Updated docs with class and method identifiers

source/_build/html/_sources/catalog.rst.txt

@@ -3,7 +3,7 @@
 Catalog
 =======
 
-Collections of :ref:`SED` objects can be stored and analyzed in a :ref:`Catalog` object. One can be initialized and populated with an :ref:`SED` object using the ``add_SED`` method.
+Collections of :py:class:`~sed.SED` objects can be stored and analyzed in a :py:class:`~catalog.Catalog` object. One can be initialized and populated with an :py:class:`~sed.SED` object using the :py:meth:`~catalog.Catalog.add_SED` method.
 
 .. code:: python
 
@@ -22,7 +22,7 @@ Catalogs can be merged with the addition operator.
     cat2.add_SED(sirius)
     cat = cat1 + cat2
 
-To check the table of data and calculated parameters, just call the ``results`` property. The wavelength and flux density units of all the SEDs can be checked and set with the ``wave_units`` and ``flux_units`` properties.
+To check the table of data and calculated parameters, just call the :py:attr:`~catalog.Catalog.results`` property. The wavelength and flux density units of all the SEDs can be checked and set with the :py:attr:`~catalog.Catalog.wave_units`` and :py:attr:`~catalog.Catalog.flux_units`` properties.
 
 .. code:: python
 
@@ -31,27 +31,27 @@ To check the table of data and calculated parameters, just call the ``results``
     cat.wave_units = q.AA
     cat.flux_units = q.W / q.m**3
 
-Additional columns of data can be added to the results table with the ``add_column`` method.
+Additional columns of data can be added to the results table with the :py:meth:`~catalog.Catalog.add_column` method.
 
 .. code:: python
 
     rv = np.array([-13.9, -5.5]) * q.km / q.s
     rv_unc = np.array([0.9, 0.1]) * q.km / q.s
     cat.add_column('radial_velocity', rv, rv_unc)
 
-Data for individual columns (and associated uncertainties when applicable) can be retrieved by passing the desired column names to the ``get_data`` method.
+Data for individual columns (and associated uncertainties when applicable) can be retrieved by passing the desired column names to the :py:meth:`~catalog.Catalog.get_data` method.
 
 .. code:: python
 
     spt_data, plx_data = cat.get_data('spectral_type', 'parallax')
 
-The ``SED`` object for a source can be retrieved with the ``get_SED`` method.
+The :py:class:`~sed.SED` object for a source can be retrieved with the :py:meth:`~catalog.Catalog.get_SED` method.
 
 .. code:: python
 
     vega = cat.get_SED('Vega')
 
-An interactive scatter plot of any two numeric columns can be made by passing the desired `x` and `y` parameter names from the results table to the ``plot`` method. Photometric colors can be calculated by passing two photometric band names with a ``-`` sign. The ``order`` argument accepts an integer and plots a polynomial of the given order. For busy plots, individual sources can be identified by passing the SED name to the ``identify`` argument. Similarly, setting the argument ``label_points=True`` prints the name of each source next to its data point.
+An interactive scatter plot of any two numeric columns can be made by passing the desired `x` and `y` parameter names from the results table to the :py:meth:`~catalog.Catalog.plot` method. Photometric colors can be calculated by passing two photometric band names with a ``-`` sign. The ``order`` argument accepts an integer and plots a polynomial of the given order. For busy plots, individual sources can be identified by passing the SED name to the ``identify`` argument. Similarly, setting the argument ``label_points=True`` prints the name of each source next to its data point.
 
 .. code:: python
 
@@ -60,20 +60,20 @@ An interactive scatter plot of any two numeric columns can be made by passing th
     cat.plot('age', 'distance', identify=['Vega'])  # Age v. Dist with Vega circled in red
     cat.plot('parallax', 'mbol', label_points=True) # Plx v. mbol with labeled points
 
-The SEDs can be plotted for visual comparison with the ``plot_SEDs`` method. The can be normalized to 1 by setting the argument ``normalize=True``.
+The SEDs can be plotted for visual comparison with the :py:meth:`~catalog.Catalog.plot_SEDs` method. The can be normalized to 1 by setting the argument ``normalize=True``.
 
 .. code:: python
 
     cat.plot_SEDs('*', normalize=True)  # Plot of all SEDs
     cat.plot_SEDs(['Vega', 'Sirius'])   # Normalized plot of Vega and Sirius
 
-The results table, photometry, and plots of each SED can be exported to a zip file or directory with the ``export`` method.
+The results table, photometry, and plots of each SED can be exported to a zip file or directory with the :py:meth:`~catalog.Catalog.export` method.
 
 .. code:: python
 
     cat.export('/path/to/target/dir', zip=True)
 
-The whole :ref:`Catalog` object can be serialized and loaded with the ``save`` and ``load`` methods, respectively.
+The whole :py:class:`~catalog.Catalog` object can be serialized and loaded with the :py:meth:`~catalog.Catalog.save` and :py:meth:`~catalog.Catalog.load` methods, respectively.
 
 .. code:: python
 
@@ -82,7 +82,7 @@ The whole :ref:`Catalog` object can be serialized and loaded with the ``save`` a
     new_cat = Catalog('A-type stars')
     new_cat.load(cat_file)
 
-A catalog can also be made from an ASCII file with column names ``name``, ``ra``, and ``dec`` by passing the filepath to the ``from_file`` method. For each source in the list, an SED is created, the methods in the ``run_methods`` argument are run, and the SED is added to the catalog.
+A catalog can also be made from an ASCII file with column names ``name``, ``ra``, and ``dec`` by passing the filepath to the :py:meth:`~catalog.Catalog.from_file` method. For each source in the list, an SED is created, the methods in the ``run_methods`` argument are run, and the SED is added to the catalog.
 
 .. code:: python
 

source/_build/html/_sources/modelgrid.rst.txt

@@ -3,9 +3,9 @@
 ModelGrid
 =========
 
-Theoretical model atmosphere grids and spectral atlases are useful tools for characterizing stellar and substellar atmospheres. These data can be managed in ``sedkit`` with the :ref:`ModelGrid` class.
+Theoretical model atmosphere grids and spectral atlases are useful tools for characterizing stellar and substellar atmospheres. These data can be managed in ``sedkit`` with the :py:class:`~modelgrid.ModelGrid` class.
 
-To use this resource, create a :ref:`ModelGrid` object, specify the parameters to track, and load it with data from a directory of XML files.
+To use this resource, create a :py:class:`~modelgrid.ModelGrid` object, specify the parameters to track, and load it with data from a directory of XML files.
 
 .. code:: python
 
@@ -15,7 +15,7 @@ To use this resource, create a :ref:`ModelGrid` object, specify the parameters t
     mgrid = ModelGrid('BT-Settl', params, q.AA, q.erg/q.s/q.cm**2/q.AA, ref='2014IAUS..299..271A', **kwargs)
     mgrid.load('/path/to/data/models')
 
-The table of model data can be viewed via the ``index`` property and the parameter values can be returned via a parameter name + '_vals' property.
+The table of model data can be viewed via the :py:attr:`~modelgrid.ModelGrid.index`` property and the parameter values can be returned via a parameter name + '_vals' property.
 
 .. code:: python
 
@@ -25,28 +25,28 @@ The table of model data can be viewed via the ``index`` property and the paramet
     mgrid.meta_vals
     mgrid.alpha_vals
 
-An individual model can be retrieved as a :ref:`Spectrum` object by passing the desired parameter values as keyword arguments to the ``get_spectrum`` method. If the given parameter values do not correspond to a point on the grid, the spectrum can be interpolated or the closest grid point spectrum can be retrieved.
+An individual model can be retrieved as a :py:class:`~spectrum.Spectrum` object by passing the desired parameter values as keyword arguments to the :py:meth:`~modelgrid.ModelGrid.get_spectrum` method. If the given parameter values do not correspond to a point on the grid, the spectrum can be interpolated or the closest grid point spectrum can be retrieved.
 
 .. code:: python
 
     spec1 = mgrid.get_spectrum(teff=3500, logg=5.5, meta=0, alpha=0)
     spec2 = mgrid.get_spectrum(teff=3534, logg=5.3, meta=0.1, alpha=0, interp=True)
     spec3 = mgrid.get_spectrum(teff=3500, logg=5.5, meta=0, alpha=0, closest=True)
 
-The :ref:`ModelGrid` can be resampled to new parameter values by passing arrays to the desired keyword arguments.
+The :py:class:`~modelgrid.ModelGrid`. can be resampled to new parameter values by passing arrays to the desired keyword arguments.
 
 .. code:: python
 
     import numpy as np
     new_mgrid = mgrid.resample_grid(teff=np.array())
 
-Models can be inspected by passing the desired parameter values to the ``plot`` method.
+Models can be inspected by passing the desired parameter values to the :py:meth:`~modelgrid.ModelGrid.plot` method.
 
 .. code:: python
 
     mgrid.plot(teff=3500, logg=5.5, meta=0, alpha=0)
 
-And a grid can be saved as a pickle file ``save`` method and loaded into a new object with the ``load_ModelGrid`` function.
+And a grid can be saved as a pickle file :py:meth:`~modelgrid.ModelGrid.save` method and loaded into a new object with the :py:func:`~modelgrid.load_ModelGrid`` function.
 
 .. code:: python
 
@@ -55,12 +55,12 @@ And a grid can be saved as a pickle file ``save`` method and loaded into a new o
     mgrid.save(mgrid_path)
     new_grid = mg.load_ModelGrid(mgrid_path)
 
-Several :ref:`ModelGrid` child classes exist for convenience.
+Several :py:class:`~modelgrid.ModelGrid`. child classes exist for convenience.
 
 .. code:: python
 
     btsettl = mg.BTSettl()          # BT-Settl model atmosphere grid
     spl = mg.SpexPrismLibrary()     # Spex Prism Library substellar spectral atlas
     fili15 = mg.Filippazzo2016()    # Substellar SED atlas from Filippazzo (2016)
 
-The true utility of the :ref:`ModelGrid` class is that it can be passed to a :ref:`Spectrum` or :ref:`SED` object to find a best fit model or best fit parameters.
+The true utility of the :py:class:`~modelgrid.ModelGrid`. class is that it can be passed to a :py:class:`~spectrum.Spectrum` or :py:class:`~sed.SED` object to find a best fit model or best fit parameters.

source/_build/html/_sources/sed.rst.txt

@@ -3,8 +3,7 @@
 SED
 ===
 
-An SED can be constructed by importing and initializing an ``SED``
-object.
+An SED (spectral energy distribution) can be constructed by importing and initializing an :py:class:`~sed.SED` object.
 
 .. code:: python
 
@@ -49,15 +48,13 @@ parameters can be set at any time.
     trap1.age = 7.6 * q.Gyr, 2.2 * q.Gyr
     trap1.radius = 0.121 * q.R_sun, 0.003 * q.R_sun
 
-Results can be calculated at any time by checking the ``results``
-property.
+Results can be calculated at any time by checking the :py:attr:`~sed.SED.results` property.
 
 .. code:: python
 
     trap1.results
 
-A variety of evolutionary model grids can be used to infer fundamental
-parameters,
+A variety of evolutionary model grids can be used to infer fundamental parameters,
 
 .. code:: python
 
@@ -84,6 +81,6 @@ Inspect the SED at any time with the interactive plotting method.
 
     trap1.plot()
 
-Entire catalogs of ``SED`` objects can also be created and their
+Entire catalogs of :py:class:`~sed.SED` objects can also be created and their
 properties can be arbitrarily compared and analyzed with the
-:ref:`catalog` object.
+:py:class:`~catalog.Catalog` object.

source/_build/html/_sources/spectrum.rst.txt

@@ -3,7 +3,7 @@
 Spectrum
 ========
 
-The :ref:`Spectrum` class handles any 1D data representing the light from an astronomical source. A :ref:`Spectrum` object is created by passing the wavelength, flux density, and (optional) uncertainty with ``astropy.units`` to the class.
+The :py:class:`~spectrum.Spectrum` class handles any 1D data representing the light from an astronomical source. A :py:class:`~spectrum.Spectrum` object is created by passing the wavelength, flux density, and (optional) uncertainty with ``astropy.units`` to the class.
 
 .. code:: python
 
@@ -15,7 +15,7 @@ The :ref:`Spectrum` class handles any 1D data representing the light from an ast
     unc = flux / 100.
     spec = Spectrum(wavelength, flux, unc, name='My spectrum')
 
-The :ref:`Spectrum` has a number of useful attributes.
+The :py:class:`~spectrum.Spectrum` has a number of useful attributes.
 
 .. code:: python
 
@@ -33,36 +33,36 @@ The :ref:`Spectrum` has a number of useful attributes.
     spec.flux_units     # The flux density units
     spec.size           # The number of data points
 
-After the :ref:`Spectrum` has been created, it be manipulated in a number of ways.
+After the :py:class:`~spectrum.Spectrum` has been created, it be manipulated in a number of ways.
 
-It can be trimmed by passing a list of lower and upper bounds to ``trim`` method. The ``include`` argument accepts bounds for wavelength regions to include and the ``exclude`` argument accepts bounds for regions to exclude. A list of :ref:`Spectrum` objects are returned unless the ``concat`` argument is set to ``True``, which simply concatenated the trimmed segment(s) into one :ref:`Spectrum`.
+It can be trimmed by passing a list of lower and upper bounds to :py:meth:`~spectrum.Spectrum.trim` method. The ``include`` argument accepts bounds for wavelength regions to include and the ``exclude`` argument accepts bounds for regions to exclude. A list of :py:class:`~spectrum.Spectrum` objects are returned unless the ``concat`` argument is set to ``True``, which simply concatenated the trimmed segment(s) into one :py:class:`~spectrum.Spectrum`.
 
 .. code:: python
 
     trim_spec_include = spec.trim(include=[(1.2 * q.um, 1.6 * q.um)])
     trim_spec_exclude = spec.trim(exclude=[(1.2 * q.um, 1.6 * q.um)], concat=True)
 
-The ``interpolate`` method accepts a new wavelength array and returns a new :ref:`Spectrum` object interpolated to those values. The ``resamp`` method accepts the same input and resamples the spectrum onto the new wavelength array while preserving the total flux.
+The :py:meth:`~spectrum.Spectrum.interpolate` method accepts a new wavelength array and returns a new :py:class:`~spectrum.Spectrum` object interpolated to those values. The :py:meth:`~spectrum.Spectrum.resamp` method accepts the same input and resamples the spectrum onto the new wavelength array while preserving the total flux.
 
 .. code:: python
 
     new_wav = np.linspace(1.2, 1.6, 50) * q.um
     new_wav_interp = spec.interpolate(new_wav)
     new_wav_resamp = spec.resamp(new_wav)
 
-The ``integrate`` method integrates the curve to caluclate the area underneath using the trapezoidal rule.
+The :py:meth:`~spectrum.Spectrum.integrate` method integrates the curve to calculate the area underneath using the trapezoidal rule.
 
 .. code:: python
 
     area = spec.integrate()
 
-The :ref:`Spectrum` can be smoothed using a Kaiser-Bessel smoothing window of narrowness ``beta`` and a given ``window`` size.
+The :py:class:`~spectrum.Spectrum` can be smoothed using a Kaiser-Bessel smoothing window of narrowness ``beta`` and a given ``window`` size.
 
 .. code:: python
 
     smooth_spec = spec.smooth(beta=2, window=11)
 
-A :ref:`Spectrum` may be flux calibrated to a given distance by passing a distance to the ``flux_calibrate`` method.
+A :py:class:`~spectrum.Spectrum` may be flux calibrated to a given distance by passing a distance to the :py:meth:`~spectrum.Spectrum.flux_calibrate` method.
 
 .. code:: python
 
@@ -79,17 +79,17 @@ A bandpass name or ``svo_filters.svo.Filter`` object can be used to convolve the
     jmag = spec.synthetic_magnitude(jband)      # Synthetic magnitude
     jflux = spec.synthetic_flux(jband)          # Synthetic flux
 
-It can also be normalized to a table of photometry weighted by the magnitude uncertainties with the ``norm_to_mags`` method. See the :ref:`SED` class for an example.
+It can also be normalized to a table of photometry weighted by the magnitude uncertainties with the :py:meth:`~spectrum.Spectrum.norm_to_mags` method. See the :ref:`SED` class for an example.
 
-A :ref:`Spectrum` object may also interact with another ``Spectrum`` object in a number of ways. The ``norm_to_spec`` method creates a new object normalized to the input :ref:`Spectrum` object and the ``__add__`` operation combines two :ref:`Spectrum` objects in their common wavelength region or concatenates the segments.
+A :py:class:`~spectrum.Spectrum` object may also interact with another :py:class:`~spectrum.Spectrum` object in a number of ways. The :py:meth:`~spectrum.Spectrum.norm_to_spec` method creates a new object normalized to the input :py:class:`~spectrum.Spectrum` object and the :py:meth:`~spectrum.Spectrum.__add__` operation combines two :py:class:`~spectrum.Spectrum` objects in their common wavelength region or concatenates the segments.
 
 .. code:: python
 
     spec2 = Spectrum(np.linspace(1.5, 2.5, 100) * q.um, flux * 1E-11, unc * 1E-11, name='Redder spectrum')
     normed_spec = spec.norm_to_spec(spec2)  # spec normalized to spec2
     combined_spec = spec + spec2            # New combined spectrum
 
-Any :ref:`Spectrum` may also be fit by a :ref:`ModelGrid` object to find the best fit model or spectrum. The ``best_fit_model`` method performs a simple goodness of fit test and returns the model with the best fit. The ``mcmc_fit` method performs a MCMC fit to the grid and returns the best fit parameters with uncertainties. The details of any fit are stored as a dictionary in the ``best_fit`` attribute.
+Any :py:class:`~spectrum.Spectrum` may also be fit by a :py:class:`~modelgrid.ModelGrid` object to find the best fit model or spectrum. The :py:meth:`~spectrum.Spectrum.best_fit_model` method performs a simple goodness of fit test and returns the model with the best fit. The :py:meth:`~spectrum.Spectrum.mcmc_fit` method performs a MCMC fit to the grid and returns the best fit parameters with uncertainties. The details of any fit are stored as a dictionary in the :py:attr:`~spectrum.Spectrum.best_fit` attribute.
 
 .. code:: python
 
@@ -98,34 +98,34 @@ Any :ref:`Spectrum` may also be fit by a :ref:`ModelGrid` object to find the bes
     spec.best_fit_model(bt)             # Goodness of fit
     spec.mcmc_fit(bt, params=['teff'])  # MCMC fit
 
-For visual inspection of an interactive ``bokeh.plotting.figure``, use the ``plot`` method.
+For visual inspection of an interactive ``bokeh.plotting.figure``, use the :py:meth:`~spectrum.Spectrum.plot` method.
 
 .. code:: python
 
     spec.plot()
 
-Several child classes are also available. The ``Blackbody`` class creates a blackbody spectrum for a given wavelength range and effective temperature.
+Several child classes are also available. The :py:class:`~spectrum.Blackbody` class creates a blackbody spectrum for a given wavelength range and effective temperature.
 
 .. code:: python
 
     from sedkit.spectrum import Blackbody
     bb_spec = Blackbody(np.linspace(1.5, 2.5, 100) * q.um, teff=2456 * q.K)
 
-The ever useful spectrum of ``Vega`` is easily created.
+The ever useful spectrum of :py:class:`~spectrum.Vega` is easily created.
 
 .. code:: python
 
     from sedkit.spectrum import Vega
     vega = Vega()
 
-And a ``Spectrum`` object can be created directly from a FITS or ASCII file with the ``FileSpectrum`` class.
+And a :py:class:`~spectrum.Spectrum` object can be created directly from a FITS or ASCII file with the :py:class:`~spectrum.FileSpectrum` class.
 
 .. code:: python
 
     from sedkit.spectrum import FileSpectrum
     file_spec = FileSpectrum('/path/to/the/file.fits', wave_units=q.um, flux_units=q.erg/q.s/q.cm**2/q.AA)
 
-Finally, the data can be exported to an ASCII file by passing a filepath to the ``export`` method.
+Finally, the data can be exported to an ASCII file by passing a filepath to the :py:meth:`~spectrum.Spectrum.export` method.
 
 .. code:: python