def test_opt_args(self, urlopen):
"""Calling get_stat_value with optional args returns specific data."""
# Call get_stat_value for specific obs
self.assertEqual(dc.get_stat_value('geoId/06', 'Count_Person', '2010'),
133)
# Call get_stat_value with all optional args
stat = dc.get_stat_value('geoId/06', 'Count_Person', '2010',
'CensusPEPSurvey', 'P1Y', 'RealPeople', 100)
self.assertEqual(stat, 103)
def __buildJsonDataFromSchema(self, dcid_to_city_name=None, category_name_to_field_name=None, outfile="dataset.json", verbose=False):
if dcid_to_city_name is None:
if self.dcid_to_city_name == {}:
self.__loadSchema()
dcids = self.dcid_to_city_name
else:
dcids = dcid_to_city_name
if category_name_to_field_name is None:
if self.category_name_to_field_name == {}:
self.__loadSchema()
category_names = self.category_name_to_field_name
else:
category_names = category_name_to_field_name
for dcid, city_name in dcids.items():
self.dataset[dcid] = {}
self.dataset[dcid]["city_name"] = city_name
if verbose:
print("############## ", city_name,
" ##############", "\n\n")
for cat_name, stat_name in category_names.items():
# if stat_name in self.handler.not_supported:
ns = self.handler.not_supported(dcid, stat_name)
if ns[0]:
f = ns[1]
x = f(dcid)
else:
x = dc.get_stat_value(dcid, stat_name)
if verbose:
print(cat_name, ":", x)
if math.isnan(x):
print("***Got NaN for", city_name, ",", cat_name)
self.dataset[dcid][cat_name] = x
print("Finished data collection for", city_name)
self.writeDatasetToJsonFile(outfile)
return
def test_basic(self, urlopen):
"""Calling get_stat_value with minimal and proper args."""
# Call get_stat_value
self.assertEqual(dc.get_stat_value('geoId/06', 'Count_Person'), 123)
def main():
param_sets = [
{
'place': 'geoId/06085',
'stat_var': 'Count_Person',
},
{
'place': 'geoId/06085',
'stat_var': 'Count_Person',
'date': '2018',
},
{
'place': 'geoId/06085',
'stat_var': 'Count_Person',
'date': '2018',
'measurement_method': 'CensusACS5yrSurvey',
},
{
'place': 'geoId/06085',
'stat_var': 'UnemploymentRate_Person',
},
{
'place': 'geoId/06085',
'stat_var': 'UnemploymentRate_Person',
'observation_period': 'P1Y',
},
{
'place': 'geoId/06085',
'stat_var': 'UnemploymentRate_Person',
'observation_period': 'P1Y',
'measurement_method': 'BLSSeasonallyUnadjusted',
},
{
'place': 'nuts/HU22',
'stat_var':
'Amount_EconomicActivity_GrossDomesticProduction_Nominal',
},
{
'place': 'nuts/HU22',
'stat_var':
'Amount_EconomicActivity_GrossDomesticProduction_Nominal',
'observation_period': 'P1Y',
'unit': 'PurchasingPowerStandard'
},
]
def call_str(pvs):
"""Helper function to print the minimal call string."""
s = "'{}', '{}'".format(pvs.get('place'), pvs.get('stat_var'))
if pvs.get('measurement_method'):
s += ", measurement_method='{}'".format(
pvs.get('measurement_method'))
if pvs.get('observation_period'):
s += ", observation_period='{}'".format(
pvs.get('observation_period'))
if pvs.get('unit'):
s += ", unit='{}'".format(pvs.get('unit'))
if pvs.get('scaling_factor'):
s += ", scaling_factor={}".format(pvs.get('scaling_factor'))
return s
for pvs in param_sets:
print('\nget_stat_value({})'.format(call_str(pvs)))
print(
'>>> ',
dc.get_stat_value(pvs.get('place'),
pvs.get('stat_var'),
date=pvs.get('date'),
measurement_method=pvs.get('measurement_method'),
observation_period=pvs.get('observation_period'),
unit=pvs.get('unit'),
scaling_factor=pvs.get('scaling_factor')))
for pvs in param_sets:
pvs.pop('date', None)
print('\nget_stat_series({})'.format(call_str(pvs)))
print(
'>>> ',
dc.get_stat_series(
pvs.get('place'),
pvs.get('stat_var'),
measurement_method=pvs.get('measurement_method'),
observation_period=pvs.get('observation_period'),
unit=pvs.get('unit'),
scaling_factor=pvs.get('scaling_factor')))
pp = pprint.PrettyPrinter(indent=4)
print(
"\nget_stat_all(['geoId/06085', 'country/FRA'], ['Median_Age_Person', 'Count_Person'])"
)
print('>>> ')
pp.pprint(
dc.get_stat_all(['geoId/06085', 'country/FRA'],
['Median_Age_Person', 'Count_Person']))
print(
"\nget_stat_all(['badPlaceId', 'country/FRA'], ['Median_Age_Person', 'Count_Person'])"
)
print('>>> ')
pp.pprint(
dc.get_stat_all(['badPlaceId', 'country/FRA'],
['Median_Age_Person', 'Count_Person']))
def handle55To64(dcid):
x1 = dc.get_stat_value(dcid, "Count_Person_55To59Years")
x2 = dc.get_stat_value(dcid, "Count_Person_60To61Years")
x3 = dc.get_stat_value(dcid, "Count_Person_62To64Years")
return x1+x2+x3
def main():
param_sets = [
{
'place': 'geoId/06085',
'stat_var': "Count_Person",
},
{
'place': 'geoId/06085',
'stat_var': "Count_Person",
'date': '2018',
},
{
'place': 'geoId/06085',
'stat_var': "Count_Person",
'date': '2018',
'measurement_method': 'CensusACS5yrSurvey',
},
{
'place': 'geoId/06085',
'stat_var': 'UnemploymentRate_Person',
},
{
'place': 'geoId/06085',
'stat_var': 'UnemploymentRate_Person',
'observation_period': 'P1Y',
},
{
'place': 'geoId/06085',
'stat_var': 'UnemploymentRate_Person',
'observation_period': 'P1Y',
'measurement_method': 'BLSSeasonallyUnadjusted',
},
{
'place': 'nuts/HU22',
'stat_var':
'Amount_EconomicActivity_GrossDomesticProduction_Nominal',
},
{
'place': 'nuts/HU22',
'stat_var':
'Amount_EconomicActivity_GrossDomesticProduction_Nominal',
'observation_period': 'P1Y',
'unit': 'PurchasingPowerStandard'
},
]
def call_str(pvs):
"""Helper function to print the minimal call string."""
s = "'{}', '{}'".format(pvs.get('place'), pvs.get('stat_var'))
if pvs.get('measurement_method'):
s += ", measurement_method='{}'".format(
pvs.get('measurement_method'))
if pvs.get('observation_period'):
s += ", observation_period='{}'".format(
pvs.get('observation_period'))
if pvs.get('unit'):
s += ", unit='{}'".format(pvs.get('unit'))
if pvs.get('scaling_factor'):
s += ", scaling_factor={}".format(pvs.get('scaling_factor'))
return s
for pvs in param_sets:
print('\nget_stat_value({})'.format(call_str(pvs)))
print(
'>>> ',
dc.get_stat_value(pvs.get('place'),
pvs.get('stat_var'),
date=pvs.get('date'),
measurement_method=pvs.get('measurement_method'),
observation_period=pvs.get('observation_period'),
unit=pvs.get('unit'),
scaling_factor=pvs.get('scaling_factor')))
for pvs in param_sets:
pvs.pop('date', None)
print('\nget_stat_series({})'.format(call_str(pvs)))
print(
'>>> ',
dc.get_stat_series(
pvs.get('place'),
pvs.get('stat_var'),
measurement_method=pvs.get('measurement_method'),
observation_period=pvs.get('observation_period'),
unit=pvs.get('unit'),
scaling_factor=pvs.get('scaling_factor')))
pp = pprint.PrettyPrinter(indent=4)
print(
'\nget_stat_all(["geoId/06085", "country/FRA"], ["Median_Age_Person", "Count_Person"])'
)
print('>>> ')
pp.pprint(
dc.get_stat_all(["geoId/06085", "country/FRA"],
["Median_Age_Person", "Count_Person"]))
print(
'\nget_stat_all(["badPlaceId", "country/FRA"], ["Median_Age_Person", "Count_Person"])'
)
print('>>> ')
pp.pprint(
dc.get_stat_all(["badPlaceId", "country/FRA"],
["Median_Age_Person", "Count_Person"]))
print('\nSTRESS TEST FOR GET_STAT_ALL')
try:
dc.get_stat_all(
dc.get_places_in(['country/USA'], 'County')['country/USA'], [
'Count_Person', 'LandAreaSqMeter',
'PopulationDensityPerSqMeter',
'Count_Person_BlackOrAfricanAmericanAlone',
'PercentBlackOrAfricanAmericanAlone', 'Count_Person_Female',
'Count_Person_Male',
'Count_Person_AmericanIndianAndAlaskaNativeAlone',
'Count_Person_AmericanIndianAndAlaskaNativeAloneOrInCombinationWithOneOrMoreOtherRaces',
'Count_Person_AmericanIndianOrAlaskaNativeAlone',
'Count_Person_AsianAlone',
'Count_Person_AsianAloneOrInCombinationWithOneOrMoreOtherRaces',
'Count_Person_BlackOrAfricanAmericanAloneOrInCombinationWithOneOrMoreOtherRaces',
'Count_Person_HispanicOrLatino',
'Count_Person_NativeHawaiianAndOtherPacificIslanderAlone',
'Count_Person_NativeHawaiianAndOtherPacificIslanderAloneOrInCombinationWithOneOrMoreOtherRaces',
'Count_Person_NativeHawaiianOrOtherPacificIslanderAlone',
'Count_Person_SomeOtherRaceAlone',
'Count_Person_SomeOtherRaceAloneOrInCombinationWithOneOrMoreOtherRaces',
'Count_Person_TwoOrMoreRaces', 'Count_Person_WhiteAlone',
'Count_Person_WhiteAloneNotHispanicOrLatino',
'Count_Person_WhiteAloneOrInCombinationWithOneOrMoreOtherRaces',
'Count_Person_Upto5Years', 'Count_Person_Upto18Years',
'Count_Person_65OrMoreYears', 'Count_Person_75OrMoreYears',
'Count_Person_ForeignBorn',
'Count_Person_USCitizenByNaturalization',
'Count_Person_NotAUSCitizen', 'Count_Person_Nonveteran',
'Count_Person_Veteran', 'Count_Person_NotWorkedFullTime',
'Count_Person_WorkedFullTime', 'Count_Person_Employed',
'Count_Person_Unemployed', 'Count_Person_InLaborForce',
'Count_Person_IncomeOf10000To14999USDollar',
'Count_Person_IncomeOf15000To24999USDollar',
'Count_Person_IncomeOf25000To34999USDollar',
'Count_Person_IncomeOf35000To49999USDollar',
'Count_Person_IncomeOf50000To64999USDollar',
'Count_Person_IncomeOf65000To74999USDollar',
'Count_Person_IncomeOf75000OrMoreUSDollar',
'Count_Person_IncomeOfUpto9999USDollar',
'Count_Person_EnrolledInSchool',
'Count_Person_NotEnrolledInSchool',
'Count_Person_EnrolledInCollegeUndergraduateYears',
'Count_Person_EnrolledInGrade1ToGrade4',
'Count_Person_EnrolledInGrade5ToGrade8',
'Count_Person_EnrolledInGrade9ToGrade12',
'Count_Person_EnrolledInKindergarten',
'Count_Person_EnrolledInNurserySchoolPreschool',
'Count_Person_GraduateOrProfessionalSchool',
'Count_Person_EducationalAttainment10ThGrade',
'Count_Person_EducationalAttainment11ThGrade',
'Count_Person_EducationalAttainment12ThGradeNoDiploma',
'Count_Person_EducationalAttainment1StGrade',
'Count_Person_EducationalAttainment2NdGrade',
'Count_Person_EducationalAttainment3RdGrade',
'Count_Person_EducationalAttainment4ThGrade',
'Count_Person_EducationalAttainment5ThGrade',
'Count_Person_EducationalAttainment6ThGrade',
'Count_Person_EducationalAttainment7ThGrade',
'Count_Person_EducationalAttainment8ThGrade',
'Count_Person_EducationalAttainment9ThGrade',
'Count_Person_EducationalAttainmentAssociatesDegree',
'Count_Person_EducationalAttainmentBachelorsDegree',
'Count_Person_EducationalAttainmentBachelorsDegreeOrHigher',
'Count_Person_EducationalAttainmentDoctorateDegree',
'Count_Person_EducationalAttainmentGedOrAlternativeCredential',
'Count_Person_EducationalAttainmentKindergarten',
'Count_Person_EducationalAttainmentMastersDegree',
'Count_Person_EducationalAttainmentNoSchoolingCompleted',
'Count_Person_EducationalAttainmentNurserySchool',
'Count_Person_EducationalAttainmentPrimarySchool',
'Count_Person_EducationalAttainmentProfessionalSchoolDegree',
'Count_Person_EducationalAttainmentRegularHighSchoolDiploma',
'Count_Person_EducationalAttainmentSomeCollege1OrMoreYearsNoDegree',
'Count_Person_EducationalAttainmentSomeCollegeLessThan1Year',
'Count_Person_Divorced', 'Count_Person_MarriedAndNotSeparated',
'Count_Person_NeverMarried', 'Count_Person_Separated',
'Count_Person_Widowed', 'Count_Person_NowMarried',
'Count_Person_AbovePovertyLevelInThePast12Months',
'Count_Person_BelowPovertyLevelInThePast12Months',
'Percent_Person_20OrMoreYears_WithDiabetes',
'Percent_Person_20OrMoreYears_Obesity',
'Percent_Person_20OrMoreYears_PhysicalInactivity',
'Percent_Person_Upto64Years_NoHealthInsurance',
'Median_Age_Person', 'Median_Income_Person', 'Count_Death',
'Count_Death_CertainInfectiousParasiticDiseases',
'Count_Death_DiseasesOfBloodAndBloodFormingOrgansAndImmuneDisorders',
'Count_Death_DiseasesOfTheRespiratorySystem'
])
except ValueError:
print('Stress test for get_stat_all FAILED!')
else:
print('Stress test for get_stat_all succeeded.')
import datacommons as dc
res = dc.get_stat_value("presinctId/271730145", "Count_Person", date="2018")
print(res)