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lines 5-81 of file: example/get_started/get_started_db.py
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# {xrst_begin get_started_db.py}
# {xrst_spell
#       ik
# }
# {xrst_comment_ch #}
#
# Create get_started Input Tables: Example and Test
# #################################################
#
# Syntax
# ******
# ``get_started_db.get_started_db`` ()
#
# Discussion
# **********
# The python command above creates the database ``get_started.db``
# in the current working directory.
# This is a very simple case where:
#
# #.  All of the rates are zero except for
#     :ref:`rate_table@rate_name@omega` .
# #.  There is only one node corresponding to the world, and hence there are no
#     :ref:`option_table@Parent Node@Children` or
#     :ref:`random effects<model_variables@Random Effects, u>` .
# #.  There is only one measurement and it is for the
#     :ref:`integrand_table@integrand_name`
#     :ref:`avg_integrand@Integrand, I_i(a,t)@susceptible`
#     and at age 50 and year 2000.
# #.  The data table has a
#     :ref:`comment column<database@Comment Columns and Tables>`
#     named ``c_data_info`` that is used as a
#     :ref:`data_extra_column<option_table@Extra Columns@data_extra_columns>` .
# #.  There is one
#     :ref:`covariate<covariate_table-name>` income and a corresponding
#     covariate multiplier on the
#     :ref:`rate value<mulcov_table@mulcov_type@rate_value>` for
#     :ref:`omega<avg_integrand@Rate Functions@omega_i(a,t)>` .
# #.  The model for other cause mortality
#     :ref:`rate_table@rate_name@omega` ,
#     and the model for the rate covariate multiplier
#     :ref:`alpha<avg_integrand@Rate Functions@Group Rate Covariate Multiplier, alpha_jk>` ,
#     are constant in age and time.
#
# Because other cause mortality and the covariate multiplier are
# constant in age and time,
# the susceptible population satisfies the
# following ODE in age :math:`a`:
#
# .. math::
#
#   S(0) = 1 \W{\R{and}} S'(a) = - \exp( \alpha x ) \omega S(a)
#
# where :math:`\omega` is the other cause mortality rate
# before the covariate effect,
# :math:`x` is the value of the income for this measurement,
# :math:`\alpha` is the covariate multiplier, and
# :math:`\exp ( \alpha x ) \omega`
# is the other cause mortality after the covariate effect;
# see :ref:`r_ik<avg_integrand@Rate Functions@Adjusted Rate, r_ik>` .
# The solution is
#
# .. math::
#
#   S(a) = \exp \left[ - \exp( \alpha x ) \omega \; a \right]
#
# Reference
# *********
# See :ref:`create_database-name` .
#
# Source Code
# ***********
# {xrst_literal
#       BEGIN PYTHON
#       END PYTHON
# }
#
# {xrst_end get_started_db.py}