:orphan: 





.. _flip_flop_good_tut:



Flip Flop tutorial with low initial estimates of KA, V and CL
#############################################################

[Generated automatically as a Tutorial summary]

Model Description
*****************


:Name: flip_flop_good

:Title: Flip Flop tutorial with low initial estimates of KA, V and CL

:Author: PoPy for PK/PD

:Abstract: 

| One compartment model with a depot leading into a central compartment.
| Here initial values of KE, KA, V are close to the true values, so we get a good fit near the true global minima.

:Keywords: flip flop; dep_one_cmp_k; one compartment model; good start values

:Input Script: :download:`flip_flop_good_tut.pyml <flip_flop_good_tut.pyml>`

:Diagram: 


.. thumbnail:: compartment_diagram.svg
    :width: 200px


Comparison
**********



True objective value
====================


.. code-block:: pyml

    86.0288



Final fitted objective value
============================


.. code-block:: pyml

    83.4768



Compare Main f[X]
=================



.. csv-table:: 
    :file: fx_comp_main.csv
    :header-rows: 1


Compare Noise f[X]
==================



.. csv-table:: 
    :file: fx_comp_noise.csv
    :header-rows: 1


Compare Variance f[X]
=====================


No Variance f[X] values to compare.

Outputs
*******



Fitted f[X] values (after fitting)
==================================


.. code-block:: pyml

    f[KA] = 0.1526
    f[KE] = 0.3014
    f[ANOISE_STD] = 0.9207



Generated data .csv file
========================


:Synthetic Data: :download:`synthetic_data.csv <synthetic_data.csv>`


Gen and Fit Summaries
=====================


* Gen: :ref:`flip_flop_good_gen` (gen)
* Fit: :ref:`flip_flop_good_fit` (fit)

Inputs
******



True f[X] values (for simulation)
=================================

.. code-block:: pyml

    f[KA] = 0.1500
    f[KE] = 0.3000
    f[ANOISE_STD] = 1.0000



Starting f[X] values (before fitting)
=====================================

.. code-block:: pyml

    f[KA] = 0.5000
    f[KE] = 0.5000
    f[ANOISE_STD] = 5.0000

