[101] | 1 | from scantable import scantable |
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| 2 | |
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[143] | 3 | def average_time(*args, **kwargs): |
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[101] | 4 | """ |
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[113] | 5 | Return the (time) average of a scan or list of scans. [in channels only] |
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| 6 | Parameters: |
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| 7 | one scan or comma separated scans |
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[143] | 8 | mask: an optional mask (only used for 'var' and 'tsys' weighting) |
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| 9 | scanav: False (default) averages all scans together, |
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| 10 | True averages each scan separately |
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| 11 | weight: Weighting scheme. 'none' (default), 'var' (variance |
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| 12 | weighted), 'tsys' |
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[113] | 13 | Example: |
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| 14 | # return a time averaged scan from scana and scanb |
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| 15 | # without using a mask |
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[129] | 16 | scanav = average_time(scana,scanb) |
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[113] | 17 | # return the (time) averaged scan, i.e. the average of |
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| 18 | # all correlator cycles |
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| 19 | scanav = average_time(scan) |
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[143] | 20 | |
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[101] | 21 | """ |
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[143] | 22 | scanAv = False |
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| 23 | if kwargs.has_key('scanav'): |
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| 24 | scanAv = kwargs.get('scanav') |
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| 25 | # |
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| 26 | weight = 'none' |
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| 27 | if kwargs.has_key('weight'): |
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| 28 | weight = kwargs.get('weight') |
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| 29 | # |
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| 30 | mask = () |
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| 31 | if kwargs.has_key('mask'): |
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| 32 | mask = kwargs.get('mask') |
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| 33 | # |
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| 34 | lst = tuple(args) |
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| 35 | from asap._asap import average as _av |
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[113] | 36 | for s in lst: |
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[101] | 37 | if not isinstance(s,scantable): |
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| 38 | print "Please give a list of scantables" |
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| 39 | return |
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[143] | 40 | return scantable(_av(lst, mask, scanAv, weight)) |
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[101] | 41 | |
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| 42 | def quotient(source, reference): |
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| 43 | """ |
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| 44 | Return the quotient of a 'source' scan and a 'reference' scan |
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| 45 | Parameters: |
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| 46 | source: the 'on' scan |
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| 47 | reference: the 'off' scan |
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| 48 | """ |
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| 49 | from asap._asap import quotient as _quot |
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| 50 | return scantable(_quot(source, reference)) |
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| 51 | |
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[150] | 52 | def scale(scan, factor, insitu=False, all=True): |
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[101] | 53 | """ |
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| 54 | Return a scan where all spectra are scaled by the give 'factor' |
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| 55 | Parameters: |
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| 56 | scan: a scantable |
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[113] | 57 | factor: the scaling factor |
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[150] | 58 | insitu: if False (default) a new scantable is returned. |
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| 59 | Otherwise, the scaling is done in-situ |
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| 60 | all: if True (default) apply to all spectra. Otherwise |
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| 61 | apply only to the selected (beam/pol/if)spectra only |
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[101] | 62 | """ |
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[141] | 63 | if not insitu: |
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| 64 | from asap._asap import scale as _scale |
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[150] | 65 | return scantable(_scale(scan, factor, all)) |
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[141] | 66 | else: |
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| 67 | from asap._asap import scale_insitu as _scale |
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[150] | 68 | _scale(scan, factor, all) |
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[141] | 69 | return |
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| 70 | |
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[101] | 71 | |
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[150] | 72 | def add(scan, offset, insitu=False, all=True): |
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[113] | 73 | """ |
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[150] | 74 | Return a scan where all spectra have the offset added |
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[113] | 75 | Parameters: |
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| 76 | scan: a scantable |
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[150] | 77 | offset: the offset |
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| 78 | insitu: if False (default) a new scantable is returned. |
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| 79 | Otherwise, the addition is done in-situ |
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| 80 | all: if True (default) apply to all spectra. Otherwise |
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| 81 | apply only to the selected (beam/pol/if)spectra only |
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[113] | 82 | """ |
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[150] | 83 | if not insitu: |
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| 84 | from asap._asap import add as _add |
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| 85 | return scantable(_add(scan, offset, all)) |
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| 86 | else: |
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| 87 | from asap._asap import add_insitu as _add |
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| 88 | _add(scan, offset, all) |
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| 89 | return |
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| 90 | |
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[101] | 91 | def bin(scan, binwidth=5): |
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| 92 | """ |
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| 93 | """ |
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| 94 | from asap._asap import bin as _bin |
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| 95 | return scantable(_bin(scan, binwidth)) |
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[113] | 96 | |
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| 97 | def average_pol(scan, mask=None): |
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| 98 | """ |
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| 99 | Average the Polarisations together. |
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| 100 | Parameters: |
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| 101 | scan - a scantable |
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| 102 | mask - an optional mask defining the region, where |
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| 103 | the averaging will be applied. The output |
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| 104 | will have all specified points masked. |
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| 105 | The dimension won't be reduced and |
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| 106 | all polarisations will contain the |
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| 107 | averaged spectrum. |
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| 108 | Example: |
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| 109 | polav = average_pols(myscan) |
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| 110 | """ |
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| 111 | from asap._asap import averagepol as _avpol |
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| 112 | from numarray import ones |
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| 113 | if mask is None: |
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| 114 | mask = tuple(ones(scan.nchan())) |
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| 115 | return scantable(_avpol(scan, mask)) |
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| 116 | |
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| 117 | def hanning(scan): |
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| 118 | """ |
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| 119 | Hanning smooth the channels. |
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| 120 | Parameters: |
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| 121 | scan - the input scan |
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| 122 | Example: |
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| 123 | none |
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| 124 | """ |
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| 125 | from asap._asap import hanning as _han |
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| 126 | return scantable(_han(scan)) |
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| 127 | |
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| 128 | |
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| 129 | def poly_baseline(scan, mask=None, order=0): |
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| 130 | """ |
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| 131 | Return a scan which has been baselined by a polynomial. |
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| 132 | Parameters: |
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| 133 | scan: a scantable |
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| 134 | mask: an optional mask |
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| 135 | order: the order of the polynomial (default is 0) |
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| 136 | Example: |
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| 137 | # return a scan baselined by a third order polynomial, |
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| 138 | # not using a mask |
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| 139 | bscan = poly_baseline(scan, order=3) |
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| 140 | """ |
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| 141 | from asap.asapfitter import fitter |
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| 142 | if mask is None: |
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| 143 | from numarray import ones |
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| 144 | mask = tuple(ones(scan.nchan())) |
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| 145 | f = fitter() |
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| 146 | f._verbose(True) |
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| 147 | f.set_scan(scan, mask) |
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| 148 | f.set_function(poly=order) |
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| 149 | sf = f.auto_fit() |
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| 150 | return sf |
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