import speclite.filters as spec_filters

my_backyard_telescope_filter = spec_filters.load_filter('bessell-r')

# NOTE:
my_backyard_telescope_filter.name

"""
Explanation: Setup
We use speclite to handle optical filters: http://speclite.readthedocs.io/en/latest/ .
Therefore, you can easily build your own custom filters, use the built in speclite filters, or use the 3ML filter library that we have built thanks to Spanish Virtual Observatory (http://svo.cab.inta-csic.es/main/index.php). 
If you use these filters, please be sure to cite the proper sources!
Simple example of building a filter
Let's say we have our own 1-m telescope with a Johnson filter and we happen to record the data. We also have simultaneous data at other wavelengths and we want to compare. Let's setup the optical plugin (we'll ignore the other data for now).
End of explanation
"""


my_backyard_telescope = PhotometryLike('backyard_astronomy',
                         filters=my_backyard_telescope_filter,
                         R=(20,.1) )

my_backyard_telescope.display_filters()

"""
Explanation: NOTE: the filter name is 'bessell-R'. The plugin will look for the name after the '-' i.e 'R'
Now let's build a 3ML plugin via PhotometryLike. 
Our data are entered as keywords with the name of the filter as the keyword and the data in an magnitude,error tuple, i.e. R=(mag,mag_err):
End of explanation
"""


threeML_filter_library.SLOAN

spec_filters.plot_filters(threeML_filter_library.SLOAN.SDSS)

spec_filters.plot_filters(threeML_filter_library.Herschel.SPIRE)

spec_filters.plot_filters(threeML_filter_library.Keck.NIRC2)

"""
Explanation: 3ML filter library
Explore the filter library. If you cannot find what you need, it is simple to add your own
End of explanation
"""


fangs_g = spec_filters.FilterResponse(
    wavelength = [3800, 4500, 5200] * u.Angstrom,
    response = [0, 0.5, 0], meta=dict(group_name='fangs', band_name='g'))
fangs_r = spec_filters.FilterResponse(
    wavelength = [4800, 5500, 6200] * u.Angstrom,
    response = [0, 0.5, 0], meta=dict(group_name='fangs', band_name='r'))

fangs = spec_filters.load_filters('fangs-g', 'fangs-r')

fangslike = PhotometryLike('fangs',filters=fangs,g=(20,.1),r=(18,.1))


fangslike.display_filters()

"""
Explanation: Build your own filters
Following the example from speclite, we can build our own filters and add them:
End of explanation
"""


grond = PhotometryLike('GROND',
                       filters=threeML_filter_library.ESO.GROND,
                       #g=(21.5.93,.23),
                       #r=(22.,0.12),
                       i=(21.8,.01),
                       z=(21.2,.01),
                       J=(19.6,.01),
                       H=(18.6,.01),
                       K=(18.,.01))

grond.display_filters()

"""
Explanation: GROND Example
Now we will look at GROND. We get the filter from the 3ML filter library.
(Just play with tab completion to see what is available!)
End of explanation
"""


spec =  Powerlaw() * XS_zdust() * XS_zdust()

data_list = DataList(grond)

model = Model(PointSource('grb',0,0,spectral_shape=spec))

spec.piv_1 = 1E-2
spec.index_1.fix=False
spec.redshift_2 = 0.347
spec.redshift_2.fix = True

spec.e_bmv_2 = 5./2.93
spec.e_bmv_2.fix = True
spec.rv_2 = 2.93
spec.rv_2.fix = True


spec.method_2 = 3
spec.method_2.fix=True



spec.e_bmv_3 = .002/3.08
spec.e_bmv_3.fix = True
spec.rv_3= 3.08
spec.rv_3.fix=True
spec.redshift_3 = 0
spec.redshift_3.fix=True
spec.method_3 = 1
spec.method_3.fix=True

jl = JointLikelihood(model,data_list)


"""
Explanation: Model specification
Here we use XSPEC's dust extinction models for the milky way and the host
End of explanation
"""


jl.set_minimizer('ROOT')
_ = jl.fit()


"""
Explanation: We compute $m_{\rm AB}$ from astromodels photon fluxes. This is done by convolving the differential flux over the filter response:
$ F[R,f_\lambda] \equiv \int_0^\infty \frac{dg}{d\lambda}(\lambda)R(\lambda) \omega(\lambda) d\lambda$
where we have converted the astromodels functions to wavelength properly.
End of explanation
"""


_=display_photometry_model_magnitudes(jl)

new_grond = grond.get_simulated_dataset()

_ = plot_point_source_spectra(jl.results,flux_unit='erg/(cm2 s keV)',
                              xscale='linear',
                              energy_unit='nm',ene_min=1E3, ene_max=1E5 )

"""
Explanation: Examine the fit
End of explanation
"""


