:py:mod:`km3flux.flux`
======================

.. py:module:: km3flux.flux

.. autoapi-nested-parse::

   Assorted Fluxes, in  (m^2 sec sr GeV)^-1

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Module Contents
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Classes
~~~~~~~

.. autoapisummary::

   km3flux.flux.BaseFlux
   km3flux.flux.PowerlawFlux
   km3flux.flux.IsotropicFlux
   km3flux.flux.HondaFlux
   km3flux.flux.Honda




Attributes
~~~~~~~~~~

.. autoapisummary::

   km3flux.flux.logger


.. py:data:: logger
   

   
















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.. py:class:: BaseFlux(**kwargs)



   
   Base class for fluxes.















   .. rubric:: Methods



   ============================================================  ==========
                              **__call__(energy, zenith=None)**  Return the flux on energy, optionally on zenith.  
      **integrate(zenith=None, emin=1, emax=100, **integargs)**  Integrate the flux via romberg integration.  
   **integrate_samples(energy, zenith=None, emin=1, emax=100)**  Integrate the flux from given samples, via simpson integration.  
   ============================================================  ==========

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   .. py:method:: integrate(self, zenith=None, emin=1, emax=100, interpolate=True, **integargs)

      
















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   .. py:method:: integrate_samples(self, energy, zenith=None, emin=1, emax=100, interpolate=True, **integargs)

      
















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.. py:class:: PowerlawFlux(gamma=2, scale=0.0001)



   
   E^-gamma flux.
















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   .. py:method:: integrate(self, zenith=None, emin=1, emax=100, **integargs)

      
      Compute analytic integral instead of numeric one.
















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.. py:class:: IsotropicFlux(data, flavors)

   
















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.. py:class:: HondaFlux(data, flavors)

   
   Base class for Honda fluxes















   .. rubric:: Methods



   ===========================================  ==========
      **make_regular_grid(axes_keys, flavor)**  Create a n_dim grid based on data.  
   **interpolation_method(axes_keys, flavor)**  Select the interpolation method.  
                       **parse_categories(f)**  Integrate the flux from given samples, via simpson integration.  
   ===========================================  ==========

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   .. py:method:: make_regular_grid(self, axes_keys, flavor)

      
      Create a n_dim grid based on data.


      :Parameters:

          **axes_keys** : list of str
              axes to be used, define dimensions order

          **flavor** : str
              column to use to fill the grid














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   .. py:method:: interpolation_method(self, axes_keys, flavor)

      
      Select the interpolation method.

      For 1D interpolation:
      - InterpolatedUnivariateSpline
      For 2D interpolation:
      - RectBivariateSpline
      For >= 3D interpolation:
      - RegularGridInterpolator

      :Parameters:

          **axes_keys** : list of str
              axes to be used, define dimensions order

          **flavor** : str
              column to use to fill the grid














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   .. py:method:: from_hondafile(cls, filepath)
      :classmethod:

      
















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   .. py:method:: parse_categories(self, f)

      
      Select the interpolation method.


      :Parameters:

          **f** : file object
              Honda file to be parsed














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.. py:class:: Honda

   
















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   .. py:method:: flux(self, year, experiment, solar='min', mountain=False, season=None, averaged=None)

      
      Return the flux for a given year and experiment.


      :Parameters:

          **year** : int
              The year of the publication.

          **experiment** : str
              The experiment name, can be one of the following:
              "Kamioka", "Gran Sasso", "Sudbury", "Frejus", "INO", "South Pole",
              "Pythasalmi", "Homestake", "JUNO"

          **solar** : str (optional)
              The solar parameter, can be "min" or "max". Default is "min" for Solar
              minimum.

          **mountain** : bool (optional)
              With or without mountain over the detector. Default is "False" => without.

          **season** : None or (int, int) (optional)
              The season of interest. If `None`, the dataset for the full period is taken.
              If a tuple is provided, the first entry is the starting and the last the
              ending month. Notice that the corresponding dataset might not be available.

          **averaged** : None or str (optional)
              The type of averaging. Default is `None`. Also available are "all" for all
              direction averaging and "azimuth" for azimuth averaging only.














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   .. py:method:: experiments(self)
      :property:

      
      Return a list of supported experiments.
















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   .. py:method:: experiment_abbr(self, experiment)

      
      Return the abbreviation used in filenames for a given experiment.
















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