diff --git a/_toc.yml b/_toc.yml index c1e3ce3..dfba5d2 100644 --- a/_toc.yml +++ b/_toc.yml @@ -90,6 +90,12 @@ parts: - caption: BUSINESS chapters: + # U.S. Bureau of Economic Analysis + - file: content/scripts/A_overview/bea_overview + sections: + - file: content/scripts/python/python_bea + title: "...in Python" + # U.S. Bureau of Labor Statistics - file: content/scripts/A_overview/bls_overview sections: @@ -182,6 +188,12 @@ parts: - file: content/scripts/R/R_CASCommon title: "...in R" + # National Weather Service + - file: content/scripts/A_overview/nws_overview + sections: + - file: content/scripts/python/python_nws + title: "...in Python" + # PubChem - file: content/scripts/A_overview/pubchem_overview sections: diff --git a/content/scripts/A_overview/bea_overview.rst b/content/scripts/A_overview/bea_overview.rst new file mode 100644 index 0000000..deab3ea --- /dev/null +++ b/content/scripts/A_overview/bea_overview.rst @@ -0,0 +1,22 @@ +U.S. Bureau of Economic Analysis +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +.. sectionauthor:: Michael T. Moen + +Brief Overview +**************** + +The U.S. Bureau of Economic Analysis (BEA) API provides programmatic access to economic data published by the BEA. A UserID is required for this API, and a rate limit of 100 requests, 100 MB, and 30 errors per minute is enforced. + +See the BEA API documentation [#bea1]_ and BEA API user guide [#bea2]_ for more information on accessing the API. Please check the terms of use [#bea3]_ for more information on the usage of this API. + +*This product uses the Bureau of Economic Analysis (BEA) Data API but is not endorsed or certified by BEA.* + +.. rubric:: References + +.. [#bea1] ``_ + +.. [#bea2] ``_ + +.. [#bea3] ``_ + diff --git a/content/scripts/A_overview/nws_overview b/content/scripts/A_overview/nws_overview new file mode 100644 index 0000000..094f5e2 --- /dev/null +++ b/content/scripts/A_overview/nws_overview @@ -0,0 +1,15 @@ +National Weather Service +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +.. sectionauthor:: Michael T. Moen + +Brief Overview +**************** + +The National Weather Service (NWS) API provides programmatic access to forecasts and alerts published by the NWS. An API key is not required to access this API, but users are required to include a User Agent with all API requests and adhere to an unpublished rate limit (we recommend sending a maximum of 1 request per second). + +See the NWS API documentation [#nws1]_ for more information on accessing the API. Note that the documentation states, "All of the information presented via the API is intended to be open data, free to use for any purpose." + +.. rubric:: References + +.. [#nws1] ``_ diff --git a/content/scripts/python/python_bea.ipynb b/content/scripts/python/python_bea.ipynb new file mode 100644 index 0000000..49dacb4 --- /dev/null +++ b/content/scripts/python/python_bea.ipynb @@ -0,0 +1,1275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# U.S. Bureau of Economic Analysis (BEA) API in Python\n", + "\n", + "by Michael T. Moen\n", + "\n", + "**BEA API documentation:** https://apps.bea.gov/API/\n", + "\n", + "**BEA API user guide:** https://apps.bea.gov/api/_pdf/bea_web_service_api_user_guide.pdf\n", + "\n", + "**BEA API terms of use:** https://apps.bea.gov/API/_pdf/bea_api_tos.pdf\n", + "\n", + "**BEA Interactive Data Application:** https://www.bea.gov/itable/ - This is a web-based application for interacting with BEA datasets\n", + "\n", + "*This product uses the Bureau of Economic Analysis (BEA) Data API but is not endorsed or certified by BEA.*\n", + "\n", + "These recipe examples were tested on February 23, 2024.\n", + "\n", + "**_NOTE:_** The BEA API limits requests to a maximum of 100 requests/minute, 100 MB/minute, and 30 errors/minute." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "### UserID Information\n", + "\n", + "Users must obtain a UserID in order to access the API. Registration can be found [here](https://apps.bea.gov/api/signup/)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "userid = ''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can save the above data in a separate python file and import it:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from userid import userid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries\n", + "\n", + "This tutorial uses the following libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import requests # Manages API requests\n", + "import matplotlib.pyplot as plt # Creates visualization of data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Finding the value added by industry in 2022\n", + "\n", + "*Note: This section uses the GDP by Industry dataset, which can be explored at https://apps.bea.gov/iTable/?reqid=150&step=2&isuri=1&categories=gdpxind&_gl=1*\n", + "\n", + "To get started, let's use the `GetDatasetList` method to get a list of all tables in the BEA database. In this example, we are interested in the `GDPbyIndustry` table.\n", + "\n", + "Note that the `ResultFormat` is set to `JSON` in this URL, indicating that the API response will be in JSON. By default, the BEA API will return data in JSON, but it can also be set to XML if that is the format you would like to receive the response in. Later examples in this tutorial will not specify a `ResultFormat`. Additionally, the `UserID` parameter is set to the value specified in the Setup part of this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NIPA : Standard NIPA tables\n", + "NIUnderlyingDetail : Standard NI underlying detail tables\n", + "MNE : Multinational Enterprises\n", + "FixedAssets : Standard Fixed Assets tables\n", + "ITA : International Transactions Accounts\n", + "IIP : International Investment Position\n", + "InputOutput : Input-Output Data\n", + "IntlServTrade : International Services Trade\n", + "IntlServSTA : International Services Supplied Through Affiliates\n", + "GDPbyIndustry : GDP by Industry\n", + "Regional : Regional data sets\n", + "UnderlyingGDPbyIndustry : Underlying GDP by Industry\n", + "APIDatasetMetaData : Metadata about other API datasets\n" + ] + } + ], + "source": [ + "method = 'GetDatasetList'\n", + "response_format = 'JSON'\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&ResultFormat={response_format}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Print tables with descriptions\n", + "for dataset in response['BEAAPI']['Results']['Dataset']:\n", + " print(f'{dataset['DatasetName']:<25}: {dataset['DatasetDescription']}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get parameter values\n", + "\n", + "In order to query the data of the database, we must first determine the parameters necessary to make a request. To do this, we must use the `GetParameterList` method, which provides parameter names, descriptions, and information about the values accepted for the parameter, including whether it is required for a request." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'ParameterName': 'Frequency',\n", + " 'ParameterDataType': 'string',\n", + " 'ParameterDescription': 'A - Annual, Q-Quarterly',\n", + " 'ParameterIsRequiredFlag': '1',\n", + " 'ParameterDefaultValue': '',\n", + " 'MultipleAcceptedFlag': '1',\n", + " 'AllValue': 'ALL'},\n", + " {'ParameterName': 'Industry',\n", + " 'ParameterDataType': 'string',\n", + " 'ParameterDescription': 'List of industries to retrieve (ALL for All)',\n", + " 'ParameterIsRequiredFlag': '1',\n", + " 'ParameterDefaultValue': '',\n", + " 'MultipleAcceptedFlag': '1',\n", + " 'AllValue': 'ALL'},\n", + " {'ParameterName': 'TableID',\n", + " 'ParameterDataType': 'integer',\n", + " 'ParameterDescription': 'The unique GDP by Industry table identifier (ALL for All)',\n", + " 'ParameterIsRequiredFlag': '1',\n", + " 'ParameterDefaultValue': '',\n", + " 'MultipleAcceptedFlag': '1',\n", + " 'AllValue': 'ALL'},\n", + " {'ParameterName': 'Year',\n", + " 'ParameterDataType': 'integer',\n", + " 'ParameterDescription': 'List of year(s) of data to retrieve (ALL for All)',\n", + " 'ParameterIsRequiredFlag': '1',\n", + " 'ParameterDefaultValue': '',\n", + " 'MultipleAcceptedFlag': '1',\n", + " 'AllValue': 'ALL'}]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "method = 'GetParameterList'\n", + "dataset = 'GDPbyIndustry'\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Display response\n", + "response['BEAAPI']['Results']['Parameter']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result above indicates that there are 4 parameters required to query the `GDPbyIndustry` dataset: `Frequency`, `Industry`, `TableID`, and `Year`.\n", + "\n", + "To find what values these parameters can take, we must use the `GetParameterValues` method. Using this method for the `Frequency` parameter returns a list of keys for each industry as well as the frequency at with which each is available (A for annually and Q for quarterly)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "output_scroll" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11 : Agriculture, forestry, fishing, and hunting (A,Q)\n", + "111CA : Farms (A,Q)\n", + "113FF : Forestry, fishing, and related activities (A,Q)\n", + "21 : Mining (A,Q)\n", + "211 : Oil and gas extraction (A,Q)\n", + "212 : Mining, except oil and gas (A,Q)\n", + "213 : Support activities for mining (A,Q)\n", + "22 : Utilities (A,Q)\n", + "23 : Construction (A,Q)\n", + "311FT : Food and beverage and tobacco products (A,Q)\n", + "313TT : Textile mills and textile product mills (A,Q)\n", + "315AL : Apparel and leather and allied products (A,Q)\n", + "31G : Manufacturing (A,Q)\n", + "31ND : Nondurable goods (A,Q)\n", + "321 : Wood products (A,Q)\n", + "322 : Paper products (A,Q)\n", + "323 : Printing and related support activities (A,Q)\n", + "324 : Petroleum and coal products (A,Q)\n", + "325 : Chemical products (A,Q)\n", + "326 : Plastics and rubber products (A,Q)\n", + "327 : Nonmetallic mineral products (A,Q)\n", + "331 : Primary metals (A,Q)\n", + "332 : Fabricated metal products (A,Q)\n", + "333 : Machinery (A,Q)\n", + "334 : Computer and electronic products (A,Q)\n", + "335 : Electrical equipment, appliances, and components (A,Q)\n", + "3361MV: Motor vehicles, bodies and trailers, and parts (A,Q)\n", + "3364OT: Other transportation equipment (A,Q)\n", + "337 : Furniture and related products (A,Q)\n", + "339 : Miscellaneous manufacturing (A,Q)\n", + "33DG : Durable goods (A,Q)\n", + "42 : Wholesale trade (A,Q)\n", + "441 : Motor vehicle and parts dealers (A,Q)\n", + "445 : Food and beverage stores (A,Q)\n", + "44RT : Retail trade (A,Q)\n", + "452 : General merchandise stores (A,Q)\n", + "481 : Air transportation (A,Q)\n", + "482 : Rail transportation (A,Q)\n", + "483 : Water transportation (A,Q)\n", + "484 : Truck transportation (A,Q)\n", + "485 : Transit and ground passenger transportation (A,Q)\n", + "486 : Pipeline transportation (A,Q)\n", + "487OS : Other transportation and support activities (A,Q)\n", + "48TW : Transportation and warehousing (A,Q)\n", + "493 : Warehousing and storage (A,Q)\n", + "4A0 : Other retail (A,Q)\n", + "51 : Information (A,Q)\n", + "511 : Publishing industries, except internet (includes software) (A,Q)\n", + "512 : Motion picture and sound recording industries (A,Q)\n", + "513 : Broadcasting and telecommunications (A,Q)\n", + "514 : Data processing, internet publishing, and other information services (A,Q)\n", + "52 : Finance and insurance (A,Q)\n", + "521CI : Federal Reserve banks, credit intermediation, and related activities (A,Q)\n", + "523 : Securities, commodity contracts, and investments (A,Q)\n", + "524 : Insurance carriers and related activities (A,Q)\n", + "525 : Funds, trusts, and other financial vehicles (A,Q)\n", + "53 : Real estate and rental and leasing (A,Q)\n", + "531 : Real estate (A,Q)\n", + "532RL : Rental and leasing services and lessors of intangible assets (A,Q)\n", + "54 : Professional, scientific, and technical services (A,Q)\n", + "5411 : Legal services (A,Q)\n", + "5412OP: Miscellaneous professional, scientific, and technical services (A,Q)\n", + "5415 : Computer systems design and related services (A,Q)\n", + "55 : Management of companies and enterprises (A,Q)\n", + "56 : Administrative and waste management services (A,Q)\n", + "561 : Administrative and support services (A,Q)\n", + "562 : Waste management and remediation services (A,Q)\n", + "6 : Educational services, health care, and social assistance (A,Q)\n", + "61 : Educational services (A,Q)\n", + "62 : Health care and social assistance (A,Q)\n", + "621 : Ambulatory health care services (A,Q)\n", + "622 : Hospitals (A,Q)\n", + "622HO : Hospitals and nursing and residential care facilities (A,Q)\n", + "623 : Nursing and residential care facilities (A,Q)\n", + "624 : Social assistance (A,Q)\n", + "7 : Arts, entertainment, recreation, accommodation, and food services (A,Q)\n", + "71 : Arts, entertainment, and recreation (A,Q)\n", + "711AS : Performing arts, spectator sports, museums, and related activities (A,Q)\n", + "713 : Amusements, gambling, and recreation industries (A,Q)\n", + "72 : Accommodation and food services (A,Q)\n", + "721 : Accommodation (A,Q)\n", + "722 : Food services and drinking places (A,Q)\n", + "81 : Other services, except government (A,Q)\n", + "FIRE : Finance, insurance, real estate, rental, and leasing (A,Q)\n", + "G : Government (A,Q)\n", + "GDP : Gross domestic product (A,Q)\n", + "GF : Federal (A,Q)\n", + "GFE : Government enterprises (A,Q)\n", + "GFG : General government (A,Q)\n", + "GFGD : National defense (A,Q)\n", + "GFGN : Nondefense (A,Q)\n", + "GSL : State and local (A,Q)\n", + "GSLE : Government enterprises (A,Q)\n", + "GSLG : General government (A,Q)\n", + "HS : Housing (A,Q)\n", + "ICT : Information-communications-technology-producing industries (A)\n", + "ICT : Information-communications-technology-producing industries 4 (A)\n", + "II : All industries (A,Q)\n", + "NABI : Not allocated by industry (A,Q)\n", + "ORE : Other real estate (A,Q)\n", + "PGOOD : Private goods-producing industries (A,Q)\n", + "PROF : Professional and business services (A,Q)\n", + "PSERV : Private services-producing industries (A,Q)\n", + "PVT : Private industries (A,Q)\n" + ] + } + ], + "source": [ + "method = 'GetParameterValues'\n", + "dataset = 'GDPbyIndustry'\n", + "parameter = 'Industry'\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&ParameterName={parameter}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Print industry keys and descriptions\n", + "for parameter in response['BEAAPI']['Results']['ParamValue']:\n", + " print(f'{parameter['Key']:<6}: {parameter['Desc']}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we use the same method to get the table IDs of the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 : Value Added by Industry (A) (Q)\n", + "5 : Value added by Industry as a Percentage of Gross Domestic Product (A) (Q)\n", + "6 : Components of Value Added by Industry (A)\n", + "7 : Components of Value Added by Industry as a Percentage of Value Added (A)\n", + "8 : Chain-Type Quantity Indexes for Value Added by Industry (A) (Q)\n", + "9 : Percent Changes in Chain-Type Quantity Indexes for Value Added by Industry (A) (Q)\n", + "10 : Real Value Added by Industry (A) (Q)\n", + "11 : Chain-Type Price Indexes for Value Added by Industry (A) (Q)\n", + "12 : Percent Changes in Chain-Type Price Indexes for Value Added by Industry (A) (Q)\n", + "13 : Contributions to Percent Change in Real Gross Domestic Product by Industry (A) (Q)\n", + "14 : Contributions to Percent Change in the Chain-Type Price Index for Gross Domestic Product by Industry (A) (Q)\n", + "15 : Gross Output by Industry (A) (Q)\n", + "16 : Chain-Type Quantity Indexes for Gross Output by Industry (A) (Q)\n", + "17 : Percent Changes in Chain-Type Quantity Indexes for Gross Output by Industry (A) (Q)\n", + "18 : Chain-Type Price Indexes for Gross Output by Industry (A) (Q)\n", + "19 : Percent Changes in Chain-Type Price Indexes for Gross Output by Industry (A) (Q)\n", + "20 : Intermediate Inputs by Industry (A) (Q)\n", + "21 : Chain-Type Quantity Indexes for Intermediate Inputs by Industry (A) (Q)\n", + "22 : Percent Changes in Chain-Type Quantity Indexes for Intermediate Inputs by Industry (A) (Q)\n", + "23 : Chain-Type Price Indexes for Intermediate Inputs by Industry (A) (Q)\n", + "24 : Percent Changes in Chain-Type Price Indexes for Intermediate Inputs by Industry (A) (Q)\n", + "25 : Composition of Gross Output by Industry (A)\n", + "26 : Shares of Gross Output by Industry (A)\n", + "27 : Cost per Unit of Real Gross Output by Industry Group (A)\n", + "29 : Contributions to Percent Changes in Chain-Type Quantity Indexes for Gross Output by Industry Group (A)\n", + "30 : Contributions to Percent Changes in Chain-Type Price Indexes for Gross Output by Industry Group (A)\n", + "31 : Chain-Type Quantity Indexes for Energy Inputs by Industry (A)\n", + "32 : Contributions to Percent Change by Industry in the Chain-Type Quantity Index for All Industries Energy Inputs (A)\n", + "33 : Chain-Type Price Indexes for Energy Inputs by Industry (A)\n", + "34 : Contributions to Percent Change by Industry in the Chain-Type Price Index for All Industries Energy Inputs (A)\n", + "35 : Chain-Type Quantity Indexes for Materials Inputs by Industry (A)\n", + "36 : Contributions to Percent Change by Industry in the Chain-Type Quantity Index for All Industries Materials Inputs (A)\n", + "37 : Chain-Type Price Indexes for Materials Inputs by Industry (A)\n", + "38 : Contributions to Percent Change by Industry in the Chain-Type Price Index for All Industries Materials Inputs (A)\n", + "39 : Chain-Type Quantity Indexes for Purchased Service Inputs by Industry (A)\n", + "40 : Contributions to Percent Change by Industry in the Chain-Type Quantity Index for All Industries Purchased Service Inputs (A)\n", + "41 : Chain-Type Price Indexes for Purchased Service Inputs by Industry (A)\n", + "42 : Contributions to Percent Change by Industry in the Chain-Type Price Index for All Industries Purchased Service Inputs (A)\n", + "208 : Real Gross Output by Industry (A) (Q)\n", + "209 : Real Intermediate Inputs by Industry (A) (Q)\n" + ] + } + ], + "source": [ + "method = 'GetParameterValues'\n", + "dataset = 'GDPbyIndustry'\n", + "parameter = 'tableid'\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&ParameterName={parameter}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Print TableIDs and descriptions\n", + "for parameter in response['BEAAPI']['Results']['ParamValue']:\n", + " print(f'{parameter['Key']:<6}: {parameter['Desc']}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we look at the years available in the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2017\n", + "2018\n", + "2019\n", + "2020\n", + "2021\n", + "2022\n" + ] + } + ], + "source": [ + "method = 'GetParameterValues'\n", + "dataset = 'GDPbyIndustry'\n", + "parameter = 'year'\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&ParameterName={parameter}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Print years\n", + "for parameter in response['BEAAPI']['Results']['ParamValue']:\n", + " print(parameter['Key'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get data\n", + "\n", + "Now that we have determined the parameters needed to retrieve the data we are looking for, we can use the `GetData` method to retrieve this data.\n", + "\n", + "In this example, we use the `all` value for the industry parameter to obtain the data for all industries. Note that usage of this value should be limited in order to adhere to the API's rate limits." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "method = 'GetData'\n", + "dataset = 'GDPbyIndustry'\n", + "\n", + "industry = 'all' # Data for all industries\n", + "frequency = 'A' # Annual data\n", + "tableid = 1 # Value Added by Industry\n", + "year = 2022 # Data for 2022\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&Industry={industry}&Frequency={frequency}&tableid={tableid}&year={year}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Display number of results\n", + "len(response['BEAAPI']['Results'][0]['Data'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "output_scroll" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Industry Value Added (in million USD)\n", + "Agriculture, forestry, fishing, and hunting 270.8\n", + "Farms 223.5\n", + "Forestry, fishing, and related activities 47.3\n", + "Mining 457.4\n", + "Oil and gas extraction 324.5\n", + "Mining, except oil and gas 78.3\n", + "Support activities for mining 54.6\n", + "Utilities 438.2\n", + "Construction 1090.1\n", + "Food and beverage and tobacco products 311.9\n", + "Textile mills and textile product mills 17.5\n", + "Apparel and leather and allied products 10.4\n", + "Manufacturing 2649.7\n", + "Nondurable goods 1242.8\n", + "Wood products 62.1\n", + "Paper products 69.2\n", + "Printing and related support activities 42.3\n", + "Petroleum and coal products 199.9\n", + "Chemical products 501.4\n", + "Plastics and rubber products 90.4\n", + "Nonmetallic mineral products 73.1\n", + "Primary metals 91.8\n", + "Fabricated metal products 164.9\n", + "Machinery 182.1\n", + "Computer and electronic products 301.8\n", + "Electrical equipment, appliances, and components 63.2\n", + "Motor vehicles, bodies and trailers, and parts 156.4\n", + "Other transportation equipment 173.0\n", + "Furniture and related products 31.2\n", + "Miscellaneous manufacturing 107.4\n", + "Durable goods 1406.9\n", + "Wholesale trade 1546.8\n", + "Motor vehicle and parts dealers 347.4\n", + "Food and beverage stores 214.6\n", + "Retail trade 1621.0\n", + "General merchandise stores 196.6\n", + "Air transportation 152.0\n", + "Rail transportation 51.5\n", + "Water transportation 21.0\n", + "Truck transportation 263.3\n", + "Transit and ground passenger transportation 57.0\n", + "Pipeline transportation 49.8\n", + "Other transportation and support activities 211.5\n", + "Transportation and warehousing 920.5\n", + "Warehousing and storage 114.5\n", + "Other retail 862.4\n", + "Information 1392.8\n", + "Publishing industries, except internet (includes software) 358.2\n", + "Motion picture and sound recording industries 100.4\n", + "Broadcasting and telecommunications 512.5\n", + "Data processing, internet publishing, and other information services 421.7\n", + "Finance and insurance 1932.9\n", + "Federal Reserve banks, credit intermediation, and related activities 856.7\n", + "Securities, commodity contracts, and investments 360.6\n", + "Insurance carriers and related activities 687.3\n", + "Funds, trusts, and other financial vehicles 28.3\n", + "Real estate and rental and leasing 3396.9\n", + "Real estate 3080.4\n", + "Rental and leasing services and lessors of intangible assets 316.5\n", + "Professional, scientific, and technical services 2013.4\n", + "Legal services 328.9\n", + "Miscellaneous professional, scientific, and technical services 1226.4\n", + "Computer systems design and related services 458.0\n", + "Management of companies and enterprises 480.4\n", + "Administrative and waste management services 820.6\n", + "Administrative and support services 749.4\n", + "Waste management and remediation services 71.2\n", + "Educational services, health care, and social assistance 2149.8\n", + "Educational services 293.5\n", + "Health care and social assistance 1856.4\n", + "Ambulatory health care services 896.4\n", + "Hospitals 599.6\n", + "Nursing and residential care facilities 178.0\n", + "Social assistance 182.4\n", + "Arts, entertainment, recreation, accommodation, and food services 1081.6\n", + "Arts, entertainment, and recreation 271.2\n", + "Performing arts, spectator sports, museums, and related activities 160.1\n", + "Amusements, gambling, and recreation industries 111.2\n", + "Accommodation and food services 810.4\n", + "Accommodation 215.8\n", + "Food services and drinking places 594.6\n", + "Other services, except government 544.4\n", + "Finance, insurance, real estate, rental, and leasing 5329.9\n", + "Government 2936.6\n", + "Gross domestic product 25744.1\n", + "Federal 939.4\n", + "Government enterprises 74.9\n", + "General government 864.5\n", + "National defense 492.4\n", + "Nondefense 372.1\n", + "State and local 1997.2\n", + "Government enterprises 158.6\n", + "General government 1838.6\n", + "Housing 2340.5\n", + "Information-communications-technology-producing industries3 1847.8\n", + "Other real estate 739.9\n", + "Private goods-producing industries1 4468.1\n", + "Professional and business services 3314.3\n", + "Private services-producing industries2 18339.4\n", + "Private industries 22807.5\n" + ] + } + ], + "source": [ + "# Print results in a table\n", + "print(f'{'Industry':<75} Value Added (in million USD)')\n", + "for industry in response['BEAAPI']['Results'][0]['Data']:\n", + " print(f'{industry['IndustrYDescription']:<75} {industry['DataValue']}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Finding wages by year through NIPA tables\n", + "\n", + "*Note: This section uses the National Income and Product Accounts (NIPA) dataset, which can be explored at https://apps.bea.gov/iTable/?reqid=19&step=2&isuri=1&categories=survey*\n", + "\n", + "In this section, we query the `NIPA` dataset. In order to find what parameters are necessary to query this dataset, we must first use the `GetParameterList`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'ParameterName': 'Frequency',\n", + " 'ParameterDataType': 'string',\n", + " 'ParameterDescription': 'A - Annual, Q-Quarterly, M-Monthly',\n", + " 'ParameterIsRequiredFlag': '1',\n", + " 'ParameterDefaultValue': '',\n", + " 'MultipleAcceptedFlag': '1',\n", + " 'AllValue': ''},\n", + " {'ParameterName': 'ShowMillions',\n", + " 'ParameterDataType': 'string',\n", + " 'ParameterDescription': 'A flag indicating that million-dollar data should be returned.',\n", + " 'ParameterIsRequiredFlag': '0',\n", + " 'ParameterDefaultValue': 'N',\n", + " 'MultipleAcceptedFlag': '0',\n", + " 'AllValue': ''},\n", + " {'ParameterName': 'TableID',\n", + " 'ParameterDataType': 'integer',\n", + " 'ParameterDescription': 'The standard NIPA table identifier',\n", + " 'ParameterIsRequiredFlag': '0',\n", + " 'MultipleAcceptedFlag': '0',\n", + " 'AllValue': ''},\n", + " {'ParameterName': 'TableName',\n", + " 'ParameterDataType': 'string',\n", + " 'ParameterDescription': 'The new NIPA table identifier',\n", + " 'ParameterIsRequiredFlag': '0',\n", + " 'MultipleAcceptedFlag': '0',\n", + " 'AllValue': ''},\n", + " {'ParameterName': 'Year',\n", + " 'ParameterDataType': 'integer',\n", + " 'ParameterDescription': 'List of year(s) of data to retrieve (X for All)',\n", + " 'ParameterIsRequiredFlag': '1',\n", + " 'ParameterDefaultValue': '',\n", + " 'MultipleAcceptedFlag': '1',\n", + " 'AllValue': 'X'}]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "method = 'GetParameterList'\n", + "dataset = 'NIPA'\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Display response\n", + "response['BEAAPI']['Results']['Parameter']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we use the `GetParameterValues` function to find what parameter values we can use in our API request. The example below finds the values for the `year` and `TableID` parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "output_scroll" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table | Annually | Quarterly | Description\n", + "T10101 | 1930-2023 | 1947-2023 | Table 1.1.1. Percent Change From Preceding Period in Real Gross Domestic Product (A) (Q)\n", + "T10102 | 1930-2023 | 1947-2023 | Table 1.1.2. Contributions to Percent Change in Real Gross Domestic Product (A) (Q)\n", + "T10103 | 1929-2023 | 1947-2023 | Table 1.1.3. Real Gross Domestic Product, Quantity Indexes (A) (Q)\n", + "T10104 | 1929-2023 | 1947-2023 | Table 1.1.4. Price Indexes for Gross Domestic Product (A) (Q)\n", + "T10105 | 1929-2023 | 1947-2023 | Table 1.1.5. Gross Domestic Product (A) (Q)\n", + "T10106 | 1929-2023 | 1947-2023 | Table 1.1.6. Real Gross Domestic Product, Chained Dollars (A) (Q)\n", + "T10107 | 1930-2023 | 1947-2023 | Table 1.1.7. Percent Change From Preceding Period in Prices for Gross Domestic Product (A) (Q)\n", + "T10108 | 1930-2023 | 1947-2023 | Table 1.1.8. Contributions to Percent Change in the Gross Domestic Product Price Index (A) (Q)\n", + "T10109 | 1929-2023 | 1947-2023 | Table 1.1.9. Implicit Price Deflators for Gross Domestic Product (A) (Q)\n", + "T10110 | 1929-2023 | 1947-2023 | Table 1.1.10. Percentage Shares of Gross Domestic Product (A) (Q)\n", + "T10111 | 0- 0 | 1948-2023 | Table 1.1.11. Real Gross Domestic Product: Percent Change From Quarter One Year Ago (Q)\n", + "T10201 | 1930-2023 | 1947-2023 | Table 1.2.1. Percent Change From Preceding Period in Real Gross Domestic Product by Major Type of Product (A) (Q)\n", + "T10202 | 1930-2023 | 1947-2023 | Table 1.2.2. Contributions to Percent Change in Real Gross Domestic Product by Major Type of Product (A) (Q)\n", + "T10203 | 1929-2023 | 1947-2023 | Table 1.2.3. Real Gross Domestic Product by Major Type of Product, Quantity Indexes (A) (Q)\n", + "T10204 | 1929-2023 | 1947-2023 | Table 1.2.4. Price Indexes for Gross Domestic Product by Major Type of Product (A) (Q)\n", + "T10205 | 1929-2023 | 1947-2023 | Table 1.2.5. Gross Domestic Product by Major Type of Product (A) (Q)\n", + "T10206 | 1929-2023 | 1947-2023 | Table 1.2.6. Real Gross Domestic Product by Major Type of Product, Chained Dollars (A) (Q)\n", + "T10301 | 1930-2023 | 1947-2023 | Table 1.3.1. Percent Change From Preceding Period in Real Gross Value Added by Sector (A) (Q)\n", + "T10303 | 1929-2023 | 1947-2023 | Table 1.3.3. Real Gross Value Added by Sector, Quantity Indexes (A) (Q)\n", + "T10304 | 1929-2023 | 1947-2023 | Table 1.3.4. Price Indexes for Gross Value Added by Sector (A) (Q)\n", + "T10305 | 1929-2023 | 1947-2023 | Table 1.3.5. Gross Value Added by Sector (A) (Q)\n", + "T10306 | 1929-2023 | 1947-2023 | Table 1.3.6. Real Gross Value Added by Sector, Chained Dollars (A) (Q)\n", + "T10401 | 1930-2023 | 1947-2023 | Table 1.4.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross Domestic Purchases, and Real Final Sales to Domestic Purchasers (A) (Q)\n", + "T10403 | 1929-2023 | 1947-2023 | Table 1.4.3. Real Gross Domestic Product, Real Gross Domestic Purchases, and Real Final Sales to Domestic Purchasers, Quantity Indexes (A) (Q)\n", + "T10404 | 1929-2023 | 1947-2023 | Table 1.4.4. Price Indexes for Gross Domestic Product, Gross Domestic Purchases, and Final Sales to Domestic Purchasers (A) (Q)\n", + "T10405 | 1929-2023 | 1947-2023 | Table 1.4.5. Relation of Gross Domestic Product, Gross Domestic Purchases, and Final Sales to Domestic Purchasers (A) (Q)\n", + "T10406 | 1929-2023 | 1947-2023 | Table 1.4.6. Relation of Real Gross Domestic Product, Real Gross Domestic Purchases, and Real Final Sales to Domestic Purchasers, Chained Dollars (A) (Q)\n", + "T10501 | 1930-2023 | 1947-2023 | Table 1.5.1. Percent Change From Preceding Period in Real Gross Domestic Product, Expanded Detail (A) (Q)\n", + "T10502 | 1930-2023 | 1947-2023 | Table 1.5.2. Contributions to Percent Change in Real Gross Domestic Product, Expanded Detail (A) (Q)\n", + "T10503 | 1929-2023 | 1947-2023 | Table 1.5.3. Real Gross Domestic Product, Expanded Detail, Quantity Indexes (A) (Q)\n", + "T10504 | 1929-2023 | 1947-2023 | Table 1.5.4. Price Indexes for Gross Domestic Product, Expanded Detail (A) (Q)\n", + "T10505 | 1929-2023 | 1947-2023 | Table 1.5.5. Gross Domestic Product, Expanded Detail (A) (Q)\n", + "T10506 | 2007-2023 | 2007-2023 | Table 1.5.6. Real Gross Domestic Product, Expanded Detail, Chained Dollars (A) (Q)\n", + "T10604 | 1929-2023 | 1947-2023 | Table 1.6.4. Price Indexes for Gross Domestic Purchases (A) (Q)\n", + "T10607 | 1930-2023 | 1947-2023 | Table 1.6.7. Percent Change From Preceding Period in Prices for Gross Domestic Purchases (A) (Q)\n", + "T10608 | 1930-2023 | 1947-2023 | Table 1.6.8. Contributions to Percent Change in the Gross Domestic Purchases Price Index (A) (Q)\n", + "T10701 | 1930-2023 | 1947-2023 | Table 1.7.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross National Product, and Real Net National Product (A) (Q)\n", + "T10703 | 1929-2023 | 1947-2023 | Table 1.7.3. Real Gross Domestic Product, Real Gross National Product, and Real Net National Product, Quantity Indexes (A) (Q)\n", + "T10704 | 1929-2023 | 1947-2023 | Table 1.7.4. Price Indexes for Gross Domestic Product, Gross National Product, and Net National Product (A) (Q)\n", + "T10705 | 1929-2023 | 1947-2023 | Table 1.7.5. Relation of Gross Domestic Product, Gross National Product, Net National Product, National Income, and Personal Income (A) (Q)\n", + "T10706 | 1929-2023 | 1947-2023 | Table 1.7.6. Relation of Real Gross Domestic Product, Real Gross National Product, and Real Net National Product, Chained Dollars (A) (Q)\n", + "T10803 | 1929-2023 | 1947-2023 | Table 1.8.3. Command-Basis Real Gross Domestic Product and Gross National Product, Quantity Indexes (A) (Q)\n", + "T10806 | 1929-2023 | 1947-2023 | Table 1.8.6. Command-Basis Real Gross Domestic Product and Gross National Product, Chained Dollars (A) (Q)\n", + "T10903 | 1929-2022 | 0- 0 | Table 1.9.3. Real Net Value Added by Sector, Quantity Indexes (A)\n", + "T10904 | 1929-2022 | 0- 0 | Table 1.9.4. Price Indexes for Net Value Added by Sector (A)\n", + "T10905 | 1929-2022 | 0- 0 | Table 1.9.5. Net Value Added by Sector (A)\n", + "T10906 | 2007-2022 | 0- 0 | Table 1.9.6. Real Net Value Added by Sector, Chained Dollars (A)\n", + "T11000 | 1929-2023 | 1947-2023 | Table 1.10. Gross Domestic Income by Type of Income (A) (Q)\n", + "T11100 | 1929-2022 | 0- 0 | Table 1.11. Percentage Shares of Gross Domestic Income (A)\n", + "T11200 | 1929-2023 | 1947-2023 | Table 1.12. National Income by Type of Income (A) (Q)\n", + "T11300 | 1948-2022 | 0- 0 | Table 1.13. National Income by Sector, Legal Form of Organization, and Type of Income (A)\n", + "T11400 | 1929-2023 | 1947-2023 | Table 1.14. Gross Value Added of Domestic Corporate Business in Current Dollars and Gross Value Added of Nonfinancial Domestic Corporate Business in Current and Chained Dollars (A) (Q)\n", + "T11500 | 1929-2023 | 1947-2023 | Table 1.15. Price, Costs, and Profit Per Unit of Real Gross Value Added of Nonfinancial Domestic Corporate Business (A) (Q)\n", + "T11600 | 1948-2022 | 0- 0 | Table 1.16. Sources and Uses of Private Enterprise Income (A)\n", + "T11701 | 1930-2023 | 1947-2023 | Table 1.17.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates (A) (Q)\n", + "T11705 | 1929-2023 | 1947-2023 | Table 1.17.5. Gross Domestic Product, Gross Domestic Income, and Other Major NIPA Aggregates (A) (Q)\n", + "T11706 | 1929-2023 | 1947-2023 | Table 1.17.6. Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates, Chained Dollars (A) (Q)\n", + "T20100 | 1929-2023 | 1947-2023 | Table 2.1. Personal Income and Its Disposition (A) (Q)\n", + "T20200A | 1929-2000 | 1947-2000 | Table 2.2A. Wages and Salaries by Industry (A) (Q)\n", + "T20200B | 1998-2023 | 2001-2023 | Table 2.2B. Wages and Salaries by Industry (A) (Q)\n", + "T20301 | 1930-2023 | 1947-2023 | Table 2.3.1. Percent Change From Preceding Period in Real Personal Consumption Expenditures by Major Type of Product (A) (Q)\n", + "T20302 | 1930-2023 | 1947-2023 | Table 2.3.2. Contributions to Percent Change in Real Personal Consumption Expenditures by Major Type of Product (A) (Q)\n", + "T20303 | 1929-2023 | 1947-2023 | Table 2.3.3. Real Personal Consumption Expenditures by Major Type of Product, Quantity Indexes (A) (Q)\n", + "T20304 | 1929-2023 | 1947-2023 | Table 2.3.4. Price Indexes for Personal Consumption Expenditures by Major Type of Product (A) (Q)\n", + "T20305 | 1929-2023 | 1947-2023 | Table 2.3.5. Personal Consumption Expenditures by Major Type of Product (A) (Q)\n", + "T20306 | 2007-2023 | 2007-2023 | Table 2.3.6. Real Personal Consumption Expenditures by Major Type of Product, Chained Dollars (A) (Q)\n", + "T20307 | 1930-2023 | 1947-2023 | Table 2.3.7. Percent Change From Preceding Period in Prices for Personal Consumption Expenditures by Major Type of Product (A) (Q)\n", + "T20308 | 1930-2023 | 1947-2023 | Table 2.3.8. Contributions to Percent Change in Prices for Personal Consumption Expenditures by Major Type of Product (A) (Q)\n", + "T20311 | 0- 0 | 1948-2023 | Table 2.3.11. Real Personal Consumption Expenditures by Major Type of Product: Percent Change from Quarter One Year Ago (Q)\n", + "T20401 | 1930-2023 | 1947-2023 | Table 2.4.1. Percent Change From Preceding Period in Real Personal Consumption Expenditures by Type of Product (A) (Q)\n", + "T20402 | 1930-2023 | 1947-2023 | Table 2.4.2. Contributions to Percent Change in Real Personal Consumption Expenditures by Type of Product (A) (Q)\n", + "T20403 | 1929-2023 | 1947-2023 | Table 2.4.3. Real Personal Consumption Expenditures by Type of Product, Quantity Indexes (A) (Q)\n", + "T20404 | 1929-2023 | 1947-2023 | Table 2.4.4. Price Indexes for Personal Consumption Expenditures by Type of Product (A) (Q)\n", + "T20405 | 1929-2023 | 1947-2023 | Table 2.4.5. Personal Consumption Expenditures by Type of Product (A) (Q)\n", + "T20406 | 2007-2023 | 2007-2023 | Table 2.4.6. Real Personal Consumption Expenditures by Type of Product, Chained Dollars (A) (Q)\n", + "T20407 | 1930-2023 | 1947-2023 | Table 2.4.7. Percent Change From Preceding Period in Prices for Personal Consumption Expenditures by Type of Product (A) (Q)\n", + "T20408 | 1930-2023 | 1947-2023 | Table 2.4.8. Contributions to Percent Change in Prices for Personal Consumption Expenditures by Type of Product (A) (Q)\n", + "T20503 | 1929-2022 | 0- 0 | Table 2.5.3. Real Personal Consumption Expenditures by Function, Quantity Indexes (A)\n", + "T20504 | 1929-2022 | 0- 0 | Table 2.5.4. Price Indexes for Personal Consumption Expenditures by Function (A)\n", + "T20505 | 1929-2022 | 0- 0 | Table 2.5.5. Personal Consumption Expenditures by Function (A)\n", + "T20506 | 2007-2022 | 0- 0 | Table 2.5.6. Real Personal Consumption Expenditures by Function, Chained Dollars (A)\n", + "T20600 | 0- 0 | 0- 0 | Table 2.6. Personal Income and Its Disposition, Monthly (M)\n", + "T20700A | 0- 0 | 0- 0 | Table 2.7A. Wages and Salaries by Industry, Monthly (M)\n", + "T20700B | 0- 0 | 0- 0 | Table 2.7B. Wages and Salaries by Industry, Monthly (M)\n", + "T20801 | 0- 0 | 0- 0 | Table 2.8.1. Percent Change From Preceding Period in Real Personal Consumption Expenditures by Major Type of Product, Monthly (M)\n", + "T20802 | 0- 0 | 0- 0 | Table 2.8.2. Contributions to Percent Change in Real Personal Consumption Expenditures by Major Type of Product, Monthly (M)\n", + "T20803 | 0- 0 | 0- 0 | Table 2.8.3. Real Personal Consumption Expenditures by Major Type of Product, Monthly, Quantity Indexes (M)\n", + "T20804 | 0- 0 | 0- 0 | Table 2.8.4. Price Indexes for Personal Consumption Expenditures by Major Type of Product, Monthly (M)\n", + "T20805 | 0- 0 | 0- 0 | Table 2.8.5. Personal Consumption Expenditures by Major Type of Product, Monthly (M)\n", + "T20806 | 0- 0 | 0- 0 | Table 2.8.6. Real Personal Consumption Expenditures by Major Type of Product, Monthly, Chained Dollars (M)\n", + "T20807 | 0- 0 | 0- 0 | Table 2.8.7. Percent Change From Preceding Period in Prices for Personal Consumption Expenditures by Major Type of Product, Monthly (M)\n", + "T20808 | 0- 0 | 0- 0 | Table 2.8.8. Contributions to Percent Change in Prices for Personal Consumption Expenditures by Major Type of Product, Monthly (M)\n", + "T20811 | 0- 0 | 0- 0 | Table 2.8.11. Real Personal Consumption Expenditures by Major Type of Product: Percent Change from Month One Year Ago (M)\n", + "T20900 | 1992-2022 | 0- 0 | Table 2.9. Personal Income and Its Disposition by Households and by Nonprofit Institutions Serving Households (A)\n", + "T21000 | 2000-2022 | 0- 0 | Table 2.10. Distributions of Personal and Disposable Income for Households (A)\n", + "T30100 | 1929-2023 | 1947-2023 | Table 3.1. Government Current Receipts and Expenditures (A) (Q)\n", + "T30200 | 1929-2023 | 1947-2023 | Table 3.2. Federal Government Current Receipts and Expenditures (A) (Q)\n", + "T30300 | 1929-2023 | 1947-2023 | Table 3.3. State and Local Government Current Receipts and Expenditures (A) (Q)\n", + "T30400 | 1929-2022 | 0- 0 | Table 3.4. Personal Current Tax Receipts (A)\n", + "T30500 | 1929-2022 | 0- 0 | Table 3.5. Taxes on Production and Imports (A)\n", + "T30600 | 1929-2022 | 0- 0 | Table 3.6. Contributions for Government Social Insurance (A)\n", + "T30700 | 1929-2022 | 0- 0 | Table 3.7. Government Current Transfer Receipts (A)\n", + "T30800 | 1960-2022 | 0- 0 | Table 3.8. Current Surplus of Government Enterprises (A)\n", + "T30901 | 1930-2023 | 1947-2023 | Table 3.9.1. Percent Change From Preceding Period in Real Government Consumption Expenditures and Gross Investment (A) (Q)\n", + "T30902 | 1930-2023 | 1947-2023 | Table 3.9.2. Contributions to Percent Change in Real Government Consumption Expenditures and Gross Investment (A) (Q)\n", + "T30903 | 1929-2023 | 1947-2023 | Table 3.9.3. Real Government Consumption Expenditures and Gross Investment, Quantity Indexes (A) (Q)\n", + "T30904 | 1929-2023 | 1947-2023 | Table 3.9.4. Price Indexes for Government Consumption Expenditures and Gross Investment (A) (Q)\n", + "T30905 | 1929-2023 | 1947-2023 | Table 3.9.5. Government Consumption Expenditures and Gross Investment (A) (Q)\n", + "T30906 | 2007-2023 | 2007-2023 | Table 3.9.6. Real Government Consumption Expenditures and Gross Investment, Chained Dollars (A) (Q)\n", + "T31001 | 1930-2023 | 1947-2023 | Table 3.10.1. Percent Change From Preceding Period in Real Government Consumption Expenditures and General Government Gross Output (A) (Q)\n", + "T31003 | 1929-2023 | 1947-2023 | Table 3.10.3. Real Government Consumption Expenditures and General Government Gross Output, Quantity Indexes (A) (Q)\n", + "T31004 | 1929-2023 | 1947-2023 | Table 3.10.4. Price Indexes for Government Consumption Expenditures and General Government Gross Output (A) (Q)\n", + "T31005 | 1929-2023 | 1947-2023 | Table 3.10.5. Government Consumption Expenditures and General Government Gross Output (A) (Q)\n", + "T31006 | 2007-2023 | 2007-2023 | Table 3.10.6. Real Government Consumption Expenditures and General Government Gross Output, Chained Dollars (A) (Q)\n", + "T31101 | 1973-2023 | 1972-2023 | Table 3.11.1. Percent Change From Preceding Period in Real National Defense Consumption Expenditures and Gross Investment by Type (A) (Q)\n", + "T31102 | 1973-2023 | 1972-2023 | Table 3.11.2. Contributions to Percent Change in National Defense Consumption Expenditures and Gross Investment by Type (A) (Q)\n", + "T31103 | 1972-2023 | 1972-2023 | Table 3.11.3. Real National Defense Consumption Expenditures and Gross Investment by Type, Quantity Indexes (A) (Q)\n", + "T31104 | 1972-2023 | 1972-2023 | Table 3.11.4. Price Indexes for National Defense Consumption Expenditures and Gross Investment by Type (A) (Q)\n", + "T31105 | 1972-2023 | 1972-2023 | Table 3.11.5. National Defense Consumption Expenditures and Gross Investment by Type (A) (Q)\n", + "T31106 | 2007-2023 | 2007-2023 | Table 3.11.6. Real National Defense Consumption Expenditures and Gross Investment by Type, Chained Dollars (A) (Q)\n", + "T31200 | 1929-2022 | 0- 0 | Table 3.12. Government Social Benefits (A)\n", + "T31300 | 1960-2022 | 0- 0 | Table 3.13. Subsidies (A)\n", + "T31400 | 1929-2022 | 0- 0 | Table 3.14. Government Social Insurance Funds Current Receipts and Expenditures (A)\n", + "T31501 | 1960-2022 | 0- 0 | Table 3.15.1. Percent Change From Preceding Period in Real Government Consumption Expenditures and Gross Investment by Function (A)\n", + "T31502 | 1960-2022 | 0- 0 | Table 3.15.2. Contributions to Percent Change in Real Government Consumption Expenditures and Gross Investment by Function (A)\n", + "T31503 | 1959-2022 | 0- 0 | Table 3.15.3. Real Government Consumption Expenditures and Gross Investment by Function, Quantity Indexes (A)\n", + "T31504 | 1959-2022 | 0- 0 | Table 3.15.4. Price Indexes for Government Consumption Expenditures and Gross Investment by Function (A)\n", + "T31505 | 1959-2022 | 0- 0 | Table 3.15.5. Government Consumption Expenditures and Gross Investment by Function (A)\n", + "T31506 | 2007-2022 | 0- 0 | Table 3.15.6. Real Government Consumption Expenditures and Gross Investment by Function, Chained Dollars (A)\n", + "T31600 | 1959-2022 | 0- 0 | Table 3.16. Government Current Expenditures by Function (A)\n", + "T31700 | 1959-2022 | 0- 0 | Table 3.17. Selected Government Current and Capital Expenditures by Function (A)\n", + "T31800A | 1952-1967 | 1959-1967 | Table 3.18A. Relation of Federal Government Current Receipts and Expenditures in the National Income and Product Accounts to the Consolidated Cash Statement, Fiscal Years and Quarters (A) (Q)\n", + "T31800B | 1968-2022 | 1968-2022 | Table 3.18B. Relation of Federal Government Current Receipts and Expenditures in the National Income and Product Accounts to the Budget, Fiscal Years and Quarters (A) (Q)\n", + "T31900 | 1959-2021 | 0- 0 | Table 3.19. Relation of State and Local Government Current Receipts and Expenditures in the National Income and Product Accounts to Census Bureau 'Government Finances' Data, Fiscal Years (A)\n", + "T32000 | 1959-2022 | 0- 0 | Table 3.20. State Government Current Receipts and Expenditures (A)\n", + "T32100 | 1959-2022 | 0- 0 | Table 3.21. Local Government Current Receipts and Expenditures (A)\n", + "T40100 | 1929-2023 | 1947-2023 | Table 4.1. Foreign Transactions in the National Income and Product Accounts (A) (Q)\n", + "T40201 | 1968-2023 | 1967-2023 | Table 4.2.1. Percent Change From Preceding Period in Real Exports and in Real Imports of Goods and Services by Type of Product (A) (Q)\n", + "T40202 | 1968-2023 | 1967-2023 | Table 4.2.2. Contributions to Percent Change in Real Exports and Real Imports of Goods and Services by Type of Product (A) (Q)\n", + "T40203A | 1967-1998 | 1967-1998 | Table 4.2.3A. Real Exports and Imports of Goods and Services by Type of Product, Quantity Indexes (A) (Q)\n", + "T40203B | 1999-2023 | 1999-2023 | Table 4.2.3B. Real Exports and Imports of Goods and Services by Type of Product, Quantity Indexes (A) (Q)\n", + "T40204A | 1967-1998 | 1967-1998 | Table 4.2.4A. Price Indexes for Exports and Imports of Goods and Services by Type of Product (A) (Q)\n", + "T40204B | 1999-2023 | 1999-2023 | Table 4.2.4B. Price Indexes for Exports and Imports of Goods and Services by Type of Product (A) (Q)\n", + "T40205A | 1967-1998 | 1967-1998 | Table 4.2.5A. Exports and Imports of Goods and Services by Type of Product (A) (Q)\n", + "T40205B | 1999-2023 | 1999-2023 | Table 4.2.5B. Exports and Imports of Goods and Services by Type of Product (A) (Q)\n", + "T40206B | 2007-2023 | 2007-2023 | Table 4.2.6B. Real Exports and Imports of Goods and Services by Type of Product, Chained Dollars (A) (Q)\n", + "T4030A | 1946-1985 | 0- 0 | Table 4.3A. Relation of Foreign Transactions in the National Income and Product Accounts to the Corresponding Items in the International Transactions Accounts (A)\n", + "T4030B | 1986-1998 | 0- 0 | Table 4.3B. Relation of Foreign Transactions in the National Income and Product Accounts to the Corresponding Items in the International Transactions Accounts (A)\n", + "T4030C | 1999-2023 | 1999-2023 | Table 4.3C. Relation of Foreign Transactions in the National Income and Product Accounts to the Corresponding Items in the International Transactions Accounts (A) (Q)\n", + "T50100 | 1929-2023 | 1947-2023 | Table 5.1. Saving and Investment by Sector (A) (Q)\n", + "T50203 | 1929-2022 | 0- 0 | Table 5.2.3. Real Gross and Net Domestic Investment by Major Type, Quantity Indexes (A)\n", + "T50205 | 1929-2022 | 0- 0 | Table 5.2.5. Gross and Net Domestic Investment by Major Type (A)\n", + "T50206 | 1967-2022 | 0- 0 | Table 5.2.6. Real Gross and Net Domestic Investment by Major Type, Chained dollars (A)\n", + "T50301 | 1948-2023 | 1947-2023 | Table 5.3.1. Percent Change From Preceding Period in Real Private Fixed Investment by Type (A) (Q)\n", + "T50302 | 1948-2023 | 1947-2023 | Table 5.3.2. Contributions to Percent Change in Real Private Fixed Investment by Type (A) (Q)\n", + "T50303 | 1947-2023 | 1947-2023 | Table 5.3.3. Real Private Fixed Investment by Type, Quantity Indexes (A) (Q)\n", + "T50304 | 1947-2023 | 1947-2023 | Table 5.3.4. Price Indexes for Private Fixed Investment by Type (A) (Q)\n", + "T50305 | 1947-2023 | 1947-2023 | Table 5.3.5. Private Fixed Investment by Type (A) (Q)\n", + "T50306 | 2007-2023 | 2007-2023 | Table 5.3.6. Real Private Fixed Investment by Type, Chained Dollars (A) (Q)\n", + "T50401 | 1930-2022 | 0- 0 | Table 5.4.1. Percent Change From Preceding Period in Real Private Fixed Investment in Structures by Type (A)\n", + "T50402 | 1930-2022 | 0- 0 | Table 5.4.2. Contributions to Percent Change in Real Private Fixed Investment in Structures by Type (A)\n", + "T50403 | 1929-2022 | 0- 0 | Table 5.4.3. Real Private Fixed Investment in Structures by Type, Quantity Indexes (A)\n", + "T50404 | 1929-2022 | 0- 0 | Table 5.4.4. Price Indexes for Private Fixed Investment in Structures by Type (A)\n", + "T50405 | 1929-2022 | 0- 0 | Table 5.4.5. Private Fixed Investment in Structures by Type (A)\n", + "T50406 | 2007-2022 | 0- 0 | Table 5.4.6. Real Private Fixed Investment in Structures by Type, Chained Dollars (A)\n", + "T50501 | 1930-2022 | 0- 0 | Table 5.5.1. Percent Change From Preceding Period in Real Private Fixed Investment in Equipment by Type (A)\n", + "T50502 | 1930-2022 | 0- 0 | Table 5.5.2. Contributions to Percent Change in Real Private Fixed Investment in Equipment by Type (A)\n", + "T50503 | 1929-2022 | 0- 0 | Table 5.5.3. Real Private Fixed Investment in Equipment by Type, Quantity Indexes (A)\n", + "T50504 | 1929-2022 | 0- 0 | Table 5.5.4. Price Indexes for Private Fixed Investment in Equipment by Type (A)\n", + "T50505 | 1929-2022 | 0- 0 | Table 5.5.5. Private Fixed Investment in Equipment by Type (A)\n", + "T50506 | 2007-2022 | 0- 0 | Table 5.5.6. Real Private Fixed Investment in Equipment by Type, Chained Dollars (A)\n", + "T50601 | 1930-2022 | 0- 0 | Table 5.6.1. Percent Change From Preceding Period in Real Private Fixed Investment in Intellectual Property Products by Type (A)\n", + "T50602 | 1930-2022 | 0- 0 | Table 5.6.2. Contributions to Percent Change in Private Fixed Investment in Intellectual Property Products by Type (A)\n", + "T50603 | 1929-2022 | 0- 0 | Table 5.6.3. Real Private Fixed Investment in Intellectual Property Products by Type, Quantity Indexes (A)\n", + "T50604 | 1929-2022 | 0- 0 | Table 5.6.4. Price Indexes for Private Fixed Investment in Intellectual Property Products by Type (A)\n", + "T50605 | 1929-2022 | 0- 0 | Table 5.6.5. Private Fixed Investment in Intellectual Property Products by Type (A)\n", + "T50606 | 2007-2022 | 0- 0 | Table 5.6.6. Real Private Fixed Investment in Intellectual Property Products by Type, Chained Dollars (A)\n", + "T50705A | 1929-1997 | 1947-1997 | Table 5.7.5A. Change in Private Inventories by Industry (A) (Q)\n", + "T50705B | 1997-2023 | 1997-2023 | Table 5.7.5B. Change in Private Inventories by Industry (A) (Q)\n", + "T50706A | 1929-1997 | 1947-1997 | Table 5.7.6A. Change in Real Private Inventories by Industry, Chained Dollars (A) (Q)\n", + "T50706B | 1997-2023 | 1997-2023 | Table 5.7.6B. Change in Real Private Inventories by Industry, Chained Dollars (A) (Q)\n", + "T50805A | 0- 0 | 1947-1997 | Table 5.8.5A. Private Inventories and Domestic Final Sales of Business by Industry (Q)\n", + "T50805B | 0- 0 | 1996-2023 | Table 5.8.5B. Private Inventories and Domestic Final Sales by Industry (Q)\n", + "T50806A | 0- 0 | 1947-1997 | Table 5.8.6A. Real Private Inventories and Real Domestic Final Sales of Business by Industry, Chained Dollars (Q)\n", + "T50806B | 0- 0 | 1996-2023 | Table 5.8.6B. Real Private Inventories and Real Domestic Final Sales by Industry, Chained Dollars (Q)\n", + "T50809A | 0- 0 | 1947-1997 | Table 5.8.9A. Implicit Price Deflators for Private Inventories by Industry (Q)\n", + "T50809B | 0- 0 | 1996-2023 | Table 5.8.9B. Implicit Price Deflators for Private Inventories by Industry (Q)\n", + "T50903 | 1929-2022 | 0- 0 | Table 5.9.3. Real Gross Government Fixed Investment by Type, Quantity Indexes (A)\n", + "T50904 | 1929-2022 | 0- 0 | Table 5.9.4. Price Indexes for Gross Government Fixed Investment by Type (A)\n", + "T50905 | 1929-2022 | 0- 0 | Table 5.9.5. Gross Government Fixed Investment by Type (A)\n", + "T50906 | 2007-2022 | 0- 0 | Table 5.9.6. Real Gross Government Fixed Investment by Type, Chained Dollars (A)\n", + "T51000 | 1951-2022 | 0- 0 | Table 5.10. Changes in Net Stock of Produced Assets (Fixed Assets and Inventories) (A)\n", + "T51100 | 1929-2022 | 0- 0 | Table 5.11. Capital Transfers Paid and Received, by Sector and by Type (A)\n", + "T60100B | 1948-1987 | 1948-1987 | Table 6.1B. National Income Without Capital Consumption Adjustment by Industry (A) (Q)\n", + "T60100C | 1987-2000 | 1987-2000 | Table 6.1C. National Income Without Capital Consumption Adjustment by Industry (A) (Q)\n", + "T60100D | 1998-2023 | 2001-2023 | Table 6.1D. National Income Without Capital Consumption Adjustment by Industry (A) (Q)\n", + "T60200A | 1929-1948 | 0- 0 | Table 6.2A. Compensation of Employees by Industry (A)\n", + "T60200B | 1948-1987 | 0- 0 | Table 6.2B. Compensation of Employees by Industry (A)\n", + "T60200C | 1987-2000 | 0- 0 | Table 6.2C. Compensation of Employees by Industry (A)\n", + "T60200D | 1998-2022 | 0- 0 | Table 6.2D. Compensation of Employees by Industry (A)\n", + "T60300A | 1929-1948 | 0- 0 | Table 6.3A. Wages and Salaries by Industry (A)\n", + "T60300B | 1948-1987 | 0- 0 | Table 6.3B. Wages and Salaries by Industry (A)\n", + "T60300C | 1987-2000 | 0- 0 | Table 6.3C. Wages and Salaries by Industry (A)\n", + "T60300D | 1998-2022 | 0- 0 | Table 6.3D. Wages and Salaries by Industry (A)\n", + "T60400A | 1929-1948 | 0- 0 | Table 6.4A. Full-Time and Part-Time Employees by Industry (A)\n", + "T60400B | 1948-1987 | 0- 0 | Table 6.4B. Full-Time and Part-Time Employees by Industry (A)\n", + "T60400C | 1987-2000 | 0- 0 | Table 6.4C. Full-Time and Part-Time Employees by Industry (A)\n", + "T60400D | 1998-2022 | 0- 0 | Table 6.4D. Full-Time and Part-Time Employees by Industry (A)\n", + "T60500A | 1929-1948 | 0- 0 | Table 6.5A. Full-Time Equivalent Employees by Industry (A)\n", + "T60500B | 1948-1987 | 0- 0 | Table 6.5B. Full-Time Equivalent Employees by Industry (A)\n", + "T60500C | 1987-2000 | 0- 0 | Table 6.5C. Full-Time Equivalent Employees by Industry (A)\n", + "T60500D | 1998-2022 | 0- 0 | Table 6.5D. Full-Time Equivalent Employees by Industry (A)\n", + "T60600A | 1929-1948 | 0- 0 | Table 6.6A. Wages and Salaries Per Full-Time Equivalent Employee by Industry (A)\n", + "T60600B | 1948-1987 | 0- 0 | Table 6.6B. Wages and Salaries Per Full-Time Equivalent Employee by Industry (A)\n", + "T60600C | 1987-2000 | 0- 0 | Table 6.6C. Wages and Salaries Per Full-Time Equivalent Employee by Industry (A)\n", + "T60600D | 1998-2022 | 0- 0 | Table 6.6D. Wages and Salaries Per Full-Time Equivalent Employee by Industry (A)\n", + "T60700A | 1929-1948 | 0- 0 | Table 6.7A. Self-Employed Persons by Industry (A)\n", + "T60700B | 1948-1987 | 0- 0 | Table 6.7B. Self-Employed Persons by Industry (A)\n", + "T60700C | 1987-2000 | 0- 0 | Table 6.7C. Self-Employed Persons by Industry (A)\n", + "T60700D | 1998-2022 | 0- 0 | Table 6.7D. Self-Employed Persons by Industry (A)\n", + "T60800A | 1929-1948 | 0- 0 | Table 6.8A. Persons Engaged in Production by Industry (A)\n", + "T60800B | 1948-1987 | 0- 0 | Table 6.8B. Persons Engaged in Production by Industry (A)\n", + "T60800C | 1987-2000 | 0- 0 | Table 6.8C. Persons Engaged in Production by Industry (A)\n", + "T60800D | 1998-2022 | 0- 0 | Table 6.8D. Persons Engaged in Production by Industry (A)\n", + "T60900B | 1948-1987 | 0- 0 | Table 6.9B. Hours Worked by Full-Time and Part-Time Employees by Industry (A)\n", + "T60900C | 1987-2000 | 0- 0 | Table 6.9C. Hours Worked by Full-Time and Part-Time Employees by Industry (A)\n", + "T60900D | 2000-2022 | 0- 0 | Table 6.9D. Hours Worked by Full-Time and Part-Time Employees by Industry (A)\n", + "T61000B | 1948-1987 | 0- 0 | Table 6.10B. Employer Contributions for Government Social Insurance by Industry (A)\n", + "T61000C | 1987-2000 | 0- 0 | Table 6.10C. Employer Contributions for Government Social Insurance by Industry (A)\n", + "T61000D | 1998-2022 | 0- 0 | Table 6.10D. Employer Contributions for Government Social Insurance by Industry (A)\n", + "T61100A | 1929-1947 | 0- 0 | Table 6.11A. Employer Contributions for Employee Pension and Insurance Funds by Industry and by Type (A)\n", + "T61100B | 1948-1987 | 0- 0 | Table 6.11B. Employer Contributions for Employee Pension and Insurance Funds by Industry and by Type (A)\n", + "T61100C | 1987-2000 | 0- 0 | Table 6.11C. Employer Contributions for Employee Pension and Insurance Funds by Industry and by Type (A)\n", + "T61100D | 1998-2022 | 0- 0 | Table 6.11D. Employer Contributions for Employee Pension and Insurance Funds by Industry and by Type (A)\n", + "T61200A | 1929-1948 | 0- 0 | Table 6.12A. Nonfarm Proprietors' Income by Industry (A)\n", + "T61200B | 1948-1987 | 0- 0 | Table 6.12B. Nonfarm Proprietors' Income by Industry (A)\n", + "T61200C | 1987-2000 | 0- 0 | Table 6.12C. Nonfarm Proprietors' Income by Industry (A)\n", + "T61200D | 1998-2022 | 0- 0 | Table 6.12D. Nonfarm Proprietors' Income by Industry (A)\n", + "T61300A | 1929-1947 | 0- 0 | Table 6.13A. Noncorporate Capital Consumption Allowances by Industry (A)\n", + "T61300B | 1948-1987 | 0- 0 | Table 6.13B. Noncorporate Capital Consumption Allowances by Industry (A)\n", + "T61300C | 1987-2000 | 0- 0 | Table 6.13C. Noncorporate Capital Consumption Allowances by Industry (A)\n", + "T61300D | 1998-2022 | 0- 0 | Table 6.13D. Noncorporate Capital Consumption Allowances by Industry (A)\n", + "T61400A | 1929-1947 | 0- 0 | Table 6.14A. Inventory Valuation Adjustment to Nonfarm Incomes by Legal Form of Organization and by Industry (A)\n", + "T61400B | 1948-1987 | 0- 0 | Table 6.14B. Inventory Valuation Adjustment to Nonfarm Incomes by Legal Form of Organization and by Industry (A)\n", + "T61400C | 1987-2000 | 0- 0 | Table 6.14C. Inventory Valuation Adjustment to Nonfarm Incomes by Legal Form of Organization and by Industry (A)\n", + "T61400D | 1998-2022 | 0- 0 | Table 6.14D. Inventory Valuation Adjustment to Nonfarm Incomes by Legal Form of Organization and by Industry (A)\n", + "T61500A | 1929-1947 | 0- 0 | Table 6.15A. Net Interest by Industry (A)\n", + "T61500B | 1948-1987 | 0- 0 | Table 6.15B. Net Interest by Industry (A)\n", + "T61500C | 1987-2000 | 0- 0 | Table 6.15C. Net Interest by Industry (A)\n", + "T61500D | 1998-2022 | 0- 0 | Table 6.15D. Net Interest by Industry (A)\n", + "T61600A | 1929-1947 | 0- 0 | Table 6.16A. Corporate Profits by Industry (A)\n", + "T61600B | 1948-1987 | 1948-1987 | Table 6.16B. Corporate Profits by Industry (A) (Q)\n", + "T61600C | 1987-2000 | 1987-2000 | Table 6.16C. Corporate Profits by Industry (A) (Q)\n", + "T61600D | 1998-2023 | 2001-2023 | Table 6.16D. Corporate Profits by Industry (A) (Q)\n", + "T61700A | 1929-1948 | 0- 0 | Table 6.17A. Corporate Profits Before Tax by Industry (A)\n", + "T61700B | 1948-1987 | 0- 0 | Table 6.17B. Corporate Profits Before Tax by Industry (A)\n", + "T61700C | 1987-2000 | 0- 0 | Table 6.17C. Corporate Profits Before Tax by Industry (A)\n", + "T61700D | 1998-2022 | 0- 0 | Table 6.17D. Corporate Profits Before Tax by Industry (A)\n", + "T61800A | 1929-1947 | 0- 0 | Table 6.18A. Taxes on Corporate Income by Industry (A)\n", + "T61800B | 1948-1987 | 0- 0 | Table 6.18B. Taxes on Corporate Income by Industry (A)\n", + "T61800C | 1987-2000 | 0- 0 | Table 6.18C. Taxes on Corporate Income by Industry (A)\n", + "T61800D | 1998-2022 | 0- 0 | Table 6.18D. Taxes on Corporate Income by Industry (A)\n", + "T61900A | 1929-1947 | 0- 0 | Table 6.19A. Corporate Profits After Tax by Industry (A)\n", + "T61900B | 1948-1987 | 0- 0 | Table 6.19B. Corporate Profits After Tax by Industry (A)\n", + "T61900C | 1987-2000 | 0- 0 | Table 6.19C. Corporate Profits After Tax by Industry (A)\n", + "T61900D | 1998-2022 | 0- 0 | Table 6.19D. Corporate Profits After Tax by Industry (A)\n", + "T62000A | 1929-1947 | 0- 0 | Table 6.20A. Net Corporate Dividend Payments by Industry (A)\n", + "T62000B | 1948-1987 | 0- 0 | Table 6.20B. Net Corporate Dividend Payments by Industry (A)\n", + "T62000C | 1987-2000 | 0- 0 | Table 6.20C. Net Corporate Dividend Payments by Industry (A)\n", + "T62000D | 1998-2022 | 0- 0 | Table 6.20D. Net Corporate Dividend Payments by Industry (A)\n", + "T62100A | 1929-1947 | 0- 0 | Table 6.21A. Undistributed Corporate Profits by Industry (A)\n", + "T62100B | 1948-1987 | 0- 0 | Table 6.21B. Undistributed Corporate Profits by Industry (A)\n", + "T62100C | 1987-2000 | 0- 0 | Table 6.21C. Undistributed Corporate Profits by Industry (A)\n", + "T62100D | 1998-2022 | 0- 0 | Table 6.21D. Undistributed Corporate Profits by Industry (A)\n", + "T62200A | 1929-1947 | 0- 0 | Table 6.22A. Corporate Capital Consumption Allowances by Industry (A)\n", + "T62200B | 1948-1987 | 0- 0 | Table 6.22B. Corporate Capital Consumption Allowances by Industry (A)\n", + "T62200C | 1987-2000 | 0- 0 | Table 6.22C. Corporate Capital Consumption Allowances by Industry (A)\n", + "T62200D | 1998-2022 | 0- 0 | Table 6.22D. Corporate Capital Consumption Allowances by Industry (A)\n", + "T70100 | 1929-2023 | 1947-2023 | Table 7.1. Selected Per Capita Product and Income Series in Current and Chained Dollars (A) (Q)\n", + "T70201A | 1948-1966 | 1947-1966 | Table 7.2.1A. Percent Change From Preceding Period in Real Auto Output (A) (Q)\n", + "T70201B | 1968-2023 | 1967-2023 | Table 7.2.1B. Percent Change From Preceding Period in Real Motor Vehicle Output (A) (Q)\n", + "T70203A | 1947-1966 | 1947-1966 | Table 7.2.3A. Real Auto Output, Quantity Indexes (A) (Q)\n", + "T70203B | 1967-2023 | 1967-2023 | Table 7.2.3B. Real Motor Vehicle Output, Quantity Indexes (A) (Q)\n", + "T70204A | 1947-1966 | 1947-1966 | Table 7.2.4A. Price Indexes for Auto Output (A) (Q)\n", + "T70204B | 1967-2023 | 1967-2023 | Table 7.2.4B. Price Indexes for Motor Vehicle Output (A) (Q)\n", + "T70205A | 1947-1966 | 1947-1966 | Table 7.2.5A. Auto Output (A) (Q)\n", + "T70205B | 1967-2023 | 1967-2023 | Table 7.2.5B. Motor Vehicle Output (A) (Q)\n", + "T70206B | 2007-2023 | 2007-2023 | Table 7.2.6B. Real Motor Vehicle Output, Chained Dollars (A) (Q)\n", + "T70303 | 1929-2022 | 0- 0 | Table 7.3.3. Real Farm Sector Output, Real Gross Value Added, and Real Net Value Added, Quantity Indexes (A)\n", + "T70304 | 1929-2022 | 0- 0 | Table 7.3.4. Price Indexes for Farm Sector Output, Gross Value Added, and Net Value Added (A)\n", + "T70305 | 1929-2022 | 0- 0 | Table 7.3.5. Farm Sector Output, Gross Value Added, and Net Value Added (A)\n", + "T70306 | 2007-2022 | 0- 0 | Table 7.3.6. Real Farm Sector Output, Real Gross Value Added, and Real Net Value Added, Chained Dollars (A)\n", + "T70403 | 1929-2022 | 0- 0 | Table 7.4.3. Real Housing Sector Output, Real Gross Value Added, and Real Net Value Added, Quantity Indexes (A)\n", + "T70404 | 1929-2022 | 0- 0 | Table 7.4.4. Price Indexes for Housing Sector Output, Gross Value Added, and Net Value Added (A)\n", + "T70405 | 1929-2022 | 0- 0 | Table 7.4.5. Housing Sector Output, Gross Value Added, and Net Value Added (A)\n", + "T70406 | 2007-2022 | 0- 0 | Table 7.4.6. Real Housing Sector Output, Real Gross Value Added, and Real Net Value Added, Chained Dollars (A)\n", + "T70500 | 1929-2023 | 1947-2023 | Table 7.5. Consumption of Fixed Capital by Legal Form of Organization and Type of Income (A) (Q)\n", + "T70600 | 1929-2022 | 0- 0 | Table 7.6. Capital Consumption Adjustment by Legal Form of Organization and Type of Adjustment (A)\n", + "T70700 | 1929-2022 | 0- 0 | Table 7.7. Business Current Transfer Payments by Type (A)\n", + "T70800 | 1948-2022 | 0- 0 | Table 7.8. Supplements to Wages and Salaries by Type (A)\n", + "T70900 | 1946-2022 | 0- 0 | Table 7.9. Rental Income of Persons by Legal Form of Organization and by Type of Income (A)\n", + "T71000 | 1946-2022 | 0- 0 | Table 7.10. Dividends Paid and Received by Sector (A)\n", + "T71100 | 1946-2022 | 0- 0 | Table 7.11. Interest Paid and Received by Sector and Legal Form of Organization (A)\n", + "T71200 | 1929-2022 | 0- 0 | Table 7.12. Imputations in the National Income and Product Accounts (A)\n", + "T71300 | 1929-2022 | 0- 0 | Table 7.13. Relation of Consumption of Fixed Capital in the National Income and Product Accounts to Depreciation and Amortization as Published by the Internal Revenue Service (A)\n", + "T71400 | 1959-2022 | 0- 0 | Table 7.14. Relation of Nonfarm Proprietors' Income in the National Income and Product Accounts to Corresponding Measures as Published by the Internal Revenue Service (A)\n", + "T71500 | 1967-2022 | 0- 0 | Table 7.15. Relation of Net Farm Income in the National Income and Product Accounts to Net Farm Income as Published by the U.S. Department of Agriculture (A)\n", + "T71600 | 1929-2022 | 0- 0 | Table 7.16. Relation of Corporate Profits, Taxes, and Dividends in the National Income and Product Accounts to Corresponding Measures as Published by the Internal Revenue Service (A)\n", + "T71700 | 1946-2022 | 0- 0 | Table 7.17. Relation of Monetary Interest Paid and Received in the National Income and Product Accounts to Corresponding Measures as Published by the Internal Revenue Service (A)\n", + "T71800 | 1982-2022 | 0- 0 | Table 7.18. Relation of Wages and Salaries in the National Income and Product Accounts to Wages and Salaries as Published by the Bureau of Labor Statistics (A)\n", + "T71900 | 1992-2022 | 0- 0 | Table 7.19. Comparison of Income and Outlays of Nonprofit Institutions Serving Households with Revenue and Expenses as Published by the Internal Revenue Service (A)\n", + "T72000 | 1984-2022 | 0- 0 | Table 7.20. Transactions of Defined Benefit and Defined Contribution Pension Plans (A)\n", + "T72100 | 1984-2022 | 0- 0 | Table 7.21. Transactions of Defined Benefit Pension Plans (A)\n", + "T72200 | 1984-2022 | 0- 0 | Table 7.22. Transactions of Private Defined Benefit Pension Plans (A)\n", + "T72300 | 1929-2022 | 0- 0 | Table 7.23. Transactions of Federal Government Defined Benefit Pension Plans (A)\n", + "T72400 | 1929-2022 | 0- 0 | Table 7.24. Transactions of State and Local Government Defined Benefit Pension Plans (A)\n", + "T72500 | 1984-2022 | 0- 0 | Table 7.25. Transactions of Defined Contribution Pension Plans (A)\n", + "T80103 | 0- 0 | 2002-2023 | Table 8.1.3. Real Gross Domestic Product, Quantity Indexes, Not Seasonally Adjusted (Q)\n", + "T80104 | 0- 0 | 2002-2023 | Table 8.1.4. Price Indexes for Gross Domestic Product, Not Seasonally Adjusted (Q)\n", + "T80105 | 0- 0 | 1947-2023 | Table 8.1.5. Gross Domestic Product, Not Seasonally Adjusted (Q)\n", + "T80106 | 0- 0 | 2002-2023 | Table 8.1.6. Real Gross Domestic Product, Chained Dollars, Not Seasonally Adjusted (Q)\n", + "T80111 | 0- 0 | 2003-2023 | Table 8.1.11. Real Gross Domestic Product: Percent Change From Quarter One Year Ago, Not Seasonally Adjusted (Q)\n", + "T80200 | 0- 0 | 2002-2023 | Table 8.2. Gross Domestic Income by Type of Income, Not Seasonally Adjusted (Q)\n", + "T80300 | 0- 0 | 1947-2023 | Table 8.3. Federal Government Current Receipts and Expenditures, Not Seasonally Adjusted (Q)\n", + "T80400 | 0- 0 | 1947-2023 | Table 8.4. State and Local Government Current Receipts and Expenditures, Not Seasonally Adjusted (Q)\n" + ] + } + ], + "source": [ + "method = 'GetParameterValues'\n", + "dataset = 'NIPA'\n", + "\n", + "# Find years available for each table\n", + "parameter = 'year'\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&ParameterName={parameter}&UserID={userid}'\n", + "year_response = requests.get(url).json()\n", + "\n", + "annual_years = {} # Years that have annual data available\n", + "quarterly_years = {} # Years that have quarterly data available\n", + "for table in year_response['BEAAPI']['Results']['ParamValue']:\n", + " annual_years[table['TableName']] = (table['FirstAnnualYear'], table['LastAnnualYear'])\n", + " quarterly_years[table['TableName']] = (table['FirstQuarterlyYear'], table['LastQuarterlyYear'])\n", + "\n", + "# Find table IDs\n", + "parameter = 'TableID'\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&ParameterName={parameter}&UserID={userid}'\n", + "table_id_response = requests.get(url).json()\n", + "\n", + "# Display table names, descriptions, and years available\n", + "print(f'{'Table':<8}| {'Annually':<9} | {'Quarterly':<9} | Description')\n", + "for id in table_id_response['BEAAPI']['Results']['ParamValue']:\n", + " name = id['TableName']\n", + " print(f'{name:<8}| {annual_years[name][0]:>4}-{annual_years[name][1]:>4} | {quarterly_years[name][0]:>4}-{quarterly_years[name][1]:>4} | {id['Description']}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the table printed above, we see that table `T60300D` has annual data from 1998 to 2023 for \"Wages and Salaries by Industry.\" Below, we use the `GetData` method to retrieve the annual data from this table for every available year:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2475" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "method = 'GetData'\n", + "dataset = 'NIPA'\n", + "\n", + "frequency = 'A' # Annual data\n", + "table_name = 'T60300D' # Wages and Salaries by Industry\n", + "year = 'X' # Data for 1998-2022\n", + "\n", + "url = f'https://apps.bea.gov/api/data?method={method}&datasetname={dataset}&Frequency={frequency}&TableName={table_name}&year={year}&UserID={userid}'\n", + "response = requests.get(url).json()\n", + "\n", + "# Display size of data [number of industries (99) x number of years (25)]\n", + "len(response['BEAAPI']['Results']['Data'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'TableName': 'T60300D',\n", + " 'SeriesCode': 'A034RC',\n", + " 'LineNumber': '1',\n", + " 'LineDescription': 'Wages and salaries',\n", + " 'TimePeriod': '1998',\n", + " 'METRIC_NAME': 'Current Dollars',\n", + " 'CL_UNIT': 'Level',\n", + " 'UNIT_MULT': '6',\n", + " 'DataValue': '4,181,616',\n", + " 'NoteRef': 'T60300D'},\n", + " {'TableName': 'T60300D',\n", + " 'SeriesCode': 'A034RC',\n", + " 'LineNumber': '1',\n", + " 'LineDescription': 'Wages and salaries',\n", + " 'TimePeriod': '1999',\n", + " 'METRIC_NAME': 'Current Dollars',\n", + " 'CL_UNIT': 'Level',\n", + " 'UNIT_MULT': '6',\n", + " 'DataValue': '4,457,926',\n", + " 'NoteRef': 'T60300D'},\n", + " {'TableName': 'T60300D',\n", + " 'SeriesCode': 'A034RC',\n", + " 'LineNumber': '1',\n", + " 'LineDescription': 'Wages and salaries',\n", + " 'TimePeriod': '2000',\n", + " 'METRIC_NAME': 'Current Dollars',\n", + " 'CL_UNIT': 'Level',\n", + " 'UNIT_MULT': '6',\n", + " 'DataValue': '4,824,946',\n", + " 'NoteRef': 'T60300D'}]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response['BEAAPI']['Results']['Data'][:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "industries = [\n", + " 'Agriculture, forestry, fishing, and hunting',\n", + " 'Mining',\n", + " 'Utilities',\n", + " 'Construction',\n", + " 'Manufacturing',\n", + " 'Wholesale trade',\n", + " 'Retail trade',\n", + " 'Transportation and warehousing',\n", + " 'Information',\n", + " 'Finance and insurance',\n", + " 'Real estate and rental and leasing',\n", + " 'Professional, scientific, and technical services',\n", + " 'Management of companies and enterprises',\n", + " 'Administrative and waste management services',\n", + " 'Educational services',\n", + " 'Health care and social assistance',\n", + " 'Arts, entertainment, and recreation',\n", + " 'Accommodation and food services',\n", + " 'Other services, except government',\n", + " 'Government'\n", + "]\n", + "\n", + "data_by_industry = []\n", + "\n", + "for industry in industries:\n", + "\n", + " years_values = []\n", + "\n", + " for item in response['BEAAPI']['Results']['Data']:\n", + " if item['LineDescription'] == industry:\n", + " year = int(item['TimePeriod'])\n", + " value = int(item['DataValue'].replace(',', ''))\n", + " value /= 1000 # Convert value to billions\n", + " years_values.append((year, value))\n", + " \n", + " data_by_industry.append((industry, years_values))\n", + "\n", + "# Display length of results (number of years)\n", + "len(data_by_industry)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note that the graphs below are not scaled together. Please check the y-axis labels to see the individual scales for each graph.\n" + ] + }, + { + "data": { + "image/png": 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JDAwUatSoIb5PTk4WTExMhD59+qjlmz17tgBA7Tg///xzwdLSUrhx44Za3k8//VQwNjYWEhMTBUEQhIkTJwpyuVzIzc197fFp82rM2dnZQoMGDYQOHTqopQMQzMzM1D6X33//XQAgLF++XEzr06ePIJPJhISEBDHt2rVrgrGxsVCYn+EzZ84UAAiWlpZC165dhS+++EK4ePGiRr7c3FwhKytLLe3JkyeCk5OT8MEHH2jEPmvWLJ3HLAjaz5uYmBgBgNCqVSuNslUtu3v3riAIgpCeni7Y2tqqnQ+C8OLztrGxEdOfPHkiABC+/PLLAsviVe+//75gb2+vkX737l0BgBATEyOmhYSECACEOXPmqOVt3Lix4OvrW+C+2rZtKwDQeHl7ewt37txRy/tqWajWb9u27WtjnDVrlto5cenSJQGAMHLkSLXtf/zxxwIA4ciRI2Kah4eHAEA4ceKEmJaamipIpVJhypQprz02VSy6PoMvv/xS43iGDh0qABDs7OyEd999V1i0aJEQHx+vsa7qu2bHjh069//2228LdnZ2GumjR48WzM3NXxu7IPz72SxevFhMy8rKEho1aiQ4OjoK2dnZgiAIwoEDBwQAwr59+9TW9/HxUftsdAEghIaGiu9Vx+bt7a32t7ds2TIBgHD58mVBEF58hzg6OgqNGjVSy7dmzRoBgNq+tZ07L+9L9Z3922+/FViugqD7WqE61wYOHKixbODAgYKrq6uQl5cnpv36668a52tBLly4oHOdixcvql1bVPbv3y8AEKysrMS0nj17Cra2toJCoVDL6+/vLwAQFi1aJKalpKQIAQEBan+jVatWFc6cOVPouF+1adMmAYCwfv16jWN7+ftRZerUqQIAQalU6tzmiRMnBIlEIsyYMaPA/Y8YMUIwNjbWuO4RERERUflgzzMiIiIiPfvpp59gbGyMCRMmqKVPmTIFgiBg3759BW7j5db5OTk5ePz4MWrWrAlbW1v8+uuvRY7J29sb9vb24lxmv//+O549e4YWLVoAeDHElapnyNmzZ5GXl6c239nL8Tx9+hR///032rZtizt37ohDi8XFxSE3Nxfjxo1T2/f48eM14tmxYwdat24NOzs7sQeQati+vLw8nDhxAgBga2uLZ8+e4dChQ0U+5pdjfvLkCZ4+fYrWrVtrLb+OHTvCy8tLfO/j4wO5XI47d+4AeNFT6cCBA+jTp4/a0F7e3t4IDAwsVDwRERHYunUrGjdujAMHDuA///kPfH190aRJE7VhvIyNjcXhxvLz8/HPP/8gNzcXTZs2LfCzL+p5M2rUqALn9Dp06BDS0tIwcOBAtc/K2NgYfn5+4nCS5ubmMDMzw7Fjx/DkyZNClYnK48ePYWdnV6R1Xp0frXXr1uLnVZDq1avj0KFDOHToEPbt24elS5fi6dOn6Nq1q9Z5vkrqp59+AgC1XpXAi+8EABq9IevVq4fWrVuL7x0cHFCnTp1CH19RxMTEYMWKFfD09BR7XXl7eyMgIOC1w/ppY2VlhfT0dI10Ozs7PH/+vFC920xMTNR6VZmZmWHMmDFITU0VeyZ17NgRrq6uar2qrly5gj/++KNEc08OHz5cbag/1WegKvdffvkFqamp+PDDD9XyDRs2DDY2NsXap2q9AwcOlKj336t/DwAwdOhQJCUlqQ35umXLFpibmyM4OLjY+3pZkyZN4OfnhwULFiAmJgb37t3Dvn37MGbMGJiamqoNFTx27FikpaXhvffew2+//YYbN25g0qRJ+OWXXwBALa+FhQXq1KmDkJAQ7NixA//973/h4uKCoKAgrUNpFkTVc83f3x8hISFiumqfUqlUYx3VfIsvx/Wy1NRUDBo0CJ6enpg2bdpr979161asX78eU6ZMQa1atYocPxERERGVHCvPiIiIiPQsISEBrq6usLa2Vkv39vYWlxfk+fPnmDlzpjgfWNWqVeHg4IC0tDSxsqooJBIJWrRoIc5tdvr0aTg6OqJmzZoA1CvPVP++XHl2+vRpdOzYEZaWlrC1tYWDgwOmT58OAGI8quNSbVOlSpUqGhUjN2/exP79++Hg4KD2Us15lZqaCuDFsIK1a9dG165dUa1aNXzwwQda5yLT5ocffkDz5s0hk8lQpUoVODg4IDo6Wmv5vVwhpmJnZydWAj169AjPnz/X+tDz5fl7CjJw4ECcPHkST548wcGDBzFo0CD89ttv6NmzJ5RKpZhv48aN8PHxgUwmg729PRwcHPDjjz8W+NkX9bzx9PQsMGbVPEQdOnTQ+LwOHjwoflZSqRQLFizAvn374OTkhDZt2mDhwoXiUGUFEV6aD7AgMpkMDg4Oamkvf14FsbS0RMeOHdGxY0d06dIFEydOxJ49e3D9+nXMnz+/0HEUVkJCAoyMjDT+NpydnWFra6vxnVDQ+VhSL88jZWRkhNDQUFy8eBF///03vv/+e3Tt2hVHjhzROqTk62RkZGh87wH/fravzgOnjaurKywtLdXSateuDQDiEKFGRkYYPHgwdu/eLVY4bdmyBTKZDP369StSzC97tdxV31uqcld9Tq9+D5iamqJGjRrF2qenpyfCwsKwbt06VK1aFYGBgVi5cmWRv+e1/S136tQJLi4uYiVjfn4+vv32W/Tu3Vvr51Rc3333Hd5++2188MEH8PT0RM+ePdG/f380btxYbY6wrl27Yvny5Thx4gSaNGmCOnXq4Mcff8QXX3wBAGp5+/Xrh8TERGzYsAF9+/bF8OHDcezYMWRnZ+M///kPgBeNGpKTk9Ve2dnZGvElJyeje/fusLGxwc6dO9UaDKgaHLw69yAA8TtZ21CTz549Q48ePZCeno7vv/9eYy60l508eRIjRoxAYGCgeKxEREREVP5YeUZERERUCYwfPx5ffPEF+vfvj+3bt+PgwYM4dOgQ7O3tkZ+fX6xttmrVCk+fPsXly5fF+c5UWrRogYSEBDx48ACnTp2Cq6ur+DD49u3bCAgIwN9//40lS5bgxx9/xKFDhzB58mQAKFY8+fn56NSpk9j759WXqleEo6MjLl26hD179qBXr144evQounbtqtZzQJuTJ0+iV69ekMlkiIqKwk8//YRDhw5h0KBBWitpdPW+KkqFTlHI5XJ06tQJW7ZsQUhICG7fvi3OR7d582YMGzYMXl5eWL9+Pfbv349Dhw6hQ4cOBZZ1Uc8bbQ+FX6Vab9OmTVo/q++//17MO2nSJNy4cQPz5s2DTCbDjBkz4O3tXeC8Qfb29kWqGCqot1xx+Pr6wsbGRuz1WBYKU3kEFP98LKinjKqiSZXvVfb29ujVqxd++ukntG3bFqdOnSpUZT/woqfjjRs3NCoIgReVTxYWFoU63wpr6NChyMjIwO7duyEIArZu3YoePXoUuwcYULrfA7o+61fnaQOAxYsX448//sD06dPx/PlzTJgwAfXr18dff/1V6P1pK1tjY2MMGjQI3333HZRKJY4ePYqkpKQS9c7T5q233sKpU6dw48YNnDhxAn/99RcWLlyI+/fvixWfKh999BFSUlJw5swZ/PLLL/jzzz/Fz0yV986dO9i/fz969eqltm6VKlXQqlUrsYHH/fv34eLiovY6c+aM2jqqHqVpaWnYv38/XF1d1Za7uLgAAB4+fKhxXA8fPkSVKlU0eqVlZ2cjKCgIf/zxB77//ns0aNBAZ9n8/vvv6NWrFxo0aICdO3fCxMREZ14iIiIiKlv8JUZERESkZx4eHjh8+DDS09PVWvf/+eef4nIVXQ9Yd+7ciZCQECxevFhMUyqVSEtLK3Zcqp5kp06dwunTpzFp0iRxma+vL6RSKY4dO4bz58+jW7du4rK9e/ciKysLe/bsUeuZ8fJQYC8f161bt9R6QTx+/FijYsTLywsZGRliT7PXMTMzQ8+ePdGzZ0/k5+dj3LhxWL16NWbMmKH1QT3woieETCbDgQMH1B58xsTEFLg/bRwcHGBubi72wnrZ9evXi7VNlaZNm2Ljxo3iw9udO3eiRo0aiI2NVTs/Zs2aVeC2yuK8UQ1n6ejoWKjPy8vLC1OmTMGUKVNw8+ZNNGrUCIsXL8bmzZt1rlO3bl1s2bIFT58+LVHlR0nl5eUhIyOj1Lfr4eGB/Px83Lx5U+yBCgApKSlIS0tT+04oCQcHB1hYWOg8J69fvw4LCwtUrVq1wG01bdoUx48fx8OHDwsV386dO/H8+XOtw5jevXtX7bhfJykpCc+ePVPrfXbjxg0AL4bbVGnQoAEaN26MLVu2oFq1akhMTMTy5csLtY/iUpXDzZs30aFDBzE9JycHd+/exdtvvy2mqXqtvfq3p6sysmHDhmjYsCE+++wznDlzBi1btsSqVaswd+5cAIWveH3V0KFDsXjxYuzduxf79u2Dg4NDoYeaLapatWqJvfKuXbuGhw8fYtiwYRr5LC0t4e/vL74/fPgwzM3N0bJlSwAv/i4A7RWNOTk5yM3NBfCi5+arQ/q+/BkolUr07NkTN27cwOHDh1GvXj2N7b311ltwcHAQh4582c8//4xGjRqppeXn52Po0KGIi4vD9u3b0bZtW21FAeBFw5MuXbrA0dERP/3002t7pxERERFR2WPPMyIiIiI969atG/Ly8rBixQq19K+++goSiQRdu3YV0ywtLbVWbBgbG2v0dli+fLnWh4mF1bRpU8hkMmzZsgUPHjxQ63kmlUrRpEkTrFy5Es+ePVMbslHVG+PleJ4+fapRERUQEAATExNER0erpb9aDgDQv39/nD17FgcOHNBYlpaWJj4cffz4sdoyIyMj+Pj4ANA+zNbLMUskErXyunfvHnbv3q1zndcxNjZGYGAgdu/ejcTERDE9Pj5e6zG8KjMzE2fPntW6TDUHnmr4R23lff78eZ3rvxpnaZ83gYGBkMvliIyMRE5OjsZy1RxhmZmZakNPAi8q0qytrV/7WQGAv78/BEEQ57TSh6NHjyIjI0Pt4XtpUVVGL126VC19yZIlAIDu3buXyn6MjY3RuXNn7N27V+08BYDExETs3bsXnTt3Fs+x5ORkXLt2TWM72dnZiIuL0zrUpDa///47Jk2aBDs7O4SGhmos//XXX9W+b14nNzcXq1evVotl9erVcHBwgK+vr1reIUOG4ODBg1i6dCns7e3VvlvLQtOmTeHg4IBVq1apDQ+4YcMGje9xVaXzyz0Z8/LysGbNGrV8CoVC/L5TadiwIYyMjNT+bnRdKwri4+MDHx8frFu3Dt999x0GDBhQ5r2f8vPzMW3aNFhYWGidi+1lZ86cQWxsLEaMGCFWnNesWRNGRkb4v//7P7Xvs7/++gsnT55E48aNAbzoQakaflX1UlVa5uXl4b333sPZs2exY8cOtcq6VwUHB+OHH37A/fv3xbS4uDjcuHFDYxjQ8ePH4//+7/8QFRWFoKAgndtMTk5G586dYWRkhAMHDmgMM0tERERE5Y89z4iIiIj0rGfPnmjfvj3+85//4N69e3j77bdx8OBBfP/995g0aZL4UBV40ePr8OHDWLJkCVxdXeHp6Qk/Pz/06NEDmzZtgo2NDerVq4ezZ8/i8OHDsLe3L3ZcZmZmaNasGU6ePAmpVKrxILpFixZij6WXK886d+4s9v4aM2YMMjIysHbtWjg6OqoNdeXk5ISJEydi8eLF6NWrF7p06YLff/8d+/btQ9WqVdV6TkydOhV79uxBjx49MGzYMPj6+uLZs2e4fPkydu7ciXv37qFq1aoYOXIk/vnnH3To0AHVqlVDQkICli9fjkaNGr22J0v37t2xZMkSdOnSBYMGDUJqaipWrlyJmjVr4o8//ihW+UVERGD//v1o3bo1xo0bh9zcXCxfvhz169cvcJuZmZlo0aIFmjdvji5dusDNzQ1paWnYvXs3Tp48iT59+ogPhHv06IHY2Fi8++676N69O+7evYtVq1ahXr16BfaKKovzRi6XIzo6GkOGDEGTJk0wYMAAODg4IDExET/++CNatmyJFStW4MaNGwgICED//v1Rr149mJiYYNeuXUhJSSlw7qxWrVrB3t4ehw8fVuvRU1aePn0q9oTLzc3F9evXER0dDXNzc3z66aelvr+3334bISEhWLNmDdLS0tC2bVv8/PPP2LhxI/r06YP27duX2r4iIyPRvHlzNGnSBKNHj0b16tVx7949rFmzBhKJBJGRkWLev/76C++88w46dOiAgIAAODs7IzU1Fd9++61YIfZqL7WTJ09CqVQiLy8Pjx8/xunTp7Fnzx7Y2Nhg165dcHZ2Vst/8eJF/PPPP+jdu3eh4nd1dcWCBQtw79491K5dG//3f/+HS5cuYc2aNTA1NVXLO2jQIEybNg27du3C2LFjNZaXNlNTU8ydOxdjxoxBhw4d8N577+Hu3buIiYnRmPOsfv36aN68OcLDw/HPP/+gSpUq2LZtm0ZF2ZEjR/DRRx+hX79+qF27NnJzc7Fp0yYYGxuLw9cCuq8VhTF06FB8/PHHAFCkIRtXrFiBtLQ0JCUlAXjRC1k1lOT48ePFyq6JEydCqVSiUaNGyMnJwdatW8Xz++XeygkJCejfvz969eoFZ2dnXL16FatWrYKPj4/aeeng4IAPPvgA69atQ0BAAIKCgpCeno6oqCg8f/4c4eHhBcY+ZcoU7NmzBz179sQ///yj0fP15XKYPn06duzYgfbt22PixInIyMjAl19+iYYNG2L48OFivqVLlyIqKgr+/v6wsLDQ2Oa7774r9pjs0qUL7ty5g2nTpuHUqVM4deqUmM/JyQmdOnUq8BiIiIiIqJQJRERERFSuQkNDhVd/hqWnpwuTJ08WXF1dBVNTU6FWrVrCl19+KeTn56vl+/PPP4U2bdoI5ubmAgAhJCREEARBePLkiTB8+HChatWqgpWVlRAYGCj8+eefgoeHh5hHEATh6NGjAgDh6NGjhYo1PDxcACC0aNFCY1lsbKwAQLC2thZyc3PVlu3Zs0fw8fERZDKZUL16dWHBggXCf//7XwGAcPfuXTFfbm6uMGPGDMHZ2VkwNzcXOnToIMTHxwv29vbChx9+qFFG4eHhQs2aNQUzMzOhatWqQosWLYRFixYJ2dnZgiAIws6dO4XOnTsLjo6OgpmZmeDu7i6MGTNGePjwYYHHun79eqFWrVqCVCoV6tatK8TExAizZs3S+KwACKGhoRrrv1rWgiAIx48fF3x9fQUzMzOhRo0awqpVq7Ru81U5OTnC2rVrhT59+ggeHh6CVCoVLCwshMaNGwtffvmlkJWVJebNz88XIiMjxXyNGzcWfvjhByEkJETw8PDQiH3WrFni+8KeNzExMQIA4cKFCxqxqpa9/LkKwotzLTAwULCxsRFkMpng5eUlDBs2TPjll18EQRCEv//+WwgNDRXq1q0rWFpaCjY2NoKfn5+wffv215aNyoQJE4SaNWuqpd29e1cAIMTExIhpISEhgqWlpcb6hfkcBEEQ2rZtKwAQXxKJRKhSpYrQq1cv4eLFiwWWRdu2bYW2bdu+NkZtseTk5AgRERGCp6enYGpqKri5uQnh4eGCUqlUy+fh4SF0795da9wv7/d14uPjhffee09wdHQUTExMBEdHR2HAgAFCfHy8Wj6FQiEsW7ZMCAwMFKpVqyaYmpoK1tbWgr+/v7B27Vq17yvVd43qZWpqKjg4OAht2rQRvvjiCyE1NVVrLJ988ong7u6u8d2nTdu2bYX69esLv/zyi+Dv7y/IZDLBw8NDWLFihc51unXrJgAQzpw5U6iyEQTNv3nVse3YsUMtn7bPVhAEISoqSvD09BSkUqnQtGlT4cSJE1o/n9u3bwsdO3YUpFKp4OTkJEyfPl04dOiQ2nf2nTt3hA8++EDw8vISZDKZUKVKFaF9+/bC4cOH1bal61qhOtcePXqk83gfPnwoGBsbC7Vr1y50GQnCi3Px5c/85dfLfxMxMTHC22+/LVhaWgrW1tZCQECAcOTIEY3t/fPPP0Lv3r0FZ2dnwczMTPD09BQ++eQTQaFQaOTNyckRli9fLjRq1EiwsrISrKyshPbt22vdrjav/p2/+nrVlStXhM6dOwsWFhaCra2tMHjwYCE5OVktT0hIyGu3+XKZvC5fYf+OiYiIiKh0SQShjGY1JyIiIiIqhrS0NNjZ2WHu3Ln4z3/+o+9wqIK6c+cO6tati3379iEgIEDf4VApyMrKQvXq1fHpp59i4sSJZbKPd999F5cvX8atW7fKZPuVwd9//w0XFxfMnDkTM2bM0Hc4RERERER6wTnPiIiIiEhvnj9/rpGmmuepXbt25RsMGZQaNWpgxIgRmD9/vr5DoVISExMDU1PTAue9Kq6HDx/ixx9/xJAhQ8pk+5XFhg0bkJeXx3IiIiIiojcae54RERERkd5s2LABGzZsQLdu3WBlZYVTp07h22+/RefOnXHgwAF9h0dElcDdu3dx+vRprFu3DhcuXMDt27c15lqjF/OpXbt2DTNmzED79u0RGxur75CIiIiIiPTGRN8BEBEREdGby8fHByYmJli4cCEUCgWcnJwwceJEzJ07V9+hEVElcfz4cQwfPhzu7u7YuHEjK850mDNnDs6cOYOWLVti+fLl+g6HiIiIiEiv2POMiIiIiIiIiIiIiIiI6H845xkRERERERERERERERHR/7DyrIIZNmwYqlevXmbb37BhAyQSCe7du1dm+ygPFy5cQIsWLWBpaQmJRIJLly7pO6QKKyMjAyNHjoSzszMkEgkmTZqEe/fuQSKRYMOGDUXaVvXq1dGjR48C8x07dgwSiQTHjh0rXtAGbPbs2ZBIJAXma9euHRo0aFAOERVNWX8HEdGbQSKRYPbs2cVat3r16hg2bFipxkNERFRY2n4Pv3pdK8p99Zt8b0RERKRNSe4XicoTK88KKSoqChKJBH5+fvoOpdRFRUUVuRJFn3JyctCvXz/8888/+Oqrr7Bp0yZ4eHjoOywNkZGR2L17t77DQGRkJDZs2ICxY8di06ZNGDJkiL5DIj1LSkrC7NmzWelMRAVSPRyUSCQ4deqUxnJBEODm5gaJRFKoxhVERERlRdWI7e+//9a6vEGDBmjXrh2Asvk9bGj31UREVHK3b9/GmDFjUKNGDchkMsjlcrRs2RLLli3D8+fP9RbXTz/9pPfKqYoQA1FJmeg7AEOxZcsWVK9eHT///DNu3bqFmjVrlsl+1q5di/z8/DLZti5RUVGoWrWqwbTyvn37NhISErB27VqMHDlS3+HoFBkZib59+6JPnz56jePIkSNo3rw5Zs2aJaYJgoDnz5/D1NS0TPbZpk0bPH/+HGZmZmWyfSqZpKQkREREoHr16mjUqJHaMn18BxFRxSeTybB161a0atVKLf348eP466+/IJVK1dKfP38OE5Pi/cy8fv06jIzYvouIiMpOSX8PDxkyBAMGDFC7/um6r+a9ERFR5fTjjz+iX79+kEqlGDp0KBo0aIDs7GycOnUKU6dOxdWrV7FmzRq9xPbTTz9h5cqVeq28el0MJblfJCpPfDJRCHfv3sWZM2ewZMkSODg4YMuWLaW+j2fPngEATE1NNR5AGSJV5UxZSE1NBQDY2tqW2jZV5a8vZbn/1NRUjbKSSCSQyWQwNjYuk30aGRlBJpPx4acBqizfQURUurp164YdO3YgNzdXLX3r1q3w9fWFs7OzWrpMJiv2zZBUKi2zxh1EREQFKczvYWNjY8hkskIN1857IyKiyufu3bsYMGAAPDw8cO3aNSxbtgyjRo1CaGgovv32W1y7dg3169fXd5iFkpubi+zs7HLdZ0nuF4nKE3+9FcKWLVtgZ2eH7t27o2/fvjorzx4/fowhQ4ZALpfD1tYWISEh+P333zXmlho2bBisrKxw+/ZtdOvWDdbW1hg8eLC47NXx1fPz87Fs2TI0bNgQMpkMDg4O6NKlC3755RcAeO38VQWNIVu9enVcvXoVx48fF4dlUg1loWvuJm3ju6vmwjpw4ACaNm0Kc3NzrF69GgCQlpaGSZMmwc3NDVKpFDVr1sSCBQuK1btl2LBhaNu2LQCgX79+avECL3pZtW7dGpaWlrC1tUXv3r0RHx+vtg3VcV27dg2DBg2CnZ2dWkv6zZs3w9fXF+bm5qhSpQoGDBiA+/fvq23j5s2bCA4OhrOzM2QyGapVq4YBAwbg6dOnAF6U+7Nnz7Bx40axXFUtEHXtPyYmBhKJBL/99pvGcUdGRsLY2BgPHjwodFmpxta/e/cufvzxRzGOe/fuaT1nkpOTMXz4cFSrVg1SqRQuLi7o3bu31nH8T506hXfeeQcymQw1atTAN998o3XfL4/rr5rj69q1a2jfvj0sLCzw1ltvYeHChRrbT0hIQK9evWBpaQlHR0dMnjwZBw4cKNFcAYsWLUKLFi1gb28Pc3Nz+Pr6YufOnRr5JBIJPvroI+zevRsNGjSAVCpF/fr1sX//fq3l0KxZM8hkMnh5eYnnfFEUVB665lMobhkfO3YMzZo1AwAMHz5cPC9U58Kr30Gqc2XRokVYs2YNvLy8IJVK0axZM1y4cEHjeHbs2IF69epBJpOhQYMG2LVrF+dRI6oEBg4ciMePH+PQoUNiWnZ2Nnbu3IlBgwZp5H/194fq2nfr1i0MGzYMtra2sLGxwfDhw5GZmam27qtznqm+B0+fPo2wsDA4ODjA0tIS7777Lh49eqS2bn5+PmbPng1XV1dYWFigffv2uHbtGudRIyIiUVF/D2vz6m/0191X65rz7Pz58+jSpQtsbGxgYWGBtm3b4vTp02p50tPTMWnSJFSvXh1SqRSOjo7o1KkTfv3115IWAxERlcDChQuRkZGB9evXw8XFRWN5zZo1MXHiRAAvKqc+//xz8XlK9erVMX36dGRlZamto3q2WtAzt5ycHERERKBWrVqQyWSwt7dHq1atxHu1YcOGYeXKlQAgXpNUz3dffsazdOlSMaZr164V6fkT8OI61q1bN9jZ2cHS0hI+Pj5YtmxZgTGo0l59Xv3bb7+ha9eukMvlsLKyQkBAAM6dO6eWpyj3hkSlgVW8hbBlyxYEBQXBzMwMAwcORHR0NC5cuCD+4AZePKzp2bMnfv75Z4wdOxZ169bF999/j5CQEK3bzM3NRWBgIFq1aoVFixbBwsJC5/5HjBiBDRs2oGvXrhg5ciRyc3Nx8uRJnDt3Dk2bNi3RsS1duhTjx4+HlZUV/vOf/wAAnJycirWt69evY+DAgRgzZgxGjRqFOnXqIDMzE23btsWDBw8wZswYuLu748yZMwgPD8fDhw+xdOnSIu1jzJgxeOuttxAZGYkJEyagWbNmYryHDx9G165dUaNGDcyePRvPnz/H8uXL0bJlS/z6668aN0D9+vVDrVq1EBkZCUEQAABffPEFZsyYgf79+2PkyJF49OgRli9fjjZt2uC3336Dra0tsrOzERgYiKysLIwfPx7Ozs548OABfvjhB6SlpcHGxgabNm3CyJEj8c4772D06NEAAC8vr9fuv2/fvggNDcWWLVvQuHFjtbxbtmxBu3bt8NZbbxW6rLy9vbFp0yZMnjwZ1apVw5QpUwAADg4OWi8owcHBuHr1KsaPH4/q1asjNTUVhw4dQmJiolrZ3bp1C3379sWIESMQEhKC//73vxg2bBh8fX0LbFXz5MkTdOnSBUFBQejfvz927tyJTz75BA0bNkTXrl0BvOiF16FDBzx8+BATJ06Es7Mztm7diqNHjxb62LVZtmwZevXqhcGDByM7Oxvbtm1Dv3798MMPP6B79+5qeU+dOoXY2FiMGzcO1tbW+PrrrxEcHIzExETY29sDAC5fvozOnTvDwcEBs2fPRm5uLmbNmlWkv5/ClEdRFbRNb29vzJkzBzNnzsTo0aPRunVrAECLFi1eu92tW7ciPT0dY8aMgUQiwcKFCxEUFIQ7d+6IPUR+/PFHvPfee2jYsCHmzZuHJ0+eYMSIEUU6b4moYqpevTr8/f3x7bffit9P+/btw9OnTzFgwAB8/fXXhdpO//794enpiXnz5uHXX3/FunXr4OjoiAULFhS47vjx42FnZ4dZs2bh3r17WLp0KT766CP83//9n5gnPDwcCxcuRM+ePREYGIjff/8dgYGBUCqVxTtwIiKqdIr7e/h1inpffeTIEXTt2hW+vr6YNWsWjIyMEBMTgw4dOuDkyZN45513AAAffvghdu7ciY8++gj16tXD48ePcerUKcTHx6NJkybFjpeIiEpm7969qFGjRqGuHSNHjsTGjRvRt29fTJkyBefPn8e8efMQHx+PXbt2qeUtzDO32bNnY968eeJzR4VCgV9++QW//vorOnXqhDFjxiApKQmHDh3Cpk2btMYUExMDpVKJ0aNHQyqVokqVKkU6/kOHDqFHjx5wcXERn93Fx8fjhx9+wMSJEwsVw8uuXr2K1q1bQy6XY9q0aTA1NcXq1avRrl07HD9+HH5+fmr5C3NvSFQqBHqtX375RQAgHDp0SBAEQcjPzxeqVasmTJw4US3fd999JwAQli5dKqbl5eUJHTp0EAAIMTExYnpISIgAQPj000819hcSEiJ4eHiI748cOSIAECZMmKCRNz8/XxAEQbh7967GPlQACLNmzRLfx8TECACEu3fvimn169cX2rZtq7HurFmzBG2niLZteHh4CACE/fv3q+X9/PPPBUtLS+HGjRtq6Z9++qlgbGwsJCYmamy/IEePHhUACDt27FBLb9SokeDo6Cg8fvxYTPv9998FIyMjYejQoRrHNXDgQLX17927JxgbGwtffPGFWvrly5cFExMTMf23337Tuv9XWVpaCiEhIRrpuvYvCIIwcOBAwdXVVcjLyxPTfv31V52fb2F4eHgI3bt3V0t79Zx58uSJAED48ssvC9wWAOHEiRNiWmpqqiCVSoUpU6aIaarP6OjRo2Ja27ZtBQDCN998I6ZlZWUJzs7OQnBwsJi2ePFiAYCwe/duMe358+dC3bp1NbZZFJmZmWrvs7OzhQYNGggdOnRQSwcgmJmZCbdu3RLTfv/9dwGAsHz5cjGtT58+gkwmExISEsS0a9euCcbGxlr/bl5V2PLQ9vcmCCUr4wsXLug8p179DlKdK/b29sI///wjpn///fcCAGHv3r1iWsOGDYVq1aoJ6enpYtqxY8cEAGrbJCLDofoOunDhgrBixQrB2tpa/D7t16+f0L59e0EQNK81r/7+UF37PvjgA7Xtv/vuu4K9vb1amoeHh9r1UxVDx44dxd8+giAIkydPFoyNjYW0tDRBEAQhOTlZMDExEfr06aO2vdmzZwsAtF6TiYio8lBdax49eqR1+cv3vUX5PSwIJbuvfvV3e35+vlCrVi0hMDBQ7bqWmZkpeHp6Cp06dRLTbGxshNDQ0NceNxERla+nT58KAITevXsXmPfSpUsCAGHkyJFq6R9//LEAQDhy5IiYVthnbm+//bbGc75XhYaGan02pXrGI5fLhdTUVLVlhX3+lJubK3h6egoeHh7CkydP1PK+fF3TFYMgaF5X+/TpI5iZmQm3b98W05KSkgRra2uhTZs2GjEWdG9IVFo4bGMBtmzZAicnJ7Rv3x7Ai26l7733HrZt24a8vDwx3/79+2FqaopRo0aJaUZGRggNDdW57bFjxxa4/++++w4SiQSzZs3SWFaY8dXLk6enJwIDA9XSduzYgdatW8POzg5///23+OrYsSPy8vJw4sSJUtn3w4cPcenSJQwbNkyttYSPjw86deqEn376SWOdDz/8UO19bGws8vPz0b9/f7VYnZ2dUatWLbHnk42NDQDgwIEDGkNNFcWr+weAoUOHIikpSa2X1ZYtW2Bubo7g4OBi76sg5ubmMDMzw7Fjx/DkyZPX5q1Xr57YOhN40ZOtTp06uHPnToH7sbKywvvvvy++NzMzwzvvvKO27v79+/HWW2+hV69eYppMJlP72yoOc3Nz8f9PnjzB06dP0bp1a61DnnTs2FGtp6CPjw/kcrkYZ15eHg4cOIA+ffrA3d1dzOft7a3xN/A6hSmPoiqLbQLAe++9Bzs7O/G96hxQbTcpKQmXL1/G0KFDYWVlJeZr27YtGjZsWKJ9E1HF0L9/fzx//hw//PAD0tPT8cMPP2gdsvF1Xr32tW7dGo8fP4ZCoShw3dGjR6v99mndujXy8vKQkJAAAIiLi0Nubi7GjRuntt748eOLFCMREVFZunTpEm7evIlBgwbh8ePH4n3ns2fPEBAQgBMnTohTHNja2uL8+fNISkrSc9RERKSiunextrYuMK/qeWRYWJhaump0qB9//FEtvTDP3GxtbXH16lXcvHmzeAeAF6NPOTg4FGvd3377DXfv3sWkSZNga2urtqw4z6rz8vJw8OBB9OnTBzVq1BDTXVxcMGjQIJw6dUrjfrGge0Oi0sLKs9fIy8vDtm3b0L59e9y9exe3bt3CrVu34Ofnh5SUFMTFxYl5ExIS4OLiojH8Ys2aNbVu28TEBNWqVSswhtu3b8PV1bXI3Wf1wdPTUyPt5s2b2L9/PxwcHNReHTt2BACkpqaWyr5VX4516tTRWObt7S3ejLwu3ps3b0IQBNSqVUsj3vj4eDFWT09PhIWFYd26dahatSoCAwOxcuVKcb6zwtJWXp06dYKLi4s4r15+fj6+/fZb9O7du1AX5eKSSqVYsGAB9u3bBycnJ7Rp0wYLFy5EcnKyRt6XK4tU7OzsCqx0A4Bq1appXEhfXTchIQFeXl4a+XT9LRXWDz/8gObNm0Mmk6FKlSpwcHBAdHS01s+toGN89OgRnj9/jlq1amnk03YO6lKY8iiqstgmoFkmqoo01XZVf4PaPqeSfnZEVDGort9bt25FbGws8vLy0Ldv3yJto6DvkpKsq+t7qEqVKmqV/0RE9OaqCA1QVQ87Q0JCNO47161bh6ysLPEeZeHChbhy5Qrc3NzwzjvvYPbs2SVuFEdERCUjl8sBvJiXsiAJCQkwMjLSuEdxdnaGra2tRmVPYZ65zZkzB2lpaahduzYaNmyIqVOn4o8//ijSMWh7JllYt2/fBgA0aNCg2Nt42aNHj5CZmanzmW5+fj7u37+vll6S+0qiouCcZ69x5MgRPHz4ENu2bcO2bds0lm/ZsgWdO3cu1ralUimMjEqn7lLXDcDLPePKY7sv9+xRyc/PR6dOnTBt2jSt69SuXbv4AZbQq/Hm5+dDIpFg3759MDY21sj/cm+axYsXY9iwYfj+++9x8OBBTJgwAfPmzcO5c+cKVSmqbf8AYGxsjEGDBmHt2rWIiorC6dOnkZSUpNaTqKxMmjQJPXv2xO7du3HgwAHMmDED8+bNw5EjR9TmYNNWNgDEeeNepyTrlsTJkyfRq1cvtGnTBlFRUXBxcYGpqSliYmKwdetWvcVZmP0U9e+wrGLX12dHRBXLoEGDMGrUKCQnJ6Nr164aLQ0LYojXECIiMgwymQwA8Pz5c63LMzMzxTz6pOpV9uWXX6JRo0Za86juPfv374/WrVtj165dOHjwIL788kssWLAAsbGxxZ4jmYiISkYul8PV1RVXrlwp9DqFbbxRmHueNm3a4Pbt2+IzyXXr1uGrr77CqlWrMHLkyELtR9szybJ6vlwWeG9I5YWVZ6+xZcsWODo6YuXKlRrLYmNjsWvXLqxatQrm5ubw8PDA0aNHkZmZqdb77NatWyWKwcvLCwcOHMA///yjs/eZqnY9LS1NLb2wXVV1fTm+vN2XH44VpQusl5cXMjIyxJ5mZcXDwwMAcP36dY1lf/75J6pWrQpLS8vXbsPLywuCIMDT07NQlXoNGzZEw4YN8dlnn+HMmTNo2bIlVq1ahblz5wIofqvGoUOHYvHixdi7dy/27dsHBweHIg0FWBJeXl6YMmUKpkyZgps3b6JRo0ZYvHgxNm/eXC77B158lteuXYMgCGplWJK/pe+++w4ymQwHDhyAVCoV02NiYoq1PQcHB5ibm2vtIq/tHCyJkv59a1MWLW5Vf4PaPqeSfg8SUcXx7rvvYsyYMTh37lyFm4z55e+hl1tSPn78mC0QiYjeAC/fE7q5uakty8zMxP3798XGr2Xxe7iw21QNDy+Xywt1n+zi4oJx48Zh3LhxSE1NRZMmTfDFF1+w8oyISI969OiBNWvW4OzZs/D399eZz8PDA/n5+bh58ya8vb3F9JSUFKSlpYnXrqKqUqUKhg8fjuHDhyMjIwNt2rTB7Nmzxcqz4lznCvv8SXUdu3LlymuvY4WNwcHBARYWFjqf6RoZGWlc14nKC4dt1OH58+eIjY1Fjx490LdvX43XRx99hPT0dOzZswcAEBgYiJycHKxdu1bcRn5+vtaKt6IIDg6GIAiIiIjQWKaqTZfL5ahatarG/GFRUVGF2oelpaXGFyPw75fhy9t99uwZNm7cWNjw0b9/f5w9exYHDhzQWJaWlobc3NxCb+t1XFxc0KhRI2zcuFHtWK5cuYKDBw+iW7duBW4jKCgIxsbGiIiI0GipIAgCHj9+DODF2Mavxt2wYUMYGRkhKytLTNNVrgXx8fGBj48P1q1bh++++w4DBgyAiUnZ1nNnZmZCqVSqpXl5ecHa2lrtmMpDYGAgHjx4IP5tAYBSqVT72yoqY2NjSCQStdYy9+7dw+7du4u9vcDAQOzevRuJiYlienx8vNZzvSS0/R3m5eVhzZo1xd6mqiK5OOenLq6urmjQoAG++eYbZGRkiOnHjx/H5cuXS20/RKRfVlZWiI6OxuzZs9GzZ099h6MmICAAJiYmiI6OVktfsWKFniIiIqLyFBAQADMzM0RHR4u9u1TWrFmD3NxcscKpLH4PF/b+z9fXF15eXli0aJHa72aVR48eAXjxm//VIeYdHR3h6upa7vdoRESkbtq0abC0tMTIkSORkpKisfz27dtYtmyZ+Dxy6dKlasuXLFkCAOjevXuR9616PqliZWWFmjVrajyTBIp2nSvs86cmTZrA09MTS5cu1dj+y89TCxuDsbExOnfujO+//x737t0T01NSUrB161a0atVKHCqTqLyx55kOe/bsQXp6Onr16qV1efPmzeHg4IAtW7bgvffeQ58+ffDOO+9gypQpuHXrFurWrYs9e/bgn3/+AVD8lm3t27fHkCFD8PXXX+PmzZvo0qUL8vPzcfLkSbRv3x4fffQRAGDkyJGYP38+Ro4ciaZNm+LEiRO4ceNGofbh6+uL6OhozJ07FzVr1oSjoyM6dOiAzp07w93dHSNGjMDUqVNhbGyM//73v3BwcFCrMHidqVOnYs+ePejRoweGDRsGX19fPHv2DJcvX8bOnTtx7949VK1aFQAwbNgwbNy4EXfv3kX16tWLXFZffvklunbtCn9/f4wYMQLPnz/H8uXLYWNjg9mzZxe4vpeXF+bOnYvw8HDcu3cPffr0gbW1Ne7evYtdu3Zh9OjR+Pjjj3HkyBF89NFH6NevH2rXro3c3Fxs2rQJxsbGCA4OVivXw4cPY8mSJXB1dYWnpyf8/PwKdSxDhw7Fxx9/DABah2w8duwY2rdvj1mzZhXq2Apy48YNBAQEoH///qhXrx5MTEywa9cupKSkYMCAASXeflGMGTMGK1aswMCBAzFx4kRxDjjVECsv/y0Vthy6d++OJUuWoEuXLhg0aBBSU1OxcuVK1KxZs8jjQqtERERg//79aN26NcaNG4fc3FwsX74c9evXL/Y2talfvz6aN2+O8PBwsQfqtm3bSlTx7OXlBVtbW6xatQrW1tawtLSEn59fica8BoDIyEj07t0bLVu2xPDhw/HkyROsWLECDRo00PpggIgMU0hIiL5D0MrJyQkTJ07E4sWL0atXL3Tp0gW///479u3bh6pVq1aIeW6IiKjsODo6YubMmfjss8/Qpk0b9OrVCxYWFjhz5gy+/fZbdO7cWWz4URa/h3XdV7/KyMgI69atQ9euXVG/fn0MHz4cb731Fh48eICjR49CLpdj7969SE9PR7Vq1dC3b1+8/fbbsLKywuHDh3HhwgUsXry42HESEVHJeXl5YevWrXjvvffg7e2NoUOHokGDBsjOzsaZM2ewY8cODBs2DBMnTkRISAjWrFmDtLQ0tG3bFj///DM2btyIPn36oH379kXed7169dCuXTv4+vqiSpUq+OWXX7Bz507xGTHw4poEABMmTEBgYCCMjY0LfL5X2OdPRkZGiI6ORs+ePdGoUSMMHz4cLi4u+PPPP3H16lWxUXlRYpg7dy4OHTqEVq1aYdy4cTAxMcHq1auRlZWFhQsXFrmMiEoLK890UD2s79Spk9blRkZG6N69O7Zs2YLHjx/D3t4eP/74IyZOnIiNGzfCyMgI7777LmbNmoWWLVuWaGz1mJgY+Pj4YP369Zg6dSpsbGzQtGlTtGjRQswzc+ZMPHr0CDt37sT27dvRtWtX7Nu3D46OjgVuf+bMmUhISMDChQuRnp6Otm3bokOHDjA1NcWuXbswbtw4zJgxA87Ozpg0aRLs7OwwfPjwQsVuYWGB48ePIzIyEjt27MA333wDuVyO2rVrIyIiAjY2NmLejIwMmJubF3n+FJWOHTti//79mDVrFmbOnAlTU1O0bdsWCxYsKPRN0KefforatWvjq6++Env7ubm5oXPnzmJF6ttvv43AwEDs3bsXDx48gIWFBd5++23s27cPzZs3F7e1ZMkSjB49Gp999hmeP3+OkJCQQleeDR48GJ988gm8vLzwzjvvaCxXVUS4uLgUansFcXNzw8CBAxEXF4dNmzbBxMQEdevWxfbt29UqBMuDlZUVjhw5gvHjx2PZsmWwsrLC0KFD0aJFCwQHB6v9LRW2HDp06ID169dj/vz5mDRpEjw9PbFgwQLcu3ev2BVdPj4+OHDgAMLCwjBz5kxUq1YNERERePjwYalWngEvvo/GjBmD+fPnw9bWFiNGjED79u11fj8VxNTUFBs3bkR4eDg+/PBD5ObmIiYmpsSVZz179sS3336L2bNn49NPP0WtWrWwYcMGbNy4EVevXi3RtomICmPBggWwsLDA2rVrcfjwYfj7++PgwYNo1apVhZjnhoiIytZ//vMfVK9eHStWrMCcOXOQm5sLT09PRERE4JNPPhHnHS+L38O67qu1adeuHc6ePYvPP/8cK1asQEZGBpydneHn54cxY8YAeHEvPW7cOBw8eBCxsbHIz89HzZo1ERUVhbFjxxY7TiIiKh29evXCH3/8gS+//BLff/89oqOjIZVK4ePjg8WLF2PUqFEAgHXr1qFGjRrYsGEDdu3aBWdnZ4SHh2PWrFnF2u+ECROwZ88eHDx4EFlZWfDw8MDcuXMxdepUMU9QUBDGjx+Pbdu2YfPmzRAEoVCN4wv7/CkwMBBHjx5FREQEFi9ejPz8fHh5eYnHXNQY6tevj5MnTyI8PBzz5s1Dfn4+/Pz8sHnz5kI/SyUqCxKBM+mVqd27d+Pdd9/FqVOn0LJlS32HU6E5OTlh6NCh+PLLL/Udit79/fffcHFxwcyZMzFjxgyN5dOmTcO3336LW7duqc3hVZktXboUkydPxl9//YW33noLwJtZDoaoUaNGcHBwwKFDh/QdChG9gdLS0mBnZ4e5c+fiP//5j77DISIiIiIiIiIDwDnPStHz58/V3ufl5WH58uWQy+Vo0qSJnqIyDFevXsXz58/xySef6DuUCmHDhg3Iy8vDkCFDtC4/evQoZsyYUWkrjF79W1IqlVi9ejVq1aolVpwBlb8cDE1OTo5Gd/5jx47h999/R7t27fQTFBG9UV69fgD/zi/A7yEiIiIiIiIiKiwO21iKxo8fj+fPn8Pf3x9ZWVmIjY3FmTNnEBkZCXNzc32HV6HVr18fCoVC32Ho3ZEjR3Dt2jV88cUX6NOnj8653y5cuFC+gZWzoKAguLu7o1GjRnj69Ck2b96MP//8E1u2bFHLV9nLwdA8ePAAHTt2xPvvvw9XV1f8+eefWLVqFZydnfHhhx/qOzwiegP83//9HzZs2IBu3brBysoKp06dEue54QgARERERERERFRYHLaxFG3duhWLFy/GrVu3oFQqUbNmTYwdO1Ztwkai12nXrh3OnDmDli1bYvPmzWq9rN4kS5cuxbp163Dv3j3k5eWhXr16mDZtGt577z19h0av8fTpU4wePRqnT5/Go0ePYGlpiYCAAMyfPx9eXl76Do+I3gC//vorpk2bhkuXLkGhUMDJyQnBwcGYO3curKys9B0eERERERERERkIVp4RERERERERERERERER/Q/nPCMiIiIiIiIiIiIiIiL6H1aeEREREREREREREREREf2Pib4DKCv5+flISkqCtbU1JBKJvsMhIqISEgQB6enpcHV1hZFR5W/7wesYEVHlwWsYEREZMl7HiIjIkBX3OlZpK8+SkpLg5uam7zCIiKiU3b9/H9WqVdN3GGWO1zEiosqH1zAiIjJk5XEde/DgAT755BPs27cPmZmZqFmzJmJiYtC0aVMALx6Azpo1C2vXrkVaWhpatmyJ6Oho1KpVS9zGP//8g/Hjx2Pv3r0wMjJCcHAwli1bBisrq0LFwOsYEVHlVNTrWKWtPLO2tgbwokDkcrmeoym8xMREuLu76zuMColloxvLRjeWjXaGWC4KhQJubm7i93tlx+tY5cOy0Y1lox3LRTdDKxtewwyHoZ1b5Yllox3LRTeWjW6GVjbldR178uQJWrZsifbt22Pfvn1wcHDAzZs3YWdnJ+ZZuHAhvv76a2zcuBGenp6YMWMGAgMDce3aNchkMgDA4MGD8fDhQxw6dAg5OTkYPnw4Ro8eja1btxYqDl7HKh+Wi24sG91YNroZWtkU9zpWaSvPVN2q5XK5QV3orK2tDSre8sSy0Y1loxvLRjtDLpc3ZdgMXscqH5aNbiwb7Vguuhlq2fAaVvEZ6rlVHlg22rFcdGPZ6GaoZVPW17EFCxbAzc0NMTExYpqnp6f4f0EQsHTpUnz22Wfo3bs3AOCbb76Bk5MTdu/ejQEDBiA+Ph779+/HhQsXxN5qy5cvR7du3bBo0SK4uroWGAevY5UPy0U3lo1uLBvdDLVsinodq/wDFRMREREREREREVGFtmfPHjRt2hT9+vWDo6MjGjdujLVr14rL7969i+TkZHTs2FFMs7GxgZ+fH86ePQsAOHv2LGxtbcWKMwDo2LEjjIyMcP78ea37zcrKgkKhUHsRERFV2p5nREREREREREREZBju3LmD6OhohIWFYfr06bhw4QImTJgAMzMzhISEIDk5GQDg5OSktp6Tk5O4LDk5GY6OjmrLTUxMUKVKFTHPq+bNm4eIiAiN9MTERIMbcjkzMxMJCQn6DqPCYbnoxrLRjWWjm6GVTXp6erHWY+UZERERERERERER6VV+fj6aNm2KyMhIAEDjxo1x5coVrFq1CiEhIWW23/DwcISFhYnvVXPjuLu7G9ywZAkJCfDw8NB3GBUOy0U3lo1uLBvdDK1situjmMM2EhERERERERERkV65uLigXr16amne3t5ITEwEADg7OwMAUlJS1PKkpKSIy5ydnZGamqq2PDc3F//884+Y51VSqVSc38wQ5zkjIqKywcozIiIiIiIiIiIi0quWLVvi+vXramk3btwQezd4enrC2dkZcXFx4nKFQoHz58/D398fAODv74+0tDRcvHhRzHPkyBHk5+fDz8+vHI6CiIgqC1aeERFRmVPmKHHn0R0oc5T6DoWIqFzx+4+IiIj0yZB+i0yePBnnzp1DZGQkbt26ha1bt2LNmjUIDQ0FAEgkEkyaNAlz587Fnj17cPnyZQwdOhSurq7o06cPgBc91bp06YJRo0bh559/xunTp/HRRx9hwIABcHV11ePRERFRcejzOsbKMyIiKlNx8XFwCnOC13QvOIU5IS4+ruCViIgqAX7/UUVUvXp1SCQSjZfqwaRSqURoaCjs7e1hZWWF4OBgjeGxiIjIMBjab5FmzZph165d+Pbbb9GgQQN8/vnnWLp0KQYPHizmmTZtGsaPH4/Ro0ejWbNmyMjIwP79+yGTycQ8W7ZsQd26dREQEIBu3bqhVatWWLNmjT4OiYiISkDf1zFWnhERUZlR5igRFBWE9Kx0AEB6VjqCooIMotUjEVFJ8PuPKqoLFy7g4cOH4uvQoUMAgH79+gF40ep/79692LFjB44fP46kpCQEBQXpM2QiIioGQ/0t0qNHD1y+fBlKpRLx8fEYNWqU2nKJRII5c+YgOTkZSqUShw8fRu3atdXyVKlSBVu3bkV6ejqePn2K//73v7CysirPwyAiohKqCNcxVp4REVGZSUpLgkKpgCAIAABBEKBQKpCUlqTnyIiIyha//6iicnBwgLOzs/j64Ycf4OXlhbZt2+Lp06dYv349lixZgg4dOsDX1xcxMTE4c+YMzp07p+/QiYioCPhbhIiIDFlFuI6x8oyIiMqMq60r5DI5JBIJgBetBOUyOVxtOdY8EVVu/P4jQ5CdnY3Nmzfjgw8+gEQiwcWLF5GTk4OOHTuKeerWrQt3d3ecPXtW53aysrKgUCjUXkREpF/8LUJERIasIlzHTMptT0RE9MaRmcoQOy4WQVFBUCgVsJZaI3ZcLGSmsoJXJiIyYPz+I0Owe/dupKWlYdiwYQCA5ORkmJmZwdbWVi2fk5MTkpOTdW5n3rx5iIiI0EhPTEyEtbV1aYZc5jIzM5GQkKDvMCoklo12LBfdWDa6lVfZRAdHY8zOMcjIyoClmSWig6ORklT0eSzT09PLIDoiIiLdKsI9tV4qz2bPnq1xc1WnTh38+eefAF5MUj1lyhRs27YNWVlZCAwMRFRUFJycnPQRLhERlUCAdwBSlqQgKS0JrraufHBMRG8Mfv9RRbd+/Xp07doVrq4la70ZHh6OsLAw8b1CoYCbmxvc3d0hl8tLGma5SkhIgIeHh77DqJBYNtqxXHRj2ehWXmXj4eGBoJZBJf4twh7FRESkD/q+p9Zbz7P69evj8OHD/wZi8m8okydPxo8//ogdO3bAxsYGH330EYKCgnD69Gl9hEpERCUkM5WhhkMNfYdBRFTu+P1HFVVCQgIOHz6M2NhYMc3Z2RnZ2dlIS0tT632WkpICZ2dnnduSSqWQSqVlGS4RERUTf4sQEZEh0+d1TG+VZyYmJlpvwFSTVG/duhUdOnQAAMTExMDb2xvnzp1D8+bNyztUIiIiIiKiSiUmJgaOjo7o3r27mObr6wtTU1PExcUhODgYAHD9+nUkJibC399fX6ESERERERGVOyN97fjmzZtwdXVFjRo1MHjwYCQmJgJAsSepJiIiIiIiooLl5+cjJiYGISEhaiOA2NjYYMSIEQgLC8PRo0dx8eJFDB8+HP7+/mzESEREREREbxS99Dzz8/PDhg0bUKdOHTx8+BARERFo3bo1rly5UuxJqrOyspCVlSW+53jMREREREREmg4fPozExER88MEHGsu++uorGBkZITg4WG3+aSIiIiIiojeJXirPunbtKv7fx8cHfn5+8PDwwPbt22Fubl6sbc6bNw8REREa6YmJibC2ti52rOUtMzMTCQkJ+g6jQmLZ6May0Y1lo11xy0WZq0RqeiocrR0hMynfSTrT09PLdX9ERESVVefOnSEIgtZlMpkMK1euxMqVK8s5KiIiIiIioopDb3OevczW1ha1a9fGrVu30KlTp2JNUh0eHo6wsDDxvUKhgJubG9zd3SGXy8sy/FKVkJAADw8PfYdRIbFsdGPZ6May0a445RIXH4egqCAolArIZXLEjotFgHdAGUWoiT2KiYiIiIiIiIiIqDzobc6zl2VkZOD27dtwcXFRm6RapTCTVEulUsjlcrUXERGVDmWOEkFRQUjPetH7Kz0rHUFRQVDmKPUcGREREREREREREVHp0kvPs48//hg9e/aEh4cHkpKSMGvWLBgbG2PgwIFqk1RXqVIFcrkc48eP5yTVRER6lJSWBIXy355fgiBAoVQgKS0JNRxq6DEyIiIiIiIiIiIiotKll8qzv/76CwMHDsTjx4/h4OCAVq1a4dy5c3BwcADASaqJiCoaV1tXyGVypGelQxAESCQSWEut4Wrrqu/QiIiIiIiIiIiIiEqVXirPtm3b9trlnKSaiKhikZnKEDsuVpzzzFpqjdhxsZCZyvQdGhEREREREREREVGp0kvlGRERGZ4A7wCkLElBUloSXG1dWXFGRERERERERERElRIrz4iIqNBkpjLOcUZERERERERERESVmpG+AyAiovKlzFEi8UkilDlKfYdCREREREREREREVOGw8oyI6A0SFx8HpzAntFnZBk5hToiLj9N3SEREREREREREREQVCivPiIjeEMocJYKigpCelQ4ASM9KR1BUEHugERERERERVXDKHCXuPLrD+zciIqJywsozIqI3RFJaEhRKBQRBAAAIggCFUoGktCQ9R2ZY8vLyMGPGDHh6esLc3BxeXl74/PPPxXIFXpTtzJkz4eLiAnNzc3Ts2BE3b97UY9RERERERGSoVCOIeE334ggiRERE5YSVZ0REbwhXW1fIZXJIJBIAgEQigVwmh6utq54jMywLFixAdHQ0VqxYgfj4eCxYsAALFy7E8uXLxTwLFy7E119/jVWrVuH8+fOwtLREYGAglEq2EiUiIiIiosLjCCJERET6wcozIqI3hMxUhthxsbCWWgMArKXWiB0XC5mpTM+RGZYzZ86gd+/e6N69O6pXr46+ffuic+fO+PnnnwG86HW2dOlSfPbZZ+jduzd8fHzwzTffICkpCbt379Zv8EREREREZFA4gggREZF+sPKMiOgNEuAdgJQlKTgRegIpS1IQ4B2g75AMTosWLRAXF4cbN24AAH7//XecOnUKXbt2BQDcvXsXycnJ6Nixo7iOjY0N/Pz8cPbsWZ3bzcrKgkKhUHsRUcXBeUaIiIhIHziCCBERkX6Y6DsAIiIqXzJTGdzt3NnjrJg+/fRTKBQK1K1bF8bGxsjLy8MXX3yBwYMHAwCSk5MBAE5OTmrrOTk5icu0mTdvHiIiIjTSExMTYW1tXYpHULYyMzORkJCg7zAqJJaNbhW9bE7fPY0xO8cgIysDVlIrrO67Gi09W5b5fit6ueiToZVNenq6vkMgIiIDpRpBJCgqCAqlgiOIEBERlRNWnhERERXB9u3bsWXLFmzduhX169fHpUuXMGnSJLi6uiIkJKTY2w0PD0dYWJj4XqFQwM3NDe7u7pDL5aURerlISEiAh4eHvsOokFg2ulXkslHmKDF20Vg8y34GAHiW/QxjvxuLlCUpZf7QqiKXi74ZWtmwNzEREZWEagSRpLQkuNq6suKMiIgMjjJHaXDXMVaeERERFcHUqVPx6aefYsCAAQCAhg0bIiEhAfPmzUNISAicnZ0BACkpKXBxcRHXS0lJQaNGjXRuVyqVQiqVlmnsRFR0qnlGVF6eZ6SGQw09RkZERERvEpmpjL89iIjIIMXFx4k9qOUyOWLHxRrEVDKc84yIiKgIMjMzYWSkfvk0NjZGfn4+AMDT0xPOzs6Ii4sTlysUCpw/fx7+/v7lGisRlRznGaHK6MGDB3j//fdhb28Pc3NzNGzYEL/88ou4fNiwYZBIJGqvLl266DFiIqLKQ5mjROKTRM6jSkREbwRljhJBUUFIz3oxlH16VjqCooIM4jrInmdERERF0LNnT3zxxRdwd3dH/fr18dtvv2HJkiX44IMPALx4sD5p0iTMnTsXtWrVgqenJ2bMmAFXV1f06dNHv8ETUZHpa54R1YM1J1cngxnSggzDkydP0LJlS7Rv3x779u2Dg4MDbt68CTs7O7V8Xbp0QUxMjPievaOJiErOUFveExERFZchj+bCyjMiIqIiWL58OWbMmIFx48YhNTUVrq6uGDNmDGbOnCnmmTZtGp49e4bRo0cjLS0NrVq1wv79+yGT8QE4kSEq73lG+GCNytKCBQvg5uamVjHm6empkU8qlYpDERMRUcnpanlfHvOoEhER6YtqNJf0rHQIggCJRAJrqbVBjObCYRuJiIiKwNraGkuXLkVCQgKeP3+O27dvY+7cuTAzMxPzSCQSzJkzB8nJyVAqlTh8+DBq166tx6iJqKRU84yUR48zQx3SggzDnj170LRpU/Tr1w+Ojo5o3Lgx1q5dq5Hv2LFjcHR0RJ06dTB27Fg8fvxYD9ESEVUeqpb3giAAUG95T0REVFmpRnOxlloDQLmN5lIa2POMiIiIiKiCMOQhLcgw3LlzB9HR0QgLC8P06dNx4cIFTJgwAWZmZggJCQHwYsjGoKAgeHp64vbt25g+fTq6du2Ks2fPwtjYWGObWVlZyMrKEt8rFAqNPEREbzpDbnlPRERUEuU9mktpYeUZEREREb0xlDnKCv2DnQ/WqKzl5+ejadOmiIyMBAA0btwYV65cwapVq8TKswEDBoj5GzZsCB8fH3h5eeHYsWMICNAcQnTevHmIiIjQSE9MTIS1tXUZHUnZyMzMREJCgr7DqJBYNtqxXHRj2WiKDo7GmJ1jkJGVAUszS0QHRyMlKUXfYRUoPT1d3yEQEZGBU43mYkhYeUZEREREbwRDmEtMNaSFKk5DGtKCDIOLiwvq1aunlubt7Y3vvvtO5zo1atRA1apVcevWLa2VZ+Hh4QgLCxPfKxQKuLm5wd3dHXK5vPSCLwcJCQnw8PDQdxgVEstGO5aLbiwbTR4eHghqGYQLVy+gWf1mBnN9Z49iIiJ6E7HyjIiIiIgqPV1ziaUsSalwD65UQ1oY2oM1MgwtW7bE9evX1dJu3Ljx2gfcf/31Fx4/fgwXFxety6VSKaRSaanGSURUWclMZXC3c+f1nYiIqIIz0ncARERERERlTTWXmCAIANTnEquI+GCNysrkyZNx7tw5REZG4tatW9i6dSvWrFmD0NBQAEBGRgamTp2Kc+fO4d69e4iLi0Pv3r1Rs2ZNBAYG6jl6IqKKQ5mjxJ1Hd6DMUeo7FCIiIioDrDwjIiIiokpPNZeYRCIBAEgkEshlcs4lRm+cZs2aYdeuXfj222/RoEEDfP7551i6dCkGDx4MADA2NsYff/yBXr16oXbt2hgxYgR8fX1x8uRJ9i4jIvqfuPg4OIU5wWu6F5zCnBAXH6fvkIiIiKiUcdhGIiIDpcxRIiktCa62ruyZQERUAM4lRvSvHj16oEePHlqXmZub48CBA+UcERGR4TCkoaCJiIio+Fh5RkRkgOLi48QHwHKZHLHjYhHgHaDvsIiIKjTVXGJseEBERETFpRoKWuXloaBrONTQY2RERERUmjhsIxGRgdHV0pFj7RMRFUxmKkMNhxqsOCMiIqJi4VDQREREbwZWnhERGRhVS0dBEACot3QkIiIiIiKisqMaCtpaag0AHAqaiIiokuKwjUREBkbV0jE9Kx2CIEAikcBaas2WjkREREREROWAQ0ETERFVfnrveTZ//nxIJBJMmjRJTGvXrh0kEona68MPP9RfkEREhaDMUeLOoztlPnwiWzoSERERERHpF4eCJiIiqtz02vPswoULWL16NXx8fDSWjRo1CnPmzBHfW1hYlGdoRERFEhcfh6CoICiUCshlcsSOi0WAd0CZ7Y8tHYmIiIiIiIiIiIjKht56nmVkZGDw4MFYu3Yt7OzsNJZbWFjA2dlZfMnlcj1ESURUMGWOEkFRQUjPSgcApGelIygqqFx6oLGlIxEREREREVUGs2fP1hiJqm7duuJypVKJ0NBQ2Nvbw8rKCsHBwUhJSVHbRmJiIrp37w4LCws4Ojpi6tSpyM3NLe9DISKq8Io7gpYyR4nEJ4ll/tyzItBb5VloaCi6d++Ojh07al2+ZcsWVK1aFQ0aNEB4eDgyMzPLOUIiosJJSkuCQqmAIAgAAEEQoFAqkJSWpOfIiIiIKq7yGu6YiIjodXg9qljq16+Phw8fiq9Tp06JyyZPnoy9e/dix44dOH78OJKSkhAUFCQuz8vLQ/fu3ZGdnY0zZ85g48aN2LBhA2bOnKmPQyEiqrDi4uPgFOYEr+lecApzQlx8XJHWa7OyTZHWM1R6GbZx27Zt+PXXX3HhwgWtywcNGgQPDw+4urrijz/+wCeffILr168jNjZW5zazsrKQlZUlvlcoFKUeNxGRNq62rpDL5EjPSocgCJBIJLCWWsPV1rVQ6ytzlBx+kYiI3ijlPdwxERGRNrweVTwmJiZwdnbWSH/69CnWr1+PrVu3okOHDgCAmJgYeHt749y5c2jevDkOHjyIa9eu4fDhw3ByckKjRo3w+eef45NPPsHs2bNhZmZW3odDRFTh6BpBK2VJymufSxZ3PUNW7pVn9+/fx8SJE3Ho0CHIZNoLdfTo0eL/GzZsCBcXFwQEBOD27dvw8vLSus68efMQERGhkZ6YmAhra+vSCb4cZGZmIiEhQd9hVEgsG91YNrqVV9lEB0djzM4xyMjKgKWZJaKDo5GSlFLgeqfvnhbXs5JaYXXf1Wjp2bLM4zXEcyY9PV3fIRARUSl4E2+6iIio4uH1qGK6efMmXF1dIZPJ4O/vj3nz5sHd3R0XL15ETk6O2ghWdevWhbu7O86ePYvmzZvj7NmzaNiwIZycnMQ8gYGBGDt2LK5evYrGjRtr3Scb5BPRm0Q1gpbKyyNo1XCoUerrGbJyrzy7ePEiUlNT0aRJEzEtLy8PJ06cwIoVK5CVlQVjY2O1dfz8/AAAt27d0ll5Fh4ejrCwMPG9QqGAm5sb3N3dDWq+tISEBHh4eOg7jAqJZaMby0a38iobDw8PBLUMKlIPMmWOEmMXjcWz7GcAgGfZzzD2u7HlcrNmiOcMb2CIiCqHN/Gmi4iIKh5ejyoePz8/bNiwAXXq1MHDhw8RERGB1q1b48qVK0hOToaZmRlsbW3V1nFyckJycjIAIDk5Wa3iTLVctUyXytIgHzDMhrLlgeWiG8tGt8paNjm5ObCSWuFZ1jMIECCBBJZSS+QocpCQqft4i7teRVDcBvnlXnkWEBCAy5cvq6UNHz4cdevWxSeffKJRcQYAly5dAgC4uLjo3K5UKoVUKi3VWImIikJmKivSTRZv1oiI6E1U0uGOiYiISgOvRxVP165dxf/7+PjAz88PHh4e2L59O8zNzctsv5WlQT5gmA1lywPLRTeWjW6VuWx2h+4Why22llkjdlws6njVKbP19K24DfLLvfLM2toaDRo0UEuztLSEvb09GjRogNu3b2Pr1q3o1q0b7O3t8ccff2Dy5Mlo06YNfHx8yjtcIqIyw5s1IiJ6E8lMZYgdF/vvTZf0xU0Xh8giIqLyxOtRxWdra4vatWvj1q1b6NSpE7Kzs5GWlqbW+ywlJUWcI83Z2Rk///yz2jZSUlLEZbqwQT4RvWkCvAOQsiSlSCNovbzehasX0Kx+s0p/zTTSdwCvMjMzw+HDh9G5c2fUrVsXU6ZMQXBwMPbu3avv0IiISpXqZs1a+mIYCN6sERHRm0J103U78jZSlqQgwDtA3yEREdEbiNejii0jIwO3b9+Gi4sLfH19YWpqiri4OHH59evXkZiYCH9/fwCAv78/Ll++jNTUVDHPoUOHIJfLUa9evXKPn4ioIlONoFXU55AyUxnc7dzfiOeX5d7zTJtjx46J/3dzc8Px48f1FwwRUTkqbksPIiIiQ1fU4Y6JiIjKAq9HFcfHH3+Mnj17wsPDA0lJSZg1axaMjY0xcOBA2NjYYMSIEQgLC0OVKlUgl8sxfvx4+Pv7o3nz5gCAzp07o169ehgyZAgWLlyI5ORkfPbZZwgNDWXPMiIiKrIKUXlGRPQm480aERERERERven++usvDBw4EI8fP4aDgwNatWqFc+fOwcHBAQDw1VdfwcjICMHBwcjKykJgYCCioqLE9Y2NjfHDDz9g7Nix8Pf3h6WlJUJCQjBnzhx9HRIRERkwVp4REREREVGxKXOU7EFNRER6xWtR5bBt27bXLpfJZFi5ciVWrlypM4+Hhwd++umn0g6NiIjeQBVuzjMiIqKKrnr16pBIJBqv0NBQAIBSqURoaCjs7e1hZWWF4OBgcaJqIqLKJC4+Dk5hTvCa7gWnMCfExccVvBIREVEp4rWIiIiIygIrz4iIiIrowoULePjwofg6dOgQAKBfv34AgMmTJ2Pv3r3YsWMHjh8/jqSkJAQFBekzZCKiUqfMUSIoKgjpWekAgPSsdARFBUGZo9RzZFSQBw8e4P3334e9vT3Mzc3RsGFD/PLLL+JyQRAwc+ZMuLi4wNzcHB07dsTNmzf1GDERkXa8FhEREVFZYeUZEdErlDlK3Hl0hzdcpJODgwOcnZ3F1w8//AAvLy+0bdsWT58+xfr167FkyRJ06NABvr6+iImJwZkzZ3Du3Dl9h05EVGqS0pKgUCogCAKAFxUuCqUCSWlJeo6MXufJkydo2bIlTE1NsW/fPly7dg2LFy+GnZ2dmGfhwoX4+uuvsWrVKpw/fx6WlpYIDAyEUsnfRkRUsfBaRERERGWFc54REb0kLj4OQVFBUCgVkMvkiB0XiwDvAH2HRRVYdnY2Nm/ejLCwMEgkEly8eBE5OTno2LGjmKdu3bpwd3fH2bNn0bx5c63bycrKQlZWlvheoVCUeexEhoxzm+ifq60r5DI50rPSIQgCJBIJrKXWcLV11Xdo9BoLFiyAm5sbYmJixDRPT0/x/4IgYOnSpfjss8/Qu3dvAMA333wDJycn7N69GwMGDCj3mImIdOG1iIiIiMoKK8+IiP5H15AfKUtS+GCWdNq9ezfS0tIwbNgwAEBycjLMzMxga2urls/JyQnJyck6tzNv3jxERERopCcmJsLa2ro0Qy5TmZmZSEhI0HcYFRLLRreils3pu6cxZucYZGRlwEpqhdV9V6OlZ8syjFA/DOGciQ6OFj8LSzNLRAdHIyWp7Od4NISyeVl6erq+QxDt2bMHgYGB6NevH44fP4633noL48aNw6hRowAAd+/eRXJyslojEBsbG/j5+eHs2bNaK8/YAISI9EVmKkPsuFixAaS11Bqx42J5/0ZEREQlxsozIqL/UQ35ofLykB81HGroMTKqyNavX4+uXbvC1bVkrVvDw8MRFhYmvlcoFHBzc4O7uzvkcnlJwyw3CQkJ8PDw0HcYFRLLRreilI0yR4mxi8biWfYzAMCz7GcY+93YStnQwRDOGQ8PDwS1DCr3XoCGUDYvq0iVSXfu3EF0dDTCwsIwffp0XLhwARMmTICZmRlCQkLEhh5OTk5q672uEUhlaQACGF7FbHli2WjHctGtvMqmpkVN/DzpZ6Smp8LR2hEyE1mF/0wM7bypSI1AiIiIygsrz4iI/odDflBRJSQk4PDhw4iNjRXTnJ2dkZ2djbS0NLXeZykpKXB2dta5LalUCqlUWpbhElUKbOhQ8chMZSx7A5Kfn4+mTZsiMjISANC4cWNcuXIFq1atQkhISLG2WVkagACGVzFbnlg22rFcdCvvsqmDOuW2r5IytPOmIjUCISKif3E6g7JlpO8AiIgqCtWQH9bSFy2kOeQHFSQmJgaOjo7o3r27mObr6wtTU1PExcWJadevX0diYiL8/f31ESZRpaJq6CCRSAAAEokEcpmcDR2ICsnFxQX16tVTS/P29kZiYiIAiA09UlLUh998XSMQqVQKuVyu9iIiIiIiorITFx8HpzAneE33glOYE+Li4wpeiYqElWdERC8J8A5AypIU3I68jZQlKQjwDtB3SFRB5efnIyYmBiEhITAx+bcjt42NDUaMGIGwsDAcPXoUFy9exPDhw+Hv74/mzZvrMWKiyoENHYhKpmXLlrh+/bpa2o0bN8QeEJ6ennB2dlZrBKJQKHD+/Hk2AiEiIiIiqgCUOUoERQUhPevFsLrpWekIigqCMkep58gqFw7bSET0Cg4/RYVx+PBhJCYm4oMPPtBY9tVXX8HIyAjBwcHIyspCYGAgoqKi9BAlUeWkaujA4SmIim7y5Mlo0aIFIiMj0b9/f/z8889Ys2YN1qxZA+BFb85JkyZh7ty5qFWrFjw9PTFjxgy4urqiT58++g2eiIiIiIg4nUE5YeUZERFRMXTu3BmCIGhdJpPJsHLlSqxcubKcoyJ6c7ChA1HxNGvWDLt27UJ4eDjmzJkDT09PLF26FIMHDxbzTJs2Dc+ePcPo0aORlpaGVq1aYf/+/ZDJWFFNRERERKRvqukM0rPSIQgCJBIJrKXWnM6glLHyjIiIiIiI6A3So0cP9OjRQ+dyiUSCOXPmYM6cOeUYFRERERERFYZqOoOgqCAolApOZ1BGWHlGRERERERERER6p8xRclhmIiKiQuB0BmXPSN8BEBERERERERHRmy0uPg5OYU7wmu4FpzAnxMXH6TskIiKiCk01nQErzsoGK8+IiIiIiIh0UOYocefRHShzlPoOhYio0lLmKBEUFYT0rHQAQHpWOoKigvjdS0RERHrDyjMiIiIiIiIt2AuCiKh8JKUlQaFUQBAEAIAgCFAoFUhKS9JzZERERPSmYuUZERERERHRK9gLgoio/LjaukIuk0MikQAAJBIJ5DI5XG1d9RwZERERvalYeUZERERERPQK9oIgIio+ZY4SiU8SC93gQGYqQ+y4WFhLrQEA1lJrxI6L5RwuREREpDcm+g6AiIiIiIioolH1gkjPSocgCJBIJLCWWrMXBBFRAeLi4xAUFQSFUgG5TI7YcbEI8A4ocL0A7wCkLElBUloSXG1dWXFGREREesWeZ0RUoSlzlLjz6A6HSCIiIqJyxV4QRERFV9Ihb2WmMtRwqMHvWiIiItI79jwjogqruC0WiYiIiEoDe0EQERWNashblZeHvK3hUEOPkREREREVDXueEVGFVNIWi6ptFGWcfSIiIqJXsRcEEVHhqYa8lUgkAACJRAK5TM4hb4mIiMjgsPKMiCokVYtFQRAAqLdYLIy4+Dg4hTmhzco2cApzQlx8XFmGS0REZPA4VDIREZUUh7wlIiKiyoKVZ0RUIZWkxWJp9FojIiJ6k6ganXhN92KjEyIiKhHVkLcnQk8gZUkKh94nIiIig8TKMyKqkErSYrGkvdaIiIjeJGx0QkREpU1mKoO7nTt7nBERERUCRwGpmPReeTZ//nxIJBJMmjRJTFMqlQgNDYW9vT2srKwQHByMlJQU/QVJRHqharF4O/J2kVoscpx9IiJ6UxXnpouNToiIiIiIiPSDo4BUXHqtPLtw4QJWr14NHx8ftfTJkydj79692LFjB44fP46kpCQEBQXpKUoi0ieZqQw1HGoUqcUix9knIqI3UXFvutjohIiIiIiIqPxxFJCKTW+VZxkZGRg8eDDWrl0LOzs7Mf3p06dYv349lixZgg4dOsDX1xcxMTE4c+YMzp07p69wicjAcJx9IqKi41ARhqskN11sdEJERERERFT+OApIxaa3yrPQ0FB0794dHTt2VEu/ePEicnJy1NLr1q0Ld3d3nD17trzDJCIDxnH2iYgKj0NFGLaS3nQVd6hkIiIiIiIiKh6OAlKx6aXybNu2bfj1118xb948jWXJyckwMzODra2tWrqTkxOSk5N1bjMrKwsKhULtRUQVB3szEBFVXBwqwvCVxk1XcYZKJsMze/ZsSCQStVfdunXF5e3atdNY/uGHH+oxYiIiIiKiyomjgFRsJuW9w/v372PixIk4dOgQZLLSOwnmzZuHiIgIjfTExERYW1uX2n7KWmZmJhISEvQdRoXEstGtopfN6bunMWbnGGRkZcBKaoXVfVejpWfLctl3RS8bfTHEcklPT9d3CESVlqrXksrLvZZqONTQY2RUWKqbrqCoICiUCt500WvVr18fhw8fFt+bmKjfFo4aNQpz5swR31tYWJRbbERUsShzlEhKS4KrrSuvKURERGVANQoIr7cVT7lXnl28eBGpqalo0qSJmJaXl4cTJ05gxYoVOHDgALKzs5GWlqbW+ywlJQXOzs46txseHo6wsDDxvUKhgJubG9zd3SGXy8vkWMpCQkICPDw89B1GhcSy0a0il40yR4mxi8biWfYzAMCz7GcY+91YpCxJKZeLQUUuG30yxHJhj2KisqPqtZSelQ5BECCRSGAtteZQEQaGN11UWCYmJq+9t7KwsHjtciJ6M8TFx4mNMuQyOWLHxXJYXyIiojKgGgWEKpZyH7YxICAAly9fxqVLl8RX06ZNMXjwYPH/pqamiIv7d56N69evIzExEf7+/jq3K5VKIZfL1V5EpH+c+JKIqOLjUBGVB4depMK4efMmXF1dUaNGDQwePBiJiYlqy7ds2YKqVauiQYMGCA8PR2Zmpp4iJSJ94ZDORERE9KYr955n1tbWaNCggVqapaUl7O3txfQRI0YgLCwMVapUgVwux/jx4+Hv74/mzZuXd7hEVELszUBEZBjYa4nozeDn54cNGzagTp06ePjwISIiItC6dWtcuXIF1tbWGDRoEDw8PODq6oo//vgDn3zyCa5fv47Y2Fid28zKykJWVpb4nr3FiQwfh3QmIiKiN125V54VxldffQUjIyMEBwcjKysLgYGBiIqK0ndYRFQMnIOFiMhwcKgIosqva9eu4v99fHzg5+cHDw8PbN++HSNGjMDo0aPF5Q0bNoSLiwsCAgJw+/ZteHl5ad1mZZl/GjDMeWHLC8tGu8paLjm5ObCSWuFZ1jMIECCBBJZSS+QocpCQWbjjraxlUxoMrWw4/zQREb2JKkTl2bFjx9Tey2QyrFy5EitXrtRPQERUqtibgSqbBw8e4JNPPsG+ffuQmZmJmjVrIiYmBk2bNgXwomXurFmzsHbtWqSlpaFly5aIjo5GrVq19Bw5ERGROltbW9SuXRu3bt3SutzPzw8AcOvWLZ2VZ5Vl/mnAMOeFLS8sG+0qc7nsDt39byNI2YtGkHW86hR6/cpcNiVlaGXDHsVERPQmKvc5z4jIsClzlLjz6E6Rx7rnHCxUWTx58gQtW7aEqakp9u3bh2vXrmHx4sWws7MT8yxcuBBff/01Vq1ahfPnz8PS0hKBgYFQKjlHBBFRSSlzlEh8ksh5d0pJRkYGbt++DRcXF63LL126BAA6lwOcf5qoslI1grwdeRspS1IQ4B2g75CIiIiIyk2F6HlGRIYhLj5ObHkol8kROy6WN1D0xlmwYAHc3NwQExMjpnl6eor/FwQBS5cuxWeffYbevXsDAL755hs4OTlh9+7dGDBgQLnHTERUWfC3SMl9/PHH6NmzJzw8PJCUlIRZs2bB2NgYAwcOxO3bt7F161Z069YN9vb2+OOPPzB58mS0adMGPj4++g6diPSAQzoTERHRm4o9z4ioUJQ5SgRFBSE968VY5+lZ6QiKCmKrb3rj7NmzB02bNkW/fv3g6OiIxo0bY+3ateLyu3fvIjk5GR07dhTTbGxs4Ofnh7Nnz+rcblZWFhQKhdqLiIj+xd8ipeOvv/7CwIEDUadOHfTv3x/29vY4d+4cHBwcYGZmhsOHD6Nz586oW7cupkyZguDgYOzdu1ffYRMREREREZUr9jwjokJJSkuCQvnvw3xBEKBQKpCUlsSWiPRGuXPnDqKjoxEWFobp06fjwoULmDBhAszMzBASEoLk5GQAgJOTk9p6Tk5O4jJt5s2bh4iICI30xMREWFtbl+5BlCFDm/y8PLFsdGPZaMdyUZf4JFHrb5ELVy/A3c5dj5EVLD09Xd8hiLZt26ZzmZubG44fP16O0RAREREREVVMrDwjokJxtXWFXCZHelY6BEGARCKBtdQarrau+g6NqFzl5+ejadOmiIyMBAA0btwYV65cwapVqxASElLs7YaHhyMsLEx8r1Ao4ObmBnd3d4OaO8bQJj8vTywb7ZQ5Sly4egHNajbjvJiv4DmjzsnVSetvkWb1K/65w97ERKRvyhwlktKS4GrrWuG/M4mIiAwVr7eVC4dtJKJCkZnKEDsuFtbSFz1grKXWiB0XywsBvXFcXFxQr149tTRvb28kJiYCAJydnQEAKSkpanlSUlLEZdpIpVLI5XK1F1FlFxcfB6cwJ7RZ2QZOYU6Ii4/Td0hUgfG3CBFR8aiut17TvXi9JSIiKiO83lY+rDwjokIL8A5AypIU3I68jZQlKQjwDtB3SETlrmXLlrh+/bpa2o0bN8TeIZ6ennB2dkZc3L8/khQKBc6fPw9/f/9yjZWoIuP8VVQcqt8iJ0JP8LcIEVEh8HpLRERU9ni9rZxYeUZERSIzlaGGQw228qY31uTJk3Hu3DlERkbi1q1b2Lp1K9asWYPQ0FAAgEQiwaRJkzB37lzs2bMHly9fxtChQ+Hq6oo+ffroN3iiCkQ1l6YgCADU59Ikeh2ZqQzudu78LUJEVAi83pIhmz9/vnh/paJUKhEaGgp7e3tYWVkhODhYY9SPxMREdO/eHRYWFnB0dMTUqVORm5tbztET0ZuE19vKiZVnRERERdCsWTPs2rUL3377LRo0aIDPP/8cS5cuxeDBg8U806ZNw/jx4zF69Gg0a9YMGRkZ2L9/P2QyPuglUlHNpSmRSAC8qHiWy+ScS5OIiOg1lDlK3Hl0p9At2Xm9JUN14cIFrF69Gj4+PmrpkydPxt69e7Fjxw4cP34cSUlJCAoKEpfn5eWhe/fuyM7OxpkzZ7Bx40Zs2LABM2fOLO9DIKI3CK+3lRMrz4iIiIqoR48euHz5MpRKJeLj4zFq1Ci15RKJBHPmzEFycjKUSiUOHz6M2rVr6ylaooqJ81cREREVTXHmUuH1lgxRRkYGBg8ejLVr18LOzk5Mf/r0KdavX48lS5agQ4cO8PX1RUxMDM6cOYNz584BAA4ePIhr165h8+bNaNSoEbp27YrPP/8cK1euRHZ2tr4OiYgqOV5vKydWnhERERGRXnD+KiIiosIpyVwqnLuaDE1oaCi6d++Ojh07qqVfvHgROTk5aul169aFu7s7zp49CwA4e/YsGjZsCCcnJzFPYGAgFAoFrl69qnV/WVlZUCgUai8ioqLi9bbyMdF3AERERET05uL8VURERAVTzaWi8vJcKjUcahS4vmruaqKKbtu2bfj1119x4cIFjWXJyckwMzODra2tWrqTkxOSk5PFPC9XnKmWq5ZpM2/ePERERGikJyYmwtraujiHoTeZmZlISEjQdxgVDstFN5aNbsUtG2MYIyUzpeCMBszQzpv09PRircfKMyIiIiIiqvSUOUokpSXB1daVlbVEZHBUc6mkZ6VDEARIJBJYS605lwpVKvfv38fEiRNx6NChcp0vOjw8HGFhYeJ7hUIBNzc3uLu7Qy6Xl1scpSEhIQEeHh76DqPCYbnoxrLRjWWjm6GVTXF7FHPYRiIiIiIiqtSKM08QEVFFwrlU6E1w8eJFpKamokmTJjAxMYGJiQmOHz+Or7/+GiYmJnByckJ2djbS0tLU1ktJSYGzszMAwNnZGSkpKRrLVcu0kUqlkMvlai8iIiJWnhERERERUaVVknmCiIgqEs6lQpVdQEAALl++jEuXLomvpk2bYvDgweL/TU1NERf3byOY69evIzExEf7+/gAAf39/XL58GampqWKeQ4cOQS6Xo169euV+TEREZLg4bCMREREREVVaJZ0niIioIuHcZVSZWVtbo0GDBmpplpaWsLe3F9NHjBiBsLAwVKlSBXK5HOPHj4e/vz+aN28OAOjcuTPq1auHIUOGYOHChUhOTsZnn32G0NBQSKXScj8mIiIyXKw8IyIiIiKiSovzBBEREVUeX331FYyMjBAcHIysrCwEBgYiKipKXG5sbIwffvgBY8eOhb+/PywtLRESEoI5c+boMWoiIjJErDwjIiIiIqJKSzVPUFBUEBRKBecJIiK9U+YokZSWBFdbV34XERXg2LFjau9lMhlWrlyJlStX6lzHw8MDP/30UxlHRkRElR0rz4iIiIioRPgQkCo61TxBPE+JSN/i4uPEyny5TI7YcbGcu4yIiIioAjLSdwBEREREZLji4uPgFOYEr+lecApzQlx8XMErEemBap4gVpwRkb4oc5QIigpCelY6ACA9Kx1BUUFQ5ij1HBkRERG9TJmjROKTRF6j33CsPCMiIiKiYuFDQCIiosJLSkuCQqmAIAgAAEEQoFAqkJSWpOfIiIiISEXVQLTNyjZsIPqGY+UZERERERULHwISEREVnqutK+QyOSQSCQBAIpFALpPD1dZVz5ERERERwAaipI6VZ0RERESVjDJHiTuP7pT5D3w+BCQyPLNnz4ZEIlF71a1bV1yuVCoRGhoKe3t7WFlZITg4GCkpKXqMmKjykJnKEDsuFtZSawCAtdQaseNiOZwsERFRBcEGovQyVp4RERERVSLlOQcZHwISGab69evj4cOH4uvUqVPissmTJ2Pv3r3YsWMHjh8/jqSkJAQFBekxWqLKJcA7AClLUnA78jZSlqQgwDtA3yERERHR/7CBKL3MRN8BEBEREVHp0DXERMqSlDKr0FI9BExKS4KrrSsrzogMgImJCZydnTXSnz59ivXr12Pr1q3o0KEDACAmJgbe3t44d+4cmjdvXt6hElVKMlMZajjU0HcYRERURpQ5St4fGShVA9GgqCAolAo2EH3DsecZERERUSWhryEmVA8BeUNBZBhu3rwJV1dX1KhRA4MHD0ZiYiIA4OLFi8jJyUHHjh3FvHXr1oW7uzvOnj2rc3tZWVlQKBRqLyIiIqI3UXmOBEJlQ9VA9EToCfYSf8Ox5xkRERFRJaEaYiI9Kx2CIEAikcBaas0hJohI5Ofnhw0bNqBOnTp4+PAhIiIi0Lp1a1y5cgXJyckwMzODra2t2jpOTk5ITk7Wuc158+YhIiJCIz0xMRHW1talfQhlKjMzEwkJCfoOo0Ji2WjHctGNZaOboZVNenq6vkMgMgj6GAmEyobMVAZ3O3d+bm84vVSeRUdHIzo6Gvfu3QPwYsz9mTNnomvXrgCAdu3a4fjx42rrjBkzBqtWrSrvUImIiIgMBoeYIKKCqO65AMDHxwd+fn7w8PDA9u3bYW5uXqxthoeHIywsTHyvUCjg5uYGd3d3yOXyEsdcnhISEuDh4aHvMCoklo12LBfdWDa6GVrZsEcxUeGoRgJReXkkkMIM18vhHokqFr1UnlWrVg3z589HrVq1IAgCNm7ciN69e+O3335D/fr1AQCjRo3CnDlzxHUsLCz0ESoRERGRQeEcZERUFLa2tqhduzZu3bqFTp06ITs7G2lpaWq9z1JSUrTOkaYilUohlUrLIVoiIiKiiqskI4HExceJjSDlMjlix8VyuEAiPdPLnGc9e/ZEt27dUKtWLdSuXRtffPEFrKyscO7cOTGPhYUFnJ2dxZehtVgkIiIi0hfOQUZEhZWRkYHbt2/DxcUFvr6+MDU1RVzcv3NzXL9+HYmJifD399djlEREREQVn2okEGvpi2GrCzsSiK7hHpU5yjKPmYh00/ucZ3l5edixYweePXumdkO2ZcsWbN68Gc7OzujZsydmzJjx2t5nWVlZyMrKEt+zSzkREREREZG6jz/+GD179oSHhweSkpIwa9YsGBsbY+DAgbCxscGIESMQFhaGKlWqQC6XY/z48fD390fz5s31HTpRmSnuMFnKHCUSnyTCydWJDVaIiAhA8UYCKelwj0RUNvRWeXb58mX4+/tDqVTCysoKu3btQr169QAAgwYNgoeHB1xdXfHHH3/gk08+wfXr1xEbG6tze5VlkmpDmzS2PLFsdGPZ6May0c4Qy4WTVBMREZXcX3/9hYEDB+Lx48dwcHBAq1atcO7cOTg4OAAAvvrqKxgZGSE4OBhZWVkIDAxEVFSUnqMmKjvFHSaLw2sREZEuqpFACqskwz0SUdnRW+VZnTp1cOnSJTx9+hQ7d+5ESEgIjh8/jnr16mH06NFivoYNG8LFxQUBAQG4ffs2vLy8tG6vskxSbWiTxpYnlo1uLBvdWDbaGWK5sEcxERFRyW3btu21y2UyGVauXImVK1eWU0RE+qNrmKyUJSmv7SlQ3PWIiIi0UQ33qGqUUdjhHomobOmt8szMzAw1a9YEAPj6+uLChQtYtmwZVq9erZHXz88PAHDr1i2dlWecpJqIiIiIiIiICqu4w2RxeC0iosqvuEP6FldxhnskorJlpO8AVPLz89XmLHvZpUuXAAAuLi7lGBFR0SlzlLjz6A4n9CSqxGbPng2JRKL2qlu3rrhcqVQiNDQU9vb2sLKyQnBwMFJSUvQYMREREdGboaj3Y6phsiQSCQBAIpFALpMXOExWcdcjIiLDEBcfB6cwJ3hN94JTmBPi4uPKZb+q4R5ZcUZUMeil8iw8PBwnTpzAvXv3cPnyZYSHh+PYsWMYPHgwbt++jc8//xwXL17EvXv3sGfPHgwdOhRt2rSBj4+PPsIlKhR9XViJqPzVr18fDx8+FF+nTp0Sl02ePBl79+7Fjh07cPz4cSQlJSEoKEiP0RIRERFVfsW5H1MNk2UtfTFPemGHySruekREVPHpGpqXDeWJ3jx6GbYxNTUVQ4cOxcOHD2FjYwMfHx8cOHAAnTp1wv3793H48GEsXboUz549g5ubG4KDg/HZZ5/pI1R6QxW1azbHvCd6s5iYmMDZ2Vkj/enTp1i/fj22bt2KDh06AABiYmLg7e2Nc+fOoXnz5uUdKhEREVGlV5L7seIOk6Va78LVC2hWvxnv+4iIKgkOzVu5lPfwm1S56KXybP369TqXubm54fjx4+UYDZG6uPg4cYJOuUyO2HGxCPAOeO06vLASvVlu3rwJV1dXyGQy+Pv7Y968eXB3d8fFixeRk5ODjh07innr1q0Ld3d3nD179rWVZ1lZWWrDFysUCp15iYiIiOhfJb0fUw2TVVQyUxnc7dz5MI6IqBJRDc2bnpUOQRAgkUhgLbXm0LwGqDjPeIleppfKM6KKqrgtFnlhJXpz+Pn5YcOGDahTpw4ePnyIiIgItG7dGleuXEFycjLMzMxga2urto6TkxOSk5Nfu9158+YhIiJCIz0xMRHW1taleQhlKjMzEwkJCfoOo0Ji2ejGstGO5aKboZVNenq6vkMgqtR4P0ZERKVFNTSvqtKFQ/MaJo4SRqWBlWdELylui8XSuLAWtxuxMkeJxCeJcHJ14pc/UTno2rWr+H8fHx/4+fnBw8MD27dvh7m5ebG3Gx4ejrCwMPG9QqGAm5sb3N3dIZfLSxRzeUpISICHh4e+w6iQWDa6sWy0Y7noZmhlw97ERGWLDzqJiKg0FXdIX6o4OEoYlQZWnhG9pCQtFktyYS1uN2J2PybSP1tbW9SuXRu3bt1Cp06d8P/s3XtYlHX+//HXcJqRw4iighSQYnm2g5aiZiammVkG1ZpuWmtaipbSwR+tWx5Sy+1rdlCz1tW2ct0sasutTCm1VIws09R1FQ0sBFoLB6UBxPv3h82sI4wCAsPA83Fdc8Xcx/f9dprP3PfnVFJSooKCApfeZ3l5eRXOkXYms9kss9lcy9ECAAA0TDzoBADUpOoO6Yv6gV7pqAk+ng4AqE8cLRZDzKeHSKtqi0VHwVrVnmMVdSO2l9prZb8z9z/408FKbw+gYsePH1dmZqZat26t7t27y9/fX2lpac71+/btU3Z2tuLi4jwYJQAAQMNXnfsxAADQ8FzoM15AoucZUE5dt1isbjfiC+l+TI81oPoeeeQRDRs2TDExMcrJydGTTz4pX19f3XXXXWratKnGjh2r5ORkNW/eXFarVZMnT1ZcXJx69erl6dABAAAAAAAaBXql40JReQZUoC67Zle3G3F192PCTODC/PDDD7rrrrt09OhRtWzZUn379lV6erpatmwpSXruuefk4+OjxMREFRcXa/DgwVq8eLGHo4a3qu58mAAAAAAANHYMv4kLwbCNgIdVtxtxdfdz9FgzDEOSa481AOe3atUq5eTkqLi4WD/88INWrVql2NhY53qLxaJFixbp559/1okTJ5Samnre+c6AiqTtTVN4crhiH49VeHK40vamnX8nAAAAAAAAXDB6ngH1QHW7ETv2y9idoas7X12p/ZgwEwDqP3oJAwAaM3peAwAAwNPoeQbUE9Wd3Nrib1F0s+hK78eEmQBQ/9FLGADQWNHzGgAAAPUBPc+ARogJMwGgfqOXMACgMaLnNQAAAOoLep4BjVR1e7oBAGofvYQBAI0RPa8BAABQX9DzDAAAoB6ilzAAoLGh5zUAAADqC3qeAQAA1FP0EgZQ255++mmZTCZNmTLFuax///4ymUwurwceeMBzQaLRoOc1AACoiL3UroM/HZS91O7pUNCI0PMMAAAAABqhjIwMLV26VN26dSu3bty4cZo1a5bzfWBgYF2GhkaMntcAgJpkL7Ur+5dshUeGU6Z4qbS9aUpYnCCb3SarxarUiamK7xjv6bDQCNDzDAAAAAAamePHj2vUqFF69dVX1axZs3LrAwMDFRER4XxZrVYPRInGip7XAICakLY3TeHJ4eq3qJ/Ck8OVtjfN0yGhiuyldiUsTlBhcaEkqbC4UAmLE+iBhjpB5RkAAAAANDJJSUkaOnSoBg4cWOH6N998Uy1atFCXLl2UkpKioqKiOo4QAACg+qh0aRhyCnJks9tkGIYkyTAM2ew25RTkeDgyNAYM2wgAAAAAjciqVav09ddfKyMjo8L1I0eOVExMjCIjI7Vz505NmzZN+/btU2pqaoXbFxcXq7i42PneZrPVStwAAACV5ah0cTiz0qVty7YejAxVERkaKavFqsLiQhmGIZPJpBBziCJDIz0dGhoBKs8AAAAAoJE4fPiwHnroIa1bt04WS8VD4o0fP975d9euXdW6dWvFx8crMzNTsbGx5bafN2+eZs6cWW55dna2QkJCai74OlBUVKSsrCxPh1EvkZuKkRf3yI173pabwsJCT4cAVBmVLg2Dxd+i1ImpzjnPQswhSp2YytDOqBNUngEAAABAI7F9+3bl5+frqquuci4rKyvTpk2b9NJLL6m4uFi+vr4u+/Ts2VOSdODAgQorz1JSUpScnOx8b7PZFBUVpejoaK+bKy0rK0sxMTGeDqNeIjcVIy/ukRv3vC039CiGN6LSpeGI7xivvAV5yinIUWRoJP+GqDNUngEAAABAIxEfH69du3a5LLv33nvVoUMHTZs2rVzFmSTt2LFDktS6desKj2k2m2U2m2s8VgAAgAvhqHTJ2J2hqztfTaWLF7P4WxhuE3WOyjMAAAAAaCRCQkLUpUsXl2VBQUEKCwtTly5dlJmZqZUrV+qmm25SWFiYdu7cqalTp6pfv37q1q2bh6IGAACoHou/RdHNoht0xZm91E6vLKAW+Hg6AKA22UvtOvjTQdlL7Z4OBQAAAKj3AgICtH79eg0aNEgdOnTQww8/rMTERH3wwQeeDg0AAABnSdubpvDkcMU+Hqvw5HCl7U3zdEhAg0HPMzRYaXvTnOMaWy1WpU5MVXzHeE+HBQAAANQrGzZscP4dFRWljRs3ei4YNCi0hAcAoPbYS+1KWJygwuJCSVJhcaESFicob0FevSx3+V0Ab0PPMzRI7goPeqABAAAAQO2jJTyAqlqyZIm6desmq9Uqq9WquLg4ffTRR871drtdSUlJCgsLU3BwsBITE5WXl+dyjOzsbA0dOlSBgYFq1aqVHn30UZ08ebKuLwWoEzkFObLZbTIMQ5JkGIZsdptyCnI8HFl5/C6AN6LyDA2SNxUeAAAAANCQ0JgRQHVcfPHFevrpp7V9+3Z99dVXGjBggG699Vbt3r1bkjR16lR98MEHWr16tTZu3KicnBwlJCQ49y8rK9PQoUNVUlKiLVu26LXXXtOKFSv0xBNPeOqSgFoVGRopq8Uqk8kkSTKZTLJarIoMjfRwZK74XQBvReUZGiRvKTwAAAAAoKGhMSOA6hg2bJhuuukmXXrppbrssss0Z84cBQcHKz09XceOHdOyZcu0YMECDRgwQN27d9fy5cu1ZcsWpaenS5I++eQT7dmzR2+88YauuOIKDRkyRLNnz9aiRYtUUlLi4asDap7F36LUiakKMYdIkkLMIUqdmFrvhkTkdwG8FZVnaJC8pfAAAAAAgIaGxowALlRZWZlWrVqlEydOKC4uTtu3b1dpaakGDhzo3KZDhw6Kjo7W1q1bJUlbt25V165dFR4e7txm8ODBstlszt5rFSkuLpbNZnN5of6xl9p18KeD9FY6S3zHeOUtyFPm3EzlLchTfMd4T4dUDr8L4K38PHHSJUuWaMmSJfr+++8lSZ07d9YTTzyhIUOGSDo9hvHDDz+sVatWqbi4WIMHD9bixYtdCj/gfByFBxNRAgAAAEDdcTRmTFicIJvdRmNGAJW2a9cuxcXFyW63Kzg4WO+++646deqkHTt2KCAgQKGhoS7bh4eHKzc3V5KUm5tb7tmh471jm4rMmzdPM2fOLLc8OztbISEhF3hFdauoqEhZWVmeDqPGbT60Wfe/fb+OFx9XsDlYS29fqj5t+lR6/4aalzP5yld5RXnn3/AsdZWbJYlLnP+GQQFBWpK4RHk5VY+3LjWGz011eVtuCgsLq7WfRyrPHGMYX3rppTIMQ6+99ppuvfVWffPNN+rcubOmTp2qf/3rX1q9erWaNm2qSZMmKSEhQZs3b/ZEuPBiFn+L2rZs6+kwAAAAAKBRoTEjgOpo3769duzYoWPHjuntt9/WmDFjtHHjxlo9Z0pKipKTk53vbTaboqKiFB0dLavVWqvnrmlZWVmKiYnxdBg1yl5q14RnJ+hEyQlJ0omSE5rwzgTlLcirdNnSEPNSU+oqNzExMUrok+BVvwv43Ljnbbmpbo9ij1SeDRs2zOX9nDlztGTJEqWnp+viiy/WsmXLtHLlSg0YMECStHz5cnXs2FHp6enq1auXJ0IGAAAAAABVQGNGAFUVEBCgdu3aSZK6d++ujIwMPf/88/rd736nkpISFRQUuPQ+y8vLU0REhCQpIiJCX375pcvx8vLynOvcMZvNMpvNNXwlqCmO+bIczpwvizLGu/C7AN7G43OeVWcM44owPjEAAAAAAADQcJw6dUrFxcXq3r27/P39lZaW5ly3b98+ZWdnKy4uTpIUFxenXbt2KT8/37nNunXrZLVa1alTpzqPHTWD+bLqH+afQ2PhkZ5n0oWNYVyRhjI+sbeNF1qXyI175MY9clMxb8xLdccnrm1PP/20UlJS9NBDD2nhwoWSmLsT5dlL7cr+JVvhkeFeMTwFAAAAUNdSUlI0ZMgQRUdHq7CwUCtXrtSGDRu0du1aNW3aVGPHjlVycrKaN28uq9WqyZMnKy4uzjlK1aBBg9SpUyfdfffdmj9/vnJzczV9+nQlJSXRs8yLMY9m/ZK2N835b2G1WJU6MVXxHeM9HRZQKzxWeVbTYxg3lPGJvW280LpEbtwjN+6Rm4p5Y17qY4/ijIwMLV26VN26dXNZztydOBM3FwAAb0cjEAB1IT8/X6NHj9aRI0fUtGlTdevWTWvXrtUNN9wgSXruuefk4+OjxMREl0aKDr6+vlqzZo0mTJiguLg4BQUFacyYMZo1a5anLgk1hHk0a0dVy3d7qV0JixNUWHy6cXNhcaESFidUaf45wJt4rPLsQsYwrgjjEwMA6tLx48c1atQovfrqq3rqqaecy48dO8bcnXDi5gIA4O1oBAKgrixbtuyc6y0WixYtWqRFixa53SYmJkYffvhhTYeGeoD5smpWdcp35p9DY+PxOc8cqjKGMQAAnpaUlKShQ4e6zNEpibk74cJxc2EYhiTXmwsAAOo7d41AmOMEAADvVd3ynfnn0Nh4pOfZhY5hDACAJ61atUpff/21MjIyyq3Lzc1l7k4vm0+vNpWeLFWwOVgnik/IkCGTTAoyB6nUVqqsIvLkwOemYuTFPW/LTX2dtxM4H1qYAwDQ8FS3fGf+OTQ2Hqk8u9AxjAEA8JTDhw/roYce0rp162Sx1NwPRObubLjeS3rvfzcXltM3F+1j23s6rHqFz03FyIt73pYbehPDWzlamBcWF8owDJlMJoWYQ2hhDgCoNnupnbnLPOxCynfmn0Nj4pHKs5oYwxgAAE/Yvn278vPzddVVVzmXlZWVadOmTXrppZe0du1a5u6EC8fNRcbuDF3d+WpuLgAAHlGdh5W0MAcA1CTm0awfLrR8Z/45NBYeqTwDqspealf2L9kKjwznRg2AR8XHx2vXrl0uy+6991516NBB06ZNU1RUlHPuzsTEREnM3YnTNxfRzaIpwwAAHnEhDytpBAIAqEhVG2W4m2crb0EeZYsHUL4D50flGeo9WqUAqE9CQkLUpUsXl2VBQUEKCwtzLmfuTgAAUF/UxMNKGoEAQMNVnZ7J1XlWxzya9Q/lO3BuPp4OADgXdzd69lK7hyMDAPeee+453XzzzUpMTFS/fv0UERGh1NRUT4cFAAAaIcfDSsMwJLk+rAQANG5pe9MUnhyu2MdjFZ4crrS9aefdp7rP6hzzbJlMJkmSyWSS1WJlHk0A9RaVZ6jXuNED4A02bNighQsXOt875u78+eefdeLECaWmpp5zvjMAADzl6aeflslk0pQpU5zL7Ha7kpKSFBYWpuDgYCUmJiovL89zQeKC8LASAFCR6laCVfdZnWOerRBziCQxjyaAeo/KM9Rr3OgBAAAAtSMjI0NLly5Vt27dXJZPnTpVH3zwgVavXq2NGzcqJydHCQkJHooSF4qHlQCAilS3EuxCntU55tnKnJupvAV5TMsCoF6j8gx1yl5q18GfDlZ62EVu9AAAAICad/z4cY0aNUqvvvqqmjVr5lx+7NgxLVu2TAsWLNCAAQPUvXt3LV++XFu2bFF6eroHI254qnpvdCF4WAkAOFt1K8Eu9Fmdxd+iti3b8mwPQL1H5RnqTHXGUZb+d6O3KWkTN3oAAABADUhKStLQoUM1cOBAl+Xbt29XaWmpy/IOHTooOjpaW7dureswvUJ1KsGqe290IXhYCQA404VUgtEoA0Bj4OfpANA4uBtHOW9BXqUKZYu/RdHNornRAwAAAC7QqlWr9PXXXysjI6PcutzcXAUEBCg0NNRleXh4uHJzcys8XnFxsYqLi53vbTZbjcZbn6XtTVPC4gTZ7DZZLValTkw97wPEC703AgCgpjgqwXIKchQZGlmlcsjRKAMAGioqz1AnHOMoO5w5jjIFLQAAAFA3Dh8+rIceekjr1q2TxVIzFTXz5s3TzJkzyy3Pzs5WSEhIjZyjrhQVFSkrK6tS29pP2jV80XCdKD4hSSq0F2r4ouH6aupXsvi5z232L9kV3htl7M5QdLPoSp03vzBfrUJanfM8Na0quWlMyIt75MY9b8tNYWGhp0NALaISDAAqRuUZ6oRjHOXC4kIZhiGTyaQQc0ilJhMFAAAAUDO2b9+u/Px8XXXVVc5lZWVl2rRpk1566SWtXbtWJSUlKigocOl9lpeXp4iIiAqPmZKSouTkZOd7m82mqKgoRUdHy2q11tq11IasrCzFxMRUatuDPx3U8eLjzveGDB0vPi5/q79iWro/RnhkeIX3Rld3vvq8Lf6r09OtplQlN40JeXGP3LjnbblpTD2KAQBwYM4z1IkLnUwUAAAAwIWLj4/Xrl27tGPHDuerR48eGjVqlPNvf39/paX9bw6uffv2KTs7W3FxcRUe02w2y2q1urwaA0cDQZPJJEkymUyyWqznbSBY3Xsjd8M9VmWuNQBAw2UvtSv7l2zKBQCoIfQ8Q525kHGUAQAAAFy4kJAQdenSxWVZUFCQwsLCnMvHjh2r5ORkNW/eXFarVZMnT1ZcXJx69erliZDrLUclmKMnWFUaCFbn3oih8AEA7niyZzIANFRUnqFOMY4yAAAAUL8999xz8vHxUWJiooqLizV48GAtXrzY02HVSxfSQLCq90YMhQ8AqIi7nsl5C/JouA4AF4DKMwAAgEqyl9rpQQ2gwdmwYYPLe4vFokWLFmnRokWeCcjL1FUDwQvp6QYAaLjomQwAtYPKMwAAgEpgKBQAaNgcc8WER4bX2wophsIHAJyNnskAUDt8PB0AAABAfeduKBQm4waAhiFtb5rCk8PVb1E/hSeHK21vmqdDcsvR042KMwCA9L+eySHmEEmiZzIA1BB6ngEAAJwHQ6EAQMPFXDEAAG/n6JmcsTtDV3e+mvILAGoAPc8aMXupXQd/OlitVvMXsi8AAN7GMRSKyWSSJJlMJlktVoZCAYAGwNFAwjAMSa4NJAAA8BYWf4uim0VTcQYANYTKswagOhVZjmFJYh+PrfKwJBeyLwAA3oihUACg4aKBBAAAAICzUXnm5apTkXUh87Yw5wsAoLFyDIWSOTdTeQvyFN8x3tMhAQBqAA0kAAAAAJyNyjMvVt2KrAsZloQhTQAAjZnF36K2LdvyQBUAGhhHA4lNSZtoIAEAAACAyrP6xF5qV/Yv2ZXuxVXdiqwLGZaEIU0AADWNeTQBAPUBc8UAAAAAcKDyrJ5wDL/Yb1G/Sg+/WN2KrAsZloQhTQAANYl5NAEAAADgNBoWAkD94efpAOB++MW8BXnnrJRyVGQlLE6QzW6rUkWWY1iSnIIcRYZGVqny60L2BQDAobrlHwAAAAA0NGl705zP+KwWq1InpjKMMAB4EJVn9YBj+EWHM4dfbNuy7Tn3vZCKLMe8LdVxIfsCACBdWPkHAAAAAA0FDQsBoP5h2MZ64ELnEXNUZFGYAgC8iafm0WQoFAAAAAD1iaNhoWEYklwbFgIAPIPKs3qAecQAAI2RJ8o/5lgDAAAAUN94qmEhAMA9j1SezZs3T1dffbVCQkLUqlUrDR8+XPv27XPZpn///jKZTC6vBx54wBPh1gnH8IubkjYpb0EeYxoDABoFR/mXOTezyuVfVXuQuRsKhR5oAAAAADyJhvUAUP94pPJs48aNSkpKUnp6utatW6fS0lINGjRIJ06ccNlu3LhxOnLkiPM1f/58T4RbZyz+FkU3i6ZgBIB6bMmSJerWrZusVqusVqvi4uL00UcfOdfb7XYlJSUpLCxMwcHBSkxMVF5engcjrv+qM/xwdXqQMRQKADQODM8LAPBGF9KwEABQ8/w8cdKPP/7Y5f2KFSvUqlUrbd++Xf369XMuDwwMVERERF2HBwCAWxdffLGefvppXXrppTIMQ6+99ppuvfVWffPNN+rcubOmTp2qf/3rX1q9erWaNm2qSZMmKSEhQZs3b/Z06A1GdSfTdgyFUlhcKMMwZDKZFGIOYSgUAGhA0vamKWFxgmx2m6wWq1InpvLwEQDgNRwNCwEAnlcv5jw7duyYJKl58+Yuy9988021aNFCXbp0UUpKioqKitweo7i4WDabzeXlKbR0BICGa9iwYbrpppt06aWX6rLLLtOcOXMUHBys9PR0HTt2TMuWLdOCBQs0YMAAde/eXcuXL9eWLVuUnp7u6dBrnb3Uruxfsmu9/KtuDzKGQgGAho3heQEAAADUFI/0PDvTqVOnNGXKFPXp00ddunRxLh85cqRiYmIUGRmpnTt3atq0adq3b59SU1MrPM68efM0c+bMcsuzs7MVEhJSa/GfbfOhzbr/7ft1vPi4gs3BWnr7UvVp06fS+xcVFSkrK6sWI/Re5MY9cuMeuamYN+alsLDQ0yGUU1ZWptWrV+vEiROKi4vT9u3bVVpaqoEDBzq36dChg6Kjo7V161b16tXL7bGKi4tVXFzsfF8TjUDspXblFOQoMjSy1iuI6rKl/4X0IHMMhVJXeQEA1B1H4wqHMxtX0IofAAAAQFV4vPIsKSlJ3333nb744guX5ePHj3f+3bVrV7Vu3Vrx8fHKzMxUbGxsueOkpKQoOTnZ+d5msykqKkrR0dGyWq21dwFnsJfaNeHZCTpRcnruthMlJzThnQnnHUbqTFlZWYqJianNML0WuXGP3LhHbirmjXnxZI/is+3atUtxcXGy2+0KDg7Wu+++q06dOmnHjh0KCAhQaGioy/bh4eHKzc095zFruhHIhTbmqAr7SbuGLxquE8Wny79Ce6GGLxqur6Z+JYtf7VROLUlc4ry+oIAgLUlcorycys8t5ytf5RXV7Vx03lhpXVfITcXIi3velpv61ABkyZIlWrJkib7//ntJUufOnfXEE09oyJAhkqT+/ftr48aNLvvcf//9evnll+s61CpjeF4AAAAANcWjlWeTJk3SmjVrtGnTJl188cXn3LZnz56SpAMHDlRYeWY2m2U2m2slzsqipSMANA7t27fXjh07dOzYMb399tsaM2ZMuQeNVVWTjUBqojFHVRz86aCOFx93vjdk6Hjxcflb/RXTsnYqaWNiYpTQJ8GrepB5Y6V1XSE3FSMv7nlbbupTA5Dzzd0pSePGjdO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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(5, 4, figsize=(17.5, 14), sharex=True)\n", + "fig.suptitle('Total Wages and Salaries (in Billion USD) by Industry 1998-2022')\n", + "\n", + "for i in range(20):\n", + " x = i // 4 # x-coordinate of graph (row number)\n", + " y = i % 4 # y-coordinate of graph (column number)\n", + "\n", + " industry_name = data_by_industry[i][0]\n", + " years, values = zip(*data_by_industry[i][1])\n", + " \n", + " axs[x, y].scatter(years, values, s=8, color='darkgreen')\n", + " axs[x, y].set_title(industry_name)\n", + " axs[x, y].grid(linewidth=0.5, color='lightgray')\n", + " axs[x, y].set_axisbelow(True)\n", + "\n", + "print(\"Note that the graphs below are not scaled together. Please check the y-axis labels to see the individual scales for each graph.\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cookbook-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/content/scripts/python/python_nws.ipynb b/content/scripts/python/python_nws.ipynb new file mode 100644 index 0000000..2065967 --- /dev/null +++ b/content/scripts/python/python_nws.ipynb @@ -0,0 +1,544 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# National Weather Service (NWS) API in Python\n", + "\n", + "by Michael T. Moen\n", + "\n", + "**NWS API documentation:** https://www.weather.gov/documentation/services-web-api\n", + "\n", + "**NWS terms of use:** From the documentation, \"All of the information presented via the API is intended to be open data, free to use for any purpose.\"\n", + "\n", + "*These recipe examples were tested on February 23, 2024.*\n", + "\n", + "**_NOTE:_** The NWS API imposes a rate limit on requests, but the amount is not published. We recommend using a 1 second delay between API requests." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "### User Agent Information\n", + "\n", + "A User Agent is required to access the NWS API. The User Agent identifies what application or program the is accessing the NWS API.\n", + "\n", + "Add your application name and email below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "user_agent = {\"User-Agent\": \"Application name, email@domain.com\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can save the above data in a separate python file and import it:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from user_agent import user_agent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries\n", + "\n", + "This tutorial uses the following libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import requests # Manages API requests\n", + "import matplotlib.pyplot as plt # Creates visualization of data\n", + "import matplotlib.dates as mdates # Provides functionality for working with dates in matplotlib plots\n", + "from time import sleep # Allows staggering of API requests to conform to rate limits\n", + "from dateutil import parser # Parses datetime strings into datetime objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Get the 12 hour forecast for a location\n", + "\n", + "The NWS publishes weather forecasts for each of its Weather Forecast Offices. A map of these offices and the regions they cover can be found here: https://www.weather.gov/srh/nwsoffices\n", + "\n", + "In order to obtain the forecast for a location, we must query that region's Weather Forecast Office using its code and the grid coordinates of the location. To determine these values for a location, we can query the `/points` endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'@context': ['https://geojson.org/geojson-ld/geojson-context.jsonld',\n", + " {'@version': '1.1',\n", + " 'wx': 'https://api.weather.gov/ontology#',\n", + " 's': 'https://schema.org/',\n", + " 'geo': 'http://www.opengis.net/ont/geosparql#',\n", + " 'unit': 'http://codes.wmo.int/common/unit/',\n", + " '@vocab': 'https://api.weather.gov/ontology#',\n", + " 'geometry': {'@id': 's:GeoCoordinates', '@type': 'geo:wktLiteral'},\n", + " 'city': 's:addressLocality',\n", + " 'state': 's:addressRegion',\n", + " 'distance': {'@id': 's:Distance', '@type': 's:QuantitativeValue'},\n", + " 'bearing': {'@type': 's:QuantitativeValue'},\n", + " 'value': {'@id': 's:value'},\n", + " 'unitCode': {'@id': 's:unitCode', '@type': '@id'},\n", + " 'forecastOffice': {'@type': '@id'},\n", + " 'forecastGridData': {'@type': '@id'},\n", + " 'publicZone': {'@type': '@id'},\n", + " 'county': {'@type': '@id'}}],\n", + " 'id': 'https://api.weather.gov/points/33.212,-87.5459',\n", + " 'type': 'Feature',\n", + " 'geometry': {'type': 'Point', 'coordinates': [-87.5459, 33.212]},\n", + " 'properties': {'@id': 'https://api.weather.gov/points/33.212,-87.5459',\n", + " '@type': 'wx:Point',\n", + " 'cwa': 'BMX',\n", + " 'forecastOffice': 'https://api.weather.gov/offices/BMX',\n", + " 'gridId': 'BMX',\n", + " 'gridX': 33,\n", + " 'gridY': 69,\n", + " 'forecast': 'https://api.weather.gov/gridpoints/BMX/33,69/forecast',\n", + " 'forecastHourly': 'https://api.weather.gov/gridpoints/BMX/33,69/forecast/hourly',\n", + " 'forecastGridData': 'https://api.weather.gov/gridpoints/BMX/33,69',\n", + " 'observationStations': 'https://api.weather.gov/gridpoints/BMX/33,69/stations',\n", + " 'relativeLocation': {'type': 'Feature',\n", + " 'geometry': {'type': 'Point', 'coordinates': [-87.5283, 33.234404]},\n", + " 'properties': {'city': 'Tuscaloosa',\n", + " 'state': 'AL',\n", + " 'distance': {'unitCode': 'wmoUnit:m', 'value': 2981.0037391988},\n", + " 'bearing': {'unitCode': 'wmoUnit:degree_(angle)', 'value': 213}}},\n", + " 'forecastZone': 'https://api.weather.gov/zones/forecast/ALZ023',\n", + " 'county': 'https://api.weather.gov/zones/county/ALC125',\n", + " 'fireWeatherZone': 'https://api.weather.gov/zones/fire/ALZ023',\n", + " 'timeZone': 'America/Chicago',\n", + " 'radarStation': 'KBMX'}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latitude = '33.211952'\n", + "longitude = '-87.545920'\n", + "\n", + "url = f'https://api.weather.gov/points/{latitude},{longitude}'\n", + "response = requests.get(url, headers=user_agent).json()\n", + "\n", + "# Display response from API\n", + "response" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Office code: BMX, Grid coordinates: 33, 69\n" + ] + } + ], + "source": [ + "office = response[\"properties\"][\"gridId\"]\n", + "gridX = response[\"properties\"][\"gridX\"]\n", + "gridY = response[\"properties\"][\"gridY\"]\n", + "\n", + "# Print grid location data\n", + "print(f'Office code: {office}, Grid coordinates: {gridX}, {gridY}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have obtained the grid coordinates and office code, we can use the `/gridpoints` endpoint to obtain the seven day forecast:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'number': 1,\n", + " 'name': 'This Afternoon',\n", + " 'startTime': '2024-02-23T13:00:00-06:00',\n", + " 'endTime': '2024-02-23T18:00:00-06:00',\n", + " 'isDaytime': True,\n", + " 'temperature': 69,\n", + " 'temperatureUnit': 'F',\n", + " 'temperatureTrend': None,\n", + " 'probabilityOfPrecipitation': {'unitCode': 'wmoUnit:percent', 'value': None},\n", + " 'dewpoint': {'unitCode': 'wmoUnit:degC', 'value': 7.222222222222222},\n", + " 'relativeHumidity': {'unitCode': 'wmoUnit:percent', 'value': 45},\n", + " 'windSpeed': '10 to 15 mph',\n", + " 'windDirection': 'NW',\n", + " 'icon': 'https://api.weather.gov/icons/land/day/few?size=medium',\n", + " 'shortForecast': 'Sunny',\n", + " 'detailedForecast': 'Sunny, with a high near 69. Northwest wind 10 to 15 mph.'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = f'https://api.weather.gov/gridpoints/{office}/{gridX},{gridY}/forecast'\n", + "response = requests.get(url, headers=user_agent).json()\n", + "\n", + "# Display first result\n", + "response[\"properties\"][\"periods\"][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can print the data we are interested in. In this case, we're looking at the temperature, probability of precipitation, and detailed forecast." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This Afternoon : 69F, 0%, Sunny, with a high near 69. Northwest wind 10 to 15 mph.\n", + "Tonight : 44F, 0%, Clear, with a low around 44. West wind 5 to 10 mph.\n", + "Saturday : 66F, 0%, Sunny. High near 66, with temperatures falling to around 61 in the afternoon. Northwest wind 5 to 15 mph.\n", + "Saturday Night : 35F, 0%, Clear, with a low around 35. Northeast wind 5 to 10 mph.\n", + "Sunday : 69F, 0%, Sunny. High near 69, with temperatures falling to around 63 in the afternoon. Southeast wind 5 to 10 mph.\n", + "Sunday Night : 48F, 0%, Mostly clear, with a low around 48.\n", + "Monday : 77F, 0%, Sunny, with a high near 77.\n", + "Monday Night : 59F, 0%, Partly cloudy, with a low around 59.\n", + "Tuesday : 80F, 0%, Partly sunny, with a high near 80.\n", + "Tuesday Night : 63F, 20%, A slight chance of rain showers after midnight. Mostly cloudy, with a low around 63. Chance of precipitation is 20%.\n", + "Wednesday : 73F, 50%, A chance of showers and thunderstorms. Mostly cloudy, with a high near 73. Chance of precipitation is 50%.\n", + "Wednesday Night : 46F, 40%, A chance of showers and thunderstorms. Mostly cloudy, with a low around 46. Chance of precipitation is 40%.\n", + "Thursday : 61F, 20%, A slight chance of showers and thunderstorms. Mostly sunny, with a high near 61. Chance of precipitation is 20%.\n", + "Thursday Night : 47F, 50%, A chance of showers and thunderstorms before midnight, then a chance of showers and thunderstorms. Partly cloudy, with a low around 47. Chance of precipitation is 50%.\n" + ] + } + ], + "source": [ + "for day in response[\"properties\"][\"periods\"]:\n", + " name = day[\"name\"]\n", + " temperature = day[\"temperature\"]\n", + " rain = day[\"probabilityOfPrecipitation\"][\"value\"] or 0\n", + " forecast = day[\"detailedForecast\"]\n", + " print(f'{name:<16}: {temperature:>3}F, {rain:>3}%, {forecast}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Get the hourly forecast for a location\n", + "\n", + "The `\\gridpoints` endpoint can also return an hourly forecast for a seven day period. The function below implements the code from the example above using this hourly forecast method:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Note that this function only works with valid latitude and longitude inputs\n", + "def getHourlyForecast(latitude, longitude, user_agent):\n", + " \n", + " points_url = f'https://api.weather.gov/points/{latitude},{longitude}'\n", + " points_response = requests.get(points_url, headers=user_agent).json()\n", + " office = points_response[\"properties\"][\"gridId\"]\n", + " gridX = points_response[\"properties\"][\"gridX\"]\n", + " gridY = points_response[\"properties\"][\"gridY\"]\n", + "\n", + " sleep(1) # Add 1 second delay between API requests\n", + "\n", + " # The addition of the \"/hourly\" to the end of this URL is the only difference between the hourly and daily forecasts\n", + " gridpoints_url = f'https://api.weather.gov/gridpoints/{office}/{gridX},{gridY}/forecast/hourly'\n", + " gridpoints_response = requests.get(gridpoints_url, headers=user_agent).json()\n", + " return gridpoints_response[\"properties\"][\"periods\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function can be easily used as such:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "156" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latitude = '33.211952'\n", + "longitude = '-87.545920'\n", + "hourlyForecast = getHourlyForecast(latitude, longitude, user_agent)\n", + "len(hourlyForecast)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we can use matplotlib to graph the temperature data:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hours = []\n", + "temperatures = []\n", + "for hourForecast in hourlyForecast:\n", + " hour = parser.parse(hourForecast[\"startTime\"][:19])\n", + " hours.append(hour)\n", + " temperatures.append(hourForecast[\"temperature\"])\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(hours, temperatures, color='coral', marker='o', s=10)\n", + "plt.title('Hourly Temperature Forecast for Next Week')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Temperature (Fahrenheit)')\n", + "plt.grid(True, which='both', color='lightgray')\n", + "plt.gca().set_axisbelow(True)\n", + "plt.gca().xaxis.set_major_locator(mdates.HourLocator(byhour=0))\n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n", + "plt.gca().xaxis.set_minor_locator(mdates.HourLocator(byhour=12))\n", + "plt.gca().xaxis.set_minor_formatter(mdates.DateFormatter(''))\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Get alerts for a location\n", + "\n", + "*This use-case is for demonstration purposes. To get current weather alerts in the U.S., be sure to also check your local NWS website directly (e.g., for Birmingham, AL area, see: https://www.weather.gov/bmx/).*\n", + "\n", + "The `/alerts` endpoint returns all alerts issued for a given location or area. The example below uses the `point` parameter to find all alerts for a pair of coordinates:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'@context': {'@version': '1.1'},\n", + " 'type': 'FeatureCollection',\n", + " 'features': [],\n", + " 'title': 'Current watches, warnings, and advisories for 33.211952 N, 87.54592 W',\n", + " 'updated': '2024-02-17T00:00:00+00:00'}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latitude = '33.211952'\n", + "longitude = '-87.545920'\n", + "\n", + "url = f'https://api.weather.gov/alerts/active?point={latitude},{longitude}'\n", + "response = requests.get(url, headers=user_agent).json()\n", + "\n", + "# Display response from API\n", + "response" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that there are no active alerts in the result above.\n", + "\n", + "The example below uses the `area` parameter to find all alerts for the state of Alabama:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'id': 'https://api.weather.gov/alerts/urn:oid:2.49.0.1.840.0.bd6153ef80e4aa2cc8ef67860f33f8dccf01d19b.001.1',\n", + " 'type': 'Feature',\n", + " 'geometry': None,\n", + " 'properties': {'@id': 'https://api.weather.gov/alerts/urn:oid:2.49.0.1.840.0.bd6153ef80e4aa2cc8ef67860f33f8dccf01d19b.001.1',\n", + " '@type': 'wx:Alert',\n", + " 'id': 'urn:oid:2.49.0.1.840.0.bd6153ef80e4aa2cc8ef67860f33f8dccf01d19b.001.1',\n", + " 'areaDesc': 'Mobile Coastal; Baldwin Coastal; Escambia Coastal; Santa Rosa Coastal; Okaloosa Coastal',\n", + " 'geocode': {'SAME': ['001097', '001003', '012033', '012113', '012091'],\n", + " 'UGC': ['ALZ265', 'ALZ266', 'FLZ202', 'FLZ204', 'FLZ206']},\n", + " 'affectedZones': ['https://api.weather.gov/zones/forecast/ALZ265',\n", + " 'https://api.weather.gov/zones/forecast/ALZ266',\n", + " 'https://api.weather.gov/zones/forecast/FLZ202',\n", + " 'https://api.weather.gov/zones/forecast/FLZ204',\n", + " 'https://api.weather.gov/zones/forecast/FLZ206'],\n", + " 'references': [],\n", + " 'sent': '2024-02-23T13:08:00-06:00',\n", + " 'effective': '2024-02-23T13:08:00-06:00',\n", + " 'onset': '2024-02-23T13:08:00-06:00',\n", + " 'expires': '2024-02-23T22:00:00-06:00',\n", + " 'ends': '2024-02-24T06:00:00-06:00',\n", + " 'status': 'Actual',\n", + " 'messageType': 'Alert',\n", + " 'category': 'Met',\n", + " 'severity': 'Moderate',\n", + " 'certainty': 'Likely',\n", + " 'urgency': 'Expected',\n", + " 'event': 'Rip Current Statement',\n", + " 'sender': 'w-nws.webmaster@noaa.gov',\n", + " 'senderName': 'NWS Mobile AL',\n", + " 'headline': 'Rip Current Statement issued February 23 at 1:08PM CST until February 24 at 6:00AM CST by NWS Mobile AL',\n", + " 'description': '* WHAT...Dangerous rip currents.\\n\\n* WHERE...In Alabama, Mobile Coastal and Baldwin Coastal\\nCounties. In Florida, Escambia Coastal, Santa Rosa Coastal and\\nOkaloosa Coastal Counties.\\n\\n* WHEN...Through late tonight.\\n\\n* IMPACTS...Rip currents can sweep even the best swimmers away\\nfrom shore into deeper water.',\n", + " 'instruction': \"Swim near a lifeguard. If caught in a rip current, relax and\\nfloat. Don't swim against the current. If able, swim in a\\ndirection following the shoreline. If unable to escape, face the\\nshore and call or wave for help.\",\n", + " 'response': 'Avoid',\n", + " 'parameters': {'AWIPSidentifier': ['CFWMOB'],\n", + " 'WMOidentifier': ['WHUS44 KMOB 231908'],\n", + " 'NWSheadline': ['HIGH RIP CURRENT RISK REMAINS IN EFFECT THROUGH LATE TONIGHT'],\n", + " 'BLOCKCHANNEL': ['EAS', 'NWEM', 'CMAS'],\n", + " 'VTEC': ['/O.CON.KMOB.RP.S.0007.000000T0000Z-240224T1200Z/'],\n", + " 'eventEndingTime': ['2024-02-24T12:00:00+00:00'],\n", + " 'expiredReferences': ['w-nws.webmaster@noaa.gov,urn:oid:2.49.0.1.840.0.70345bf491fe21f0db1b5964f2b8364de86995f8.001.1,2024-02-23T04:56:00-06:00 w-nws.webmaster@noaa.gov,urn:oid:2.49.0.1.840.0.828dd06944269a0d0ad4ec031f4aa1b7ae372686.001.1,2024-02-22T21:28:00-06:00 w-nws.webmaster@noaa.gov,urn:oid:2.49.0.1.840.0.6855a2a47102d526aae1769aef222329cccf8bd5.001.1,2024-02-22T13:07:00-06:00 w-nws.webmaster@noaa.gov,urn:oid:2.49.0.1.840.0.f566540bba690d5159ffe102b5f70192c708c39a.001.1,2024-02-22T06:11:00-06:00 w-nws.webmaster@noaa.gov,urn:oid:2.49.0.1.840.0.ea0d41539efa6eb01e5c7831c7b16b35dee0a259.001.1,2024-02-21T21:51:00-06:00 w-nws.webmaster@noaa.gov,urn:oid:2.49.0.1.840.0.762c6681e40b5427a745a7b08d271b70f9ceb41d.001.1,2024-02-21T13:57:00-06:00']}}}]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state = 'AL'\n", + "\n", + "url = f'https://api.weather.gov/alerts/active?area={state}'\n", + "response = requests.get(url, headers=user_agent).json()\n", + "\n", + "# Display response from API\n", + "response['features']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the example above, there is one alert in the state of Alabama:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Rip Current Statement issued February 23 at 1:08PM CST until February 24 at 6:00AM CST by NWS Mobile AL'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response['features'][0]['properties']['headline']" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cookbook-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}