..  _sample notebook

Sample Notebook
========================================================

We provide a sample notebook for interacting with the API. The ipynb file for this notebook can be found in ``[luptai-installation-directory]/src/python/notebooks/model_runner.ipynb``, and can be executed in a Jupyter environment (eg. Jupyter Lab).


.. note::

   To run the notebook the python LUPTAI modules must be in *PYTHONPATH*


This can be achieved by editing the *enivronment variable* PYTHONPATH
to include the directory that contains LUPTAI python source code.
Alternatively, add just for this notebook by uncommenting the below cell
and replace the path str with the path to the python source code on your
local machine

.. code:: ipython3

    # import sys
    # sys.path.append(r'D:\src\LUPTAI\luptai_core\src\python')

.. code:: ipython3

    import os

.. code:: ipython3

    from luptai.model import ModelRunner
    from luptai.model.parameters import InputParameters, BatchSettings, MajorMode, PTMode
    from luptai.database.scenario_data import ScenarioDataConnection
    from luptai.model.model_data.export_data import export_model_data, AttributedActivitiesExport
    from luptai.model.model_data.import_data import ResultsImport

Assign parameter values
-----------------------

Adjust each of these variables as needed for your specific run.

.. code:: ipython3

    # The scenario we are interested in
    scenario_id = 'Scenario558'

.. code:: ipython3

    # Point to the directories that contain the scenario info
    working_dir = r'D:\LUPTAI\projects\test_project'
    paras_path = os.path.join(working_dir, 'Parameters/default.json')
    scenario_path = os.path.join(working_dir, f'Scenarios/{scenario_id}.sqlite')
    data_path = os.path.join(working_dir, r'ModelData')

.. code:: ipython3

    # Assembly directory, where the LUTPAI DLLs and EXEs are located on your local machine
    assembly_directory = r'D:\LUPTAI\codebase\luptai\bin'

.. code:: ipython3

    # Load up the parameters file
    input_paras = InputParameters.load_from_json(paras_path)

.. code:: ipython3

    # Connect to the scenario database
    sdc = ScenarioDataConnection(scenario_path)

.. code:: ipython3

    # The purposes we want to run, a list of the purpose types, as strings, to be used for this run.
    # Alternatively use the sdc.get_activities_list function to get all activities available in the scenario.
    purposes = ["BANK"]
    # purposes = sdc.get_activities_list()

.. code:: ipython3

    # Modes for the run
    major_modes = [MajorMode.WALK_ONLY, MajorMode.PT_AND_WALK]
    pt_modes = [PTMode.BUS, PTMode.TRAIN, PTMode.BUSWAY, PTMode.FERRY, PTMode.LIGHT_RAIL]

.. code:: ipython3

    # Time Periods
    time_periods = [input_paras.get_am_peak()]

.. code:: ipython3

    # init the needed objects
    runner = ModelRunner(assembly_directory)
    bs = BatchSettings()

Setup for the run
-----------------

No need to change anything here, it is just setting up the Model inputs
based on the above variables

.. code:: ipython3

    # Populate the run settings for this batch
    bs.input_parameters = input_paras
    bs.major_modes = major_modes
    bs.pt_modes = pt_modes
    bs.time_periods = time_periods
    bs.project_id = "test"
    bs.scenario_id = scenario_id
    bs.scenario_model_data_root = data_path
    # Before adding purposes we check that they are in the scenario and turn them from strings to PyPurposes
    purps_in_scenario = sdc.get_activities_list()
    all_purposes = input_paras.get_purposes_as_py_purpose_list()
    bs.purposes = [p for p in all_purposes if p.get_activity_type() in purposes and p.get_activity_type() in [ap.get_activity_type() for ap in all_purposes]]

Do the Run
----------

No changes necessary here this just runs the model.

.. code:: ipython3

    # export the data for the run
    export_model_data(sdc, data_path)

.. code:: ipython3

    # This actually does the run
    runner.do_batch_settings_run(bs)

Get the results
---------------

Here we save the results to the scenario database, and collect them in a
dictionary of dataframes, which we can analyze in this very notebook.

.. code:: ipython3

    # get a dictionary of dataframes containing the results from the model runs.
    results = runner.get_results()

.. code:: ipython3

    # save the results to the scenario database
    for cr in results.keys():
        result_file = os.path.join(data_path, f'{cr}.xml')
        if os.path.isfile(result_file):
            ResultsImport.from_xml(result_file).to_scenario_db(sdc)

Analyse the results
-------------------

Feel free to do any analysis of the results below

.. code:: ipython3

    first_result = next(iter(results))
    print(f'Results for first 20 nodes of model run {first_result}:')
    results[first_result].head(20)