.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_5_postprocforcefft.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_plot_5_postprocforcefft.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_5_postprocforcefft.py:


Time series of postProcessing coefficient force
===============================================

This example reads and plots one postProcessing coefficient force and compute and plot the
fft of the lift coefficient force

.. GENERATED FROM PYTHON SOURCE LINES 10-16

Read the postProcessing files
-----------------------------

.. note:: In this example it reads one postProcessing file for the
          statistically steady state of the flow around a rectangular
          cylinder.

.. GENERATED FROM PYTHON SOURCE LINES 16-63

.. code-block:: Python


    from fluidfoam.readpostpro import readforce
    import numpy as np

    # ****************Selection of case, repository and parameters*************** #
    rep = '../output_samples/'
    case = 'ascii'

    time = '100'                        # Simulation start time for fft
    force_file_name = 'coefficient'     # Variable on which fft is applied
    index_Cl = 2                        # Lift force
    index_Cd = 1                        # Drag force
    n0 = 0                              # Start point for the fft
    nfft = 500                          # Number of point for the fft
    calculdeltat = 0.01                 # Delta t used in the calculation
    fftdeltat = 0.2                     # Sampling Delta t for the fft, note that Tfft = nfft * fftdeltat =< Ttot = endTime - time
    timestart = 0                       # Start time for sampling from time
    display_temp = True

    # *************************************************************************** #

    # reading Data
    force = readforce(rep + case, namepatch = 'forces', time_name = time, name = force_file_name)
    timevec = np.zeros(len(force))
    varC = np.zeros((len(force),2))
    for i in range(len(force)):
        timevec[i] = force[i,0]
        varC[i,0] = force[i,index_Cl]
        varC[i,1] = force[i,index_Cd]

    # Sampling
    i = 0

    varCi = np.zeros((nfft,2))
    vart = np.zeros(nfft)
    while i < nfft:
        for k in range(2):
            varCi[i,k] = varC[int(i*fftdeltat/calculdeltat+timestart/calculdeltat), k]
        vart[i] = timevec[int(i*fftdeltat/calculdeltat+timestart/calculdeltat)]
        i += 1

    # fft calculation
    y1 = np.zeros((nfft,2),dtype = np.complex128)
    for k in range(2):
    # Division by nfft to correct the spectral amplitude
        y1[:, k] = np.fft.fft(varCi[n0:n0+nfft,k] - np.mean(varCi[n0:n0+nfft,k]))/nfft 








.. GENERATED FROM PYTHON SOURCE LINES 64-67

Now plots the lift and drag coefficient forces
---------------------------------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 67-96

.. code-block:: Python


    import matplotlib.pyplot as plt

    plt.rcParams.update({'font.size': 20})
    # Temporal plot section
    colorList = ['r', 'b']
    labelList = ['Lift', 'Drag']
    if display_temp:
        for k in range(2):
            plt.plot(vart[n0:n0+nfft], varCi[n0:n0+nfft,k],
                 color = colorList[k], label = labelList[k])
        plt.legend(loc='best')
        plt.xlabel('time (s)')
        plt.ylabel('Coefficient (-)')
        plt.show()

    # frequency plot section
    f = np.arange(nfft)*1/(fftdeltat*nfft)

    plt.figure(figsize=(16, 8))
    for k in range(2):
        plt.semilogy(f[n0:n0+nfft], abs(y1[n0:n0+nfft,k]), color = colorList[k], label = labelList[k])

    plt.axis([0.0, 1/(2*fftdeltat), 1e-5,  2*np.max(abs(y1[n0:n0+nfft]))])
    plt.grid()
    plt.legend(loc='upper right')
    plt.xlabel(r'f (Hz)')
    plt.ylabel(r'Amplitude ($m^2/s^2$)')
    plt.show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_5_postprocforcefft_001.png
         :alt: plot 5 postprocforcefft
         :srcset: /auto_examples/images/sphx_glr_plot_5_postprocforcefft_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_5_postprocforcefft_002.png
         :alt: plot 5 postprocforcefft
         :srcset: /auto_examples/images/sphx_glr_plot_5_postprocforcefft_002.png
         :class: sphx-glr-multi-img






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

   **Total running time of the script:** (0 minutes 0.475 seconds)


.. _sphx_glr_download_auto_examples_plot_5_postprocforcefft.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_5_postprocforcefft.ipynb <plot_5_postprocforcefft.ipynb>`

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

      :download:`Download Python source code: plot_5_postprocforcefft.py <plot_5_postprocforcefft.py>`

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

      :download:`Download zipped: plot_5_postprocforcefft.zip <plot_5_postprocforcefft.zip>`


.. only:: html

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

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