def lifeExpectancyTotal(sex, region, dob):
if not isinstance(dob, date):
raise TypeError('One or more arguments did not match the expected parameter type')
if dob < date(1920, 1, 1) or dob > date(2059, 12, 31):
raise BirthdateOutOfRangeError(dob, 'between 1920-01-01 and 2059-12-31')
age = relativedelta(years=35) # set an arbitrary age that keeps our calculation in the boundaries of lifeExpectancyRemaining()
age_float = relativedelta_to_decimal_years(age)
refdate = dob + age
return age_float + lifeExpectancyRemaining(sex, region, refdate, age)
def lifeExpectancyTotal(sex, region, dob):
if not isinstance(dob, date):
raise TypeError(
'One or more arguments did not match the expected parameter type')
if dob < date(1920, 1, 1) or dob > date(2059, 12, 31):
raise BirthdateOutOfRangeError(dob,
'between 1920-01-01 and 2059-12-31')
age = relativedelta(
years=35
) # set an arbitrary age that keeps our calculation in the boundaries of lifeExpectancyRemaining()
age_float = relativedelta_to_decimal_years(age)
refdate = dob + age
return age_float + lifeExpectancyRemaining(sex, region, refdate, age)
def calculateMortalityDistribution(country, sex, age):
# check that all arguments have the right type (even though it's not very pythonic)
if not isinstance(sex, basestring) or not isinstance(
country, basestring) or not isinstance(age, relativedelta):
raise TypeError(
'One or more arguments did not match the expected parameter type')
# confirm that sex and region contain valid values
if sex not in SEXES_LIFE_EXPECTANCY:
raise InvalidSexError(sex)
if country not in dataStore.countries:
raise InvalidCountryError(country)
age_float = relativedelta_to_decimal_years(age)
if age_float > 120:
raise AgeOutOfRangeError(age)
# helper function
def setInterpDate(x, offset):
""" ??? """
idate = datetime.strptime(
str(x + offset + 3) + "/1" + "/1", "%Y/%m/%d")
return (idate - datetime(1970, 1, 1)).days
def rounddown(x, base=5):
return int(base * math.floor(float(x) / base))
# get columns which correspond to the inputs
idate = datetime.utcnow().date()
iage = age_float
iyear = idate.year
flr_yr = rounddown(iyear, base=5)
# get closest age in 5 year windows
flr_age = rounddown(iage, base=5)
# Get the age cohort
if flr_age >= 5:
cohort_st = list(
dataStore.survival_ratio.columns).index("X" + str(flr_age - 5))
else:
cohort_st = 4
cohort_end = len(dataStore.survival_ratio.columns)
#cohort = dataStore.survival_ratio.loc[(dataStore.survival_ratio.region==country) & (dataStore.survival_ratio.sex==SEXES_LIFE_EXPECTANCY[sex]) & (dataStore.survival_ratio.Begin_prd >=(flr_yr-5))].ix[:,cohort_st:cohort_end]
# get older and younger cohort
cohort_old = dataStore.survival_ratio.loc[
(dataStore.survival_ratio.region == country)
& (dataStore.survival_ratio.sex == SEXES_LIFE_EXPECTANCY[sex]) &
(dataStore.survival_ratio.Begin_prd >=
(flr_yr - 10))].ix[:, cohort_st:cohort_end]
#cohort_young = dataStore.survival_ratio.loc[(dataStore.survival_ratio.region==country) & (dataStore.survival_ratio.sex==SEXES_LIFE_EXPECTANCY[sex]) & (dataStore.survival_ratio.Begin_prd >=(flr_yr))].ix[:,cohort_st:cohort_end]
# get dates for the jan 1st for 3 years --> then to Unix timestamp
dates = [
setInterpDate(flr_yr, -5),
setInterpDate(flr_yr, 0),
setInterpDate(flr_yr, +5)
]
# make the output dataStore.survival_ratiotable
temp = np.zeros(shape=(len(cohort_old.columns), 7))
odata = pd.DataFrame(temp,
columns=[
"lower_age", "pr0", "pr1", "pr2", "pr_sx_date",
"death_percent", "dth_pc_after_exact_age"
])
# fill in with existing values
if iage >= 5:
odata['lower_age'] = np.arange(flr_age - 5, 130, 5)
else:
odata['lower_age'] = np.arange(0, 130, 5)
odata['pr0'] = np.matrix(cohort_old).diagonal().T
odata['pr1'] = np.matrix(cohort_old).diagonal(-1).T
odata['pr2'] = np.matrix(cohort_old).diagonal(-2).T
# Interpolate for the input date (idate)
odata["pr_sx_date"] = np.array([
InterpolatedUnivariateSpline(dates, list(odata.ix[i, 1:4]),
k=2)(inPosixDays(idate))
for i in np.arange(0, len(cohort_old.columns), 1)
])
clen = len(odata)
# calc the % deaths
odata["death_percent"][1] = 100
for i in np.arange(2, clen, 1):
odata["death_percent"][i] = odata["death_percent"][
i - 1] * odata["pr_sx_date"][i]
# percentage deaths
for i in np.arange(1, clen - 1, 1):
odata["dth_pc_after_exact_age"][
i] = odata["death_percent"][i] - odata["death_percent"][i + 1]
odata["dth_pc_after_exact_age"][clen - 1] = odata["death_percent"][clen -
1]
# proportion of people who will die before iage
beforeDod = odata["dth_pc_after_exact_age"][1] * (iage - flr_age) / 5
odata["dth_pc_after_exact_age"][
1] = odata["dth_pc_after_exact_age"][1] - beforeDod
odata["dth_pc_after_exact_age"] = odata[
"dth_pc_after_exact_age"] * 100 / odata["dth_pc_after_exact_age"].sum(
)
# add 5 to each of the "ages"
if iage >= 5:
odata["lower_age"] = odata["lower_age"] + 5
else:
odata["lower_age"] = odata["lower_age"] + iage
output = odata.ix[0:clen - 1, ['lower_age', 'dth_pc_after_exact_age']]
return list(output.values)
def lifeExpectancyRemaining(sex, region, refdate, age):
# check that all arguments have the right type (even though it's not very pythonic)
if not isinstance(sex, basestring) or not isinstance(
region, basestring) or not isinstance(
refdate, date) or not isinstance(age, relativedelta):
raise TypeError(
'One or more arguments did not match the expected parameter type')
# confirm that sex and region contain valid values
if sex not in SEXES_LIFE_EXPECTANCY:
raise InvalidSexError(sex)
if region not in dataStore.countries:
raise InvalidCountryError(region)
# check the various date requirements
if refdate < date(1955, 1, 1) or refdate >= date(2095, 1, 1):
raise CalculationDateOutOfRangeError(refdate,
'from 1955-01-01 to 2094-12-31')
age_float = relativedelta_to_decimal_years(age)
if age_float > 120:
raise AgeOutOfRangeError(age)
if refdate - age > date(2095, 6, 30):
raise EffectiveBirthdateOutOfRangeError(invalidValue=(refdate - age))
# find beginning of 5 yearly period for the le_date
le_yr = refdate.year
lowest_year = math.floor(int(le_yr) / 5) * 5
# extract a row corresponding to the time-period
life_exp_prd_5below = dataStore.life_expectancy_ages[
(dataStore.life_expectancy_ages.region == region)
& (dataStore.life_expectancy_ages.sex == SEXES_LIFE_EXPECTANCY[sex]) &
(dataStore.life_expectancy_ages.Begin_prd == lowest_year - 5)]
life_exp_prd_ext = dataStore.life_expectancy_ages[
(dataStore.life_expectancy_ages.region == region)
& (dataStore.life_expectancy_ages.sex == SEXES_LIFE_EXPECTANCY[sex]) &
(dataStore.life_expectancy_ages.Begin_prd == lowest_year)]
life_exp_prd_5above = dataStore.life_expectancy_ages[
(dataStore.life_expectancy_ages.region == region)
& (dataStore.life_expectancy_ages.sex == SEXES_LIFE_EXPECTANCY[sex]) &
(dataStore.life_expectancy_ages.Begin_prd == lowest_year + 5)]
# life_exp_prd
life_exp_prd = pd.concat(
[life_exp_prd_5below, life_exp_prd_ext, life_exp_prd_5above])
life_exp_prd = life_exp_prd.ix[:, 4:len(life_exp_prd.columns)]
# Place holder for Agenames and values for three consecutive periods of interest
life_exp_ = np.zeros((len(life_exp_prd.columns), 4))
# Age group starting at and less than the next value: 0, 1, 5, 10
life_exp_[:, 0] = np.insert((np.arange(5, 130, 5)), 0, [0, 1])
# transpose the dataframe - prep for assinging life expectancy vals
life_exp_prd = life_exp_prd.T
# Assigning life expectancy values
life_exp_[:, 1] = life_exp_prd[life_exp_prd.columns[0]].values
life_exp_[:, 2] = life_exp_prd[life_exp_prd.columns[1]].values
life_exp_[:, 3] = life_exp_prd[life_exp_prd.columns[2]].values
# interpolations
xx_interp1 = InterpolatedUnivariateSpline(life_exp_[:, 0], life_exp_[:, 1])
xx_interp2 = InterpolatedUnivariateSpline(life_exp_[:, 0], life_exp_[:, 2])
xx_interp3 = InterpolatedUnivariateSpline(life_exp_[:, 0], life_exp_[:, 3])
# predictions
x_interp1 = xx_interp1(
age_float) #interpolated value for AGE in earlier 5 yearly period
x_interp2 = xx_interp2(
age_float
) #interpolated value for AGE in the 5 yearly period of interest
x_interp3 = xx_interp3(
age_float) #interpolated value for AGE in 5 yearly period after
# matrix of vals
life_exp_yr = np.zeros((3, 2))
#The mid point of period 2010-2015 which is from 1st July 2010 to June 30 of 2015, therefore, the mid point is 1st Jan 2013
#In the following we turn the year to the date and then to numeric. We will use these to interpolate between periods and then predict the le for exact date
addDate = lambda d: inPosixDays(date(int(d) + 3, 1, 1))
life_exp_yr[:, 0] = [
addDate(lowest_year - 5),
addDate(lowest_year),
addDate(lowest_year + 5)
]
life_exp_yr[:, 1] = [x_interp1, x_interp2, x_interp3]
life_exp_spl = InterpolatedUnivariateSpline(life_exp_yr[:, 0],
life_exp_yr[:, 1],
k=2)
return life_exp_spl(inPosixDays(refdate))[()]
def lifeExpectancyRemaining(sex, region, refdate, age):
# check that all arguments have the right type (even though it's not very pythonic)
if (
not isinstance(sex, basestring)
or not isinstance(region, basestring)
or not isinstance(refdate, date)
or not isinstance(age, relativedelta)
):
raise TypeError("One or more arguments did not match the expected parameter type")
# confirm that sex and region contain valid values
if sex not in SEXES_LIFE_EXPECTANCY:
raise InvalidSexError(sex)
if region not in dataStore.countries:
raise InvalidCountryError(region)
# check the various date requirements
if refdate < date(1955, 1, 1) or refdate >= date(2095, 1, 1):
raise CalculationDateOutOfRangeError(refdate, "from 1955-01-01 to 2094-12-31")
age_float = relativedelta_to_decimal_years(age)
if age_float > 100:
raise AgeOutOfRangeError(age)
# find beginning of 5 yearly period for the le_date
le_yr = refdate.year # (le_date.loc['1'])[0:4]
lowest_year = math.floor(int(le_yr) / 5) * 5
# extract a row corresponding to the time-period
life_exp_prd_5below = dataStore.life_expectancy_ages[
(dataStore.life_expectancy_ages.region == region)
& (dataStore.life_expectancy_ages.sex == SEXES_LIFE_EXPECTANCY[sex])
& (dataStore.life_expectancy_ages.Begin_prd == lowest_year - 5)
]
life_exp_prd_ext = dataStore.life_expectancy_ages[
(dataStore.life_expectancy_ages.region == region)
& (dataStore.life_expectancy_ages.sex == SEXES_LIFE_EXPECTANCY[sex])
& (dataStore.life_expectancy_ages.Begin_prd == lowest_year)
]
life_exp_prd_5above = dataStore.life_expectancy_ages[
(dataStore.life_expectancy_ages.region == region)
& (dataStore.life_expectancy_ages.sex == SEXES_LIFE_EXPECTANCY[sex])
& (dataStore.life_expectancy_ages.Begin_prd == lowest_year + 5)
]
life_exp_prd = pd.concat([life_exp_prd_5below, life_exp_prd_ext, life_exp_prd_5above])
life_exp_prd = life_exp_prd.ix[:, 7 : len(life_exp_prd.columns)]
# Place holder for Agenames and values for three consecutive periods of interest
life_exp_ = np.zeros((len(life_exp_prd.columns), 4))
# Age group starting at and less than the next value: 0, 1, 5, 10
life_exp_[:, 0] = np.insert((np.arange(5, 105, 5)), 0, [0, 1])
# transpose the dataframe - prep for assinging life expectancy vals
life_exp_prd = life_exp_prd.T
# Assigning life expectancy values
life_exp_[:, 1] = life_exp_prd[life_exp_prd.columns[0]].values
life_exp_[:, 2] = life_exp_prd[life_exp_prd.columns[1]].values
life_exp_[:, 3] = life_exp_prd[life_exp_prd.columns[2]].values
# interpolations
xx_interp1 = InterpolatedUnivariateSpline(life_exp_[:, 0], life_exp_[:, 1])
xx_interp2 = InterpolatedUnivariateSpline(life_exp_[:, 0], life_exp_[:, 2])
xx_interp3 = InterpolatedUnivariateSpline(life_exp_[:, 0], life_exp_[:, 3])
# predictions
x_interp1 = xx_interp1(age_float) # interpolated value for AGE in earlier 5 yearly period
x_interp2 = xx_interp2(age_float) # interpolated value for AGE in the 5 yearly period of interest
x_interp3 = xx_interp3(age_float) # interpolated value for AGE in 5 yearly period after
# matrix of vals
life_exp_yr = np.zeros((3, 2))
# The mid point of period 2010-2015 which is from 1st July 2010 to June 30 of 2015, therefore, the mid point is 1st Jan 2013
# In the following we turn the year to the date and then to numeric. We will use these to interpolate between periods and then predict the le for exact date
addDate = lambda d: inPosixDays(date(int(d) + 3, 1, 1))
life_exp_yr[:, 0] = [addDate(lowest_year - 5), addDate(lowest_year), addDate(lowest_year + 5)]
life_exp_yr[:, 1] = [x_interp1, x_interp2, x_interp3]
return life_exp_yr[0, 1]
def calculateMortalityDistribution(country, sex, age):
# check that all arguments have the right type (even though it's not very pythonic)
if not isinstance(sex, basestring) or not isinstance(country, basestring) or not isinstance(age, relativedelta):
raise TypeError('One or more arguments did not match the expected parameter type')
# confirm that sex and region contain valid values
if sex not in SEXES_LIFE_EXPECTANCY:
raise InvalidSexError(sex)
if country not in dataStore.countries:
raise InvalidCountryError(country)
age_float = relativedelta_to_decimal_years(age)
if age_float > 120:
raise AgeOutOfRangeError(age)
# helper function
def setInterpDate(x, offset):
""" ??? """
idate = datetime.strptime(str(x+offset+3)+"/1"+"/1", "%Y/%m/%d")
return (idate - datetime(1970, 1, 1)).days
def rounddown(x, base=5):
return int(base * math.floor(float(x)/base))
# get columns which correspond to the inputs
idate = datetime.utcnow().date()
iage = age_float
iyear = idate.year
flr_yr = rounddown(iyear, base=5)
# get closest age in 5 year windows
flr_age = rounddown(iage, base=5)
# Get the age cohort
if flr_age >= 5:
cohort_st = list(dataStore.survival_ratio.columns).index("X"+str(flr_age-5))
else:
cohort_st = 4
cohort_end = len(dataStore.survival_ratio.columns)
#cohort = dataStore.survival_ratio.loc[(dataStore.survival_ratio.region==country) & (dataStore.survival_ratio.sex==SEXES_LIFE_EXPECTANCY[sex]) & (dataStore.survival_ratio.Begin_prd >=(flr_yr-5))].ix[:,cohort_st:cohort_end]
# get older and younger cohort
cohort_old = dataStore.survival_ratio.loc[(dataStore.survival_ratio.region==country) & (dataStore.survival_ratio.sex==SEXES_LIFE_EXPECTANCY[sex]) & (dataStore.survival_ratio.Begin_prd >=(flr_yr-10))].ix[:,cohort_st:cohort_end]
#cohort_young = dataStore.survival_ratio.loc[(dataStore.survival_ratio.region==country) & (dataStore.survival_ratio.sex==SEXES_LIFE_EXPECTANCY[sex]) & (dataStore.survival_ratio.Begin_prd >=(flr_yr))].ix[:,cohort_st:cohort_end]
# get dates for the jan 1st for 3 years --> then to Unix timestamp
dates = [setInterpDate(flr_yr, -5), setInterpDate(flr_yr, 0), setInterpDate(flr_yr, +5)]
# make the output dataStore.survival_ratiotable
temp = np.zeros(shape=(len(cohort_old.columns),7))
odata = pd.DataFrame(temp, columns=["lower_age","pr0","pr1","pr2","pr_sx_date","death_percent","dth_pc_after_exact_age"])
# fill in with existing values
if iage>=5:
odata['lower_age'] = np.arange(flr_age-5, 130, 5)
else:
odata['lower_age'] = np.arange(0, 130, 5)
odata['pr0'] = np.matrix(cohort_old).diagonal().T
odata['pr1'] = np.matrix(cohort_old).diagonal(-1).T
odata['pr2'] = np.matrix(cohort_old).diagonal(-2).T
# Interpolate for the input date (idate)
odata["pr_sx_date"] = np.array([InterpolatedUnivariateSpline(dates,list(odata.ix[i,1:4]),k=2)(inPosixDays(idate)) for i in np.arange(0, len(cohort_old.columns),1)])
clen = len(odata)
# calc the % deaths
odata["death_percent"][1] = 100
for i in np.arange(2,clen,1):
odata["death_percent"][i] = odata["death_percent"][i-1]*odata["pr_sx_date"][i]
# percentage deaths
for i in np.arange(1,clen-1,1):
odata["dth_pc_after_exact_age"][i] = odata["death_percent"][i] - odata["death_percent"][i+1]
odata["dth_pc_after_exact_age"][clen-1] = odata["death_percent"][clen-1]
# proportion of people who will die before iage
beforeDod = odata["dth_pc_after_exact_age"][1] * (iage - flr_age)/5
odata["dth_pc_after_exact_age"][1] = odata["dth_pc_after_exact_age"][1] - beforeDod
odata["dth_pc_after_exact_age"] = odata["dth_pc_after_exact_age"]* 100/odata["dth_pc_after_exact_age"].sum()
# add 5 to each of the "ages"
if iage>=5:
odata["lower_age"] = odata["lower_age"]+5
else:
odata["lower_age"] = odata["lower_age"]+iage
output = odata.ix[0:clen-1,['lower_age', 'dth_pc_after_exact_age']]
return list(output.values)
