Doc strings reformatting

Reformat doc strings so that they match the required format for the LINCC Framesworks incubator template setup for our documentation sphinx setup (which follows the numpy standard https://numpydoc.readthedocs.io/en/latest/format.html)

src/sorcha/lightcurves/base_lightcurve.py

@@ -64,7 +64,7 @@ def _log_error_message(self, error_msg: str) -> None:
 
         Parameters
         ----------
-        error_msg : str
+        error_msg : string
             The string to be appended to the error log
         """
         logger.error(error_msg)

src/sorcha/lightcurves/identity_lightcurve.py

@@ -50,7 +50,7 @@ def name_id() -> str:
 
         Returns
         -------
-        str
+        string
             Unique identifier for this light curve calculator
         """
         return "identity"

src/sorcha/modules/PPAddUncertainties.py

@@ -29,13 +29,15 @@ def degCos(x):
     """
     Calculate cosine of an angle in degrees.
 
-    Parameters:
+    Parameters
     -----------
-    x (float): angle in degrees.
+    x : float
+        angle in degrees.
 
-    Returns:
+    Returns
     -----------
-    The cosine of x.
+    float
+        The cosine of x.
     """
 
     return np.cos(x * np.pi / 180.0)
@@ -45,13 +47,15 @@ def degSin(x):
     """
     Calculate sine of an angle in degrees.
 
-    Parameters:
+    Parameters
     -----------
-    x (float): angle in degrees.
+    x : float
+        angle in degrees.
 
-    Returns:
+    Returns
     -----------
-    The sine of x.
+    float
+        The sine of x.
     """
 
     return np.sin(x * np.pi / 180.0)
@@ -62,19 +66,25 @@ def addUncertainties(detDF, configs, module_rngs, verbose=True):
     Generates astrometric and photometric uncertainties, and SNR. Uses uncertainties
     to randomize the photometry. Accounts for trailing losses.
 
-    Parameters:
+    Parameters
     -----------
-    detDF (Pandas dataframe): Dataframe of observations.
+    detDF : Pandas dataframe)
+        Dataframe of observations.
 
-    configs (dictionary): dictionary of configurations from config file.
+    configs : dictionary
+        dictionary of configurations from config file.
 
-    module_rngs (PerModuleRNG): A collection of random number generators (per module).
+    module_rngs : PerModuleRNG
+        A collection of random number generators (per module).
 
-    Returns:
-    -----------
-    detDF (Pandas dataframe): dataframe of observations, with new columns for observed
-    magnitudes, SNR, and astrometric/photometric uncertainties.
+    verbose: Boolean, optional
+    Verbose Logging Flag. Default = True
 
+    Returns
+    -----------
+    detDF : Pandas dataframe
+        dataframe of observations, with new columns for observed
+        magnitudes, SNR, and astrometric/photometric uncertainties.
     """
 
     pplogger = logging.getLogger(__name__)
@@ -126,23 +136,48 @@ def uncertainties(
     """
     Add astrometric and photometric uncertainties to observations.
 
-    Parameters:
+    Parameters
     -----------
-    detDF (Pandas dataframe): dataframe containing observations.
+    detDF : Pandas dataframe
+        dataframe containing observations.
 
-    configs (dictionary): dictionary of configurations from config file.
+    configs : dictionary
+        dictionary of configurations from config file.
 
-    limMagName, seeingName, filterMagName, dra_name, ddec_name, dec_name (strings): column
-    names of the limiting magnitude, seeing, magnitude, RA rate, DEC rate and DEC.
+    limMagName : string, optional
+        pandas dataframe column name of the limiting magnitude.
+        Default = "fiveSigmaDepthAtSource"
 
-    Returns:
-    -----------
-    astrSigDeg (numpy array): astrometric uncertainties in degrees.
+    seeingName : string, optional
+        pandas dataframe column name of the seeing
+        Default = "seeingFwhmGeom"
+
+    filterMagName : string, optional
+        pandas dataframe column name of the object magnitude
+        Default = "TrailedSourceMag"
 
-    photometric_sigma (numpy array): photometric uncertainties in magnitude.
+    dra_name : string, optional
+        pandas dataframe column name of the object RA rate
+        Default = "AstRARate(deg/day)"
+
+    ddec_name: string, optional
+        pandas dataframe column name of the object declination rate
+        Default = "AstDecRate(deg/day)"
+
+    dec_name : string, optional
+        pandas dataframe column name of the object declination
+        Default = "AstDec(deg)"
+
+    Returns
+    -----------
+    astrSigDeg: numpy array
+        astrometric uncertainties in degrees.
 
-    SNR (numpy array): signal-to-noise ratio.
+    photometric_sigma: numpy array
+        photometric uncertainties in magnitude.
 
+    SNR: numpy array
+        signal-to-noise ratio.
     """
 
     if configs.get("trailing_losses_on", False):
@@ -165,47 +200,68 @@ def calcAstrometricUncertainty(
     mag, m5, nvisit=1, FWHMeff=700.0, error_sys=10.0, astErrCoeff=0.60, output_units="mas"
 ):
     """Calculate the astrometric uncertainty, for object catalog purposes.
-    The effective FWHMeff MUST BE given in miliarcsec (NOT arcsec!).
-    Systematic error, error_sys, must be given in miliarcsec.
-    The result corresponds to a single-coordinate uncertainty.
-    Note that the total astrometric uncertainty (e.g. relevant when
-    matching two catalogs) will be sqrt(2) times larger.
-    Default values for parameters are based on estimates for LSST.
 
-    Parameters:
+
+    Parameters
     -----------
-    mag (float/array of floats): magnitude of the observation.
+    mag : float or array of floats)
+        magnitude of the observation.
 
-    m5 (float/array of floats): 5-sigma limiting magnitude.
+    m5 : float or array of floats
+        5-sigma limiting magnitude.
 
-    nvisit (int): number of visits to consider.
+    nvisit :int, optional
+        number of visits to consider.
+        Default = 1
 
-    FWHMeff (float): effective Full Width at Half Maximum of Point Spread Function [mas].
+    FWHMeff : float, optional
+        effective Full Width at Half Maximum of Point Spread Function [mas].
+        Default = 700.0
 
-    error_sys (float): systematic error [mas].
+    error_sys : float, optional
+        systematic error [mas].
+        Default = 10.0
 
-    output_units (string): 'mas' (default): milliarcseconds, 'arcsec': arcseconds.
+    astErrCoeff : float, optional
+        Astrometric error coefficient
+        (see calcRandomAstrometricErrorPerCoord description).
+        Default = 0.60
 
-    Returns:
+    output_units : string, optional
+       Default: "mas"  (milliarcseconds)
+        other options: "arcsec" (arcseconds)
+
+    Returns
     -----------
-    astrom_error (float/array of floats): astrometric error.
+    astrom_error : float or array of floats)
+        astrometric error.
 
-    SNR (float/array of floats): signal to noise ratio.
+    SNR : float or array of floats)
+        signal to noise ratio.
 
-    error_rand (float/array of floats): random error.
+    error_rand : float or array of floats
+        random error.
 
-    Description:
+    Notes
     ------------
+
+    The effective FWHMeff MUST BE given in miliarcsec (NOT arcsec!).
+    Systematic error, error_sys, must be given in miliarcsec.
+    The result corresponds to a single-coordinate uncertainty.
+    Note that the total astrometric uncertainty (e.g. relevant when
+    matching two catalogs) will be sqrt(2) times larger.
+    Default values for parameters are based on estimates for LSST.
+
     The astrometric error can be applied to parallax or proper motion (for nvisit>1).
     If applying to proper motion, should also divide by the # of years of the survey.
     This is also referenced in the LSST overview paper (arXiv:0805.2366, ls.st/lop)
 
+
     - assumes sqrt(Nvisit) scaling, which is the best-case scenario
     - calcRandomAstrometricError assumes maxiumm likelihood solution,
       which is also the best-case scenario
     - the systematic error, error_sys = 10 mas, corresponds to the
       design spec from the LSST Science Requirements Document (ls.st/srd)
-
     """
 
     # first compute SNR
@@ -229,26 +285,35 @@ def calcAstrometricUncertainty(
 
 def calcRandomAstrometricErrorPerCoord(FWHMeff, SNR, AstromErrCoeff=0.60):
     """Calculate the random astrometric uncertainty, as a function of
-    effective FWHMeff and signal-to-noise ratio SNR
-    Returns astrometric uncertainty in the same units as FWHM.
+    effective FWHMeff and signal-to-noise ratio SNR and return
+    the astrometric uncertainty in the same units as FWHM.
+
     ** This error corresponds to a single-coordinate error **
     the total astrometric uncertainty (e.g. relevant when matching
     two catalogs) will be sqrt(2) times larger.
 
-    Parameters:
+    Parameters
     -----------
-    FWHMeff (float/array of floats): Effective Full Width at Half Maximum of Point Spread Function [mas].
+    FWHMeff : float or array of floats
+        Effective Full Width at Half Maximum of Point Spread Function [mas].
 
-    SNR (float/array of floats): Signal-to-noise ratio.
+    SNR : float or array of floats
+        Signal-to-noise ratio.
 
-    AstromErrCoeff (float): Astrometric error coefficient (see description below).
+    AstromErrCoeff : float, optional
+        Astrometric error coefficient (see description below).
+        Default =0.60
 
-    Returns:
+    Returns
     -----------
-    RandomAstrometricErrorPerCoord (float/array of floats): random astrometric uncertainty per coordinate.
+    RandomAstrometricErrorPerCoord: float or array of floats
+        random astrometric uncertainty per coordinate.
 
-    Description:
+    Returns astrometric uncertainty in the same units as FWHMeff.
+
+    Notes
     ------------
+
     The coefficient AstromErrCoeff for Maximum Likelihood
     solution is given by
 
@@ -281,14 +346,15 @@ def calcPhotometricUncertainty(snr):
     """
     Convert flux signal to noise ratio to an uncertainty in magnitude.
 
-    Parameters:
+    Parameters
     -----------
-    snr (float/array of floats): The signal-to-noise-ratio in flux.
+    snr : float or array of floats
+        The signal-to-noise-ratio in flux.
 
-    Returns:
+    Returns
     -----------
-    magerr (float/array of floats): The resulting uncertainty in magnitude.
-
+    magerr : float or rray of floats
+        The resulting uncertainty in magnitude.
     """
 
     # see e.g. www.ucolick.org/~bolte/AY257/s_n.pdf section 3.1

src/sorcha/modules/PPApplyColourOffsets.py

@@ -11,21 +11,30 @@ def PPApplyColourOffsets(observations, function, othercolours, observing_filters
     If phase model variables exist for each colour, this function also selects the
     correct variables for each observation based on filter.
 
-    Parameters:
+    Parameters
     -----------
-    observations (Pandas dataframe): dataframe of observations.
+    observations: Pandas dataframe
+        dataframe of observations.
 
-    function (string): string of desired phase function model. Options are HG, HG12, HG1G2, linear, H.
+    function : string
+        string of desired phase function model. Options are HG, HG12, HG1G2, linear, H.
 
-    othercolours (list of strings): list of colour offsets present in input files.
+    othercolours : list of strings
+        list of colour offsets present in input files.
 
-    observing_filters (list of strings): list of observation filters of interest.
+    observing_filters : list of strings
+        list of observation filters of interest.
 
-    mainfilter (string): the main filter in which H is given and all colour offsets are calculated against.
+    mainfilter : string
+        the main filter in which H is given and all colour offsets are calculated against.
 
-    Returns:
+    Returns
     -----------
-    observations (Pandas dataframe): dataframe of observations with H calculated in relevant filter.
+    observations : Pandas dataframe
+        observations dataframe modified with H calculated in relevant filter (H_filter)
+        and renames the column for H in the main filter as H_original.
+        The dataframe has also been modified to have the appropriate phase curve filter specific values/columns.
+
 
     """