--------------------------------------------------
lines 18-254 of file: example/csv/root_node_sex.py
--------------------------------------------------

{xrst_begin csv.root_node_sex}
{xrst_spell
  const
  dage
  delim
  dtime
  eps
  haqi
  meas
  sincidence
  std
  uniform uniform
}

Start Fitting a a Particular Node and Sex
#########################################

csv_file
********
This dictionary is used to hold the data corresponding to the
csv files for this example:
{xrst_code py}"""
csv_file = dict()
"""{xrst_code}

node.csv
********
{xrst_code py}"""
csv_file['node.csv'] = \
'''node_name,parent_name
n0,
n1,n0
n2,n1
n3,n1
'''
r"""{xrst_code}
The following is a diagram of this node tree::

        n0
        |
        n1
       /  \
     n2    n3


option_fit.csv
**************
This example uses the default value for all the options in option_fit.csv
except for:

#. The root node name is n1 and root sex is female.
#. refit_split is set to false
#. random_seed is chosen using the python time package

{xrst_code py}"""
csv_file['option_fit.csv']  = \
'''name,value
root_node_name,n1
root_node_sex,female
refit_split,false
tolerance_fixed,1e-8
'''
random_seed    = str( int( time.time() ) )
csv_file['option_fit.csv'] += f'random_seed,{random_seed}\n'
"""{xrst_code}

option_predict.csv
******************
This example uses the default value for all the options in option_predict.csv.
{xrst_code py}"""
csv_file['option_predict.csv']  = 'name,value\n'
"""{xrst_code}

covariate.csv
*************
This example has one covariate called haqi.
Other cause mortality, omega, is constant and equal to 0.02.
The covariate depends on the node and sex
{xrst_code py}"""
csv_file['covariate.csv'] = \
'''node_name,sex,age,time,omega,haqi
n0,female,50,2000,0.02,1.0
n1,female,50,2000,0.02,1.0
n2,female,50,2000,0.02,0.5
n3,female,50,2000,0.02,1.5
n0,male,50,2000,0.02,1.2
n1,male,50,2000,0.02,1.2
n2,male,50,2000,0.02,0.7
n3,male,50,2000,0.02,1.7
'''
"""{xrst_code}

fit_goal.csv
************
The goal is to fit the model for nodes n2 and n3.
{xrst_code py}"""
csv_file['fit_goal.csv'] = \
'''node_name
n2
n3
'''
"""{xrst_code}

predict_integrand.csv
*********************
For this example we want to know the values of Sincidence
and prevalence for each of the goal nodes.
(Note that Sincidence is a direct measurement of iota.)
{xrst_code py}"""
csv_file['predict_integrand.csv'] = \
'''integrand_name
Sincidence
prevalence
'''
"""{xrst_code}

prior.csv
*********
We define three priors:

.. csv-table::
   :widths: auto
   :delim: ;

   uniform_1_1; a uniform distribution on [ -1, 1 ]
   uniform_eps_1; a uniform distribution on [ 1e-6, 1 ]
   gauss_01; a mean 0 standard deviation 1 Gaussian distribution

{xrst_code py}"""
csv_file['prior.csv'] = \
'''name,lower,upper,mean,std,density
uniform_-1_1,-1.0,1.0,0.5,1.0,uniform
uniform_eps_1,1e-6,1.0,0.5,1.0,uniform
gauss_01,,,0.0,1.0,gaussian
'''
"""{xrst_code}

parent_rate.csv
***************
The only non-zero rates are omega and iota
(omega is known and specified by the covariate.csv file).
The model for iota is constant (with respect to age and time).
Its value prior is uniform_eps_1.
It does not have any dage or dtime priors because it is constant
(so there are no age or time difference between grid values).
{xrst_code py}"""
csv_file['parent_rate.csv'] = \
'''rate_name,age,time,value_prior,dage_prior,dtime_prior,const_value
iota,0.0,0.0,uniform_eps_1,,,
'''
"""{xrst_code}

child_rate.csv
**************
The child rates are random effects that represent the difference
between the rate for a node being fit and the rate for one of its child nodes.
These random effects are different for each child node.
The are constant in age and time so age and time do not appear
in child_rate.csv.
In this example, when fitting n1, the child nodes are n2 and n3.
When fitting n2 and n3, there are no child nodes (no random effects).
Our prior for the random effects is gauss_01.
{xrst_code py}"""
csv_file['child_rate.csv'] = \
'''rate_name,value_prior
iota,gauss_01
'''
r"""{xrst_code}

mulcov.csv
**********
There is one covariate multiplier,
it multiplies haqi and affects iota.
The root level prior for this multiplier is either uniform\_-1,1
or constant and equal to the true value.
{xrst_code py}"""
true_mulcov_haqi           = 0.5
root_mulcov_prior_constant = True
csv_file['mulcov.csv']  = 'covariate,type,effected,value_prior,const_value\n'
if root_mulcov_prior_constant :
   csv_file['mulcov.csv'] += f'haqi,rate_value,iota,,{true_mulcov_haqi}\n'
else :
   csv_file['mulcov.csv'] += 'haqi,rate_value,iota,uniform_-1_1,\n'
"""{xrst_code}

root_mulcov_prior_constant
==========================
If the root level prior is not constant ( uniform on [-1,+1] ),
it is frozen (constant) for the n1 and n2 fits
using the value found by the n0 fit.
Hence the prior for the n1 and n2 fits has the covariate multiplier constant.
On the other hand, the n1 and n2 fit priors for iota
have random variation do to the root level fit for the covariate multiplier
not being constant.


data_in.csv
***********
The data_in.csv file has one point for each combination of node and sex.
The integrand is Sincidence (a direct measurement of iota.)
The age intervals do not really matter because the true iota
for this example is constant.
The measurement standard deviation is 1e-4 during the fitting.
None of the data is held out.
The zero values in the meas_value column below get replaced; see below
{xrst_code py}"""
header  = 'data_id, integrand_name, node_name, sex, age_lower, age_upper, '
header += 'time_lower, time_upper, meas_value, meas_std, hold_out, '
header += 'density_name, eta, nu'
csv_file['data_in.csv'] = header + \
'''
0, Sincidence, n1, female, 0,  10, 1990, 2000, 0.0000,  1e-4, 0, gaussian, ,
1, Sincidence, n1, male,   0,  10, 1990, 2000, 0.0000,  1e-4, 0, gaussian, ,
2, Sincidence, n2, female, 10, 20, 2000, 2010, 0.0000,  1e-4, 0, gaussian, ,
3, Sincidence, n2, male,   10, 20, 2000, 2010, 0.0000,  1e-4, 0, gaussian, ,
4, Sincidence, n3, female, 20, 30, 2010, 2020, 0.0000,  1e-4, 0, gaussian, ,
5, Sincidence, n3, male,   20, 30, 2010, 2020, 0.0000,  1e-4, 0, gaussian, ,
'''
csv_file['data_in.csv'] = csv_file['data_in.csv'].replace(' ', '')
"""{xrst_code}
The measurement value meas_value is 0.0000 above gets replaced
the true value for iota with no measurement noise,
even though the measurement standard deviation is modeled as 1e-4.
See the following code:
{xrst_literal
   # BEGIN_MEAS_VALUE
   # END_MEAS_VALUE
}

Source Code
***********
{xrst_literal
   BEGIN_PROGRAM
   END_PROGRAM
}

{xrst_end csv.root_node_sex}