doc update

doc/help/pypeit_coadd_1dspec.rst

@@ -95,7 +95,7 @@
                             file for the echelle setup in question
                              
                             setup_id: string identifier for the echelle setup in
-                            question, i.e. 'VIS', 'NIR', or '
+                            question, i.e. 'VIS', 'NIR', or 'UVB'
                              
                             If the coaddfile is not given the output file will be
                             placed in the same directory as the first spec1d file.

pypeit/core/wavecal/wvutils.py

@@ -99,7 +99,7 @@ def get_sampling(waves, pix_per_R=3.0):
     Computes the median wavelength sampling of wavelength vector(s)
 
     Args:
-        waves (list or `numpy.ndarray`_):
+        waves (list, `numpy.ndarray`_):
             List of `numpy.ndarray`_ wavelength arrays or a single 1d
             'numpy.ndarray'_
         pix_per_R (float):

pypeit/scripts/flux_setup.py

@@ -19,6 +19,9 @@
 from pypeit.spectrographs.util import load_spectrograph
 
 def match_spec1ds_to_sensfuncs(spectrograph_name, spec1dfiles, sensfiles):
+    """
+    This needs a docstring
+    """
     result_map = {}
     spectrograph = load_spectrograph(spectrograph_name)
 

pypeit/sensfunc.py

@@ -60,8 +60,9 @@ class SensFunc(datamodel.DataContainer):
         par (:class:`~pypeit.par.pypeitpar.SensFuncPar`):
             The parameters required for the sensitivity function computation.
         par_fluxcalib (:class:`~pypeit.par.pypeitpar.FluxCalibPar`, optional):
-            The parameters required for flux calibration. These are only used for
-            flux calibration of the standard star spectrum for the QA plot. If None, defaults will be used.
+            The parameters required for flux calibration. These are only used
+            for flux calibration of the standard star spectrum for the QA plot.
+            If None, defaults will be used.
         debug (:obj:`bool`, optional):
             Run in debug mode, sending diagnostic information to the screen.
     """
@@ -145,7 +146,10 @@ def empty_sensfunc_table(norders, nspec, nspec_in, ncoeff=1):
             nspec (:obj:`int`):
                 The number of spectral pixels for the zeropoint arrays.
             nspec_in (:obj:`int`):
-                The number of spectral pixels on the detector for the input standard star spectrum.
+                The number of spectral pixels on the detector for the input
+                standard star spectrum.
+            ncoeff (:obj:`int`, optional):
+                Number of coefficients for smooth model fit to zeropoints
 
         Returns:
             `astropy.table.Table`_: Instance of the empty sensitivity
@@ -285,11 +289,9 @@ def compute_blaze(self, wave, trace_spec, trace_spat, flatfile, box_radius=10.0,
             debug (:obj:`bool`, optional):
                 Show plots useful for debugging. Default=False
 
-
         Returns:
-            log10_blaze_function (`numpy.ndarray`_):
-                The log10 blaze function. Shape = (nspec, norddet) if norddet > 1, else shape = (nspec,)
-
+            `numpy.ndarray`_: The log10 blaze function. Shape = (nspec, norddet)
+            if norddet > 1, else shape = (nspec,)
         """
 
 

pypeit/specobjs.py

@@ -551,7 +551,7 @@ def apply_flux_calib(self, par, spectrograph, sens):
         sensitivity function (``sens``).
 
         Args:
-            par (pypeit.par.pypeitpar.FluxCalibrate):
+            par (:class:`~pypeit.par.pypeitpar.FluxCalibratePar`):
                 Parset object containing parameters governing the flux calibration.
             spectrograph (:class:`~pypeit.spectrographs.spectrograph.Spectrograph`):
                 PypeIt Spectrograph class

pypeit/utils.py

@@ -1112,14 +1112,6 @@ def robust_meanstd(array):
 def polyfitter2d(data, mask=None, order=2):
     """
     2D fitter
-
-    Args:
-        data:
-        mask:
-        order:
-
-    Returns:
-
     """
     x, y = np.meshgrid(np.linspace(0.0, 1.0, data.shape[1]), np.linspace(0.0, 1.0, data.shape[0]))
     if isinstance(mask, (float, int)):
@@ -1154,15 +1146,6 @@ def polyfitter2d(data, mask=None, order=2):
 def polyfit2d(x, y, z, order=3):
     """
     Generate 2D polynomial
-
-    Args:
-        x:
-        y:
-        z:
-        order:
-
-    Returns:
-
     """
     ncols = (order + 1)**2
     G = np.zeros((x.size, ncols))
@@ -1176,14 +1159,6 @@ def polyfit2d(x, y, z, order=3):
 def polyval2d(x, y, m):
     """
     Generate 2D polynomial
-
-    Args:
-        x:
-        y:
-        m:
-
-    Returns:
-
     """
     order = int(np.sqrt(len(m))) - 1
     ij = itertools.product(range(order+1), range(order+1))
@@ -1199,6 +1174,7 @@ def subsample(frame):
 
     Args:
         frame (`numpy.ndarray`_):
+            Array of data to subsample.
 
     Returns:
         `numpy.ndarray`_: Sliced image
@@ -1262,7 +1238,7 @@ def yamlify(obj, debug=False):
 
     Returns
     -------
-    obj: :class:`object`
+    obj : :class:`object`
         An object suitable for yaml serialization.  For example
         `numpy.ndarray`_ is converted to :class:`list`,
         :class:`numpy.int64` is converted to :class:`int`, etc.