source: trunk/python/asapmath.py @ 180

from scantable import scantable

def average_time(*args, **kwargs):
    """
    Return the (time) average of a scan or list of scans. [in channels only]
    Parameters:
        one scan or comma separated  scans
        mask:     an optional mask (only used for 'var' and 'tsys' weighting)
        scanav:   False (default) averages all scans together,
                  True averages each scan separately
        weight:   Weighting scheme. 'none' (default), 'var' (variance
                  weighted), 'tsys'
    Example:
        # return a time averaged scan from scana and scanb
        # without using a mask
        scanav = average_time(scana,scanb)
        # return the (time) averaged scan, i.e. the average of
        # all correlator cycles
        scanav = average_time(scan)

    """
    scanAv = False
    if kwargs.has_key('scanav'):
       scanAv = kwargs.get('scanav')
#
    weight = 'none'
    if kwargs.has_key('weight'):
       weight = kwargs.get('weight')
#
    mask = ()
    if kwargs.has_key('mask'):
        mask = kwargs.get('mask')
#
    lst = tuple(args)
    from asap._asap import average as _av
    for s in lst:
        if not isinstance(s,scantable):
            print "Please give a list of scantables"
            return
    return scantable(_av(lst, mask, scanAv, weight))

def quotient(source, reference):
    """
    Return the quotient of a 'source' scan and a 'reference' scan
    Parameters:
        source:        the 'on' scan
        reference:     the 'off' scan
    """
    from asap._asap import quotient as _quot
    return scantable(_quot(source, reference))

def scale(scan, factor, insitu=False, all=True):
    """
    Return a scan where all spectra are scaled by the give 'factor'
    Parameters:
        scan:        a scantable
        factor:      the scaling factor
        insitu:      if False (default) a new scantable is returned.
                     Otherwise, the scaling is done in-situ
        all:         if True (default) apply to all spectra. Otherwise
                     apply only to the selected (beam/pol/if)spectra only
    """
    if not insitu:
        from asap._asap import scale as _scale
        return scantable(_scale(scan, factor, all))
    else:
        from asap._asap import scale_insitu as _scale
        _scale(scan, factor, all)
        return
        

def add(scan, offset, insitu=False, all=True):
    """
    Return a scan where all spectra have the offset added
    Parameters:
        scan:        a scantable
        offset:      the offset
        insitu:      if False (default) a new scantable is returned.
                     Otherwise, the addition is done in-situ
        all:         if True (default) apply to all spectra. Otherwise
                     apply only to the selected (beam/pol/if)spectra only
    """
    if not insitu:
        from asap._asap import add as _add
        return scantable(_add(scan, offset, all))
    else:
        from asap._asap import add_insitu as _add
        _add(scan, offset, all)
        return
        
def bin(scan, width=5, insitu=False):
    """
    Return a scan where all spectra have been binned up
        width:       The bin width (default=5) in pixels
        insitu:      if False (default) a new scantable is returned.
                     Otherwise, the addition is done in-situ
    """
    if not insitu:
        from asap._asap import bin as _bin
        return scantable(_bin(scan, width))
    else:
        from asap._asap import bin_insitu as _bin
        _bin(scan, width)
        return

def average_pol(scan, mask=None, insitu=False):
    """
    Average the Polarisations together.
    Parameters:
        scan:        The scantable
        mask:        An optional mask defining the region, where the
                     averaging will be applied. The output will have all 
                     specified points masked. 
        insitu:      If False (default) a new scantable is returned.
                     Otherwise, the averaging is done in-situ
    Example:
        polav = average_pols(myscan)
    """
    if mask is None:
        mask = ()
    if not insitu:
        from asap._asap import averagepol as _avpol
        return scantable(_avpol(scan, mask))
    else:
        from asap._asap import averagepol_insitu as _avpol
        _avpol(scan, mask)
        return
    
def smooth(scan, kernel="hanning", width=5.0, insitu=False, all=True):
    """
    Smooth the spectrum by the specified kernel (conserving flux).
    Parameters:
        scan:       The input scan
        kernel:     The type of smoothing kernel. Select from
                    'hanning' (default), 'gaussian' and 'boxcar'.
                    The first three characters are sufficient.
        width:      The width of the kernel in pixels. For hanning this is
                    ignored otherwise it defauls to 5 pixels.
                    For 'gaussian' it is the Full Width Half
                    Maximum. For 'boxcar' it is the full width.
        insitu:     If False (default) a new scantable is returned.
                    Otherwise, the scaling is done in-situ
        all:        If True (default) apply to all spectra. Otherwise
                    apply only to the selected (beam/pol/if)spectra only
    Example:
         none
    """
    if not insitu:
        from asap._asap import smooth as _smooth
        return scantable(_smooth(scan,kernel,width,all))
    else:
        from asap._asap import smooth_insitu as _smooth
        _smooth(scan,kernel,width,all)
        return
    
def poly_baseline(scan, mask=None, order=0):
    """
    Return a scan which has been baselined (all rows) by a polynomial. 
    Parameters:
        scan:    a scantable
        mask:    an optional mask
        order:   the order of the polynomial (default is 0)
    Example:
        # return a scan baselined by a third order polynomial,
        # not using a mask
        bscan = poly_baseline(scan, order=3)
    """
    from asap.asapfitter import fitter
    if mask is None:
        from numarray import ones
        mask = tuple(ones(scan.nchan()))
    f = fitter()
    f._verbose(True)
    f.set_scan(scan, mask)
    f.set_function(poly=order)    
    sf = f.auto_fit()
    return sf