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

.. only:: html

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

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

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

.. _sphx_glr__examples_visualization_plot_pyplot_timeseries.py:


Plotly - Timeseries
-------------------

.. todo:: Explain and Simplify

.. todo: Frequency might not be working?
         Frequency can be computed as sum of columns.

.. GENERATED FROM PYTHON SOURCE LINES 11-88




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

 .. code-block:: none


    '\n\n# --------------------------------------------------------------------\n#                               Main\n# --------------------------------------------------------------------\n# Load data\ndata, antibiotics, organisms = load_data_nhs()\n\n# Count records per specimen code\nspecimen_code_count = data     .groupby(\'laboratory_number\').head(1)     .specimen_code.value_counts(normalize=True)     .sort_values(ascending=False)\n\n# Filter most frequent specimens\ndata = data[data.specimen_code.isin(     specimen_code_count.index.values[:1])]\n\n# -------------------------------------------\n# Compute Freq and SARI\n# -------------------------------------------\n# Create instance\nfreq = Frequency(column_antibiotic=\'antimicrobial_code\',\n                 column_organism=\'microorganism_code\',\n                 column_date=\'date_received\',\n                 column_outcome=\'sensitivity\')\n\n# Create daily frequency\ndaily = freq.compute(data, strategy=\'ITI\',\n                      by_category=\'pairs\',\n                      fs=\'1M\')\n\ndaily[\'freq\'] = daily.sum(axis=1)\n\nimport plotly.express as px\nimport pandas as pd\n\nprint(daily)\n\ndaily = daily.reset_index()\ndaily = daily[daily.SPECIE.isin([\'ECOL\', \'PSEUD\']) &\n              daily.ANTIBIOTIC.isin([\'AAUG\'])]\n\n\n\nfig = px.bar(daily, x=\'DATE\', y=\'freq\',\n    title=\'Time Series with Range Slider and Selectors\',\n    facet_col=\'SPECIE\')\n\nfig.update_xaxes(\n    rangeslider_visible=True,\n    rangeselector=dict(\n        buttons=list([\n            dict(count=1, label="1m", step="month", stepmode="backward"),\n            dict(count=6, label="6m", step="month", stepmode="backward"),\n            dict(count=1, label="YTD", step="year", stepmode="todate"),\n            dict(count=1, label="1y", step="year", stepmode="backward"),\n            dict(step="all")\n        ])\n    )\n)\n\nfig.show()\n'





|

.. code-block:: default
   :lineno-start: 12


    # Libraries
    import sys
    import glob
    import numpy as np
    import pandas as pd
    import seaborn as sns
    import matplotlib as mpl
    import matplotlib.pyplot as plt

    # Import own libraries
    from pyamr.core.freq import Frequency
    from pyamr.datasets.load import load_data_nhs

    """

    # --------------------------------------------------------------------
    #                               Main
    # --------------------------------------------------------------------
    # Load data
    data, antibiotics, organisms = load_data_nhs()

    # Count records per specimen code
    specimen_code_count = data \
        .groupby('laboratory_number').head(1) \
        .specimen_code.value_counts(normalize=True) \
        .sort_values(ascending=False)

    # Filter most frequent specimens
    data = data[data.specimen_code.isin( \
        specimen_code_count.index.values[:1])]

    # -------------------------------------------
    # Compute Freq and SARI
    # -------------------------------------------
    # Create instance
    freq = Frequency(column_antibiotic='antimicrobial_code',
                     column_organism='microorganism_code',
                     column_date='date_received',
                     column_outcome='sensitivity')

    # Create daily frequency
    daily = freq.compute(data, strategy='ITI',
                          by_category='pairs',
                          fs='1M')

    daily['freq'] = daily.sum(axis=1)

    import plotly.express as px
    import pandas as pd

    print(daily)

    daily = daily.reset_index()
    daily = daily[daily.SPECIE.isin(['ECOL', 'PSEUD']) &
                  daily.ANTIBIOTIC.isin(['AAUG'])]



    fig = px.bar(daily, x='DATE', y='freq',
        title='Time Series with Range Slider and Selectors',
        facet_col='SPECIE')

    fig.update_xaxes(
        rangeslider_visible=True,
        rangeselector=dict(
            buttons=list([
                dict(count=1, label="1m", step="month", stepmode="backward"),
                dict(count=6, label="6m", step="month", stepmode="backward"),
                dict(count=1, label="YTD", step="year", stepmode="todate"),
                dict(count=1, label="1y", step="year", stepmode="backward"),
                dict(step="all")
            ])
        )
    )

    fig.show()
    """

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

   **Total running time of the script:** ( 0 minutes  0.001 seconds)


.. _sphx_glr_download__examples_visualization_plot_pyplot_timeseries.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_pyplot_timeseries.py <plot_pyplot_timeseries.py>`

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

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


.. only:: html

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

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