.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ridge.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_ridge.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_ridge.py:


========
i. ridge
========
.. currentmodule:: easy_mpl

This file shows the usage of :func:`ridge` function.

.. GENERATED FROM PYTHON SOURCE LINES 10-21

.. code-block:: Python


    # sphinx_gallery_thumbnail_number = 2

    import numpy as np
    import pandas as pd
    from easy_mpl import ridge
    import matplotlib.pyplot as plt
    from easy_mpl.utils import version_info

    version_info()  # print version information of all the packages being used





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

 .. code-block:: none


    {'easy_mpl': '0.21.5', 'matplotlib': '3.9.4', 'numpy': '2.0.2', 'pandas': '2.2.3', 'scipy': '1.13.1'}



.. GENERATED FROM PYTHON SOURCE LINES 22-26

.. code-block:: Python


    data_ = np.random.random(size=100)
    _ = ridge(data_)




.. image-sg:: /auto_examples/images/sphx_glr_ridge_001.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_001.png, /auto_examples/images/sphx_glr_ridge_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 27-31

.. code-block:: Python


    data_ = np.random.random((100, 3))
    _ = ridge(data_)




.. image-sg:: /auto_examples/images/sphx_glr_ridge_002.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_002.png, /auto_examples/images/sphx_glr_ridge_002_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 32-33

specifying colormap

.. GENERATED FROM PYTHON SOURCE LINES 33-36

.. code-block:: Python


    _ = ridge(data_, color="Blues")




.. image-sg:: /auto_examples/images/sphx_glr_ridge_003.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_003.png, /auto_examples/images/sphx_glr_ridge_003_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 37-38

The data can also be in the form of pandas DataFrame

.. GENERATED FROM PYTHON SOURCE LINES 38-41

.. code-block:: Python


    _ = ridge(pd.DataFrame(data_))




.. image-sg:: /auto_examples/images/sphx_glr_ridge_004.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_004.png, /auto_examples/images/sphx_glr_ridge_004_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 42-43

if we don't want to fill the ridge, we can specify the color as white

.. GENERATED FROM PYTHON SOURCE LINES 43-46

.. code-block:: Python


    _ = ridge(np.random.random(100), color=["white"])




.. image-sg:: /auto_examples/images/sphx_glr_ridge_005.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_005.png, /auto_examples/images/sphx_glr_ridge_005_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 47-48

we can draw all the ridges on same axes as below

.. GENERATED FROM PYTHON SOURCE LINES 48-52

.. code-block:: Python


    df = pd.DataFrame(np.random.random((100, 3)), dtype='object')
    _ = ridge(df, share_axes=True, fill_kws={"alpha": 0.5})




.. image-sg:: /auto_examples/images/sphx_glr_ridge_006.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_006.png, /auto_examples/images/sphx_glr_ridge_006_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 53-54

we can also provide an existing axes to plot on

.. GENERATED FROM PYTHON SOURCE LINES 54-58

.. code-block:: Python


    _, ax = plt.subplots()
    _ = ridge(df, ax=ax, fill_kws={"alpha": 0.5})




.. image-sg:: /auto_examples/images/sphx_glr_ridge_007.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_007.png, /auto_examples/images/sphx_glr_ridge_007_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 59-60

The data can also be in the form of list of arrays

.. GENERATED FROM PYTHON SOURCE LINES 60-65

.. code-block:: Python


    x1 = np.random.random(100)
    x2 = np.random.random(100)
    _ = ridge([x1, x2], color=np.random.random((3, 2)))




.. image-sg:: /auto_examples/images/sphx_glr_ridge_008.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_008.png, /auto_examples/images/sphx_glr_ridge_008_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 66-67

The length of arrays need not to be constant/same. We can use arrays of different lengths

.. GENERATED FROM PYTHON SOURCE LINES 67-72

.. code-block:: Python


    x1 = np.random.random(100)
    x2 = np.random.random(90)
    _ = ridge([x1, x2], color=np.random.random((3, 2)))




.. image-sg:: /auto_examples/images/sphx_glr_ridge_009.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_009.png, /auto_examples/images/sphx_glr_ridge_009_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 73-77

.. code-block:: Python

    f = "https://raw.githubusercontent.com/AtrCheema/AI4Water/master/ai4water/datasets/arg_busan.csv"
    df = pd.read_csv(f, index_col='index')
    print(df.shape)
    df.head()




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

 .. code-block:: none

    (1446, 25)


.. 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>tide_cm</th>
          <th>wat_temp_c</th>
          <th>sal_psu</th>
          <th>air_temp_c</th>
          <th>pcp_mm</th>
          <th>pcp3_mm</th>
          <th>pcp6_mm</th>
          <th>pcp12_mm</th>
          <th>wind_dir_deg</th>
          <th>wind_speed_mps</th>
          <th>air_p_hpa</th>
          <th>mslp_hpa</th>
          <th>rel_hum</th>
          <th>ecoli</th>
          <th>16s</th>
          <th>inti1</th>
          <th>Total_args</th>
          <th>tetx_coppml</th>
          <th>sul1_coppml</th>
          <th>blaTEM_coppml</th>
          <th>aac_coppml</th>
          <th>Total_otus</th>
          <th>otu_5575</th>
          <th>otu_273</th>
          <th>otu_94</th>
        </tr>
        <tr>
          <th>index</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>6/19/2018 0:00</th>
          <td>36.407149</td>
          <td>19.321232</td>
          <td>33.956058</td>
          <td>19.780000</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>159.533333</td>
          <td>0.960000</td>
          <td>1002.856667</td>
          <td>1007.256667</td>
          <td>95.000000</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 0:30</th>
          <td>35.562515</td>
          <td>19.320124</td>
          <td>33.950508</td>
          <td>19.093333</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>86.596667</td>
          <td>0.163333</td>
          <td>1002.300000</td>
          <td>1006.700000</td>
          <td>95.000000</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 1:00</th>
          <td>34.808016</td>
          <td>19.319666</td>
          <td>33.942532</td>
          <td>18.733333</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>2.260000</td>
          <td>0.080000</td>
          <td>1001.973333</td>
          <td>1006.373333</td>
          <td>95.000000</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 1:30</th>
          <td>30.645216</td>
          <td>19.320406</td>
          <td>33.931263</td>
          <td>18.760000</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>62.710000</td>
          <td>0.193333</td>
          <td>1001.776667</td>
          <td>1006.120000</td>
          <td>95.006667</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>6/19/2018 2:00</th>
          <td>26.608980</td>
          <td>19.326729</td>
          <td>33.917961</td>
          <td>18.633333</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>0.0</td>
          <td>63.446667</td>
          <td>0.510000</td>
          <td>1001.743333</td>
          <td>1006.103333</td>
          <td>95.006667</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 78-91

.. code-block:: Python


    cols = ['air_temp_c',
            'wat_temp_c',
            'sal_psu',
            'tide_cm',
            'rel_hum',
            'pcp12_mm',
            'wind_dir_deg',
            'wind_speed_mps'
            ]

    _ = ridge(df[cols])




.. image-sg:: /auto_examples/images/sphx_glr_ridge_010.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_010.png, /auto_examples/images/sphx_glr_ridge_010_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 92-97

.. code-block:: Python

    f = 'https://media.githubusercontent.com/media/HakaiInstitute/essd2021-hydromet-datapackage/main/2013-2019_Discharge1015_5min.csv'
    df = pd.read_csv(f)
    df.index = pd.to_datetime(df.pop('Datetime'))
    print(df.shape)
    df.head()




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

 .. code-block:: none

    (543568, 12)


.. 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>Watershed</th>
          <th>Qlevel</th>
          <th>Qflag</th>
          <th>Qrate</th>
          <th>Qrate_min</th>
          <th>Qrate_max</th>
          <th>Qvol</th>
          <th>Qvol_min</th>
          <th>Qvol_max</th>
          <th>Qmm</th>
          <th>Qmm_min</th>
          <th>Qmm_max</th>
        </tr>
        <tr>
          <th>Datetime</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2014-07-31 13:50:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>0.0995</td>
          <td>13.26</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 13:55:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>0.0995</td>
          <td>13.26</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 14:00:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>0.0995</td>
          <td>13.26</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 14:05:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>0.0995</td>
          <td>13.26</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
        <tr>
          <th>2014-07-31 14:10:00+00:00</th>
          <td>WTS1015</td>
          <td>2</td>
          <td>AV</td>
          <td>0.0442</td>
          <td>0.0025</td>
          <td>0.0995</td>
          <td>13.26</td>
          <td>0.75</td>
          <td>29.85</td>
          <td>0.004</td>
          <td>0.0002</td>
          <td>0.009</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 98-104

.. code-block:: Python

    groupby_year = df.groupby(lambda x: x.year)

    _ = ridge(
        [grp['Qrate'].resample('D').interpolate(method='linear') for _, grp in groupby_year],
        labels=[name for name, _ in groupby_year],
        )



.. image-sg:: /auto_examples/images/sphx_glr_ridge_011.png
   :alt: ridge
   :srcset: /auto_examples/images/sphx_glr_ridge_011.png, /auto_examples/images/sphx_glr_ridge_011_2_00x.png 2.00x
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 6.188 seconds)


.. _sphx_glr_download_auto_examples_ridge.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/sphinx-gallery/sphinx-gallery.github.io/master?urlpath=lab/tree/notebooks/auto_examples/ridge.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. only:: html

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

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