Y\n", + "1 090Y\n", + "2 090Y\n", + "0 077Y\n", + "0 090Y\n", + "1 090Y\n", + "0 071Y\n", + "0 079Y\n", + "Name: PatientAge, dtype: object" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "metadata.PatientAge" ] @@ -429,7 +1143,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8415b5f3-a194-4f52-a5bc-e570204b2830", + "id": "b692c150", "metadata": {}, "outputs": [], "source": [] @@ -437,7 +1151,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -451,7 +1165,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.9.2" } }, "nbformat": 4, diff --git a/MIDRC_Clinical_Data.ipynb b/MIDRC_Clinical_Data.ipynb index f1b4ad0..d50ae55 100644 --- a/MIDRC_Clinical_Data.ipynb +++ b/MIDRC_Clinical_Data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "fe51540e-c6e4-4ece-9690-9e1dea2e42ee", + "id": "01f878af", "metadata": {}, "source": [ "# MIDRC Open-R1 Clinical Data\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "c9547961-f249-4068-b9b0-acfa48b089f4", + "id": "5dfa1c83", "metadata": {}, "source": [ "In this notebook we will visualize the distribution of subjects accross a variety demographics and their COVID-19 status in the Open-R1 dataset from The Medical Imaging and Data Resource Center. (MIDRC - https://data.midrc.org/)" @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "73905708-1ff4-4e7e-808a-6d1a4a47dfbd", + "id": "7bff7dca", "metadata": {}, "source": [ "The Open-R1 data set has 1,169 subjects, this notebook will compare the distribution of COVID-19 positive and negative patients across multiple demographic classes. In particular we will focus on the subjects' age groups (-20, 21-30, ..., 90+), sex (Male or Female), race (Black or African American, White, Asian, Pacific Islander, American Indian, Other, or Not Reported), and whether the subject is Hispanic or Latino. Below is a subset of the dataset." @@ -30,42 +30,117 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "8498c432-b062-4e67-b5a3-a8569d9f6ff3", + "execution_count": 1, + "id": "d214a58d", "metadata": {}, "outputs": [], "source": [ - "!pip install gen3 --user" + "# The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n", + "\n", + "#!pip install --upgrade pandas\n", + "#!pip install --upgrade --ignore-installed PyYAML\n", + "#!pip install --upgrade pip\n", + "#!pip install --upgrade gen3 --user --upgrade\n", + "#!pip install cdiserrors\n", + "#!pip install --upgrade pydicom" ] }, { "cell_type": "code", - "execution_count": null, - "id": "1dcd402a-9876-4d96-97e3-0042897507df", + "execution_count": 2, + "id": "112c887d", "metadata": { "tags": [] }, "outputs": [], "source": [ - "import gen3\n", "import pandas as pd\n", - "import numpy as np\n", + "import sys, os, webbrowser\n", + "import gen3\n", + "import pydicom\n", "import matplotlib.pyplot as plt\n", - "from scipy.stats import chi2_contingency\n", - "from sklearn import tree\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import accuracy_score\n" + "\n", + "from gen3.submission import Gen3Submission\n", + "from gen3.auth import Gen3Auth\n", + "from gen3.index import Gen3Index\n", + "from gen3.query import Gen3Query" ] }, { "cell_type": "code", - "execution_count": null, - "id": "d034641b-330f-4809-90bf-27a8f95b6b83", + "execution_count": 3, + "id": "a005ec13", + "metadata": {}, + "outputs": [], + "source": [ + "# Import some custom Python scripts from personal GitHub repo.\n", + "# Change these directory paths to reflect your local working directory.\n", + "\n", + "home_dir = \"/Users/christopher\" \n", + "demo_dir = \"{}/Documents/Notes/MIDRC/tutorials\".format(home_dir)\n", + "\n", + "os.chdir(demo_dir)\n", + "\n", + "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O {}/expansion.py\".format(demo_dir))\n", + "%run expansion.py\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e4b39ef9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Getting all project_ids you have access to in the data commons.\n", + "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1', 'TCIA-RICORD']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christopher/Documents/Notes/MIDRC/tutorials/expansion.py:213: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead.\n", + " df = json_normalize(res[\"data\"][\"project\"])\n" + ] + }, + { + "data": { + "text/plain": [ + "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1', 'TCIA-RICORD']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initiate instances of the Gen3 SDK Classes using credentials file for authentication.\n", + "# Change the directory path in \"cred\" to reflect the location of your credentials file.\n", + "\n", + "api = \"https://data.midrc.org\"\n", + "cred = \"{}/Downloads/midrc-credentials.json\".format(home_dir)\n", + "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n", + "sub = Gen3Submission(api, auth) # submission class\n", + "query = Gen3Query(auth) # query class\n", + "exp = Gen3Expansion(api,auth,sub) # class with some custom scripts\n", + "exp.get_project_ids()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ab7c8d1a", "metadata": { "tags": [] }, "outputs": [], "source": [ + "#Function to sort subjects into various age groups\n", "def age_group(agelist):\n", " min_age = min(agelist)\n", " groups = [\"-20 yr\", \"21-30 yr\", \"31-40 yr\", \"41-50 yr\", \"51-60 yr\", \"61-70 yr\", \"71-80 yr\", \"81-90 yr\", \"90+ yr\"]\n", @@ -92,6 +167,7 @@ " \n", " return grouplist\n", "\n", + "#Function to represent various demographics into a precent positivity statistic\n", "def percent_representation(df, demographic_type, demographics):\n", "\n", " positive_df = df[df['covid19_positive'] == 'Yes']\n", @@ -119,26 +195,103 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "33a05293-0927-4e7e-a30f-df1435982fcb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!gen3 drs-pull object dg.MD1R/2e3b3d14-bac8-4d84-a119-f452df3b33ac" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c60f160-b93b-4ee9-ba77-c440a262bdf8", + "execution_count": 6, + "id": "c4095057", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " covid19_positive age_group sex ethnicity race\n", + "0 No 71-80 yr Male Not Hispanic or Latino White\n", + "1 No 61-70 yr Male Not Hispanic or Latino Not Reported\n", + "2 No 71-80 yr Male Not Hispanic or Latino White\n", + "3 No 51-60 yr Male Not Hispanic or Latino Black or A.A.\n", + "4 No 71-80 yr Female Not Hispanic or Latino Not Reported" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df = pd.read_csv('MIDRC_Open_R1_case.tsv', sep='\\t')\n", + "#Using the Gen3 connection \"sub\" data for project R1 is downloaded and converted into a data frame\n", + "cases = sub.export_node(program='Open',project='R1',node_type='case',fileformat='tsv')\n", + "df = pd.read_csv(StringIO(cases), sep='\\t', header=0)\n", "df['zip'] = df['zip'].astype(str)\n", "df['age_group'] = age_group(df['age_at_index'])\n", "\n", @@ -146,12 +299,12 @@ "df.loc[df.race == 'American Indian or Alaskan Native', 'race'] = 'American Indian' \n", "df.loc[df.race == 'Black or African American', 'race'] = 'Black or A.A.' \n", "df = df[['covid19_positive', 'age_group', 'sex', 'ethnicity', 'race']]\n", - "df.head()" + "df.head()\n" ] }, { "cell_type": "markdown", - "id": "f22aa8d5-eb9c-4165-b384-ad35c37e8893", + "id": "91a95e69", "metadata": {}, "source": [ "### Subjects' COVID-19 Status" @@ -159,29 +312,40 @@ }, { "cell_type": "markdown", - "id": "be86e8d9-c6c0-4b42-b678-af970ac30aed", + "id": "ce373785", "metadata": {}, "source": [ - "Approximately 17% of the subjects in the Open-R1 dataset were COVID-19 positive at the time of the dataset indexing. " + "Approximately 22% of the subjects in the Open-R1 dataset were COVID-19 positive at the time of the dataset indexing. " ] }, { "cell_type": "code", - "execution_count": null, - "id": "459f5a10-4cab-4a52-9849-e0fa4215e61e", + "execution_count": 7, + "id": "a28b5a37", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Number of COVID-19 positive subjects': 1385, 'Number of COVID-19 negative subjects': 6331}\n", + "Positivity percentage = 21.9%\n" + ] + } + ], "source": [ "covid_breakdown = {'Number of COVID-19 positive subjects': len(df[df['covid19_positive'] == 'Yes']['covid19_positive']), \n", " 'Number of COVID-19 negative subjects': len(df[df['covid19_positive'] == 'No']['covid19_positive']), }\n", - "covid_breakdown" + "print(covid_breakdown)\n", + "\n", + "print(\"Positivity percentage = {}%\".format(round(list(covid_breakdown.items())[0][1]/list(covid_breakdown.items())[1][1]*100,1)))" ] }, { "cell_type": "markdown", - "id": "2df7d95d-e4ed-4b14-8228-c5a258e57006", + "id": "99eed950", "metadata": {}, "source": [ "## Subject Distribution" @@ -189,12 +353,25 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "c6b85a43-ba0b-4493-a3b6-704e283f0095", + "execution_count": 8, + "id": "93b2dd2b", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "races = ['Black or A.A.', \n", " 'White', \n", @@ -224,20 +401,33 @@ }, { "cell_type": "markdown", - "id": "0958f4bd-12d8-424f-b329-11324f362882", + "id": "10bc59b7", "metadata": {}, "source": [ - "Here we can see that the number of Black and African American (A.A.) subjects are nearly evenly represented amongst both COVID-19 positve and negative populations, while White and Asian subjects are significantly under represented amongst the COVID-19 positive population. Additionally the number of subjects who chose not to report their race (Not Reported) was much higher amongst the COVID-19 positive population." + "Users can examine the ratio of Negative and Positive COVID cases amoungst various demographics. At many points thoughout the first two years of the pandemic, desparities of COVID positivity ratios were often noted. Additionally, since subjects possess the ability to not report their race (Not Reported), differences in positivity ratios can be observed if present." ] }, { "cell_type": "code", - "execution_count": null, - "id": "eb73c6b7-4e6d-4b20-9775-1e82bdbf31bd", + "execution_count": 9, + "id": "f1e11218", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ethnicities = ['Not Hispanic or Latino', 'Hispanic or Latino'] \n", "\n", @@ -262,22 +452,35 @@ }, { "cell_type": "markdown", - "id": "27428af3-56ac-45ff-a7bd-788c8cf1a33e", + "id": "b6589c91", "metadata": { "tags": [] }, "source": [ - "Hispanic and Latino subjects are slightly over represented amongst COVID-19 positive population and slightly under represented in the COVID-19 negative population." + "When reduced to only two groups (Not Hispanic or Latino verse Hispanic or Latino), differences in COVID positivity can be observed if present." ] }, { "cell_type": "code", - "execution_count": null, - "id": "1a01fd47-4dfd-40aa-8736-8affc2dbca3f", + "execution_count": 10, + "id": "12f528d5", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sexes = ['Male', 'Female'] \n", "\n", @@ -302,20 +505,33 @@ }, { "cell_type": "markdown", - "id": "522a4e50-0db7-45c3-a921-d3cd5540b8a1", + "id": "3834ea41", "metadata": {}, "source": [ - "The majority of the COVID-19 postive subjects are male and the majority of COVID-19 negative subjects are female." + "If present, a disparity of COVID positivity can be noted between sexes." ] }, { "cell_type": "code", - "execution_count": null, - "id": "b89533cd-86cc-40e2-b7ff-be55f9f830a1", + "execution_count": 11, + "id": "486dfd08", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure()\n", "ax = fig.add_axes([0,0,1,1])\n", @@ -344,16 +560,16 @@ }, { "cell_type": "markdown", - "id": "5c9d4b78-1083-49d9-8e4f-72411c84453d", + "id": "0112e249", "metadata": {}, "source": [ - "The majority of COVID-19 positive subjects are between the ages of 51 and 70, while the COVID-19 negative subjects are more evenly distributed between the 51-60, 61-70, and 71-80 age groups. The COVID-19 positive and negative subjects appear to be nearly evenly represented accross most of the age groups with the exception of the 71-80 and 81-90 year old age group which have a much higher proportion of COVID-19 negative subjects." + "The affect age plays in the prevalence of COVID positivity is displayed above. It should be noted that this chart is not normalized by the age distribution of the general population. Typically though, individuals <20 years represent a significant portion of most general populations." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -367,7 +583,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.9.2" } }, "nbformat": 4, diff --git a/Querying and Accessing MIDRC Data.ipynb b/Querying and Accessing MIDRC Data.ipynb deleted file mode 100644 index 0a391f0..0000000 --- a/Querying and Accessing MIDRC Data.ipynb +++ /dev/null @@ -1,40135 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Query and Accessing Data in a Gen3 Data Commons\n", - "---\n", - "by Chris Meyer, PhD\n", - "\n", - "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n", - "\n", - "June 2022\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction\n", - "---\n", - "* This notebook is intended to demonstrate a variety of ways to access file objects and structured data (aka \"metadata\") in a Gen3 data commons.\n", - "* File objects are accessed via their \"data GUID\" aka \"object_id\", which is a unique identifier that is associated with a storage_url in the file index (https://data.midrc.org/index/index). Users must be authorized to access a file in order to download it via the object_id. \n", - "* Structured data in a Gen3 Data Commons is imported into Postgres via the \"sheepdog\" service and must conform to the data model. The data model is a relational model that consists of tables or \"nodes\" that are related to one another via foreign keys so that the model can be thought of as a graph of nodes that are linked to each other. Each node in the model contains certain properties (keys) that store data of a particular type (values).\n", - "* The \"sheepdog\" service can export tables of data from a particular node of a data project. This is the simplest way to access \"all\" the data in a Gen3 data commons.\n", - "* Queries can be constructed to target specific types of data in Postgres and are handled by the \"peregrine\" graphQL service.\n", - "* Structured data can also be transformed via an \"ETL\" (extract, transform, load) process that takes the complex relationships between nodes and \"flattens\" the data into a single table, which is stored in an ElasticSearch (ES) database that can be queried using the \"guppy\" graphQL service. These ES tables are what the data exploration app of the Gen3 data-portal is based on.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting PyYAML\n", - "\u001b[?25l Downloading https://files.pythonhosted.org/packages/b3/85/79b9e5b4e8d3c0ac657f4e8617713cca8408f6cdc65d2ee6554217cedff1/PyYAML-6.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (603kB)\n", - "\u001b[K 100% |████████████████████████████████| 604kB 24.3MB/s ta 0:00:01\n", - "\u001b[?25hInstalling collected packages: PyYAML\n", - "Successfully installed PyYAML-6.0\n", - "Collecting pip\n", - "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a4/6d/6463d49a933f547439d6b5b98b46af8742cc03ae83543e4d7688c2420f8b/pip-21.3.1-py3-none-any.whl (1.7MB)\n", - "\u001b[K 100% |████████████████████████████████| 1.7MB 20.4MB/s ta 0:00:01\n", - "\u001b[?25hInstalling collected packages: pip\n", - 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" Attempting uninstall: requests\n", - " Found existing installation: requests 2.20.0\n", - " Uninstalling requests-2.20.0:\n", - " Successfully uninstalled requests-2.20.0\n", - " Attempting uninstall: PyYAML\n", - " Found existing installation: PyYAML 6.0\n", - " Uninstalling PyYAML-6.0:\n", - " Successfully uninstalled PyYAML-6.0\n", - " Attempting uninstall: jsonschema\n", - " Found existing installation: jsonschema 2.6.0\n", - " Uninstalling jsonschema-2.6.0:\n", - " Successfully uninstalled jsonschema-2.6.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "jupyterlab-launcher 0.13.1 requires jsonschema>=2.6.0, but you have jsonschema 2.5.1 which is incompatible.\u001b[0m\n", - "Successfully installed PyYAML-5.4.1 aiofiles-0.8.0 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asyncio-3.4.3 asynctest-0.13.0 attrs-21.4.0 backoff-1.11.1 cdislogging-1.1.1 charset-normalizer-2.0.12 click-7.1.2 contextvars-2.4 dataclasses-0.8 dataclasses-json-0.5.7 dictionaryutils-3.4.4 drsclient-0.1.6 fastavro-1.4.7 frozenlist-1.2.0 gdcdictionary-1.2.0 gen3-4.9.4 h11-0.9.0 httpcore-0.11.1 httpx-0.15.5 humanfriendly-10.0 idna-ssl-1.1.0 immutables-0.18 importlib-metadata-1.7.0 importlib-resources-5.4.0 indexclient-2.1.0 jsonschema-2.5.1 marshmallow-3.14.1 marshmallow-enum-1.5.1 multidict-5.2.0 mypy-extensions-0.4.3 pypfb-0.5.15 python-json-logger-0.1.11 requests-2.27.1 rfc3986-1.5.0 sniffio-1.2.0 tqdm-4.64.0 typing-extensions-4.1.1 typing-inspect-0.7.1 yarl-1.7.2 zipp-3.6.0\n" - ] - } - ], - "source": [ - "## May need to install these Python packages. \n", - "## We should add them to the base workspace image so this is unnecessary..\n", - "\n", - "!pip install --upgrade --ignore-installed PyYAML\n", - "!pip install --upgrade pip\n", - "!pip install --upgrade gen3\n", - "!pip install pydicom" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python Packages and scripts\n", - "import pandas as pd\n", - "import sys, os\n", - "import gen3\n", - "import pydicom\n", - "from io import StringIO\n", - "\n", - "\n", - "from gen3.submission import Gen3Submission\n", - "from gen3.auth import Gen3Auth\n", - "from gen3.index import Gen3Index\n", - "from gen3.query import Gen3Query" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Import some custom Python scripts from personal GitHub repo\n", - "home_dir = \"/home/jovyan/pd\"\n", - "gen3_dir = \"{}/gen3\".format(home_dir)\n", - "os.system('mkdir -p {}'.format(gen3_dir))\n", - "os.chdir(gen3_dir)\n", - "\n", - "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O /home/jovyan/pd/gen3/expansion.py\")\n", - "%run /home/jovyan/pd/gen3/expansion.py\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2022-06-22 21:14:57,584][WARNING] Unable to find refresh_file\n", - "Getting all project_ids you have access to in the data commons.\n", - "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1']\n" - ] - }, - { - "data": { - "text/plain": [ - "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1']" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Initiate instances of the Gen3 SDK Classes using the WTS (no need for uploading credentials.json)\n", - "\n", - "api = 'https://data.midrc.org'\n", - "auth = Gen3Auth(api) # authentication class; \n", - "## if using outside of a Gen3 workspace, you must initialize by passing your credentials.json file downloaded from your Profile page. See below:\n", - "# cred = '/home/jovyan/gen3/midrc-credentials.json'\n", - "# auth = Gen3Auth(api, refresh_file=cred)\n", - "sub = Gen3Submission(api, auth) # submission class, sheepdog and peregrine (postgres) functions\n", - "query = Gen3Query(auth) # query class, guppy (elasticsearch) functions\n", - "exp = Gen3Expansion(api,auth,sub) # class with some custom scripts that I wrote\n", - "exp.get_project_ids()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# # for local runs:\n", - "# %run /Users/christopher/Documents/GitHub/cgmeyer/gen3sdk-python/expansion/expansion.py\n", - "# api = 'https://data.midrc.org'\n", - "# cred = '/Users/christopher/Downloads/midrc-credentials.json'\n", - "# auth = Gen3Auth(api,refresh_file=cred)\n", - "# sub = Gen3Submission(api, auth)\n", - "# query = Gen3Query(auth)\n", - "# exp = Gen3Expansion(api,auth,sub)\n", - "# exp.get_project_ids()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessing structured data in Postgres using sheepdog exports\n", - "---\n", - "* Probably the most straight-forward way to access structured data in a Gen3 Data Commons is to simply export the table of data using the sheepdog service (https://petstore.swagger.io/?url=https://raw.githubusercontent.com/uc-cdis/sheepdog/master/openapi/swagger.yml#/export/post__program___project__export).\n", - "* The Gen3SDK has a function `Gen3Submission.export_node()` for exporting entire tables of data from Postgres: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/submission.py#L361\n", - " * This function will export all records in a particular node of a specified project, and one can then use standard Python / R (etc.) tools to do the filtering and cohort building.\n", - "* Note: This export function is also accesible in the data-portal by navigating to a data project's URL, e.g., https://data.midrc.org/Open-A1, clicking a node in the graph, and then clicking the \"Download All\" button.\n", - " * For example: https://data.midrc.org/Open-A1/search?node_type=measurement\n", - " * Or, you can enter this URL in your browser, for example: https://data.midrc.org/api/v0/submission//Open/A1/export?node_label=measurement&format=tsv" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typeidproject_idsubmitter_idage_at_indexage_at_index_gt89case_idscountry_of_origincovid19_positivedata_submission_guid...icu_indicatorindex_eventracesexsite_idtreatment_infoventilator_indicatorzipdatasets.iddatasets.submitter_id
0case00019c92-8598-4d94-84d2-3abf2b132dc9Open-A110000364-116407127.0NaNNaNNaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
1case000465e0-4c4f-48e4-8974-95c045e333cbOpen-A110000364-522270955.0NaNNaNNaNNoNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
2case0010129a-8aa1-4916-834a-c4ebcdaf44d5Open-A110003752-Zlh3OsFFjEC7bnFqpHZgIw76.0NaN10003752-Zlh3OsFFjEC7bnFqpHZgIwNaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
3case001c584d-b7a1-42d0-a204-c3336683d4e9Open-A110000364-164702056.0NaN10000364-1647020NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN274.014b6b707-15a8-4379-bdca-b7d9eea1b35e,f7bb0d11-...ACR_20210823,ACR_20211020
4case00373c30-18f5-4afa-9a96-525bec5e76f0Open-A110000364-583726921.0NaN10000364-5837269NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.0f670ecfb-bc34-4c2d-b15d-4c2762268904ACR_20210827
5case003ef620-52c8-4cdc-a7a9-c8106edc51bdOpen-A110000364-151619848.0NaN10000364-1516198United States of AmericaYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN0.09bf8adc9-008a-4863-9e8e-bd051555eea6ACR_20210915
6case00456adc-9ed7-44f9-b6d1-50284656e20eOpen-A110000364-65530171.0NaN10000364-655301NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.0f7bb0d11-a6e8-4504-bc3b-e57bf164e633ACR_20211020
7case00458e57-b582-4bdd-9134-bcabe90c6d2eOpen-A110000364-261164483.0NaN10000364-2611644NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN0.0f7bb0d11-a6e8-4504-bc3b-e57bf164e633,2d761222-...ACR_20211020,ACR_20211115
8case004b0d6f-8ff5-4549-b0fc-a14354b737ebOpen-A110000364-94839439.0NaN10000364-948394NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN271.014b6b707-15a8-4379-bdca-b7d9eea1b35eACR_20210823
9case00516d40-dcae-4f33-801c-34838e96d24cOpen-A110003752-ihwNEEhSFkmIx0dTcN3w72.0NaNNaNNaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
10case0058eb1d-aaa2-4d64-8db5-2e292852a661Open-A110000364-596804377.0NaN10000364-5968043NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN0.02d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-...ACR_20211115,ACR_20211020
11case005e3d9a-0a15-4f86-b5e3-e8502c95bde9Open-A110000364-237981165.0NaN10000364-2379811NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN0.02d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-...ACR_20211115,ACR_20211020
12case00731046-e22f-46e0-b69e-961d911fe1ecOpen-A110003752-BDZwCSpvl0KgMPTRYWHAwg68.0NaNNaNNaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
13case0077951b-38f9-48f6-9791-4fc897b2ca14Open-A110003752-ynnax8heZ0cbuEqZMOVg72.0NaNNaNNaNNoNaN...NaNFirst COVID testBlack or African AmericanMale10003752.0NaNNaN606.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
14case007c3ba2-630f-4347-b3d9-7612eb4fc2f6Open-A110000364-658576073.0NaN10000364-6585760NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.0f7bb0d11-a6e8-4504-bc3b-e57bf164e633ACR_20211020
15case007cafb8-437d-4e1e-bbea-71b4912d1ba7Open-A110003752-IsAEaMll3EOuDHg9zbiXhA54.0NaNNaNNaNYesNaN...NaNFirst COVID testWhiteFemale10003752.0NaNNaN0.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
16case008db922-2329-47cb-b50b-003176e65464Open-A110000364-68627335.0NaN10000364-686273NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.014b6b707-15a8-4379-bdca-b7d9eea1b35eACR_20210823
17case009575e7-6c35-44e5-8435-e4b72205d416Open-A110000364-110200278.0NaNNaNNaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN270.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
18case00a6a45b-d261-44fb-9eae-87adb7ac6933Open-A110000364-269732064.0NaN10000364-2697320NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40,f670ecfb-...ACR_20211115,ACR_20210827
19case00a7e1ef-3358-42bc-91bb-e01569f12623Open-A110000364-236645140.0NaN10000364-2366451NaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10000364.0NaNNaN274.0f670ecfb-bc34-4c2d-b15d-4c2762268904,2d761222-...ACR_20210827,ACR_20211115
20case00aa2d23-6eff-47c6-9d09-47a4c0e56807Open-A110000364-96859283.0NaN10000364-968592NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.0f7bb0d11-a6e8-4504-bc3b-e57bf164e633ACR_20211020
21case00af0a97-21ef-4b5f-9dd9-03225968c159Open-A110003752-1pHuLYLiMUWoqcp3B6Nhw34.0NaN10003752-1pHuLYLiMUWoqcp3B6NhwNaNNoNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
22case00ce1f51-3c12-479d-a42d-dcdb4a1b6affOpen-A110000364-5265871.0NaN10000364-52658NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.014b6b707-15a8-4379-bdca-b7d9eea1b35eACR_20210823
23case00d68288-b45a-4024-9009-493f941fc8a1Open-A110000364-129578856.0NaNNaNNaNNoNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN0.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
24case00d7bc1b-fbe7-440b-90b2-5f6462b3b501Open-A110000364-653589034.0NaN10000364-6535890NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.0f670ecfb-bc34-4c2d-b15d-4c2762268904ACR_20210827
25case00dde7cf-97b6-4634-9be8-fc524f913689Open-A110000364-523341452.0NaNNaNNaNNoNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN0.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
26case00df5184-0af6-415c-ba59-f44dae263f2bOpen-A110000364-145049864.0NaNNaNNaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN286.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
27case00e00391-70f9-4279-9826-d5d94f17409aOpen-A110000364-562294870.0NaN10000364-5622948NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN0.014b6b707-15a8-4379-bdca-b7d9eea1b35eACR_20210823
28case00e0618d-b638-4d81-b871-157840d1865cOpen-A110000364-156332550.0NaNNaNNaNNoNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
29case00ea40da-b44c-42fa-83dc-c70953bb3506Open-A110000364-543571071.0NaN10000364-5435710NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.0f670ecfb-bc34-4c2d-b15d-4c2762268904,f7bb0d11-...ACR_20210827,ACR_20211020
..................................................................
7504caseff4e5bae-88c4-47ec-a42b-a6abd1f77c0aOpen-A110003752-IOOvSLTyE6c0aZvKMx5jQ1.0NaN10003752-IOOvSLTyE6c0aZvKMx5jQNaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7505caseff513b74-c6e1-4b9c-951d-0431b05ccac8Open-A110000364-261743057.0NaN10000364-2617430NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.0f670ecfb-bc34-4c2d-b15d-4c2762268904ACR_20210827
7506caseff52abdc-8b71-4066-a921-a226ec93925bOpen-A110000364-656090255.0NaNNaNNaNYesNaN...NaNFirst COVID testBlack or African AmericanMale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7507caseff558b07-ce01-4ec4-8fae-a3c4e931311dOpen-A110003752-GhZQEAnaw0K2SjyHCSRLrw61.0NaN10003752-GhZQEAnaw0K2SjyHCSRLrwNaNNoNaN...NaNFirst COVID testWhiteMale10003752.0NaNNaN0.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7508caseff626041-5fd9-4164-9705-cf31dd7526f6Open-A110000364-201272957.0NaNNaNNaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7509caseff6f794f-91ea-4de0-946a-3d4f3fcaba6dOpen-A110000364-20779617.0NaNNaNNaNNoNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN0.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7510caseff72faf5-f7e9-4a31-ba4a-9baa81b4a66cOpen-A110008204-znWVaidhQEynkQtQ3wMlRA72.0NaN10008204-znWVaidhQEynkQtQ3wMlRANaNNoNaN...NaNFirst COVID testWhiteMale10008204.0NaNNaN772.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7511caseff739871-3a04-4aa2-a86d-11454bda6663Open-A110008204-bevB7abQOEqSfKiF1pFqDQ66.0NaN10008204-bevB7abQOEqSfKiF1pFqDQNaNNoNaN...NaNFirst COVID testWhiteMale10008204.0NaNNaN772.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7512caseff756807-17bc-460e-be5a-f1503105bfebOpen-A110003752-Lz796PtOUOgXaTykRH27g34.0NaNNaNNaNNoNaN...NaNFirst COVID testBlack or African AmericanMale10003752.0NaNNaN606.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7513caseff8b6c96-5e45-4795-a9fd-e24c48833784Open-A110003752-pmO4UKtBXkKXpUJRbpUdnw1.0NaN10003752-pmO4UKtBXkKXpUJRbpUdnwNaNNoNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7514caseff92b2ca-0fd5-4177-90a7-dbdef31cb23dOpen-A110000364-246868364.0NaN10000364-2468683NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN271.0f670ecfb-bc34-4c2d-b15d-4c2762268904ACR_20210827
7515caseffa7ec23-bef4-496d-ae82-fbd3796238b6Open-A110003752-6USsNwNCm0iyfIAkbI4Uug65.0NaNNaNNaNYesNaN...NaNFirst COVID testBlack or African AmericanMale10003752.0NaNNaN606.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7516caseffa9b862-e8aa-4525-94ae-247d395946c8Open-A110003752-AcsDqmM60qJQRfqv2gfg57.0NaN10003752-AcsDqmM60qJQRfqv2gfgNaNNoNaN...NaNFirst COVID testWhiteFemale10003752.0NaNNaN604.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7517caseffb0298c-d87b-4841-b398-a8ea41009b7cOpen-A110000364-172599344.0NaNNaNNaNNoNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7518caseffb042f6-01c9-4b81-98d1-cc55b9d544d8Open-A110000364-654757344.0NaN10000364-6547573NaNYesNaN...NaNFirst COVID testNot ReportedMale10000364.0NaNNaN286.02d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-...ACR_20211115,ACR_20211020
7519caseffb21de7-7d59-46d3-b497-3788f9f10d42Open-A110000364-139570558.0NaN10000364-1395705NaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10000364.0NaNNaN271.014b6b707-15a8-4379-bdca-b7d9eea1b35e,f7bb0d11-...ACR_20210823,ACR_20211020,ACR_20211115
7520caseffb420ef-b198-4f93-bca2-07f2c0546e7eOpen-A110008204-qeRymORTUaySqAH1ry8PQNaNYes10008204-qeRymORTUaySqAH1ry8PQNaNNoNaN...NaNFirst COVID testWhiteMale10008204.0NaNNaN0.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7521caseffb6a26f-fd18-402e-a5c6-57cb50d1601dOpen-A110000364-115907722.0NaN10000364-1159077NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN270.014b6b707-15a8-4379-bdca-b7d9eea1b35e,2d761222-...ACR_20210823,ACR_20211115
7522caseffb9a6cb-80ca-431d-aa7d-61c7037be3b7Open-A110003752-DSd2inbH0GOcAxuLlIw22.0NaNNaNNaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7523caseffc56220-524c-44f8-941b-171a619b82a9Open-A110003752-SwYygM5C0aZu7wCMy5qA53.0NaN10003752-SwYygM5C0aZu7wCMy5qAUnited States of AmericaNoNaN...NaNFirst COVID testBlack or African AmericanMale10003752.0NaNNaN606.09bf8adc9-008a-4863-9e8e-bd051555eea6ACR_20210915
7524caseffcc96e6-380e-4b61-a317-018e24efce0cOpen-A110000364-22758276.0NaN10000364-227582NaNYesNaN...NaNFirst COVID testBlack or African AmericanMale10000364.0NaNNaN271.02d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-...ACR_20211115,ACR_20211020,ACR_20210827
7525caseffce4d8c-b684-4edf-80a5-e7e37845fb1fOpen-A110000364-28725886.0NaN10000364-287258NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN0.014b6b707-15a8-4379-bdca-b7d9eea1b35e,f7bb0d11-...ACR_20210823,ACR_20211020
7526caseffcfe964-b9e2-4ce7-8946-b72138c97fc3Open-A110000364-166424018.0NaN10000364-1664240NaNYesNaN...NaNFirst COVID testWhiteFemale10000364.0NaNNaN272.0013383a2-32ca-4c50-97bb-cfd281ea1556,f7bb0d11-...ACR_20210804,ACR_20211020
7527caseffd6130e-c2fb-4f25-92a5-27288ec331d9Open-A110000364-126000161.0NaN10000364-1260001NaNYesNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN0.0f7bb0d11-a6e8-4504-bc3b-e57bf164e633,14b6b707-...ACR_20211020,ACR_20210823
7528caseffe2ad86-0c31-4d1a-a7a1-48a5934e1a06Open-A110003752-Xbd0tHsXEqknBoX4N5HA58.0NaN10003752-Xbd0tHsXEqknBoX4N5HAUnited States of AmericaNoNaN...NaNFirst COVID testBlack or African AmericanMale10003752.0NaNNaN606.09bf8adc9-008a-4863-9e8e-bd051555eea6ACR_20210915
7529caseffe6718c-8f07-44cb-985e-633c2a260071Open-A110003752-tUj4JW5dIkKKhf6w9GUCqw57.0NaN10003752-tUj4JW5dIkKKhf6w9GUCqwNaNNoNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN604.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7530caseffe96970-8a14-48fc-8cd1-2fffd305c948Open-A110003752-CCj8Obz5c0q1o2Amtfluw65.0NaN10003752-CCj8Obz5c0q1o2AmtfluwNaNNot ReportedNaN...NaNFirst COVID testBlack or African AmericanMale10003752.0NaNNaN606.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
7531caseffece2b4-eb81-4f1a-960c-dd18d96feb3bOpen-A110000364-94074967.0NaNNaNNaNNoNaN...NaNFirst COVID testWhiteMale10000364.0NaNNaN272.02d761222-3669-4a77-9473-342c369fcc40ACR_20211115
7532casefff76aad-25ca-4d06-9da5-abd0a18acb42Open-A110003752-PUN2Iozze0iuaH96b6xS2g74.0NaN10003752-PUN2Iozze0iuaH96b6xS2gUnited States of AmericaYesNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.09bf8adc9-008a-4863-9e8e-bd051555eea6ACR_20210915
7533casefffb8626-c305-4e66-9744-ab8c7eba88c3Open-A110003752-1xOeCuwEj0O1PNBpK90HBg55.0NaN10003752-1xOeCuwEj0O1PNBpK90HBgNaNYesNaN...NaNFirst COVID testBlack or African AmericanFemale10003752.0NaNNaN606.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
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"5 10000364-1516198 48.0 NaN \n", - "6 10000364-655301 71.0 NaN \n", - "7 10000364-2611644 83.0 NaN \n", - "8 10000364-948394 39.0 NaN \n", - "9 10003752-ihwNEEhSFkmIx0dTcN3w 72.0 NaN \n", - "10 10000364-5968043 77.0 NaN \n", - "11 10000364-2379811 65.0 NaN \n", - "12 10003752-BDZwCSpvl0KgMPTRYWHAwg 68.0 NaN \n", - "13 10003752-ynnax8heZ0cbuEqZMOVg 72.0 NaN \n", - "14 10000364-6585760 73.0 NaN \n", - "15 10003752-IsAEaMll3EOuDHg9zbiXhA 54.0 NaN \n", - "16 10000364-686273 35.0 NaN \n", - "17 10000364-1102002 78.0 NaN \n", - "18 10000364-2697320 64.0 NaN \n", - "19 10000364-2366451 40.0 NaN \n", - "20 10000364-968592 83.0 NaN \n", - "21 10003752-1pHuLYLiMUWoqcp3B6Nhw 34.0 NaN \n", - "22 10000364-52658 71.0 NaN \n", - "23 10000364-1295788 56.0 NaN \n", - "24 10000364-6535890 34.0 NaN \n", - "25 10000364-5233414 52.0 NaN \n", - "26 10000364-1450498 64.0 NaN \n", - "27 10000364-5622948 70.0 NaN \n", - "28 10000364-1563325 50.0 NaN \n", - "29 10000364-5435710 71.0 NaN \n", - "... ... ... ... \n", - "7504 10003752-IOOvSLTyE6c0aZvKMx5jQ 1.0 NaN \n", - "7505 10000364-2617430 57.0 NaN \n", - "7506 10000364-6560902 55.0 NaN \n", - "7507 10003752-GhZQEAnaw0K2SjyHCSRLrw 61.0 NaN \n", - "7508 10000364-2012729 57.0 NaN \n", - "7509 10000364-2077961 7.0 NaN \n", - "7510 10008204-znWVaidhQEynkQtQ3wMlRA 72.0 NaN \n", - "7511 10008204-bevB7abQOEqSfKiF1pFqDQ 66.0 NaN \n", - "7512 10003752-Lz796PtOUOgXaTykRH27g 34.0 NaN \n", - "7513 10003752-pmO4UKtBXkKXpUJRbpUdnw 1.0 NaN \n", - "7514 10000364-2468683 64.0 NaN \n", - "7515 10003752-6USsNwNCm0iyfIAkbI4Uug 65.0 NaN \n", - "7516 10003752-AcsDqmM60qJQRfqv2gfg 57.0 NaN \n", - "7517 10000364-1725993 44.0 NaN \n", - "7518 10000364-6547573 44.0 NaN \n", - "7519 10000364-1395705 58.0 NaN \n", - "7520 10008204-qeRymORTUaySqAH1ry8PQ NaN Yes \n", - "7521 10000364-1159077 22.0 NaN \n", - "7522 10003752-DSd2inbH0GOcAxuLlIw 22.0 NaN \n", - "7523 10003752-SwYygM5C0aZu7wCMy5qA 53.0 NaN \n", - "7524 10000364-227582 76.0 NaN \n", - "7525 10000364-287258 86.0 NaN \n", - "7526 10000364-1664240 18.0 NaN \n", - "7527 10000364-1260001 61.0 NaN \n", - "7528 10003752-Xbd0tHsXEqknBoX4N5HA 58.0 NaN \n", - "7529 10003752-tUj4JW5dIkKKhf6w9GUCqw 57.0 NaN \n", - "7530 10003752-CCj8Obz5c0q1o2Amtfluw 65.0 NaN \n", - "7531 10000364-940749 67.0 NaN \n", - "7532 10003752-PUN2Iozze0iuaH96b6xS2g 74.0 NaN \n", - "7533 10003752-1xOeCuwEj0O1PNBpK90HBg 55.0 NaN \n", - "\n", - " case_ids country_of_origin \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 10003752-Zlh3OsFFjEC7bnFqpHZgIw NaN \n", - "3 10000364-1647020 NaN \n", - "4 10000364-5837269 NaN \n", - "5 10000364-1516198 United States of America \n", - "6 10000364-655301 NaN \n", - "7 10000364-2611644 NaN \n", - "8 10000364-948394 NaN \n", - "9 NaN NaN \n", - "10 10000364-5968043 NaN \n", - "11 10000364-2379811 NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 10000364-6585760 NaN \n", - "15 NaN NaN \n", - "16 10000364-686273 NaN \n", - "17 NaN NaN \n", - "18 10000364-2697320 NaN \n", - "19 10000364-2366451 NaN \n", - "20 10000364-968592 NaN \n", - "21 10003752-1pHuLYLiMUWoqcp3B6Nhw NaN \n", - "22 10000364-52658 NaN \n", - "23 NaN NaN \n", - "24 10000364-6535890 NaN \n", - "25 NaN NaN \n", - "26 NaN NaN \n", - "27 10000364-5622948 NaN \n", - "28 NaN NaN \n", - "29 10000364-5435710 NaN \n", - "... ... ... \n", - "7504 10003752-IOOvSLTyE6c0aZvKMx5jQ NaN \n", - "7505 10000364-2617430 NaN \n", - "7506 NaN NaN \n", - "7507 10003752-GhZQEAnaw0K2SjyHCSRLrw NaN \n", - "7508 NaN NaN \n", - "7509 NaN NaN \n", - "7510 10008204-znWVaidhQEynkQtQ3wMlRA NaN \n", - "7511 10008204-bevB7abQOEqSfKiF1pFqDQ NaN \n", - "7512 NaN NaN \n", - "7513 10003752-pmO4UKtBXkKXpUJRbpUdnw NaN \n", - "7514 10000364-2468683 NaN \n", - "7515 NaN NaN \n", - "7516 10003752-AcsDqmM60qJQRfqv2gfg NaN \n", - "7517 NaN NaN \n", - "7518 10000364-6547573 NaN \n", - "7519 10000364-1395705 NaN \n", - "7520 10008204-qeRymORTUaySqAH1ry8PQ NaN \n", - "7521 10000364-1159077 NaN \n", - "7522 NaN NaN \n", - "7523 10003752-SwYygM5C0aZu7wCMy5qA United States of America \n", - "7524 10000364-227582 NaN \n", - "7525 10000364-287258 NaN \n", - "7526 10000364-1664240 NaN \n", - "7527 10000364-1260001 NaN \n", - "7528 10003752-Xbd0tHsXEqknBoX4N5HA United States of America \n", - "7529 10003752-tUj4JW5dIkKKhf6w9GUCqw NaN \n", - "7530 10003752-CCj8Obz5c0q1o2Amtfluw NaN \n", - "7531 NaN NaN \n", - "7532 10003752-PUN2Iozze0iuaH96b6xS2g United States of America \n", - "7533 10003752-1xOeCuwEj0O1PNBpK90HBg NaN \n", - "\n", - " covid19_positive data_submission_guid \\\n", - "0 Yes NaN \n", - "1 No NaN \n", - "2 Yes NaN \n", - "3 Yes NaN \n", - "4 Yes NaN \n", - "5 Yes NaN \n", - "6 Yes NaN \n", - "7 Yes NaN \n", - "8 Yes NaN \n", - "9 Yes NaN \n", - "10 Yes NaN \n", - "11 Yes NaN \n", - "12 Yes NaN \n", - "13 No NaN \n", - "14 Yes NaN \n", - "15 Yes NaN \n", - "16 Yes NaN \n", - "17 Yes NaN \n", - "18 Yes NaN \n", - "19 Yes NaN \n", - "20 Yes NaN \n", - "21 No NaN \n", - "22 Yes NaN \n", - "23 No NaN \n", - "24 Yes NaN \n", - "25 No NaN \n", - "26 Yes NaN \n", - "27 Yes NaN \n", - "28 No NaN \n", - "29 Yes NaN \n", - "... ... ... \n", - "7504 Yes NaN \n", - "7505 Yes NaN \n", - "7506 Yes NaN \n", - "7507 No NaN \n", - "7508 Yes NaN \n", - "7509 No NaN \n", - "7510 No NaN \n", - "7511 No NaN \n", - "7512 No NaN \n", - "7513 No NaN \n", - "7514 Yes NaN \n", - "7515 Yes NaN \n", - "7516 No NaN \n", - "7517 No NaN \n", - "7518 Yes NaN \n", - "7519 Yes NaN \n", - "7520 No NaN \n", - "7521 Yes NaN \n", - "7522 Yes NaN \n", - "7523 No NaN \n", - "7524 Yes NaN \n", - "7525 Yes NaN \n", - "7526 Yes NaN \n", - "7527 Yes NaN \n", - "7528 No NaN \n", - "7529 No NaN \n", - "7530 Not Reported NaN \n", - "7531 No NaN \n", - "7532 Yes NaN \n", - "7533 Yes NaN \n", - "\n", - " ... icu_indicator index_event \\\n", - "0 ... NaN First COVID test \n", - "1 ... NaN First COVID test \n", - "2 ... NaN First COVID test \n", - "3 ... NaN First COVID test \n", - "4 ... NaN First COVID test \n", - "5 ... NaN First COVID test \n", - "6 ... NaN First COVID test \n", - "7 ... NaN First COVID test \n", - "8 ... NaN First COVID test \n", - "9 ... NaN First COVID test \n", - "10 ... NaN First COVID test \n", - "11 ... NaN First COVID test \n", - "12 ... NaN First COVID test \n", - "13 ... NaN First COVID test \n", - "14 ... NaN First COVID test \n", - "15 ... NaN First COVID test \n", - "16 ... NaN First COVID test \n", - "17 ... NaN First COVID test \n", - "18 ... NaN First COVID test \n", - "19 ... NaN First COVID test \n", - "20 ... NaN First COVID test \n", - "21 ... NaN First COVID test \n", - "22 ... NaN First COVID test \n", - "23 ... NaN First COVID test \n", - "24 ... NaN First COVID test \n", - "25 ... NaN First COVID test \n", - "26 ... NaN First COVID test \n", - "27 ... NaN First COVID test \n", - "28 ... NaN First COVID test \n", - "29 ... NaN First COVID test \n", - "... ... ... ... \n", - "7504 ... NaN First COVID test \n", - "7505 ... NaN First COVID test \n", - "7506 ... NaN First COVID test \n", - "7507 ... NaN First COVID test \n", - "7508 ... NaN First COVID test \n", - "7509 ... NaN First COVID test \n", - "7510 ... NaN First COVID test \n", - "7511 ... NaN First COVID test \n", - "7512 ... NaN First COVID test \n", - "7513 ... NaN First COVID test \n", - "7514 ... NaN First COVID test \n", - "7515 ... NaN First COVID test \n", - "7516 ... NaN First COVID test \n", - "7517 ... NaN First COVID test \n", - "7518 ... NaN First COVID test \n", - "7519 ... NaN First COVID test \n", - "7520 ... NaN First COVID test \n", - "7521 ... NaN First COVID test \n", - "7522 ... NaN First COVID test \n", - "7523 ... NaN First COVID test \n", - "7524 ... NaN First COVID test \n", - "7525 ... NaN First COVID test \n", - "7526 ... NaN First COVID test \n", - "7527 ... NaN First COVID test \n", - "7528 ... NaN First COVID test \n", - "7529 ... NaN First COVID test \n", - "7530 ... NaN First COVID test \n", - "7531 ... NaN First COVID test \n", - "7532 ... NaN First COVID test \n", - "7533 ... NaN First COVID test \n", - "\n", - " race sex site_id treatment_info \\\n", - "0 Black or African American Female 10000364.0 NaN \n", - "1 White Female 10000364.0 NaN \n", - "2 Black or African American Female 10003752.0 NaN \n", - "3 White Female 10000364.0 NaN \n", - "4 White Female 10000364.0 NaN \n", - "5 White Male 10000364.0 NaN \n", - "6 White Male 10000364.0 NaN \n", - "7 White Female 10000364.0 NaN \n", - "8 White Male 10000364.0 NaN \n", - "9 Black or African American Female 10003752.0 NaN \n", - "10 White Female 10000364.0 NaN \n", - "11 White Female 10000364.0 NaN \n", - "12 Black or African American Female 10003752.0 NaN \n", - "13 Black or African American Male 10003752.0 NaN \n", - "14 White Female 10000364.0 NaN \n", - "15 White Female 10003752.0 NaN \n", - "16 White Female 10000364.0 NaN \n", - "17 White Female 10000364.0 NaN \n", - "18 White Female 10000364.0 NaN \n", - "19 Black or African American Female 10000364.0 NaN \n", - "20 White Male 10000364.0 NaN \n", - "21 Black or African American Female 10003752.0 NaN \n", - "22 White Male 10000364.0 NaN \n", - "23 White Female 10000364.0 NaN \n", - "24 White Male 10000364.0 NaN \n", - "25 White Female 10000364.0 NaN \n", - "26 White Female 10000364.0 NaN \n", - "27 White Male 10000364.0 NaN \n", - "28 White Male 10000364.0 NaN \n", - "29 White Female 10000364.0 NaN \n", - "... ... ... ... ... \n", - "7504 Black or African American Female 10003752.0 NaN \n", - "7505 White Male 10000364.0 NaN \n", - "7506 Black or African American Male 10000364.0 NaN \n", - "7507 White Male 10003752.0 NaN \n", - "7508 White Female 10000364.0 NaN \n", - "7509 White Male 10000364.0 NaN \n", - "7510 White Male 10008204.0 NaN \n", - "7511 White Male 10008204.0 NaN \n", - "7512 Black or African American Male 10003752.0 NaN \n", - "7513 Black or African American Female 10003752.0 NaN \n", - "7514 White Female 10000364.0 NaN \n", - "7515 Black or African American Male 10003752.0 NaN \n", - "7516 White Female 10003752.0 NaN \n", - "7517 White Male 10000364.0 NaN \n", - "7518 Not Reported Male 10000364.0 NaN \n", - "7519 Black or African American Female 10000364.0 NaN \n", - "7520 White Male 10008204.0 NaN \n", - "7521 White Male 10000364.0 NaN \n", - "7522 Black or African American Female 10003752.0 NaN \n", - "7523 Black or African American Male 10003752.0 NaN \n", - "7524 Black or African American Male 10000364.0 NaN \n", - "7525 White Male 10000364.0 NaN \n", - "7526 White Female 10000364.0 NaN \n", - "7527 White Male 10000364.0 NaN \n", - "7528 Black or African American Male 10003752.0 NaN \n", - "7529 Black or African American Female 10003752.0 NaN \n", - "7530 Black or African American Male 10003752.0 NaN \n", - "7531 White Male 10000364.0 NaN \n", - "7532 Black or African American Female 10003752.0 NaN \n", - "7533 Black or African American Female 10003752.0 NaN \n", - "\n", - " ventilator_indicator zip \\\n", - "0 NaN 272.0 \n", - "1 NaN 272.0 \n", - "2 NaN 606.0 \n", - "3 NaN 274.0 \n", - "4 NaN 272.0 \n", - "5 NaN 0.0 \n", - "6 NaN 272.0 \n", - "7 NaN 0.0 \n", - "8 NaN 271.0 \n", - "9 NaN 606.0 \n", - "10 NaN 0.0 \n", - "11 NaN 0.0 \n", - "12 NaN 606.0 \n", - "13 NaN 606.0 \n", - "14 NaN 272.0 \n", - "15 NaN 0.0 \n", - "16 NaN 272.0 \n", - "17 NaN 270.0 \n", - "18 NaN 272.0 \n", - "19 NaN 274.0 \n", - "20 NaN 272.0 \n", - "21 NaN 606.0 \n", - "22 NaN 272.0 \n", - "23 NaN 0.0 \n", - "24 NaN 272.0 \n", - "25 NaN 0.0 \n", - "26 NaN 286.0 \n", - "27 NaN 0.0 \n", - "28 NaN 272.0 \n", - "29 NaN 272.0 \n", - "... ... ... \n", - "7504 NaN 606.0 \n", - "7505 NaN 272.0 \n", - "7506 NaN 272.0 \n", - "7507 NaN 0.0 \n", - "7508 NaN 272.0 \n", - "7509 NaN 0.0 \n", - "7510 NaN 772.0 \n", - "7511 NaN 772.0 \n", - "7512 NaN 606.0 \n", - "7513 NaN 606.0 \n", - "7514 NaN 271.0 \n", - "7515 NaN 606.0 \n", - "7516 NaN 604.0 \n", - "7517 NaN 272.0 \n", - "7518 NaN 286.0 \n", - "7519 NaN 271.0 \n", - "7520 NaN 0.0 \n", - "7521 NaN 270.0 \n", - "7522 NaN 606.0 \n", - "7523 NaN 606.0 \n", - "7524 NaN 271.0 \n", - "7525 NaN 0.0 \n", - "7526 NaN 272.0 \n", - "7527 NaN 0.0 \n", - "7528 NaN 606.0 \n", - "7529 NaN 604.0 \n", - "7530 NaN 606.0 \n", - "7531 NaN 272.0 \n", - "7532 NaN 606.0 \n", - "7533 NaN 606.0 \n", - "\n", - " datasets.id \\\n", - "0 2d761222-3669-4a77-9473-342c369fcc40 \n", - "1 2d761222-3669-4a77-9473-342c369fcc40 \n", - "2 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "3 14b6b707-15a8-4379-bdca-b7d9eea1b35e,f7bb0d11-... \n", - "4 f670ecfb-bc34-4c2d-b15d-4c2762268904 \n", - "5 9bf8adc9-008a-4863-9e8e-bd051555eea6 \n", - "6 f7bb0d11-a6e8-4504-bc3b-e57bf164e633 \n", - "7 f7bb0d11-a6e8-4504-bc3b-e57bf164e633,2d761222-... \n", - "8 14b6b707-15a8-4379-bdca-b7d9eea1b35e \n", - "9 2d761222-3669-4a77-9473-342c369fcc40 \n", - "10 2d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-... \n", - "11 2d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-... \n", - "12 2d761222-3669-4a77-9473-342c369fcc40 \n", - "13 2d761222-3669-4a77-9473-342c369fcc40 \n", - "14 f7bb0d11-a6e8-4504-bc3b-e57bf164e633 \n", - "15 2d761222-3669-4a77-9473-342c369fcc40 \n", - "16 14b6b707-15a8-4379-bdca-b7d9eea1b35e \n", - "17 2d761222-3669-4a77-9473-342c369fcc40 \n", - "18 2d761222-3669-4a77-9473-342c369fcc40,f670ecfb-... \n", - "19 f670ecfb-bc34-4c2d-b15d-4c2762268904,2d761222-... \n", - "20 f7bb0d11-a6e8-4504-bc3b-e57bf164e633 \n", - "21 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "22 14b6b707-15a8-4379-bdca-b7d9eea1b35e \n", - "23 2d761222-3669-4a77-9473-342c369fcc40 \n", - "24 f670ecfb-bc34-4c2d-b15d-4c2762268904 \n", - "25 2d761222-3669-4a77-9473-342c369fcc40 \n", - "26 2d761222-3669-4a77-9473-342c369fcc40 \n", - "27 14b6b707-15a8-4379-bdca-b7d9eea1b35e \n", - "28 2d761222-3669-4a77-9473-342c369fcc40 \n", - "29 f670ecfb-bc34-4c2d-b15d-4c2762268904,f7bb0d11-... \n", - "... ... \n", - "7504 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7505 f670ecfb-bc34-4c2d-b15d-4c2762268904 \n", - "7506 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7507 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7508 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7509 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7510 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7511 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7512 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7513 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7514 f670ecfb-bc34-4c2d-b15d-4c2762268904 \n", - "7515 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7516 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7517 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7518 2d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-... \n", - "7519 14b6b707-15a8-4379-bdca-b7d9eea1b35e,f7bb0d11-... \n", - "7520 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7521 14b6b707-15a8-4379-bdca-b7d9eea1b35e,2d761222-... \n", - "7522 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7523 9bf8adc9-008a-4863-9e8e-bd051555eea6 \n", - "7524 2d761222-3669-4a77-9473-342c369fcc40,f7bb0d11-... \n", - "7525 14b6b707-15a8-4379-bdca-b7d9eea1b35e,f7bb0d11-... \n", - "7526 013383a2-32ca-4c50-97bb-cfd281ea1556,f7bb0d11-... \n", - "7527 f7bb0d11-a6e8-4504-bc3b-e57bf164e633,14b6b707-... \n", - "7528 9bf8adc9-008a-4863-9e8e-bd051555eea6 \n", - "7529 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7530 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "7531 2d761222-3669-4a77-9473-342c369fcc40 \n", - "7532 9bf8adc9-008a-4863-9e8e-bd051555eea6 \n", - "7533 b1487107-9f44-40bd-b1bf-4144415db825 \n", - "\n", - " datasets.submitter_id \n", - "0 ACR_20211115 \n", - "1 ACR_20211115 \n", - "2 ACR_20220107 \n", - "3 ACR_20210823,ACR_20211020 \n", - "4 ACR_20210827 \n", - "5 ACR_20210915 \n", - "6 ACR_20211020 \n", - "7 ACR_20211020,ACR_20211115 \n", - "8 ACR_20210823 \n", - "9 ACR_20211115 \n", - "10 ACR_20211115,ACR_20211020 \n", - "11 ACR_20211115,ACR_20211020 \n", - "12 ACR_20211115 \n", - "13 ACR_20211115 \n", - "14 ACR_20211020 \n", - "15 ACR_20211115 \n", - "16 ACR_20210823 \n", - "17 ACR_20211115 \n", - "18 ACR_20211115,ACR_20210827 \n", - "19 ACR_20210827,ACR_20211115 \n", - "20 ACR_20211020 \n", - "21 ACR_20220107 \n", - "22 ACR_20210823 \n", - "23 ACR_20211115 \n", - "24 ACR_20210827 \n", - "25 ACR_20211115 \n", - "26 ACR_20211115 \n", - "27 ACR_20210823 \n", - "28 ACR_20211115 \n", - "29 ACR_20210827,ACR_20211020 \n", - "... ... \n", - "7504 ACR_20220107 \n", - "7505 ACR_20210827 \n", - "7506 ACR_20211115 \n", - "7507 ACR_20220107 \n", - "7508 ACR_20211115 \n", - "7509 ACR_20211115 \n", - "7510 ACR_20220107 \n", - "7511 ACR_20220107 \n", - "7512 ACR_20211115 \n", - "7513 ACR_20220107 \n", - "7514 ACR_20210827 \n", - "7515 ACR_20211115 \n", - "7516 ACR_20220107 \n", - "7517 ACR_20211115 \n", - "7518 ACR_20211115,ACR_20211020 \n", - "7519 ACR_20210823,ACR_20211020,ACR_20211115 \n", - "7520 ACR_20220107 \n", - "7521 ACR_20210823,ACR_20211115 \n", - "7522 ACR_20211115 \n", - "7523 ACR_20210915 \n", - "7524 ACR_20211115,ACR_20211020,ACR_20210827 \n", - "7525 ACR_20210823,ACR_20211020 \n", - "7526 ACR_20210804,ACR_20211020 \n", - "7527 ACR_20211020,ACR_20210823 \n", - "7528 ACR_20210915 \n", - "7529 ACR_20220107 \n", - "7530 ACR_20220107 \n", - "7531 ACR_20211115 \n", - "7532 ACR_20210915 \n", - "7533 ACR_20220107 \n", - "\n", - "[7534 rows x 22 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Example of exporting a table of data using the `Gen3Submission.export_node()` function\n", - "cases = sub.export_node(program='Open',project='A1',node_type='case',fileformat='tsv')\n", - "df = pd.read_csv(StringIO(cases), sep='\\t', header=0)\n", - "display(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "140" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "['10000364-1261658',\n", - " '10003752-WGPvINkkL0CIrSueIVq1Jg',\n", - " '10003752-uXv1gUN50k6mv7tUjnA4iA',\n", - " '10000364-6412754',\n", - " '10000364-1445371',\n", - " '10003752-fgcG89lFo029tEOaehRqg',\n", - " '10003752-Qusqy0HUn0ebZ0uNUpPZ4Q',\n", - " '10003752-nTeM3GIJOkSAtdqFYGZM0w',\n", - " '10003752-DM1ytb31mESVcGstOv6pg',\n", - " '10003752-7RqcKWJlcUOyqHQQXQdKg',\n", - " '10003752-SIQrULeYu0aa2wj0Vlc8ZQ',\n", - " '10003752-dnp2GNsIYEqYNg0Tz0LNuA',\n", - " '10000364-1261808',\n", - " '10000364-2080600',\n", - " '10000364-1380451',\n", - " '10000364-1749434',\n", - " '10003752-WRHmgFHch00GU4s3Cn1SA',\n", - " '10003752-yLzAgX2qDE69PIuW4djGSQ',\n", - " '10003752-Hfy4w9COeEuCM6LR4gwnbw',\n", - " '10003752-Z2wiaR2e4EqPqBQcWBNp9w',\n", - " '10003752-LdWIbKc4CkGdL3JbZApXiQ',\n", - " '10003752-yCjv2IrU5kqAI05tltP3xQ',\n", - " '10003752-JsWe1KAEumMRVwpm5LpQ',\n", - " '10003752-TForj4izLkqZ5W8bsPlY5Q',\n", - " '10003752-z4uukcRJ7EazThNHZYsvCw',\n", - " '10003752-jYqP2dXmUuXzdgQTpTeg',\n", - " '10003752-IptE89okUqdFb6cysr0hA',\n", - " '10000364-1350445',\n", - " '10003752-fPrVyUm40q14GE1kfoxA',\n", - " '10003752-ir82OJFQcUK2JMzcDpBRQ',\n", - " '10003752-sUmpJYfSokqMel7Q5dzQ',\n", - " '10003752-PPOeCrjLe0ymDrbWOtI80Q',\n", - " '10000364-66216',\n", - " '10000364-1485801',\n", - " '10003752-jTxE4cap6EeoE9DZgexRoA',\n", - " '10008204-937Ivt3Bukq2hPT20U0A',\n", - " '10000364-1432396',\n", - " '10000364-1319431',\n", - " '10003752-dYN3RzoP4kYJ7IhSsJhxA',\n", - " '10003752-oPmRWCiKlUM6SyI05jBzQ',\n", - 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"## One can then use standard tools in any programming language to do cohort building. \n", - "## Here I'm using the \"pandas\" Python package to select a cohort based on demographic information stored in the case node.\n", - "cohort = list(df.loc[(df['sex']=='Female') & (df['race']==\"Black or African American\") & (df['age_at_index']>79)]['submitter_id'])\n", - "display(len(cohort))\n", - "cohort" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a Python wrapper to get all the data in a particular node\n", - "---\n", - "* I've written a wrapper script called `Gen3Expansion.get_node_tsvs()` that uses the `Gen3Submission.export_node()` function to export the same node across all projects you have access to in the data commons and then merges the results into a single master table for that node:\n", - "https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L219" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File previously downloaded.\n", - "node_tsvs/case_tsvs/Open-R1_case.tsv has 3508 records.\n", - "File previously downloaded.\n", - "node_tsvs/case_tsvs/Open-A1_case.tsv has 7534 records.\n", - "File previously downloaded.\n", - "node_tsvs/case_tsvs/Open-A1_PETAL_REDCORAL_case.tsv has 857 records.\n", - "length of all dfs: 11899\n", - "Master node TSV with 11899 total records written to master_case.tsv.\n" - ] - }, - { - "data": { - "text/html": [ - "
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0case00190c16-38d5-4156-b95e-38bbbb5f501bOpen-R1419639-01105979.0419639-011059United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteMale419639.0NaNNaN949.05ecc9107-5d51-40e1-bc22-50599ade61bfRSNA_20211214
1case0019ec59-9ed7-4e4c-81f9-c268f04564d5Open-R1419639-01085864.0419639-010858United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNNot ReportedMale419639.0NaNNaN0.038a6adc3-59bf-49dd-bbd6-0d2177ad203dRSNA_20220114
2case002443fc-a489-499f-8252-1f75b0c40cb1Open-R1419639-00404879.0419639-004048United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteMale419639.0NaNNaN941.004e67623-9b88-4755-bc28-5288210b1389RSNA_20211026
3case002b361e-5afe-4ce0-b6c3-61f194d392c2Open-R1419639-00001259.0419639-000012United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNBlack or African AmericanMale419639.0NaNNaN941.09c23f602-8be9-4582-bae9-7026e6f446fd,5ecc9107-...RSNA_20210820,RSNA_20211214
4case00305c7f-86dc-4d55-afe1-4655dc1a1f19Open-R1419639-01030773.0419639-010307United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNNot ReportedFemale419639.0NaNNaN0.0af721fd1-49a7-42d7-9f5f-b5f83d3ef0eeRSNA_20211117
5case004f9807-c0db-404a-9f40-30b36ea85361Open-R1419639-00007674.0419639-000076United States of AmericaNoNaNHispanic or Latino...NaNNaNNot ReportedMale419639.0NaNNaN941.05ecc9107-5d51-40e1-bc22-50599ade61bfRSNA_20211214
6case00532213-0169-4aa0-97db-78aece75df31Open-R1419639-00153145.0419639-001531United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNBlack or African AmericanMale419639.0NaNNaN946.06220bbaa-5671-43ab-8b60-f58a52dd8f46,473968d5-...RSNA_20210922,RSNA_20211006
7case0061c678-c972-4da5-a73d-49bd6e17d1edOpen-R1419639-00965672.0419639-009656United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNOtherMale419639.0NaNNaN0.0aa324c95-1b01-46c9-81c1-a0f4b924e5d5,af721fd1-...RSNA_20210902,RSNA_20211117,RSNA_20211006
8case0062852e-9ed0-4070-8ee5-5648c0ca53b6Open-R1419639-00946034.0419639-009460United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNAsianMale419639.0NaNNaN941.0af721fd1-49a7-42d7-9f5f-b5f83d3ef0ee,aa324c95-...RSNA_20211117,RSNA_20210902
9case006948e8-4e46-409e-a4da-c24609515a1aOpen-R1419639-01039868.0419639-010398United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNBlack or African AmericanFemale419639.0NaNNaN941.0af721fd1-49a7-42d7-9f5f-b5f83d3ef0eeRSNA_20211117
10case007d2b0b-9e46-4912-a8be-cfa30dff5373Open-R1419639-01132881.0419639-011328United States of AmericaYesNaNNot Hispanic or Latino...NaNNaNNot ReportedFemale419639.0NaNNaN0.038a6adc3-59bf-49dd-bbd6-0d2177ad203dRSNA_20220114
11case00a086ee-471b-4da4-8b65-4baef8bae353Open-R1419639-00981471.0419639-009814United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteMale419639.0NaNNaN941.0473968d5-82eb-4b37-95d7-31f72c1c188fRSNA_20211006
12case00dde440-de60-4763-be99-032b7b7bc3d0Open-R1419639-00987683.0419639-009876United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteMale419639.0NaNNaN941.0473968d5-82eb-4b37-95d7-31f72c1c188f,04e67623-...RSNA_20211006,RSNA_20211026
13case00faf540-e353-4fdc-bd6c-2fd33c953c8dOpen-R1419639-01060252.0419639-010602United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteMale419639.0NaNNaN941.0af721fd1-49a7-42d7-9f5f-b5f83d3ef0eeRSNA_20211117
14case0117a301-e8de-4dd3-9b4d-a418b59a9996Open-R1419639-00313383.0419639-003133United States of AmericaNoNaNHispanic or Latino...NaNNaNNot ReportedFemale419639.0NaNNaN941.05ecc9107-5d51-40e1-bc22-50599ade61bfRSNA_20211214
15case011b091f-cbe8-4fd8-895c-eae9f93ac2cbOpen-R1419639-00805585.0419639-008055United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNAsianMale419639.0NaNNaN941.09c23f602-8be9-4582-bae9-7026e6f446fdRSNA_20210820
16case0125631f-f9ff-40d9-b36e-724e90bb9eb0Open-R1419639-00974971.0419639-009749United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteFemale419639.0NaNNaN0.0af721fd1-49a7-42d7-9f5f-b5f83d3ef0ee,aa324c95-...RSNA_20211117,RSNA_20210902
17case01410ef5-516e-4aeb-86de-efea06cd7b58Open-R1419639-00803561.0419639-008035United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteMale419639.0NaNNaN946.0aa324c95-1b01-46c9-81c1-a0f4b924e5d5,af721fd1-...RSNA_20210902,RSNA_20211117,RSNA_20210922
18case0142668f-befa-4ec7-a024-c85788e50468Open-R1419639-00309727.0419639-003097United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNOtherMale419639.0NaNNaN941.038a6adc3-59bf-49dd-bbd6-0d2177ad203dRSNA_20220114
19case017944c3-0673-4478-900a-b255db0f6f54Open-R1419639-00155681.0419639-001556United States of AmericaNoNaNHispanic or Latino...NaNNaNNot ReportedMale419639.0NaNNaN941.038a6adc3-59bf-49dd-bbd6-0d2177ad203d,22d66d9d-...RSNA_20220114,RSNA_20220211
20case0198754e-b5f3-4e7a-9ace-ace0c2879c56Open-R1419639-00019657.0419639-000196United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNWhiteFemale419639.0NaNNaN941.05ecc9107-5d51-40e1-bc22-50599ade61bfRSNA_20211214
21case019ab7ac-2c92-410e-b30a-6c45055cccd4Open-R1419639-01021677.0419639-010216United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNNot ReportedFemale419639.0NaNNaN0.0af721fd1-49a7-42d7-9f5f-b5f83d3ef0eeRSNA_20211117
22case01b054bf-b351-4d61-a910-d29c9ecdc955Open-R1419639-00326321.0419639-003263United States of AmericaNoNaNHispanic or Latino...NaNNaNNot ReportedFemale419639.0NaNNaN941.05ecc9107-5d51-40e1-bc22-50599ade61bfRSNA_20211214
23case01b10b01-77d2-499e-982c-9d6259c1b438Open-R1419639-00886846.0419639-008868United States of AmericaNoNaNHispanic or Latino...NaNNaNNot ReportedMale419639.0NaNNaN953.06220bbaa-5671-43ab-8b60-f58a52dd8f46RSNA_20210922
24case01c63700-af92-4693-bba3-9d6beee6d365Open-R1419639-00455864.0419639-004558United States of AmericaNoNaNHispanic or Latino...NaNNaNNot ReportedMale419639.0NaNNaN0.09c23f602-8be9-4582-bae9-7026e6f446fd,af721fd1-...RSNA_20210820,RSNA_20211117,RSNA_20210922,RSNA...
25case01c71b7f-df19-4866-8d9b-4b80a9e17232Open-R1419639-00682763.0419639-006827United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNAsianMale419639.0NaNNaN941.0aa324c95-1b01-46c9-81c1-a0f4b924e5d5,af721fd1-...RSNA_20210902,RSNA_20211117
26case01e307ca-6754-4767-90be-314ea9a5d8adOpen-R1419639-01071880.0419639-010718United States of AmericaYesNaNNot Hispanic or Latino...NaNNaNWhiteFemale419639.0NaNNaN0.0af721fd1-49a7-42d7-9f5f-b5f83d3ef0eeRSNA_20211117
27case01e99ca4-6071-42b9-a416-b5f2f88645bbOpen-R1419639-00956580.0419639-009565United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNBlack or African AmericanMale419639.0NaNNaN941.09c23f602-8be9-4582-bae9-7026e6f446fdRSNA_20210820
28case01f31b93-8c89-4c9d-9063-a67d043cd992Open-R1419639-00311934.0419639-003119United States of AmericaYesNaNNot Hispanic or Latino...NaNNaNWhiteFemale419639.0NaNNaN941.05ecc9107-5d51-40e1-bc22-50599ade61bfRSNA_20211214
29case01f9ded2-51d2-404c-bdba-5f405206d515Open-R1419639-00077956.0419639-000779United States of AmericaNoNaNNot Hispanic or Latino...NaNNaNAsianFemale419639.0NaNNaN940.0be97ed98-dea1-44b1-808a-4d1039972434RSNA_20220105
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11869casef6891891-253a-49cc-9d5b-a2c076966fe5Open-A1_PETAL_REDCORALU05-00315NaNU05-00315NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN72fe76f3-27ea-4d92-b480-ac74d9af197cACR_PETAL_REDCORAL_20211220
11870casef6b22247-1628-4f5b-ae6b-2c5924b245d2Open-A1_PETAL_REDCORALU01-01662NaNU01-01662NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN72fe76f3-27ea-4d92-b480-ac74d9af197cACR_PETAL_REDCORAL_20211220
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11875casef916eee6-109c-4605-8b3b-8a02a8ed1fc2Open-A1_PETAL_REDCORALD01-01655NaND01-01655NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN72fe76f3-27ea-4d92-b480-ac74d9af197cACR_PETAL_REDCORAL_20211220
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11877casef97a81f3-b6e2-4d1c-a77d-881b0da707aeOpen-A1_PETAL_REDCORALD04-01095NaND04-01095NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN72fe76f3-27ea-4d92-b480-ac74d9af197cACR_PETAL_REDCORAL_20211220
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11879casefa08b6e8-fc27-4426-9ef5-64a3b65fed23Open-A1_PETAL_REDCORALB02-01501NaNB02-01501NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN72fe76f3-27ea-4d92-b480-ac74d9af197cACR_PETAL_REDCORAL_20211220
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11892casefe0806c3-80aa-4ee5-86a8-ac6a6f4c1837Open-A1_PETAL_REDCORALV02-00314NaNV02-00314NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN72fe76f3-27ea-4d92-b480-ac74d9af197cACR_PETAL_REDCORAL_20211220
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11899 rows × 21 columns

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"11871 case f70df33d-af11-42af-8300-efd3ce7521ad Open-A1_PETAL_REDCORAL \n", - "11872 case f74e8047-a393-49c2-a113-6853017545bd Open-A1_PETAL_REDCORAL \n", - "11873 case f78dda1c-ab8a-4b0b-a8fc-ff81c5dfbb46 Open-A1_PETAL_REDCORAL \n", - "11874 case f889a407-1a7b-481b-9dde-ef69d39e6aef Open-A1_PETAL_REDCORAL \n", - "11875 case f916eee6-109c-4605-8b3b-8a02a8ed1fc2 Open-A1_PETAL_REDCORAL \n", - "11876 case f955540c-dd77-42c0-9b50-8222540efb85 Open-A1_PETAL_REDCORAL \n", - "11877 case f97a81f3-b6e2-4d1c-a77d-881b0da707ae Open-A1_PETAL_REDCORAL \n", - "11878 case f9f7da55-8a6b-460e-8e69-4393e4d58c68 Open-A1_PETAL_REDCORAL \n", - "11879 case fa08b6e8-fc27-4426-9ef5-64a3b65fed23 Open-A1_PETAL_REDCORAL \n", - "11880 case fa7e183b-0ad2-4df7-84f9-b41c64a831e8 Open-A1_PETAL_REDCORAL \n", - "11881 case fa8c00f5-2533-41f2-9897-47a05c7b93d4 Open-A1_PETAL_REDCORAL \n", - "11882 case fab09373-5b3f-4822-a630-275fb057b171 Open-A1_PETAL_REDCORAL \n", - "11883 case fb0e9858-e70f-4f47-ac34-95ffb6103b94 Open-A1_PETAL_REDCORAL \n", - "11884 case fbbd37b1-770d-466d-be8a-c73b61d4cc1a Open-A1_PETAL_REDCORAL \n", - "11885 case fbf5ad0d-0548-47da-97d6-886bbd23f809 Open-A1_PETAL_REDCORAL \n", - "11886 case fc7a9b5d-a031-4ee8-bea0-5ef38526b787 Open-A1_PETAL_REDCORAL \n", - "11887 case fce04c54-6833-4a85-b0be-ac42433dfc6c Open-A1_PETAL_REDCORAL \n", - "11888 case fd3244ed-9d58-459a-b56c-a6ba49cb6e18 Open-A1_PETAL_REDCORAL \n", - "11889 case fd538a5c-effb-4af0-a944-052f571510a5 Open-A1_PETAL_REDCORAL \n", - "11890 case fd910a2a-0429-4a62-b083-50f9d55bccc6 Open-A1_PETAL_REDCORAL \n", - "11891 case fdacdbf1-0cab-4a19-bcba-c016b7dc95fe Open-A1_PETAL_REDCORAL \n", - "11892 case fe0806c3-80aa-4ee5-86a8-ac6a6f4c1837 Open-A1_PETAL_REDCORAL \n", - "11893 case fe93e9de-e8fc-4617-a3c5-6eb0492bac0a Open-A1_PETAL_REDCORAL \n", - "11894 case ff254815-f395-4bd7-b83d-397d2922283d Open-A1_PETAL_REDCORAL \n", - "11895 case ff4fb4cf-5e5d-44ab-9f29-7d8a33b09c6f Open-A1_PETAL_REDCORAL \n", - "11896 case ff5c429c-dd78-48cb-9170-bf0c67c78c50 Open-A1_PETAL_REDCORAL \n", - "11897 case ffb5c4cb-d9e2-4f77-8e78-308cb1c2e408 Open-A1_PETAL_REDCORAL \n", - "11898 case ffc28c74-616e-4722-969e-3a1ccfa4da4d Open-A1_PETAL_REDCORAL \n", - "\n", - " submitter_id age_at_index case_ids country_of_origin \\\n", - "0 419639-011059 79.0 419639-011059 United States of America \n", - "1 419639-010858 64.0 419639-010858 United States of America \n", - "2 419639-004048 79.0 419639-004048 United States of America \n", - "3 419639-000012 59.0 419639-000012 United States of America \n", - "4 419639-010307 73.0 419639-010307 United States of America \n", - "5 419639-000076 74.0 419639-000076 United States of America \n", - "6 419639-001531 45.0 419639-001531 United States of America \n", - "7 419639-009656 72.0 419639-009656 United States of America \n", - "8 419639-009460 34.0 419639-009460 United States of America \n", - "9 419639-010398 68.0 419639-010398 United States of America \n", - "10 419639-011328 81.0 419639-011328 United States of America \n", - "11 419639-009814 71.0 419639-009814 United States of America \n", - "12 419639-009876 83.0 419639-009876 United States of America \n", - "13 419639-010602 52.0 419639-010602 United States of America \n", - "14 419639-003133 83.0 419639-003133 United States of America \n", - "15 419639-008055 85.0 419639-008055 United States of America \n", - "16 419639-009749 71.0 419639-009749 United States of America \n", - "17 419639-008035 61.0 419639-008035 United States of America \n", - "18 419639-003097 27.0 419639-003097 United States of America \n", - "19 419639-001556 81.0 419639-001556 United States of America \n", - "20 419639-000196 57.0 419639-000196 United States of America \n", - "21 419639-010216 77.0 419639-010216 United States of America \n", - "22 419639-003263 21.0 419639-003263 United States of America \n", - "23 419639-008868 46.0 419639-008868 United States of America \n", - "24 419639-004558 64.0 419639-004558 United States of America \n", - "25 419639-006827 63.0 419639-006827 United States of America \n", - "26 419639-010718 80.0 419639-010718 United States of America \n", - "27 419639-009565 80.0 419639-009565 United States of America \n", - "28 419639-003119 34.0 419639-003119 United States of America \n", - "29 419639-000779 56.0 419639-000779 United States of America \n", - "... ... ... ... ... \n", - "11869 U05-00315 NaN U05-00315 NaN \n", - "11870 U01-01662 NaN U01-01662 NaN \n", - "11871 V01-01647 NaN V01-01647 NaN \n", - "11872 A01-00902 NaN A01-00902 NaN \n", - "11873 H03-00247 NaN H03-00247 NaN \n", - "11874 U01-01615 NaN U01-01615 NaN \n", - "11875 D01-01655 NaN D01-01655 NaN \n", - "11876 M01-00901 NaN M01-00901 NaN \n", - "11877 D04-01095 NaN D04-01095 NaN \n", - "11878 D01-01659 NaN D01-01659 NaN \n", - "11879 B02-01501 NaN B02-01501 NaN \n", - "11880 H03-00243 NaN H03-00243 NaN \n", - "11881 W01-01339 NaN W01-01339 NaN \n", - "11882 U01-01636 NaN U01-01636 NaN \n", - "11883 C05-00781 NaN C05-00781 NaN \n", - "11884 A01-00941 NaN A01-00941 NaN \n", - "11885 C06-00610 NaN C06-00610 NaN \n", - "11886 C05-00779 NaN C05-00779 NaN \n", - "11887 B01-01548 NaN B01-01548 NaN \n", - 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"12 No NaN Not Hispanic or Latino \n", - "13 No NaN Not Hispanic or Latino \n", - "14 No NaN Hispanic or Latino \n", - "15 No NaN Not Hispanic or Latino \n", - "16 No NaN Not Hispanic or Latino \n", - "17 No NaN Not Hispanic or Latino \n", - "18 No NaN Not Hispanic or Latino \n", - "19 No NaN Hispanic or Latino \n", - "20 No NaN Not Hispanic or Latino \n", - "21 No NaN Not Hispanic or Latino \n", - "22 No NaN Hispanic or Latino \n", - "23 No NaN Hispanic or Latino \n", - "24 No NaN Hispanic or Latino \n", - "25 No NaN Not Hispanic or Latino \n", - "26 Yes NaN Not Hispanic or Latino \n", - "27 No NaN Not Hispanic or Latino \n", - "28 Yes NaN Not Hispanic or Latino \n", - "29 No NaN Not Hispanic or Latino \n", - "... ... ... ... \n", - "11869 NaN NaN NaN \n", - "11870 NaN NaN NaN \n", - "11871 NaN NaN NaN \n", - "11872 NaN NaN NaN \n", - "11873 NaN NaN NaN \n", - "11874 NaN NaN NaN \n", - "11875 NaN NaN NaN \n", - "11876 NaN NaN NaN \n", - "11877 NaN NaN NaN \n", - "11878 NaN NaN NaN \n", - "11879 NaN NaN NaN \n", - "11880 NaN NaN NaN \n", - "11881 NaN NaN NaN \n", - "11882 NaN NaN NaN \n", - "11883 NaN NaN NaN \n", - "11884 NaN NaN NaN \n", - "11885 NaN NaN NaN \n", - "11886 NaN NaN NaN \n", - "11887 NaN NaN NaN \n", - "11888 NaN NaN NaN \n", - "11889 NaN NaN NaN \n", - "11890 NaN NaN NaN \n", - "11891 NaN NaN NaN \n", - "11892 NaN NaN NaN \n", - "11893 NaN NaN NaN \n", - "11894 NaN NaN NaN \n", - "11895 NaN NaN NaN \n", - "11896 NaN NaN NaN \n", - "11897 NaN NaN NaN \n", - "11898 NaN NaN NaN \n", - "\n", - " ... icu_indicator \\\n", - "0 ... NaN \n", - "1 ... NaN \n", - "2 ... NaN \n", - "3 ... NaN \n", - "4 ... NaN \n", - "5 ... NaN \n", - "6 ... NaN \n", - "7 ... NaN \n", - "8 ... NaN \n", - "9 ... NaN \n", - "10 ... NaN \n", - "11 ... NaN \n", - "12 ... NaN \n", - "13 ... NaN \n", - "14 ... NaN \n", - "15 ... NaN \n", - "16 ... NaN \n", - "17 ... NaN \n", - "18 ... NaN \n", - "19 ... NaN \n", - "20 ... NaN \n", - "21 ... NaN \n", - "22 ... NaN \n", - "23 ... NaN \n", - "24 ... NaN \n", - "25 ... NaN \n", - "26 ... NaN \n", - "27 ... NaN \n", - "28 ... NaN \n", - "29 ... NaN \n", - "... ... ... \n", - "11869 ... NaN \n", - "11870 ... NaN \n", - "11871 ... NaN \n", - "11872 ... NaN \n", - "11873 ... NaN \n", - "11874 ... NaN \n", - "11875 ... NaN \n", - "11876 ... NaN \n", - "11877 ... NaN \n", - "11878 ... NaN \n", - "11879 ... NaN \n", - "11880 ... NaN \n", - "11881 ... NaN \n", - "11882 ... NaN \n", - "11883 ... NaN \n", - "11884 ... NaN \n", - "11885 ... NaN \n", - "11886 ... NaN \n", - "11887 ... NaN \n", - "11888 ... NaN \n", - "11889 ... NaN \n", - "11890 ... NaN \n", - "11891 ... NaN \n", - "11892 ... NaN \n", - "11893 ... NaN \n", - "11894 ... NaN \n", - "11895 ... NaN \n", - "11896 ... NaN \n", - "11897 ... NaN \n", - "11898 ... NaN \n", - "\n", - " index_event race sex site_id \\\n", - "0 NaN White Male 419639.0 \n", - "1 NaN Not Reported Male 419639.0 \n", - "2 NaN White Male 419639.0 \n", - "3 NaN Black or African American Male 419639.0 \n", - "4 NaN Not Reported Female 419639.0 \n", - "5 NaN Not Reported Male 419639.0 \n", - "6 NaN Black or African American Male 419639.0 \n", - "7 NaN Other Male 419639.0 \n", - "8 NaN Asian Male 419639.0 \n", - "9 NaN Black or African American Female 419639.0 \n", - "10 NaN Not Reported Female 419639.0 \n", - "11 NaN White Male 419639.0 \n", - "12 NaN White Male 419639.0 \n", - "13 NaN White Male 419639.0 \n", - "14 NaN Not Reported Female 419639.0 \n", - "15 NaN Asian Male 419639.0 \n", - "16 NaN White Female 419639.0 \n", - "17 NaN White Male 419639.0 \n", - "18 NaN Other Male 419639.0 \n", - "19 NaN Not Reported Male 419639.0 \n", - "20 NaN White Female 419639.0 \n", - "21 NaN Not Reported Female 419639.0 \n", - "22 NaN Not Reported Female 419639.0 \n", - "23 NaN Not Reported Male 419639.0 \n", - "24 NaN Not Reported Male 419639.0 \n", - "25 NaN Asian Male 419639.0 \n", - "26 NaN White Female 419639.0 \n", - "27 NaN Black or African American Male 419639.0 \n", - "28 NaN White Female 419639.0 \n", - "29 NaN Asian Female 419639.0 \n", - "... ... ... ... ... \n", - "11869 NaN NaN NaN NaN \n", - "11870 NaN NaN NaN NaN \n", - "11871 NaN NaN NaN NaN \n", - "11872 NaN NaN NaN NaN \n", - "11873 NaN NaN NaN NaN \n", - "11874 NaN NaN NaN NaN \n", - "11875 NaN NaN NaN NaN \n", - "11876 NaN NaN NaN NaN \n", - "11877 NaN NaN NaN NaN \n", - "11878 NaN NaN NaN NaN \n", - "11879 NaN NaN NaN NaN \n", - "11880 NaN NaN NaN NaN \n", - "11881 NaN NaN NaN NaN \n", - "11882 NaN NaN NaN NaN \n", - "11883 NaN NaN NaN NaN \n", - "11884 NaN NaN NaN NaN \n", - "11885 NaN NaN NaN NaN \n", - "11886 NaN NaN NaN NaN \n", - "11887 NaN NaN NaN NaN \n", - "11888 NaN NaN NaN NaN \n", - "11889 NaN NaN NaN NaN \n", - "11890 NaN NaN NaN NaN \n", - "11891 NaN NaN NaN NaN \n", - "11892 NaN NaN NaN NaN \n", - "11893 NaN NaN NaN NaN \n", - "11894 NaN NaN NaN NaN \n", - "11895 NaN NaN NaN NaN \n", - "11896 NaN NaN NaN NaN \n", - "11897 NaN NaN NaN NaN \n", - "11898 NaN NaN NaN NaN \n", - "\n", - " treatment_info ventilator_indicator zip \\\n", - "0 NaN NaN 949.0 \n", - "1 NaN NaN 0.0 \n", - "2 NaN NaN 941.0 \n", - "3 NaN NaN 941.0 \n", - "4 NaN NaN 0.0 \n", - "5 NaN NaN 941.0 \n", - "6 NaN NaN 946.0 \n", - "7 NaN NaN 0.0 \n", - "8 NaN NaN 941.0 \n", - "9 NaN NaN 941.0 \n", - "10 NaN NaN 0.0 \n", - "11 NaN NaN 941.0 \n", - "12 NaN NaN 941.0 \n", - "13 NaN NaN 941.0 \n", - "14 NaN NaN 941.0 \n", - "15 NaN NaN 941.0 \n", - "16 NaN NaN 0.0 \n", - "17 NaN NaN 946.0 \n", - "18 NaN NaN 941.0 \n", - "19 NaN NaN 941.0 \n", - "20 NaN NaN 941.0 \n", - "21 NaN NaN 0.0 \n", - "22 NaN NaN 941.0 \n", - "23 NaN NaN 953.0 \n", - "24 NaN NaN 0.0 \n", - "25 NaN NaN 941.0 \n", - "26 NaN NaN 0.0 \n", - "27 NaN NaN 941.0 \n", - "28 NaN NaN 941.0 \n", - "29 NaN NaN 940.0 \n", - "... ... ... ... \n", - "11869 NaN NaN NaN \n", - "11870 NaN NaN NaN \n", - 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"25 aa324c95-1b01-46c9-81c1-a0f4b924e5d5,af721fd1-... \n", - "26 af721fd1-49a7-42d7-9f5f-b5f83d3ef0ee \n", - "27 9c23f602-8be9-4582-bae9-7026e6f446fd \n", - "28 5ecc9107-5d51-40e1-bc22-50599ade61bf \n", - "29 be97ed98-dea1-44b1-808a-4d1039972434 \n", - "... ... \n", - "11869 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11870 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11871 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11872 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11873 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11874 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11875 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11876 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11877 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11878 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11879 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11880 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11881 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11882 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11883 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11884 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11885 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11886 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11887 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11888 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11889 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11890 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11891 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11892 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11893 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11894 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11895 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11896 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11897 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "11898 72fe76f3-27ea-4d92-b480-ac74d9af197c \n", - "\n", - " datasets.submitter_id \n", - "0 RSNA_20211214 \n", - "1 RSNA_20220114 \n", - "2 RSNA_20211026 \n", - "3 RSNA_20210820,RSNA_20211214 \n", - "4 RSNA_20211117 \n", - "5 RSNA_20211214 \n", - "6 RSNA_20210922,RSNA_20211006 \n", - "7 RSNA_20210902,RSNA_20211117,RSNA_20211006 \n", - "8 RSNA_20211117,RSNA_20210902 \n", - "9 RSNA_20211117 \n", - "10 RSNA_20220114 \n", - "11 RSNA_20211006 \n", - "12 RSNA_20211006,RSNA_20211026 \n", - "13 RSNA_20211117 \n", - "14 RSNA_20211214 \n", - "15 RSNA_20210820 \n", - "16 RSNA_20211117,RSNA_20210902 \n", - "17 RSNA_20210902,RSNA_20211117,RSNA_20210922 \n", - "18 RSNA_20220114 \n", - "19 RSNA_20220114,RSNA_20220211 \n", - "20 RSNA_20211214 \n", - "21 RSNA_20211117 \n", - "22 RSNA_20211214 \n", - "23 RSNA_20210922 \n", - "24 RSNA_20210820,RSNA_20211117,RSNA_20210922,RSNA... \n", - "25 RSNA_20210902,RSNA_20211117 \n", - "26 RSNA_20211117 \n", - "27 RSNA_20210820 \n", - "28 RSNA_20211214 \n", - "29 RSNA_20220105 \n", - "... ... \n", - "11869 ACR_PETAL_REDCORAL_20211220 \n", - "11870 ACR_PETAL_REDCORAL_20211220 \n", - "11871 ACR_PETAL_REDCORAL_20211220 \n", - "11872 ACR_PETAL_REDCORAL_20211220 \n", - "11873 ACR_PETAL_REDCORAL_20211220 \n", - "11874 ACR_PETAL_REDCORAL_20211220 \n", - "11875 ACR_PETAL_REDCORAL_20211220 \n", - "11876 ACR_PETAL_REDCORAL_20211220 \n", - "11877 ACR_PETAL_REDCORAL_20211220 \n", - "11878 ACR_PETAL_REDCORAL_20211220 \n", - "11879 ACR_PETAL_REDCORAL_20211220 \n", - "11880 ACR_PETAL_REDCORAL_20211220 \n", - "11881 ACR_PETAL_REDCORAL_20211220 \n", - "11882 ACR_PETAL_REDCORAL_20211220 \n", - "11883 ACR_PETAL_REDCORAL_20211220 \n", - "11884 ACR_PETAL_REDCORAL_20211220 \n", - "11885 ACR_PETAL_REDCORAL_20211220 \n", - "11886 ACR_PETAL_REDCORAL_20211220 \n", - "11887 ACR_PETAL_REDCORAL_20211220 \n", - "11888 ACR_PETAL_REDCORAL_20211220 \n", - "11889 ACR_PETAL_REDCORAL_20211220 \n", - "11890 ACR_PETAL_REDCORAL_20211220 \n", - "11891 ACR_PETAL_REDCORAL_20211220 \n", - "11892 ACR_PETAL_REDCORAL_20211220 \n", - "11893 ACR_PETAL_REDCORAL_20211220 \n", - "11894 ACR_PETAL_REDCORAL_20211220 \n", - "11895 ACR_PETAL_REDCORAL_20211220 \n", - "11896 ACR_PETAL_REDCORAL_20211220 \n", - "11897 ACR_PETAL_REDCORAL_20211220 \n", - "11898 ACR_PETAL_REDCORAL_20211220 \n", - "\n", - "[11899 rows x 21 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cases = exp.get_node_tsvs(node='case')\n", - "display(cases)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a Python wrapper to get all the data in a particular project\n", - "---\n", - "* Similar to the above example, I've written a wrapper script called `Gen3Expansion.get_project_tsvs()` that uses the `Gen3Submission.export_node()` function to export every node in every project (or a particular project) in the data commons.\n", - "https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L298\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Downloading 7534 records in node 'case' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_case.tsv\n", - "\t0 records in node 'condition' of project 'Open-A1'.\n", - "\n", - "Downloading 6 records in node 'core_metadata_collection' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_core_metadata_collection.tsv\n", - "\n", - "Downloading 18814 records in node 'cr_series_file' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_cr_series_file.tsv\n", - "\n", - "Downloading 36657 records in node 'ct_series_file' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_ct_series_file.tsv\n", - "\n", - "Downloading 7 records in node 'dataset' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_dataset.tsv\n", - "\n", - "Downloading 3464 records in node 'dx_series_file' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_dx_series_file.tsv\n", - "\n", - "Downloading 20608 records in node 'imaging_study' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_imaging_study.tsv\n", - "\n", - "Downloading 21113 records in node 'measurement' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_measurement.tsv\n", - "\t0 records in node 'medication' of project 'Open-A1'.\n", - "\t0 records in node 'mg_series_file' of project 'Open-A1'.\n", - "\n", - "Downloading 676 records in node 'mr_series_file' of project 'Open-A1'.\n", - "\n", - "Output written to file: project_tsvs/Open-A1_tsvs/Open-A1_mr_series_file.tsv\n", - "\t0 records in node 'nm_series_file' of project 'Open-A1'.\n", - "\t0 records in node 'observation' of project 'Open-A1'.\n", - "\t0 records in node 'procedure' of project 'Open-A1'.\n", - "\t0 records in node 'pt_series_file' of project 'Open-A1'.\n", - "\t0 records in node 'rf_series_file' of project 'Open-A1'.\n", - "\t0 records in node 'supplementary_file' of project 'Open-A1'.\n", - "\t0 records in node 'us_series_file' of project 'Open-A1'.\n", - "\t0 records in node 'visit' of project 'Open-A1'.\n", - "\t0 records in node 'xa_series_file' of project 'Open-A1'.\n" - ] - }, - { - "data": { - "text/plain": [ - "'Open-A1_case.tsv\\nOpen-A1_core_metadata_collection.tsv\\nOpen-A1_cr_series_file.tsv\\nOpen-A1_ct_series_file.tsv\\nOpen-A1_dataset.tsv\\nOpen-A1_dx_series_file.tsv\\nOpen-A1_imaging_study.tsv\\nOpen-A1_measurement.tsv\\nOpen-A1_mr_series_file.tsv\\n'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## This example gets all the data in every node of the data model in the project Open-A1\n", - "## If \"projects\" is not specific, all data across all projects you have access to will be downloaded.\n", - "exp.get_project_tsvs(projects='Open-A1')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 49076\r\n", - "-rw-r--r-- 1 jovyan users 1776810 Jun 22 21:15 Open-A1_case.tsv\r\n", - "-rw-r--r-- 1 jovyan users 1332 Jun 22 21:15 Open-A1_core_metadata_collection.tsv\r\n", - "-rw-r--r-- 1 jovyan users 12225991 Jun 22 21:15 Open-A1_cr_series_file.tsv\r\n", - "-rw-r--r-- 1 jovyan users 23389777 Jun 22 21:15 Open-A1_ct_series_file.tsv\r\n", - "-rw-r--r-- 1 jovyan users 1216 Jun 22 21:15 Open-A1_dataset.tsv\r\n", - "-rw-r--r-- 1 jovyan users 2186247 Jun 22 21:15 Open-A1_dx_series_file.tsv\r\n", - "-rw-r--r-- 1 jovyan users 6419409 Jun 22 21:15 Open-A1_imaging_study.tsv\r\n", - "-rw-r--r-- 1 jovyan users 3790634 Jun 22 21:15 Open-A1_measurement.tsv\r\n", - "-rw-r--r-- 1 jovyan users 445637 Jun 22 21:15 Open-A1_mr_series_file.tsv\r\n" - ] - } - ], - "source": [ - "!ls -l project_tsvs/Open-A1_tsvs" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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8ct_series_file001a2fe3-9ef0-4e2d-aeb3-3a47ef5c8cb5Open-A12.16.840.1.114274.1818.50444111535957281371522...CTDCMDICOM10000364-1429707/2.16.840.1.114274.1818.559175...209772368b47ec344a9e2a2f330a5763e653f820...NaNSagittal10.0 MIP2.16.840.1.114274.1818.50444111535957281371522...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_20210827c9721d8c-6436-4b54-8621-a712a18457842.16.840.1.114274.1818.55917510878621680632489...
9ct_series_file001b93e7-91ab-41ea-8738-d1c60a60d25bOpen-A12.16.840.1.114274.1818.49568246924437508717848...CTDCMDICOM10000364-1432081/2.16.840.1.114274.1818.530726...296058762f0c1880ed7c04c64e5aa058708d3fa1...NaNSAG BODY2.16.840.1.114274.1818.49568246924437508717848...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_202108273e4d5680-5302-4b1e-a6f5-84b21f6b1c4b2.16.840.1.114274.1818.53072664876741205902722...
10ct_series_file001cef65-519f-4cad-a0f3-ada2c3b8bd35Open-A12.16.840.1.114274.1818.50783701110696030245437...CTDCMDICOM10000364-1259756/2.16.840.1.114274.1818.481538...192122eb6577c19d468ed618590d6bca18d7cf...NaNSmart Prep Series2.16.840.1.114274.1818.50783701110696030245437...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_2021082787e018ca-72ff-42cd-a763-e7f210fe7cec2.16.840.1.114274.1818.48153856909943962611251...
11ct_series_file00230d84-a9a6-4305-b0c9-9954652ce3e0Open-A12.16.840.1.114274.1818.55051502853362002701452...CTDCMDICOM10003752-v7AOAbI1WkOWxd5w6HVOQ/2.16.840.1.1142...20680718b056442a416cc0c2eb31025b6137fce3...NaNMinIP, iDose (2)2.16.840.1.114274.1818.55051502853362002701452...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107d9ae2dbe-1e60-475f-bc5b-8203542b47092.16.840.1.114274.1818.52573342776289661131695...
12ct_series_file00237df2-b061-48f8-9785-09f9f8397f8aOpen-A12.16.840.1.114274.1818.55890235514104568851395...CTDCMDICOM2.16.840.1.114274.1818.57262165786098327381143...2387596635fa22105259d99a25d007c5445051d0...NaNART SAG DLIR MED2.16.840.1.114274.1818.55890235514104568851395...NaNNaNNaNNaNNaNb7481b2a-7332-4b4a-b0db-e757aab3d6032.16.840.1.114274.1818.57262165786098327381143...
13ct_series_file002a7182-3c8a-4513-940a-afbb6551ccdcOpen-A12.16.840.1.114274.1818.50544969920006524349223...CTDCMDICOM10000364-1521591/CT_2.16.840.1.114274.1818.510...133097945db4041779d83e481e8981886b335e8b...NaNCTA MIPS CORONAL2.16.840.1.114274.1818.50544969920006524349223...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_20211020e1720f15-81a1-4148-9dd8-c1e7bc1d8c622.16.840.1.114274.1818.51086294230658947977370...
14ct_series_file002bc05f-09e8-4620-aec9-f9c4ae7e547bOpen-A12.16.840.1.114274.1818.46901483095924786198355...CTDCMDICOM2.16.840.1.114274.1818.50339192942619654751362...41874874111cc68bdfabca661a673c5f416a749e...NaNSAGITTAL2.16.840.1.114274.1818.46901483095924786198355...NaNNaNNaNNaNNaN9c734bab-56a2-4e79-9b62-9086a77b43422.16.840.1.114274.1818.50339192942619654751362...
15ct_series_file002bd07f-f0a5-45ab-b684-7b2d64df7908Open-A12.16.840.1.114274.1818.53852316720529246871304...CTDCMDICOM2.16.840.1.114274.1818.57272482713393876731593...34868058e0621331b50fdbc1456737bc8a20c411...NaNDE AP BLEND With 3.0 Br40 2 F_0.52.16.840.1.114274.1818.53852316720529246871304...NaNNaNNaNNaNNaN3ecc2698-0f88-49f3-b6b9-da1fcfe059412.16.840.1.114274.1818.57272482713393876731593...
16ct_series_file002da9eb-4fa9-40e4-8914-760045e5a894Open-A12.16.840.1.114274.1818.47788547502293258367012...CTDCMDICOM2.16.840.1.114274.1818.53858790051352121871692...312754142906a85d1c7609c0e9782b8bfbe025e7...NaNcoronal abdomen2.16.840.1.114274.1818.47788547502293258367012...NaNNaNNaNNaNNaNa44ec9d3-3cad-41a1-a78f-0e50523080222.16.840.1.114274.1818.53858790051352121871692...
17ct_series_file0031222a-5f5e-47b9-ae00-1202b9149a3dOpen-A12.16.840.1.114274.1818.50646714107291197723664...CTDCMDICOM2.16.840.1.114274.1818.46352567840725257505915...27363843359174b405a1034e4a18650a1b7b981...NaNSmart Prep Series2.16.840.1.114274.1818.50646714107291197723664...NaNNaNNaNNaNNaNdfe98f60-9dc5-43db-82d0-806d03a45d7e2.16.840.1.114274.1818.46352567840725257505915...
18ct_series_file00333556-f6e3-41d0-8577-15598ae21040Open-A12.16.840.1.114274.1818.53838950720515509061928...CTDCMDICOM10003752-9fbsIMn6cUyofDlSakKuhg/2.16.840.1.114...17833978b498beeb3e804e3cf1f9bf33f84be0f9...NaNAXL ART, iDose (2)2.16.840.1.114274.1818.53838950720515509061928...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107a394afe0-c836-4410-a26a-50855493f7702.16.840.1.114274.1818.52114092694237991004384...
19ct_series_file0033eb67-2371-43a3-a81b-2cba69922167Open-A12.16.840.1.114274.1818.48519652760864609681298...CTDCMDICOM2.16.840.1.114274.1818.47023052143718685801473...23850546e525340baee22aaa6a3f3a268d9f76f4...NaNLung2.16.840.1.114274.1818.48519652760864609681298...NaNNaNNaNNaNNaN755ea54a-5b74-420a-8391-dde9e302ff472.16.840.1.114274.1818.47023052143718685801473...
20ct_series_file00351284-e4a3-42d7-af9d-1fd2b8ff9d99Open-A12.16.840.1.114274.1818.49305487510397925131693...CTDCMDICOM2.16.840.1.114274.1818.52162114235094682361434...157263020eb903f2945ba8416e8c80e3d53bac62f...NaNtop down rib to rib2.16.840.1.114274.1818.49305487510397925131693...NaNNaNNaNNaNNaN5f0f86df-6376-4fee-a409-e98dea4800392.16.840.1.114274.1818.52162114235094682361434...
21ct_series_file00398cbf-8255-4287-bf9d-45d11bc189adOpen-A12.16.840.1.114274.1818.50830320114023229651721...CTDCMDICOM10000364-5739783/2.16.840.1.114274.1818.538796...80507682fb0152cdc25033e7737b02ad72ad2dfd...NaNPE 1.0mm Thins2.16.840.1.114274.1818.50830320114023229651721...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_20210827730245ed-5b94-4fd7-94d3-b7c2c50d09ac2.16.840.1.114274.1818.53879679040957546491255...
22ct_series_file0039f8ec-42a7-4043-b60c-d92f2b2a5431Open-A12.16.840.1.114274.1818.49005225458506140691352...CTDCMDICOM2.16.840.1.114274.1818.56929896772386057438939...273024662e265c1b6029fde2512149ea4309b629...NaNLUNGS iDose (2)2.16.840.1.114274.1818.49005225458506140691352...NaNNaNNaNNaNNaNdb69e3e9-6d1d-4ceb-8bd0-f51e34a802612.16.840.1.114274.1818.56929896772386057438939...
23ct_series_file003aa6f8-7eaa-4ff1-a1b2-6bbdcd8ed69eOpen-A12.16.840.1.114274.1818.47795015415414848191488...CTDCMDICOM2.16.840.1.114274.1818.46638061106650275419844...41880808b2b6d6e2f01ec1b7b145bc2819276a8e...NaNsagittal abdomen2.16.840.1.114274.1818.47795015415414848191488...NaNNaNNaNNaNNaN58601920-ef3b-40d6-87bb-ec3b579ef54e2.16.840.1.114274.1818.46638061106650275419844...
24ct_series_file003b41b2-3081-4101-922c-02af368b7964Open-A12.16.840.1.114274.1818.49563997547211370316329...CTDCMDICOM10000364-5203243/CT_2.16.840.1.114274.1818.538...34277244eea88381ecc557feb1401b90b138642e...NaNCor2.16.840.1.114274.1818.49563997547211370316329...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_202110205136665a-c314-45f7-a858-843cc3b1aa392.16.840.1.114274.1818.53864404740166466509189...
25ct_series_file003cc676-901d-4d8f-8824-56675b34f9afOpen-A12.16.840.1.114274.1818.53431952655932754076149...CTDCMDICOM10003752-l6zXbbK3c068a7a37KyWw/2.16.840.1.1142...611274725f049fb9ba9aa77c50bb57439d5d4098...NaNCHEST COR, iDose (2)2.16.840.1.114274.1818.53431952655932754076149...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_2022010716a365bc-98ad-43c0-9880-c432122003ee2.16.840.1.114274.1818.49706829240474143967564...
26ct_series_file003d7dbe-da0b-4edc-9920-8980aec67c47Open-A12.16.840.1.114274.1818.52708517308744556571563...CTDCMDICOM10003752-GaNatus5c0eNy1txIytdyA/2.16.840.1.114...769282426df7f757ec4f514986a32504b2682e0a...NaNHIGH RES, iDose (2)2.16.840.1.114274.1818.52708517308744556571563...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107e5bb7207-9c44-4f65-9b69-a0f12f0b13b02.16.840.1.114274.1818.57276554732880748299032...
27ct_series_file00430d03-8ffc-4f3b-8f45-2ecd3f733585Open-A12.16.840.1.114274.1818.57443694093781255711482...CTDCMDICOM10003752-udJLnHWQUECW4u8VcGosLg/2.16.840.1.114...1002534c6e75ce61fea57c76ebd125809a35fda...NaNSCOUT2.16.840.1.114274.1818.57443694093781255711482...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107df235fcc-68be-429d-8a3d-3d156883ffde2.16.840.1.114274.1818.54454184266889817543229...
28ct_series_file00435260-cdbe-4299-b675-fd1fd3365ac1Open-A12.16.840.1.114274.1818.56289009156633266231891...CTDCMDICOM10000364-1067510/CT_2.16.840.1.114274.1818.565...240975607abdf2e23870eed93095e34f203a5ffe...NaNSAG CHEST 3MM2.16.840.1.114274.1818.56289009156633266231891...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_202110202a01973b-0f20-46d2-bbfc-b9e467fb6c972.16.840.1.114274.1818.56562285241690637041749...
29ct_series_file004386de-23df-4980-b2c1-fb8635215426Open-A12.16.840.1.114274.1818.49487280544236776531575...CTDCMDICOM10003752-3YdcOrFwskyQo1jJYMUI7w/2.16.840.1.114...1430632083c42a41ae0f01f0faaabd4e6d484690...NaNMIP, iDose (4)2.16.840.1.114274.1818.49487280544236776531575...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107a1f00bf8-98b1-488e-b4bc-4375ae7d00192.16.840.1.114274.1818.56095822575185594662034...
..................................................................
36627ct_series_fileffd57103-c0ee-45a3-8a7f-b195b4140f81Open-A12.16.840.1.114274.1818.50543575489997508864757...CTDCMDICOM2.16.840.1.114274.1818.54700166749634173131091...170000d5217f0157a88db605d6d6c9c346ec32...NaNTopogram 0.6 T20f2.16.840.1.114274.1818.50543575489997508864757...NaNNaNNaNNaNNaN0b647d5d-4447-4489-a917-b6d99c0ef8a22.16.840.1.114274.1818.54700166749634173131091...
36628ct_series_fileffd8c93d-4bf7-4cb3-bfc5-f16a38b22618Open-A12.16.840.1.114274.1818.49428524375576773818979...CTDCMDICOM2.16.840.1.114274.1818.48943829450915622837191...212784981380f29ed0d33bbd4cb67a8ee26ea8b4...NaNCORONAL2.16.840.1.114274.1818.49428524375576773818979...NaNNaNNaNNaNNaN17a07a00-fd77-4620-8e58-f9363274cb572.16.840.1.114274.1818.48943829450915622837191...
36629ct_series_fileffda357d-3a70-4ef1-9564-a6b34287f44aOpen-A12.16.840.1.114274.1818.48506951762211287641801...CTDCMDICOM2.16.840.1.114274.1818.47181537340210687507484...18377846ae4b0d4a696a5e0b35497126e8ed8a9c...NaNAXL iDose (2)2.16.840.1.114274.1818.48506951762211287641801...NaNNaNNaNNaNNaNd68f3f8f-168a-4fba-884d-5f89456db5882.16.840.1.114274.1818.47181537340210687507484...
36630ct_series_fileffdabfbb-0f0b-428e-9401-38a9b98e1d0dOpen-A12.16.840.1.114274.1818.53976293854785801717680...CTDCMDICOM2.16.840.1.114274.1818.55068505393176182881436...3115646247ca7c3cb462afbf7992837d7b0bd420...NaNCAP WITH 5.00 Bl56 S2 Lung2.16.840.1.114274.1818.53976293854785801717680...NaNNaNNaNNaNNaN4259a1f6-e293-4331-b85c-2c389b8076d22.16.840.1.114274.1818.55068505393176182881436...
36631ct_series_fileffdac164-4ed8-475b-8b44-d6e66e030e8bOpen-A12.16.840.1.114274.1818.53164750272845914766785...CTDCMDICOM10000364-2613055/2.16.840.1.114274.1818.533211...39913920a85c1aad0e09d17fff2b0c7dc6beb7e1...NaNAX CHEST PE STD +C F_0.52.16.840.1.114274.1818.53164750272845914766785...NaNNaNNaN9de1d49d-ad56-44b3-be2e-a2795ce41ff3ACR_20210915519abfb8-de35-433a-ba6e-efa283f92b312.16.840.1.114274.1818.53321136013187983131761...
36632ct_series_fileffdbd2d0-b010-4aad-ac63-be2a0308c561Open-A12.16.840.1.114274.1818.57220952741527326011015...CTDCMDICOM10003752-FTrypNMZECIjF730AFXfQ/2.16.840.1.1142...13788924e20fea4184cf77f8a1d69474fb36691c...NaNCHEST MiPS2.16.840.1.114274.1818.57220952741527326011015...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107ec5767eb-607a-4c46-aa4f-794760f181f72.16.840.1.114274.1818.52855318792067369166696...
36633ct_series_fileffdc79f5-5434-4cfa-a6b1-69b7d034f67cOpen-A12.16.840.1.114274.1818.49998392691251620621543...CTDCMDICOM10003752-0rkAAluz3EuvIj3aFfpWSw/2.16.840.1.114...31588304536eec2f24e29fecfb0cdc0f85979a64...NaNVENOUS RECON2.16.840.1.114274.1818.49998392691251620621543...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107db3a49fb-cac6-4e52-b2fe-b427bef1cba72.16.840.1.114274.1818.47978731145840567707608...
36634ct_series_fileffe00d01-ac4c-4c57-b3b2-578d0f3f8501Open-A12.16.840.1.114274.1818.48606589163784907614417...CTDCMDICOM2.16.840.1.114274.1818.50243181985210264931432...461333166c32da7a7883dae75788f8e01fd0816c...NaNSag2.16.840.1.114274.1818.48606589163784907614417...NaNNaNNaNNaNNaN94fa2d03-4af1-4cff-a177-384112979b702.16.840.1.114274.1818.50243181985210264931432...
36635ct_series_fileffe085d8-dabd-43eb-ada1-ba33a0d221aeOpen-A12.16.840.1.114274.1818.51979215377779423361128...CTDCMDICOM2.16.840.1.114274.1818.52627481775334240721795...29986326114039aad2ad5af1448441dc4a2cde53...NaNRoutine Chest2.16.840.1.114274.1818.51979215377779423361128...NaNNaNNaNNaNNaNaa3d141f-b07b-4b68-ae47-4434402327a92.16.840.1.114274.1818.52627481775334240721795...
36636ct_series_fileffe11de4-455f-4be2-90a1-3f015d35ab71Open-A12.16.840.1.114274.1818.50673217044353872091121...CTDCMDICOM10000364-6101237/CT_2.16.840.1.114274.1818.464...973224942352d0e37386801eac5fd811156c4647...NaN1.25x.625 standard2.16.840.1.114274.1818.50673217044353872091121...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_20211020c6823abe-a991-4b01-9e90-ac0f5843a3152.16.840.1.114274.1818.46496126341619658791061...
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36644ct_series_fileffee10f0-d21f-4a66-8a80-358f2774bc0eOpen-A12.16.840.1.114274.1818.51170213715360990881107...CTDCMDICOM10003752-fPHT8uqA0q5qRuHLkYPTA/2.16.840.1.1142...14770614b6ff8b3724c2031a92c848f990a1ca00...NaNMinIP, iDose (2)2.16.840.1.114274.1818.51170213715360990881107...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_202201077308986a-3fd4-44b4-8dc6-5eea0f1811532.16.840.1.114274.1818.48700906795823839421389...
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36649ct_series_filefff1ccf3-0917-49f2-adff-3f8b02f735e3Open-A12.16.840.1.114274.1818.51521500690102685029326...CTDCMDICOM10000364-650687/2.16.840.1.114274.1818.5101995...2782471256855ea31ffe4bf11eaf0e114febcd37...NaNCOR BODY2.16.840.1.114274.1818.51521500690102685029326...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_20210827bc753f0d-da5a-4177-88d4-bbe113e318722.16.840.1.114274.1818.51019951011241647966797...
36650ct_series_filefff3bc2e-a188-4713-899d-85a5d3831519Open-A12.16.840.1.114274.1818.56804893493554909195004...CTDCMDICOM2.16.840.1.114274.1818.49223169490672576991068...4324906005d22aa82da3bee7be2296974a929504...NaNSagittal MPR 3.02.16.840.1.114274.1818.56804893493554909195004...NaNNaNNaNNaNNaNd4aaba88-4adc-4de6-8d92-52dee5473f062.16.840.1.114274.1818.49223169490672576991068...
36651ct_series_filefff3cd4a-9929-4668-8e2d-77e7aef8752bOpen-A12.16.840.1.114274.1818.46130115274371592312353...CTDCMDICOM2.16.840.1.114274.1818.56931261911713855351136...23492854a4095d6067c9fc8f71d5a3dbc8e04b2e...NaNCoronal 3.0 MPR2.16.840.1.114274.1818.46130115274371592312353...NaNNaNNaNNaNNaN2b7aa84c-e515-44a6-ba88-007f34148c702.16.840.1.114274.1818.56931261911713855351136...
36652ct_series_filefff4ac21-8fe4-4ac1-9e41-3c9ee5e1b712Open-A12.16.840.1.114274.1818.49929890207265010191125...CTDCMDICOM2.16.840.1.114274.1818.52146263260855674077294...26016868d04ee65e0bb76ee3921dceeb44f2be43...NaNSTD AXL2.16.840.1.114274.1818.49929890207265010191125...NaNNaNNaNNaNNaN0b8ff751-72e6-4504-b976-5b98130c34762.16.840.1.114274.1818.52146263260855674077294...
36653ct_series_filefff52d87-5c5a-4a11-92f5-f80d6b00862eOpen-A12.16.840.1.114274.1818.48936021023819044245648...CTDCMDICOM10000364-1606954/2.16.840.1.114274.1818.524255...1129993053c58821a20e1b24629a3e21b08dc20a...NaNCHEST 70keV2.16.840.1.114274.1818.48936021023819044245648...NaNNaNNaNe6c5fb12-7467-4a7c-979a-b1e84c5c53dfACR_20210823820b8580-1dff-4a41-a960-912a42a3de3c2.16.840.1.114274.1818.52425549090119014511974...
36654ct_series_filefff8b097-040d-470e-932e-59ead163a32bOpen-A12.16.840.1.114274.1818.56806844052065594631125...CTDCMDICOM10000364-6101237/CT_2.16.840.1.114274.1818.464...22389206671f6f8e55d3a36ad4bf7219a181dc3b...NaNchest coronal2.16.840.1.114274.1818.56806844052065594631125...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_20211020c6823abe-a991-4b01-9e90-ac0f5843a3152.16.840.1.114274.1818.46496126341619658791061...
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36657 rows × 34 columns

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" - ], - "text/plain": [ - " type id project_id \\\n", - "0 ct_series_file 0000ecf4-0982-44a4-9bf9-3da508aa8254 Open-A1 \n", - "1 ct_series_file 0001ea29-d145-46b3-b655-fdf54d376561 Open-A1 \n", - "2 ct_series_file 00077049-9d86-43be-b0c9-dc4b826c785e Open-A1 \n", - "3 ct_series_file 0008c3b1-a442-4d0d-939b-556e3cb3abec Open-A1 \n", - "4 ct_series_file 0008dff4-0074-4a4f-bb7d-488d69a57867 Open-A1 \n", - "5 ct_series_file 000d4373-2468-4803-ad95-dd535616b9d7 Open-A1 \n", - "6 ct_series_file 00181318-8477-45c7-8bdc-a324c1ea9fb6 Open-A1 \n", - "7 ct_series_file 0018a73d-d0e0-4849-98eb-962c9eabfacf Open-A1 \n", - "8 ct_series_file 001a2fe3-9ef0-4e2d-aeb3-3a47ef5c8cb5 Open-A1 \n", - "9 ct_series_file 001b93e7-91ab-41ea-8738-d1c60a60d25b Open-A1 \n", - "10 ct_series_file 001cef65-519f-4cad-a0f3-ada2c3b8bd35 Open-A1 \n", - "11 ct_series_file 00230d84-a9a6-4305-b0c9-9954652ce3e0 Open-A1 \n", - "12 ct_series_file 00237df2-b061-48f8-9785-09f9f8397f8a Open-A1 \n", - "13 ct_series_file 002a7182-3c8a-4513-940a-afbb6551ccdc Open-A1 \n", - "14 ct_series_file 002bc05f-09e8-4620-aec9-f9c4ae7e547b Open-A1 \n", - "15 ct_series_file 002bd07f-f0a5-45ab-b684-7b2d64df7908 Open-A1 \n", - "16 ct_series_file 002da9eb-4fa9-40e4-8914-760045e5a894 Open-A1 \n", - "17 ct_series_file 0031222a-5f5e-47b9-ae00-1202b9149a3d Open-A1 \n", - "18 ct_series_file 00333556-f6e3-41d0-8577-15598ae21040 Open-A1 \n", - "19 ct_series_file 0033eb67-2371-43a3-a81b-2cba69922167 Open-A1 \n", - "20 ct_series_file 00351284-e4a3-42d7-af9d-1fd2b8ff9d99 Open-A1 \n", - "21 ct_series_file 00398cbf-8255-4287-bf9d-45d11bc189ad Open-A1 \n", - "22 ct_series_file 0039f8ec-42a7-4043-b60c-d92f2b2a5431 Open-A1 \n", - "23 ct_series_file 003aa6f8-7eaa-4ff1-a1b2-6bbdcd8ed69e Open-A1 \n", - "24 ct_series_file 003b41b2-3081-4101-922c-02af368b7964 Open-A1 \n", - "25 ct_series_file 003cc676-901d-4d8f-8824-56675b34f9af Open-A1 \n", - "26 ct_series_file 003d7dbe-da0b-4edc-9920-8980aec67c47 Open-A1 \n", - "27 ct_series_file 00430d03-8ffc-4f3b-8f45-2ecd3f733585 Open-A1 \n", - "28 ct_series_file 00435260-cdbe-4299-b675-fd1fd3365ac1 Open-A1 \n", - "29 ct_series_file 004386de-23df-4980-b2c1-fb8635215426 Open-A1 \n", - "... ... ... ... \n", - "36627 ct_series_file ffd57103-c0ee-45a3-8a7f-b195b4140f81 Open-A1 \n", - "36628 ct_series_file ffd8c93d-4bf7-4cb3-bfc5-f16a38b22618 Open-A1 \n", - "36629 ct_series_file ffda357d-3a70-4ef1-9564-a6b34287f44a Open-A1 \n", - "36630 ct_series_file ffdabfbb-0f0b-428e-9401-38a9b98e1d0d Open-A1 \n", - "36631 ct_series_file ffdac164-4ed8-475b-8b44-d6e66e030e8b Open-A1 \n", - "36632 ct_series_file ffdbd2d0-b010-4aad-ac63-be2a0308c561 Open-A1 \n", - "36633 ct_series_file ffdc79f5-5434-4cfa-a6b1-69b7d034f67c Open-A1 \n", - "36634 ct_series_file ffe00d01-ac4c-4c57-b3b2-578d0f3f8501 Open-A1 \n", - "36635 ct_series_file ffe085d8-dabd-43eb-ada1-ba33a0d221ae Open-A1 \n", - "36636 ct_series_file ffe11de4-455f-4be2-90a1-3f015d35ab71 Open-A1 \n", - "36637 ct_series_file ffe152e2-5174-4c99-8392-1d5849cae1be Open-A1 \n", - "36638 ct_series_file ffe293eb-2e2c-456b-875f-dd444f513d8a Open-A1 \n", - "36639 ct_series_file ffe2f056-8166-47ce-b090-379a4bdc1460 Open-A1 \n", - "36640 ct_series_file ffe440b7-78d6-4acc-ad0d-b2f35c65c5f0 Open-A1 \n", - "36641 ct_series_file ffe6eb1f-7979-439e-82ea-e2941f3fa0e0 Open-A1 \n", - "36642 ct_series_file ffea39ac-a4d4-40d4-bf9e-5f3779743a87 Open-A1 \n", - "36643 ct_series_file ffea85a1-cf64-4359-9d14-3de91b7f34cd Open-A1 \n", - "36644 ct_series_file ffee10f0-d21f-4a66-8a80-358f2774bc0e Open-A1 \n", - "36645 ct_series_file ffee587f-c7a2-416a-89f5-8446eea5b3a1 Open-A1 \n", - "36646 ct_series_file ffee73bf-8987-4b5b-aa1b-3f83d0dcfcbd Open-A1 \n", - "36647 ct_series_file ffef3899-d58f-4861-9760-07c67beed0e4 Open-A1 \n", - "36648 ct_series_file fff01d22-2bd5-4185-ab77-d5c4b9ac0d61 Open-A1 \n", - "36649 ct_series_file fff1ccf3-0917-49f2-adff-3f8b02f735e3 Open-A1 \n", - "36650 ct_series_file fff3bc2e-a188-4713-899d-85a5d3831519 Open-A1 \n", - "36651 ct_series_file fff3cd4a-9929-4668-8e2d-77e7aef8752b Open-A1 \n", - "36652 ct_series_file fff4ac21-8fe4-4ac1-9e41-3c9ee5e1b712 Open-A1 \n", - "36653 ct_series_file fff52d87-5c5a-4a11-92f5-f80d6b00862e Open-A1 \n", - "36654 ct_series_file fff8b097-040d-470e-932e-59ead163a32b Open-A1 \n", - "36655 ct_series_file fffbd30e-44af-4d1b-b743-ae2de0acd5ee Open-A1 \n", - "36656 ct_series_file fffe5fec-e036-4b35-aaa4-1f8c506fbff1 Open-A1 \n", - "\n", - " submitter_id data_category \\\n", - "0 2.16.840.1.114274.1818.49957484958482183452775... CT \n", - "1 2.16.840.1.114274.1818.47452056560618671341191... CT \n", - "2 2.16.840.1.114274.1818.51148225759453066365970... CT \n", - "3 2.16.840.1.114274.1818.49471856660770992231274... CT \n", - "4 2.16.840.1.114274.1818.54441193239846891335001... CT \n", - "5 2.16.840.1.114274.1818.52155290829062416411654... CT \n", - "6 2.16.840.1.114274.1818.49528130810496449261224... CT \n", - "7 2.16.840.1.114274.1818.55615157599364385421040... CT \n", - "8 2.16.840.1.114274.1818.50444111535957281371522... CT \n", - "9 2.16.840.1.114274.1818.49568246924437508717848... CT \n", - "10 2.16.840.1.114274.1818.50783701110696030245437... CT \n", - "11 2.16.840.1.114274.1818.55051502853362002701452... CT \n", - "12 2.16.840.1.114274.1818.55890235514104568851395... CT \n", - "13 2.16.840.1.114274.1818.50544969920006524349223... CT \n", - "14 2.16.840.1.114274.1818.46901483095924786198355... CT \n", - "15 2.16.840.1.114274.1818.53852316720529246871304... CT \n", - "16 2.16.840.1.114274.1818.47788547502293258367012... CT \n", - "17 2.16.840.1.114274.1818.50646714107291197723664... CT \n", - "18 2.16.840.1.114274.1818.53838950720515509061928... CT \n", - "19 2.16.840.1.114274.1818.48519652760864609681298... CT \n", - "20 2.16.840.1.114274.1818.49305487510397925131693... CT \n", - "21 2.16.840.1.114274.1818.50830320114023229651721... CT \n", - "22 2.16.840.1.114274.1818.49005225458506140691352... CT \n", - "23 2.16.840.1.114274.1818.47795015415414848191488... CT \n", - "24 2.16.840.1.114274.1818.49563997547211370316329... CT \n", - "25 2.16.840.1.114274.1818.53431952655932754076149... CT \n", - "26 2.16.840.1.114274.1818.52708517308744556571563... CT \n", - "27 2.16.840.1.114274.1818.57443694093781255711482... CT \n", - "28 2.16.840.1.114274.1818.56289009156633266231891... CT \n", - "29 2.16.840.1.114274.1818.49487280544236776531575... CT \n", - "... ... ... \n", - "36627 2.16.840.1.114274.1818.50543575489997508864757... CT \n", - "36628 2.16.840.1.114274.1818.49428524375576773818979... CT \n", - "36629 2.16.840.1.114274.1818.48506951762211287641801... CT \n", - "36630 2.16.840.1.114274.1818.53976293854785801717680... CT \n", - "36631 2.16.840.1.114274.1818.53164750272845914766785... CT \n", - "36632 2.16.840.1.114274.1818.57220952741527326011015... CT \n", - "36633 2.16.840.1.114274.1818.49998392691251620621543... CT \n", - "36634 2.16.840.1.114274.1818.48606589163784907614417... CT \n", - "36635 2.16.840.1.114274.1818.51979215377779423361128... CT \n", - "36636 2.16.840.1.114274.1818.50673217044353872091121... CT \n", - "36637 2.16.840.1.114274.1818.52314624481119033341148... CT \n", - "36638 2.16.840.1.114274.1818.50929911502023812091367... CT \n", - "36639 2.16.840.1.114274.1818.52424787368047838846273... CT \n", - "36640 2.16.840.1.114274.1818.52488403809436029421106... CT \n", - "36641 2.16.840.1.114274.1818.55529830906975846191111... CT \n", - "36642 2.16.840.1.114274.1818.48559459829862204641321... CT \n", - "36643 2.16.840.1.114274.1818.47788242157270206265069... CT \n", - "36644 2.16.840.1.114274.1818.51170213715360990881107... CT \n", - "36645 2.16.840.1.114274.1818.46456861740571239258465... CT \n", - "36646 2.16.840.1.114274.1818.52087799792970052711109... CT \n", - "36647 2.16.840.1.114274.1818.55779880859255759781439... CT \n", - "36648 2.16.840.1.114274.1818.56517423875777975239789... CT \n", - "36649 2.16.840.1.114274.1818.51521500690102685029326... CT \n", - "36650 2.16.840.1.114274.1818.56804893493554909195004... CT \n", - "36651 2.16.840.1.114274.1818.46130115274371592312353... CT \n", - "36652 2.16.840.1.114274.1818.49929890207265010191125... CT \n", - "36653 2.16.840.1.114274.1818.48936021023819044245648... CT \n", - "36654 2.16.840.1.114274.1818.56806844052065594631125... CT \n", - "36655 2.16.840.1.114274.1818.50032364059379807521614... CT \n", - "36656 2.16.840.1.114274.1818.48823377996653460923277... CT \n", - "\n", - " data_format data_type \\\n", - "0 DCM DICOM \n", - "1 DCM DICOM \n", - "2 DCM DICOM \n", - "3 DCM DICOM \n", - "4 DCM DICOM \n", - "5 DCM DICOM \n", - "6 DCM DICOM \n", - "7 DCM DICOM \n", - "8 DCM DICOM \n", - "9 DCM DICOM \n", - "10 DCM DICOM \n", - "11 DCM DICOM \n", - "12 DCM DICOM \n", - "13 DCM DICOM \n", - "14 DCM DICOM \n", - "15 DCM DICOM \n", - "16 DCM DICOM \n", - "17 DCM DICOM \n", - "18 DCM DICOM \n", - "19 DCM DICOM \n", - "20 DCM DICOM \n", - "21 DCM DICOM \n", - "22 DCM DICOM \n", - "23 DCM DICOM \n", - "24 DCM DICOM \n", - "25 DCM DICOM \n", - "26 DCM DICOM \n", - "27 DCM DICOM \n", - "28 DCM DICOM \n", - "29 DCM DICOM \n", - "... ... ... \n", - "36627 DCM DICOM \n", - "36628 DCM DICOM \n", - "36629 DCM DICOM \n", - "36630 DCM DICOM \n", - "36631 DCM DICOM \n", - "36632 DCM DICOM \n", - "36633 DCM DICOM \n", - "36634 DCM DICOM \n", - "36635 DCM DICOM \n", - "36636 DCM DICOM \n", - "36637 DCM DICOM \n", - "36638 DCM DICOM \n", - "36639 DCM DICOM \n", - "36640 DCM DICOM \n", - "36641 DCM DICOM \n", - "36642 DCM DICOM \n", - "36643 DCM DICOM \n", - "36644 DCM DICOM \n", - "36645 DCM DICOM \n", - "36646 DCM DICOM \n", - "36647 DCM DICOM \n", - "36648 DCM DICOM \n", - "36649 DCM DICOM \n", - "36650 DCM DICOM \n", - "36651 DCM DICOM \n", - "36652 DCM DICOM \n", - "36653 DCM DICOM \n", - "36654 DCM DICOM \n", - "36655 DCM DICOM \n", - "36656 DCM DICOM \n", - "\n", - " file_name file_size \\\n", - "0 10003752-FTrypNMZECIjF730AFXfQ/2.16.840.1.1142... 1630002 \n", - "1 2.16.840.1.114274.1818.57601668720416007191725... 42499230 \n", - "2 10003752-1Pj5lmhqK0G7OJDm8wlilA/2.16.840.1.114... 13520868 \n", - "3 2.16.840.1.114274.1818.50059223583904198521565... 28610238 \n", - "4 2.16.840.1.114274.1818.50606639569211286313615... 177471568 \n", - "5 2.16.840.1.114274.1818.53764936885997250101628... 29964882 \n", - "6 2.16.840.1.114274.1818.52909850575691355561481... 22380960 \n", - "7 10000364-366240/CT_2.16.840.1.114274.1818.5555... 31455812 \n", - "8 10000364-1429707/2.16.840.1.114274.1818.559175... 20977236 \n", - "9 10000364-1432081/2.16.840.1.114274.1818.530726... 29605876 \n", - "10 10000364-1259756/2.16.840.1.114274.1818.481538... 192122 \n", - "11 10003752-v7AOAbI1WkOWxd5w6HVOQ/2.16.840.1.1142... 20680718 \n", - "12 2.16.840.1.114274.1818.57262165786098327381143... 23875966 \n", - "13 10000364-1521591/CT_2.16.840.1.114274.1818.510... 13309794 \n", - "14 2.16.840.1.114274.1818.50339192942619654751362... 41874874 \n", - "15 2.16.840.1.114274.1818.57272482713393876731593... 34868058 \n", - "16 2.16.840.1.114274.1818.53858790051352121871692... 31275414 \n", - "17 2.16.840.1.114274.1818.46352567840725257505915... 2736384 \n", - "18 10003752-9fbsIMn6cUyofDlSakKuhg/2.16.840.1.114... 17833978 \n", - "19 2.16.840.1.114274.1818.47023052143718685801473... 23850546 \n", - "20 2.16.840.1.114274.1818.52162114235094682361434... 157263020 \n", - "21 10000364-5739783/2.16.840.1.114274.1818.538796... 80507682 \n", - "22 2.16.840.1.114274.1818.56929896772386057438939... 27302466 \n", - "23 2.16.840.1.114274.1818.46638061106650275419844... 41880808 \n", - 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"36649 10000364-650687/2.16.840.1.114274.1818.5101995... 27824712 \n", - "36650 2.16.840.1.114274.1818.49223169490672576991068... 43249060 \n", - "36651 2.16.840.1.114274.1818.56931261911713855351136... 23492854 \n", - "36652 2.16.840.1.114274.1818.52146263260855674077294... 26016868 \n", - "36653 10000364-1606954/2.16.840.1.114274.1818.524255... 11299930 \n", - "36654 10000364-6101237/CT_2.16.840.1.114274.1818.464... 22389206 \n", - "36655 2.16.840.1.114274.1818.54642616033529162091218... 31716034 \n", - "36656 10000364-1052751/CT_2.16.840.1.114274.1818.497... 13196690 \n", - "\n", - " md5sum \\\n", - "0 5aff7eef47137bcd418dcf21b38ac96b \n", - "1 910821c0599ea333c4be2de47f183136 \n", - "2 af9c12fb02d887bcad79edcf8b89caa2 \n", - "3 320cd3091aa0caa700d1a0a71e254658 \n", - "4 567c35e00d413d4c815d7f2f315b30cf \n", - "5 3240c1e31f49966c720c3ad08987b72d \n", - "6 ded4bd6a84b10718547adce771139566 \n", - "7 522824026385ddc22ecdb6a92017882e \n", - "8 8b47ec344a9e2a2f330a5763e653f820 \n", - "9 2f0c1880ed7c04c64e5aa058708d3fa1 \n", - "10 eb6577c19d468ed618590d6bca18d7cf \n", - "11 b056442a416cc0c2eb31025b6137fce3 \n", - "12 35fa22105259d99a25d007c5445051d0 \n", - "13 5db4041779d83e481e8981886b335e8b \n", - "14 111cc68bdfabca661a673c5f416a749e \n", - "15 e0621331b50fdbc1456737bc8a20c411 \n", - "16 2906a85d1c7609c0e9782b8bfbe025e7 \n", - "17 3359174b405a1034e4a18650a1b7b981 \n", - "18 b498beeb3e804e3cf1f9bf33f84be0f9 \n", - "19 e525340baee22aaa6a3f3a268d9f76f4 \n", - "20 eb903f2945ba8416e8c80e3d53bac62f \n", - "21 fb0152cdc25033e7737b02ad72ad2dfd \n", - "22 2e265c1b6029fde2512149ea4309b629 \n", - "23 b2b6d6e2f01ec1b7b145bc2819276a8e \n", - "24 eea88381ecc557feb1401b90b138642e \n", - "25 5f049fb9ba9aa77c50bb57439d5d4098 \n", - "26 6df7f757ec4f514986a32504b2682e0a \n", - "27 c6e75ce61fea57c76ebd125809a35fda \n", - "28 7abdf2e23870eed93095e34f203a5ffe \n", - "29 83c42a41ae0f01f0faaabd4e6d484690 \n", - "... ... \n", - "36627 d5217f0157a88db605d6d6c9c346ec32 \n", - "36628 1380f29ed0d33bbd4cb67a8ee26ea8b4 \n", - "36629 ae4b0d4a696a5e0b35497126e8ed8a9c \n", - "36630 47ca7c3cb462afbf7992837d7b0bd420 \n", - "36631 a85c1aad0e09d17fff2b0c7dc6beb7e1 \n", - "36632 e20fea4184cf77f8a1d69474fb36691c \n", - "36633 536eec2f24e29fecfb0cdc0f85979a64 \n", - "36634 6c32da7a7883dae75788f8e01fd0816c \n", - "36635 114039aad2ad5af1448441dc4a2cde53 \n", - "36636 2352d0e37386801eac5fd811156c4647 \n", - "36637 48ae335479a508c6812ebaa45a38b745 \n", - "36638 0db2de2aee37689bf041f112417f8e46 \n", - "36639 9210503af030a8f93ae8321d0a544927 \n", - "36640 b42c685cbd7ad9febd30cc2079c32d69 \n", - "36641 ac1af7df8fb301ab6791538c7c6bd153 \n", - "36642 3c7045d96fb1263ae079b5bd460766a4 \n", - "36643 1e0d70f72a79e94a1573d7a94258825e \n", - "36644 b6ff8b3724c2031a92c848f990a1ca00 \n", - "36645 cf29d64165af6fc602f84c5e8b12d255 \n", - "36646 b8eddf89742b1994432ca7f26599f0f5 \n", - "36647 4a401932a98168bafa404cbe9873e6e3 \n", - "36648 321ee723751efaf2c1ba734d8060347a \n", - "36649 56855ea31ffe4bf11eaf0e114febcd37 \n", - "36650 05d22aa82da3bee7be2296974a929504 \n", - "36651 a4095d6067c9fc8f71d5a3dbc8e04b2e \n", - "36652 d04ee65e0bb76ee3921dceeb44f2be43 \n", - "36653 53c58821a20e1b24629a3e21b08dc20a \n", - "36654 671f6f8e55d3a36ad4bf7219a181dc3b \n", - "36655 ff9522be1a76556686f113273a2b1276 \n", - "36656 86b8ca90f474f62ae2965a4823f34d50 \n", - "\n", - " ... pixel_spacing \\\n", - "0 ... NaN \n", - "1 ... NaN \n", - "2 ... NaN \n", - "3 ... NaN \n", - "4 ... NaN \n", - "5 ... NaN \n", - "6 ... NaN \n", - "7 ... NaN \n", - "8 ... NaN \n", - "9 ... NaN \n", - "10 ... NaN \n", - "11 ... NaN \n", - "12 ... NaN \n", - "13 ... NaN \n", - "14 ... NaN \n", - "15 ... NaN \n", - "16 ... NaN \n", - "17 ... NaN \n", - "18 ... NaN \n", - "19 ... NaN \n", - "20 ... NaN \n", - "21 ... NaN \n", - "22 ... NaN \n", - "23 ... NaN \n", - "24 ... NaN \n", - "25 ... NaN \n", - "26 ... NaN \n", - "27 ... NaN \n", - "28 ... NaN \n", - "29 ... NaN \n", - "... ... ... \n", - "36627 ... NaN \n", - "36628 ... NaN \n", - "36629 ... NaN \n", - "36630 ... NaN \n", - "36631 ... NaN \n", - "36632 ... NaN \n", - "36633 ... NaN \n", - "36634 ... NaN \n", - "36635 ... NaN \n", - "36636 ... NaN \n", - "36637 ... NaN \n", - "36638 ... NaN \n", - "36639 ... NaN \n", - "36640 ... NaN \n", - "36641 ... NaN \n", - "36642 ... NaN \n", - "36643 ... NaN \n", - "36644 ... NaN \n", - "36645 ... NaN \n", - "36646 ... NaN \n", - "36647 ... NaN \n", - "36648 ... NaN \n", - "36649 ... NaN \n", - "36650 ... NaN \n", - "36651 ... NaN \n", - "36652 ... NaN \n", - "36653 ... NaN \n", - "36654 ... NaN \n", - "36655 ... NaN \n", - "36656 ... NaN \n", - "\n", - " series_description \\\n", - "0 SCOUT \n", - "1 HELICAL ABD/PELVIS \n", - "2 MIP, iDose (2) \n", - "3 Stone Study 3.0 3 \n", - "4 THINS \n", - "5 CORONAL \n", - "6 STONE STUDY THICK SLICES \n", - "7 SAG BODY \n", - "8 Sagittal10.0 MIP \n", - "9 SAG BODY \n", - "10 Smart Prep Series \n", - "11 MinIP, iDose (2) \n", - "12 ART SAG DLIR MED \n", - "13 CTA MIPS CORONAL \n", - "14 SAGITTAL \n", - "15 DE AP BLEND With 3.0 Br40 2 F_0.5 \n", - "16 coronal abdomen \n", - "17 Smart Prep Series \n", - "18 AXL ART, iDose (2) \n", - "19 Lung \n", - "20 top down rib to rib \n", - "21 PE 1.0mm Thins \n", - "22 LUNGS iDose (2) \n", - "23 sagittal abdomen \n", - "24 Cor \n", - "25 CHEST COR, iDose (2) \n", - "26 HIGH RES, iDose (2) \n", - "27 SCOUT \n", - "28 SAG CHEST 3MM \n", - "29 MIP, iDose (4) \n", - "... ... \n", - "36627 Topogram 0.6 T20f \n", - "36628 CORONAL \n", - "36629 AXL iDose (2) \n", - "36630 CAP WITH 5.00 Bl56 S2 Lung \n", - "36631 AX CHEST PE STD +C F_0.5 \n", - "36632 CHEST MiPS \n", - "36633 VENOUS RECON \n", - "36634 Sag \n", - "36635 Routine Chest \n", - "36636 1.25x.625 standard \n", - "36637 Sag \n", - "36638 MIN IP \n", - "36639 LUNG (CHEST) DLIR MED \n", - "36640 UNINFUSED 3.00 Br40 S3 sag \n", - "36641 CTAC 70 DFOV \n", - "36642 SAG, iDose (2) \n", - "36643 ABD/PELVIS W/O \n", - "36644 MinIP, iDose (2) \n", - "36645 Sag \n", - "36646 SCOUT \n", - "36647 Cor MPR \n", - "36648 sagittal abdomen \n", - "36649 COR BODY \n", - "36650 Sagittal MPR 3.0 \n", - "36651 Coronal 3.0 MPR \n", - "36652 STD AXL \n", - "36653 CHEST 70keV \n", - "36654 chest coronal \n", - "36655 chest sagital \n", - "36656 Fl_LUNG 1.5 Bl57 \n", - "\n", - " series_uid slice_thickness \\\n", - "0 2.16.840.1.114274.1818.49957484958482183452775... NaN \n", - "1 2.16.840.1.114274.1818.47452056560618671341191... NaN \n", - "2 2.16.840.1.114274.1818.51148225759453066365970... NaN \n", - "3 2.16.840.1.114274.1818.49471856660770992231274... NaN \n", - "4 2.16.840.1.114274.1818.54441193239846891335001... NaN \n", - "5 2.16.840.1.114274.1818.52155290829062416411654... NaN \n", - "6 2.16.840.1.114274.1818.49528130810496449261224... NaN \n", - "7 2.16.840.1.114274.1818.55615157599364385421040... NaN \n", - "8 2.16.840.1.114274.1818.50444111535957281371522... NaN \n", - "9 2.16.840.1.114274.1818.49568246924437508717848... NaN \n", - "10 2.16.840.1.114274.1818.50783701110696030245437... NaN \n", - "11 2.16.840.1.114274.1818.55051502853362002701452... NaN \n", - "12 2.16.840.1.114274.1818.55890235514104568851395... NaN \n", - "13 2.16.840.1.114274.1818.50544969920006524349223... NaN \n", - "14 2.16.840.1.114274.1818.46901483095924786198355... NaN \n", - "15 2.16.840.1.114274.1818.53852316720529246871304... NaN \n", - "16 2.16.840.1.114274.1818.47788547502293258367012... NaN \n", - "17 2.16.840.1.114274.1818.50646714107291197723664... NaN \n", - "18 2.16.840.1.114274.1818.53838950720515509061928... NaN \n", - "19 2.16.840.1.114274.1818.48519652760864609681298... NaN \n", - "20 2.16.840.1.114274.1818.49305487510397925131693... NaN \n", - "21 2.16.840.1.114274.1818.50830320114023229651721... NaN \n", - "22 2.16.840.1.114274.1818.49005225458506140691352... NaN \n", - "23 2.16.840.1.114274.1818.47795015415414848191488... NaN \n", - "24 2.16.840.1.114274.1818.49563997547211370316329... NaN \n", - "25 2.16.840.1.114274.1818.53431952655932754076149... NaN \n", - "26 2.16.840.1.114274.1818.52708517308744556571563... NaN \n", - "27 2.16.840.1.114274.1818.57443694093781255711482... NaN \n", - "28 2.16.840.1.114274.1818.56289009156633266231891... NaN \n", - "29 2.16.840.1.114274.1818.49487280544236776531575... NaN \n", - "... ... ... \n", - "36627 2.16.840.1.114274.1818.50543575489997508864757... NaN \n", - "36628 2.16.840.1.114274.1818.49428524375576773818979... NaN \n", - "36629 2.16.840.1.114274.1818.48506951762211287641801... NaN \n", - "36630 2.16.840.1.114274.1818.53976293854785801717680... NaN \n", - "36631 2.16.840.1.114274.1818.53164750272845914766785... NaN \n", - "36632 2.16.840.1.114274.1818.57220952741527326011015... NaN \n", - "36633 2.16.840.1.114274.1818.49998392691251620621543... NaN \n", - "36634 2.16.840.1.114274.1818.48606589163784907614417... NaN \n", - "36635 2.16.840.1.114274.1818.51979215377779423361128... NaN \n", - "36636 2.16.840.1.114274.1818.50673217044353872091121... NaN \n", - "36637 2.16.840.1.114274.1818.52314624481119033341148... NaN \n", - "36638 2.16.840.1.114274.1818.50929911502023812091367... NaN \n", - "36639 2.16.840.1.114274.1818.52424787368047838846273... NaN \n", - "36640 2.16.840.1.114274.1818.52488403809436029421106... NaN \n", - "36641 2.16.840.1.114274.1818.55529830906975846191111... NaN \n", - "36642 2.16.840.1.114274.1818.48559459829862204641321... NaN \n", - "36643 2.16.840.1.114274.1818.47788242157270206265069... NaN \n", - "36644 2.16.840.1.114274.1818.51170213715360990881107... NaN \n", - "36645 2.16.840.1.114274.1818.46456861740571239258465... NaN \n", - "36646 2.16.840.1.114274.1818.52087799792970052711109... NaN \n", - "36647 2.16.840.1.114274.1818.55779880859255759781439... NaN \n", - "36648 2.16.840.1.114274.1818.56517423875777975239789... NaN \n", - "36649 2.16.840.1.114274.1818.51521500690102685029326... NaN \n", - "36650 2.16.840.1.114274.1818.56804893493554909195004... NaN \n", - "36651 2.16.840.1.114274.1818.46130115274371592312353... NaN \n", - "36652 2.16.840.1.114274.1818.49929890207265010191125... NaN \n", - "36653 2.16.840.1.114274.1818.48936021023819044245648... NaN \n", - "36654 2.16.840.1.114274.1818.56806844052065594631125... NaN \n", - "36655 2.16.840.1.114274.1818.50032364059379807521614... NaN \n", - "36656 2.16.840.1.114274.1818.48823377996653460923277... NaN \n", - "\n", - " spacing_between_slices storage_urls \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "17 NaN NaN \n", - "18 NaN NaN \n", - "19 NaN NaN \n", - "20 NaN NaN \n", - "21 NaN NaN \n", - "22 NaN NaN \n", - "23 NaN NaN \n", - "24 NaN NaN \n", - "25 NaN NaN \n", - "26 NaN NaN \n", - "27 NaN NaN \n", - "28 NaN NaN \n", - "29 NaN NaN \n", - "... ... ... \n", - "36627 NaN NaN \n", - "36628 NaN NaN \n", - "36629 NaN NaN \n", - "36630 NaN NaN \n", - "36631 NaN NaN \n", - "36632 NaN NaN \n", - "36633 NaN NaN \n", - "36634 NaN NaN \n", - "36635 NaN NaN \n", - "36636 NaN NaN \n", - "36637 NaN NaN \n", - "36638 NaN NaN \n", - "36639 NaN NaN \n", - "36640 NaN NaN \n", - "36641 NaN NaN \n", - "36642 NaN NaN \n", - "36643 NaN NaN \n", - "36644 NaN NaN \n", - "36645 NaN NaN \n", - "36646 NaN NaN \n", - "36647 NaN NaN \n", - "36648 NaN NaN \n", - "36649 NaN NaN \n", - "36650 NaN NaN \n", - "36651 NaN NaN \n", - "36652 NaN NaN \n", - "36653 NaN NaN \n", - "36654 NaN NaN \n", - "36655 NaN NaN \n", - "36656 NaN NaN \n", - "\n", - " core_metadata_collections.id \\\n", - "0 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "1 NaN \n", - "2 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "8 5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85 \n", - "9 5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85 \n", - "10 5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85 \n", - "11 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "12 NaN \n", - "13 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "14 NaN \n", - "15 NaN \n", - "16 NaN \n", - "17 NaN \n", - "18 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "19 NaN \n", - "20 NaN \n", - "21 5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85 \n", - "22 NaN \n", - "23 NaN \n", - "24 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "25 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "26 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "27 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "28 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "29 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "... ... \n", - "36627 NaN \n", - "36628 NaN \n", - "36629 NaN \n", - "36630 NaN \n", - "36631 9de1d49d-ad56-44b3-be2e-a2795ce41ff3 \n", - "36632 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "36633 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "36634 NaN \n", - "36635 NaN \n", - "36636 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "36637 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "36638 NaN \n", - "36639 NaN \n", - "36640 NaN \n", - "36641 NaN \n", - "36642 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "36643 NaN \n", - "36644 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "36645 NaN \n", - "36646 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "36647 5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85 \n", - "36648 NaN \n", - "36649 5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85 \n", - "36650 NaN \n", - "36651 NaN \n", - "36652 NaN \n", - "36653 e6c5fb12-7467-4a7c-979a-b1e84c5c53df \n", - "36654 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "36655 NaN \n", - "36656 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "\n", - " core_metadata_collections.submitter_id \\\n", - "0 ACR_20220107 \n", - "1 NaN \n", - "2 ACR_20220107 \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 ACR_20211020 \n", - "8 ACR_20210827 \n", - "9 ACR_20210827 \n", - "10 ACR_20210827 \n", - "11 ACR_20220107 \n", - "12 NaN \n", - "13 ACR_20211020 \n", - "14 NaN \n", - "15 NaN \n", - "16 NaN \n", - "17 NaN \n", - "18 ACR_20220107 \n", - "19 NaN \n", - "20 NaN \n", - "21 ACR_20210827 \n", - "22 NaN \n", - "23 NaN \n", - "24 ACR_20211020 \n", - "25 ACR_20220107 \n", - "26 ACR_20220107 \n", - "27 ACR_20220107 \n", - "28 ACR_20211020 \n", - "29 ACR_20220107 \n", - "... ... \n", - "36627 NaN \n", - "36628 NaN \n", - "36629 NaN \n", - "36630 NaN \n", - "36631 ACR_20210915 \n", - "36632 ACR_20220107 \n", - "36633 ACR_20220107 \n", - "36634 NaN \n", - "36635 NaN \n", - "36636 ACR_20211020 \n", - "36637 ACR_20211020 \n", - "36638 NaN \n", - "36639 NaN \n", - "36640 NaN \n", - "36641 NaN \n", - "36642 ACR_20220107 \n", - "36643 NaN \n", - "36644 ACR_20220107 \n", - "36645 NaN \n", - "36646 ACR_20211020 \n", - "36647 ACR_20210827 \n", - "36648 NaN \n", - "36649 ACR_20210827 \n", - "36650 NaN \n", - "36651 NaN \n", - "36652 NaN \n", - "36653 ACR_20210823 \n", - "36654 ACR_20211020 \n", - "36655 NaN \n", - "36656 ACR_20211020 \n", - "\n", - " imaging_studies.id \\\n", - "0 25076607-be7f-47d3-816b-dcb5fa7363c7 \n", - "1 ad885b5b-715b-48c9-a418-907a495de131 \n", - "2 523ad313-380b-43aa-8a5f-aeed4bf51570 \n", - "3 cd0aa457-f676-4888-94d2-438c1c55e1fc \n", - "4 65ba9271-5756-4a71-8fcc-f1cd5836671e \n", - "5 54eae512-138c-4570-9586-62c715ce2a54 \n", - "6 25884807-c3b8-4ca6-bbb7-91fee879fcca \n", - "7 3f94f8f2-1722-489f-a698-e39bc88044ab \n", - "8 c9721d8c-6436-4b54-8621-a712a1845784 \n", - "9 3e4d5680-5302-4b1e-a6f5-84b21f6b1c4b \n", - 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1514ct_series_file0ac1f5ac-948e-484f-abbe-15dfdb3c5ab6Open-A12.16.840.1.114274.1818.52139856230859676691361...CTDCMDICOM2.16.840.1.114274.1818.47546942155557792113367...2618937804cd254193a2add4f14a2ef8fa6bf0a8...NaNSAG iDose (2)2.16.840.1.114274.1818.52139856230859676691361...NaNNaNNaNNaNNaNb1ccf140-c713-49a1-8ed0-e0c9a54bf6382.16.840.1.114274.1818.47546942155557792113367...
1634ct_series_file0b8b4506-9e60-42fa-9abd-a36f37141369Open-A12.16.840.1.114274.1818.55796402103412404711436...CTDCMDICOM10003752-WGPvINkkL0CIrSueIVq1Jg/2.16.840.1.114...2632872069163820da9562541641a9e25b3cf0b8...NaNLUNGS, iDose (2)2.16.840.1.114274.1818.55796402103412404711436...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107ed36c0cb-d9d9-44c3-a8d2-5cfd0fdaf8692.16.840.1.114274.1818.51055078868275614981128...
1791ct_series_file0c99ef80-1fa8-4568-a26d-5a85fbe2f872Open-A12.16.840.1.114274.1818.55152454794275602154480...CTDCMDICOM10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427...24220430804d17d27ace478bcb074b83733b9b5a...NaNVEN SAG CHEST2.16.840.1.114274.1818.55152454794275602154480...NaNNaNNaN9de1d49d-ad56-44b3-be2e-a2795ce41ff3ACR_20210915ac540136-37c8-47f8-a692-128ba3e3b3392.16.840.1.114274.1818.47548839393717393311310...
1886ct_series_file0d625da5-6509-49e2-b6f0-fa7752653861Open-A12.16.840.1.114274.1818.56531355311072326471027...CTDCMDICOM10003752-BRjhzfVUa0unnKEp9MeyAA/2.16.840.1.114...823170343a7ca99bf5872c39a5f243e92f5da82f...NaNHI RES, iDose (2)2.16.840.1.114274.1818.56531355311072326471027...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107c5f338ef-1e8d-4e46-8cef-290e171d6aba2.16.840.1.114274.1818.46938907313235410118693...
1923ct_series_file0d9c8ac3-95f4-434d-b55f-3f34b399655eOpen-A12.16.840.1.114274.1818.52546362306442159622974...CTDCMDICOM10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427...10555512870cc072ae1eb167fc74dad3cf5d98cfa...NaNCHEST SOURCE WO2.16.840.1.114274.1818.52546362306442159622974...NaNNaNNaN9de1d49d-ad56-44b3-be2e-a2795ce41ff3ACR_2021091545dbe3d9-25dd-4bdb-b77f-a4c29ada82592.16.840.1.114274.1818.48324760574904930945338...
2035ct_series_file0e545904-3ef8-4a82-aa9f-ead20ec91a63Open-A12.16.840.1.114274.1818.47062791267923794261751...CTDCMDICOM2.16.840.1.114274.1818.50232371834041182001774...1553584e3c40e6f456270709e4a5b984a65be8a...NaNSCOUT2.16.840.1.114274.1818.47062791267923794261751...NaNNaNNaNNaNNaNc3badb66-676f-43bd-becf-bc58608d31972.16.840.1.114274.1818.50232371834041182001774...
2089ct_series_file0ed788a2-dce9-429a-b111-21efae900331Open-A12.16.840.1.114274.1818.54346279024422850746055...CTDCMDICOM2.16.840.1.114274.1818.47546942155557792113367...206813504e79fe1537c3a44d9cc8c41547dd2739...NaNAXL iDose (2)2.16.840.1.114274.1818.54346279024422850746055...NaNNaNNaNNaNNaNb1ccf140-c713-49a1-8ed0-e0c9a54bf6382.16.840.1.114274.1818.47546942155557792113367...
2201ct_series_file0fa5b77e-a71a-4538-a10f-38abc89b6edeOpen-A12.16.840.1.114274.1818.53418313407643202564340...CTDCMDICOM2.16.840.1.114274.1818.49162333685696322481506...32686758b0b1ab8550fa90a52ec335dffda44390...NaNCOR iDose (2)2.16.840.1.114274.1818.53418313407643202564340...NaNNaNNaNNaNNaN2ab3f394-c819-4fe0-ae9c-cf286ab32b172.16.840.1.114274.1818.49162333685696322481506...
2216ct_series_file0fbec308-196d-4bec-9081-57cb05600d96Open-A12.16.840.1.114274.1818.56923453410353910621162...CTDCMDICOM10000364-1749434/CT_2.16.840.1.114274.1818.517...49938244fb614691bf3154c9770bb10414535039...NaNLUNG2.16.840.1.114274.1818.56923453410353910621162...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_2021102040ed42e4-d19f-4f3d-bd55-e0a72874bfbd2.16.840.1.114274.1818.51756275185682361958967...
2327ct_series_file1096077e-1cda-44b5-93af-70c977b0b618Open-A12.16.840.1.114274.1818.47450795471739653425584...CTDCMDICOM2.16.840.1.114274.1818.51339555956390258931766...237050378b618c0e0551f3ecc67e685c0be3a21d6...NaNAXIAL LUNG B60f 1.52.16.840.1.114274.1818.47450795471739653425584...NaNNaNNaNNaNNaN051a7ef7-9137-4c2c-8b72-7672511d5bdd2.16.840.1.114274.1818.51339555956390258931766...
2328ct_series_file109760b8-d095-4118-8f3b-5a60842e1b01Open-A12.16.840.1.114274.1818.53802420710852412653261...CTDCMDICOM10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427...182453506f4dadfd447c9ccd5a5d9940f5e193c4...NaNART COR CHEST2.16.840.1.114274.1818.53802420710852412653261...NaNNaNNaN9de1d49d-ad56-44b3-be2e-a2795ce41ff3ACR_2021091545dbe3d9-25dd-4bdb-b77f-a4c29ada82592.16.840.1.114274.1818.48324760574904930945338...
2367ct_series_file10c06fd3-a6de-4eb4-ae19-7dd679372762Open-A12.16.840.1.114274.1818.54548482228493910589530...CTDCMDICOM2.16.840.1.114274.1818.50543623976916621593618...320856061fa5ae5626f3356a7769e96b172af340...NaN2x2 cor abd2.16.840.1.114274.1818.54548482228493910589530...NaNNaNNaNNaNNaNc535df5c-88a8-4328-a8a8-5a7ad0129dd52.16.840.1.114274.1818.50543623976916621593618...
2419ct_series_file112ca118-40af-4298-aee8-e8f963ff1b1aOpen-A12.16.840.1.114274.1818.47247454113117857471988...CTDCMDICOM2.16.840.1.114274.1818.49162333685696322481506...12195588222f9da4bda439c4fd78b221c6a3558e...NaNMIP iDose (2)2.16.840.1.114274.1818.47247454113117857471988...NaNNaNNaNNaNNaN2ab3f394-c819-4fe0-ae9c-cf286ab32b172.16.840.1.114274.1818.49162333685696322481506...
2491ct_series_file11bfed4c-307a-47e3-a60b-b0a2ac454fceOpen-A12.16.840.1.114274.1818.51827490655301756669271...CTDCMDICOM2.16.840.1.114274.1818.57135063690911574026567...3452312296d368d656cd364690d1ec3941ee2f60...NaNSag2.16.840.1.114274.1818.51827490655301756669271...NaNNaNNaNNaNNaN81de2138-d56d-43ee-b79e-4da9bc23828e2.16.840.1.114274.1818.57135063690911574026567...
2558ct_series_file121b72e0-debf-43e0-abd8-314fffb11bc4Open-A12.16.840.1.114274.1818.57532792989429822161041...CTDCMDICOM10000364-6147957/2.16.840.1.114274.1818.474057...44255728651d11a1441e83f9e1059c25398f06f9...NaNSag MPR2.16.840.1.114274.1818.57532792989429822161041...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_20210827b8e40ac4-e051-4e12-85e7-f96841da32902.16.840.1.114274.1818.47405767010972118591122...
2659ct_series_file12d2d363-a5b8-4384-bd62-7820a6879223Open-A12.16.840.1.114274.1818.48341341055219706652697...CTDCMDICOM10000364-1749434/CT_2.16.840.1.114274.1818.562...40454726750ac6302c8290a25aab353137133c38...NaNAXIALS2.16.840.1.114274.1818.48341341055219706652697...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_20211020c35746bf-9ca6-4c4b-807d-3d2b7c81663d2.16.840.1.114274.1818.56236958931622562032480...
2660ct_series_file12dc68c2-8a15-4dcf-9d28-03c1933ef108Open-A12.16.840.1.114274.1818.48194208187608996421285...CTDCMDICOM2.16.840.1.114274.1818.48497422923503503389003...1293608d7ed310cb84440da3a196a2d2082987b...NaNSCOUT2.16.840.1.114274.1818.48194208187608996421285...NaNNaNNaNNaNNaN6ddc58c9-ca3e-42d2-9167-655cace7436e2.16.840.1.114274.1818.48497422923503503389003...
2706ct_series_file1332c384-25a6-4573-9703-bcce6026dd38Open-A12.16.840.1.114274.1818.55984719531389594114093...CTDCMDICOM2.16.840.1.114274.1818.52980787041006160911466...375404c4ac6d899529a0306a1ab3a7c5a5deb9...NaNSURVIEW2.16.840.1.114274.1818.55984719531389594114093...NaNNaNNaNNaNNaN1471390f-a523-4656-9c79-f90c2417472c2.16.840.1.114274.1818.52980787041006160911466...
..................................................................
34288ct_series_filef02a84e6-86f4-4bcf-86f8-cf1f6c5449cdOpen-A12.16.840.1.114274.1818.54196933174925358285960...CTDCMDICOM10000364-1380451/2.16.840.1.114274.1818.576184...69649710bedfbf82b37a576c01f3c1b391a8a0f5...NaNCOR CHEST PE STD MIP +C F_0.62.16.840.1.114274.1818.54196933174925358285960...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_20210827d646ae78-6f8c-48e2-b179-242060af43a92.16.840.1.114274.1818.57618452499506629833358...
34309ct_series_filef04c5e73-79d9-43ba-a7d5-b236babd7609Open-A12.16.840.1.114274.1818.56073649327926434991462...CTDCMDICOM10000364-1319431/CT_2.16.840.1.114274.1818.540...1863574414992b03b4af154e4d0b90c16ae0043...NaNMonitoring 10.0 Br382.16.840.1.114274.1818.56073649327926434991462...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_2021102008f71e93-ebc9-4e1e-a4ee-5013e595d3dd2.16.840.1.114274.1818.54095403294717579441711...
34483ct_series_filef16a446a-000f-4149-a1cb-882394aed86dOpen-A12.16.840.1.114274.1818.49253156750623446381395...CTDCMDICOM2.16.840.1.114274.1818.55800604469442512806545...19400978509f5335362f9eb0592bc5e7eb14cf58...NaNChest Sag2.16.840.1.114274.1818.49253156750623446381395...NaNNaNNaNNaNNaNa7e7db95-50d4-4c55-bec1-5a61f14b48522.16.840.1.114274.1818.55800604469442512806545...
34484ct_series_filef16ac215-67db-4872-abdd-777501c064baOpen-A12.16.840.1.114274.1818.57131787984902380351364...CTDCMDICOM10000364-1261658/2.16.840.1.114274.1818.515636...221100986799185cb1194332e48965ca345c3e39...NaNSAGITTAL MPR2.16.840.1.114274.1818.57131787984902380351364...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_202108270ec9de60-e1bf-492c-b7b5-d60bcf5acfd82.16.840.1.114274.1818.51563634460029048314588...
34582ct_series_filef2121ead-5527-4c42-b6a9-e9853315d6a4Open-A12.16.840.1.114274.1818.50719568566687396181626...CTDCMDICOM10003752-9fVajlRxk06kNBV8GCiR6g/2.16.840.1.114...1789874491dccebfa13fe49b55bddca9c3e9f814...NaNMinIP, iDose (2)2.16.840.1.114274.1818.50719568566687396181626...NaNNaNNaN9de1d49d-ad56-44b3-be2e-a2795ce41ff3ACR_20210915d4d5ef53-e937-4c60-a9e0-e6923e3205be2.16.840.1.114274.1818.49755462096846527711347...
34833ct_series_filef3b807c8-1733-4425-874c-91b45d6dfeddOpen-A12.16.840.1.114274.1818.55892374236772729211324...CTDCMDICOM10003752-LjN5UD29KE6UXIPjxQ4Mw/2.16.840.1.1142...8814744a21b109a2c23b616822e27867d8f9c5...NaNScout2.16.840.1.114274.1818.55892374236772729211324...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107230ce6b6-9d5f-42b4-a93f-5cc21da36a992.16.840.1.114274.1818.56722407795165087528672...
34931ct_series_filef45769a8-db17-4a9e-93d6-5b413b7ad8acOpen-A12.16.840.1.114274.1818.54036272945275104811569...CTDCMDICOM2.16.840.1.114274.1818.52072902194468673511281...36541080f9d5e1f71b97aca680f02174b5d0491d...NaNSAG mips2.16.840.1.114274.1818.54036272945275104811569...NaNNaNNaNNaNNaN0e457e95-33af-48ee-9cc9-ba5c410982072.16.840.1.114274.1818.52072902194468673511281...
34933ct_series_filef45a87e3-4402-4a1a-ae8f-a674f69649d5Open-A12.16.840.1.114274.1818.53533151318719223819997...CTDCMDICOM10000364-6492944/CT_2.16.840.1.114274.1818.463...30212946739ec15e6ec83e56c90eeb8c65012966...NaNCor MIP2.16.840.1.114274.1818.53533151318719223819997...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_202110203b3ecb3d-e4d6-4c13-950a-eec9eea349732.16.840.1.114274.1818.46370819974182057886888...
34966ct_series_filef48d7064-842f-458e-afe9-8e74df06714fOpen-A12.16.840.1.114274.1818.57116847183581503751372...CTDCMDICOM10000364-1749434/CT_2.16.840.1.114274.1818.517...414750ee3c672401ec1e3de55a18498b400fa9...NaNSmart Prep Series2.16.840.1.114274.1818.57116847183581503751372...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_2021102040ed42e4-d19f-4f3d-bd55-e0a72874bfbd2.16.840.1.114274.1818.51756275185682361958967...
35005ct_series_filef4dbda7b-cd35-41a1-8987-a662e64b4f89Open-A12.16.840.1.114274.1818.52937350291748289925686...CTDCMDICOM2.16.840.1.114274.1818.52336546464586758891957...36878704249b0b3d71f3af03e3111b08377ad0fa...NaNABD SAG 2x22.16.840.1.114274.1818.52937350291748289925686...NaNNaNNaNNaNNaNcd15c4ec-5c48-47a7-826f-7cadf233198a2.16.840.1.114274.1818.52336546464586758891957...
35166ct_series_filef5f9cc4b-7fd0-40c3-b9ae-b38afe98e074Open-A12.16.840.1.114274.1818.48769881958854835438888...CTDCMDICOM10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427...219386647f29c4a6ff3d317a9ccbce881d0430a0...NaNVEN COR CHEST2.16.840.1.114274.1818.48769881958854835438888...NaNNaNNaN9de1d49d-ad56-44b3-be2e-a2795ce41ff3ACR_202109155087e689-35e3-4fb5-93b9-9896d6cf21de2.16.840.1.114274.1818.48688028118645704643913...
35180ct_series_filef6130b7c-fcbc-4f72-bd49-cf850a670384Open-A12.16.840.1.114274.1818.49522960612900783455913...CTDCMDICOM2.16.840.1.114274.1818.55737110823469461175134...51936220dd5be357a284f37e73b057d7e29a1cdb...NaNHI RES2.16.840.1.114274.1818.49522960612900783455913...NaNNaNNaNNaNNaN1a9f00b1-be35-4293-b80c-031c00b789072.16.840.1.114274.1818.55737110823469461175134...
35231ct_series_filef6655af5-4ae5-4aad-bfdb-ee908f35b6aaOpen-A12.16.840.1.114274.1818.51372862075384294291311...CTDCMDICOM10003752-dYN3RzoP4kYJ7IhSsJhxA/2.16.840.1.1142...5804686a651d7397bbc40f5811a8474d5f07f24...NaNSTD AXL2.16.840.1.114274.1818.51372862075384294291311...NaNNaNNaN9de1d49d-ad56-44b3-be2e-a2795ce41ff3ACR_2021091585534fe4-fbb9-4c2e-b6cc-297cc1f6876d2.16.840.1.114274.1818.55292279166442805687676...
35243ct_series_filef681be3d-2c48-4a90-9e18-e3b6f7d3e5b7Open-A12.16.840.1.114274.1818.53929921450115012801214...CTDCMDICOM10000364-6147957/2.16.840.1.114274.1818.474057...465916369dab7e6cada6450e711128e89ff44ac3...NaNPE Lungs2.16.840.1.114274.1818.53929921450115012801214...NaNNaNNaN5f596eb7-d79b-47d3-8ff0-1d4ba3d2ea85ACR_20210827b8e40ac4-e051-4e12-85e7-f96841da32902.16.840.1.114274.1818.47405767010972118591122...
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482 rows × 34 columns

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CT \n", - "35250 2.16.840.1.114274.1818.54224394692577902551673... CT \n", - "35341 2.16.840.1.114274.1818.57006587110347619497516... CT \n", - "35377 2.16.840.1.114274.1818.50354021659339484474916... CT \n", - "35395 2.16.840.1.114274.1818.48227151261486612789842... CT \n", - "35399 2.16.840.1.114274.1818.49265557179383310379151... CT \n", - "35556 2.16.840.1.114274.1818.54099736554534158663855... CT \n", - "35637 2.16.840.1.114274.1818.50892074178737076771500... CT \n", - "35658 2.16.840.1.114274.1818.49632659233623505791789... CT \n", - "35693 2.16.840.1.114274.1818.50800570938328320834464... CT \n", - "35718 2.16.840.1.114274.1818.54230946313288245235004... CT \n", - "35920 2.16.840.1.114274.1818.56589161309160872506680... CT \n", - "36017 2.16.840.1.114274.1818.54961336870100270459213... CT \n", - "36256 2.16.840.1.114274.1818.48681936645061680929956... CT \n", - "36433 2.16.840.1.114274.1818.48439609856238435821718... CT \n", - "36505 2.16.840.1.114274.1818.57442183453554543434456... CT \n", - "36652 2.16.840.1.114274.1818.49929890207265010191125... CT \n", - "\n", - " data_format data_type \\\n", - "93 DCM DICOM \n", - "499 DCM DICOM \n", - "633 DCM DICOM \n", - "761 DCM DICOM \n", - "772 DCM DICOM \n", - "914 DCM DICOM \n", - "916 DCM DICOM \n", - "1101 DCM DICOM \n", - "1178 DCM DICOM \n", - "1259 DCM DICOM \n", - "1414 DCM DICOM \n", - "1415 DCM DICOM \n", - "1514 DCM DICOM \n", - "1634 DCM DICOM \n", - "1791 DCM DICOM \n", - "1886 DCM DICOM \n", - "1923 DCM DICOM \n", - "2035 DCM DICOM \n", - "2089 DCM DICOM \n", - "2201 DCM DICOM \n", - "2216 DCM DICOM \n", - "2327 DCM DICOM \n", - "2328 DCM DICOM \n", - "2367 DCM DICOM \n", - "2419 DCM DICOM \n", - "2491 DCM DICOM \n", - "2558 DCM DICOM \n", - "2659 DCM DICOM \n", - "2660 DCM DICOM \n", - "2706 DCM DICOM \n", - "... ... ... \n", - "34288 DCM DICOM \n", - "34309 DCM DICOM \n", - "34483 DCM DICOM \n", - "34484 DCM DICOM \n", - "34582 DCM DICOM \n", - "34833 DCM DICOM \n", - "34931 DCM DICOM \n", - "34933 DCM DICOM \n", - "34966 DCM DICOM \n", - "35005 DCM DICOM \n", - "35166 DCM DICOM \n", - "35180 DCM DICOM \n", - "35231 DCM DICOM \n", - "35243 DCM DICOM \n", - "35250 DCM DICOM \n", - "35341 DCM DICOM \n", - "35377 DCM DICOM \n", - "35395 DCM DICOM \n", - "35399 DCM DICOM \n", - "35556 DCM DICOM \n", - "35637 DCM DICOM \n", - "35658 DCM DICOM \n", - "35693 DCM DICOM \n", - "35718 DCM DICOM \n", - "35920 DCM DICOM \n", - "36017 DCM DICOM \n", - "36256 DCM DICOM \n", - "36433 DCM DICOM \n", - "36505 DCM DICOM \n", - "36652 DCM DICOM \n", - "\n", - " file_name file_size \\\n", - "93 2.16.840.1.114274.1818.47546942155557792113367... 13324066 \n", - "499 10000364-1319431/CT_2.16.840.1.114274.1818.540... 43928694 \n", - "633 10003752-WGPvINkkL0CIrSueIVq1Jg/2.16.840.1.114... 469918 \n", - "761 10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427... 64672416 \n", - "772 10003752-BRjhzfVUa0unnKEp9MeyAA/2.16.840.1.114... 13105028 \n", - "914 2.16.840.1.114274.1818.52468874831763838151313... 19040976 \n", - "916 2.16.840.1.114274.1818.52072902194468673511281... 69860 \n", - "1101 2.16.840.1.114274.1818.52072902194468673511281... 34612422 \n", - "1178 10003752-WGPvINkkL0CIrSueIVq1Jg/2.16.840.1.114... 21140310 \n", - "1259 10000364-6492944/CT_2.16.840.1.114274.1818.463... 52298106 \n", - "1414 10000364-1319431/CT_2.16.840.1.114274.1818.540... 48807050 \n", - "1415 2.16.840.1.114274.1818.47546942155557792113367... 25982368 \n", - "1514 2.16.840.1.114274.1818.47546942155557792113367... 26189378 \n", - "1634 10003752-WGPvINkkL0CIrSueIVq1Jg/2.16.840.1.114... 26328720 \n", - "1791 10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427... 24220430 \n", - "1886 10003752-BRjhzfVUa0unnKEp9MeyAA/2.16.840.1.114... 82317034 \n", - "1923 10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427... 105555128 \n", - "2035 2.16.840.1.114274.1818.50232371834041182001774... 1553584 \n", - "2089 2.16.840.1.114274.1818.47546942155557792113367... 20681350 \n", - "2201 2.16.840.1.114274.1818.49162333685696322481506... 32686758 \n", - "2216 10000364-1749434/CT_2.16.840.1.114274.1818.517... 49938244 \n", - "2327 2.16.840.1.114274.1818.51339555956390258931766... 237050378 \n", - "2328 10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427... 18245350 \n", - "2367 2.16.840.1.114274.1818.50543623976916621593618... 32085606 \n", - "2419 2.16.840.1.114274.1818.49162333685696322481506... 12195588 \n", - "2491 2.16.840.1.114274.1818.57135063690911574026567... 34523122 \n", - "2558 10000364-6147957/2.16.840.1.114274.1818.474057... 44255728 \n", - "2659 10000364-1749434/CT_2.16.840.1.114274.1818.562... 40454726 \n", - "2660 2.16.840.1.114274.1818.48497422923503503389003... 1293608 \n", - "2706 2.16.840.1.114274.1818.52980787041006160911466... 375404 \n", - "... ... ... \n", - "34288 10000364-1380451/2.16.840.1.114274.1818.576184... 69649710 \n", - "34309 10000364-1319431/CT_2.16.840.1.114274.1818.540... 1863574 \n", - "34483 2.16.840.1.114274.1818.55800604469442512806545... 19400978 \n", - "34484 10000364-1261658/2.16.840.1.114274.1818.515636... 22110098 \n", - "34582 10003752-9fVajlRxk06kNBV8GCiR6g/2.16.840.1.114... 17898744 \n", - "34833 10003752-LjN5UD29KE6UXIPjxQ4Mw/2.16.840.1.1142... 881474 \n", - "34931 2.16.840.1.114274.1818.52072902194468673511281... 36541080 \n", - "34933 10000364-6492944/CT_2.16.840.1.114274.1818.463... 30212946 \n", - "34966 10000364-1749434/CT_2.16.840.1.114274.1818.517... 414750 \n", - "35005 2.16.840.1.114274.1818.52336546464586758891957... 36878704 \n", - "35166 10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427... 21938664 \n", - "35180 2.16.840.1.114274.1818.55737110823469461175134... 51936220 \n", - "35231 10003752-dYN3RzoP4kYJ7IhSsJhxA/2.16.840.1.1142... 5804686 \n", - "35243 10000364-6147957/2.16.840.1.114274.1818.474057... 46591636 \n", - "35250 10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427... 25202000 \n", - "35341 2.16.840.1.114274.1818.52072902194468673511281... 36124288 \n", - "35377 2.16.840.1.114274.1818.56770327176963468622571... 77259644 \n", - "35395 10000364-1749434/CT_2.16.840.1.114274.1818.562... 405472 \n", - "35399 10003752-jYqP2dXmUuXzdgQTpTeg/2.16.840.1.11427... 11595286 \n", - "35556 10000364-6492944/CT_2.16.840.1.114274.1818.463... 36221524 \n", - "35637 2.16.840.1.114274.1818.55423042293544820171375... 104404 \n", - "35658 2.16.840.1.114274.1818.57135063690911574026567... 16819028 \n", - "35693 2.16.840.1.114274.1818.49959742781222587766338... 20915564 \n", - "35718 2.16.840.1.114274.1818.48424175505031677266944... 53514656 \n", - "35920 10000364-1319431/CT_2.16.840.1.114274.1818.540... 191022 \n", - "36017 10003752-8NkCyxAx0W4beO3Liivyg/2.16.840.1.1142... 386150 \n", - "36256 2.16.840.1.114274.1818.53129077508732444431454... 20340926 \n", - "36433 2.16.840.1.114274.1818.55737110823469461175134... 1528872 \n", - "36505 10003752-WGPvINkkL0CIrSueIVq1Jg/2.16.840.1.114... 7790730 \n", - "36652 2.16.840.1.114274.1818.52146263260855674077294... 26016868 \n", - "\n", - " md5sum \\\n", - "93 6eb3dcf365021935f8186b1752dbf0e0 \n", - "499 48e07096e513efa9c7815dfeada1063b \n", - "633 cbee0716c15a9a7f9233550b6647d1b4 \n", - "761 79eec0a0c0898d6cf425983f78d2c5e5 \n", - "772 fbc18badef75b5621fea2003bc92ac9f \n", - "914 1d9b8883fa3828539883fd7198cda6a5 \n", - "916 037df94c1b18afdc211c1d60492bf889 \n", - "1101 d533202bad43e717ff70efaf0e3526c1 \n", - "1178 46c0e664a16934dc8a29fdb1dc482c7d \n", - "1259 0172ef3bdbf53b8e3d5a70db4282fd3e \n", - "1414 ec7041bb5dfea1573ed0448d09666ffa \n", - "1415 24e348db4f0607d2838048bdf0295f91 \n", - "1514 04cd254193a2add4f14a2ef8fa6bf0a8 \n", - "1634 69163820da9562541641a9e25b3cf0b8 \n", - "1791 804d17d27ace478bcb074b83733b9b5a \n", - "1886 3a7ca99bf5872c39a5f243e92f5da82f \n", - "1923 70cc072ae1eb167fc74dad3cf5d98cfa \n", - "2035 e3c40e6f456270709e4a5b984a65be8a \n", - "2089 4e79fe1537c3a44d9cc8c41547dd2739 \n", - "2201 b0b1ab8550fa90a52ec335dffda44390 \n", - "2216 fb614691bf3154c9770bb10414535039 \n", - "2327 b618c0e0551f3ecc67e685c0be3a21d6 \n", - "2328 6f4dadfd447c9ccd5a5d9940f5e193c4 \n", - "2367 1fa5ae5626f3356a7769e96b172af340 \n", - "2419 222f9da4bda439c4fd78b221c6a3558e \n", - "2491 96d368d656cd364690d1ec3941ee2f60 \n", - "2558 651d11a1441e83f9e1059c25398f06f9 \n", - "2659 750ac6302c8290a25aab353137133c38 \n", - "2660 d7ed310cb84440da3a196a2d2082987b \n", - "2706 c4ac6d899529a0306a1ab3a7c5a5deb9 \n", - "... ... \n", - "34288 bedfbf82b37a576c01f3c1b391a8a0f5 \n", - "34309 414992b03b4af154e4d0b90c16ae0043 \n", - "34483 509f5335362f9eb0592bc5e7eb14cf58 \n", - "34484 6799185cb1194332e48965ca345c3e39 \n", - "34582 91dccebfa13fe49b55bddca9c3e9f814 \n", - "34833 4a21b109a2c23b616822e27867d8f9c5 \n", - "34931 f9d5e1f71b97aca680f02174b5d0491d \n", - "34933 739ec15e6ec83e56c90eeb8c65012966 \n", - "34966 ee3c672401ec1e3de55a18498b400fa9 \n", - "35005 249b0b3d71f3af03e3111b08377ad0fa \n", - "35166 7f29c4a6ff3d317a9ccbce881d0430a0 \n", - "35180 dd5be357a284f37e73b057d7e29a1cdb \n", - "35231 a651d7397bbc40f5811a8474d5f07f24 \n", - "35243 9dab7e6cada6450e711128e89ff44ac3 \n", - "35250 593370a0db3bf4ac18beec30762e1caf \n", - "35341 b082f2467399d512d54f7d383da843e4 \n", - "35377 636fec68c654ad6def024f96d90cc446 \n", - "35395 916a4e96b6bc95f72c26c7b0728263da \n", - "35399 ab6aa87685181a09c68f134d9644113d \n", - "35556 12c7bef1276ace41cd9d1e74e2f48c69 \n", - "35637 9d3adccda90845de3aafdb5f43e14bb9 \n", - "35658 ec785b71199f815aa2dec7ff65da7231 \n", - "35693 3dfbd5307d5bfe2d5ac1dafdd05b0a75 \n", - "35718 ec81b9eb9308e7c720a2dbe39cd052af \n", - "35920 6960ab96967c8651dcc728d748767b54 \n", - "36017 266bbab282a47aa334825a10dc805274 \n", - "36256 2fd3eb2ea5ef52f61a97a720705e56fb \n", - "36433 95ac788429830c0cb5f7c7fcb8ea1e4d \n", - "36505 a48d8e5db5e85c3a64f6e84608dc2a3c \n", - "36652 d04ee65e0bb76ee3921dceeb44f2be43 \n", - "\n", - " ... pixel_spacing \\\n", - "93 ... NaN \n", - "499 ... NaN \n", - "633 ... NaN \n", - "761 ... NaN \n", - "772 ... NaN \n", - "914 ... NaN \n", - "916 ... NaN \n", - "1101 ... NaN \n", - "1178 ... NaN \n", - "1259 ... NaN \n", - "1414 ... NaN \n", - "1415 ... NaN \n", - "1514 ... NaN \n", - "1634 ... NaN \n", - "1791 ... NaN \n", - "1886 ... NaN \n", - "1923 ... NaN \n", - "2035 ... NaN \n", - "2089 ... NaN \n", - "2201 ... NaN \n", - "2216 ... NaN \n", - "2327 ... NaN \n", - "2328 ... NaN \n", - "2367 ... NaN \n", - "2419 ... NaN \n", - "2491 ... NaN \n", - "2558 ... NaN \n", - "2659 ... NaN \n", - "2660 ... NaN \n", - "2706 ... NaN \n", - "... ... ... \n", - "34288 ... NaN \n", - "34309 ... NaN \n", - "34483 ... NaN \n", - "34484 ... NaN \n", - "34582 ... NaN \n", - "34833 ... NaN \n", - "34931 ... NaN \n", - "34933 ... NaN \n", - "34966 ... NaN \n", - "35005 ... NaN \n", - "35166 ... NaN \n", - "35180 ... NaN \n", - "35231 ... NaN \n", - "35243 ... NaN \n", - "35250 ... NaN \n", - "35341 ... NaN \n", - "35377 ... NaN \n", - "35395 ... NaN \n", - "35399 ... NaN \n", - "35556 ... NaN \n", - "35637 ... NaN \n", - "35658 ... NaN \n", - "35693 ... NaN \n", - "35718 ... NaN \n", - "35920 ... NaN \n", - "36017 ... NaN \n", - "36256 ... NaN \n", - "36433 ... NaN \n", - "36505 ... NaN \n", - "36652 ... NaN \n", - "\n", - " series_description \\\n", - "93 MIP iDose (2) \n", - "499 #PP DE_Thorax 1.5 Qr40 2 B_Sn140kV \n", - "633 SURVIEW \n", - "761 HI RES \n", - "772 MIP, iDose (2) \n", - "914 Chest Sag \n", - "916 Topogram 0.6 T20s \n", - "1101 COR mips \n", - "1178 AXL, iDose (2) \n", - "1259 Sag MPR \n", - "1414 AX CHEST PE STD THIN +C F_0.6 \n", - "1415 LUNGS iDose (2) \n", - "1514 SAG iDose (2) \n", - "1634 LUNGS, iDose (2) \n", - "1791 VEN SAG CHEST \n", - "1886 HI RES, iDose (2) \n", - "1923 CHEST SOURCE WO \n", - "2035 SCOUT \n", - "2089 AXL iDose (2) \n", - "2201 COR iDose (2) \n", - "2216 LUNG \n", - "2327 AXIAL LUNG B60f 1.5 \n", - "2328 ART COR CHEST \n", - "2367 2x2 cor abd \n", - "2419 MIP iDose (2) \n", - "2491 Sag \n", - "2558 Sag MPR \n", - "2659 AXIALS \n", - "2660 SCOUT \n", - "2706 SURVIEW \n", - "... ... \n", - "34288 COR CHEST PE STD MIP +C F_0.6 \n", - "34309 Monitoring 10.0 Br38 \n", - "34483 Chest Sag \n", - "34484 SAGITTAL MPR \n", - "34582 MinIP, iDose (2) \n", - "34833 Scout \n", - "34931 SAG mips \n", - "34933 Cor MIP \n", - "34966 Smart Prep Series \n", - "35005 ABD SAG 2x2 \n", - "35166 VEN COR CHEST \n", - "35180 HI RES \n", - "35231 STD AXL \n", - "35243 PE Lungs \n", - "35250 CHEST RECON WO \n", - "35341 SAG mips \n", - "35377 AXL SOURCE iDose (2) \n", - "35395 Smart Prep Series \n", - "35399 ART COR MIP CHEST \n", - "35556 PE 2mm \n", - "35637 Topogram 0.6 T20f \n", - "35658 A/P W \n", - "35693 Cor \n", - "35718 HIGH RES \n", - "35920 TopoBody_lat 1.0 Tr20 \n", - "36017 SURVIEW \n", - "36256 CORONAL \n", - "36433 Scout \n", - "36505 MIN IP \n", - "36652 STD AXL \n", - "\n", - " series_uid slice_thickness \\\n", - "93 2.16.840.1.114274.1818.55964346378096610171168... NaN \n", - "499 2.16.840.1.114274.1818.48959334245767654136969... NaN \n", - "633 2.16.840.1.114274.1818.47761396116332639857774... NaN \n", - "761 2.16.840.1.114274.1818.52447249543864220461728... NaN \n", - "772 2.16.840.1.114274.1818.46261309264012416081379... NaN \n", - "914 2.16.840.1.114274.1818.49672260042487025331314... NaN \n", - "916 2.16.840.1.114274.1818.52088358400569405888781... NaN \n", - "1101 2.16.840.1.114274.1818.51618659123100181941648... NaN \n", - "1178 2.16.840.1.114274.1818.52789230929689933181196... NaN \n", - "1259 2.16.840.1.114274.1818.47500625575938939955885... NaN \n", - "1414 2.16.840.1.114274.1818.50397103575092183821247... NaN \n", - "1415 2.16.840.1.114274.1818.54927525279730560581390... NaN \n", - "1514 2.16.840.1.114274.1818.52139856230859676691361... NaN \n", - "1634 2.16.840.1.114274.1818.55796402103412404711436... NaN \n", - "1791 2.16.840.1.114274.1818.55152454794275602154480... NaN \n", - "1886 2.16.840.1.114274.1818.56531355311072326471027... NaN \n", - "1923 2.16.840.1.114274.1818.52546362306442159622974... NaN \n", - "2035 2.16.840.1.114274.1818.47062791267923794261751... NaN \n", - "2089 2.16.840.1.114274.1818.54346279024422850746055... NaN \n", - "2201 2.16.840.1.114274.1818.53418313407643202564340... NaN \n", - "2216 2.16.840.1.114274.1818.56923453410353910621162... NaN \n", - "2327 2.16.840.1.114274.1818.47450795471739653425584... NaN \n", - "2328 2.16.840.1.114274.1818.53802420710852412653261... NaN \n", - "2367 2.16.840.1.114274.1818.54548482228493910589530... NaN \n", - "2419 2.16.840.1.114274.1818.47247454113117857471988... NaN \n", - "2491 2.16.840.1.114274.1818.51827490655301756669271... NaN \n", - "2558 2.16.840.1.114274.1818.57532792989429822161041... NaN \n", - "2659 2.16.840.1.114274.1818.48341341055219706652697... NaN \n", - "2660 2.16.840.1.114274.1818.48194208187608996421285... NaN \n", - "2706 2.16.840.1.114274.1818.55984719531389594114093... NaN \n", - "... ... ... \n", - "34288 2.16.840.1.114274.1818.54196933174925358285960... NaN \n", - "34309 2.16.840.1.114274.1818.56073649327926434991462... NaN \n", - "34483 2.16.840.1.114274.1818.49253156750623446381395... NaN \n", - "34484 2.16.840.1.114274.1818.57131787984902380351364... NaN \n", - "34582 2.16.840.1.114274.1818.50719568566687396181626... NaN \n", - "34833 2.16.840.1.114274.1818.55892374236772729211324... NaN \n", - "34931 2.16.840.1.114274.1818.54036272945275104811569... NaN \n", - "34933 2.16.840.1.114274.1818.53533151318719223819997... NaN \n", - "34966 2.16.840.1.114274.1818.57116847183581503751372... NaN \n", - "35005 2.16.840.1.114274.1818.52937350291748289925686... NaN \n", - "35166 2.16.840.1.114274.1818.48769881958854835438888... NaN \n", - "35180 2.16.840.1.114274.1818.49522960612900783455913... NaN \n", - "35231 2.16.840.1.114274.1818.51372862075384294291311... NaN \n", - "35243 2.16.840.1.114274.1818.53929921450115012801214... NaN \n", - "35250 2.16.840.1.114274.1818.54224394692577902551673... NaN \n", - "35341 2.16.840.1.114274.1818.57006587110347619497516... NaN \n", - "35377 2.16.840.1.114274.1818.50354021659339484474916... NaN \n", - "35395 2.16.840.1.114274.1818.48227151261486612789842... NaN \n", - "35399 2.16.840.1.114274.1818.49265557179383310379151... NaN \n", - "35556 2.16.840.1.114274.1818.54099736554534158663855... NaN \n", - "35637 2.16.840.1.114274.1818.50892074178737076771500... NaN \n", - "35658 2.16.840.1.114274.1818.49632659233623505791789... NaN \n", - "35693 2.16.840.1.114274.1818.50800570938328320834464... NaN \n", - "35718 2.16.840.1.114274.1818.54230946313288245235004... NaN \n", - "35920 2.16.840.1.114274.1818.56589161309160872506680... NaN \n", - "36017 2.16.840.1.114274.1818.54961336870100270459213... NaN \n", - "36256 2.16.840.1.114274.1818.48681936645061680929956... NaN \n", - "36433 2.16.840.1.114274.1818.48439609856238435821718... NaN \n", - "36505 2.16.840.1.114274.1818.57442183453554543434456... NaN \n", - "36652 2.16.840.1.114274.1818.49929890207265010191125... NaN \n", - "\n", - " spacing_between_slices storage_urls \\\n", - "93 NaN NaN \n", - "499 NaN NaN \n", - "633 NaN NaN \n", - "761 NaN NaN \n", - "772 NaN NaN \n", - "914 NaN NaN \n", - "916 NaN NaN \n", - "1101 NaN NaN \n", - "1178 NaN NaN \n", - "1259 NaN NaN \n", - "1414 NaN NaN \n", - "1415 NaN NaN \n", - "1514 NaN NaN \n", - "1634 NaN NaN \n", - "1791 NaN NaN \n", - "1886 NaN NaN \n", - "1923 NaN NaN \n", - "2035 NaN NaN \n", - "2089 NaN NaN \n", - "2201 NaN NaN \n", - "2216 NaN NaN \n", - "2327 NaN NaN \n", - "2328 NaN NaN \n", - "2367 NaN NaN \n", - "2419 NaN NaN \n", - "2491 NaN NaN \n", - "2558 NaN NaN \n", - "2659 NaN NaN \n", - "2660 NaN NaN \n", - "2706 NaN NaN \n", - "... ... ... \n", - "34288 NaN NaN \n", - "34309 NaN NaN \n", - "34483 NaN NaN \n", - "34484 NaN NaN \n", - "34582 NaN NaN \n", - "34833 NaN NaN \n", - "34931 NaN NaN \n", - "34933 NaN NaN \n", - "34966 NaN NaN \n", - "35005 NaN NaN \n", - "35166 NaN NaN \n", - "35180 NaN NaN \n", - "35231 NaN NaN \n", - "35243 NaN NaN \n", - 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"35341 NaN \n", - "35377 NaN \n", - "35395 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "35399 9de1d49d-ad56-44b3-be2e-a2795ce41ff3 \n", - "35556 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "35637 NaN \n", - "35658 NaN \n", - "35693 NaN \n", - "35718 NaN \n", - "35920 23e8c279-92d2-47da-99de-97dedc454bb4 \n", - "36017 9de1d49d-ad56-44b3-be2e-a2795ce41ff3 \n", - "36256 NaN \n", - "36433 NaN \n", - "36505 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", - "36652 NaN \n", - "\n", - " core_metadata_collections.submitter_id \\\n", - "93 NaN \n", - "499 ACR_20211020 \n", - "633 ACR_20220107 \n", - "761 ACR_20210915 \n", - "772 ACR_20220107 \n", - "914 NaN \n", - "916 NaN \n", - "1101 NaN \n", - "1178 ACR_20220107 \n", - "1259 ACR_20211020 \n", - "1414 ACR_20211020 \n", - "1415 NaN \n", - "1514 NaN \n", - "1634 ACR_20220107 \n", - "1791 ACR_20210915 \n", - "1886 ACR_20220107 \n", - "1923 ACR_20210915 \n", - "2035 NaN \n", - "2089 NaN \n", - "2201 NaN \n", - "2216 ACR_20211020 \n", - "2327 NaN \n", - "2328 ACR_20210915 \n", - "2367 NaN \n", - "2419 NaN \n", - "2491 NaN \n", - "2558 ACR_20210827 \n", - "2659 ACR_20211020 \n", - "2660 NaN \n", - "2706 NaN \n", - "... ... \n", - "34288 ACR_20210827 \n", - "34309 ACR_20211020 \n", - "34483 NaN \n", - "34484 ACR_20210827 \n", - "34582 ACR_20210915 \n", - "34833 ACR_20220107 \n", - "34931 NaN \n", - "34933 ACR_20211020 \n", - "34966 ACR_20211020 \n", - "35005 NaN \n", - "35166 ACR_20210915 \n", - "35180 NaN \n", - "35231 ACR_20210915 \n", - "35243 ACR_20210827 \n", - "35250 ACR_20210915 \n", - "35341 NaN \n", - "35377 NaN \n", - "35395 ACR_20211020 \n", - "35399 ACR_20210915 \n", - "35556 ACR_20211020 \n", - "35637 NaN \n", - "35658 NaN \n", - "35693 NaN \n", - "35718 NaN \n", - "35920 ACR_20211020 \n", - "36017 ACR_20210915 \n", - "36256 NaN \n", - "36433 NaN \n", - "36505 ACR_20220107 \n", - "36652 NaN \n", - "\n", - " imaging_studies.id \\\n", - "93 b1ccf140-c713-49a1-8ed0-e0c9a54bf638 \n", - "499 08f71e93-ebc9-4e1e-a4ee-5013e595d3dd \n", - 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"2089 2.16.840.1.114274.1818.47546942155557792113367... \n", - "2201 2.16.840.1.114274.1818.49162333685696322481506... \n", - "2216 2.16.840.1.114274.1818.51756275185682361958967... \n", - "2327 2.16.840.1.114274.1818.51339555956390258931766... \n", - "2328 2.16.840.1.114274.1818.48324760574904930945338... \n", - "2367 2.16.840.1.114274.1818.50543623976916621593618... \n", - "2419 2.16.840.1.114274.1818.49162333685696322481506... \n", - "2491 2.16.840.1.114274.1818.57135063690911574026567... \n", - "2558 2.16.840.1.114274.1818.47405767010972118591122... \n", - "2659 2.16.840.1.114274.1818.56236958931622562032480... \n", - "2660 2.16.840.1.114274.1818.48497422923503503389003... \n", - "2706 2.16.840.1.114274.1818.52980787041006160911466... \n", - "... ... \n", - "34288 2.16.840.1.114274.1818.57618452499506629833358... \n", - "34309 2.16.840.1.114274.1818.54095403294717579441711... \n", - "34483 2.16.840.1.114274.1818.55800604469442512806545... \n", - "34484 2.16.840.1.114274.1818.51563634460029048314588... \n", - 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"object_ids = list(cohort_ct['object_id'])\n", - "object_ids" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Queries to Postgres using Peregrine graphQL query service\n", - "---\n", - "* Peregrine GitHub Docs: https://github.com/uc-cdis/peregrine\n", - "* Peregrine swagger docs: https://petstore.swagger.io/?url=https://raw.githubusercontent.com/uc-cdis/peregrine/master/openapis/swagger.yaml\n", - "\n", - "---\n", - "* Most structured data (aka \"metadata\") submitted to a Gen3 system is stored in Postgres tables using the \"sheepdog\" service. This data must conform to the data commons' data model (https://data.midrc.org/dd), and is queryable via the \"peregrine\" service, which converts graphQL queries to SQL queries and returns the data requested. The Postgres tables are considered the \"source-of-truth\" for data in a Gen3 system (vs. the derived data in ElasticSearch, covered below).\n", - "\n", - "* On the data commons' website, peregrine queries can be sent to the API using the \"graphiQL\" query builder: https://data.midrc.org/query (click on \"Switch to Graph Model\"; if button says \"Switch to Flat Model\" you're in the correct spot).\n", - "\n", - "* Alternatively, you can send queries to the peregrine API using the Gen3SDK `Gen3Submission.query()` function, which uses the Python `requests` package to send queries as API requests: https://github.com/uc-cdis/gen3sdk-python/blob/31751633ba621b35f39eda7295f131245fb92728/gen3/submission.py#L399\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example graph model query \\#1\n", - "* This query is running across all records in the `case` node and returns data from any dataset in the data commons you are authorized to access. Remember, the properties in the `case` node are essentially table headers for variables whose values are of a specific data type (string, enumeration, integer, number, boolean, array, etc.).\n", - "* The argument `covid19_positive: \"Yes\"` returns only case records that have the value \"Yes\" for the property `covid19_positive`, which indicates whether a case in MIDRC has ever had a positive COVID-19 test result.\n", - "* The `first` argument defines how many `case` records we want returned. Using the argument `first: 0`, all the records we have access to will be returned. If we leave the \"first\" argument out, only the first 10 records are returned by default. Setting `first: 2000` will return the first 2000 records in the table, etc.\n", - "* If your query is timing out, you will need to paginate the query (covered in next section) using a combination of \"first\" and \"offset\" arguments. This is only necessary if the tables being queries are very large, or the query traverses many nodes in the graph model.\n", - "* Properties we want returned from the API are enclosed in brackets. The possibilities and exact syntax are constrained by the data model (data.midrc.org/dd). \n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "### Define the query\n", - "\n", - "## Here we're asking for the `project_id`, `submitter_id`, and some demographic data for every `case` record.\n", - "## We're also asking for the `study_uid` for every `imaging_study` record belonging to those cases, and for all `dx_series_file` records for those `imaging_studies`.\n", - "## Finally, we're asking for the `file_name` and `object_id` of any Digital X-ray files (node `dx_series_file`, backref: `dx_series_files`) they may have.\n", - "\n", - "## Note: \"submitter_id\" is a required property on every node, which is the human-readable (string), unique identifier for a record in a data table / node. So, the \"submitter_id\" of a record in the case node is the de-identified patient's \"ID\".\n", - "\n", - "query_txt = \"\"\"\n", - "{\n", - " case(first: 0, covid19_positive: \"Yes\") {\n", - " project_id\n", - " submitter_id\n", - " ethnicity\n", - " sex\n", - " race\n", - " imaging_studies (study_modality: \"DX\") {\n", - " study_uid\n", - " dx_series_files {\n", - " object_id\n", - " file_name\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "\"\"\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'ethnicity': 'Not Hispanic or Latino',\n", - " 'imaging_studies': [{'dx_series_files': [{'file_name': '2.16.840.1.114274.1818.46462464228338035604085382488501697711/2.16.840.1.114274.1818.57119669613829751891125487061413366952.zip',\n", - " 'object_id': 'dg.MD1R/5eeb7280-7d5b-4bac-a34b-a0eadb65e033'}],\n", - " 'study_uid': '2.16.840.1.114274.1818.46462464228338035604085382488501697711'}],\n", - " 'project_id': 'Open-A1',\n", - " 'race': 'Black or African American',\n", - " 'sex': 'Female',\n", - " 'submitter_id': '10000364-1163342'},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'imaging_studies': [],\n", - " 'project_id': 'Open-A1',\n", - " 'race': 'White',\n", - " 'sex': 'Female',\n", - " 'submitter_id': '10000364-1560988'},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'imaging_studies': [{'dx_series_files': [{'file_name': '2.16.840.1.114274.1818.462269544687285955216049365983114851242/2.16.840.1.114274.1818.5266294379737328289107155289649633448.zip',\n", - " 'object_id': 'dg.MD1R/1e1a8278-4fb8-46cc-8ca4-53e1931de592'}],\n", - " 'study_uid': '2.16.840.1.114274.1818.462269544687285955216049365983114851242'},\n", - " {'dx_series_files': [{'file_name': '10000364-1605860/2.16.840.1.114274.1818.49175781962090677039777902775594799259/2.16.840.1.114274.1818.51284993071612923214127329616632366210.zip',\n", - " 'object_id': 'dg.MD1R/5b06fbfb-a92d-466f-9d93-6b3fab850e5c'}],\n", - 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"metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Send the query using the Gen3 SDK Gen3Submission.query() function\n", - "## The response will be in JSON format.\n", - "\n", - "response = sub.query(query_txt)\n", - "if 'data' in response:\n", - " data = response['data']['case']\n", - " display(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['dg.MD1R/5eeb7280-7d5b-4bac-a34b-a0eadb65e033',\n", - " 'dg.MD1R/1e1a8278-4fb8-46cc-8ca4-53e1931de592',\n", - " 'dg.MD1R/5b06fbfb-a92d-466f-9d93-6b3fab850e5c',\n", - " 'dg.MD1R/ea6ad8e7-1dc9-4916-8e75-38abb66c6416',\n", - " 'dg.MD1R/a0c8e617-3430-45d0-b1e8-da193161f6f3',\n", - " 'dg.MD1R/68c0d3bf-06d9-4561-9f38-926c3948902a',\n", - " 'dg.MD1R/06f0d3ab-113b-45b2-84a4-8ba9242af0b6',\n", - " 'dg.MD1R/f1242600-47d1-4ce0-9851-8c0a976a0e29',\n", - " 'dg.MD1R/27acfaeb-bf00-4cbb-a5cc-4b74ed3c9c1d',\n", - " 'dg.MD1R/9bd41c93-7e4a-4267-84ad-51970dc802c1',\n", - 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" ...]" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## the \"object_id\" field is the file's data GUID (or globally unique identifier), which can be used to access the file.\n", - "\n", - "object_ids = []\n", - "for case in data:\n", - " studies = case['imaging_studies']\n", - " for study in studies:\n", - " files = study['dx_series_files'] \n", - " if len(files)>0:\n", - " for file in files:\n", - " object_id = file['object_id']\n", - " object_ids.append(object_id)\n", - "object_ids" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Take a look at one of the file objects\n", - "object_id = object_ids[0]\n", - "os.system(\"gen3 drs-pull object {}\".format(object_id))" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Archive: 10000364-1163342/2.16.840.1.114274.1818.46462464228338035604085382488501697711/2.16.840.1.114274.1818.57119669613829751891125487061413366952.zip\n", - " extracting: 2.16.840.1.114274.1818.57119669613829751891125487061413366952/2.16.840.1.114274.1818.48835068669285345335314100396583670180.dcm \n", - " extracting: 2.16.840.1.114274.1818.57119669613829751891125487061413366952/2.16.840.1.114274.1818.50562724593808174072277459636544006786.dcm \n", - " extracting: 2.16.840.1.114274.1818.57119669613829751891125487061413366952/2.16.840.1.114274.1818.545101927693002822610905640249661878717.dcm \n", - " extracting: 2.16.840.1.114274.1818.57119669613829751891125487061413366952/2.16.840.1.114274.1818.572332279246432020710965140097547833765.dcm \n" - ] - } - ], - "source": [ - "!unzip 10000364-1163342/2.16.840.1.114274.1818.46462464228338035604085382488501697711/2.16.840.1.114274.1818.57119669613829751891125487061413366952.zip\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dataset.file_meta -------------------------------\n", - "(0002, 0000) File Meta Information Group Length UL: 220\n", - "(0002, 0001) File Meta Information Version OB: b'\\x00\\x01'\n", - "(0002, 0002) Media Storage SOP Class UID UI: Digital X-Ray Image Storage - For Presentation\n", - "(0002, 0003) Media Storage SOP Instance UID UI: 2.16.840.1.114274.1818.48835068669285345335314100396583670180\n", - "(0002, 0010) Transfer Syntax UID UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", - "(0002, 0012) Implementation Class UID UI: 1.3.6.1.4.1.25403.1.1.1\n", - "(0002, 0013) Implementation Version Name SH: 'Dicom 0.1'\n", - "(0002, 0016) Source Application Entity Title AE: 'CPV-ACUOAPP1'\n", - "-------------------------------------------------\n", - "(0008, 0005) Specific Character Set CS: ''\n", - "(0008, 0008) Image Type CS: ['ORIGINAL', 'PRIMARY', '']\n", - 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"(0040, a032) Observation DateTime DT: '18390126000000.000000'\n", - "(0040, a491) Completion Flag CS: ''\n", - "(0040, a493) Verification Flag CS: ''\n", - "(0045, 0010) Private Creator LO: 'GEMS_SEND_03'\n", - "(0054, 0220) View Code Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: 'R-10236'\n", - " (0008, 0102) Coding Scheme Designator SH: 'SRT'\n", - " (0008, 0104) Code Meaning LO: 'left lateral'\n", - " ---------\n", - "(0054, 0410) Patient Orientation Code Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: 'F-10450'\n", - " (0008, 0102) Coding Scheme Designator SH: 'SRT'\n", - " (0008, 0104) Code Meaning LO: 'recumbent'\n", - " ---------\n", - "(0054, 0414) Patient Gantry Relationship Code Sequence 1 item(s) ---- \n", - " (0008, 0100) Code Value SH: 'F-10470'\n", - " (0008, 0102) Coding Scheme Designator SH: 'SRT'\n", - " (0008, 0104) Code Meaning LO: 'head-first'\n", - " ---------\n", - "(0070, 0080) Content Label CS: ''\n", - "(0070, 0081) Content Description LO: ''\n", - "(0070, 0084) Content Creator's Name PN: ''\n", - "(0400, 0500) Encrypted Attributes Sequence 1 item(s) ---- \n", - " (0033, 1215) Private tag data OB: b'\\xb8\\xaf\\xc7\\x01\\xa5>\\xd0\\xec'\n", - " (0400, 0510) Encrypted Content Transfer Syntax U UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", - " (0400, 0520) Encrypted Content OB: Array of 720 elements\n", - " ---------\n", - "(2050, 0020) Presentation LUT Shape CS: 'IDENTITY'\n", - "(4008, 0040) Results ID SH: ''\n", - "(7fe0, 0010) Pixel Data OB: Array of 16518594 elements" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pydicom import dcmread\n", - "\n", - "fpath = \"2.16.840.1.114274.1818.57119669613829751891125487061413366952/2.16.840.1.114274.1818.48835068669285345335314100396583670180.dcm\"\n", - "ds = dcmread(fpath)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Counts with peregrine\n", - "---\n", - "* Peregrine is able to provide counts of records in nodes. A simple example is to quickly get the count of the numbers of cases and imaging studies in the data commons.\n", - "* You can also add arguments to the counts to, for example, get the number of cases in a particular project or get the imaging studies of a particular modality." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'data': {'_case_count': 11899}}\n", - "{'data': {'_imaging_study_count': 31819}}\n", - "{'data': {'CT_studies': 6776}}\n", - "{'data': {'Open_A1_cases': 7534}}\n" - ] - } - ], - "source": [ - "query_txt = \"{_case_count}\"\n", - "print(sub.query(query_txt))\n", - "query_txt = \"{_imaging_study_count}\"\n", - "print(sub.query(query_txt))\n", - "query_txt = '{CT_studies: _imaging_study_count(study_modality:\"CT\")}'\n", - "print(sub.query(query_txt))\n", - "query_txt = '{Open_A1_cases: _case_count(project_id:\"Open-A1\")}'\n", - "print(sub.query(query_txt))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Queries of \"datanode\" using peregrine\n", - "---\n", - "Another handy trick with peregrine queries is the \"datanode\" query. \"Datanode\" isn't a real node in the data model, but is useful way to query all nodes that store file information. For example, if you have a patient ID, you can get all the files associated with that case.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "query_txt = \"\"\"\n", - "{\n", - " datanode(first: 0, case_ids: \"10000364-1163342\") {\n", - " object_id\n", - " file_name\n", - " modality\n", - " }\n", - "}\n", - "\n", - "\"\"\"\n", - "response = sub.query(query_txt)\n", - "if 'data' in response:\n", - " display(response['data']['datanode'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Queries to ElasticSearch using Guppy graphQL query service\n", - "---\n", - "* Guppy Documentation: https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filters\n", - "* Guppy Download instructions: https://github.com/uc-cdis/guppy/blob/master/doc/download.md\n", - "* ETL (Tube) Documentation: https://github.com/uc-cdis/tube#gen3-etl---a-process-from-postgresql-to-es\n", - "---\n", - "* The Gen3 platform includes services for running an ETL process (Extract, Transform, Load), which is done by the Gen3 ETL service \"tube\", on the data in Postgres to create flattened tables of the same data in ElasticSearch (ES) for rapid querying performed by the Gen3 query service \"guppy\".\n", - "* Guppy runs graphql-like queries against the ES database, and can rapidly return derived data like histograms, statistics, aggregations, counts, etc. The tube service uses Spark to create these new tables of data in ES via an ETL mapping, which defines the structure of the new tables and is based on the data model. \n", - "* Since the structure of the data changes via the ETL process, peregrine queries to Postgres will not run using guppy. To explore what is possible to query, use the graphiQL interface / documentation.\n", - "* The \"Exploration\" app aka \"Data Explorer\" (data.midrc.org/explore), which uses faceted search to filter the flat data tables in ES, runs off of guppy queries.\n" - ] - }, - { - "attachments": { - "Screen%20Shot%202022-06-22%20at%2012.56.01%20PM.png": { - "image/png": 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M870V8tnE9g0LZOfGRfaqqjY5OeLVbVk1Q9YtHCcpW5ZJ2s51UqREJSlRrq5UrNdNKtXrEXG70AW7U7fIlpVTzfvaNNm6ZrYUKVZWylY/QsrX6iBlqx1m3usKh27iPd+7e6dsWPKDfa7vFfqekVkJBPbJ+n++lcC+PVK6SkspWb5+Zquz7CAF0naul80rfrNbl6/dWYqWqBh2T9n1Gtq3N022rv7dHHOq6D737dst5aq1lXK1Okr5Gu2lUNFSYY/vZq5bNN6+JsyLTao2PtHNjvio16bXWKx0dSlX44iI67EgbwRSt+2WVfO32IPXb1/Jfl7SJ9vXp8o68xlLPz/p/KyW1Qu2SMrW3VK+ZkmpUOvAZ+dIPyMinUdWj5tX66duW2m/r/T4RUtVMdfdMa9OJWGP6z4rlazYSEpXauZdx9Y1syR16wrzuaKm+Xl3uDc/lgn9OabvWeZNy/7M839O3rj0J9mTtl1CjxfpPGI5XrKsM3fuXElJSZHq1atLrVq1vMuONN9b4VAmAiL/ztoo65dulz2pe6XOYRWlRosDnz8PZddsm/gCBFsx1GHanr3S5Yo37JoPDTpGTu7aOIatoq+yYOkG2RsISN1qZaV0iaLRN4jDNVLT9sg/KzdLAXNuzepWkoLmw0+4EjDXecstt8iKFStk4MCB0rdv33CrZXneL+/8I+Ofn+NtV8F8eLrh0172ecqWNHnlgh9l69pUb/mZDx0hrY8/8ObrLcjhiY3Ldoh+YCtdqbj5xTN7g7WVcz6URZOesFfQoNONUrvtwGy5mr1pGk78buq2gPkg0Ml8sC2ULfv17yRS/anXC2d9Z1e9+KWuUu8gPlD7jxPL9G7zA3LdP9ukQIECUr1ZWe+DfSzbxrLO5DeOFg0VChYuLkddOjGWTWJaZ/u6uZKWusl+2CtZvkFM22Rlpcy+jz5/4Hf5fewyqd+uklz0yoGwLtJ8PW5Ofi9k5bqyc90Zo/rLjo0L7S67Xf6beQ0VDNq9Bl/zxt9u3gNWBM33Pylaqqq06P2oeX9o658dNL1vzy6ZM+4mE2hNDZof9MQcu1GXW6Vm63ODZrsnuu0fX1ztnsrhp40wYX9r73noxM5N/8j0j86xs2u3vUgadLo+dJV89XzHxl2yZXWKCZYKS+UGpXPt2hb/+rwsn/2mPV6L3o9J5Ya9g46dXa8h3emSqS/JspmvB+0/9EmVhsdLs2Mfivi+//OrHbxN6rW/Quqa/5mVicO7mN8pd0upik3kiLPey2xVluWBwO9fLJPPH/rdHvmeiX2kUJH972FTP1oiY5/6w86/b0rWP7c9deJ42bFpl/S8opkcfVlT78oi/YyIdB66YSL87ND3Vu/92bwXH3XJz+ZnfjHvupmILjBxeCfzXrFX9D2oee9HvA2mfXCG+aPQv+azTi3peN7n3vxYJvRz0sxPL7SrNjt2SFAYH+l4kebrTjT0CgT2SpnKLcwfqML/ESKW80r0dQYMGCC7d++Wk046SS655BLvciLN91Y4hIn3bv1N/pq4xttDh9PrSZ87DvOeJ9NEXn1eiWdjgq0Yaiengq2jrxkpO1J3yy3ndpIBJ7SJ4Uzib5W/l22U/vd9Yk/srf/2k5YNqoQ9yS+//FJGjhwpZcqUkVdffVUKFTr0kGR3yl556uTx5q/Ae+wxG7SvbD40F5Mzh+z/a/DktxbJN/+ba5dVqlvK/DW6pHTq30CadjvQoivsyebAzI/unC5zv1spTbpWk/OfybyFRFYPn1PBlv8X4FYnPisV63bL6qllun5m9ZcXwdaav7faIFRP+vLXu0vNVuUzPf+sLsypYGv6h2eZ1opLbLDV8bzRWT2tqOtn9n0U6ZeTSPP1YDn5vRD1YnJohcyCLf0LvtaRtpCxxfyyU6JsHSlctLRtwbJrx4EPaAUKFpGO/T+1rVpCT1XrePboy0Rba3nF7qu2/Wuztgb0l0r1jpYWxz1hgong1lv+72tdXz+Ud7pgXIYwzu0r2YKtqaPML/JPHvwv8s4tq4+ZBVvZ9RrSYP3PsdfZVn7+89NQtaB57aVuX2VbNLhlxUrXkMP7vWZanlR1s7xHf7BlXmTmdfuZfQ/yVgiZINgKAYmzp/5A6b+T+pgwJu+DLf95KFe8/+zQPwZOHtkz6HuoYZdbpFab8+OstuP7dCIFSvEUbP38f+ZzvGn9Vb35adLk6HviGzQHzy5SgBVp/qGeyvI/Nslrg/b/YbhUhWJSvWlZadixinQd2OhQd52Q2+fV55V4xiLYiqF2CLYiI8USbGmaf/HFF9tU/8orr5RevXpF3mEWlsydsEo+umua3SJcq57/nf2dbDBdokJbk2ThENm2ak5+IEvUYCuz+tO/Qvzw6gLr3/WCRlLBdDHN6UKwFV44s+8j/WVoxZ+bpEqDMnLkuQdaixFsHWixNfPjAaKtbbRod8PmvR62rfactoYWc7++xWvxVbJCQ2l/9odusX3UbhK/vtnL/hVbZxQtWUlanjBUypiugWLby4rs25Mqa/4aKwsnPeb9YqXdYNqc8rLdh/sSGmzpfP3FS38BC1cItsKpZP+8zIKt7HgN6Rm7EFyntQWutsiqXL+nmd7f1Vu762xZZf4I8/WttnWprleoSEnpctH3dn197kpQsGVmhnvdunX1kWDLrxF/08tM157ZXy23J3bS4DZSsFABO32oLba+/d882bV9tzTrUV0adzkQkEb6GRHpPPRkcvJzlL3YQ/yirSC1NaS/aLe5nPiDk/8Y+W16zlc3mj/WbJNqzfqa4Oh07/IOJdjSn7MLvtsfQDU66nYpXbm5t99IQVqk89ANCbb280UKsCLN99APcuKrJ/+U30Yttlvf+f1J5rNQ8B/uDnK3CbsZwVbGqiPYymiSYQ7BVgYSb0YswdaECRPs2FraSmvEiBFSrFj2NMuePXa5fPrATHsud//cx7SAOPDLpM50TeCPGdRMelx+oAm8d/K5OJGTH8gSNdiKVn+5WD32UARb4cUP5vso0i8teoSc/F4IfwU5PzezFlvuQ3NhMw5Wl4v2d68NPSPtYjjl7RNsyytt/dJt0K9BLaj++uEBE1qNsZuVqtjYdh/ULq3hyra1f8is0VfYbl+6vN3pb9qxsdy64YItXaZhmoYToYVgK1QkZ55nFmxlx2tow9IfbWClZ69h1eHmdRFpvLTdpmvzzI8vENeasO4RV0i9DsFdDUODLd1vQ9MFtlab83QyQyHYykCSEDMONdiKdJGZ/YyItE28/+yY8vZJ6WMnVjR/jOssa/8eay8l0ntrpOtkfniBQwm2wu9x/1z3/hra9TGzbQi29utECrAizc/MNJZlox+aJTO/+FfqtKkolw4/KpZN8vU6BFsZq5dgy2eybO1WmbdkvejYVwHzr3GtitKtbR0pXqxw1DG21m/eKZNmL5M1m3bI5u2pUrp4UalZpYwcdVidoMHmU3btkenzTXN/U2587mv72MeM2XV8x0aiw1N1blPbPO7/S5ldaL5oeDTzr9Wy1uw7xYxpVa5UMXNuFey6JYuFH1R707ZU+fn3f2W5uaadabulYpkSUr96OXs+xYpGTrj37tsn85dskPlL18vSNVusQdsm1cw4YOXML1rujERWb9guC5dvkpXrt8nj70y2C244q6M0MmblSheTNo0O/GXuhhtukNWrV0vHjh1l8ODBB3bim9LuhKsXbJU1C7facRQq1illm5hWb1rOdJUJ7ra4btE22bx6p/zz23r59f1/7F76P9HRtIIoYManKWG6WaTYeaPumWG7KbYxY2q1OXH/uFqNOpsuF+l/iTRVLEtmbJCVczfbbYqYMVXKVClmm7VGG1tFB1RdYbZzg63WbVtRarepkOGvB/9MWSd79+yTn177W5bP2d+y5bjrW9jzq23emEukD4q+adMmmTdvnp1fpUoVadKkiZ2O5UtmwZa2FNm2+g/TlaSyVKp/jPlFZa1sXPKjHR9g754Uc72tpVrzvrZrlDuW/iK7ZeUM073tH9F9a6napI+UrdpGipaukqVBrt0+/Y+Z1V+FWqVMC4LSprXAPlO/6+xmtVtXMANsF7XTOqj8ol/XmbENzNh0h1eSYqUKmzEX0mw96l96e1/XwutCoQOe/2364a9bvM20hkkzv8QVteObaVdU7bLqio6ns3bRVnuDATeWSO9rWpgxGMoYl6K2XnXdvXv3ypQpU+xmGs62b9/e7SKmx0hdEbUL2fp/9gce1VueIft2p5j6+UU2LP3Z/KV7lZSq0FgqN+qdYcBlDTr27d4lS6e/bLun6S+rDY68wZ5LpfpHh+0+FNOJmpX8/pl9H7m69DvpMcL90hLr94I7x317A/b7S7/HNvy7Xao2KiP6fVaprhn3yPde5NbX+k81LQO09ZgOVKxjpmlrsiXTN9hx9fR7eunSpXacP92mUaNGUq1aNbf5IT1GCrZ0PBD9MK5FB17XkClS+funIbJ6/md2cVvT/cuNtaXfs7+9e4pthaWtbLpe/GNQi69w+9PXhoZhWkJb0viDLR1oXgef1xKpZUFOB1t684h/0m/woedRunIxqdmivNRrV9FkfGEqWlcyJSs/M3T9betSZfVfW8xA+4Wkfof9N9jYZG5+sGT6ejP22R5p0KGSbDPv63/9vEamfbpUN5Hzhx5pHyvVKW26Yu9vNXoo79V2ZxG+RAq2suM1pIf89a3j7c0rdPqwvsPM+0nm719p5nU35Z2TdXVTD0VMKDvBBmJ2hvnigi0Nx3aa17l2y9HX55Hnfxn2BiY5HWzpHyb+/X2j/VmeZr73S5qfGfqe0ahzlQw/l9016GOqGdhc32NWmdfGNjMep95cpop5rwj6rODfIH36YLfL6vuaHi7Wn2VBp5nFzzf6mWaVGehduyA26nRgSInQYEuN9LOPDgqvP4drmO/VmubGIfrZK1xZaj5jpaXssTf2qVTPvHenl3A/I3RRuPOI9rNDP9P9+/sGu+fyNUqasZnKpB8l+EFbgq+ct9nOrH9EZfvZMru+n/1jONU5/BL7M1tbWmqp0ugE21LXPsnki77Xbl7+mxnjb4ZtMak3FtEbSWi38szKwW6XsnWZbFo6yR5P91+xXnf7+U7/CBOp6GeSVXM/Fj2mdm3WQdhLmQHea7Q8M+j9wb+9rqfbbF83z7wW1psb6OhnujpSvcUZYbsv6yDve1K3mp+ZzYPGfwwNtvSmF5tXTDPv7X/am/KUNze+qFC7S9j3H/3j0ZoF+/84VNF8Pirm614dKdgKPQ9tRbbu7/2/t9mW0eYiS1Vqam6cc4Z57ytoPkufZm/klLp1pb38as1PNb9v7P/s6vfQab0hy+6UzVK8XG0bgoYuz6vnCxculFmzZsm6deskNTVVypYtKw0aNJDOnTtLiRIZv8cjBViR5h/sdbn3ER2Xd7H5ma3Dy5xwUyu7u1otK5jPOcHO+r2u7+v6M3/n5t3283z1JmXNY9kDvwP6Tmat+d1Tb3hUtmoJqWbW09819GfK4mnrRX8fbXZ0ddHPC+uXbPN+L9BjLDefMZf/sdmMyVnI/q5Qx3xGdY0sNi3fIctmb7LnUaJcEfu+pPvxfgf1HV8n9ZiLp643vwdvM6/pFPP+aj6/mZ9j+rm3zuEVg7bT99+8/LwScupx9TTWYOv/AQAA//9OBeGhAABAAElEQVTsnQd8FMUXxx+9hNBCQu+h9w6hdwFBsaB0kKJ/VLAhdkUsYEMsiCCCIqCAiCJdpUivofdA6CUhBAgkhPJ/v9nMZu5yl1wugYC84UN2d8ru3HfbzG/fvEl3+Ur0TfIg3LwZn02vY+nq/40bN0j/v3kT6zepUMECHhwldbPEXrtBo39ZS7/8vSvBjn2yZqJXezakNyYsU2kj+jej9kGBdj783DEz1tGURdvtOOeVd55oQh0blVXRoaci6aHXZjpnsbeXftmTcvpkUdsXL1+l18b9Q6t3HLPTzRXUbcKw+6lccT8zmn5csI3GzFzvEKc3UOazZ9tQ7QoFdZS9PBV+iV4fv5SC95+24/RK6UK5aexL7Slf7uwq6o9/99HwSSt0ssOybNG8NH34QyouLCyMBg0apNYHDx5MjRo1csiLjUMbwujXNzdTVERMgjSfPFmoy4e1qViNvHbaotE7ae3PIfa2udKgaylaM911GvK9tqw9ZcqWgS6cvkI/DVlHZw9dNIvb6xVbFKKHR9Sg9BnT23Fqhc/3xtmhNO+jbY7xcVv3PV+J6j1eyk775L7FLn8XMnT9tC6VbZRf5V23bh19+umnar1gwYI0ZswYte7JnxM7Z9DBVR+prCXrDaEi1Xraxfb89SqdDVlCmbP7Ucn6Q2jvP2/ZafZKuvRUpvGrVKB8ZxV1eP1XdDR4sp1srmTIlJ2C+ro+72a+xNYTO39N+pah5k+Vp3NHo+jLR/5Ru+kzNoiK17Ku8UvhMfRp+8Uq/qmfmtL2Rcdp1ZQD9uFe/7cDZcycng6tD6Ofh22gq5ev2WnmSutnK1JQj9IqassfR+iP97eayfZ6/tI56alpTdX2lStXqHfv3nba1KlTKVOmTPZ2UiurJzWh67GX+ZrKSg2fWGlnP3Ngvn1eajw8lbbO6Us3rl+10/VKQGB7KtfiXb1JK7+rRzdvXLe3zZXSDV+mQpW6mFHJWr8ccZU+vm+R2zL6Pvp9eDAFzz9KJWr4Ue9xQXZ+V/Ge3gvYSeSpK/TrG5vp6PZz9j71in9JX+r1VX3KkS+rjlLLrx79h8KPRFH7oVXIl9N+4fOvQ6+vGlDJOvlowoQJtGTJEhXdrl076tu3r86SouXmWY9T1DnrOmw0YD2l43sK4ca1aFr1vfXMS5c+A9XrsZAyZc3j8lhXo87Q+ZObVZpf8SaEew0hZPVndHzHNLVeoPyDVKbJG2o98T83ac3k5nTt6iWVrW63uZQlh/XMP39iA23/838qvlT95yns0D/8PLSu/+K1nqRitQY47PpyRAhtmmldS0Wq9aaS9Z51SPd24/rVG/TbO1to598nXO7Cr5gP9eZ739ff8Twjc3LfGSiz+5+TNOPVjfwszEgv/NmaZgzbSCEbziKJilbJS3mKZKdtC1y/a4O6l6bWgyuqvCl5VqsduPlzaO0XdGzbjyq1QquRlK9UK7WeGtdQJF9X2+YOVPvL6luI6nT9w00tHKN3LBhMEUdXq8hSDV6gwlW62Rn+HV9brecuVJtyF65Hhzd8rbZ9AypT9Qcn2/n0ysrvGvDzKpZ88pahmo9M19EpXkZfjKVZr2+ig+usc+m8Q5zvvvxsKlAul3MS7V1+imbzNejqPZGnUHZ64I3q9rvHLOxtOW+ea8l5l+k6etO+Cf7zKP0+Iljt4s1VHey2z4aZh2n+J1b7tscX9emnwWv1YRyWzQeWp8Z9Ayld+nQO8fq533xgOWrSr6yd5uodgURX9dD7sAsbK2hHleJn+8fc1sJ5LFQhNw2Y3NjIEb+6dNxeWjFpn4p4aUEbvhazUGrdz7sXv0xhh602C57zmbPno7U/tqbY6Ahmkkm98/EOcBXwHt/7zxuqreYqHe23Kvd/S9lzl3BI9rYc0U1+r4y23ysOO+WNghUeosDGrzlH075lw+n0vrkJ4lUEv/PKNX+H0E4xw+l9f9K+5dxu4T6eq5C/XCcq29SxXarbNv6l2lD5Vh/YxTb+8hBdiTxCWX0LE54zZw+6aKNwPQKDXqaClR6xy2Hl0tldtOW3XiquXIv3uJ732enujuccf/HMDgqe08cu57yC8x66cRyd2jNHJZVp8rrdrjbzXr1yjtZNaaOicheuS1U6jDWT02T94sWL9O6771JoaKjL42fIkIHeeecdKleunEN69+7dKTY2lpzbU+7iHQonY2Ps48vc9tfwXCpdz9/aG/fR1s84RAs+2+Fy73jXP/xeTcpVIJtD+oJPdtD6mYcIfb/mT5ajKc+uoQtnolWeZv3LUdMBZWn5d/to2YS9qr0b1LM0TXthvcM+sFGuSQF6bFRt+nfSflo6fm+CdLxX+k1spJ49ZuLp/Rfol5c3UMSJy2a0vY7nGn5ntpxWnwNtp7Rsr9gVuwNXLl92zdC5qun+q8IWhKlBn8yn9btPqN8M4ad2+YKUJXNG2rTnJIVfuOLAwlnY+u6PLfTNnE12HpQNLJKXjp65QKu2HbXjF3zalQLy+FDY+cs08KN5Kh4iFwKOqQWjH998gHJky8xCYMJ61alQiAr45aCtLDztDg1TZYv4+9Lvox5T6/jz14ZDNOybv9U29tusZnEllO0NDafN+07Z+eZ/0pXy5/Wxt1Hf7u/8RlHRsSquLh+rEO8b5fSxihfIReNf7qDqupp/2yc/r6XomGt0OiJKlcnPvy9rloxUunAe+vhpq1G+dOlS+uabb1T6hx9+SKVLW2KCPrDZiEEcHjr5SuTgF/xFOrYzQmdjkakmVW5TWG2vnLyfgucdpYthMXaDFJ0ghGrti9JW7mgjoJOLgIatbz5LLHxySlPKlCUDfdXF6gQjHccsVD4XxURdo13LTtr7bNC1NLV5zurMIB/CQn74reOHH0LOgKxUolY+brhcV41q3Thu+1wlqs8CGwIagedPXrbrgjhd1/uHVaUStfMhKkWNK0+ELW5Z2Q2LTNnyUpbs/iy4HYgXRji9Qe+/WRTypZO7ZlPI2s9UGjoiCGiYoVGWxSeAaj82W8V5+yex81f7oRKKnSfCVnU+1xBVzIAGeeTpaPriIeseQBpeNHiZ4Dzs4Y6MDo9/XEelHVh9hhaO3qHOo36R4dxmypqBIKI89lEdVeR2CFvgDOYQNbLmLELRF44pMUzXuVK7MZS3aEO1uX5qB4qNiVTiiU6HaIZQpslrCRqYOo8ny+gLsfRdv39VVrf3EfNx1zlxFe/pvYBz/22vFfZ9WJLvsdx8/k7tjaST+6xnJu6hPt8EOYhbWtiq2q5Ighf+gO8bs9CX+7YLWwC4bmp7gmiFgHuoaPUnuLHbyRaaVEIifyBCQYxCMAWqRIqoJFMQq9DmI36utlDxzsJWQJl2XMd21rOAnwN1Hp/DHYdC9u5vlbA1f9R22jD7sDoO7k80TNOlS0chG8/az0tTWNYV8uadgbKmsIWOsPksKBOUnwJK+dKeFSddvldqdCxGDXsFqiqkVkdY/x69dCdsIT2l19DxbdPUMx37chaoEOcumIJYQJkO3HEdbmc1ha0q94+jjT93pisXrOdx2WbvUP6y99t5sXJLhC1uJ/349Bo6tMlqD+FdX5LfqbnyZ6OjW8/ZzwtcX4N/a+lQnw2zWLD52BJskICOQ77iOdS73PzI9r9pzSigtK9d1tty3jzXIo5fTta7TFWSmXjTvjHvK3fCloYAnkWr5aWL3PHT7JFW4/5i1OnNajqbWmpRKiXClifvjsWf7+KPmgfVMZ+d2YLyxrUJ7cowl9GdlqjOaoXmBanLSEuYTY37Ge9sfMCA0JQ9Tymq9egMddiQNSwebZ+q1ss2e4vviU52dcyVTTMeocvnD9tRWXzyqw9gVyLjRQa0Cep0ncvtkniB1tty2/98it8pG9Xx0ObInqcktyNirPs3ToAqUP4h1Y7QlToW/AN/MPzS2uT3hE/eQG5L5+R2ZAjFslCjA347GCBcCt9LW37trpO4U16UsvgWpJhLp5RApRNKN3iJClV5XG/aH+3cCVt2Rl7JmDkHt5WKclvpqP0hB+nFavan4rWfsrOmhrCF59vmWV3VPvHBQQVmkT5DZrValz8YXOf4DdOt8+zjV5ZqPjzNymf8PbJpPIXyf4Qq93/D7RurjWlkua2rMDx56aWX6OhR6/kNEatUqVLk7+9Pe/fupfDwcFWf7Nmz0+TJkx3q5k7AchfvUDgZGzNe2UhnDl6w2wYoqvtS+ACB5xGCbnuqDf4T2CCAYCBxJDjcFo3wnhgwqbHqa+p8WtgqVcefTh+44GCQ0GZwJWrQvZQtbOkyWKr2acHsdHD9GVsIQ78BRhM4TmD9AO43ZeT3Snx61fuKUOfhNezdXAqLpnE9VtjH1H3LTJkzcJvklB1fh/tH7YdVUeX++nJ3mrZX7MrfgSv3vLC1fEsovfDlEnVqIErBmsknm6WI3uCbfdzsTTRxnvUVC5lMYQviU9Onf1BiEISg0UPaUFYWxHRYvD6EXmWLK4SPB7WkFrVL6iS1bDLIKvvCY/Woe1vrYtUZDp04T4+8MUttDuhYgwY+WJPSc6Nfh1E/raYZ/+xSm4tHdye/XJb6/Nq3S2nRuoNKLIPglcfX6ugi4z+bDtPQr/9SZd5mK7JOcVZkiHjhi8W0PPiIKjf2pXZUuVSAyoc/plj2SPMKyoJNJ+4/eo4ef9sSOqawKFexpL9OUsvPP/+cVq9erda///57ypEjh52OzvPoB/5SHVg8AB7jhkYprbpzrv2rTtMsttqAYIQH0+DZLdSDQu9g2/xj9NvwLWrzDbbUycCWOmbQDapmA1ht71/WToJoNrbrMrXt3NjCsX4avE5ZiuCYLy1sY5c7vvM8ffeE1dmv3bkEtX2hkrIOQoaoczE0+60ttgXAc7+3cvgiMPPVTbTrnxOEDlS30XXtfeqVffv20bfffqs2ixUrRkOGDNFJSS49ErawF34Bl2/5HqGxgHDzBgt5i15k66hVahvWG4WrxjdCzA5wpfs+54aiZXmiMqfCn8TOnyfClq5CU/4KXKlVIcpbxEddA5t+C6U/R1oWdaa1F/KHh16i8X3+VdcUBJDO78S/XPDFZFyP5Wq3WgjRx8ASX6VeffVVZX2K7VGjRlHGjPH3O+ISC55YbKF84crdqFTQ87xm3e/Ht0+nkDWfql3DOgKdSDPoxm1yLDHM8kmtu7uPUE43Ijyx2NLHSepemP7ietq38rS613t9WZ955NFFaRdb98x8bZPart25OHV4paqdpoUtHVG+aQFq3KcM+XFnFQ0LhAULFtBff1nPwBYtWlCHDh109hQt3VlsYacXz+6krb8/YQlHxlHQIM/hX5EtheqTP389hmjsKkDAjIk6re7fxmwN5mnAl3V8YUcwG/nmfa3v+ZM7Z9GBVSNVXrNjhohbIWzdZOvskS0XqvsQgla3z+raFiJsTECLPo+3yjU7qSl5Z2hhS/1I/oN3TvuXqlDx6nkpV8Fs/Hi07jdTuHh7XUed3V6m5Flt78TFSmLCVkqvob1L36Yz++epo1bt+C3/3louapAw6vrVKFo9ualKyJGvAtV4aIqdyVnYQsdcWfZxxxid5fq9FqsPJbrArRC2wg5doq8fX6oO0aRvWWo2sKx9HhFpiqcvzm9DOfysD1zmdYT3fE9+zuQvk1PtB9fm1nnH6Pf3rHYfPpY9+HZ1leZtORT25rnmzbvM2/aNp8KW7uQpIPwn8uQVmvp8vPW787tTvz+c21ru3h3u6oHjJfbuSOx3o+wJbr9NiGu/4XlTpmF+RFNq3M/m8zOw4TC2FnpU7RsCzvpp96t15+eqiuQ/Zw4sYGutN9UmrHlhzQhrLwRYeG+fN4gFRMv6BB9FStQdpNK8LXcudAXtXPSC2kfO/NWocvsvbevg6IsnaOsf/e0PMaZIhXsb7wK0I+t2/d3hw8zxbVNZOB+t9lm0el+u49Nq3RT2nK2klPUTvxf5xUjOApCzpZTaGf/RFltqm+tRrvm7DpZXyvp96TvWR1xOb9DrL8rI4htCaghbakdxf/6dwO14rrsrK2qbFedt0Psfuw66PIQvsEYboEGfZTo60WVUVJTd9kw0o5Ho4+OjPhYZUS5XDx8+TC+//LJKa926NfXv39+h3GeffUZr165V6TBU8PPzs/fjTsByF28X9HLljxFbacufR6gYC1l9xzd02MuRLedo0lNWf6Zg2Vz06Ie1uH3lo/Lgub5qykH6e+xutY3RMrD21EELW3ob4hRG4OC9AMtOBG2xhXXn9wbeDV91WWqLUPgo131MPdviHH3L8b1XKGHOuW+5eU4ozf3Q6rN0H12PLQ7j24KxV67TpIGr1EcaCHnPsGhvhrRqr5h1uNPW73lhq9eI32nnobPqvCz/upeylnI+SRCDIAohmMIWhu51GPqzih8zpC01qlZUres/l9n6qTGLVwgvd2tAj7WqpJPUMjFha9Hag/QaDwtEMIcnqgj+A4upZz+3zHCnvd3ZHo74JFuDbWRLM1hrLf68u4PQhrJvfbeMIi5EU0sW2R5sUk7tbtuB09T3g7lq3ZUAh4Txv2+mb/k/woqve9viX1LC1osvvqi+AuALwPTp01V5/WfFxH22qeZDw2tSlfsK6yR7ueV3Hib2wVa1bQ4fQ0RiwgjSdYPKWdgK3RROkwetRhZqw8NLGvAwEzOErDtLq6ceVA3kRz+oZYtpU55Zq4QrPLAG/tg4viMWVxji1hcP/6M6a86NuMQaZOaxvVn3VNgqUrUXD0cc7HCIqHP77S9Qzl/ozA7wnSpswZoKAoYZzOvKuZGNfGumhqivJ35Fc1C7oZXtokkJW3ZGL1c8EbbwFbTmI9YzJf4w/EKe2FANT4QJfh1uVJrhvyRswcrie36JI2AIcoUWBc2fqtaXT2Bz8O/2qvVX/mlni1amsFWfhwO35UbJ7QqJCVuoA4YpHlw5iodYWkK8q3plZmELw/wCAts5JK+aGKTOfXKHAUMMCf6tt9qXX/GmVLGtJY6a97UWtpAJX6LxPEAwv6DfCmELVq4fNJ2vjuVK7MeQ4zlxHy3wLNXipnlvJ/ed4SxsOX98UJXhP0k1FHW+1F4mJmzhWCm5hiCsXjhtNZzr91zMw/Hzelx93cmEpUj9XvGWsM7CFnZ4aN2XdGyr1ebJw8MTK3f42j7OrRC2dvBQ9F/fstolw5bcx5Yb1kdJfVBY40J0QXjyxyb2cMQVEzFMZI+K7/UlD1OuawkJKgJ/bhJ9P2CV+sAFAfSVv+9T7QFvy3n7XDOvd0/fZd62b9wJSuZQxJqdilHH16vZmPTKebYsGxNnJQ0LaVhD66DbYc5totQWtnC87/tb50xZ6M1uqb8NqarAwmHVTwdUh/SFea3ZysYSsnU9U7K0BRcWUxr2/ZfbhlZHGPs0LRldWdyu+6kttxnD1eH1EEazLjGXTrI4ZgnsGIpYq4v1wdvrcnEWxHif1GfhR1sb6WNCcNnw84NKtDEtpuzjQTCCWMOijBk2/9qNrkVHkn/ptnY7c89fr/Hoi8Uqm6s25P4V7/FQ5zV83xamqh0tCyZk1s8c8/iItznzuqth88hzZPNEHg74DVYdhlTeTmHrzH52L7H0LVWHErUHUdGaLODFBVPsxNBuWNB6Erp27UrXr1/3JKud54cffqBs2SyjBzvSxQrcM8BNA8LEiRPJ19fXIdeaNWto9GhLuBwxYoTDcER3Apa7eIcde7GRmLD1w1Or6fAW614a/GsLW9QyD/Pb2zyEb+ExFdWfhwTqtoUpbMF6t++3QSxIZjCLOghbXT9hVzKNLXFcZ9LPGGw/waKbtiLT6St/OGALa6ZV7J8fbKNNv4cSxLiBU5ro7Pbyr6/42RXncsX5Y1tatVfsyt2BK/e0sHWdfXzV7f+9Oi3Olkjmudq89xQNGPWnijKFLUTAPxdCxgzpWeFWq+oPRK+fFm+n6Ut2qu3kCluwBrt23dp3JsPPE+p88Ph5Gs3DAPXwSVPYmrZ4B33KaQgYptinfTWqV6kwFcrn+KBSGeL+zGDfYqOmWkIPRLRMGR1vZmQ7wsMmuw3/TZUwxa+khK0BAwZQZGQk5cuXj8aOHRt3RGsx7fn1tH/1aTWkD50M/cXczHTj+k36rMMSpYJj7DMUeB28Fbaux96gj9osUgIU9lXrgeI8zLEQFamSJ8GDTB8LDd3h9S3xD0IZfK64CrAg2730pDKRNZX1O0HYcteh0R0ODFXCkCUdzA6wq0aJzuftMrHz54nFljtfGid28ZdZblzqgAZ1ucYFWDDwdXmN6Xx3grAV2Og1KljxIV0lewmBAkIFvujW77XEjsfKf0nYUr4RPrW+TqOjmiGToxUmfm/4kUtqqCLWTfHLFLaGLmzLwyEyI8ttCUkJW7oS8K0RFvIXhR9epr4gax9YOh1LZ6s8DHHBsAdn32xmGVfr5v0LfyL4Yo5gxpvClmpwYwiFsrjJQHW7L6DMLIDcCmEL9dAWLFiHOX+dR0qoodnahwTinUNK3hmmsNWoVxlq+XR5592r7bRqKCYlbOnKenMNwb8WhhUiwL+WOdRU79fdUlsmZObh6/V6LLCzuRK2oAit+4mH3l62PhZWbPsZW0xaDXX9nklVH1v8Xr4e1wYznxVoN5w9eFFZ/umhcqawpa8jVwKI/oH4An/lwlX1AUv7Y/G2nLfPNW/eZV61b/hHeyJsmQw1J72cxZa08JcHIfDVpfHi/O0UtrYvPE6z37au8/48/LwwDz9HgLXGp+2tdmTj3mWoxSDX977+LclZwucTBBcEDCnD0DIzHAv+kYfwfaGinH1XwbfwSlj+cMiZvypVe8Dqj6gI4w+OAestiMvwlehtOdyf/463REf/Uq0pkF0WuArBv/XhoYKhDu+c/SveZ99RVh8AYhh8eAaU7cDWLGVc7ULFYTTAzgVD7HQIVQUqPKgsRt35G0PmJIUtFwKiPog5LNT8UHg7hS2cn9V4b7OvVGdLetOHrSshU/8O52W/fv3I0w67LotRMp4IWxiKeO3aNVXM9BsLIS0kJITGjRtnD1O8U4Ut3OPvNrD66XBT8kCcla1moZd4L4zttkxtdni5CtV+uIRaN4Wt7p+z1RQPYXQOpsXWW2vuT9CfWDsthBaNsfr82t+vuY/guezHMM4S+I2VPMoorn2LuuOdhT6wKbhfi7lOocHn6A8uo92kiLBlEnW97ul98p/0sXXi7EXqOOwXReb1Xo3ooWauX3YXomKo+bNTVD5nYQuRl65cVRZUWw+eoX1Hwmkv/9e+qlQh/pNcYUuXO8kC2Zrtx2h7yBmCiKT9Xel0LE1h6xz7BHtuzGLbCk3n88uZjYKqFKFGVYspv1sQ4nT4cMoqmrV0t95McjmsexB1aVlR5UtK2NJfGSpWrKgcD5o7H93R8nfg/IXPzIN1bSkF09BBPzezkxMTRpBJN6icLbaQZpp+YlsH+NsKbOCv/HnlLeqjo5Wz+dGdrCFMdmQSK+YDKM2FLW4IuBvGpDscd5uw1eJJdlb7hItGFXd2fn/XcmpuniI0uEvU9FMvrCrsr8356/6dIGxVe2AiN3CrmdVW63BYCtP9/7qwNY+HkG7koaSeBgwlq/NoCZVdC1sQSXqNbeDpLlIln6fClvPBrsVcYGe88+jU7l8dfKyYX3LNr9SmY3rnfTlvO1pyDuZJJXqpLO6ELSSaflR0R+tWCVvHtkfQz0M32Kb7uv4wt4cVV4VmBR0mDUF6St4ZprDVd1zDBPvWx7/ThS1dT7305BrC5CK4HhCS85Hi6uUwFqruU+VyFazpYFXhWtji9lDYHtoyu4cqo6xC2EIMoqx+z6SqsKWOYg2HO7D2DDvfj1D+UbQvvrhktTBFmS86/638rVRqWYgeYatsT4O35bx+rnn5Lktu+wa/3xNhy7QycGa2mof5LPlql4o2red0O+x2WGzBEvSTdpYTedNq17SYe2ZGczU83bn+3m7DMfrpvX+o4rjOM2fzc9jVjevRtkWW9XECH92sr+CmKGa5IHjBoay7DW/Lmc9yd/t2jm88cKOKgiUX7ms8b8wAgconTyBbarVhP1ldnSzAbnKZXvxMcOpfcHs0Kwt0eYs3Zp+Tfeyhl3q/SQlbziK7LqeXsHCDpZs51O92Cluoh3ldmB8TtL9E5+GXuu5puTx16pTy97tr1y46cuSI7VvLrNOdKmxhYo7P2bUNgvMkXmb9ISCNCLIEsPqPsWU/u5RBMIUtd885LWw5DyXU+1/Hk5ot5MnNEMz+n07HEPc5725Rm6awpdNP7o6kQxvD6Dh/nD99INLBn5jO47zftGqv6Prcict7WtiCY3RthTRiAM922CDQ5TnCbI11+k9Uac7C1tyV++jjaWsSCFmYGbBK6QD6dZll7p5cYQv+vT7h/bqaqbEq7xe+s+ATC8EUtrANS685K/bS3xsP2VZdiNcBTt6/Gdqe4AwewRxqqfMkthzYqQY9+aDVGPRU2KpSpQq9+eabDrv9sPkCZTWVmLqOAtp01PlLYEqELez3DDsI3DArlHazw3jTWSzSEBr1DKSWz1RQ63BcDYfWyQmvLecZGNnBNkJaC1tofDTqv85l9XWH424Tth58qwZV61DE5W9CJHy0Bf95TPk2c5UJzmPhRFaHO0HYwqyIOfysIcK6XljeK8IWZoUxnXqbDFytN32C/erwDDYIWthK6nniaj9JxWHWwOuxV7gB7ufyC7U7YQud/Njo85SBOzw5C1h+etwd6+jm7+nwRsuq1Wy472J/KOHsFwUBX/QhOHkSzGEgmHUJsy8hJCZsId0cOgMfJjnylb8lsyLiWJfPX6X1vxyiXWzl6mqGWvjfemxUHTWTLfKn5J1hClvuhingGLeqoXj5/CHubJ1WHT+IRM7BncVWalxDp/b+TvuXj1CHTM7MlmcOLFSztaEgLDQw26oO7oQtpB/49wM6uXu2ygrLkPKtPrwlwha+dC/8dCetn3VIV8teFqmUR1ltwl8fgilsDa83V8WZznhVRBJ/vC2XkucaqpTcdxnKJKd9g/yeCFvOnSqU08EcsvjsLHbeHvdh8HYKW6iLnnUZ7cVhS9oqizs96Q8+XD7xXUNd5VRYxrsJ8HRn5kQemHEUM48ilKz7LBWp3tuj3Xhd7tha2jH/GY+OoTMF9VnOvkutj7ywQDrMw43PstWxtsrU+bCEb71qncbzjIVVzGg6uXMmC+u/OHy8MTOYlsOIT0rYyparOE9i9Ku5C4f1jb88rCzOzHbv7Ra2MPGPGtLJNStU+XEqHfSScs6P9yuC8g9Wpr1aT+s/sNiCP2QMOXQOAQEBamjiwYMHVdKdKmzhefdN9+Wqjg+8WZ2q31/U+afY27odgQ/cD/HEZAha2HInWiHPrRK2rkReVbM062GUOBYC6lKgbE6+bmLpxO7zKs75GXyr2ivqYHfpn3ta2Iq8FEMtBluWWK4cuOtzipkM274wTW2awlbw/lPU70NL+YVY9L/OtahWuYJq5sL0cY5oaz3xnSqXXGFr8ryt9OWvG1TZmmULUM/7qrJDd3/Ky5ZXCHtCw6j78Dlq3VnYUpFxf6KuxCrrLczwOP2vHbYAZ86m+OXMDTR5wVbll2vW+4+YxV2uZ8uSiXyzW8N8khK29FBEPBy/+uorh/2N67acTvMsF0lZWEx8YqWaIREN1X5s3qtDSoUtvR8MM8TwpqNsQbBj8XE1K5JOe+S9WlSpdSF29BhLo1otVNHw9VW5dWGdxe3S1z+LbaoqwlZCTImdP0+GIpoOYBPuPT4GQzNO77tAR7ado42zDzt8BRnCM2Vhxj0EEbbimZlrumPiyvLRnZ8Ud/HYb2L3gvYlgE7J0z83N6vhcj1z9gxs6m/51dHCVsMegdTq2Qou83sbqf2jufNz5U7YMn0bNeTnGL7YJxb0sEPkgRCNhrk5lMXZasbdvjBsZfXkZmpYIfI06LPUduSdlLClrAF4li4MScSQE4hicCiMkBxRRBVIxp+LZ6MJE3RAiNgy1/pog+KYqKPDK1ZHKSXvDFPYMi1KnKt4qxqKcAJ9/vh6dTjzC74+vjthKzWuIeXYHeeUA67BoL4r2HVCen1ot0vTWrB8qw/siUdQIDFhCxOTwCdPLPvcQYDD+u3znuFLKlYJw3CQnRrB9FkCZ8IN+WMUhp9pZ78n90Qqh704lils6WcF/DPqWW+d64Mv+xgKguEh+gOVt+VS8lwz6+Xpu8ws40n7Bvk9EbYwxBDPZldBd/qQZn7U0++P22GxhWOf4aFG33RbhlXCkKJSdf1V2w3Om5P6GKYKJeMP/EfhAwIChghmz13cZelrMReVKwEk+vpXouqdf1D5TMsrZ+HY3BGGokNUwvMY96+35TCMed2UNmrX+ct14ud5T/MwLtchIrl6VsRGR9C5I6so/NBSblOxFRq/LxBQx4b9VrvcF2ZexPsn/PAKFl7nO8zojGeCHtaYlLAFh/Dw8+UurOXfiJkaTQv32y1soW7aibyub8jqT+n4julKAER7AO/3OyFMmTKF5s61xP6CBQvSo48+ShhlkzdvXlU9zIyoDRPuVGEL4hBczCC0eIpHc/R1MZqD0+CM/YNm81W+JpynOedF0MKWOz9XyKOfce7EL28ttvSoJBwDfgyrdSiqnNbrSY8S2++taq+gLndruKeFLZw07cC9Pvuh+vrFdi7P4/w1B+jNCctUmilsfT1rA30/f6uKn/fx40rQMndwOYadx//PeoElV9jSTu1hVTXzvYcpQ3rHRuj6Xcfpf59Y/i60sBV99Rr9s/GwqkLtCgUpgMU2M8Rw+oBR8+xhinN51sRC7IcLVmfvfL9CZf37ix6UO0fCzheGW/69wfoqCp9dBfwsx5FJCVuYQhYmra6cx//KMx7uWHJcNZRemNuKGwZWB9Wss/mwcp5KOjFhBPvQDSrnDjmGwEA4wVA0zIzhHGAxgi+sCJjmvdMb1tAwvb8GXUtRm+cqORdT24fZjBRTdGfLndnBqXlinXmXO0pGpOOQoyEOjZU9f73KX9eWqBfof81iy52whfN3Neqamsq3UMXcjiRZxFzMY+DXTA9R8ZgVEbMjIoiwpTAk+KOve+f7CBndCVju4lEmsXvB7FwNXcR+svg+cg4xl2J5dsSTKhqzqGr/N7rTeSuErbU/tuZOeoSaEcrVkF4tfKFSjQfi2ZFO1e/w+q/paPAktV6h9UeUr2QLte7uz5ofWlhDPdTQ4XWcLZ0SA1ZPbm53AuA8GE6EEwu6AY082mJG509K2EI+s96Yoh3TnCOklrAFZ9NHeKKA9BnTEYaia+FAHYT/mMMKMPX183Nbq6SUvDPSWtjaveRlCjtkdcZcWd6Zwhcc/cPhP4J5Lry9hrAfUyArVf8FngG3G6LdhsiTm2jb3CdVOpzNw0ejGRITtpDvwqlgWxBFx+56LM/qdeN6qgpbE3iGW3zJxvDVQSyEm/5JUAdMBDNl8FqsOghbGAK7l6dRRwfl+T9aJZhRGfm15Q/WtVDjbTlvn2vevMu8bd+YdTSH4piWWI/zZC3lnCZrAR8EfS6cO336/XG7hC3URTuRx7u9OncSf3zGskRJTJhDueSGLbN72sPsaj82m7LlKuZyF/C5hAlgIOwiwE8mRBfT55V7K6SbtHpSU8vHln0fxvvKSl65eGso58kdVMXi/kB0igo/qCyUMSwes6Me2/aTSvUPbMPvn5JmdlU3zP6ohynWfHiamunw+PZpHHeRfPNXZgs+Z0u5m7SDxe6I43jPYebegVS89kC1npSwhUy1H/+N8G5yDlejzhCG+yGYM7mmhbBlzkwMMXPngudUOwLDNsu3/MC56olua2fsiWZySoRglSVLFqfYhJuDBg2isLAwZZk1fvx41V8zc23YsIE+/vhjFXWnCluonLbESsxYwnwndH6b2//trfZ/WglbMdxXGdnC6svD3/P9ryW0yF86bi+tmLRP8ReLLYUh0T/3vLD1/g8rafZya7jghGEdqCZbXJnhSsw16j1iDh08YZkBmsKWHsKHGQhXjO1tFlPrv/y1kz7i4YQIL3WtT11bV1br+o8W1Z7rUo8tshxNd7WlV7v6pem9gc11EbWEY/mXx8bP1Dj17QepfPF8BMfyzZ+ZoqyyerOF1+Au1tATs7DpXH7OyC5UNCAnnQhjX2Mv/6KydW9TmV54vL5ZRK1/xg7pp7JjegQtiGHdFLZ+eKMTW5UFINoOH330EW3caI3Rd56hY9uCY/TbO9Z4Y3xpbRU37M8uzCvahBxxj35Qmyq2jD8/3gpbGPqy4DPrt7jqPLtzQmj6yXj+j9Y8JMhRALxwOppGd1qiqm8KYojQnXk4JMRXROcQGxtrO4bMmDEjYZpeT8PtELZMJ8C6XjBfvnDB8reQjmdOyJkzp07yaJnY+UuJxZbufKgvLzwTVpzGYNfJdMhrmiybwpY5W4oumNLfq8UPy8/GSr1b9eVy7z9vqe2UDEXEF2PMuuQcoqOjKSYmRkVnzZrVo4aOuQ/dMUltYcvVvXD+BM+uxf5vEEwfKWZ9zA7nEJ75Kndhy+LOE2HLWxbaeT/qUf3BSQ5DLdQsUnC6zsH53JoiEtLqPD6HOwv5VF7nP6YQ4Ox/4/j26RSy5lNVBF/Eq3X6jnL4V3TehdoO3TieZ4Yab6WxQFa/56K4TpQVZdbJeQhI/A7ZCfjUDva07zreWdjy9rllDu3GlNuuPjBo5/JmRzkl74zUEra8/c1HNo2nUP6P4MwRcaa1ntlJNs9XSq4h02oLxwts+AoVrGRZcWHbDBE8ZGnnwiFKiEK8KbTpfEkJW8i35+/X2Ym79RVdl3P2sZWS56oeGmgOKdHHgaXSL8PihzYP/KEJFSxvuV9Yw7MeL/5il8ra8dWqVPPB4nYxrFyOuEof32fV25xW3tty3j7XvHmXedu+8UTYUrOF8WxfGTM7fmg1Z2Ks3bk4W1jGd870+yM1hS1X7w7zBJrPCfhRg1N7d51Gb+9n0/+cu3evWSf9kRFx5v2/gd8deIcgYGZA52HKp3hI734e2otgfqTwtpwpcNfqMjOBSIUh05tmPKqOp63LYIG5kmfn5QcC5SkaRJXbfaHSzT+mc/kanX9U7yc9rN3ydbWUs1sffHQ507l80RpPUIk6g1SSJ8KWK0f9KGwOwTdnJLxVwhYs38o2tdpv+nfppSloQmTTvsYw8zUc2ycnaH/FySnj3OdyV7ZLly4qyZXLGDyfYa21b58lrKRU2PL2ftN1T2xWxD/e22pbe3cfzQ7ggxz7ozd4opHxvf5VI4WwP3Nm5LQStsy2kKt3EUYLwQLVW+fxKeWtud9Ny3te2IJz9vuH/myfsxEDmrGD9aLkky0THTgaQSN/WkXb2Cm8Dqaw9RVbbE2Ks9iCQ3U4n4dT9rM8dHH2st00/g9LtEHZ3u1YaHq0rt6NWrZ5biqFs7P3oMpF6OmHa/MxMyuhCYnaYgvr41/uQLXKF1TCVeipCzT+9020JM56CuljhrSlRtWsLxcvfrmElm0JRTShTvc3LEPZWXiDz65dh87S698upWPsNB/O5Bd/3l3lwx9TuMIMkRDGCvJMiqfOXVKO8T9gB/MIGBY54ZX71Tr+HD1zgR58ZYba7tOuGrVrUJpyscWXf+7sKs6cRnbUqFFUsmT8lx4ISBN6/0va0WvdR0pSoz6BbKqdlR1lRyuzT0yBiqCGIfLUrOZ7MTFhBGV0g8q5Q24OT4DPhfZDK9vTgGMmpDU8s4VWx03hA3X6jB3eI6Cz9eBb1al4DT/1lRjDGP+dtJ9CNpxV6b3HBlGJWn5qHX+0BQvKwbcTZv7yK57D/sK8bt06+vRTq+NauHBhe2pdeweJrNwqYctsCOBrW5HqvbhT7m9/lbxy5Qr17t3brtnUqVPJnFHFTnCzktj5S4mwZZrtojHbtH9ZFiIsETLy5BWa99F2NRsnqmX6AjGPCaG16n2F+bdmVtcj8qb0994qYUt/NYZZe/kW7yvhxDegkjJ3R70xjTPuQ4R27dpR37591bqnf9zdRyivr+sSfB/0HscN37jgLh7JOs3dvWAKV+gk4VxguCisePavPsPnb5s6itnhRIQnwpa3LMyZjPClHUOrsucppYZa7Vr0PE8uYdXJ1VBB7R8NdYQoVahyV/7/GGXxsRpd+MJ8YucsOrp1sj2cI6HocJN9Xz1kW07xyVU+j/IWa8RsalEsfxWP4BmozrAj+vMnrA8JOJ6rKdFNocS9sMVOwMP30pZf498R2J/ZIcO2t88tDPPC0G4MD8LQpkffr0WwvoPFzfWrN5SftVlvbMIhqBrPcPRg3AxHKXlneCpsmR18DEUPKOWrPmJoi2JvfzOuEXQqEdS92upDfgc0477iNYLodTR4skqzxFEe2mO87FLnGuIPUcbMZjgYrML8A9vytOj11bWJoZLhh5cTfHLpoCcR0Nt66YmwhaFHa6e0VhYdupyzsJWS56q2EsK+9TsX11Z46CVaNmGfg39F08IXTsbHPPi37Vuz/dAqasIYvJchQi3gmVm1by7zq7635VA/b55r3rzLvG3fmNe9O4st/A7dZgoIzMnDeq5RyPowmvFq/DPnuTmtWJzJhqwq6PdHaghbSb079DFNJ/I67gkW5IrycFXn4O39HLJmNB3fPlXtzhP/WJj8Bfcxgh6ahnXcc7DWVIGf62WavE7+JVtR+kzZ6Bz7Vtzzzxu2ta75UcXbcqbAjedQ6aBh/AxoTenZP1bY4aV0iH1o4Z2EUK7FCJ5Nup1a1yIVNjBsslitJ/mDSS41Q+O5IytZUHpFDZfEPvXoANPXHp4jZVgA0tbGUecOEPxHalFPi2HYvyfCFvLlKlCDSjV4nkW0Cjw88yiFbvzWFtLxHIV/MNQHwWzPYoZgzBSsg7vjuYtHOZ2WxSe/OmeZsuZ2+bFp37J32BXGn/pQ/N7PzzMOz7O3PV3RsxZ6mh/58JHck6AttpD3lVdeoZo1axJmRMRom8mTJ9Pu3bvt3QwZMoQaNmxob2tLMue2pbt4b+83fcDEhC2zj4b89w9jFz484z3aGBiiPG/UdnY5c07tyhyGiIi0ErYwAmFkS8vNTf7SOenxj+uoj7UYBn9sx3n2IbnDFuJQT2c3CuZzOzXbKzjW3RrueWELJ84ciufqRMIiS89yaApbO0POUq/34huAzmUhHmXLklEJSUirW6GQctqu8z372UJaveOY3lTLZV/1Uv6rpi/ZQZ9MX2unmXVAJBzIm4Lb/9iZe3926n745Hkliun6Ii/qAQHNDBDwTGf58Df2wpeLKXj/aTObwzr2M5mtsgqx4KUDhjcGPTVZb6plBbYe+4mtyBDCw8Ppf//7n1p//vnnqUGDBmpd/4GVzLQX1tlqtI43l5iSu9tn9XgYTw4zmhITRpBRN6ichS2kLfh4RwKHs+hom07kYfGD2dW0Dx+UM2e1wLarYDqd1+mrfjhAf42Nfzkg3mxomw97jHEfM2aMLprk8lYJW8pPzyS2eDKCOctMSjok2GVi588UmfqwSFg8TiS8FB7D03YvVjUy+RlVVJ3lHwatsZ0tIk37BUFHWgeMZe/4ejW9qfypvN9kvr2NFXO8fUp/760StvYufZvO7HdsKJnCiLdijgaR2H2kOxrJEbaSuhcw/Hj6ixvsBoiuh7nEvdqf/e1p/2hIu5XCFoaQYDjitauXzGokWHc1PAL+UdbzV3n4/EgQuCODr+BmKFK1F5WsP9iMUusYxrXn79d4OJtl0ZYggxnB+1XOaY3Gu072VNhC/gMrR9LJXbN00USFreQ+t3YuOUFavNIHcH4G475FhzR/mZw6ixoy7M07w1Nhy5xBTR/UtChOybPaHG6o9+28DGz0GhWs+JBDdGpdQ9gphgeFrP08wXXncMC4jcT8/ngibGE3pqNrbKemsGUKP9g3rhfzGY8PYsd2RiBJBVNcOcAi+dTn1+kkl0tXsyZ6W86b5xp+izfvMm/aN2YHyZ2w5Xx/OkNzZXGg3x8me5Rz9+5wVw+USerdgTw6LPpsJ639JURtYqjqMzN4GHg6nRq/9PZ+XjO5mfU+4GdtUO+ltoP1+D0nXLOHmnNSlfu/4fdXHZXJtDJKWMqKMWfK1Xm8LRe6cRxb9X6nd+NymbdYY55BdbSdpoR5+Fo03lcQjfBeMoM5pBBDE+FAXQ9RVPnwzkMw9uMbUJktoSeraPzRopF/KR6y1yp+yJ7t88/Fe9MujBVOr9jmExbu49uvqS1srWeL5pgox/5SPR6unZmHi5oBw/i1w3jEeyKCmuVvx/qMGTNo1qz49zxcx0DY0gE+ks+cscROxJkiljsBy128t/ebrktiwhbyuGpX6LJ6CYvPR3k2XN0vQHxaCVs4trbMxTqC83sMH3HhukEH/REH27eyvaKPd7ctRdiKO2Pb2SoLVkumWISkhmy9BcunTsN+UTlNYQsRSzcdps9nrLPFK5WJ/7SpW4pe6dGQVgSH2v6rqpfJTxNf7aiz0MHjbBHGllCb952y45Z+2ZNy+mThryDsJ+DPLfTDgm22qIZMELh6tKlC/TpVp/cmr6Q/2D8WgvNMhd/PC6bF662XusoQ9wd16M2WVU2qFzOj1TpmU/x+bjDNYmszZyGsI1t+9e9Yg4rw0EXnAAZwdB96KlIlYUbI6cPjG+bPPPOMeig2b97cFrnMfUCxXjxmF+3464RDoxQ3d9W2han14IoODyBdFo7ef31zs9o0G2I6XU/N7cqRIL7qrp9xiK2zDiYQ1XBc+GVAQwx+uJwDHJHD6kcr/zodvmCaP1meLQyK8HvVsQWFcdQYyriXHSPrhrc5BGfTpk0EizaE5HYQb5Wwhboc3zZNzdSGjhWC6Twbw9t69uyp4vEnuRZbiZ0/U9gyv7SawlaPL+oTZk1zFTDb2srJBwgWf5q3zgehtM4jJag++0pzPk97lp2iv77eZTuYxxeUp6Y1VUVT+ns9EbZqPTpDWQLpuuqlttgwnaHqNPh+2v7n/1iU5fs9rrGI2cvQKUWYNGkSLViwQK2bDRIV4cGfxO4j3TkpVcefen4VP4TZXTwOl9S9gDwwGV/x/X529h/qIDYjDRY8TfuVYSsTx+G6Wthy/hKHMjqkhAUaqLuXDGPfI9YzV+8TS5yXyu3GuPxii3RY5eBeOrLlOwfrFaTpgC/ZxdjHCBrziYVTe35jC5/vKOYyW4canQOUwaxUPmxJhlno3Pl7MYWt0kFDlfWYu+Oh07LuJ3bEG+cE3Nliy3xuJdfSFMfc9+9pZelqig+6LhVbFFLWlgGl4z+k6DRv3hnwqYQGJEJSvnbgJBZOWfWHjqDupdV7CGXN35zcZzU+Fuxf8R5bFVjiPPanAzqJZZq8QfnLxrcRdBqWqXkNoZO3ly0JrkSG8n7jOzDqeNwpzJqjAFtCvEB+JZqpKFd//p3AFuh8/cHaq3L7r1xlseN2LnyeHU3DCi2hsJWi5yq3k/CcWPXTAYfnPN7h8IXZpF9ZmsvDU4LnWz7izFlUURf4w5z/8XY6sCa+04Z4lG81qALV6lyMhxfHdcSREBe8LefNc82bd5k37Rt3gpLpYwsWzos+32lbs2keGKJ43/OVXFpEaWGrJfNs1DtQF7GFLed3h7t6oKAn7w59ANPlQBtuQzbge9hV8OZ+Nq0v3Vk0ujpWyOrP2Hn4NJUE0QUuHnQ4s3+++pCAZ4QZYHlUOuhFKlC+sxltr3tbDhZfGCqsfEfae7PeIUWq9qTidZ5K4DQe746Dqz7moboJ+xZ4Bxar2T/B+yTm0in1zIvAxBlO7ytYMMMiLJCt1EwH9VrYCuBZA/GBRgctbGX1LcwWYwO4Lh8leJ/C71Ylfhc7v/9MYcvZV6G747mLR30iT26m3WylZn6wqtdjIT878unq2ssN0x9gy7TjSnAL6rNMtaPtxDtgBcMNf/zxR1q4cKGDoAWBq1WrVtSnTx+CW5ktW6xRSG3btqV+/fqpmmsBq2PHjg79AXfx5v3mTZtBu4RJzI8WLHZhdXuQfSyaAW3/ul1KUr3HSiZo+y/m5xp87zpPUmaWT8p5/MZfD6u+Ico4+8JC3I5F3Gd9K67Puvp+e8QOhhuivhhCbQbUt9UzFal8swJsYfyX3VftyX0fWLjrcKvaK3r/d9tShC2nM3b12nU6dPw8ZeLGTDF23I6hhUmFGzyk7hQPaQyLvEx52KqpMFs06VkRURZDE8M5rVThPJQ5Y4akdueQDosoDJe8EBWjhgbqIX46Uwj7/srAIkrR/DkpPfs5MsPl6Fg6ExFF5y9FsyCWmfzzZHfpGN4so9dhwRV66jxbj2Xh4+agrJkz6qRkL//++2/69ttv1TA1PDzxsHQXMDMWhgLkYb85OfJZw8fc5U2NeAxtuRQWwzPW8KwzvO6TJzMPO8nGLx4PzjuLY+eORFE0C3NwYO2L+jqegtSo4h2/j/fff1+ZLOMc32kBw5ou8Lm9FBbNfkEysLVAFoIAea+dp9DQUBo6dCgNHjyYGjXiIb13UYClQ9jhSzx8N7Ma4uLsaPx2/xTMSHUpbI8a+pAtV1EeqlqRBfAiHlbjJg8nPMYdg0N0hf+ny5CJJ5oorqxY9NBED3ekhoBEcT3On9hEmbLlVl//s7AgcbsDGsU9evSgoKAgevrpp706PHwaXeR7FCI0hv/iHoW44Em43e8MT+qUVB7MToaOVlT4fnXu4Msme57S3Nh2/26M32fqXUPYJwTbyOMb6DoPG8xdmF0iKN8vt/9FlpL3CIZtnOdh5nhWwIoT7gzMcDbkoupE5C3qk6BDg3wQgjChwWUun5x3ubflcMzkPte8eZelpH2DOroLcNkQzhPwZODJH/KV9E3gc8tdudsZb344e3Fe69vSnkyN3wdhC8MWIWTD16IrocTVcbwtB2vki/wsio0+r/xt4Z2WVAMJHzogimPIYkYejggxKal3Dz6OXuYyEHgyZGBfn76YRbJEksdy9VvNOIhsGF6Zxcefz3FFD5+h5h5u7Tr8bK1m63LMaunOL9itrYHne8dHhlOnTinfufhoky+fo0h36NAh7tty37hYMRYivX9HpEabwZNfhedfxLHL/I67qiaT0u4EPCmbFnnwXIXLjWtXryeoL941p3iWX7hVcX6/pUVd7+RjirB1J5+d/0jdMDYcvpjgxA6d6zp16vxHfpn8jJ07d9Lw4cPJnTWeEEp7ArjvPvjgA8K5mjhxopr5Ju1rJTX4rxCYOXMm4b+roeb/ld8ov+PWEpD3yK3ley/ufdLAVWr4TnmexfExns1RghBICwKwqNu79C11aFi2wsL1Xg/SZrjXr4Bb+/tF2Lq1fGXvcQTmzJlD06ZNo6JFi9pO0gXO3U0AgmW3bt2UBR7MlHFuJdx5BDAzzrx586hx48b07LPP3nkVlBrdtQSCg4OVaAofHJ988glh1k0JQiA5BOQ9khxakjcxAmGHLqmJHjbPOUKLxuxUWU0fnYmVlTQhkFoEYNUNtx2waNs6d6By/p85ux/V6+E4Q2xqHe9u2o+0Ge6ms3V31lWErbvzvN11tYYjQsy8ERERQS+++CLVq1fvrvsNUmFHAjdu3KApU6ZQhw4dEpgsO+aUrbQkAGed6DxiqFhKzMfT8jfIse9MAidOnFAzI8J3m4had+Y5utNrJe+RO/0M3T31+7D5Agdfa/DBCV+cEoTA7SRgzpipj1uh1UjKV6qV3rxnl9JmuGdP/W374SJs3TbUciA4HoRlT4ECBWj06PjZVoSMEBACQkAICAEhIASEgBDwloApbGGm3kd45jP41ZQgBG4nAWdhq2j1J6hE3UG3swpyLCFwzxIQYeuePfXyw4WAEBACQkAICAEhIASEwN1PADNIYjKJ7Lkz37OT+dz9Z/Hu/wVw/I8JZm7evM4TUgVShsw+d/+Pkl8gBO4SAiJs3SUnSqopBISAEBACQkAICAEhIASEgBAQAkJACAgBIeBIQIQtRx6yJQSEgBAQAkJACAgBaOPgnwAAQABJREFUISAEhIAQEAJCQAgIASFwlxAQYesuOVFSTSEgBISAEBACQkAICAEhIASEgBAQAkJACAgBRwIibDnykC0hIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgbuEgAhbd8mJkmoKASEgBISAEBACQkAICAEhIASEgBAQAkJACDgSEGHLkYdsCQEhIASEgBAQAkJACAgBISAEhIAQEAJCQAjcJQRE2LpLTpRUUwgIASEgBISAEBACQkAICAEhIASEgBAQAkLAkYAIW448ZEsICAEhIASEgBAQAkJACAgBISAEhIAQEAJC4C4hIMLWXXKipJpCQAgIASEgBISAEBACQkAICAEhIASEgBAQAo4ERNhy5CFbQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJ3CQERtu6SEyXVFAJCQAgIASEgBISAEBACQkAICAEhIASEgBBwJCDCliMP2RICQkAICAEhIASEgBAQAkJACAgBISAEhIAQuEsIiLB1l5woqaYQEAJCQAgIASEgBISAEBACQkAICAEhIASEgCMBEbYceciWEBACQkAICAEhIASEgBAQAkJACAgBISAEhMBdQkCErbvkREk1hYAQEAJCQAgIASEgBISAEBACQkAICAEhIAQcCdzzwtaNazEUduhvRyqJbGXI7EN+xZsmkkOS0orA3r176dKlS1SgQAEqXLhwWlXDq+NOmjSJDh48SAMHDqRixYp5tY/ECu3atYumTZvmkKVy5cr0+OOPU2L3QIZM2Sl73tKULWcRLpvOobxseE5g69atNHPmTIcCderUoQceeMAhTjaEgBAQAkJACAgBISAEhIAQEAJCIHkE7nlh68LprbT1936eU0uXnhoPWO95/v9YzuiLJ+jy+UOUMXMOypm/2h3167p3706xsbHUrl076tu37x1Vt6Qqs2jRIpo4cSKVLFmSRo0alVT2ZKevWrWKxowZ41CuSpUq9Oabb5Kn9wDOef6ynahk/WcpXfpMDvtK6cb1q1EUeTqYpbN0lLtwPd5/hpTuMk3Ku7s/Fi5cSN9//71DnYKCgui5555ziJMNISAEhIAQEAJCQAgIASEgBISAEEgeARG2RNhK1hVzcPUndGLHz6pM44Ebk1X2Vme+m4Wt69ev01NPPUWRkZFK7IDokZrBFLaGDh2qdp03b14qXbq0x8KWrg+suMo2fYvylWqlo1K8PH9iA23/839qP5Xu+5zyFmuU4n2mxQ7c3R8RERF04MABVaXvvvuOsC3CVlqcITmmEBACQkAICAEhIASEgBAQAv81AiJsGcKWf+m2VK75O0me49S2VknygHdQBncd9zuhit988w1FRUVRo0aNqH79+ndClZJVh3/++YfGjRtHuXLlovHjx1O6dKk39M8UtmbMmOFQL9Niy/keiI25SJfO7qKLZ3bSyV0zKDY60i5b85Hp5JO3jL2dkpX/urBlsnn77bdp9+7dImyZUGRdCAgBISAEhIAQEAJCQAgIASHgJQERtgxhK6BMexa23vUS5b1R7E4Wtu72MxATE0N9+vQhWG8NGzaMatWqlWo/yVNhK7F74Mb1q7R78VA6d3SVqldW30JUp+sfqVJHEbZSBaPsRAgIASEgBISAEBACQkAICAEhcM8REGErlYQt+NY5f2wtRZ4MZquWc5TDvyLlKliTcheqzf6CMia4sCJPbqLL5w5RtjzFOU8dunnjOqFzH3ZwibKCKVTlcboUvpcuntpOmX3ykV+JZoRjnDu8giKOr6cMmbLxcK2GKh5DwxAuhe2h8MPL6cKpYMqUNQ/5BlSiQpW7uPWHdPPmDTqzfx7n30oxF0/STf6HcnmLNyb/Ui0dykXwb4uOPEZnDsznoWvb1PECG76iljkCKpCvfyW1npZ/NmzYoHxsFSlSJIED9ps3b9KWLVuUpUxYWBhlzZqV/Pz8qHbt2lSiRIlEq33u3DmCY3oMJbtw4YLygxUYGKiG8WXIkNAX1PHjxyk0NJRy5sxJcNCO4YU7duwgOHCPjo6mUqVKKYsyHN85fPTRR7Rx40a17w8//NAhGcPXYOmD4O/vT2XKeG4tlRrClq7M2h9b8zUeoTbLNH2TCpRL6AAd13L4oWXqmr0ee5mvq9x8PVamAuUfoIxZcupd0eWIEIo8sZn9toXQiZ2WJVlAmQ6UM6AKZc7hn2CihuRcs/ZBeAVWaWf3L6IrF46o6Mw+AezLqw7BQi0d+81zF2ChFsFC3vnjG+jqlXB1r+Yr2ZyyKmf68aWSc3+IxVY8N1kTAkJACAgBISAEhIAQEAJCQAiklIAIWykWtm7SgZWjeJjWLJfnAkJR1Y7fUvY8pRzSt819kkWwTZQ9dwkqw/6KsH3zRqzKk69kS6rQehTt+etVOhuyhDJn96NiNQbQgVUjHfaBjWw5i1KtLrNo7z9vct7FCdLTZ8is0mFdYwYIDzsXDCFY4bgKEB9qPfIzi2oBKnnzr90oKnyfq6yUp2gQVW73hcu02xnpzsfW6dOn6Y033lACk6v6VKhQQTlRz5jRUYCEGDZr1qwEs9npfWD2xddff53y58+vo9QSMxwuWLBAzczYo0cPl87gIYgNGTIkwZDJlStX0hdfWCzhTN7X19fe97p16+jTTz9V2wULFkzgDN7O6GIlNYWtk7tm8zX/gToKhKHyLd+3j3j1yjkKnt2TYqJO23EOKywi4VrJU8QaKnp4/Vd0NHiyQxa9AcE2qO8KvamE3+Rcsyh441o0bfmtlxLQ7B0ZK+kzZqWaD0+jbLkSzkR59uBi2rv0TSU6G0XUqk/eQKre+UfC/YWQnPtDhC2FTP4IASEgBISAEBACQkAICAEhIARShYAIWykUtrZwJ/5SmGVFgzOSJUdBysSiEGYOtEUj7sxX6/QdzyJY1T5pWtiCcBTLliCw2NKhQPkHqUyTN2xhS8djmTm7P4tN/iwy7beFMAgAsIphEyvKzh30DJl82Nprn50OP0jwh6TDlQtHadOMR+xjwmdY9jwlKQN38i+e3WnH5ypQg6p2mqCK7VgwmIW4zdZvYksvBIgCCP7sRLxss3fUelr+cSVsQZzq378/Xbx4UVUNYhQcpuPC37Ztmxr2h4SmTZvS008/7VB9WEzByksHzFgI/1f79u1T5REPgWrkyJFUvHhxnY20sIU0DCtEgBAFAWznzp3KqgxxSMdMedmyZcOmCgcPHqRXX31VrQ8ePFj5C4tLojtF2MJ1vWpikKqWj19ZJQxZdbxJ635qT1cvn7U2+XrM5ltYiaNRbH147eolFQ8xqGG/1WodIlnI2s/UNaeFXVyP6XhWxCx8b9R+bLbK5801i4Lb/hhAkaesc4jj5shXni20MtBFvmcheiE4C2iIO7b1Rzq0Lk6sxe9gCy1c75cjDtn3FUTpGiyKYb/JuT9E2AJhCUJACAgBISAEhIAQEAJCQAgIgdQhIMKWIWzBMso3IF58ckYMq46S9Z61o2Ehteev19R2xsw5qMr941THGREQqvYtH85D/ear9Kzcwa/T9Xe1jj9a2NIR6FyXbvgSD1+sTdq6SltsIQ86+pXbf6WGQmH76uUwWj+tgy1CoXyNh36yLU8whArp6LyjbKP+61BMhUNrP6dj235S62Uav0YFKjwUl0IsPlykDdM6KhECHfmGT6y007CSEh9bGIZ37do1h/0ltQHRBwKQJ8GVsHXo0CHlrwrl27VrR3379rV3deXKFZV26tQpypQpE02dOtVOCw4Opg8+sKySMGQQFl+FCxdW6Tdu3FB5586dq7YhlJnDBrWwhUTUHf6yqlevrvJC6IJj+KVLl6ptiGkQ1XSA83tdx4YNGyqrLp0GQe3bb79Vm8WKFXNI03ncLVPTYgvHWPldPXXtwbKvQe9/1GFNJ/R+JZorq0NzmJ8pMtV5fI7DcL6kfGx5c81i2OLK79gyjJdZfPJT7cd/sy2sOJK2z3uahxiuV3Wv3vkHezgthlmu++k+9ftgcVntgYn2fXXjWgztWjKUhydawlyJ2oOoaM0n1D7wx5P7Q4QtG5esCAEhIASEgBAQAkJACAgBISAEUkxAhC1D2EqKJoSvej0W2dnW/dSWBaZwtQ1RCdYgzmHzrK4UdW6/iq7Y9jP2GdRErZvCFsSBut3+VJYjZnlT2CodNJT9ZT1mJisrEd3BrtBqJOVjyykz7F36li2swUJGD5va+vsTyk8WxLgGfZaZRdQ6hntpB+GNB250SPek4+5QwNgYPny4slgyopJcffHFF6levXpJ5kMGV8IWLK606NS5c2fq2rWrw77glwuzBKZPn57eeust8vHxUenPP/88wVcWwujRo21RS0XE/RkxYgRt375dbUGsqFTJ8jNmCls9e/akjh07msXo5MmTtij18MMP02OPOZ5X1BECWNGiRe2hhw478GIjtYWtNT+0oGsxFxxE0yObJ1Loxm9U7er1WMjWhfkcamoOYYRIq4cjIlNSwpY31+z1q1G0erIlGrpydB994ZgSmHH80o2G2femed/U5OG4GHZoBghm66e2U/e+8zPBk/tDhC2TpqwLASEgBISAEBACQkAICAEhIARSRkCELUPYgvCTkZ1cuws+7CcLHXIEZQ0yoa5az5GvAltLTVHrzn8wfG/b3IEqunDlrlQq6EW1bgpbZZq8zk61OzsXdRiKCIsrWF6ZYf+K9+nUnt9UFHwRwWrLDIfXf83+iyapqIZPrOKhVFnUOqzJbt64xvtLz/8z2UUwnBE+vQ78O5LTLX9fqSlswT8UHKMnJ7z00ksezw7oStiKjY2lXr162UMC69SpQy1atFBO3bNksXg41wfDF7XYVKNGDXtooHM+0xoMYhSEMwRT2MJQwxw5cjgXVXWCBVvbtm2pX79+DumDBg0iOLjHsMcJE6yhoA4ZvNhIbWFr1feNlDWgabEFK6gb1yyfbfpaQ1VxreE+2Lv0bXuYYnKFLW+v2Q3TH2AH9pZAiWG8hat0o4DAtrbvOFcoN/7yEF2JPKKG/dbqYjm0d86He0v71TOtz0TYciYl20JACAgBISAEhIAQEAJCQAgIgVtLQIQtQ9gKKNOeyjV/1yPimM1t08wuKm9BHsoXyEP6XAUIRCu/a6CSMPthlfstixZT2DKtqcx9aIst56GEOs+Bfz+gk7stH0TOAhTyhG4cR0c2f6eym8KWLg9rr7M8C+PFM9sp+tIp2+eQTsfSeb+edNzN8rdz3ZWwheP//vvvDsMMdZ3gbwvCVatWrZR1lI6Hs/lnn7WGnD7yyCPUpYt1nnW6XsKqSluANWrUiOATC0ELWxiGOH16vG8zXQ5LiG3uhC1tCZZYeXNfnqynprBlirqYFKHWo47iT9S5A3R671wlZkWzPzftW8usZ3KFLV02udds+OFltPuvYWpYod4HlhhmC593BSo8yD7i2phJyn+Y7R/PIcX1RpUOY3mGRUvk9uT+EIst1xwlVggIASEgBISAEBACQkAICAEh4A0BEba8FLYwoyHEKYSiNZ6gEnUGueX/Lyy7ePgShjRhaBOCFrbciVbIc6uErZioMxT8W097GCWOhYC6ZM6Wj65fu6KGmSHuvyBs4XfAKfucOXNo8+bNtvN2xOsAK66nnnpKbZoO3Pv06UPt27fX2RIs9bDBKlWqqJkVkUELW85+u8zCiQlbH3/8MWGIJAKGSaZGSE1h61LYHtoyu4eqVt5ijanSfaPVOgSv7X8+pQQt5zpnypqLLQpz2NZTyRW2UnLNxrBoG7L6M4o4vtaaZMGpcvC/VfPRnyljZl+V8u/42k45Et8s2+xtyl/WGm4qwlbirCRVCAgBISAEhIAQEAJCQAgIASGQ2gRE2PJS2EJHe/1US/AICLyPyrV4z+W5gTP2NZObqzS/4k2pYttP1boWttz5uUKmWyVsreN6X+X6I/gGVKYiVXtQrkK1CI6yERKzBPOk4652kgZ/3FlsmVXBMMMjR44oX19w4B4aGmonP/nkk9SyZUuKjIykAQMGqPj7779fWVfZmYwVOJ/v3bu3imndurVdJqXC1tChQ1W9smbNSj/++KNxRO9XU1PYMn2wFavZn4rXtgRB0zdVpmx5qViNfpS3eGN7MgRYW2H2QITkClspuWZNahhieC50JVsqLlIzgOq0nPmrKSfx2F4zuZmyMoM1WoXWo3QWt0sIY3oYsCf3h1hsuUUpCUJACAgBISAEhIAQEAJCQAgIgWQTEGHLS2ELpLUlFnz31OuxwCX80/v+oH3LrOGNxWoOYBHAsvJKK2ELM76t/bG1qiuGYlV74PsE9d61eCiFH16q4lPTYksPsUtwwEQiXnnlFapZs2YiOeKTXAlbO3fuVEJWzpw5CbMMOofly5fT119/raJr1aplz6CoLbESc+AOqypYVyFgJkPMuoiQUmELVmK4MfPly0djx45V+0zpn9QStpS11m+9lAUiLPwa9F5qizraoTyG+QX1Wergvw31N++F5Ahb3l6zUeH7KOzQUp4YIBMVrNyFLbIcfZ2Zw4nha65R/zUK85bZPelS2G71u+C7zlW4cHob3yPLVVLxWgNt/3UibLmiJXFCQAgIASEgBISAEBACQkAICIFbR0CErRQIW8G/9batPgIbvUYFKz7kcKbgp2ftj63s4U/wRQQrEIS0ErYijq2lHfOfUXWApVbJ+s+pdf3n6pVzbInW4ZY4j0+LWRFnzpxJ+I8wfvx4yp3bcXIAd47iR40aRZs2bVLlMENigwaWnzQVwX+uXbtGTz/9NEVERKioL7/8kvLnz6/WUyJsmX67KlasSO+8847aJ/7AEb6+YTNmzGjP4GhnSGQlNYQtzFy4Y/5g+9ooWr0vlaj7tH1UPYTPHHJrJ7Jj+Y2/PKycsiOucrsvKE/RIDvZnBXRnD0UGby9Zs1ypRu+TIUqJfSVpp3Lm0OCj275ng5vsATFUg1eUA7n7YryCpzh476G7zBLEFvNselUFhG2TFKyLgSEgBAQAkJACAgBISAEhIAQuPUEdD85qSOlu3wl+mZSmZAOoUAHvY6lq/83btwg/R/+eW7cuEmFChbQxW/L8kIKhK3L5w/TphmP2PXEjGtFq/ehjFlyUeSpzTyU8DWCtQmCOQwR22klbMWycLV2iuUsG8OnqnX6jnz8yirxLTx0OQ9D/NAW4lDPej0W8uxw+bCqAjr86PgjlKjzNOUsUI2y5y5BGHqWVIBoo6+JpPLqdDhQT5fOEg10nLulK4utvXv32r6v4Cz+mWeeobJly6pdXLx4kX7++WdasmSJ2sawwg4dOqj1s2fPKuFKH6tbt27KyXz27NkpJCSEvvjiCzp16pRKNochIiIlwhb2qZ3QY2bGhx9+WFeB1q1bR5hZEqFw4cI0erTl28rOkMiKp8IWRKlCVbrae7p5LVaJUeeOrrRFKSTCSrFutz8crLK0xRbSy7UYwbMPtlMi0MUzO2j/ivcI94sOgQ2HUcFKj+pNunR2F22BJRiHvEUbUpHqvdQxsuUqRt5esxCgVk5k8YyfLVxRCmz4Cs8+2olXM6qJEo5vn84ClmWtl8OvHNV4eKo6PmZgXPNDc/s+gCBWpFpPnkkxP0We2MgTMkxkP2KW6OlfqjWVb/WhKoc/ntwfMhTRxiUrQkAICAEhIASEgBAQAkJACAiBFBMQYSsFwhboH90yye4cuzsbWXIUpBoP/cQ+rHLZWdJK2EIFNv7cma7wbHV24E6/6vzHRUCkgpiggxYpsB12+B/avfhlnaSWECIqtRvjEJcWG66ELdQDAtCaNdYwM10vOHaHBZQOfn5+9NFHH5Gvr6+Oor///pu+/fZbe9vVSsmSJemtt95ysJ5KibAVHBxMH3zwgToUloGBgfZhTWGrYMGCNGaM58w9FbbsgyWyUrDiI1Q6aCgLRBkccpm+2VSC83XF139sdKRdBrMRlm9l/dbrsZdp9aQmdhpWTP9z3l6zru5P1BvilR24nlU6fE2YtVQH+APbueh5x3w6MW6J+6TO43PsoZiI9uT+EGHLCaRsCgEhIASEgBAQAkJACAgBISAEUkBAhK0UCltgD4uU3UuGUUzUaYdTkT5DZgoocz8FNn6FrY5YPDLCjnk8jO34OiV21e/1t5ESv5qU8/iQ1Z/S8R3TVQFnX1iIhGVJ6MZvVDr8B2HYFAKGG2I4InwQmQH1hSPwwmyxs+r7Jtypt4Qfc9Y35Ee90IHX4gCGlGFoWVoHd8IWLMVmzZpF8+bNo+joaIdqwiKsXr161K9fPwdRS2eCo3kMMzSdzCMNllvwqdWlS5cEFmUpEbamTJlCc+fOJQhvWE+fPv66wdBIDJFEuJ3CFgTZrL5FlGVfgQoPkq9/JVWHhH9u0p6/X6ezIX85CKWwlspXohmVb/k+Bc95QvmvQtl8JVpQhTYf2bs5vm0aHd44VllTIRIWhdrHVUqu2RM7fuF7YbyDqKYPCmvDwMavUa6CCf24YTbFnQufo6iIEMffw4XzlWyp7ms94YLeH5ZJ3R8ibJm0ZF0ICAEhIASEgBAQAkJACAgBIZAyAve8sJUyfI6lIfRcCt9DVy+HU06ebdCT4XmOe7i9W1cvhxEcaF+/Fp2gvhjGBR9FGAqG/3dDcCds6bpj2Gt4eDiFhYURxK48efJQQECAEpF0HndLlD1+/DhduHCBihcvTjlyODoid1cuufEQ2DBEsmPHjtSzZ8/kFnebPzGLLbeFvEyA9VXUuQN8H4QRhjY6Xz8XTgUra68cLJA5C75JHTIl12zMpZNsqXiMrrHVWGafADWENmOWnEkdktNv8n1ySA2lzJazCPvJK2mLxB4UTpBFhK0ESCRCCAgBISAEhIAQEAJCQAgIASHgNQERtrxGJwXvJAKm0/VOnTpRjx497qTqeVSX3bt3E0QPBAyBhPCWWuF2ClupVef/6n5E2Pqvnln5XUJACAgBISAEhIAQEAJCQAikBQERttKCuhwzVQkcPHiQINz8+eefar+mE/hUPdAt3tmbb75JcHbfqFEj24F8ah3SFLYwxBEBwxwx5FHCrSeASROuXr2qDjRixAjat28fBQUF0XPPOc5KeutrIkcQAkJACAgBISAEhIAQEAJCQAj8twiIsPXfOp/35K+BnyszwCdW/vz5zag7fn3nzp00fPhwJTSNHz/ewRl9alTeFLb0/qpUqWLPGKnjZHlrCCxatIgmTpzosHMRthxwyIYQEAJCQAgIASEgBISAEBACQsArAiJseYVNCt1JBHr16qV8ZkEoaNu2rcNMgndSPROrC6x4tm/fTkOGDKGGDRsmltWrtM2bN9PIkSMdytasWZNeeeUVhzjZuDUEli5dSt98Y03koI/QpEkTeuaZZ/SmLIWAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9CkiBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA2hMQYSvtz4HUQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLACwIibHkBTYoIASEgBISAEBACQkAICAEhIASEgBAQAkJACKQ9ARG20v4cSA2EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBLwgIMKWF9Du1iI//PAD7d+/nx577DGqUqVKqvyMa1dv0KENZ+n8iSuULn06Ktc4P/kGZLX3HXboEp3Yc54un79KeYv4UFlO/6+FiGNrKfbKOcqUNTflKRp0V/08d+fv/PHLFHHiMmX1zUQFy+e6q36Tq8pe5fNzns8TQu7CdSlz9nyust2ZcTeJjmw9R2Ghl+ha9HUqWjUvFayQi27euElHtpyjG7wsWC4XZc2ZSdX/xrUbFMrxCGa8ivgP/rl54zqdDVlMdPMm+eQNJB+/si5/5fXYy3Ry50y6FLaHLkeGqjzZfItQ9rylqVDlLnz/5nFZLmHkTd7HXr6e1lPkyU0UE3Waj1mOr6s6lAfXlk9AwiJGzPkTG+hq1FkVk6dogySPG3XuAEWF76P0GTJTvlKtjD3JamoRuP3XEFH0xRN0/vg6/r+BcI6z5SxKuQrVojxF6lP2PKUS/WmXI0LUdYxMOfKVTzI/3k94TyHkLd6EMmbOodblz51BwN2zHLU7ys9+vKfzlchBvv7xbStPah59MZZO7olUWUvU8lNtNGwk9o44tTeSrlyIpdyFsvPzLLsnh7mj8oSHLqfrV6NUnfIWa0QZs+S8o+p3p1fGbCv5B95H6dKlV1XG+zP88DK17le8KWXI7JOsn4J3GJ5zzu3kmEsn+T26ha/NjORfuo29T3f1sDPICkVERFBISAilT5+eatSoYRNxF29nSOFK1LkY1ca8ePYKZcmRiSq3LsT3WYYU7lWKC4HUISDCVupwvOP3snnzZho5ciRlzZqVJkyYQFmyZElxna9EXqVxPZbThTPR9r4eHlGTKrcprLbXTA2hxV/stNPycENp8G8t7e3btRLLYsDZkIv8gk5HBcrltBt3qXX81ZOaEF766TNmpYZPrEyt3dKls7voanQEi0uFKHvukqm2X72jxM7f78ODKXj+USpRw496j7s9Yt25o1Hc2YulHH5ZKWf+5DXg9W9ytzyxcwYdXPWRSi5ZbwgVqdbTXdZkxaMBHXk6mNLxv9yF6/G1lfov9+kvrqd9K0/b9arduTh1eKUqgdeXj/yj4vuMDaLi3HFBcBePtFt9L+AYtztcOL2Ntv7+hDpsQGB7Ktfi3QRVOLjqYzq5exaLgdcTpKkIbrznL9OByjZ7izfTuc7DsedCV9CuJcN4P7Fu82TLVZyqdppAmbPldZln4y8P05U4Yc3XvxJV7/yDy3w6Er8Nv5EvLmo8YL2O/s8u0bG+cf0m+RXzUY3m2/FDb+c1FH3hGG39YwBdvWyJm65+X4ZM2aly+6/4OVjVVTI/yz6mEzt/UWl479TvuZhQxl04snkihW78RiVXf3ASf3xKnQ9b7o4n8ckjkNgze3i9uWpnbYdUovrdEhc8nY8a/OdR+n1EsIp+Y2UHvkYskSKx431y32KKioih5gPLUZN+8R8J7oZ3x5ULR2njz51tDIUqPUalGw61t2UlaQKHN4ylo1u+VxkbPrGK27VWX+H88fW0fd4gFV+p3RjKW7Rh0jszcmyZ3ZPF+N38UdGP6vVYZKc4HK/favUBB4kO8UY9kIaPApfPH1ICfc781RB1T4b58+fT5MmT1W+fNm0aZcyYUa27i08NSPiYOumpVQ67ev6PVvyuyuYQd69spEV75V5h6+3vFGHLW3J3Ubno6Gh66qn/s3cd4FUUXfvQQg2dFHrvvRNAkF5EVBBFuhSxd7GXz08ULHx2RUAFAX9FURGQDtJ77zXUACGQBAid/7yzzGbu5t6bm5uEJHAmT+7uTt9328w7pwwlXOzBgwdT27ZtU6T3yybspTlfbFN1YRKSLzgXNXqwDFVsFswSTFfpo06zefB+RaWXqVeYpSmyU7f/1k2RtpNSyfHdMYqAQ5nB45pT0Wr5k1I80bypRWyt/aU7f7wPKGKrQc+/Eu1HUjN4u35pQWz9+spa2jb/KFUIC6aHRjVM6ul4zZ9axBakbzb//ahqu1qH/xFWiFMyHN58msYOssjS3AWyU0jFvFS2QREK61POI4HlbdKS2s9CSp67r3UlRkrs/vc9itgx1a4uW458TJgUZfLkohogX7sST8yHVOpKFVq8Yec1d/Yt/4SObJ5kRilpABBYcTFHXMguSFdh8J+/aAOX/DgwiS0cV2z5NgVXvAu7bsPtRmy9f+dM9d1o91Q1atIraRN5twD6EHmz7qHIfXNpx/zXXAhW3Cs58hani2cj1AKJ2d0yDZ+i4rX7mlFq3yS2EFG4TCuq0tYi7hNk5gghttyhkn7ivL2zU4rYemNpZyYp/Ce2MsK3Y9ei/9DxnfFjJZC9YQP+TT8XOgP0xIVQMoimNCW2jH4Awr3LPqKjW35WaDYfsiYDoJo6XfREYHmKT4leTHxmJe1ZfkJVVbRKfiYXM9ODHzVkiWNLYyAl2shIdaTFeCUj4ZMWfRViKy1Qv8ltTp06lSZPnkyBgYFKWgtiqykRvrh/Pp06eM6tVM+2ecfo11etD44pTZIS7Sa1jtQekGVUYsvb9cNK75Etp6lImUBq+EDKS4u5u4ZCbCVEZeaHW2jVlP0q4ZUFHXm101qRQwTEwReO3qnSwnqXYxUmSzXA2yQptZ8F1Zmb/OONlIg9uZU2TO2nepQpczaq0flLyhdqkuvX6di2qbRnyXC71zW7jHbkIdq37GN+HibbeUrUeVhJ/WUNCLTjQELvmPeqUhvUkfXu/yWBmpiT2EK/GvedzQPE+Lp0eWyF2DLRSJ39m3EPmRNDnAVUoivc8bpauNBnBfWb8NXfMBH7u46iso2fo2I1H7KPseMkthBX466v3RKpSBNiCyik3+DpXY4eJ4fYghrjppmH1Yl3fLEGS8NY0qjevhFzv9jOJOtlqtQihMo3iVerTv/fjuu0dGxTXrC45HKhq7X/RKnfukTKgUcEIvfPpyObflLp+BZCRRDBfH/5I7F1YNVXrHK4RpkKKN/sZVUnfjwRaZ76gTJCbAEFIk8Elqd4q5T/v2cjL9DHneeoCpwSnf7XmrFLCrGV/q6fEFvp75qkaI+us92ZQYMGUWxsLHXt2pV69eqVYvVrkfWWgypRi8HxIutoYNOMwzT1nfWqrdcWd1asfoo1nMSKUntAllGJLW/XL4kQp0h2IbYSwvjXuxtp/d8HqUSNgvTwGN9E/71NWlL7WUh4Bqkf442UwGD60AZLraLCHa9RSOV4NRWzZ0c2TaR9K0apqKLVerD6ykt28mVWB14xoT3b8Lqm1AErt/4vFSnbzk533blO22a9QLDzguBO1dBJbCEfSI4anb/CboIgxFYCSFI8IrXvIXR41aQuTBgcU30PrdqdzMmd84SObv6Z9i7/SEVDoqtJ/4W2ig4i3RFbsJvVuO8cnogmXDkXYsuJcMY5Tg6x5eksvX0jPJVJ798O2FncMfdV1f3yTYfRnqUj1D7UeWt1tb4Bns5N4hNHILnElqcWPBFbnvIjXogtCx1PBJaneG+Y+pJ2mm3vfnbfPJW17xdNqEyDDGSr1pcT9COPEFt+gJbKRYTYMgA+HnWOdoZH0o7wU3T+4mUqWjiQqpYpTNXLxq9aGdnV7jUmjtZsP0q7D0XRiTPn2X7xdcqfOwfVrhhMtSoEUxYP0lGnYy/Q4g0H6fCJGDp/6TIVDMxJpUPyUdOaJSh7QLxUhrO9q9eu0Y4Dp7iPkRR+PJrKFyuo2ikZnI9tSDlzE61evZo+/PBDlfD5559TcLB74+1RLHkVsTuaInbG8MCYbVGxylNwhbysR+9qIPLq5Wu0b5VlG2TK6+uUykgNtqlVo0Mx1UbeoJxscyuO80TSip/3qbgHRzZgEfhMbIg0tzKAqnt5/vQl2rk4go3anlf15C6UnQqXykMVmgZ5NUQI+yswiIr/UwfPUlC5QCpZqyDbZMnjYh4nOiKOTuyNUYbtZ3y0WTXb5rEqFFQ+kMVmA6h4jQIq7urVq7Ry5Uq1D9tj9erV0130aeuJ2ILB3sh981UdIVXvo2uX4/hcl/Okd7Ga4OQuUJ4Kl2uTQDrk+K5pnJeNM679mm0iRSvbKVBJQShU+o5EjVOrjB5+Ert+5RoHqVXdk3tj6UzEeRecUCVWgC/wai4kuWBcFnY3INl1YO0pZVutcBm+Bghs8PzAulN0dNsZtg8UR9lyZuVJfnalRmfnsXLSvpUn6SobPP937G46vNWSEmv7ZBWVWpwJHS3iHB4eTkeOHFHx5cqV83gv36jWZeNNFTHywHy6fC6K8obUVKuJMHKKawTD4FnYfk3ekNqEiahpwwbGm6OPrmNV0X1s6+YX1VYQ22jKy7ZrAvIUIRhXTU4IZ+wuxV0h2KnbvzZS2Rtq/0w1VWWxqgVYCiiA1Zbin8Xi1QtQznwBKt3dpMXXZ8HsM1bnjvD108aH8YzhmTGlxnT+E3ti2NZYHJ9/TvXegDFkTIT2r4lU75BKd4RQcp8z3ZZz642U2MS2jKIjLIK9TreJbL+tkrO4OsYq/9Kxli05GKCv291Sc0DijrmvsI0+a5WyWPWeVDbsebd1mJErxrflZ+O0inJKgGliC7aRIKWlbS1Vbf+x2/smNYktGKXezfbbTu6PZQnAS2zLL0DZt4M6OVTHvQVfvxm6Dm/vDkiHnD1lqYROem6VhVvH4sooLWwjlmtcRH2XkLBx40Y6d84yDl23bl1lN1K34e82te+hE3tm0M75b6ru5cpfmur1mJJoV7fPfonwbkIoVqMXlW3yrF3GJLYwcUf/EYIr3U0VW1jt2Jl5J7WJLbwrdi89oextwlFM9txZmazNxWrlQd4NnvN3AvYvI/hdEbErWi2A5QvNRWXrF1blzXNw2fe3HFeSlPeabhNOcPbdcJKDuDyFsxPUcErVKWjflzqv3iZlfOPpXY66TGKrYY/SdGTrGTrO71tIy2N8VpSduwRXzOd28RDneoxt1kEFsVyjIrprHtXYkUF/e+DspxCPx3z5duzlbzgM0mfPnY1K1i5ot2PuYNy2d4WlwhRcPq9tkyelnmdtwwl2Lps+vIw2/DlA2SlFH5r0m5+oEflL5yOVg4XoY+voYuwxyh4YSoGFq1BIlXv5GnsZk7N91TNH1/B4YA1L6+7msQ5/n9kmamjVbrzvHgv06fq1K3T60DKWhOJyp/fwc1KVx3ct2f4dvvNuBvMoxAOrE3v+UY5LQJJnzpqLcuQJoSIV2jOetVSOhD9JKwM7ZWcOraRMWbOxTdqudnVOYgvYRB1aosZBcIYSyO8hOFDBeEkbnLcL8w5wPR+1j8evhdV56jRPxJa7fsABxoXow4zBDPudV76pJf2VJ6iKkn6N3GsRL7kLlVd90e2YW9joOn1wmYoCdp6kpc0yN2MfZmOWLl1KR48epVOnTilbyAULFqT69esTxrzO4InA8hTvLO/rsX6PnI28SH8N36iKtRpaWc0Tc7Dx+BI8NjQDnnWMCfFexxZmNIJ5TglnRu7GFRgv7l1xUs2fS9YupL4fsAGMOQTGDW2eqKLes1oFEmNRzCmO74qhwzz/iDp8TtVdomYBW3NBzU34XXlwA89Z2HZv/tCcVJ6/R845rdlvX79jMLVzYF2kKpoW4xWzz7KfEAEhthgTeBUbO20DffPn2oQIcUz9yqH07uCWFFTAleQBmfXCF3Po8MlYt+WqlSlCXzzXgfLmdp0gjJ+5iT791Rq8OwvmzpGNPnmyHdWvEupMoohTZ+m10Qtow+7jCdLKFc1PX73QiQrnz+WSNmrUKFq+fDkVKFCAvv32W5c0HMAG1j+fbKX10w4mSEMEDFW34wl1thyWUWwM1j7sEG/40VmoYfcytuqUM+2OARXoTn4ZIpj2nZz5MHHu+WEDKs2DW2fAIOs3JtQObY5yJimype8XjXnQaRkeX//XQfrrPesl7MwcXC4vDZ1kkQ9xcXHUr18/O8vEiRMpW7aEq952BseOJ2LLnMxgUr3xjwEJxORRldPg9ZIxjVxssJjNQZIEEiX+hsSu36sLO/EHIwt5srGl1Rc7sVpDIOP8f8NW213RKzggsn56eqWaLNuJxk7VVkWp27t1bHsfWnLMyGLv9vy4obLZhgg4PZgzxyIYOnbsSAMGDLDzJbbjjdhaMaGd8moJKZxMWbLSid0zElQHUqvmXd9QHh58IhxY9QVLAv2g9p0/KWHX46sHF3rEr/dnjdUkxR2Bhb64i/f1WVDnwpPGNb+H0/SR1mTZeX4dnq3GtvTKukTP/IhVJn/dT7i2dz5SiSY8udx2KqElOpP7nLk0aBx4IyV2LniLr+d0lTuk8j1K9cso6rKLScblizFMpBZzMa69+LuGtrSWmiD54FHOvN+c0lgmsVXr7jG0/vfeqh+ejICnFrG1nxcffubnV9tBdAGDD9o+WZWg4uoMSf1m6PLe3h33vFmb/viPZeRa5ze3w+Z0sL1+PvHEE3TihDVBHjZsWJIXIsx69X5q30Nrf+1BIMMRfLXFByPzq3++R5VxvlNMYgvqrhv+6G/b54IzAkgKmiHViC1+V8z+dBstn7zXbM5lv+sbtan2XSVc4nBwgT3v/cGGzXf+G5EgDRH1upaiTi9Vt78TOpO/5bDYktT32lUmfqe+vZ62zjuqm3fZwrZoP3bc4fRWmNTxjbt3tm5IE1tN+5S3FpHWn9JJ9hb96Ml2bkBEmcE0Hu+vjS1fvh2Tnl1Fu5dZ41LzWTX7ggWsCU+tUFFY7ASZjZASzzNIqZU/dVD1YVEJiwRYINy18B0VB9Xx0g0eU/vufo5snmxJ7EIq1xEgMVmByeIg9hDoDDHHN7JB9ceZ1LNIeZd0dvahpH/DXnCJxgHeBZumPWIvfpgZ8KxjvAgvqWZAGfM5N9OwD8K8TrdJLpKd/pTxRDSZxFax6g/xouX/uR2n5sxXkiXkxibw9quJR7+Mx9+wsbXut4dcVP1NDOCRvEqbD2jZDy3V9xreMPG9dhe2zX6RPTwuUEmwXwsHTWkdMLYdN26cWgB015cGDRrQ888/rzwg6nRPBJaneF0uqdsdiyLo/16KH+ub5YtXK0ADx8XbloVU1+9vrFOL1GY+vd/5pZpU/75SLtzt2VMX6WO2yYww9KcWtHnWEVo6YY8uQtD6AdX73+bWWO6hTxryePOAbevLzsg7A8c0ozy8KDf+ieXKs7uZhn23TjiS+B07xR7Kv+hh3T/O+nFsvgNT4v3mrg2J84yAEFuMzctfz6M5q/fbKIVVL045mWCatyY+rmKJgjT2lS6Ui+MRIlk666G3p9KpmDh1HMykF8ioHCxttXBduB3fo1VVGtbbkgRAxrnczjBuDwEkVsu6pRTxtZOlxNbtih/gzfioJwUXjCfSDrFkVy9u79yFy6pswypFqWiRQJYwO0XbWXoLoRRLfI1+qbMLuTVw4EClhlilShV65x3rI68y8w8Y7W/7LFIrf4gDq166biFmzYn28MqanvCABBr0Q3O1IohB5ZiBi1UVWDFEABEVyCuYCDXaF+eX0mGKZWZfl8egC6H+faWpcc+ytI0Hib++apGIKFuZBzg5mfU/xiu2B5md18HpaQODv2/7/mvXC4P0kBqCVwqURUBb/b8OU+TWnmUn6J9RW9R5as+NeYNyKJIOEkcP8OAKIbkTbl+ILaiHwJMaBi0wFIxJCzwp6mDaLFg1sTNPsNkzmDFYwqQXocIdryoiTJdL6jax6/fIhBYKn8SIrZosTaFtd+g+KMP8VfPzC9+yvYZ4qNBhRfniuSu0beEx+9o16VmOCVOLJPqJB7tnjrEr6Rv3E8rpe+auYTVtgjO1iS2s9GqvednzhPK1ymlPRtGnHIHFqEHPP7HLdpl+54HwJyq/9pCHa4w6sucOovoPxNvIUQWS+PPLy2uUtKE7TLq+XlutkHmaDLmL9/VZQDf/YZJqJZNUCHheSvNzhncFVuX1Mw3pMTzLOmhiC4btIU0Ar1o6aCPgyX3OdH3OrTdSAsa6t8+1VnRRDhP+knUHUgH26IRrlVjAyu7qyXerbE6CylvZ6zxBWjKmsRpgZ88dTA17WQMylDGJLXhR3f3vcNumUuEyrdkI+AiXqlOD2DJVCtAYJOrgtRbPIQaxOjzICwxI08Gfb4Yuq4ktd++OXqMaqXc18up73vyuDP6+ue0hMTUGiql5D+Gc9DfCSVAhzVswCbFmg5bzPWuNP0xiCxMzTGC3/vOMqgpSIo16/+MiNZFaxNa/Y3fRght2/tA4vOgGsTQOVtD16jrin5vWlsli6xuGY6ygj+Zv+emj1jcQY49SXPY8vzcOGMRNnS4l6e7X4yVR/C2HNv15r80YsZlW/34AxdXzAaknSBDuW3PSvk/NRTLk82d84+6djboQNLFlHVm/GPsAT0gzaAzxvDw8uqmSmNV5U4LY8uXbsWvxcZr8wirVLL5Ptbu4kjJI+Ou/rFbPC6jo50uz2/P3NbPKnxLP877lo9ixx0RVn5aQhUTU0nFN1TcaDkMa97XG3SqT8WOqtSEahAje2ZDG1uMBxOt6sY8AD7lbZz1nHfAvxmggdZxju5J1h1Cp+kPsfPB2vZ6JaK5cxQXkKqLGDPCUe+XSWRWHuup2n2yQW2w/jB0f6TFhthwFWPKlHOc/x5KuO+26ChRvrLypWo35U8azzSuT2LLq518m73LyeBYhjse0+pzQf7yDoB6tQ0oQW1tmPqUkv5QdtRv46bFxkbJtlCOWLTOeUJJ3aNcdyc+d5G9zGHf1Ml+vUjxW+013Mc22a9eupREj4r/7EESoUKGCmpts3rzZ7tcjjzxCrVu3to89EVie4u2CSdwBKQ2tlwuxV+yxHd7ZOQKzUkiFfNR9eD1VI6T0xw1Zao8TMY4vUb0gxbI0NsaPOtS/tzR79q6hD/kejie2ancqoTyy24m8A1Iel1sTWzpNj0/joi/bxDreL9myZ1H9DGVJVkjWxpxkieIbxDvKPv7znexwJf7eTOp3DJJdPzxqSfylxXhFn79s3SNw2xNba3ccoyEjrUlHbVYdHPVUO1vC6uKlK/TR5BX0+6IdCr23H76DujSrqPanctx/f1yi9j9/pj2FsQqhDnEXr9DgD/5WhBPIpt+H36+T6NVvF9CslXsVqfXniAeoQGD8YG/+2gP04pdzVd63uK27b7SFiOc+m02LWHURZNhXL3R0UY80ybLud1ahV/o0VXVAlPXRRx9V+3feeae9ryL4Z/H3u2n+N9a51exQnDoPYzUqfikgwHjoX+9tUl7qcOyO5daSNi0Hs42tQRYuyItg2th6ndn2LOw5QwdIXG2Zc0S19fTvrZValU7bvuAYYVKP4BwgTX5+Fe1itRn0se/njakYq1/pYA4mIWXW+eWaOkmpRH3Te5E6ducV8fLly/TKK68oMVhkwgdGu821K/Gyoyct+MBioqqDKbGFOKxylQ17lvew9kA8EONVwuUfq/38Reuz8d9v1L7+SW2viN6uX2LElu4jSMnm/SuolWKon0Ct5KueC1Wy07gkSJGfnlqppO3wUXzhn3a6GrVNzMbWzJkzae5c6/lo1aoVde7c2aW8twNTgqZMo6eV4W+dX0ts4RiDWkjR5CpgkTZQBV376/1KogvpztW91PaKqG1sQfR6AE9azOBpMuQpHmUTs5MCNZcxD1vENQYf7Z+rZqu4wMDx72+uVyo5qOuZP1mNNiQndkkTW+qAf0AcQ7IL6sxa9Dy5z5mu27n1RkogLwbDkMZyCTcG5PlC6lDhsq1ZhaKRW6ILqmBQCUPwVQ1Rt6PVEUGgNRu0UkcnILYwAcM9eIWlxRCcRsBTg9haOzWc/v7AkshzOvfAauTo/ovV4BQk1L1v17H7npxvhia2dGXOd4eOT8xmBVTqDxw4oLIPGTKEKlWqpIv6vU3NewgTqCVjmqi++aqGqE/EVEesc99PvGBTWSU5iS1IHGyd+TRLay5V6U7VxVQhtngB7P1WlgdLEC09eRVdS3ajE1vnHKUpr1sLWD3er09VWoWqvuFn0ZhdtPA7npBzgJR3h+er8fNnfRehyjj+seV0nM0IIDw5pZWtPuJvOX/ea1CP+aD1P+o5AKEFKQHtVRDSX7P+t9U2t/Dkr9zHGwt4/oxvvL2zTWIL79XenzZW6sIKHP5ZPnEvzf5smzp0ehROCWJLt+Pt2wHVo0/YqDQWNEBu9vsmfjEX5aHy/GH7WQpLSJ5BtUiHlHiel//QUpFCIFKa9F+oq3Z59zuJKWRSkl4TOylCBu/p6p2+sB0wYHEifPXXLJn9vaoPauyQpNLBHDeUqvcIlaw3WCexamK8t2Tr/b+C06z7e/XkrkwQHFGkUNV2H/HY6Q673OENP9L+VZ+rY9M2WEzEBtr41yAVD8nyym2G22Xw3cBiCVTfzW+NP2VQqS8SW8iHMVKde8crQg/HWKiFRBlIdgSnhHRKEFuqYv4xyUinV0Rv543yUQeX2IsAUGMMrdZdV+txi7HLxYsXPaa7S4DWB0yb+BLeffddAoGFMjAdU7RovATZ4cOH6bnnLAI1LCyMnnnGWsBAvZ4ILE/xvvTFWx5zQazfl03sRWddBu9tmM5AwJyx0YNl7Pc6xhX/N2yNrYlgzsVMYkvX1WJgRarWpihBJRrzR0jPmsRWY9YaaPtUVdshxopJ+2jWp1t1cer8Ug2q3620fWy+C12kiJPxHUPlaTFesU9KdtwicNsTW48wqbWGyS2EBZ/3sUktjRZsYbV5+id12LZBGfrgUYstHz5+Cf22cAdVKVWYfnrrHp3d3n7+62r6YeZGdbx2nPVBwoFuDwTV7P/1UhJediHeeXPMQjodc4Fa1y9D99xhDdg37TlOA67RHB4AAEAASURBVIZPU9k+fKw1teI0Zxj95zr6lv8R/v2yH+XOmU3ZInnvvfdUXM+ePenee+9V+/iBBM0HPChFAKv+2OSW8YM2FWsNRj7vNk+pFIFMemFmO5eBqzdixBux9ePQZWpV1l2daBqi/5hEV21dlOp2Lal6g5VJrAQgOAfJKpJ/Fn3Hg+Ux1mD55fkdeRUsq0ryNiDTZZOz9YXYctrtsdqL9+JjSgPpvqR3YgsflvZMXpghnG1t/fCYRSK0449Ok17lzGRlT2sZD8QxibmfV3k0kYpMiRFbLhUl8cBXYgukFmxEmOHg2u/Y3tm3Kqp6x89Y2id+0G4OYH1VMTLrTmz/ZhNbE55YoYgrSCEMGd88wTsBz+Vn3earCYpJXJrEFlbIBnwbxiRhlsROL0XSEyMlsEJ7cO0YOrxpgouUpEvjTHQVYWkp2M8KyBWvAn1o/Tg10Efesk2eZ1tHPV2KeTsw1Saa9F9g2/FwSmyhDtgf2TRtiKoO5GrjPrP4GcmmjlOD2DJXKM0BpmqQf2Dbbe/KE1SoRB7q+GJ1FZ3cb4ZJbLl7d+i2Exso6nwpuU3NewgTPUheISRF6g/5TZXnCi3esG3euCO2IO2xkglS7RXO9MiZGsQWzAL8r6u10ADSp0JTV/udWMjAtUTo+Fx127uueR95cophqq1B7b1B99IuY5aklEP7/rzXIJ04vMUMFGdbYcH00KiGal//YDL2xw0HOXgX6oU2f8Y3vhJbWAiE7TJn+Hv4Jlr7Z7iKHvLjHRTKUtII5mTOX1VEVRH/JDaOMsdf5qIHyu9eepy0LZrHJrWkImwXNaUC7C5BSgehaFV2/NHsJbtqLGhgYQMB3218v80AL7Yn985WURVbvs12gO4yk3n/Oq0Yz6YKYC+RvxHNBq1QkpAog7IIhcu0YinbkWrf/DHV3fRiBTz9bZ9j9c+5wKbLbp8zjCL3W9JlcAYB6SzY1do5/3WVBXav8C4wA9Qu4U0VfQQBDgk1f8qgTl+ILahnhg34l5uzxti6L1ikwXhYvYO4L005j5aoulnEFvqiF5XQz6YDMW+wSEWk6YUukIBYiNbfWaR5Cn/++SfBRElSQps2bQgLL76Evn37EuxruRNAQHkIKEBQAVJcek6HeE8Elqd4lElO8EZsmWP/uneXpC6vxUva6jbNhW94XO31v0YqyUlsQZsGC19mMIktSGIN/rE5X7v46wptlBFt/1FFoDnQh83SmAE2AN9tagmxmGNXf79juu60GK/otmXrHoHbmtiC4fcGA8cqZLq1rEyv9m3mFqWTrHYI6a3cOQNsCSvY5brK/7ANbxqIR771u47Tf77/l46fPqfqM4mtSbO30Mc/Y/WGqDirEvbvVIsaVSumDNWrSDc/v8zbRiMmWmQByLdsWRNOGA9GRNND70xVpTX5tWjRIvryyy9V3LPPPktNmjSxaz/Kkhnf3ZDMuOfNOlSrc3E7zdxZy7Z2/h6xSUU9OrGFUjPQ6f4SWyazDvWXZv3KU9mGRZRaoa7buV31y36a+fEWFQ39ZS3GbuaDIXmoKiKY5FdiAzKzDn/2fSG2yjd7lY2J3peg+g1T+1Hsya1q8IJBjBnSO7H14j/tXaTt0HcYwR3ZzlqZxTHspFRvV1QZHU+M6EhrYsu52ov+I8CYPEgKhIot3+LBbxe1j59bitjilat3GlsEOqQwwxykpD5peDuFZCUI8SdYUgHBJLYwWDHdxOtyqbVNnJTQLV9XRn5P7VtAp4+scFGd0DkwMajN9kEC2QkAwuGN42n/SmsyVLbxs1SsZi87a2I7+vlFvqYPL+UBvrV6647YQh5zkmVOXlKD2IJTh+8GLEazKmCgV6l5CL/fA10Gizod2+R+M0xiy927Q7eVFgPF1LyHYAR5zc/WopI7yVx93u62pmQC7McUZnUbBHfEFuLNySykuCBhipAaxBbqxfseIQsbJzfmjsrg+PKf9toqzSaxZd5H3f5Tl6q3L6bqcP5gEgW7CJD4xAKIv+UgXeXve01LiaNvkEoDwQbbn9qhibPPOPZnfOMLsYWJnjaf4GwXzl6+emihiu7ySk2qe08ptX8zia0zrFb66b0WIeNc1MJiJUwXYNFjMJu1SMmw+e+h6r2OOhs8+Icy9WDWv5Tt/ygVPgfRgjxQM4e6uTsCRNcBqS78I4+W5N79739ZffwPlcUkkHUZbCHBFBd9UEVhYRMk0N4lI9k21S8qDqp6+pugIm78xJ7YahN1mvy6duUiLWV1bK3qB2kuSBoVKtVSmbcwy+t9f8qgrC/ElpNA1G1iu2/5J6yNMElFwdaWNmp/M4ktczGyWvtPqOANqTioli4dBzXEq0pSriqn+RJmzZpFP/zwgy9Z7TwdOnRwsd1rJ7jZgWOda+wYLEuWLC42tGJiYgjzuAkTJqhS6ZnYwmLY7M+2qn4+9Vsr24i783SnsBka2CzEO/2VBR1VsklseXpHmMSWOw0iVKSlW91pESHd05zVn+8Y6kNIi/GK1bL8ekLgtia2QFh1eM56Ab/K6nvdWI0vqWH7gUhazV4Rtx04SbvYmHw4E0zOYBJbUWyT65lPZ9PW/fH6xshfKG9OCqtRnJrVLKnsbmXNwgPFG+H9CUtpyoLt+jDR7bBeYdSjdVWaNm2a/UKEel2ZMvGSXlvYON9vb1oSXuYKn7NyU1LqgRHMoreMZ9E9vSRQhzeJLUh9YPXu6PYzLs1BPQ0T4orNghRbb4v9c67prDazhtVnfA2dXuBV3vtLq+zpgdgyP/DmOUB0O/bElgxHbGGQ3/ereKLUPKd1f4TTtPctMtSMxyp7+SZFlPdEd55J0prYci9Vx8RW1B5aN+VBdSq3MrEFo/+j7p5rXrJE999aaZF8JrFlSgYkWkEKZPCdlHA2dp1iIjbSse1TedX+HzXYRQ6s4IJkBtFp2hVxqlY4a3MeeyK8PRFbmIismNDWliqDmgecFaQGsYXJ/p9srH3DjEMu3cZgE3YW8R6Gt9sceS2pMWRK7jdDE1ve3h1oJy0Giql5D0HqY/HoBjg1ZaRYk00qIpEfLV2AbPUfnGrb3PFEbCGf6Qm0VP1HlU251CK20B7MFuxme5aHNp1Wng3hCUvb4kM6gklsmWYDHmXpHXg19iX4Wy4577XDm0/Tzy+utu3K6H6C1IcUV5WWoVSSPSOawZ/xjS/EVqtHKlPzhyuYTdn7UJv8T5O/1XHT3qzqd8Or8M0kttD4xGdWKttqUJl87OeWqj+m5NvdLMVRh6U5UipcZRtTy3680yZ8IPnuDBfYe6AmhMo0eorNEPS1s8ATLqSLQFiBoPI12M8Yk2XNB6/ytVj8u9zHEsr4PDsMQti/4n8sdfxTgpKQ6MpftAGfV2/buY3O5E8ZX4gtqEJCJdJdOBW+iLbNel4llW86jAm4+9X+zSS2zPvClJI1bW7Wvud7ewHL3Xnc7DiQW/BiD5XEPXv2EFQQoQJphvRMbJlSo2+t4HFhvDCVeQq05Mc9NO8raz4LLSAsXJjElqf3nEls3ftWHarZKaEwhia2zO+N2bi3OWtSv2O63rQYr+i2Zesegdua2NrBRtd7vWOtuoxgFcM2rGroa4g+e5Fe+mqurcaoy4GgqlSyEEWfu2iTVyaxhXxXrl6jP/7dqYzTr2JSzBlgiP7rFzspY/BIg90t2N/yNQy5uw49ck89MsVnobddqpS1iod6Vv0fS0B9YklAeWPXTe8P5kog6vD2kvBGbKEsxELX/XmQDa0es3WyEa8DjALCy5728gOPHKZRY53P07bFwxWpJXtnQ0gPxBZsM8BGgzNkVGILBh67vuWqsmeeG1z8rp4STtsXHkswKUC+Zmxno7VhZwNxaU1sgUQAmeAMtwuxBScMWuLRiYGn41cXsRdN9piqiS13ttM8lfU1/lJclO0JKR+riWrVBl3eEymBwW3Mic0qG0hLU8VQl9Xbi2cj2DthH0vlhCO1dAzslyz/sZVdR93uP+siXrdmORgUNp0JeCK2UCFsJMFWEgK8RzXsNZOJikGWa/MkTqJUJYn8QEVow9+HbVuKzuw9PmDbSHeGqujkfjM0sZXYuyM1BoppeQ8BPNxDuCcs9ZelSnrDibW745U/tWeS6JRKaj5kNW+t2YI3YusyPy8rJ3a0iFq+ZyDFcmL3TApf87WqJyUndCBOIEntJLKgylycbWBq9ThzorHy533sKMBa2X/ubzYqXySHu1NPEOdvueS819AJ2PzCvb+NpVRP7o9N0C/Y38KiH7wJ65DU8Y0vxJa5WKfbMbd6UleLv8333Pg232xiayc7nvj5hvc0eDeDjUXTburL8zrYTiDMvvu7f2gdq4qv+crn4k5HHotH11dlYWux5t3f+VwPJDAhiWlJei3zvdz/3WdLcflSqHBpVnNsN9LOGn1sLasnf8VS/lvshRg7kXcKlmzOXldHmVGs5p60Mr4QW1q10qWhGwemFHuJ2uyNsuFjKuVmEltocPP0x9TCFL9slcohrpWW7gMZ6NSQuNH9NNls375d2fZ1Tshz5cql5m0gukBypWdiS4/fExsDrud531/DLTM92oi7SWx50iBKTWLLn++YvlFSY7yi65atfwg4nyNPtWQ6H3eB13gTD9fhUu9G0PvYuvuH6KX+h6FGqPcVDY2XCNL1pNbWlNjSUk7u2rp05aoioyBFFXBDDfDxj2fSiq1HVPb7WlSmzmHlqULxQsq2FSInz9miDM9j30lsIU6Hc3GXFQEGI/aT526xvR5CTRHG5RG0vS7Y5ZryXndd1OM2Z/ZsFJgrQImwalVEuIlt1KiRXQYutrESiTDg26ZUsrbrqqPOaNq66P1ZY8IgTofkEFu6DmxhcwNqBgfWRdIKHkDqQTLUFJ+a2lplnfvFduX+FRIFeBkmFgJyZeHVcUvaQIgt92h5u36JGY83V4Xd134jll8HUBE9xCvfW2YfcfGM0v2/9aha26J2cf1hdGfPxM7k544vNrZud2ILHsdGtPlHIdz2yapUvW3C1W8n/IFFsvO4MZNNbMH2wZAJdzizJevYdLJQtvFzrA5oqYXqSj0RW+fP7Ke1v1irxUHlO1GlVv/RRdxuj2yaaLl859SQyveyB9LXVD5blYWPPKmdOCs0VU6KlGtPlVu/Z2fxRmwhk2kEvHjN3orUwjligJ4U6QC7QR92IIp/fFcMHdwURWt+P2B7fEPRp/kdDO+zyf1maGIrsXdHagwU0/oegv002FFDKNOQpUZqx0uNqEg3P6btIHhOa9R7pp3LG7GFTPDaumfJcJUfpG7hsm1TnNg6uCGKvn9kqWoDC1F3skQRDIfnC82p3glI0GSLSWyZkldD2QtvcMW8qg7nDyR9IIkEFfbMWTK5eBtMSrnkvNecfYpl71owRA8nNvDwp4PTy5eOx9aX8Y0vxFazvhWo9eOVzartfXPSZy7q3WxiC4TeJ3fNVYtZur/6u54YoW2fTBJ2Vv7UkceLrP3A78YCxRp6LBnNxte1R8G63SZR7kIVVV79bncuPpgVwfnD1ctxqg3t5c98np3Gy+PLXmcy2yJCs2aHVGKm+Hc797fe/f8Xn9XDXtbs+SiAvZwmDNeVpP+p8MV0Ytd0vseO21lKN3icStQZYB/H7/hWxhdiC99GfCPdBdNpkinhfrOJLdNuJfoL+2lLxjZV0nsl6w5iT5VD3XX/psdB3RDeDiGxhdClSxdlNgaCCDAmj/Daa6/R7t270zWxNZudaSyfvE/115vU/oJvdtK/3+9S+V5dyAujvCBgElvu7DUis/mOS0mJLX+/Y+oE+Cc1xiu6btn6h8BtTWyBg6s/cIxCDsbaR7JhdmdAngfemEJ7j56huhVD6LuX7yKQUXc8/qPK6sk21zdT19J309arPJrYusD2t+avOaDi6lcJpSCWzDID7HMNHjHdlvSaxsRWUSa4pi3ZRW+P+1dlnfdZb8qfJ+EK59m4SzRv9X6VBza7QgrlcTEe36tXL+ratavdnGnEr9VQFnEfUMFOM3fMl9BTbLi0gGG41Bsx4kliC4PV7fNZNJxDaVZnyxvsei5XLl7lwfIyW01RG0s1B2gvzmLbTvkDzG6qfYiSQgIMoSwTcNpjmxBbCpIEP96un7/EFlQ3MEiHClPFZsEJ2oTUHaTvEJyu3PUAWIiteNhutvF4fU806VmW2j1TLb4jxt6BNZFso+o85eRnUBv41BJbqUFsHd/1N+1a+LbqQfGafalM46eM3rh6F4X9MwymdVgyppFa2Q7IHUSNes3Q0W63ETum0u5/LQLKbMeUCoDKB1arvQVIii0f38qWmGn00N+E9nVIjNhCeagkaiPgIDX05C2liC08h5d4QaFw6TxscDm/7pq15W/e7E/jB6nwigjviMn9ZqQlsZXW99DZUztp/W+9FL6YHENaIDGjxTA4rz2MgZQFOatDYsQW8pnOC3LmLaEkTBCfUhJb877cQUvG70aVLh5SVQT/eDIef2xHNI3uZ41nPEkhgUD65K45qiptL8XfcqjEn/faGX7HHWSnNZmzZqJKd4QoyVTVoRs/ptFhEHvPTmvLEp/+jW98IbYgBTeU7Zy6U/HZu/IkexxeoXpmevwyx03mZNNbexor08AyKvZ1HLXw2520aNwuguTGE7/caRt07v91GJViNeeUCmdPbqP1Uy2COF9oXYLXQ0/h4NrR7ADGSi/CJG/lNu+rrFryCpKUYf0XJZAGRiZb8of34XERz++excNZhf13VYcn6SXYmYK9KQT9/JqkUUP+LmTPk3AhHxKXh3mRBSG4YmelJgn1vrMndzAJUICgnugMZluBRapR7Xt/JH/KoF6zj00HLlNSaYg31fJN9T6kmWHbrOe4bev51ur0SL/ZxBba1EbkQWRikWjngjcRTU36zWfC3D2hrjI4fkyzLo4kj4cdO3akAQPcEYyuRVasWEGffGLdJ4MHD6a2bdu6ZuCjJ554gk6cOJGuiS3T07I3YYlxg5Ym8IyelsSWv98xfZGE2NJIpJ/tbU1s4TI88+ksWrzRsjPy67vdqGyxAi5XZ9mmQ/Tk/2apuKFd69HgrnVoZ/gp21D76/2a0b0ssWWG2POXmAz7LYHx+KssoXbnExOUVFa/DjXpqR4JV5hM4/J/fNCDSgTlpaORsdTlJWt1p1e76vTcg43N5tT+J2yQfiIbpkfQhFhERAQ99dRTKs7poQMra1/cz0aU2eAnwvPT27IrcVeSyRy4wWbCo5Na2CuxKKMHQHrgiTgdPBFbcAsNqRAMemGcGu5ancE0vqpdfZuGST151JrF6g0rWM0BQRNi2DcHZIPGNrO9FyENAdKEWDVByJQpE+XN6/sHD2U82dIxV66So4qYPU8oNXxoGppyCfCiol0Q58iRw2fXwroSb9fPX2LLVFdyR0CatkCcK7ia2IJ9H+0tRfcV2+Sc782Q2IIhUtN1N/qc3HsrNYktd8+Cacvu2b/aJiCeY45fYDtc1oTTJCZ9Ibb8xeJc1G62cdYTcLI9hgpUt/tkta9/tHoBjp3SMJpEQlqx6g+x18PnsOsmXGcioJet8oiJASYICDA0u5ztuMAYMII1MYI0TCZ1bP44VRqdRBvy6j5BpRJemdwF0+uWne5GYis2NlZJPSNPYGCgi+FZu5ybHUjrQgJLEZHj70hwKqZxeT1RTu43I6nEFqQGw3qXS9B7f845re8hnIQ5QYYtoDr3TXA7uYJnMeTVEl7u3v++EFu4F1excWy+gV0wdBJb/r5XtXkA0wiw2ZD5LejA3nMbsRddBJA/H3WcrcYAkMqGnS1TjQ955rGE9pIJe7BL/b4K40WwQn6XQx3+vNdMFcaeHzd0u1Cjjctr9Rt/xzfeiCYt9YbzcNo5RRzanPj0SuXNFsfm2Ce1iC133w60jWB6TsM3Hnb8cJ2fZIPSphczKzeRP88zyppeB919e3X92MKrIUgOBFMdePeidyli558qHsRHmcbPqH39c5Htc62axPaCOGRjyanGfWar/SgmbrYygYNg2ebEN8n1e6DVj5FHe8U130OeyCHzPaGly/Ys+YClMKegKh4LJiTEoPGy5DtrPpGncBX1bvGnDOr3hdhCPnfSywqvybyAzv3B9w1kIfBGSAtiK3zNaHacYRGasKOGhQIY3q/VdZzqk68/plkXX8s451yeyk2ZMoV++eUXlTxy5EgqXbq0S1aoKb71lrVYlxKqiP4+b+iU+Wz3+7KJcqShO2u+w4pXK0ADvmuqJG11OramWnLD7mVsj8tpSWz5+x3T56WJrZQcr+i6ZesfArc9sQWD7z3f+l2hB1W/kY+1odoVgykgWxbauu8kvTV2kW0Q/vfh9yu7V5COavH4eFWmYomC9PETbZVkFSSutnCZjyYvV4bk9SWBJ8O8ubOrw+c/n0ML14erfag/3tW0AuXiduGhcRsblH/t2wV0+GSsMiY/+3/WCi8ym8RVdzZyD2IstHAgRUSdJZBvw9nAPIKWKlMH/NO/f3/CRa5WrZr9ctRppmoJVhy7vl6bSrEqAS/Ys92CSPqDjQufO31RZXeqISLSGzHiidhCOT2pwj5WbOGREYNjkB6YUP32xjpFuOnBIvIhmMRV/XtLUVO20wQVGRBwMF47feQmla9krYI0YHRTtY8f84WLMjU7FKOc+QJs2x5xcXEu3kvg1leLANuVeNlJLWJLDwQwMKjc6j1lIygwqBoPFLKp3nz33Xc0Z45FMvi6OmSehrfr5y+xZa6qw1h8pxers4v6fKpZuONdPmmfLYasJ8y6T7pNXHfY9oHnKdhYgxoKQnLON7WILXPVuGCJpkrFCBI2UG1ASO69ldLEVmLPQuwJlpToYt1TuA73vGm9E3ANoE66+Pvd9gRKTzhxnr4QW/5igYn+0nFNFcGEtkrUeZgNNj/MA+fsFLlvHm2H2/Ubk3fnYDuGVVA2so0qHTDoL93wcVaXqqtWojEhgEt4eD7U0jFYkcfKvBlMo7OIxyA5tGp3tmnSTK26x7Itr6iDS+kIG/fVklZZsuViKbGZlCXAVTLXF2ILbZjqLjjmBz+BKqJeyUXyK6+8QnXq1MFuosG0VwTPpS0GVWRDutbCRvSxOH6XbuZ3qqXeohcXUGlyvhm+Elv6vQS199aPVaHsubOS6WzCn3NOD/cQJn249vr+wH1WrGYfPrcmbPS5CttyCqfTB5fRkS2T2SD7Mesa8jWvedc36n41L6ovxBbyH97AXj1XfWYWTSCx5e971SSfOj5fnerfV4qfycxs/+cCwZsypHZ0cKqfrv71AM34aLNKhhcsGBWHEXmMAbaxRDfGAAggRB5nqR/tBdnfcv6810ySCuOT+9+rpyTB8S6EWgykHqe8vlb107Rr5c/4xnwv92cirxQTeTqYxBbiOr9Uk+3ehSip9VMHz9EClpDaNv+oyt7gvtLUaVgNXZTt5x2iP9/doI6TK7Fl9tHdOMpulHcgPQYpMh3a8HPclL1fuwv+Pc+X+ZvQTH0TrAWCxVy1K7HkbMuUYKzQ/FUKqXIfXbl0lgmv1va3pWj1B6kEG5eHhC1IqG2zXmCviUdUVSXrDmb1tUfsavV7HBEgS8o3f0UtvEDiavfi9+nUgQUqr9PEgUlcqXLNXmbVyArc3l46vuMvfv4nqXImkYZvFBxJIMA+VKVW77LXOWuBGx4b9ywZYben7Vr5Uwb1+0psYVxanvteuExr/pZmp9OHl9OO+a/bKp9lGj7J46F+qFIFPZ6F7chGvS1hASR4as9TvLMMVC/zhtTi56G0Ih9VYzd+cH2Vjcwb4wNEw2YZbJclJWBRTqsK+louc+bMPi00LV++nEaNGqWqrVWrlhJGwCIVCKglS5bQ+PHj7bYLFy6sPN1jAR5hxowZ9MMPP6j9SZMmUdasWdW+p3gk+vO8qUr5xxuxhTymOiLU0ju9WIPvjzxKHRteUbVne+TVhuOxn5bEVnK+Y+h7aoxXUK8E/xG47YktQDfmr/X09R/WAMUTlK/1bUb3tYyXzDIJKpQBKXbuwmW7OAimdbsi7OPRL3WmepVD6cCxM9T33T9d8sLg/Cn2lmiGdwe3pE5N4gcCMFb/3OezacPueF16Mz/2Uc8Pr99NRZnw0gEvTLw48UL86quvdLS9NV9EdqRjp3m/CtTqsfhz18n6gU6KxBbKRh44q1zNa1taiMMkWpNoOEbQKjDWEZME0Zdo8vOrlRirjnNuUc8gHvCA8NIB6o3v3TFDH6qtqTLl74RbV5haxNbOBW+x0d/puhm1Ld/0ZeXmGQf+Tkh0hd6unyaZ8HHq902YLsJSfvOV/R3nRMXOwDszP9xCq6bsN6MSXF/gD6+K2g4aMi9lbylzb3hL0YVNffvknG9qEVuQ4sH1N4NJjCT33kppYiuxZwHnsXH6YSa115unlGDfafw/NYktNG7aSErQmRsRIBardfw0QfKxrb/SnqUjEsSDKNKEmE4EGVWvxxQmU4J0lL0FSbZ5xhP2wN1OcLODyUzNzl8nILWQVU+IvElsIR+kDFb+1MGedCVGbA0bNozq1auHookGvHt/fGy5rfKNApi8I5jv5brswawLkw5m8Peb4cu7A+04J8WIGza3g/2uMAfmSTnn9HAPYRIKj2owPJ1YgKoM1KsgEeIMvhJbKLfGYbDaKbHl73sVtqbGPAxCwX3AtziA7adoqXDTGyZsuoEA2rP8hPvCN2IfYWlCvTCCKH/Loaw/77Wtc47a5BXqQHCOVfDcPMwLaTCUjuDP+MYkjTwRW2jHfDZVY8YPCEJ8L+FlTIeUJLZ8+XbodncsZJMDw1brQ3In/asT/XmezXc6bCdVbPm2rs7jFpJZkNBCyJmvFDv0+E3tR7BK4W5WLfQW3HlNhHrxxj8G2ES1u/IwWN6w13RFRun0i+dOKLVkvN89BZBGde77SRFlOk+ChQ5OQD5IFOuAsUe9B3637XL5U8YToWSqIrr7duo+YIsFpNr3/KD6p+NTktiKPDCfts9+SVettp6+/5v/fpRg0B7BVxJUZb5JPyCwhg4d6uIBMUuWLDaZhW5g/hYZGWn3CGQXtDQ8EVie4lGBP8+bbjgxYguL17+8vMatQzBdB7ZOydO0JLaS8x3DuaTWeAV1S/APASG2buC2mr0TQioKElxmKBWSj17o2YTCari6FoW64YeTltH0ZZa4vC4Do+9P3d+QWtYtRV1e/D9bHfHrFzpSw6rFVLbd3Ma46Rto9qp9upi9rV0hmPp1rEV31LYkPuwE3oE3xXHTNtCUhdsTEGFdWPJrUJc6VJxVF82wcuVK+vjjj1UUBrD58uUzk9X+HpZ2gnH243tjXNJAPsBznWkw3szw2b3z1KDVnY0uGArXq67mSqEuD/VASH9snWetNOp4bCHp04xX9yo2T2ijCeow/47bzQaOwxMQYVg1bTGwAq9kuUpIoE4MtOZ+uc02iqzsVbBqJQLU+fr06aP28ZMaEltOSRLdmDeviBj44KN8jsWn9QS8HLt+1jYWvv/+e5o5c6aqyh+JLW/XTxNbZRsUoT5fxKu+6snpHWyT7U62zeYuYKV71S/7WTprL3ulu+CSBYPz2p1LEOx3wA6XGWBkFyojO9kwrx7AmyogyTlfb8SWNkDrSUTdm1dE9P/IpknKM5M2TgtyJGzAv+rUkntvaRUac1KoMTMnQ5hYlWBJRQRP8bqct2dB54EhcUjtHNrs+j7URqJrsatlU61Ekx0QQR/IxLK7kFwsDm/4UeFsDuR1O0Wr9mA1w+ddBtI6DVtcQ5AB8BDlLkAKMqhCRyrd4DElGekuD+IuX4imHXNfVt4W9fU282KVPaTyPaoeM97c17aTzPvETDf3XSZdbiS2nn76aTp2zJLuSYrEFtqAt7clP+xRnuv086bbhqRMg+6lqTHbWjOvs07355vhy7sD9Z/YG0vTR2xS9o10e8PmMLF1431hnnNSiC3UlR7uIUgJ7mXVosj989T9pM9Rb3FfwJYbHA5gIuYu7Fs+isneiSoJtuNMG27O/HHRB2nNL+x05obUgpPYSs57Fe8S2GPT5JVuu1rrotSZJYd2Lj5uSwzhu/7wmKY6C3fnOq35LVzZ6XJ+J2Bnsd3TVZUNOLvAjR1/y6F4Ut9rKLOLzwFjlcNbExIRVVsVVdKOkDYzQ1LHN97e2Vpiq91T1Vh1NTMtGrPLZewDoq1G+2JsWL4K239ist4IvhBb5rcDRfWCF6QlMQ4zgy/fDuQHATmy3Sz1HU/MZqY/z/O6KQ+qdzra8jS2QpoZYAReS3khHmqFeF8jgKSCdJYtKali+YffuUWr3s8qik8rCV8drbfwdLpt1vMUfXyj/XzpNKgaVmLCzd2zCanNnSzdBDtYzu8ZvGdXaPEGk0Ou4ytIne5ZMpLv4WlcJn4RXbXH/YRnR7wzTO+//pTxhdgCkXhi9wzL66A+Yd6CyMP41KnSiSw2seWwd+mpPU/xurkdc19hEnm+jV+BEmFUvaOrdCrymp6GQyp1VdjqOtLLdu/evfTZZ5/Z33Ldr6CgIHrmmWcIRBe+dTokh9gyn7ekjhlijsexKYq5qhue7Gjh/bx84j7lhMb5XYCZEUj3mhLYqMwkttxpByEP5n7vNrUW+ru9W5eqt7Pm00jTQasFms5KdBq2o1gbAd+a1o9Wpmb9K9hJyfmOpeZ4xe6g7CQJASG2HHBBnTD8eAyz5deoGJNUWoXQkc0+jDl3kdUBz9Gly1eodGh+ypMzwE6DTS3Y4yrCRuKL5I+XINIZzrOE14nT5+jM2Qss8RXA+XhA68YwvM5vbiHBFR5xhr0fZmeVxDyUIyCrmWzvQ4R20KBBSqzVaUDeznRj5wqL159iaSpIdBdmFbAsjkGSM39KHGNChRcNJllQOYHrb3eG4d21BQkurI7mzBugPDFly5HFXbZbOi48PJxefPFFJb7crJl7QiGtAMAH7mzkRaWaAo+nuQsEsMh+TlutJK36lRbtvvfee3Tw4EH69ttv06J5v9sESRnF6i4X2DEDnDEEwg6fd40Pv9vypSAk5M5Gbud/VnPid1ueIpX5nVHVIwHgrBOG2c+d3svvG/a+ei6SpYBCldooVpix+p2UAOI5+uhaliQ9pFQh8gbX4Dpcydqk1OdvXm3Y1tPCRWL1Qq0qhtXHzkZe4IlxFiXxAQLTl+ucFt+MxM4nsfT0dA9du3KRVenXs2HobZSrYDn+jtVTxqkTO4eUTk/OdwTveaivxvL9A2khOJgxyVCoJmLiElQ20OOYAiv9keFnKTsvfOTn8r5+y/0t58977fzpS+ocMWaBKQM8I1rK0dP1SM74xlOdwBtEGMZM+UNz2SrEnvKnRbxpn9UpnZEW/fG1TRBBILkunTup1M1z5ivBRX374IFAxgJKjsCi/ByU8/lbAAmu2JNbWMqqELdZLtHnH0QYJD4vxBxmUueKUoXPlb8MfwPjJfWc5+tPGWcd7o7xLo09sUXZnwxkdUz3Hhzdlbx5cYfWj1Pqjmix/oNTeb6Aa5o+A6SyYCQ+ICCATK+I6C2k//ft20clS5ZU9jSTcwbJHTP42jY80mKOhjmd87vgax03K19KfMduVl+lHe8ICLHlHZ9bIlUbPgwJCVGrArfESclJKNHl4cOH09atW2ns2LHJ/tgJpKmDAK7PO++8Q3feeSc9+uijqdOI1HpbIhAVFaWIbQyEv/7669sSAznp5CFw+fJlku9I8jCU0q4ILPpuFy0cs1MRfy+xF+ubsUjq2gM5EgQsBFZMaEewewa7p/VZTfN2DzJmuN3vgFv//IXYuvWvsfImN2TIELVNqujpbQBPhj3FH3/8kaZPn07NmzenJ598MsOex63c8StXrtBDDz2kRMnh8aZEifS7WngrX4db9dyef/55OnToED3++OPUooWlWn2rnqucV+ogIN+R1MH1dqsVUmSQuD/MTkbGP7FcnT5MDtwxsOLtBoWcbxojEMOqoZBi27/yc4rYMVX1plKr/1JQ+Q5p3LO0b17GDGl/DaQHqYuAEFupi2+6qX3BggVqRR86259//jlprxrppoPSkSQjAPtpIE7CwsLkeiYZvZtT4BqrI0+YMIE6d+6sDIDenFalldsFAUjjVqlShSpWlMnj7XLNU/o85TuS0ojenvX99vo62jLniH3ysP0Fj5bwbixBELiZCCz+rqGLzbPsuYOVEf+b2Yf02paMGdLrlZF+pRQCQmylFJLpvB7Y2oK0FnS0YZOpQYMG6bzH0j1BQBAQBAQBQUAQEAQEgfSOgElswf5Yjw8aULFq+dN7t6V/tyACJrEVkKsQ1er6vbJ9dgueqpySICAIOBAQYssBiBwKAoKAICAICAKCgCAgCAgCgoBvCFy+cJVOHzlP2dhzYz42ap85i29G132rXXIJAr4jcPFshDKwD0mtpDgA8L0FySkICALpFQEhttLrlZF+CQKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCHhFQIgtr/BIoiAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgkB6RUCIrfR6ZaRfgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAl4REGLLKzySKAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIJBeERBiK71eGemXICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgFcEhNjyCo8kCgKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCKRXBITYSq9XRvolCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAg4BUBIba8wiOJgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAukVASG20uuVkX4JAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAIeEVAiC2v8EiiICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQHpFQIit9HplpF+CgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCXhEQYssrPJIoCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgkF4REGIrvV4Z6ZcgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAVwSE2PIKjyQKAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAIpFcEhNhKr1dG+iUICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCDgFQEhtrzCI4mCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgC6RWB257YunblIkXun+fz9ckSkJsKlWrhc37JePMQ2LlzJ509e5ZCQkKoWLFiN6/hFGjp+++/p71799KQIUOoZMmSKVCjaxXbtm2jSZMmuURWr16dHnzwQZc4fRAXfZDOHFlD0cfW0LmoPZQtRwHKVaAM5S9anwqXbaOzZbjtJ598QlFRUS79HjZsGAUGBrrEyYEgIAgIAoKAICAICAKCgCAgCAgCgkDGQOC2J7Zijm+kjX8O9P1qZcpMzQev8j3/LZbzQuxROn9mP2UNyEN5g2ulq7Pr1asXXb58mTp27EgDBgxIV31LrDOzZs2isWPHUpkyZWjEiBGJZU9y+tKlS+nTTz91KVejRg164403XOIuno2gjX8Npotnj7nEmwfZcuSjsmHPU1D5TmZ0htjv27cvXbhwwaWvX3/9NRUqVMglTg4EAUFAEBAEBAFBQBAQBAQBQUAQEAQyBgJCbAmxlaQ7de+yj+jolp9VmeZD1iSpbGpnzsjE1tWrV2no0KEUHR1NzzzzDIWFhaUoXCax9eKLL6q6CxYsSOXKlbPbOXVgIW2fO4yuX7tqx2XOmoNy5i1BVy6dpYvnjhMn2mnlm75ModW628cZYWfr1q2El97+/ftpypQpqstCbGWEKyd9FAQEAUFAEBAEBAFBQBAQBAQBQcA9AkJsGcRWkXLtqdKdb7tHyojNlDmbcXR77aZnYgsExblz56hZs2bUuHHjDHdh5s+fT9988w3ly5ePRo8eTZkyZUqxczCJrV9++SVBveeidtO6KT3t+PxFG/Cz8A4F5A6y465ciqX9y/9HETv/tONqdvmW8oXWs48zys6ePXvo1VdfVd0VYiujXDXppyAgCAgCgoAgIAgIAoKAICAICAIJERBiyyC2gip04sn8fxKiJDE2AumZ2LI7mUF3Ll68SP379ydIb8HuU716KUcYJUZsrf+tF509tVMhV7RqDyrX7CWPKO5f8T86vOknlQ6bWzXu+sZj3vSaIMRWer0y0i9BQBAQBAQBQUAQEAQEAUFAEBAEkoaAEFspRGzB9tSZwyvY2PYGunwhivIUqcqSLHWVse1MmbMmuCrRx9bS+aj9lLNAKc7TQKl/nTm6miL3zqHcBStQ0RoPKqIhNmIzS80UpkKlWxLaiDrwL50+soqyZMtJBUs2VfFZsuVS9Z+N3EGnDiyimIgNyth3YFA1Klq9B3mSMLvOamUndk/n/BvpYuwxus5/MBJesFRzKlK2tUu503xuF6IP04k9Myjm+CbVHlTREPIEVaHAItXUflr+rF69WtnYKl68eAID7NevX6f169fT9u3bKTIyknLkyKHsKtWvX59Kly7ttdswNg7D9CBDYmJilB2s8uXLKzW+LFmyJCh75MgRCg8Pp7x58xIMtEO9cMuWLQQD7rDvVLZsWSVR5s6u08iRI2nNmjWq7vfff9+l7tOnT6v+I7JIkSJUoUIFl3RvB96ILdx3m/9+VBWHhFajXjO8VUW4b5aNa0bXrl4ivkmo2cBlvHG9x6G6GLHtNzY8v1epMGbOmp2y5w6moAod1XPhvoHrfH/9w8/RKmXjK3PWXJQjTwgVqdA+UXtucTGH6HT4UoqOWKeqxj0MJw9Zs+d125QQW25hkUhBQBAQBAQBQUAQEAQEAUFAEBAEMhwCQmwlm9i6TnuWjKBj2yx7Pc47AEQR1LVyFSjrkrRp2iNMgq2lXPlLU4UWbxKOr1+7rPIULtOaqrQdQTvmvkIn982hgFyFqGSdwbRn6QcudeAA9o/q9ZhCO+e/wXlnJ0jPnCVApecILOqSBjJj68ynLXLCJcU6ACFQr/vPtiraut8eonOndrnJSVSgRBhV7/iZ27SbGenJxtbx48fp9ddfVwSTu/5UqVJFGVHPmtWVnAEZBjtMv/76q7tiyvvia6+9RsHBwS7p8HA4c+ZM5Zmxd+/ebo3BgxB7+umnE6hMLlmyhD77zMISxuRNb30rV66kjz/+WLUVGhqawBi8SyccB96ILdwHUYeWqhKVWv3HJ6PwkfvmUuzJ7awumZlK1h1EIK50OLhuLIWv/dbFHpdOwzZvcE1+Jr5jMiyeFDx/eh9t+IOl1S6fN7Pa+3hO6nSbRLifXcN12rdsFB3Z4urxUecJrXIflW/+qj60t0Js2VDIjiAgCAgCgoAgIAgIAoKAICAICAIZGgEhtpJJbK3/vQ+djdxu3wTZ84RSNiaF4DlQSbQghSf/te4eoyb0OqMmtiAhcznulIvB7pDK91CFO163iS1dBtuAXEWYbCrCJNNumwiDxJYiBLidXPlKsjRXbpb22mWnQwKsbvfJdjWQbln7S3e7TUh05SpQhrKwofDYk1vt+Hwhdajm3d+pcltmPsVE3DrrnG4YEIdhcYQiZdtQxZZvq/20/HFHbIGcGjRoEMXGxqquhYSEKGko3PibNm1San9IaNGiBT3++OMu3YfEFKS8dIDHQti/2rVrlzJAjngQVB988AGVKlVKZyNNbCENaoUIIKJAgMF4OTw3IiB93LhxlDNnTnWMn71799Irr7yijp966illL0wnphaxtXpyV5YGPKLu0+R6/DyxewbtXPCm7jLfi7lYArG8uj/PRe2x48s0fJKK1+534/g6LR3XnK5dsbwVggzOXbAcG6w/Z6lH3rjfChRvTNU7fWHXgZ3Nfw+lM0ctJwb6Pr525SLhHucbWeUNqXwfP0+u5JYQWy4wyoEgIAgIAoKAICAICAKCgCAgCAgCGRYBIbYMYguSUYFBNT1ezJxMGpVp9KSdDgmpHXOtCXPWgDzK1lCewpVVOjzL7Vr0Dqv6WWpdOQKLUYOe8Ua3NbGlKwMBUK7pC6ymVZ+0dJWW2EIeSLdgUg+1RYRL5yNp1aTONgmF8nXu+4nQR4TLF6JVOsgClG02aKWKx49pI6kCS7OEsFSLDjAQvnpSF+UFD8RV04eX6CS1TY6NLajhXblyxaW+xA5A+oAA8iW4I7bg/Q72qhA6duxIAwYMsKuKi4tTaREREZQtWzaaOHGinbZhwwYaPny4OobKICS+ihUrpo6vXbum8k6bNk0dw7OgqTaoiS0kou9ov3bt2ioviC4Yhl+wYIE6BpkGUk0HGL/XfWzatKmS6tJpINS+/ZYloTiULFnSJU3n8bT1JrG1dGyYIiyz5chHjfvO81SFT/HrpjzI6od71D1X594JlLtQRbvc2ZPbaP3UvuoYBuchyYgA1dmNfw1S+0XKtqPKbSzcEXHlYgyt+b9ufD+fTnAfR4X/S1tnPafK5Q2uxc/H54pIQwTUdlHnpXMnVHq9+39xkZoUYkvBIj+CgCAgCAgCgoAgIAgIAoKAICAIZHgEhNgyiK3EriaIr0a9Z9nZVv7UngmmU+oYpJImtewMvANPc/A4h1C1/Sds9+cOtW8SW1D7a/jQ3/akXGXgH5PYKhf2ItvLekAnqS2kqE4fWqb2q7T5gAqz5JQZIDmjibWmbAdJq3Ft/PNhZScLZFyT/gvNImrfVE1rPsSShtGZkkNsvfPOO0piSdfly/b555+nRo0a+ZKV3BFbkLjSpNO9995LPXv2dKkLdrngJTBz5sz05ptvUu7cuVX6s88+S7CVhTBq1Cib1FIRN37effdd2rx5szp66623qFo1y86YSWz16dOHunTpYhajY8eO2aRUt27d6IEHXK8r+ggCrESJErbqoUsFfhx4IrYg3bR0XFNVI9RlQQAlJywZ04TJ1stUsERTqtbx0wRVLf+hpSJNoUJb/8GpKh12tXbOf13th1Tqyqq5b7iUO75rGoWvZgP1LJGI5wwEHMLKiZ0UcQVSt3Hfufb9rQuD3Fr98z1KcstJmAmxpVGSrSAgCAgCgoAgIAgIAoKAICAICAIZGwEhtgxiC8RP1hz5PV7R3Dzx16pQMKC95LuGKm+ewlV4wj3BbTmo722aNkSlFavek8qGPa/2TWKrwh2vUUjlexOUN4ktSFyZNomQefe/71HEDoscCBvwbwJi7MCqL+nQhu9VvU0fXmrbQYI02fVrV7i+zPyfzW4X6oyw6bVn8QeKnEBCShJbsA8Fw+hJCS+88ILP3gHdEVtQ++vbt6+tEtigQQNq1aqVMuqePXu8XSizT1Bf1GRTnTp1bNVAMw/2TWkwkFEgzhBMYguqhnny5FHx5g/6BAm29u3b08CBA80keuyxx5SBe6g9fvfddy5p/h54IrZwHywZ01hVC0nBBj3/8rcJVQ6k1nWWaIMxefN+hYThkc2T6fDGH1U+k9hS5Nr3zW3VQdjgCq3WnUnglgnu6fjOXafFoy3pxSJl21J5h6qhzrdhan+Kiw7ne99V+lCILY2QbAUBQUAQEAQEAUFAEBAEBAFBQBDI2AgIsWUQW0EVOlGlO//j0xWFseu1v/ZQeT0ZqEYiJvqQYkGAGmGNu75W+yaxZUpTqcQbP5rYcqoS6jx7Fg+nY9t/V4dOAgqR4Wu+oYPrxqh0k9hSEfwDaa+T7IUx9sRmunA2wrZxpNOxddabHIkts97U2HdHbKGdP//800XNULcNe1sgrtq0aaOko3Q8jM0/+aSlctq9e3fq0cO6zjpdbyFVpSXAmjVrRrCJhaCJLaghTp4cb9tMl8PWG7GlJcG8lTfr8mXfE7GFskvh4ZBVVp3kj7d6YT8Ong8RIPmnpQFxDLIMUlZR4UuUrbdL509w3FUk2cEkthBpqsfamXgH9rbw3BSv1Vt5GtVp5vOn4xLbmveyEFuJoSXpgoAgIAgIAoKAICAICAKCgCAgCGQMBITY8pPYgkdDkFMIJeo8TKUbPObxii+GZBdLeMGIdl32NIigiS1PpBXypBaxdZHtDm2Y2sdWo0RbCOhLQM7CdPVKnLJthDiTDMBxRiS2VL/ZKPsff/xB69ats423I14HSHENHTpUHZoG3Pv370+dOnXS2RJstdpgjRo1lGdFZNDEltNul1nYG7H14YcfElQkEaAmmRLBG7G15ud7LWPr3BBUU0FUJRZMacEqbUdS4TKtVJGog0to+5yX4h0n3KgIxBckwmDUHSSXk9hCNjxTB1Z9xQ4MtiQgwpBesGRzqtZhFHbp9OEVtGXGE2rf15+w/osoS4ClairElq+oST5BQBAQBAQBQUAQEAQEAUFAEBAE0jcCQmz5SWyBHFrFNn4Qgsp3oEqt/uv2SsMY+/If7lRphUq1YDtbH6t9TWx5snOFTKlFbGnbRGgjMKg6Fa/Zm/IVraekYxDnTRIsoxJbOC8EqBkePHhQ2fqCAffw8HArgX8feeQRat26NUVHR9PgwYNV/F133aWkq+xMxg6Mz/frZ3n2a9u2rV0mucTWiy++qPqVI0cOGj9+vNGi/7veiK2t/zxLUQcXq8qL1ehFZZs8m2hD8Kp5/swBlU+rwULdEPcWSFyEoPKdKLhyF8obVENJgyFOk2juiC2kW+E6SxFuoVPhi+nErul08dxxncAE8uNMJA+gS3FRtHJCOxUfXOlulujqY+fxtJMzXynKxHa6EITY8oSSxAsCgoAgIAgIAoKAICAICAKCgCCQsRAQYstPYguXWUtiBeQqwkblZ7q98sd3/UW7FlrqjSXrDqZS9S0pr7QituBdbsX4tqqvsGVUq+u4BP3eNvtFOnXA8tqXkhJbWsUuQYNeIl5++WWqW7eulxzxSe5UEbdu3aqIrLx58xK8DDrDokWL6Msvv1TR9erVsz0oakksbwbcIVUF6SoEeDKE10WE5BJbkBLDg1m4cGH66quvVJ3J/fFGbJneCiFZFTZgkYvtNWfbZyN30Prfe6vogNxB1KjXDLUPe2+Q5EIoVf9RKlnX1XYY4leMb608dprE1qnwRXT25A7KlrMAFa2WUO3zyOZJtG/5JyhOgUWqUe17LTtdS8Y0UpJdBYo1ouqdrWuoMhk/J/bMoHOn9hIcPxSr8ZCdIsSWDYXsCAKCgCAgCAgCgoAgIAgIAoKAIJChERBiKxnE1oap/Vhtaqu6Aco3e5VCq97ncjPADtGK8W0IRtkR4HEOnucQ0orYMlW4IKlVpvEzqj/6B5IwqyZ2ZsLgsopKSWIrLbwi/vrrr4R/hNGjR1P+/K7OATwZih8xYgStXbtWlYOHxCZNLDtpKoJ/rly5Qo8//jidPn1aRX3++ecUHBys9pNDbJl2u6pWrUpvv/22qhM/MISvH9isWbPaHhztDF52vBFbKKY9ZWI/R2AxJo/G294HEacD7Gqt/eV+VmM9qaJK13+MStR9WO3vXTKSjm6zVCdrdR1LeYNr6WJqG3VoKcHjJoJJbO1Z8gEd2zZFxcM7aPY8IWpf/3hy1GD2uV6PXylX/jK6iNqeP7Nf9RUHJiGGYyG2gIIEQUAQEAQEAUFAEBAEBAFBQBAQBDI+AnqenNiZZDofd+F6YpmQDqJAB72Prbv/a+w9Tf9j8nrt2nUqGuo6qdV1pdY2JhnEFlSxoJKlAyRCStTuT1mz56PoiHWsSvgqS6dYxIephoj8aUVsXWbiasUNFa4s2XJRrbvHUO5CFRX5BsmZPYvft4k49LNR739Y2qUwdlU4sPorOrTekvKCWljekFpMKJRmaZuCOovHLUgbfU94zORIgAH1TJkyOWLdH7qT2Nq5c6dt+wrG4p944gmqWLGiqiA2NpZ+/vlnmjNnjjqGWmHnzp3V/smTJxVxpVt66KGHlJH5XLly0b59++izzz6jiIgIlWyqISIiOcQW6tRG6OGZsVu3broLtHLlSoJnSYRixYrRqFGj7LRhvmM3AABAAElEQVTEdhIjthQJ9OsDthoh7o2SdQZS/uKNKE/hSsoeG4ipvUs/tJ0MgKQFWatDBDsy2M0ODRBwT8GDaADfFyBLj239lQ6uH2vXnzV7Xmrcd65SDYQTgy0zLcP7MBZfqdW7VKC45akR6o17loywJQhL1GZ7dg0fU22Yzx/sw5ULG0ZFyrelzOzpM5IlDvev/JwuscowAuoMKm9J1OFYiC2gIEEQEAQEAUFAEBAEBAFBQBAQBASBjI+AEFvJILZw+Q+t/54OrHavBqVvj+x5QqnOfT+5SMCkFbGFPmk7R7p/rHdmEw6IA0kFAkwHkxSIPDCfts9+SSepbcESTalax09d4tLiwB2xhX6AAFq+fLlLl2DYHRJQOhQqVIhGjhxJgYGBOormzZtH3377rX3sbqdMmTL05ptvukhPJYfY2rBhAw0fbpFD2JYvX95u1iS2QkND6dNPfcc8MWILjZyL2q0I1ysXY+w2Pe1ky5GP6nSbTNlZFVEHS9qvo6vhd8e9BULLrF8bdN80bQgbj1+nq1JbkFWmN0XYo6v3wO+KLNMZTc+fOs65NY3O6zQhtjQSshUEBAFBQBAQBAQBQUAQEAQEAUEgYyMgxFYyiS1cfhi63j5nmIuRa8TDXlFQhbuofPOXbaPViEfYMp3V2I6sVGRX477zrEjHb2LG4/ct+5iObJmsSjlVBhF5cN1YCl/ztUpvNmi5bTcJBAQ8yp07tUul6R/0t1T9oWyLqCctHXeHrY5YseVbFFyxi86mjNqD4NKkQ4ESYVS942d2elrteCK2ICk2ZcoUmj59Ol24cMGle5AIa9SoEQ0cONCF1NKZYGgeaoamkXmkQXILNrV69OiRQKIsOcTWhAkTaNq0aQTiDfuZMzPpeCNANRIqkgipQWyh3mtXLtD2ua/QmaOrbcksxOsAsql4rX7qPtGG2HUatvBsiPImMYp4EGGV23zAWGVh8mwIolTQXhivX7vCklkj6fiuafZ9p/OAeM0XUocqt37PRXpQp585sop2zHvNlo7U8ZlYcqt4zT5UqsHQBM+fEFsaJdkKAoKAICAICAKCgCAgCAgCgoAgkLERuO2JrZS8fCB6zp7aodS28rK3QV/U81Ky/aTWBTWv86f30VUmM5z9BdEAe1w585VU/0mtOy3yeyK2dF+g9nrq1CmKjIwkkF0FChSgoKAgRSLpPJ62KHvkyBGKiYmhUqVKUZ48eTxlTVY8CDaoSHbp0oX69Enc05+vjfkiseWsC2q00cfWU9zpA5SzQGlWSazM9reKOrO5PY6LPkhxrKqbOVtOF6+IyAyJreiI9eyRs4aL9BXS8AzFxRyiCzGHef+KsrcF21mZs2ZHstcAu3CxJ7cxwXVG2dvKma8E53evxirEllcoJVEQEAQEAUFAEBAEBAFBQBAQBASBDIOAEFsZ5lJJR70hYBpdv/vuu6l3b8trn7cy6S1t+/bt9NZbb6luQQUSxFtKBX+IrZRqOz3WI8RWerwq0idBQBAQBAQBQUAQEAQEAUFAEBAEko6AEFtJx0xKpDME9u7dSyBu/v77b9Uz0wh8Ouuq1+688cYbBGP3zZo1sw3Iey2QhEST2IKKIwLUHKHyeDsF2FWD9B0cAGgS8euvvybYWJMgCAgCgoAgIAgIAoKAICAICAKCgCCQ8RAQYivjXTPpsQMB2LkyA2xiBQcHm1Hpfn/r1q30zjvvKKJp9OjRLsboU6LzJrGl66tRo4btMVLH3erbvn37JrCzJsTWrX7V5fwEAUFAEBAEBAFBQBAQBAQBQeBWRkCIrVv56t4m5wayAqqIYWFh1L59exdPghkFgnfffZc2b95MTz/9NDVt2jTFu71u3Tr64IMPXOqtW7cuvfzyyy5xt/rB4MGDKTo62uU0v/nmGypYsKBLnBwIAoKAICAICAKCgCAgCAgCgoAgIAhkDASE2MoY10l6KQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIOBAQIgtByByKAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIJAxEBBiK2NcJ+mlICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgAMBIbYcgMihICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQMZAQIitjHGdpJeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCDgSE2HIAIoeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCGQMBIbYyxnWSXgoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAg4EBBiywGIHAoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAhkDASE2MoY10l6KQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIOBAQIgtByByKAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIJAxEBBiK2NcJ+mlICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgAMBIbYcgMihICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQMZAQIitjHGdpJeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCDgSE2HIAIoeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCGQMBIbYyxnWSXgoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAg4EBBiywGIHAoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAhkDASE2MoY10l6KQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIOBAQIgtByByKAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIJAxEBBiK2NcJ+mlICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgAMBIbYcgMihICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQMZAQIitjHGdpJeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCDgSE2HIAIoeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCGQMBIbYyxnWSXgoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAg4EBBiywGIHAoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAhkDASE2MoY10l6KQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIOBAQIgtByByKAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIJAxEBBiK2NcJ+mlICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgAMBIbYcgMihICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQMZAQIitjHGdpJeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCDgSE2HIAIoeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCGQMBIbYyxnWSXgoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAg4EBBiywGIHAoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAhkDASE2MoY10l6KQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIOBAQIgtByByKAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIJAxEBBiK2NcJ+mlICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgAMBIbYcgMihICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQMZAQIitjHGdpJeCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCDgRue2Ir9vwl2hEeqWCpVzmUMmfK5IDo1jjcGX6KYs5fpKKFA6lYkcBb46TcnMX+o2coMvo8FcqXi8oWze8mx60RdenyVVq17SgdjYylLJkzUfPaJSmoQG46ejKWjnBcYK4AqlyqsH2yp2Mv0J7DUZw3M9WtFGLHp8TO5cuXaeTIkRQXF0cvvfQS5c2bNyWqpXNRFyl8fRTFnoyj7HmyUfW2RSlr9ixW3deJDm6Mosjws3TlwlUqUbMghVbJlyLtpp9KrtOJPf8QXb9OuQqWozyFKqWfrvnQE0/XL2JnNMXFXKb8RXNRgWK5fKgpfWc5F7Wbzp3aTcSfjiLlOlCmTJnTd4eN3l25dI32rz5JZ47GUSZ+j1RqHkyBQTnoQuxlOrYjWuUsXa+QSsPB+dOX6PieGMqcJROVqlvIqOnW3L10PpLOHFmlTi5/8cYUkLOg2xO9EHuUjm2dwvjspQtnj1GWbLkoZ96SjGU1Cq3ajfHL6racM/La1UsUE7GB21xN0cfW0bVrlylfcC3KV6wB5Q+tR1kCcjuLuByf3Dubrl+7wvdiZgoq38Elzd0Bzg3nmD1PCOULresui8QlE4GbfQ/xB4PORu6kM4dX8T20li6eO065+duRn++hAsUaUkDuIK9ndOboarp07qTKU6BEE8qWo4DX/Oei9vD7bxe/EwKocNk2XvNK4s1H4Pq163SQx1HXeBtaKR/lyJtNdQLv/kM8hkIoXqMAX+cbYysVk/jPmSPn6fTR85QjMBuFVo4fe3n6RnjqR+ItpY8c165cpMj981Rn8D4vUq5d+uhYBuoF3hN4X2TLkZ8KlAizex4Xc4hij2/m71Ym/m51tON93Tl9eAVdjotKME6OPbmV4s6EJ2jPUz98be92yHfo0KH/Z+864KMovvD7UxNKQguEEnrvvYSOCAIiRVR6F7EgKioWFBXFil1AEBCpIiq99x56L6HXQCCEkEAIIfB/32xmM7e5u9xdEggy8/vd7ey0nX3bZr5573sUGhpK2bJlozJlEuYejtLTkkweeWBr/oZg+mjiOnFNtozrSxkzPDyTEndupBavTaOw69H0Yvsa1P+pau5UfajKPv/FAtoZfJHaNSxNH/ZplCJ9Pxt6nQCA5vH1FuBRijSajEYiomKoy/B/6FL4DbOVz19oSi3qlKCP+V6ex/d0TQZpf327jZmv3udB4/tShvQpd59PmjSJFi9eTJUqVaIPPvjAPGZyIhiITRq40aaJ1+c1J5983iJtxpCtFLzhkplfs0MRavNOZXP/fkUA3kRcjOYJbwbKUyxbih4WE6Kgqcbk1JcntZXb/ppi7WMgcO9eHGXPU44yOpisJ+dgzq7fN08soxvhMdR0QBlq1K90cg7jcl2AaXfj7lHuwlkFSOpyRRcK7l/8KoWf3SRK1uo8hycOhVyolXQRgCU3r52kDJmy8X1fJekKbpaIjrhNY7uvpeuht8yaT4+oThVbFKTdC87S3BG7RfqwDW0YqDHeF2r6BxvbUDrle5maz4LZwfscObnlRzq39w9x1HLNv0g0cY+7fYMOLH1dgFCOupYugxcVrzOY8ld4xlERkX5q22g6u2ui0zJ+xVtQmWYjGLeyPwldP66mWb9IjQFUmH/Owobf6jEQFktZc5Wi6p1mOCv60OfF8gLI5RORPHf6H/mX8THB2tQ+sft5D109vY4OLh8qrqmj8/L2LUKVnxrvEKTd/ufTFB1xWlTP7leBqnaY7Kgpkb5nbl+6fmmvAFMbPm+AwE4r6Mz7KoGrZ2/QT51WiWP2Hh1IRXihAuFS8HUa22OtiD8/qSEVKO/eQvDcj3fT7kVnqWi13NRrbAJI4egb4agf6MDD8O04v3c6ndjyrZAX/io9OYYX52qZ+zqStAR2/dODQfdDlClLbqrTfalZ4fjGr+jCgVliv+GA7Wa6q5ENv9Xhd14c4ftYtvlIs5qj4zlKR8Woywfp9q1wBmwLUJYcxcy2HrXI559/Trt27aKCBQvSd999Z56+o3SzQBqIaGBLAbZSesKfBq6v2QUNbJmicDvyzpiVtHzbSWpYJYC+H9zS7fopXeGPxXvph7+MAWQRf1/KnzsbdW5egftX+L4DWydPnqShQ4eKU8TLDy/BlAjTXguiY5tDRVMFyuXgyX066vxNbdaCyEjn9oXThP4bRF7WnJnJv7QPFa/lR4E9SqTEod1qY9vsU7Toa15p4jA8qK1bdZMqnJrA1vrxtXlh/y75l21PpRoNS6orbuc7u34PAtj6vOli1ky5Qy1erUD1uhV3+3ycVUgtYOv4pm/owv6Z4tCeDPac9Rl5m6Ycp+U/HxTFAPj55stCdToXo9IN8tkAWyqA5WjSgkZS81kQnXwAf85ACWhG7fjrWQYBzpg9y5wtvwAM7sRcZ82tC2KwLTPLtfiK8hRtJnfNbVzsTdq/6BUDHDBTSWjWpEuXUWiA4VmVAceo2m6CXc0bFdiC1pYAWnmA7ig8SsDWpaM8kWcgF+H5iTyRr+DeRN6RDJNKvx/3EPpwYvO3dH7fdJvuZMjsI+7H6Ovn+RaKNfOgXVWh1Q92J+YqsIUKpZt8RPlKP2nWtUY0sGWVSNradwQopSVg62H4dmyd3pZiWBtXhhys/VipzWi5q7cuSMARoJSWgK0dszrxguIpAWzV6jLPhbP6bxZxBGA5Sk9LUnjkga29xy7Rws3HxDV5u1s9YaqVli5QSvXlp7+2UdSt29S4ahEKrJQyGgUp1beUbCc1NLbSGrDV8b2/6PTFiERaWZAjNLP2n7xMxfPnoOcY7JIhtTS2hg8fTocOHaK6devSG2+8IQ+XrG3UlVs0qs1y0YY9rZ7FX++nrbNPivx3V7fi1R/XzHyS1SkHlVNzQPawAltJXb8VPx/iAWIslWnsTyXrOTeLcSB2t5M1sJVYZD8/s4rCztxItOKOkjBR2bv4nKjU6q1KwvQQOxrYai5kgr9TW1nDarehYZU5az6q0n4SZVbMvABYAZy8dMQYHEPLqn7fDYw3GaZAsiE5kMY+ykAjK0/Rpma5ewxqwaTs4NIhhDYRYOpYr9dqUV4kxP/ZAFucliVncarxjLESrpaTcQ1sSUmk3tYZsJVS99CJTaPo/P4EjbuAan2pUJUevCCUQDuBydrhle8Js0F5trg3cI+owQps4X6t23OZTVtqeQ1sqdJIe3FoQ60Zd0R0LLB7CcpZKKuIJxfYwrfg/P5w8iuWnWo/l6DZ4ugb4agf6ExqjqPEySbz72b4CbGIYdMMLxwE9l4j3sU26XrHoQTwvosI2c6m0aWpZIN3zHLJBbYOLB5Md25HUr4ybXmxtoPZriMgzVE/UFF+j6GxpYEtrbElbqZ7zAcjg4xja+939+5dtvs2fhi8wQa8QP6U5f+RfdHbR0MCjwKwJbXvXmhXnQbwz5WQGsDWpUuXaNCgQeLwALgqVEgA0lzpk6My4czd8GNHg8ug58/1qFitBK4w1Jk3Yg/tWnCGAirlor6/1XfUzH1JT80B2cMKbCV1/e7LhbEcRANbFoHwrtSca9K/DDV+vnTiAnZSHE1aUDQ1nwU7XbkvSc5AiV1/d6OoMGPCWKfHMoemXfsWDGQOM8O8Aiv8WOmXIez0WgFYYR9gVdUOf7D5Q1GZbbONZfOIXX93F5xJyChcfQAVqTnApowV2EJm8XpDqGClLjbl5I4GtqQkUm+b2vcQ7ostU1iTHFp9PNku+9inwiTH/hnd4/vtTcJ9h2DP1NAKbKGcM+0UDWxBQg9fSC6w5eiMnX0jHNVJ69+Owyvfp8vHDdO5kg3eo2MbRopTKVrrJQKIrEPyJJBcYMvR0R0BW47KI10DW4Z0HGlmOUp3JtP7nffIa2xduXaTyePDBOdQ3YoJZlRR0bdpd7DB4RNYuRDF3I6jAycu074ToXSD8yoWz0vVSjPharbM4ppdi7pFe49eoj3HQgWZd6G8PtSkehHyyWrk27uwR89epV3MBxXKXEnRt++QL5ctWTAn1WWNqiyZbVd11fo3b8UKwvt9x0Pp+o3bVLlkXv7l42Nlos37zjMBPlHNcgUoU8YEHo6dRy4S6gXk8yGYryHcZcBx015jVb5q6XzimMfPhxO02I5fCGdOqSxUqUReqsVtOQue9MdZe0nlXWWusMOnrtCBk1coMjqGCvn5CC00yDwpYAvnvP3QBYLsQ/naA3DNkdWLcP5VSuWz0djbevA83Y69SxMW7KK9LOsSTEb/6jN1RPcgc+u1xb20ce9ZwX2F+yGbVyYqwET99SsHkF+OLEmdltP82Dt3KejAeVHmvV9X0Q2+lq3qlqAn6pQUafUqFRR9P8HX78KVKHFf4trJ4Aqwhet4gLW9DrFsrzHZfMXifiwTfybiN3itZFtyO2HCBFq6dCllyZKFwLMF7hJrAK9RKJNNX2QzEGxhOpiPTQdBYpo1l+2zAU2fEOZCiroSQ/NG7hFNNRtYVpgaejF5PNq6HX2HNk87QSd3XBF8SS1fM8C0guVz8spzJvPwV05G0Yl4UmwkZsuTmWDSWKRaLh77J+6nrAhztfMHrgnibPBAFaqYkwKq5GLidtu+grMpkvsbvP4Sbf/3tKje9Vtjwpo7IBvlYtMuhPDwcKHRhrifnx+VKlUKUZeCM2ALRJlXThi8Gf7lO9Ld2GgKP7eZJyzrhbp81pwlKU+J5jaE0FjRunzUGJwd2/iF6ANWzvKX7cgySUf52CwxOcTnSV0/yBHh9M4wcR1z8cpx7iIJvGTQErrFmlxYAQaxPDhxsCJ8akeY4H2SHGYgvT3K/GqXT0YyN8dtBgIyMQeVlzChU++p2Og4OrXzijjm9DcM093KrQoJJwS4V0vU9TPvhT179tCNGwZnXfXq1cnLy0vUc+XPkSkiFmsuHvxHNJGneDN2fODLfGw76eqp9QyIHBbaPSBQzVuqtc1hwH92K+IcPy+LTPO0kvWN1c1secuJyahNBTd24vh9dmLrZVFj9rCdwjyzEnNqVXrC+PaVqJtXaGfJawkOrRJ1/Mwj2Ju0uPosmI1w5MqpKPGMXQyO4GfLi4pUzSV4j1TOLlnelfsiOc+ZPI69rTNQYuOEQH4n3eZ7KD016B9kr7pIu3pmAx1Y8pqIF6rcnYrVNeJI2DKlhSC9RRwceuDScxZu3wiloGnG/QJNmnq9VtpoDEhgC+DYTZhI8j2I/tXuupA1W20XCHCc1AS2QCJ9ZP1Ffi/dFPdZVn6H5uHnvVT9vAlOQOycrDvfDFQHKfXxLZfFt7xw1dz8XGVg89Db/OyHCc3DWp2KCicjcJCw6BvDbLz5S+WYpDg7m7ZnEsTZaCcuLo6CgozrmDlzZqpRw/m1QB1XQmrfQ4dXvMvcYYaGc8GKXah44JAku7Xlj8f5/RouylVuO87mOyGBLXDDQePr9k3jfVG+5Sh+XzdO1HZqA1swIT2z+yq/C6OZ+yaOsvhmorwlsov3tzNtbXwnLvH75SJzSeF948314KykdIO8TnkWPa0HwbjzXhOCZD0A3KcXDl4T55eRuTqz+2UW1Arye5dI4DiOG+Mb9Z2P8QzkgGAFtmCOLsY+PLaBdpV/aaa54HEa+mFvzHT5eCRdu3jT5hlCu/a+EUi314+kvh14Z5zZHYbq7DwjCwO2CRqIIjH+D/29cOia2CtaPQ/zhqZPsecZ3E0bJzYQprwYL1XrOJU2Tqgv9jNl8WOuqMVqV+zGofElnDnw9x9at96+hQnOSHIXaWS3vEzEGBDjATgSiYkMoczZ8wteVP9yHfiaZJDFEm1jb0Uw7+dG4YTkdnSYMDnOU6ypU/5P9Cvk4N/MMXWIx2dXhNMIb98A8i/XUZjlJToIJ7hbB+dx8+oJNqPPQ7mLNjGbtAJb0C4NP7NJOFIBF6xvgRqsaVg3kXapbABOU+7cuk7Z/Mqyw5aKMpkcAVv2+nEpeD6Po9lh1Y4x/G6MEN/VYrVfFW3lLtqIYmMi6HqIMS/JWTjQoUzg/OVG2DFKl9HLqQm32cn7FAkLC6NNmzbR5cuXKSIigrJmzUr58+enevXqUZ48iccGjgAsR+n36TRcOswjD2w5mvBvPxRCL3y9UAhx9qed6OVRi23IupGRj73QTf+4A4MsYfTyt4lfblm9MtIPr7UUAJh6NUBE/t7YVbRpvwEqqXmIo974oU9SmSK5rVm0LOg4vfvr6kTpqPPtoBZmn39//ykBSsmCUstHJY+/fSeO6g2YJIr88kYrmrXqAK3dzYNhS3i8VjH6qF9j8sqU+EXqaX8sh3B5F8ToIEi3F/q1qUo7gkNoNwOM9sjjAWa9+fNyOseeA+2FCsX86Oc3njABKykze2W/H9xCcFohj7Ex+mFWEE1Zus9eUZH2Ud9G1LZBaYf5SWXAq2HzwVMdFtswpjd5Z87gMccWgE+YXMLBgDUAQMP1txLO9+vXjyIjI6lx48b08ssvW6sRNHf++WAnnTtgDKCtBdq8XZlqdizCq8xGzuG1F+nPt7dZi4n9QhVyUgyDTgAz7IXuP9YVE/A4Hsz++9EuOrDygr1iAgzrxQSq2f0SAxcAXP56b4cgNrdWxuS//fCqJlk2jiFNtaxlA7uVoMdfLS+SMVEaNWqUiOMj8sMPP1iLO9x3BmwB9Diy6kNRt9rT02jPnD5ikm1tLG/J1mza9IlIjgzdT7vn9LYWMffrdF9id/JrFkgiktT168eDQwSpKWQ1M5Wmca3Z9C17Hi/6c2jCvSA1905uvUIzOR0ApL3w+KDyBHMLhDD2mPnzs4nflbLe0OVPmB6iXnnlFeEBBnngjHNnUusI2Lp755YYEKPNUo0/oLM7J7K3QQOcRpoMACGqtP+dJ5EGyLfz76425kKyHLYAwiq2+lFNcisOsOHrJwxw017F99a0FpMCR5MTe+muPgs4HsDKZd8fMMFgtQ+YpHb/vo4AktV0V+6L5Dxn6rGscWegxNZpbUztqQpPfM9gtnF/W9sAF9flEytEsk++yuaAGIPrvfMHiHR3zB7U+614vTdYG6ureUgJbOUoUJO1bOrQqW2/iDwM9qvyPWYNqQVsqfxt1mPiOnf5uhYVrZl4MO3uNwNtR4XF0KjWy8RhBk5tTPuWnqeNU46Zh32SnYos+GKvua9G8pXwoYHTG4skePXt1auXmT1t2jTKmNHxAqNZMIlIat5DOLTkS+SZLgOdq8z3iLNugagZk0kEqzaWCmxVeeo3nhx2F+UAdNVlzURoFqohtYAteGad/f4OOh5kAGvqMRHHfdSHScv9GXyxBgA/M9/eKkytrXmo1/zlcgTA0xo8refJew1A3dTBQQ7HNOWbFaCnR1QzxxzoqyfjG1c4tjAWWzP+iN2xT1mmDGg/vJoAjFV5pQR5fFLfjqYvlOHv1TLxvcfC5PO/N1S7YMZXjz1C6yYFi/03F7cQi6Yp9TwD8Ahe87FoG99x/zLt6NCytxnENBYWq7GWbTY/Y7xndig+AlDsyKphJvBszQeJeqUnf7WrpXt+3wwmq//O0MS0VARHXqnGH9r1fAuQ58jqDxh4i7PU4rllrpJCKxj11XApeAEFr+VxIrQ+7YR8ZZ6i0nw8NXhSxxHQpAJbkPHRtSPUQ5lxeKIs0+zTRAuwKUEeL9swD6ZEStR/W3y35QJVrsKNqMITCY4ElKK05Y/HTGAssI/9uapaPrXjUN4YPXo0rV271uGhevfuTa1bt7bJdwRgOUq3qfyAdzSw5YA8XgW2ABpBQ6ZckTwCbDrCGl6HThuaANB+At8RQmDFQkJDBxov0HxByO3jTUu+7Urp4rVEAIK89M0i2spaQwhoGxpR/kwAvocBGdluIdb0mfvlc6KM/Ju18iB9OW2T3KXaXM8vZxbWQApJBLq5C2yhnwA1sK3BHvWu34ihLfEaQjjgkM51qWuLBCQcacnpD+q7GybM30Wj/91hVqvKGlaF8/myJl0oa5gZqzUy0wpsQZuq60f/msANQMma5fILsG7NztNm+rPNytPQ7oGimUHfLqHzVyLN64tEqe32Xo8Goj7Sfpu3i8bMSegXPBKWLJSL4E0RGlwyLB7VxWOvirgevT+bJ5qS9xvunTzxmmDTP+ogzsUTr4grt5+kt0cbpn9os06FggIkU+8rgJufDmhqgltA/1988UXRny5dulCHDh3kaYotVlknDthoAhBYDQyomIsiw27ZDFRrdijK3gwriToneACLFfVbkXfMARY0vLyyZyD/Ur7CVDn0+HWbwSraRWg3rKqYEC/6ch9t++eUSMvJWj/QNoF2zontl8166mRGFOS/gytDGNTaLnYx8C3O5o/YnmLNMOk1Th1orvjpEB1eF8JaWzE254gGqrUtTPV7lhRtJWfC7SqwBc0NEANjwgGPfLeunzO5eNAJkATnCqhPcKm8c7ZhkgTQRQSeDMlBTu0u85LlITGp69dpZA1xyKSALWhVWUFDkD17s2aWNFFFQ2Ua+ROu8bUQ1rplUFSGzjxpRh60jn5/0XhfgksKAdc0O2vvIcATVGbWBERIbWALWjMYaGILr2R3bkcRtG9kAC9EqUbvi10AFwA9oA0kB5qYVCL4FW8uyJzFjgd/t67H0m/91oua9mTywpTGwu27PQALleylu/osYMV+AnNMhbAWBQKeQ3jigldRaFfK0OfX+lSYNbhkkMCWo/sCJODJec7kcextnYESAJYBMMuQt+QTAmRyNMmR5eRW9bJlBahkGXtbFRDLW6oNlWlqTLpQVgW2Kj05lrbP7CCee+TZIwFPDWDrIC8qYIEAAc8bJsbe/Jzhup9hrUwZVC+3SPPkm4F6KrBVtXWA8NSGdBm6sNORZT8eEKCqfJf75PUS9zm0Q5/7qpYomlITYXlcuU3NewieU7fNeEocygpQyePb20KbdMNvdcX7BfxwtbstNIupwBY44Y6uG0kXD/8j8vMUe4zKPf6lWRaRVAG2eJz8x8ubhWY2joH7qBgDob7sFRkanPIdgvf/q/8+hiJmgNbRb32NdxwSMUYoyAtjIYcjbECkDh9VI7xTZPC0nkfvNT6/n581OA5xfNAqFCjry0D5HTq4JsQcU9TrUoJavJYAmngyvnEF2JIygJwx9kE4se2K2Q/IUH4bZNmUALZc+XYs+/4gbZ5xXBx20F/NTG142Q9iWX731HIxTivXND89+0VNkZVSzzMchEDjCt9uyZEIrZw98/qL4+RmPsTyLb42u6NGpFmbTMOzhm+59DqKdIzbanWZz++jBIBWdRyDMnACgbo3r50Q4wikIVi1Lc/t+YNOBsUvfPHYzpvHgzjezfCTYoyIOlhIq/b0dHPcB3N6mNXL4O0TIDTDYqIu2jhGKVHvTSpQqbMo5kkdVHQF2JL9wLjUm/saGx3O92HC+ADfV4CJapCgVHK8ImKhClpZ5tiYDyDHXaUavccgYmvaNKmRGFsb98ImcU+o/bhx9ag5xi5Q4TkqUf8tNfuBxKXXenlwOPgqUqQIhYSEEJx/yfDLL78IqxK57wjAcpQu66WFrQa2XAC2cKE+f6EptahTQlwzcIENZe2WVTtOiX38TR72lDBPRBzg1SeT1hG0ixDmfPEsBeT1EfGTDMB0GjZbxJ9vW40GtK/OpoPxKiuc+uXUTaw5dVDkL/uum2kGBtO2x141NHYAev08pJXZJo738+xt9PviPaIe/twFtlAHYNDb3VjFMl4z62JYFLV5ayayBKDzz8hnRBx/ye2P2ZCLkcsMTD3xxnRRGuDbr2+3oWJsGigDwKkhrI0lgxXY+nftYfp08gaR/RNr0QWyeaAM0TF3hAkjQEUAV+p5oowz8njIvvHLkwXwCaDxO9bkkvJD3WVbT9C7rJ2H8PVLj1GzmsVEPDl/UpNsYLsa9Hy7ajZNuQts3YiOpQ7vzhLAHoDCL158zDSbhPnjmH+20+Qlxmq3+gysXr2axowZI479+uuvC3VWtSN/vJQwKG05uILwtibV2aFJ8+fQ7eYg0+qhSuVo6vVLvUQr+5JjqzCbtvUZV988LExSvnhsiRiMAdCCaaBp2sTXaSlrimyZeUKUVwdIGEz+9DSbd7LpIQaYz3xew9TowqB11ZjDtGmaMbB6ekR1YRYnD5oUN0RwcDD9+uuvonjhwoVp8ODBsmqSW1eBLTRUsGJXNkF5nWPGu0Ss9m0eJY4B7Q1MctUgV/lTwytiUtcvKWBL9hMT4oa9SwlzRZgX7WCTT6l5obotR3ncU+N6rxfXHpMVTFrUkBTH1k8//USnTp0SVQYMGEBlypRRqzuNqxo0wgsdDyYRVI0t7GNAVrnNGEqfyQBjVUJaDKAwaFaDOrhNDa+I8jo0eZ45tvqXVg9tF8BCAXvAlqyY1LMAE2IADAgAoqu2TXgHwyRlxptbxeRETFb/4clq/GdRAlvyONb7AunJec5ku/a2zkAJmPZi4nL7ZphNVQzIs+QsITRh/Eq2YFNL+/fSkdXDKfSoASi4YoYoDxJ3+wZt+r2x2M2WpxybxkyRWYmALZh0YGIGgNQeCXhqAFt/s4nr/uXnBRgxmK+jaiJ+aHUIzXrHWECw3gOefjNUYEsKonG/0lSheQGCuXN69qiLAODMmVfE2NhYevfdd4VJI8p/+eWXlCFDBkSTFVLzHoLWCLRHEFw1Q5QnI80RMUlTTWmtwBY0DmEyC0+fCJWeHGPjTTE1gC1oTv3SebU4XqM+panJgNJ8/yaMk1WAZ8giPGPGYgUqTOIFNQmgdvy4umlmjTz1HsCiGbR75HvG03qevNcun4ik0V3WoEtk1VyGNvLUV4Po7L6rgrrhzSXcRw6ejm9cBbYqPFZAfDfl83InJo7mfLzb1H63ehROCWBLnBj/Oft2OJMV6l9gIHN8PJCJMV+p+vlEsynxPAPc2Tr9SdGeVVvaFuRI7BQk9Nhi1tb6QNTN6JWTqneawe9EAzSECd++hS8RtOgRAqr2paK1XxJxMe6DuXm8GXnF1j+bzxsA6dPbxrDTkkmiLL4t0NhHgGlx0NQnuFqcMCOswp5zYfKIcPdODB1c/habJ24S+0VrMjdY9b4ifmLzd+xN1WgD2lBYoJFBaPnP5XJ8XJhhVmdADMGTOqjnKrBlHZfeuHpMAOiQG4JVQzolgC3RMP9JMNKeFrWz80b9o+s+5UWAOaKp2l0X8MKpv4g7+7t16xbduXPHWZFEed7e3pQ+ffpE6dYEaGt17dpVmOUGBATQZ599ZkOzsXLlSnN+AkWFpk2bmk04ArAcpZsV00BEA1suAFs9Wlai156rY3O5Nuw5S4N/MEw6Xnm6FvVpU8UmHzxVfUbOF2njh7ah6mXyi/jSLcfpvXHGB3v1Tz1MszdZeRNr+Az63mh3+vAOpjnixAW76RcGGRBUEE3Ww7bniLmmppi7wBY0mADAqbxcaPMzBoP+YVAIYcfE/mKLv+T2x2zIxQiAu0mLDOBuHINa0CqzBlWDzApsjfxjA/295rDQups6vL21KsFrpAQG1fNEQWfAlgr+/TC4JTWokjBZQ13wVjV8aTKi9HbXejaeCkWiB38pCWz9vnAP/fT3NtGLBV93pvysOagGcJK9+PUi2n44xMYL4x9//EELFiwQRfGiK1HCAH2RcJo5kX5/yfiAVn+qMLV93/bZQBl1sALPeN3YBEmGpIARR8AWzAFGNl4kmikVmI+6fldbNim2mADN+XiXiGMwWZD5JhA2TD5GK0cfEvHX5oCXylvE5R8GlJh0ndoVlsiDnLMBmazv6dZVYAvq5dU7GQB0wrHuCR4IaP14ZS/IK4JzE7I4ltaBrbqdi1PL1yvY9HndhGBaHe/dyQqGoiAmGMeDQgkcZ63eqmhTNylgy6awmzuuAFv2+JBwGJiGysGtFbz6LwFb6rNZu1OxRNcHsjjJGgJ/vLIZUVKBSxXYsndfiAqp9OcMlMAhMdk/vvEbfp8tFRMKe90AaAluLZAMS+1IlDMBAY7DxCujd4KWmr121DQ5kMcqf92eK80sq8YWMk4G/UTn9hjfoJxsnlixzS9m+dQAtiYP3CTeldD+AHCQ0ct2AA7zI3DilOeJdPV2xsQrOd8MK7AFDSyAn9agghr23h/W8im1n5r30NldE9ncdLToqjMnAfbORTV5rtd7ten10Apsoa6qJQjtkbo9lgqgFHnmfcwaIg2f34qkZIf9bE7694c7RTuqybhs+NimUJr2epDYfeGPRqY5IugEpJYuwM0m/J23BtVsDaar+Ur5CN5HT+p5+l5T7/cWTFtQj+kL1ADtZyymAcx7hjWd8Sypx3JnfOMKsIXFhFdmNxP8imo/wHf3c6dVFH7hpujD20tbmkDx/QK20J+J/TcKoM+66IE8aH1tnHpMgIBvLHw80TmgjKfh+IavmP9slqgOgAk8TzIcWz+SQg4ZmowwVStQ4VmZJbZBU1uaix72aB5iokIYNGsrykKLqsazs0UcnkthTohQuslHdnia7rG5G3MzgiOPn7kG/bcI07wjq1mD+Kgx/sV4EONCNQAU2zqtlegTTCDrdDfmmYdX8PFOGMezAkaoD7Am/OxmtgYoKDTEkOZJHdRzBdhyZDZ/IyyY8M5CgFZZzc7/ijj+5PcwORpbsjFnwNZt5rYNYpAfAbQCVdpNlNXEdvPvTYQ2flLeiNVKH3/8MR04YCz4qenO4kOGDKE6dRLmTI7Kqg6+XnvtNQoMDLQpCl7ZPn36iLRnnnmG8JPBEYDlKF3WSwtbDWy5AGxN+bA9lS9qIO3yoh1kc8MenxjIrD2g5cylCNaE+UsUV/Oh4XMn7q5Iz8jEvDLEsXfI4+ev0Xczt5hmiiqwBXAB5oulA3LRjI87ymo2W5UvzF1gq3erKjTomVo27WFn0eZj9MH4NSJ949jepjZScvsjGnTjDxxnMI2EttqcL57jF3niyjdjGER60RjAW4EtaNnF8Y85sm0I4mOYtH8XOwmAht0lJvFHcAfYQnloNiGAg0rtF0Cvqcv20YzlxksrLQJbb/2yQmgeQtvs21eNF7Y4GeVv3voj9NV0Y8IptQih4bJ+/XpRCqquICKUQV3BfPXvZqZ7aZkvt7PZXAVcWBi0vbu6lUwW3FzS5MwdjS00MGPIVgpmYnGEYjXyCB4NcLl4+zjmSgGvF0zZUL7LKFswTDTEf7vmnaHF3xqra+oKcVoAtuClJz8TyFvD7n97UeTlA2L1rm7PBG1GlEvrwNZbS1raaHqgzyDYHd/HuOewD3CyTEN/QQKtruYjzxoeNLAFU1CYhFrD6e3j6MzOcSI5sPdaU5sLCf8lYAsaWb/2XCfOs+dP9UzCbpEQ/4fVxVFtlgutu9rPMPj1pgFOqsCWvftCbSOl40mBEvJ4WBEPO7OWwk6sZtPK3XwOl2WWuQUIVbPzHBNAAL8WAAMEuBXHCrGrQT6/VvJie8AWL7fzSn5rs0/lW35rkhanBrC1ZfoJWvqD8c3DRLRBr5JUvLafcAjh6PyS881QgS1nPDxpFdiSMvHkHlJNj4rXfZ0KVu4mm0tyKydwKFi/70bWbja0nuwBWyijTrjBMwQuHITUALZgXhYXP65KnzFhnAygBaTl0L6GAxkEFdhS76PX5vIilb/tIhXKgxw+MjQaUV7EyiKAEE/refpegyb4Vy2Wincd+lGjXRHWBC8g3osZMtsCwciXwZPxjSvAVqshFan2s8XkYWy2O/5hTekv94q0l2c2FWTy2LmfwNa+Jefpn+HGu7I/0xIUZPNzBCw6jmq9XGjbN+xVipq9VFakp9Sf1MqCuaCVLymanXNs/9MYd1mBFmHqO94YS9oDQGT/0Aa0kPBtyJzNWKyHaTFMjLEIUr/fRi6aeLKDRU/8UAYgCgL6gvbwTajxrAHGiQzl79TWX5gg3gDQpHb5VSaZP7B4sFkK4JB/ufbCkQm0Oe0FT+qgHVeALSuAqB5fmoUC0FNB9PsFbKEv5mIk9yGw9xqTc1DlsJVcbGrfHcXBwbt9+3ZH2XbT33zzTZd5YKG5iADtY9XBF0CvmTNn0saNuMdIgFoa2BKiSPyHwakMMo6tvd9dBnPkDy8CgA8F8ideaZPtpcZWBYOCxvc1OYRUjq15zHVVkAEVNRxms7VuHxvAlgpAyTLnmF+p3TvGy0UFtmR+CIMem/edE14WQWouubVkPrZqu62HzBDAS5vAkvRJ/yZqMTMOL4mSh8ldYGtYrwbUoXHij8JiBraG2QG2ktsfs9MuRuTxWtQuTp8PbOawVpfh/1Awy9MKbMkK4D/bxgDhwVOXRTnJVyXzsXUX2EIdeNGEtt0evgbBZ8LoCP/Ay6aGtAhstRv6p0MyfbXvMg5QFeDqiBEjaN++fUIddsaMGTJbbBeM3Es75p4W8eFbeEUq8XdZ5KmaUpLwExmeamyh7rl94TTzrW0mRxfSEMARgVXOck3yU2H2jKiGHzusFCuSapqz+EDmIYJnR4S0AGxB5dwnX5VEXZYfYKjBP0zAFgDGnqPrJTofTHbmfrI7EYcOgNGi1XMTNP9A8u9lB8R80MCW1RuePLkzO8azhuOvYve/DGztX8YaGOxIwtUAjRvJfSSBLYf3hauNelDOVWDL2jQ478JOraWLh/7l95mhWYIy6kquSphrb6Xc2qbct9XkrG6uoiPfPrDF36crh3lSYZCAY5IGDTFokqUGsAVtLHghlV7KZL9h+oVnFF7pcH1NM3EukJxvhgpsNXuhLDXsW0oe0mab1oEtm87yjiv30LXzW4VJE+pazXes7Vn35aTdagbtCNgC8LZlyuMmf6MkzU4VYCu+sxEh0XRsSyid2xtOl9irsuTWUs9FBbakiaJ1sUwtby/uab3kvNd2zjlN8z83ACO1T6BDKFnPT1Ae5ApIWDBEGU/GN64AW33GMq+hZVwk+4RjTui/Qeyqpn73E9iCtto3rQwSeVVrF3xr4HJFeGVWU0FbIHZS4C/s9Do6uPQNoyUGMbzigSe1adUZTM3n/jFN/1TQy6CJiG9HrewgLr3tqt8KB0VtkmU9m0QnO5XajBbm8lj42PVPT/5GGFYLZpX4c85VpCGbSvZmwExV7vCkjmvAVv1+mwRgZ/ZDiYDEH2T+CKoW3P0EttT7QuXGlJybBv/WRsbeMig9f7DRqKgo2rJlC+3fv1/waoWGhgrzRLVXWmNLlYYlLsEsJMu4PVALaRLUwvZRAbZg2vUNa7/8yUTw1lC5RF7Kmd3L9EyoAls1+v4mindqWo7e7VHfWlXsH2TC+h5sjojgLrA1gsGy1gyaWYMjYCu5/bEeJ6l9ebz2jcrQB70bOiwuNbuswFZEVAwTpK8QJnVqZfB1lSmcmyKYnF0S/rsLbAEc/ZqvqRXIAgBUia8pTCAR0iKwJeWqysRZfMybrah2+YJOga2/3t1BB1ddsOGHsNfmrrlnaN7IPSJLXQlMDrCFxm5eu01b/zxJB5nPxZ4XRfBvPfdlLeH9DeU/rmN8KBF3JfRgD4zFuQ2EtABsgWPBHo/PwwpsgQC6HXugdBSObrzEXE/nxD1mrwzIY0Eiq4YHDWwVrv48Fan5gtolEX9UgK2ts07S4lGGxmMiIdhJwOSu72/Gd04CW0ndF3aaSTLp5rWTFBN1SQykffNXT1TeEbCFFXVMXhBQTzUxtDZy7cI2Bh9eBlIhssClBjDh4pG5pvenQlV6UbE6g6xV7e6HHlsiPG0hE+YvMIORwRGwhXzVdMav+ONUtvnnqQJs4Vh3WdtmJ7/f4ZRDatYgXQaQt8PTae4i2URScr4ZKrDV/sNqVKWNwXEnjyW3qQVsPch7CKawmycbC332TdLl2dtu1Xrg4cGkXAZHwBbyVU0NmDPV7raY9jKJ9vVLDNBYtChke55soYmzZNQB2jr7ZKLq8JAM3japma0CW1jUOrLuonAqYiWVT9SQkuBpveS813D4UAbrts0+TYfWhCRajEN+gx4l6bFXyiFqBnfHN64AW9Ik0zyIElGfm3YfMDfikwEi934CWzjg0u8MjlSAlkOXtxTA+JJv9lPQXycFN6r8XihdT1bUBGxdbEV15AEuK1AUIBSrPYgKVe3lYisJixO+/uzc4KnxbtdztULpJsN5gdYwhUSdkAN/MV/Znzx+PmW3CXsaoe7WcUVjy0rJoHbm+MavRR+RJoF1xO8nsIXjyUUB1YR0I3v9Bq+qPQcbqPOgwsKFC2nq1KmJgKycOXNSyZIladu2baJrGthycoUkmIUiMq6BrQROKJXTqHppf+rxRGUmnfejXAywIDjSBOv43l/CO1/jqoUdmoytYu92b8V7t0ttYCu5/REn68af1MRqyBxW3zOXlaMg+2UFtiTghXodWTMNmm+lCuWmrN4ZRVMzlu+nb2ZsEXF3gK3dRy9Sv88XiHrgKXuxQw2qwXxq8HIpPWFK8CgtAlvdWesQ2oLQhHvdwiMnTsry55s1M2Vm5wI//vgjbdhgrOJNnjyZQGYowzI2E9g844TY/WBjG5uVeVkGW5Xr4r01rU2gKbnAlnqMyMu3CN6OMAjeNd+YiCJf9cY4rsc6sRIM8tQWgyuo1e3Gs+TIyF5qDBVtDWwlFlFS10+SlltJcyWAUb97SWo+yHYwn/gobKrC5hyXgq/Tmb1Xafs/p0yvlyg7mD1l5WAzKBk0sCUlkbCV1+F+kMernDi9xwRSzoK2mggJvTJi6TL8zySEdve+sLblbB8EvtB6QbBnDugI2FK5jco1/4LysLdKZ2HfgoF07YJhbiC9lApidyafRwDQBVOX/zE4kFSQJicoV7b5SPaUmWBC7gzYAgk4eF9ib0WIQ4Cwft/CV4RmUNZcpQS5cVLH9iQfzjlA8Hxq5xXawgsOIMdGEHw58R7tkvPNUIEtVaPE2ld1gp6SHFsP8h7COcoJFeI1npllmiZh31FQuYP8SrSkso99ZhZ1BmyhEMyWAHAhQBMVoFZKA1uqNjecxNRngAfmZ1lzGeaS8HA4rtc60QcV2Fr89X4TDBselDBpFwXlH2v+3o427sFM3qxVwRrlntZLzntNdkdsuU9hZ6KYRyqcoAV2POiymd3p0xpU4XH7ZsqujG9cAbbAcQptSnvh1PYrNJk9VCKoi3r3G9gKZRPUMV3XiH6gvzBv/rK54SzIGaAtKrj5h3fklj8eE7Wg4QpzQkch/Jwxb8DiRiAvWuAdrmpsWRcf1HYAhIAHFXXxDUCQz7MVcFbrQZszLpbNaflYGTIZiwMqv5PVc6laV8bhZRHnZg3QzMRiTNipdcLrr+olEAT4+FZYg6t1XAG2wBmpeohUj3V4xbvMB7ZcJNVjM0B57vcb2Dqx6Vs6v3+66Ac0x+Dlcu98Y+GyWseplC1PYusn9TzuV3zPnj2CMB7H8/Lyos6dO1PVqlXJ3581psHLw+HZZ58VWw1sCTHY/5NgFnJlXANbCcCWJHiH972/Pn3ahu8JMtt68Dy9+M1iRG1MESUXEtLXj+5FWbwMQAb7Mrz362paGnRc7KY2sJXc/sg+u7qFZ0F4GMzK5714VFcTkFLrn78cSU+xaR2CCmzB818j9lyI8HSTsvRezwYirv6N/XcHjZ+/SyS5A2z9wqT2E+NJ7Rcy+ToALTWovF9pEdiSXhSdcbedCrlGe45eEqfVOrAUgRtuypQpNH++oen01VdfUdGiRc3TVr3X9fmVVdyr5jLz1IgkBBXeieI9/yA/KWDEEXn8tfM3hTckTIrLNPJPRFoccTGavm+3QnQBGgOvz39cxOUALV8JHxo4vbFIs/5dOcWDTlZ7R6jcuhAPCIyPgga2rJJK+vpJQMVdYAs8aLd5kpynaDYqUN7g2DCPzhODZczrIwFVqyt3DWyZkjIj8jrcD2BLnVx1/rqWeD7NjigRcKnAG1feEtlN5w6pCWwdWv42XTm5SvQARLDWCYwKWpRvOYo1jIz3A7jrwGGHkLdUayrT9BMRd/R3YMkbdPWMMRFXj6NqBRSv+wZzJHV11IRIjwjZYQ6gQTYPk0I1OAO2UE51Uw8S8LjYGwxsxYnJCiYtyQ0wGTq0KkQ0U5RNin3yGZM12S6u7aQXNplmivCamKNgFhuPp+5+Mx40sPWg76GzO5lAfvtoIeIcBWoJr4VS3va28Kq5+Y9m4rpjYlyHvXdlypoAaiQFbKE+TBIxIUcAp4/glOO2VN4be8d2NW08e7iFKSsoBF5iXqd06W35DECuPuVVA1BQga0g9nq8hDV7EBxpIeE7Al5Nvs0e8gAAQABJREFUBMnh6Wk9T99rMO9DXZjNl26QT/RF/VP7WK1tYXpqWBUG4D0b36h9VJ1yYFFobI+14rCqeZ/aD8RXjT5M6ycfFcmDmGBemkfKcVPRarmp19hAs5ojz7mO+oGKro6j5JgRno+rtgkwnY2AoxWaXCkV4JABixcIxWq/yhpXPR02LblMUaBMs0+Y87M1x+6xWXgtUcfbtwhrRP4t4rZ/91jzp7Ew7VXf5dtndqDo62f50UzPHE5rTcBLrat+lyTAI0Eje3xgsi4AaJjIIxSpMYDbzsweEafzNzeSsueryNfW0JKW5XEe+3nxQ5rTF64+gDXPB3hUB23KPqrk9UhXzfKdOcGAdiq0TSEb1ZPr/Qa2VDqAgKp9BJB55eRKvgcTSPlxXq4ESeviSllZ5p133qHq1RNrmMt8uYVHdng+RAAvcr58tu+a6Oho6tXLGMdoYEtKzc5WglnIknENbCUAW1J7p1XdEvTpgKY2EgQ9GczlVu04JdKnsfe+skUMu+aFG4/ShxOMF9JLrBXUr201m7oHTlymnp8aZojISG1gK7n9sem8CzuqSeSL7WtQ/6dszx9NjPh9Pc1Zd0S0pgJbR06HUdePDQ8a9rjEIm/epuc++DtJ8vj6lQPox9da2vRWAnwA3NYx4GgNf644YBKvv9mlLnV5vKK1iNv7KekVcRkDoe8yIIow6pXHqUn1Ijb9gVODXiPmCa0uaKQt/KYLr0ixWvjSpTRhwgRR1uqhQx3AwGygz/j6iQamqtt3q5c0T4EtlcgVJPD2BoySfFUF01SeDHsetUBY+xsPtMHvIQCxeQyIsQwQkhqQgbhReugAeaNKsm+04Phf/Xj65q/BXDoGFxNqhB5bxOZIH4rKyTFFzFfmKSrd2GhH7UlkZKQwFUda9uzZzdUdtYyjeFLXTwIq7gJb0lwkf2lfGsCesOQ1kP1QyeVVkwnkS2Dr8UHlKbB7CVnF3CbnfF3xipgapojJubdw4vI63A9gC6ZFvzy3WmjV4fr1/71honfCEZ5wzoyfcKrApCvAlqeyOLNjHHOcjRP3gT1zQLl6jgIqh4ogB/6NPWTFmxdWbPUjwR28vQDvVfB+hZVvY0COCbnxAlG1tlC3ZP13KH8FQ4vL2hY0Aw4sGWwAEpypAm2ybFLAFsodXvk+E3AvlVXE1qqxhTHb9evXRR4IZ318fGzKO9rBu1JqUQSyp7fH2eObNajk8nKinJxvhifAVv8JDUzgVPbP03N+0PcQgMnNk5uKCTLOxTAzHcmx+I+UPEHexkRdFJNL4VGN92GKBJMkNSQFbKEsvLaBTN4m2AG2PH2vSnoAcCZ2HGGZwN0j+nOo4fAFxx8wuRHlL+srugLt7N/6rhdxUfcTrmsRgwTNUEhqintaz9P3GqgSpDOat9jTYJYcmUSf5R/a/aSeYQkgTbA9Hd+oz5YjYAvHtUe2fzP8Nv3QcaXQsgTI+PKfTfkdZgj0QQBbexefI3hVRYCGPZwPgXj/yfcSa1R5+jyjbWhrCc1WvqdVgnDkWUPoscU8FvtAJKvvUUkCj4zKbccJk3W17kX2qHiUPSsiSNNwxI+uHSHM1BG3x82pelNUATFVi1jlfkI7CNDY3fJHc+G1D16aG/TfxKn/IwmkQfsJ3lGtD4xqfgzPvkVrveRRHfTBFWALCy61GWy3apOpJvhWQv7UALZA5l+7q2OaEgloAswCMAigv0jNF6lw9X44VZdDanpFHDZsGAUHB9vlQkYH//rrL/FDvFOnTqb2FvYdeT90lJ6c5w3HS8kg51xJtfm/m9G3+HOSdMDJySDj2Nr7PYocW1JjCzKSpPIAD05fvE7j5u6g5dtOSvHRD2xy14BN7xBQ5plhfwtzROz3bV2FHqtVTHgp3M3aNAB11JDawFZy+6P21ZW49Xj92lSlTs3KkV+OrEx+fp2mLt1Hs1cfMptSgS0Quzd++Q+RB80kADgF2BkAPCLuZ0DwmxmbBZG8rLz6px7kwyZ3MkitJvBxffXyY5znRUX8fYS23c+ssTUpXmNraLdA6sgaYfCOePnaTfpnzSEaN8/4CKOtXq0q06vP1JbNerxNSWALDhu6MegHwn0EnMPjbJbomy0zHTsXTnPZI+JMBucQBrarQc+3MwBF1Y1st27dqF27dqKM/FNNS7Ci1/qtSsKbDsxSMDhR+XZU4njUTwoYcaSxpU6qsHr3zGc1BBcWVnvj2BMSVkFnD9shuliFeZzax/M4YRD5a3f2inncmMi1frMSVWhegIlAMzKAFEk72SPiVuZxQLCCAOrqJEwG8hbPLrQUMmczNCqDgoIIXk8QChYsSN99952Iu/KXmsCWHAhAHb1Uo/dZuy0HZfNLmIS+8sorBIJJhHfffZeqVUsMJDs6h6SunwRU3AW21FV1DGYb9y9N2VnzDgEkwwu/2kdHNxmahXLCLPsojwl+tcdeKkeZs2YwV55RJjnnm1rAlrpqXLTWy+TjX4UnQEXZZDeXOK3k3FtoQMrEek8jT72vVXNiR+nWOvaeBRW4AodW67cqCs2s2zfj2EwtjN8J++h66C00Re+saiWuEeKuAFueygIr2NCaQgDoBN6p3EWaiEkAAIuzu38XeQbBNr6xCbNklR+NK7MmYRMqzKvgIP2FOQqArEtHFtCpbT+LyQQasucd8+i6z+ji4X/FcfAHrTC/ki2Fa3mYqMBUUhDRMyeXDNaBvUx3BdiykoCjrjohw766kov9adOmUcaMibXEkWcNEoBGOt6l4LzC+xjvWYDPcCIQfuFmIg5GT78ZrgJb6gQfpm2VnyjI7/hMlN3PeId4es5p4R66cmIFHVrxjnkpcA/mL9+JchVuwN7W/NkL4D7WGNxI5/dONTWtMHGswxxZ6TPZmgW7AmzhQKpXT3FgO8CWp+9VFXzqNTqQitbIzf2+R2Gno2jN+GAbfkWr+emsd7YTFs4Q8J1o0LukMEuPiYqlTVNP0LpJwSKv2pOsCfVBFRHHn6f1PHmvqaaU8l3oX8ZX9OXW9VjazJ5FZT/lIo2n4xv1vncGbGGxD2MimH5CIx19nPPJLtPE/5mRNag8g0kypCSwpX5X7H075DFVEnmZ1ndcfQrgPluDp88zPNXi3kZwhecKwPLGiYHmgkOdbouEBqTq2AHfB4yz/Io1p3QZvekqE9MfXjVMfCNwnKrtJ/FYphKi4lsBYA3tIhSo2JnPr6do88bVo0xo/yZ7TTwv8tQFMyvADRPIQlV6cL18FMFm8Gd2TuBxkjH+VYE0lXsR35VSvNCJcQbCjavHBIE+OCURJK+VJ3VQ3xVgC+Xg8KhMsxH83NYQoNGl4IV0MugHZIlQvdNM/maVlLspyrEl+yjGA80+429XHr42FfgS2n7/wk6toYPL3jT7gGtcr9cq0zwyIcN5LC4uzlQCcl4yITd9+vQ8xkgYiyTk2MbGjh1Lq1atEokwOWzfvr3wjnjlyhWaO3euUFCQNZo2bUovvvii3HUb2PL0eTMPmIIRDWwx8fdHEw0TgfvhFVHlcsJ1hKaPSjoOAvm97FlPBlU7CeDDa98vNTWLZBm57d6ykgB4sJ/awBaOkZz+oL67AR4NB/P5h12PTrKqCmyh8JCfltOaXafNela5g+9sZ/BFM1+CjkiYzMDVjwxgqUGCjlZNObUM4gDDvDNnMD0P1i5XgMa81dpazK39lAS2cGBX5Ar5/DKE1b0zpjf72q9fP8KKbLNmzWjgwIFmOiIYnGGgaI84WC0IEveyTfzVJI+BLTRyYPkFE7ySjWLAdiM8Ru6KSRYGQ/lKJWghhByKoGmvB9mUMyvERzDg68GExxkypTOzVK88MhGTpubxhK/qhDt//vz0ww8JH2dZ3tE2NYGtrdPaUMwNAwSSx6/DZk2Z4kETdUIydOhQl10Lo63UArbAzTP5pc2mGROOJc0PJG8P0qo/VZjavp8wYUHaVDZbUXlLkDZ0xRPkld0YrCTnfFML2LpyahUdWvY2umoGFRhJzr2FBlMa2ErqWWCrBlr45V7a/m/Ce9g8MSVinai6C2y5+5ypZh1KN2yiJRu8x0BBR5s07KgmhjaZPMjlmYlNEhw8VO3wB49/E96hsgDMQU5s+T5RHZmvbp1xtrgCbKEtldwY+ykJbMFse3yf9SaXlmjf8g5GmqqVh31PvxmuAlswg/ys0SIcygxC+3MKa39ySM7APC3cQzAz3bfoFXOybJ6knQgWMSq3GZMI1EJRV4EtaH0FTX3CnIBjYmc1RfT0vaouYqBPeM+r73hogp87EI4sEdRFEnx/pryy2amnY/C7vTC1sQmeoxFP63n6XlN5vYyz4OfQ8pzg/oSHYPmd8mR84wqwZZWv7I/cQmsMIKDU1kJ6SgJbSX47ZEd4u/RbJpH/84RIgRbZK7OaqesNZklPn2f1e16+xTeUu2gTs01HEXhPhLc8hAIVnmOHHm+J+OEV7zEn1DIRd/RXsFJXgoaVGlRtLjVdjdvzmoj3+oGlryc8k2qF+DgWxmp1nmNqRMG0b9vM9sLEzyyO7xeC8g3LnrciA3C/i2RP6qCiBI2sJnuqKSK+jxLUEwez/NnTYpMLteCbBO+kDI6O5ygd9Y6sHk6hRxfKJsTWkSa1JJFHIVdAUJtG78POwYMH6aOPPnJ4JCxW4SeBoICAAHMh3pFmlqN0T583h51LRoY8n6SaeKQ1thax+VW+XLarWsfOXaXnPvxHyE31XigFeTEsitq8NVPsTny3LVUpZdi2QqFt4oJdNHnxXhtAC0BL9xaVqN9TVenT3zfQPAbcEAawyd0LbHonA7z7jZ2zg3YcvkDHL1wTyQDDWtUtSTUZNHlm2GyR9s/IZ1iryFgBQoIEQwY9XYt6tzEmfXfi7lKd5yeK8p+/0JRa1Ckh4uqfagK4cWxvoSGm5nvaH7UNd+JXWBPqy2mbKOjAeRv5lSiQg4axt8SfGIACQGUFtmBu+PX0TbRw0zGbwxVizS1oUcEEr+1bf5qgofT+h8Lg6Pp8ygZat/uMeczvB7eghlUKi7ZW7zhF388KMsEreQAQsr/TvT7XO22Cp1X5PpjA90NyQjvmETvHfGIvd6xJfZ+09R4ntcvqVigogCh5HHhutAfgyvxrUbeEt071HGXeC+2qUze+NyXRvkwfP348LV++nHLnzk1jxoyRyeYWK/Sbp50QxN5YoVcDiEpbDYFNv+1zhTLXL0XTd08ZXFj2+FYWfmFMjosxhwsGfdYQvP4SrZ901GbgK8uUb1ZAaPqAw8ca4G1oCXtuO8JE8+ogGuWa9C9DdbsWtxkIy/prfwsWJokSPFNNcHbs2EFffvmlKOruhNtVYMsRYbAzr4hYlcQKf2y0oamHDqrukwcPHkwhIcaqt7saW0ldPwmoQHOqQa+EVTcJYDTqU4qaDiwrxWuzxTXa8Psx2jH3dKJrhMlKrU5FqW4X1piJN5eQlUE8C1DlTDxPGtKHLmdgizlOENTzdRfIUwfCGDB6+RQSbUI1HS64EYrWfIkCqvcVcfVP1foBn4ZVewJkqQC45EAP5m4we0NQ7y13tQFR/8cOK8XkrxnLuiHLXA3qCrqrGluo7+xZkO0fZPORVWMPm5oAMh2aC4+9VJaKVM8tk8TWlftClYW7z1lc7E06uu5TYV5lc2DewSC7VKNhNt6jrGXCTq8lkMnKVXRrPohwC1TgVfdqfbg9xzwwUZcP0pE1HwkiWnm9zbZ4ouHFmjeYADmbaK0fzxrBPBnJWaguVWz9s1ndXuTAktdZiwdaaImBrZiYGOrRo4dZzR2NLVQCUTvewTAVsgZcZzz3pRsaYyI135NvhgpsdWePtdDKdBQOr7lIK345aN57Kq9ics45rdxDMJ86zO/166yhpZI+S3lgUutftr0wJ5Jp1u2Ov56lm+EnxMQXDg2cBZsJuB1gS32vuvUd4XHyuolHaePUYzbveQAw9fj93qhfaZr/6R7aveis6F7jvqWpyQtlzK7i+73ip0O0Z8k5m/ooAD4p1PeOf/eblTjiaT204e57DRpY8Kq4efpxU1NV9gXnCQ4pAHbyGyXz3B3fqMCWqt2kcmz1/KkeBbO34S3MUaYGfFPxrFZvX0RNFnEJbBWv5ccLfmyWHR8cfTsc9UPWc+XbgbIq5UALNnWuxybP9oJnzzNzY42vI96h0Jat328jN520Zoyq5QVTOmjuyBB6dBEd2/CFaSos06EFXCJwCD+PHWSSzTYq7IjQzoLpoU3g56xA+WeoWN3BgnTeJo93YG58YMlrvEjL11IBplAOHvtKNnxHaESp9VAH38BwOFKx1IEc8pZsRSVZ40x1buJJHRNQYk4/aLbJoAJb0Ao7xOMe6/cUci3VeBhrRjeT1cytBLasfJeOjucoHQ0CsN+34EUb+cHzMBaUrEHVtq7Q8lvKVaSRtcgD31+7di1NmjTJBK9kh8qXL0+vv/66cAIGB2AIIJWHYzAECWAVLlyYvvnmG5HmLN2z581sNkUjjzywlaLSdKMxmMGFMPh1/UYM5c+TnU3qstjUPsGgVXqenAXk86F0DlQOb96KFZ73vNhLHcIKNmMcOmaliG9iEAre6+5nuJ/9ucsI4dlL1wXQVCx/DqEV5cq5Qt4Xr96g27F3qCjXy+adyawGc0fwcfkxl5T1epiFHERg0gcw80rETcrJWloF+ZpKr4ioAtPEMM4rXjAnZcqQ3kErDz4ZwOulq1EUGn6D8vhmoXy5syZycCB7qZojWgnkZRm5vRUZyxP0KMElkZPJgq3AgyyXklvwQ0ReuSUGqzA5AT8WBoxJBpZBBINrAGiy5/FiEzDvRHxASbbxHygA5wBwEgAA09c3ASRPC6cG09Lr7PEyiq9vhkzpWeMks7i+LoxB00L3U6wPMPHv3r07BQYG0ssvv5xi7d6PhmBWcuVkFL8LiHLkz5JoAnc/+qAe4zaDvACXboQdZXPPHGyiVoHNCksIcEst5ygOIBqAwI2rxwWo4O0bIMwSsbLubgB5cMT5bRTHHqpyFKwZb3aR9ATL3eMkVf6zzz6jM2fOEEhoPQkACmBeCkAa5r8w+7PyCTlq90F8Mxz1xdX0tHQPYYIWcWEHA6VnhRmzT75KfC8bQL6r55MS5ZLzHYGW3TU2M4+OuC3MCaXZqOzX5ROR4tuMBTJHYwp4DrzGC2vZcmdmnqMsLn/LPa3n7nsNYG7UlRjC8TCOzJqTxyr5vIU5oDxPe1uPxzf2GotPu8Pf1TAep8Wy3OGkRWqKOaly37NUTtQhCx9nD3Re970PnhwQ4Hdk6H7Gje5Q1tyleSyax6VmUB4g1+0bl8X3BN8VV8A2Rqj4e3SS372nGMQtxHWLJfn8Awy/yR7+ACqlT+9FmbPnjzdLdPzt8aSOKyeO7ym+x/9jYA0mklbOLVfaSO0y0vmLM8L+1O6DK+2D9unixYsUFhZGOXPmpAIFCtjw5sI0EXnFixd3mXLA0XGTO2Zw1K476RrYckdaD7Dshj1n6ds/t4geQOMnZ3bblzkAif5fzCfwbYHke9GoLqnaW0/7A8+GAMBcDQCHnmpQ2tXiD0W5XaxRdvpihFt9bVytSKJr7lYDqVRYevRo2bIlwTRRh/+GBK5evUpvvfUWZcqUya423n/jLB/+s5Dkn1h5q1cvsfbiw3+G+gwelAQOHDhAILa1cm88qP7o4z58EtDfkYfvmqX1Hk8asFFoXpdt7E9w9qODlsCDkAAWMYKmtBCHLlD+WSrR4O0H0Y00dcy0MmbQwFaaui0cd+Zc6HVq984sUaBx1cL0Xs8GlCdeywtA0RdTN5pmdu9zHkjMUzN42p/WQ2aY5n6u9m/HxP6uFn0oyqneG13tsJUzzdV6qV3u+HH2qsjk4iAzHDdunPCgl9rH1O2nvgTg6fLs2bNCC6hx48apf0B9BLclsHv3bho5ciTlzZtXqIp7edkudrjdoK6gJRAvgTt37lDXrl3Fex3auODe0EFLwF0J6O+IuxLT5e1JAJq9Pvm8aOecM7T0hwOiiEqEb6+OTtMSSGkJ3LkdxaaeIYIgHqb8INdHqMv8tNKpT0of82FpLy2NGTSw9bDcNdzPr6Ztoj9XHjR7DA9/ccyTJbm2kFGhmB9Ner+tQ/Mxs3IKRDzpD7icbsfGuXX0vKyB9l8KACLhqdGdkDO7N2XMwDY7aTB8//33tGnTJnr88cfp+eefT4M91F1yVwLwmFKuXDkqXfq/pS3prhzScvkLFy4QCORbtWpFGtRKy1fq4esbTBdghtymTRvKk8c1k5mH7yx1j1NbAvo7ktoSfjTa/7zpYhuuNPDogU9PBy2B+ykBOOjYM89W0SJvqTZUpunH97MbafJYaWnMoIGtNHmL2O8UzA3/XXuYZq7YbwNmoTTI5/u3rUZdW1SkDOnvDwCS1vpjX2o6NbUlEBERQYMGDaLY2FjBxeLjk+BtMLWPrdvXEtAS0BLQEtAS0BLQEtAS+G9KQAW2ilbLTZ1G1hC8mv/Ns9VnlVYlYAW2fPNXp0rsXdaep+O0eg6PQr80sPWQXmV4BwRROUIBJir3yZr5gZ5JWuvPAxWGPriWgJaAloCWgJaAloCWgJaAloCWQLIkAOcTINeH4wk49HnUnMQkS3i6copKAOaHcMyRJUcxlx0ApGgHdGNJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW0mKSBfQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEtAS0BLQEkiLEtDAVlq8KrpPWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJaAloCWgJJSkADW1JE94jO7LlKV05H0Z1bcRRQORflL+crc/U2hSRw904MXTm50uXW0mfKSrmLNHa5vC54/yRw5MgRioqKIn9/fypYsOD9O3AKHGnSpEl0/PhxGjBgABUuXDgFWrRtYuXKlbR69WqbxLZt21KdOnU47R6FHlvCm3uUJVcJypa7jE25/8rOvXt36fLxZXyedylHwdqUKUue/8qpJTqP65f20K3r58krewHy8a+aKP+/khAXe5NCgxfRzWsn6X//S0/+5TtSlhxF6UZYMN24eowyeuWgnAGB5ulGXj5A0ddOJ0o3C7gR+fbbb+nq1as2NYYOHUrZs2e3SdM7WgJaAloCWgJaAloCWgJaAo+eBDSwFX/NZwzZSsEbLpl3QM0ORajNO5XN/UcpcuNqDEVcjKZM3hkoT7FsKXrqmADumdvP9Tb/l44aPr/V9fL/sZK3Ii+ISWSGTNnIJ1+VNHV23bp1o9jYWGrVqhX16dMnTfUtqc4sXbqUJkyYQMWKFaMvv/wyqeJu5//yyy+0du1am3o9evQggFu3b16hoKlPiDzf/DWocttfbcr9V3aiLh+kXf/2FKdTptmnlLekcc7/lfNTz2PLlBYUG32VsvmVp2od/lCzPI6Hn9vC2GccZc9TjjJ65/K4nZSqGHMjlLbNaMc4ZazZZNGaL1FA9b60658eFHXlEIOXualO96VmvqN0s4AbkZ49e9KtW7dsaowZM4Zy585tk6Z3tAS0BLQEtAS0BLQEtAS0BB49CWhgi6/5uX3hNKH/BnH1s+bMTP6lfah4LT8K7FHi0bsj+Iy3zT5Fi77eJ859eFDbFJWBBrbcE+fxTd/Qhf0zRaWGA7a7VzmVSz/MwFZcXBwNHDiQIiIi6LXXXqPAwAQtk5QQmwS28uTJY4J+RYsWJT8/Pw1spYSA01gbqQFsrR9fW2i7+ZdtT6UaDXvgZxy85mO6FDxf9CNdBi/KkNmHAqr2pgIVnr0vwNaBAwcIA5aTJ0/S7NmzRT80sPXAbwvdAS0BLQEtAS0BLQEtAS2BNCEBDWzxZVj89X7aOvukuCDvrm7Fq84Z0sTFeVCduF/All+JllSm6UdJnub/0mVMssx/tUBaBrYwqbxx4wY1aNCA6tat+9BdglWrVtHYsWPJ19eXxo0bx6ZV/0uxc5DAVokSJejzzz+3afdR0diCtuGRVQYgU6L+25QtT1kbOfyXdh4FYGvjxAZ0986tRFpZuI6nto6miJDtlDV3aSrZ4B3z0qakxpZs9NixY/Tee++JXQ1sSanorZaAloCWgJaAloCWgJbAoy0BDWzx9Z83Yg/tWnCGAirlor6/1X+07wg++/sFbOUt1ZqBrU8eeXk7E0BaBrac9fthyIuJiaHevXsTtLfA1VOjRo0U67YGtlJMlA9FQ48CsLXhtzpshhjH5umPUbnHXTPf1cDWQ3H76k5qCWgJaAloCWgJaAloCTz0Enikga3TO8PodvQd2jztBJ3ccYVyF85KLV+rIC5qwfI5KUvOTDYXGNxTIYcj6GJwBPMexTJnTHbyL+XDWx9Klz6xtkfosesUcSmafPJ6Uz4ud+/uPbp09Dqd3H6FcgVkpTKN/Cn8/E26ciqSvH0yUaFKOZmAN4bO7Q9n88hrzHGVXqQFVMlFGTKlE30JP3eDzu4NF/3w9s1IfsWzi3bsHR8VcMyT267QpWORFHk5GnzVlMU3ExXmNgOq5rLp98UjERR55RYFr79E2/89LY7X9Vs2h+GQOyAb5WL5IISHh9OhQ4dEHKZVpUqVEnFX/lRTxOQAW9AGucYcNBEhuyn2lsFt45u/OuUoUJP+ly6xxl1EyA66efUkeecswmVqiQnatQvb6Mrx5ZQ1VykqUKkzRYUdociL+yhT1jyUu2gTwjGunlrH12grpc/ozedfX6Snz5hFnGrUlcMUdmotXb+4m8mRc1L2vBWoQMVn+fj2NcxAph16dCGX30MxkSFMIX5P1MtVpCFfx8ds6oFf51bEOSYZX0TXL+0VxytZ39CEyJa3HGX3M+5TV2SeWmW2bdsmOLYKFSqUiID9Ht9ou3btEvfJlStXyMvLS3Dh1KxZk2CS5yyAIBrE9NDMuH79uuDBKlmyJEH7KX369Imqnj9/nk6fPk0+Pj5UsWJFYV64f/9+OnjwoODkKV68uNAos8fF89VXX9H27dtF21bNquTc554CW2npHrQKOvZWBIWf20zhZzezg41rlKNQXQY5mrAGjx9dPDSH+AamvKWe4GfFeD7gKOLSEcN0LVfRRpQ5a17RJJ6Diwf/EfE8xZuxSZsvc/rt5GdtPT+Dh0U5EJDj/eAs3Lp+jq6e2UjX+PlMlyEz5SrSgN/hjSk2hvt5ZhOlz5yV382tnDXhVh7eB+Fn+Z3D7w0cI4tvUcpX9imhiZYUsHXndhSf89/8fj9OMTcuif5mzpqPz7EV4b0lw53bkXT5qMFRdWzjFyIZWlD5y3Zk8abj47VnzULjWyDr4D0WdnKNeF+B3B0E7tnzViT/su2EuaAs58lWvYbHNn0lTCOz5irJjlU6ieb8y3F/+H0bEbKT368nzHenPJYrwFb09bMUfnqjuAdQD+9DOAyBqaO9oDW27ElFp2kJaAloCWgJaAloCWgJPNoSeKSBrdGd19Dlk5F274DuP9alEnX8jDwGg7bOOkmLv91vtyw0vZ7+tDr5+nvb5C/+hk0c/zpJ5ZsVoKYvlKEpgzbT9VCD/LZJ/zLU+PnStPa3YFoz/ggVrZZbcHpNf2OrTRvYAQD23Jc1af2ko7R63JFE+TkLZKF+ExowQJPZJg8g2p9vb6PwCzdt0uVOgXI5COfp7WMAMf9+tIv2Lj4ns222gd1K0OOvlhdpQUFBNGrUKBHPnz8//fDDDzZlne0kH9i6R8c2fEkhBw2OFeuxADCBjDtLzuI2WXvnv8CTrx3Cg1epxh8S9iUJstRAOLziXbp8YrkwtSlc7XmSE0u1IW+fAKrx7Gw2sfqAy7LHN0tIlz6TyId3NDVg8nlg8WC6G3dbTTbjmMTV6DSTJ4bG5H/n312FpzGzgBLBpL9iqx+VlAcTdcSxdenSJRo2bJgAmOz1rFy5cvTBBx9Qhgy2ACTAMHDn/PXXX/aqCe+L77//PuXLl88mHx4OFy9eLDwzdu/e3S4ZPACxwYMHJzKZ3LBhA/34oyFLkMmrHtaSc597CmylpXtQFfLZnRPp1PbRapIRZ5AF5sRHVn0o9su3+EaAv9hxRB4PczaYtSGUavwBoe1bkefFvvoHb3tV2v/OoL6tAwtoDe1fPEgAWmp5xAGqFazUnc7sHCeyUoqX7tTWX+js7knWw4n93EUaMfi8jwH2cLvk8Wd2TqDTO34VoJC9BnzyVeZ31ngGiNJTZOh+2j2nt71iIq1O9yX8fjK8S95msvrdTNoOoMxu4GuD90ROBiA9DTFRIbR1umOexXq9V/P1ye4hx9Y9OrHpOzq/f7rd7uUv15FKNnwvUZ4GthKJRCdoCWgJaAloCWgJaAloCTzyEnikga1Z72yn0OPXKezMDfNGgNYWQrthVQmaUghzP95NuxedFXH8layXl0Ayf2Z3mAkagZfr+UkNKU/RhEmYBLZARH+JtbduhMeYbbR4tQLV61bcBLbMDI4Uq5GHcuTPQse3hppAmF+x7AKEw3FK1s3LWg0Z6HhQQn7lJwpRh4+rmc1EsebV2O7rzGP65PWiotxuxkzp6fC6i2Z6rY5FqfXQSqLeip8OcV4Ia23FMMH1HZEm5VGtbWGq37OkSEvOhD+5wJbUAJAnmjlbfsrIoBDcz5ugEU/oqjz1G3sRTPBqKYEtAEex0WFCY0u2IcmZJagg07GFNkqmrH4MMh01gTBMnqEZAQ2VLL6FeTKdlTVNgs18aIBV7zTDbAYaCTtmdTKPCY2uLDmLUXomYI68fMBM9/WvRpWfGi/q7V/8qtCCEOfEGi4IIGxG8CvenEo3+UjEH+SfPWAL4FT//v0pMtIAjP39/YU2FF40e/fuFWZ/6HPjxo3p5Zdftuk+NKag5SUDPBaC/yo4OFiQRiMdANUXX3xBRYoUkcVIAlvIg1khAgBXAGAgnIbnRgTkT5w4kby9EwDo48eP07vvvivyX331VcEXJnb4Lzn3eXKBLdkHbB/UPSj7cHzDV3Th4Cy5K/qT0Tsn3Qw/ad7zMtNdYAtgDoAqbL19ixA0m26z9z0Z/Mt2YOL09+Uua5zepZ1/PcvP+ykjjZ9Bb59CIh7NGlzcmFkWkZQAtg6vfI8uH19mtgvwPHM2f4qOOG28B8wcSgRshR5dREdWG6AfiuHdAY0nvD9uXD1m1ixWexAVqtqL8K7YObuLSAcAKAKfIwBzhNpd5sV7SLzHnjVb83v6skjHu8g7e0EBjN9gzVPIEQH16vfbZJTx4B98cNtmthc17fWnLntATJ8pq0fA1r4FA+nahe2ibflOhIYYZCCvoz9rqpVqZAtuaWDLgwupq2gJaAloCWgJaAloCWgJ/Mcl8EgDW/LaSo4tmOf1GWfLsXVm11WaNHCjKJq/tC8983kNXgE3wC+Y+W2ccpxWjjbM8ko3yEddRtWWzZIEtmQCwKknXq8gzBKldpXU2EIZgGU9fqor8rF/63os/fzsahOEylfCh7r9UIfN0AyAA+DTuF7rBDCHum8uaYFqIuycc5rmf75XxLt9V4dKBhqaQEiIjY6jSQM2UgibVAK4euWvZqKc/EuKYwtAw6+/sgYCh8KFCwtNGFk3qa0KbME1fPa8CeCTta43g0bF6gwyk6EhdXiFMcmBFkelJ8eahNSYHAev/ZhN/RaJ8l48yavVZa5ZVwJbMgETzBL132QzoJoktatUYAsT7YqtfxZmi6iDCd7W6W3EJBz7qF+t41SejBfGLmtrRIh8TP5Qt0H/IJGOv5Nbvqdze6eK/VKsgeDPmggywPRoG2tEYCIK4Kp+3w0yS2yTw7F169YtunPHAChtGnWyA9AHAJArwR6wBY9l4KtCaNWqlekREPvR0dEi7+LFi5QxY0aaNm0akkXYvXs3jRw5UsRhMgiNr4IFC4r9u3fvirLz588X+1ZCdglsIRN9x/GrVq0qygLoAjH86tWrxT7ANIBqMoD8vk+fPmK3fv36Nvdycu7zlAC20sI9GBN1ke/rJ4V8AJIAMM7mZ2huMvrAWoiv0dWzxvsRhdwFtlAH7VVuM0YAJNi/GX6CdjB4hWB9Ji4e/peOrvtM5OEZr9Zximmyhmdw5+zOCWAPl0ousBUdcYa2/2k8r7gelduOY8C8ijg+/s7vm04nNn9r7uNcqnX4w9xHfwBgoW61DlMEubrMVDXafPPXEJqmMg9bZ14R1fdo7qJNBeeVaqK4d97zbNpngMS1Os8hr3jwT23f3bjk2PIr/jiVbW7rEEEuOOCdXofBLhkcpV89vY4OLH1DFIM8K7b+SbxTkQCTzz3z+psAZ41nZtlo4GpgS0pXb7UEtAS0BLQEtAS0BLQEtASkBDSwxZJwBmxNHriJTu0KE/J69e9mJqglBYjtv8PZhG8Jawtw6M8mgQUr5hRxFdiC2V+fXwN5EmYLGqjAVpdvalPphrZmVtCi2jjVWNnvy6Cb1CITB+C/DZOPmcDaBxvb8ETQ4F9ZMHIv7Zh7mgDGDZjSSBY3tyt+5nanGO0OD7I1NUkK2DIb8SCiTsiSqm6dJAVNbcmTVuNaAFSy52UN2g43rh4VTZdv+S1ztRjnrgJbMPur3XWBOZGS/VCBrRKBbzFf1nMyS2yhRRV+1tB+KNf8C8rDmlNqgGaGBNagJSG1LPbM7St4sgDG1eu9Rq0i4jBRlOCAdSKeHGDr448/FhpLiQ7oJGHIkCFUp04dJyUSsuwBW9C4klxVHTp0oC5dDO0TWQu8XLNmzaJ0zBf04YcfUtasBkj8+uuvE7iyEL777jsT1JL1sB0xYgTt27dPJA0fPpwqVKgg4iqw1aNHD2rb1vZ+DgkJMQGrp59+mp57zva6oo8AwAICAkwTW9FwMv5SAthKC/cgvBqGHlsiJKE+T6poNk9uRndirhtl3DRFhKZOvV4rEz2LMMeDWR6C+kwETW1lAFesoYR6MINTA7S9gqa1NpPUumaiG5EDS15nHq/1okaZZiPscnYd38gabQcMjTYrsLXht3oMhscyp2J9qtDqh0RH3vx7EwFqw8S5Zud/bfKdAVvCvHH7GFFeNU+UDYQwh9mxDQZQDIA+OeaIss2UBLZwjXCtsEBQt+cK810pjwVwS2iKsQaeX/EWDKQZ54J8DWxJKemtloCWgJaAloCWgJaAloCWgJSABrZYEo6ALWhkfVJvgZBV1dYB1G64oQUihSe3l49H0uiua8Rum7crUc2ni4q4Cmx1+561ptiE0RpUYOvDzU/yyr4tCf2W6Sdo6Q8HRLX317cxSeRlO7vnn6W5n+4Wu8M2tOGJggFsoe934+6J9lRi+TsxcXR691Wax3Uk39eDArYA/GRgomNHISvzZGFShgATpA3ja4t4tjzlhKaG2LH8gcR47/wBIrVgxS5UPHCIiKvAFkybYOJkDSqwBY0raFmoAZoi0BhBCOyzLtFkXOXhqd93I4OMBucZtMnu3b3D7aXjX0azSZgjgdPr2PovxOQXGdaJeHKALfCggRjdnfDmm2+67B3QHrAFs7+ePXuaJoG1atWiZs2aCVL3zJltOeBkv2C+KMGmatWqmaaBMl9uVW0wgFEAzhBUYAumhtmyJZgDy7roEzTYWrZsSf369ZPJYvvSSy8RCO5h9jh+/HibPE93UgLYSgv3oASSAEDgnrcXTm0dzfxTE0WWuxpbjgCf09vHmTxZgb3XCm0umOVunBAojuOMZ27X392EIwgUtD5PorIbfxKAMUz6oJlm+35GUwD1AO4hWIEtgFr3WOMQBOvq+wQaoOf3zaBzeyaLeu4CW9CWu3vntqgr3zPYwXsG78Ajq4ebmmtpD9i6R+vH1RJ9h/ZXSYupocjgv93/9hbmnlatPQ1sSQnprZaAloCWgJaAloCWgJaAloCUgAa2WBKOgK2Ii9H0fbsVQlYwIazTubiUm80WANKIQAMAq/tccWr5hqFJogJbqjaVWlkCW1ZTQlkmaOYJWvKdAWxZASiU2bPwHM35xDA5UYEtWT/kUITwwnj+4DXm+Yqw4ROTZazt3i+NLXe8IqrmSY5IhXE+mEhCSwIB3g8rPWloNajAlqpNJQrG/0lgCxNQ1ZRQljm2fiSFHPpH7NqbMJ/ePpYn47+JfBXYkvWh7XWZvTBGhu6jW2ziZXLWyAK8tbabHGBLaTZVovaALRxo7ty5NmaG8uDg2wJw1bx5c6EdJdNBNj9o0CCx26lTJ3r2WcMMTebLLbSqpAZYgwYNCJxYCBLYghnijBkJ3GayHrbOgC2pCeasvtqWK/HkAltp5R6UGkfwzFf96el2Tz3s1Bo6uOxNkecusFWocncqVve1RO2e2THeIFznHAlsQRNT8k8FVO1DRWu/nKgeEoLXfESXgo33sfV5slvBSaI8fzijgEmco7BpUiPBm2UFtlAeYNOl4Pl09fQGwcV3+2YopxlccLI994EtoybMHOF5EmDWLeamktxasl1s0xqwpb7L1X46i6vXUQNbziSl87QEtAS0BLQEtAS0BLQEHk0JaGCLr7sjYCuUCd/HdFsr7ox2H1Slqk8GOLxLPm+6mFfI71ClFgWp44jqopwEthyBViiUWsBWdMRtmjV0u2lGKTuOvviX9mGC3li6cOiaSH4YgC14NAQ4hRBQrS8VrfWSiNv7kyY8IGmuzp4GESSw5QgwQJnUArZi2ORm9789TDNKHAsBfcnknYfi7kSbplzqBA5lHkZgS/SbSdnnzJlDO3fuNMnbkS4DtLgGDhwodlUC9969e1Pr1gmmZLK83EqzwUqVKgnPikiXwJaVt0vWwdYZsPX1118TTCQRYCaZEiGtAVue3oPrx9UU4oAjhirtDK0sq3yuntlAB5YY4JS7wFbh6s9TkZrGc622aw/YAjAMc2AEPP94D9gLR9d9ypqVc0SW9XmyV95Zmjz/7H4VqGoHQ7vKXnmp2WUFtiCbQ8vfTnBsEV8ZGmDg9gNROkAud4EtaLCCfB2AljVk9PJlbdJspqfJtAZshZ/bQvsXvWLtttN9CW6ikAa2nIpKZ2oJaAloCWgJaAloCWgJPJIS0MAWX3ZHwBbAoa9aGES4zQaWpYZ9Stm9SUDGPrLJIpHXiMs05bIIEthyxHOFMqkFbE15ZQud2HYZh6DqTxWmKm0CBCk9vCkiONMES4saW5iYb43nzslb8gkq0+xTcR7WP5Cxb/69qUjOXaQxlW85SsQlsOWI5wqFUgvYkpNeHCN73ooELRXfAjUI3tUQnGmCPazAljgx/oOZ4ZkzZ/7P3nXAR1G87ZfeA4FA6L33KhBQiggCoqDiX6UKig17Qf1U7Iq9gyCgdBU7RUB6b1JDJ/SeEEICBEL53mc2s5nb3F0udwlJ4J38cjs7O22fbTPPvEXZ+oIB93379ulD9PDDD9PNN99MMTEx9NBDD6n02267TZFQdiYjAuPz/fr1Uym33HKLXSZQYuuFF15Q/cqbNy+NGzfOaNH/aGYjtvy9B5eOaa0kC53OGExkDm4YT3tWWvaj0pPYUnaXJt+umnZ6SzT7o591pAVKbGlJLJBQzdgjoaegcTKJLagbAndmrlSxElW7UGjNbhRUop4yio/ENVN6KHIrtcTWdsOeX658Ral8o4FUtMKNtiMMkwTMbMTWhXMnaeX4jgqT0Bq3U9kGfVTc2w88Zmrj+EJseUNKjgkCgoAgIAgIAoKAIHB9IiDEFl93T8QWbgktiVWpSQj1/dZScXPeKhErT9D4J1eo5B5DG1H9LmVVPKOIrfNnLtIH7WeqPjS5owLd9kp9Z5dp/ojttGjsDpWeFSS20FEtiZU7f3H2vGWdn/PEju34i1WR3lLJpjSInuxebWIrIT6aVoy7RfXHk9TLltkvUNRey2ufcyIeCLGlVeycGHnbf+mll6hxY0vi0Fs+HHOnihgeHq6IrKCgIIKXQWdYuHAhgfRBaNKkie1BUUtieTPgDqkqSFchwJMhvC4iBEpsQUoML8KQkBD69ttvVZ2B/mQmYiuQexAeAeEZEKFF37lMxhZOBg08GEK9DCE9iS3UryWonI4lcAwBdriWjb3RVvVzPk9WLt9/7XNjY/Ut+syyyWizhrio7QS7XggmsWV6cKzQ9FEq39jVthvyrxh3s/KomlpiSxvsh/2psP7zXWz3oV7zPZjZiC30TxuiDy7TnOp2td4HSDfD8V0z6EzUbsK1LlPvfvuQEFs2FBIRBAQBQUAQEAQEAUFAEEhEQIgtBsIbsfXXOxto3d/WxK7XZ2wAPqyEy81z+eJlGtl3MR3bfVqlP/1nBypcMp+KZxSxdXR7DH3Xd5HqQ7eX61Pj7hVc+hwfm0DD2di9v8bjYSBc3zg5c+a0Pdu5NOJhx/SKmBobW6hu/e/9KPaEZW+sautXqFTtO11awaR2xbgOytYNDphu4jOK2DLVbtzZE4L0wqqJXXkinqDOxTkRD4TYygiviL/88gvhH2HkyJFUpEgRFdc/ngzFDxs2jNauXauywUNiy5auJPLFixfp8ccfp+joaJXnq6++otDQUBUPhNgy7XbVrl2b3njjDVUnfgK5zzMTsRXIPWjajStehb3T3fyejQ8iJ/ctovBZz9pp6U1srfutD8VFblXtQWoT0ptmMKUfke58nsy8vsRNlUindz5d3nwvmcTW7iXsLXGLpdra4I7RFBTaQBdRW3hChUdUBG/EFqSaqrd5XeXTP5rgM9Wt9TEWEaM1P91lE5J1O39JMLYfaNBkFAy+1+zwvkt1+ro4CUdP6dpTLCppcs8vlL9IJZf6zp7aQ2t/7qnSnGqgQmy5QCU7goAgIAgIAoKAICAICAKMgOYnUgIj29lz8VdSyoTjmLjqoOPYuvu/zN6i9D9shlxmT36lS5XUxa/a1huxFXs8nj7tNsfuy21D6lPdjqV5FTknHWdviNOHbaIDm06q46YaIhIyitg6H5dAH9z8j+pTaJUguvejZlSkTH6243SJDm4+Rf98stkm4pBpyJxbKW9QLpUfP+unsafFt9er/bvfaUIlKhfiSVleylPQyrNy5UqCxz2EMmXK0GeffabivvwEQmydPbWXJzt3281gFb9cw/6UM09hijn6H6sSvsLSDxbxYaohokBGEVsJTFytSFS7gWe5Brd/TzDEDY+IUfsWshri+zYRh3427/0P31shiKqwdzV7nFtn2Taq2OxxCirZgCeBFQnqRykFkDb6GUwprz4OA+rZsmXTu1637iS2tm/fbtu+grH4wYMHU/Xq1VU9sbGxNGXKFJozx3qeoFbYtWtXdezEiROKuNIN3n///crIfP78+SkiIoK+/PJLOnr0qDpsqiEiIRBiC3VqI/TwzHjXXXfpLlAg93lmIrYCuQdBuC5j1V7t6KBo+ZuodJ272UthQYras4AObhxn44VIehNbZ6J20H+/JknvlK3fl0Iqt+N3Wywd2TKVnymL0NedCpTYguH3ZT+0tc+/WIWbqGLzJ9QzGHdiq7KBd/rYRt2cq8QWO5rYyQ4nEPDMQ3IqNz+3ILOPhP9C+9eNttUUc+YJYom4f211O5TRRFKeAqEET6652IMsiDMELbGFeI32bzPB15nJ8YvsmGIzwcYY3pU6VG01hErVsUginebPVvcnLYgt810OW4NVwoZQ8aq3UHb2GhvJ0qt7Vn5FF1j9HEGfn+6zEFsaCdkKAoKAICAICAKCgCAgCGgEhNhiJLwRWwAqfM5hmvqqJU2igXNuq7YsQT3fa6IIL30so4gttD/lhdW0fZFFBGAfRByM2+tQvkFR2r/BIuSQ1u/bMKrYpJg6fIDTxwxaqrOqbas+VanD4Foqbk74S5UqRV98YdnXcSngYScQYgtVHlg3lvaudq+6opvMU7AUNbpzgovaVEYRW+iTtqOj+8d6Q/aEFmkgqUA+6GBO5CL3zqOts1/Uh9S2aLlWVKez75i7FE7DHXfEFqoH0bl8+XKXlmDYHRJQOhQrVow+/PBDKlSokE6iuXPn0nfffWfvu4tUqlSJXn/9dRcpwUCIrfXr19N771nkA7ZVq1a1mw3kPs9MxBZOKJB78NShVWy0/SlbqtAGKDECdbLoQyvVXnoTW2jkwH9jaO8azyqjhUs1to2qB0psoT3Yqwqf9Yyt3og0T8GU2LKkMUE4GR4QHc8+CK2L50/b1ZlG0iHJef7MMfsYIs37zFbkmFMyLdk7hVVGE+Jj7LKepM3sDD5E0pLYQnOmNKCn5ouWv5Hq3Oq6cCLElie0JF0QEAQEAUFAEBAEBIHrFwEhtvjaT/9gI635fR95s6MVtS+OZrKk0262p2WG4NL56YZ7KlHz/1XiuYWrpMvsz8Np+eQIKlsnmAayEWZ3ISXj8Wt+3UvTP9ykijptYSFx86xD9Ovr/6njry27jbLnsPoAdUP0d+PMg+qY/kF/OwyuTTXblqQvuv9rqyP2+bIFVW5eXGdTRu1XT91LZ6LPq7SwXlXolictaQGojEF1DOFqE1toE1IJW+cMSTbpg6exEtVuo6o3vuQi+YAym6ezGhtPvmEjCLaC3IWUjMdHLPuEDm2erIq6mzDv/280T9aGq+OtH1xu273BBBdewCBtYgb0t0LTR9h+zH20dMxNNnFQve1QCq3ezc6KfoHg0hNkqBVBvSijgydiC5JiU6dOpenTp1N8fLxLNyER1rx5cxo4cKALqaUzwdA81AxNI/M4Bskt2NS65557kkmUBUJsjR8/nv7++28C8YZ49uxMOiaGQO7z9CK2MuoehPOG7XP/j2BPCtKGCHiWilfpxP8dacNfD6q0ht1/UA4SsBN3Ygut+72vSq91y4cUUqm9ikNleOloSzWuYlP2bth4gEo3f0wVQJPs0XlOHV5Nu5d+xOp2++znAoR2haYPKbtMhzZN5OcvB7V+0CLcdDl/t/BeCLXBc6f5fZpoDB51QQqzers3WPLyA0VOm8QWjsOb61Z+fk3iGunArmaHD/hezsHSpIOQpELL/gsIdgAR4PFw678vuZRNkui8Qtv4epyI+NelPyC4Qiq2ZZXRd2n9HwNstc2Qiu2pVscPVb3+/uCa4dq5c95hqxwWKEHNe82wm/CUrjOANMV5aElbnZ6NJbfK1u9DFZo9kuxdLsSWRkm2goAgIAgIAoKAICAICAIaASG2NBI+bq+wumT0wbM8wbnAE4iCtnqej8Wverb40wmspneOLl64lKy/ly9doaPbYngimpcKFc971fsWaIMgeuKitrEkWhR7Gqvrk3peoG0GUh5e0mBk+9LF+GT9hRoRbCHlK1xe/QfSztUq64nY0u1DzTgqKnfjqV8AAEAASURBVIoiIyMJZFdwcDCVKFFCkUg6j6ctyh46dIhOnz5NFSpUoIIFrcm+p/z+poNgg4pkt27dqE+flL2z+dqON2LL1zrSI19a3IOQBAJppAmYg+vH0Z5VFtEa9sAiRfakR9891QnSDSp+2bLnVFlAFIEUgtfRFn2T1Mg9lU9NOlTmQdglnI9h9ez6jEGSxKG3emCA/xyrB2bPlc/FKyLKQGIr5ug6fg/XU+fhrR7nMZCMZ07u4ndgJMHeFt4fZjh9dL26VgWL10lGEJn5MjoOlddY4Bp/Stnbyle4HHfJdaFI91GILY2EbAUBQUAQEAQEAUFAEBAENAJCbGkkZCsICAI+I2AaXb/99tupd+/ePpfNLBm3bt1KQ4cOVd2BCiSIt7QKmZXYSu35HQmfShErP1fFmrKRb0hFuYYr7LCho5K4gZQNJBXTM0BSE0bXQWQ1u++vZE3BLtPKiV1UeiEmuyFBdvFCLKsvjk2W11tCviLlqGTNHt6yZLljJyJmU9zxbanqd+m6Pd1c81RVkeaZhdhKc0ilQkFAEBAEBAFBQBAQBLI8AkJsZflLKCcgCFxdBHbv3k1Lly6ladOmqYZNI/BXtyeBtfbaa68RjN23bt3aNiAfWI1JpTWxVblyZYJ3SgR4EIUqZlYKcZHbaN1vFmmZt1AZqtv1a+XBD+cAya1N0x+1VWzL1OtFlVs+k66nZ9plCql0M1Vr85otOQZVX0hrXbwQp/pQv9tIgr0tp8F5XzoIg+039JruS9Ysk8f03uhrp02bab6WSa98sNEHSU44k9CE9PDhwwn2+iQIAoKAICAICAKCgCAgCFzfCAixdX1ffzl7QSDVCMDOlRlgEys0NNRMyvTx8PBwRTjBttbIkSNdjNGnRec1sWXWBVVHqDxmtaBV+3S/YVcKQdvaQlx59eszi9XekryrIj2tA2wxwTOqaRgd9qouXjhr26hDm8FlWygvhLp9qAJCjdDXkCMnvMBefQ+9vvbPn3zAzGnLKqV68rKEXvaceVLKdlWO9+3bN5nNPiG2rgr00oggIAgIAoKAICAICAKZHgEhtjL9JZIOCgKZCwFMMKGKGBYWRp06dXLxJJi5euq5N2+//TZt2rSJnnrqKWrVqpXnjH4egWojvDyaoX///tSli6UmZ6Zn/vgVilj+Gdvj+8OFzFL9ZmPlodW6ssOGl9lxRe6rciqwSbV9/lClkmgackfj2ZmQqtRsMJWud+9V6Ys0cvUQeOihhygmJsnTI1oeMWIEFS1a9Op1QloSBAQBQUAQEAQEAUFAEMiUCAixlSkvi3RKEBAEBIHMhwC8A8bHHFAdK1CsOuXOH5JhnYTB8bNskD0+9jDlylOYjadXoxy5C2RYf6RhQUAQEAQEAUFAEBAEBAFBQBDIGASE2MoY3KVVQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEgQAREGIrQACluCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgkDGICDEVsbgLq0KAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAIBIiAEFsBAijFBQFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBDIGASG2MgZ3aVUQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUEgQASE2AoQQCkuCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgkDEICLGVMbhLq4KAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAIBIiDEVoAASnFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBASBjEFAiK2MwV1aFQQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEAgQASG2AgRQigsCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAhkDAJCbGUM7tKqICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQIAICLEVIIBSXBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQSBjEBBiK2Nwl1YFAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEAkRAiK0AAZTigoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAhmDgBBbGYO7tCoICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAQIAJCbAUIoBQXBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQyBgEhNjKGNylVUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBIEAERBiK0AApbggIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoJAxiAgxFbG4C6tCgKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCASIgBBbAQIoxQUBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQyBgEhtjIGd2lVEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBIEAEhNgKEEApLggIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIJAxCAixlTG4S6uCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCASIgxFaAAEpxQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEgYxBQIitjMFdWhUEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAIEAEhtgIEUIoLAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAIZAwCQmxlDO7SqiAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgkCACAixFSCAUlwQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUEgYxAQYitjcJdWBQFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAJEQIitAAGU4oKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAIZg4AQWxmDu7QqCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgECACQmwFCKAUFwQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEMgYBITYyhjcpVVBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBASBABEQYitAAKW4ICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCQMYgIMRWxuAurQoCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgEiIAQWwECKMUFAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEMgYBIbYyBndpVRAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQSBABK57Yiv27AXati9SwdikZinKni1bgJBmzuLb90XR6bPnqXRIISpTvFDm7OQ12KsLCZdo467jdIX/6lUpQXlz50yTs0xISKAPP/yQzp07Ry+++CIFBQWlSb1nTp6nfetOUuyJc5SnYC6qe0tpypknh1X3FaL9G05S5L44uhh/icrVL0qlahVOk3YzTyVX6Piuf4iuXKH8RatQwWI1Mk/XfOiJp+t3dHsMnTudQEVK56fgMvl9qClzZzlzciedidpJxK/r4lVupWzZsmfuDpu98/Acxccm0JFtMSpnxSbFKFt261t0+eJl9UziQKkahSlvUC6ztmsufuFsJJ06tEqdV5GyLSh3vqJuzzE+9jAdCZ9KZ6N3U3zcEcqRKz/lCypPhUrUoVK172L8fHvXXr50gU4fXc9trqaYI//R5csJVDi0ARUu04yKlGpCOXIXcNu+TjyxezZduXyR78XsVKLqrTrZ4xbnhnPMU7AkFS7V2GM+OeA/AlcuX6ITEbPVe7xA0apUoFh1t5Wl1T3EDVFc5HY6dXAV30Nr6fyZY9xmDSrC91BwmRsod4ESbtvXiacOr6YLZ06o3eByLSlX3mB9yO32zMld/P7bQdlz5KaQyh3c5pHEjEPgyuUrtJ/HUZd5a76zL164TAd4DIVQtl4wX+fEsZWPXfX0jTgbfYGO7TrN90M2qtC4mF2bp37YGTJ55PLF8xS5Z67qJd7nxat0zOQ9znzdw3sC74tceYtQcLkwu4PnTh+g2GOb+LuVjb9bne10XyPRB1dQwrmTycbJsSfC6dypfcnau8B5T3EZhOL8ncxSYzZfQQkw34EDB+j48eNUsGBBqlEjae4RHR1NERERlD17dmrUqFGArVz7xa97YuvvJTvojTGL1JVeMXIA5cqZhSZIqbg/Oz49kaJOn6NHuzehB2+/fh6M+AsXKeLwKcx/qUb5YvxiuLrE5ZqtR+jhj6arK/XXsP+lGak4duxYmjlzJtWrV49ee+21VNwJnrNiIDb2kaUuGZ75qwMFheZTaZOfW0U7lhyzjzftUYG6vlTf3r9akQQm1U5ExPKHMRuVrBFkEwBp0T4mnCsnWJPTwjyprd/tu7SoVtWBgcCVK5eoUEgtyuVhsh5IY96u38e3zqYz0eep3aAadNNA95O8QNp2VxZk2uVLV6hY+QKKJHWXx9+0zTOfpOgDy1TxZvf+wWRPWX+rcimHie7ZU3soZ+6CfN83cDmWVjuenqP10w7Qn2+vV828uqQrEzXWt+jkgTP01d3zVHr/b8OoApNeOoDIjDl6jsmfnBRSqaBOztLbPSu+pIMbx6lzqNXhg2QT90sXzlD4rGcUCeXpRLPnzEuVmz9Fper09JRFpe9d/S0dWDfGa57ilTtSjfZv83vG/SR08cimdvkKTQZRef73FpZ835KJsAQqULQaNb57sresWf5Yer6rvYFz+thG2vDnAJWlRNUufP3ecsmelvfQyX2LaMucIeqaujRi7OQrXIHq3z7KI0m75qe76FzMPlWiUPE61LDHj0bp5FGcG84RZOqND1kkcPJckpJRCHh6Zx/bcZpG9FmouvXQ2BupdO0iqeqip2+Emf7a0q6UPXEe46kfaDSjns3UnPChjZMoYsWndpF6tw3nxblm9r5EUkZg3W99mHTfSrnzF6PmvWfZBXYv/ZAOh/+s9m8ctMZO9zWy5Pvm/M67RPg+1uzwnl3MU3vmt7bVgKV8j+axy8Sd2EIX4qMpb6HSlL9IJTv9eou8//77tG7dOipTpgx99tln9unPmDGDfvjhB7U/adIkypnTt0U7u4LrLCLElkFsrRw1gHLmsCYT19p9cL0SWzsPnKR7h/6mLuf41+6g2pWKX9VLmx7E1p49e2jIkCHqPPDyw0swLcLEp1fSruXHVVWlaxXhyX12uvfjG1gKIhcd3BRNox9coo4VCM5DJasHUeVmxSmsT5W0aDpVdRzbyYPD3omDwzE8OKyTusGht8bSk9haPOoGXti/TCVrdqdqN73qrRt+HfN2/TKC2Hq/3UyWTLlIHZ+sQy17VfbrnDwVSi9ia/eyj+nw5imqWX8Ge576q9O9PUf+TE5WT91LMz7iVVcOQ1d2081k6a03YguSUWt/uYdJgP32OeYpWEoRBhfPn2bJrcNqsK0P1ur4IYVUbK937e2lhLO0ecZgixywU0lJ1mTPnktJgOFZ1QFtNLxjtFvJG5PYAtGgiFYeoHsK1xOxlZ7vak/4It0bsZVW9xDaiVj+KR3aNAlRO+TME6Tux3OnD/EtlGCnQ7qqTucv3E7MTWILBaq3fYNCq99ml3VGhNhyIpK59j0RSmlJbJkElj/fjox6NlNzpVZN6kbnWRpXhyIs/Viv67d6V7Y+IOCJaMpQYmvgMiVtqru/9ue7eUFxryK2mt33l06+7rZCbKXNJb/uia2Nu47R9OW7FJov9mpJOVjU71oMX/2ymuLiL1CbhhUorF7aSDdkBZyuRWJr6NChtHXrVmrRogU9++yzaXIZ4iLj6ZOuc1Rd7qR6Zn60mVZN3aOOvzy/M6/+ZNyKQXoOyLIqsZXS9fv36608QEygGm1KUtWW3tVi0uSG4kqE2EqOpLfnCCoqG2ceVIU6v1BPqZVgx9MkCceuN2Jr7yqWsFpvSVjlKRBKDbqPpTyGmhcIK5CTx7Zbg2NIWbUasIT5Jlf1TT2QBobIA4mskIrt7HxXmNSCStmWWc8R6kSAqmPLfvNVfpWQ+ONCbHFa/uDK1KSntRJu5tNxIbY0Eum39UZspdU9FLHsEzq0OUnirlyjAVS2QR9eEEoy9YDJ2ra5ryi1QX22uDdwj5jBSWzhfm3Rd7ZLXWZ+IbZMNDJfHJK0C0ZuVx0L612Fgsta6syBEluevhGeiC1P/UDH0nMclRZX5Gx0hFrEcKmLFw7C+i9Q72KXdNnxiADedzFH1ih17KqtX7LzBUpshc98ii5eiKXQGt14sbaHXa8nIi1yzzw6tHGCyle/20j+jibNIfT3GBJbQmyJxJZ9M/kZue6JLT9xk2JZBIFrjdg6duwYPfHEEwp9EFx16tRJkysRfegsfXmnZcug79ctqVKzEJd6/3p7A62btp/K1StKA75v5XLsau+k54AsqxJbKV2/q32N0J4QW8lR9+c5EmKrgw3kul97UVyUNWFs3me2R9WuTdMeoVOHLfUKrPBjpV+HqH0LFWGFfZBVDXuMY/WHivqwyzaB1SPW/dpb2UzCgfKNB1GFpq6qhk5iC/kqt3yOytS7D9FkQYitZJCkeYI3Yist7iHcFyvGd1ISuDxDo5o3v6NUctyfyBW+354n3HcI7lQNncQW8nmTThFiCwhlvRAoseXpjD0RW57yIz09x1He2vX12La5/0cndluqc1Vbv0K7lljqbhWbPUYgkSUEhkCgxJan1j0RW57yI12ILQsdkdjydpf4fuy6J7YiT51l4/FRSgWxRd0kla64cxdo/Q7LnlBY/bJ0/sIlCo84QZsijtMZPla3cglqVJ2Nvxa09IRPxcXTxp3HaAMbCs/BdpzKlgiito0rUFAB67i7SwLSZd2Oo3Q8+gydY1tQhTlv1TLB1IIlqvLncV1hNsufjU9QBu837T5Op89coPpVS/B/KLeVm5ZvOsQG8Ima1ipNuXMl2QT5b/tRQrlyoUFUoaRl8PsyG8hettGSEGhYPVS1uftQNBs7P0a7D0dTSOH8yuB5M67LW/CnP97q83YsOjaeFq/fTwePn6azFxKoaKF8VJHPp1X9cpTHMMx+NCqOdh2MpsORsTRs4jJV5ZN3N6MqZYqqawZD7gj7jsbQgWOnXdLUAeNn9dbD6vpXK1eUQotaK2/GYbp0+TLtOhCtrglww7XD/dG8ThnawPeE08bW1r2RFBVzTlWBe86T+uv6nUcp7mwCFS+Sn2pUsOzqjB49mmbNmkX58+cn2NmCnSlngF2j42xI9Cir7GEL1cFQVh2EEdMCRV3vR0j6HGFbSHGR5+mv9zaoqto/UlOpGuZl4/Go68K5i7R8YgTtWRup7CV1etoi08rUDuaV59x285F74ihi9QmeUFrnVjAkD0GlsUKjojz2T95PXRDqaofCTynD2bADVbZuMJVrUJQNt7v2FbaEju8+reqf8bGlftXhsVps+LIQq0vmVsZYUeelS5do5cqVqvo8efJQkyZNdFMpbr0RWzCUGRlh2ToqWftOupxwjqIPLucJy2IlLl8guCqFVOngYhAaK1ondlqDs11LP1Dtw5BxqZp3MibZKZTVEgMxopnS9QOOCPv+i1LXsSivHBerkGSLCSvA8SzJVbxSIWVYHnY3Dm2Opr1ro6huxzJJdpvY4PleruPwllOs5nOObYTl5AlaHqWOatp2Sjh3ifNFqjYnPWvZfqnfuaxyQoB7tUqL4va9sGHDBjpz5ozK27hxY8qbN6+K+/LjTRUxcu88SjhzkoJK1lerlDCeimsECZwcbHspqGRDNi5+t8uqL+yfxccc5Odlhq2eVrWVtbpZsEQtNRn1pV+e8mj8vT1H+lrCPkqV5kkq0+6ILdgvi+Vnd8fiY7Tm932q2fs/tcibYuUKUlG2a+YMkXvj1DN2dEcMP1t5qULDospGnbbHYubHeyOGr3NQiXwUWi2IVaquqInQnjWRVLRcAapxU8mAnjOzLWfcmyri0tFh/E66wPdQDmr9oPWMO8tj/+T+JRT+z9PqUNn6valSCyuOhBXjOyqjt4jDhh5s6XkLF84cp5UTu6gskKRp2W+uy72jiS2QY2ehIsnSXujfDfdPZ8lW1wUCVJKexBaMSG9ffJTfS2eVGnABfoeG8PNerVWJJCcgbk42Nd8MFMf9sHvFCbYZeIXKNyzGUnM5WT30gnpH4J3S7O6KyskIvgXp9a52cxp2kjdiKy3uoW3/vsx2Hi0J5zJ176PKYc/ZbXuKrBh3C9s1ilaHIbFgOg7QxBZsw0Hi68LZEypf7U6f8Pu6TbIq05vYwrto51IeXx6PZxWhC+r6FmGnI9XCSvC70PN7GsbRj/H75SjbksL7Jl/h3MpZSfXWJbzaWfS3HIBJzXtNAenjt8wJemrGN5cSLlPEKusaYjwDHBCcxBbsT6qxD7/PIV1VsnphNU7DN9XdmMnTN8ITseWuH76Mo3avPEFwWpKnQC5+vq0xhBMPvDN2r7BMV4RWDVK2WAMZe5n1w3bT0jGtlSovxkuN7pxAS0e3Uvu58xdnW1Ezzexu4xjL4bsOhyDnY49QnkKllH3TkrV6MLZJ0kLOwv6Wg4oz7H6eOrSG7Znu4uekNhWr2FY5M2G5YGczah/SwEe2/EpxJ7by+CxSOY3IV7gclax1p1LLc1uIHVXAwREcVUBNM3vO/JSXHZEUr9bJrV1QnP/ZkxGsRh+i+qPrdBJbkC6N3r9MOVKBLdjCpZuwpGGLZNKlujycplyMP00Fi9fkc6yrk8kTsQVj9acOrKRsOXPxuOMOlf/Yjr95HM0Oq9YO53djjPquVrrhSXWsWMWbKOF8DJ0+Ys1LgsuHecQEzl/ORO2i7LnyelXhtjt5lSJRUVG0bNkyOnHiBMXExFCBAgWoVKlS1LJlSwoJST42EGIrbS7MdU9smcbjTRtbpm2kqe/cTY9/MpOOMQFlhtDgAjTpzR60bW8UPf5p8hdtgby56IunOykCzCwHT4yvjJhHyzZbpJJ5DHGUGzXkNpvMMI/PXrmbXv5uvpmk4ijz6RMdbRLlh/+7XZFSOqM7G1sXLl6iloPGqizfPNuZfp4XTguZNHKGW5pVojcGtnHr0c/f/jjb8GV/3MyN9MUv1mTZmV+ff9NapdShvxbvoDfHLnJmU/vVmaCa/OadKj7qz3U04s+1VCwoH83+vJfb/F2em6yu/SA2uv8wG983A4jRl/la/scEpTOAQOzZrhZ9PHmFOqSNx0+ctYk+/cmalH31zK1uVUNxj7QdPE6V69WxLj17bwsVHzhwIMXGxlKbNm3o8ccfdzZJkNz57bX/6GC4NYB2Zuj6Yn1qemcF+zu7beFR+unF1c5sar9snWA6z6TTiT2xbo/3/rKFmoBf4sHs72+so/C5h93mw+CtHxu9djcgxoT/l1fWKsPmzsL1mFjpPrShbQh13V/76a93rY+cM29olSB6ZFIblQxPkf369bOzTJw4kXLl8kwU2xk54o3YAumxfd7rKnujuybShj8eUJNsszziprHi2OObaf0f/Z1Z7P3mvf9xO/m1M6QQSen6DeTBIYInG1tf95xHUfvPUBdWfSsUkpd+GpJ0L2jJPRBZE55a6fE+qN2+NN31diN1naLYY+bX98z32Oshc261vfoNHjxYeYBBZtiMSw0B6Y3Y0sQFjJpmy5GTju+ckaw/kNSpf9sIHpTVVsf++/V+F3UhswA8CdXt/KWZlOr4t/cu8Iiffo48TU7cEVt43rTaorMzYb2q0C1PWueFYyArZ38ebhNgZn6oFPf+vLkiks30mR+z6vEvewjXtt3DNWj8E8vVJBd52j5Yg9o8VF15ZPX3OTPbcsa9EVurJna1pafq3Po5E3jW/e2sA5OMExH/quSg0Pr2gBiD/I1/D1LpqVF7MO+3yi2fZWms++0mNbFVpHRTlrJpTntXf6OOYbDfsPsPdj4dSS9ia9n43TTn6y26GZctrvN9HzWjik2TD6ZT+81AxXFR5+mTLrNVG49MaEObZh2ipeN32W3exk5Fpn2w0d43I2n1rjbrdMa9EVuB3kNoS9tL5BkyE53zmIxKWixw9kXvw1AzJpMITmksk9hqcPv3PDnsrfKB6GrBkol4X5kh3YitK0Szv9hCyyfvNptzid/xWkNqeFs5lzTsgPiZ8uIq9T1xHsT91+HxWorwdB7zt5w/77XUfMt0P/0Z37h7Z6M+k9jCWGzBqO1uxz412WRA96GNFKGo+4Gtp2+Ep3R3/fBlHDXpmVW0c5m1sG9+s82+RDD5Nf5Ja2x774fNlJmDQMZeZt0gPHYseFMlVWvzmiJCts5+kUlMa2GxEUvZ6m+3WU7HD22azEbnP7MkKnVi4ha27qq1ed2tB1t/y0FtcuPfD9vEtdkknl2MF/MFuT4zx3ZMox0L33LbR5QPrXE7Ved+mgHtYDyp1ePNY4hjcaXRXZNc7Fd5IppMYgsY71z4trM6tQ9PlDXav5NsATYg4/GJNrZ0He4artLqRfXd1gtURcvfRHVuTXIkYJZZMe5mmxgLe8D9vM/Mn95xLPh8++23tHDhQo9N9e/fn7p06eJyXIgtFzj83hFiy4PxeJPYAmlyhqWdalUIUWTTdpbw2rrPkkoAeQGpH4SwumWpdPFCBImc8D3Wag0Ik38+vd/2xsf3Oz328QxaxVJACKgbElElixVU0j263rJcz5/sRc8MP8/dYksfIf0GLlc8OD+hr07SLbXEFvoJr4nYNqlZiiXBztOK8EN2888xsXI/EyxmCKQ/Zj2+xP9dzQbTh1uqcsBMS8PhWpik0oyP71NSVcs2HqCPp7AUxvmLNjYgIvPmyclSW8H00eOWeksgxBakru5jw/TADQHY3VC7NJ1mUmo9E124Z8ygia2TnP8W9lKJ0DWsKr31YFsVN39msN2310YtUElTmISDtBjY/0cffVSl3XfffdSjRw+ziJKoGDNoqVqpxwEQSuXqFqXYqHjCCpwOTXtUZG+G9dQuBidYUY+PvWgPsCDhlbdQTipZrbByVw0pKZAfOqBehDtebagmxDOGbaLVv+1VacGl8yuyC9I5EWtO2OXMyYzKyD9b5h5hUmuN2sXAtzKrP2K7lyXDsFKMYJImu5Ydp38+26wm6vp4UIm8ymU2JI7+x4MrhEAGV74SW5DcgGFgDFrgkS/+9EGXwQaMBBct14qwSvXfVEsl6fJF65wwGcLgCuEGNpQZiIfElK7f3e9ZRGxKxBakqpxEyUMwzM9em76+xyK/0F+oopauWZjJhYu0ZcER+15reV8V6vh0bZb8i6cfHl2GrPa1xzUtxNJ7CPAElYclARHSm9iC1AxWfhFg/DtHrnyEgaEOeQuVYXsOf6pdEBcgPSANxIVUGiaVCMUrd1DGnNWOnz8/v7RGSRt6e45SMzn596uttG3REZbaOm9fA/1cNupWnlr1rap6ihX70Wxj6ghLUSDgOcQ1xao9pCt1eOC7Vi4r85rYgoMIuJGHJKUO2hlAIM+Zrsvd1huxBWIZBLMOJdhtOEgmb5McnRdb08uWk6Ay8znjJiFWolpXqtHOmnQhn0ls1WOydM2UHuq5xzF3RsDTg9jawosKWCBAwPOGiXE+fs5w3fezBJUOppdbpEEdKbXfDJQzia2GXcrR+hkHkGyH+9jpyOwvw9P1XW035ibijdgK9B6C59TVk29XrToJKjddsZNgt23J97xAxVvYh7uh13T7mElswSbczkXv0dFtv6njIZVuplq3DLPzIpJexNai0TtofqJtKLRTsVExJgCC6OTBM7ZjGaQ/+/ctLKVhvR+xD6mj7wcsRlQFvIvK8MLYkW0xLoR+jzcaEb41Ovhbzq/3Go+9U/Mt0330Z3zjjlBCfSaxpevXYx/sR6yOdHmfPzy+jRrf6LyevhGe0t31w5dxFCSBJz+/SjWLcV7Dbq6kDA789Q6bp/h7v3rfvDi7E39fswc09tLniC0chOBbjW+4tpEIqZwNfz2oshVje4i1O35kFrHjpgMYJMKZA563s6ci7PEA0p1Sk/6Wg0e/dVi8TBw3QKIMdh/h5fTihTg0xYt+eZUHXE1uQZ0eKtE6IB0SZefjjro4RqnS8nkqXe/exGxXWIrtRpaks8aRufIGswZGFW7jjKWen9g+pKzqdvlaV+1RgsoktnRmjEvzFanIEs3RfB8mjQ/wfQWZaAZNSvnlFTGR2MIiA6Sy9Dmhfj3uqnbTK2qReNnYm9TY2roXlql7wuzHmZM77TF26Tr/oyqtXjAPZ0hce63XjcPBV4UKFejIkSME5186fPPNN1S8eHG9S0Js2VAEFBFiywdiCwi//3A76ti8igL7Movhg2SZt3av2sfPj6/ertTPEAd59RZLC/3FdSP88cE9VK5EkIrvOXyK7n51qoo/1K0RDeremFUHk8RUh01YxpJT1qrr7M96UbHC+VReqDre/OQEFQfp9fVzne060d7XU1fTDzM3qOP4SS2xhTJ33FidXuzF4p6JKn1Q5+v6whQcUuqLv73XU8XxE2h/7Ip8jLzCUmqzWFoNpBYIv+BCSYMqXIcXvrFW54cOuIlub13drjUlG1uBEFufMnE2cfZm1VafTvXoqXua86qG1TRUWV8e7iqVp4kt5NDng/ji4f2SqZ4+/cUsWrzhgCJTJwztjmw0f/58Gj58uIo/88wzSpxV7ST+jHtsuVIXxG6np+pQ83sr8UfA6hAkaX4assYeZCrSwvAmaNpo6vdNy2Qr+9o2UHlWbXtgZCu7WaikfHDzP2owBvUpqEPZqk18X85iSZEVUywy4Ylf2tsqUiBGvrprnpowgyzp+X4TW6ILg9Z5w7fRsom7VTt3vd1YqcXpRlOyDZGQkEAvv/yyUpNBmWHDhvnsHtdXYgv1lql7P6ugPMMxC2O12rf8Exxitb6mhEmuGfQqf3p4RUzp+qVEbOl+YkJ8Y/9qSl0R6kUnImLp2/sWqMNOpwJQIZ3w5Eo6sOmkUnd9/p+Ouhq1TcnG1ldffUV79+5VeQcNGkQ1atRwKe9tx5SgUV7omFzUQUtsYR+DWkhAaGPNEHdf+0tPWxUNhkohuaODObhND6+Inp4jtJ+ayYnu7+qp3r0iQvURBAOCc4ICdUZMXkASg5B+8reb9a1MmtjS7YA4vvWZOkotUaszB/Kc6Xrdbb0RW1DthT2OC2ejXIpiQJ4/uIqShCletSOrWrq/l7bPH8oSfNNVWV/UEHUjl3jysOyHNmq3YEgtVo0Zrw8lI7ag0oGJGSY67oyApwex9eur/9HmOYfUJPMpvo6mivjW+UcIxCqC8x7w95thElsaiDYDq1OdDqUJ6s452KMuQnq+q3W77rbeiK1A7yFIjUB6BMFXNUTdR62OiEmaqUrrJLYgcYj3GDx9ItS7bbiLN8V0Ibb4e/1+e8uTbaUmIXQff8tz5c2hu07hcw7T1Fct8vSe95tSrfaWdDwyjOUFNU2g3vlmY6p3axm7nHkPYNHs+Zn8nUgcJ/lbzp/3mj/fMn/HN+4IJQDiJLbq3FyaQPbp5+Xi+Uv0x5vrbel3vYigwfT0jfCU7qkfqi9ManvyLg01w0/ZmRAWNEBu9hsRprugtlAd/ajTLDXua9WnKnUYXEulp8U3AeTOqkm3qfqc0tKuJEdypyBq/Aa1cfXuzaEIniKlm6m6QCzvWz2cnY+MVfv4RkCSCsHfcii7evIdvDDMQgC8YFm748c8droJySocXP8j7Vn1lYpDcrjBHWNUPGL5Z+xN1Wob0lBYoNFBSfn/OUCdA9QwG7MEFoJJ7DnJJLwn8A6BqrPz3eKLxBbqd45Lz5zcpQh0LR3mlJBOC2IL7SJ4s7HlDSuU3bnoHV4E+ANRVv+fxouYJVXc2098fDxdvHjRW5Zkx/Lly0c5ciS9D5NlSEyAtNb999+vTDWUK1eO3n33XRczG3PnzqXvvvtO5YagQrt27eyqhNiyoQgoIsSWD8QWSIun/9fcBeglTDo8xeQDwuC7mtEDXRu4HIedqgfe+1uljRrSlRrXsAYBs1bspldGzlfp87/qk8wGFySNnvjcqnfS0B62OuKYaevpm9+swalJopmN9n37T1tSLLXEFqSZQMCZdrlQ97s/LqHfFm5TzawdY62WYCfQ/qgKU/Hz8IfTac22I4rYgsqgJt90Fa9/v4CiTzP517QSdb8paVKTXsRWTNx5av+kNblpzLbWRr1kfYh1f7A9z3bTur34ky3RZRJbsNv1yEeW5IFJmqKcSRr+X9/WdGfbmkimcePG0bRp01QcL8AqVaqoOH72sU2kHx5bpvYb316euv2f6/2IA+bADp7xerEKkg4pESOeJuRQB3ivjXUe1cJC6f7PbtBVqi0mQH+8uU7FQYyUYXsTCEt+3EVzv92q4k//AbtUFoGrEvgHA0pMuvaui0o2sDIHyk6CTpf3d+srsVWgaFVegbNI36S2eEWN7UBA6seUBtLHMzux1eLeytSJyQszmPdVR1Zva8lqbmaAxBgISBCoPVk6DKvPOqREbOl8/mx9JbZAasGmlhn2rx3Fz4s1sICKIQbPOlxLxJb5bN5wdyXq/EJdfZr2dg9LCIwbvFzt92d14QpNiqm4SWzBTt4D34UxSZjyoM6uOICIN2IL1WIQv3vpx/w+m8XvCUsiz9kcVn1hWwtGhrV0JPLYhADHoeKVGmlJPZDPlbcwe6ybazfplNjCgT0rv6KDG35UeYJZPbFu12/s/OlBbP34yDL1rsTzB+LAJCTQMFRXYcOnNk+kG99RXvXFfLZT+81wEluQlgUp7gzp+a52tmXueyO2kC+Qe+jAujGsbvqtas6bkwCzPzpuqjy37D/f9nroJLaQ35QSBEHfos8sRZTimH0f82T6xocsyRqkBxIgxfn5HdYCIRaoqrUKdakOCxl4pyN0frYu3fC/SioOcwJaShfkZlv+zjvD/BHbadHYHSoZqquw2+dvOX/fa+b97uu3zGwrNeMbT4SSSWxhMWHw1Pa291uNGUilr++eR9GHz1rSULNYGiqRKPZEYHlK99QPtJXSs7lw1A5a8P121a2n/+QxWsmkMdrOpcdI29B8bFJbKl6lkO5+wNvdSz5kW54/q3ogeQQJJB12LX6Pjmy1JBmhqla6zj36kNrCAylsPyG4k5blkSWtGMc2FmHrjp+d1g+uUCp2/paDp7+tc15U7VVq/pTyiqp2jJ+tc4awmq71vWjRdw6/m4Np27/czwirn07CCEVB1kQfWM7aAGWUZBnSYFdr+7xXEVWqmVAfNAPUN/etHqHOCzbJ8J1C8IXY8qQ2D/ukeGchQKqs6b2/qzh+9PfQSbJ5ag/vTLw7EVolSmypHf7xRmxdYNu2K5nkRzDJQZXAP8t/aKsk41LyRqzzY/vmm29SeLi14Geme4s/99xz1Lx50pzJU17TwdfTTz9NYWFhLllhV/aBBx5QaT179iT86yDElkYisK0QWz4QW+Nf7061K4a4IL2F1Q37vGWxxCNf7KrU98wM+4/FUI+Xf1FJ5nFIV128dFml52IjwTrAAPnuQ6foM5YC0mqKJrH1KJMgSDftQ+myemvaC0stsdW/cwN6oqe1sqHrw9ZUiVs6or9NKAXaH7MNX+KTWDLqE8YGARJr/bs0UMbZS4d4/6CmF7EFo/AD3reISycxZZ7P6L/X0be/W6ucJrEFw/09XvqZDp6IpRsblKPPn+pkFzPtg5nkJyRcFi9erPJB1BWGCHUwVzCf/LW97V5aH9fbqayuAltYmAC9PL+zTla2ubRXxNRIbKGCyc+toh1Ljqm6sNILw8Gw5ZIvyFI5sxsxIrDrBftQamX4E1cyTGeDLYiZn1oScc/NgBRGHnUopQGZLu/P1ldiC156SrEBeWdY/3s/ij0RrgYvGMSYIbMTWy/808lF0gN9h/Tchx2tlVnsN7mjAkvPlVaG+lMiOjKa2ILdm5b9F6DbLsEcrFVvO5SNjXazj19LxBYksr7ru0idW9+vWtrOFeyT5QhWFz/hlXlMWm/oyeTX8xb5ZRJbIMBBhF+tkBKxpftx+eJ5VnddSFER81m1cj2fwwl9yN5icN/03j9sAgH2tUAYIDil9exCHiL6+XUaL3ZHbGECtXJCF7tPtTt9aq/kpwextWJSBM36whqoY8Lcul9VqnxDceUQwsPpKIcgWpovtd8Mk9gC8fnQDze6bSY939VuG0xMTInY0mX9uYcObhjHxKVlc69yi2eoTP1euroUt3oCh4ytBixl6Wbrm+aO2EIec8INg8t6MpsexBbaw/seIQfGptlUVP2A9Fo+YTetZLt7CCaxZY49nASIysw/kPCJPX5O7RYulV+ROf6W8/e95u+3zJ/xjSdCySS2Oj/H5OA9FjmocdLbtb/to2nDLBt1j09pZztx8URgeUr31A+0k9KzeYqJtS96WISMkwjUNh69Pfv6XFK71VJZMPPgtJd0jp1zrPnJGnc5iRa0AxVhqApjMaPVwKWcYtzEiR3BGA//lpRvZZXqbzmThIO9VP08JzalNrHHw2nzjMEqrsmvkweWUvjMp+xsIIdK1uquHJlA4spdwLtqKZtygDQaAkieUnXu5u9KW2USw10ZpHkimkxVRCeBaNal1UJBBJok+tUittAX2BWDJBv6EMZjOm1z0LRhq22xmX33FP/kk09ozZo1ng67TX/++ed9tgMLyUWEnDlzMnGadA+C9JoyZQotXYp7kxSpJcSWgiJNf4TY8oHYMgkJjf42trHV602L2DIJKH0cXvvuYOICwSS29PEjrOa3fNNB5WUR5Iu2raWPY2vWqw2Ye7LJhPzwktj/3b8QTbUq4qv9WlOPNpZkkKog8Wcm23p6ddQCtWcSW4H2x2zDlzjsUj39xWxbIk2XgV2rMPYi2bp+eWV3y+lhML2ILZPw++Xtu6gy2+1yFxZv2K/6jWPO+2j8P5vo859XqmJzWAqtKJ8LAhwVwL6Z81q//fbbtGnTJiUOO3nyZJVX/0x7byOt/XOf2h26gifqSe9SnUVtTUkprOxrtSJ/JbZQ6cFN0TTlhdUudniQDjsbWOWs1bYUlWfPiGb4kgdMWJH0NTzCtibg2REhpQGZr3W6y+crsdXgjtFuPdDoDzBW5bISsQWCse+3Ld1BQv/9sY/+ft8aZJsZoEJatWVxpSYKT3nOkNHElnupOmLJlV1sk+Fe1d1rmdjaPPsQ/cqOJHwNkLjRdupMYuu1pV2T1It9rSyAfL4SW84mYPMuau9COrr1dybqrfcq8pgrueZg3t1KubNOve/6Xmhsr6LjuHtii+1QRW6zjYBjIA4JMUiSpQexBWksSFAc3npKd1ltofoFUhJe6XB9bTVxPhrIN8Mktto/XJNuHFDNpV29k57vat2Gu62vxJazrC/30KlDq2jT9MdUUaf6jrM+576etOM+gO0gHTwRW5jMrhh/i22/URvNTi9iC/05z15yd7I9ywMbo9m7IXs4ZJU1EN9mMIktbYPKuVhm5ncX97dcIO81f75l/oxvPBFKJrH1wAi2a+gYF2mc0OboB637w5Se80RgeUr31A+048uzOfHplcq2GlTRH5vSVnXPlGK7nTUDGrGGQFqFqH2LaMusZ63qmMTIy7YxnUGp/SUmNv3fb+xxMql97fHUfOc7y7vb97ec/Ry6q9RNGiTMIGmGhY91v/Xlb8RW11yJ51y0wo1UrmF/XoB2FajYs+JzOrhxgmsZ3sN4EyqXZRv0TmZv0hdiyylBZTYAI/6QBkMwnR1dTWLLvC9M25jaXiLIQCwUePN2aZ7T1YjHxcXRihUraPPmzcqu1vHjx5V6otm2SGyZaKRdXIitq0xsQVLn40nL6Sc2BO8M9auUULajtGdCk9hqMuB7lf1u9rL3cp9WzqJqfwsbrO/D6ogIqZXYevvBttSFDZk7gydiK9D+ONvxZR+Sbn8s2k5z1+yxpdrMclCnHP5CF2UPTKenF7H107/h9CFfR4RpH91Lpdj4v7tgSnY5iS0Yn+/4zERV7KXeYdSzfW2Cl8VOz05SaSP4XOBYQAdvxNYvL6+lLfMOu7V1pMtju+5P9iz43gaVZK4EBkJsoTK4BF/10x7awvZc3HlRhP2t/w1rxqo/1mrUm82tD6XqiA8/fdgDY2WuA8GXAZkPVbrN4jqBbcITWEtlDZmdXhHd2fHJqsQWDEDfwR4oPYXjbEB89dR9tHXBkWQEJsq0ZjsbNyfa2dB1ZDSx5c7gKfp2vRBbq37eQzM/sSQe9TXxtgVROeB769uiiS1lE8dhO81bHb4cO3tqD0+ej6kV88KlGicr4onYwko8VuwRUM5UMXRWcurwaiYfHrdXt0EigEw4uv1P2/tT2Qb9qFLzJ5xF3e6baiBJkxMrqydiC0dN1ZnilW+hmh3eTxdiC21dvniZ/uP3O5xy7GEHHM4ARxvwdFqsgvWtCuSbYRJb3V9vRA26lnU2p/bT610N9RRIXiIUZlVjXFszeCK20uIeghrj8h/bq+Y8kedmX3TcLIfJOCblOngitnDclO7Inb8YG52fSRvZiDbO0SlFoevzdwuCBO8MJ5EFpxNl2YyAXjwziS0sam1fdNSy0/f7zT437W+5QN5r6Jw/37LUjm88EUomsaVVMt0BZj43phdKTwSWp3RP/UCbZhueTDpsZ6n6KYles3V/TZt9L8291XYG4+48UpuWWqLIkyOPwiXZScHto3xuXr/DU1sO0mP6m+RLYyEV21Otjh/aWY+E/0KHw3/i8fNeO82MuJMIjTmylvau+pY1Aza7VcUvWv5G9h74mV2NL8SWN3uiu5d+pPqICjWxjvjVJLbQnl4UgOfHJvdMRRIb02+tDM+7c7ChMmTQz/Tp02nChAnJiKzg4GCqWrUqrV69WvVMiK30uUBCbF1lYuuH6Rvoq1+tmxq2mfrcWp+Nzhe3pXU8SYLd+covyvtim4bl6dMnO7q9G+Yx4fPCt5bocHoTW4H2x+0JpCLxzLkEJb21lu1uTf53s+2B0OlNMi2ILU3iDbq9ET3cvYnq5aJ1++iZr+ao+PjX7qDalSzSxXkKIOFeTLwmTmILeV8eMY9mr4qgOlx+HNczdf5Wen/8UgJJB8Ise6Lxd+T98ssvackSaxXvxx9/JBgz1GE2G2lfPjlC7XqTsDBtXbyyoItNNAVKbOl+YBt7Il55SYJ6Irzm6GB6YxzZZ5Hy2AXjqR3Z0H1KIX+RXLaNH18GZCnV5+n49UpsterNBmCfqOUJlqR0VqWO2h/HBuOjCSvnprfNu99pQnVuSSJihdhKgk3HPNmqw3F/JifejMfD+9XEZyzJpf7Dwyi4TAHdDbfb7Dmz2eq+mtgqVb0wDRp/k9v8/iZC2gVSLwju1AE9EVumbaNaHT6gEPZW6S1smvYInTq8RmXRXkqVYXc2Po8AMgSqLtl4lTylYE5ganZ4jz1lJn2D9aTIncMIGAFfOaET23SJUU2AKN80fTBPSBJYWraa8pSVUtv+HIdzjsPsqW7vf5G0ghccNFGhnAQkkg+BfDNMYsuUKHH2Nb3e1aajjsotnmV1wPtdmvZEbKXFPYSG9IQK8SY9f1ZSgYh7C6baUvEqnajmze/a2b0RW8gEtSUQXAiwHYfzS2tia//6kzT2YasNkKDtWBIPhsNh/1I7odELUiaxNfOjzbRqqqWiOHRlklq36qz+4e/GhXOW1FfufDmVRLm/5QJ5r+nuqG0qvmVmOV/GN54IJZPY8qbivXdNJP34uLVwai7qefpGeEr31A+cjy/PJsjyT2/7Vy1mte5bjW5+vCZpQjylxTATM1/ieEeuGGcRo5BwhaqdpxB90DJLgsWNMF600O9w/Vw6iWOzHrx7LyWwWiy/92GuAMHfcvZzyXU16fmT2YzbeM48hSl3vqLJjkEyE4sxUXsXqQVU00tg47snq29FskIs9QVVvKh9i+n4junsqfqYnaVis8fZvuQDat8XYgs2I7VNLruSxMi2f19me2DWXAemHTRmV5vYilj2KR3abC36Q3IMXic3/v2w6iVsihUMSa5x5DyXq7G/YcMGZTAebeXNm5fuvfdeatiwIZUsyRLT2a2xxj333KO6IsRW+lwRIbauMrGlDbxXKFmYfnnnLsqReKPry7tqyyF69OOZateU2ILXP+2FcfG37EWPvQM6g+lpL72JrUD74+y7t/14NsI+b81elaVprVJUgkkfM8BI+0PDpttqin+z18TSbIcLISViS9vAgrfFBd/0dfFQifLHo89Q5+cstT+T2Np9KJruee1XZHHrPEAd4J+3f1ispMyw747YMq/37+/3pDfHLKL1bL/rsR5NaCB7zTTD+PHj6e+/LUmnDz/8kCpWrGgfXvs722T4gFdwOTzwHYu4N0z+8cSxMQ8udevFzl9i69Shs8obEibFNW4qyR9HV/sApkFaDJafYTfhCH+y5x+4iMdK8COT2qg050/kXiZQNpxUyfW7lFXupLHjy4DMWZev+0JsuSIFlQgMjvOyvbTqrUNdD/Ie7KTBXhpCo27l6fZXG6g4foTYsqGwI1eT2DInNfd+1Ew9n3ZHjMimfw6xMe1LVIINAGvnDulJbMHYLozuIsBLlHMCYxJftTt9whJG1vsBtutgww6hRLUuVKPdWyru6Sf8n2fp5P5F6rDZjikV4I4UcdaHFXI9gIaxeagUmsEbsYV8pjcrGAG/lHBGrbSnFbEF1aCt846oLlVkleKgUFfpJVzbsQ8vs9UU4TWxSJn8FMg3I6OJrWM7ptGOBW+ocy5bvy9VavGkiusfk/iCDT2oHCOk1T104D82IL/mW1UnVIDgtdBbgFfN5ePaWxIWPAluzt67chcoYRdJidhCeagkwjEJAuy8KZtyXJdp98au0I/I3G+20ZJxO1VJd7ayQI7inY5gElsr2evxP59Z9t20VI/KZPyY3wltw9Pfcv6+1/z5lvk7vjH7aDrlMIktd85aNGTzvt1Gi3+0rsUTbGBeq/p7IrA8pXvqB9rxdRy14LvttHDMDqUNMPjndjTsln9UN7FYUqFxMd3lgLemcfFKNzxJZRv29VintmWKDDXav8VeBbuovGum9KBzpw8wZ5WDbTEtTCbJiUzm90UTNf6WM/vsySNfAkuXHtw4UfUvtHpXRYIf2jSJv7mxVCi0Ll/bVupY0s8V2syLH1qdvnzjQVSh6SAmsBZS3IltvBgdnMxoPsqizojln6pqChWvQw17/KjivhBb3pxgQDoV0qbA1PTkerWJLXNsXq7hA0pSDkb5IcXavPcsda6+/mjtF1/zI99LL71EjRs3TrEIPB7C8yEC7CKHhrqOm8+dO0f9+lnjGCG2UoTTrwxCbF1lYktL/3RuUYXeGdTO5aLBsPyL3yYRWBOHdqeaFSwd6+lLd9Lroxeq/O5Ij/CIE9T3nT/t+tKb2Aq0P3ZHfYjAsH67weOVVFY/lnB78p4bkpUyjcvDu2O5EkEqj0lsufMmCdVGkE8Io4bcxt4rS6q4/vmUDdZPZMP1CCaxhT71fPVXJUUHUuxPJtOCC7lOKEwHAijvjtiCauptz0+hY0yg3d66Ov3F9yOCO/XGWbNm0ejRo9Vxp4cOcwBTtk4wPTCqFavqZFN59Y8pQu70kuYvsWUacr2PjcC7Iz+08VVTrcm0k+HOoxY8A33ff7GS6lKE2F9MiCWejjkge3B0a3syrs8TBrFPnz6tdmG4MSjIuhf0cW9b8+NZuFT6qCKG1ridqrd5PVk3YmNj6TLfVwiFChWyV3eSZXSTkNL1+/jW2WrVFZ4pb2LPVTp83XMeS2CdIU8SW1Av1Qb8X2DvTPmL5NZF1RbeK99qOU3FnSu4mti65YnaFNa7iks57ARyvr54RUwPVUQYBdUfTRgGNR04JDtBNwlXk9jCtfnmf/PV9YXk1YNs4Nv5TjBVTeB2vn5nS6XMF2LL3+ds/9qR7JVypELHnTqgXj1HBtOGCty1L/m+ha1e6PRoacINr1erJnVTagrWgBwr/NYLxJTaQpmqrV5SRnjN8joOyYDwf56yVT5Mok0FKFXzAABAAElEQVTnSYnYQr5tc/+PvXW5Dr7dEVv+PBN4Vw7r8I+Sygpjr6W3sPdSZzCNy+uJciDfDH+IrbR8V585uZPt5N2nTtMdjqa0njlJTqt7CN44l//YzrZ9ZamZvsf9cf3mooPn444qA87KExvvm0QbjiOkRGwhD7y9wZi8S3BDbPlzD6FO7dDFk60s81tw6zN1qDl70UU4xJKB3w+wxlD1OpahO9/iyZ8DhlH8Ldf237SkuL/l/H2vmf339Vvm7/jGfLY8EVvAzh2BeDb6An1x51z1PMNO6eM/tbMl5jwRWJ7SPfUDbac0jkIeBHNsgW88FiQh+fkEOynSknxWTn41BzD2grSWkmzle9o0EK7rNrfHd81kD4GvqSTz+d+58G2lbo4DkGys1OJpsxg/i0fUdwGJ5iKFv+XM91CRMjdQva4W2W02ahJpje+aRAWKVSdNpFkObuZzdtcHxlQ/hmffis0eo11LPqAjWywVPHckmnq3jbLmRgVDalGjO8erbvhCbGHBBXVqo+y6/6YKvtMjYXoQW3nYptoN93s2U6IJTZBZIAZB9Fdo+iiVbzxQd9mnbXp6RXz11Vdpx44dbm0ho3O//PKL+kf87rvvJi29hf3UekUM5HlDe9dq0GP0lM4v29lz8Uy7pBwAtA46jq27f0zi9D8eyss8EC9dypVY0HWl19b0JLhy1ADSxsfXbD1CD380XTXrjpDwpDKo++nJeLyW2EI+bVQeJMm+o6dp5J9rac7qPboK+oI95bVmj3kIJpGC/QHsFfDmZpWUl0JI+GhyBscQ0pvYCrQ/Vi99/32O1f4WsPofwpBeYXRbq2pKag3EEGyL/d9385WHQRiTn82G2HU4wEb8uyca8Yfnx84tq1DhgnmpeJH8KotpA6tpzVIsKdWU6lUtQadi42nirM30w8wNuioXYguJ//K1GjLcYuahAvnGwDZKpfAS38frth+hl1jN8Ex8gl3e3X2Eg+NmbKQvplqqOdgPq1uWvnr2VkRdgulGtlevXnTHHXe4HDdVS6BC0OWFesqbDtRSNs486GJvxzQcj0rMwYteUTUr9zQhNydVGBD3fLeJsoWFCfQl9oSEldqpr65VVTXgQVH3RDtOGJx+13sRHdttEVBdnq9HdTqUZkOguVgUO5b+Y4+Iq36xnoW2D9WgNg8mkTHmYK0V23aqf2sZLpebChW3iEVzRQQNT5w4kXLlSi7haJ6fjqcnsaUHAnkKhFK1m/6PpduKuBj6HDx4MMHAJMLLL79MjRq5SuzpPrrbpnT9/CW2jmyLoZH9LKkX2GDq8kJddjVdWHUh/nQCLWdvbNqNu2kLBBl0m7CvdvNjtShPgZz2yjOOB3K+6UVsmSuwEOcPKtmAybyKahCMPq9cuZLgUQehTJky9NlnSbYsVGIKP56eIxTzZ3JiloEqaInKhZTETp6C1v1uElf6+kEy68LZS6ymFsXvhE10+ni86vVL8zqra4QdX4gtf58zU00MpBPsTsGzE9T2QHodWP+D6o9lYBsT5qRB//61o5gUS7R7xxOgkIptqXyTQWoVHOooUOE4tn0a7V39tXIBjoqwIg5VRDPsXPQuHd32u50EqbDiVTsp1/JQb4GqpDJEzza5dHAO7HW6L8SW0wg4ypoTMl2Xv8+EtleEevAuhc0rvI/xnj285ZRyIgBnHebiAvL6+83wldhKr3c17pWlY1rZhCMmf+X5H17JIiPm0lYQQInew5yqgml1D0VG/Etb/30JMKoAg9Wlat9NRcu3ZrtDJVlNaBNLDC6lQ2zsWUtaYeLYnG1k5cjtKnXuC7GFRkyvnqpRN8SWv/fQ3K+30pLxu1S18NjX9M4KjGd2ZVoAXvogtaODcyHk55fWEBbOEOA5t3X/qsojJwzRL5uQ9I1odBtL9b7WQFdD/pbz573mz7fM3/GNed97I7bwPGJMVL5BUSWRjj7+8dY6tRgBkHq+14Rqs7kGHcz3vWlywlO6p36gPvOYu3GUbhPbCU+ucDE70IG/563Y86oz+PtNgKda3NsIvti5ArG8dEyY/fw37zVDSUBevMAGu5kgw3GE0nXvpXIN+qpjIKG2zHqevSYeUsfKN36ISRFLlc3fcqjIJK7wjaja+iUmr6qxLc/ddGzbX7b6nEmkmbYXUaYaL3RinIEAG6AwoA97gAjarlX0gWWEcQ8CjMXXaP+2+l5hH+PWXUuG8TdrPnbZ8DyTYTc8puK+EFvIqOssUrqJIo2OsXrjnpVJ383Gd0/hb1bSNdfjWajlQz1fB0/tmWMrp7F6XUaNB9q/q4zmFypRh4lT13F71N4FtGX287opHhpkp5b95tnqkUkHvMcuXbqkOAnvuVyP5siRw8XDoevRpL0RI0bQvHmWRDpIq+7duyvviJGRkfTnn38SBBR0aNeuHT366KN6N9XElr/Pm93gNRoRYusqS2xNnrOZPp6M1WMrQNrHJD9gQH4jezfU4VG26fQg23ZC2MHeE5/+fJaS7tHHzW3vTvVowqxNKim9iS00Ekh/zH77Et975BSBFDSxAokVxd4SzfD2Q2wEv2XSyxdqimGP/GBmoVosBTeBpeF0eIA9SZqY63Rsq5crygYAL9Puw6eSEVsgYodNXKbsYpllPMU9EVumwXiUHfbozdSBSUt3YeDAgUrSpX379vTII4+4ZAHRgIGiO8PBZkYYca/Z1pVATokY8TYhD59z2CavdDsYsJ2JPq931SRrwMhWFFotSXrqyNYYZQPIzGcXSIxgwNeHDR7nzJ3dPgTVmndvmmHvI2LaAgrkZZ+exNaqiV1d7CCg381ZrUnbXDAnJEOGDPHZtTDqSen6aZIptRJbqNu0hYJ9BOf1Bf7wqpi3UNJAxDkYRrkh/95q5wnkfNOL2IrcO4+2zn4RXbWDSYyYxFapUqXoiy+SBn52AS8Rb8+RP5MTqOqOGbTUpUVMUjpoQ/68zjR92EZaw6rK3oLTTlJ6ElvohzkZ8NSvqq1fYaLgzmSHTRVDl4M8yNVkhk6Hg4eGPcbx+DeHTrK3SnWDvUw5y9gZjIjTYLxxyKNXRDMP4ubEBPspEVupeQdAbXvUA4uVlAfqRnA+o0gzpfKw7+83w1diKz3f1aa6Ic7FXTCfXfN4Wt1DUDPdNGOwIlTN+t3FIUFav+vwZKQW8vpKbEHqa+WEW+2JOyZ2TlVEf9+rpgSVu/7jfsrNzl+0N2PTky6+P+MHL7ePuSsPKZ+HJ7SxyXPk8bccmxfy771m2APTfXQ+J85vmT/jG5M08kRsgXjWtu90X8wtpKNAAppSUZ6+EZ7SPfUD7aT0bJp92baATQ4MsUwOIP0ZlqB3qjwj3d+xl/k9r93xYyrGCxYpBZA/8JaHULrO/9jb4AsqfnTrb7RzcRLRohIdP+68Jvpb7vyZ47Tu114sbRbtaCVpF98f2IHCOx8Bqn2rp3RXWzsXvl8IiYQ8ooVK1KWG3X9AVIVkxDanom5N5CETpMCasGMKPa7UpJFTZc/0EOysQzVm/LiTfktLYmv7/KF0fKclSKKb9SRJrY3II58vJKiu72ptt2zZQm+88YbH5rDIjn9NvpQrV85eLE2txJa/z5vHzl0jBzS2KZ3OdS2xNePj+yi0qOsK266DJ+l/r/+mcDNtYWkgj0bFUdcXpqjdMS93owbVLD1bCLSNmbaOfpy50YWkAcHVu2M9Gnh7Q3rnhyW2Spqp/obKYuLO04g/1tLabYcV2YI0kGGdW1SlpuxBr+erlqjqb+/1dPEO2PHpiYoEeuKuZtS/q7ViBi+DzR8agyro/YfbUcfmVVTc/PHkFVHn8bc/unxqtlArHDN9vTK27izXkPHtxxJZN7FxfWeYv3avMti/72iMOgSyavKbSROmk0yOwWC7tmGmy9/I0nJDB7ShoawCunTjgWTEls731+IdSrJL1490XM9urFrYiqWvnmAyEsETsYVjz345m+ANE+XmsMRZntw5kZwsjBo1iubMmUPFihWj4cOHJzuOFfrlEyNozW97kw0y4fYdK7HaXoNZ+PSxc/TZ7f+qJHc2uqaz/S5Mjs3BrFl+x+JjtHjsTjoYnvzjXrt9aSVxBUkRZ4C3oX/YC9N2NjTvHOS1fbAGtbi/sstAWJfHQOvfb7bYK5umra7z589Tnz59dNZ0kdhySgHoxrx5RcSqJFb4YXNBB9N98lNPPUVHjlir3qmV2Erp+mliC5JTrY2VVq2KeNMD1ajdIzV1t1y2WLWGJ6rlk3bb0j06AwbnDbuWIxBmsMNlhuO7Y9XkYz+TLzoMmcPEVmI+83xTM4lHXeZAuNm9f3Cdlgodjq2c0JnvpRPKdhNsKzmDN6+IyAtjqSC49GAxuFwYQe0NYe3atTRs2DAV90diy9tz5MvkBORwOSZ7zbDw+x3ssXKvTSS7U0fbMvcwzRuxzX5edHlIcd38WM1ktlK0JA/Umgey5yF3IZDn7FLCWdq56B2lXuWsG4Psaje9qlS2nMf0PuyNwJisXn3X6XoLQ7il6/BqPRvQ9eYCPO7EFtq+4A1liFZfb10HSIO8LHkD9+LeJlqLof7Bk5Hgsi2obpev7eLuIuH/PMNSPJBCc09smc9Eat8BUC3COzicr7Uz4Drjua9+o6u9D+Tz55thElu92WMtpDI9hfR6V6O9g+t/VLaukl07Pla69j1UOew5NfFz17e0uoegPrWN3+unWULLNPqs24SkRsma3ZU6kU5zbtf+cg+djY5QqkBwaOAtuEzA3RBbgdxDuFazvwhPNnaAk5euQ+rRdv7O//n2etU93FPaiyoS8P3+96uttOGfg8m+5bAnBRX4fI5vRCDlUDa17zV/v2WpHd+YhJL5zjZtbPX9qiXtWHqMVrCNMjOAAMSz2rh7BTNZxT19Izyle+qHrtjbs6nzYHsp4TJ92HGWuq7VwkLp/s8slTczD+L+fROu0OJRzdU7FNKyrQZioSZJStfZht43pbygSgfJHR3iorYr6SyoHroEfl5K1+7JKopPufWq6285SGRun/eqsoPlfBdhcaVam9eSGTeHijK+gdFwpGKQWegvcChRtTNVZcl+bRgf6ZBU3bXkQzq242+OJ2mD4Bi+VyB64JQid/4QlYQfm9him36QbNPBJLYgFbaVxz3O7ylwrdbmVZaMbq+L2VtNbDntXXpqz5vEFkjBTdMe5TEMPwuJWFRp9aJbW2KmtHWdTp9S0Qo32X3KLJGFCxfS2LFjbfJK96t27dr0zDPPKCdgcACGAKPycAyGoImt8uXL08cff6zS8DNjxgz64Ycf1P6kSZOUBBh2/HveVDXX9M91T2xl1NWFJNERJr9OnzlPpUIK2apxuj8RLCGUgz3ilQsNSmbQXOc5y2pu8JqXN5EEMVXjlo3o75Ec0eXTenu1+oN2YNT9VFw8E0G5qXhwfirC6oWBBlwLkFO5c+agKmWDbbVUX+uNO3eB9vB1K1Y4H5UqVog/SL6VhDplD1aXPHgilnp1rEvP3tvCY0FTHdFpQN5ZKD42gSfoccouUjAbCzZX/px502of9iFiI+PVAAjqgbCPBfIjxcCEbwyTayBoCoXkZRWwfMnsAaVYxzWQAc4B4CQABGbhwoUz1RlhAhwXeV6ppkBasUAwX9/QfLZR/0zV2XTsDNTqe/fuTWFhYfT444+nY0tpXzUMjUfuicMYmIqUym+TjGnfkm81XmCSF+TSmaidrO5ZhNWJ67BaYRWPZISzVkhYghCA2gdIhXyFyym1RKzIpzbA6HDModV0iT1UFSnTNFHtwseXeGob85I/0HcACAaol2LRAOq/UNF22sbz1HxGfDM89cXXdJCkcZFb+Z9V5fjZLFi8Jp9zbbeGo93VmZb3ECZoMYfXMlF6QKkxB4XW43vZlfB314e0TgvkHsJ7PubIOfUdL1A0D3tTdR07wCsgiE2oPecwJKnNc0CeU6z6WrBYHvaqmN/nb7m/5VL7XvP3W+b3+MYExxG/yCYboniclsCS6CEVC9oSzY5sGbprOgFyJ/GfoZ3z0jiIIJBVF86cUN8FfB98Ic38LYeuQIIr9sRmlpgqpr5l2ougp27iu3WWPfyBVMqRIy/lKVQqUS3R87cH5Bm+V/GnDyqyC6rP+YtUUqrYntrxJR3vQnyPszGxBhVJp80tX+pI7zza+Qv6ltIiQHr3xVv9MLN09OhRioqKouBgNvhfurSL3VyoJuJY5cqVfTaV4qm9d999l/bv308wXC+BkhGKnjC5ZiW2PJ1wZktfsuEAffrTCtWt0SwB5jRUDkmwBz/4W3nUC2WvgTM+uS9dT8Hf/sxeFUEgpnwNIO5gVP1aDqu3HqZHPrJWUtxJ/znPXXv06NSpE0E1UcK1gcDJkyfphRdeoNy5c7uVxrs2zjLrn4U2/omVt5YtW2b9E5IzyDQIyDsg01yKLNsRuYey7KXLtB1fOGoHLfh+u1qkfJGdyHgiNDPtCUjHrgkEsBC2cnxHdS6QyK3S+sVr4rwCOYnw8HCCMXynva5A6szqZUViK4tcQdMYfRtWt3ulb2sKSTSADqLogwlLafoyy/Dn//GxO9u6VytKq9P1tz9dnpvs0UaYp76tHfOgp0NZNv3EqbOUJ1cOimEpsadYVRGSYjBe/92LXVM8p927dyvj4jBmOHLkSOVBL8VCkiHTIwBPlwcOHFBSQG3atMn0/b0eO7h+/Xp67733qESJEkpUPG/ewCVFr0cc5ZzdIyDvAPe4SKrvCMg95DtWktMzAlBjhLTnwU3RNI7tpyE4bXR6Li1HBIG0QQCG/aFSCsk3qPLDlARCC7ZPCzXv6zlcvHiR7r//fuWBERo8sNclQSS2stQ98OHEZfTT3C12n03D5jqxTqXiNPb/urEaI+uapHPwpz9QH7yQYHks8bV7JVgC7VoLvy3YRu+OW+JyWqYtNpcDbnY+//xzWrZsGd1yyy300EMPuckhSVkNAXhMqVWrFlWvfm1LKGa162L29/Dhw8ozYufOnUlILRMZiacFAvIOSAsUr+865B66vq9/Wp39r6/+R5vnHLKrg5H9x39u59ZOmp1JIoJAGiMABx0b/nIVbihRrSvVaPdmGreU9aqDuiNMl3Tt2pVCQpLsqmW9M0nbHovEVtrima61Qd3w94XbaMq/m23D8bpBGB1/sFsjup9tNOXMkf6kFtrNbP3RWGSFrZPYemPATcrYvK99j4mJoSeeeIISEhKUXnVQUJK3QV/rkHyCgCAgCAgCgoAgIAgIAoKAiYBJbMFW6j0fNKMydYqYWSQuCKQ7Ak5iq3CpxlSPvcvCyYwEQcAdAkJsuUMlC6RFsipbZMxZ1dPSbHw+qECeDO11ZutPhoLhQ+OXmGk/eDyWYIS7VAgbDPXgBdGHqiSLICAICAKCgCAgCAgCgoAgkCYIwCB/9KGzlCsPe9tLhQOANGlcKhEEDASgfgjHHDCQb3p8NLJIVBCwERBiy4ZCIoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAJZCQEhtrLS1ZK+CgKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCNgICLFlQyERQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEgayEgBBbWelqSV8FAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEbASE2LKhkIggIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoJAVkJAiK2sdLWkr4KAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAI2AkJs2VBIRBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQSArISDEVla6WtJXQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEARsBIbZsKCQiCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgkJUQEGIrK10t6asgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAjYAQWzYUEhEEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBDISggIsZWVrpb0VRAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQcBGQIgtGwqJCAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCGQlBITYykpXS/oqCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgYCMgxJYNhUQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEshICQmzpq3WFaP+GkxS5L44uxl+icvWLUqlahfVR2aYRApcvnqfIPXN9ri1H7gJUrEIbn/NLxquHwPbt2ykuLo5KlixJZcqUuXoNp0FLY8eOpd27d9OgQYOofPnyaVCjaxVz586l+fPnuyR269aNmjdvzmlX6Piuf3hzhfIXrUIFi9VwyXet7Fy5cplO7J7N53mZipS5gXLnD7lWTi3Tn8e50wco9tgmyp4zL4VUau93fz/99FM6efKkS/khQ4ZQoUKFXNJkRxAQBAQBQUAQEAQEAUFAEMhIBITYSkR/8nOraMeSY/a1aNqjAnV9qb69fz1Fzpw8TzFHz1HufDl5UlQwTU/99LENtOHPgb7XmS073fjQKt/zX2M542MP09lTeyhn7oIUFNogU51dr169KCEhgTp37kwPPPBApupbSp2ZNWsWjR49mipVqkTDhg1LKXuqj3/zzTe0cOFCl3J9+vQhkFsXzkbSygm3qmOFSzWh+t2+c8l3rezEndhC637vq06nRvt3qERV65yvlfPzdh6XLpyhmGPrKRv/FSnTnLJlz+Ete5of2/bvy3QiYo5qt/WDK/2uv2/fvhQfH+9Sfvjw4VSsWDGXNNkRBAQBQUAQEAQEAUFAEBAEMhIBIbYY/YObomn0g0vUdSgQnIdKVg+iys2KU1ifKhl5bTKs7dVT99KMjzap9oeu7Jam/RBiK3Vw7l72MR3ePEUVunHQmtQVTufcWZnYunTpEj3yyCMUExNDTz/9NIWFhaUpWprYCgkJsUm/ihUrUvHixYXYSlOkM2dlpw6vpk3THlWdq3Pr51S0fOur2tG0IrbCw8MJg4Q9e/bQ1KlT1TkIsXVVL6U0JggIAoKAICAICAKCgCDgAwJCbDFIMz/aTKum7lFwvTy/M6vM5PQBums3y9UitopX6UQ12r2RIpDZsudKMc+1miEzE1uY4J45c4Zat25NLVq0yHKXYN68eTRixAgqXLgwjRw5krJly5Zm56CJrSpVqtD777/vUu/1IrEFacPt815V516l1YtUMKSmCw7X8s61Qmzpa7Rr1y565ZVX1K4QWxoV2QoCgoAgIAgIAoKAICAIZBYEhNjiK/HX2xto3bT9VK5eURrwfavMcm0yrB9Xi9gqUa0LE1tvZdh5ZoWGMzOxlRXw89bH8+fPU//+/QnSW7Ab1KRJE2/ZU3VMiK1UwXXNZRZi65q7pHJCgoAgIAgIAoKAICAICAKZGIHrmtja918UXTh3kZZPjKA9ayOpWPkC1OnpOupylakdTPmDc7tcOtieOrItho7uiGG7RwlsM6YQlawWxNsgyp4jubTH8V2n2c7KOQoqkY9COd+Vy1fo2M7TtGdNJBUtV4Bq3FSSog+dpci9sZQvKDeVrRdMaOPg5mhWjzzFNq5yqLRyDYqyjaXsqi/RB8/QgY3Rqh/5Cuei4pULqXrctf//7J0HXFdVG8cfFREEwcFwD8S9xYkzzb1HmjNn0/awrLTha9nQppWWmTs1y8zMvffCvRFUFBFEQGSI8J7nXM/1/JcioEL9zvvx3rPPud/7/9Pn/3uf5zk8gNc8syuSLp2Ko7jLCRyvmvJ7OlNpMWep2oUt9h1+PIbiIhPpxKZLtPv3ULle/0kN5L1IKXfhTuMm89HR0XT06FGZZ9eqChUqyHx6LrorYmaELbYGuXp+O8VcDKIbiVfI3bsqeRarSwWL1xNxZWwt7mIu7qHrV86Qa6Eyok99weUm8Y/PyNOryK1wBSpe43G6FnWc4sIPkrObFxUp25J4jSshG8U72kl58rqK528i6/PkzS8f9VrkMYoK2UCx4UGU16UQFfCpRsWr9xHr27cw42DaESeXif77KSnuogghnibHFS7TTLzH1hbjosWzJcacF0HG/6bYSwfkev5N3pR3d58qVMDb+Jymh/n96rNr1y4ZY6tkyZI2AdjTxAdt37598nMSGRlJLi4uMi5PvXr1iF3y7pQ4WDUHpmcrkdjYWBkHy9/fn9j6KU8e21hFYWFhFBoaSh4eHlS9enXpXnjo0CE6cuSIjA/k5+cnLcrsxQX65JNPaPfu3XJua8uqzHzOMypsZafPoPU7upEYQ9Hnt1H0uW3igI2rVLBkIxGDr6WwcPWm8KN/kPgAk0+F9uK7Ynw/+KCIS8eXymkKl21O+dx8ZJ6/B+FHFsu8l18rcsrnKWL67RXftU3iO3hM9itUKlDM1dF6CxblxNjzdOXsFroqvp+5nfJR4TJNxd/wFnQjSezz7FbKk89N/G3uYDEmo4Wkaxfp4pHf6Hp0MN28cV0GwncTQf+LVe1lPi/Pze0xF/aK/z4E04XDC+RyPhU6if8G1CBnd2/zIIy4iEN07fIx82+NvX1FnPybbiZfJ8/idcV/i/xsuqQkx8n3cfX8bkqICRXcvMmrfBu5hj1XxPDjSygt5QbldS1IXn6P2szHFWmpN8S7XCLb9HVhsWUXFypBAARAAARAAARAAASyCYH/tLA15fH1dPlMnN1XMfCrRlS+obfRJsSgnQvO0PJJh+z2ZUuvXuPrkmdRV4v25Z8JF8eFZ6hqq+L0yFOVaNbz2yg2wgjE23JEJWoxsiJt+PEErZ92nMrWKSJjes19ZafFHFxgAazvxHq06eeTtG7qcZv2QsXz0/CfmgqBJp9FG4tov76xi6IvXLeoV4XiVQoSP6erhyHE/P7ePjqw/LxqtrgHDihPbV6oKut27NhBn3/+ucwXK1aMvvzyS4u+dypkXthKo1ObJ4ofmUa8F+u1WGDiYNzWPwQPLH1KiGB7KH/BslShxVjiMv+I4+RVrjVVaTOR1I9B5/xFqHSdkXRqy8fW0wtWpSigzyLhYvWuCM4sTnyzSrnzOMt2lwLFLVpYRDu8/EVKvZlsUa8KTvk8KKD3fPFD1/jxv/e3/hQfdUI1W9z5R3/1Dl9Z1D2MgqMYW5cuXaJ33nlHCkz29lWlShV69913ycnJUoBkMYzj+CxcuNDeMHn64ttvv02+vr4W7XzC4fLly+XJjAMHDrQbDJ4FsRdffNHGZXLz5s301VcGSw4mr5/2lpnPeUaFrez0GdQhn9s7nUJ2T9GrjLwQs9id+PjasbJcte1nUvzlgqPg8akpibRluhFzqkKLd4nnTowLM+bTrvxdrdV9hjw4QauWovSh5c9LQUuv5zyLaiVqDKSze6fKpqyIS3di/ft06YQh0Fmvx2IeP7+PvyHChez8hs4FzbDpxhW8t8ChG2Wbes8cVN5RcPfNPzaWf6O8yraiKm0/sZgzKnQjHV31umRh0SAK+dyLkatnKclHn3/XvK5SrOf+DQf+I8U567GXTvxJJ9Z/IKv9Gr8qWPaTeQhb1qRQBgEQAAEQAAEQAAEQyE4E/tPC1oI3d1PE6ViKOhtvvhO22uLU7Z3axJZSnJa8H0RBf5+Teb74N/YhDjJ/NijKFI04LtfIn5uRV9nbpwgqYYsD0V8S1lvx0UnmHG1fqEaNB/iZwpbZIDLlAryoYLH8dHpnhCmEeZcrIEU4Xse/kY/4f+ed6PSO2+0125ekHu/XMae5Jiyvvh+40VzTw8eFyop58zrnoWMbw836+j3LUsfRNeS41V8fFW0XhdVWkghwnSLrFI86XUpTk8H+si4zP/gzK2ztWzyIrkUa1mK8Gf4Rl1eIQnxyoCkaiR+btbr+KE4RvH2qpRK2WDi6kRBl8YOwaOXuVKH5O6awJR/y1oWtUZyFJUR81En5I5Or+QcqW23wj9r8nqVF2U1Ympww29kCrG7veeY0CbHnaM+C3uaabNGVv1A5yuPkIqzoDpv1nkXrUM2u0+S4Q8tfEELcXuOZhIULp9yiPydvYW1RseV7Mv8wL/aELRanRowYQXFxhmBctGhRaQ3Ff2gOHDgg3f54zy1atKDnnnvOYvtsMcVWXirxiYUc/+rEiRMygDXXs0D18ccfU5kyZVQ3UsIWt7FbIScWXFkA4+DXfHIjJ26fPn06ubreFqBPnz5Nb731lmx/4YUXZLwwWRCXzHzOMytsqT3w/WF9BtUeTm/+hC4cWaCKcj95XQsJ66Qz5mdeNd6rsMXCC1tP8t3VswylJF+j5PgINR0VrdxDfDffNsts7bV3YR/xfQ8x6sR30NWjpMwnCAsuMZnZlzOZFbbOB/1CZ3Z+bcwp1nIr7C//3sQLy6wbCVfMtQIeWyDF9IvCEi14+yT5TEo45+87Px9brNXra1iqZUbYunTiLyE+vWeuzX+PmF3StXBhvRpt1nOG11XC2YVDv9LprZ/K9lK1h1LZBpbfP24I+v0J+TeJ/7Y1GbpJWsJxPYQtpoAEAiAAAiAAAiAAAiCQXQn8p4Ut9VJUjC12zxs6tYmqlvez+67Qz09vkfliFT3psY8CqFBJQ/xiN78ts07TmimG0FKxqS/1+7yBOV4JW6qCxan2L1eTbonKukpZbHEfFssGfd1ItnM5MfYGfdNnnSlC+Zb3oAFfNhRuaIbAweLT1Cc2SmGOx772T1seJtPeP0Jp6UeGC9uAyQ3JP9CwBOLGGwk36ecnt9BF4VLJwtWoha2MQbeud4uxxULDDz/8IHuXLl1aWsJYTHCHgi5ssWVUAZ/b4pP1MFchGpVr+LxZzRZSx1aPkWUnZ3eq0fl7MyA1/zg+seF94er3t2x3KVCC6vdbYo5Vwpaq4B+D5Zu8JtwX65GyrlI/NrkP/yCs3vEb6bbIZQ74vXNuJ/mDlcs8vk7P2eIHZWkuih+UMbKdrVH0H5Pcdmb7F3T+wGzOUoVmY6holZ4yzxd2J9o1t4v8Qc/CVZNhm802zmQmxlZiYiKlpBgCpcWkdyiw6MMCUHqSPWGLT0/jeFWcOnToYJ4IyOWEhATZFh4eTnnz5qU5c+ZwtUxBQUE0YcIEmWeXQbb4KlGihCynpqbKvkuXGlYz1gHZlbDFnXnvvH7t2rXlWBa6ODD8unXrZJnFNBbVVOLg90OHDpXFJk2aWHyWM/M5zwphKzt8Blks2Tm3s+TD1ogsGLPbr5HShBXiS3TlnPH3kevuVdjiMTxfzU7fUR5n4+8qu/PtEeIVJ+vvRPix3+nkxv/JNv6O1+k5S7gyesgyfwf3LnpcfFcvyzJfMits8T54Pyz0NBB/T1hIVynswBwhYk2WRWuh6G4xttTfGuu/FWpuvtuz2GJhb9uMloawLvqUa/gilaw1yBzGf18PLH1a/J0yxFx9fq5jSzn+W8l/exsOXGGO4wyL9VvF3KKDcJVvQtU6fGm2Q9gyUSADAiAAAiAAAiAAAiCQDQlA2BIv5U7C1i9Pb6WQfVHy1b3wWytT1NLf5e/jhAvfP8JaQKQRwiWwRPVCMq8LW+z2N/SHQPEjzFI00IWtfp81oIrNfOVYdWErqi2zT8niMCG6KSsy1b75l1OmsPbulk7ih2Bu2fTXhAO0Z0kosRj35Kzmqrt5X/2NmHeWMe+4HV3Mes7cTdiy6HyPBV3YuttQ6x9fO2a3Ez9ajXfBopK9U9b2Luon4pSdlFNXbTdJxJsxnl0XtviHcIP+f0lxSt+D+rHJdeUDXxfxsvrqzcRWVNHntsq6Ko9+bBOn5vi6saaw1mT4VhG/zIjRtn/JMBkni8W4xkPWW8zJBXZRVOKA9Q/xzAhb77//vrRYslnwDhWvvvoqNWzY8A49bjfZE7bY4krFqurRowf169fv9gCR47hcCxYsoNy5c9PYsWPJzc0QM15++WXiWFmcJk+ebIpasuLW5cMPP6SDBw/K0rhx46hatWoyrwtbgwYNoi5dLD/PFy9eNAWrXr16Ud++lu+V98gCWKlSpUwXW33djOSzQtjKDp9BPtUw4tQ/EoH+fdKZbPulFaUkxRp97tEVka2ZGj+xxua7GPTHEOI4VJz078SO2R0M4UoITTzOybmA7KMubO21Y05HVbQYa1beQ8b8myPXW2vjFskuwylCUOMTXss1esGc+X4JW+FHF9PJTYYA7O3Xhio/anniJm+AY44dXPas3IsubHHF0VWjKfLMGtmmrMxkQVwuHl4o3K8nymLNLlNlzELVBmFLkcAdBEAABEAABEAABEAgOxKAsCXeiiNhiy2yPmj8l3xvtTuWom7jDCsQ6xd5+XQcTem/XlZ3eqMG1etVVuZ1YWvAF8JqSrgwWidd2Bq7rbMwDLAMQr99bjCt+PKwHPb2pk5mEHk1T9DSc7RkfJAsvrO5k/iBaAhbvPfUm2lyPj2wfErSTQoNukJ/ijEq3tfDErZY+HFyKagexebuJgIms9UUJ7ZU2Dytgcy7e1WRlhqyYHVh970DS5+UtSWq9yO/wFdlXhe22LWJXZysky5ssfsO/yjUE1uKsMUIJ46Vw1ZbegrZ+a2Ir/OzrGoybIvpxsMWEmmpKWK+3OJfXnMIW0hcDl5FpzZ9bFpY6D/iuWNmhC2Og8aB0e8lvfbaa+k+HdCesMVuf4MHDzZdAuvXr0+tWrWSQd3z5bOMAaf2xe6LSmyqU6eO6Rqo2tVdtwZjMYqFM066sMWuhu7ut92B1VjeE1uwtWvXjoYPH66q5f3ZZ58lDnDPbo/Tpk2zaMtoISuErezwGVRCEn/WVXwoayYhO6eIz/10WX2vFlvWlkFq7tDdU804WYFDNkhrLnY13vJToOxypzhz+34bIA+C4I7W3yc1f3rv+nee/14Vr9aHfCp2kgdO3GmO+yVscSwzPlCCU0CfhSJmYDm721CCnLWwxaI/i/+cilbqJuINvmuOV3H9WPhv/MRas54zELYscKAAAiAAAiAAAiAAAiCQzQhA2BIvxJGwFROeQF90Wy1fGbsQNnzcz+7rYwHpw0BDAGvU14/avWJYkujClm5NpU+ihC1rV0LVZ8f8YPpnsiFsWQtQ3Gf/svP0xwdGXCJd2FLjLx6Nkacwhh25KuJ8xVjEE1N9rOd9UBZb93Iqou6eVEy48vkLlz57id1t2IWHE59+WKPzdzKvC1u6NZVsvHVRwpb1j0HV55SwlLgoLCY42fvBHLr7e/Fj/EfZrgtbskJc2NrrsjiFMS7iICUKFy92W7RO1vNmRtiynjury/aELV5jyZIlFm6Gal2Ot8XC1aOPPiqto1Q9B5t//vnnZbF3797Up4/hhqba1Z2tqpQFWNOmTYljYnFSwha7Ic6bdzu2mRrH9zsJW8oS7E7j9bnSk8+ssJVdPoPKHc6tSEWq22uu3UePCllPR1a+JtvuVdgqWXOgsHR6yWbes3umUeieH2S9ErZ0Ucba9U+fgONPcRwqTtbfJ71fevJ8Muq+xQNNizQ1ht+PWyF/YanVVpyo2s+0zlTt90vY0mNgNRu5Uy1nc1fWpfY+RzuF6zOf8qiLlRzbjF0cOZWqPUTE3xol8+oCYUuRwB0EQAAEQAAEQAAEQCA7EoCwJd6KI2ErQgR8/27ABvneur1bm2p3LuXwHX70yHLhIpNCNdqWoJ4f1pX9lLDlSLTiTvdL2EqISaYFo3ebbpRq47yXohU9KEHE77pw9KqszgnCFp9oyOIUp1J1hlHZ+oarjaywumxiyy5h4cWBnuuKkwY5KWHL3g89Nfx+CVtJwj0q6PdBphulWo/34uzqRTdTEswfztY/xHOisMXPx0HZ//jjD9q7d68ZvF09N9/Ziuvpp5+WVXoA9yFDhlDHjh31rhZ55TZYo0YNebIiNyphyzpulz7wTsLWp59+Kl0kuT+7SWZFym7CVkY/g5um1pM4+CCGWt0MqyxrPlfObqbD/xji1L0KW6XrjqQy9YzvtT6vPWGLhWEWbDjx95//DthLJzeOF5aVf8gm6++Tvf53q2NLsZAdXwvrytWGG6TVALbCrNV1qogXWMNsuV/C1u75PcTf7nPS8rPpiG3metYZZdll7+9d2MF5FLztczmEhX/+PwD0eGGNBq2kvK6FLaaEsGWBAwUQAAEQAAEQAAEQAIFsRgDClnghjoQtFoc+abtCvrJWT1emZkMr2H19HIx9QkvDPaS56POI6MtJCVuO4lxxn/slbM0atZ2CdxlBlOt2LU21OpWSQen5NEVOd7IEy44WW/zDfOet2Dk+/u2pUqvx8jmsLxyMfduMR2R1kTItqGo74wecErYcxbniAfdL2OKYP+qktwI+1YmtVDyLB1BeFyMW250swXKqsKXeC7sZnj17Vsb64gDuoaGhqomeeuopat26NcXExNDIkSNlfefOnaV1ldlJy3Dw+SeeeELWtGnTxhyTWWHr9ddfl/tycXGhmTNnaitmPJvdhK2MfgY52DhbFlofxqCTOb9/Fp3Z8aWsup/CFltP7ZrXVa5jfVqivh/1Xee6rBC29Ln51MErZ7dQ1Jl1wvp1k3kKI7spsiWoSlkhbClR0atsK6rS9hM59aFlz1F02A6ZbyostnKJ2F/2Ertjs1u2PWGLhbqt4gRfdpEuXLo5VWs/SZ7ayidNOnLzhrBljzLqQAAEQAAEQAAEQAAEsgsBCFviTTgStvglKUuscgFeNHiK4eJm/fKCd1ymWS9sl9U9xtWhmh1LyvzDEraS4lPo41bL5R4CupWhzmNqWm+Z1n1/nDb+fELW5wSLLd6ossRyzu8tTvQyns/6wS6d+JNOrP9AVuvWIOrH7oMWtviH8PaZbeR+HFm9HFn5OkWFGKf2Wf8Qz4ywpVzsrBndqfzmm29S3bqGxeGd+nGbPVfEw4cPSyHLw8OD+JRB67RhwwZi0YdTQECAeYKissS6UwB3DjzP1lWc+CRDPnWRU2aFLbYS4z+EXl5eNGXKFDlnZi/ZSdjKzGdw9689KSHmrMTRaPAaIcZ62qAxTw4ULfdT2OKFldhjfbCE2pQu2nCd9fdJ9UvP/WZyvHmaqbd/W5t4Vhwjj0+MVIHz2VWTXTY53VXYWjNGuCWvlKctNh2x3Uagun71jBCbHpNz6cJW8NZJFHbIcAm1d4AFD2DBioUrZmFP2OI+R1e+QZEha2V7g/7LaMfs9lxNlVtPkO6VsqBdIGxpMJAFARAAARAAARAAARDIdgQgbIlXcidh68/x+2nfUuOH3YDJIgB8oI/FS0xNSaWpgzfRpdOxsv6lJY+SZ1FXmX9Ywlb48Rj6YfBGuYcub9Wkut3LWOw5Me4GfSeC3Wc0eDwHCFcfHCcnJ/NkO4tFHBT0UxHvJcYWT2fGlxF5/6ZjqFjVnhar8A+57TMflcfWc4N+6tfDEraiz2+nQ38b8WrsxRNKTrgiLNE63Zfg8Q/jVMSFCxcS/+M0depUKliwoMyri6NA8RMnTqQ9e/bIbnxCYuPGliJySkoKPfecsFaJjpZ9vv76a/L19ZX5zAhbetyuqlWr0nvvvSfn5EtmPufZSdjKzGdQjxvH8aRY+NDTldCNdHjFK2bV/Ra29i0eRNcij8r12GqTrTf1pFs/cn1mhC0+8GEzB6sXbs2OgtXrweXr9JhJ7t5V5XZ0YcveaZJntn8lRLOZsq89MYlPNeTTDTnpwta1qOPEwfE5Obv5UIN+S6Rboqy4ddED7zsStvR52EqLmbLVWeCwzTYiG08LYUsnjDwIgAAIgAAIgAAIgEB2I6D0ibvtK9f1hMS0u3Xidv7hqpLK893ev9TUVFL/+NS7VHGSX/FiRdXwB3a/k7AVF5FIk7qsMvfSeXRNqt62ODnnd6IIcRrisokH6dzBK7Jdd0PkioclbCVdu0Eft/5H7sm3vAc9/ml9Klgiv7AsuEnnD12lfz4/ZApx3Gn0qvbk4nH7tL6gv8RJix8GyfG9xweQj18B8vB1oXzuRp8dO3YQn7jHqUSJEjR58mSZT88lM8IWu8rsWdDbXKZEjf4y0LFTPk+KCd8rXAnHEFuncNLdELn8sIStG0K42j6rLW9BBmuu1fVHadXB1h5RoRvEiYgfmUIc92k48B/x2fLirEwhu8SJc/uM2EZl6z9HHkVrCcuRsjYxcFR//c6ijfoO6vV3ynMA9Vy5ct2pi9lmz2Lr+PHjZuwrDhY/atQoqljRsGKJi4uj+fPn06pVxveJ3Qo7deok57t8+bIUrtTk/fv3l0Hm8+fPT8HBwfTVV19ReHi4bNbdELkiM8IWz6mC0PPJjL169VJboMx8zrOTsJWZzyAfxrBVuPaqgw7Yda14td7ilEJ34Y633hRnFLT7LWzFR50gPr1PpZI1B5OX3yPib1scXTyySHynDEFftWdG2OI5VEwrzvOJiKUDnpJWa/zfK44tdmz1m3Yto65dPkL7fh/Mw4hPfixZe7D4XnuTq2dpWRcp4nUdFWM5sfVZ+cDXqUi5ViKoezid3jyRrpzbItv4ogtbXD7w50jx924fZ4WLaHGq9MgHVEDEQOOxYQfn0oVD82UbXxwJW9zGgnpS/CXOyuRToZOY631VtLhD2LLAgQIIgAAIgAAIgAAIgEA2IwBhS7yQOwlb/L4Or7pAi94xrEkcvT//xj702IQA8SPFiGHF/R6WsMVrz399Fx3faAgBXOZ9cXB7lUrXKkxn9xuCHNc9MSWQygYUkc3nRP30J2//sOLKJoP86dFRVWS7/oO/WLFi9OWXRnwd2XiXS2aELZ763L6fKWSX4crmaKl87sWoTs/ZFm5TD0vY4j3qP47lnjkujvhhrBIHambxQaVKrT4UliiGmx27C7HbkJ74h3K1Dulnro/Nyrw9YYvnZ6Fz2zbLwNYc2J0toFQqUqQIffLJJ1SgQAFVRWvWrKEffvjBLNvLlCtXjsaOHWthJZgZYSsoKIgmTDCskPju7+9vLpuZz3l2Erb4gTLzGWTLoUPLXzStCk1AtzKFSjQ04z7db2GLlzy3dzqF7HbsMupZrK6ML8V9MytsxV46QPv/HGHxfWWxiN399FS67pMiCP6TZhUL11t/bm6WOWPtBr19ZmshxMdY9FEFPrGQE89jLWzxiYb7Fg82RXw1xt79TsJW2IG5FLx9kjksoM9CG3dL1QhhS5HAHQRAAARAAARAAARAIDsSgLAl3sqyjw/Q7t9D6U5xtKJCr9FyYel0WsTT0lOh4vmpQZ9y1LBvOfH/jltauqz84jBtmxdMJasVouEiCLO9dLfg8bt/C6FlnxyUQ61jYXHloRVh9NvYvbL93a2dhTuJsQd2N+T9Hlh+XrapC+/30VFVqXLLovRl99WmO+KgrxqRX0Nv1U0Gtd+1KITio5NkXeCA8tTmBcPNhl3G2HWM04MWtnjNuIhDdHTVaAtrA65nVxqfCp3Jv9mbNu40KugyxwjiWEH20t2Cxwdv/VzEt5knh9r7wXx2708Uuvs72c4nlvFpaZzY3ZDdEdnaRE+83zL1nqYSNfrRlunNTeGgYstx5Fuxi9mV98UCl/ox7cgtyhzwgDKOhC22FFu0aBEtW7aMEhMTLXbDFmENGzak4cOHW4haqhMHmmc3Qz3IPLex5RbH1OrTp4+NRVlmhK1Zs2bR0qVLiYU3zufOLUTHWykzn/P7JWw9rM8gH95wfM3bxC5sLLZw4u+Sd/l2MiaTFH9EXe3uM8TpgNVlu261VKXNJ+QlLJI4scvwFnbxE6lsPXG6Yd1hMq9f7J2KqLezq9/pLZ+K+F+h5veCBe0y9UaK79lpYbk0547WSvpcd8urta5HB9t05QMgStcdQcWr97VpY+GIBThl7cZiVeDQjWY/DobPfxdUDDPVwFZYNbv8IAS1J4UV1kUbYYv78ZxHVr0h3BV3mM/P9SxkFa/2uDys4nLwqjsySE1Joi0iFpeYQFp+1e/3J09hN0HYsosFlSAAAiAAAiAAAiAAAtmEAISte3wRacJdMvr8dXHkerL4weFuuufd4zQPrHti7A3htpJAKck3bfabejONwo/FiB+iLlTA2+WB7SmrFmKh51rUMWGJFkUe4se09RH1WbVOVs2TfD2S+MfxTfGj1Hq/HM+HYyGxq5JyV8qqde/XPI6ELbUeuxlHRUVRZGQksdhVqFAh8vHxkSKS6uPozmPDwsIoNjaWypQpQ+7u7o66ZqqeBTZ2kezSpQsNGjQoU3Ppg+8kbOn9HnQ+Kz6DbGXEAgpbIHE6HzSTzuz8SuZZuFHWRrLiAVxYdHMWVo+5chvWsupEQBadGg2+7Uae2a3wc7OQxiecOglRz9WjlPj7n3nXeX4nLNbndnIhtjZjwfteEgtj8dGnqICIlcXiXnoT/y3iwP+c/Bq9QiVq3nbxtJ4DwpY1EZRBAARAAARAAARAAASyEwEIW9npbWAvIJBDCOhB17t27UoDBw7MITu/vc2jR4/SuHHjZAW7QLLwllUpuwpb9/p8Fw8vouAdX8hh9YSrmq1wkiYObGgr3eLYQpEtFe9nYktNjj/FQpY9CyMWnXbM6Si3wJZjbEGWkhwn3Bd/vqdtuRYsRUUr97inMTmtM7OMPCOsV4VrdBMhSLKw5ihB2HJEBvUgAAIgAAIgAAIgAALZgQCErezwFrAHEMhBBE6fPk1btmyhv/76S+5aDwKfgx5DBrnnYPdNmzY1A8hn1f6VsOXn50d8OiUnPkGUXTFzUroWeUzEczJES5cCJah6p2+kpRI/A1swHVz2jOliW6LGAPJr/PJ9fTz9lEavcq2pQot3TcsxdvVla62U5GtyDzW7TJUWUNYB59OzwXxuvtRgwLL0dM1Rfdg91EVYhfJplsfXjZV79/YTp10+asSZs34YjovH1pN8gIMSgb/77jviGHlIIAACIAACIAACIAACIJBdCEDYyi5vAvsAgRxCgONc6YljYvn6+upV2T5/+PBhKThxbK2pU6daBKPPis0rYUufi10d2eUxpyXl2qf2rVwNVawtrnfK50GNBq0Qxj9GXDnVN6vvfOopn4yqB13nWF8pyddFqKjbhxMUKtmIqnf8xlye3fX4JMP0pjzCeikr3AzTu96D6seuh3qsMHYpbdBvKTm7+djdwuDBg23i5EHYsosKlSAAAiAAAiAAAiAAAg+RAISthwgfS4NATiTAP3bZFTEwMJDatWtncZJgTnmeDz/8kA4ePEgvvvgiNWnSJMu3za6NfMqjnoYMGUIdOxpucnp99s+nUfC2ySIe3x9m4Hhzz8KNzbdCJ3Fgw1v3HBvKnOMeMylJscLaaJx0SeTA53pid7py9UdR8RqP69XI3yKgC1ssQlZtM5EKl2nukM/IkSMpJsby5Mbvv/+eChcu7HAMGkAABEAABEAABEAABEDgQROAsPWgiWM9EAABEMihBBJiz1FizDm5e7ciFck5v9dDexK20Lp+NYT4dMG8+TzJrXAFyuPs9tD2kxMWZjdNds1kqzu3wv5mwP2csHfsEQRAAARAAARAAARAAAQcEYCw5YgM6kEABEAABEAABEAABEAABEAABEAABEAABLI1AQhb2fr1YHMgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAKOCEDYckQG9SAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAtmaAIStbP16sDkQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAFHBCBsOSKDehAAARAAARAAARAAARAAARAAARAAARAAgWxNAMJWtn492BwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIAjAhC2HJFBPQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAQLYmAGErW78ebA4EQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMARAQhbjsigHgRAAARAAARAAARAAARAAARAAARAAARAIFsTgLCVrV8PNgcCIAACIAACIAACIAACIAACIAACIAACIOCIAIQtR2RQDwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgkK0JQNjK1q8HmwMBEAABEAABtUySAQAAQABJREFUEAABEAABEAABEAABEAABEHBEAMKWIzKoBwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQyNYEIGxl69eDzYEACIAACIAACIAACIAACIAACIAACIAACDgiAGHLERnUgwAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIZGsCELay9evB5kAABEAABEAABEAABEAABEAABEAABEAABBwRgLDliAzqQQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEsjUBCFvZ+vVgcyAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAo4IQNhyRAb1IAACIAACIAACIAACIAACIAACIAACIAAC2ZoAhK1s/XqwORAAARAAARAAARAAARAAARAAARAAARAAAUcEIGw5IoN6EAABEAABEAABEAABEAABEAABEAABEACBbE0Awla2fj3YHAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgCMCELYckUE9CIAACIAACIAACIAACIAACIAACIAACIBAtiYAYStbvx5sDgRAAARAAARAAARAAARAAARAAARAAARAwBEBCFuOyKAeBEAABEAABEAABEAABEAABEAABEAABEAgWxOAsJWtXw82BwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIg4IgAhC1HZFAPAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiCQrQlA2MrWrwebAwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQcEQAwpYjMqgHARAAARAAARAAARAAARAAARAAARAAARDI1gQgbGXr14PNgQAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIOCIAYcsRGdSDAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAhkawIQtrL168HmQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEHBGAsOWIDOpBAARAAARAAARAAARAAARAAARAAARAAASyNQEIW9n69WBzIAACIAACIAACIAACIAACIAACIAACIAACjghA2HJEBvUgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAALZmgCErWz9erA5EAABEAABEAABEAABEAABEAABEAABEAABRwQgbDkig3oQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIFsTQDCVrZ+PdhcRglcuBxHYZFxVCC/M1Uu45XRabLtuLiIQ5QQc1buz92rMuUv5Jdt9/pf2lhywhW6en67fGRv//aUK1fu/9Lj41lBAARAAARAAARAAARAAARA4IETgLD1wJFjwawgcCU2gcKvxJOrsxOVK17QZsr3p2+kPzefoHqVi9EPb3Syac/pFVt/bk43b1yXj+HqWYbq9f0tpz9Sjtj/zeR4irkURLnE/wqWaEi5cuex2HfIril0bt90Wddk2BbK7ZTPoh0FEAABEAABEAABEAABEAABEACBrCUAYStreWK2B0Rg4doj9PHsrXK1PdNH2Kz6bxa2okI30pEVr1g8c/3H/yAXj5IWdShkPYGrF3bRwb+ekRNXa/8FFS7d1GIRC2Fr+FbKncfZoh0FEAABEAABEAABEAABEAABEACBrCUAYStreWK2B0TgbsLWUmGtdejMZfIrVpD6PlrtAe3qwSyzf8kwir10wGKxopW6UYUW71rUoZD1BO4mbEWeWUthB2bLhWt2mSosupyyfhOYEQRAAARAAARAAARAAARAAARAwCQAYctEgUxOInA3YSsnPcu97DUlKZa2/dJKDvH2ayPc4vZTcnyEtAxqMnyLqM91L9Oh7z0SuJuwdY/ToTsIgAAIgAAIgAAIgAAIgAAIgEAmCUDYyiTAk+eu0L4T4RQRHU8JySnk6ZaP/EsUokY1SlL+fHkdzn498QYdC42kg6cjKDY+mWr6+4h/vuTh5kzbDoZRbqFP1KtSnJzzWsbw4Qnvdezlq9fpeGgUuYh4VPWqFJN7ChPB1fccu0hx15NoQLsaFvuMFP2PCGunY2IMp9oVfal6eZ87Pg/3i4pJoMNi3OHgy2LfuamWf1Exzlvu98iZSHLN50QBIuaVdUpNS6PdRy8Qs4wQa6eJckE3F7lurQq+lCf37QDc/Bz8PJsOnKVF647Kqb58sZ28ly7qQaV9PWU+OCyaLkReI0/3fFRD7N1eOnsphk6cvSL+RVFuAbxiqcJUoXQRKuXjYa87HTh1Sb4rPxHTq7h3AeIA9fz+DodcJpe8TlSpTBFqWrMU5ROcrVNoaCiFhYXJ6vLly5Ovr691l3SVQ3d/T2f3/ij71u7xC10J3SzK02S58qMTyNuv7R3nSb2ZTDEXdlP0uW0UH32aPHxrUpEyzcnduypFBq+mGwlXqWDJBuTqWdpmnnsdm5Z6k8KPLSFKSyWv8q0pr0shShIiXOTpVWL9rVS1/WQLV70biTGifgtdDdtFyQlRVLB4ffIq98hdXSzluPPb5DOlJPL+G4lxLck5vzeFH/1DaH25yadCe8qTN7/NM7FQFXVmPSXGXZAxy/K6FKQCPtWpaOVu5JTv9ufgenSw4LaXrl8NpguHF8h5fCp0Ig+fGuTs7i0YtpB1CbHn6Oq5HZTLKS+xFZ29xGtxgPmYi0F0I/GKZO9ZrK543np2LbyuRR2nuPCD5OzmRUXKtpQMr4RsoGgxx82UBCrgXZ18K3chV49S9pZDHQiAAAiAAAiAAAiAAAiAAAj8qwlA2Mrg6427nkxjvl9LWw+dtzuDm0temja6sxQ7rDus3HGa3vphnXU18ZhJz7elpz5dJttmvN3VRpTJyNi1u8/Q61PWyPmXf96f3piymrYfNkSW2kI4+umtLnI9oSfR4vVHacIstvyxTa/1a0T92lS3aUhNTaMvFuygOSsP2bSxWDSofU16d9p62WYdD4vFrNe+WUXnhUhkL1Ur503fvNJeCH5GEO6xP66nZVtP2etKT4h1XujTQLbdKcbW9aQb9Pm87fTHxuN25+n9SBV6uW9DKQTqHUZ+/BftFSLmM90DyClPbvr6t116s8yXF6LX5y+0tRHHpk2bRqtWrZJ9OnToQEOHDrUZm56K7TPbCDEkWohEntRo8BohRF2h7bMMMcvdqwrV6TnL4TQs4hz6e5TQmW7a9ClZ6wkhQv1ObBHmW7EzVWz5nkWfjIxNjD1Pu+Z3l/PU6Pwdhe2fTVeEcKVS4NCNpth0+fRKOr7uXbt7cyvsT7V7zLQQwdQc5/ZOp5DdU1Tx9l2IWZUeeY+Orx0r66q2/UyKQqoDn14YtHiQEIkuqSrLuxhfvcNXVEiIZJxCdn5D54JmyLz1hQUzfhZOd46xlUanNk+ki0cWWU8hyyz81ezyg80Jl8dWv0WXg1cJoa4IlWv0ovlMFpOI/VZo9pYQ5HpYVKMAAiAAAiAAAiAAAiAAAiAAAv92AhC2MvCGWQB69rO/aaewMuLEglR9YV1VtIg77T95iY4KSyxOJYVVz5KJfWVeXRasOUIT52xVRWogxnkXyi8sli7SJWH1pSdrYSujY3Vhq2G1ErR2T4i5TLNapeiLWxZPn83bRvNWHZZtvoXcpHVXYvJN2i7Eu3hhYcbp1ccbUf+2t8UttrZigW/VrjOynVnwGpx2CPFMjZMV4qILW2wZ1v+93ylKnHDISa3JlmXr94aa9X1aVaXRAwNln68W7qT1+0KJx6q5yxQ1rLS6N61EgzvWlP0cCVuJwqqO1wwNj5H9ini4SisythLbevD2c7Ig98s73Sws5pSwxWPUnpsICy1+5pU7g+V8fGlRuzRNEuKWnrJC2IoND6L9f46Q05ao0Z/8GhsB5Pcs7ENsUcSp4cB/hADiJfP6hWM/HV31hlnl5OwuLKFKUaKwMEpJvmbWc8Za2MroWF3YYtHtWqRhYacWa3IruPr5/TPpzI6vjGoh0LiKIPi5nVzEM50RQpfxuctfsCzV6TXXQtw6vfkTunDEsJ7iwWyhlde1kMU4tZalsJVGO2Z3pOTrl2+vWaCEsIjyoXhhHaV4cOB33iOni0cWU/D2SVJ4U3vKlTuvPBUxnxhXr+9i2e9OwtY+IaTpDPK5F6O8wirs+tUzxNZwMonnr9X1R2lJZ1QQKWGLLc/EBmR1XtfClE88b3z0qdtioGhv/MQacnIuoIbiDgIgAAIgAAIgAAIgAAIgAAL/egIQtjLwis9cuEq93zGsLkZ2qUNPdq8rXAdvxzaaKE7rWyBO7eO0cvIAKuLpKvNXryVS6xdmyzyLXt+82sG07GGx7JtFu2jG8v2ynS+6sJWZsUrYUhOzEDN6QCDVqVhUinHshsfug4PHL5FdHhMWS68IAUu5QV4RwhNbXCkrr2WfPi7HcWcWg56f/I8cx4LO+KceMV0W2WXy1a9XmQIgd9KFrd83HKPxv2yWY79+qR0FCpFIpYSkFGIhiUVCFq4WT3hMNcn73WJsORK2pv8VRN8u3i3n6NTYn94c1ITyCx6criUk0/gZm0yRzlrEU8IW92VLsk+ebW1y4Gd98YsV0qKL21d9MYAKCwFMpeXLl9Pq1atlsVWrVtSpUyfVlO774eUvmhZPDfr/Rfnci8qx7Bp3essnMq8LXvrEHJeLrbE4+TcdQ8Wq9jSbww7OpeBtk8yytbCV0bG6sKUm9yrXiniP7l6VpXjF1mc7ZreX4gxbLNXq9pPpBpmakkRHVr0u3RZ5fNl6z1KpusPkVEnXwmnn3M4yzwIUi0HsTmmkNDq8/CWTFdfpwlasiEu2f8lw2bVI2UeoSpuJlItFo1vpwJ8jKSZ8nyxZnzZ5txhbjoSty8ErhUA1Rs7JomKNzt9LBlzBFnQnNrxPESf/lu0uQmSr38/4LnKFKWxxQeyzcuvxpstpWmqKOCHzVfNZ/Rq9TCVqDuCeSCAAAiAAAiAAAiAAAiAAAiDwnyAAYSsDr3nF9tM0Zuo6OXLd14NMNzk11dYD5+h5IXJwmjuuh+mOqIsqv7zTlar7+agh5n3wh0tknCqu0IWtzIy1FrZ0YUot/Nzny6VwxZZKs8Z2l652qo3vLG51f3OBtJJiV7wRXevI5mc+NSzXWCxbIUQ8jqOlp9j4JHrk+dvucbqwNWHmZvpt/TGqUsaLZo8zXNb0sV8vvC306eO4T0aErfiEG9T8uV/kEiyWLfiwl81zJt+4KZ+TreekJZYQqNiCjJMubC0c35s41paelm87Re/ccrmcLRhWKWtrOaX3v5d8akoibfm5ubTY4fhXykKI59DbWDRpPGS9xdQcO+vo6jdlnW+lrlSxxViLdi4cWfEKRYUa7nS6sJWZsdbCFluYsailp+PrxpqCTt3e84ndDvWUJiyUds7pIKyroqQrXsOBxvfq+Np3KOKUIahWbTdJxgnTx3FeF+R0Yevs3p8odPd3srs9Cze2zjq1eYJsr97xG9MdkSsyKmztmN1OPgPPUafnbFPU4rJKexf1o/grJ2VRfyZd2CpZc7BwR3xBDZF3HsNjORWt3JMqNDcENFmBCwiAAAiAAAiAAAiAAAiAAAj8ywlA2MrAC2brqpSbt1yCnG5betxMTaXTYVdp8vztppWSLmwpEYjFo3nv37aY0bewdPMJem+6ITDowlZmxurC1rCOtei53vX1JUWwdhGofrgRkPzpbgE0sL1lMHnVedxPG2iNiNelLKiYQcOR02WzHt9K9Vf398S4pVuMH+y6QMWxuW6KfxwbXg8QnyTcBfeduEQf/LzRdM/Ux/G8GRG2OCD+ICEccvpgeAvq1KSCzFtfFgux7X9CdOP06wc9yb9kYZlXwpYjIY6DyXcZ/avs+91rHahB1RIynxWXsANzhCvcZDmVXyMhENW0FIgOLH1SBCPfK9tlbKhSgeayx9a8TZdPG4KQPSGHO167fIT2/T5YjtGFrcyM1YUtDsTe+Im15p5UZvevPSkh5qx0Iwzoc9utULXzPWTnt2ZcKmVBtWM2i12XZYwuFd9KH2OMmyLiYhmfT13YEuqgEAOTZffcTkbsNi6w9RMzPL5unOmmmBXCFotzm6c1kOvdKQ4ar83vkVOJ6v3IL/BVmdeFrUaDVgp3S+PzKBtvXTb/2Fjs/wZ5lW1FVdoa1nt6O/IgAAIgAAIgAAIgAAIgAAIg8G8lAGErE2/2YtQ1cYLheToYHCFP9FOxtfQpdWGr46vzpFDTKdCfPhjRUu9m5vmUvSH/+1OWdWErM2N1YevHNztLF0RzQZG5dCWeOr42T6+6a56FpguRQsh5wxByxg1rTl2bVrQ7bubfB+jLRTtlm7VAxZVHQyJpl4hXdkScLnhCBJNX8a/0yazHZUTY0i3t5ggLscrCUsxe4tMPh05YKps+e+5ReiSgrMwrYatDo/I0/slHbIby56Hz6/NlfVYLWzvndqGkaxfl3BxLit3v9MQij4rTxCfs1ewy1WwO+v0Jirt8WMaDajpih1mvZ9gdbvOPDWWVLmxlZqwubHmXbydc6P6nLynzW34KNPdt02inokanKVSwRAOxV0PIcStSkeqK2Fv2UlTIejqy8jXZZClsGb3jr5yiS8eXSjHLXqwx7pUVwhbHP+M4aJyKVRFCabMxMm99YWGKn4sTnwjJAfc5mcKWcENsNtL4HskG7aJ4QNjSoCALAiAAAiAAAiAAAiAAAiDwnyAAYSsDr5kDpn82dxv9KgLBW6ea5X2oUAEX2hB0VjbpwlbAMMMqik/de0vEdrKXdKsiXdjKzFhd2FrycR8q6eNhsfTx0Cjq//7vFnV3K2z5fgiFXLxKA97/Q3b9SMTWatuwvN1hc1YcpEm/GoKKLlDFXEuSJzTuPmYINmowB2evVLoIxQg3xsPCyoqTPo7LGRG2fl19mD4R742TPQ6yQVxYWOs5ZqEsvvNEU+rRorLMK2GrW7OKNHZoc9XdvN8vYYsFmL2LHjfXuWtGBhFfK4KIu8uuu+f3oAQRJF4Phm47RxptmmpY8unCVmbG6sJW6bojqEy9p22W3TS1nk3dnSoqthwngtt3EXs1xnn41hRxuQyrLOtxV85upsP/vCSrdWGLLagO/vW0aeGmj+PTJvPkdafEuDBZnRXCVszFPcIS6yk5X6k6w6hs/Wf1JS3ym9iyS+yPXTLZNZOTErZy5c5DjoRJCFsWGFEAARAAARAAARAAARAAARD4DxGAsJWBlz1j2X76+rddcmRdEYB9UPuaIl6Wtxks/JgIeK4EH13YYrGERRN7p+apbegilC5sZWasPqe9mGBx15Op5aiZcgsv9WlI7Rr6qe04vHsVzE9X4xKpzUtzZB/rQOv6wE+FmDRfiEqcdIFKxfXi+p5CPGJLtgoli5Cba16uEic0HqLP5m2XeX0cV2RE2NooTlN8WQSz5/TTW52pdgUj+Lqs0C47j4TRM58tlzXfvtKBGlU3XAoflrCluwOykJMnb35tt7ezfIpgUvwlWVEm4EkqLf5xYhHn6gUjYH5TYfGjB0qXHcRFF6F0YSszY/U5/ZuMpmLVLA8A4LW3zWgpTyHMX8hPBnFX+3F0z+fmK59/y/SmMraYdaB1fdz5/bPEaYtfyipd2NLjerFbX+k6w6lwmWbkUqC47Bt9bisdWm7EscoKYSspPkLECeso5/bxb0+VWo3Xt2nmU5LjBA/DErBImRZUtd3nsg3ClokIGRAAARAAARAAARAAARAAARCwIQBhywbJ3StUgHeONbVwfC+L+FA8WhdGdGHr9W9X09o9IXKBTVOeME/jkxW3LmN+WEcrdpyWJV3YyszYuwlbvFhbIVBFiQDxA9vVoJf7Gi5pt7Zk3nYfvUhhl2PJ092FWtYtI+uVJVm9ysXohzdsT/rjOFzsRslzc1IClR7IvVfLyjRmcFPZrl++/30PTVtqnE6nxqn2jAhbweI0y8dunWb5XM96NKxzbTWdxV1f98+JfamEOMGS08MQtthFkEUcdlPL7eRCTYYZsb8sNnyrEB91gvb+1l+WWLDheEyc+MREPjmRE4sqLK5Yp+BtkynsoCFS6sJWZsamR9jat3gQXYs8KsUqR7GyYi8doKiQDXLLLNhxXCwVm4srGw1eQ2xpZZ3Y/Y/dADnpwpYKKs88A4esEy6ahpCqxl868SedWP+BLGaFsMUTKUssdiNtONAQTdV66q6vW7ruSGHhZlh5QdhShHAHARAAARAAARAAARAAARAAAVsCELZsmdy1Rok59mItcSD2N6bcFrD0WE7LRAD1sSKQOqdnewTQ8C7GyYJqwcPBl2nweCO4OdfpwlZmxqZH2Ppo1hZatO6o3Mryz/uRTyE3tS15jxCnBHYQAhWn7s0r0btDmsn8+yLQ/Z8i4D2naaM7Ud1KxWReXXSrK65TApXu/qi7+6lxbEXW993fsjR4PItsvYTV3HkR5J3Tikn9iS3P9BQu4mR1uhUnq7w49XD+B71EcPtcsktmhK3ExERKSkqS87i4uFC+fPn0ZR3mdbHD0YmG+uDts9rSjYQrsqp295+pgE8NGV+LY2VxYouneo//bhGji2N37f61lxnrShe2ODZXRsemR9g6t286heyaIvdm79REDui+feaj0qqLBaimI7aKvrnEqYbf09m9hmuvd/m2In7XBDmHulwRJzweFic9qqQLW8qNUXf3U/04sDyz4ID2nKwD8eunIuonF6rx/Cz8TJyaDN9qclaxyrjev+kYKla1J2fNxPHR+Dlv3rgu6wIeW0BsxcYJwpbEgAsIgAAIgAAIgAAIgAAIgAAI2CUAYcsuljtXKost7jVVWCkFCGslPhExNDyWpi7ZQ6t2nTEn+PLFdtS0VilZ5j6PvfObGRydTyhsXb8cuTg7UdDJS/ThjE3mOM7owlZmxqZH2NKFK45x9Z4IBl9HiFR58uSig6ciaPqyINp+OEzuTz0zF/STALnMglcd4Z55PfEGrRPWaT+JcXpSwta1hGRq8dxM2cSnRH4+qg0VF5ZRfCLiISHwfTZvmwwkr8Zau1Dqp0d+9HQr8i9RiHwKu5G7q7McogQ3a0sy3R3RV4h37w1vTrXFfoWeQbtFAHs+kVJZl+luiDxpZoStadOm0apVhhtkhw4daOjQoerR7njXrY4C+iyk/AXL3bF/yM5vxEmAM2SfwqWbUbX2xkmK+5cMI7Z84uTqUYpK1xtJboX8pYsii0RKUOF2XdjickbHpkfYYou0bb88Yq5fvJqIAVdrEDkLAS5GuE+e3fuTiIW1h7dB3n5tqPKjH8k8W7BtFW57qSmJsly4dHMqXq035RFxxaLOrKfzB4zPlmwUF13YUhZb3Fap1YfCgq2DPBExLuIQndw4nq5fDeEmmaxdKPXTIwuXakIlaw+WJzq6epaW/R0JWzznngW9b80qTj2s0Z9K1R5CTvk8KSZ8rxCvxtCNxGjZrrshcgWELRMbMiAAAiAAAiAAAiAAAiAAAiBgQwDClg2Su1dYWyG5ueSleCHkqMQB5A+I0w1VeqZ7AI3oalhn8al/L32xwrREUn3UnV0BZ4tg65x0YYvLGR2bHmGL59etwrhsLw0VYtyo3kaQcdW+UrhOviVcKB2lvq2rmoH2lbDFfV8V8a7Wi7hXKllz5Phle0+Eq2ZTROQK/eRC1WFIh1r0/GPG3hwJW9x3sghkrxirsdb34Z1q07O9LAObP2hhiy2p+DRETmxp1WDAMutt2pSTr0fSjtm3XA1FEPkmQzdJ1z2O88RWQ3x6or3EsaqSE6KkUGQtbGV0bHqELd4Lx7Q6vOJlIS7dtLc1WceulfUf/0O6LKpOV8N2ilhYL0o3TVWn3wuVaEjRYcahBbqwdWrTBLp4dPHtroKTmMQss1vjjcQYs+ztJyzCHjUswlgA3Ppzc7ONMxykv/GQ9bLOkbDFjef2/Sys076V/Rxd8rkXozo9Z1u4VkLYckQL9SAAAiAAAiAAAiAAAiAAAiBABGErA58Cdjec/tc++mX5AQtBi4WZgW1r0PCutWn8jM2mi96TQtR6SohbKvFpgN//sYf2HLtAp0XcJ04shnVo5E/1qhQ340AtnvAYcRwvPWVkrG6l5Ci2l1qDxbOJs7dICzJVx3e2bnpGuE92alKBcucyXPP09pNiHFt17REnHCqLpyY1Swke1el8RBz9b+ZmYkuwlV8MMIexu+Gnc7fSsq2nzDrOlBSWWy881kDG8ery+q+mCPjdax2oQVUjkDv3m7ZkHy1cd8Rc7wkRxP+FPuJUOZGUsNWoWgn69tUOsk6/bD1wTh4AwM+rpyplvGhUr/pmwHi97ZlP/6adwqrL0amWdzoV8eeff6bly43YSum12Dqz/SvT8ohP0uMT9dKTdCuvCi3epaKVuslhbOV0YsOHQkjaYgo3HGeKA9Kz8LN1Rksp8LAlUdkGoyyWyshYXdjS92Ex8a1C0rVweYJhPMfE0kQmbvYq15r8m70pxJ5CNkNZdDu+5m26FnXctPpiYcq7fDvxry3t/3OEHFO7+wzhlln91vg0kgH5g1dbriUELq+yLYVb4/8o6I9hMvYXD/Aq24qqtP3k1liisANzKWT3FNNajIP5q/hgdxK2eAK2Cju6arQZ5F9NyidW+lToLJ/TOrg/hC1FCXcQAAEQAAEQAAEQAAEQAAEQsCUAYcuWSbpr2G2OxYzY+CQq5lWAvK3iNXGw8jwiPlMpXw+7YhAvxC57HMOJ3RE5rRZujKO/WyPzW78fQvlu1csKq0tmxlpNZVNk18ezwrWSXQaLFnEnL8/84jQ9m252K1h8c8+f1wyq/8mcrdJiq3YFX3EaoWGBpA9kfuFX4in5RgqVLVbQdCfkPrwPjsflLYQ1a776HBnNJ9+4SSHipEp+tDLFPMnZKU9Gp8pR41JTkkTcqjjhRucl980xpTggO6dKj3wgRJaOMm/vkpmx9uazrEsTAd/PSHdAV4+SIs5UOZvg7pb9b5fYyipX7jzSgoprzwfNpDM7v5IdWHiyPk2Sra/ir5wSVmyRxPG2lDuhmjE2PEjO5+5dze5JkqpfRu5snXYt6phYO4o8hODGFmlIIAACIAACIAACIAACIAACIAAC907gPytsJSSlmKcPphcbB1QPrFEyvd1t+m3ef44m/bpd1rPAU6iAi0UftgQb8fFSaS3FFlJ/iyDuKmVmrJrjftynLtlL/whXxNJCvPtCxBOzTixaPfL8LFnds0VlevuJptZdUL7PBBJiz9HeRcZnqXyT100LLn3ZIytfFycPGu6kdXvPl0IPt2dmrD5/VucvHl5EwTu+kNPWE7HH2IXPMqWJYOwikL6IW2UEnd9m2YwSCIAACIAACIAACIAACIAACIDAv4LAf1bYsg56np63GVi9JH39yq34RekZYNXnfEQsdXtzgaxtUbs0jRnc1DyVj62vPhYugMot723R1rNlZXOGzIw1J7kPGT0u11Pd6tLgDjVN6zNm/MaUNXQ0NFKuvODDXlReBHlHetAE0mjzj41kDCt2eePT/AqVbCQ3wS6GwVsn04UjxueST+LjE/lup8yMvT1LVueuRR6jfYsHymk5Plj1Tt/IoPhcwZZbB5c9Q/FRxmmdJWoMIL/GL2f1FjAfCIAACIAACIAACIAACIAACIBANiDwnxW2mP3lq9cpjc2k0pmc8+ahgu6WVlbpHGp2U255qoJPBLx5M9WMtcX11cp5089vdzFd+VTfzIxVc2T1PT7hBo2atNwiWD7HC7skXAsvRcebyw0SQfFf6tvQLCPzYAmc2zddBC6fYi7KAhfH10pJijXrhGmTCFw+i9yLVLpdJ3KZGWsxURYXDix9UpyYuNecVbka6ic8OuXzoEaDVkirLbMjMiAAAiAAAiAAAiAAAiAAAiAAAv8aAv9pYethvEXW0X7fcIzmrz5kIWbxXjj4/Igudai/CLjulEec1GaVMjPWaqosLV5PukHTlwbRgrVHLILp8yIc/P4lEQi+eZ0yWbomJrt3ApdO/CXFrWQRcN06FRBxpKq0/VScvuhj3STLmRlrd8IsqUyj4G2TKfzYH2bgeHNaIdL5VugkgrG/RSziIYEACIAACIAACIAACIAACIAACPw7CUDYeojvNVJYjEXGXJc7KC6Cz3u45Uv3bjIzNt2L3GNHDvTOllpRMQnk5upMxb3cTbfEe5wK3e8jAQ6WzsHiOXg8ux5ykHaS4fPvvmhmxt599oz34FhgiTHn5ARuRSqaQfEzPiNGggAIgAAIgAAIgAAIgAAIgAAI5AQCELZywlvCHkEABEAABEAABEAABEAABEAABEAABEAABGwIQNiyQYIKEAABEAABEAABEAABEAABEAABEAABEACBnEAAwlZOeEvYIwiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgA0BCFs2SFABAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiCQEwhA2MoJbwl7BAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQsCEAYcsGCSpAAARAAARAAARAAARAAARAAARAAARAAARyAgEIWznhLWGPIAACIAACIAACIAACIAACIAACIAACIAACNgQgbNkgQQUIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgEBOIABhKye8JewRBEAABEAABEAABEAABEAABEAABEAABEDAhgCELRskqAABEAABEAABEAABEAABEAABEAABEAABEMgJBCBs5YS3hD2CAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjYEICwZYMEFSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjmBAIStnPCWsEcQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEbAhC2bJCgAgRAAARAAARAAARAAARAAARAAARAAARAICcQgLCVE94S9ggCIAACIAACIAACIAACIAACIAACIAACIGBDAMKWDRJUgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAI5AQCELZywlvCHkEABEAABEAABEAABEAABEAABEAABEAABGwIQNiyQYIKEAABEAABEAABEAABEAABEAABEAABEACBnEAAwlZOeEvYIwiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgA0BCFs2SFABAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiCQEwhA2MoJbwl7BAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQsCEAYcsGCSpAAARAAARAAARAAARAAARAAARAAARAAARyAgEIWznhLWGPIAACIAACIAACIAACIAACIAACIAACIAACNgQgbNkgQQUIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgEBOIABhKye8JewRBEAABEAABEAABEAABEAABEAABEAABEDAhgCELRskqAABEAABEAABEAABEAABEAABEAABEAABEMgJBCBs5YS3hD2CAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjYEICwZYMEFSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjmBAIStnPCWsEcQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEbAhC2bJCgAgRAAARAAARAAARAAARAAARAAARAAARAICcQgLCVE94S9ggCIAACIAACIAACIAACIAACIAACIAACIGBDAMKWDRJUgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAI5AQCELZywlvCHkEABEAABEAABEAABEAABEAABEAABEAABGwIQNiyQYIKEAABEAABEAABEAABEAABEAABEAABEACBnEAAwlZOeEvYIwiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgA0BCFs2SFABAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiCQEwhA2MoJbwl7BAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQsCEAYcsGCSrulcDx0CiKvZ5Exb0KUAnvAvc6PFv1v3b5CF2/GmLsKVcu8vHvkK79paXepMvBK4jSjO6uBctQAe9q6RqbXTslJ1yhq+e3y+15+7enXLlyZ9etYl8gAAIgAAIgAAIgAAIgAAIgAAL/UQIQtv6jLz4rH7vtS3MoKjaBnukeQCO61snKqR/4XPuXDKPYSwfMdau1/4IKl25qlh1lIk7+TcfXjTWbC5UKpOodvjLLOTETsmsKnds3XW69ybAtlNspX058DOwZBEAABEAABEAABEAABEAABEDgX0wAwta/+OU+qEf7NwtbnsUCqGaXH+6Kct9vA+ha1HGz379O2Bq+lXLncTafDxkQAAEQAAEQAAEQAAEQAAEQAAEQyA4EIGxlh7eQw/fw9cJddC0xmVrULkOBNUrm6Kexttgi4X4XOGQ95cmb3+Fz3RAue9tntbVo/zcIW5Fn1lLYgdnyuWp2mSpQOFk8IwogAAIgAAIgAAIgAAIgAAIgAAIg8LAJQNh62G8A62crAjbClthduYYvUslagxzuM2Tnt3Qu6GeL9n+DsGXxQCiAAAiAAAiAAAiAAAiAAAiAAAiAQDYkAGErAy/lWkIyBZ24JEcG1ixJSck36XDwZToYHEHxoq26nw/VqViUPN2NmERXryXSgZOXaP+pCMqTOxeV9PGglnXLkIeb45hFJ89doX0nwikiOp4SklPIU/T1L1GIGgmLqPz58lrsOlG07z56UdYFVC5GN1NT6ciZy3QsJIqi4hKoUqkiVLlMESpbvCDlFgHRrROvceLsFdFGFFizlFyTn+fEuSgSU1GVsl7yn29hN+uhsrz3eDhdT7xBpXw9qExRT1mXmpZGWw+cl/naFX3lnk+HRdOBU5fo9IVo8vLMTzXK+1D9KsXtzqkqed5joZF08HQExcYnU01/H/HPV7Bzpm0Hw+Se64k5nPPmUUNo//79FB8fL8t169YlFxcXs+1uGVPYEpZazvm9KDk+glwKFKf6/f50OHT7zDZ0IzGaXD1LU0LMWdnPkbCVlpZKESeXUWz4fkqKuyhizadRXpdCVLhMM/L2ay2soizf7fWrZygmbI+oz01Fq/SUge2jz24V44MoLe0meRYPoEIlG1H+Qn5298exv24mXxf9BAePknTl3GaKvbCP4qNPyzEFSzSggsXr2bVIS4g9R1fP7aBcTnmpaKVu5vxsoRYZvFaWi1btSak3Eij6/DaKCt1ESdcuklshf/Iq/yh5FqtrjrHNpFGcCNSvnsXVs4xg0JQKlmgo6g9RfORJyl+4nJgjwHYoakAABEAABEAABEAABEAABEAABEDgFgEIWxn4KLCI9NSny+TIReN703OfL6dLQhzSk28hN5r7fg8pLj03abneJPNuLnnpy5faSQFMb4y7nkxjvl9LWw8ZopDexnkeN210Z6okhCqVTggRrN+4xbI4ZlAT+mHJXhnMXbWre6uAsvTesBbk5mopnixYc4Qmztkqu337Sgeyt19ufLZHAA3tXNtGHLMXYys55SY1ftKwYuI5F6w9TBuCDNFH7YfvbeqXo/eGtyAXZ1s3t5U7TtNbP6zTu8s8M5j0fFvzHcx4u6sUyVTHUaNGUUREhCyOHj2aAgLSL47owlaZgKcodPd3cp56fRdL4Uqtoe5xEYco6I8hslg+8HU6vfVTmbcnbF29sIsOL3+RUm8mq+EWd6d8HhTQez45u/mY9ae3fEIXDi+Q5Qot3qWTGz402/SMd/m2VKnVeJuTCzdNrSe7FS7VRMQAO0bJ16P0YTKf28mFanb+ngr4VLdoswger8XYijglAuWvNQLl1+k1h/b/MdTuM/n4dxR7+sBiTi4kX4+kvb/1JxbIrJO7VxVydi0sBLgtlM/NlxoMML5n1v1QBgEQAAEQAAEQAAEQAAEQAAEQAAEmAGErA58DXdhikSVeWBVVKeMlxabjoVF0VFgYcWLrpdDwGJkPrF6SinsXoKMhkXRYWFNxKuLhSv9M6k+52VRKJGHkRM9+9jftPHpBlnlutmgqWsSd9guLLzVvSTHPkol9ZR++6MKWquSxDauVkMUdh8PkHrnAe5r7Xg8LIUkXttR4XqNWBV+6HH3d3A+3dWtWkcYOba66yfvdhC1+Tj41ke9sURYbn0TbxZ5UevXxRtS/raWoYr2nBoKDd6H80jLNWkS8X8JW4yfW0LYZj8htFq3cnSo0f0dt2bwfXfkGRYYI6yVh4dVk6CbaMr2JbLMWttj6ac+C3pSWelO2s2VW/kLlKI8QleIuHzbrPYvWoZpdp5nz68KWquQg7q4FywphKFqIRMZnidvcvatSnR4zVTd5V8KWXumc35vyuhaihKshtwUpsf8anb4V1lv1za7pEbb4OdJSb0iLL7YIS4w9TzdvXDfnqNbhS2JRTaXEuAu0Z2EfSk1JlFWOnoUbIWwpariDAAiAAAiAAAiAAAiAAAiAAAg4IgBhyxGZO9TrwhZ3++ipR6htw/JyRGpqGo3+bg2t3RMiy3z55Z2u0j2R8yxeffDzRvpz8wku0h8f96FSwjWR05kLV6n3O4tkfmSXOvRk97oW1lETZ28Vlk9HZPvKyQOoiKerzFsLW20b+NH7I1qQs5PhnpckXBXfm76RVu4Mlv1f6duQBrSrIfN8sRaRrNsvRl2jFyf/I1wIr8oxs97tRlXLeZvj7yZscUcWxN4YEGgKauFizk6vz5dzsNi2eMJj5nzsutn6hdmyzALbN692MBkxv28W7aIZy/eb/a2Fra+//ppCQkJk+5NPPkmVKlUy+94to1tsNRu5k1SZrZqaDNtsMTwtNUUKWSxWsTtg9Y7fkBKSrIWtM9u/oPO3ArFXaDZGuhWqyVKS42jX3C6UknyNrNexFrasBbb4K6fkHpWYVK39F1S4dFM1tbkfruAA+LV7/EL5C5Yz29kCLPz4Elm2drlMj7DFA0tU709+gS+LnCHQhh2cR8HbPpdzsptjDWENptLhf16hK2c3yqK3Xxuq/OgEkTfGxUUcpP1/jjBFPghbihruIAACIAACIAACIAACIAACIAACjghA2HJE5g71urA1SAhELwmhSE+b95+jF79cIatG9apPQzvV0ptlnKmhE5bKummjO1HdSsVkfsX20zRmquF6t+7rQTYxuLYeOEfPf2HMO3dcD9MdURe2WAha/NFjIpZXbos1Oe5Wz7cW0vnLcdKdcfVXA03hSxe2eraoTG8/cVsYUZNcEOO6jP5VFlvWKUOfP99GNdHdhC12y2QBT4+DxYP/98tmWrzhmJxnz/QR5nzT/wqibxfvlmVdFDQ7iMzgD5eYlm/Wwpbe717zSshiCywWtiKDV9PR1W/KaaxFo0sn/qQT6w1Xuxqdpoj4UA1MIcla2FLzOjm7U+Mh6222xS6K7H7HqdmTxrNzXhe22FWwdvcZXG2R4qNOSNc+rnT1KEX1Hv/dbFdCG1fU7TWX3IpUNNtURu2Ny9U7fEW8d07pEbbcCvtTXeE+aZnSaMtPTaQ1mEuBEiI+mSGccRyy7bPaCXU3lfK5F6MG/Y3vgD6WRTblbglhSyeDPAiAAAiAAAiAAAiAAAiAAAiAgD0CELbsUblLnS5szRrbnaqK4Op6OiLcDQd98IesmvpGJ+l+p7efvRRDPYTIxElvZ2uklJupsj6v021hikWp02FXafL87aZboCNha7SwiurTuqqcw/qyeP0x+t9Mw+qIY4OVE8HkOenClj6v9fi3ROwvtvpiN8eNU54wm+8mbA3pUIuef+y2i5sa+Pe2U/TutPWyuOX7IaY11zOfGu6YFUsVpnnv91TdLe5LhcUbW6Fxup/CllBhTJGGA5nX7PKDuY+9i/pR/JWT0hIqcKixFyUkWQtbbNXFFl4cBF4PEM+WVpeDV9GpTR9Llz6e3JGwxRZhbBlmL7F73/VoYZF3S5BTfdR+OLA9xwmzl2Iu7qUDS5+UTSVrDqRyjV6S+fQIW/5Nx1AxEUDeOgX9/oR0seTA+I0Gr5LNl4NX0rHVY2S+fJM3qHi1PtbDhEVjquDdWFptQdiywYMKEAABEAABEAABEAABEAABEAABKwIQtqyApKeoC1t/ilhXJYSVlJ74FL8B7xvClj2h6HxELHV70wgIrgtbag52/dt28Lw8ZZFPR1SxtVQ73/V5dYutH9/sbBOQXo3jkwWH/O9PWfzyxXbUtFYpmdeFrR3ThpFTntuimhrL95nLD9CXC3fKKt2i7G7C1jvCAqyHsASzTsuFsPWOHWGr46vzZDD+ToH+9MGIltbDZFl/lvsrbJEMlM4B01k0ChTWVuzSxwHQd8xuL/fCAg0LNZyUkGQtbMlGcYk+t5Uun15F7HaXeC3cjDWl2vnuSNhqogVw1/tz/sT69+nSCcMCquHAf+SJjlyv9uNdvh1Vbv0/rrJJLLpt/tGwOtT3nR5hq1a3n8jD19IikRfggPocWF8Xts7umUqh4h8ndoks4F1N5q0vO+d0oqT4S4ixZQ0GZRAAARAAARAAARAAARAAARAAARsCELZskNy94n4JW6nCZOuzudvoV3FKoXWqWd6HChVwMU8WdCRszRcWThWEpZO9xCLZ47dOT3xvWHPq0tRwS9OFLd0l0HoOvZ8eG+xuwtaHQpzqKEQq6+RI2AoY9qPs2vuRKvSWOOXRXjoiAvAPEu6InO63sMVxrPYuelyuVa7hi1Sy1iA6s/0rETNrpqxjlzp2reOkhCRdIOL6pPgICvp9kM2phLly5xGnAHrRzZQESkmK5a4OhS1d8JIdtcvpLZ+K0xMNV1EOIM+B5Dmp/RSv1leIb69rIyyzqp97kUrEJx1ySo+wxX15jHWyJ2yd2jSBLh41rMYC+iy0iPWlj981rytxkHlYbOlUkAcBEAABEAABEAABEAABEAABELBHAMKWPSp3qbtfwtaMZfvp6992ydXrVixKg9rXFEHnvamwOE2QkyNLMN1i6+uX21NgjZKyv/VF3/d3r3WgBlVLyC66YLVJuBjmF66G9tK0Jfvo+yV7ZJPuOpjVwlbPMQvlaZItapemSS+0tbcVWrv7DL0+ZY1su9/CFi+yY05HShbilAqwvn1ma7qRGEOunmWEi99v5h6VQGQtbKnx3JFjZbHLn2fxAGnRxHW66KMLWHqMrUaD14j+ntzdJh1b/ZZ0aeQGjuHFsbw4qf0ULt2cqrWfJOusL3xC4ZbpRlw1r7KtqErbT2SXrBa2wg7MoeDtk+Xc1qcl6nvivfCeIGzpVJAHARAAARAAARAAARAAARAAARCwRwDClj0qd6nTBaKsdEVUAdH5lMCF43vZBIDfeSSMnvlsudydI4utAW2r0yuP24/DNOW33fTTsiA5Xre40oWtSSIofAsRHN5eUvsrIoS2lV8MMLtktbD1+rerzVMlHQltY35YRyt2nJZ7eBDC1rm90ylk9xS5XsWW46TrHxesY0UpIUkXtmTQ9Jlt5FgP35pUq9t0mdcvR1a+TlEhxsEBjoQtv8avUoka/fRhZn7bL62kxRdbgDUdscOsV/th98nAoRtEvXECodlBZPSA7aXqDKOy9Z+VzVktbF0N20kHlxlze/u1vXUior4TotjwIHkyItdC2LJkgxIIgAAIgAAIgAAIgAAIgAAIgIAtAQhbtkzuWnO/hC3lgtehUXka/+QjFvvgwPJvTLkt+MwZ150qlzGC1usWWzxo2aePU9EihsWOmiQ6LpG6iVMN4xNvEAtnHDw+d25D5NCFrWrlvInjdFmfYLj3+EUaOXGZnM7aRTCrha1lW07S2J9YhCF6tkcADe9SR+bV5XDwZRo83nBD5DprYSsuLo5SRcB9TgUKFBDPaT9mmOxgdTFPCLQKwp6SHEfbZli+E4651WToJsrtlM+cRQlJurAVfX47Hfp7lOyjB2dXg5ITrhDHlUpLvSGrHAlbTvk8xEmCf8kYX2os3yNO/SPigL0jq6yFM7UfbvRr9AqVqNlf9lMXDmi/U7j+sTUaJ/3kxKwWtlJvJguGLeVpiSzABfRZJE9x1PfCLp/Xr4bIKghbigzuIAACIAACIAACIAACIAACIAACjghA2HJE5g7190vYUhZRvLQKKs8nIoaGx9JU4QK4atcZc1d68HdrYYstqt4b3oJqV/SlvHnySBfGcUIoCg2PkeMnPtOaHq1fzpxLF7a4snYFX3qjfyD5lypECUlC+DgcZrr9cftfQjgrpglnWS1s8TM/9s5v5n6HdaxFrcV+XZydKOjkJfpwxibehpmsha1Ro0ZRRIQh1Lz11ltUp46lMGYOtJNxJGxxV7Pt1jhdvFJTKSFJb7shhKvtswyXSracqtX1R3IrUpH4RMSo0A3CDfEjmVdz6MHfdVdEbudg7JVafUgFhRtjSlKcCBi/jM7s+FINpbq955Nb4dvxzNR+VIcS1ftT8RqPi5hgRWVw95MbPjCFJM+idahm12mqa5bH2OKJdcu33E4u0jqsYPF64nTJU3R274+UEHPWXB/ClokCGRAAARAAARAAARAAARAAARAAAQcEIGw5AHOn6vslbM1bdYg+m7fdXNpNxLpiCyuVOID8AXGyoUrPdA+gEV3rkC5sWY9RfdW9qwgY/+6QZqa1FtfrwhaLYlGxCaq7zd3eCYdZLWzxovxML32xQp6OaLMJUTGwXQ2aveKgbLqTsDV69GgKCAiwN4XdOlO8srLY4s6RZ9bS0VXG6YdcrtH5OyEw1eesmZSQpAtb3Lh7fg9KiD1n9mNrL2GiZZbzuhYmFsBUYvHKx78D6cIWWznxCYaOkj1rMLUf6/Ws52BrsDo9Z8sYYqotqy22eF7e/+HlL1B02G13SbUe33Plziv3kBAT+v/27j5Wy7KOA/gFJ146BBwUIwh58YUhaVpY+FKQc9GLO6U1K+1FtjD8p9icrRfKXlZozeyPtnRrko7ckMqamW5NVxGw/iEnI1Ax5hI5IlCCh7MOinVd9+E5PA/nHD1nySPXuT7PBs/z3M99n/t3fX7/fXdf12UqYj2MzwQIECBAgAABAgQIECDQr4Bgq1+WVz5YH2w9cMtVYcpJ4xoueHLnv8Inb+zZ/a1+LazaSc/u6wyXfXlN9XXV19rDufEJqfRK0w1X3f9IuOvBzQ2BVgqrPrP4nPD5j5wXvnfn+nDf+ieq878QQ61lMdyqD7Zuu+HDYf3mf4a7/7ClOqf23/RTxocl8cmnKxbNrR3qfa8PttLaWz9e89fe3RdrJ6UpijdcdUF4+xk9tdaOp/dasPXFj78rLLns3Oqnlw6/HBZc27OW1E3LLgmLF5xef0n1eaBdEWsn7u/sDrf/dlPY9Niu8I9dz1eHU7j3oQvOCOefNS0+1fWr6ti9K6+splfWrlu+fHno6Oiovr6WwVbsUNhwx8XVVLqeNavW1W7Z+14Lkk6euTDM+8DRxdrTdMM0HfHgvp7e1S4Y2TI6zDz/umrtrA2rFvZOR0zreE2Z094QbKXdDrfFReL/88Iztcur9xRKnbnoGyEt/H7sq1bPW+ZeHqdMjg0dW3/ZEI6lsGzi1Plh3uJb+kxxHEywNf/KtaF10mnH3jb0tyti/Uk7H10dd3FcG3eL3F0FfKmO1rbZYc4l34nTKr8Zuv69o3qqLU2N9CJAgAABAgQIECBAgAABAgMJCLYGknkdj3cfeil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['dg.MD1R/de27485c-5478-4319-9547-2fab97327798']},\n", - " {'case_ids': ['419639-008501'],\n", - " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.662382812487955583445512079829',\n", - " 'study_description': 'XR CHEST 1 VIEW AP',\n", - " 'object_id': ['dg.MD1R/600c0202-00fa-470f-abf7-c0dce5e12fab']},\n", - " {'case_ids': ['419639-011382'],\n", - " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.215519496553565613550958544153',\n", - " 'study_description': 'XR CHEST 1 VIEW AP',\n", - " 'object_id': ['dg.MD1R/94a4451b-31bf-4d48-864b-cb5ea83d9925']},\n", - " {'case_ids': ['419639-009825'],\n", - " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.268741306941057438464842870387',\n", - " 'study_description': 'XR CHEST 1 VIEW AP',\n", - " 'object_id': ['dg.MD1R/a11605f1-f90f-4431-aaa2-829c55d36f8a']},\n", - " {'case_ids': ['419639-011556'],\n", - " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.294580064854427244193346246565',\n", - " 'study_description': 'XR CHEST 1 VIEW AP',\n", - " 'object_id': ['dg.MD1R/6c0412ca-cc4c-4720-9879-512c2c851b0c',\n", - " 'dg.MD1R/f798a4d1-575b-4781-8778-1240618807a3']},\n", - " ...]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## an example guppy query, which hits the ElasticSearch database\n", - "\n", - "## define some parameters\n", - "pid = 'Open-R1'\n", - "node = 'imaging_study'\n", - "fields = [\"study_uid\",\n", - " \"study_description\",\n", - " \"case_ids\",\n", - " \"object_id\"]\n", - "filters = {\"project_id\": pid,\n", - " \"covid19_positive\" : \"Yes\",\n", - " \"body_part_examined\" : \"CHEST\",\n", - " \"study_modality\" : \"DX\"}\n", - "\n", - "## send the guppy query with the SDK class Gen3Query\n", - "## Note the \"first: 100000\", which makes sure we don't just get the default first 10 records\n", - "response = query.query(\n", - " data_type=node,\n", - " first=100000,\n", - " fields=fields,\n", - " filters=filters,\n", - " sort_object={\"submitter_id\": \"asc\"},\n", - ")\n", - "\n", - "# display the returned data\n", - "if 'data' in response:\n", - " study_data = response['data'][node]\n", - " display(study_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'project_id': 'Open-A1',\n", - " 'submitter_id': '10000364-1599931',\n", - " 'object_id': ['dg.MD1R/b5cebe0d-907a-4ab2-aef7-b96cebd7d448']},\n", - " {'project_id': 'Open-A1',\n", - " 'submitter_id': '10008204-ONymhwnpKUy5dlM6b4a5Ng',\n", - " 'object_id': ['dg.MD1R/1355977e-81ed-4ac5-934d-b20407eab280',\n", - " 'dg.MD1R/ed0954b6-f76d-4e74-bb7e-3818d69d8001',\n", - " 'dg.MD1R/109daa01-5969-4c2c-bb44-25efe88856e6',\n", - " 'dg.MD1R/6f330232-cee6-4fef-8d11-e5d04a5951f0',\n", - " 'dg.MD1R/c32a89c9-eca8-426a-a6b3-a22b7700ecbc',\n", - " 'dg.MD1R/9e1a772a-f5b8-485d-9f30-600e1c052313',\n", - " 'dg.MD1R/8ca85564-7d98-4312-8507-64f173333da9']}]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## another example guppy query, which hits the ElasticSearch database\n", - "\n", - "## define some parameters\n", - "node = 'case'\n", - "\n", - "fields = [\"project_id\",\n", - " \"submitter_id\",\n", - " \"object_id\"]\n", - "\n", - "filters = {\"sex\":\"Female\",\n", - " \"race\" : \"Asian\",\n", - " \"ethnicity\" : \"Hispanic or Latino\"}\n", - "\n", - "## send the guppy query with the SDK class Gen3Query\n", - "## Note the \"first: 100000\", which makes sure we don't just get the default first 10 records\n", - "response = query.query(\n", - " data_type=node,\n", - " first=100000,\n", - " fields=fields,\n", - " filters=filters,\n", - " sort_object={\"submitter_id\": \"asc\"},\n", - ")\n", - "\n", - "# display the returned data\n", - "if 'data' in response:\n", - " case_data = response['data'][node]\n", - " display(case_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - 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'dg.MD1R/cfa28aee-3175-4713-bdb4-d5073d09248e',\n", - " 'dg.MD1R/2cdad3e0-0bc0-4a39-ade2-8132729d82a5',\n", - " 'dg.MD1R/576dd8f6-641d-4c9b-8539-b3a513789e9d',\n", - " 'dg.MD1R/4d8a8124-2b22-49cc-b6de-f82ce1250bc3',\n", - " 'dg.MD1R/98c5c5c1-8b53-46de-8d9a-a99b5f54f14e',\n", - " 'dg.MD1R/6c82df16-c68a-4cc5-b9a0-686082b23dbf',\n", - " 'dg.MD1R/8063524d-0188-4c4e-aa71-f078ad82c13e',\n", - " 'dg.MD1R/6cae2fac-5ec5-4749-b820-90c70bc4215f',\n", - " 'dg.MD1R/6eb68a22-6d32-441b-b34a-d8eff8b46330',\n", - " 'dg.MD1R/c6f307ae-bee5-455a-bdb0-47e46acbbc5b',\n", - " 'dg.MD1R/47b3492e-9731-4a07-a123-e09769834d33',\n", - " 'dg.MD1R/38b31967-3c04-4fa7-a24c-0a20d93b0d2f',\n", - " 'dg.MD1R/0cb5439e-734f-4e8f-9843-6298c92ee7d6',\n", - " 'dg.MD1R/ea259dd7-5b4b-4b86-9299-8e33f10c37b1',\n", - " ...]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Elastic search is handy for accessing files for a cohort since object_ids associated with each study or case are joined to the table \n", - "study_object_ids = []\n", - "for study in study_data:\n", - " if 'object_id' in study:\n", - " object_id_list = study['object_id']\n", - " for object_id in object_id_list:\n", - " study_object_ids.append(object_id)\n", - "display(study_object_ids)" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['dg.MD1R/b5cebe0d-907a-4ab2-aef7-b96cebd7d448',\n", - " 'dg.MD1R/1355977e-81ed-4ac5-934d-b20407eab280',\n", - " 'dg.MD1R/ed0954b6-f76d-4e74-bb7e-3818d69d8001',\n", - " 'dg.MD1R/109daa01-5969-4c2c-bb44-25efe88856e6',\n", - " 'dg.MD1R/6f330232-cee6-4fef-8d11-e5d04a5951f0',\n", - " 'dg.MD1R/c32a89c9-eca8-426a-a6b3-a22b7700ecbc',\n", - " 'dg.MD1R/9e1a772a-f5b8-485d-9f30-600e1c052313',\n", - " 'dg.MD1R/8ca85564-7d98-4312-8507-64f173333da9']" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Elastic search is handy for accessing files for a cohort since object_ids associated with each study or case are joined to the table \n", - "case_object_ids = []\n", - "for case in case_data:\n", - " if 'object_id' in case:\n", - " object_id_list = case['object_id']\n", - " for object_id in object_id_list:\n", - " case_object_ids.append(object_id)\n", - "display(case_object_ids)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sending aggregations with guppy\n", - "---\n", - "* Guppy has the ability to return some useful statistics (e.g., histograms) using aggregations.\n", - "* The `Gen3Query.graphql_query()` function can be used to send aggregations and other more complex queries that the basic `Gen3Query.query()` function can't support: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/query.py#L112" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': {'_aggregation': {'case': {'sex': {'histogram': [{'key': 'Female',\n", - " 'count': 5931},\n", - " {'key': 'Male', 'count': 5109},\n", - " {'key': 'Not Reported', 'count': 2},\n", - " {'key': 'no data', 'count': 857}]},\n", - " 'race': {'histogram': [{'key': 'White', 'count': 5112},\n", - " {'key': 'Black or African American', 'count': 3082},\n", - " {'key': 'Not Reported', 'count': 1814},\n", - " {'key': 'Asian', 'count': 640},\n", - " {'key': 'Other', 'count': 117},\n", - " {'key': 'American Indian or Alaska Native', 'count': 41},\n", - " {'key': 'Native Hawaiian or other Pacific Islander', 'count': 29},\n", - " {'key': 'no data', 'count': 1064}]},\n", - " 'ethnicity': {'histogram': [{'key': 'Not Hispanic or Latino',\n", - " 'count': 10061},\n", - " {'key': 'Hispanic or Latino', 'count': 923},\n", - " {'key': 'Not Reported', 'count': 58},\n", - " {'key': 'no data', 'count': 857}]}}}}}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## A more complex example using Python requests\n", - "query_txt = \"\"\"{\n", - " _aggregation {\n", - " case {\n", - " sex {\n", - " histogram {\n", - " key\n", - " count\n", - " }\n", - " }\n", - " race {\n", - " histogram {\n", - " key\n", - " count\n", - " }\n", - " }\n", - " ethnicity {\n", - " histogram {\n", - " key\n", - " count\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\"\"\"\n", - "response = query.graphql_query(query_string=query_txt)\n", - "display(response)" - ] - }, - { - "cell_type": "code", - "execution_count": 212, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requesting 'https://data.midrc.org/guppy/graphql': {'query': '{_aggregation {case {race {histogram {key count} }}}}', 'variables': None}\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " key count\n", - "0 White 5112\n", - "1 Black or African American 3082\n", - "2 Not Reported 1814\n", - "3 Asian 640\n", - "4 Other 117\n", - "5 American Indian or Alaska Native 41\n", - "6 Native Hawaiian or other Pacific Islander 29\n", - "7 no data 1064" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Here is an example simple script for sending a basic aggregation request that will return the data as a DataFrame (\"TSV\")\n", - "## https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L3511\n", - "\n", - "data = exp.guppy_aggregation(node='case', prop='race', format='TSV')\n", - "display(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sex': {'histogram': [{'key': 'Female', 'count': 5931},\n", - " {'key': 'Male', 'count': 5109},\n", - " {'key': 'Not Reported', 'count': 2},\n", - " {'key': 'no data', 'count': 857}]},\n", - " 'race': {'histogram': [{'key': 'White', 'count': 5112},\n", - " {'key': 'Black or African American', 'count': 3082},\n", - " {'key': 'Not Reported', 'count': 1814},\n", - " {'key': 'Asian', 'count': 640},\n", - " {'key': 'Other', 'count': 117},\n", - " {'key': 'American Indian or Alaska Native', 'count': 41},\n", - " {'key': 'Native Hawaiian or other Pacific Islander', 'count': 29},\n", - " {'key': 'no data', 'count': 1064}]},\n", - " 'ethnicity': {'histogram': [{'key': 'Not Hispanic or Latino', 'count': 10061},\n", - " {'key': 'Hispanic or Latino', 'count': 923},\n", - " {'key': 'Not Reported', 'count': 58},\n", - " {'key': 'no data', 'count': 857}]}}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## A more complex example using Python requests\n", - "query_txt = \"\"\"{\n", - " _aggregation {\n", - " case {\n", - " sex {\n", - " histogram {\n", - " key\n", - " count\n", - " }\n", - " }\n", - " race {\n", - " histogram {\n", - " key\n", - " count\n", - " }\n", - " }\n", - " ethnicity {\n", - " histogram {\n", - " key\n", - " count\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\"\"\"\n", - "query_json = {\"query\": query_txt}\n", - "guppy_url = \"{}/guppy/graphql\".format(api)\n", - "response = requests.post(guppy_url, json=query_json, auth=auth)\n", - "display(json.loads(response.text)['data']['_aggregation']['case'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': {'_aggregation': {'imaging_data_file': {'project_id': {'histogram': [{'key': 'Open-A1',\n", - " 'count': 59611},\n", - " {'key': 'Open-R1', 'count': 25003},\n", - " {'key': 'Open-A1_PETAL_REDCORAL', 'count': 6037}]}}}}}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Count the number of files in each project\n", - "files_by_project = \"\"\"\n", - "{\n", - " _aggregation {\n", - " imaging_data_file {\n", - " project_id {\n", - " histogram {\n", - " key\n", - " count\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\"\"\"\n", - "response = query.graphql_query(files_by_project)\n", - "display(response)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use the guppy download endpoint to access ElasticSearch tables.\n", - "---\n", - "* Tables of data from ES can be exported from the data exploration app (https://data.midrc.org/explore) by using the \"Download Table\" button.\n", - "* To get these sorts of tables using the API, you can use the guppy download function: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/query.py#L146" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'case_ids': ['10003752-DDqm9M6Q0OeSyzuCJPMw'],\n", - 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" 'study_description': 'CTA CHEST (PE STUDY) W CONTRAST',\n", - " 'object_id': ['dg.MD1R/797d9d81-d90a-40ee-8c7f-36e49c2c1e93',\n", - " 'dg.MD1R/fe6dc83f-c7b1-457a-b3cd-deea6b67d36d',\n", - " 'dg.MD1R/4be7747d-f3fa-44ff-8a9c-57d83526fa7c',\n", - " 'dg.MD1R/fd82a957-6878-46e8-88df-4749dd070f37',\n", - " 'dg.MD1R/f009f0be-9a61-48b8-aca3-27a81cba241d',\n", - " 'dg.MD1R/c7220010-2e4b-479c-8cce-26a04741fd69',\n", - " 'dg.MD1R/fb23b1d5-5a7b-46c7-b359-96c3c7060519',\n", - " 'dg.MD1R/b5c3e851-7eb7-49af-b7ef-df2ce187e6e9']},\n", - " {'case_ids': ['10003752-SeA11jeiNUeRUGflaoTGhQ'],\n", - " 'study_uid': '2.16.840.1.114274.1818.551336256374412277512106171150896859796',\n", - " 'project_id': 'Open-A1',\n", - " 'study_description': '\"CT CHEST AND UPPER ABD W\"',\n", - " 'object_id': ['dg.MD1R/61dc24b3-9ccd-48ee-bdc8-b5f7436cc185',\n", - " 'dg.MD1R/18e7ec2c-2a0e-498d-a710-9a230c22e5ec',\n", - " 'dg.MD1R/c1c998e7-f04a-4d6b-8c29-a4d9169f7680',\n", - " 'dg.MD1R/fe04a682-3bdd-4a72-b1e9-1c3e778b5bf0',\n", - " 'dg.MD1R/494e5c51-151c-4e15-ad2e-2f95b493e689',\n", - " 'dg.MD1R/8eaac70f-b423-403c-a304-a84da1328bd8',\n", - " 'dg.MD1R/aa108466-d0ae-4402-aa18-95128fe79d8b',\n", - " 'dg.MD1R/9bb0333a-53d6-4230-a3ca-b9c617c58874',\n", - " 'dg.MD1R/77e8d7b2-ef14-476b-9bf9-2a52855d331b']},\n", - " {'case_ids': ['10000364-1400306'],\n", - " 'study_uid': '2.16.840.1.114274.1818.50633512417267835389923634424582602420',\n", - " 'project_id': 'Open-A1',\n", - " 'study_description': 'CT CHEST WO CONTRAST',\n", - " 'object_id': ['dg.MD1R/fd04200b-5475-4681-a8fd-34acd93abb9a',\n", - " 'dg.MD1R/d1a5a009-0c3f-4928-908a-2525eb03f4c2',\n", - " 'dg.MD1R/29a79708-0e77-4ba6-8c3a-ffea790cfdf9',\n", - " 'dg.MD1R/6cad6165-ec4e-44e4-80f9-0c78c6f24dcb',\n", - " 'dg.MD1R/10424881-0cf5-4a03-8879-464bea6494f0',\n", - " 'dg.MD1R/c6b34419-dd0b-4730-be7d-4addaaf3a243',\n", - " 'dg.MD1R/04a0fa03-3fab-41bd-8f61-d439c1a07206',\n", - " 'dg.MD1R/5491d338-6147-4e2c-b973-77915c2a2a4b']},\n", - " {'case_ids': ['H03-00245'],\n", - " 'study_uid': '2.16.840.1.114274.1818.52232235843765190763291559831499102394',\n", - " 'project_id': 'Open-A1_PETAL_REDCORAL',\n", - " 'study_description': 'CT CT-CHEST W/O CONTRAST',\n", - " 'object_id': ['dg.MD1R/6f1e94b7-fb6f-457b-8337-efa62ccb4bea',\n", - " 'dg.MD1R/694d19ba-0bc6-43de-84c8-232c74c0bfc7',\n", - " 'dg.MD1R/43e79171-0afc-426a-b4d8-9cc6774ffbee',\n", - " 'dg.MD1R/649534d8-d28f-411d-92e1-f8deb68999e6',\n", - " 'dg.MD1R/ae9ca792-3938-4e85-8dff-f60dbf0576bd']},\n", - " {'case_ids': ['419639-006430'],\n", - " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.120095542073716122218042560568',\n", - " 'project_id': 'Open-R1',\n", - " 'study_description': 'CT CHEST WITHOUT CONTRAST',\n", - " 'object_id': ['dg.MD1R/dfdcb4b4-d16f-40ca-aef9-355a840dd9ae',\n", - " 'dg.MD1R/41c60163-d4a1-4e35-9142-7a6e9f233c7a',\n", - " 'dg.MD1R/68e61f1e-c01a-4883-a970-c45d32e428c6',\n", - " 'dg.MD1R/8a0ca070-a9f2-4fac-a04f-0464051b121c',\n", - " 'dg.MD1R/3c0f459e-e4de-4269-85df-99fafd108a62',\n", - " 'dg.MD1R/08bd14ee-40a0-4a03-b4c7-55da0d6712e1',\n", - " 'dg.MD1R/f04b3003-34e1-405c-b4fe-b943dd580aa8']},\n", - " {'case_ids': ['10000364-5484661'],\n", - " 'study_uid': '2.16.840.1.114274.1818.46887999779091619554120536691548091067',\n", - " 'project_id': 'Open-A1',\n", - " 'study_description': 'Thorax^CHEST_ABD_PEL_WITH (Adult)',\n", - " 'object_id': ['dg.MD1R/cd78bdaa-4b65-46a9-a894-64c77e92b1a1',\n", - " 'dg.MD1R/0baccfbd-28f7-406a-b732-ef5a69e70d6b',\n", - " 'dg.MD1R/16caf269-4efc-439d-8bff-85befb1e0720',\n", - " 'dg.MD1R/560fafa0-c1f0-4cc8-bfe5-308e28087d72',\n", - " 'dg.MD1R/67719162-bfeb-43bc-b974-b4f7f0bbf23a',\n", - " 'dg.MD1R/2af92d9b-442e-449f-a6ab-cff85fe3d579']},\n", - " {'case_ids': ['10000364-1825488'],\n", - " 'study_uid': '2.16.840.1.114274.1818.569799973924248904811447866112707172528',\n", - " 'project_id': 'Open-A1',\n", - " 'study_description': 'Abdomen^ABDOMEN_PELVIS_W_ROUTINE (Adult)',\n", - " 'object_id': ['dg.MD1R/f0a2c444-a451-411a-986e-2112b0269807',\n", - " 'dg.MD1R/3f419ae1-453f-44ee-8683-4546733184ff',\n", - " 'dg.MD1R/cdde5830-3154-4fe3-9f13-929bd1379ebd',\n", - " 'dg.MD1R/0cb5f711-eac5-40d1-b831-4d9f5ff68053',\n", - " 'dg.MD1R/5ff1a5a6-1fc5-40d2-89a6-f0ceff368fc4']},\n", - " {'case_ids': ['419639-009296'],\n", - " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.264224639489762580960133252508',\n", - " 'project_id': 'Open-R1',\n", - " 'study_description': 'CT CHEST PULMONARY EMBOLISM (CTPE)',\n", - " 'object_id': ['dg.MD1R/ed04458e-3afe-49b8-8cbb-23a0963a119d',\n", - " 'dg.MD1R/5a5a8d5e-23a4-437e-adb5-6a83f0e926f1',\n", - " 'dg.MD1R/8d3164ff-47c6-4d8e-b850-cfa1b30c59b7',\n", - " 'dg.MD1R/62defb09-b21f-43b5-b50e-031eba6f7e1e',\n", - " 'dg.MD1R/f9f0a648-5dea-45fb-8c61-80ea9d93247a',\n", - " 'dg.MD1R/5312dc8a-6aee-4bd1-9bde-541824b6ef4f',\n", - " 'dg.MD1R/6289640f-1892-4c58-80e5-0816c8146894',\n", - " 'dg.MD1R/4e3ec3a6-c244-46bc-9fe4-f7997ef295b4']},\n", - " ...]" - ] - }, - "execution_count": 232, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## This query gets all the imaging studies of modality \"CT\"\n", - "\n", - "query.raw_data_download(\n", - " data_type=\"imaging_study\",\n", - " fields=[\n", - " \"study_uid\",\n", - " \"project_id\",\n", - " \"study_description\",\n", - " \"body_part_examined\",\n", - " \"case_ids\",\n", - " \"object_id\"\n", - " ],\n", - " filter_object={\"=\": {\"study_modality\": \"CT\"}}\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'White',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Male',\n", - " 'age_at_index': 47,\n", - " 'submitter_id': '10000364-206421',\n", - " 'object_id': ['dg.MD1R/f99825c3-68ea-4a2a-a466-9052fbe6d80a',\n", - " 'dg.MD1R/44348bdb-a831-44ec-907c-1f7002bbeff9',\n", - " 'dg.MD1R/cd02ad3c-208e-47d3-8c49-132e64939458',\n", - " 'dg.MD1R/ad7ca02e-44fc-48ba-92da-e9d2ba12941a',\n", - " 'dg.MD1R/6de50ee5-f71f-4400-98ea-416fb8e909b8',\n", - " 'dg.MD1R/72725cc9-4d7d-41c9-a052-c00385cdcc22',\n", - " 'dg.MD1R/a2c28f50-a4e1-4d19-9fe1-915060baacd0',\n", - " 'dg.MD1R/2a36fa46-c03f-4580-84af-c43d2a5ae9a6',\n", - " 'dg.MD1R/98b37604-d2b7-486b-a49b-4e3864f199b6',\n", - " 'dg.MD1R/8522ea81-9b14-4877-b455-66a63c9f43b3',\n", - " 'dg.MD1R/fd6dd641-f65c-473c-bdd7-518afa0dae11',\n", - " 'dg.MD1R/402505b9-dd29-430f-aa3e-afe1d3115d40',\n", - " 'dg.MD1R/15a058a6-8331-4493-9fdb-76517392f1e9',\n", - " 'dg.MD1R/d4d46fcd-0d3c-4e2c-b35c-13627c99b4f9',\n", - " 'dg.MD1R/07d10b9e-5bcd-4b03-86da-75cef411a230',\n", - " 'dg.MD1R/6139aa55-923b-4318-9d4d-a1e2ce0b7a7c',\n", - " 'dg.MD1R/1d024937-2866-417e-b176-7f12ac7dfbc5',\n", - " 'dg.MD1R/35d73f75-c80e-4407-9adc-903c7ade327d',\n", - " 'dg.MD1R/c6ce4e7d-a73d-4d88-9687-37f1e15b0d40']},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'Black or African American',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Female',\n", - " 'age_at_index': 46,\n", - " 'submitter_id': '10000364-1288962',\n", - " 'object_id': ['dg.MD1R/1b7a4aca-a724-45fa-bdd9-7894d1de6259',\n", - " 'dg.MD1R/5f362efd-418b-47e9-bdd8-b88ec801b0ca']},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'White',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Male',\n", - " 'age_at_index': 46,\n", - " 'submitter_id': '10008204-oLAFzo7d0MacboPylxhg',\n", - " 'object_id': ['dg.MD1R/d3ab6406-1e72-4c4e-a65a-a5ca3e3ee63c',\n", - " 'dg.MD1R/df6474ba-8a2a-4a5f-a0af-45df2b0f509f']},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'Black or African American',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Female',\n", - " 'age_at_index': 45,\n", - " 'submitter_id': '10003752-7abGbWvV6UaEc0nAAWvfYQ',\n", - " 'object_id': ['dg.MD1R/8d741781-36f6-4a2b-9b63-ba6d15976707',\n", - " 'dg.MD1R/4571c711-58e9-4079-bbb0-4b18ed698b29']},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'White',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Male',\n", - " 'age_at_index': 45,\n", - " 'submitter_id': '10000364-2732012',\n", - " 'object_id': ['dg.MD1R/976498e7-cda1-4e2e-bf3f-9102cba06b97',\n", - " 'dg.MD1R/b3aed8e7-b25b-4e19-ac03-eb5d411b0d6b',\n", - " 'dg.MD1R/c2a48bcf-4ae6-4300-bec3-89fdb0455405',\n", - " 'dg.MD1R/27f213b7-b40e-4a08-8b3a-b1d116635769',\n", - " 'dg.MD1R/a41440f7-1036-4204-933a-6bd65c83b791',\n", - " 'dg.MD1R/84ef98c9-0d38-4184-ae2d-76199521d1c2',\n", - " 'dg.MD1R/bf8f4f7e-5d4f-4991-8bb1-5f01522925ce',\n", - " 'dg.MD1R/3396bc04-a787-40d9-8dca-cd3694d1cc16',\n", - " 'dg.MD1R/e67c88c8-e9d0-40de-9bd3-c1cc2fd08434',\n", - " 'dg.MD1R/522d277b-3cb2-4c43-9072-c08426ac0404',\n", - " 'dg.MD1R/884e5e86-1a1c-4636-a8b4-2b3def5dda3b',\n", - " 'dg.MD1R/830f5d85-331b-4ba8-9133-8af912fac3d0',\n", - " 'dg.MD1R/b6999741-0c06-4685-ba41-fc691bf8a3cb',\n", - " 'dg.MD1R/ac4bcdd3-2d24-4994-92b3-6b702cb47a40',\n", - " 'dg.MD1R/e6a83505-a2a2-44e3-b729-dac2f535b4b8',\n", - " 'dg.MD1R/f92fcf04-564e-4123-9ca3-725eab953e10']},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'Black or African American',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Female',\n", - " 'age_at_index': 47,\n", - " 'submitter_id': '10000364-1510060',\n", - " 'object_id': ['dg.MD1R/fc4f55b9-a2e8-4be5-be02-efb27d259885',\n", - " 'dg.MD1R/35174e99-e613-4212-9e7d-c7c561741ec5',\n", - " 'dg.MD1R/9f5e3657-538e-455f-94b9-d1a86591069f',\n", - " 'dg.MD1R/182e71a9-dd68-4228-9d3a-c6afe79fd239',\n", - " 'dg.MD1R/bf369fb2-a3a7-42d3-a378-020c360203f3',\n", - " 'dg.MD1R/2ecf7cbe-350f-4cdc-bf5d-ef4bf82ebd1b',\n", - " 'dg.MD1R/b278bcf2-18d4-45c4-b7fd-87cd1df87155',\n", - " 'dg.MD1R/12acee8c-1276-452f-83f9-88a59cfed5c9',\n", - " 'dg.MD1R/2a91db36-38bb-46de-b1fa-4fb347fd890c',\n", - " 'dg.MD1R/96ceee5b-f4d1-4f80-9005-67b7151ccf1b',\n", - " 'dg.MD1R/2301720b-fafd-48b4-8e7e-52563071463b',\n", - " 'dg.MD1R/0ab2af7d-777a-4e96-ba0a-70ad379b5599',\n", - " 'dg.MD1R/b2f96e1f-3ed9-4f1f-b67d-7943e195fe9f',\n", - " 'dg.MD1R/8cc12391-e11b-47ac-8830-ca7c824f3479',\n", - " 'dg.MD1R/f362c2e4-914c-44ee-8b62-f16b2eb4fa20',\n", - " 'dg.MD1R/8327e539-564a-49c8-a0c6-4b9d2fba5f71',\n", - " 'dg.MD1R/d9e4eeca-233a-43fb-b63f-405b60418bd6',\n", - 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" 'dg.MD1R/9fb685a1-c767-45d2-81a4-f738426f2fc9',\n", - " 'dg.MD1R/829910a5-6399-4a07-b327-d4a8e6d7742f',\n", - " 'dg.MD1R/50229ac7-41dd-4339-b4d5-bff7a3be6b72',\n", - " 'dg.MD1R/6889fde0-8235-4725-96d5-117686d66b73',\n", - " 'dg.MD1R/609fd92f-66be-4106-ba94-b5198941f355',\n", - " 'dg.MD1R/2721d1f8-cbd6-41af-aee5-0577efa570a4']},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'Black or African American',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Male',\n", - " 'age_at_index': 47,\n", - " 'submitter_id': '10003752-r673U7PdgUCpx4HKshaE3A',\n", - " 'object_id': ['dg.MD1R/79d03ce9-4ddb-4dc7-9a04-eaa5a51aa419']},\n", - " {'ethnicity': 'Not Hispanic or Latino',\n", - " 'race': 'White',\n", - " 'project_id': 'Open-A1',\n", - " 'sex': 'Female',\n", - " 'age_at_index': 47,\n", - " 'submitter_id': '10000364-5199670',\n", - " 'object_id': ['dg.MD1R/0cd5e5de-42af-4b1e-b4c2-3a9659715b2e']}]" - ] - }, - "execution_count": 227, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Here is an example getting all the cases in a particular project between ages of 45 and 47\n", - "\n", - "query.raw_data_download(\n", - " data_type=\"case\",\n", - " fields=[\n", - " \"submitter_id\",\n", - " \"project_id\",\n", - " \"race\",\n", - " \"sex\",\n", - " \"ethnicity\",\n", - " \"age_at_index\",\n", - " \"object_id\"\n", - " ],\n", - " filter_object={\"AND\": [{\">=\": {\"age_at_index\": 45}},\n", - " {\"<=\": {\"age_at_index\": 47}},\n", - " {\"=\": {\"project_id\": \"Open-A1\"}}]},\n", - " sort_fields=[{\"submitter_id\": \"asc\"}],\n", - " accessibility=\"accessible\"\n", - " )\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Querying_and_Accessing_MIDRC_Data.ipynb b/Querying_and_Accessing_MIDRC_Data.ipynb new file mode 100644 index 0000000..8828556 --- /dev/null +++ b/Querying_and_Accessing_MIDRC_Data.ipynb @@ -0,0 +1,49833 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d8ef63c3", + "metadata": {}, + "source": [ + "# Query and Accessing Data in a Gen3 Data Commons\n", + "---\n", + "by Chris Meyer, PhD\n", + "\n", + "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n", + "\n", + "June 2022\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5db7f87c", + "metadata": {}, + "source": [ + "### Introduction\n", + "---\n", + "* This notebook is intended to demonstrate a variety of ways to access file objects and structured data (aka \"metadata\") in a Gen3 data commons.\n", + "* File objects are accessed via their \"data GUID\" aka \"object_id\", which is a unique identifier that is associated with a storage_url in the file index (https://data.midrc.org/index/index). Users must be authorized to access a file in order to download it via the object_id. \n", + "* Structured data in a Gen3 Data Commons is imported into Postgres via the \"sheepdog\" service and must conform to the data model. The data model is a relational model that consists of tables or \"nodes\" that are related to one another via foreign keys so that the model can be thought of as a graph of nodes that are linked to each other. Each node in the model contains certain properties (keys) that store data of a particular type (values).\n", + "* The \"sheepdog\" service can export tables of data from a particular node of a data project. This is the simplest way to access \"all\" the data in a Gen3 data commons.\n", + "* Queries can be constructed to target specific types of data in Postgres and are handled by the \"peregrine\" graphQL service.\n", + "* Structured data can also be transformed via an \"ETL\" (extract, transform, load) process that takes the complex relationships between nodes and \"flattens\" the data into a single table, which is stored in an ElasticSearch (ES) database that can be queried using the \"guppy\" graphQL service. These ES tables are what the data exploration app of the Gen3 data-portal is based on.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8e1a7935", + "metadata": {}, + "outputs": [], + "source": [ + "# The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n", + "\n", + "#!pip install --upgrade pandas\n", + "#!pip install --upgrade --ignore-installed PyYAML\n", + "#!pip install --upgrade pip\n", + "#!pip install --upgrade gen3\n", + "#!pip install pydicom" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7ea2fa09", + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python Packages and scripts\n", + "\n", + "import pandas as pd\n", + "import sys, os\n", + "import gen3\n", + "import pydicom\n", + "from io import StringIO\n", + "\n", + "\n", + "from gen3.submission import Gen3Submission\n", + "from gen3.auth import Gen3Auth\n", + "from gen3.index import Gen3Index\n", + "from gen3.query import Gen3Query\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8d6e9922", + "metadata": {}, + "outputs": [], + "source": [ + "# Import some custom Python scripts from personal GitHub repo\n", + "# change these directory paths to reflect your local working directory\n", + "\n", + "home_dir = \"/Users/christopher\" \n", + "demo_dir = \"{}/Documents/Notes/MIDRC/tutorials\".format(home_dir)\n", + "\n", + "os.chdir(demo_dir)\n", + "\n", + "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O {}/expansion.py\".format(demo_dir))\n", + "%run expansion.py\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d316dcdf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Getting all project_ids you have access to in the data commons.\n", + "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1', 'TCIA-RICORD']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christopher/Documents/Notes/MIDRC/tutorials/expansion.py:213: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead.\n", + " df = json_normalize(res[\"data\"][\"project\"])\n" + ] + }, + { + "data": { + "text/plain": [ + "['Open-A1', 'Open-A1_PETAL_REDCORAL', 'Open-R1', 'TCIA-RICORD']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n", + "# Change the directory path in \"cred\" to reflect the location of your credentials file.\n", + "\n", + "api = \"https://data.midrc.org\"\n", + "cred = \"{}/Downloads/midrc-credentials.json\".format(home_dir)\n", + "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n", + "sub = Gen3Submission(api, auth) # submission class\n", + "query = Gen3Query(auth) # query class\n", + "exp = Gen3Expansion(api,auth,sub) # class with some custom scripts\n", + "exp.get_project_ids()\n" + ] + }, + { + "cell_type": "markdown", + "id": "85fc475d", + "metadata": {}, + "source": [ + "## Accessing structured data in Postgres using sheepdog exports\n", + "---\n", + "* Probably the most straight-forward way to access structured data in a Gen3 Data Commons is to simply export the table of data using the sheepdog service (https://petstore.swagger.io/?url=https://raw.githubusercontent.com/uc-cdis/sheepdog/master/openapi/swagger.yml#/export/post__program___project__export).\n", + "* The Gen3SDK has a function `Gen3Submission.export_node()` for exporting entire tables of data from Postgres: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/submission.py#L361\n", + " * This function will export all records in a particular node of a specified project, and one can then use standard Python / R (etc.) tools to do the filtering and cohort building.\n", + "* Note: This export function is also accesible in the data-portal by navigating to a data project's URL, e.g., https://data.midrc.org/Open-A1, clicking a node in the graph, and then clicking the \"Download All\" button.\n", + " * For example: https://data.midrc.org/Open-A1/search?node_type=measurement\n", + " * Or, you can enter this URL in your browser, for example: https://data.midrc.org/api/v0/submission//Open/A1/export?node_label=measurement&format=tsv" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1373e86d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2case0007d748-b2b6-4728-9462-42458c5ff6f8Open-A110008204-OQoEQrW3dECxziJyv9TIhwNaNYes10008204-OQoEQrW3dECxziJyv9TIhwNaNYesNot Hispanic or Latino...NaNWhiteFemale10008204.0NaNNaNNaN772.0da3a84c5-aa32-45cc-ab67-af674eb3d425ACR_20220415
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4case00279427-46c6-477c-a175-684b634c4e45Open-A110008204-PjxPFmCaESCPAUG14fSww60.0No10008204-PjxPFmCaESCPAUG14fSwwNaNNoNot Hispanic or Latino...NaNAsianFemale10008204.0NaNNaNNaN0.0586f8831-85cf-4f0a-8752-97acf18811b2ACR_20220715
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17213casefffb8626-c305-4e66-9744-ab8c7eba88c3Open-A110003752-1xOeCuwEj0O1PNBpK90HBg55.0NaN10003752-1xOeCuwEj0O1PNBpK90HBgNaNYesNot Hispanic or Latino...NaNBlack or African AmericanFemale10003752.0NaNNaNNaN606.0b1487107-9f44-40bd-b1bf-4144415db825ACR_20220107
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" + ], + "text/plain": [ + " type id project_id \\\n", + "0 case 00019c92-8598-4d94-84d2-3abf2b132dc9 Open-A1 \n", + "1 case 000465e0-4c4f-48e4-8974-95c045e333cb Open-A1 \n", + "2 case 0007d748-b2b6-4728-9462-42458c5ff6f8 Open-A1 \n", + "3 case 0010129a-8aa1-4916-834a-c4ebcdaf44d5 Open-A1 \n", + "4 case 00279427-46c6-477c-a175-684b634c4e45 Open-A1 \n", + "... ... ... ... \n", + "17209 case ffece2b4-eb81-4f1a-960c-dd18d96feb3b Open-A1 \n", + "17210 case ffede6ba-4724-4bf7-9a5c-d9c28b9a87d6 Open-A1 \n", + "17211 case fff0e332-8092-4c38-80fb-357babcefbde Open-A1 \n", + "17212 case fff76aad-25ca-4d06-9da5-abd0a18acb42 Open-A1 \n", + "17213 case fffb8626-c305-4e66-9744-ab8c7eba88c3 Open-A1 \n", + "\n", + " submitter_id age_at_index age_at_index_gt89 \\\n", + "0 10000364-1164071 27.0 NaN \n", + "1 10000364-5222709 55.0 No \n", + "2 10008204-OQoEQrW3dECxziJyv9TIhw NaN Yes \n", + "3 10003752-Zlh3OsFFjEC7bnFqpHZgIw 76.0 NaN \n", + "4 10008204-PjxPFmCaESCPAUG14fSww 60.0 No \n", + "... ... ... ... \n", + "17209 10000364-940749 67.0 No \n", + "17210 10022779-6AEZNqauUiGiBlHxxY8w 15.0 No \n", + "17211 10008204-nqiOkmBe0WXOr2ZzVFTQ 86.0 No \n", + "17212 10003752-PUN2Iozze0iuaH96b6xS2g 74.0 NaN \n", + "17213 10003752-1xOeCuwEj0O1PNBpK90HBg 55.0 NaN \n", + "\n", + " case_ids country_of_origin \\\n", + "0 10000364-1164071 NaN \n", + "1 10000364-5222709 NaN \n", + "2 10008204-OQoEQrW3dECxziJyv9TIhw NaN \n", + "3 10003752-Zlh3OsFFjEC7bnFqpHZgIw NaN \n", + "4 10008204-PjxPFmCaESCPAUG14fSww NaN \n", + "... ... ... \n", + "17209 10000364-940749 NaN \n", + "17210 10022779-6AEZNqauUiGiBlHxxY8w NaN \n", + "17211 10008204-nqiOkmBe0WXOr2ZzVFTQ NaN \n", + "17212 10003752-PUN2Iozze0iuaH96b6xS2g United States of America \n", + "17213 10003752-1xOeCuwEj0O1PNBpK90HBg NaN \n", + "\n", + " covid19_positive ethnicity ... linked_external_data \\\n", + "0 Yes Not Hispanic or Latino ... NaN \n", + "1 No Not Hispanic or Latino ... NaN \n", + "2 Yes Not Hispanic or Latino ... NaN \n", + "3 Yes Not Hispanic or Latino ... NaN \n", + "4 No Not Hispanic or Latino ... NaN \n", + "... ... ... ... ... \n", + "17209 No Not Hispanic or Latino ... NaN \n", + "17210 Yes Not Hispanic or Latino ... NaN \n", + "17211 No Not Hispanic or Latino ... NaN \n", + "17212 Yes Not Hispanic or Latino ... NaN \n", + "17213 Yes Not Hispanic or Latino ... NaN \n", + "\n", + " race sex site_id token_record_id \\\n", + "0 Black or African American Female 10000364.0 NaN \n", + "1 White Female 10000364.0 NaN \n", + "2 White Female 10008204.0 NaN \n", + "3 Black or African American Female 10003752.0 NaN \n", + "4 Asian Female 10008204.0 NaN \n", + "... ... ... ... ... \n", + "17209 White Male 10000364.0 NaN \n", + "17210 Black or African American Female 10022779.0 NaN \n", + "17211 White Male 10008204.0 NaN \n", + "17212 Black or African American Female 10003752.0 NaN \n", + "17213 Black or African American Female 10003752.0 NaN \n", + "\n", + " treatment_info ventilator_indicator zip \\\n", + "0 NaN NaN 272.0 \n", + "1 NaN NaN 272.0 \n", + "2 NaN NaN 772.0 \n", + "3 NaN NaN 606.0 \n", + "4 NaN NaN 0.0 \n", + "... ... ... ... \n", + "17209 NaN NaN 272.0 \n", + "17210 NaN NaN 0.0 \n", + "17211 NaN NaN 772.0 \n", + "17212 NaN NaN 606.0 \n", + "17213 NaN NaN 606.0 \n", + "\n", + " datasets.id \\\n", + "0 2d761222-3669-4a77-9473-342c369fcc40 \n", + "1 2d761222-3669-4a77-9473-342c369fcc40,5d4579ce-... \n", + "2 da3a84c5-aa32-45cc-ab67-af674eb3d425 \n", + "3 b1487107-9f44-40bd-b1bf-4144415db825 \n", + "4 586f8831-85cf-4f0a-8752-97acf18811b2 \n", + "... ... \n", + "17209 4797be61-6666-45d6-9771-913ab5bd0163,2d761222-... \n", + "17210 586f8831-85cf-4f0a-8752-97acf18811b2 \n", + "17211 da3a84c5-aa32-45cc-ab67-af674eb3d425 \n", + "17212 9bf8adc9-008a-4863-9e8e-bd051555eea6 \n", + "17213 b1487107-9f44-40bd-b1bf-4144415db825 \n", + "\n", + " datasets.submitter_id \n", + "0 ACR_20211115 \n", + "1 ACR_20211115,ACR_20220314 \n", + "2 ACR_20220415 \n", + "3 ACR_20220107 \n", + "4 ACR_20220715 \n", + "... ... \n", + "17209 ACR_20220218,ACR_20211115 \n", + "17210 ACR_20220715 \n", + "17211 ACR_20220415 \n", + "17212 ACR_20210915 \n", + "17213 ACR_20220107 \n", + "\n", + "[17214 rows x 23 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Example of exporting a table of data using the `Gen3Submission.export_node()` function\n", + "cases = sub.export_node(program='Open',project='A1',node_type='case',fileformat='tsv')\n", + "df = pd.read_csv(StringIO(cases), sep='\\t', header=0)\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ff227add", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "197" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "['10000364-1261658',\n", + " '10003752-WGPvINkkL0CIrSueIVq1Jg',\n", + " '10003752-uXv1gUN50k6mv7tUjnA4iA',\n", + " '10000364-6412754',\n", + " '10000364-1445371',\n", + " '10022779-G8aTV7ABUa0enutMB0LLg',\n", + " '10003752-fgcG89lFo029tEOaehRqg',\n", + " '10003752-Qusqy0HUn0ebZ0uNUpPZ4Q',\n", + " '10003752-nTeM3GIJOkSAtdqFYGZM0w',\n", + " '10003752-DM1ytb31mESVcGstOv6pg',\n", + " '10000364-5879162',\n", + " '10003752-7RqcKWJlcUOyqHQQXQdKg',\n", + " '10003752-SIQrULeYu0aa2wj0Vlc8ZQ',\n", + " 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'10003752-CJ2LiofUqNSCrOvrKy2A',\n", + " '10003752-Yvr5gA17Qkm6C3Nr6ioNKQ',\n", + " '10000364-1842777',\n", + " '10003752-51S1voqe2UqgRfAdngn5kw',\n", + " '10022779-BqGlLrjKf0T7iomPhuZUA',\n", + " '10003752-dE3qiYHJ0WF8WIQtj79lA',\n", + " '10003752-6b3wGi34EmpXamCHL9Z9g',\n", + " '10003752-FulUjEunx0mjzokYb3LR9w']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## One can then use standard tools in any programming language to do cohort building. \n", + "## Here I'm using the \"pandas\" Python package to select a cohort based on demographic information stored in the case node.\n", + "cohort = list(df.loc[(df['sex']=='Female') & (df['race']==\"Black or African American\") & (df['age_at_index']>79)]['submitter_id'])\n", + "display(len(cohort))\n", + "cohort" + ] + }, + { + "cell_type": "markdown", + "id": "a27e781d", + "metadata": {}, + "source": [ + "### Using a Python wrapper to get all the data in a particular node\n", + "---\n", + "* I've written a wrapper script called `Gen3Expansion.get_node_tsvs()` that uses the `Gen3Submission.export_node()` function to export the same node across all projects you have access to in the data commons and then merges the results into a single master table for that node:\n", + "https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L219" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fdc0977e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christopher/Documents/Notes/MIDRC/tutorials/expansion.py:248: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead.\n", + " json_normalize(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File previously downloaded.\n", + "node_tsvs/case_tsvs/Open-A1_case.tsv has 17214 records.\n", + "File previously downloaded.\n", + "node_tsvs/case_tsvs/Open-A1_PETAL_REDCORAL_case.tsv has 857 records.\n", + "File previously downloaded.\n", + "node_tsvs/case_tsvs/TCIA-RICORD_case.tsv has 524 records.\n", + "File previously downloaded.\n", + "node_tsvs/case_tsvs/Open-R1_case.tsv has 7716 records.\n", + "length of all dfs: 26311\n", + "Master node TSV with 26311 total records written to master_case.tsv.\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " type id project_id \\\n", + "0 case 00019c92-8598-4d94-84d2-3abf2b132dc9 Open-A1 \n", + "1 case 000465e0-4c4f-48e4-8974-95c045e333cb Open-A1 \n", + "2 case 0007d748-b2b6-4728-9462-42458c5ff6f8 Open-A1 \n", + "3 case 0010129a-8aa1-4916-834a-c4ebcdaf44d5 Open-A1 \n", + "4 case 00279427-46c6-477c-a175-684b634c4e45 Open-A1 \n", + "... ... ... ... \n", + "26306 case ffc5fdd4-37fe-4fd3-9c03-ef12e75bfe94 Open-R1 \n", + "26307 case ffc72804-b7fa-4b3e-8b73-ca90a5daa4b7 Open-R1 \n", + "26308 case ffca55f0-f620-4556-95b1-d91213a4cfd9 Open-R1 \n", + "26309 case ffccf300-010e-4c4c-a119-63ba28002cdf Open-R1 \n", + "26310 case ffe09dbc-c8e7-4ee7-9afc-75c53c4d52c9 Open-R1 \n", + "\n", + " submitter_id age_at_index age_at_index_gt89 \\\n", + "0 10000364-1164071 27.0 NaN \n", + "1 10000364-5222709 55.0 No \n", + "2 10008204-OQoEQrW3dECxziJyv9TIhw NaN Yes \n", + "3 10003752-Zlh3OsFFjEC7bnFqpHZgIw 76.0 NaN \n", + "4 10008204-PjxPFmCaESCPAUG14fSww 60.0 No \n", + "... ... ... ... \n", + "26306 419639-009758 63.0 NaN \n", + "26307 419639-006813 34.0 No \n", + "26308 419639-009211 81.0 No \n", + "26309 419639-002015 73.0 No \n", + "26310 419639-011547 66.0 NaN \n", + "\n", + " case_ids country_of_origin \\\n", + "0 10000364-1164071 NaN \n", + "1 10000364-5222709 NaN \n", + "2 10008204-OQoEQrW3dECxziJyv9TIhw NaN \n", + "3 10003752-Zlh3OsFFjEC7bnFqpHZgIw NaN \n", + "4 10008204-PjxPFmCaESCPAUG14fSww NaN \n", + "... ... ... \n", + "26306 419639-009758 United States of America \n", + "26307 419639-006813 United States of America \n", + "26308 419639-009211 United States of America \n", + "26309 419639-002015 United States of America \n", + "26310 419639-011547 United States of America \n", + "\n", + " covid19_positive ethnicity ... linked_external_data \\\n", + "0 Yes Not Hispanic or Latino ... NaN \n", + "1 No Not Hispanic or Latino ... NaN \n", + "2 Yes Not Hispanic or Latino ... NaN \n", + "3 Yes Not Hispanic or Latino ... NaN \n", + "4 No Not Hispanic or Latino ... NaN \n", + "... ... ... ... ... \n", + "26306 No Not Hispanic or Latino ... NaN \n", + "26307 Yes Not Hispanic or Latino ... NaN \n", + "26308 No Not Hispanic or Latino ... NaN \n", + "26309 No Not Hispanic or Latino ... NaN \n", + "26310 No Not Hispanic or Latino ... NaN \n", + "\n", + " race sex site_id token_record_id \\\n", + "0 Black or African American Female 10000364.0 NaN \n", + "1 White Female 10000364.0 NaN \n", + "2 White Female 10008204.0 NaN \n", + "3 Black or African American Female 10003752.0 NaN \n", + "4 Asian Female 10008204.0 NaN \n", + "... ... ... ... ... \n", + "26306 NaN Male 419639.0 NaN \n", + "26307 Asian Male 419639.0 NaN \n", + "26308 Not Reported Male 419639.0 NaN \n", + "26309 White Female 419639.0 NaN \n", + "26310 White Male 419639.0 NaN \n", + "\n", + " treatment_info ventilator_indicator zip \\\n", + "0 NaN NaN 272.0 \n", + "1 NaN NaN 272.0 \n", + "2 NaN NaN 772.0 \n", + "3 NaN NaN 606.0 \n", + "4 NaN NaN 0.0 \n", + "... ... ... ... \n", + "26306 NaN NaN 0.0 \n", + "26307 NaN NaN 941.0 \n", + "26308 NaN NaN 0.0 \n", + "26309 NaN NaN 940.0 \n", + "26310 NaN NaN 941.0 \n", + "\n", + " datasets.id \\\n", + "0 2d761222-3669-4a77-9473-342c369fcc40 \n", + "1 2d761222-3669-4a77-9473-342c369fcc40,5d4579ce-... \n", + "2 da3a84c5-aa32-45cc-ab67-af674eb3d425 \n", + "3 b1487107-9f44-40bd-b1bf-4144415db825 \n", + "4 586f8831-85cf-4f0a-8752-97acf18811b2 \n", + "... ... \n", + "26306 6220bbaa-5671-43ab-8b60-f58a52dd8f46 \n", + "26307 c378d9a4-e1ac-4317-aa66-6958ecc706bc \n", + "26308 c378d9a4-e1ac-4317-aa66-6958ecc706bc \n", + "26309 2ec8acaf-ae7f-4035-82a9-8d3f19c1c91d,38a6adc3-... \n", + "26310 d4d45224-01bf-4784-b7d1-6769bcedbc46 \n", + "\n", + " datasets.submitter_id \n", + "0 ACR_20211115 \n", + "1 ACR_20211115,ACR_20220314 \n", + "2 ACR_20220415 \n", + "3 ACR_20220107 \n", + "4 ACR_20220715 \n", + "... ... \n", + "26306 RSNA_20210922 \n", + "26307 RSNA_20220329 \n", + "26308 RSNA_20220329 \n", + "26309 RSNA_20220308,RSNA_20220114,RSNA_20220329 \n", + "26310 RSNA_20220214 \n", + "\n", + "[26311 rows x 23 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cases = exp.get_node_tsvs(node='case')\n", + "display(cases)" + ] + }, + { + "cell_type": "markdown", + "id": "b150f734", + "metadata": {}, + "source": [ + "### Using a Python wrapper to get all the data in a particular project\n", + "---\n", + "* Similar to the above example, I've written a wrapper script called `Gen3Expansion.get_project_tsvs()` that uses the `Gen3Submission.export_node()` function to export every node in every project (or a particular project) in the data commons.\n", + "https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L298\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "71757809", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christopher/Documents/Notes/MIDRC/tutorials/expansion.py:324: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead.\n", + " json_normalize(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_case.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_condition.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_core_metadata_collection.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_cr_series_file.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_ct_series_file.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_dataset.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_dx_series_file.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_imaging_study.tsv'\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_measurement.tsv'\n", + "\t0 records in node 'medication' of project 'Open-A1'.\n", + "\t0 records in node 'mg_series_file' of project 'Open-A1'.\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_mr_series_file.tsv'\n", + "\t0 records in node 'nm_series_file' of project 'Open-A1'.\n", + "\t0 records in node 'observation' of project 'Open-A1'.\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_procedure.tsv'\n", + "\t0 records in node 'pt_series_file' of project 'Open-A1'.\n", + "\t0 records in node 'rf_series_file' of project 'Open-A1'.\n", + "\t0 records in node 'supplementary_file' of project 'Open-A1'.\n", + "\t0 records in node 'us_series_file' of project 'Open-A1'.\n", + "\tPreviously downloaded: 'project_tsvs/Open-A1_tsvs/Open-A1_visit.tsv'\n", + "\t0 records in node 'xa_series_file' of project 'Open-A1'.\n" + ] + }, + { + "data": { + "text/plain": [ + "'Open-A1_case.tsv\\nOpen-A1_condition.tsv\\nOpen-A1_core_metadata_collection.tsv\\nOpen-A1_cr_series_file.tsv\\nOpen-A1_ct_series_file.tsv\\nOpen-A1_dataset.tsv\\nOpen-A1_dx_series_file.tsv\\nOpen-A1_imaging_study.tsv\\nOpen-A1_measurement.tsv\\nOpen-A1_mr_series_file.tsv\\nOpen-A1_procedure.tsv\\nOpen-A1_visit.tsv\\n'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## This example gets all the data in every node of the data model in the project Open-A1\n", + "## If \"projects\" is not specific, all data across all projects you have access to will be downloaded.\n", + "exp.get_project_tsvs(projects='Open-A1')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "51fc6b41", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 261264\r\n", + "-rw-r--r-- 1 christopher staff 4334153 Oct 7 11:53 Open-A1_case.tsv\r\n", + "-rw-r--r-- 1 christopher staff 4053963 Oct 7 11:54 Open-A1_condition.tsv\r\n", + "-rw-r--r-- 1 christopher staff 2478 Oct 7 11:54 Open-A1_core_metadata_collection.tsv\r\n", + "-rw-r--r-- 1 christopher staff 41708674 Oct 7 11:54 Open-A1_cr_series_file.tsv\r\n", + "-rw-r--r-- 1 christopher staff 45465032 Oct 7 11:54 Open-A1_ct_series_file.tsv\r\n", + "-rw-r--r-- 1 christopher staff 1891 Oct 7 11:54 Open-A1_dataset.tsv\r\n", + "-rw-r--r-- 1 christopher staff 3261754 Oct 7 11:54 Open-A1_dx_series_file.tsv\r\n", + "-rw-r--r-- 1 christopher staff 16621300 Oct 7 11:54 Open-A1_imaging_study.tsv\r\n", + "-rw-r--r-- 1 christopher staff 8164530 Oct 7 11:55 Open-A1_measurement.tsv\r\n", + "-rw-r--r-- 1 christopher staff 546834 Oct 7 11:55 Open-A1_mr_series_file.tsv\r\n", + "-rw-r--r-- 1 christopher staff 731954 Oct 7 11:55 Open-A1_procedure.tsv\r\n", + "-rw-r--r-- 1 christopher staff 8853830 Oct 7 11:55 Open-A1_visit.tsv\r\n" + ] + } + ], + "source": [ + "!ls -l project_tsvs/Open-A1_tsvs" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f7e40ba9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidproject_idsubmitter_iddata_categorydata_formatdata_typefile_namefile_sizemd5sum...pixel_spacingseries_descriptionseries_uidslice_thicknessspacing_between_slicesstorage_urlscore_metadata_collections.idcore_metadata_collections.submitter_idimaging_studies.idimaging_studies.submitter_id
0ct_series_file0000e493-6158-446d-8e0e-73fe7717ea3dOpen-A12.16.840.1.114274.1818.55280523935754766246057...CTDCMDICOM10000364-902955/2.16.840.1.114274.1818.4848051...147081085b52b91b04ce014c2e91ab53d4ab7e64...NaNLUNG MIPS2.16.840.1.114274.1818.55280523935754766246057...NaNNaNNaNb2f0484a-2536-49b7-8739-c1056a9ce85cACR_202202185436bbf6-7505-4ba6-a5e8-90fe390721772.16.840.1.114274.1818.48480514094995143579745...
1ct_series_file0000ecf4-0982-44a4-9bf9-3da508aa8254Open-A12.16.840.1.114274.1818.49957484958482183452775...CTDCMDICOM10003752-FTrypNMZECIjF730AFXfQ/2.16.840.1.1142...16300025aff7eef47137bcd418dcf21b38ac96b...NaNSCOUT2.16.840.1.114274.1818.49957484958482183452775...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_2022010725076607-be7f-47d3-816b-dcb5fa7363c72.16.840.1.114274.1818.46251060062627182281552...
2ct_series_file0001ea29-d145-46b3-b655-fdf54d376561Open-A12.16.840.1.114274.1818.47452056560618671341191...CTDCMDICOM2.16.840.1.114274.1818.57601668720416007191725...42499230910821c0599ea333c4be2de47f183136...NaNHELICAL ABD/PELVIS2.16.840.1.114274.1818.47452056560618671341191...NaNNaNNaNb0dc067c-b7e8-41ee-a594-a857f033f716ACR_20211115ad885b5b-715b-48c9-a418-907a495de1312.16.840.1.114274.1818.57601668720416007191725...
3ct_series_file00034623-4a38-4c11-aea5-2b298aa0e9e9Open-A12.16.840.1.114274.1818.55127517796029680761427...CTDCMDICOM10008204-0nJwEB77qUq3a2uabyflrg/2.16.840.1.114...66050106244c8c9621798e6c26663ab6fb87be0...NaNCOR MIP 102.16.840.1.114274.1818.55127517796029680761427...NaNNaNNaNb2f0484a-2536-49b7-8739-c1056a9ce85cACR_202202181fe6c945-9ba6-41f2-99ee-c80c1c2882fa2.16.840.1.114274.1818.56883258560556874514740...
4ct_series_file00052c2d-89d4-44cd-97df-6de2566b993cOpen-A12.16.840.1.114274.1818.47155464547112396948848...CTDCMDICOM10000364-2059687/2.16.840.1.114274.1818.499762...351131302b518cedb9d2a5171c3ab1b070c9524e...NaNCAP WITH 5.00 Bl56 S2 Lung2.16.840.1.114274.1818.47155464547112396948848...NaNNaNNaN4a5dd86d-7965-48bb-9c2b-c6d36089c780ACR_202203146c8ab96e-7dfd-4dfa-8b42-3c226e91b94d2.16.840.1.114274.1818.49976247630990486627334...
..................................................................
67674ct_series_filefffa6f1d-c3da-4a85-81ef-bae883509a87Open-A12.16.840.1.114274.1818.56750137356834730091538...CTDCMDICOM10000364-5439827/2.16.840.1.114274.1818.528965...29287140fba11dc36836a50e2e0383706cdf7528...NaNChest 2.0 B31f2.16.840.1.114274.1818.56750137356834730091538...NaNNaNNaN4a5dd86d-7965-48bb-9c2b-c6d36089c780ACR_202203147b70c74e-2973-4c5d-aa01-ef7e4bf18bb22.16.840.1.114274.1818.52896557293505301851152...
67675ct_series_filefffab4cd-121b-444a-bc2a-523e8fd44d26Open-A12.16.840.1.114274.1818.48828824781586614918835...CTDCMDICOM10008204-Q9piW8nKM0bXUaT0jo6yw/2.16.840.1.1142...275931561a60deadec8092f132cd44d61a12d956...NaNCHEST COR X22.16.840.1.114274.1818.48828824781586614918835...NaNNaNNaN4a5dd86d-7965-48bb-9c2b-c6d36089c780ACR_20220314c2dfd332-dce0-401f-ab55-84d6f1f3f5232.16.840.1.114274.1818.47176250726742885511773...
67676ct_series_filefffbad2c-8d29-443d-a069-bf47bb53eca1Open-A12.16.840.1.114274.1818.50452763299441297771610...CTDCMDICOM10000364-1326503/2.16.840.1.114274.1818.563257...158230943fb971342afe5884ce1158554e727da8...0.587891,0.587891I MAPPING2.16.840.1.114274.1818.50452763299441297771610...2.52.5NaN59f32345-e328-4e77-8813-6fdff0a106e3ACR_20220415a1216056-7bfc-41bd-ad5d-4a558c63366f2.16.840.1.114274.1818.56325786198642448731400...
67677ct_series_filefffbd30e-44af-4d1b-b743-ae2de0acd5eeOpen-A12.16.840.1.114274.1818.50032364059379807521614...CTDCMDICOM2.16.840.1.114274.1818.54642616033529162091218...31716034ff9522be1a76556686f113273a2b1276...NaNchest sagital2.16.840.1.114274.1818.50032364059379807521614...NaNNaNNaNb0dc067c-b7e8-41ee-a594-a857f033f716ACR_20211115fe61f903-6a51-462d-9e55-0bdad49cd1bb2.16.840.1.114274.1818.54642616033529162091218...
67678ct_series_filefffe5fec-e036-4b35-aaa4-1f8c506fbff1Open-A12.16.840.1.114274.1818.48823377996653460923277...CTDCMDICOM10000364-1052751/CT_2.16.840.1.114274.1818.497...1319669086b8ca90f474f62ae2965a4823f34d50...NaNFl_LUNG 1.5 Bl572.16.840.1.114274.1818.48823377996653460923277...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_20211020afe6128d-e9ae-4caa-b890-ce4059fb8a822.16.840.1.114274.1818.49754138310293527441582...
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67679 rows × 34 columns

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" + ], + "text/plain": [ + " type id project_id \\\n", + "0 ct_series_file 0000e493-6158-446d-8e0e-73fe7717ea3d Open-A1 \n", + "1 ct_series_file 0000ecf4-0982-44a4-9bf9-3da508aa8254 Open-A1 \n", + "2 ct_series_file 0001ea29-d145-46b3-b655-fdf54d376561 Open-A1 \n", + "3 ct_series_file 00034623-4a38-4c11-aea5-2b298aa0e9e9 Open-A1 \n", + "4 ct_series_file 00052c2d-89d4-44cd-97df-6de2566b993c Open-A1 \n", + "... ... ... ... \n", + "67674 ct_series_file fffa6f1d-c3da-4a85-81ef-bae883509a87 Open-A1 \n", + "67675 ct_series_file fffab4cd-121b-444a-bc2a-523e8fd44d26 Open-A1 \n", + "67676 ct_series_file fffbad2c-8d29-443d-a069-bf47bb53eca1 Open-A1 \n", + "67677 ct_series_file fffbd30e-44af-4d1b-b743-ae2de0acd5ee Open-A1 \n", + "67678 ct_series_file fffe5fec-e036-4b35-aaa4-1f8c506fbff1 Open-A1 \n", + "\n", + " submitter_id data_category \\\n", + "0 2.16.840.1.114274.1818.55280523935754766246057... CT \n", + "1 2.16.840.1.114274.1818.49957484958482183452775... CT \n", + "2 2.16.840.1.114274.1818.47452056560618671341191... CT \n", + "3 2.16.840.1.114274.1818.55127517796029680761427... CT \n", + "4 2.16.840.1.114274.1818.47155464547112396948848... CT \n", + "... ... ... \n", + "67674 2.16.840.1.114274.1818.56750137356834730091538... CT \n", + "67675 2.16.840.1.114274.1818.48828824781586614918835... CT \n", + "67676 2.16.840.1.114274.1818.50452763299441297771610... CT \n", + "67677 2.16.840.1.114274.1818.50032364059379807521614... CT \n", + "67678 2.16.840.1.114274.1818.48823377996653460923277... CT \n", + "\n", + " data_format data_type \\\n", + "0 DCM DICOM \n", + "1 DCM DICOM \n", + "2 DCM DICOM \n", + "3 DCM DICOM \n", + "4 DCM DICOM \n", + "... ... ... \n", + "67674 DCM DICOM \n", + "67675 DCM DICOM \n", + "67676 DCM DICOM \n", + "67677 DCM DICOM \n", + "67678 DCM DICOM \n", + "\n", + " file_name file_size \\\n", + "0 10000364-902955/2.16.840.1.114274.1818.4848051... 14708108 \n", + "1 10003752-FTrypNMZECIjF730AFXfQ/2.16.840.1.1142... 1630002 \n", + "2 2.16.840.1.114274.1818.57601668720416007191725... 42499230 \n", + "3 10008204-0nJwEB77qUq3a2uabyflrg/2.16.840.1.114... 6605010 \n", + "4 10000364-2059687/2.16.840.1.114274.1818.499762... 35113130 \n", + "... ... ... \n", + "67674 10000364-5439827/2.16.840.1.114274.1818.528965... 29287140 \n", + "67675 10008204-Q9piW8nKM0bXUaT0jo6yw/2.16.840.1.1142... 27593156 \n", + "67676 10000364-1326503/2.16.840.1.114274.1818.563257... 15823094 \n", + "67677 2.16.840.1.114274.1818.54642616033529162091218... 31716034 \n", + "67678 10000364-1052751/CT_2.16.840.1.114274.1818.497... 13196690 \n", + "\n", + " md5sum ... pixel_spacing \\\n", + "0 5b52b91b04ce014c2e91ab53d4ab7e64 ... NaN \n", + "1 5aff7eef47137bcd418dcf21b38ac96b ... NaN \n", + "2 910821c0599ea333c4be2de47f183136 ... NaN \n", + "3 6244c8c9621798e6c26663ab6fb87be0 ... NaN \n", + "4 2b518cedb9d2a5171c3ab1b070c9524e ... NaN \n", + "... ... ... ... \n", + "67674 fba11dc36836a50e2e0383706cdf7528 ... NaN \n", + "67675 1a60deadec8092f132cd44d61a12d956 ... NaN \n", + "67676 3fb971342afe5884ce1158554e727da8 ... 0.587891,0.587891 \n", + "67677 ff9522be1a76556686f113273a2b1276 ... NaN \n", + "67678 86b8ca90f474f62ae2965a4823f34d50 ... NaN \n", + "\n", + " series_description \\\n", + "0 LUNG MIPS \n", + "1 SCOUT \n", + "2 HELICAL ABD/PELVIS \n", + "3 COR MIP 10 \n", + "4 CAP WITH 5.00 Bl56 S2 Lung \n", + "... ... \n", + "67674 Chest 2.0 B31f \n", + "67675 CHEST COR X2 \n", + "67676 I MAPPING \n", + "67677 chest sagital \n", + "67678 Fl_LUNG 1.5 Bl57 \n", + "\n", + " series_uid slice_thickness \\\n", + "0 2.16.840.1.114274.1818.55280523935754766246057... 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NaN \n", + "\n", + " spacing_between_slices storage_urls \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "67674 NaN NaN \n", + "67675 NaN NaN \n", + "67676 2.5 NaN \n", + "67677 NaN NaN \n", + "67678 NaN NaN \n", + "\n", + " core_metadata_collections.id \\\n", + "0 b2f0484a-2536-49b7-8739-c1056a9ce85c \n", + "1 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", + "2 b0dc067c-b7e8-41ee-a594-a857f033f716 \n", + "3 b2f0484a-2536-49b7-8739-c1056a9ce85c \n", + "4 4a5dd86d-7965-48bb-9c2b-c6d36089c780 \n", + "... ... \n", + "67674 4a5dd86d-7965-48bb-9c2b-c6d36089c780 \n", + "67675 4a5dd86d-7965-48bb-9c2b-c6d36089c780 \n", + "67676 59f32345-e328-4e77-8813-6fdff0a106e3 \n", + "67677 b0dc067c-b7e8-41ee-a594-a857f033f716 \n", + "67678 23e8c279-92d2-47da-99de-97dedc454bb4 \n", + "\n", + " core_metadata_collections.submitter_id \\\n", + "0 ACR_20220218 \n", + "1 ACR_20220107 \n", + "2 ACR_20211115 \n", + "3 ACR_20220218 \n", + "4 ACR_20220314 \n", + "... ... \n", + "67674 ACR_20220314 \n", + "67675 ACR_20220314 \n", + "67676 ACR_20220415 \n", + "67677 ACR_20211115 \n", + "67678 ACR_20211020 \n", + "\n", + " imaging_studies.id \\\n", + "0 5436bbf6-7505-4ba6-a5e8-90fe39072177 \n", + "1 25076607-be7f-47d3-816b-dcb5fa7363c7 \n", + "2 ad885b5b-715b-48c9-a418-907a495de131 \n", + "3 1fe6c945-9ba6-41f2-99ee-c80c1c2882fa \n", + "4 6c8ab96e-7dfd-4dfa-8b42-3c226e91b94d \n", + "... ... \n", + "67674 7b70c74e-2973-4c5d-aa01-ef7e4bf18bb2 \n", + "67675 c2dfd332-dce0-401f-ab55-84d6f1f3f523 \n", + "67676 a1216056-7bfc-41bd-ad5d-4a558c63366f \n", + "67677 fe61f903-6a51-462d-9e55-0bdad49cd1bb \n", + "67678 afe6128d-e9ae-4caa-b890-ce4059fb8a82 \n", + "\n", + " imaging_studies.submitter_id \n", + "0 2.16.840.1.114274.1818.48480514094995143579745... \n", + "1 2.16.840.1.114274.1818.46251060062627182281552... \n", + "2 2.16.840.1.114274.1818.57601668720416007191725... \n", + "3 2.16.840.1.114274.1818.56883258560556874514740... \n", + "4 2.16.840.1.114274.1818.49976247630990486627334... \n", + "... ... \n", + "67674 2.16.840.1.114274.1818.52896557293505301851152... \n", + "67675 2.16.840.1.114274.1818.47176250726742885511773... \n", + "67676 2.16.840.1.114274.1818.56325786198642448731400... \n", + "67677 2.16.840.1.114274.1818.54642616033529162091218... \n", + "67678 2.16.840.1.114274.1818.49754138310293527441582... \n", + "\n", + "[67679 rows x 34 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## You can then read in the TSVs of data exported from a node to do cohort building / research\n", + "tsv_dir = 'project_tsvs/Open-A1_tsvs'\n", + "ct = pd.read_csv(\"{}/Open-A1_ct_series_file.tsv\".format(tsv_dir),sep='\\t',dtype=str)\n", + "display(ct)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a3e9628c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidproject_idsubmitter_iddata_categorydata_formatdata_typefile_namefile_sizemd5sum...pixel_spacingseries_descriptionseries_uidslice_thicknessspacing_between_slicesstorage_urlscore_metadata_collections.idcore_metadata_collections.submitter_idimaging_studies.idimaging_studies.submitter_id
175ct_series_file00b0e0dc-170f-4863-a207-6838fdf1e1f3Open-A12.16.840.1.114274.1818.55964346378096610171168...CTDCMDICOM2.16.840.1.114274.1818.47546942155557792113367...133240666eb3dcf365021935f8186b1752dbf0e0...NaNMIP iDose (2)2.16.840.1.114274.1818.55964346378096610171168...NaNNaNNaNb0dc067c-b7e8-41ee-a594-a857f033f716ACR_20211115b1ccf140-c713-49a1-8ed0-e0c9a54bf6382.16.840.1.114274.1818.47546942155557792113367...
414ct_series_file019ac39b-ece4-4fbe-8fdd-dad6d14b76f0Open-A12.16.840.1.114274.1818.48529565050476535451230...CTDCMDICOM10008204-1KQ44WmzGkq4jdkHfyo5gQ/2.16.840.1.114...20873724cb2c99385ec40d07d2cd246cf2245ab4...NaNCOR L2.16.840.1.114274.1818.48529565050476535451230...NaNNaNNaNb2f0484a-2536-49b7-8739-c1056a9ce85cACR_20220218aaa83fe3-f825-4bf0-a5ad-8b339850e41c2.16.840.1.114274.1818.46752283805707555701690...
752ct_series_file02ecfc2c-f3b2-409c-85e2-fcbf25db548dOpen-A12.16.840.1.114274.1818.52621518601439517932967...CTDCMDICOM10000364-1490659/2.16.840.1.114274.1818.560807...25281408c2de213e3147c1ea7d4f4dd17e6d9d7b...NaNchest coronal2.16.840.1.114274.1818.52621518601439517932967...NaNNaNNaNb2f0484a-2536-49b7-8739-c1056a9ce85cACR_2022021821f75cac-8d92-494a-af37-0ee41b17fe592.16.840.1.114274.1818.56080742760726638561677...
765ct_series_file02fcfa2b-2737-4475-92d3-9a6832093830Open-A12.16.840.1.114274.1818.47899984674779750371141...CTDCMDICOM10022779-0MObYYkvKkKrHfkwQT9kA/2.16.840.1.1142...29211206cb1a1e69d471e54a53b900a60cacd171...0.7383099,0.7383099LUNGS MIP, iDose (3)2.16.840.1.114274.1818.47899984674779750371141...52.5NaN27d81499-fc2f-4677-8d4b-fae20e054946ACR_20220715c7930d40-7f64-4079-8162-637140e343412.16.840.1.114274.1818.49193912772239556361726...
904ct_series_file03a16d8b-658b-4fc6-a9eb-9567c96d2e4eOpen-A12.16.840.1.114274.1818.48959334245767654136969...CTDCMDICOM10000364-1319431/CT_2.16.840.1.114274.1818.540...4392869448e07096e513efa9c7815dfeada1063b...NaN#PP DE_Thorax 1.5 Qr40 2 B_Sn140kV2.16.840.1.114274.1818.48959334245767654136969...NaNNaNNaN23e8c279-92d2-47da-99de-97dedc454bb4ACR_2021102008f71e93-ebc9-4e1e-a4ee-5013e595d3dd2.16.840.1.114274.1818.54095403294717579441711...
..................................................................
67409ct_series_filefedd02de-c32e-4206-bca9-83cb65ce1146Open-A12.16.840.1.114274.1818.57442183453554543434456...CTDCMDICOM10003752-WGPvINkkL0CIrSueIVq1Jg/2.16.840.1.114...7790730a48d8e5db5e85c3a64f6e84608dc2a3c...NaNMIN IP2.16.840.1.114274.1818.57442183453554543434456...NaNNaNNaN06406925-1d98-4e8f-a0ce-c52b678bcb2aACR_20220107f27cc79e-d8b1-4b96-a883-73da2210ce602.16.840.1.114274.1818.48752907436280297562336...
67538ct_series_fileff6aa265-6ab0-412c-abfd-6f43031dce70Open-A12.16.840.1.114274.1818.52468334977846042031093...CTDCMDICOM10000364-1404165/2.16.840.1.114274.1818.529432...62924048c4cd3ae36fe9bc5145d3438b4a11094...NaNHigh kVp Stnd2.16.840.1.114274.1818.52468334977846042031093...NaNNaNNaNb2f0484a-2536-49b7-8739-c1056a9ce85cACR_202202184fa257ab-a4d0-4281-aca9-d4c09069341b2.16.840.1.114274.1818.52943247670294330431389...
67540ct_series_fileff6b88f2-981f-4da8-8f6f-9f059c133be3Open-A12.16.840.1.114274.1818.56922523991554735871257...CTDCMDICOM10000364-5879162/2.16.840.1.114274.1818.547064...132524325dda60f022f09840ae96e94e7d13f6...NaNTopogram 0.6 T20f2.16.840.1.114274.1818.56922523991554735871257...NaNNaNNaN4a5dd86d-7965-48bb-9c2b-c6d36089c780ACR_20220314c8b1a6da-28c7-4f00-80d4-4c4bc3ca53132.16.840.1.114274.1818.54706480863499007832743...
67541ct_series_fileff6e68db-95aa-4daa-85a8-acfa342e504eOpen-A12.16.840.1.114274.1818.56981222797237409122046...CTDCMDICOM10000364-1265706/2.16.840.1.114274.1818.484499...57263698a2ed781194d52f192211c087c2fa46a9...NaNSag2.16.840.1.114274.1818.56981222797237409122046...NaNNaNNaNb2f0484a-2536-49b7-8739-c1056a9ce85cACR_202202184fa5428b-2866-4004-a7b0-b427eee75c992.16.840.1.114274.1818.48449940367163446134194...
67667ct_series_filefff4ac21-8fe4-4ac1-9e41-3c9ee5e1b712Open-A12.16.840.1.114274.1818.49929890207265010191125...CTDCMDICOM2.16.840.1.114274.1818.52146263260855674077294...26016868d04ee65e0bb76ee3921dceeb44f2be43...NaNSTD AXL2.16.840.1.114274.1818.49929890207265010191125...NaNNaNNaNb0dc067c-b7e8-41ee-a594-a857f033f716ACR_202111150b8ff751-72e6-4504-b976-5b98130c34762.16.840.1.114274.1818.52146263260855674077294...
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688 rows × 34 columns

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CT \n", + "752 2.16.840.1.114274.1818.52621518601439517932967... CT \n", + "765 2.16.840.1.114274.1818.47899984674779750371141... CT \n", + "904 2.16.840.1.114274.1818.48959334245767654136969... CT \n", + "... ... ... \n", + "67409 2.16.840.1.114274.1818.57442183453554543434456... CT \n", + "67538 2.16.840.1.114274.1818.52468334977846042031093... CT \n", + "67540 2.16.840.1.114274.1818.56922523991554735871257... CT \n", + "67541 2.16.840.1.114274.1818.56981222797237409122046... CT \n", + "67667 2.16.840.1.114274.1818.49929890207265010191125... CT \n", + "\n", + " data_format data_type \\\n", + "175 DCM DICOM \n", + "414 DCM DICOM \n", + "752 DCM DICOM \n", + "765 DCM DICOM \n", + "904 DCM DICOM \n", + "... ... ... \n", + "67409 DCM DICOM \n", + "67538 DCM DICOM \n", + "67540 DCM DICOM \n", + "67541 DCM DICOM \n", + "67667 DCM DICOM \n", + "\n", + " file_name file_size \\\n", + "175 2.16.840.1.114274.1818.47546942155557792113367... 13324066 \n", + "414 10008204-1KQ44WmzGkq4jdkHfyo5gQ/2.16.840.1.114... 20873724 \n", + "752 10000364-1490659/2.16.840.1.114274.1818.560807... 25281408 \n", + "765 10022779-0MObYYkvKkKrHfkwQT9kA/2.16.840.1.1142... 29211206 \n", + "904 10000364-1319431/CT_2.16.840.1.114274.1818.540... 43928694 \n", + "... ... ... \n", + "67409 10003752-WGPvINkkL0CIrSueIVq1Jg/2.16.840.1.114... 7790730 \n", + "67538 10000364-1404165/2.16.840.1.114274.1818.529432... 6292404 \n", + "67540 10000364-5879162/2.16.840.1.114274.1818.547064... 132524 \n", + "67541 10000364-1265706/2.16.840.1.114274.1818.484499... 57263698 \n", + "67667 2.16.840.1.114274.1818.52146263260855674077294... 26016868 \n", + "\n", + " md5sum ... pixel_spacing \\\n", + "175 6eb3dcf365021935f8186b1752dbf0e0 ... NaN \n", + "414 cb2c99385ec40d07d2cd246cf2245ab4 ... NaN \n", + "752 c2de213e3147c1ea7d4f4dd17e6d9d7b ... NaN \n", + "765 cb1a1e69d471e54a53b900a60cacd171 ... 0.7383099,0.7383099 \n", + "904 48e07096e513efa9c7815dfeada1063b ... NaN \n", + "... ... ... ... \n", + "67409 a48d8e5db5e85c3a64f6e84608dc2a3c ... NaN \n", + "67538 8c4cd3ae36fe9bc5145d3438b4a11094 ... NaN \n", + "67540 325dda60f022f09840ae96e94e7d13f6 ... NaN \n", + "67541 a2ed781194d52f192211c087c2fa46a9 ... NaN \n", + "67667 d04ee65e0bb76ee3921dceeb44f2be43 ... NaN \n", + "\n", + " series_description \\\n", + "175 MIP iDose (2) \n", + "414 COR L \n", + "752 chest coronal \n", + "765 LUNGS MIP, iDose (3) \n", + "904 #PP DE_Thorax 1.5 Qr40 2 B_Sn140kV \n", + "... ... \n", + "67409 MIN IP \n", + "67538 High kVp Stnd \n", + "67540 Topogram 0.6 T20f \n", + "67541 Sag \n", + "67667 STD AXL \n", + "\n", + " series_uid slice_thickness \\\n", + "175 2.16.840.1.114274.1818.55964346378096610171168... NaN \n", + "414 2.16.840.1.114274.1818.48529565050476535451230... NaN \n", + "752 2.16.840.1.114274.1818.52621518601439517932967... NaN \n", + "765 2.16.840.1.114274.1818.47899984674779750371141... 5 \n", + "904 2.16.840.1.114274.1818.48959334245767654136969... NaN \n", + "... ... ... \n", + "67409 2.16.840.1.114274.1818.57442183453554543434456... NaN \n", + "67538 2.16.840.1.114274.1818.52468334977846042031093... NaN \n", + "67540 2.16.840.1.114274.1818.56922523991554735871257... NaN \n", + "67541 2.16.840.1.114274.1818.56981222797237409122046... NaN \n", + "67667 2.16.840.1.114274.1818.49929890207265010191125... NaN \n", + "\n", + " spacing_between_slices storage_urls \\\n", + "175 NaN NaN \n", + "414 NaN NaN \n", + "752 NaN NaN \n", + "765 2.5 NaN \n", + "904 NaN NaN \n", + "... ... ... \n", + "67409 NaN NaN \n", + "67538 NaN NaN \n", + "67540 NaN NaN \n", + "67541 NaN NaN \n", + "67667 NaN NaN \n", + "\n", + " core_metadata_collections.id \\\n", + "175 b0dc067c-b7e8-41ee-a594-a857f033f716 \n", + "414 b2f0484a-2536-49b7-8739-c1056a9ce85c \n", + "752 b2f0484a-2536-49b7-8739-c1056a9ce85c \n", + "765 27d81499-fc2f-4677-8d4b-fae20e054946 \n", + "904 23e8c279-92d2-47da-99de-97dedc454bb4 \n", + "... ... \n", + "67409 06406925-1d98-4e8f-a0ce-c52b678bcb2a \n", + "67538 b2f0484a-2536-49b7-8739-c1056a9ce85c \n", + "67540 4a5dd86d-7965-48bb-9c2b-c6d36089c780 \n", + "67541 b2f0484a-2536-49b7-8739-c1056a9ce85c \n", + "67667 b0dc067c-b7e8-41ee-a594-a857f033f716 \n", + "\n", + " core_metadata_collections.submitter_id \\\n", + "175 ACR_20211115 \n", + "414 ACR_20220218 \n", + "752 ACR_20220218 \n", + "765 ACR_20220715 \n", + "904 ACR_20211020 \n", + "... ... \n", + "67409 ACR_20220107 \n", + "67538 ACR_20220218 \n", + "67540 ACR_20220314 \n", + "67541 ACR_20220218 \n", + "67667 ACR_20211115 \n", + "\n", + " imaging_studies.id \\\n", + "175 b1ccf140-c713-49a1-8ed0-e0c9a54bf638 \n", + "414 aaa83fe3-f825-4bf0-a5ad-8b339850e41c \n", + "752 21f75cac-8d92-494a-af37-0ee41b17fe59 \n", + "765 c7930d40-7f64-4079-8162-637140e34341 \n", + "904 08f71e93-ebc9-4e1e-a4ee-5013e595d3dd \n", + "... ... \n", + "67409 f27cc79e-d8b1-4b96-a883-73da2210ce60 \n", + "67538 4fa257ab-a4d0-4281-aca9-d4c09069341b \n", + "67540 c8b1a6da-28c7-4f00-80d4-4c4bc3ca5313 \n", + "67541 4fa5428b-2866-4004-a7b0-b427eee75c99 \n", + "67667 0b8ff751-72e6-4504-b976-5b98130c3476 \n", + "\n", + " imaging_studies.submitter_id \n", + "175 2.16.840.1.114274.1818.47546942155557792113367... \n", + "414 2.16.840.1.114274.1818.46752283805707555701690... \n", + "752 2.16.840.1.114274.1818.56080742760726638561677... \n", + "765 2.16.840.1.114274.1818.49193912772239556361726... \n", + "904 2.16.840.1.114274.1818.54095403294717579441711... \n", + "... ... \n", + "67409 2.16.840.1.114274.1818.48752907436280297562336... \n", + "67538 2.16.840.1.114274.1818.52943247670294330431389... \n", + "67540 2.16.840.1.114274.1818.54706480863499007832743... \n", + "67541 2.16.840.1.114274.1818.48449940367163446134194... \n", + "67667 2.16.840.1.114274.1818.52146263260855674077294... \n", + "\n", + "[688 rows x 34 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Now we can use Python to get the CT series files for the cohort of cases we built earlier\n", + "cohort_ct = ct.loc[ct['case_ids'].isin(cohort)]\n", + "cohort_ct" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "20261819", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['dg.MD1R/e6352608-31ae-42fe-9191-69c4ee140a78',\n", + " 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https://petstore.swagger.io/?url=https://raw.githubusercontent.com/uc-cdis/peregrine/master/openapis/swagger.yaml\n", + "\n", + "---\n", + "* Most structured data (aka \"metadata\") submitted to a Gen3 system is stored in Postgres tables using the \"sheepdog\" service. This data must conform to the data commons' data model (https://data.midrc.org/dd), and is queryable via the \"peregrine\" service, which converts graphQL queries to SQL queries and returns the data requested. The Postgres tables are considered the \"source-of-truth\" for data in a Gen3 system (vs. the derived data in ElasticSearch, covered below).\n", + "\n", + "* On the data commons' website, peregrine queries can be sent to the API using the \"graphiQL\" query builder: https://data.midrc.org/query (click on \"Switch to Graph Model\"; if button says \"Switch to Flat Model\" you're in the correct spot).\n", + "\n", + "* Alternatively, you can send queries to the peregrine API using the Gen3SDK `Gen3Submission.query()` function, which uses the Python `requests` package to send queries as API requests: https://github.com/uc-cdis/gen3sdk-python/blob/31751633ba621b35f39eda7295f131245fb92728/gen3/submission.py#L399\n" + ] + }, + { + "cell_type": "markdown", + "id": "311a6d7e", + "metadata": {}, + "source": [ + "### Example graph model query \\#1\n", + "* This query is running across all records in the `case` node and returns data from any dataset in the data commons you are authorized to access. Remember, the properties in the `case` node are essentially table headers for variables whose values are of a specific data type (string, enumeration, integer, number, boolean, array, etc.).\n", + "* The argument `covid19_positive: \"Yes\"` returns only case records that have the value \"Yes\" for the property `covid19_positive`, which indicates whether a case in MIDRC has ever had a positive COVID-19 test result.\n", + "* The `first` argument defines how many `case` records we want returned. Using the argument `first: 0`, all the records we have access to will be returned. If we leave the \"first\" argument out, only the first 10 records are returned by default. Setting `first: 2000` will return the first 2000 records in the table, etc.\n", + "* If your query is timing out, you will need to paginate the query (covered in next section) using a combination of \"first\" and \"offset\" arguments. This is only necessary if the tables being queries are very large, or the query traverses many nodes in the graph model.\n", + "* Properties we want returned from the API are enclosed in brackets. The possibilities and exact syntax are constrained by the data model (data.midrc.org/dd). \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1a7994cb", + "metadata": {}, + "outputs": [], + "source": [ + "### Define the query\n", + "\n", + "## Here we're asking for the `project_id`, `submitter_id`, and some demographic data for every `case` record.\n", + "## We're also asking for the `study_uid` for every `imaging_study` record belonging to those cases, and for all `dx_series_file` records for those `imaging_studies`.\n", + "## Finally, we're asking for the `file_name` and `object_id` of any Digital X-ray files (node `dx_series_file`, backref: `dx_series_files`) they may have.\n", + "\n", + "## Note: \"submitter_id\" is a required property on every node, which is the human-readable (string), unique identifier for a record in a data table / node. So, the \"submitter_id\" of a record in the case node is the de-identified patient's \"ID\".\n", + "\n", + "query_txt = \"\"\"\n", + "{\n", + " case(first: 0, covid19_positive: \"Yes\") {\n", + " project_id\n", + " submitter_id\n", + " ethnicity\n", + " sex\n", + " race\n", + " imaging_studies (study_modality: \"DX\") {\n", + " study_uid\n", + " dx_series_files {\n", + " object_id\n", + " file_name\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0cecf28b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'ethnicity': 'Not Hispanic or Latino',\n", + " 'imaging_studies': [],\n", + " 'project_id': 'Open-A1',\n", + " 'race': 'White',\n", + " 'sex': 'Female',\n", + " 'submitter_id': '10008204-b1dlUCvREymgv39kYBKiQ'},\n", + " {'ethnicity': 'Not Hispanic or Latino',\n", + " 'imaging_studies': [],\n", + " 'project_id': 'Open-A1',\n", + " 'race': 'Black or African 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{'ethnicity': 'Not Hispanic or Latino',\n", + " 'imaging_studies': [],\n", + " 'project_id': 'Open-A1',\n", + " 'race': 'White',\n", + " 'sex': 'Male',\n", + " 'submitter_id': '10003752-FJmm7reLMEqHgsAuyXHAfw'},\n", + " {'ethnicity': 'Not Hispanic or Latino',\n", + " 'imaging_studies': [],\n", + " 'project_id': 'Open-A1',\n", + " 'race': 'White',\n", + " 'sex': 'Female',\n", + " 'submitter_id': '10000364-1265178'},\n", + " {'ethnicity': 'Not Hispanic or Latino',\n", + " 'imaging_studies': [],\n", + " 'project_id': 'Open-A1',\n", + " 'race': 'White',\n", + " 'sex': 'Female',\n", + " 'submitter_id': '10000364-1269513'},\n", + " ...]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Send the query using the Gen3 SDK Gen3Submission.query() function\n", + "## The response will be in JSON format.\n", + "\n", + "response = sub.query(query_txt)\n", + "if 'data' in response:\n", + " data = response['data']['case']\n", + " display(data)" + ] + }, + { + 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'dg.MD1R/d4c252b0-be55-4848-95e1-c139cba368c9',\n", + " 'dg.MD1R/d28a4bb2-1b55-487c-b482-6d991147391f',\n", + " 'dg.MD1R/976ccb82-f2f4-494f-a12b-42699142ee1d',\n", + " 'dg.MD1R/f96c8614-0ee0-4f44-9d96-154d1ca4b17f',\n", + " 'dg.MD1R/defc03dd-02df-4431-a679-d0fef684b5a4',\n", + " 'dg.MD1R/fac33691-060d-4dd2-a11f-ccd5201676dd',\n", + " 'dg.MD1R/63eebbf8-8569-470c-9ca4-d1360bf7620d',\n", + " 'dg.MD1R/4b934c06-2a61-4970-a4c6-c6fe11c0aad4',\n", + " 'dg.MD1R/ceddb278-d2ec-45ae-93fe-ffd80e2382ba',\n", + " 'dg.MD1R/53e56f3e-9d2c-453c-aa67-7384fbc7d0cc',\n", + " 'dg.MD1R/f1c95c6a-47e7-4857-9b18-ebcf2658cd64',\n", + " 'dg.MD1R/95687806-6c5d-437e-9c1e-2e7b058fcde7',\n", + " 'dg.MD1R/e44a74fe-2b08-4313-8582-5e653dd9a61d',\n", + " 'dg.MD1R/eb5f8eb6-9768-40ef-b99a-de9da21c02fc',\n", + " 'dg.MD1R/e78ed3d5-8e84-407d-9bd1-037607ca2d15',\n", + " 'dg.MD1R/6980fb28-f216-4251-89ba-aa1c9c6a9f07',\n", + " 'dg.MD1R/b98f998b-07b7-4663-9f49-c6ad3cb76fd1',\n", + " 'dg.MD1R/7e2b288c-8303-4d45-9e3c-2c0c335230ef',\n", + " 'dg.MD1R/950c7a0d-0902-44e3-9657-42f6aa9b5f7f',\n", + " 'dg.MD1R/b58053a6-2a2e-4e1d-91c6-fd4f4452a1a5',\n", + " 'dg.MD1R/9e97eba2-020c-44b5-8e18-c9be3a4dffaa',\n", + " 'dg.MD1R/50c5a805-cbe2-4638-ba8d-e7b7d73f9563',\n", + " 'dg.MD1R/83060cea-0508-4278-ad10-a7efcd16563f',\n", + " 'dg.MD1R/0fb03d7d-f162-44dc-b0b5-eb2934821220',\n", + " 'dg.MD1R/a6af8d62-82e0-4f68-a1fd-e21b66736a3e',\n", + " 'dg.MD1R/79be8b99-20eb-46a4-b476-723f2ffda4eb',\n", + " 'dg.MD1R/3516b23b-a04c-4573-bd9e-0db62ce36605',\n", + " 'dg.MD1R/8b9e9d4e-6175-4ab6-adb4-2cf456688e55',\n", + " 'dg.MD1R/c32275a0-4a85-45dc-896f-b3a6bda38682',\n", + " 'dg.MD1R/d8c7d928-90c1-4fcd-ab40-17acc9fae38b',\n", + " 'dg.MD1R/de5f8bc2-bc8e-4c58-b36e-2ae0d3916c9f',\n", + " 'dg.MD1R/b69ede20-56c6-4f85-a03a-1ce541f6e554',\n", + " 'dg.MD1R/268a13fc-f1b7-4ca6-b5b6-f42e9609c553',\n", + " 'dg.MD1R/25d3071c-a94b-446a-8f32-826ac7875cb3',\n", + " 'dg.MD1R/92144675-9f11-46a9-be3f-e59681e71af3',\n", + " 'dg.MD1R/3b456274-2bf3-4e13-b344-b5f9e1498f89',\n", + " 'dg.MD1R/a34900de-a817-45d0-b6ac-f833481ee4c8',\n", + " 'dg.MD1R/41ddcaa7-23d8-40db-941c-a989274f1104',\n", + " 'dg.MD1R/fded3114-7807-429b-8230-3a1292638d1e',\n", + " 'dg.MD1R/f5cfa8bd-aec2-4378-9a09-312b5e9ab82a',\n", + " 'dg.MD1R/a79c667b-c5c6-46a2-99df-287d1f3ce071',\n", + " 'dg.MD1R/b2c95abb-035e-4cee-949c-ad22776f2e3b',\n", + " 'dg.MD1R/d0c43583-641a-46a7-afbd-f47cdd294d85',\n", + " 'dg.MD1R/aa1229cb-a951-4b9b-9860-c45f3340b337',\n", + " 'dg.MD1R/ae46ec2e-3d7f-4cf8-9c47-664e04649f70',\n", + " 'dg.MD1R/414c8d73-1253-4826-b82d-8034cfd1a4c7',\n", + " 'dg.MD1R/ecc10d5b-ceba-4779-ac00-767cd7e0c070',\n", + " 'dg.MD1R/6ea71884-fb0f-4fb5-a2f5-40504e40ad89',\n", + " 'dg.MD1R/07a5d84d-f483-4a93-805b-98cf4d31810d',\n", + " 'dg.MD1R/59ec154d-c222-4f0a-b151-b16ec20b0dec',\n", + " 'dg.MD1R/f316aaa1-7e6e-4b6b-b3ef-b6567c10de7c',\n", + " 'dg.MD1R/b30030d3-0b90-4171-986e-d1a72c3e9839',\n", + " 'dg.MD1R/fb36147c-1acf-4879-883d-2c00dcfbffb7',\n", + " 'dg.MD1R/83b0fccc-0620-46a1-a31b-977f6213d482',\n", + " 'dg.MD1R/5306006c-17c4-4a70-9bdd-7702896b30fc',\n", + " 'dg.MD1R/a60d787f-cd82-4706-a9a8-56e08eebe7e1',\n", + " 'dg.MD1R/9099ad79-b87b-4c64-a6e9-a8d6b3dddcb6',\n", + " 'dg.MD1R/eccc5f2f-4333-417e-9455-e53151f78ffe',\n", + " 'dg.MD1R/e45074f9-4a7e-49f3-bdeb-5e17d71c41e5',\n", + " 'dg.MD1R/e7c6a58b-cf44-4b99-8ef6-29ad7accd005',\n", + " 'dg.MD1R/666c6b8e-50e5-499e-9ff7-4e5beea6059c',\n", + " 'dg.MD1R/7b64723d-cca5-4561-91c5-30f007650d29',\n", + " 'dg.MD1R/1ec65ccc-7d44-41a4-a816-48e961eebc08',\n", + " 'dg.MD1R/4ba424b6-7d27-4abc-8828-c01dbc3a6b7b',\n", + " 'dg.MD1R/aab32d06-2786-4b26-a396-d35d72288178',\n", + " 'dg.MD1R/55312f93-d668-4355-a011-4eb432a4c183',\n", + " 'dg.MD1R/b87f8818-49dc-4dc7-8d21-b68c857d6d75',\n", + " ...]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## the \"object_id\" field is the file's data GUID (or globally unique identifier), which can be used to access the file.\n", + "\n", + "object_ids = []\n", + "for case in data:\n", + " studies = case['imaging_studies']\n", + " for study in studies:\n", + " files = study['dx_series_files'] \n", + " if len(files)>0:\n", + " for file in files:\n", + " object_id = file['object_id']\n", + " object_ids.append(object_id)\n", + "object_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "60b02d88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Take a look at one of the file objects\n", + "#object_id was originally selected from the list above. If the object id is not above use an object id from above.\n", + "\n", + "object_id = 'dg.MD1R/ea6ad8e7-1dc9-4916-8e75-38abb66c6416'\n", + "os.system(\"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "15fe0b88", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 0\r\n", + "drwxr-xr-x 4 christopher staff 128 Oct 17 12:34 \u001b[34m2.16.840.1.114274.1818.52236113359126249589212595743121753735\u001b[m\u001b[m\r\n" + ] + } + ], + "source": [ + "!ls -l 10000364-1958844/" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b97a8336", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archive: 10000364-1958844/2.16.840.1.114274.1818.52236113359126249589212595743121753735/2.16.840.1.114274.1818.54309100269617797736626917868992258958.zip\n", + " extracting: 2.16.840.1.114274.1818.54309100269617797736626917868992258958/2.16.840.1.114274.1818.46312267929568121457864041736105067915.dcm \n", + " extracting: 2.16.840.1.114274.1818.54309100269617797736626917868992258958/2.16.840.1.114274.1818.514319985161150694215877153407679914378.dcm \n", + " extracting: 2.16.840.1.114274.1818.54309100269617797736626917868992258958/2.16.840.1.114274.1818.55717158006175236544657254052638663083.dcm \n", + " extracting: 2.16.840.1.114274.1818.54309100269617797736626917868992258958/2.16.840.1.114274.1818.49586797551840017217771275425236445833.dcm \n" + ] + } + ], + "source": [ + "#The object downloaded above was zipped and must be unzipped to process further\n", + "#The path and file names may have changed and those changes should be reflected below\n", + "!unzip 10000364-1958844/2.16.840.1.114274.1818.52236113359126249589212595743121753735/2.16.840.1.114274.1818.54309100269617797736626917868992258958.zip" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9ab6b52a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset.file_meta -------------------------------\n", + "(0002, 0000) File Meta Information Group Length UL: 222\n", + "(0002, 0001) File Meta Information Version OB: b'\\x00\\x01'\n", + "(0002, 0002) Media Storage SOP Class UID UI: Digital X-Ray Image Storage - For Presentation\n", + "(0002, 0003) Media Storage SOP Instance UID UI: 2.16.840.1.114274.1818.46312267929568121457864041736105067915\n", + "(0002, 0010) Transfer Syntax UID UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", + "(0002, 0012) Implementation Class UID UI: 1.3.6.1.4.1.25403.1.1.1\n", + "(0002, 0013) Implementation Version Name SH: 'Dicom 0.1'\n", + "(0002, 0016) Source Application Entity Title AE: 'CPV-ACUOPRIAPP'\n", + "-------------------------------------------------\n", + "(0008, 0005) Specific Character Set CS: ''\n", + "(0008, 0008) Image Type CS: ['DERIVED', 'SECONDARY', '']\n", + "(0008, 0012) Instance Creation Date DA: ''\n", + "(0008, 0013) Instance Creation Time TM: '000000'\n", + "(0008, 0016) SOP Class UID UI: Digital X-Ray Image Storage - For Presentation\n", + "(0008, 0018) SOP Instance UID UI: 2.16.840.1.114274.1818.46312267929568121457864041736105067915\n", + "(0008, 0020) Study Date DA: '19591229'\n", + "(0008, 0021) Series Date DA: '19591229'\n", + "(0008, 0022) Acquisition Date DA: '19591229'\n", + "(0008, 0023) Content Date DA: '19591229'\n", + "(0008, 0030) Study Time TM: '112622.000000'\n", + "(0008, 0031) Series Time TM: '113012.000000'\n", + "(0008, 0032) Acquisition Time TM: '113213'\n", + "(0008, 0033) Content Time TM: '113213.000000'\n", + "(0008, 0050) Accession Number SH: ''\n", + "(0008, 0060) Modality CS: 'DX'\n", + "(0008, 0061) Modalities in Study CS: ['', 'DX', '']\n", + "(0008, 0064) Conversion Type CS: 'WSD'\n", + "(0008, 0068) Presentation Intent Type CS: 'FOR PRESENTATION'\n", + "(0008, 0070) Manufacturer LO: 'GE MEDICAL SYSTEMS'\n", + "(0008, 0090) Referring Physician's Name PN: ''\n", + "(0008, 1010) Station Name SH: '[Station Name.8]'\n", + "(0008, 1030) Study Description LO: 'XR CHEST PA AND LATERAL'\n", + "(0008, 103e) Series Description LO: 'XR CHEST PA AND LATERAL'\n", + "(0008, 1090) Manufacturer's Model Name LO: 'Revolution XRd ADS_28.2'\n", + "(0008, 2112) Source Image Sequence 2 item(s) ---- \n", + " (0008, 1150) Referenced SOP Class UID UI: Digital X-Ray Image Storage - For Processing\n", + " (0008, 1155) Referenced SOP Instance UID UI: 2.16.840.1.114274.1818.522904073378240663510778052673726600632\n", + " ---------\n", + " (0008, 1150) Referenced SOP Class UID UI: Digital X-Ray Image Storage - For Processing\n", + " (0008, 1155) Referenced SOP Instance UID UI: 2.16.840.1.114274.1818.480400972398743971513202718324881760163\n", + " ---------\n", + "(0008, 2218) Anatomic Region Sequence 1 item(s) ---- \n", + " (0008, 0100) Code Value SH: 'T-D3000'\n", + " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", + " (0008, 0104) Code Meaning LO: 'Chest'\n", + " ---------\n", + "(0010, 0010) Patient's Name PN: '1958844^10000364^^^'\n", + "(0010, 0020) Patient ID LO: '10000364-1958844'\n", + "(0010, 0030) Patient's Birth Date DA: ''\n", + "(0010, 1010) Patient's Age AS: '082Y'\n", + "(0010, 1020) Patient's Size DS: None\n", + "(0010, 1030) Patient's Weight DS: None\n", + "(0012, 0010) Clinical Trial Sponsor Name LO: '[Clinical Trial Sponsor Na]'\n", + "(0012, 0020) Clinical Trial Protocol ID LO: '[Clinical Trial Protocol ]'\n", + "(0012, 0021) Clinical Trial Protocol Name LO: 'CIRR'\n", + "(0012, 0030) Clinical Trial Site ID LO: '[Clinical Trial Site ]'\n", + "(0012, 0031) Clinical Trial Site Name LO: '[Clinical Trial Site Na]'\n", + "(0012, 0040) Clinical Trial Subject ID LO: '10000364-1958844'\n", + "(0012, 0050) Clinical Trial Time Point ID LO: '[Clinical Trial Time Point ]'\n", + "(0012, 0060) Clinical Trial Coordinating Center LO: '[Clinical Trial Coordinating Center Na]'\n", + "(0012, 0062) Patient Identity Removed CS: 'YES'\n", + "(0012, 0063) De-identification Method LO: ['Basic Application Confidentiality Profile', 'Retain Longitudinal Temporal Information Modified Dates', 'TRIAD Anonymization; Date=20210312; Time=095435']\n", + "(0012, 0064) De-identification Method Code Sequence 3 item(s) ---- \n", + " (0008, 0100) Code Value SH: '113100'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'Basic Application Confidentiality Profile'\n", + " ---------\n", + " (0008, 0100) Code Value SH: '113107'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'Retain Longitudinal Temporal Information Modified Dates Option'\n", + " ---------\n", + " (0008, 0100) Code Value SH: '900901'\n", + " (0008, 0102) Coding Scheme Designator SH: 'WIF'\n", + " (0008, 0104) Code Meaning LO: 'BASIC PROFILE OVERRIDES POTENTIALLY VIOLATE SECURITY STANDARD'\n", + " (0008, 0108) Extended Code Meaning LT: Array of 229 elements\n", + " ---------\n", + "(0018, 0010) Contrast/Bolus Agent LO: ''\n", + "(0018, 0015) Body Part Examined CS: 'CHEST'\n", + "(0018, 1010) Secondary Capture Device ID LO: ''\n", + "(0018, 1020) Software Versions LO: 'Ads Application Package VERSION ADS_28.2'\n", + "(0018, 1110) Distance Source to Detector DS: '1840.0'\n", + "(0018, 1111) Distance Source to Patient DS: '1791.0'\n", + "(0018, 1147) Field of View Shape CS: 'RECTANGLE'\n", + "(0018, 1149) Field of View Dimension(s) IS: [404, 404]\n", + "(0018, 1160) Filter Type SH: 'NONE'\n", + "(0018, 1164) Imager Pixel Spacing DS: [0.19467391, 0.19467391]\n", + "(0018, 1166) Grid CS: ['FIXED', 'FOCUSED']\n", + "(0018, 1190) Focal Spot(s) DS: '1.25'\n", + "(0018, 1401) Acquisition Device Processing Code LO: '8#1211151091001009080100#0000#1040#20350#35220#0.440000'\n", + "(0018, 1508) Positioner Type CS: ''\n", + "(0018, 1511) Positioner Secondary Angle DS: '90.0'\n", + "(0018, 1700) Collimator Shape CS: 'POLYGONAL'\n", + "(0018, 1720) Vertices of the Polygonal Collimato IS: [2047, 2037, -3, 2037, -3, -12, 2047, -12]\n", + "(0018, 5101) View Position CS: 'PA'\n", + "(0018, 6000) Sensitivity DS: '0.1312872'\n", + "(0018, 7001) Detector Temperature DS: '32.099998'\n", + "(0018, 7004) Detector Type CS: 'SCINTILLATOR'\n", + "(0018, 7005) Detector Configuration CS: 'AREA'\n", + "(0018, 701a) Detector Binning DS: [1, 1]\n", + "(0018, 7020) Detector Element Physical Size DS: [0.2, 0.2]\n", + "(0018, 7022) Detector Element Spacing DS: [0.2, 0.2]\n", + "(0018, 7024) Detector Active Shape CS: 'RECTANGLE'\n", + "(0018, 7026) Detector Active Dimension(s) DS: [409.6, 409.6]\n", + "(0018, 7030) Field of View Origin DS: [13, 13]\n", + "(0018, 7032) Field of View Rotation DS: '0.0'\n", + "(0018, 7034) Field of View Horizontal Flip CS: 'YES'\n", + "(0018, 7050) Filter Material CS: 'COPPER'\n", + "(0018, 7052) Filter Thickness Minimum DS: '0.0'\n", + "(0018, 7054) Filter Thickness Maximum DS: '0.0'\n", + "(0018, 7060) Exposure Control Mode CS: 'AUTOMATIC'\n", + "(0018, 7062) Exposure Control Mode Description LT: 'AEC_all_cells'\n", + "(0018, a001) Contributing Equipment Sequence 1 item(s) ---- \n", + " (0008, 0070) Manufacturer LO: 'Claron'\n", + " (0008, 1010) Station Name SH: 'CPV-ACUOPRIAPP'\n", + " (0008, 1090) Manufacturer's Model Name LO: 'Deidentification'\n", + " (0018, 1020) Software Versions LO: ['WIF 2.1.17', '1.0.0.0']\n", + " (0018, a002) Contribution DateTime DT: '20210824090022.548428'\n", + " (0018, a003) Contribution Description ST: 'Cleaned'\n", + " (0040, a170) Purpose of Reference Code Sequence 1 item(s) ---- \n", + " (0008, 0100) Code Value SH: '109104'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'De-identifying Equipment'\n", + " ---------\n", + " ---------\n", + "(0020, 000d) Study Instance UID UI: 2.16.840.1.114274.1818.52236113359126249589212595743121753735\n", + "(0020, 000e) Series Instance UID UI: 2.16.840.1.114274.1818.54309100269617797736626917868992258958\n", + "(0020, 0010) Study ID SH: ''\n", + "(0020, 0011) Series Number IS: '34212'\n", + "(0020, 0013) Instance Number IS: '3'\n", + "(0020, 0020) Patient Orientation CS: ['L', 'F']\n", + "(0020, 0022) Overlay Number IS: None\n", + "(0020, 0024) Curve Number IS: None\n", + "(0020, 0026) LUT Number IS: None\n", + "(0020, 0062) Image Laterality CS: 'U'\n", + "(0028, 0002) Samples per Pixel US: 1\n", + "(0028, 0004) Photometric Interpretation CS: 'MONOCHROME2'\n", + "(0028, 0008) Number of Frames IS: '1'\n", + "(0028, 0010) Rows US: 2022\n", + "(0028, 0011) Columns US: 2022\n", + "(0028, 0100) Bits Allocated US: 16\n", + "(0028, 0101) Bits Stored US: 14\n", + "(0028, 0102) High Bit US: 13\n", + "(0028, 0103) Pixel Representation US: 0\n", + "(0028, 0300) Quality Control Image CS: 'NO'\n", + "(0028, 0301) Burned In Annotation CS: 'NO'\n", + "(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'\n", + "(0028, 1040) Pixel Intensity Relationship CS: 'LOG'\n", + "(0028, 1041) Pixel Intensity Relationship Sign SS: -1\n", + "(0028, 1050) Window Center DS: [4119, 4119, 4119]\n", + "(0028, 1051) Window Width DS: [2917, 2187, 4375]\n", + "(0028, 1052) Rescale Intercept DS: '0.0'\n", + "(0028, 1053) Rescale Slope DS: '1.0'\n", + "(0028, 1054) Rescale Type LO: 'US'\n", + "(0028, 1055) Window Center & Width Explanation LO: ['NORMAL', 'HARDER', 'SOFTER']\n", + "(0028, 2110) Lossy Image Compression CS: '00'\n", + "(0028, 3010) VOI LUT Sequence 3 item(s) ---- \n", + " (0028, 3002) LUT Descriptor US: [16384, 0, 16]\n", + " (0028, 3003) LUT Explanation LO: 'NORMAL'\n", + " (0028, 3006) LUT Data US: Array of 16384 elements\n", + " ---------\n", + " (0028, 3002) LUT Descriptor US: [16384, 0, 16]\n", + " (0028, 3003) LUT Explanation LO: 'HARDER'\n", + " (0028, 3006) LUT Data US: Array of 16384 elements\n", + " ---------\n", + " (0028, 3002) LUT Descriptor US: [16384, 0, 16]\n", + " (0028, 3003) LUT Explanation LO: 'SOFTER'\n", + " (0028, 3006) LUT Data US: Array of 16384 elements\n", + " ---------\n", + "(0032, 000a) Study Status ID CS: 'COMPLETED'\n", + "(0040, 0009) Scheduled Procedure Step ID SH: ''\n", + "(0040, 0244) Performed Procedure Step Start Date DA: ''\n", + "(0040, 0245) Performed Procedure Step Start Time TM: '000000'\n", + "(0040, 0310) Comments on Radiation Dose ST: '80 %'\n", + "(0040, a032) Observation DateTime DT: '18390614000000.000000'\n", + "(0040, a491) Completion Flag CS: ''\n", + "(0040, a493) Verification Flag CS: ''\n", + "(0045, 0010) Private Creator LO: 'GEMS_SENO_02'\n", + "(0045, 101b) [Clinical View] CS: 'postero-anterior'\n", + "(0054, 0220) View Code Sequence 1 item(s) ---- \n", + " (0008, 0100) Code Value SH: 'R-10214'\n", + " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", + " (0008, 0104) Code Meaning LO: 'postero-anterior'\n", + " (0054, 0222) View Modifier Code Sequence 1 item(s) ---- \n", + " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", + " ---------\n", + " ---------\n", + "(0054, 0410) Patient Orientation Code Sequence 1 item(s) ---- \n", + " (0008, 0100) Code Value SH: 'F-10450'\n", + " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", + " (0008, 0104) Code Meaning LO: 'recumbent'\n", + " ---------\n", + "(0054, 0414) Patient Gantry Relationship Code Sequence 1 item(s) ---- \n", + " (0008, 0100) Code Value SH: 'F-10470'\n", + " (0008, 0102) Coding Scheme Designator SH: 'SNM3'\n", + " (0008, 0104) Code Meaning LO: 'head-first'\n", + " ---------\n", + "(0070, 0080) Content Label CS: ''\n", + "(0070, 0081) Content Description LO: ''\n", + "(0070, 0084) Content Creator's Name PN: ''\n", + "(0400, 0500) Encrypted Attributes Sequence 1 item(s) ---- \n", + " (0033, 1215) Private tag data OB: b'\\x04\\xa4\\x91\\xe2\\xb4#$\\xa4'\n", + " (0400, 0510) Encrypted Content Transfer Syntax U UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", + " (0400, 0520) Encrypted Content OB: Array of 736 elements\n", + " ---------\n", + "(2050, 0020) Presentation LUT Shape CS: 'IDENTITY'\n", + "(4008, 0040) Results ID SH: ''\n", + "(7fe0, 0010) Pixel Data OB: Array of 4460448 elements" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pydicom import dcmread\n", + "\n", + "fpath = \"2.16.840.1.114274.1818.54309100269617797736626917868992258958/2.16.840.1.114274.1818.46312267929568121457864041736105067915.dcm\"\n", + "ds = dcmread(fpath)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "7308d00f", + "metadata": {}, + "source": [ + "### Counts with peregrine\n", + "---\n", + "* Peregrine is able to provide counts of records in nodes. A simple example is to quickly get the count of the numbers of cases and imaging studies in the data commons.\n", + "* You can also add arguments to the counts to, for example, get the number of cases in a particular project or get the imaging studies of a particular modality." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1e80ace7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'data': {'_case_count': 26311}}\n", + "{'data': {'_imaging_study_count': 75722}}\n", + "{'data': {'CT_studies': 11342}}\n", + "{'data': {'Open_A1_cases': 17214}}\n" + ] + } + ], + "source": [ + "query_txt = \"{_case_count}\"\n", + "print(sub.query(query_txt))\n", + "query_txt = \"{_imaging_study_count}\"\n", + "print(sub.query(query_txt))\n", + "query_txt = '{CT_studies: _imaging_study_count(study_modality:\"CT\")}'\n", + "print(sub.query(query_txt))\n", + "query_txt = '{Open_A1_cases: _case_count(project_id:\"Open-A1\")}'\n", + "print(sub.query(query_txt))" + ] + }, + { + "cell_type": "markdown", + "id": "5dcd297c", + "metadata": {}, + "source": [ + "### Queries of \"datanode\" using peregrine\n", + "---\n", + "Another handy trick with peregrine queries is the \"datanode\" query. \"Datanode\" isn't a real node in the data model, but is useful way to query all nodes that store file information. For example, if you have a patient ID, you can get all the files associated with that case.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "097f8def", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.50145587602717585115358391564826314149.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/1489432d-0418-4f05-bc5d-c878f1b18ad3'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.464520502732852002914285869982724642223.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/8fedd1a6-620f-4016-8091-6cb7ca1dff67'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.503335697103443967215559096020248425118.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/22acfa24-fea1-48b8-bc8c-f6ce2118e922'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.514121201817181423017344112428574827650.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/609189f3-4d14-4e30-89fd-049a82bd11b3'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.53509866604124156787936249200744822971.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/db42ae24-e124-48cf-998f-fd97d95f0113'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.549820400218250368212928484985432269501.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/3d6af337-d4e1-41ed-a24e-61c0170a0317'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.514539387161268363314607880262141534380.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/bb432cae-c619-4c5b-bae4-f826d08952ad'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.462233723090313909315476916956571315859.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/d401243b-430d-4c7d-85d8-74f742979041'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.47919587858183454067064513746535711622.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/2cb053ea-1294-48dc-804d-7de15715721e'},\n", + " {'file_name': '10000364-1163342/2.16.840.1.114274.1818.470657568449812436615849982694926045881/2.16.840.1.114274.1818.48146691736384049415177108764944419714.zip',\n", + " 'modality': 'CT',\n", + " 'object_id': 'dg.MD1R/c50a25d8-0439-4c9f-856b-beaec5fef140'},\n", + " {'file_name': '2.16.840.1.114274.1818.46462464228338035604085382488501697711/2.16.840.1.114274.1818.57119669613829751891125487061413366952.zip',\n", + " 'modality': 'DX',\n", + " 'object_id': 'dg.MD1R/5eeb7280-7d5b-4bac-a34b-a0eadb65e033'}]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query_txt = \"\"\"\n", + "{\n", + " datanode(first: 0, case_ids: \"10000364-1163342\") {\n", + " object_id\n", + " file_name\n", + " modality\n", + " }\n", + "}\n", + "\n", + "\"\"\"\n", + "response = sub.query(query_txt)\n", + "if 'data' in response:\n", + " display(response['data']['datanode'])" + ] + }, + { + "cell_type": "markdown", + "id": "3ba72458", + "metadata": {}, + "source": [ + "## Queries to ElasticSearch using Guppy graphQL query service\n", + "---\n", + "* Guppy Documentation: https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filters\n", + "* Guppy Download instructions: https://github.com/uc-cdis/guppy/blob/master/doc/download.md\n", + "* ETL (Tube) Documentation: https://github.com/uc-cdis/tube#gen3-etl---a-process-from-postgresql-to-es\n", + "---\n", + "* The Gen3 platform includes services for running an ETL process (Extract, Transform, Load), which is done by the Gen3 ETL service \"tube\", on the data in Postgres to create flattened tables of the same data in ElasticSearch (ES) for rapid querying performed by the Gen3 query service \"guppy\".\n", + "* Guppy runs graphql-like queries against the ES database, and can rapidly return derived data like histograms, statistics, aggregations, counts, etc. The tube service uses Spark to create these new tables of data in ES via an ETL mapping, which defines the structure of the new tables and is based on the data model. \n", + "* Since the structure of the data changes via the ETL process, peregrine queries to Postgres will not run using guppy. To explore what is possible to query, use the graphiQL interface / documentation.\n", + "* The \"Exploration\" app aka \"Data Explorer\" (data.midrc.org/explore), which uses faceted search to filter the flat data tables in ES, runs off of guppy queries.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "bf8d06d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'case_ids': ['419639-008501'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.307786171314313794552620787256',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/8060548a-9884-456a-8613-32bc21a8690d',\n", + " 'dg.MD1R/40a107b9-e132-4ad5-b3c4-c5a78d9457af']},\n", + " {'case_ids': ['419639-007659'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.312148817844334972300139053236',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/581a97c7-7a49-4461-a202-5478c4722468',\n", + " 'dg.MD1R/f7e6c8fc-af8e-444e-9826-eea158b2a112']},\n", + " {'case_ids': ['419639-008731'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.872698731607279627200457505502',\n", + " 'study_description': 'XR CHEST 1 VIEW AP'},\n", + " {'case_ids': ['419639-011864'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.812420976676333103393759444784',\n", + " 'study_description': 'XR CHEST 2 VIEWS PA AND LATERAL',\n", + " 'object_id': ['dg.MD1R/ac34ef1a-a3bf-475e-a7f4-add431dd7cad']},\n", + " {'case_ids': ['419639-011490'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.201386903613340413216649280083',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/259e638c-6356-4716-8b97-cd915b8ebe05',\n", + " 'dg.MD1R/57e92230-8ae5-419c-9e47-1b361b0bcfbf']},\n", + " {'case_ids': ['419639-011913'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.278107455876066137171677606988',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/00fd7845-9463-4749-bbdd-9e653b4c4746']},\n", + " {'case_ids': ['419639-005739'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.255215587837651294056379356790',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/8d0564d9-e6d0-4bfc-92a0-f02ce0e8ee95']},\n", + " {'case_ids': ['419639-007007'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.158274748531862656644206451575',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/eeeab5db-3293-4a48-8dd5-3330619c0680']},\n", + " {'case_ids': ['419639-010002'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.181388069998600102535202353077',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/8f4b5374-c4c9-4757-81ad-4c9d19f4c368']},\n", + " {'case_ids': ['419639-002750'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.141322415832565615027484122991',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/6e0d6d48-d583-44e7-82fc-2b78121771f5']},\n", + " {'case_ids': ['419639-010865'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.294533389428789566303145980656',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/e0edc5a3-ed4c-48ad-811c-d4a20073307d']},\n", + " {'case_ids': ['419639-006595'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.301221051683079724740696994818',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/35ef0ebd-ac84-4c72-9e55-5a84dadc9c07']},\n", + " {'case_ids': ['419639-006196'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.206040740172547974312718015775',\n", + " 'study_description': 'XR CHEST 1 VIEW AP',\n", + " 'object_id': ['dg.MD1R/bbf0e6a0-6255-4f29-8746-397786a6149d']},\n", + " {'case_ids': ['419639-005784'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.324213754138858622909060628223',\n", + " 'study_description': 'XR CHEST 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AP',\n", + " 'object_id': ['dg.MD1R/eea5e217-4a70-4f20-b14e-0138c17674de']},\n", + " ...]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## an example guppy query, which hits the ElasticSearch database\n", + "\n", + "## define some parameters\n", + "pid = 'Open-R1'\n", + "node = 'imaging_study'\n", + "fields = [\"study_uid\",\n", + " \"study_description\",\n", + " \"case_ids\",\n", + " \"object_id\"]\n", + "filters = {\"project_id\": pid,\n", + " \"covid19_positive\" : \"Yes\",\n", + " \"body_part_examined\" : \"CHEST\",\n", + " \"study_modality\" : \"DX\"}\n", + "\n", + "## send the guppy query with the SDK class Gen3Query\n", + "## Note the \"first: 100000\", which makes sure we don't just get the default first 10 records\n", + "response = query.query(\n", + " data_type=node,\n", + " first=100000,\n", + " fields=fields,\n", + " filters=filters,\n", + " sort_object={\"submitter_id\": \"asc\"},\n", + ")\n", + "\n", + "# display the returned data\n", + "if 'data' in response:\n", + " study_data = response['data'][node]\n", + " display(study_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d4459fa2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'project_id': 'Open-A1',\n", + " 'submitter_id': '10008204-55f52JyJrEyqWpi6PpYbkw',\n", + " 'object_id': ['dg.MD1R/fe590043-51f4-47f9-9e65-2740b53d95df']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10000364-1599931',\n", + " 'object_id': ['dg.MD1R/b5cebe0d-907a-4ab2-aef7-b96cebd7d448']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10022779-LVMy6iR1mUmz3rqVksMLA',\n", + " 'object_id': ['dg.MD1R/6209450f-3f9c-4417-95f2-df32851160bf',\n", + " 'dg.MD1R/3a7971be-4e30-4381-b854-2daa858c38e9']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10008204-ONymhwnpKUy5dlM6b4a5Ng',\n", + " 'object_id': ['dg.MD1R/8ca85564-7d98-4312-8507-64f173333da9',\n", + " 'dg.MD1R/ed0954b6-f76d-4e74-bb7e-3818d69d8001',\n", + " 'dg.MD1R/c32a89c9-eca8-426a-a6b3-a22b7700ecbc',\n", + " 'dg.MD1R/109daa01-5969-4c2c-bb44-25efe88856e6',\n", + " 'dg.MD1R/1355977e-81ed-4ac5-934d-b20407eab280',\n", + " 'dg.MD1R/9e1a772a-f5b8-485d-9f30-600e1c052313',\n", + " 'dg.MD1R/6f330232-cee6-4fef-8d11-e5d04a5951f0']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10000364-1882460',\n", + " 'object_id': ['dg.MD1R/dec79d31-3b31-41e2-8b94-8b914633ed58',\n", + " 'dg.MD1R/3bddc221-64ca-465d-a5c2-43f922e95794',\n", + " 'dg.MD1R/7409a87a-f917-4e10-9274-b011f3284572',\n", + " 'dg.MD1R/907c2a54-42d2-41b4-ba20-ca80272dcc10',\n", + " 'dg.MD1R/58ed4452-d28d-4e34-b68e-b125665488f4',\n", + " 'dg.MD1R/ae054cad-c96b-4d55-8d5d-6517d1f96ac9',\n", + " 'dg.MD1R/3d8d4049-f78e-4f84-92a8-67098f9dd0c1']}]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## another example guppy query, which hits the ElasticSearch database\n", + "\n", + "## define some parameters\n", + "node = 'case'\n", + "\n", + "fields = [\"project_id\",\n", + " \"submitter_id\",\n", + " \"object_id\"]\n", + "\n", + "filters = {\"sex\":\"Female\",\n", + " \"race\" : \"Asian\",\n", + " \"ethnicity\" : \"Hispanic or Latino\"}\n", + "\n", + "## send the guppy query with the SDK class Gen3Query\n", + "## Note the \"first: 100000\", which makes sure we don't just get the default first 10 records\n", + "response = query.query(\n", + " data_type=node,\n", + " first=100000,\n", + " fields=fields,\n", + " filters=filters,\n", + " sort_object={\"submitter_id\": \"asc\"},\n", + ")\n", + "\n", + "# display the returned data\n", + "if 'data' in response:\n", + " case_data = response['data'][node]\n", + " display(case_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f9aa5639", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['dg.MD1R/8060548a-9884-456a-8613-32bc21a8690d',\n", + " 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study in study_data:\n", + " if 'object_id' in study:\n", + " object_id_list = study['object_id']\n", + " for object_id in object_id_list:\n", + " study_object_ids.append(object_id)\n", + "display(study_object_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c3a0d6b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'project_id': 'Open-A1',\n", + " 'submitter_id': '10008204-55f52JyJrEyqWpi6PpYbkw',\n", + " 'object_id': ['dg.MD1R/fe590043-51f4-47f9-9e65-2740b53d95df']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10000364-1599931',\n", + " 'object_id': ['dg.MD1R/b5cebe0d-907a-4ab2-aef7-b96cebd7d448']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10022779-LVMy6iR1mUmz3rqVksMLA',\n", + " 'object_id': ['dg.MD1R/6209450f-3f9c-4417-95f2-df32851160bf',\n", + " 'dg.MD1R/3a7971be-4e30-4381-b854-2daa858c38e9']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10008204-ONymhwnpKUy5dlM6b4a5Ng',\n", + " 'object_id': ['dg.MD1R/8ca85564-7d98-4312-8507-64f173333da9',\n", + " 'dg.MD1R/ed0954b6-f76d-4e74-bb7e-3818d69d8001',\n", + " 'dg.MD1R/c32a89c9-eca8-426a-a6b3-a22b7700ecbc',\n", + " 'dg.MD1R/109daa01-5969-4c2c-bb44-25efe88856e6',\n", + " 'dg.MD1R/1355977e-81ed-4ac5-934d-b20407eab280',\n", + " 'dg.MD1R/9e1a772a-f5b8-485d-9f30-600e1c052313',\n", + " 'dg.MD1R/6f330232-cee6-4fef-8d11-e5d04a5951f0']},\n", + " {'project_id': 'Open-A1',\n", + " 'submitter_id': '10000364-1882460',\n", + " 'object_id': ['dg.MD1R/dec79d31-3b31-41e2-8b94-8b914633ed58',\n", + " 'dg.MD1R/3bddc221-64ca-465d-a5c2-43f922e95794',\n", + " 'dg.MD1R/7409a87a-f917-4e10-9274-b011f3284572',\n", + " 'dg.MD1R/907c2a54-42d2-41b4-ba20-ca80272dcc10',\n", + " 'dg.MD1R/58ed4452-d28d-4e34-b68e-b125665488f4',\n", + " 'dg.MD1R/ae054cad-c96b-4d55-8d5d-6517d1f96ac9',\n", + " 'dg.MD1R/3d8d4049-f78e-4f84-92a8-67098f9dd0c1']}]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "case_data" + ] + }, + { + "cell_type": "markdown", + "id": "d842abd3", + "metadata": {}, + "source": [ + "### Sending aggregations with guppy\n", + "---\n", + "* Guppy has the ability to return some useful statistics (e.g., histograms) using aggregations.\n", + "* The `Gen3Query.graphql_query()` function can be used to send aggregations and other more complex queries that the basic `Gen3Query.query()` function can't support: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/query.py#L112" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "27035fe3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': {'_aggregation': {'case': {'sex': {'histogram': [{'key': 'Female',\n", + " 'count': 13080},\n", + " {'key': 'Male', 'count': 12363},\n", + " {'key': 'Not Reported', 'count': 11},\n", + " {'key': 'no data', 'count': 857}]},\n", + " 'race': {'histogram': [{'key': 'White', 'count': 12348},\n", + " {'key': 'Black or African American', 'count': 6487},\n", + " {'key': 'Not Reported', 'count': 4335},\n", + " {'key': 'Asian', 'count': 1631},\n", + " {'key': 'Other', 'count': 330},\n", + " {'key': 'Native Hawaiian or other Pacific Islander', 'count': 72},\n", + " {'key': 'American Indian or Alaska Native', 'count': 68},\n", + " {'key': 'no data', 'count': 1040}]},\n", + " 'ethnicity': {'histogram': [{'key': 'Not Hispanic or Latino',\n", + " 'count': 22150},\n", + " {'key': 'Hispanic or Latino', 'count': 2655},\n", + " {'key': 'Not Reported', 'count': 649},\n", + " {'key': 'no data', 'count': 857}]}}}}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## A more complex example using Python requests\n", + "query_txt = \"\"\"{\n", + " _aggregation {\n", + " case {\n", + " sex {\n", + " histogram {\n", + " key\n", + " count\n", + " }\n", + " }\n", + " race {\n", + " histogram {\n", + " key\n", + " count\n", + " }\n", + " }\n", + " ethnicity {\n", + " histogram {\n", + " key\n", + " count\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\"\"\"\n", + "response = query.graphql_query(query_string=query_txt)\n", + "display(response)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a6bb9084", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requesting 'https://data.midrc.org/guppy/graphql': {'query': '{_aggregation {case {race {histogram {key count} }}}}', 'variables': None}\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " key count\n", + "0 White 12348\n", + "1 Black or African American 6487\n", + "2 Not Reported 4335\n", + "3 Asian 1631\n", + "4 Other 330\n", + "5 Native Hawaiian or other Pacific Islander 72\n", + "6 American Indian or Alaska Native 68\n", + "7 no data 1040" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Here is an example simple script for sending a basic aggregation request that will return the data as a DataFrame (\"TSV\")\n", + "## https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L3511\n", + "\n", + "data = exp.guppy_aggregation(node='case', prop='race', format='TSV')\n", + "display(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "afcdb969", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sex': {'histogram': [{'key': 'Female', 'count': 13080},\n", + " {'key': 'Male', 'count': 12363},\n", + " {'key': 'Not Reported', 'count': 11},\n", + " {'key': 'no data', 'count': 857}]},\n", + " 'race': {'histogram': [{'key': 'White', 'count': 12348},\n", + " {'key': 'Black or African American', 'count': 6487},\n", + " {'key': 'Not Reported', 'count': 4335},\n", + " {'key': 'Asian', 'count': 1631},\n", + " {'key': 'Other', 'count': 330},\n", + " {'key': 'Native Hawaiian or other Pacific Islander', 'count': 72},\n", + " {'key': 'American Indian or Alaska Native', 'count': 68},\n", + " {'key': 'no data', 'count': 1040}]},\n", + " 'ethnicity': {'histogram': [{'key': 'Not Hispanic or Latino', 'count': 22150},\n", + " {'key': 'Hispanic or Latino', 'count': 2655},\n", + " {'key': 'Not Reported', 'count': 649},\n", + " {'key': 'no data', 'count': 857}]}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## A more complex example using Python requests\n", + "query_txt = \"\"\"{\n", + " _aggregation {\n", + " case {\n", + " sex {\n", + " histogram {\n", + " key\n", + " count\n", + " }\n", + " }\n", + " race {\n", + " histogram {\n", + " key\n", + " count\n", + " }\n", + " }\n", + " ethnicity {\n", + " histogram {\n", + " key\n", + " count\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\"\"\"\n", + "query_json = {\"query\": query_txt}\n", + "guppy_url = \"{}/guppy/graphql\".format(api)\n", + "response = requests.post(guppy_url, json=query_json, auth=auth)\n", + "display(json.loads(response.text)['data']['_aggregation']['case'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1b30f1eb", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': {'_aggregation': {'imaging_data_file': {'project_id': {'histogram': [{'key': 'Open-A1',\n", + " 'count': 132888},\n", + " {'key': 'Open-R1', 'count': 48075},\n", + " {'key': 'Open-A1_PETAL_REDCORAL', 'count': 6037},\n", + " {'key': 'TCIA-RICORD', 'count': 1590}]}}}}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Count the number of files in each project\n", + "files_by_project = \"\"\"\n", + "{\n", + " _aggregation {\n", + " imaging_data_file {\n", + " project_id {\n", + " histogram {\n", + " key\n", + " count\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\"\"\"\n", + "response = query.graphql_query(files_by_project)\n", + "display(response)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3597b07f", + "metadata": {}, + "source": [ + "### Use the guppy download endpoint to access ElasticSearch tables.\n", + "---\n", + "* Tables of data from ES can be exported from the data exploration app (https://data.midrc.org/explore) by using the \"Download Table\" button.\n", + "* To get these sorts of tables using the API, you can use the guppy download function: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/query.py#L146" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "84aae2a8", + "metadata": {}, + 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'dg.MD1R/15b31761-9282-4db4-844f-53c0548d0c60',\n", + " 'dg.MD1R/aa36ef9c-bc91-42e2-91b0-81c976068224',\n", + " 'dg.MD1R/2750eb0d-f22c-45e4-9a0a-4bea1368a562',\n", + " 'dg.MD1R/4df978cf-f98c-4345-9ba6-c7d5869ca83e',\n", + " 'dg.MD1R/f178ff91-670c-410f-9657-745d1eebb350']},\n", + " {'case_ids': ['H03-00231'],\n", + " 'study_uid': '2.16.840.1.114274.1818.50376001950748783266811600552927618718',\n", + " 'project_id': 'Open-A1_PETAL_REDCORAL',\n", + " 'study_description': 'CT CT PULMONARY ANGIOGRAPHY',\n", + " 'object_id': ['dg.MD1R/382581f1-1b4a-4126-87b0-6aa33580f087',\n", + " 'dg.MD1R/53059389-aa58-4304-8fb0-a1da7179fcb4',\n", + " 'dg.MD1R/85e90967-3c7b-47ac-bbd7-863461e16b22',\n", + " 'dg.MD1R/7c8c5520-d4ac-4613-b373-6effd2f84384',\n", + " 'dg.MD1R/c84e1a99-2e76-49df-87b0-83bf292bfce8',\n", + " 'dg.MD1R/d9f5935e-b1bb-47bc-b12a-904678751b4a',\n", + " 'dg.MD1R/665aa0b4-bcf8-4908-8539-f51da2d0ed80']},\n", + " {'case_ids': ['10000364-1492496'],\n", + " 'study_uid': '2.16.840.1.114274.1818.56060079969005366644952998144843290774',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'Thorax^CT_PE (Adult)',\n", + " 'body_part_examined': ['CHEST'],\n", + " 'object_id': ['dg.MD1R/ba7d81da-3df0-4804-ae4d-ab25aa7e7aa7',\n", + " 'dg.MD1R/e8a6cd75-7981-454d-bbe0-09f29242f382',\n", + " 'dg.MD1R/44e89dd2-6eae-44e2-bbe9-e33507e1269d',\n", + " 'dg.MD1R/0da873f9-85b6-4334-b26a-73c790bd2c08',\n", + " 'dg.MD1R/55e31dcd-a58f-4f3b-a066-9babc93d6a76',\n", + " 'dg.MD1R/4c586c9c-13b7-4a9e-85c5-5c23fa7e0691',\n", + " 'dg.MD1R/5d36b2a1-ac13-4ab6-9de0-5387899ec7f5']},\n", + " {'case_ids': ['419639-000880'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.273286366529828459267276338216',\n", + " 'project_id': 'Open-R1',\n", + " 'study_description': 'CT CHEST PULMONARY EMBOLISM (CTPE)',\n", + " 'object_id': ['dg.MD1R/13e440f5-0a0d-4427-96fc-b4727ae5e88a',\n", + " 'dg.MD1R/f9a95691-0fee-41a0-87c5-feea22a8c4b4',\n", + " 'dg.MD1R/b23df943-0de3-4652-a2fb-9632ea25d39a',\n", + " 'dg.MD1R/a62f4f79-1f2e-42b9-a947-496d3fb39705',\n", + " 'dg.MD1R/02c9191a-3684-4d46-b665-232f2badf082',\n", + " 'dg.MD1R/bf036cd6-05c9-468f-aa7d-c7f6989576bb',\n", + " 'dg.MD1R/12b36fce-676e-4f90-8f39-4cdb5c947714',\n", + " 'dg.MD1R/5c6c4d84-b472-45d8-9d50-6d867db04fef']},\n", + " {'case_ids': ['10000364-1839931'],\n", + " 'study_uid': '2.16.840.1.114274.1818.48998288796252115887738222222994348445',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CTA CHEST (PE STUDY) W CONTRAST',\n", + " 'object_id': ['dg.MD1R/ccaf90af-0270-4ead-a677-d8d381699aa2',\n", + " 'dg.MD1R/23452ccd-bfc8-4d4f-9e2e-a284e464a4b3',\n", + " 'dg.MD1R/ddf20995-c2c1-4aea-be20-6ae660868ca9',\n", + " 'dg.MD1R/53c0389b-5a8a-4a9b-ad93-8541530cdeab',\n", + " 'dg.MD1R/9daa489a-cd0e-4bf1-a866-aae07451c51c',\n", + " 'dg.MD1R/4b019e55-1ae8-4207-9933-3b9f313c58b3',\n", + " 'dg.MD1R/3b60709a-e390-451e-82f1-3729900bc015',\n", + " 'dg.MD1R/5d8d7030-b9d7-47ea-a93a-90da0027371d',\n", + " 'dg.MD1R/7dc17f73-b24f-44a3-ab37-b3f1d144e7ca']},\n", + " {'case_ids': ['440808-000001'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.440808.3254',\n", + " 'project_id': 'TCIA-RICORD',\n", + " 'study_description': 'THORAX PE',\n", + " 'object_id': ['dg.MD1R/9d92cec1-09e0-4bd0-9520-a802c3510149']},\n", + " {'case_ids': ['10000364-899490'],\n", + " 'study_uid': '2.16.840.1.114274.1818.48223726785512834277411968652554273203',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'Abdomen^ABD_PEL_WITH (Adult)',\n", + " 'object_id': ['dg.MD1R/cf5b681c-ff35-4cba-9b66-854f819bc40b',\n", + " 'dg.MD1R/77398ed3-202e-4644-97b2-c3f2e4b4b069',\n", + " 'dg.MD1R/85159ac4-fc16-4219-82e3-55cfcca07da0',\n", + " 'dg.MD1R/43b6a759-8080-4265-b786-07c5ba03d9d8',\n", + " 'dg.MD1R/ab81b3b4-cdc4-4aea-8b17-6f7962b745d6',\n", + " 'dg.MD1R/7a1150f3-2a23-4fd7-bed1-13a729d5641b',\n", + " 'dg.MD1R/b1318bcf-a037-4a18-bf85-577e4a55677f']},\n", + " {'case_ids': ['10003752-4qJGqNLpQkqO3MOZaM2uBQ'],\n", + " 'study_uid': '2.16.840.1.114274.1818.475260413194907902910212819043567636377',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT ANGIO CHEST WWO',\n", + " 'object_id': ['dg.MD1R/625fc8eb-34ca-41be-b707-7d18be5171cf',\n", + " 'dg.MD1R/4463d5b6-bb78-4df9-9a2b-4b12348b0790',\n", + " 'dg.MD1R/13e6375b-115c-44b4-ba03-89d6dfd5b238',\n", + " 'dg.MD1R/21e78ae9-5261-4a65-aedf-74a2c4b6aee4',\n", + " 'dg.MD1R/c01ce79d-db81-460c-af26-a4ed55288b66',\n", + " 'dg.MD1R/f8882d38-596f-48cc-8a75-da98b4bb365f',\n", + " 'dg.MD1R/b3a927db-57e1-4135-9b5f-890bcbdcf5e7',\n", + " 'dg.MD1R/6e23b3f8-de31-43eb-920a-0a7f8e264621',\n", + " 'dg.MD1R/b9ab74a6-a854-43f1-91be-ff56016a56a7',\n", + " 'dg.MD1R/6da035d0-afbc-4c67-b02d-68eada57dffd',\n", + " 'dg.MD1R/460b9ecb-474a-4e06-9797-5521769cf384',\n", + " 'dg.MD1R/75ace50e-d2de-49fb-a8dd-57fc6f936fb8',\n", + " 'dg.MD1R/1056465b-b30e-4d88-a10a-00c744da3ca4',\n", + " 'dg.MD1R/1b4b8b68-ab23-4ff0-af33-ecef1dbeb071',\n", + " 'dg.MD1R/12232eea-0f8c-4ddf-891c-400150fe3243',\n", + " 'dg.MD1R/5f2cbc12-d31c-4e9c-87c9-c2a8588e02bc',\n", + " 'dg.MD1R/e041ea96-3adf-4823-b3e7-aacf1af4ad8d']},\n", + " {'case_ids': ['10000364-1542478'],\n", + " 'study_uid': '2.16.840.1.114274.1818.475984178692755569410513036599458344346',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT CHEST WO CONTRAST',\n", + " 'object_id': ['dg.MD1R/0ad97cea-b834-49a4-8117-96127148f94a',\n", + " 'dg.MD1R/d97ae0a5-a396-4766-9c01-db4ec6876769',\n", + " 'dg.MD1R/c4f0b742-853e-4da4-acc4-e4f7720606d5',\n", + " 'dg.MD1R/b5070521-d58b-463e-ac2c-3787cf20288f',\n", + " 'dg.MD1R/847a52a7-ecee-4cfc-9bc5-d5186eb79459',\n", + " 'dg.MD1R/ec8d68cf-edd6-4e15-9a44-18ec191f81df',\n", + " 'dg.MD1R/d403e3a0-5f32-4ff5-827e-6db395d0b201']},\n", + " {'case_ids': ['10000364-1557150'],\n", + " 'study_uid': '2.16.840.1.114274.1818.53740367069289401852749965980018749592',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT ABDOMEN PELVIS WO CONTRAST (STONE)',\n", + " 'object_id': ['dg.MD1R/27852078-15cc-4b8a-a8f6-3821911e3586',\n", + " 'dg.MD1R/c4d2d5f7-0d1b-4098-af28-9a7e260e605f',\n", + " 'dg.MD1R/d9de7067-2511-4df6-97ae-dfd9aaee0f45',\n", + " 'dg.MD1R/ea3cc15e-3ff8-4032-b263-1276d4f62261',\n", + " 'dg.MD1R/8230ee40-aad7-48c8-ad06-f60d6dfde841']},\n", + " {'case_ids': ['419639-010998'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.145859209440257632566762693091',\n", + " 'project_id': 'Open-R1',\n", + " 'study_description': 'CT CHEST PULMONARY EMBOLISM (CTPE)',\n", + " 'object_id': ['dg.MD1R/4296aecb-e0ff-48f9-abbd-9b4ae30b29fd',\n", + " 'dg.MD1R/be2cd44d-ad7e-4489-a001-bc03430c82e9',\n", + " 'dg.MD1R/767b14a8-d908-469f-b366-0b2119e44c5f',\n", + " 'dg.MD1R/a2e98cf8-7538-4265-b502-1ef16688cc7b',\n", + " 'dg.MD1R/e3ce414c-0881-4018-afa4-cfd2a7178728',\n", + " 'dg.MD1R/fccb446b-3cd8-40c7-9044-5e6e1c360fa3',\n", + " 'dg.MD1R/9ab8450a-afb1-4cf5-8565-ca6f53a1f4a0']},\n", + " {'case_ids': ['10000364-1140611'],\n", + " 'study_uid': '2.16.840.1.114274.1818.467901708470669329714767538567635463851',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT CHEST WO CONTRAST',\n", + " 'object_id': ['dg.MD1R/ce051e9d-1d56-428d-8ebe-93913e082926',\n", + " 'dg.MD1R/391aaea1-11fd-4bc4-84f8-965c08882ed0',\n", + " 'dg.MD1R/735c5eb7-3584-47b7-937b-a53afee347f2',\n", + " 'dg.MD1R/471ec100-6262-4885-83fa-4f9240064db9',\n", + " 'dg.MD1R/3ca6edbc-01bf-4a88-b3cf-4abbbfa36875',\n", + " 'dg.MD1R/f293bd37-39b9-4d35-9b4f-47c7e077ebdd',\n", + " 'dg.MD1R/f7f52f0a-a3a8-4970-984e-5bc2c9c8b6f4',\n", + " 'dg.MD1R/76277871-44c2-4a0f-be52-3b4542858e49',\n", + " 'dg.MD1R/6448804c-08a0-4d8c-8d32-5b4d6f4b1600']},\n", + " {'case_ids': ['419639-000861'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.488331024349243636870870700681',\n", + " 'project_id': 'Open-R1',\n", + " 'study_description': 'CT CHEST PULMONARY EMBOLISM (CTPE)',\n", + " 'object_id': ['dg.MD1R/6b0b8f89-7208-42d6-9a96-518413aeb495',\n", + " 'dg.MD1R/a7b37ede-7d9a-4647-a09c-fcd3fdf36b0d',\n", + " 'dg.MD1R/fed53b9c-cf1e-4f9c-b4ff-2c6b65336002',\n", + " 'dg.MD1R/0a17ae0d-d1c8-4838-8946-9ec6b787175f']},\n", + " {'case_ids': ['10003752-DeMtZYCbyEGzStHxM8kbA'],\n", + " 'study_uid': '2.16.840.1.114274.1818.53744049778588943153054314490000814994',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT CHEST WO',\n", + " 'object_id': ['dg.MD1R/9336f75a-9e08-4134-bb7e-db5f732f3561',\n", + " 'dg.MD1R/000eaeea-bcdb-40e2-9fc3-d20074162db5',\n", + " 'dg.MD1R/427f812b-c929-4e66-8e15-5603cd05c225',\n", + " 'dg.MD1R/8ae97a10-20a5-46ed-a679-051ba69c039b',\n", + " 'dg.MD1R/5e2960ce-9bc4-49cc-a03f-01b391e41614',\n", + " 'dg.MD1R/14a72edb-1782-42d3-b069-a4c23738713f',\n", + " 'dg.MD1R/22a86459-d9d3-438d-b5f6-5c3517405a57',\n", + " 'dg.MD1R/7f063d9b-496f-4518-a410-66259687618c',\n", + " 'dg.MD1R/de8792d3-e234-4e88-9e17-0a8e080d53f1']},\n", + " {'case_ids': ['10000364-1450463'],\n", + " 'study_uid': '2.16.840.1.114274.1818.517859522867219612516707445367532843394',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT CHEST ABDOMEN PELVIS W CONTRAST (ROUTINE)',\n", + " 'object_id': ['dg.MD1R/12e9ee7f-ed7e-4caf-baa8-44d840717c41',\n", + " 'dg.MD1R/698dcaa2-bb3c-4133-aae9-29ac19d97980',\n", + " 'dg.MD1R/22868e9d-95f8-4bb2-9f01-c6b6835985f4',\n", + " 'dg.MD1R/43d38e0c-9a2a-4e8c-932d-5f64e914252c',\n", + " 'dg.MD1R/cdd61c62-be66-44ad-9fec-20bc4cc2664e',\n", + " 'dg.MD1R/f5713765-9380-4890-9cca-33c78e48b5b1',\n", + " 'dg.MD1R/7ef32c3c-4e44-46be-bedd-e733595b7389']},\n", + " {'case_ids': ['10003752-N5vyaKcBY0qrhK1EwRel9g'],\n", + " 'study_uid': '2.16.840.1.114274.1818.5194613981898766008804686336430008198',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT CHEST ABDOMEN PELVIS W',\n", + " 'object_id': ['dg.MD1R/09012858-4475-4bea-b827-473ad5b664df',\n", + " 'dg.MD1R/02a2bdad-b654-4a4a-a6c3-f63bec9f6dd8',\n", + " 'dg.MD1R/ea843707-ac2b-4804-a0dc-67573a8470a4',\n", + " 'dg.MD1R/0d4bcc88-2c0c-495e-ab57-85f74699361f',\n", + " 'dg.MD1R/ccc40615-62be-4e85-9a49-91fc5adfcabc',\n", + " 'dg.MD1R/0b57a347-e191-4b68-b30b-8e9684d06ce8',\n", + " 'dg.MD1R/1b79faee-bb94-41f8-9464-5d10c367c96b',\n", + " 'dg.MD1R/b99ee3ae-3d8d-4921-a208-621aca09b725',\n", + " 'dg.MD1R/d4bfc96b-9312-4925-a6c7-31212fb31991']},\n", + " {'case_ids': ['10003752-MojpBGJVZ0u3qseqcbaN3w'],\n", + " 'study_uid': '2.16.840.1.114274.1818.509083401764981882712201954819835729318',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT CHEST ABDOMEN PELVIS W',\n", + " 'object_id': ['dg.MD1R/be82368c-14d3-4b86-b552-bb968c5fb4c2',\n", + " 'dg.MD1R/6407b9c7-105c-455f-a7e5-14c24f2836bf',\n", + " 'dg.MD1R/fa7fc058-ab0c-4f50-af5f-ed0287faf53f',\n", + " 'dg.MD1R/a7be2c61-2dbb-44b7-881f-ba1fa0738314',\n", + " 'dg.MD1R/89d9e3f3-3a50-4b44-85e1-99cad6553a9f',\n", + " 'dg.MD1R/e241ec6e-1330-4385-9a0e-4e0468ea8f19',\n", + " 'dg.MD1R/52242cb2-5519-432f-98a9-1cb9abfd248d',\n", + " 'dg.MD1R/1621cfe4-82cd-4dcb-8622-e91d509f1e26']},\n", + " {'case_ids': ['10000364-1490588'],\n", + " 'study_uid': '2.16.840.1.114274.1818.561169883351552454013802626109061014930',\n", + " 'project_id': 'Open-A1',\n", + " 'study_description': 'CT ABDOMEN PELVIS WO CONTRAST (STONE)',\n", + " 'object_id': ['dg.MD1R/eea8b073-bb79-47f6-b419-29ac41f84b5c',\n", + " 'dg.MD1R/09784145-3243-4b0a-9040-864e2fe58597',\n", + " 'dg.MD1R/3b8b5321-eb1b-49e8-a75d-159ea7178124',\n", + " 'dg.MD1R/006aaa2a-4616-41e6-be93-505b62fc28d3']},\n", + " ...]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## This query gets all the imaging studies of modality \"CT\"\n", + "\n", + "query.raw_data_download(\n", + " data_type=\"imaging_study\",\n", + " fields=[\n", + " \"study_uid\",\n", + " \"project_id\",\n", + " \"study_description\",\n", + " \"body_part_examined\",\n", + " \"case_ids\",\n", + " \"object_id\"\n", + " ],\n", + " filter_object={\"=\": {\"study_modality\": \"CT\"}}\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "279428e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10000364-1659647',\n", + " 'object_id': ['dg.MD1R/94b00ef4-06da-4415-b99f-b0ec005f15e3']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10003752-va894vbO0ut1evSelAXQ',\n", + " 'object_id': ['dg.MD1R/41f04389-a9b9-41b3-baa9-d2fa1477a966']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10003752-kfqUyPohA0OcAh700bIyfQ',\n", + " 'object_id': ['dg.MD1R/e4644d7a-b95e-4733-88f9-330c49d3237f',\n", + " 'dg.MD1R/1988219a-f33b-4964-bd29-552f1b2d1d33',\n", + " 'dg.MD1R/d01cd6aa-977f-4450-84f5-5c0b7f089700',\n", + " 'dg.MD1R/60ed8ff1-1177-433d-8975-2fce318113fb',\n", + " 'dg.MD1R/b2f7354e-287c-4306-8cb2-1988704f06b8',\n", + " 'dg.MD1R/a591b056-8eb4-46e5-a52d-b525a65bfa87',\n", + " 'dg.MD1R/bccb20c7-2479-432e-8969-c3a7070e5e78',\n", + " 'dg.MD1R/637f56c9-b7f3-42b7-868c-6bfe316143cf',\n", + " 'dg.MD1R/41a06a21-46ab-4b97-a2c7-c9d252c51010',\n", + " 'dg.MD1R/365fb30c-04c5-48b2-8f66-c95fa9d11af9',\n", + " 'dg.MD1R/1f050a8e-dc8a-4625-9987-5b6928bd7ecf',\n", + " 'dg.MD1R/e7cdcfff-02ea-4e16-b876-3cb19dd38c36',\n", + " 'dg.MD1R/0074f951-fbc7-4014-8887-321b1ee10617',\n", + " 'dg.MD1R/158b54dd-1007-4e5c-92be-92d102426bf2',\n", + " 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'dg.MD1R/74a571ed-c9d0-45ee-9eee-4f11f1a0b81e',\n", + " 'dg.MD1R/ee70e04a-99bc-47b1-89a0-486b56ad21dd',\n", + " 'dg.MD1R/e86b5ce5-2a0f-49d3-8d22-e6b9714b8dcb',\n", + " 'dg.MD1R/89288db6-1000-48a4-8f17-07ab40873fe7',\n", + " 'dg.MD1R/70abc856-93f6-476e-a942-e138798ccad2',\n", + " 'dg.MD1R/83570ed3-3ecd-4c3d-a939-c036f2fa9ae7',\n", + " 'dg.MD1R/9eea3203-2784-48a4-847c-4fd561cc9271',\n", + " 'dg.MD1R/bb288535-81f4-4a16-84fe-c7bb61a3ddc3',\n", + " 'dg.MD1R/1b1b244f-15aa-4276-8e31-0938349c074c',\n", + " 'dg.MD1R/bf8cbdad-fb49-402d-b084-1d837cbb30e3',\n", + " 'dg.MD1R/92ab7644-a933-4136-9803-aa3336f00675',\n", + " 'dg.MD1R/1f372ca5-6a41-4880-b427-b5706c4209cb',\n", + " 'dg.MD1R/1f9c6ec7-def8-49a8-82cb-854bc21032d8',\n", + " 'dg.MD1R/113c39ff-649c-43b7-884c-81ba911064b9',\n", + " 'dg.MD1R/ade055c1-581a-4495-a01f-4be7a317cce1',\n", + " 'dg.MD1R/3abb682e-1ac5-4f01-aed2-9838ddbc25d0',\n", + " 'dg.MD1R/a4175aa0-318d-4025-89dd-b0ba48d6830e',\n", + " 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'dg.MD1R/24f88bb3-27d0-4847-a168-ee397ebe57a6',\n", + " 'dg.MD1R/128b24ab-9dae-4e8e-8090-e8f9febb1269',\n", + " 'dg.MD1R/1a39576c-416f-42b1-8118-3dcfcbe8bd77',\n", + " 'dg.MD1R/dc07e982-5756-4f8c-8bc6-01b4da804d0d',\n", + " 'dg.MD1R/49dea332-a36b-42aa-ae08-adbe0d5b1e41',\n", + " 'dg.MD1R/b638bb53-d321-4f4f-9cc3-874639a2b780',\n", + " 'dg.MD1R/cfce0105-32f8-4bea-b134-44a44b2903e6',\n", + " 'dg.MD1R/a3a03c06-1735-49db-8c47-295636244989',\n", + " 'dg.MD1R/ce13a15c-9a40-4cb5-ad65-b11e7a32d82f',\n", + " 'dg.MD1R/9ea06b04-583b-4aef-a793-6a9c17cdc420',\n", + " 'dg.MD1R/ff17a9ac-a959-4613-8fec-3e0d1bbee885',\n", + " 'dg.MD1R/284b3e85-0456-4f86-a5d7-110bf5f698fe',\n", + " 'dg.MD1R/304b84f2-d294-4466-a05a-cad5bc7c901f',\n", + " 'dg.MD1R/7d181850-cfb4-4f02-b8be-f841efb0233d',\n", + " 'dg.MD1R/4e371fd8-9f77-492c-8725-7ca9cb1d4276']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10000364-1308159',\n", + " 'object_id': ['dg.MD1R/4fdf87e7-58ae-4e17-bba8-cd4523e10e55',\n", + " 'dg.MD1R/e1362311-df62-4775-9227-a4a5899e4af5',\n", + " 'dg.MD1R/c6a575e5-99dd-43df-bc86-4575f6965cd0',\n", + " 'dg.MD1R/d79f06ff-8d80-4fc6-b80c-0afc721850d1',\n", + " 'dg.MD1R/70d0ad12-d32c-4fca-b32d-57581afef51f',\n", + " 'dg.MD1R/a103a714-6c14-4a53-9923-3cb711652d2a',\n", + " 'dg.MD1R/75d65446-9277-4d76-a22e-a100b48311a9',\n", + " 'dg.MD1R/55c24281-2f27-4164-9c6d-086374f06c13',\n", + " 'dg.MD1R/58aa4524-0c30-4e93-b352-544107110e28',\n", + " 'dg.MD1R/e91e2b3e-0aba-4e66-b7eb-2237228c0688']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10003752-saqN5hF8yUGGd7Wkz9xWJg',\n", + " 'object_id': ['dg.MD1R/80a5cb40-46d2-41d4-9ff6-2d146ba8c6e1',\n", + " 'dg.MD1R/b036798f-a5dc-4c70-a835-c4e92aa19531',\n", + " 'dg.MD1R/537976ab-3126-47b4-a806-23e229061327',\n", + " 'dg.MD1R/c2265c45-8526-4b6a-a214-39725738f24f',\n", + " 'dg.MD1R/44085f5e-8101-4728-9450-2ddd70f92485',\n", + " 'dg.MD1R/dfed79ec-a15e-4c3d-a2fd-d17d6b3358ec',\n", + " 'dg.MD1R/6d22412b-179e-4699-83e6-3fabee6f984b',\n", + " 'dg.MD1R/6861a8ce-0d5e-4458-95b7-7f7b0e05ee81',\n", + " 'dg.MD1R/34bf392a-ff41-48f9-9907-353eeb854de3',\n", + " 'dg.MD1R/0fa902e7-3de6-46db-9125-432cef525f3e',\n", + " 'dg.MD1R/2baf1371-a585-4f70-b375-bb1b3a642ff0',\n", + " 'dg.MD1R/cca1ac3b-979c-44ed-801c-905015e47253',\n", + " 'dg.MD1R/8a46d446-5a42-4e09-9200-9c6bfef53fb8',\n", + " 'dg.MD1R/e0f993de-31dc-4fde-a39a-14f03c64fa50',\n", + " 'dg.MD1R/927a69ad-7918-4d2b-87c2-7ed2019691d1',\n", + " 'dg.MD1R/fbe3b6fe-0f66-4396-ab22-022aa937cd81',\n", + " 'dg.MD1R/b0322300-46a7-4d34-81c7-a16aefdf42ab',\n", + " 'dg.MD1R/4ab6472b-906c-4485-baa4-e40937615312',\n", + " 'dg.MD1R/131a0619-7ff1-4c51-8f29-9b17c82a48b8',\n", + " 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'dg.MD1R/f3ac076a-e8dd-42f4-beb8-a4bf0131271a']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 47,\n", + " 'submitter_id': '10000364-1537249',\n", + " 'object_id': ['dg.MD1R/cfd8c8b3-3efc-4655-93f3-876572b7850d',\n", + " 'dg.MD1R/7ebc274d-fd53-4029-a7e1-592276623c14',\n", + " 'dg.MD1R/d70ede0c-e46c-4aed-97ce-0122597968bc']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10022779-pVNV9sBEmEFvZ1SQaKhA',\n", + " 'object_id': ['dg.MD1R/6545e16b-4b32-43f5-83c8-81eeb491694a',\n", + " 'dg.MD1R/d5b578ad-8289-4c4c-b9d5-bc81ed7a713d',\n", + " 'dg.MD1R/b26bb981-0c41-4d84-91c8-b0f4c5a92c1f',\n", + " 'dg.MD1R/8a583566-c01a-48f2-8c03-3f7b1713e546',\n", + " 'dg.MD1R/afbcfdff-8620-41f5-b290-e36ec54e4a03',\n", + " 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'dg.MD1R/e25b9646-1eca-48be-abed-1c8bd6332c6a',\n", + " 'dg.MD1R/1a0c2557-f3b4-4f18-8c4a-2afe6fd40553',\n", + " 'dg.MD1R/ec4f4cca-0995-4add-ad0d-b94086308645',\n", + " 'dg.MD1R/c273b2d8-f248-40b9-a5ba-fcf88c9e1566',\n", + " 'dg.MD1R/6b2cc108-22da-40da-8996-44e4aa4c6e70',\n", + " 'dg.MD1R/25fb2709-3a99-4899-9e35-29acfd4c0f9f',\n", + " 'dg.MD1R/5ff93839-186a-461d-a251-8762bcdf57b5',\n", + " 'dg.MD1R/9d9bcd60-d285-4906-a77c-fa25f45c1cb6',\n", + " 'dg.MD1R/ad22e765-f963-4c34-893b-cf7075447881',\n", + " 'dg.MD1R/661c7ce9-8139-4bc5-8fc5-be783842d516',\n", + " 'dg.MD1R/bc851745-bbb5-4b42-8d95-d66efbb5ea5e',\n", + " 'dg.MD1R/5d9a8ab9-58f2-42f5-8961-39c7253d5385',\n", + " 'dg.MD1R/a6a0df4e-44e4-4ff0-9736-199c8c622e20',\n", + " 'dg.MD1R/7ba51807-359d-45df-b891-57313827698b',\n", + " 'dg.MD1R/1636eb42-9be7-4621-9bd0-b7d071ce8233']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10022779-GucmEiOiHkmLFrU22gNl6g',\n", + " 'object_id': ['dg.MD1R/fa1f5dfc-e9f8-4d1a-a982-92429e7c7992',\n", + " 'dg.MD1R/89e71965-bc26-40cb-b646-28e3cb91b592']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10022779-IOJbZ2EE2c2LG5I0eQ',\n", + " 'object_id': ['dg.MD1R/1f928eae-6405-4583-952e-67ee593a3c3d',\n", + " 'dg.MD1R/95b6f67c-4b17-4192-b14b-54510965f14e',\n", + " 'dg.MD1R/72a25435-e61b-4cd4-8d05-176bf4d6ec48',\n", + " 'dg.MD1R/f06677f2-1ad5-465f-9f9a-af9555d4967d',\n", + " 'dg.MD1R/590cd68f-5e99-4786-8806-6e08cd272a14',\n", + " 'dg.MD1R/f2ab5a33-ff97-4d9e-91a7-8749b1e209d0',\n", + " 'dg.MD1R/04db5712-278a-4d96-90bd-57a6e9ab2337',\n", + " 'dg.MD1R/93b5d9c0-d1dc-441f-a4e5-59ab5645195c']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 47,\n", + " 'submitter_id': '10000364-1457766',\n", + " 'object_id': ['dg.MD1R/76da4ee6-c176-4496-9789-0ba8b91722dd',\n", + " 'dg.MD1R/76885c5c-5c1e-433b-b703-7a785fa0ba5d']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 47,\n", + " 'submitter_id': '10022779-vN5TEOflEq9SwRrghakgA',\n", + " 'object_id': ['dg.MD1R/d9686db2-7337-41d3-9bc9-d86b5a82e6c6',\n", + " 'dg.MD1R/03d83f5e-ca34-49ec-a970-022426b5498d']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 47,\n", + " 'submitter_id': '10000364-1213935',\n", + " 'object_id': ['dg.MD1R/27d1b82f-2a8c-42e9-9cb7-acf1ed9eb412']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 47,\n", + " 'submitter_id': '10008204-U5ooDxLg0qCxCSLlMd2A',\n", + " 'object_id': ['dg.MD1R/27039c66-59a9-47eb-a709-76ccae77136d',\n", + " 'dg.MD1R/9285a97b-4aec-4a42-b150-7894adb2a97b',\n", + " 'dg.MD1R/5980c658-ea91-46dc-9b5c-ec948bdfd587',\n", + " 'dg.MD1R/999e63ac-dd58-4d72-b4cc-45a373388d67',\n", + " 'dg.MD1R/d42a5858-4370-4526-8289-a8a775779318',\n", + " 'dg.MD1R/748da2ce-c409-41db-a383-fbbebf2d5dca',\n", + " 'dg.MD1R/f243c615-1a5d-444f-8e25-67413fb81e67',\n", + " 'dg.MD1R/e5ffada6-c792-4c6a-bf72-a12ebcac6d76',\n", + " 'dg.MD1R/51f91e40-b247-4acd-a412-c10dd6a37fec',\n", + " 'dg.MD1R/49484750-5e4c-439e-9487-4e486da96220',\n", + " 'dg.MD1R/a99a43fb-8a41-489c-8122-7e5f607adbb6',\n", + " 'dg.MD1R/d994fbbe-0835-4856-a2db-48dc95c968d3',\n", + " 'dg.MD1R/483d8c87-f2c8-4bfc-bc5b-eb0dba52b257',\n", + " 'dg.MD1R/f5f4930f-7d9c-4f31-8e40-98475c472101',\n", + " 'dg.MD1R/468bca02-1490-4f7a-b475-5686c1956a5d',\n", + " 'dg.MD1R/56309c94-77e9-45de-a7f7-3d712827a7b0',\n", + " 'dg.MD1R/e7e9fad1-4505-43f7-b98f-2358e83d5a8b',\n", + " 'dg.MD1R/3f5e4816-33b0-492d-8045-d326bd337d63',\n", + " 'dg.MD1R/26cacd5d-484f-4254-a7e1-0f4531b55b24',\n", + " 'dg.MD1R/b5c30830-a344-429d-bc04-61c35455f785',\n", + " 'dg.MD1R/887aa5ca-6875-426d-b62b-83cf9a79c3e6',\n", + " 'dg.MD1R/f9d4e332-aa2b-4371-b010-98267847b5dc',\n", + " 'dg.MD1R/b9bee501-6cea-4766-800d-19d9fa01026a',\n", + " 'dg.MD1R/00d58c1e-19ee-4d50-b128-457316c4243d',\n", + " 'dg.MD1R/bc49f431-59de-4716-a722-4700e00a6e69',\n", + " 'dg.MD1R/027a9393-ecbe-4c8e-9d49-a06940fbbd41']},\n", + " {'race': 'American Indian or Alaska Native',\n", + " 'ethnicity': 'Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10022779-pADEDx7vNUO2eaep7KIqQ',\n", + " 'object_id': ['dg.MD1R/8f6540e4-43bc-4382-995b-e5ef3ba0d29b',\n", + " 'dg.MD1R/4ef104a2-6e07-4e59-9461-e6a22eed826c',\n", + " 'dg.MD1R/41a8e941-7c25-4380-9f67-b6e6cb04d148',\n", + " 'dg.MD1R/63c73ee4-4334-453a-ad6e-c5d5705b06f8',\n", + " 'dg.MD1R/e6a39ea7-5e9b-4ab1-80ce-8609e4a030be',\n", + " 'dg.MD1R/30e279f9-7eb0-407c-a0ab-12ef53abd785',\n", + " 'dg.MD1R/8f688e8c-c3e8-40d6-80aa-d67ebace732b']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10022779-N0wYl3E50aaQY94MoRg9w',\n", + " 'object_id': ['dg.MD1R/241a6692-f1a4-42b3-a3e2-5ddffeec7fc3',\n", + " 'dg.MD1R/6971ed6b-797c-4f28-8502-7a8c27c65067',\n", + " 'dg.MD1R/2d054789-dfd7-4686-aa08-5714c1b0eb67']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10022779-8EB31MWieEemPo0dfCPkg',\n", + " 'object_id': ['dg.MD1R/7b0b1bc9-6b5d-47e3-9d48-4528d167d148',\n", + " 'dg.MD1R/986fb246-aca4-468b-bd64-2468a23f945a']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10008204-c21AZ89cFkqLGTHKd4mxBA',\n", + " 'object_id': ['dg.MD1R/e394d68d-27b4-4c80-b777-75f6774a00c1',\n", + " 'dg.MD1R/765b62a0-78dd-4802-b16f-0d156a6c8c0b',\n", + " 'dg.MD1R/a3ed4692-ac74-41cd-94aa-a8a368d7ce95',\n", + " 'dg.MD1R/17030194-e9a0-4cb1-88b9-feac3d3436dc']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 47,\n", + " 'submitter_id': '10003752-DFWYoLtVkWjXxqmdOSGuw',\n", + " 'object_id': ['dg.MD1R/8acc3326-b9d9-45db-84df-3b1ad8fe854d',\n", + " 'dg.MD1R/f8e18248-3c8f-4e01-b50f-c482224fe297',\n", + " 'dg.MD1R/fe409344-a2c0-4e2b-8f4f-d0813d082ab2',\n", + " 'dg.MD1R/8acf4afa-bea2-47b6-9f3c-5a8dda8351a1',\n", + " 'dg.MD1R/2aeac1b4-0d4d-44c9-b9f9-15c844b40f81',\n", + " 'dg.MD1R/2677de68-af90-4115-beb5-7f8b1ae08ee3',\n", + " 'dg.MD1R/2db0a988-0abf-458b-8e3e-7e0584d34bea',\n", + " 'dg.MD1R/20403818-6000-4141-82f3-3f4bb8f0c4d3',\n", + " 'dg.MD1R/96dc806a-db2d-45f6-b3f0-6871d26d7b2c',\n", + " 'dg.MD1R/4329be8d-2e3b-4db4-8baf-b10d338499b7',\n", + " 'dg.MD1R/9069f501-2d13-4779-b831-8755e2aaaad4',\n", + " 'dg.MD1R/3c383fbc-c96e-4bb3-bbe0-7bf573df3111',\n", + " 'dg.MD1R/6022c660-9ef4-4f37-bec3-e56128442e16',\n", + " 'dg.MD1R/6b193ee2-42b3-4787-929f-3e0878ee4582',\n", + " 'dg.MD1R/7d640ad6-d2fb-415a-8455-d3f0dc5a99a9',\n", + " 'dg.MD1R/0f1aa163-b5d3-4ced-94be-9747a925b41a',\n", + " 'dg.MD1R/938c4021-be23-4fba-96c6-641728d240fc',\n", + " 'dg.MD1R/108f2bba-5e76-4b24-9702-62a5aac6c6a6',\n", + " 'dg.MD1R/e0c38850-e416-4c86-bde1-2593fd31c7da',\n", + " 'dg.MD1R/d38a1bdf-055c-4206-848e-0b9658c36d0e',\n", + " 'dg.MD1R/1fa98e21-1d4a-4177-ad2d-9ad0689a273c',\n", + " 'dg.MD1R/1a31b927-9353-499b-879f-1dc9e29d9656',\n", + " 'dg.MD1R/0492800c-21d3-4f59-9862-aa8b79d49815',\n", + " 'dg.MD1R/1ce4af29-5f16-4e35-8c92-2c95340764c8',\n", + " 'dg.MD1R/19e0e21f-005e-411c-ba0c-9b9db435c7e9',\n", + " 'dg.MD1R/1d308279-6056-4535-8590-d9af9c3271bf',\n", + " 'dg.MD1R/ab086a80-5048-47a3-89e1-8133033413a6',\n", + " 'dg.MD1R/a35960c9-f990-49d1-8f05-3bdeb766e86f']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 46,\n", + " 'submitter_id': '10000364-922982',\n", + " 'object_id': ['dg.MD1R/ec38dc98-b05f-45b4-9569-b46d0a4eaf27',\n", + " 'dg.MD1R/e8ec33d7-3c61-4176-9d90-779ac1923fdf',\n", + " 'dg.MD1R/2941920f-5622-4788-8b26-af76bebd6174',\n", + " 'dg.MD1R/b4ef985c-0e2c-47cd-bc47-40d525e3f0c4',\n", + " 'dg.MD1R/f5285a33-f10e-4559-80f8-dcd8f64c0123',\n", + " 'dg.MD1R/034fad46-4f5d-490b-9142-0c2e25dfdafd',\n", + " 'dg.MD1R/3dd94491-263b-467e-8164-50df1a605b9b',\n", + " 'dg.MD1R/03ed87db-b196-492f-8d37-b1dceb016e16',\n", + " 'dg.MD1R/13a7dc74-1660-4573-9875-d01579ee08b0',\n", + " 'dg.MD1R/fe3712a3-e7e3-468d-8f1a-0dfe4a20a8e2',\n", + " 'dg.MD1R/a7a20dd4-de4e-4698-b750-e92dcf220b25',\n", + " 'dg.MD1R/9d0549d9-2f5e-4c77-ba91-dcbdb815050f']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10000364-5465296',\n", + " 'object_id': ['dg.MD1R/32c523f7-9b36-4864-a452-3c2ae85788fe',\n", + " 'dg.MD1R/11cf0c0d-e6d1-4f53-9239-b9f33df666a2']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10008204-feADbiA0q7oMrftxLxmg',\n", + " 'object_id': ['dg.MD1R/9d888195-efd2-4994-a1b0-1b5c4f7eafd0',\n", + " 'dg.MD1R/778fb25d-f4ef-44e3-b3da-5ff60f3175b4']},\n", + " {'race': 'Black or African American',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Female',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10022779-exjGl8nVnUzazJReDyS3Q',\n", + " 'object_id': ['dg.MD1R/e5ce9d52-6e3a-49d2-9700-3df2ccba01a3',\n", + " 'dg.MD1R/5628103b-bca4-4210-9128-0c52d1402e8e',\n", + " 'dg.MD1R/64f37918-ad05-49d5-8837-b471b9d01757']},\n", + " {'race': 'White',\n", + " 'ethnicity': 'Not Hispanic or Latino',\n", + " 'project_id': 'Open-A1',\n", + " 'sex': 'Male',\n", + " 'age_at_index': 45,\n", + " 'submitter_id': '10000364-1971739',\n", + " 'object_id': ['dg.MD1R/b298c86f-2ffc-4884-b097-c8753b17fe56',\n", + " 'dg.MD1R/1b47dc7f-ee4c-483a-beb8-9e2048cd508b',\n", + " 'dg.MD1R/7671f6f4-95db-4e41-bffd-ccd1fe6be417',\n", + " 'dg.MD1R/0e147765-5236-4611-973c-88818f789416']}]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Here is an example getting all the cases in a particular project between ages of 45 and 47\n", + "\n", + "query.raw_data_download(\n", + " data_type=\"case\",\n", + " fields=[\n", + " \"submitter_id\",\n", + " \"project_id\",\n", + " \"race\",\n", + " \"sex\",\n", + " \"ethnicity\",\n", + " \"age_at_index\",\n", + " \"object_id\"\n", + " ],\n", + " filter_object={\"AND\": [{\">=\": {\"age_at_index\": 45}},\n", + " {\"<=\": {\"age_at_index\": 47}},\n", + " {\"=\": {\"project_id\": \"Open-A1\"}}]},\n", + " sort_fields=[{\"submitter_id\": \"asc\"}],\n", + " accessibility=\"accessible\"\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c6570ebd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'study_modality': ['CT'],\n", + " 'case_ids': ['10003752-JAtkcSczhECkwIeFkMkUA'],\n", + " 'study_uid': '2.16.840.1.114274.1818.51823901133181661835638047342706386856',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Black or African American'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [54],\n", + " 'object_id': ['dg.MD1R/f036ad90-ebaa-4933-b336-0dbc8c991056',\n", + " 'dg.MD1R/f09bb121-5e6d-4de2-8de0-036b77febb60',\n", + " 'dg.MD1R/05db8ab8-7cfb-4771-b641-5f44d3b92330',\n", + " 'dg.MD1R/ce5a00cc-3d48-4ead-9e20-01411ca2bce6',\n", + " 'dg.MD1R/707a3c0d-acea-4c10-86e0-17b269af8ce1',\n", + " 'dg.MD1R/e71baa13-6840-4330-9b29-ddb9a3ff0ffa',\n", + " 'dg.MD1R/a89a45ee-3dec-4f9d-8af6-ea3d3594168e',\n", + " 'dg.MD1R/e9c579d7-6e94-4e0e-ae73-3a623f125a87',\n", + " 'dg.MD1R/c6e753ed-bbbb-4a25-a5bb-d090daae410c',\n", + " 'dg.MD1R/24872336-2522-4cc8-bdff-7f92f3c55ead',\n", + " 'dg.MD1R/e36b3422-66c1-4d5c-b7cc-b588e427be8e',\n", + " 'dg.MD1R/252492ba-6f1a-4394-b8b6-8da204a1d4cf',\n", + " 'dg.MD1R/bc5b0feb-f559-40b8-83a3-c305c1fd2f2d']},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['10000364-5377321'],\n", + " 'study_uid': '2.16.840.1.114274.1818.562393802736199220211696540889043926152',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [85],\n", + " 'object_id': ['dg.MD1R/3905b507-532d-44c3-846c-3fd92f271c1e',\n", + " 'dg.MD1R/54f1571e-ff4a-44c8-a2e6-6d29fa3afda7',\n", + " 'dg.MD1R/4d05be47-656c-4740-a062-cdb2b6fbfb4d',\n", + " 'dg.MD1R/90b41520-ed8c-43a2-965a-6856d4fa7c81',\n", + " 'dg.MD1R/44e3ee42-f416-4f78-a925-de7cbdb18be3',\n", + " 'dg.MD1R/6429731a-138e-4bc0-9817-841d053df8b7',\n", + " 'dg.MD1R/b985586e-95ff-43d8-b903-1f86c39eeb83',\n", + " 'dg.MD1R/f1e631cd-558b-48db-bb14-646f3d45305f',\n", + " 'dg.MD1R/6e63854c-5db9-4618-bdb3-03219efaf559',\n", + " 'dg.MD1R/df3ab185-43ee-4311-aa6f-db63ec2ce58a',\n", + " 'dg.MD1R/e3570d4f-cc06-457e-8538-f961f62b9154',\n", + " 'dg.MD1R/7e704fca-0b26-4aed-9b38-9c615ec24188',\n", + " 'dg.MD1R/4b488c16-3c27-4ac5-ad2f-41ae1452cc20']},\n", + " {'study_modality': ['CR'],\n", + " 'case_ids': ['10003752-rQKuhWtxk2nqc2g6hFLIw'],\n", + " 'study_uid': '2.16.840.1.114274.1818.49758574023046102482389533212652191890',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Black or African American'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [56],\n", + " 'object_id': ['dg.MD1R/412ce916-3cf6-4ddb-84fd-cbaa76410d87']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-010774'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.998625199684256323608132905330',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [65],\n", + " 'object_id': ['dg.MD1R/dfe90c75-a733-4d1c-a2c7-e7c00f6a16c1']},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['419639-007258'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.268538303296837992654182760797',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Other'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [37],\n", + " 'object_id': ['dg.MD1R/d263cdf1-9479-44a8-9716-f3a62091d802',\n", + " 'dg.MD1R/c52b4496-652c-43cf-8e9d-ac7091b1fa46',\n", + " 'dg.MD1R/28e6bd5d-765b-4e9a-8f73-c3578eeb0fee',\n", + " 'dg.MD1R/14b17276-2807-4594-b229-553ed3e8b7d7',\n", + " 'dg.MD1R/5c0cbd3d-1900-4eeb-9bbb-ac0764716dd2',\n", + " 'dg.MD1R/317d1f4e-cb61-45d7-b158-5dac152e3f89',\n", + " 'dg.MD1R/5526a7c5-6743-4ac0-b4ee-5b917cf6a70d']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-010503'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.226953097105207979063573043465',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [39],\n", + " 'object_id': ['dg.MD1R/7d7a7ae9-dbd8-4466-865b-9ab768303feb']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-010791'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.229012848586685209603768289405',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [19],\n", + " 'object_id': ['dg.MD1R/6c82df16-c68a-4cc5-b9a0-686082b23dbf']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-002749'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.266242159133537930703566322236',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [81],\n", + " 'object_id': ['dg.MD1R/2a53bf60-3ad8-48e8-8ef4-dbd70689d294']},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['10000364-1727850'],\n", + " 'study_uid': '2.16.840.1.114274.1818.536361470305235062311787686323003300994',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [66],\n", + " 'object_id': ['dg.MD1R/9338fd09-ee65-4cb4-82c9-bb0356d24e52',\n", + " 'dg.MD1R/9321a212-eda7-46b0-acc7-a15a1ee55d28',\n", + " 'dg.MD1R/82f3ac63-28da-4a2a-a5c0-af9366beceaa',\n", + " 'dg.MD1R/39177483-605e-4616-8ac2-aba6b7ee2ccb',\n", + " 'dg.MD1R/7e15c844-e3dd-4f8e-a7cf-3a932ffadfe8',\n", + " 'dg.MD1R/64eb9138-6ab6-492d-89bd-f5d7233c7d61']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-006920'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.270085499102516805881914515322',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': []},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['419639-011143'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.208975474417633865758609502425',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [73],\n", + " 'object_id': ['dg.MD1R/3aff525b-25b2-4d5f-8d76-b65e2a0d725c',\n", + " 'dg.MD1R/1821298a-642f-437e-8c1f-1039da736272',\n", + " 'dg.MD1R/d7d475ad-c286-4f96-b840-23b73263948a',\n", + " 'dg.MD1R/d220ec12-756c-4b5c-9b74-abc9ccc12997',\n", + " 'dg.MD1R/95084ded-5222-4631-994f-7efede3f844d',\n", + " 'dg.MD1R/d7f7b004-0e25-44e1-974d-629123f0cb95',\n", + " 'dg.MD1R/898d9ac6-bb2d-42f7-8737-185d03ef1b3b',\n", + " 'dg.MD1R/784e2278-6c8c-43d2-96c4-86e652379ee0']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-010999'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.316243344515413515846901257761',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [31],\n", + " 'object_id': ['dg.MD1R/ec0c5f89-3011-4c64-87ae-2c421dc6d5b8']},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['10000364-1329267'],\n", + " 'study_uid': '2.16.840.1.114274.1818.508100376880809777512198095595279104402',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [61],\n", + " 'object_id': ['dg.MD1R/c749f869-a650-412a-ab69-f4737c8f638d',\n", + " 'dg.MD1R/d2d2c141-ce7a-494a-806c-fec6ee0ecf68',\n", + " 'dg.MD1R/5dc9fa48-21f3-40e7-aceb-8c245ffabff5',\n", + " 'dg.MD1R/ce9d3ae2-bcca-4a58-8134-ecea931c24c1',\n", + " 'dg.MD1R/ab121ce5-7c37-4d41-8638-cc08162c208d',\n", + " 'dg.MD1R/2c0f88c8-3cfd-4b57-83d5-e2c67590d2f0',\n", + " 'dg.MD1R/071e3b1c-3888-4f0b-95eb-6e582a545403']},\n", + " {'study_modality': ['CR'],\n", + " 'case_ids': ['10003752-bAyaJFkdXkfmMaRCLk2Ew'],\n", + " 'study_uid': '2.16.840.1.114274.1818.482546375968461797912503776329717388732',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Black or African American'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [17],\n", + " 'object_id': ['dg.MD1R/52a483f5-adae-4748-9a67-cd8d9fb1b9f0']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-007579'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.117471514540799318722896815965',\n", + " 'ethnicity': ['Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [66],\n", + " 'object_id': ['dg.MD1R/08b7563a-0aa6-4a3b-b3bb-26b6747f481e']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-010767'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.164794439957306821400969834057',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [56],\n", + " 'object_id': ['dg.MD1R/2e2c2d5e-e573-42fa-8c2f-ee2913a66f7d']},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['10000364-2697209'],\n", + " 'study_uid': '2.16.840.1.114274.1818.511432023638509718217473157864919521173',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Asian'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [65],\n", + " 'object_id': ['dg.MD1R/c68670bd-a21d-42f1-b78a-001bdf93b3e8',\n", + " 'dg.MD1R/3b71dd18-d849-4421-b603-722a6b75fe7b',\n", + " 'dg.MD1R/d1f3fa83-c854-417d-aaf2-ccb9389ad360',\n", + " 'dg.MD1R/e055cfc3-a293-4bea-abae-2e935098eb65',\n", + " 'dg.MD1R/56f4ba77-6bcf-4086-906d-3dd6ab74021c',\n", + " 'dg.MD1R/fa2ecc63-9846-42a4-a7a7-0fd946531c77']},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['10000364-1235791'],\n", + " 'study_uid': '2.16.840.1.114274.1818.50339628437889570989965736075188094619',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [88],\n", + " 'object_id': ['dg.MD1R/77c3c640-c33f-4bf0-8936-12cbf6d7a025',\n", + " 'dg.MD1R/1afe1c1d-45df-4498-9553-be159921f288',\n", + " 'dg.MD1R/358172da-487c-42be-a388-a56fb5e2c96a',\n", + " 'dg.MD1R/e64663e6-cc72-40fc-ac28-c0bf185cb929',\n", + " 'dg.MD1R/41445e39-225d-4207-9234-1c1b38c325d7',\n", + " 'dg.MD1R/ea237b20-a0b4-45c9-af55-bdf4508bb864',\n", + " 'dg.MD1R/3014c477-142d-4224-8eb2-d508f78060a9']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-010512'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.254625786058461186964645575350',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [34],\n", + " 'object_id': ['dg.MD1R/66b40ea2-5f7a-4308-81f2-46b7f18aeeb8',\n", + " 'dg.MD1R/6af92c37-7651-44f4-a7b0-917f3d6552b7']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-007192'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.291431155243018683452205154085',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Asian'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': []},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['10000364-1721955'],\n", + " 'study_uid': '2.16.840.1.114274.1818.536681334896030858114156659672737643168',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [49],\n", + " 'object_id': ['dg.MD1R/637d735b-c1c7-4baa-8042-8e55e545fb98',\n", + " 'dg.MD1R/f8618553-cc4a-4cc4-b684-f00a2f50ccda',\n", + " 'dg.MD1R/577fdc0f-562f-4eef-aadf-ec0d6c577396',\n", + " 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'Open-A1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [],\n", + " 'object_id': ['dg.MD1R/d8ff3d22-0ea7-485b-8a5e-dc397e64d694',\n", + " 'dg.MD1R/7205f01f-d5eb-494f-ada3-f0eaf0b339c9',\n", + " 'dg.MD1R/3626feae-8e57-4d73-811f-9cec9406802b']},\n", + " {'study_modality': ['CTPT'],\n", + " 'case_ids': ['10000364-952373'],\n", + " 'study_uid': '2.16.840.1.114274.1818.501151603194204870816186988725236765056',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Male'],\n", + " 'age_at_index': [63],\n", + " 'object_id': ['dg.MD1R/2d57d510-3d54-42bd-8c8d-55b91178aa07',\n", + " 'dg.MD1R/1ab91cfb-8371-46bb-994b-66ad17a59dd8',\n", + " 'dg.MD1R/68769fa2-f479-434b-9fbc-98f25eb6ef40']},\n", + " {'study_modality': ['CT'],\n", + " 'case_ids': ['10000364-1710349'],\n", + " 'study_uid': '2.16.840.1.114274.1818.46987393695811467285142549296785380245',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['White'],\n", + " 'project_id': 'Open-A1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [65],\n", + " 'object_id': ['dg.MD1R/e8cb1ba7-6d8a-418d-82a4-1487ef484ced',\n", + " 'dg.MD1R/85a49e93-6da1-4d35-81e6-44191aeb4b22',\n", + " 'dg.MD1R/b26c3bea-b164-4e57-99be-033beb2e3994',\n", + " 'dg.MD1R/b500e1f4-ac96-40bc-9e6e-88f8364d0c4a',\n", + " 'dg.MD1R/1c45f0ec-0f6f-4031-a77a-ecfc2731372f',\n", + " 'dg.MD1R/c7b79edf-2ece-4f05-952a-5d37ea32095c',\n", + " 'dg.MD1R/55060691-e1f8-4a68-bc1f-6ecca8d8080a',\n", + " 'dg.MD1R/865e920b-9c62-4ad4-b9ff-dc6fd6fc724a',\n", + " 'dg.MD1R/df1adb44-4da8-41ba-ad48-0ab6165759a0']},\n", + " {'study_modality': ['DX'],\n", + " 'case_ids': ['419639-011284'],\n", + " 'study_uid': '1.2.826.0.1.3680043.10.474.419639.285735607680683167783836458346',\n", + " 'ethnicity': ['Not Hispanic or Latino'],\n", + " 'race': ['Not Reported'],\n", + " 'project_id': 'Open-R1',\n", + " 'sex': ['Female'],\n", + " 'age_at_index': [48],\n", + " 'object_id': ['dg.MD1R/603908d9-41c1-467a-82ed-6f522e545f17',\n", + " 'dg.MD1R/72525698-640e-4934-bf3e-8cab4f7f76de']},\n", + " ...]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Here is an example getting all the imaging studies where the patient had a positive COVID-19 test result within a week of the study date.\n", + "\n", + "response = query.raw_data_download(\n", + " data_type=\"imaging_study\",\n", + " fields=[\n", + " \"study_uid\",\n", + " \"study_modality\",\n", + " \"case_ids\",\n", + " \"project_id\",\n", + " \"race\",\n", + " \"sex\",\n", + " \"ethnicity\",\n", + " \"age_at_index\",\n", + " \"object_id\"\n", + " ],\n", + " filter_object={\"AND\": [{\">=\": {\"days_from_study_to_pos_covid_test\": -7}},\n", + " {\"<=\": {\"days_from_study_to_pos_covid_test\": 7}}]},\n", + " sort_fields=[{\"submitter_id\": \"asc\"}],\n", + " accessibility=\"accessible\"\n", + " )\n", + "\n", + "display(response)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fc104d89", + "metadata": {}, + "source": [ + "## Use the Gen3 SDK \"drs-pull\" commands to access the files themselves.\n", + "---\n", + "\n", + "Next, we'll use the Gen3 SDK command `gen3 drs-pull object` to access the imaging file using it's \"object_id\" aka \"data GUID\".\n", + "\n", + "See the detailed documentation to learn more about the Gen3 SDK drs-pull command: https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "db38edda", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10003752-JAtkcSczhECkwIeFkMkUA\n", + "dg.MD1R/f036ad90-ebaa-4933-b336-0dbc8c991056\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Take a look at one of the file objects\n", + "\n", + "data = response[0]\n", + "case_id = data['case_ids'][0]\n", + "study_uid = data['study_uid']\n", + "object_id = data['object_id'][0]\n", + "print(case_id)\n", + "print(object_id)\n", + "\n", + "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id)\n", + "os.system(cmd)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "f7d6c19f", + "metadata": {}, + "outputs": [], + "source": [ + "cmd = \"ls -l {}/{}\".format(case_id,study_uid)\n", + "stout = subprocess.check_output(cmd, shell=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "449e2c69", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.16.840.1.114274.1818.506401487277029714118212710130274577294.zip\n", + "2.16.840.1.114274.1818.506401487277029714118212710130274577294\n" + ] + } + ], + "source": [ + "## Grab the filename and series UID of the downloaded file using RegEx\n", + "import re\n", + "\n", + "m = re.search(' ([0-9\\.]+.zip)', str(stout))\n", + "\n", + "if m:\n", + " zip_file = m.group(1)\n", + " print(zip_file)\n", + "else:\n", + " print(\"No zip found.\")\n", + "\n", + "series_uid = re.sub(\"(\\.zip)\", \"\", zip_file)\n", + "print(series_uid)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "08ec040b", + "metadata": {}, + "outputs": [], + "source": [ + "# Unzip the imaging series package\n", + "from zipfile import ZipFile\n", + "\n", + "with ZipFile('{}/{}/{}/{}'.format(demo_dir,case_id,study_uid,zip_file), 'r') as zipObj:\n", + " zipObj.extractall()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c08956a8", + "metadata": {}, + "outputs": [], + "source": [ + "#Input the name of the newly create .dcm file\n", + "cmd = \"ls -l {}/{}/{}\".format(case_id,study_uid,series_uid)\n", + "stout = subprocess.run(cmd, shell=True, capture_output=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "e024d412", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.16.840.1.114274.1818.461926081131899494311494385126978422156.dcm\n" + ] + } + ], + "source": [ + "# Get the name of the first DICOM file in the extracted imaging series\n", + "m = re.search(' ([0-9\\.]+.dcm)', str(stout))\n", + "\n", + "if m:\n", + " dcm_file = m.group(1)\n", + " print(dcm_file)\n", + "else:\n", + " print(\"No DCM files found.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "90575758", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset.file_meta -------------------------------\n", + "(0002, 0000) File Meta Information Group Length UL: 218\n", + "(0002, 0001) File Meta Information Version OB: b'\\x00\\x01'\n", + "(0002, 0002) Media Storage SOP Class UID UI: CT Image Storage\n", + "(0002, 0003) Media Storage SOP Instance UID UI: 2.16.840.1.114274.1818.461926081131899494311494385126978422156\n", + "(0002, 0010) Transfer Syntax UID UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", + "(0002, 0012) Implementation Class UID UI: 1.3.6.1.4.1.25403.1.1.1\n", + "(0002, 0013) Implementation Version Name SH: 'Dicom 0.1'\n", + "(0002, 0016) Source Application Entity Title AE: 'CPV-ACUOAPP1'\n", + "-------------------------------------------------\n", + "(0008, 0005) Specific Character Set CS: ''\n", + "(0008, 0008) Image Type CS: ['ORIGINAL', 'PRIMARY', 'AXIAL']\n", + "(0008, 0012) Instance Creation Date DA: ''\n", + "(0008, 0013) Instance Creation Time TM: '000000'\n", + "(0008, 0016) SOP Class UID UI: CT Image Storage\n", + "(0008, 0018) SOP Instance UID UI: 2.16.840.1.114274.1818.461926081131899494311494385126978422156\n", + "(0008, 0020) Study Date DA: '19600223'\n", + "(0008, 0021) Series Date DA: '19600223'\n", + "(0008, 0022) Acquisition Date DA: '19600223'\n", + "(0008, 0023) Content Date DA: '19600223'\n", + "(0008, 002a) Acquisition DateTime DT: '19600517151525.060000'\n", + "(0008, 0030) Study Time TM: '150759.982'\n", + "(0008, 0031) Series Time TM: '151459.119'\n", + "(0008, 0032) Acquisition Time TM: '151723'\n", + "(0008, 0033) Content Time TM: '151723.647'\n", + "(0008, 0050) Accession Number SH: ''\n", + "(0008, 0060) Modality CS: 'CT'\n", + "(0008, 0061) Modalities in Study CS: ['', 'CT', '']\n", + "(0008, 0070) Manufacturer LO: 'Philips'\n", + "(0008, 0090) Referring Physician's Name PN: ''\n", + "(0008, 1010) Station Name SH: '[Stati.3272]'\n", + "(0008, 1030) Study Description LO: 'CT CHEST WO'\n", + "(0008, 103e) Series Description LO: 'INSPIR PRONE, iDose (2)'\n", + "(0008, 1090) Manufacturer's Model Name LO: 'Brilliance 64'\n", + "(0008, 1140) Referenced Image Sequence 1 item(s) ---- \n", + " (0008, 1150) Referenced SOP Class UID UI: CT Image Storage\n", + " (0008, 1155) Referenced SOP Instance UID UI: 2.16.840.1.114274.1818.50085903529714352811863521297766962088\n", + " ---------\n", + "(0008, 3010) Irradiation Event UID UI: 2.16.840.1.114274.1818.47843956609754041809924814337498004117\n", + "(0010, 0010) Patient's Name PN: 'JAtkcSczhECkwIeFkMkUA^10003752^^^'\n", + "(0010, 0020) Patient ID LO: '10003752-JAtkcSczhECkwIeFkMkUA'\n", + "(0010, 0030) Patient's Birth Date DA: ''\n", + "(0010, 1010) Patient's Age AS: '054Y'\n", + "(0010, 1020) Patient's Size DS: None\n", + "(0010, 1030) Patient's Weight DS: None\n", + "(0012, 0010) Clinical Trial Sponsor Name LO: '[Clinical Trial Sponsor Na]'\n", + "(0012, 0020) Clinical Trial Protocol ID LO: '[Clinical Trial Protocol ]'\n", + "(0012, 0021) Clinical Trial Protocol Name LO: 'CIRR'\n", + "(0012, 0030) Clinical Trial Site ID LO: '[Clinical Trial Site ]'\n", + "(0012, 0031) Clinical Trial Site Name LO: '[Clinical Trial Site Na]'\n", + "(0012, 0040) Clinical Trial Subject ID LO: '10003752-JAtkcSczhECkwIeFkMkUA'\n", + "(0012, 0050) Clinical Trial Time Point ID LO: '[Clinical Trial Time Point ]'\n", + "(0012, 0060) Clinical Trial Coordinating Center LO: '[Clinical Trial Coordinating Center Na]'\n", + "(0012, 0062) Patient Identity Removed CS: 'YES'\n", + "(0012, 0063) De-identification Method LO: ['Basic Application Confidentiality Profile', 'Retain Longitudinal Temporal Information Modified Dates', 'TRIAD Anonymization; Date=20210822; Time=161143']\n", + "(0012, 0064) De-identification Method Code Sequence 3 item(s) ---- \n", + " (0008, 0100) Code Value SH: '113100'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'Basic Application Confidentiality Profile'\n", + " ---------\n", + " (0008, 0100) Code Value SH: '113107'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'Retain Longitudinal Temporal Information Modified Dates Option'\n", + " ---------\n", + " (0008, 0100) Code Value SH: '900901'\n", + " (0008, 0102) Coding Scheme Designator SH: 'WIF'\n", + " (0008, 0104) Code Meaning LO: 'BASIC PROFILE OVERRIDES POTENTIALLY VIOLATE SECURITY STANDARD'\n", + " (0008, 0108) Extended Code Meaning LT: Array of 229 elements\n", + " ---------\n", + "(0018, 0010) Contrast/Bolus Agent LO: ''\n", + "(0018, 0015) Body Part Examined CS: 'CHEST'\n", + "(0018, 0022) Scan Options CS: 'AXIAL'\n", + "(0018, 0050) Slice Thickness DS: '1.25'\n", + "(0018, 0060) KVP DS: '120.0'\n", + "(0018, 0088) Spacing Between Slices DS: '1.25'\n", + "(0018, 0090) Data Collection Diameter DS: '500.0'\n", + "(0018, 1010) Secondary Capture Device ID LO: ''\n", + "(0018, 1020) Software Versions LO: '4.1'\n", + "(0018, 1030) Protocol Name LO: '[Protocol Na]'\n", + "(0018, 1100) Reconstruction Diameter DS: '340.0'\n", + "(0018, 1110) Distance Source to Detector DS: '1040.0'\n", + "(0018, 1111) Distance Source to Patient DS: '570.0'\n", + "(0018, 1120) Gantry/Detector Tilt DS: '0.0'\n", + "(0018, 1130) Table Height DS: '143.1'\n", + "(0018, 1143) Scan Arc DS: '360.0'\n", + "(0018, 1150) Exposure Time IS: '500'\n", + "(0018, 1151) X-Ray Tube Current IS: '360'\n", + "(0018, 1152) Exposure IS: '180'\n", + "(0018, 1160) Filter Type SH: 'C'\n", + "(0018, 1210) Convolution Kernel SH: 'C'\n", + "(0018, 5100) Patient Position CS: 'FFP'\n", + "(0018, 9302) Acquisition Type CS: 'SEQUENCED'\n", + "(0018, 9305) Revolution Time FD: 0.5\n", + "(0018, 9306) Single Collimation Width FD: 0.625\n", + "(0018, 9307) Total Collimation Width FD: 1.25\n", + "(0018, 9309) Table Speed FD: 0.0\n", + "(0018, 9323) Exposure Modulation Type CS: 'NONE'\n", + "(0018, 9324) Estimated Dose Saving FD: 180.0\n", + "(0018, 9345) CTDIvol FD: 2.6\n", + "(0018, a001) Contributing Equipment Sequence 1 item(s) ---- \n", + " (0008, 0070) Manufacturer LO: 'Claron'\n", + " (0008, 1010) Station Name SH: 'CPV-ACUOPRIAPP'\n", + " (0008, 1090) Manufacturer's Model Name LO: 'Deidentification'\n", + " (0018, 1020) Software Versions LO: ['WIF 2.1.17', '1.0.0.0']\n", + " (0018, a002) Contribution DateTime DT: '20211105073412.905110'\n", + " (0018, a003) Contribution Description ST: 'Cleaned'\n", + " (0040, a170) Purpose of Reference Code Sequence 1 item(s) ---- \n", + " (0008, 0100) Code Value SH: '109104'\n", + " (0008, 0102) Coding Scheme Designator SH: 'DCM'\n", + " (0008, 0104) Code Meaning LO: 'De-identifying Equipment'\n", + " ---------\n", + " ---------\n", + "(0020, 000d) Study Instance UID UI: 2.16.840.1.114274.1818.51823901133181661835638047342706386856\n", + "(0020, 000e) Series Instance UID UI: 2.16.840.1.114274.1818.506401487277029714118212710130274577294\n", + "(0020, 0010) Study ID SH: ''\n", + "(0020, 0011) Series Number IS: '501'\n", + "(0020, 0012) Acquisition Number IS: '6'\n", + "(0020, 0013) Instance Number IS: '24'\n", + "(0020, 0022) Overlay Number IS: None\n", + "(0020, 0024) Curve Number IS: None\n", + "(0020, 0026) LUT Number IS: None\n", + "(0020, 0032) Image Position (Patient) DS: [-170, 54.02, -266.2]\n", + "(0020, 0037) Image Orientation (Patient) DS: [1, 0, 0, 0, -1, 0]\n", + "(0020, 0052) Frame of Reference UID UI: 2.16.840.1.114274.1818.53211343523704081467448296588687099267\n", + "(0020, 1040) Position Reference Indicator LO: ''\n", + "(0020, 1041) Slice Location DS: '266.2'\n", + "(0028, 0002) Samples per Pixel US: 1\n", + "(0028, 0004) Photometric Interpretation CS: 'MONOCHROME2'\n", + "(0028, 0008) Number of Frames IS: '1'\n", + "(0028, 0010) Rows US: 512\n", + "(0028, 0011) Columns US: 512\n", + "(0028, 0030) Pixel Spacing DS: [0.6640625, 0.6640625]\n", + "(0028, 0100) Bits Allocated US: 16\n", + "(0028, 0101) Bits Stored US: 12\n", + "(0028, 0102) High Bit US: 11\n", + "(0028, 0103) Pixel Representation US: 0\n", + "(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'\n", + "(0028, 1050) Window Center DS: [-450, -450]\n", + "(0028, 1051) Window Width DS: [2000, 2000]\n", + "(0028, 1052) Rescale Intercept DS: '-1024.0'\n", + "(0028, 1053) Rescale Slope DS: '1.0'\n", + "(0032, 000a) Study Status ID CS: 'COMPLETED'\n", + "(0040, 0009) Scheduled Procedure Step ID SH: ''\n", + "(0040, 0244) Performed Procedure Step Start Date DA: ''\n", + "(0040, 0245) Performed Procedure Step Start Time TM: '000000'\n", + "(0040, a032) Observation DateTime DT: '18390126000000.000000'\n", + "(0040, a491) Completion Flag CS: ''\n", + "(0040, a493) Verification Flag CS: ''\n", + "(0070, 0080) Content Label CS: ''\n", + "(0070, 0081) Content Description LO: ''\n", + "(0070, 0084) Content Creator's Name PN: ''\n", + "(00e1, 1040) Private tag data UN: b'INSPIR PRONE'\n", + "(0400, 0500) Encrypted Attributes Sequence 1 item(s) ---- \n", + " (0033, 1215) Private tag data OB: b'\\x99w\\xf2\"1\\xdfg\\x94'\n", + " (0400, 0510) Encrypted Content Transfer Syntax U UI: JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14 [Selection Value 1])\n", + " (0400, 0520) Encrypted Content OB: Array of 872 elements\n", + " ---------\n", + "(4008, 0040) Results ID SH: ''\n", + "(7fe0, 0010) Pixel Data OB: Array of 290216 elements" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read in the DCM file using the python DICOM package pydicom\n", + "dimg = pydicom.dcmread(\"{}/{}/{}/{}\".format(case_id,study_uid,series_uid,dcm_file),force=True)\n", + "dimg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3dd85f59", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}