:py:mod:`km3modules.plot`
=========================

.. py:module:: km3modules.plot

.. autoapi-nested-parse::

   A collection of plotting functions and modules.

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Module Contents
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Classes
~~~~~~~

.. autoapisummary::

   km3modules.plot.IntraDOMCalibrationPlotter



Functions
~~~~~~~~~

.. autoapisummary::

   km3modules.plot.plot_dom_parameters
   km3modules.plot.make_dom_map
   km3modules.plot.ztplot
   km3modules.plot.trim_axes
   km3modules.plot.cumulative_run_livetime



.. py:function:: plot_dom_parameters(data, detector, filename, label, title, vmin=0.0, vmax=10.0, cmap='cividis', under='deepskyblue', over='deeppink', underfactor=1.0, overfactor=1.0, missing='lightgray', hide_limits=False)

   
   Creates a plot in the classical monitoring.km3net.de style.


   :Parameters:

       **data: dict((du, floor) -> value)**
           ..

       **detector: km3pipe.hardware.Detector() instance**
           ..

       **filename: filename or filepath**
           ..

       **label: str**
           ..

       **title: str**
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       **underfactor: a scale factor for the points used for underflow values**
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       **overfactor: a scale factor for the points used for overflow values**
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       **hide_limits: do not show under/overflows in the plot**
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.. py:function:: make_dom_map(pmt_directions, values, nside=512, d=0.2, smoothing=0.1)

   
   Create a mollweide projection of a DOM with given PMTs.

   The output can be used to call the `healpy.mollview` function.















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.. py:class:: IntraDOMCalibrationPlotter(name=None, **parameters)




   
   The module which can be attached to the pipeline
















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   .. py:method:: configure()

      
      Configure module, like instance variables etc.
















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   .. py:method:: process(blob)

      
      Knead the blob and return it
















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   .. py:method:: create_plot(calibration)


   .. py:method:: save_hdf5(calibration)



.. py:function:: ztplot(hits, filename=None, title=None, max_z=None, figsize=(16, 8), n_dus=4, ytick_distance=200, max_multiplicity_entries=10, grid_lines=[])

   
   Creates a ztplot like shown in the online monitoring
















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.. py:function:: trim_axes(axes, n)

   
   little helper to massage the axes list to have correct length...
















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.. py:function:: cumulative_run_livetime(qtable, kind='runs')

   
   Create a figure which plots the cumulative livetime of runs


   :Parameters:

       **qtable: pandas.DataFrame**
           A table which has the run number as index and columns for
           'livetime_s', 'timestamp' and 'datetime' (pandas datetime).

       **kind: str**
           'runs' to plot for each run or 'timeline' to plot based
           on the actual run time.

   :Returns:

       matplotlib.Figure
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