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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_plot_manila.py:


Example on Quantitative Precipitation Estimate (QPE)
====================================================

This example demonstrates how to perform QPE,
using raingauge data from Manila.

.. GENERATED FROM PYTHON SOURCE LINES 8-19

.. code-block:: default


    import os
    import pandas
    import numpy as np
    from swirlspy.qpe.rfmap import raingauge
    from swirlspy.qpe.rfmap import to_map_coordinates
    from swirlspy.qpe.rfmap import epsilon
    from swirlspy.qpe.rfmap import rbf_interpolation
    from swirlspy.qpe.rfmap import save_fig









.. GENERATED FROM PYTHON SOURCE LINES 20-25

Read files and declare raingauge object
---------------------------------------

Read files in consecutive timesteps to obtain accumulated rainfall,
return as a raingauge object.

.. GENERATED FROM PYTHON SOURCE LINES 25-60

.. code-block:: default


    THIS_DIR = '../tests/test_qpe'
    os.chdir(THIS_DIR)
    
    filelist = ["rf60m_201808102340_qced", "rf60m_201808102350_qced", 
                "rf60m_201808110000_qced", "rf60m_201808110010_qced", 
                "rf60m_201808110020_qced", "rf60m_201808110030_qced",
                "rf60m_201808110040_qced"]
    table = pandas.read_csv(filepath_or_buffer=filelist[0], header=None, 
    						delim_whitespace=True, skiprows=[0], 
    						usecols=[1,2,3], dtype=float, na_values=3276.7, 
    						low_memory=False) #override nan values
    nrow, ncol = table.shape
    accu_rf = np.zeros(nrow)
    #accumulate rainfall
    for i in range (0,len(filelist)):
        file = open(filelist[i])
        if i == 0:
            START_time = file.readline()
        elif i == len(filelist)-1:
            END_time = file.readline()
        table = pandas.read_csv(filepath_or_buffer=filelist[i], header=None,  
    							delim_whitespace=True,  skiprows=[0],
    							usecols=[1,2,3], dtype=float, na_values=3276.7, 
    							low_memory=False) #override nan values
        table = table.dropna()
        gauge_lat = table[1].values
        gauge_lon = table[2].values
        accu_rf = accu_rf + table[3].values
    
    #Declare raingauge object
    rg_object = raingauge(START_time, END_time, gauge_lat, 
    					    gauge_lon, accu_rf)   









.. GENERATED FROM PYTHON SOURCE LINES 61-65

Interpolation
-------------

Perform RBF interpolation.

.. GENERATED FROM PYTHON SOURCE LINES 65-80

.. code-block:: default

    lllon = 119.8
    urlon = 122.0
    lllat = 13.6
    urlat = 16.5
    RESOLUTION = 1000
    MODE= "RBF"
    m_rg_lon, m_rg_lat, m_lllon, m_lllat, m_urlon, m_urlat, \
    RBF_gx, RBF_gy = to_map_coordinates(lllon, urlon, lllat, urlat, 
                                        rg_object.gauge_lon, 
                                        rg_object.gauge_lat,
                                        RESOLUTION) 
    epsilon_value = epsilon(urlon, lllon, RESOLUTION, rg_object.rg_number)
    interpolated_rf = rbf_interpolation(m_rg_lon, m_rg_lat, RBF_gx, 
                                        RBF_gy, rg_object.rainfall, 
                                        epsilon_value) 




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

 .. code-block:: none

    /opt/conda/envs/swirlspy/lib/python3.6/site-packages/scipy/interpolate/rbf.py:257: LinAlgWarning: Ill-conditioned matrix (rcond=2.85228e-19): result may not be accurate.
      self.nodes = linalg.solve(self.A, self.di)




.. GENERATED FROM PYTHON SOURCE LINES 81-87

Generate rainfall map
---------------------

Here, we create a rainfall map with Cartopy basemap and
showing raingauge values. Please refer to 
`swirlspy.qpe.rfmap.save_fig` for customization.

.. GENERATED FROM PYTHON SOURCE LINES 87-92

.. code-block:: default


    #Options here: showing raingauge values, Cartopy basemap
    save_fig(MODE, rg_object.START_time, rg_object.END_time, lllon, urlon, lllat, 
            urlat, m_rg_lon, m_rg_lat, rg_object.rainfall, 
            RBF_gx, RBF_gy, interpolated_rf, True, True)



.. image-sg:: /auto_examples/images/sphx_glr_plot_manila_001.png
   :alt: plot manila
   :srcset: /auto_examples/images/sphx_glr_plot_manila_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  5.285 seconds)


.. _sphx_glr_download_auto_examples_plot_manila.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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