tkp.utility.sigmaclip
– Generic sigma clipping routine¶
Generic kappasigma clipping routine.
Note: this does not replace the specialized sigma_clip function in utilities.py

tkp.utility.sigmaclip.
calcmean
(data, errors=None)[source]¶ Calculate the mean and the standard deviation of the mean

tkp.utility.sigmaclip.
calcsigma
(data, errors=None, mean=None, axis=None, errors_as_weight=False)[source]¶ Calculate the sample standard deviation
Parameters: data (numpy.ndarray) – Data to be averaged. No conversion from eg a list to a numpy.array is done. Kwargs:
 errors (numpy.ndarray, None): Eerrors for the data. Errors
 needs to be the same shape as data (this is different than for numpy.average). If you want to use weights instead of errors as input, set errors_as_weight=True. If not given, all errors (and thus weights) are assumed to be equal to 1.
 mean (float): Provide mean if you don’t want the mean to be
 calculated for you. Pay careful attention to the shape if you provide ‘axis’.
 axis (int): Specify axis along which the mean and sigma are
 calculated. If not provided, calculations are done over the whole array
errors_as_weight (bool): Set to True if errors are weights.
Returns: (2tuple of floats) mean and sigma

tkp.utility.sigmaclip.
clip
(data, mean, sigma, siglow, sighigh, indices=None)[source]¶ Perform kappasigma clipping of data around mean
Parameters: Kwargs:
indices (numpy.ndarray): data selection by indicesReturns: (numpy.ndarray) indices of nonclipped data

tkp.utility.sigmaclip.
sigmaclip
(data, errors=None, niter=0, siglow=3.0, sighigh=3.0, use_median=False)[source]¶ Remove outliers from data which lie more than siglow/sighigh sample standard deviations from mean.
Parameters: data (numpy.ndarray) – Numpy array containing data values. Kwargs:
 errors (numpy.ndarray, None): Errors associated with the data
 values. If None, unweighted mean and standard deviation are used in calculations.
 niter (int): Number of iterations to calculate mean & standard
 deviation, and reject outliers, If niter is negative, iterations will continue until no more clipping occurs or until abs(‘niter’) is reached, whichever is reached first.
 siglow (float): Kappa multiplier for standard deviation. Std *
 siglow defines the value below which data are rejected.
 sighigh (float): Kappa multiplier for standard deviation. Std *
 sighigh defines the value above which data are rejected.
use_median (bool): Use median of data instead of mean.
Returns: (2tuple) Boolean numpy array of indices indicating which elements are clipped (False), with the same shape as the input; number of iterations Return type: tuple