def fetch(self,
location: str,
include_fields: List,
limit: int = None) -> pd.DataFrame:
"""
Fetch General Data about various locations supported by the C3 API
Args:
location (str): Location ID (i.e. "UnitedStates" or "Alameda_California_UnitedStates")
include_fields (List): List of fields to return (reference API) parameter is a powerful way to fetch data from multiple C3.ai Types
limit (int, optional): [description]. Max Limit of rows
Returns:
pd.DataFrame: [description]
"""
assert len(location) > 0, "location must be specified"
spec = {"filter": "id == '{}'".format(location)}
if len(include_fields) > 0:
spec["include"] = ", ".join(include_fields)
if limit:
spec["limit"] = limit
return datalake.fetch("outbreaklocation", {"spec": spec})
def fetch(self,
parent,
year : int = None,
month : int = None,
laborForce : int = None,
employedPopulation : int = None,
unemployedPopulation : int = None,
unemployentRate : float = None,
origin : str = ""
) -> pd.DataFrame:
field_names = [ 'year',
'month',
'laborForce',
'employedPopulation',
'unemployedPopulation',
'unemployentRate',
'origin']
fields = [id_ for id_ in
[year,
month,
laborForce,
employedPopulation,
unemployedPopulation,
unemployentRate,
origin]
]
clause = '&&'.join([f" {name} == '{field}'" for name, field in zip(field_names, fields) if field])
assert parent, "location must be specified"
print(parent)
print(clause)
spec = {
"filter" : f"contains(parent, '{parent}')" + " && " + clause,
}
if limit:
spec['limit'] = limit
print(spec)
return datalake.fetch(
"labordetail",
{
"spec":spec
},
get_all=True
)
def fetch(self,
location: str,
name: str = "",
hospitalType: str = "",
address: str = "",
lat: float = None,
lon: float = None,
liscensedBeds: int = None,
staffedBeds: int = None,
icuBeds: int = None,
icuBedsAdult: int = None,
icuBedsPedi: int = None,
icuBedsPotential: int = None,
ventilatorUsage: int = None,
bedUtilization: float = None,
limit: int = None) -> pd.DataFrame:
'''fetch following the c3ai. api https://c3.ai/covid-19-api-documentation/#tag/Hospital/paths/~1api~11~1hospital~1fetch/post
gets information about hospitals from c3ai's datalake
Args:
name (str): Hosptial name
location (OutbreakLocation): C3.ai Type OutbreakLocation associated with hospital.
hospitalType (str): Hospital type. Short Term Acute Care Hospital, Critical Access Hospital, VA Hospital, etc.
address (str): Hospital address
lat (float): Hospital's lat
lon (float): Hospital's lon
liscensedBeds (int): Maximum number of beds the hospital holds the license to operate
staffedBeds (int): Number of adult, pediatric, birthing room, and ICU beds maintained in patient care areas of the hospital.
icuBeds (int): Number of intensive care unit (ICU) beds in the hospital
icuBedsAdult (int): Number of ICU beds for adults in the hospital
icuBedsPedi (int): Number of pediatric ICU beds in the hospital
icuBedsPotential (int): Potential increase in bed capacity in the hospital, computed as the number of licensed beds minus the number of staffed beds
ventilatorUsage (int): Hospital's average number of ventilators in use
bedUtilization (float): Hospital's average bed utilization rate, computed based on the Medicare Cost Report as the total number of patient days (excluding nursery days) divided by the available bed days.
'''
field_names = [
'name', 'hospitalType', 'address', 'lat', 'lon', 'liscensedBeds',
'staffedBeds', 'icuBeds', 'icuBedsAdult', 'icuBedsPedi',
'icuBedsPotential', 'ventilatorUsage', 'bedUtilization'
]
fields = [
id_ for id_ in [
name, hospitalType, address, lat, lon, liscensedBeds,
staffedBeds, icuBeds, icuBedsAdult, icuBedsPedi,
icuBedsPotential, ventilatorUsage, bedUtilization
]
]
clause = '&&'.join([
f" {name} == '{field}'"
for name, field in zip(field_names, fields) if field
])
# super(HospitalDataLoader, self).fetch(
# location,
# include_fields = include_fields,
# limit = limit
# )
assert location, "location must be specified"
print(location)
print(clause)
spec = {
"filter": f"contains(location, '{location}')" + " && " + clause,
}
if limit:
spec['limit'] = limit
print(spec)
return datalake.fetch("hospital", {"spec": spec}, get_all=True)
class OutbreakLocationLoader(GeneralDataLoader):
def __init__(self):
super(LaborDetailLoader, self).__init__()
#returns 2000 entries max
def fetch(self,
parent,
id : str = None, # The location ID of the country, state or province, and county to fetch for the COVID-19 outbeak. Should be used with the key filter, e.g., "filter": 'id == "Afghanistan"'.
name : str = None, # Actual name of the country, state or province, and county corresponding to the location ID.
fips : str = None, # FIPS code for the country, and for the county and county-equivalents in the United States.
'locationType', 'populationCDS', 'id', 'name', 'typeIdent',
'location.value.id', 'location.timestamp', 'hospitalIcuBeds',
'hospitalStaffedBeds', 'hospitalLicensedBeds', 'latestTotalPopulation',
'populationOfAllChildren', 'latestLaborForce',
'latestEmployedPopulation', 'latestUnemployedPopulation',
'latestUnemploymentRate', 'laborForceOfAllChildren', 'fips.id',
'hospitalPrediction.timestamp', 'countryArea', 'countryCode',
'population2018', 'population2019', 'latestKindergartenPopulation',
'elementarySchoolCount', 'kindergartenCount', 'universityCount',
'nursingHomeCount', 'elderlyPopulationRatio', 'elderlyAloneRatio'
) -> pd.DataFrame:
# id string The location ID of the country, state or province, and county to fetch for the COVID-19 outbeak. Should be used with the key filter, e.g., "filter": 'id == "Afghanistan"'.
# name string Actual name of the country, state or province, and county corresponding to the location ID.
# fips string FIPS code for the country, and for the county and county-equivalents in the United States.
# lineList LineListRecord List of C3.ai Type LineListRecord objects associated with the location.
# assets BiologicalAsset List of C3.ai Type BiologicalAsset objects associated with the location.
# diagnoses Diagnosis List of C3.ai Type Diagnosis objects associated with the location.
# hospitals Hospital List of C3.ai Type Hospital objects associated with the location.
# policy LocationPolicySummary List of C3.ai Type LocationPolicySummary objects associated with the location.
# populationData PopulationData List of C3.ai Type PopulationData objects associated with the location.
# laborDetail LaborDetail List of C3.ai Type LaborDetail objects associated with the location.
# vaccineCoverage VaccineCoverage List of C3.ai Type VaccineCoverage objects associated with the location.
# locationExposures LocationExposure List of C3.ai Type LocationExposure objects with this location as locationTarget.
# locationExposuresVisited LocationExposure List of C3.ai Type LocationExposure objects with this location as locationVisited.
# latestTotalPopulation int Most recent population of the location based on data from The World Bank or the US Census Bureau. Data available at county-level for locations in the United States and country-level globally.
# population2019 int Population of the location for the year 2019, based on data from the European Centre for Disease and Control.
# populationCDS int Population of the location, based on data from Corona Data Scraper.
# hospitalIcuBeds int Total number of hospital intensive care unit (ICU) beds. Available for locations in the United States.
# hospitalStaffedBeds int Total number of staffed hospital beds. Available for locations in the United States.
# hospitalLicensedBeds int Total number of licensed hospital beds. Available for locations in the United States.
# populationOfAllChildren int Most up-to-date total population of all sub-locations (e.g. for all counties in a state) based on available demographic data. Available for locations in the United States.
# latestLaborForce int Most up-to-date labor force population of the location based on available Bureau of Labor Statistics data. Available for county locations in the United States.
# latestEmployedPopulation int Most up-to-date employed population of the location based on available Bureau of Labor Statistics data. Available for county locations in the United States.
# latestUnemployedPopulation int Most up-to-date unemployed population of the location based on available Bureau of Labor Statistics data. Available for county locations in the United States.
# latestUnemploymentRate double Most up-to-date unemployment rate of the location based on available Bureau of Labor Statistics data, in percent. Available for county locations in the United States.
# laborForceOfAllChildren int Most up-to-date labor force population of all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. Available for US state- and country-level locations.
# employedPopulationOfAllChildren int Most up-to-date employed population of all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. Available for US state- and country-level locations.
# unemployedPopulationOfAllChildren int Most up-to-date unemployed population of all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. Available for US state- and country-level locations.
# unemploymentRateOfAllChildren double Most up-to-date unemployment rate, in percent, over all sub-locations (e.g. for all counties in a state) based on available Bureau of Labor Statistics data. This value is unemployedPopulationOfAllChildren divided by laborForceOfAllChildren, in percent. Available for US state- and country-level locations.
# elementarySchoolCount int Total number of elementary schools. Available for locations in South Korea.
# kindergartenCount int Total number of kindergartens. Available for locations in South Korea.
# universityCount int Total number of universities. Available for locations in South Korea.
# nursingHomeCount int Total number of nursing homes. Available for locations in South Korea.
# elderlyPopulationRatio double Proportion of population that is elderly, as percent (0-100). Available for locations in South Korea.
# elderlyAloneRatio double Proportion of households that are elderly people living alone, as percent (0-100). Available for locations in South Korea.
# publicHealthCareCenterBeds int Total number of hospital beds available in public facilities. Available for locations in India.
field_names = [ 'year',
'month',
'laborForce',
'employedPopulation',
'unemployedPopulation',
'unemployentRate',
'origin']
fields = [id_ for id_ in
[year,
month,
laborForce,
employedPopulation,
unemployedPopulation,
unemployentRate,
origin]
]
clause = '&&'.join([f" {name} == '{field}'" for name, field in zip(field_names, fields) if field])
assert parent, "location must be specified"
print(parent)
print(clause)
spec = {
"filter" : f"contains(parent, '{parent}')" + " && " + clause,
}
if limit:
spec['limit'] = limit
print(spec)
return datalake.fetch(
"labordetail",
{
"spec":spec
},
get_all=True
)
def fetch(self,
parent,
year : int = None,
populationAge: str = "",
gender: str = "",
race: str = "",
ethnicity: str = "",
estimate: bool = None,
median: bool = None,
value: float = None,
minAge: int = None,
maxAge: int = None,
origin: str = ""
) -> pd.DataFrame:
field_names = [ 'year',
'populationAge',
'gender',
'race',
'ethnicity',
'estimate',
'median',
'value',
'minAge',
'maxAge',
'origin',]
fields = [id_ for id_ in
[year,
populationAge,
gender,
race,
ethnicity,
estimate,
median,
value,
minAge,
maxAge,
origin]
]
clause = '&&'.join([f" {name} == '{field}'" for name, field in zip(field_names, fields) if field])
# super(PopulationDataLoader, self).fetch(
# parent,
# include_fields = include_fields,
# limit = limit
# )
assert parent, "location must be specified"
print(parent)
print(clause)
spec = {
"filter" : f"contains(parent, '{parent}')" + " && " + clause,
}
if limit:
spec['limit'] = limit
print(spec)
return datalake.fetch(
"populationdata",
{
"spec":spec
},
get_all=True
)