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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_gallery_plot_cyclone.py>`
        to download the full example code

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

.. _sphx_glr_gallery_plot_cyclone.py:


Cyclone removal
================

.. GENERATED FROM PYTHON SOURCE LINES 6-58



.. image:: /gallery/images/sphx_glr_plot_cyclone_001.png
    :alt: plot cyclone
    :class: sphx-glr-single-img





.. code-block:: default


    import pandas as pd
    import toto
    from toto.core.cyclone_mask import Cyclone,binaries_directory
    import os
    import matplotlib.pyplot as plt

    from pandas.plotting import register_matplotlib_converters
    register_matplotlib_converters()

    # Read a Txt file
    #filename=r'../_tests/txt_file/cyclones_115.3085_19.8892.txt'
    filename='https://raw.githubusercontent.com/calypso-science/Toto/master/_tests/txt_file/cyclones_115.3085_19.8892.txt'

    df0=pd.read_csv(filename,delimiter='\t',header=0)
    df0.rename(columns={'Year':'year','Month':'month','Day':'day',
    	                                   'H[UTC]':'hour','Min':'minute','Sec':'second'},
    	       inplace=True)

    time=pd.to_datetime(df0[['year','month','day','hour','minute','second']])

    # Time series location
    lon=115.3085
    lat=19.8892
    # import Cyclone module
    CYCLONE_FILE=os.path.join(binaries_directory(),'IBTrACS.ALL.v04r00.nc')
    cy=Cyclone(cyclone_file=CYCLONE_FILE)
    cy.min_cat=1 # Minimum category to use
    cy.rmw=None # radius_of_tropical_cyclone_maximum_sustained_wind_speed if not set it will use radius in meters
    cy.radius=500 # radius around the cente to mask in meter
    cy.mask_before=12/24. # hours before a cyclone to mask
    cy.mask_after=12/24. # hours after a cyclone to mask



    # mask all timestep within cy.radius with cyclone above or equal the minimum category 
    cy.limit_categories_within_radius([lon,lat]) 

    # create the mask
    msk=cy.remove_cyclones(time,[lon,lat])

    # Apply the mask
    df_no_cyclone=df0.loc[~msk]

    # Plot the results
    fig, ax = plt.subplots(1)
    ax.plot(time,df0['hs[m]'],label='Real')
    ax.plot(time[~msk],df_no_cyclone['hs[m]'],'r',label='Cyclone removed')
    #plt.xlim(datetime.datetime(2010,11,1),datetime.datetime(2010,12,1))
    plt.legend()
    fig.autofmt_xdate()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  2.182 seconds)


.. _sphx_glr_download_gallery_plot_cyclone.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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