"""Handle remote datasets."""

import warnings

import requests
from bs4 import BeautifulSoup

from .base import Example

# remote_dict holds the metadata for remote datasets from the geoda center
# to update prior to release run _remote_data()

_remote_dict = {
    "AirBnB": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/airbnb.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//airbnb/",
        "n": "77",
        "k": "20",
        "description": "Airbnb rentals, socioeconomics, and crime in Chicago",
    },
    "Atlanta": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab/" "/data/atlanta_hom.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//atlanta_old/",
        "n": "90",
        "k": "23",
        "description": "Atlanta, GA region homicide counts and rates",
    },
    "Baltimore": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/baltimore.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//baltim/",
        "n": "211",
        "k": "17",
        "description": "Baltimore house sales prices and hedonics",
    },
    "Bostonhsg": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/boston.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//boston-housing/",
        "n": "506",
        "k": "23",
        "description": "Boston housing and neighborhood data",
    },
    "Buenosaires": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/buenosaires.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//buenos-aires_old/",
        "n": " 209",
        "k": " 21",
        "description": " Electoral Data for 1999 Argentinean Elections",
    },
    "Cars": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab/"
            "/data/Abandoned_Vehicles_Map.csv"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab//1-source-and-description/"
        ),
        "n": "137,867",
        "k": "21",
        "description": "2011 abandoned vehicles in Chicago (311 complaints).",
    },
    "Charleston1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/CharlestonMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//charleston-1_old/",
        "n": " 117",
        "k": " 30",
        "description": " 2000 Census Tract Data for Charleston, SC MSA and counties",
    },
    "Charleston2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/CharlestonMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//charleston2/",
        "n": " 44",
        "k": " 97",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns (Census Bureau)"
            " for Charleston, SC MSA"
        ),
    },
    "Chicago Health": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/comarea.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//comarea_vars/",
        "n": " 77",
        "k": " 86",
        "description": " Chicago Health + Socio-Economics",
    },
    "Chicago commpop": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/chicago_commpop.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//commpop/",
        "n": " 77",
        "k": " 8",
        "description": (
            " Chicago Community Area Population Percent Change for 2000 and 2010"
        ),
    },
    "Chicago parcels": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab/"
            "/https://uchicago.box.com/s/j2d2ch5uvckse24y8l7vh9198wnq216i"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//parcels/",
        "n": " 592,521",
        "k": " 5",
        "description": " Tax parcel polygons of Cook county",
    },
    "Chile Labor": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/flma.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//FLMA/",
        "n": "141",
        "k": "62",
        "description": "Labor Markets in Chile (1982-2002)",
    },
    "Chile Migration": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab/"
            "/https://uchicago.box.com/s/yqc97nq23hoeeqo5lkc2grlg98skokgk"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//CHIM/",
        "n": " 304",
        "k": " 10",
        "description": " Internal Migration in Chile (1977-2002)",
    },
    "Cincinnati": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/walnuthills_updated.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//walnut_hills/",
        "n": " 457",
        "k": " 89",
        "description": " 2008 Cincinnati Crime + Socio-Demographics",
    },
    "Cleveland": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/cleveland.zip"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab//clev_sls_154_core/"
        ),
        "n": " 205",
        "k": " 9",
        "description": " 2015 sales prices of homes in Cleveland, OH.",
    },
    "Columbus": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/columbus.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//columbus/",
        "n": " 49",
        "k": " 20",
        "description": " Columbus neighborhood crime",
    },
    "Elections": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/election.zip"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab/"
            "/county_election_2012_2016-variables/"
        ),
        "n": " 3,108",
        "k": " 74",
        "description": " 2012 and 2016 Presidential Elections",
    },
    "Grid100": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/grid100.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//grid100/",
        "n": " 100",
        "k": " 34",
        "description": " Grid with simulated variables",
    },
    "Groceries": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/grocery.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//chicago_sup_vars/",
        "n": " 148",
        "k": " 7",
        "description": " 2015 Chicago supermarkets",
    },
    "Guerry": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/guerry.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//Guerry/",
        "n": " 85",
        "k": " 23",
        "description": " Moral statistics of France (Guerry, 1833)",
    },
    "Health+": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/income_diversity.zip"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab/"
            "/co_income_diversity_variables/"
        ),
        "n": " 3,984",
        "k": " 64",
        "description": " 2000 Health, Income + Diversity",
    },
    "Health Indicators": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/healthIndicators.zip"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab//healthindicators-variables/"
        ),
        "n": " 77",
        "k": " 31",
        "description": " Chicago Health Indicators (2005-11)",
    },
    "Hickory1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/HickoryMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//hickory1/",
        "n": " 68",
        "k": " 30",
        "description": " 2000 Census Tract Data for Hickory, NC MSA and counties",
    },
    "Hickory2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/HickoryMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//hickory2/",
        "n": " 29",
        "k": " 55",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns "
            "(Census Bureau) for Hickory, NC MSA"
        ),
    },
    "Home Sales": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/kingcounty.zip"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab//KingCounty-HouseSales2015/"
        ),
        "n": " 21,613",
        "k": " 21",
        "description": " 2014-15 Home Sales in King County, WA",
    },
    "Houston": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/houston_hom.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//houston/",
        "n": " 52",
        "k": " 23",
        "description": " Houston, TX region homicide counts and rates",
    },
    "Juvenile": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/juvenile.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//juvenile/",
        "n": " 168",
        "k": " 3",
        "description": " Cardiff juvenile delinquent residences",
    },
    "Lansing1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/LansingMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//lansing1/",
        "n": " 117",
        "k": " 30",
        "description": " 2000 Census Tract Data for Lansing, MI MSA and counties",
    },
    "Lansing2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/LansingMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//lansing2/",
        "n": " 46",
        "k": " 55",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns "
            "(Census Bureau) for Lansing, MI MSA"
        ),
    },
    "Laozone": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/laozone.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//ozone/",
        "n": " 32",
        "k": " 8",
        "description": " Ozone measures at monitoring stations in Los Angeles basin",
    },
    "LasRosas": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/lasrosas.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//lasrosas/",
        "n": " 1,738",
        "k": " 34",
        "description": (
            " Corn yield, fertilizer and field data for precision "
            "agriculture, Argentina, 1999"
        ),
    },
    "Liquor Stores": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/liquor.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//liq_chicago/",
        "n": " 571",
        "k": " 2",
        "description": " 2015 Chicago Liquor Stores",
    },
    "Malaria": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/malariacolomb.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//colomb_malaria/",
        "n": " 1,068",
        "k": " 50",
        "description": (
            " Malaria incidence and population (1973, 95, 93 "
            "censuses and projections until 2005) \xa0 \xa0 \xa0"
        ),
    },
    "Milwaukee1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/MilwaukeeMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//milwaukee1/",
        "n": " 417",
        "k": " 31",
        "description": " 2000 Census Tract Data for Milwaukee, WI MSA",
    },
    "Milwaukee2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/MilwaukeeMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//milwaukee2/",
        "n": " 83",
        "k": " 55",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns "
            "(Census Bureau) for Milwaukee, WI MSA"
        ),
    },
    "NCOVR": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/ncovr.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//ncovr/",
        "n": "3,085",
        "k": " 69",
        "description": " US county homicides 1960-1990",
    },
    "Natregimes": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/natregimes.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//natregimes/",
        "n": " 3,085",
        "k": " 73",
        "description": " NCOVR with regimes (book/PySAL)",
    },
    "NDVI": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/ndvi.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//ndvi/",
        "n": " 49",
        "k": " 5",
        "description": " Normalized Difference Vegetation Index grid",
    },
    "Nepal": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/nepal.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//nepal/",
        "n": " 75",
        "k": " 61",
        "description": " Health, poverty and education indicators for Nepal districts",
    },
    "NYC": {
        "download_url": "https://geodacenter.github.io/data-and-lab///data/nyc.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//nyc/",
        "n": " 55",
        "k": " 34",
        "description": (
            " Demographic and housing data for New York City subboroughs, 2002-09"
        ),
    },
    "NYC Earnings": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/lehd.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//LEHD_Data/",
        "n": " 108,487",
        "k": " 70",
        "description": " Block-level Earnings in NYC (2002-14)",
    },
    "NYC Education": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/nyc_2000Census.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//NYC-Census-2000/",
        "n": " 2,216",
        "k": " 56",
        "description": " NYC Education (2000)",
    },
    "NYC Neighborhoods": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/nycnhood_acs.zip"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab//NYC-Nhood-ACS-2008-12/"
        ),
        "n": " 195",
        "k": " 98",
        "description": " Demographics for New York City neighborhoods",
    },
    "NYC Socio-Demographics": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/nyctract_acs.zip"
        ),
        "explain_url": (
            "https://geodacenter.github.io/data-and-lab//NYC_Tract_ACS2008_12/"
        ),
        "n": " 2,166",
        "k": " 113",
        "description": " NYC Education + Socio-Demographics",
    },
    "Ohiolung": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/ohiolung.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//ohiolung/",
        "n": " 88",
        "k": " 42",
        "description": " Ohio lung cancer data, 1968, 1978, 1988",
    },
    "Orlando1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/OrlandoMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//orlando1/",
        "n": " 328",
        "k": " 30",
        "description": " 2000 Census Tract Data for Orlando, FL MSA and counties",
    },
    "Orlando2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/OrlandoMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//orlando2/",
        "n": " 94",
        "k": " 59",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns "
            "(Census Bureau) for Orlando, FL MSA"
        ),
    },
    "Oz9799": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/oz9799.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//oz96/",
        "n": " 30",
        "k": " 78",
        "description": " Monthly ozone data, 1997-99",
    },
    "Phoenix ACS": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/phx2.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//phx/",
        "n": " 685",
        "k": " 17",
        "description": (
            " Phoenix American Community Survey Data (2010, 5-year averages)"
        ),
    },
    "Pittsburgh": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/pittsburgh.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//pitt93/",
        "n": " 143",
        "k": " 8",
        "description": " Pittsburgh homicide locations",
    },
    "Police": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/police.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//police/",
        "n": " 82",
        "k": " 21",
        "description": " Police expenditures Mississippi counties",
    },
    "Sacramento1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/sacramento.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//sacramento1/",
        "n": " 403",
        "k": " 30",
        "description": " 2000 Census Tract Data for Sacramento MSA",
    },
    "Sacramento2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/SacramentoMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//sacramento2/",
        "n": " 125",
        "k": " 53",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns "
            "(Census Bureau) for Sacramento MSA"
        ),
    },
    "SanFran Crime": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab/"
            "/data/SFCrime_July_Dec2012.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//SFcrimes_vars/",
        "n": " 3,384",
        "k": " 13",
        "description": (
            " July-Dec 2012 crime incidents in San Francisco "
            "(points + area) - for CAST"
        ),
    },
    "Savannah1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/SavannahMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//savannah1/",
        "n": " 77",
        "k": " 30",
        "description": " 2000 Census Tract Data for Savannah, GA MSA and counties",
    },
    "Savannah2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/SavannahMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//savannah2/",
        "n": " 24",
        "k": " 55",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns "
            "(Census Bureau) for Savannah, GA MSA"
        ),
    },
    "Scotlip": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/scotlip.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//scotlip/",
        "n": " 56",
        "k": " 11",
        "description": " Male lip cancer in Scotland, 1975-80",
    },
    "Seattle1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/SeattleMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//seattle1/",
        "n": " 664",
        "k": " 30",
        "description": " 2000 Census Tract Data for Seattle, WA MSA and counties",
    },
    "Seattle2": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/SeattleMSA2.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//seattle2/",
        "n": " 145",
        "k": " 59",
        "description": (
            " 1998 and 2001 Zip Code Business Patterns "
            "(Census Bureau) for Seattle, WA MSA"
        ),
    },
    "SIDS": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/sids.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//sids/",
        "n": " 100",
        "k": " 13",
        "description": " North Carolina county SIDS death counts",
    },
    "SIDS2": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/sids2.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//sids2/",
        "n": " 100",
        "k": " 17",
        "description": " North Carolina county SIDS death counts and rates",
    },
    "Snow": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/snow.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//snow/",
        "n": " NA",
        "k": " NA",
        "description": " John Snow & the 19th Century Cholera Epidemic",
    },
    "South": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/south.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//south/",
        "n": " 1,412",
        "k": " 69",
        "description": " US Southern county homicides 1960-1990",
    },
    "Spirals": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/spirals.csv",
        "explain_url": "https://geodacenter.github.io/data-and-lab//spirals/",
        "n": " 301",
        "k": " 2",
        "description": " Synthetic spiral points",
    },
    "StLouis": {
        "download_url": "https://geodacenter.github.io/data-and-lab//data/stlouis.zip",
        "explain_url": "https://geodacenter.github.io/data-and-lab//stlouis/",
        "n": " 78",
        "k": " 23",
        "description": " St Louis region county homicide counts and rates",
    },
    "Tampa1": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/TampaMSA.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//tampa1/",
        "n": " 547",
        "k": " 30",
        "description": " 2000 Census Tract Data for Tampa, FL MSA and counties",
    },
    "US SDOH": {
        "download_url": (
            "https://geodacenter.github.io/data-and-lab//data/us-sdoh-2014.zip"
        ),
        "explain_url": "https://geodacenter.github.io/data-and-lab//us-sdoh/",
        "n": " 71,901",
        "k": " 25",
        "description": " 2014 US Social Determinants of Health Data",
    },
}


def _remote_data():
    """Helper function to get remote metadata for each release.

    Returns
    -------
    datasets : dict
        Remote data sets keyed by the dataset name.
        Values are dictionaries with the following keys
        'download_url', 'explain_url', 'n', 'k', 'description'.
    """

    url = "https://geodacenter.github.io/data-and-lab//"
    try:
        page = requests.get(url, timeout=(10, None))
    except:  # noqa: E722
        warnings.warn("Remote data sets not available. Check connection.")  # noqa: B028
        return {}
    soup = BeautifulSoup(page.text, "html.parser")
    samples = soup.find(class_="samples")
    rows = samples.find_all("tr")
    datasets = {}
    for row in rows[1:]:
        data = row.find_all("td")
        name = data[0].text.strip()
        description = data[1].text
        n = data[2].text
        k = data[3].text
        targets = row.find_all("a")
        download_url = url + targets[1].attrs["href"]
        explain_url = url + targets[0].attrs["href"]
        datasets[name] = {
            "download_url": download_url,
            "explain_url": explain_url,
            "n": n,
            "k": k,
            "description": description,
        }
    return datasets


def _build_remotes():
    """Build remote meta data.

    Returns
    -------
    datasets : dict
        Example datasets keyed by the dataset name.
    """

    datasets = {}
    for name in _remote_dict:
        description = _remote_dict[name]["description"]
        n = _remote_dict[name]["n"]
        k = _remote_dict[name]["k"]
        download_url = _remote_dict[name]["download_url"]
        explain_url = _remote_dict[name]["explain_url"]
        datasets[name] = Example(name, description, n, k, download_url, explain_url)

    # Other Remotes
    # rio
    name = "Rio Grande do Sul"
    description = "Cities of the Brazilian State of Rio Grande do Sul"
    n = 497
    k = 3
    download_url = "https://github.com/sjsrey/rio_grande_do_sul/archive/master.zip"
    explain_url = (
        "https://raw.githubusercontent.com/sjsrey/rio_grande_do_sul/master/README.md"
    )
    datasets[name] = Example(name, description, n, k, download_url, explain_url)

    # nyc bikes
    name = "nyc_bikes"
    description = "New York City Bike Trips"
    n = 14042
    k = 27
    download_url = "https://github.com/sjsrey/nyc_bikes/archive/master.zip"
    explain_url = "https://raw.githubusercontent.com/sjsrey/nyc_bikes/master/README.md"
    datasets[name] = Example(name, description, n, k, download_url, explain_url)

    # taz
    name = "taz"
    description = "Traffic Analysis Zones in So. California"
    n = 4109
    k = 14
    download_url = "https://github.com/sjsrey/taz/archive/master.zip"
    explain_url = "https://raw.githubusercontent.com/sjsrey/taz/master/README.md"
    datasets[name] = Example(name, description, n, k, download_url, explain_url)

    # clearwater
    name = "clearwater"
    description = "mgwr testing dataset"
    n = 239
    k = 14
    download_url = "https://github.com/sjsrey/clearwater/archive/master.zip"
    explain_url = "https://raw.githubusercontent.com/sjsrey/clearwater/master/README.md"
    datasets[name] = Example(name, description, n, k, download_url, explain_url)

    # newHaven
    name = "newHaven"
    description = "Network testing dataset"
    n = 3293
    k = 5
    download_url = "https://github.com/sjsrey/newHaven/archive/master.zip"
    explain_url = "https://raw.githubusercontent.com/sjsrey/newHaven/master/README.md"
    datasets[name] = Example(name, description, n, k, download_url, explain_url)

    # Chicago SDOH
    name = "chicagoSDOH"
    description = "Chicago census tract SDOH variables"
    n = 791
    k = 65
    download_url = "https://github.com/lanselin/spreg_sample_data/archive/master.zip"
    explain_url = (
        "https://raw.githubusercontent.com/lanselin/spreg_sample_data/master/README.md"
    )
    datasets[name] = Example(name, description, n, k, download_url, explain_url)

    # remove Cars dataset as it is broken
    datasets.pop("Cars")

    return datasets


class Remotes:
    """Remote datasets."""

    def __init__(self):
        """Initialize Remotes."""
        self._datasets = None

    @property
    def datasets(self):
        """Create dictionary of remotes."""
        if self._datasets is None:
            self._datasets = _build_remotes()
        return self._datasets


datasets = Remotes()
