{
"cells": [
{
"source": [
"# PDB search\n",
"\n",
"This interactive Python notebook will guide you through various ways of programmatically accessing Protein Data Bank in Europe (PDBe) data using REST API\n",
"\n",
"The REST API is a programmatic way to obtain information from the PDB and EMDB. You can access details about:\n",
"\n",
"* sample\n",
"* experiment\n",
"* models\n",
"* compounds\n",
"* cross-references\n",
"* publications\n",
"* quality\n",
"* assemblies\n",
"and more...\n",
"For more information, visit https://www.ebi.ac.uk/pdbe/pdbe-rest-api\n",
"\n",
"This notebook is a part of the training material series, and focuses on getting information from the PDBe search API. Retrieve this material and many more from [GitHub](https://github.com/PDBeurope/pdbe-api-training)\n",
"\n",
"## 1) Making imports and setting variables\n",
"First, we import some packages that we will use, and set some variables.\n",
"\n",
"Note: Full list of valid URLs is available from https://www.ebi.ac.uk/pdbe/api/doc/"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": " \n "
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/html": " \n "
},
"metadata": {}
}
],
"source": [
"import requests # used for getting data from a URL\n",
"from pprint import pprint # pretty print\n",
"import pandas as pd # used for turning results into mini databases\n",
"from solrq import Q # used to turn result queries into the right format\n",
"\n",
"import cufflinks as cf\n",
"import plotly.offline as py\n",
"\n",
"cf.go_offline() # required to use plotly offline (no account required).\n",
"py.init_notebook_mode() # graphs charts inline (IPython).\n",
"\n",
"search_url = \"https://www.ebi.ac.uk/pdbe/search/pdb/select?\" # the rest of the URL used for PDBe's search API."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2) a function to get data from the search API\n",
"Let's start with defining a function that can be used to GET data from the PDBe search API.\n"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [],
"source": [
"def make_request(search_dict, number_of_rows=10):\n",
" \"\"\"\n",
" makes a get request to the PDBe API\n",
" :param dict search_dict: the terms used to search\n",
" :param number_of_rows: number or rows to return - limited to 10\n",
" :return dict: response JSON\n",
" \"\"\"\n",
" if 'rows' not in search_dict:\n",
" search_dict['rows'] = number_of_rows\n",
" search_dict['wt'] = 'json'\n",
" # pprint(search_dict)\n",
" response = requests.post(search_url, data=search_dict)\n",
"\n",
" if response.status_code == 200:\n",
" return response.json()\n",
" else:\n",
" print(\"[No data retrieved - %s] %s\" % (response.status_code, response.text))\n",
"\n",
" return {}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3) formatting the search terms \n",
"This will allow us to use human readable search terms and this function will make a URL that the search API can handle."
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [],
"source": [
"def format_search_terms(search_terms, filter_terms=None):\n",
" ret = {'q': str(search_terms)}\n",
" if filter_terms:\n",
" fl = '{}'.format(','.join(filter_terms))\n",
" ret['fl'] = fl\n",
" return ret"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4) Getting useful data out of the search\n",
"\n",
"This function will run the search and will return a list of the results"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [],
"source": [
"def run_search(search_terms, filter_terms=None, number_of_rows=100):\n",
" search_term = format_search_terms(search_terms, filter_terms)\n",
"\n",
" response = make_request(search_term, number_of_rows)\n",
" results = response.get('response', {}).get('docs', [])\n",
" print('Number of results: {}'.format(len(results)))\n",
" return results\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5) running a search\n",
"\n",
"Now we are ready to actually run a search against the PDB API for entries containing human Dihydrofolate reductase in the PDB. This will return a list of results - only 10 to start with.\n",
"\n",
"A list of search terms is available at:\n",
"https://www.ebi.ac.uk/pdbe/api/doc/search.html\n",
"\n",
"This will return details of human Dihydrofolate reductase's in the PDB\n",
"\n",
"We are going to use the Python package \"solrq\" to make sure we get the search in the right format.\n",
"\n",
"Q(molecule_name=\"Dihydrofolate reductase\")\n",
"\n",
"Here we are searching for molecules named Dihydrofolate reductase.\n",
"If we search for two terms i.e. molecule_name and organism_scientific_name then we will get molecules that match both search terms.\n",
"\n",
"We will return the number of results for two searches.\n",
"\n",
"The first one will hit the limit of 100. There are more than 100 Dihydrofolate reductase structures. \n",
"We have to add the argument \"number_of_rows\" to a higher number, say 1000, to find all the examples. "
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "1st search\nNumber of results: 100\n"
}
],
"source": [
"print('1st search')\n",
"search_terms = Q(molecule_name=\"Dihydrofolate reductase\")\n",
"\n",
"first_results = run_search(search_terms)\n"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "1st search - more rows\nNumber of results: 405\n"
}
],
"source": [
"print('1st search - more rows')\n",
"search_terms = Q(molecule_name=\"Dihydrofolate reductase\")\n",
"\n",
"first_results_more_rows = run_search(search_terms, number_of_rows=1000)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will add organism_name of Human to the query to limit the results to only return those that are structures of Human Dihydrofolate reductase."
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "2nd search\nNumber of results: 79\n"
}
],
"source": [
"print('2nd search')\n",
"search_terms = Q(molecule_name=\"Dihydrofolate reductase\",organism_name=\"Human\")\n",
"second_results = run_search(search_terms)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How did we know which search terms to use?\n",
"\n",
"We will then look at the last result.\n",
"We will print the data we have for the first result.\n",
"\n",
"This will be the first item of the list \"second_results\"\n",
"i.e. second_results[0]\n",
"\n",
"We are using \"pprint\" (pretty print) rather than \"print\" to make the result easier to read.\n",
"\n",
"All of the \"keys\" on the left side of the results can be used as a search term."
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"tags": [
"outputPrepend"
]
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "L',\n 'q_struct_asym_id': ['A'],\n 'q_structure_determination_method': ['MOLECULAR REPLACEMENT'],\n 'q_structure_solution_software': ['MOLREP'],\n 'q_superkingdom': ['Eukaryota'],\n 'q_tax_id': [9606],\n 'q_tax_query': [9606],\n 'q_title': 'Preferential Selection of Isomer Binding from Chiral Mixtures: '\n 'Alternate Binding Modes Observed for the E- and Z-isomers of a '\n 'Series of 5-Substituted 2,4-Diaminofuro[2,3-d]pyrimidines as '\n 'Ternary Complexes with NADPH and Human Dihydrofolate Reductase',\n 'q_uniprot': ['P00374 : DYR_HUMAN'],\n 'q_uniprot_accession': ['P00374', 'P00374-2'],\n 'q_uniprot_accession_best': ['P00374'],\n 'q_uniprot_best': ['P00374 : DYR_HUMAN'],\n 'q_uniprot_coverage': [0.99],\n 'q_uniprot_features': ['Protein has possible alternate isoforms',\n 'DHFR',\n 'Protein has possible natural variant ',\n 'Nucleotide binding - NADP'],\n 'q_uniprot_id': ['DYR_HUMAN', 'DYR_HUMAN'],\n 'q_uniprot_id_best': ['DYR_HUMAN'],\n 'q_uniprot_non_canonical': ['P00374-2 : DYR_HUMAN'],\n 'q_unp_count': 1,\n 'q_unp_nf90_accession': ['B0YJ76',\n 'A0A2K6C3Y8',\n 'S5WD14',\n 'S5VM81',\n 'P00374',\n 'A0A024RAQ3'],\n 'q_unp_nf90_id': ['B0YJ76_HUMAN',\n 'A0A2K6C3Y8_MACNE',\n 'S5WD14_SHISS',\n 'S5VM81_ECO57',\n 'DYR_HUMAN',\n 'A0A024RAQ3_HUMAN'],\n 'q_unp_nf90_organism': ['Homo sapiens (Human)',\n 'Macaca nemestrina (Pig-tailed macaque)',\n 'Shigella sonnei (strain Ss046)',\n 'Escherichia coli O157:H7',\n 'Homo sapiens (Human)',\n 'Homo sapiens (Human)'],\n 'q_unp_nf90_protein_name': ['Dihydrofolate reductase',\n 'DHFR domain-containing protein',\n 'Dihydrofolate reductase',\n 'Dihydrofolate reductase',\n 'Dihydrofolate reductase',\n 'Dihydrofolate reductase'],\n 'q_unp_nf90_tax_id': ['9606', '9545', '300269', '83334', '9606', '9606'],\n 'r_factor': 0.19536,\n 'r_free': 0.2456,\n 'r_work': [0.19278],\n 'rank': ['species',\n 'genus',\n 'subfamily',\n 'family',\n 'order',\n 'class',\n 'phylum',\n 'kingdom',\n 'superkingdom',\n 'species',\n 'genus',\n 'family',\n 'order',\n 'class',\n 'phylum',\n 'superkingdom'],\n 'reactant_id': ['NDP'],\n 'refinement_software': ['REFMAC'],\n 'release_date': '2010-12-01T01:00:00Z',\n 'release_year': 2010,\n 'resolution': 1.9,\n 'revision_date': '2017-11-08T01:00:00Z',\n 'revision_year': 2017,\n 'sample_preparation_method': ['Engineered'],\n 'seq_100_cluster_number': '24820',\n 'seq_100_cluster_rank': 39,\n 'seq_30_cluster_number': '162',\n 'seq_30_cluster_rank': 65,\n 'seq_40_cluster_number': '16985',\n 'seq_40_cluster_rank': 65,\n 'seq_50_cluster_number': '28892',\n 'seq_50_cluster_rank': 65,\n 'seq_70_cluster_number': '25856',\n 'seq_70_cluster_rank': 65,\n 'seq_90_cluster_number': '15408',\n 'seq_90_cluster_rank': 57,\n 'seq_95_cluster_number': '4071',\n 'seq_95_cluster_rank': 51,\n 'serial_crystal_experiment': ['N'],\n 'spacegroup': 'H 3',\n 'status': 'REL',\n 'struct_asym_id': ['A'],\n 'structure_determination_method': ['MOLECULAR REPLACEMENT'],\n 'structure_solution_software': ['MOLREP'],\n 'superkingdom': ['Eukaryota'],\n 't_abstracttext_unassigned': ['The crystal structures of six human '\n 'dihydrofolate reductase (hDHFR) ternary '\n 'complexes with NADPH and a series of mixed E/Z '\n 'isomers of 5-substituted '\n '5-[2-(2-methoxyphenyl)-prop-1-en-1-yl]furo[2,3-d]pyrimidine-2,4-diamines '\n 'substituted at the C9 position with propyl, '\n 'isopropyl, cyclopropyl, butyl, isobutyl and '\n 'sec-butyl (E2-E7, Z3) were determined and the '\n 'results were compared with the resolved E and '\n 'Z isomers of the C9-methyl parent compound. '\n 'The configuration of all of the inhibitors, '\n 'save one, was observed as the E isomer, in '\n 'which the binding of the furopyrimidine ring '\n 'is flipped such that the 4-amino group binds '\n 'in the 4-oxo site of folate. The Z3\\xa0isomer '\n 'of the C9-isopropyl analog has the normal '\n '2,4-diaminopyrimidine ring binding geometry, '\n 'with the furo oxygen near Glu30 and the '\n '4-amino group interacting near the cofactor '\n 'nicotinamide ring. Electron-density maps for '\n 'these structures revealed the binding of only '\n 'one isomer to hDHFR, despite the fact that '\n 'chiral mixtures (E:Z ratios of 2:1, 3:1 and '\n '3:2) of the inhibitors were incubated with '\n 'hDHFR prior to crystallization. Superposition '\n 'of the hDHFR complexes with E2 and Z3 shows '\n "that the 2'-methoxyphenyl ring of E2 is "\n 'perpendicular to that of Z3. The most potent '\n 'inhibitor in this series is the isopropyl '\n 'analog Z3 and the least potent is the isobutyl '\n 'analog E6, consistent with data that show that '\n 'the Z isomer makes the most favorable '\n 'interactions with the active-site residues. '\n 'The isobutyl moiety of E6 is observed in two '\n 'orientations and the resultant steric crowding '\n 'of the E6 analog is consistent with its weaker '\n 'activity. The alternative binding modes '\n 'observed for the furopyrimidine ring in these '\n 'E/Z isomers suggest that new templates can be '\n 'designed to probe these binding regions of the '\n 'DHFR active site.'],\n 't_all_compound_names': ['Nicotinamide-adenine dinucleotide',\n 'NDP',\n 'D2F : '\n '5-[(1E)-2-(2-methoxyphenyl)hex-1-en-1-yl]furo[2,3-d]pyrimidine-2,4-diamine',\n 'NDP : NADPH '\n 'DIHYDRO-NICOTINAMIDE-ADENINE-DINUCLEOTIDE PHOSPHATE',\n 'SO4 : SULFATE ION',\n 'NDP : Dihydronicotinamide-adenine dinucleotide '\n 'phosphate',\n 'NDP : Reduced nicotinamide adenine dinucleotide '\n 'phosphate',\n 'NDP : TPNH',\n 'SO4 : Sulfate',\n 'SO4 : Sulfate dianion',\n 'SO4 : Sulfate(2-)',\n 'SO4 : Sulfuric acid ion(2-)',\n 'D2F : '\n '5-[(1E)-2-(2-methoxyphenyl)hex-1-en-1-yl]furo[2,3-d]pyrimidine-2,4-diamine',\n 'SO4 : sulfate',\n 'D2F : '\n '5-[(E)-2-(2-methoxyphenyl)hex-1-enyl]furo[2,3-d]pyrimidine-2,4-diamine',\n 'NDP : '\n '[[(2R,3S,4R,5R)-5-(3-aminocarbonyl-4H-pyridin-1-yl)-3,4-dihydroxy-oxolan-2-yl]methoxy-hydroxy-phosphoryl] '\n '[(2R,3R,4R,5R)-5-(6-aminopurin-9-yl)-3-hydroxy-4-phosphonooxy-oxolan-2-yl]methyl '\n 'hydrogen phosphate'],\n 't_all_enzyme_names': ['Oxidoreductases',\n 'Acting on the CH-NH group of donors',\n 'With NAD(+) or NADP(+) as acceptor',\n 'Dihydrofolate reductase',\n '1.5.1.3 : Dihydrofolate reductase',\n '5,6,7,8-tetrahydrofolate:NADP(+) oxidoreductase'],\n 't_all_go_terms': ['cytoplasm',\n 'mitochondrion',\n 'cytosol',\n 'folic acid binding',\n 'oxidoreductase activity',\n 'NADPH binding',\n 'RNA binding',\n 'sequence-specific mRNA binding',\n 'mRNA binding',\n 'dihydrofolate reductase activity',\n 'NADP binding',\n 'methotrexate binding',\n 'translation repressor activity, mRNA regulatory element '\n 'binding',\n 'drug binding',\n 'one-carbon metabolic process',\n 'negative regulation of translation',\n 'folic acid metabolic process',\n 'response to methotrexate',\n 'regulation of removal of superoxide radicals',\n 'tetrahydrobiopterin biosynthetic process',\n 'tetrahydrofolate metabolic process',\n 'tetrahydrofolate biosynthetic process',\n 'oxidation-reduction process',\n 'regulation of transcription involved in G1/S transition '\n 'of mitotic cell cycle',\n 'positive regulation of nitric-oxide synthase activity',\n 'axon regeneration',\n 'dihydrofolate metabolic process'],\n 't_all_sequence_family': ['IPR001796 : Dihydrofolate reductase domain',\n 'IPR024072 : Dihydrofolate reductase-like domain '\n 'superfamily',\n 'PF00186 : DHFR_1',\n 'CL0387 : DHFred'],\n 't_all_structure_family': ['3-Layer(aba) Sandwich',\n 'Alpha Beta',\n '3.40.430.10',\n 'Dihydrofolate Reductase, subunit A',\n 'Dihydrofolate Reductase, subunit A'],\n 't_citation_authors': ['Cody V', 'Piraino J', 'Pace J', 'Li W', 'Gangjee A'],\n 't_citation_title': ['Preferential selection of isomer binding from chiral '\n 'mixtures: alternate binding modes observed for the E '\n 'and Z isomers of a series of 5-substituted '\n '2,4-diaminofuro[2,3-d]pyrimidines as ternary complexes '\n 'with NADPH and human dihydrofolate reductase.'],\n 't_entry_authors': ['Cody V'],\n 't_entry_info': ['POTASSIUM PHOSPHATE',\n 'AMMONIUM SULFATE',\n 'ETHANOL',\n 'MOSFLM',\n 'SCALA',\n 'Image plate',\n 'RIGAKU RAXIS IV',\n 'X-ray diffraction',\n 'REFMAC',\n 'MOLECULAR REPLACEMENT',\n 'MOLREP'],\n 't_entry_title': ['Preferential Selection of Isomer Binding from Chiral '\n 'Mixtures: Alternate Binding Modes Observed for the E- and '\n 'Z-isomers of a Series of 5-Substituted '\n '2,4-Diaminofuro[2,3-d]pyrimidines as Ternary Complexes '\n 'with NADPH and Human Dihydrofolate Reductase'],\n 't_expression_organism_name': ['Escherichia coli',\n 'Bacterium Coli',\n 'Enterococcus Coli',\n 'Bacterium 10a',\n 'Escherichia Coli',\n 'Escherichia Sp. 3_2_53faa',\n 'Escherichia/Shigella Coli',\n 'Ecolx',\n 'Bacterium E3',\n 'Bacterium Coli Commune',\n 'E. Coli',\n 'Bacillus Coli',\n 'Escherichia Sp. Mar',\n 'Escherichia coli',\n 'Escherichia',\n 'Enterobacteriaceae',\n 'Enterobacterales',\n 'Gammaproteobacteria',\n 'Proteobacteria',\n 'Bacteria'],\n 't_journal': ['Acta Crystallogr. D Biol. Crystallogr.'],\n 't_mesh_terms': ['Crystallography, X-Ray,Humans,Hydrophobic and Hydrophilic '\n 'Interactions,Models, Molecular,NADP,Protein Structure, '\n 'Tertiary,Pyrimidines,Stereoisomerism,Tetrahydrofolate '\n 'Dehydrogenase'],\n 't_molecule_info': ['protein structure',\n 'homo',\n 'monomer',\n 'Dihydrofolate reductase',\n 'Dihydrofolate reductase',\n 'protein structure',\n 'homo',\n 'monomer',\n 'DHFR',\n 'P00374',\n 'P00374-2',\n 'Protein has possible alternate isoforms',\n 'DHFR',\n 'Protein has possible natural variant ',\n 'Nucleotide binding - NADP',\n 'DYR_HUMAN',\n 'DYR_HUMAN'],\n 't_molecule_sequence': 'VGSLNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQNLVIMGKKTWFSIPEKNRPLKGRINLVLSRELKEPPQGAHFLSRSLDDALKLTEQPELANKVDMVWIVGGSSVYKEAMNHPGHLKLFVTRIMQDFESDTFFPEIDLEKYKLLPEYPGVLSDVQEEKGIKYKFEVYEKND',\n 't_organism_name': ['Homo sapiens',\n 'Man',\n 'Homo Sapiens (Human)',\n 'Human',\n 'Homo Sapiens',\n 'Homo sapiens',\n 'Homo',\n 'Homininae',\n 'Hominidae',\n 'Primates',\n 'Mammalia',\n 'Chordata',\n 'Metazoa',\n 'Eukaryota'],\n 'tax_id': [9606],\n 'tax_query': [9606],\n 'title': 'Preferential Selection of Isomer Binding from Chiral Mixtures: '\n 'Alternate Binding Modes Observed for the E- and Z-isomers of a '\n 'Series of 5-Substituted 2,4-Diaminofuro[2,3-d]pyrimidines as '\n 'Ternary Complexes with NADPH and Human Dihydrofolate Reductase',\n 'uniprot': ['P00374 : DYR_HUMAN'],\n 'uniprot_accession': ['P00374', 'P00374-2'],\n 'uniprot_accession_best': ['P00374'],\n 'uniprot_best': ['P00374 : DYR_HUMAN'],\n 'uniprot_coverage': [0.99],\n 'uniprot_features': ['Protein has possible alternate isoforms',\n 'DHFR',\n 'Protein has possible natural variant ',\n 'Nucleotide binding - NADP'],\n 'uniprot_id': ['DYR_HUMAN', 'DYR_HUMAN'],\n 'uniprot_id_best': ['DYR_HUMAN'],\n 'uniprot_non_canonical': ['P00374-2 : DYR_HUMAN'],\n 'unp_count': 1,\n 'unp_nf90_accession': ['B0YJ76',\n 'A0A2K6C3Y8',\n 'S5WD14',\n 'S5VM81',\n 'P00374',\n 'A0A024RAQ3'],\n 'unp_nf90_id': ['B0YJ76_HUMAN',\n 'A0A2K6C3Y8_MACNE',\n 'S5WD14_SHISS',\n 'S5VM81_ECO57',\n 'DYR_HUMAN',\n 'A0A024RAQ3_HUMAN'],\n 'unp_nf90_organism': ['Homo sapiens (Human)',\n 'Macaca nemestrina (Pig-tailed macaque)',\n 'Shigella sonnei (strain Ss046)',\n 'Escherichia coli O157:H7',\n 'Homo sapiens (Human)',\n 'Homo sapiens (Human)'],\n 'unp_nf90_protein_name': ['Dihydrofolate reductase',\n 'DHFR domain-containing protein',\n 'Dihydrofolate reductase',\n 'Dihydrofolate reductase',\n 'Dihydrofolate reductase',\n 'Dihydrofolate reductase'],\n 'unp_nf90_tax_id': ['9606', '9545', '300269', '83334', '9606', '9606']}\n"
}
],
"source": [
"pprint(second_results[0])"
]
},
{
"cell_type": "markdown",
"source": [
"A full list of available search terms is available using the following command."
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 170,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "There are 448 available search terms\ndict_keys(['abstracttext_unassigned', 't_abstracttext_unassigned', 'q_abstracttext_unassigned', 'all_assembly_composition', 't_molecule_info', 'q_all_assembly_composition', 'all_assembly_form', 'q_all_assembly_form', 'all_assembly_id', 'q_all_assembly_id', 'all_assembly_mol_wt', 'q_all_assembly_mol_wt', 'all_assembly_type', 'q_all_assembly_type', 'all_authors', 'q_all_authors', 'all_molecule_names', 'q_all_molecule_names', 'all_num_interacting_entity_id', 'q_all_num_interacting_entity_id', 'assembly_composition', 'q_assembly_composition', 'assembly_form', 'q_assembly_form', 'assembly_id', 'q_assembly_id', 'assembly_mol_wt', 'q_assembly_mol_wt', 'assembly_num_component', 'q_assembly_num_component', 'assembly_type', 'q_assembly_type', 'beam_source_name', 'q_beam_source_name', 'biological_cell_component', 'all_go_terms', 'q_all_go_terms', 't_all_go_terms', 'q_biological_cell_component', 'biological_function', 'q_biological_function', 'biological_process', 'q_biological_process', 'bound_compound_id', 'q_bound_compound_id', 'bound_compound_name', 'q_bound_compound_name', 'bound_compound_synonym', 'q_bound_compound_synonym', 'bound_compound_systematic_name', 'q_bound_compound_systematic_name', 'bound_compound_weight', 'q_bound_compound_weight', 'cath_architecture', 'all_structure_family', 'q_all_structure_family', 't_all_structure_family', 'q_cath_architecture', 'cath_class', 'q_cath_class', 'cath_code', 'q_cath_code', 'cath_homologous_superfamily', 'q_cath_homologous_superfamily', 'cath_topology', 'q_cath_topology', 'cell_a', 'q_cell_a', 'cell_alpha', 'q_cell_alpha', 'cell_b', 'q_cell_b', 'cell_beta', 'q_cell_beta', 'cell_c', 'q_cell_c', 'cell_gamma', 'q_cell_gamma', 'chain_id', 'q_chain_id', 'citation_authors', 't_citation_authors', 'q_citation_authors', 'citation_doi', 'q_citation_doi', 'citation_title', 't_citation_title', 'q_citation_title', 'citation_year', 'q_citation_year', 'cofactor_class', 't_all_compound_names', 'q_cofactor_class', 'cofactor_id', 'q_cofactor_id', 'compound_id', 'q_compound_id', 'compound_name', 'all_compound_names', 'q_all_compound_names', 'q_compound_name', 'compound_synonym', 'q_compound_synonym', 'compound_systematic_name', 'q_compound_systematic_name', 'compound_weight', 'q_compound_weight', 'crystallisation_cond', 'q_crystallisation_cond', 'crystallisation_method', 'q_crystallisation_method', 'crystallisation_ph', 'q_crystallisation_ph', 'crystallisation_reservoir', 't_entry_info', 'q_crystallisation_reservoir', 'crystallisation_temperature', 'q_crystallisation_temperature', 'data_collection_date', 'q_data_collection_date', 'data_collection_year', 'q_data_collection_year', 'data_quality', 'q_data_quality', 'data_reduction_software', 'q_data_reduction_software', 'data_scaling_software', 'q_data_scaling_software', 'deposition_date', 'q_deposition_date', 'deposition_site', 'q_deposition_site', 'deposition_year', 'q_deposition_year', 'detector', 'q_detector', 'detector_type', 'q_detector_type', 'diffraction_protocol', 'q_diffraction_protocol', 'diffraction_source_type', 'q_diffraction_source_type', 'diffraction_wavelengths', 'q_diffraction_wavelengths', 'ec_hierarchy_name', 'all_enzyme_names', 'q_all_enzyme_names', 't_all_enzyme_names', 'q_ec_hierarchy_name', 'ec_number', 'q_ec_number', 'entity_id', 'q_entity_id', 'entity_weight', 'q_entity_weight', 'entry_author_list', 'q_entry_author_list', 'entry_authors', 't_entry_authors', 'q_entry_authors', 'entry_entity', 'entry_lig_entity', 'q_entry_lig_entity', 'entry_organism_scientific_name', 'entry_uniprot', 'q_entry_uniprot', 'entry_uniprot_accession', 'q_entry_uniprot_accession', 'entry_uniprot_id', 'q_entry_uniprot_id', 'enzyme_name', 'q_enzyme_name', 'enzyme_num_name', 'q_enzyme_num_name', 'enzyme_systematic_name', 'q_enzyme_systematic_name', 'experiment_data_available', 'q_experiment_data_available', 'experimental_method', 'q_experimental_method', 'expression_host_sci_name', 'expression_organism_name', 'q_expression_organism_name', 't_expression_organism_name', 'q_expression_host_sci_name', 'expression_host_synonyms', 'q_expression_host_synonyms', 'expression_host_tax_id', 'q_expression_host_tax_id', 'gene_name', 'q_gene_name', 'genus', 'q_genus', 'go_id', 'q_go_id', 'go_mapping', 'q_go_mapping', 'has_bound_molecule', 'q_has_bound_molecule', 'has_carb_polymer', 'q_has_carb_polymer', 'has_cofactor', 'q_has_cofactor', 'has_modified_residues', 'q_has_modified_residues', 'has_reactant', 'q_has_reactant', 'homologus_pdb_entity_id', 'q_homologus_pdb_entity_id', 'int_lig_entity', 'q_int_lig_entity', 'interacting_ligands', 'q_interacting_ligands', 'interpro', 'all_sequence_family', 'q_all_sequence_family', 't_all_sequence_family', 'q_interpro', 'interpro_accession', 'q_interpro_accession', 'interpro_dom', 'q_interpro_dom', 'interpro_dom_acc', 'q_interpro_dom_acc', 'interpro_dom_name', 'q_interpro_dom_name', 'interpro_name', 'q_interpro_name', 'interpro_supfam', 'q_interpro_supfam', 'interpro_supfam_acc', 'q_interpro_supfam_acc', 'interpro_supfam_name', 'q_interpro_supfam_name', 'inv_overall_quality', 'q_inv_overall_quality', 'journal', 't_journal', 'q_journal', 'journal_page', 'q_journal_page', 'journal_volume', 'q_journal_volume', 'matthews_coefficient', 'q_matthews_coefficient', 'max_observed_residues', 'q_max_observed_residues', 'mesh_terms', 't_mesh_terms', 'q_mesh_terms', 'model_quality', 'q_model_quality', 'modified_residue_flag', 'q_modified_residue_flag', 'molecule_name', 'q_molecule_name', 'molecule_sequence', 'q_molecule_sequence', 't_molecule_sequence', 'molecule_synonym', 'q_molecule_synonym', 'molecule_type', 'q_molecule_type', 'mutation', 'q_mutation', 'nigli_cell_a', 'q_nigli_cell_a', 'nigli_cell_alpha', 'q_nigli_cell_alpha', 'nigli_cell_b', 'q_nigli_cell_b', 'nigli_cell_beta', 'q_nigli_cell_beta', 'nigli_cell_c', 'q_nigli_cell_c', 'nigli_cell_gamma', 'q_nigli_cell_gamma', 'nigli_cell_symmetry', 'q_nigli_cell_symmetry', 'num_interacting_entity_id', 'q_num_interacting_entity_id', 'num_r_free_reflections', 'q_num_r_free_reflections', 'number_of_bound_entities', 'q_number_of_bound_entities', 'number_of_bound_molecules', 'q_number_of_bound_molecules', 'number_of_copies', 'q_number_of_copies', 'number_of_models', 'q_number_of_models', 'number_of_polymer_entities', 'q_number_of_polymer_entities', 'number_of_polymer_residues', 'q_number_of_polymer_residues', 'number_of_polymers', 'q_number_of_polymers', 'number_of_protein_chains', 'q_number_of_protein_chains', 'organism_scientific_name', 'organism_name', 'q_organism_name', 't_organism_name', 'q_organism_scientific_name', 'organism_synonyms', 'q_organism_synonyms', 'overall_quality', 'q_overall_quality', 'pdb_accession', 'q_pdb_accession', 'pdb_format_compatible', 'q_pdb_format_compatible', 'pdb_id', 'q_pdb_id', 'percent_solvent', 'q_percent_solvent', 'pfam', 'q_pfam', 'pfam_accession', 'q_pfam_accession', 'pfam_clan', 'q_pfam_clan', 'pfam_clan_name', 'q_pfam_clan_name', 'pfam_description', 'q_pfam_description', 'pfam_name', 'q_pfam_name', 'pivot_resolution', 'q_pivot_resolution', 'polymer_length', 'q_polymer_length', 'prefered_assembly_id', 'q_prefered_assembly_id', 'primary_wavelength', 'q_primary_wavelength', 'processing_site', 'q_processing_site', 'pubmed_author_list', 'q_pubmed_author_list', 'pubmed_authors', 'q_pubmed_authors', 'pubmed_id', 'q_pubmed_id', 'r_factor', 'q_r_factor', 'r_free', 'q_r_free', 'r_work', 'q_r_work', 'rank', 'q_rank', 'reactant_id', 'q_reactant_id', 'refinement_software', 'q_refinement_software', 'release_date', 'q_release_date', 'release_year', 'q_release_year', 'resolution', 'q_resolution', 'revision_date', 'q_revision_date', 'revision_year', 'q_revision_year', 'sample_preparation_method', 'q_sample_preparation_method', 'seq_100_cluster_number', 'q_seq_100_cluster_number', 'seq_100_cluster_rank', 'q_seq_100_cluster_rank', 'seq_30_cluster_number', 'q_seq_30_cluster_number', 'seq_30_cluster_rank', 'q_seq_30_cluster_rank', 'seq_40_cluster_number', 'q_seq_40_cluster_number', 'seq_40_cluster_rank', 'q_seq_40_cluster_rank', 'seq_50_cluster_number', 'q_seq_50_cluster_number', 'seq_50_cluster_rank', 'q_seq_50_cluster_rank', 'seq_70_cluster_number', 'q_seq_70_cluster_number', 'seq_70_cluster_rank', 'q_seq_70_cluster_rank', 'seq_90_cluster_number', 'q_seq_90_cluster_number', 'seq_90_cluster_rank', 'q_seq_90_cluster_rank', 'seq_95_cluster_number', 'q_seq_95_cluster_number', 'seq_95_cluster_rank', 'q_seq_95_cluster_rank', 'serial_crystal_experiment', 'q_serial_crystal_experiment', 'spacegroup', 'q_spacegroup', 'status', 'q_status', 'struct_asym_id', 'q_struct_asym_id', 'structure_determination_method', 'q_structure_determination_method', 'structure_solution_software', 'q_structure_solution_software', 'superkingdom', 'q_superkingdom', 'tax_id', 'q_tax_id', 'tax_query', 'q_tax_query', 'title', 't_entry_title', 'q_title', 'uniprot', 'q_uniprot', 'uniprot_accession', 'q_uniprot_accession', 'uniprot_accession_best', 'q_uniprot_accession_best', 'uniprot_best', 'q_uniprot_best', 'uniprot_coverage', 'q_uniprot_coverage', 'uniprot_features', 'q_uniprot_features', 'uniprot_id', 'q_uniprot_id', 'uniprot_id_best', 'q_uniprot_id_best', 'uniprot_non_canonical', 'q_uniprot_non_canonical', 'unp_nf90_accession', 'q_unp_nf90_accession', 'unp_nf90_id', 'q_unp_nf90_id', 'unp_nf90_organism', 'q_unp_nf90_organism', 'unp_nf90_protein_name', 'q_unp_nf90_protein_name', 'unp_nf90_tax_id', 'q_unp_nf90_tax_id', '_version_', 'q_unp_count', 'unp_count'])\n217\n"
}
],
"source": [
"print('There are {} available search terms'.format(len(second_results[0].keys())))\n",
"print(second_results[0].keys())\n",
"keys_without_q = [q for q in second_results[0].keys() if not (q.startswith('q_') or (q.startswith('t_')))]\n",
"print(len(keys_without_q))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
},
"tags": []
}
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see we get lots of data back about the individual molecule we have searched for and the PDB entries in which it is contained. \n",
"\n",
"We can get the PDB ID and experimental method for this first row as follows.\n",
"Note that experimental method is a list"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "3nxv\n['X-ray diffraction']\n"
}
],
"source": [
"print(second_results[0].get('pdb_id'))\n",
"print(second_results[0].get('experimental_method'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can restrict the results to only the information we want using a filter so its easier to see the information we want."
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "3rd search\nNumber of results: 79\n[{'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nxv'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hsu'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5ht4'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5ht5'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hqz'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hvb'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hui'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hpb'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4m6k'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4qjc'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4keb'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4m6j'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4g95'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4qhv'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4kfj'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4m6l'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4kak'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4kbn'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4kd7'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hsr'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1kms'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1kmv'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1drf'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1pd9'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1dhf'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1hfr'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1hfq'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '4ddr'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hve'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '5hqy'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1dls'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1dlr'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1hfp'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1ohk'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1pd8'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1mvs'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1ohj'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1s3w'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3f8y'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3ghc'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '2w3a'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '2w3m'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3s7a'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3f8z'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3s3v'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nzd'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nxo'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3n0h'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1u71'},\n {'experimental_method': ['Solution NMR'], 'pdb_id': '1yho'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1mvt'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1boz'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1s3v'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1pdb'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1u72'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '1s3u'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '2dhf'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '2c2t'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '2c2s'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '2w3b'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3ghw'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3eig'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3ghv'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3gi2'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nxy'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3oaf'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3fs6'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nxt'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3ntz'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nxr'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nxx'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3l3r'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3gyf'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3f91'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '3nu0'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '6de4'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '6dav'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '6a7e'},\n {'experimental_method': ['X-ray diffraction'], 'pdb_id': '6a7c'}]\n"
}
],
"source": [
"print('3rd search')\n",
"search_terms = Q(molecule_name=\"Dihydrofolate reductase\",organism_name=\"Human\")\n",
"filter_terms = ['pdb_id', 'experimental_method']\n",
"third_results = run_search(search_terms, filter_terms)\n",
"pprint(third_results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6) Analysing and plotting the results\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to use a Python package called Pandas to help us sort and visualise the results\n",
"\n",
"\n",
"First we have to do a bit of housekeeping, some of the results are lists (a PDB entry can have more than one experimental method or organism for example) so we need to change them into strings so we can use them in a graph"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [],
"source": [
"def change_lists_to_strings(results):\n",
" \"\"\"\n",
" input - list of results from search\n",
" output - list of results with lists changed into strings\n",
" \"\"\"\n",
" for row in results:\n",
" for data in row:\n",
" if type(row[data]) == list:\n",
" # if there are any numbers in the list change them into strings\n",
" row[data] = [str(a) for a in row[data]]\n",
" # unique and sort the list and then change the list into a string\n",
" row[data] = ','.join(sorted(list(set(row[data]))))\n",
" \n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "[{'experimental_method': 'X-ray diffraction', 'pdb_id': '3nxv'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hsu'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5ht4'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5ht5'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hqz'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hvb'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hui'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hpb'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4m6k'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4qjc'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4keb'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4m6j'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4g95'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4qhv'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4kfj'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4m6l'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4kak'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4kbn'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4kd7'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hsr'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1kms'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1kmv'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1drf'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1pd9'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1dhf'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1hfr'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1hfq'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '4ddr'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hve'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '5hqy'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1dls'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1dlr'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1hfp'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1ohk'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1pd8'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1mvs'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1ohj'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1s3w'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3f8y'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3ghc'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '2w3a'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '2w3m'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3s7a'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3f8z'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3s3v'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3nzd'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3nxo'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3n0h'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1u71'},\n {'experimental_method': 'Solution NMR', 'pdb_id': '1yho'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1mvt'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1boz'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1s3v'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1pdb'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1u72'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '1s3u'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '2dhf'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '2c2t'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '2c2s'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '2w3b'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3ghw'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3eig'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3ghv'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3gi2'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3nxy'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3oaf'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3fs6'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3nxt'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3ntz'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3nxr'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3nxx'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3l3r'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3gyf'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3f91'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '3nu0'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '6de4'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '6dav'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '6a7e'},\n {'experimental_method': 'X-ray diffraction', 'pdb_id': '6a7c'}]\n"
}
],
"source": [
"results = change_lists_to_strings(third_results)\n",
"pprint(results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the only thing that changed is ['X-ray diffraction'] is now 'X-ray diffraction'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we wanted to know the experimental methods used to determine structures of Human Dihydrofolate reductase we could loop through the results and count how many entries use each experimental method. \n",
"\n",
"We can use a Python package called Pandas to do this for us. \n",
"It changes the results into a mini database - called a DataFrame. \n"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": " experimental_method pdb_id\n0 X-ray diffraction 3nxv\n1 X-ray diffraction 5hsu\n2 X-ray diffraction 5ht4\n3 X-ray diffraction 5ht5\n4 X-ray diffraction 5hqz\n.. ... ...\n74 X-ray diffraction 3nu0\n75 X-ray diffraction 6de4\n76 X-ray diffraction 6dav\n77 X-ray diffraction 6a7e\n78 X-ray diffraction 6a7c\n\n[79 rows x 2 columns]\n"
}
],
"source": [
"def pandas_dataset(list_of_results):\n",
" results = change_lists_to_strings(list_of_results) # we have added our function to change lists to strings\n",
" df = pd.DataFrame(results)\n",
"\n",
" return df\n",
"\n",
"df = pandas_dataset(list_of_results=results)\n",
"print(df)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use the this to count how many PDB codes there are for each experimental method\n",
"This groups PDB IDs by experimental method and then counts the number of unique PDB IDs per method."
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "experimental_method\nSolution NMR 1\nX-ray diffraction 78\nName: pdb_id, dtype: int64\n"
}
],
"source": [
"ds = df.groupby('experimental_method')['pdb_id'].nunique()\n",
"print(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can find which experimental method has the greatest (max) or lowest (min) number of entries."
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"tags": []
},
"outputs": [
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"output_type": "stream",
"name": "stdout",
"text": "78\n1\n"
}
],
"source": [
"dt = ds.max()\n",
"print(dt)\n",
"dt = ds.min()\n",
"print(dt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can sort the results so its in decending order and then the first value is the experimental method with the highest number of results"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "'X-ray diffraction'"
},
"metadata": {},
"execution_count": 178
}
],
"source": [
"ds.sort_values(ascending=False).index[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or sort ascending so the experimental method with the lowest number of results is given"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "'Solution NMR'"
},
"metadata": {},
"execution_count": 179
}
],
"source": [
"ds.sort_values(ascending=True).index[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or we can then very easily plot these results as a bar chart"
]
},
{
"cell_type": "code",
"execution_count": 180,
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" df = pandas_dataset(list_of_results)\n",
" ds = df.groupby(column_to_group_by)['pdb_id'].nunique()\n",
" return ds\n",
" \n",
"def pandas_min_max(list_of_results, column_to_group_by, get_min=True):\n",
" df = pandas_dataset(list_of_results)\n",
" if get_min:\n",
" ds = df.groupby(column_to_group_by)['pdb_id'].min()\n",
" else:\n",
" ds = df.groupby(column_to_group_by)['pdb_id'].max()\n",
" return ds\n",
" \n",
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" ds = pandas_count(list_of_results=list_of_results, column_to_group_by=column_to_group_by)\n",
" ds.iplot(kind=graph_type)\n"
]
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"text": "updated search\nNumber of results: 79\n"
},
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"text/plain": "resolution\n1.050 1kmv\n1.090 1kms\n1.201 4m6j\n1.210 5hsr\n1.230 3fs6\n1.240 3ghw\n1.270 2w3b\n1.300 3ghc\n1.350 3ntz\n1.396 4m6k\n1.400 2c2s\n1.450 3f8y\n1.460 5hqy\n1.500 2c2t\n1.530 3gi2\n1.550 6dav\n1.600 2w3m\n1.610 4qhv\n1.620 4qjc\n1.650 5hpb\n1.700 3eig\n1.760 4kfj\n1.800 1mvt\n1.840 4kbn\n1.850 6a7e\n1.900 1mvs\n1.920 3n0h\n2.000 1drf\n2.010 3f8z\n2.050 4ddr\n2.060 6a7c\n2.100 1boz\n2.200 1pd9\n2.300 1dhf\n2.411 6de4\n2.500 1ohj\n2.715 4kd7\nName: pdb_id, dtype: object"
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"filter_terms = ['pdb_id', 'resolution']\n",
"new_results = run_search(search_terms, filter_terms)\n",
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},
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},
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"cell_type": "markdown",
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"Then we can count the results using our pandas function above"
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},
{
"cell_type": "code",
"execution_count": 186,
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{
"cell_type": "markdown",
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
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"output_type": "execute_result",
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"source": [
"pandas_plot(list_of_results=results, column_to_group_by='release_year', graph_type='line')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try changing the term you want to search for and see if you get interesting results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7) searching for two terms at once\n",
"\n",
"It would be interesting to see how many PDB entries were solved by each experimental method per year. \n",
"\n",
"we can use the tag \"release_year\" to get the year of release of each entry\n",
"\n",
"We have to define a new function to group entries by two terms.\n",
"\n",
"When we do the search we have to filter the results by the terms we want to plot otherwise it takes too long to run. "
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Number of results: 1975\n"
}
],
"source": [
"search_terms = Q(all_enzyme_names=\"Lysozyme\")\n",
"filter_results = ['beam_source_name','release_year', 'pdb_id']\n",
"results = run_search(search_terms, filter_results, number_of_rows=10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will take a while as it will return lots of results. \n",
"We can then define a function to group the results by two terms. "
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [],
"source": [
"def pandas_plot_multi_groupby(results, first_column_to_group_by, second_column_to_group_by, y_axis='pdb_id', graph_type='line'):\n",
" \n",
" df = pandas_dataset(results)\n",
" new_df = df.groupby([first_column_to_group_by, second_column_to_group_by])\n",
" ds = new_df.count().unstack().reset_index(first_column_to_group_by)\n",
" ds.plot(x=first_column_to_group_by, y=y_axis, kind=graph_type).legend(bbox_to_anchor=(1,1))\n",
" \n",
"def pandas_plot_multi_groupby_min(results, first_column_to_group_by, second_column_to_group_by, graph_type='line', use_min=False, use_max=False):\n",
" \n",
" df = pandas_dataset(results)\n",
" new_df = df.groupby([first_column_to_group_by])[second_column_to_group_by]\n",
" ds = None\n",
" if use_min:\n",
" ds = new_df.min()\n",
" elif use_max:\n",
" ds = new_df.max()\n",
" else:\n",
" print('specify either use_min or use_max')\n",
" return None\n",
" ds.plot(x=first_column_to_group_by, y=second_column_to_group_by, kind=graph_type)\n",
"\n",
"def pandas_box_plot(results, first_column_to_group_by, second_column_to_group_by):\n",
" df = pandas_dataset(results)\n",
" df.boxplot(column=second_column_to_group_by,by=first_column_to_group_by)"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"tags": []
},
"outputs": [
{
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"source": [
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},
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"metadata": {},
"source": [
"This shows us that rotating anodes were used as the major source of radiation until around 2004 when Synchrotron's overtook as the major source of radiation. \n",
"\n",
"Try editing the queries to plot interesting trends within the PDB"
]
},
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"metadata": {},
"source": [
"## Questions to answer"
]
},
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"source": [
"1) Electron Microscopy is going through a revolution. Is this leading to a growth in Electron Microscopy PDB entries?"
]
},
{
"cell_type": "code",
"execution_count": 193,
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"source": [
"search_terms = Q(experimental_method='X-ray diffraction',release_year='2018')\n",
"filter_results = ['structure_determination_method','release_year', 'pdb_id']\n",
"results = run_search(search_terms, filter_results, number_of_rows=1000000)\n",
"pandas_plot(list_of_results=results, column_to_group_by='structure_determination_method')"
],
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"source": [
"2) New refinement programs have got better and there are more methods to validate the quality of strucures in the PDB.\n",
"Have structures got better over time?\n",
"We can use \"overall_quality\" to judge this\n",
"This could be plotted as a groupby or a box plot. "
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Number of results: 61831\n"
},
{
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"data": {
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fOhDPH9IoIAiCeIo039Hiu6wyBHk5wpKsTfAnX3/9tWDQoEESmUwmnThx4sD6+noWAKSmpnIVCoVELBbLvLy8pNXV1eyYmBinY8eO9dWtaBwVFeUUEhIyYPDgwZKwsLABTU1N1LRp09xompZJpVLZsWPH+MDdHu7x48e7BwQEiFxdXT0XL17c7XBNWFhYTVJSkhAA9u/fbzV58uQ63baCggIzkUgkB+7+4H/99dddRCKRnKZpma4H2NnZ2WvJkiXOMplMmpiY2Dc9PZ3j4+MjoWlaNm7cOPfKyko2AGzZssXO3d1dTtO0bNKkSQMBoL6+nqWrP03Tsj179lgBgEqlEtI0LROJRPIlS5Z0rgrN5XIV8+fP7+fh4SEfNmwYffPmTZPc3FxzmUwm1eXJycn50+veCA0Nddu3b5+V7vWUKVMG7N+/3woASktLTZVKpdjV1dVz5cqVjrrPxc3NzTM0NNSNpml5UVGRmbOzs5euhzs6OtrBzc3N09fXV3z58mVzY/usqKgwdXV1vaN7rVQqmwGgu3OqT38EIz8/32zQoEESmqZly5cvd+ruGBMTE/vqVo728/MTt7S0UIbXV0pKCnfQoEESqVQqUygUkqysLHNj+RoaGlivvPKKm5eXl1Qqlcp0n5UhoVCo8ff3b9y5c6fRR4+FhYXVHD58WAgAx48f5w8ZMkRtYmJidDXaoUOH3i4tLTXr7vgIQof8xSEIgniKfJ97C42tT3BtgiPL+qEi79FOZLCTNSFk5/WesrS2trIkEolM93rlypVlCxcurNW9LisrM9m6datjWlpaoUAgaH/nnXcc3n//ffstW7bcCg8Pd09KSioKDAxsqqmpYfH5/Pa33377ZkZGhuXevXtLACAqKopz+fJli3PnzuXzeDxm48aN9hRFobCwMO/ixYsWQUFBoqKioksAkJeXx83KysrjcDjtHh4enqtWrSr38PDoMrwwfvz4xsWLF7tqNBocOnRImJiYeC02NrbL8M6OHTtsS0pKzPLy8nJNTU1RXl7eGcphbW2tycvL+x0AaJqWxcbGlgQHB6sjIyOdoqOjnRITE6/HxcU5XLt2LYfD4TC6MJC1a9c6CgQCbWFhYR4AVFZWsouLi003bdrknJmZ+butra0mICCA3rdvn9Xs2bPrmpubWX5+frcTEhKur1q1ynHt2rVOe/fuLeHz+dr09HTO8OHDm1UqlU14eHi1sfOzfv16l+3bt3c5tgULFlTFxsbaz549u666upqdmZnJ++qrr/749NNPrbOzsy1zcnJyeTxeu0KhkE2dOrXe3t5eU1JSYp6QkPDHmDFjivXL+umnn7jffPONMCcnJ6+trQ2DBg2SKRSKJsN9Llu2rDwoKIhWKBS3x4wZU79s2bJqGxsb7fbt2+26O6fGLF26tP+CBQsq33jjjeqYmJhue9K3bdvm+MMPPxQOGDCgraqqim1hYcEYXl81NTWsX3/9Nd/U1BRHjhzhr1mzxuX7778vMsz3xhtvOI8aNarh0KFDxVVVVWw/Pz/plClTGgQCQbvhftevX18WFBREv/XWW11WGxeLxa0nT560qqysZB84cEA4e/bs6jNnznQZkdBoNEhJSeHPnz+frFhO3BMZKSAIgniKHM68gX5CDpRuwiddlcfKMHxIv0EAAGfOnLEsKiqyUCqVEolEIjt48KB1SUmJWXZ2toWdnV1bYGBgEwAIhcJ2U1NTo/uYMGFCHY/HYwAgPT2dN3v27GoAUCgULU5OTndycnIsAMDf37/B2tpay+VyGQ8Pj5aioiKjPdYmJiaMUqlUx8fHC1taWlhisfiOsXynT58WLFq0qEpXL3t7+85QmYiIiFoAqK6uZjc2NrKDg4PVALBw4cLqX375hQcAYrG4OTQ0dMCuXbuEpqamDACkpaUJ9MNmbG1ttWfPnrUcOnRoo5OTk8bU1BTTp0+vSU1N5QEAi8XCggULagBg3rx51efPn+cBwNy5c6vi4+NtNBoNjh492nf+/PlGGwVbtmy5oX9+dOnBwcHq4uJii5s3b5okJCQIg4ODa3XH6e/v3+Dg4KDl8XhMcHBw7ZkzZ3gA4OjoeGfMmDG3DfeRkpLCCwoKquPz+e1CobB9/PjxdYZ5AOCtt96qzsnJyQ0LC6tJS0vjDxkyRNLc3Ez1dE6NuXDhAm/hwoU1ALBo0SKjxw0Afn5+6vDwcLcdO3bYdBfmVVNTww4KCnIXiUTyNWvW9CssLDS63zNnzghiY2MdJRKJzN/fX9za2kpduXLFaC++TCa7o1Ao1CqVyuiXweTJk2sTExOFFy5csJwwYYJaf5uukW1ra+tTWVlpGhIS0tDd8RGEDhkpIAiCeEqU1jXj56IqRI6hn9zaBPfo0X9SGIaBv79/w7Fjx/7QTz9//jynt2VYWlp26Y01xszMrDMMg81mM21tbd2ejPDw8JoZM2Z4rF69+mZv66GPz+ffs04pKSmXT548yT969Gifjz76yLGgoCD3Qfalj6LuHtKcOXNqt2/f7nTw4MFGLy+vJgcHh17H9utMnz69Oj4+XvjVV18Jd+/eXWy4D8PXXC63V+ehJ25ubm2RkZHVkZGR1SKRSJ6RkdHr60Afi8XqEnLz5ptvOicnJ/cBgPz8/LwDBw6UnD592vLbb7/t4+vrK8vMzMwzfE90dLRzYGBgY3JyclFBQYHZ6NGjxcb2xzAMDh8+fMXHx6e1N/XbsGHDrX/84x/uQ4cObTTcFhERUfvCCy9Ip02bVs1m/3kesa6R3djYyBo5cqRo27ZtduvXr+/13Avi+URGCgiCIJ4SX2feAMMAYYOd7535OTNy5MjbGRkZvEuXLpkDQENDAys7O9vc29u7paKiwjQ1NZULALW1tay2tjYIBAKtWq3u9m/ciBEj1Pv37xcCQHZ2tnlZWZmZt7d3y/3W66WXXlIvX768bN68eTXd5RkzZkyDSqWy0U1w1g8f0rG2ttYKBALtqVOneACQkJBgPWzYMLVWq0VRUZHZ5MmTG3fu3FmqVqvZ9fX17MDAwIbY2NjOp9NUVlayAwICbp87d45fVlZmogtpGjlypBq4O6lX9wShPXv2WCuVykYA4HK5TGBgYH1UVFT/uXPndoaYLFu2zHnv3r1G490NLV68uEqlUtkDgK+vb+dnePbsWUF5eTlbrVZTJ06csAoMDFR3XwowevRo9YkTJ6zUajVVW1vLSk5O7tz/1q1bbbdu3WoLAIcPHxa0trZSAFBSUmJSV1fHdnV1vXO/53Tw4MHq+Ph4IQDEx8d3xu5/8sknpfojIrm5ueajR4++/fHHH9/s27ev5urVq2aG11dDQwPbxcXlDgCoVCobXbphvlGjRjXs2LHDXvfUqp9//rnHxoxCoWgRiUTNP/74Y5fQIJqm76xbt640MjKysrv38/n89ri4uJJdu3bZP6oJ9sSz656NAoqiEimKqqAoqktcHkVRKymKYiiKsul4TVEUFUdR1BWKorIpihqsl3cORVGXO/7N0Uv3pSgqp+M9cZRh1wJBEMRzgGEYHL5wA8MGWqOf8Plbm0AX7qD7t3Tp0j+1jJycnDQqlar41VdfHUjTtMzPz0+Sk5NjYWFhwSQlJRUtX768v1gslo0cOZJuampiTZw4sbGwsJCjm+BpuL81a9ZUtLe3UzRNy6ZPn+6uUqmKORyO0YmaPWGxWNi8eXO5o6Njt48PWrFiRaWLi8sdiUQiF4vFsoSEBKPhILt37/4jOjrahaZpWXZ2Nmfbtm03NRoNNXPmzAE0Tcs8PT1lCxYsqLCxsdHGxMSU1dXVsXUTYE+cOMF3dXVt27hxY2lgYCAtlUrlPj4+t2fNmlUHABwOp/38+fOWIpFInpaWxo+JiSnT7TciIqKGoiiEhYV1hpjk5eVxnJycevUrsl+/fhp3d/eWWbNm/SkEx9vb+/aUKVPc5XK5fPLkybUvvvhil/kB+vz9/ZtCQ0NrPD095WPHjhV5e3t3hhjl5+dzrK2ttQBw6tQpgVgslovFYtm4cePo995770b//v0193tOd+3aVfL555/b0TQtKy0tNR5zBmDFihUuusnbQ4YMUQ8dOrTZ8PqKjo6+tWnTJhepVCrTDzEyzKc7pxKJRObh4SFfv379PXsA3n333bLy8nKjIUarV6+uksvlPY46jBgxolkikTR//vnnz1dMInHfKIbp+TuQoqgXAagB7GUYxlMvvR+AfwGQAPBlGKaKoqggAG8CCALwAoB/MgzzAkVRQgAZAPwAMAAyO95TS1HUeQDLAZwDcAJAHMMwJ+9VcT8/PyYjI+O+D5ggCOJp9GtxDV757D/Y8YoPXv4LJxlTFJXJMIyfflpWVlaxj48PmYj4DONyuYqmpqaLxrZt2LDBvr6+nv3Pf/6zMwTK399fdPbs2cu9KbuxsZElk8lkv/322++6H+6P2qhRozxOnjxZZGFhcd8NN4Ig/iwrK8vGx8fHzTD9niMFDMOkATA2LBoLYA3u/sjXmYq7jQeGYZhfAFhRFOUI4CUAyQzD1DAMUwsgGcCEjm0ChmF+Ye62TvYCCLnPYyMIgvjbO5RxHZZmbEz0MrquEkH8JcaNG+d+8OBB67Vr1/4p3ry3DYIjR47wxWKxfOHChRV/VYMAAFJSUq6QBgFB/LUeaKIxRVFTAZQyDJNlEO3jDEB/ktqNjrSe0m8YSScIgnhuNN3R4Hh2GYK9HcE1I89/IB697kYJkpOTix6m3JCQkMaQkJCchymDIIinw33/9aEoigtgHYDxj74699z36wBeB4D+/Xu1ejpBEMRT79SlW7h9R4tpvv2edFUIgiCI59SDPH3IHcAAAFkURRUDcAFwgaIoBwClAPT/qrl0pPWU7mIk3SiGYT5nGMaPYRg/W1uyYjdBEM+Gw5k34GrNxRC3LvNhCYIgCOKxuO9GAcMwOQzD2DEM48YwjBvuhvwMZhjmFoBvAUR0PIVoKIB6hmHKAHwPYDxFUX0piuqLu6MM33dsa6AoamjHU4ciABx9RMdGEATx1Lte04T0ompMG+zS5bnuBEEQBPG49OaRpF8A+A8AMUVRNyiKmt9D9hMArgK4AiAewFIAYBimBsD7AH7t+Le5Iw0def7V8Z4iAPd88hBBEMSz4usLpaAoIOwvfOIQQRAEQdxLb54+NINhGEeGYUwZhnFhGCbBYLsbwzBVHf9nGIZZxjCMO8MwXgzDZOjlS2QYxqPj32699AyGYTw73vMGc69npBIEQTwj2tsZHL5wHSPcbeBs9UALsj4z2Gy2r/46BevWrXMAAKVSKU5LS7vvhRvS09M5X375ZZcFnx4VpVIpdnR09NItQgUAY8eOdedyuQoAKC4uNp0wYcLAv2r/j9PLL7/splv4TEd3nN2Ji4uzjoiI6NXkv8DAQI+qqqouC7pFRUU5bdiwwd4wPSsry1ypVIolEols4MCB8hkzZrj2VP53333HHzVqlAcAJCUl9dFdW4budUz6ent9Pch1WFBQYEZRlO8HH3zQuThdRERE/7i4OGvg7vngcDiK2trazt9w8+bN60dRlG9ZWZkJ8N/7SSQSyUePHm308wWA6OhoBw8PDzlN0zKJRCI7ffq0ZU916839uG/fPqvMzEwL3evIyEinI0eO8Ht39N377rvv+BRF+R44cKDz8xw1apTHd999x9fVrad7sqCgwMzCwmKwRCKRubu7y0NDQ910i+ARd5EVjQmCIJ6Q88U1uF7TjGlklADm5ubtulVk8/Pz87Zu3XrrYcrLyMjgHj9+3OiPsUe1siufz9cmJyfzAKCqqopdUVHRuQCWm5tb26lTp672tiz9Ba+eF+3t7dBqtUhNTb1iY2PT68eZLlu2rP/y5cvL8/Pz865evZq7YsWKinu/667w8PD6h722gJ6vrwfJZ0goFGpUKpVdS0uL0R+t/fr1a/3iiy+sAECr1eLs2bN8Ozu7zgtbdz9dvnw518rKSvPhhx92mYj5448/Wn7//fdWOTk5eYWFhXkpKSmFAwcOvHO/dTV05MgRq+zs7M5ejo8//vhmSEhI48OWCwD29vZt27dvd+xue0/3JHD3c8vPz88rKCjILSsrM0tMTCQTufSQRgFBEMQTcjjzBvjmJnhJTtYm6I2vv/5aMGjQIIlMJpNOnDhxYO345IgAACAASURBVH19PQsAUlNTuQqFQiIWi2VeXl7S6upqdkxMjNOxY8f66laSjYqKcgoJCRkwePBgSVhY2ICmpiZq2rRpbjRNy6RSqezYsWN84G4P9/jx490DAgJErq6unosXL+62xRYWFlaTlJQkBID9+/dbTZ48uU63raCgwEwkEsmBuz/4X3/9dReRSCSnaVqm6wF2dnb2WrJkibNMJpMmJib2TU9P5/j4+EhompaNGzfOvbKykg0AW7ZssXN3d5fTNC2bNGnSQACor69n6epP07Rsz549VgCgUqmEutV3lyxZ0vmIby6Xq5g/f34/Dw8P+bBhw+ibN2+a5ObmmstkMqkuT05Ozp9e90ZoaKjbvn37rHSvp0yZMmD//v1WAFBaWmqqVCrFrq6unitXrnTUfS5ubm6eoaGhbjRNy4uKisycnZ29dD3c0dHRDm5ubp6+vr7iy5cvmxvbZ0VFhamrq2vnj1elUtkMAN2dU336Ixj5+flmgwYNktA0LVu+fLlTd8eYmJjYV7dytJ+fn7ilpYUyvL5SUlK4gwYNkkilUplCoZBkZWWZG8vX0NDAeuWVV9y8vLykUqlUpvusDAmFQo2/v3/jzp07rY1tDwsLqzl8+LAQAI4fP84fMmSI2sTExGikxdChQ2+XlpZ2WRG5tLTUVCgUanSrPjs6Omrc3NzaAODo0aN8qVQqo2la9sorr7g1Nzd3aZzoj6zs3r2778svv+yWnJxs+eOPP1qtX7/eRSKRyHJzc831R5q6K9fZ2dlrxYoVTjKZTErTtOzixYsWhvsDAKlU2sTn87XffPONoLvPpbt7Up+JiQkGDx58u6eVrJ9H5IHYBEEQT8DtVg1O5JRh6iAncMyMjuw/Ee/+/G6/K7VX7jtcpycefT2a3h/x/vWe8rS2trIkEolM93rlypVlCxcurNW9LisrM9m6datjWlpaoUAgaH/nnXcc3n//ffstW7bcCg8Pd09KSioKDAxsqqmpYfH5/Pa33377ZkZGhuXevXtLACAqKopz+fJli3PnzuXzeDxm48aN9hRFobCwMO/ixYsWQUFBoqKioksAkJeXx83KysrjcDjtHh4enqtWrSr38PDoMrwwfvz4xsWLF7tqNBocOnRImJiYeC02NrZLL+aOHTtsS0pKzPLy8nJNTU1RXl7eecKtra01eXl5vwMATdOy2NjYkuDgYHVkZKRTdHS0U2Ji4vW4uDiHa9eu5XA4HEYXBrJ27VpHgUCgLSwszAOAyspKdnFxsemmTZucMzMzf7e1tdUEBATQ+/bts5o9e3Zdc3Mzy8/P73ZCQsL1VatWOa5du9Zp7969JXw+X5uens4ZPnx4s0qlsgkPD682dn7Wr1/vYqyHdsGCBVWxsbH2s2fPrquurmZnZmbyvvrqqz8+/fRT6+zsbMucnJxcHo/XrlAoZFOnTq23t7fXlJSUmCckJPwxZsyYYv2yfvrpJ+4333wjzMnJyWtra8OgQYNkCoWiyXCfy5YtKw8KCqIVCsXtMWPG1C9btqzaxsZGu337drvuzqkxS5cu7b9gwYLKN954ozomJqbbRxpu27bN8YcffigcMGBAW1VVFdvCwoIxvL5qampYv/76a76pqSmOHDnCX7Nmjcv3339fZJjvjTfecB41alTDoUOHiquqqth+fn7SKVOmNAgEgnbD/a5fv74sKCiIfuutt7qsNi4Wi1tPnjxpVVlZyT5w4IBw9uzZ1WfOnOkyIqHRaJCSksKfP39+lzJCQkIaYmJinNzc3Dz9/f0bZsyYURMcHKxuamqiFi1aNOCHH34o8Pb2bg0NDXX78MMPbTds2HDPEZlx48bdHjt2bN2kSZPqX3vttVr9bfcq18bGRpOXl/f7tm3bbLdt22b/5ZdfXjO2j3feeadsw4YNzqGhoQ2G23p7TzY1NVGZmZmWcXFxPX4vPW/ISAFBEMQTcCKnDE13tCR0qINh+JB+gwAAzpw5Y1lUVGShVColEolEdvDgQeuSkhKz7OxsCzs7u7bAwMAmABAKhe2mpsY7/yZMmFDH4/EYAEhPT+fNnj27GgAUCkWLk5PTnZycHAsA8Pf3b7C2ttZyuVzGw8OjpaioyGiPtYmJCaNUKtXx8fHClpYWllgsNhp6cfr0acGiRYuqdPWyt7fvDJWJiIioBYDq6mp2Y2MjOzg4WA0ACxcurP7ll194ACAWi5tDQ0MH7Nq1S2hqasoAQFpamkA/bMbW1lZ79uxZy6FDhzY6OTlpTE1NMX369JrU1FQeALBYLCxYsKAGAObNm1d9/vx5HgDMnTu3Kj4+3kaj0eDo0aN958+fb7RRsGXLlhv650eXHhwcrC4uLra4efOmSUJCgjA4OLhWd5z+/v4NDg4OWh6PxwQHB9eeOXOGBwCOjo53xowZc9twHykpKbygoKA6Pp/fLhQK28ePH2+0l/ett96qzsnJyQ0LC6tJS0vjDxkyRNLc3Ez1dE6NuXDhAm/hwoU1ALBo0SKjxw0Afn5+6vDwcLcdO3bYdBfmVVNTww4KCnIXiUTyNWvW9CssLDS63zNnzghiY2MdJRKJzN/fX9za2kpduXKlSy8+AMhksjsKhUKtUqmExrZPnjy5NjExUXjhwgXLCRMmqPW36RrZtra2PpWVlaYhISFdfkD36dOn/dKlS3n/+7//e83W1lYzZ84c97i4OOusrCwLFxeXVm9v71YAmDt3bvXZs2cfek7AvcqdOXNmLQAolcqm69evG73nAGDixIlqAPj+++95htvudU9ev37dXCKRyOzt7X3s7OzaXnjhheaHPa5nCRkpIAiCeAKO/nYTA2wsMbj/0xXSeq8e/SeFYRj4+/s3HDt27A/99PPnz/d6hralpWWX3lhjzMzMOsMw2Gw209bW1u1kxPDw8JoZM2Z4rF69+mZv66GPz+ffs04pKSmXT548yT969Gifjz76yLGgoCD3QfalT/f42zlz5tRu377d6eDBg41eXl5NDg4OvY7t15k+fXp1fHy88KuvvhLu3r272HAfhq+5XG6vzkNP3Nzc2iIjI6sjIyOrRSKRPCMj44Fm6rNYrC4hN2+++aZzcnJyHwDIz8/PO3DgQMnp06ctv/322z6+vr6yzMzMPMP3REdHOwcGBjYmJycXFRQUmI0ePVpsbH8Mw+Dw4cNXfHx8WntTvw0bNtz6xz/+4T506NAuMfkRERG1L7zwgnTatGnVbPafRxt1jezGxkbWyJEjRdu2bbNbv359l55+ExMTTJo0qXHSpEmN3t7ezfv27bMeMmRIl9EZY/TPr7HwovtlYWHBdNSJ0Wg0PZb39ttvl23ZssXRWMhUT/ekbk5BWVmZybBhwyRJSUl9wsPD6x+27s8KMlJAEATxmDW0tOGXq9WY4OlA1ibopZEjR97OyMjgXbp0yRwAGhoaWNnZ2ebe3t4tFRUVpqmpqVwAqK2tZbW1tUEgEGjVanW3f+NGjBih3r9/vxAAsrOzzcvKysy8vb1b7rdeL730knr58uVl8+bNq+kuz5gxYxpUKpWNboKzfviQjrW1tVYgEGhPnTrFA4CEhATrYcOGqbVaLYqKiswmT57cuHPnzlK1Ws2ur69nBwYGNsTGxnY+naayspIdEBBw+9y5c/yysjITXfjEyJEj1cDdSb26uO49e/ZYK5XKRgDgcrlMYGBgfVRUVP+5c+d2hpgsW7bMee/evUbj3Q0tXry4SqVS2QOAr69v52d49uxZQXl5OVutVlMnTpywCgwMVHdfCjB69Gj1iRMnrNRqNVVbW8tKTk7u3P/WrVttt27dagsAhw8fFuieGlNSUmJSV1fHdnV1vXO/53Tw4MHq+Ph4IQDEx8d3xu5/8sknpfojIrm5ueajR4++/fHHH9/s27ev5urVq2aG11dDQwPbxcXlDgCoVCobXbphvlGjRjXs2LHDXveEnJ9//rnHxoxCoWgRiUTNP/74Y5fQIJqm76xbt640MjKysrv38/n89ri4uJJdu3bZ666/AQMGyIG7T3HKycnp7JG/ePEix8XF5Y6Pj09LaWmpme5e27t3r3VAQECXRom1tXXbhQsXLLRaLY4ePdrZu8Hj8bQNDQ1d7r3eltsbYWFhDfX19ez8/Pwun19v7klHR0fN5s2bb3z44YfdTlp+HpFGAUEQxGOWWlAJTTuDsdIuT1t8bunCHXT/li5d6qy/3cnJSaNSqYpfffXVgTRNy/z8/CQ5OTkWFhYWTFJSUtHy5cv7i8Vi2ciRI+mmpibWxIkTGwsLCzm6CZ6G+1uzZk1Fe3s7RdO0bPr06e4qlapYN+HyfrBYLGzevLnc0dGx28cHrVixotLFxeWORCKRi8ViWUJCgtFwkN27d/8RHR3tQtO0LDs7m7Nt27abGo2Gmjlz5gCapmWenp6yBQsWVNjY2GhjYmLK6urq2LoJsCdOnOC7urq2bdy4sTQwMJCWSqVyHx+f27NmzaoDAA6H037+/HlLkUgkT0tL48fExJTp9hsREVFDURTCwsI6Q0zy8vI4Tk5OvXpMU79+/TTu7u4ts2bN+lMIjre39+0pU6a4y+Vy+eTJk2tffPHFHnug/f39m0JDQ2s8PT3lY8eOFXl7e3eGGOXn53Osra21AHDq1CmBWCyWi8Vi2bhx4+j33nvvRv/+/TX3e0537dpV8vnnn9vRNC3racLpihUrXHSTt4cMGaIeOnRos+H1FR0dfWvTpk0uUqlUph9iZJhPd04lEonMw8NDvn79eufu9qvz7rvvlpWXlxsNMVq9enWVXC7vcdRhxIgRzRKJpPnzzz8XlpWVmTAMQwF3GzIREREDdJPY8/PzOdu3b7/J5XKZzz77rPiVV15xp2laxmKxsGrVqi4Nj/fee6906tSpHoMHD5bY29t3Xivh4eE1cXFxDlKpVJabm9vZ6Ohtub0VHR1dduvWrS6fS2/uSQCYNWtWXXNzM0vXECcA6u+6LICfnx+TkZFx74wEQRBPmbcOXsTZy1U4/85YsFmPd6SAoqhMhmH89NOysrKKfXx8ukxEJJ4dXC5X0dTUdNHYtg0bNtjX19ez//nPf3aGW/j7+4vOnj17uTdlNzY2smQymey33377XffD/VEbNWqUx8mTJ4t0ISbEg/niiy/6FBUVmRsLJSKeH1lZWTY+Pj5uhulkTgFBEMRj1KZtR0p+BV6SOzz2BgFBGBo3bpz7tWvXzFNTUwv103vbIDhy5Ah/6dKlbosXLy7/qxoEAJCSknLlryr7eTJjxgwSP090izQKCIIgHqNfi2vQ0KLBWBkJHSIen+5GCZKTk4septyQkJDGkJCQnIcpgyCIpwOZU0AQBPEY/ZhXATMTFgJENvfOTBAEQRCPCWkUEARBPCYMw+D/8svh72EDrhkZqCUIgiCeHqRRQBAE8ZhcqVDjWnUTeeoQQRAE8dQhjQKCIIjHJPn3cgDAGKndPXISBEEQxONFGgUEQRCPyY955fB26QN7gcWTrspTh81m++qvU7Bu3ToHAFAqleK0tDTu/ZaXnp7O+fLLL7ss+PSoKJVKsaOjo5duESoAGDt2rDuXy1UAQHFxsemECRMG/lX7f5xefvllN93CZzq64+xOXFycdURERP/elB8YGOhRVVXVZUG3qKgopw0bNnQZVsvKyjJXKpViiUQiGzhwoHzGjBmuPZX/3Xff8UeNGuUBAElJSX1015ahex2Tvt5eXw9yHRYUFJhRFOX7wQcfdPYeRERE9I+Li7MG7p4PDoejqK2t7fwNN2/evH4URfmWlZWZAP+9n0QikXz06NFGP98rV66YOjs7e+kW06usrGQ7Ozt7FRQUGF0T4UnpzXnZvHmzXWNjY+fn0d019STExcVZFxcXd66DYVjX3oqMjHQ6cuQI/9HW7s9Io4AgCOIxqGxsxcXrdSR0qBvm5ubtulVk8/Pz87Zu3XrrYcrLyMjgHj9+3OiPMd3Krg+Lz+drk5OTeQBQVVXFrqio6PzD7+bm1nbq1KmrvS1Lf8Gr50V7ezu0Wi1SU1Ov2NjY9PpxpsuWLeu/fPny8vz8/LyrV6/mrlixotfP3A8PD69/2GsL6Pn6epB8hoRCoUalUtm1tLQYfW5xv379Wr/44gsrANBqtTh79izfzs6u88LW3U+XL1/OtbKy0nz44Ye2hmV4eHi0vfbaaxWRkZEuAPDWW2+5REREVIrF4js91e1R3T+PkkqlstdfOfp+r6m/0v79+21KSko6vxsM69obGo0GH3/88c2QkJAHWgG6t+5ZKYqiEimKqqAo6pJe2ocUReVTFJVNUdQ3FEVZ6W17m6KoKxRFFVAU9ZJe+oSOtCsURa3VSx9AUdS5jvQvKYp6qlqoBEEQj0JKfgUYBqRR8BC+/vprwaBBgyQymUw6ceLEgfX19SwASE1N5SoUColYLJZ5eXlJq6ur2TExMU7Hjh3rq1tJNioqyikkJGTA4MGDJWFhYQOampqoadOmudE0LZNKpbJjx47xgbu9euPHj3cPCAgQubq6ei5evNilu/qEhYXVJCUlCQFg//79VpMnT67TbSsoKDATiURy4O4f9Ndff91FJBLJaZqW6XqAnZ2dvZYsWeIsk8mkiYmJfdPT0zk+Pj4SmqZl48aNc6+srGQDwJYtW+x0q85OmjRpIADU19ezdPWnaVq2Z88eKwBQqVRC3eq7S5Ys6Vwtl8vlKubPn9/Pw8NDPmzYMPrmzZsmubm55jKZTKrLk5OT86fXvREaGuq2b9++zt8AU6ZMGbB//34rACgtLTVVKpViV1dXz5UrVzrqPhc3NzfP0NBQN5qm5UVFRWbOzs5euh7u6OhoBzc3N09fX1/x5cuXzY3ts6KiwtTV1bXzh6tSqWwGgO7OqT79EYz8/HyzQYMGSWiali1fvtypu2NMTEzsq1s52s/PT9zS0kIZXl8pKSncQYMGSaRSqUyhUEiysrLMjeVraGhgvfLKK25eXl5SqVQq031WhoRCocbf379x586d1sa2h4WF1Rw+fFgIAMePH+cPGTJEbWJiYnRht6FDh94uLS01+tvq3XffrcjMzLTcvHmz3fnz53mbNm0qN5bv5Zdfdps5c2Z/b29vyZIlS1yMHS8A+Pn5idPT0zm69/n6+or/85//cPTLysjIsPDy8pJKJBIZTdOynJwccwDYtGmTvUgkkotEIvnmzZu7xFjqj/gA/x092bJli11FRYVpYGAg/cILL9DA3XtLd00ZK7egoMBs4MCB8ldffdXVw8NDPmLECJFare7SADM890D39/OqVascPT09pSKRSD5jxgzX9vZ27N69u++lS5e4ERERAyUSiez999/vUtfuvtcMvx/0R+ycnZ29VqxY4SSTyaQ0TcsuXrxoAQA3b940GT58uMjDw0M+ffp0Vycnp87PoTd6k3EPgP8FsFcvLRnA2wzDaCiK2g7gbQDRFEXJALwKQA7ACcCPFEXRHe/ZCWAcgBsAfqUo6luGYfIAbAcQyzDMQYqiPgMwH8CnvT0AgiCIv4Mffy+HsxUHUse/dPT3od1c906/1suX7ztcpyfmIlGT09YPrveUp7W1lSWRSGS61ytXrixbuHBhre51WVmZydatWx3T0tIKBQJB+zvvvOPw/vvv22/ZsuVWeHi4e1JSUlFgYGBTTU0Ni8/nt7/99ts3MzIyLPfu3VsCAFFRUZzLly9bnDt3Lp/H4zEbN260pygKhYWFeRcvXrQICgoSFRUVXQKAvLw8blZWVh6Hw2n38PDwXLVqVbmHh0eX7tHx48c3Ll682FWj0eDQoUPCxMTEa7GxsY6G+Xbs2GFbUlJilpeXl2tqagpduAYAWFtba/Ly8n4HAJqmZbGxsSXBwcHqyMhIp+joaKfExMTrcXFxDteuXcvhcDiMLiRi7dq1jgKBQFtYWJgH3A39KC4uNt20aZNzZmbm77a2tpqAgAB63759VrNnz65rbm5m+fn53U5ISLi+atUqx7Vr1zrt3bu3hM/na9PT0znDhw9vVqlUNuHh4dXGzs/69etdtm/f3uXYFixYUBUbG2s/e/bsuurqanZmZibvq6+++uPTTz+1zs7OtszJycnl8XjtCoVCNnXq1Hp7e3tNSUmJeUJCwh9jxowp1i/rp59+4n7zzTfCnJycvLa2NgwaNEimUCiaDPe5bNmy8qCgIFqhUNweM2ZM/bJly6ptbGy027dvt+vunBqzdOnS/gsWLKh84403qmNiYrr0pOts27bN8YcffigcMGBAW1VVFdvCwoIxvL5qampYv/76a76pqSmOHDnCX7Nmjcv3339fZJjvjTfecB41alTDoUOHiquqqth+fn7SKVOmNAgEgnbD/a5fv74sKCiIfuutt7qsNi4Wi1tPnjxpVVlZyT5w4IBw9uzZ1WfOnOkyIqHRaJCSksKfP3++0RXLzc3NmZiYmBvTpk0Tff3115fNzc27XTG6rKzM7MKFC/kmJibdHu+cOXOq/vWvf9kMHz78enZ2tnlraytr2LBhzfrlfPLJJ7ZLly4tX7JkSU1LSwul0Wjw008/cQ8cOGCdmZn5O8Mw8PX1lY4ZM6ZxxIgRzd3VR+9zqvj000/tU1NTCx0dHf805NZduTY2NtqSkhKL/fv3Xx0+fPi1oKCggXv37u27dOnSGv33G557oPv7efXq1RUfffRRGQCEhIQMOHjwYJ/XXnut9tNPP7X76KOPrr/44otNAPDZZ5911rW77zVdOfrfD99///2fzq+NjY0mLy/v923bttlu27bN/ssvv7y2du1ap8DAwMaYmJhbhw8fFvz73/++r2df33OkgGGYNAA1Bmk/MAyj++B/AaDrSZkK4CDDMK0Mw/wB4AoAZce/KwzDXGUY5g6AgwCmUhRFARgN4HDH+/8fgJD7OQCCIIinXUubFj9drsJYqR3ufu0RhgzDh/QbBABw5swZy6KiIgulUimRSCSygwcPWpeUlJhlZ2db2NnZtQUGBjYBgFAobDc1NTW6jwkTJtTxeDwGANLT03mzZ8+uBgCFQtHi5OR0JycnxwIA/P39G6ytrbVcLpfx8PBoKSoqMtpjbWJiwiiVSnV8fLywpaWF1V3YxenTpwWLFi2q0tXL3t6+M6whIiKiFgCqq6vZjY2N7ODgYDUALFy4sPqXX37hAYBYLG4ODQ0dsGvXLqGpqSkDAGlpaQL9sBlbW1vt2bNnLYcOHdro5OSkMTU1xfTp02tSU1N5AMBisbBgwYIaAJg3b171+fPneQAwd+7cqvj4eBuNRoOjR4/2nT9/vtFGwZYtW27onx9denBwsLq4uNji5s2bJgkJCcLg4OBa3XH6+/s3ODg4aHk8HhMcHFx75swZHgA4OjreGTNmzG3DfaSkpPCCgoLq+Hx+u1AobB8/fnydYR4AeOutt6pzcnJyw8LCatLS0vhDhgyRNDc3Uz2dU2MuXLjAW7hwYQ0ALFq0yOhxA4Cfn586PDzcbceOHTbdhXnV1NSwg4KC3EUikXzNmjX9CgsLje73zJkzgtjYWEeJRCLz9/cXt7a2UleuXDHaiy+Tye4oFAq1SqUSGts+efLk2sTEROGFCxcsJ0yYoNbfpmtk29ra+lRWVpqGhIQ0dHd8x48f72Nra9uWnZ3d42SnsLCwWhMTkx6Pd+7cubU//vhjn9bWVuqzzz6zmTlzZpfGyLBhw27v2LHD8Z133nG4fPmyGY/HY86cOcMLCgqqEwgE7X369GkPDg6uTUlJeegelJ7KdXZ2bh0+fHgzACgUiqbi4uIu97mxc9/d/Xzy5Em+t7e3hKZpWXp6Ov/SpUscw/KM1M/o95puu+77wZiZM2fWAoBSqWy6fv26OQCcP3+eN2fOnBoAmDZtWoNAILivEKpH8aDseQC+7Pi/M+42EnRudKQBwHWD9BcAWAOo02tg6OfvgqKo1wG8DgD9+/dq/hJBEMQTl15UheY2Lcb8DUKH7tWj/6QwDAN/f/+GY8eO/aGffv78+Xv+4dWxtLTs0htrjJmZWWdvKZvNZtra2rptyYWHh9fMmDHDY/Xq1Td7Ww99fD7/nnVKSUm5fPLkSf7Ro0f7fPTRR44FBQW5D7IvfbrG6Zw5c2q3b9/udPDgwUYvL68mBweH+47Dnj59enV8fLzwq6++Eu7evbvYcB+Gr7lcbq/OQ0/c3NzaIiMjqyMjI6tFIpE8IyOj19eBPhaL1aVn/M0333ROTk7uAwD5+fl5Bw4cKDl9+rTlt99+28fX11eWmZmZZ/ie6Oho58DAwMbk5OSigoICs9GjR4uN7Y9hGBw+fPiKj49Pa2/qt2HDhlv/+Mc/3IcOHdolljwiIqL2hRdekE6bNq2azf7znFpdI7uxsZE1cuRI0bZt2+zWr1/fZe5Feno6Jy0tTfDzzz/nv/jii+K5c+fWurq6thl+BgDA4/E6z1t3x8vn89sDAgIaDhw4YPXtt98KL1682OWzWrx4cU1AQMDtb775ps+kSZNEn3zyybXefBampqaM/sT+1tbWh+phMbzPm5ubu3SU9+bcA3dD11auXOl67ty5PA8Pj7aoqCinlpaW3nS8G/1e0+np+8HCwoIB7nZOaDSaR9Lb9FATjSmKegeABkDSo6jMvTAM8znDMH4Mw/jZ2nY70kcQBPFUSc6rAM/cBC8MNNrhR/TCyJEjb2dkZPAuXbpkDgANDQ2s7Oxsc29v75aKigrT1NRULgDU1tay2traIBAItD1N5hsxYoR6//79QgDIzs42LysrM/P29m6533q99NJL6uXLl5fNmzevprs8Y8aMaVCpVDa6CZr64UM61tbWWoFAoD116hQPABISEqyHDRum1mq1KCoqMps8eXLjzp07S9VqNbu+vp4dGBjYEBsb2xl3XVlZyQ4ICLh97tw5fllZmYkupGnkyJFq4O6kXl088p49e6yVSmUjAHC5XCYwMLA+Kiqq/9y5czt7dZctW+a8d+9eo/HuhhYvXlylUqnsAcDX17fzMzx79qygvLycrVarqRMnTlgFBgaq9mdKVwAAIABJREFUuy8FGD16tPrEiRNWarWaqq2tZSUnJ3fuf+vWrbZbt261BYDDhw8LdD8IS0pKTOrq6tiurq537vecDh48WB0fHy8EgPj4+M7Y/U8++aRUf0QkNzfXfPTo0bc//vjjm3379tVcvXrVzPD6amhoYLu4uNwBAJVK1RmyYZhv1KhRDTt27LDX/bj9+eefe2zMKBSKFpFI1Pzjjz92CQ2iafrOunXrSiMjIyu7ez+fz2+Pi4sr2bVrl73u+hswYIAcuHtNLF261PXDDz+8LhKJ7rzxxhvlb775pouxz8BQd8cL3L0eoqOj+/n4+Ny2tbXt0sjMy8szk0qlrevXr6946aWX6n777TfOqFGj1CdOnLBqbGxkNTQ0sE6cONF31KhRf2oIubu7t165coXT3NxMVVVVsc+ePSvQbbO0tNTqYvH19abcnhg798bu56amJhYAODg4aOrr61nHjh3rfFoXj8fT1tfXd97z+nXt7nutt/UzNGTIEPW+ffuEwN25Cg0NDff1BKYHbhRQFDUXwCQA4QzD6FpbpQD66WVz6UjrLr0agBVFUSYG6QRBEM+E9nYG//d7OQJpW5ibPBVPyHsq6cIddP+WLl36p1FjJycnjUqlKn711VcH0jQt8/Pzk+Tk5FhYWFgwSUlJRcuXL+8vFotlI0eOpJuamlgTJ05sLCws5OgmeBrub82aNRXt7e0UTdOy6dOnu6tUqmIOh9NtPHV3WCwWNm/eXG4Yy6xvxYoVlS4uLnckEolcLBbLEhISjLYOd+/e/Ud0dLQLTdOy7OxszrZt225qNBpq5syZA2ialnl6esoWLFhQYWNjo42JiSmrq6tj6yZBnjhxgu/q6tq2cePG0sDAQFoqlcp9fHxuz5o1qw4AOBxO+/nz5y1FIpE8LS2NHxMTU6bbb0RERA1FUQgLC+sMMcnLy+M4OTn16jEz/fr107i7u7fMmjXrTyE43t7et6dMmeIul8vlkydPrtXFVHfH39+/KTQ0tMbT01M+duxYkbe3d2eIUX5+Psfa2loLAKdOnRKIxWK5WCyWjRs3jn7vvfdu9O/fX3O/53TXrl0ln3/+uR1N07LS0lLjMWcAVqxY4aKbvD1kyBD10KFDmw2vr+jo6FubNm1ykUqlMv0QI8N8unMqkUhkHh4e8vXr13cbHaHz7rvvlpWXlxsNMVq9enWVXC7vcdRhxIgRzRKJpPnzzz8XlpWVmTAMQwHA//zP/9g4OzvfCQ0NbQCA6OjoiitXrlgcP36cd686dXe8ABAQENBkaWmpfe2114zOY9i/f7+Qpmm5RCKR/f7775xFixZV+/v7N82cObN68ODBUl9fX+ns2bMrDecTeHh4tE2ePLlWIpHIp06dOlAul3deT3PmzKmaMGFC5+Rdnd6U2xNj597Y/WxjY6MNDw+vlEql8lGjRtE+Pj6d125ERETVm2++6SqRSGRqtZrSr2t332u9rZ+hbdu23Tx9+rRAJBLJ//3vf/e1sbFps7Ky6vXoH/Xf3/M9ZKIoNwDfMQzj2fF6AoD/ARDIMEylXj45gAO4O4fACcD/ARABoAAUAhiDuz/6fwUwk2GYXIqiDgH4Sm+icTbDMLvuVSc/Pz8mIyOjt8dJEATxRGRdr8PUnT8jdroPQhXdPsjmsaEoKpNhGD/9tKysrGIfHx+jf8CJZwOX+/+zd+fRUVRp/8C/T3f2jZAQQhIgQZJO6LAYyETRYEAWUXY3VARREVkcR9ERXEZmRkA9R2X0dXSiAyrL64oO4qDzwxEIvAxgWJJAWIMhQELIvm/dfX9/dHWmDQmEpJMQ8v2ck9Ndt6puPV1dDfVU3VvXI6aysvJAY/NefvnlwJKSEv3bb79d3wQqPj4+YufOnSeaU3dZWZnOaDQaDx48eMR24u5oo0aNCv/+++8zbE0mqGU+/fTTbhkZGa6NNSVylMzMTOeRI0dGZmRkHGrYrInaVlVVlTg5OSlnZ2f8+OOPnk888URoY3d7UlJSegwZMiSsYfll+xSIyKcARgLoISJnASyF9WlDrgC2aG0Edyul5mkn+V8ASIe1WdFCpZRZq+cJAP8CoAewWillaxO5GMBnIrIMwAEAq65sFxARXb3+fSQXep1gVCRHMaarz9ixY/ufPn3adfv27cfty5ubEPzjH//wXrBgQdi8efNy2yohAICtW7eebKu6u5L777+/pC3rf/fdd/2XLVsWsmLFijNMCNrfyZMnXe69997+FosFzs7OKjExMfNK1m/WnYKrEe8UEFFncPvbO+Dt5oQvHh/e0aEA4J0CIqKurqk7BRzRmIiojZwtqsSRnFKM7QRPHSIioq6NSQERURv59xFrs90xRiYFRER0dWNSQETURn48kov+AZ7o18Ozo0MhIiK6JCYFRERtoKy6DrtPFfAuARERdQpMCoiI2kDS8XzUmRX7EzSTXq8fZj9OwQsvvNALAOLi4iKTkpI8rrS+Xbt2uX/++ecXDfjkKHFxcZFBQUGD7EdYHTNmTH8PD48YwPpYxvHjx1/XVttvT3fddVeYbeAzG9vnbMo777zjP2vWrL7NqT8hISE8Pz//okfVLFq0KPjll1++6AeUkpLiGhcXFxkVFWW87rrrou+///7QS9X/3XffeY8aNSr8Usvk5+frX3vttfpRUR35/cXFxUUOHDhwgG06KSnJIy4uLtIWm4gMe+utt+oHANu1a5e7iAyzffa77rorLCQkZFBUVJQxMjLSuHHjRm9HxEXUEJMCIqI28OORXPh5uiCm70XjZlEjXF1dLbYRVI8ePZq+YsWK862pLzk52eOf//xno0mBbSTS1vL29jZv2bLFC7CeVF64cKF+AKywsLC6H3744VRz62o4AFRXYLFYYDabsX379pM9evRo9uNMFy5c2PfJJ5/MPXr0aPqpU6cOP/30061+5n5BQYF+1apV9c8NvtLvrxn1O33xxRc+jc2LiIio2rBhQ/0/FGvXrvWLjIz81QBby5YtO3v06NH0N95448yTTz55ySSIqKWYFBAROZjJbMFPRy9gVGRP6HXS0eFcM77++muf66+/PspoNA64/fbbryspKdEBwPbt2z1iYmKiIiMjjYMGDRpQUFCgf/XVV4M3bdrU3TaS7KJFi4KnTp3ab+jQoVF33nlnv8rKSrn77rvDDAaDccCAAcZNmzZ5A9Yr3OPGjes/YsSIiNDQ0IHz5s1rcsS5O++8s3D9+vV+ALBu3TrfSZMmFdvmHTt2zCUiIiIasJ7wz507t3dERES0wWAwLl++vCcAhISEDJo/f36I0WgcsHr16u67du1yHzJkSJTBYDCOHTu2f15enh4Ali1b1rN///7RBoPBOHHixOsAoKSkRGeL32AwGD/++GNfAEhMTPSzjcA6f/78+tFyPTw8Yh599NE+4eHh0cOHDzdkZ2c7HT582NVoNNZfwU5LS/vVdHNMmzYtbO3atb626cmTJ/dbt26dLwCcO3fOOS4uLjI0NHTgM888E2TbL2FhYQOnTZsWZjAYojMyMlxCQkIG5eTkOAHA4sWLe4WFhQ0cNmxY5IkTJ1wb2+aFCxecQ0NDa23TcXFxVQDQ1Hdqr+Hdh4iIiOhjx465PPPMM73PnDnjGhUVZXz88cd7239/jjhWnnjiidzXX389qLF5ISEhtTU1NbozZ844WSwW/PTTT91Gjx7d6HgCo0ePLrdPPokc6bKDlxER0ZVJPl2Ekqo6jDV2vgHL/r3mSJ/Cc+VX3FznUvxCvCpHzxpw5lLL1NTU6KKiooy26WeeeSbnscceK7JN5+TkOK1YsSIoKSnpuI+Pj+XFF1/s9corrwQuW7bs/IwZM/qvX78+IyEhobKwsFDn7e1tef7557OTk5M916xZkwUAixYtcj9x4oTbnj17jnp5eamlS5cGigiOHz+efuDAAbc77rgjIiMj4xAApKene6SkpKS7u7tbwsPDBz777LO54eHhF91eGDduXNm8efNCTSYTvvzyS7/Vq1efXrly5UUnfm+++WZAVlaWS3p6+mFnZ2fk5ubWN5Xx9/c3paenHwEAg8FgXLlyZdaECRPKn3rqqeDFixcHr169+sw777zT6/Tp02nu7u7K1sxmyZIlQT4+Pubjx4+nA0BeXp4+MzPT+Y9//GPIvn37jgQEBJhGjBhhWLt2re/MmTOLq6qqdLGxsRWrVq068+yzzwYtWbIkeM2aNVne3t7mXbt2ud90001ViYmJPWbMmFHQ2Pfz0ksv9W7spHbOnDn5K1euDJw5c2ZxQUGBft++fV4bNmz45f333/dPTU31TEtLO+zl5WWJiYkxTpkypSQwMNCUlZXlumrVql9Gjx6daV/Xjh07PL755hu/tLS09Lq6Olx//fXGmJiYyobbXLhwYe4dd9xhiImJqRg9enTJwoULC3r06GF+/fXXezb1nV7Om2++eXbixInuttFfjx075mKbd6l6m3usjBgxonzTpk2+mzZt8u7WrdtFd0WmTp1atHbt2u6xsbGVgwYNqnR1dW10EKkNGzZ0GzNmTHFj84hai3cKiIgc7Mf0XLjodRgREXD5hQnAxc2H7BMCANi2bZtnRkaGW1xcXFRUVJTxs88+88/KynJJTU1169mzZ11CQkIlAPj5+VmcnRu/kDp+/PhiLy8vBQC7du3ymjlzZgEAxMTEVAcHB9empaW5AUB8fHypv7+/2cPDQ4WHh1dnZGQ0esXayclJxcXFlX/44Yd+1dXVusjIyNrGlvvpp598Hn/88XxbXIGBgfUnhbNmzSoCrM1XysrK9BMmTCgHgMcee6xg9+7dXgAQGRlZNW3atH7vvfeen7OzswKApKQkH/tmMwEBAeadO3d63njjjWXBwcEmZ2dnTJ8+vXD79u1eAKDT6TBnzpxCAHjkkUcK9u7d6wUAs2fPzv/www97mEwmbNy4sfujjz7aaFJga75i+7OVT5gwoTwzM9MtOzvbadWqVX4TJkwosn3O+Pj40l69epm9vLzUhAkTirZt2+YFAEFBQbWjR4+uaLiNrVu3et1xxx3F3t7eFj8/P8u4ceMaPfn93e9+V5CWlnb4zjvvLExKSvL+zW9+E1VVVSWX+k5bwxHHCgC88MILOStWrGj0bsGsWbMKv/nmG79169b5P/DAA4UN57/00ku9w8LCBs6dO7ffCy+80KqmdURN4Z0CIiIHUkphy5Fc3BTuD0/XzvdP7OWu6HcUpRTi4+NLN23a9It9+d69e92bW4enp6fl8ksBLi4u9Vdp9Xq9qqura7IN2IwZMwrvv//+8N///vfZzY3Dnre392Vj2rp164nvv//ee+PGjd3eeOONoGPHjh1uybbsiVg/0kMPPVT0+uuvB3/22WdlgwYNquzVq1ez2/bbTJ8+veDDDz/027Bhg99HH32U2XAbDac9PDya9T1cSlhYWN1TTz1V8NRTTxVEREREJycnN+s4cHJyUvadw2tqalrVvu9KjpXJkyeXLV26NGTnzp0XPaO4b9++JmdnZ5WUlOSzevXqrJ07d3rZz1+2bNnZhx9+uGj58uU958yZE3b48OEjrYmbqDG8U0BE5EAZeeU4XVCJMXzqkEONHDmyIjk52evQoUOuAFBaWqpLTU11HTx4cPWFCxect2/f7gEARUVFurq6Ovj4+JjLy8ub/D/u5ptvLl+3bp0fAKSmprrm5OS4DB48uPpK47rtttvKn3zyyZxHHnnkoqu7NqNHjy5NTEzsYevgbN98yMbf39/s4+Nj/uGHH7wAYNWqVf7Dhw8vN5vNyMjIcJk0aVLZX//613Pl5eX6kpISfUJCQunKlSvr26fl5eXpR4wYUbFnzx7vnJwcJ1uTppEjR5YD1k69ticIffzxx/5xcXFlAODh4aESEhJKFi1a1Hf27Nn5tvoWLlwYsmbNGl80w7x58/ITExMDAWDYsGH1+3Dnzp0+ubm5+vLyctm8ebNvQkJC+aXqufXWW8s3b97sW15eLkVFRbotW7bUb3/FihUBK1asCACAr776ysd2Mp+VleVUXFysDw0NrW3OdxoWFlZz8OBBTy0+j3PnzrkCQLdu3cwVFRWNHi+OOlYAYMmSJTn/8z//06uxeX/605/OvfLKK2ednJq+mPD8889fsFgssmHDhkY7LRO1BpMCIiIH+lEbxXj0gM7Xn6Aj2foU2P4WLFgQYj8/ODjYlJiYmHnfffddZzAYjLGxsVFpaWlubm5uav369RlPPvlk38jISOPIkSMNlZWVuttvv73s+PHj7raOxg2399xzz12wWCxiMBiM06dP75+YmJjp7u7eaDvuS9HpdPjzn/+cGxQU1OTjg55++um83r1710ZFRUVHRkYaV61a5dfYch999NEvixcv7m0wGIypqanur732WrbJZJIHHnign8FgMA4cONA4Z86cCz169DC/+uqrOcXFxfqIiIjoyMhI4+bNm71DQ0Prli5dei4hIcEwYMCA6CFDhlQ8+OCDxQDg7u5u2bt3r2dERER0UlKS96uvvppj2+6sWbMKRQR33nlnqa0sPT3dPTg4uFmPaerTp4+pf//+1Q8++OCvmh4NHjy4YvLkyf2jo6OjJ02aVHTLLbdc1D/AXnx8fOW0adMKBw4cGD1mzJiIwYMH1zcxOnr0qLu/v78ZAH744QefyMjI6MjISOPYsWMNf/rTn8727dvX1JzvdNasWUVFRUX68PDw6LfffrtnaGhoNQD06tXLPGzYsPKIiIjoxx9//Fcdhh11rADA9OnTS/z8/Bo9VsaOHVsxc+bMS/YX0Ol0WLx4cfYbb7zRaGJB1BqiVIuO6w4XGxurkpOTOzoMIqJfufv9Xag2mfHdb0d0dCiNEpF9SqlY+7KUlJTMIUOG5De1DnV+Hh4eMZWVlQcam/fyyy8HlpSU6N9+++36JlDx8fERO3fuPNGcusvKynRGo9F48ODBI7YTd0cbNWpU+Pfff5/h5ubWOU9aiK4iKSkpPYYMGRLWsLzzNXglIrpKFZTXYF9WEX43OqKjQyFqlrFjx/Y/ffq06/bt24/blzc3IfjHP/7hvWDBgrB58+bltlVCAABbt2492VZ1E5EVkwIiIgf56egFKAX2J6CrTlN3CbZs2ZLRmnqnTp1aNnXq1LTW1EFEVwf2KSAicpAfj+QiqJsbooPZB5CIiDqXyyYFIrJaRC6IyCG7Mj8R2SIiJ7TX7lq5iMg7InJSRFJFZKjdOg9py58QkYfsyoeJSJq2zjvS8BlmRESdQHWdGUnH8zFmQOBFj2IkIiK62jXnTsHHAMY3KFsC4N9KqQgA/9amAeB2ABHa31wA7wPWJALAUgA3AIgDsNSWSGjLPGa3XsNtERFd9f5zqgBVdWY+dYiIiDqlyyYFSqkkAA2fvzwFwCfa+08ATLUrX6OsdgPwFZEgALcB2KKUKlRKFQHYAmC8Ns9HKbVbWR+DtMauLiKiTuPH9Fx4uugxvL9/R4dCRER0xVrapyBQKWV7xvF5ALZedSEA7EfDPKuVXar8bCPljRKRuSKSLCLJeXl5LQydiMixlFL48UgubjEEwNXponGpqBn0ev0w+3EKXnjhhV4AEBcXF5mUlORxpfXt2rXL/fPPP+/m+Eit4uLiIoOCggbZj447ZsyY/h4eHjEAkJmZ6Tx+/Pjr2mr77emuu+4Ksw18ZmP7nE155513/GfNmtW3OfUnJCSE5+fnX/TDWbRoUfDLL798Ua/92bNn93n22WeDbNOLFy/uNXPmzGZtq700Fbu9hsfo+vXru9mO+4527Ngxl7/97W/142m09Pd0Lf0OuoJWP31IKaVEpF2eG6yU+gDAB4B1nIL22CYR0eUcOleK3NIaPnWoFVxdXS1Hjx5Nd1R9ycnJHsnJyZ7Tp08vaTivrq4Ozs7Ord6Gt7e3ecuWLV633XZbeX5+vv7ChQv1lYaFhdX98MMPp5pbl8lkwqVGsr0WWSwWKKWwffv2K3rc6FtvvXVu8ODB0Y888kgBAKxbty4gJSXlkseObVt6/dWTtDc8RmfMmFEC4KLjtSOcOHHC9fPPP/ebN29eIXDp31NT6urqrvh3QB2rpXcKcrWmP9BeL2jl5wD0sVuut1Z2qfLejZQTEXUaW47kQifAqCj2J2hLX3/9tc/1118fZTQaB9x+++3XlZSU6ABg+/btHjExMVGRkZHGQYMGDSgoKNC/+uqrwZs2bepuG9F40aJFwVOnTu03dOjQqDvvvLNfZWWl3H333WEGg8E4YMAA46ZNm7wB6xXucePG9R8xYkREaGjowHnz5vVuKp4777yzcP369X4AsG7dOt9JkybVj0Z77Ngxl4iIiGjAesI/d+7c3hEREdEGg8G4fPnyngAQEhIyaP78+SFGo3HA6tWru+/atct9yJAhUQaDwTh27Nj+eXl5egBYtmxZz/79+0cbDAbjxIkTrwOAkpISnS1+g8Fg/Pjjj30BIDEx0c9gMBgjIiKi58+fX3/n3cPDI+bRRx/tEx4eHj18+HBDdna20+HDh12NRuMA2zJpaWm/mm6OadOmha1du9bXNj158uR+69at8wWAc+fOOcfFxUWGhoYOfOaZZ4Js+yUsLGzgtGnTwgwGQ3RGRoZLSEjIoJycHCfAetU/LCxs4LBhwyJPnDjh2tg2/fz8LH/4wx/OzZ8/v+/8+fP7vvDCC+d69Ohx0RgJjW1rxowZfQcOHDggPDw8+umnnw4GgG+//dZ7zJgx/W3rffPNNz5jx47t37C+BQsWhNi+h7lz5/a2bePGG280GAwG4/Dhww0nTpxwabie/d2unJwcp5CQkEHV1dXS8Bi1v7vSVL133XVX2OzZs/vExMRE9e7de1DDOzgAUFpaqhs5cmR4ZGSkMSIiIto2onfD30lRUZHu2LFjLsOGDYs0Go0DjEbjgC1btngCwIsvvhiSnJzsFRUVZXzxxRd7NYy1tLRUd88994QNGjRowIABA4y27/ydd97xv/XWW8NvvPFGw0033RRp/zu41G9r5cqVPcLCwgYOGjRowH333Rfa3LtM5FgtvSzxLYCHALymvW60K39CRD6DtVNxiVIqR0T+BWCFXeficQCeV0oVikipiNwIYA+AWQD+p4UxERF1iB/TcxEb6gc/z4vOBzqdf73/lz75Z05fcXOdS+nRJ7TytvlPnbnUMjU1NbqoqCijbfqZZ57Jeeyxx4ps0zk5OU4rVqwISkpKOu7j42N58cUXe73yyiuBy5YtOz9jxoz+69evz0hISKgsLCzUeXt7W55//vns5ORkzzVr1mQBwKJFi9xPnDjhtmfPnqNeXl5q6dKlgSKC48ePpx84cMDtjjvuiMjIyDgEAOnp6R4pKSnp7u7ulvDw8IHPPvtsbnh4eF3DmMeNG1c2b968UJPJhC+//NJv9erVp1euXBnUcLk333wzICsryyU9Pf2ws7MzcnNz6y9X+/v7m9LT048AgMFgMK5cuTJrwoQJ5U899VTw4sWLg1evXn3mnXfe6XX69Ok0d3d3ZWtms2TJkiAfHx/z8ePH0wEgLy9Pn5mZ6fzHP/4xZN++fUcCAgJMI0aMMKxdu9Z35syZxVVVVbrY2NiKVatWnXn22WeDlixZErxmzZosb29v865du9xvuummqsTExB4zZswoaOz7eemll3q//vrrF322OXPm5K9cuTJw5syZxQUFBfp9+/Z5bdiw4Zf333/fPzU11TMtLe2wl5eXJSYmxjhlypSSwMBAU1ZWluuqVat+GT16dKZ9XTt27PD45ptv/NLS0tLr6upw/fXXG2NiYiobi+fxxx8vfP/993vq9Xq1cOHChv0e6zXc1ltvvXUuMDDQbDKZcNNNN0Xu2bPHfeLEiWW/+93v+mZnZzsFBwebVq9e7f/www//aoTv8+fP6zdv3tz91KlTh3Q6HWzfw/z58/vOmDGj4Le//W3BX/7yF//58+f3+fHHHy879oObm5tqeIy+88479R2SLlVvbm6uc3Jy8tGDBw+6TZs2Lfzhhx8usq/766+/9unVq1fdtm3bTgJAQUGBvrq6Whr+Try8vCxOTk6mHTt2HPfw8FBpaWmu999//3WHDh06snz58nNvvvlmoG3QuMDAwDr7WJ944omQUaNGlX755ZeZ+fn5+tjY2AGTJ08uBYDDhw97pKamHg4MDDQfO3bsV/8oNvbbcnJywhtvvBG0f//+dF9fX8tNN91kiI6OrrrcPiTHa84jST8F8B8AkSJyVkQehTUZGCsiJwCM0aYBYDOAUwBOAvgQwAIAUEoVAngFwM/a35+1MmjL/F1bJwPA9475aEREbS8zvwLpOaUYF82mQ61haz5k+7NPCABg27ZtnhkZGW5xcXFRUVFRxs8++8w/KyvLJTU11a1nz551CQkJlYD1KnJTTYPGjx9f7OXlpQBg165dXjNnziwAgJiYmOrg4ODatLQ0NwCIj48v9ff3N3t4eKjw8PDqjIyMRq9YOzk5qbi4uPIPP/zQr7q6WhcZGVnb2HI//fSTz+OPP55viyswMLD+qvasWbOKAOuJW1lZmX7ChAnlAPDYY48V7N692wsAIiMjq6ZNm9bvvffe83N2dlYAkJSU5PP000/b7tIjICDAvHPnTs8bb7yxLDg42OTs7Izp06cXbt++3QsAdDod5syZUwgAjzzySMHevXu9AGD27Nn5H374YQ+TyYSNGzd2f/TRRxtNCpYtW3bW/vuxlU+YMKE8MzPTLTs722nVqlV+EyZMKLJ9zvj4+NJevXqZvby81IQJE4q2bdvmBQBBQUG1o0ePrmi4ja1bt3rdcccdxd7e3hY/Pz/LuHHjihsuY5ORkeGcl5fnnJub62K7Y9SYhtv65JNP/LSr4sYTJ064paSkuOl0Otx7770FH374oV9+fr5+//79Xvfcc8+vmsn4+/ubXV1dLdOnTw/75JNPfL28vCwAcODAAc+5c+cWAsD8+fML9+3b59VULFfiUvVOnjy5WK/XY9iwYdUFBQUXHexDhw51bZ7lAAAgAElEQVSt2rFjh8/8+fNDfvjhBy9/f39zU7+T2tpaeeCBB8IMBoPxnnvu6Z+RkeHWnPi2bdvms3LlyqCoqChjfHx8ZE1NjZw8edIFAEaMGFFqf4zba+y3tWPHDs8bbrihLDAw0Ozq6qqmTZtW1Ni61PYue6dAKXV/E7NGN7KsArCwiXpWA1jdSHkygIGXi4OI6Gr0XWo2AOCOQRddRO2ULndFv6MopRAfH1+6adOmX+zL9+7d697cOjw9PS2XXwpwcXGp77Om1+tVXV1dkwNPzJgxo/D+++8P//3vf5/d3DjseXt7XzamrVu3nvj++++9N27c2O2NN94IOnbs2OGWbMuebSyNhx56qOj1118P/uyzz8oGDRpU2atXr0ZP5i5l+vTpBR9++KHfhg0b/D766KPMhttoOO3h4dGs7+FSFixY0HfJkiXZR44ccXvuueeCExMTz548edJ54sSJEQDwyCOP5E2ZMqXEfltHjx51effddwO1Oynmu+66K6y6uloHAPPnzy+YMGFCuJubm5o0aVJRw8TS2dkZBw8ePPLtt9/6fPXVV93ff//9nrt37z7enFidnJyU2WzdrZWVla0exMTNza3++LSedv3a4MGDa/bv35++YcOGbn/4wx9Cfvzxx9J777230QRr+fLlgT179qzbsGHDLxaLBe7u7sOaE4NSCl999dXJIUOG1NiX79y50/NS3++V/Lao/XFEYyKiVtiUkoPfhHVHsG+zz02pBUaOHFmRnJzsdejQIVfA2m46NTXVdfDgwdUXLlxw3r59uwcAFBUV6erq6uDj42MuLy9v8v+4m2++uXzdunV+AJCamuqak5PjMnjw4Oorjeu2224rf/LJJ3MeeeSRJpuwjB49ujQxMbFHXZ21BZJ98yEbf39/s4+Pj/mHH37wAoBVq1b5Dx8+vNxsNiMjI8Nl0qRJZX/961/PlZeX60tKSvQJCQmlK1eurO/EkpeXpx8xYkTFnj17vHNycpxsTZpGjhxZDlg72tran3/88cf+cXFxZQDg4eGhEhISShYtWtR39uzZ9U1mFi5cGLJmzRpfNMO8efPyExMTAwFg2LBh9ftw586dPrm5ufry8nLZvHmzb0JCQvml6rn11lvLN2/e7FteXi5FRUW6LVu21G9/xYoVAStWrAgAgC+++MInPz/faeHChQWvvfZazubNm3337dvnFh4eXme7k/Hcc89d9IjCoqIivbu7u8XPz8985swZp23bttU/TScsLKwuMDCw7s033wyaO3dufsN1S0pKdIWFhfrp06eX/O1vfztz9OhRDwCIiYmp+Pvf/94dsPbniI2Nvegz9unTp2bv3r2eALB+/fr6PgCXOkabU29TMjMznb29vS0LFiwoXLRo0fmDBw96NPU7KSkp0QcFBdXp9Xq89957/rbkpVu3buby8vL647RhrKNGjSp98803A21P3/q///u/Fv8DGB8fX7Fnzx7vvLw8fV1dHTZu3HhRPwlqH0wKiIha6HhuGY7llmHi4OCODqXTs/UpsP0tWLDgV4+nDg4ONiUmJmbed9991xkMBmNsbGxUWlqam5ubm1q/fn3Gk08+2TcyMtI4cuRIQ2Vlpe72228vO378uLutY2TD7T333HMXLBaLGAwG4/Tp0/snJiZmuru7X/FT7XQ6Hf785z/nBgUFmZpa5umnn87r3bt3bVRUVHRkZKRx1apVfo0t99FHH/2yePHi3gaDwZiamur+2muvZZtMJnnggQf6GQwG48CBA41z5sy50KNHD/Orr76aU1xcrI+IiIiOjIw0bt682Ts0NLRu6dKl5xISEgwDBgyIHjJkSMWDDz5YDADu7u6WvXv3ekZEREQnJSV5v/rqq7bHimPWrFmFIoI777yz1FaWnp7uHhwcfFE/isb06dPH1L9//+oHH3zwV02PBg8eXDF58uT+0dHR0ZMmTSq65ZZbGu0fYBMfH185bdq0woEDB0aPGTMmYvDgwfXNfo4ePeru7+9vrqyslOeee67v+++/n6XT6eDj42NZtmzZ2YULF162Y+rw4cOrBg4cWNm/f/+B995773XDhg371Yn2fffdVxAUFFQ7dOjQi5LD4uJi/fjx4yO0jr+Rr7zyyhkA+Nvf/pa1du3aHgaDwfjpp5/6v/feexfdaVuyZEnuqlWrAgYMGGDMz8+vb6FxqWO0OfU2Zd++fe7XX3/9gKioKOPy5cuDX3755ZymfidPPfXUhU8//dQ/MjLSePToUTd3d3cLAMTFxVXp9XoVGRlp/NOf/tSzYay2YzMqKsoYHh4e/dJLLzX5OPnL6devX93TTz+dExsbO2DYsGFRffr0qenWrdsV37Gi1pPGbj11BrGxsSo5ObmjwyCiLuyt/3cM7249id0vjEZP72Y1xe1wIrJPKRVrX5aSkpI5ZMiQi66O0rXDw8MjprKy8kBj815++eXAkpIS/dtvv13fBCo+Pj5i586dJ5pTd1lZmc5oNBoPHjx4xN/fv01O5kaNGhX+/fffZ9g3nXG0WbNm9Y2Jial8+umn+VtoZyUlJbpu3bpZ6urqcNttt4XPnj07f9asWU32KaHWSUlJ6TFkyJCwhuVd66HIREQOopTCd6k5GN7fv9MkBEQNjR07tv/p06ddt2/f/qv28c1NCP7xj394L1iwIGzevHm5bZUQAIDtKThtJTo6eoC7u7slMTHxquxTc637/e9/H5yUlORTU1MjCQkJpbY7XNS+mBQQEbXA4exSnMqvwGO3cLBOuvo1dZdgy5Ytl3185qVMnTq1bOrUqWmtqeNqcPjw4SMdHUNX9sEHH5zt6BiIfQqIiFpkU2o2nHSC8dG9OjoUR7BYLBY+BYSI6Bqn/Vvf6BOimBQQEV0hpRS+S8lBfEQPdL8GBiwDcCgvL68bEwMiomuXxWKRvLy8bgAONTafzYeIiK7QgTPFOFdchUVjDR0dikOYTKY558+f//v58+cHgheLiIiuVRYAh0wm05zGZjIpICK6QptSsuHipMPYa2QU42HDhl0AMLmj4yAioo7DK0JERFfAbFH4Z2oORhoC4OPmfPkViIiIOgEmBUREV+DnzEJcKKvBpCEcsIyIiK4dTAqIiK7AppRsuDvrMXpAz44OhYiIyGGYFBARNZPJbMH3h85j9ICe8HBhlywiIrp2MCkgImqmXRkFKKyoZdMhIiK65jApICJqpk0p2fB2dUKCIaCjQyEiInKoViUFIvK0iBwWkUMi8qmIuIlIPxHZIyInReRzEXHRlnXVpk9q88Ps6nleKz8mIre17iMRETlejcmMfx0+j7HRgXBz1nd0OERERA7V4qRAREIAPAkgVik1EIAewH0AXgewUikVDqAIwKPaKo8CKNLKV2rLQUSM2nrRAMYDeE9E+D8uEV1VdhzPR2m1iU2HiIjomtTa5kNOANxFxAmAB4AcALcC+Eqb/wmAqdr7Kdo0tPmjRUS08s+UUjVKqV8AnAQQ18q4iIgc6rvUbPh6OCM+vEdHh0JERORwLU4KlFLnALwBIAvWZKAEwD4AxUopk7bYWQAh2vsQAGe0dU3a8v725Y2sQ0TU4apqzdiSnovbB/aCs55dsYiI6NrTmuZD3WG9yt8PQDAAT1ib/7QZEZkrIskikpyXl9eWmyIiqrf12AVU1JoxcTCbDhER0bWpNZe8xgD4RSmVp5SqA/A1gJsB+GrNiQCgN4Bz2vtzAPoAgDa/G4AC+/JG1vkVpdQHSqlYpVRsQACf/kFE7eO71Gz08HLFjdf5d3QoREREbaI1SUEWgBtFxEPrGzAaQDqArQDu1pZ5CMBG7f232jS0+T8ppZRWfp/2dKJ+ACIA7G1FXEREDlNeY8K/j1zAhEG9oNdJR4dDRETUJlo8JKdSao+IfAVgPwATgAMAPgDwTwCficgyrWyVtsoqAGtF5CSAQlifOASl1GER+QLWhMIEYKFSytzSuIiIHOnH9FzUmCyYyKcOERHRNazFSQEAKKWWAljaoPgUGnl6kFKqGsA9TdSzHMDy1sRCRNQWvkvNRlA3Nwzr272jQyEiImozfIwGEVETSirrsP14HiYODoKOTYeIiOgaxqSAiKgJ/zp8HnVmxacOERHRNY9JARFREzalZqOvnwcG9+7W0aEQERG1KSYFRESNyC+vwa6MAkwcHATrA9aIiIiuXUwKiIga8f2h8zBbFCbxqUNERNQFMCkgImrEdynZCO/phahe3h0dChERUZtjUkBE1EBuaTX2Zhay6RAREXUZTAqIiBr4Z2oOlAKfOkRERF0GkwIiogY2pWbDGOSD8J5eHR0KERFRu2BSQERk50xhJQ5kFWPikKCODoWIiKjdMCkgIrLzz7QcAMAkNh0iIqIuhEkBEZGdTSnZGNLHF338PDo6FCIionbj1NEBEBF1pDqzBQeyirHjRB6STuTjcHYpXpowoKPDIiIialdMCoioS1FK4XRBJZJO5CHpeD52nypAeY0Jep3g+j6+eHacAbOGh3V0mERERO2KSQERXfNKqurwn4x8JJ3Ix44TeThTWAUA6OPnjinXB2NERACG9/dHN3fnDo6UiIioYzApIKJrUlWtGR/uOIXtx/Nw8EwxzBYFL1cnDO/vj7kjrsOIiACE+ntwcDIiIiK0MikQEV8AfwcwEIAC8AiAYwA+BxAGIBPAvUqpIrH+z/s2gDsAVAKYrZTar9XzEICXtGqXKaU+aU1cRETvb8/AO/8+gSG9u2HByP4YERGAmL6+cNbz+QpEREQNtfZOwdsAflBK3S0iLgA8ALwA4N9KqddEZAmAJQAWA7gdQIT2dwOA9wHcICJ+AJYCiIU1sdgnIt8qpYpaGRsRdVFKKXyXko2b+vvjfx+7saPDISIiuuq1+JKZiHQDcAuAVQCglKpVShUDmALAdqX/EwBTtfdTAKxRVrsB+IpIEIDbAGxRShVqicAWAONbGhcR0eHsUpzKr8CkIRxrgIiIqDlacx+9H4A8AB+JyAER+buIeAIIVErlaMucBxCovQ8BcMZu/bNaWVPlFxGRuSKSLCLJeXl5rQidiK5lm1Kz4aQTjI/u1dGhEBERdQqtSQqcAAwF8L5SKgZABaxNheoppRSsTYIcQin1gVIqVikVGxAQ4KhqiegaYm06lIP4iB7o7unS0eEQERF1Cq1JCs4COKuU2qNNfwVrkpCrNQuC9npBm38OQB+79XtrZU2VExFdsQNninGuuAqTBrPpEBERUXO1OClQSp0HcEZEIrWi0QDSAXwL4CGt7CEAG7X33wKYJVY3AijRmhn9C8A4EekuIt0BjNPKiIiu2KaUbLg46TA2OvDyCxMRERGA1j996LcA1mtPHjoF4GFYE40vRORRAKcB3KstuxnWx5GehPWRpA8DgFKqUEReAfCzttyflVKFrYyLiLogs0Xhn6k5GGkIgI8bByIjIiJqrlYlBUqpg7A+SrSh0Y0sqwAsbKKe1QBWtyYWIqKfMwtxoayGTx0iIiK6QhzFh4iuGZtSsuHurMfoAT07OhQiIqJOhUkBEV0TTGYLvj90HmOMgfBwaW3LSCIioq6FSQERXRN2ZRSgsKIWEwcHdXQoREREnQ6TAiJqVz8dzcWhcyUOr3dTSja8XZ2QYOAYJkRERFeKSQERtRuLReF3nx7EE/+7HyazxWH11pjM+OHweYyL7gU3Z73D6iUiIuoqmBQQUbs5caEcZTUmZBZU4uv9jhujMOl4PsqqTZg0hE2HiIiIWoJJARG1m/1ZRQCAEF93vP3vE6g1OeZuwXep2eju4Yybw3s4pD4iIqKuhkkBEbWb/aeL4OfpguXTBuJccRU+Tz7T6jqras3Ykp6L8QOD4KznP2lEREQtwf9Biajd7M8qQkwfXyQYAvCbsO5496cTqK4zt6rOn45eQGWtmU2HiIiIWoFJARG1i+LKWmTkVWBoaHeICBaNjURuaQ3W7T7dqnq/S81GgLcrbujn76BIiYiIuh4mBUTULg6cKQYAxPT1BQAM7++Pm8P98f62DFTUmFpUZ1l1HX46egETBgVBrxOHxUpERNTVMCkgonZx4HQRdAIM6e1bX7ZobCQKKmrxyX8yW1Tnj0dyUWOysOkQERFRKzEpIKJ2sT+rGFG9fODp6lRfNiy0O0ZFBiBx+ymUVtddcZ2bUnIQ4uuOmD7dHRkqERFRl8OkgIjanNmicPBMMYaG+l40b9HYSJRU1WHVjl+uqM7iylrsOJGHCYODoGPTISIiolZhUkBEbe7EhTKU15gwtO/FV/QH9e6G8dG9sHrnLyiqqG12nf86fB51ZoVJg4MdGSoREVGXxKSAiNrc/tPWTsaNJQUA8PRYA8prTfhgx6lm17kpJQdh/h4YGOLjkBiJiIi6slYnBSKiF5EDIvKdNt1PRPaIyEkR+VxEXLRyV236pDY/zK6O57XyYyJyW2tjIqKry/4s66Blof4ejc6P7OWNSYOD8fH/ZSKvrOay9eWV1WBXRj4mDQmGCJsOERERtZYj7hT8DsARu+nXAaxUSoUDKALwqFb+KIAirXylthxExAjgPgDRAMYDeE9E9A6Ii4iuEvuzijC0r+8lT+CfGhOBGpMZf9uecdn6fjiUA4sCJrLpEBERkUO0KikQkd4AJgD4uzYtAG4F8JW2yCcApmrvp2jT0OaP1pafAuAzpVSNUuoXACcBxLUmLiK6ehRX1uJUXgVimmg6ZHNdgBfuGtoba3efxvmS6ksuuyklB4ZAL0T28nZkqERERF1Wa+8U/AXAcwAs2rQ/gGKllG0korMAQrT3IQDOAIA2v0Rbvr68kXV+RUTmikiyiCTn5eW1MnQiag8Hsi7dn8Dek6MjYLEovLv1RJPL5JRUYW9mITsYExEROVCLkwIRmQjgglJqnwPjuSSl1AdKqVilVGxAQEB7bZaIWmF/VhH0OsGQPt0uu2wfPw9M/00ffP7zGZwprGx0mX+m5gAAJg5hUkBEROQorblTcDOAySKSCeAzWJsNvQ3AV0RsoxP1BnBOe38OQB8A0OZ3A1BgX97IOkTUye3PKkJUL294uDhdfmEAT9waDhHB//zU+N2CTak5GBjig349PB0ZJhERUZfW4qRAKfW8Uqq3UioM1o7CPymlZgDYCuBubbGHAGzU3n+rTUOb/5NSSmnl92lPJ+oHIALA3pbGRURXD7NF4WBWcbOaDtkEdXPHjBv6YsP+c/glv+JX87IKKpFypphNh4iIiBysLcYpWAxgkYichLXPwCqtfBUAf618EYAlAKCUOgzgCwDpAH4AsFApZW6DuIionR3PLUNFrbnRkYwvZf7I/nDR6/D2j8d/Vb4pNRsAMGFwkMNiJCIiIqB59/MvQym1DcA27f0pNPL0IKVUNYB7mlh/OYDljoiFiK4e+7OKADSvk7G9nt5ueOimMCQmZWDBqHAYAq1PGfouNQdD+/qid/fGxzsgIiKiluGIxkTUZvafLoa/pwv6+l35Sfzjt1wHTxcnrNxivVtw8kIZjuSUYhI7GBMRETkckwIiajMHsooQ07d7i0Yd7u7pgkfi++H7Q+dx6FwJNqXkQASYMIhNh4iIiByNSQERtYmiilqcyq+44v4E9h6N74du7s54a8txbErNxo39/NHTx82BURIRERHApICI2siBMy3rT2Cvm7sz5t5yHX46egGn8iowcQjvEhAREbUFJgVE1Cb2ny6GXicY3Pvyg5ZdyuybwuDv6QK9TnD7QCYFREREbcEhTx8iImpof1YRBgQ1f9Cypni6OuG1uwbjdEEF/DxdHBQdERER2WNSQEQOZ7YopJwpxl3DejukvrHGQIfUQ0RERI1j8yEicrhj57VBy1rRn4CIiIjaD5MCInK4lg5aRkRERB2DSQEROdz+rCL08HJBHz/3jg6FiIiImoFJARE53IGs4hYPWkZERETtj0kBETlUYUUtfsmvYNMhIiKiToRJARE51IH6/gQtH8mYiIiI2heTAiJyqP1ZRXDSCQb3ZlJARETUWTApICKH2n+6GAOCfODuou/oUIiIiKiZWpwUiEgfEdkqIukiclhEfqeV+4nIFhE5ob1218pFRN4RkZMikioiQ+3qekhb/oSIPNT6j0VEHcFktiDlbDGbDhEREXUyrblTYALwjFLKCOBGAAtFxAhgCYB/K6UiAPxbmwaA2wFEaH9zAbwPWJMIAEsB3AAgDsBSWyJBRJ3LsdwyVNaaMTSUP2EiIqLOpMVJgVIqRym1X3tfBuAIgBAAUwB8oi32CYCp2vspANYoq90AfEUkCMBtALYopQqVUkUAtgAY39K4iKjj7M8qBsBBy4iIiDobh/QpEJEwADEA9gAIVErlaLPOAwjU3ocAOGO32lmtrKnyxrYzV0SSRSQ5Ly/PEaETkQMdOF2EHl6u6N2dg5YRERF1Jq1OCkTEC8AGAE8ppUrt5ymlFADV2m3Y1feBUipWKRUbEBDgqGqJyEH2ZxVhaF9fDlpGRETUybQqKRARZ1gTgvVKqa+14lytWRC01wta+TkAfexW762VNVVORJ1IQXkNMgsq2Z+AiIioE2rN04cEwCoAR5RSb9nN+haA7QlCDwHYaFc+S3sK0Y0ASrRmRv8CME5EumsdjMdpZUTUiRxgfwIiIqJOy6kV694MYCaANBE5qJW9AOA1AF+IyKMATgO4V5u3GcAdAE4CqATwMAAopQpF5BUAP2vL/VkpVdiKuIioA/x30LJuHR0KERERXaEWJwVKqZ0Ammo4PLqR5RWAhU3UtRrA6pbGQkQdb39WEYzBPnBz5qBlREREnQ1HNCaiVjOZLUg5U8KmQ0RERJ0UkwIiarWj58tQVWdGDEcyJiIi6pSYFBBRqx3IKgLATsZERESdFZMCImq1/VnFCPDmoGVERESdFZMCImo1DlpGRETUuTEpIKJWyS+vwemCSjYdIiIi6sSYFBBRq9QPWsaRjImIiDotJgVE1Cq2QcsGhXDQMiIios6KSQERtcr+00WI5qBlREREnRqTAiJqMZPZgtSzJYhhfwIiIqJOjUkBEbWYbdAy9icgIiLq3JgUEFGL7a8ftIwjGRMREXVmTAqIqMX2ny5CT29XhPhy0DIiIqLOjEkBEbWIUgr7s4oxtG93DlpGRETUyTl1dABEdPVSSiGvrAaZBZXILKjA6YIKZBZU4nRBBU7nV6KsxoSZN4Z2dJhERETUSkwKiJpJKYXc0hocOV+K0/kVcHPWw9vNGd5uTtrff9+7O+vb7Oq5UgrFlXU4V1yFs0WVOFtUhXPFVThney2uQlm1CV6uv47Lp0GM/31vfXXW6XC2qLL+pN/2Wllrrt+2k07Qx88Dof4eiA31Q78enrhrWO82+ZxERETUfq6apEBExgN4G4AewN+VUq91cEh0FVNKocZkQVWtGdUmM6pqzaiqM8NZr4O/pwt8PVyg17X8pLyy1oTjueU4dr4UR3LKcPR8KY6eL0NxZV2z1nfSCbxsJ9+u/z35dnXWwUWvg5NO4Oykg7NO4KzXwUmvg4te4KTXwVmvg7PeVi4orTLhXLF28q+d+NufqAOAh4sevbu7I8TXHTF9fdHN3Rnl1SaUVZtQWl2H0moTzhVXo6y6DGXVJpRV18GiGo/dWW898e/n74nh1/kjrIcHQv09EebvgRBfdzjp2eqQiIjoWnNVJAUiogfwVwBjAZwF8LOIfKuUSu/YyNqG2aJQWWtCVa0ZFbXm+veV2omtk07g6qSHi5MOrk66+hNJV2c9XJ109eUuet2vrkbXmS0orzahvMZ6MlheY0J5Td1/3zeYV1VnhslsgcmsUGdRMFssqDMra5lFoc5sLbPOt74qZT0B9XDVw8PFCZ4ueni4aq8uTvC8qNwJ7i461JmtJ/E1dWZUa681Jov2Z0ZNnd1723J1FlTVmVFdZ90v1dr+sZVfik6A7h4u8Pdygb+nK/y9XNDDyxX+ni7w97JNW+cBwLHcMhy1O/nPLKiA0k6aPVz0iOzljdsHBmFAkDeievngugBP1Jos9SfYtpPvMu1EvKz+/X/LzhZVotZsQZ1tn5v/u7/rzAq1ZkuTn8fXwxkhvu7o18MT8RE90Lu79QTdlgj4ejhf0Z0JpRQqa831MZZWm1BrsqB3d3cE+7q3KqEiIiKizueqSAoAxAE4qZQ6BQAi8hmAKQAcnhT8/ssUnCuucnS1jTJbFKrqtJN97eS/otaMWlPTJ39XysVJB1e9DrVm60n15egEWrMSZ7g5W69K63XaFWqdQK8TeLg4wUkvcNJZr2g7aVet9TqBAKisM6OyxoTKWjPyy2tRUViJyhozKmqtZeamLkE3wbU++dHXv3dx0sPdWQd3Fz26ezjD1VkPd9ufix5uznq4Oet+VebqpEed2YLCiloUlNcgX3stKK/F4exS5JfXoKza1GQcIkConweievlgyvXBiOrlgwFB3ujT3QO6djhJVkrBbFEwWawJgi1x8HR1gperY3+qIgJPVyd4ujqhVze3llVSUQB89oCtQlvNl5lG49Nt6Z5PAA+/Nt3Eji+OI/9M+RWv16OPF0bca2iDiIiIiK7M1ZIUhAA4Yzd9FsANDRcSkbkA5gJA3759W7Qh6xVwx52UX4qIoLuHC3p318Pd2cl6hd3FegJrvYKutyuzXl13c9bDbLFeVa+1u3Jue19bf3Xd8qtyF70O3m7Wk0cvN+f69uTWaSd4a69t2dYd+HWzHluSUFlrhrPeevfDdufD1cl6Ut/wbkdbqzGZtaShFvnlNSisqEWd2QJDoDcMgd7wdPDJ95UQsSZgTnrAzVnfYXFcESdXAFoSqBokg/XTyvrefro9NYyLiIiILiLqKvgPU0TuBjBeKTVHm54J4Aal1BNNrRMbG6uSk5PbK0QiomuCiOxTSsV2dBxERHR1uVp6DJ4D0MduurdWRkREREREbexqSQp+BhAhIv1ExAXAfQC+7eCYiIiIiIi6hKuiT4FSyiQiTwD4F6yPJF2tlDrcwWEREREREXUJV0VSAABKqc0ANsXuI2AAAAfLSURBVHd0HEREREREXc3V0nyIiIiIiIg6CJMCIiIiIqIujkkBEREREVEXx6SAiIiIiKiLuyoGL2sJEckDcLqj42gjPQDkd3QQVwHuByvuByvuB6vW7odQpVSAo4IhIqJrQ6dNCq5lIpLMEUe5H2y4H6y4H6y4H4iIqC2w+RARERERURfHpICIiIiIqItjUnB1+qCjA7hKcD9YcT9YcT9YcT8QEZHDsU8BEREREVEXxzsFRERERERdHJMCIiIiIqIujklBOxCRPiKyVUTSReSwiPxOK/cTkS0ickJ77a6Vi4i8IyInRSRVRIba1fWQtvwJEXmooz5TSzh4P5hF5KD2921HfaaWaMF+iBKR/4hIjYg826Cu8SJyTNtHSzri87SUg/dDpoikacdDckd8npZqwX6Yof0e0kRkl4gMsaur0x4PRETUsdinoB2ISBCAIKXUfhHxBrAPwFQAswEUKqVe0/4D766UWiwidwD4LYA7ANwA4G2l1A0i4gcgGUAsAKXVM0wpVdT+n+rKOWo/aHWVK6W8OuSDtFIL9kNPAKHaMkVKqTe0evQAjgMYC+AsgJ8B3K+USm/3D9UCjtoPWl2ZAGKVUp1ucLMW7IebABxRShWJyO0A/qj9+9CpjwciIupYvFPQDpRSOUqp/dr7MgBHAIQAmALgE22xT2A9EYBWvkZZ7Qbgq5043AZgi1KqUEsEtgAY344fpVUcuB86tSvdD0qpC0qpnwHUNagqDsBJpdQppVQtgM+0OjoFB+6HTq0F+2GX3YWA3QB6a+879fFAREQdi0lBOxORMAAxAPYACFRK5WizzgMI1N6HADhjt9pZrayp8k6nlfsBANxEJFlEdovIVHRSzdwPTelqx8OlKAD/T0T2icjcNgmyHbRgPzwK4Hvt/TVzPBARUftz6ugAuhIR8QKwAcBTSqlSEamfp5RSItIl2nI5aD+EKqXOich1AH4SkTSlVEYbhdwmeDxYOWg/xGvHQ08AW0TkqFIqqY1CbhNXuh9EZBSsSUF8uwZKRETXJN4paCci4gzrf/jrlVJfa8W5tuYw2usFrfwcgD52q/fWypoq7zQctB+glLK9ngKwDdarq53GFe6HpnS146FJdsfDBQDfwNqUptO40v0gIoMB/B3AFKVUgVbc6Y8HIiLqOEwK2oFYL/mtgrVz4Ft2s74FYHuC0EMANtqVzxKrGwGUaM0I/gVgnIh0155EMk4r6xQctR+0z++q1dkDwM0AOk1nyhbsh6b8DCBCRPqJiAuA+7Q6OgVH7QcR8dQ66EJEPGH9XRxyfMRt40r3g4j0BfA1gJlKqeN2y3fq44GIiDoWnz7UDkQkHsAOAGkALFrxC7C2G/4CQF8ApwHcq5Qq1E4S3oW1E3ElgIeVUslaXY9o6wLAcqXUR+32QVrJUftBe/pKolaHDsBflFKr2vXDtEIL9kMvWJ865aMtXw7AqDUxuQPAXwDoAaxWSi1v1w/TCo7aDwB6wHp3ALA2ifzfa3w//B3AXVoZAJiUUrFaXZ32eCAioo7FpICIiIiIqItj8yEiIiIioi6OSQERERERURfHpICIiIiIqItjUkBERERE1MUxKSAiIiIi6uKYFBARERERdXFMCqhLE5FtIhLb0XEQERERdSQmBXTN00ZE7hLHuog4dXQMRERE1Pl0iRMl6npEJExEjonIGgCHAMwUkf+IyH4R+VJEvBpZZ1xjy4jIyyLys4gcEpEPtJGWISJPiki6iKSKyGdamaeIrBaRvSJyQESmXCLGJBG53m56p4gMaaoO7TPt0OLbr43sDBEZqZV/CyDdgbuRiIiIugiOaEzXJBEJA3AKwE0ATgL4GsDtSqkKEVkMwFUp9WcR2QbgWQCZl1jGTylVqNW7FsAXSqlNIpINoJ9SqkZEfJVSxSKyAkC6UmqdiPgC2AsgRilV0UiMD2nznhIRA4D/VUrFNlUHAAXAopSqFpEIAJ9qy48E8E8AA5VSv7TB7iQiIqJrHJsa0LXstFJqt4hMBGAE8H/aRX4XAP9psOyNl1hmlIg8B8ADgB+AwwA2AUgFsF5E/gHgH9qy4wBMFpFntWk3AH0BHGkkvi8B/EFEfg/gEQAfX6aObADvancXzAAMdnXtZUJARERELcWkgK5ltqvzAmCLUur+Syzb6DIi4gbgPQCxSqkzIvJHWE/SAWACgFsATALwoogM0uq5Syl17HLBKaUqRWQLgCkA7gUwzC6Wi+rQtp0LYAisTf+qG/msRERERFeMfQqoK9gN4GYRCQfq2/0bmrmMLQHI1/oY3K3N1wHoo5TaCmAxgG4AvAD8C8Bv7fod/P/27R0lgiCKwvB/GIzNXYGpqzB2AT72YWhkOoHJCCaCazAzlUHEEWNDAwPFBcydoDuQwfEBSoP1f1lfqotLZYdbtfVFb6fAGJhW1UtfW7XHOvBUVXNgFxj98BwkSZI+ZCjQv1dVz8ABcJFkRnctaPM7a6rqFZjQPVa+BKb9LyPgPMk9cAuM+7VHwBowS/LQf3/W2w3wBpy9K6/a4wTYT3LX9+90QJIk/QofGksDSrIBXNEFkPnA7UiSpEY5KZAGkmQPuAYODQSSJGlITgqkP5ZkGzheKj9W1c4Q/UiSJC0zFEiSJEmN8/qQJEmS1DhDgSRJktQ4Q4EkSZLUOEOBJEmS1LgFolDBjGpxKpgAAAAASUVORK5CYII=\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"search_terms = Q(experimental_method='Electron Microscopy')\n",
"filter_results = ['experimental_method','release_year', 'pdb_id']\n",
"results = run_search(search_terms, filter_results, number_of_rows=1000000)\n",
"pandas_plot_multi_groupby(results, 'release_year', 'experimental_method')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3) Electron Microscopy resolution has been said to be improving. Is this true? \n",
"hint - the search term and filter can be different.\n",
"pandas_plot_multi_groupby_min with use_min would be useful to plot this or maybe a box plot?"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Number of results: 61831\n"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/svg+xml": "\n\n\n\n",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/svg+xml": "\n\n\n