.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/RunModel/matlab_example.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_RunModel_matlab_example.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_RunModel_matlab_example.py:


Third-party - Matlab
==================================

.. GENERATED FROM PYTHON SOURCE LINES 8-40

The RunModel class is capable of passing input in different formats into a single computational model. This means that
the samples passed into a model can be passed as:
- floating point values
- numpy arrays
- lists
- tuples
- lists of other iterables
- numpy arrays of other iterables
- or any combination of the above

In the examples below, we demonstrate the use of a third-party computational model (in this case, Matlab) with inputs
that are combinations of the above.

Some notes on their use:

1. UQpy converts all sample input to a numpy array with at least two dimensions. The first dimension,
i.e. len(samples) must correspond to the number of samples being passed for model execution. The second dimension,
i.e. len(samples[0]) must correspond to the number of variables that each sample possesses.

2. Each individual sample, i.e. sample[j], may be composed of multiple data types -- with each variable having a
different data type. For example, sample[j][k] may be a floating point value and sample[j][l] may be an array of
arbitrary dimension.

3. If a specific variable has multiple dimensions, the user may specify the index to be return in the input file.
For example, the place holder for a variable x1 corresponding to sample[j][l] that is an array of shape (1,4) can be
read as <x1[0, 3]>, which will return the final (0,3) component of samples[j][l].

4. If the user does not specify the index for a multidimensional variable, then the entire multidimensional variable
is flattened and written with comma delimiters.

All examples are run using Matlab execution through the command line. The user will need to modify the model_script
in order to provide the correct path to the Matlab application on his/her computer.

.. GENERATED FROM PYTHON SOURCE LINES 45-60

Examples 1-2:
The provided Matlab models take the sum of three random variables:

.. math:: s = \sum_{i=1}^3 x_i

.. math:: x_i \sim N(0,1)

Example 3:
The provided Matlab model takes the product of a random variable and the determinant of a random matrix:

.. math::z = x \det(Y)

.. math:: x \sim N(0,1)

:math:`y` is a :math:`3x3` matrix of standard normal random variables.

.. GENERATED FROM PYTHON SOURCE LINES 63-71

.. code-block:: default


    from UQpy.sampling import MonteCarloSampling
    from UQpy.run_model.RunModel import RunModel
    from UQpy.run_model.model_execution.ThirdPartyModel import ThirdPartyModel
    from UQpy.distributions import Normal
    import time
    import numpy as np


.. GENERATED FROM PYTHON SOURCE LINES 72-80

Pick which model to run
-----------------------

Options:
- 'all'
- 'scalar'
- 'vector'
- 'mixed'

.. GENERATED FROM PYTHON SOURCE LINES 83-86

.. code-block:: default


    pick_model = 'all'


.. GENERATED FROM PYTHON SOURCE LINES 87-94

Example 1: Three scalar random variables
------------------------------------------
In this example, we pass three scalar random variables. Note that this is different from assigning a single variable
with three components, which will be handled in the following example.

Here we will pass the samples both as an ndarray and as a list. Recall that UQpy converts all samples into an ndarray
of at least two dimensions internally.

.. GENERATED FROM PYTHON SOURCE LINES 97-121

.. code-block:: default


    if pick_model == 'scalar' or pick_model == 'vector' or pick_model == 'all':
        # Call MCS to generate samples
        d = Normal(loc=0, scale=1)
        x_mcs = MonteCarloSampling(distributions=[d, d, d], nsamples=5, random_state=987979)
        names = ['var1', 'var11', 'var111']

        # UQpy returns samples as an ndarray. Convert them to a list for part 1.2
        x_mcs_list = list(x_mcs.samples)
        print("Monte Carlo samples of three random variables from a standard normal distribution.")
        print('Samples stored as an array:')
        print('Data type:', type(x_mcs.samples))
        print('Number of samples:', len(x_mcs.samples))
        print('Dimensions of samples:', np.shape(x_mcs.samples))
        print('Samples')
        print(x_mcs.samples)
        print()
        print('Samples stored as a list:')
        print('Data type:', type(x_mcs_list))
        print('Number of samples:', len(x_mcs_list))
        print('Dimensions of samples:', np.shape(x_mcs_list))
        print('Samples:')
        print(x_mcs_list)


.. GENERATED FROM PYTHON SOURCE LINES 122-129

1.1 Pass sampled as ndarray, specify format in generated input file, serial execution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:

- model_script = matlab_model_sum_scalar.py
- input_template = sum_scalar.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 132-149

.. code-block:: default


    if pick_model == 'scalar' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_sum_scalar.py',
                                input_template='sum_scalar.m', var_names=names, model_object_name="matlab",
                                output_script='process_matlab_output.py', output_object_name='read_output',
                                model_dir='Matlab_Model', fmt="{:>10.4f}")
        m = RunModel(ntasks=1, model=model)
        m.run(x_mcs.samples)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)


.. GENERATED FROM PYTHON SOURCE LINES 150-157

1.2 Samples passed as list, no format specification, parallel execution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:

- model_script = matlab_model_sum_scalar.py
- input_template = sum_scalar.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 160-176

.. code-block:: default


    if pick_model == 'scalar' or pick_model == 'all':
        # Call to RunModel with samples as a list - Again we run the model while instantiating the RunModel object.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_sum_scalar.py',
                                input_template='sum_scalar.m', var_names=names, model_object_name="matlab",
                                output_script='process_matlab_output.py', output_object_name='read_output',
                                model_dir='Matlab_Model')
        m = RunModel(samples=x_mcs_list, ntasks=2, model=model)
        t_par_matlab = time.time() - t
        print("\nTime for parallel execution:")
        print(t_par_matlab)
        print()
        print("The values retured from the Matlab simulation:")
        print(m.qoi_list)


.. GENERATED FROM PYTHON SOURCE LINES 177-184

Example 2: Single tri-variate random variable
-----------------------------------------------
In this example, we pass three random variables in as a trivariate random variable. Note that this is different from
assigning three scalar random variables, which was be handled in Example 1.

Again, we will pass the samples both as an ndarray and as a list. Recall that UQpy converts all samples into an
ndarray of at least two dimensions internally.

.. GENERATED FROM PYTHON SOURCE LINES 189-193

Restructure the samples
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To pass the samples in as a single tri-variate variable, we need reshape the samples from shape (5, 3) to
shape (5, 1, 3)

.. GENERATED FROM PYTHON SOURCE LINES 196-216

.. code-block:: default


    if pick_model == 'vector' or pick_model == 'all':
        x_mcs_tri = x_mcs.samples.reshape(5, 1, 3)
        x_mcs_tri_list = list(x_mcs_tri)

        print("Monte Carlo samples of three random variables from a standard normal distribution.")
        print('Samples stored as an array:')
        print('Data type:', type(x_mcs_tri))
        print('Number of samples:', len(x_mcs_tri))
        print('Dimensions of samples:', np.shape(x_mcs_tri))
        print('Samples')
        print(x_mcs_tri)
        print()
        print('Samples stored as a list:')
        print('Data type:', type(x_mcs_tri_list))
        print('Number of samples:', len(x_mcs_tri_list))
        print('Dimensions of samples:', np.shape(x_mcs_tri_list))
        print('Samples:')
        print(x_mcs_tri_list)


.. GENERATED FROM PYTHON SOURCE LINES 217-224

2.1 Pass samples as ndarray, specify format in generated input file, serial execution, index samples in input_template
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:

- model_script = matlab_model_sum_vector_indexed.py
- input_template = sum_vector_indexed.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 227-244

.. code-block:: default



    if pick_model == 'vector' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_sum_vector_indexed.py',
                                input_template='sum_vector_indexed.m', model_object_name="matlab",
                                output_script='process_matlab_output.py', output_object_name='read_output',
                                model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0'])
        m = RunModel(samples=x_mcs_tri, ntasks=1, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)


.. GENERATED FROM PYTHON SOURCE LINES 245-251

2.2 Samples passed as list, no format specification, parallel execution, index samples
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:
- model_script = matlab_model_sum_vector_indexed.py
- input_template = sum_vector_indexed.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 254-270

.. code-block:: default


    if pick_model == 'vector' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_sum_vector_indexed.py',
                                input_template='sum_vector_indexed.m', model_object_name="matlab",
                                output_script='process_matlab_output.py', output_object_name='read_output',
                                model_dir='Matlab_Model', var_names=['x0'])
        m = RunModel(samples=x_mcs_tri_list, ntasks=2, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for parallel execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)


.. GENERATED FROM PYTHON SOURCE LINES 271-277

2.3 Samples passed as a ndarray, specify format in generated input file, serial execution, do not index samples
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:
- model_script = matlab_model_sum_vector_indexed.py
- input_template = sum_vector_indexed.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 280-298

.. code-block:: default


    if pick_model == 'vector' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        # Notice that we do not specify var_names. This will default to a single variable with name x0. In this case,
        # we will read them in by indexing in the input_template.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_sum_vector.py',
                                input_template='sum_vector.m', model_object_name="matlab",
                                output_script='process_matlab_output.py', output_object_name='read_output',
                                model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0'])
        m = RunModel(samples=x_mcs_tri, ntasks=1, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)


.. GENERATED FROM PYTHON SOURCE LINES 299-305

Example 3: Passing a scalar and an array to RunModel
-----------------------------------------------------
In this example, we pass a single scalar random variable as well as an array into a Matlab model.

Again, we will pass the samples both as an ndarray and as a list. Recall that UQpy converts all samples into an
ndarray of at least two dimensions internally.

.. GENERATED FROM PYTHON SOURCE LINES 310-311

Create the input samples to be passed

.. GENERATED FROM PYTHON SOURCE LINES 314-371

.. code-block:: default


    if pick_model == 'mixed' or pick_model == 'vector' or pick_model == 'all':

        # Call MCS to generate samples
        # First generate the scalar random variable
        x_mcs1 = MonteCarloSampling(distributions=d, nsamples=5, random_state=843765)
        # Next generate a 3x3 random matrix
        x_mcs2 = MonteCarloSampling(distributions=[d, d, d], nsamples=15, random_state=438975)
        x_mcs_array = x_mcs2.samples.reshape((5, 3, 3))
        # -------------------------------------------------------------------------------------------------------------

        print("Monte Carlo samples of a single random variable from a standard normal distribution.")
        print('Samples stored as an array:')
        print('Data type:', type(x_mcs1.samples))
        print('Number of samples:', len(x_mcs1.samples))
        print('Dimensions of samples:', np.shape(x_mcs1.samples))
        print('Samples')
        print(x_mcs1.samples)
        print()
        print("Monte Carlo samples of a 3x3 matrix of standard normal random variables.")
        print('Samples stored as an array:')
        print('Data type:', type(x_mcs_array))
        print('Number of samples:', len(x_mcs_array))
        print('Dimensions of samples:', np.shape(x_mcs_array))
        print('Samples')
        print(x_mcs_array)
        print()

        # Create a set of samples to be passed into RunModel
        # Here we need to create the mixed samples such that each sample has a single scalar and a single 3x3 matrix.
        # This data structure is essential to passing the input to UQpy correctly.
        x_mixed = []
        for i in range(5):
            x_mixed.append([x_mcs1.samples[i], x_mcs_array[i]])

        print("Combined samples with a scalar and a 3x3 matrix of standard normal random variables.")
        print('Samples stored as a list:')
        print('Data type:', type(x_mixed))
        print('Number of samples:', len(x_mixed))
        print('Dimensions of samples:', np.shape(x_mixed))
        print('Samples')
        print(x_mixed)
        print()

        x_mixed_array = np.atleast_2d(np.asarray(x_mixed))
        print("Combined samples with a scalar and a 3x3 matrix of standard normal random variables.")
        print('Samples stored as ndarray:')
        print('Data type:', type(x_mixed_array))
        print('Number of samples:', len(x_mixed_array))
        print('Dimensions of samples:', np.shape(x_mixed_array))
        print('Samples')
        print(x_mixed_array)
        print()

        # Notice that, in both the ndarray case and the list case, the samples have dimension (5,2). That is, there
        # are five samples of two variables. The first variable is a scalar. The second variable is a 3x3 matrix.


.. GENERATED FROM PYTHON SOURCE LINES 372-379

3.1 Pass samples as ndarray, specify format in generated input file, serial execution, do not index samples in
input_template
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:
- model_script = matlab_model_det.py
- input_template = prod_determinant.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 382-401

.. code-block:: default


    if pick_model == 'mixed' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        # Notice that we do not specify var_names. This will default to two variables with names x0 and x1. In this
        # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in without indexing in the
        # input_template.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_det.py',
                                input_template='prod_determinant.m', model_object_name="matlab", var_names=['x0', 'x1'],
                                output_script='process_matlab_output.py', output_object_name='read_output',
                                model_dir='Matlab_Model', fmt="{:>10.4f}")
        m = RunModel(samples=x_mixed_array, ntasks=1, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)


.. GENERATED FROM PYTHON SOURCE LINES 402-408

3.2 Pass samples as ndarray, specify format in generated input file, serial execution, index samples in input_template
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:
- model_script = matlab_model_det_index.py
- input_template = prod_determinant_index.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 411-428

.. code-block:: default


    if pick_model == 'mixed' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        # We will read the matrix in with indexing in the input_template.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_det_index.py',
                     input_template='prod_determinant_index.m', model_object_name="matlab",
                     output_script='process_matlab_output.py', output_object_name='read_output',
                     model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0', 'x1'])
        m = RunModel(samples=x_mixed_array, ntasks=1, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)


.. GENERATED FROM PYTHON SOURCE LINES 429-436

3.3 Pass samples as list, do not specify format in generated input file, parallel execution, do not index samples in
input_template
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:
- model_script = matlab_model_det.py
- input_template = prod_determinant.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 439-458

.. code-block:: default



    if pick_model == 'mixed' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_det.py',
                     input_template='prod_determinant.m', model_object_name="matlab",
                     output_script='process_matlab_output.py', output_object_name='read_output',
                    model_dir='Matlab_Model', var_names=['x0', 'x1'])
        m = RunModel(samples=x_mixed, ntasks=2, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)

        # Notice that the solution changes slightly due to the increased precision by not specifying a fmt.


.. GENERATED FROM PYTHON SOURCE LINES 459-466

3.4 Pass samples as list, do not specify format in generated input file, parallel execution, index samples in
input_template
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:
- model_script = matlab_model_det_index.py
- input_template = prod_determinant_index.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 469-490

.. code-block:: default


    if pick_model == 'mixed' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        # Notice that we do not specify var_names. This will default to two variables with names x0 and x1. In this
        # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in with indexing in the
        # input_template.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_det_index.py',
                     input_template='prod_determinant_index.m', model_object_name="matlab",
                     output_script='process_matlab_output.py', output_object_name='read_output',
                     model_dir='Matlab_Model', var_names=['x0', 'x1'])
        m = RunModel(samples=x_mixed, ntasks=2, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Matlab simulation:")
        print(m.qoi_list)

        # Notice that the solution changes slightly due to the increased precision by not specifying a fmt.


.. GENERATED FROM PYTHON SOURCE LINES 491-498

3.5 Pass samples as ndarray, specify format in generated input file, serial execution, partially index samples in
input_template
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This examples uses the following files:
- model_script = matlab_model_det_partial.py
- input_template = prod_determinant_partial.m
- output_script = process_matlab_output.py

.. GENERATED FROM PYTHON SOURCE LINES 501-519

.. code-block:: default


    if pick_model == 'mixed' or pick_model == 'all':
        # Call to RunModel - Here we run the model while instantiating the RunModel object.
        # Notice that we do not specify var_names. This will default to two variables with names x0 and x1. In this
        # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in with indexing in the
        # input_template.
        t = time.time()
        model = ThirdPartyModel(model_script='matlab_model_det_partial.py',
                     input_template='prod_determinant_partial.m', model_object_name="matlab",
                     output_script='process_matlab_output.py', output_object_name='read_output',
                    model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0', 'x1'])
        m = RunModel(samples=x_mixed_array, ntasks=1, model=model)
        t_ser_matlab = time.time() - t
        print("\nTime for serial execution:")
        print(t_ser_matlab)
        print()
        print("The values returned from the Python simulation:")
        print(m.qoi_list)


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

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.. _sphx_glr_download_auto_examples_RunModel_matlab_example.py:

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        :width: 150 px



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

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