Source code for basiclife.BasicTerm_SC.Data
"""
The :mod:`~basiclife.BasicTerm_SC.Data` space is for reading
input data from internal files, and provides the data
to :mod:`~basiclife.BasicTerm_SC.Projection` in the form
of numpy arrays. Below is the list the references
for input data and associated files in this space.
.. list-table:: Input Fiels and References
:header-rows: 1
* - Reference
- Input File
* - :attr:`model_point_table`
- *model_point_table.xlsx*
* - :attr:`mort_table`
- *mort_table.xlsx*
* - :attr:`disc_rate_ann`
- *disc_rate_ann.xlsx*
.. rubric:: Parameters and References
(In all the sample code below,
the global variable `BasicTerm_SC` refers to the
:mod:`~basiclife.BasicTerm_SC` model.)
Attributes:
model_point_table: This reference holds model point data
as a pandas DataFrame read from
the internal associated file, *model_point_table.xlsx*.
.. code-block::
>>> BasicTerm_SC.Data.model_poit_table
age_at_entry sex policy_term policy_count sum_assured
point_id
1 47 M 10 1 622000
2 29 M 20 1 752000
3 51 F 10 1 799000
4 32 F 20 1 422000
5 28 M 15 1 605000
... .. ... ... ...
9996 47 M 20 1 827000
9997 30 M 15 1 826000
9998 45 F 20 1 783000
9999 39 M 20 1 302000
10000 22 F 15 1 576000
[10000 rows x 5 columns]
The columns of the DataFrame represents model point attributes,
such as ``age_at_entry``, ``sex``, ``policy_term``,
``policy_count`` and ``sum_assured``.
The columns are then referenced from the cells with the same names.
Each of the cells then returns its attribute for all model points
as a numpy array.
Unlike :mod:`~basiclife.BasicTerm_S`, ``point_id`` is not used
as the model point identifier. Instead, the array index is used.
.. seealso::
* :func:`age_at_entry`
* :func:`sex`
* :func:`policy_term`
* :func:`sum_assured`
disc_rate_ann: This refernce holds annual discount rates
by duration as a pandas Series read from
the internal associated file, *model_point_table.xlsx*.
.. code-block::
>>> BasicTerm_SC.Data.disc_rate_ann
year
0 0.00000
1 0.00555
2 0.00684
3 0.00788
4 0.00866
146 0.03025
147 0.03033
148 0.03041
149 0.03049
150 0.03056
Name: disc_rate_ann, Length: 151, dtype: float64
This is referenced from :func:`disc_rate_ann_array`,
which converts the Series into a numpy array, and is
referenced from the :mod:`~basiclife.BasicTerm_SC.Projection` space.
.. seealso::
* :func:`disc_rate_ann_array`
mort_table: This reference holds a mortality table
by age and duration as a DataFrame.
The table is read from the associated internal file, *mort_table.xlsx*.
.. code-block::
>>> BasicTerm_SC.Data.mort_table
0 1 2 3 4 5
Age
18 0.000231 0.000254 0.000280 0.000308 0.000338 0.000372
19 0.000235 0.000259 0.000285 0.000313 0.000345 0.000379
20 0.000240 0.000264 0.000290 0.000319 0.000351 0.000386
21 0.000245 0.000269 0.000296 0.000326 0.000359 0.000394
22 0.000250 0.000275 0.000303 0.000333 0.000367 0.000403
.. ... ... ... ... ... ...
116 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
117 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
118 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
119 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
120 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
[103 rows x 6 columns]
This is referenced from :func:`mort_table_array`,
which converts the DataFrame into a numpy array.
:func:`mort_table_array` adds rows filled with ``nan`` to
align the row index with age.
:func:`mort_table_array` is referenced from
the :mod:`~basiclife.BasicTerm_SC.Projection` space.
.. seealso::
* :func:`mort_table_array`
np: The `numpy`_ module.
pd: The `pandas`_ module.
.. _numpy:
https://numpy.org/
.. _pandas:
https://pandas.pydata.org/
.. _new_pandas:
https://docs.modelx.io/en/latest/reference/space/generated/modelx.core.space.UserSpace.new_pandas.html
"""
from modelx.serialize.jsonvalues import *
_formula = None
_bases = []
_allow_none = None
_spaces = []
# ---------------------------------------------------------------------------
# Cells
[docs]
def age_at_entry():
"""Age at entry
Returns the age at entry attribute of all model points
as a numpy array of type *int64*.
"""
return model_point_table["age_at_entry"].to_numpy(dtype='int64')
[docs]
def sex():
"""Sex
Returns the age at entry attribute of all model points
as a numpy array. This is not used by default.
"""
# Not used
return model_point_table['sex'].to_numpy()
[docs]
def policy_term():
"""Policy term
Returns the policy term attribute of all model points
as a numpy array of type *int64*.
"""
return model_point_table['policy_term'].to_numpy(dtype='int64')
[docs]
def policy_count():
"""Policy counts
Returns the policy count attribute of all model points
as a numpy array of type *float64*.
"""
return model_point_table['policy_count'].to_numpy(dtype='float64')
[docs]
def sum_assured():
"""Sum assured
Returns the sum assured attribute of all model points
as a numpy array of type *float64*.
"""
return model_point_table['sum_assured'].to_numpy(dtype='float64')
[docs]
def point_id():
"""Point ID
Returns the point id attribute of all model points
as a numpy array of type *int64*.
This attribute is not used, and the index of the numpy array is
used as the model point identifier.
"""
return model_point_table.index.to_numpy(dtype='int64')
[docs]
def mort_table_array():
"""Mortality table as a numpy array
This cells reference :attr:`mort_table`, and
returns a numpy array holding mortality rates by age and duration.
Rows filled with the ``nan`` values are inserted to
make the row index integers align with the age at entry.
"""
start_age = mort_table.index[0]
return np.concatenate(
(np.full((start_age, len(mort_table.columns)), np.nan), mort_table.to_numpy()),
axis=0)
[docs]
def disc_rate_ann_array():
"""Annual discount rates
This cells reference :attr:`disc_rate_ann`, and
returns a numpy array holding annual discount rates by duration.
"""
return disc_rate_ann.to_numpy()
# ---------------------------------------------------------------------------
# References
disc_rate_ann = ("IOSpec", 1924149079376, 1924149572048)
mort_table = ("IOSpec", 1924172456224, 1924171574272)
model_point_table = ("IOSpec", 1924172272160, 1926288025184)
np = ("Module", "numpy")
