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Section Navigation

  • pharmpy.model Package
    • get_and_check_dataset
    • get_and_check_odes
    • to_compartmental_system
    • Assignment
    • Bolus
    • ColumnInfo
    • Compartment
    • CompartmentalSystem
    • CompartmentalSystemBuilder
    • DataInfo
    • DatasetError
    • DatasetWarning
    • Distribution
    • EstimationStep
    • ExecutionSteps
    • FiniteDistribution
    • Infusion
    • JointNormalDistribution
    • Model
    • ModelError
    • ModelfitResultsError
    • ModelSyntaxError
    • NormalDistribution
    • Parameter
    • Parameters
    • RandomVariables
    • SimulationStep
    • Statement
    • Statements
    • VariabilityHierarchy
    • VariabilityLevel
  • pharmpy.modeling Package
    • add_admid
    • add_allometry
    • add_bioavailability
    • add_cmt
    • add_covariate_effect
    • add_derivative
    • add_effect_compartment
    • add_estimation_step
    • add_iiv
    • add_individual_parameter
    • add_iov
    • add_indirect_effect
    • add_lag_time
    • add_metabolite
    • add_parameter_uncertainty_step
    • add_pd_iiv
    • add_peripheral_compartment
    • add_pk_iiv
    • add_population_parameter
    • add_predictions
    • add_residuals
    • add_time_after_dose
    • append_estimation_step_options
    • bin_observations
    • bump_model_number
    • calculate_aic
    • calculate_bic
    • calculate_corr_from_cov
    • calculate_corr_from_prec
    • calculate_cov_from_corrse
    • calculate_cov_from_prec
    • calculate_epsilon_gradient_expression
    • calculate_eta_gradient_expression
    • calculate_eta_shrinkage
    • calculate_individual_parameter_statistics
    • calculate_individual_shrinkage
    • calculate_prec_from_corrse
    • calculate_prec_from_cov
    • calculate_parameters_from_ucp
    • calculate_pk_parameters_statistics
    • calculate_se_from_cov
    • calculate_se_from_prec
    • calculate_ucp_scale
    • check_dataset
    • check_high_correlations
    • check_parameters_near_bounds
    • cleanup_model
    • convert_model
    • create_basic_pk_model
    • create_config_template
    • create_joint_distribution
    • create_rng
    • create_symbol
    • deidentify_data
    • display_odes
    • drop_columns
    • drop_dropped_columns
    • evaluate_epsilon_gradient
    • evaluate_eta_gradient
    • evaluate_expression
    • evaluate_individual_prediction
    • evaluate_population_prediction
    • evaluate_weighted_residuals
    • expand_additional_doses
    • filter_dataset
    • find_clearance_parameters
    • find_volume_parameters
    • fix_or_unfix_parameters
    • fix_parameters
    • fix_parameters_to
    • get_admid
    • get_baselines
    • get_bioavailability
    • get_central_volume_and_clearance
    • get_cmt
    • get_concentration_parameters_from_data
    • get_config_path
    • get_covariate_effects
    • get_covariate_baselines
    • get_doses
    • get_doseid
    • get_dv_symbol
    • get_evid
    • get_ids
    • get_individual_parameters
    • get_individual_prediction_expression
    • get_initial_conditions
    • get_lag_times
    • get_mdv
    • get_model_code
    • get_model_covariates
    • get_number_of_individuals
    • get_number_of_observations
    • get_number_of_observations_per_individual
    • get_observations
    • get_observation_expression
    • get_omegas
    • get_parameter_rv
    • get_pd_parameters
    • get_pk_parameters
    • get_population_prediction_expression
    • get_rv_parameters
    • get_sigmas
    • get_thetas
    • get_unit_of
    • get_zero_order_inputs
    • greekify_model
    • has_additive_error_model
    • has_combined_error_model
    • has_covariate_effect
    • has_first_order_absorption
    • has_first_order_elimination
    • has_instantaneous_absorption
    • has_linear_odes
    • has_linear_odes_with_real_eigenvalues
    • has_michaelis_menten_elimination
    • has_mixed_mm_fo_elimination
    • has_mu_reference
    • has_odes
    • has_presystemic_metabolite
    • has_proportional_error_model
    • has_random_effect
    • has_seq_zo_fo_absorption
    • has_weighted_error_model
    • has_zero_order_absorption
    • has_zero_order_elimination
    • get_number_of_peripheral_compartments
    • get_number_of_transit_compartments
    • is_linearized
    • is_real
    • list_time_varying_covariates
    • load_dataset
    • load_example_model
    • make_declarative
    • get_mu_connected_to_parameter
    • mu_reference_model
    • omit_data
    • plot_abs_cwres_vs_ipred
    • plot_cwres_vs_idv
    • plot_dv_vs_ipred
    • plot_dv_vs_pred
    • plot_eta_distributions
    • plot_individual_predictions
    • plot_iofv_vs_iofv
    • plot_transformed_eta_distributions
    • print_model_code
    • print_model_symbols
    • read_dataset_from_datainfo
    • read_model
    • read_model_from_string
    • rename_symbols
    • remove_bioavailability
    • remove_covariate_effect
    • remove_derivative
    • remove_error_model
    • remove_estimation_step
    • remove_iiv
    • remove_iov
    • remove_lag_time
    • remove_loq_data
    • remove_parameter_uncertainty_step
    • remove_peripheral_compartment
    • remove_predictions
    • remove_residuals
    • remove_unused_parameters_and_rvs
    • replace_fixed_thetas
    • replace_non_random_rvs
    • resample_data
    • sample_individual_estimates
    • sample_parameters_from_covariance_matrix
    • sample_parameters_uniformly
    • set_additive_error_model
    • set_baseline_effect
    • set_combined_error_model
    • set_covariates
    • set_dataset
    • set_description
    • set_direct_effect
    • set_dtbs_error_model
    • set_dvid
    • set_estimation_step
    • set_evaluation_step
    • set_first_order_absorption
    • set_first_order_elimination
    • set_iiv_on_ruv
    • set_initial_condition
    • set_initial_estimates
    • set_instantaneous_absorption
    • set_lloq_data
    • set_lower_bounds
    • set_michaelis_menten_elimination
    • set_mixed_mm_fo_elimination
    • set_name
    • set_ode_solver
    • set_peripheral_compartments
    • set_power_on_ruv
    • set_proportional_error_model
    • set_reference_values
    • set_seq_zo_fo_absorption
    • set_simulation
    • set_time_varying_error_model
    • set_tmdd
    • set_transit_compartments
    • set_upper_bounds
    • set_weighted_error_model
    • set_zero_order_absorption
    • set_zero_order_elimination
    • set_zero_order_input
    • simplify_expression
    • solve_ode_system
    • split_joint_distribution
    • transform_blq
    • transform_etas_boxcox
    • transform_etas_john_draper
    • transform_etas_tdist
    • translate_nmtran_time
    • unfix_parameters
    • unfix_parameters_to
    • update_initial_individual_estimates
    • use_thetas_for_error_stdev
    • write_csv
    • write_model
    • unconstrain_parameters
    • undrop_columns
    • unload_dataset
    • plot_vpc
  • pharmpy.tools Package
    • create_context
    • create_report
    • fit
    • is_strictness_fulfilled
    • load_example_modelfit_results
    • predict_influential_individuals
    • predict_influential_outliers
    • predict_outliers
    • print_fit_summary
    • print_log
    • read_results
    • read_modelfit_results
    • retrieve_model
    • retrieve_modelfit_results
    • retrieve_models
    • run_allometry
    • run_amd
    • run_bootstrap
    • run_covsearch
    • run_estmethod
    • run_iivsearch
    • run_iovsearch
    • run_linearize
    • run_modelfit
    • run_modelsearch
    • run_retries
    • run_ruvsearch
    • run_structsearch
    • run_tool
    • run_simulation
    • summarize_modelfit_results
    • write_results
  • pharmpy.workflows Package
    • execute_workflow
    • split_common_options
    • default_model_database
    • default_context
    • LocalDirectoryDatabase
    • LocalModelDirectoryDatabase
    • LocalDirectoryContext
    • Log
    • NullModelDatabase
    • ModelDatabase
    • ModelEntry
    • ModelfitResults
    • Results
    • SimulationResults
    • Task
    • Context
    • Workflow
    • WorkflowBuilder
  • API Reference
  • pharmpy.modeling Package
  • print_model_code

print_model_code#

pharmpy.modeling.print_model_code(model)[source]#

Print the model code of the underlying model language to the console

Parameters:

model (Model) – Pharmpy model

Examples

>>> from pharmpy.modeling import print_model_code, load_example_model
>>> model = load_example_model("pheno")
>>> print_model_code(model)
$PROBLEM PHENOBARB SIMPLE MODEL
$DATA pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV FA1 FA2
$SUBROUTINE ADVAN1 TRANS2
$ABBREV REPLACE ETA_CL=ETA(1)
$ABBREV REPLACE ETA_VC=ETA(2)

$PK
TVCL = THETA(1)*WGT
TVV = THETA(2)*WGT
IF(APGR.LT.5) TVV = TVV*(1 + THETA(3))
CL = TVCL*EXP(ETA_CL)
VC = TVV*EXP(ETA_VC)
V = VC
S1 = VC

$ERROR
Y = F + F*EPS(1)

$THETA  (0,0.00469307) ; POP_CL
$THETA  (0,1.00916) ; POP_VC
$THETA  (-.99,.1) ; COVAPGR

$OMEGA  0.0309626 ; IIV_CL
$OMEGA  0.031128 ; IIV_VC

$SIGMA  0.0130865  ; SIGMA

$ESTIMATION METHOD=1 INTERACTION MAXEVALS=99999
$COVARIANCE UNCONDITIONAL PRINT=E
$TABLE ID TIME DV CIPREDI PRED RES CWRES NOAPPEND NOPRINT ONEHEADER FILE=pheno.tab

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