[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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[167] | 91 | def bin(scan, width=5, insitu=False): |
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[101] | 92 | """ |
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[167] | 93 | Return a scan where all spectra have been binned up |
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[172] | 94 | width: The bin width (default=5) in pixels |
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[167] | 95 | insitu: if False (default) a new scantable is returned. |
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| 96 | Otherwise, the addition is done in-situ |
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[101] | 97 | """ |
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[167] | 98 | if not insitu: |
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| 99 | from asap._asap import bin as _bin |
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| 100 | return scantable(_bin(scan, width)) |
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| 101 | else: |
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| 102 | from asap._asap import bin_insitu as _bin |
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| 103 | _bin(scan, width) |
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| 104 | return |
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[113] | 105 | |
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[166] | 106 | def average_pol(scan, mask=None, insitu=False): |
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[113] | 107 | """ |
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| 108 | Average the Polarisations together. |
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| 109 | Parameters: |
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[172] | 110 | scan: The scantable |
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| 111 | mask: An optional mask defining the region, where the |
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| 112 | averaging will be applied. The output will have all |
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| 113 | specified points masked. |
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| 114 | insitu: If False (default) a new scantable is returned. |
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[166] | 115 | Otherwise, the averaging is done in-situ |
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[113] | 116 | Example: |
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| 117 | polav = average_pols(myscan) |
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| 118 | """ |
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| 119 | if mask is None: |
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[166] | 120 | mask = () |
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| 121 | if not insitu: |
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| 122 | from asap._asap import averagepol as _avpol |
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| 123 | return scantable(_avpol(scan, mask)) |
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| 124 | else: |
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| 125 | from asap._asap import averagepol_insitu as _avpol |
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| 126 | _avpol(scan, mask) |
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| 127 | return |
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[113] | 128 | |
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[174] | 129 | def hanning(scan, insitu=False, all=True): |
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[113] | 130 | """ |
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| 131 | Hanning smooth the channels. |
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| 132 | Parameters: |
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[172] | 133 | scan: The input scan |
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| 134 | insitu: If False (default) a new scantable is returned. |
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| 135 | Otherwise, the scaling is done in-situ |
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[174] | 136 | all: if True (default) apply to all spectra. Otherwise |
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| 137 | apply only to the selected (beam/pol/if)spectra only |
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[113] | 138 | Example: |
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| 139 | none |
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| 140 | """ |
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[172] | 141 | if not insitu: |
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| 142 | from asap._asap import hanning as _hann |
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[174] | 143 | return scantable(_hann(scan,all)) |
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[172] | 144 | else: |
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| 145 | from asap._asap import hanning_insitu as _hann |
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[174] | 146 | _hann(scan,all) |
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[172] | 147 | return |
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[113] | 148 | |
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| 149 | def poly_baseline(scan, mask=None, order=0): |
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| 150 | """ |
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[160] | 151 | Return a scan which has been baselined (all rows) by a polynomial. |
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[113] | 152 | Parameters: |
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| 153 | scan: a scantable |
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| 154 | mask: an optional mask |
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| 155 | order: the order of the polynomial (default is 0) |
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| 156 | Example: |
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| 157 | # return a scan baselined by a third order polynomial, |
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| 158 | # not using a mask |
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| 159 | bscan = poly_baseline(scan, order=3) |
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| 160 | """ |
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| 161 | from asap.asapfitter import fitter |
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| 162 | if mask is None: |
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| 163 | from numarray import ones |
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| 164 | mask = tuple(ones(scan.nchan())) |
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| 165 | f = fitter() |
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| 166 | f._verbose(True) |
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| 167 | f.set_scan(scan, mask) |
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| 168 | f.set_function(poly=order) |
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| 169 | sf = f.auto_fit() |
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| 170 | return sf |
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