.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/hybrid_choice_specs/plot_b01_model_specification.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_hybrid_choice_specs_plot_b01_model_specification.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_hybrid_choice_specs_plot_b01_model_specification.py:


Generate files from latent-variable measurement specifications.

This script illustrates the latent-variables specification API. It does not
estimate any model and does not use data. Instead, it starts from pure
specification objects, resolves them into semantic ``ResolvedModel`` objects, and
uses the resolved specifications to generate files that can be inspected or used
elsewhere.

The script shows how to:

- define pure latent-variable and measurement specifications,
- define normalization plans in expert mode, using explicit parameter fixings,
- resolve a specification into a semantic ``ResolvedModel``,
- build live Biogeme expressions from the resolved specification,
- generate files from the resolved specification:
   - pedagogical runnable Python code,
   - a LaTeX scientific report,
   - a static HTML report.

The same specification is resolved under several build contexts:

- maximum likelihood and Bayesian estimation modes,
- log/exp and lower-bound parameterizations for positive parameters,
- automatic normalization and an explicit normalization plan.

Only specification files and reports are generated. No estimation algorithm is
run in this example.

Michel Bierlaire
Sat Jun 06 2026, 15:20:15

.. GENERATED FROM PYTHON SOURCE LINES 33-364




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ====================
    SPEC loaded
    ====================
    - #latent_variables:  2
    - #likert_indicators: 14
    - #likert_types:      1

    ====================
    Normalization plans ready
    ====================
    - plan_empty:   (no fixings)
    - plan_example: (some intercept/loadings/sigma fixed)

    ============================================================
    CASE 1: ML + log/exp positivity + EMPTY plan
    ============================================================
    - estimation mode: maximum_likelihood
    - positivity mode: log_exp
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Beta name=measurement_intercept_Envir01 value=0.0 status=0>) + (<Beta name=measurement_coefficient_car_centric_attitude_Envir01 value=0.0 status=0> * (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (exp(<Beta name=struct_car_centric_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_car_centric_attitude_draws>)))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (exp(<Beta name=struct_environmental_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_environmental_attitude_draws>)))) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), [(UnaryMinus((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>))) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), (UnaryMinus(exp(<Beta name=likert_delta_0_log value=-0.86 status=0>)) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), (exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), ((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>)) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>))])
    - generated Python file: b01_generated_files/case_1_ml_plus_log_exp_positivity_plus_empty_plan.py
    - generated LaTeX file: b01_generated_files/case_1_ml_plus_log_exp_positivity_plus_empty_plan.tex
    - generated HTML file: b01_generated_files/case_1_ml_plus_log_exp_positivity_plus_empty_plan.html
    - normalization rules: none
    - warnings:
      * No obvious reference indicator could be inferred for latent variable 'car_centric_attitude'.
      * No obvious reference indicator could be inferred for latent variable 'environmental_attitude'.

    ============================================================
    CASE 2: ML + log/exp positivity + EXAMPLE plan
    ============================================================
    - estimation mode: maximum_likelihood
    - positivity mode: log_exp
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Numeric value=0.0>) + (<Numeric value=-1.0> * (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (exp(<Beta name=struct_car_centric_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_car_centric_attitude_draws>)))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (exp(<Beta name=struct_environmental_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_environmental_attitude_draws>)))) / <Numeric value=1.0>), [(UnaryMinus((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>))) / <Numeric value=1.0>), (UnaryMinus(exp(<Beta name=likert_delta_0_log value=-0.86 status=0>)) / <Numeric value=1.0>), (exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) / <Numeric value=1.0>), ((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>)) / <Numeric value=1.0>)])
    - generated Python file: b01_generated_files/case_2_ml_plus_log_exp_positivity_plus_example_plan.py
    - generated LaTeX file: b01_generated_files/case_2_ml_plus_log_exp_positivity_plus_example_plan.tex
    - generated HTML file: b01_generated_files/case_2_ml_plus_log_exp_positivity_plus_example_plan.html
    - normalization rules:
      * MeasurementIntercept(indicator_name='Envir01') = 0.0 (reference indicator (location))
      * MeasurementIntercept(indicator_name='Envir02') = 0.0 (reference indicator (location))
      * MeasurementLoading(latent_name='car_centric_attitude', indicator_name='Envir01') = -1.0 (reference indicator (scale))
      * MeasurementLoading(latent_name='environmental_attitude', indicator_name='Envir02') = 1.0 (reference indicator (scale))
      * MeasurementSigma(indicator_name='Envir01') = 1.0 (ordinal threshold scale)
    - warnings: none

    ============================================================
    CASE 3: ML + BOUNDED positivity + EMPTY plan (non-standard but illustrative)
    ============================================================
    - estimation mode: maximum_likelihood
    - positivity mode: lower_bound
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Beta name=measurement_intercept_Envir01 value=0.0 status=0>) + (<Beta name=measurement_coefficient_car_centric_attitude_Envir01 value=0.0 status=0> * (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (<Beta name=struct_car_centric_attitude_sigma value=10.0 status=0> * <Draws name=struct_car_centric_attitude_draws>)))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (<Beta name=struct_environmental_attitude_sigma value=10.0 status=0> * <Draws name=struct_environmental_attitude_draws>)))) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), [(UnaryMinus((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>)) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), (UnaryMinus(<Beta name=likert_delta_0 value=1.0 status=0>) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), (<Beta name=likert_delta_0 value=1.0 status=0> / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), ((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>)])
    - generated Python file: b01_generated_files/case_3_ml_plus_bounded_positivity_plus_empty_plan_non_standard_but_illustrative.py
    - generated LaTeX file: b01_generated_files/case_3_ml_plus_bounded_positivity_plus_empty_plan_non_standard_but_illustrative.tex
    - generated HTML file: b01_generated_files/case_3_ml_plus_bounded_positivity_plus_empty_plan_non_standard_but_illustrative.html
    - normalization rules: none
    - warnings:
      * No obvious reference indicator could be inferred for latent variable 'car_centric_attitude'.
      * No obvious reference indicator could be inferred for latent variable 'environmental_attitude'.

    ============================================================
    CASE 4: ML + BOUNDED positivity + EXAMPLE plan (non-standard but illustrative)
    ============================================================
    - estimation mode: maximum_likelihood
    - positivity mode: lower_bound
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Numeric value=0.0>) + (<Numeric value=-1.0> * (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (<Beta name=struct_car_centric_attitude_sigma value=10.0 status=0> * <Draws name=struct_car_centric_attitude_draws>)))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (<Beta name=struct_environmental_attitude_sigma value=10.0 status=0> * <Draws name=struct_environmental_attitude_draws>)))) / <Numeric value=1.0>), [(UnaryMinus((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>)) / <Numeric value=1.0>), (UnaryMinus(<Beta name=likert_delta_0 value=1.0 status=0>) / <Numeric value=1.0>), (<Beta name=likert_delta_0 value=1.0 status=0> / <Numeric value=1.0>), ((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>) / <Numeric value=1.0>)])
    - generated Python file: b01_generated_files/case_4_ml_plus_bounded_positivity_plus_example_plan_non_standard_but_illustrative.py
    - generated LaTeX file: b01_generated_files/case_4_ml_plus_bounded_positivity_plus_example_plan_non_standard_but_illustrative.tex
    - generated HTML file: b01_generated_files/case_4_ml_plus_bounded_positivity_plus_example_plan_non_standard_but_illustrative.html
    - normalization rules:
      * MeasurementIntercept(indicator_name='Envir01') = 0.0 (reference indicator (location))
      * MeasurementIntercept(indicator_name='Envir02') = 0.0 (reference indicator (location))
      * MeasurementLoading(latent_name='car_centric_attitude', indicator_name='Envir01') = -1.0 (reference indicator (scale))
      * MeasurementLoading(latent_name='environmental_attitude', indicator_name='Envir02') = 1.0 (reference indicator (scale))
      * MeasurementSigma(indicator_name='Envir01') = 1.0 (ordinal threshold scale)
    - warnings: none

    ============================================================
    CASE 5: BAYESIAN + BOUNDED positivity + EMPTY plan
    ============================================================
    - estimation mode: bayesian
    - positivity mode: lower_bound
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Beta name=measurement_intercept_Envir01 value=0.0 status=0>) + (<Beta name=measurement_coefficient_car_centric_attitude_Envir01 value=0.0 status=0> * DistributedParameter(car_centric_attitude, (((((((((((`0.0` + Beta('struct_car_centric_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_top_manager', 0.0, None, None, 0) * top_manager)) + (Beta('struct_car_centric_attitude_age_30_less', 0.0, None, None, 0) * age_30_less)) + (Beta('struct_car_centric_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_car_centric_attitude_car_oriented_parents', 0.0, None, None, 0) * car_oriented_parents)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_car_centric_attitude_male', 0.0, None, None, 0) * male)) + (Beta('struct_car_centric_attitude_used_to_go_to_school_by_car', 0.0, None, None, 0) * used_to_go_to_school_by_car)) + (Beta('struct_car_centric_attitude_sigma', 10.0, 1e-15, None, 0) * Draws("struct_car_centric_attitude_draws", "Normal"))), (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (<Beta name=struct_car_centric_attitude_sigma value=10.0 status=0> * <Draws name=struct_car_centric_attitude_draws>))))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * DistributedParameter(environmental_attitude, (((((((((`0.0` + Beta('struct_environmental_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_environmental_attitude_childSuburb', 0.0, None, None, 0) * childSuburb)) + (Beta('struct_environmental_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_environmental_attitude_city_center_as_kid', 0.0, None, None, 0) * city_center_as_kid)) + (Beta('struct_environmental_attitude_artisans', 0.0, None, None, 0) * artisans)) + (Beta('struct_environmental_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_environmental_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_environmental_attitude_single', 0.0, None, None, 0) * single)) + (Beta('struct_environmental_attitude_sigma', 10.0, 1e-15, None, 0) * Draws("struct_environmental_attitude_draws", "Normal"))), (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (<Beta name=struct_environmental_attitude_sigma value=10.0 status=0> * <Draws name=struct_environmental_attitude_draws>))))) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), [(UnaryMinus((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>)) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), (UnaryMinus(<Beta name=likert_delta_0 value=1.0 status=0>) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), (<Beta name=likert_delta_0 value=1.0 status=0> / <Beta name=measurement_Envir01_sigma value=10.0 status=0>), ((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>) / <Beta name=measurement_Envir01_sigma value=10.0 status=0>)])
    - generated Python file: b01_generated_files/case_5_bayesian_plus_bounded_positivity_plus_empty_plan.py
    - generated LaTeX file: b01_generated_files/case_5_bayesian_plus_bounded_positivity_plus_empty_plan.tex
    - generated HTML file: b01_generated_files/case_5_bayesian_plus_bounded_positivity_plus_empty_plan.html
    - normalization rules: none
    - warnings:
      * No obvious reference indicator could be inferred for latent variable 'car_centric_attitude'.
      * No obvious reference indicator could be inferred for latent variable 'environmental_attitude'.

    ============================================================
    CASE 6: BAYESIAN + BOUNDED positivity + EXAMPLE plan
    ============================================================
    - estimation mode: bayesian
    - positivity mode: lower_bound
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Numeric value=0.0>) + (<Numeric value=-1.0> * DistributedParameter(car_centric_attitude, (((((((((((`0.0` + Beta('struct_car_centric_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_top_manager', 0.0, None, None, 0) * top_manager)) + (Beta('struct_car_centric_attitude_age_30_less', 0.0, None, None, 0) * age_30_less)) + (Beta('struct_car_centric_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_car_centric_attitude_car_oriented_parents', 0.0, None, None, 0) * car_oriented_parents)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_car_centric_attitude_male', 0.0, None, None, 0) * male)) + (Beta('struct_car_centric_attitude_used_to_go_to_school_by_car', 0.0, None, None, 0) * used_to_go_to_school_by_car)) + (Beta('struct_car_centric_attitude_sigma', 10.0, 1e-15, None, 0) * Draws("struct_car_centric_attitude_draws", "Normal"))), (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (<Beta name=struct_car_centric_attitude_sigma value=10.0 status=0> * <Draws name=struct_car_centric_attitude_draws>))))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * DistributedParameter(environmental_attitude, (((((((((`0.0` + Beta('struct_environmental_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_environmental_attitude_childSuburb', 0.0, None, None, 0) * childSuburb)) + (Beta('struct_environmental_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_environmental_attitude_city_center_as_kid', 0.0, None, None, 0) * city_center_as_kid)) + (Beta('struct_environmental_attitude_artisans', 0.0, None, None, 0) * artisans)) + (Beta('struct_environmental_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_environmental_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_environmental_attitude_single', 0.0, None, None, 0) * single)) + (Beta('struct_environmental_attitude_sigma', 10.0, 1e-15, None, 0) * Draws("struct_environmental_attitude_draws", "Normal"))), (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (<Beta name=struct_environmental_attitude_sigma value=10.0 status=0> * <Draws name=struct_environmental_attitude_draws>))))) / <Numeric value=1.0>), [(UnaryMinus((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>)) / <Numeric value=1.0>), (UnaryMinus(<Beta name=likert_delta_0 value=1.0 status=0>) / <Numeric value=1.0>), (<Beta name=likert_delta_0 value=1.0 status=0> / <Numeric value=1.0>), ((<Beta name=likert_delta_0 value=1.0 status=0> + <Beta name=likert_delta_1 value=1.0 status=0>) / <Numeric value=1.0>)])
    - generated Python file: b01_generated_files/case_6_bayesian_plus_bounded_positivity_plus_example_plan.py
    - generated LaTeX file: b01_generated_files/case_6_bayesian_plus_bounded_positivity_plus_example_plan.tex
    - generated HTML file: b01_generated_files/case_6_bayesian_plus_bounded_positivity_plus_example_plan.html
    - normalization rules:
      * MeasurementIntercept(indicator_name='Envir01') = 0.0 (reference indicator (location))
      * MeasurementIntercept(indicator_name='Envir02') = 0.0 (reference indicator (location))
      * MeasurementLoading(latent_name='car_centric_attitude', indicator_name='Envir01') = -1.0 (reference indicator (scale))
      * MeasurementLoading(latent_name='environmental_attitude', indicator_name='Envir02') = 1.0 (reference indicator (scale))
      * MeasurementSigma(indicator_name='Envir01') = 1.0 (ordinal threshold scale)
    - warnings: none

    ============================================================
    CASE 7: BAYESIAN + log/exp positivity + EMPTY plan (non-standard but illustrative)
    ============================================================
    - estimation mode: bayesian
    - positivity mode: log_exp
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Beta name=measurement_intercept_Envir01 value=0.0 status=0>) + (<Beta name=measurement_coefficient_car_centric_attitude_Envir01 value=0.0 status=0> * DistributedParameter(car_centric_attitude, (((((((((((`0.0` + Beta('struct_car_centric_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_top_manager', 0.0, None, None, 0) * top_manager)) + (Beta('struct_car_centric_attitude_age_30_less', 0.0, None, None, 0) * age_30_less)) + (Beta('struct_car_centric_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_car_centric_attitude_car_oriented_parents', 0.0, None, None, 0) * car_oriented_parents)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_car_centric_attitude_male', 0.0, None, None, 0) * male)) + (Beta('struct_car_centric_attitude_used_to_go_to_school_by_car', 0.0, None, None, 0) * used_to_go_to_school_by_car)) + (exp(Beta('struct_car_centric_attitude_sigma_log', 2.302585092994046, None, None, 0)) * Draws("struct_car_centric_attitude_draws", "Normal"))), (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (exp(<Beta name=struct_car_centric_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_car_centric_attitude_draws>))))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * DistributedParameter(environmental_attitude, (((((((((`0.0` + Beta('struct_environmental_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_environmental_attitude_childSuburb', 0.0, None, None, 0) * childSuburb)) + (Beta('struct_environmental_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_environmental_attitude_city_center_as_kid', 0.0, None, None, 0) * city_center_as_kid)) + (Beta('struct_environmental_attitude_artisans', 0.0, None, None, 0) * artisans)) + (Beta('struct_environmental_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_environmental_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_environmental_attitude_single', 0.0, None, None, 0) * single)) + (exp(Beta('struct_environmental_attitude_sigma_log', 2.302585092994046, None, None, 0)) * Draws("struct_environmental_attitude_draws", "Normal"))), (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (exp(<Beta name=struct_environmental_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_environmental_attitude_draws>))))) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), [(UnaryMinus((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>))) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), (UnaryMinus(exp(<Beta name=likert_delta_0_log value=-0.86 status=0>)) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), (exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>)), ((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>)) / exp(<Beta name=measurement_Envir01_sigma_log value=2.302585092994046 status=0>))])
    - generated Python file: b01_generated_files/case_7_bayesian_plus_log_exp_positivity_plus_empty_plan_non_standard_but_illustrative.py
    - generated LaTeX file: b01_generated_files/case_7_bayesian_plus_log_exp_positivity_plus_empty_plan_non_standard_but_illustrative.tex
    - generated HTML file: b01_generated_files/case_7_bayesian_plus_log_exp_positivity_plus_empty_plan_non_standard_but_illustrative.html
    - normalization rules: none
    - warnings:
      * No obvious reference indicator could be inferred for latent variable 'car_centric_attitude'.
      * No obvious reference indicator could be inferred for latent variable 'environmental_attitude'.

    ============================================================
    CASE 8: BAYESIAN + log/exp positivity + EXAMPLE plan (non-standard but illustrative)
    ============================================================
    - estimation mode: bayesian
    - positivity mode: log_exp
    - latent variables: 2
    - indicators: 14
    - ordinal threshold systems: 1
    - measurement models present: ['ordered_probit']
    - resolved parameters: 66
    - structural expressions: ['car_centric_attitude', 'environmental_attitude']
    - threshold systems: ['likert']
    - measurement terms: 14 indicators
      example term (Envir01): OrderedProbit(((((<Numeric value=0.0> + <Numeric value=0.0>) + (<Numeric value=-1.0> * DistributedParameter(car_centric_attitude, (((((((((((`0.0` + Beta('struct_car_centric_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_top_manager', 0.0, None, None, 0) * top_manager)) + (Beta('struct_car_centric_attitude_age_30_less', 0.0, None, None, 0) * age_30_less)) + (Beta('struct_car_centric_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_car_centric_attitude_car_oriented_parents', 0.0, None, None, 0) * car_oriented_parents)) + (Beta('struct_car_centric_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_car_centric_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_car_centric_attitude_male', 0.0, None, None, 0) * male)) + (Beta('struct_car_centric_attitude_used_to_go_to_school_by_car', 0.0, None, None, 0) * used_to_go_to_school_by_car)) + (exp(Beta('struct_car_centric_attitude_sigma_log', 2.302585092994046, None, None, 0)) * Draws("struct_car_centric_attitude_draws", "Normal"))), (((((((((((<Numeric value=0.0> + <Beta name=struct_car_centric_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_top_manager value=0.0 status=0> * <Variable name=top_manager>)) + (<Beta name=struct_car_centric_attitude_age_30_less value=0.0 status=0> * <Variable name=age_30_less>)) + (<Beta name=struct_car_centric_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_car_centric_attitude_car_oriented_parents value=0.0 status=0> * <Variable name=car_oriented_parents>)) + (<Beta name=struct_car_centric_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_car_centric_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_car_centric_attitude_male value=0.0 status=0> * <Variable name=male>)) + (<Beta name=struct_car_centric_attitude_used_to_go_to_school_by_car value=0.0 status=0> * <Variable name=used_to_go_to_school_by_car>)) + (exp(<Beta name=struct_car_centric_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_car_centric_attitude_draws>))))) + (<Beta name=measurement_coefficient_environmental_attitude_Envir01 value=0.0 status=0> * DistributedParameter(environmental_attitude, (((((((((`0.0` + Beta('struct_environmental_attitude_intercept', 0.0, None, None, 0)) + (Beta('struct_environmental_attitude_childSuburb', 0.0, None, None, 0) * childSuburb)) + (Beta('struct_environmental_attitude_ScaledIncome', 0.0, None, None, 0) * ScaledIncome)) + (Beta('struct_environmental_attitude_city_center_as_kid', 0.0, None, None, 0) * city_center_as_kid)) + (Beta('struct_environmental_attitude_artisans', 0.0, None, None, 0) * artisans)) + (Beta('struct_environmental_attitude_high_education', 0.0, None, None, 0) * high_education)) + (Beta('struct_environmental_attitude_low_education', 0.0, None, None, 0) * low_education)) + (Beta('struct_environmental_attitude_single', 0.0, None, None, 0) * single)) + (exp(Beta('struct_environmental_attitude_sigma_log', 2.302585092994046, None, None, 0)) * Draws("struct_environmental_attitude_draws", "Normal"))), (((((((((<Numeric value=0.0> + <Beta name=struct_environmental_attitude_intercept value=0.0 status=0>) + (<Beta name=struct_environmental_attitude_childSuburb value=0.0 status=0> * <Variable name=childSuburb>)) + (<Beta name=struct_environmental_attitude_ScaledIncome value=0.0 status=0> * <Variable name=ScaledIncome>)) + (<Beta name=struct_environmental_attitude_city_center_as_kid value=0.0 status=0> * <Variable name=city_center_as_kid>)) + (<Beta name=struct_environmental_attitude_artisans value=0.0 status=0> * <Variable name=artisans>)) + (<Beta name=struct_environmental_attitude_high_education value=0.0 status=0> * <Variable name=high_education>)) + (<Beta name=struct_environmental_attitude_low_education value=0.0 status=0> * <Variable name=low_education>)) + (<Beta name=struct_environmental_attitude_single value=0.0 status=0> * <Variable name=single>)) + (exp(<Beta name=struct_environmental_attitude_sigma_log value=2.302585092994046 status=0>) * <Draws name=struct_environmental_attitude_draws>))))) / <Numeric value=1.0>), [(UnaryMinus((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>))) / <Numeric value=1.0>), (UnaryMinus(exp(<Beta name=likert_delta_0_log value=-0.86 status=0>)) / <Numeric value=1.0>), (exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) / <Numeric value=1.0>), ((exp(<Beta name=likert_delta_0_log value=-0.86 status=0>) + exp(<Beta name=likert_delta_1_log value=-0.43 status=0>)) / <Numeric value=1.0>)])
    - generated Python file: b01_generated_files/case_8_bayesian_plus_log_exp_positivity_plus_example_plan_non_standard_but_illustrative.py
    - generated LaTeX file: b01_generated_files/case_8_bayesian_plus_log_exp_positivity_plus_example_plan_non_standard_but_illustrative.tex
    - generated HTML file: b01_generated_files/case_8_bayesian_plus_log_exp_positivity_plus_example_plan_non_standard_but_illustrative.html
    - normalization rules:
      * MeasurementIntercept(indicator_name='Envir01') = 0.0 (reference indicator (location))
      * MeasurementIntercept(indicator_name='Envir02') = 0.0 (reference indicator (location))
      * MeasurementLoading(latent_name='car_centric_attitude', indicator_name='Envir01') = -1.0 (reference indicator (scale))
      * MeasurementLoading(latent_name='environmental_attitude', indicator_name='Envir02') = 1.0 (reference indicator (scale))
      * MeasurementSigma(indicator_name='Envir01') = 1.0 (ordinal threshold scale)
    - warnings: none

    ====================
    END OF SCRIPT
    ====================






|

.. code-block:: Python


    from __future__ import annotations

    from pathlib import Path

    from likert_spec import likert_indicators, likert_types
    from two_latent_variables_spec import latent_variables

    from biogeme.latent_variables import (
        BuildContext,
        EstimationMode,
        Fixing,
        IndicatorMeasurementSpec,
        MeasurementConfiguration,
        MeasurementIntercept,
        MeasurementLoading,
        MeasurementModel,
        MeasurementSigma,
        NormalizationPlan,
        PositiveParameterSpec,
        PositivityMode,
        build_biogeme_model,
        generate_html_report,
        generate_latex_report,
        generate_python_code,
        resolve_model,
        save_html_report,
        save_latex_report,
        save_python_code,
    )

    OUTPUT_DIR = Path('b01_generated_files')
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

    print('\n====================')
    print('SPEC loaded')
    print('====================')

    print(f'- #latent_variables:  {len(latent_variables)}')
    print(f'- #likert_indicators: {len(likert_indicators)}')
    print(f'- #likert_types:      {len(likert_types)}')

    # =============================================================================
    # Homogeneous ordinal measurement configuration used by the resolver
    # =============================================================================
    DEFAULT_SIGMA_START = 10.0

    measurement_configuration = MeasurementConfiguration(
        specifications=[
            IndicatorMeasurementSpec(
                indicator_name=indicator.name,
                measurement_model=MeasurementModel.ORDERED_PROBIT,
                measurement_sigma=PositiveParameterSpec(start=DEFAULT_SIGMA_START),
            )
            for indicator in likert_indicators
        ]
    )

    # =============================================================================
    # Normalization plans used when resolving the specifications
    # =============================================================================
    # Plan A: empty plan. The resolver determines the required normalizations.
    plan_empty = NormalizationPlan()

    # Plan B: explicit plan. These fixings illustrate how expert-specified
    # normalizations are passed to the resolver.
    plan_example = NormalizationPlan()

    # --- LV normalizations by reference-indicator strategy (examples)
    # car_centric_attitude anchored on Envir01
    plan_example.add(
        Fixing(MeasurementIntercept('Envir01'), 0.0, note='reference indicator (location)')
    )
    plan_example.add(
        Fixing(
            MeasurementLoading('car_centric_attitude', 'Envir01'),
            -1.0,
            note='reference indicator (scale)',
        )
    )

    # environmental_attitude anchored on Envir02
    plan_example.add(
        Fixing(MeasurementIntercept('Envir02'), 0.0, note='reference indicator (location)')
    )
    plan_example.add(
        Fixing(
            MeasurementLoading('environmental_attitude', 'Envir02'),
            1.0,
            note='reference indicator (scale)',
        )
    )

    # --- Ordinal scale normalization (one per ordinal threshold system)
    # With a single ordinal threshold system ("likert"), fix one measurement sigma.
    plan_example.add(
        Fixing(MeasurementSigma('Envir01'), 1.0, note='ordinal threshold scale')
    )

    print('\n====================')
    print('Normalization plans ready')
    print('====================')
    print('- plan_empty:   (no fixings)')
    print('- plan_example: (some intercept/loadings/sigma fixed)')


    # =============================================================================
    # Helpers
    # =============================================================================
    def make_context(
        estimation_mode: EstimationMode,
        positivity_mode: PositivityMode,
    ) -> BuildContext:
        """Create a build context used to resolve and generate a specification."""
        base = BuildContext.default(estimation_mode)
        return BuildContext(
            estimation_mode=base.estimation_mode,
            draw_type=base.draw_type,
            positivity_mode=positivity_mode,
            naming=base.naming,
            ordinal_eps=base.ordinal_eps,
            ordinal_enforce_order=base.ordinal_enforce_order,
        )


    def title_to_file_stem(title: str) -> str:
        """Convert a case title into a filesystem-friendly stem."""
        stem = title.lower()
        replacements = {
            ' ': '_',
            '/': '_',
            '+': 'plus',
            ':': '',
            ',': '',
            '(': '',
            ')': '',
            '-': '_',
        }
        for source, target in replacements.items():
            stem = stem.replace(source, target)
        while '__' in stem:
            stem = stem.replace('__', '_')
        return stem.strip('_')


    def summarize_case(
        *, title: str, context: BuildContext, plan: NormalizationPlan
    ) -> None:
        """Resolve one specification, generate files, and print a compact summary.

        No estimation is performed. The generated Python, LaTeX, and HTML files are
        derived from the resolved specification.
        """
        print('\n' + '=' * 60)
        print(title)
        print('=' * 60)

        resolved = resolve_model(
            latent_variables=latent_variables,
            likert_indicators=likert_indicators,
            likert_types=likert_types,
            measurement_configuration=measurement_configuration,
            context=context,
            normalization_plan=plan,
        )
        built = build_biogeme_model(resolved)
        python_code = generate_python_code(resolved)
        latex_report = generate_latex_report(resolved)
        html_report = generate_html_report(resolved)

        stem = title_to_file_stem(title)
        python_path = OUTPUT_DIR / f'{stem}.py'
        latex_path = OUTPUT_DIR / f'{stem}.tex'
        html_path = OUTPUT_DIR / f'{stem}.html'

        save_python_code(python_code, python_path)
        save_latex_report(latex_report, latex_path)
        save_html_report(html_report, html_path)

        measurement_models = [
            model.value for model in resolved.metadata.measurement_models_present
        ]

        print(f'- estimation mode: {context.estimation_mode.value}')
        print(f'- positivity mode: {context.positivity_mode.value}')
        print(f'- latent variables: {resolved.metadata.n_latent_variables}')
        print(f'- indicators: {resolved.metadata.n_indicators}')
        print(f'- ordinal threshold systems: {resolved.metadata.n_threshold_systems}')
        print(f'- measurement models present: {measurement_models}')
        print(f'- resolved parameters: {len(resolved.parameters)}')
        print(f'- structural expressions: {list(built.latent_expressions.keys())}')
        print(f'- threshold systems: {list(built.threshold_expressions.keys())}')
        print(f'- measurement terms: {len(built.measurement_terms)} indicators')
        print(f'  example term (Envir01): {built.measurement_terms.get("Envir01")}')
        print(f'- generated Python file: {python_path.as_posix()}')
        print(f'- generated LaTeX file: {latex_path.as_posix()}')
        print(f'- generated HTML file: {html_path.as_posix()}')

        if resolved.normalization.rules:
            print('- normalization rules:')
            for rule in resolved.normalization.rules:
                print(f'  * {rule.target_name} = {rule.value} ({rule.reason})')
        else:
            print('- normalization rules: none')

        if resolved.normalization.warnings:
            print('- warnings:')
            for warning in resolved.normalization.warnings:
                print(f'  * {warning}')
        else:
            print('- warnings: none')


    # =============================================================================
    # Enumerate build contexts and normalization plans explicitly
    # =============================================================================
    # We generate files for 8 cases:
    #   modes: maximum likelihood vs Bayesian,
    #   positivity: log/exp vs lower-bound parameterization,
    #   normalization plan: automatic vs explicit.
    #
    # Some combinations are not intended as recommended estimation settings. They
    # are included to exercise the specification resolver and code generators.

    # -------------------------------------------------------------------------
    # CASE 1: ML + log/exp positivity + empty plan
    # -------------------------------------------------------------------------
    context_1 = make_context(
        EstimationMode.MAXIMUM_LIKELIHOOD,
        PositivityMode.LOG_EXP,
    )
    summarize_case(
        title='CASE 1: ML + log/exp positivity + EMPTY plan',
        context=context_1,
        plan=plan_empty,
    )

    # -------------------------------------------------------------------------
    # CASE 2: ML + log/exp positivity + example plan
    # -------------------------------------------------------------------------
    context_2 = make_context(
        EstimationMode.MAXIMUM_LIKELIHOOD,
        PositivityMode.LOG_EXP,
    )
    summarize_case(
        title='CASE 2: ML + log/exp positivity + EXAMPLE plan',
        context=context_2,
        plan=plan_example,
    )

    # -------------------------------------------------------------------------
    # CASE 3: ML + bounded positivity + empty plan
    # -------------------------------------------------------------------------
    context_3 = make_context(
        EstimationMode.MAXIMUM_LIKELIHOOD,
        PositivityMode.LOWER_BOUND,
    )
    summarize_case(
        title='CASE 3: ML + BOUNDED positivity + EMPTY plan (non-standard but illustrative)',
        context=context_3,
        plan=plan_empty,
    )

    # -------------------------------------------------------------------------
    # CASE 4: ML + bounded positivity + example plan
    # -------------------------------------------------------------------------
    context_4 = make_context(
        EstimationMode.MAXIMUM_LIKELIHOOD,
        PositivityMode.LOWER_BOUND,
    )
    summarize_case(
        title='CASE 4: ML + BOUNDED positivity + EXAMPLE plan (non-standard but illustrative)',
        context=context_4,
        plan=plan_example,
    )

    # -------------------------------------------------------------------------
    # CASE 5: Bayesian + bounded positivity + empty plan
    # -------------------------------------------------------------------------
    context_5 = make_context(
        EstimationMode.BAYESIAN,
        PositivityMode.LOWER_BOUND,
    )
    summarize_case(
        title='CASE 5: BAYESIAN + BOUNDED positivity + EMPTY plan',
        context=context_5,
        plan=plan_empty,
    )

    # -------------------------------------------------------------------------
    # CASE 6: Bayesian + bounded positivity + example plan
    # -------------------------------------------------------------------------
    context_6 = make_context(
        EstimationMode.BAYESIAN,
        PositivityMode.LOWER_BOUND,
    )
    summarize_case(
        title='CASE 6: BAYESIAN + BOUNDED positivity + EXAMPLE plan',
        context=context_6,
        plan=plan_example,
    )

    # -------------------------------------------------------------------------
    # CASE 7: Bayesian + log/exp positivity + empty plan
    # -------------------------------------------------------------------------
    context_7 = make_context(
        EstimationMode.BAYESIAN,
        PositivityMode.LOG_EXP,
    )
    summarize_case(
        title='CASE 7: BAYESIAN + log/exp positivity + EMPTY plan (non-standard but illustrative)',
        context=context_7,
        plan=plan_empty,
    )

    # -------------------------------------------------------------------------
    # CASE 8: Bayesian + log/exp positivity + example plan
    # -------------------------------------------------------------------------
    context_8 = make_context(
        EstimationMode.BAYESIAN,
        PositivityMode.LOG_EXP,
    )
    summarize_case(
        title='CASE 8: BAYESIAN + log/exp positivity + EXAMPLE plan (non-standard but illustrative)',
        context=context_8,
        plan=plan_example,
    )

    print('\n====================')
    print('END OF SCRIPT')
    print('====================')


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.058 seconds)


.. _sphx_glr_download_auto_examples_hybrid_choice_specs_plot_b01_model_specification.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_b01_model_specification.ipynb <plot_b01_model_specification.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_b01_model_specification.py <plot_b01_model_specification.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_b01_model_specification.zip <plot_b01_model_specification.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_