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 b_operate(left, right, op='add'): |
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53 | """ |
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54 | Apply simple mathematical binary operations to two |
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55 | scan tables, returning the result in a new scan table. |
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56 | The operation is applied to both the correlations and the TSys data |
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57 | Parameters: |
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58 | left: the 'left' scan |
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59 | right: the 'right' scan |
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60 | op: the operation: 'add' (default), 'sub', 'mul', 'div' |
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61 | """ |
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62 | from asap._asap import b_operate as _bop |
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63 | return scantable(_bop(left, right, op)) |
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64 | |
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65 | def scale(scan, factor, insitu=False, all=True): |
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66 | """ |
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67 | Return a scan where all spectra are scaled by the give 'factor' |
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68 | Parameters: |
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69 | scan: a scantable |
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70 | factor: the scaling factor |
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71 | insitu: if False (default) a new scantable is returned. |
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72 | Otherwise, the scaling is done in-situ |
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73 | all: if True (default) apply to all spectra. Otherwise |
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74 | apply only to the selected (beam/pol/if)spectra only |
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75 | """ |
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76 | if not insitu: |
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77 | from asap._asap import scale as _scale |
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78 | return scantable(_scale(scan, factor, all)) |
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79 | else: |
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80 | from asap._asap import scale_insitu as _scale |
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81 | _scale(scan, factor, all) |
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82 | return |
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83 | |
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84 | |
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85 | def add(scan, offset, insitu=False, all=True): |
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86 | """ |
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87 | Return a scan where all spectra have the offset added |
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88 | Parameters: |
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89 | scan: a scantable |
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90 | offset: the offset |
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91 | insitu: if False (default) a new scantable is returned. |
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92 | Otherwise, the addition is done in-situ |
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93 | all: if True (default) apply to all spectra. Otherwise |
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94 | apply only to the selected (beam/pol/if)spectra only |
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95 | """ |
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96 | if not insitu: |
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97 | from asap._asap import add as _add |
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98 | return scantable(_add(scan, offset, all)) |
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99 | else: |
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100 | from asap._asap import add_insitu as _add |
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101 | _add(scan, offset, all) |
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102 | return |
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103 | |
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104 | def convert_flux(scan, area, eta=1.0, insitu=False, all=True): |
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105 | """ |
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106 | Return a scan where all spectra are converted to either Jansky or Kelvin |
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107 | depending upon the flux units of the scan table. |
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108 | Parameters: |
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109 | scan: a scantable |
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110 | area: the illuminated area of the telescope (m**2) |
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111 | eta: The efficiency of the telescope (default 1.0) |
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112 | insitu: if False (default) a new scantable is returned. |
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113 | Otherwise, the conversion is done in-situ |
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114 | all: if True (default) apply to all spectra. Otherwise |
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115 | apply only to the selected (beam/pol/if)spectra only |
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116 | """ |
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117 | if not insitu: |
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118 | from asap._asap import convertflux as _convert |
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119 | return scantable(_convert(scan, area, eta, all)) |
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120 | else: |
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121 | from asap._asap import convertflux_insitu as _convert |
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122 | _convert(scan, area, eta, all) |
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123 | return |
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124 | |
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125 | def gain_el(scan, poly=None, filename="", method="linear", insitu=False, all=True): |
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126 | """ |
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127 | Return a scan after applying a gain-elevation correction. The correction |
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128 | can be made via either a polynomial or a table-based interpolation |
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129 | (and extrapolation if necessary). |
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130 | You specify polynomial coefficients, an ascii table or neither. |
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131 | If you specify neither, then a polynomial correction will be made |
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132 | with built in coefficients known for certain telescopes (an error will |
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133 | occur if the instrument is not known). |
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134 | Parameters: |
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135 | scan: a scantable |
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136 | poly: Polynomial coefficients (default None) to compute a gain-elevation |
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137 | correction as a function of elevation (in degrees). |
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138 | filename: The name of an ascii file holding correction factors. |
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139 | The first row of the ascii file must give the column |
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140 | names and these MUST include columns |
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141 | "ELEVATION" (degrees) and "FACTOR" (multiply data by this) somewhere. |
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142 | The second row must give the data type of the column. Use 'R' for |
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143 | Real and 'I' for Integer. An example file would be: |
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144 | |
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145 | TIME ELEVATION FACTOR |
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146 | R R R |
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147 | 0.1 0 1.5 |
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148 | 0.2 20 1.4 |
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149 | 0.3 40 1.3 |
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150 | 0.4 60 1.2 |
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151 | 0.5 80 1.1 |
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152 | 0.6 90 1.0 |
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153 | method: Interpolation method when correcting from a table. Values |
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154 | are "nearest", "linear" (default), "cubic" and "spline" |
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155 | insitu: if False (default) a new scantable is returned. |
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156 | Otherwise, the conversion is done in-situ |
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157 | all: if True (default) apply to all spectra. Otherwise |
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158 | apply only to the selected (beam/pol/if)spectra only |
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159 | """ |
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160 | if poly is None: |
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161 | poly = () |
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162 | if not insitu: |
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163 | from asap._asap import gainel as _gainEl |
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164 | return scantable(_gainEl(scan, poly, filename, method, all)) |
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165 | else: |
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166 | from asap._asap import gainel_insitu as _gainEl |
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167 | _gainEl(scan, poly, filename, method, all) |
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168 | return |
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169 | |
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170 | def opacity(scan, tau, insitu=False, all=True): |
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171 | """ |
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172 | Return a scan after applying an opacity correction. |
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173 | Parameters: |
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174 | scan: a scantable |
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175 | tau: Opacity from which the correction factor is exp(tau*ZD) |
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176 | where ZD is the zenith-distance |
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177 | insitu: if False (default) a new scantable is returned. |
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178 | Otherwise, the conversion is done in-situ |
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179 | all: if True (default) apply to all spectra. Otherwise |
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180 | apply only to the selected (beam/pol/if)spectra only |
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181 | """ |
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182 | if not insitu: |
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183 | from asap._asap import opacity as _opacity |
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184 | return scantable(_opacity(scan, tau, all)) |
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185 | else: |
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186 | from asap._asap import opacity_insitu as _opacity |
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187 | _opacity(scan, tau, all) |
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188 | return |
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189 | |
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190 | def bin(scan, width=5, insitu=False): |
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191 | """ |
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192 | Return a scan where all spectra have been binned up |
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193 | width: The bin width (default=5) in pixels |
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194 | insitu: if False (default) a new scantable is returned. |
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195 | Otherwise, the addition is done in-situ |
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196 | """ |
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197 | if not insitu: |
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198 | from asap._asap import bin as _bin |
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199 | return scantable(_bin(scan, width)) |
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200 | else: |
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201 | from asap._asap import bin_insitu as _bin |
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202 | _bin(scan, width) |
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203 | return |
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204 | |
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205 | def average_pol(scan, mask=None, insitu=False): |
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206 | """ |
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207 | Average the Polarisations together. |
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208 | Parameters: |
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209 | scan: The scantable |
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210 | mask: An optional mask defining the region, where the |
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211 | averaging will be applied. The output will have all |
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212 | specified points masked. |
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213 | insitu: If False (default) a new scantable is returned. |
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214 | Otherwise, the averaging is done in-situ |
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215 | Example: |
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216 | polav = average_pols(myscan) |
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217 | """ |
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218 | if mask is None: |
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219 | mask = () |
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220 | if not insitu: |
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221 | from asap._asap import averagepol as _avpol |
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222 | return scantable(_avpol(scan, mask)) |
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223 | else: |
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224 | from asap._asap import averagepol_insitu as _avpol |
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225 | _avpol(scan, mask) |
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226 | return |
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227 | |
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228 | def smooth(scan, kernel="hanning", width=5.0, insitu=False, all=True): |
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229 | """ |
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230 | Smooth the spectrum by the specified kernel (conserving flux). |
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231 | Parameters: |
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232 | scan: The input scan |
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233 | kernel: The type of smoothing kernel. Select from |
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234 | 'hanning' (default), 'gaussian' and 'boxcar'. |
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235 | The first three characters are sufficient. |
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236 | width: The width of the kernel in pixels. For hanning this is |
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237 | ignored otherwise it defauls to 5 pixels. |
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238 | For 'gaussian' it is the Full Width Half |
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239 | Maximum. For 'boxcar' it is the full width. |
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240 | insitu: If False (default) a new scantable is returned. |
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241 | Otherwise, the scaling is done in-situ |
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242 | all: If True (default) apply to all spectra. Otherwise |
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243 | apply only to the selected (beam/pol/if)spectra only |
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244 | Example: |
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245 | none |
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246 | """ |
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247 | if not insitu: |
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248 | from asap._asap import smooth as _smooth |
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249 | return scantable(_smooth(scan,kernel,width,all)) |
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250 | else: |
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251 | from asap._asap import smooth_insitu as _smooth |
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252 | _smooth(scan,kernel,width,all) |
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253 | return |
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254 | |
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255 | def poly_baseline(scan, mask=None, order=0): |
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256 | """ |
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257 | Return a scan which has been baselined (all rows) by a polynomial. |
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258 | Parameters: |
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259 | scan: a scantable |
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260 | mask: an optional mask |
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261 | order: the order of the polynomial (default is 0) |
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262 | Example: |
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263 | # return a scan baselined by a third order polynomial, |
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264 | # not using a mask |
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265 | bscan = poly_baseline(scan, order=3) |
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266 | """ |
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267 | from asap.asapfitter import fitter |
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268 | if mask is None: |
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269 | from numarray import ones |
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270 | mask = tuple(ones(scan.nchan())) |
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271 | f = fitter() |
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272 | f._verbose(True) |
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273 | f.set_scan(scan, mask) |
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274 | f.set_function(poly=order) |
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275 | sf = f.auto_fit() |
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276 | return sf |
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