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

.. only:: html

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

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

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

.. _sphx_glr_examples_plot_polinjus.py:


=====================================================
Processamento dos dados do polinjus.csv
=====================================================

.. GENERATED FROM PYTHON SOURCE LINES 8-9

Para realizar o processamento do polinjus.csv, será utilizado o módulo plotly

.. GENERATED FROM PYTHON SOURCE LINES 9-16

.. code-block:: Python

    import plotly.graph_objects as go
    import plotly.io as pio
    from numpy.polynomial import Polynomial

    pio.templates.default = "ggplot2"









.. GENERATED FROM PYTHON SOURCE LINES 17-21

O polinjus.csv é um arquivo de entrada das LIBS, utilizado pelo modelo NEWAVE particularmente
em casos com estágios individualizados, ou para execução da simulação final individualizada.
Para processar a sua informação, o usuário deve importar a class `UsinasHidreletricas`, que contém
todos os métodos necessários

.. GENERATED FROM PYTHON SOURCE LINES 21-25

.. code-block:: Python

    from inewave.libs import UsinasHidreletricas

    arq_polinjus = UsinasHidreletricas.read("./libs/polinjus.csv")








.. GENERATED FROM PYTHON SOURCE LINES 26-30

A implementação de todos os arquivos das LIBS segue o formato de arquivos por registro,
contendo informações em linhas de comentários e com delimitador ';'. Além disso,
a primeira coluna sempre identifica qual o registro cadastrado na linha em questão. O
número total de colunas em cada linha é variável, de acordo com o valor da primeira coluna.

.. GENERATED FROM PYTHON SOURCE LINES 32-34

Estão disponíveis métodos para cada registro, com argumentos opcionais para realizar filtros
nos objetos a serem processados

.. GENERATED FROM PYTHON SOURCE LINES 34-38

.. code-block:: Python

    registros = arq_polinjus.hidreletrica_curvajusante(codigo_usina=1)
    registros






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

 .. code-block:: none


    [<inewave.libs.modelos.usinas_hidreletricas.HidreletricaCurvaJusante object at 0x7f134349d310>, <inewave.libs.modelos.usinas_hidreletricas.HidreletricaCurvaJusante object at 0x7f13434888c0>, <inewave.libs.modelos.usinas_hidreletricas.HidreletricaCurvaJusante object at 0x7f1343488aa0>, <inewave.libs.modelos.usinas_hidreletricas.HidreletricaCurvaJusante object at 0x7f1343488b90>, <inewave.libs.modelos.usinas_hidreletricas.HidreletricaCurvaJusante object at 0x7f1343488c80>]



.. GENERATED FROM PYTHON SOURCE LINES 39-41

É possível realizar a edição dos objetos retornados relacionados
a cada registro. Para tal, basta atribuir valores a cada propriedade:

.. GENERATED FROM PYTHON SOURCE LINES 41-44

.. code-block:: Python

    registros[0].indice_familia = 5









.. GENERATED FROM PYTHON SOURCE LINES 45-47

Outra possibilidade é gerar uma visualização dos conteúdos do arquivo
no formato de DataFrame. Neste caso, a informação é somente leitura

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: Python

    df = arq_polinjus.hidreletrica_curvajusante(df=True)
    df.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>codigo_usina</th>
          <th>indice_familia</th>
          <th>nivel_montante_referencia</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>5</td>
          <td>885.3052</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>2</td>
          <td>885.4000</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1</td>
          <td>3</td>
          <td>885.7000</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1</td>
          <td>4</td>
          <td>885.8000</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1</td>
          <td>5</td>
          <td>886.0000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 51-54

Com as informações do polinjus.csv é possível gerar uma visualização para os polinômios
de jusante. Em particular, o registro HIDRELETRICA-CURVAJUSANTE-POLINOMIOPORPARTES-SEGMENTO
contém as informações desejadas. No caso, por exemplo, para a usina de código 7

.. GENERATED FROM PYTHON SOURCE LINES 54-58

.. code-block:: Python

    df = arq_polinjus.hidreletrica_curvajusante_polinomio_segmento(df=True)
    df = df.loc[df["codigo_usina"] == 7]
    df.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>codigo_usina</th>
          <th>coeficiente_a0</th>
          <th>coeficiente_a1</th>
          <th>coeficiente_a2</th>
          <th>coeficiente_a3</th>
          <th>coeficiente_a4</th>
          <th>indice_familia</th>
          <th>indice_polinomio</th>
          <th>limite_inferior_vazao_jusante</th>
          <th>limite_superior_vazao_jusante</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>32</th>
          <td>7</td>
          <td>617.7467</td>
          <td>0.002571</td>
          <td>-1.632490e-07</td>
          <td>0.000000e+00</td>
          <td>0.000000e+00</td>
          <td>1</td>
          <td>1</td>
          <td>0.000</td>
          <td>4332.009</td>
        </tr>
        <tr>
          <th>33</th>
          <td>7</td>
          <td>620.8103</td>
          <td>0.001156</td>
          <td>-1.888075e-15</td>
          <td>0.000000e+00</td>
          <td>0.000000e+00</td>
          <td>1</td>
          <td>2</td>
          <td>4332.009</td>
          <td>13081.880</td>
        </tr>
        <tr>
          <th>34</th>
          <td>7</td>
          <td>619.4653</td>
          <td>0.000017</td>
          <td>-2.809288e-07</td>
          <td>1.586390e-09</td>
          <td>-5.900055e-13</td>
          <td>2</td>
          <td>1</td>
          <td>0.000</td>
          <td>1319.244</td>
        </tr>
        <tr>
          <th>35</th>
          <td>7</td>
          <td>617.7467</td>
          <td>0.002571</td>
          <td>-1.632490e-07</td>
          <td>0.000000e+00</td>
          <td>0.000000e+00</td>
          <td>2</td>
          <td>2</td>
          <td>1319.244</td>
          <td>4332.009</td>
        </tr>
        <tr>
          <th>36</th>
          <td>7</td>
          <td>620.8103</td>
          <td>0.001156</td>
          <td>-1.888075e-15</td>
          <td>0.000000e+00</td>
          <td>0.000000e+00</td>
          <td>2</td>
          <td>3</td>
          <td>4332.009</td>
          <td>13081.880</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-61

Para a visualização, vamos gerar 100 amostras para cada segmento, que é aproximado
por um polinômio.

.. GENERATED FROM PYTHON SOURCE LINES 61-86

.. code-block:: Python

    num_pontos = 100
    ordem_polinomios = 4
    cols_coeficientes = [f"coeficiente_a{i}" for i in range(ordem_polinomios + 1)]

    num_curvas = len(df["indice_familia"].unique())
    vazoes_limites = []
    pontos_familias = {}
    for indice_familia in range(num_curvas):
        df_familia = df.loc[df["indice_familia"] == indice_familia]
        pontos_familias[indice_familia] = {"x": [], "y": []}
        indices_polinomios = df_familia["indice_polinomio"].unique().tolist()
        for indice_polinomio in indices_polinomios:
            df_polinomio = df_familia.loc[
                df_familia["indice_polinomio"] == indice_polinomio
            ]
            lim_inf = df_polinomio["limite_inferior_vazao_jusante"].iloc[0]
            lim_sup = df_polinomio["limite_superior_vazao_jusante"].iloc[0]
            vazoes_limites += [lim_inf, lim_sup]
            coeficientes = df_polinomio[cols_coeficientes].to_numpy().flatten()
            polinomio = Polynomial(coeficientes)
            x, y = polinomio.linspace(n=num_pontos, domain=[lim_inf, lim_sup])
            pontos_familias[indice_familia]["x"] += list(x)
            pontos_familias[indice_familia]["y"] += list(y)









.. GENERATED FROM PYTHON SOURCE LINES 87-88

Para visualizar:

.. GENERATED FROM PYTHON SOURCE LINES 88-104

.. code-block:: Python


    fig = go.Figure()
    for familia, pontos in pontos_familias.items():
        fig.add_trace(
            go.Scatter(
                x=pontos["x"],
                y=pontos["y"],
                mode="lines",
                name=str(familia),
            )
        )

    fig.update_xaxes(title="Vazão Defluente (m3/s)")
    fig.update_yaxes(title="Nível de Jusante (m)")
    fig.update_layout(legend_title_text="Família")
    fig





.. raw:: html

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

   **Total running time of the script:** (0 minutes 0.145 seconds)


.. _sphx_glr_download_examples_plot_polinjus.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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