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