aegis_sim.utilities.analysis.survival 

 1# BASIC
 2
 3# TODO add functions that convert mortality to survival to survivorship to counts, etc.
 4
 5
 6def get_mortality(survival):
 7    # TODO Ensure that you are slicing the phenotype array at right places
 8    y = 1 - survival
 9    return y
10
11
12def get_survivorship(survival):
13    # TODO Ensure that you are slicing the phenotype array at right places
14    y = survival.cumprod(axis=1)
15    return y
16
17
18def get_total_mortality(interval_life_table):
19    total_survivorship = get_total_survivorship(interval_life_table)
20    total_mortality = -total_survivorship.pct_change(axis=1)
21    return total_mortality
22
23
24def get_total_survivorship(interval_life_table):
25    # additive age structure records viability of each individual during its lifetime
26    # when applied to a discrete cohort, additive age structure have the same shape as the survivorship curve
27    # furthermore, if normalized, it is equivalent to the survivorship curve
28    # when not applied to a cohort, the same holds if the population is stationary which approximately holds at short time scales
29    total_survivorship = interval_life_table.div(interval_life_table.iloc[:, 0], axis=0)  # normalize
30    return total_survivorship
31
32
33# COMPLEX
34
35
36def get_life_expectancy(phenotypes, AGE_LIMIT):
37    # TODO Ensure that you are slicing the phenotype array at right places
38    pdf = phenotypes.iloc[:, :AGE_LIMIT]
39    survivorship = pdf.cumprod(1)
40    y = survivorship.sum(1)
41    return y
42
43
44def get_longevity(survivorship, proportion_alive):
45    """
46    Longevity (omega) .. age at which proportion_alive of the population is still alive.
47    Usually, this is set to 95% or 99%, sometimes called exceptional lifespan.
48    """
49    return survivorship.ge(proportion_alive).sum(1)
def get_mortality(survival): 
 7def get_mortality(survival):
 8    # TODO Ensure that you are slicing the phenotype array at right places
 9    y = 1 - survival
10    return y
def get_survivorship(survival): 
13def get_survivorship(survival):
14    # TODO Ensure that you are slicing the phenotype array at right places
15    y = survival.cumprod(axis=1)
16    return y
def get_total_mortality(interval_life_table): 
19def get_total_mortality(interval_life_table):
20    total_survivorship = get_total_survivorship(interval_life_table)
21    total_mortality = -total_survivorship.pct_change(axis=1)
22    return total_mortality
def get_total_survivorship(interval_life_table): 
25def get_total_survivorship(interval_life_table):
26    # additive age structure records viability of each individual during its lifetime
27    # when applied to a discrete cohort, additive age structure have the same shape as the survivorship curve
28    # furthermore, if normalized, it is equivalent to the survivorship curve
29    # when not applied to a cohort, the same holds if the population is stationary which approximately holds at short time scales
30    total_survivorship = interval_life_table.div(interval_life_table.iloc[:, 0], axis=0)  # normalize
31    return total_survivorship
def get_life_expectancy(phenotypes, AGE_LIMIT): 
37def get_life_expectancy(phenotypes, AGE_LIMIT):
38    # TODO Ensure that you are slicing the phenotype array at right places
39    pdf = phenotypes.iloc[:, :AGE_LIMIT]
40    survivorship = pdf.cumprod(1)
41    y = survivorship.sum(1)
42    return y
def get_longevity(survivorship, proportion_alive): 
45def get_longevity(survivorship, proportion_alive):
46    """
47    Longevity (omega) .. age at which proportion_alive of the population is still alive.
48    Usually, this is set to 95% or 99%, sometimes called exceptional lifespan.
49    """
50    return survivorship.ge(proportion_alive).sum(1)

Longevity (omega) .. age at which proportion_alive of the population is still alive. Usually, this is set to 95% or 99%, sometimes called exceptional lifespan.