1 | from scantable import scantable |
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2 | |
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3 | def average_time(*args, **kwargs): |
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4 | """ |
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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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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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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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16 | scanav = average_time(scana,scanb) |
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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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20 | |
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21 | """ |
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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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36 | for s in lst: |
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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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40 | return scantable(_av(lst, mask, scanAv, weight)) |
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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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52 | def scale(scan, factor, insitu=False): |
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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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57 | factor: the scaling factor |
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58 | Note: |
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59 | This currently applies the all beams/IFs/pols |
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60 | """ |
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61 | if not insitu: |
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62 | from asap._asap import scale as _scale |
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63 | return scantable(_scale(scan, factor)) |
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64 | else: |
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65 | from asap._asap import scale_insitu as _scale |
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66 | _scale(scan, factor) |
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67 | return |
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68 | |
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69 | |
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70 | def add(scan, offset): |
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71 | """ |
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72 | Return a scan where the offset is added. |
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73 | Parameters: |
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74 | scan: a scantable |
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75 | offset: the value to add |
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76 | Note: |
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77 | This currently applies the all beams/IFs/pols |
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78 | """ |
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79 | from asap._asap import add as _add |
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80 | return scantable(_add(scan, offset)) |
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81 | |
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82 | |
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83 | def bin(scan, binwidth=5): |
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84 | """ |
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85 | """ |
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86 | from asap._asap import bin as _bin |
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87 | return scantable(_bin(scan, binwidth)) |
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88 | |
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89 | def average_pol(scan, mask=None): |
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90 | """ |
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91 | Average the Polarisations together. |
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92 | Parameters: |
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93 | scan - a scantable |
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94 | mask - an optional mask defining the region, where |
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95 | the averaging will be applied. The output |
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96 | will have all specified points masked. |
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97 | The dimension won't be reduced and |
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98 | all polarisations will contain the |
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99 | averaged spectrum. |
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100 | Example: |
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101 | polav = average_pols(myscan) |
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102 | """ |
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103 | from asap._asap import averagepol as _avpol |
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104 | from numarray import ones |
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105 | if mask is None: |
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106 | mask = tuple(ones(scan.nchan())) |
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107 | return scantable(_avpol(scan, mask)) |
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108 | |
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109 | def hanning(scan): |
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110 | """ |
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111 | Hanning smooth the channels. |
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112 | Parameters: |
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113 | scan - the input scan |
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114 | Example: |
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115 | none |
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116 | """ |
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117 | from asap._asap import hanning as _han |
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118 | return scantable(_han(scan)) |
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119 | |
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120 | |
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121 | def poly_baseline(scan, mask=None, order=0): |
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122 | """ |
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123 | Return a scan which has been baselined by a polynomial. |
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124 | Parameters: |
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125 | scan: a scantable |
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126 | mask: an optional mask |
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127 | order: the order of the polynomial (default is 0) |
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128 | Example: |
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129 | # return a scan baselined by a third order polynomial, |
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130 | # not using a mask |
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131 | bscan = poly_baseline(scan, order=3) |
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132 | """ |
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133 | from asap.asapfitter import fitter |
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134 | if mask is None: |
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135 | from numarray import ones |
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136 | mask = tuple(ones(scan.nchan())) |
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137 | f = fitter() |
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138 | f._verbose(True) |
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139 | f.set_scan(scan, mask) |
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140 | f.set_function(poly=order) |
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141 | sf = f.auto_fit() |
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142 | return sf |
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