1 | """This module defines the scantable class.""" |
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2 | |
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3 | import os |
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4 | import tempfile |
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5 | import numpy |
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6 | try: |
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7 | from functools import wraps as wraps_dec |
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8 | except ImportError: |
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9 | from asap.compatibility import wraps as wraps_dec |
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10 | |
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11 | from asap.env import is_casapy |
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12 | from asap._asap import Scantable |
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13 | from asap._asap import filler, msfiller |
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14 | from asap.parameters import rcParams |
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15 | from asap.logging import asaplog, asaplog_post_dec |
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16 | from asap.selector import selector |
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17 | from asap.linecatalog import linecatalog |
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18 | from asap.coordinate import coordinate |
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19 | from asap.utils import _n_bools, mask_not, mask_and, mask_or, page |
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20 | from asap.asapfitter import fitter |
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21 | |
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22 | |
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23 | def preserve_selection(func): |
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24 | @wraps_dec(func) |
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25 | def wrap(obj, *args, **kw): |
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26 | basesel = obj.get_selection() |
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27 | try: |
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28 | val = func(obj, *args, **kw) |
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29 | finally: |
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30 | obj.set_selection(basesel) |
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31 | return val |
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32 | return wrap |
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33 | |
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34 | def is_scantable(filename): |
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35 | """Is the given file a scantable? |
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36 | |
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37 | Parameters: |
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38 | |
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39 | filename: the name of the file/directory to test |
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40 | |
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41 | """ |
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42 | if ( os.path.isdir(filename) |
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43 | and os.path.exists(filename+'/table.info') |
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44 | and os.path.exists(filename+'/table.dat') ): |
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45 | f=open(filename+'/table.info') |
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46 | l=f.readline() |
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47 | f.close() |
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48 | #if ( l.find('Scantable') != -1 ): |
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49 | if ( l.find('Measurement Set') == -1 ): |
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50 | return True |
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51 | else: |
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52 | return False |
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53 | else: |
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54 | return False |
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55 | ## return (os.path.isdir(filename) |
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56 | ## and not os.path.exists(filename+'/table.f1') |
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57 | ## and os.path.exists(filename+'/table.info')) |
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58 | |
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59 | def is_ms(filename): |
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60 | """Is the given file a MeasurementSet? |
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61 | |
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62 | Parameters: |
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63 | |
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64 | filename: the name of the file/directory to test |
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65 | |
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66 | """ |
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67 | if ( os.path.isdir(filename) |
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68 | and os.path.exists(filename+'/table.info') |
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69 | and os.path.exists(filename+'/table.dat') ): |
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70 | f=open(filename+'/table.info') |
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71 | l=f.readline() |
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72 | f.close() |
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73 | if ( l.find('Measurement Set') != -1 ): |
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74 | return True |
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75 | else: |
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76 | return False |
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77 | else: |
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78 | return False |
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79 | |
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80 | def normalise_edge_param(edge): |
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81 | """\ |
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82 | Convert a given edge value to a one-dimensional array that can be |
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83 | given to baseline-fitting/subtraction functions. |
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84 | The length of the output value will be an even because values for |
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85 | the both sides of spectra are to be contained for each IF. When |
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86 | the length is 2, the values will be applied to all IFs. If the length |
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87 | is larger than 2, it will be 2*ifnos(). |
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88 | Accepted format of edge include: |
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89 | * an integer - will be used for both sides of spectra of all IFs. |
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90 | e.g. 10 is converted to [10,10] |
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91 | * an empty list/tuple [] - converted to [0, 0] and used for all IFs. |
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92 | * a list/tuple containing an integer - same as the above case. |
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93 | e.g. [10] is converted to [10,10] |
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94 | * a list/tuple containing two integers - will be used for all IFs. |
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95 | e.g. [5,10] is output as it is. no need to convert. |
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96 | * a list/tuple of lists/tuples containing TWO integers - |
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97 | each element of edge will be used for each IF. |
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98 | e.g. [[5,10],[15,20]] - [5,10] for IF[0] and [15,20] for IF[1]. |
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99 | |
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100 | If an element contains the same integer values, the input 'edge' |
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101 | parameter can be given in a simpler shape in the following cases: |
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102 | ** when len(edge)!=2 |
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103 | any elements containing the same values can be replaced |
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104 | to single integers. |
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105 | e.g. [[15,15]] can be simplified to [15] (or [15,15] or 15 also). |
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106 | e.g. [[1,1],[2,2],[3,3]] can be simplified to [1,2,3]. |
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107 | ** when len(edge)=2 |
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108 | care is needed for this case: ONLY ONE of the |
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109 | elements can be a single integer, |
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110 | e.g. [[5,5],[10,10]] can be simplified to [5,[10,10]] |
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111 | or [[5,5],10], but can NOT be simplified to [5,10]. |
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112 | when [5,10] given, it is interpreted as |
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113 | [[5,10],[5,10],[5,10],...] instead, as shown before. |
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114 | """ |
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115 | from asap import _is_sequence_or_number as _is_valid |
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116 | if isinstance(edge, list) or isinstance(edge, tuple): |
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117 | for edgepar in edge: |
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118 | if not _is_valid(edgepar, int): |
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119 | raise ValueError, "Each element of the 'edge' tuple has \ |
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120 | to be a pair of integers or an integer." |
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121 | if isinstance(edgepar, list) or isinstance(edgepar, tuple): |
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122 | if len(edgepar) != 2: |
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123 | raise ValueError, "Each element of the 'edge' tuple has \ |
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124 | to be a pair of integers or an integer." |
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125 | else: |
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126 | if not _is_valid(edge, int): |
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127 | raise ValueError, "Parameter 'edge' has to be an integer or a \ |
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128 | pair of integers specified as a tuple. \ |
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129 | Nested tuples are allowed \ |
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130 | to make individual selection for different IFs." |
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131 | |
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132 | |
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133 | if isinstance(edge, int): |
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134 | edge = [ edge, edge ] # e.g. 3 => [3,3] |
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135 | elif isinstance(edge, list) or isinstance(edge, tuple): |
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136 | if len(edge) == 0: |
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137 | edge = [0, 0] # e.g. [] => [0,0] |
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138 | elif len(edge) == 1: |
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139 | if isinstance(edge[0], int): |
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140 | edge = [ edge[0], edge[0] ] # e.g. [1] => [1,1] |
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141 | |
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142 | commonedge = True |
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143 | if len(edge) > 2: commonedge = False |
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144 | else: |
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145 | for edgepar in edge: |
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146 | if isinstance(edgepar, list) or isinstance(edgepar, tuple): |
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147 | commonedge = False |
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148 | break |
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149 | |
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150 | if commonedge: |
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151 | if len(edge) > 1: |
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152 | norm_edge = edge |
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153 | else: |
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154 | norm_edge = edge + edge |
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155 | else: |
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156 | norm_edge = [] |
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157 | for edgepar in edge: |
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158 | if isinstance(edgepar, int): |
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159 | norm_edge += [edgepar, edgepar] |
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160 | else: |
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161 | norm_edge += edgepar |
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162 | |
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163 | return norm_edge |
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164 | |
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165 | def raise_fitting_failure_exception(e): |
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166 | msg = "The fit failed, possibly because it didn't converge." |
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167 | if rcParams["verbose"]: |
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168 | asaplog.push(str(e)) |
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169 | asaplog.push(str(msg)) |
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170 | else: |
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171 | raise RuntimeError(str(e)+'\n'+msg) |
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172 | |
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173 | def pack_progress_params(showprogress, minnrow): |
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174 | return str(showprogress).lower() + ',' + str(minnrow) |
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175 | |
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176 | class scantable(Scantable): |
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177 | """\ |
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178 | The ASAP container for scans (single-dish data). |
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179 | """ |
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180 | |
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181 | @asaplog_post_dec |
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182 | def __init__(self, filename, average=None, unit=None, parallactify=None, |
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183 | **args): |
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184 | """\ |
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185 | Create a scantable from a saved one or make a reference |
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186 | |
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187 | Parameters: |
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188 | |
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189 | filename: the name of an asap table on disk |
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190 | or |
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191 | the name of a rpfits/sdfits/ms file |
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192 | (integrations within scans are auto averaged |
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193 | and the whole file is read) or |
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194 | [advanced] a reference to an existing scantable |
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195 | |
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196 | average: average all integrations withinb a scan on read. |
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197 | The default (True) is taken from .asaprc. |
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198 | |
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199 | unit: brightness unit; must be consistent with K or Jy. |
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200 | Over-rides the default selected by the filler |
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201 | (input rpfits/sdfits/ms) or replaces the value |
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202 | in existing scantables |
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203 | |
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204 | antenna: for MeasurementSet input data only: |
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205 | Antenna selection. integer (id) or string |
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206 | (name or id). |
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207 | |
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208 | parallactify: Indicate that the data had been parallactified. |
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209 | Default (false) is taken from rc file. |
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210 | |
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211 | """ |
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212 | if average is None: |
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213 | average = rcParams['scantable.autoaverage'] |
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214 | parallactify = parallactify or rcParams['scantable.parallactify'] |
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215 | varlist = vars() |
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216 | from asap._asap import stmath |
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217 | self._math = stmath( rcParams['insitu'] ) |
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218 | if isinstance(filename, Scantable): |
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219 | Scantable.__init__(self, filename) |
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220 | else: |
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221 | if isinstance(filename, str): |
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222 | filename = os.path.expandvars(filename) |
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223 | filename = os.path.expanduser(filename) |
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224 | if not os.path.exists(filename): |
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225 | s = "File '%s' not found." % (filename) |
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226 | raise IOError(s) |
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227 | if is_scantable(filename): |
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228 | ondisk = rcParams['scantable.storage'] == 'disk' |
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229 | Scantable.__init__(self, filename, ondisk) |
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230 | if unit is not None: |
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231 | self.set_fluxunit(unit) |
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232 | if average: |
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233 | self._assign( self.average_time( scanav=True ) ) |
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234 | # do not reset to the default freqframe |
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235 | #self.set_freqframe(rcParams['scantable.freqframe']) |
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236 | elif is_ms(filename): |
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237 | # Measurement Set |
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238 | opts={'ms': {}} |
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239 | mskeys=['getpt','antenna'] |
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240 | for key in mskeys: |
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241 | if key in args.keys(): |
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242 | opts['ms'][key] = args[key] |
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243 | self._fill([filename], unit, average, opts) |
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244 | elif os.path.isfile(filename): |
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245 | self._fill([filename], unit, average) |
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246 | # only apply to new data not "copy constructor" |
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247 | self.parallactify(parallactify) |
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248 | else: |
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249 | msg = "The given file '%s'is not a valid " \ |
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250 | "asap table." % (filename) |
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251 | raise IOError(msg) |
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252 | elif (isinstance(filename, list) or isinstance(filename, tuple)) \ |
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253 | and isinstance(filename[-1], str): |
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254 | self._fill(filename, unit, average) |
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255 | self.parallactify(parallactify) |
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256 | self._add_history("scantable", varlist) |
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257 | |
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258 | @asaplog_post_dec |
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259 | def save(self, name=None, format=None, overwrite=False): |
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260 | """\ |
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261 | Store the scantable on disk. This can be an asap (aips++) Table, |
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262 | SDFITS or MS2 format. |
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263 | |
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264 | Parameters: |
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265 | |
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266 | name: the name of the outputfile. For format "ASCII" |
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267 | this is the root file name (data in 'name'.txt |
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268 | and header in 'name'_header.txt) |
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269 | |
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270 | format: an optional file format. Default is ASAP. |
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271 | Allowed are: |
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272 | |
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273 | * 'ASAP' (save as ASAP [aips++] Table), |
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274 | * 'SDFITS' (save as SDFITS file) |
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275 | * 'ASCII' (saves as ascii text file) |
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276 | * 'MS2' (saves as an casacore MeasurementSet V2) |
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277 | * 'FITS' (save as image FITS - not readable by |
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278 | class) |
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279 | * 'CLASS' (save as FITS readable by CLASS) |
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280 | |
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281 | overwrite: If the file should be overwritten if it exists. |
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282 | The default False is to return with warning |
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283 | without writing the output. USE WITH CARE. |
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284 | |
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285 | Example:: |
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286 | |
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287 | scan.save('myscan.asap') |
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288 | scan.save('myscan.sdfits', 'SDFITS') |
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289 | |
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290 | """ |
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291 | from os import path |
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292 | format = format or rcParams['scantable.save'] |
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293 | suffix = '.'+format.lower() |
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294 | if name is None or name == "": |
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295 | name = 'scantable'+suffix |
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296 | msg = "No filename given. Using default name %s..." % name |
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297 | asaplog.push(msg) |
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298 | name = path.expandvars(name) |
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299 | if path.isfile(name) or path.isdir(name): |
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300 | if not overwrite: |
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301 | msg = "File %s exists." % name |
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302 | raise IOError(msg) |
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303 | format2 = format.upper() |
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304 | if format2 == 'ASAP': |
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305 | self._save(name) |
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306 | elif format2 == 'MS2': |
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307 | msopt = {'ms': {'overwrite': overwrite } } |
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308 | from asap._asap import mswriter |
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309 | writer = mswriter( self ) |
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310 | writer.write( name, msopt ) |
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311 | else: |
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312 | from asap._asap import stwriter as stw |
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313 | writer = stw(format2) |
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314 | writer.write(self, name) |
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315 | return |
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316 | |
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317 | def copy(self): |
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318 | """Return a copy of this scantable. |
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319 | |
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320 | *Note*: |
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321 | |
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322 | This makes a full (deep) copy. scan2 = scan1 makes a reference. |
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323 | |
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324 | Example:: |
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325 | |
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326 | copiedscan = scan.copy() |
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327 | |
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328 | """ |
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329 | sd = scantable(Scantable._copy(self)) |
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330 | return sd |
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331 | |
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332 | def drop_scan(self, scanid=None): |
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333 | """\ |
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334 | Return a new scantable where the specified scan number(s) has(have) |
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335 | been dropped. |
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336 | |
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337 | Parameters: |
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338 | |
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339 | scanid: a (list of) scan number(s) |
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340 | |
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341 | """ |
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342 | from asap import _is_sequence_or_number as _is_valid |
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343 | from asap import _to_list |
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344 | from asap import unique |
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345 | if not _is_valid(scanid): |
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346 | raise RuntimeError( 'Please specify a scanno to drop from the' |
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347 | ' scantable' ) |
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348 | scanid = _to_list(scanid) |
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349 | allscans = unique([ self.getscan(i) for i in range(self.nrow())]) |
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350 | for sid in scanid: allscans.remove(sid) |
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351 | if len(allscans) == 0: |
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352 | raise ValueError("Can't remove all scans") |
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353 | sel = selector(scans=allscans) |
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354 | return self._select_copy(sel) |
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355 | |
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356 | def _select_copy(self, selection): |
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357 | orig = self.get_selection() |
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358 | self.set_selection(orig+selection) |
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359 | cp = self.copy() |
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360 | self.set_selection(orig) |
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361 | return cp |
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362 | |
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363 | def get_scan(self, scanid=None): |
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364 | """\ |
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365 | Return a specific scan (by scanno) or collection of scans (by |
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366 | source name) in a new scantable. |
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367 | |
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368 | *Note*: |
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369 | |
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370 | See scantable.drop_scan() for the inverse operation. |
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371 | |
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372 | Parameters: |
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373 | |
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374 | scanid: a (list of) scanno or a source name, unix-style |
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375 | patterns are accepted for source name matching, e.g. |
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376 | '*_R' gets all 'ref scans |
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377 | |
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378 | Example:: |
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379 | |
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380 | # get all scans containing the source '323p459' |
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381 | newscan = scan.get_scan('323p459') |
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382 | # get all 'off' scans |
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383 | refscans = scan.get_scan('*_R') |
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384 | # get a susbset of scans by scanno (as listed in scan.summary()) |
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385 | newscan = scan.get_scan([0, 2, 7, 10]) |
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386 | |
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387 | """ |
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388 | if scanid is None: |
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389 | raise RuntimeError( 'Please specify a scan no or name to ' |
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390 | 'retrieve from the scantable' ) |
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391 | try: |
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392 | bsel = self.get_selection() |
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393 | sel = selector() |
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394 | if type(scanid) is str: |
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395 | sel.set_name(scanid) |
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396 | return self._select_copy(sel) |
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397 | elif type(scanid) is int: |
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398 | sel.set_scans([scanid]) |
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399 | return self._select_copy(sel) |
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400 | elif type(scanid) is list: |
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401 | sel.set_scans(scanid) |
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402 | return self._select_copy(sel) |
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403 | else: |
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404 | msg = "Illegal scanid type, use 'int' or 'list' if ints." |
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405 | raise TypeError(msg) |
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406 | except RuntimeError: |
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407 | raise |
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408 | |
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409 | def __str__(self): |
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410 | tempFile = tempfile.NamedTemporaryFile() |
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411 | Scantable._summary(self, tempFile.name) |
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412 | tempFile.seek(0) |
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413 | asaplog.clear() |
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414 | return tempFile.file.read() |
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415 | |
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416 | @asaplog_post_dec |
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417 | def summary(self, filename=None): |
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418 | """\ |
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419 | Print a summary of the contents of this scantable. |
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420 | |
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421 | Parameters: |
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422 | |
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423 | filename: the name of a file to write the putput to |
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424 | Default - no file output |
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425 | |
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426 | """ |
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427 | # info = Scantable._summary(self) |
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428 | if filename is not None: |
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429 | if filename is "": |
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430 | filename = 'scantable_summary.txt' |
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431 | from os.path import expandvars, isdir |
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432 | filename = expandvars(filename) |
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433 | # if not isdir(filename): |
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434 | # data = open(filename, 'w') |
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435 | # data.write(info) |
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436 | # data.close() |
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437 | # else: |
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438 | if isdir(filename): |
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439 | msg = "Illegal file name '%s'." % (filename) |
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440 | raise IOError(msg) |
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441 | else: |
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442 | filename = "" |
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443 | Scantable._summary(self, filename) |
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444 | # info = Scantable._summary(self, filename) |
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445 | # return page(info) |
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446 | |
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447 | def get_spectrum(self, rowno): |
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448 | """Return the spectrum for the current row in the scantable as a list. |
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449 | |
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450 | Parameters: |
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451 | |
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452 | rowno: the row number to retrieve the spectrum from |
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453 | |
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454 | """ |
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455 | return self._getspectrum(rowno) |
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456 | |
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457 | def get_mask(self, rowno): |
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458 | """Return the mask for the current row in the scantable as a list. |
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459 | |
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460 | Parameters: |
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461 | |
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462 | rowno: the row number to retrieve the mask from |
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463 | |
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464 | """ |
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465 | return self._getmask(rowno) |
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466 | |
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467 | def set_spectrum(self, spec, rowno): |
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468 | """Set the spectrum for the current row in the scantable. |
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469 | |
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470 | Parameters: |
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471 | |
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472 | spec: the new spectrum |
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473 | |
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474 | rowno: the row number to set the spectrum for |
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475 | |
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476 | """ |
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477 | assert(len(spec) == self.nchan(self.getif(rowno))) |
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478 | return self._setspectrum(spec, rowno) |
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479 | |
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480 | def get_coordinate(self, rowno): |
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481 | """Return the (spectral) coordinate for a a given 'rowno'. |
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482 | |
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483 | *Note*: |
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484 | |
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485 | * This coordinate is only valid until a scantable method modifies |
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486 | the frequency axis. |
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487 | * This coordinate does contain the original frequency set-up |
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488 | NOT the new frame. The conversions however are done using the user |
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489 | specified frame (e.g. LSRK/TOPO). To get the 'real' coordinate, |
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490 | use scantable.freq_align first. Without it there is no closure, |
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491 | i.e.:: |
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492 | |
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493 | c = myscan.get_coordinate(0) |
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494 | c.to_frequency(c.get_reference_pixel()) != c.get_reference_value() |
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495 | |
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496 | Parameters: |
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497 | |
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498 | rowno: the row number for the spectral coordinate |
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499 | |
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500 | """ |
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501 | return coordinate(Scantable.get_coordinate(self, rowno)) |
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502 | |
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503 | def get_selection(self): |
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504 | """\ |
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505 | Get the selection object currently set on this scantable. |
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506 | |
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507 | Example:: |
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508 | |
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509 | sel = scan.get_selection() |
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510 | sel.set_ifs(0) # select IF 0 |
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511 | scan.set_selection(sel) # apply modified selection |
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512 | |
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513 | """ |
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514 | return selector(self._getselection()) |
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515 | |
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516 | def set_selection(self, selection=None, **kw): |
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517 | """\ |
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518 | Select a subset of the data. All following operations on this scantable |
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519 | are only applied to thi selection. |
---|
520 | |
---|
521 | Parameters: |
---|
522 | |
---|
523 | selection: a selector object (default unset the selection), or |
---|
524 | any combination of "pols", "ifs", "beams", "scans", |
---|
525 | "cycles", "name", "query" |
---|
526 | |
---|
527 | Examples:: |
---|
528 | |
---|
529 | sel = selector() # create a selection object |
---|
530 | self.set_scans([0, 3]) # select SCANNO 0 and 3 |
---|
531 | scan.set_selection(sel) # set the selection |
---|
532 | scan.summary() # will only print summary of scanno 0 an 3 |
---|
533 | scan.set_selection() # unset the selection |
---|
534 | # or the equivalent |
---|
535 | scan.set_selection(scans=[0,3]) |
---|
536 | scan.summary() # will only print summary of scanno 0 an 3 |
---|
537 | scan.set_selection() # unset the selection |
---|
538 | |
---|
539 | """ |
---|
540 | if selection is None: |
---|
541 | # reset |
---|
542 | if len(kw) == 0: |
---|
543 | selection = selector() |
---|
544 | else: |
---|
545 | # try keywords |
---|
546 | for k in kw: |
---|
547 | if k not in selector.fields: |
---|
548 | raise KeyError("Invalid selection key '%s', " |
---|
549 | "valid keys are %s" % (k, |
---|
550 | selector.fields)) |
---|
551 | selection = selector(**kw) |
---|
552 | self._setselection(selection) |
---|
553 | |
---|
554 | def get_row(self, row=0, insitu=None): |
---|
555 | """\ |
---|
556 | Select a row in the scantable. |
---|
557 | Return a scantable with single row. |
---|
558 | |
---|
559 | Parameters: |
---|
560 | |
---|
561 | row: row no of integration, default is 0. |
---|
562 | insitu: if False a new scantable is returned. Otherwise, the |
---|
563 | scaling is done in-situ. The default is taken from .asaprc |
---|
564 | (False) |
---|
565 | |
---|
566 | """ |
---|
567 | if insitu is None: |
---|
568 | insitu = rcParams['insitu'] |
---|
569 | if not insitu: |
---|
570 | workscan = self.copy() |
---|
571 | else: |
---|
572 | workscan = self |
---|
573 | # Select a row |
---|
574 | sel = selector() |
---|
575 | sel.set_rows([row]) |
---|
576 | workscan.set_selection(sel) |
---|
577 | if not workscan.nrow() == 1: |
---|
578 | msg = "Could not identify single row. %d rows selected." \ |
---|
579 | % (workscan.nrow()) |
---|
580 | raise RuntimeError(msg) |
---|
581 | if insitu: |
---|
582 | self._assign(workscan) |
---|
583 | else: |
---|
584 | return workscan |
---|
585 | |
---|
586 | @asaplog_post_dec |
---|
587 | def stats(self, stat='stddev', mask=None, form='3.3f', row=None): |
---|
588 | """\ |
---|
589 | Determine the specified statistic of the current beam/if/pol |
---|
590 | Takes a 'mask' as an optional parameter to specify which |
---|
591 | channels should be excluded. |
---|
592 | |
---|
593 | Parameters: |
---|
594 | |
---|
595 | stat: 'min', 'max', 'min_abc', 'max_abc', 'sumsq', 'sum', |
---|
596 | 'mean', 'var', 'stddev', 'avdev', 'rms', 'median' |
---|
597 | |
---|
598 | mask: an optional mask specifying where the statistic |
---|
599 | should be determined. |
---|
600 | |
---|
601 | form: format string to print statistic values |
---|
602 | |
---|
603 | row: row number of spectrum to process. |
---|
604 | (default is None: for all rows) |
---|
605 | |
---|
606 | Example: |
---|
607 | scan.set_unit('channel') |
---|
608 | msk = scan.create_mask([100, 200], [500, 600]) |
---|
609 | scan.stats(stat='mean', mask=m) |
---|
610 | |
---|
611 | """ |
---|
612 | mask = mask or [] |
---|
613 | if not self._check_ifs(): |
---|
614 | raise ValueError("Cannot apply mask as the IFs have different " |
---|
615 | "number of channels. Please use setselection() " |
---|
616 | "to select individual IFs") |
---|
617 | rtnabc = False |
---|
618 | if stat.lower().endswith('_abc'): rtnabc = True |
---|
619 | getchan = False |
---|
620 | if stat.lower().startswith('min') or stat.lower().startswith('max'): |
---|
621 | chan = self._math._minmaxchan(self, mask, stat) |
---|
622 | getchan = True |
---|
623 | statvals = [] |
---|
624 | if not rtnabc: |
---|
625 | if row == None: |
---|
626 | statvals = self._math._stats(self, mask, stat) |
---|
627 | else: |
---|
628 | statvals = self._math._statsrow(self, mask, stat, int(row)) |
---|
629 | |
---|
630 | #def cb(i): |
---|
631 | # return statvals[i] |
---|
632 | |
---|
633 | #return self._row_callback(cb, stat) |
---|
634 | |
---|
635 | label=stat |
---|
636 | #callback=cb |
---|
637 | out = "" |
---|
638 | #outvec = [] |
---|
639 | sep = '-'*50 |
---|
640 | |
---|
641 | if row == None: |
---|
642 | rows = xrange(self.nrow()) |
---|
643 | elif isinstance(row, int): |
---|
644 | rows = [ row ] |
---|
645 | |
---|
646 | for i in rows: |
---|
647 | refstr = '' |
---|
648 | statunit= '' |
---|
649 | if getchan: |
---|
650 | qx, qy = self.chan2data(rowno=i, chan=chan[i]) |
---|
651 | if rtnabc: |
---|
652 | statvals.append(qx['value']) |
---|
653 | refstr = ('(value: %'+form) % (qy['value'])+' ['+qy['unit']+'])' |
---|
654 | statunit= '['+qx['unit']+']' |
---|
655 | else: |
---|
656 | refstr = ('(@ %'+form) % (qx['value'])+' ['+qx['unit']+'])' |
---|
657 | |
---|
658 | tm = self._gettime(i) |
---|
659 | src = self._getsourcename(i) |
---|
660 | out += 'Scan[%d] (%s) ' % (self.getscan(i), src) |
---|
661 | out += 'Time[%s]:\n' % (tm) |
---|
662 | if self.nbeam(-1) > 1: out += ' Beam[%d] ' % (self.getbeam(i)) |
---|
663 | if self.nif(-1) > 1: out += ' IF[%d] ' % (self.getif(i)) |
---|
664 | if self.npol(-1) > 1: out += ' Pol[%d] ' % (self.getpol(i)) |
---|
665 | #outvec.append(callback(i)) |
---|
666 | if len(rows) > 1: |
---|
667 | # out += ('= %'+form) % (outvec[i]) +' '+refstr+'\n' |
---|
668 | out += ('= %'+form) % (statvals[i]) +' '+refstr+'\n' |
---|
669 | else: |
---|
670 | # out += ('= %'+form) % (outvec[0]) +' '+refstr+'\n' |
---|
671 | out += ('= %'+form) % (statvals[0]) +' '+refstr+'\n' |
---|
672 | out += sep+"\n" |
---|
673 | |
---|
674 | import os |
---|
675 | if os.environ.has_key( 'USER' ): |
---|
676 | usr = os.environ['USER'] |
---|
677 | else: |
---|
678 | import commands |
---|
679 | usr = commands.getoutput( 'whoami' ) |
---|
680 | tmpfile = '/tmp/tmp_'+usr+'_casapy_asap_scantable_stats' |
---|
681 | f = open(tmpfile,'w') |
---|
682 | print >> f, sep |
---|
683 | print >> f, ' %s %s' % (label, statunit) |
---|
684 | print >> f, sep |
---|
685 | print >> f, out |
---|
686 | f.close() |
---|
687 | f = open(tmpfile,'r') |
---|
688 | x = f.readlines() |
---|
689 | f.close() |
---|
690 | asaplog.push(''.join(x), False) |
---|
691 | |
---|
692 | return statvals |
---|
693 | |
---|
694 | def chan2data(self, rowno=0, chan=0): |
---|
695 | """\ |
---|
696 | Returns channel/frequency/velocity and spectral value |
---|
697 | at an arbitrary row and channel in the scantable. |
---|
698 | |
---|
699 | Parameters: |
---|
700 | |
---|
701 | rowno: a row number in the scantable. Default is the |
---|
702 | first row, i.e. rowno=0 |
---|
703 | |
---|
704 | chan: a channel in the scantable. Default is the first |
---|
705 | channel, i.e. pos=0 |
---|
706 | |
---|
707 | """ |
---|
708 | if isinstance(rowno, int) and isinstance(chan, int): |
---|
709 | qx = {'unit': self.get_unit(), |
---|
710 | 'value': self._getabcissa(rowno)[chan]} |
---|
711 | qy = {'unit': self.get_fluxunit(), |
---|
712 | 'value': self._getspectrum(rowno)[chan]} |
---|
713 | return qx, qy |
---|
714 | |
---|
715 | def stddev(self, mask=None): |
---|
716 | """\ |
---|
717 | Determine the standard deviation of the current beam/if/pol |
---|
718 | Takes a 'mask' as an optional parameter to specify which |
---|
719 | channels should be excluded. |
---|
720 | |
---|
721 | Parameters: |
---|
722 | |
---|
723 | mask: an optional mask specifying where the standard |
---|
724 | deviation should be determined. |
---|
725 | |
---|
726 | Example:: |
---|
727 | |
---|
728 | scan.set_unit('channel') |
---|
729 | msk = scan.create_mask([100, 200], [500, 600]) |
---|
730 | scan.stddev(mask=m) |
---|
731 | |
---|
732 | """ |
---|
733 | return self.stats(stat='stddev', mask=mask); |
---|
734 | |
---|
735 | |
---|
736 | def get_column_names(self): |
---|
737 | """\ |
---|
738 | Return a list of column names, which can be used for selection. |
---|
739 | """ |
---|
740 | return list(Scantable.get_column_names(self)) |
---|
741 | |
---|
742 | def get_tsys(self, row=-1): |
---|
743 | """\ |
---|
744 | Return the System temperatures. |
---|
745 | |
---|
746 | Parameters: |
---|
747 | |
---|
748 | row: the rowno to get the information for. (default all rows) |
---|
749 | |
---|
750 | Returns: |
---|
751 | |
---|
752 | a list of Tsys values for the current selection |
---|
753 | |
---|
754 | """ |
---|
755 | if row > -1: |
---|
756 | return self._get_column(self._gettsys, row) |
---|
757 | return self._row_callback(self._gettsys, "Tsys") |
---|
758 | |
---|
759 | |
---|
760 | def get_weather(self, row=-1): |
---|
761 | """\ |
---|
762 | Return the weather informations. |
---|
763 | |
---|
764 | Parameters: |
---|
765 | |
---|
766 | row: the rowno to get the information for. (default all rows) |
---|
767 | |
---|
768 | Returns: |
---|
769 | |
---|
770 | a dict or list of of dicts of values for the current selection |
---|
771 | |
---|
772 | """ |
---|
773 | |
---|
774 | values = self._get_column(self._get_weather, row) |
---|
775 | if row > -1: |
---|
776 | return {'temperature': values[0], |
---|
777 | 'pressure': values[1], 'humidity' : values[2], |
---|
778 | 'windspeed' : values[3], 'windaz' : values[4] |
---|
779 | } |
---|
780 | else: |
---|
781 | out = [] |
---|
782 | for r in values: |
---|
783 | |
---|
784 | out.append({'temperature': r[0], |
---|
785 | 'pressure': r[1], 'humidity' : r[2], |
---|
786 | 'windspeed' : r[3], 'windaz' : r[4] |
---|
787 | }) |
---|
788 | return out |
---|
789 | |
---|
790 | def _row_callback(self, callback, label): |
---|
791 | out = "" |
---|
792 | outvec = [] |
---|
793 | sep = '-'*50 |
---|
794 | for i in range(self.nrow()): |
---|
795 | tm = self._gettime(i) |
---|
796 | src = self._getsourcename(i) |
---|
797 | out += 'Scan[%d] (%s) ' % (self.getscan(i), src) |
---|
798 | out += 'Time[%s]:\n' % (tm) |
---|
799 | if self.nbeam(-1) > 1: |
---|
800 | out += ' Beam[%d] ' % (self.getbeam(i)) |
---|
801 | if self.nif(-1) > 1: out += ' IF[%d] ' % (self.getif(i)) |
---|
802 | if self.npol(-1) > 1: out += ' Pol[%d] ' % (self.getpol(i)) |
---|
803 | outvec.append(callback(i)) |
---|
804 | out += '= %3.3f\n' % (outvec[i]) |
---|
805 | out += sep+'\n' |
---|
806 | |
---|
807 | asaplog.push(sep) |
---|
808 | asaplog.push(" %s" % (label)) |
---|
809 | asaplog.push(sep) |
---|
810 | asaplog.push(out) |
---|
811 | asaplog.post() |
---|
812 | return outvec |
---|
813 | |
---|
814 | def _get_column(self, callback, row=-1, *args): |
---|
815 | """ |
---|
816 | """ |
---|
817 | if row == -1: |
---|
818 | return [callback(i, *args) for i in range(self.nrow())] |
---|
819 | else: |
---|
820 | if 0 <= row < self.nrow(): |
---|
821 | return callback(row, *args) |
---|
822 | |
---|
823 | |
---|
824 | def get_time(self, row=-1, asdatetime=False, prec=-1): |
---|
825 | """\ |
---|
826 | Get a list of time stamps for the observations. |
---|
827 | Return a datetime object or a string (default) for each |
---|
828 | integration time stamp in the scantable. |
---|
829 | |
---|
830 | Parameters: |
---|
831 | |
---|
832 | row: row no of integration. Default -1 return all rows |
---|
833 | |
---|
834 | asdatetime: return values as datetime objects rather than strings |
---|
835 | |
---|
836 | prec: number of digits shown. Default -1 to automatic |
---|
837 | calculation. |
---|
838 | Note this number is equals to the digits of MVTime, |
---|
839 | i.e., 0<prec<3: dates with hh:: only, |
---|
840 | <5: with hh:mm:, <7 or 0: with hh:mm:ss, |
---|
841 | and 6> : with hh:mm:ss.tt... (prec-6 t's added) |
---|
842 | |
---|
843 | """ |
---|
844 | from datetime import datetime |
---|
845 | if prec < 0: |
---|
846 | # automagically set necessary precision +1 |
---|
847 | prec = 7 - \ |
---|
848 | numpy.floor(numpy.log10(numpy.min(self.get_inttime(row)))) |
---|
849 | prec = max(6, int(prec)) |
---|
850 | else: |
---|
851 | prec = max(0, prec) |
---|
852 | if asdatetime: |
---|
853 | #precision can be 1 millisecond at max |
---|
854 | prec = min(12, prec) |
---|
855 | |
---|
856 | times = self._get_column(self._gettime, row, prec) |
---|
857 | if not asdatetime: |
---|
858 | return times |
---|
859 | format = "%Y/%m/%d/%H:%M:%S.%f" |
---|
860 | if prec < 7: |
---|
861 | nsub = 1 + (((6-prec)/2) % 3) |
---|
862 | substr = [".%f","%S","%M"] |
---|
863 | for i in range(nsub): |
---|
864 | format = format.replace(substr[i],"") |
---|
865 | if isinstance(times, list): |
---|
866 | return [datetime.strptime(i, format) for i in times] |
---|
867 | else: |
---|
868 | return datetime.strptime(times, format) |
---|
869 | |
---|
870 | |
---|
871 | def get_inttime(self, row=-1): |
---|
872 | """\ |
---|
873 | Get a list of integration times for the observations. |
---|
874 | Return a time in seconds for each integration in the scantable. |
---|
875 | |
---|
876 | Parameters: |
---|
877 | |
---|
878 | row: row no of integration. Default -1 return all rows. |
---|
879 | |
---|
880 | """ |
---|
881 | return self._get_column(self._getinttime, row) |
---|
882 | |
---|
883 | |
---|
884 | def get_sourcename(self, row=-1): |
---|
885 | """\ |
---|
886 | Get a list source names for the observations. |
---|
887 | Return a string for each integration in the scantable. |
---|
888 | Parameters: |
---|
889 | |
---|
890 | row: row no of integration. Default -1 return all rows. |
---|
891 | |
---|
892 | """ |
---|
893 | return self._get_column(self._getsourcename, row) |
---|
894 | |
---|
895 | def get_elevation(self, row=-1): |
---|
896 | """\ |
---|
897 | Get a list of elevations for the observations. |
---|
898 | Return a float for each integration in the scantable. |
---|
899 | |
---|
900 | Parameters: |
---|
901 | |
---|
902 | row: row no of integration. Default -1 return all rows. |
---|
903 | |
---|
904 | """ |
---|
905 | return self._get_column(self._getelevation, row) |
---|
906 | |
---|
907 | def get_azimuth(self, row=-1): |
---|
908 | """\ |
---|
909 | Get a list of azimuths for the observations. |
---|
910 | Return a float for each integration in the scantable. |
---|
911 | |
---|
912 | Parameters: |
---|
913 | row: row no of integration. Default -1 return all rows. |
---|
914 | |
---|
915 | """ |
---|
916 | return self._get_column(self._getazimuth, row) |
---|
917 | |
---|
918 | def get_parangle(self, row=-1): |
---|
919 | """\ |
---|
920 | Get a list of parallactic angles for the observations. |
---|
921 | Return a float for each integration in the scantable. |
---|
922 | |
---|
923 | Parameters: |
---|
924 | |
---|
925 | row: row no of integration. Default -1 return all rows. |
---|
926 | |
---|
927 | """ |
---|
928 | return self._get_column(self._getparangle, row) |
---|
929 | |
---|
930 | def get_direction(self, row=-1): |
---|
931 | """ |
---|
932 | Get a list of Positions on the sky (direction) for the observations. |
---|
933 | Return a string for each integration in the scantable. |
---|
934 | |
---|
935 | Parameters: |
---|
936 | |
---|
937 | row: row no of integration. Default -1 return all rows |
---|
938 | |
---|
939 | """ |
---|
940 | return self._get_column(self._getdirection, row) |
---|
941 | |
---|
942 | def get_directionval(self, row=-1): |
---|
943 | """\ |
---|
944 | Get a list of Positions on the sky (direction) for the observations. |
---|
945 | Return a float for each integration in the scantable. |
---|
946 | |
---|
947 | Parameters: |
---|
948 | |
---|
949 | row: row no of integration. Default -1 return all rows |
---|
950 | |
---|
951 | """ |
---|
952 | return self._get_column(self._getdirectionvec, row) |
---|
953 | |
---|
954 | @asaplog_post_dec |
---|
955 | def set_unit(self, unit='channel'): |
---|
956 | """\ |
---|
957 | Set the unit for all following operations on this scantable |
---|
958 | |
---|
959 | Parameters: |
---|
960 | |
---|
961 | unit: optional unit, default is 'channel'. Use one of '*Hz', |
---|
962 | 'km/s', 'channel' or equivalent '' |
---|
963 | |
---|
964 | """ |
---|
965 | varlist = vars() |
---|
966 | if unit in ['', 'pixel', 'channel']: |
---|
967 | unit = '' |
---|
968 | inf = list(self._getcoordinfo()) |
---|
969 | inf[0] = unit |
---|
970 | self._setcoordinfo(inf) |
---|
971 | self._add_history("set_unit", varlist) |
---|
972 | |
---|
973 | @asaplog_post_dec |
---|
974 | def set_instrument(self, instr): |
---|
975 | """\ |
---|
976 | Set the instrument for subsequent processing. |
---|
977 | |
---|
978 | Parameters: |
---|
979 | |
---|
980 | instr: Select from 'ATPKSMB', 'ATPKSHOH', 'ATMOPRA', |
---|
981 | 'DSS-43' (Tid), 'CEDUNA', and 'HOBART' |
---|
982 | |
---|
983 | """ |
---|
984 | self._setInstrument(instr) |
---|
985 | self._add_history("set_instument", vars()) |
---|
986 | |
---|
987 | @asaplog_post_dec |
---|
988 | def set_feedtype(self, feedtype): |
---|
989 | """\ |
---|
990 | Overwrite the feed type, which might not be set correctly. |
---|
991 | |
---|
992 | Parameters: |
---|
993 | |
---|
994 | feedtype: 'linear' or 'circular' |
---|
995 | |
---|
996 | """ |
---|
997 | self._setfeedtype(feedtype) |
---|
998 | self._add_history("set_feedtype", vars()) |
---|
999 | |
---|
1000 | @asaplog_post_dec |
---|
1001 | def set_doppler(self, doppler='RADIO'): |
---|
1002 | """\ |
---|
1003 | Set the doppler for all following operations on this scantable. |
---|
1004 | |
---|
1005 | Parameters: |
---|
1006 | |
---|
1007 | doppler: One of 'RADIO', 'OPTICAL', 'Z', 'BETA', 'GAMMA' |
---|
1008 | |
---|
1009 | """ |
---|
1010 | varlist = vars() |
---|
1011 | inf = list(self._getcoordinfo()) |
---|
1012 | inf[2] = doppler |
---|
1013 | self._setcoordinfo(inf) |
---|
1014 | self._add_history("set_doppler", vars()) |
---|
1015 | |
---|
1016 | @asaplog_post_dec |
---|
1017 | def set_freqframe(self, frame=None): |
---|
1018 | """\ |
---|
1019 | Set the frame type of the Spectral Axis. |
---|
1020 | |
---|
1021 | Parameters: |
---|
1022 | |
---|
1023 | frame: an optional frame type, default 'LSRK'. Valid frames are: |
---|
1024 | 'TOPO', 'LSRD', 'LSRK', 'BARY', |
---|
1025 | 'GEO', 'GALACTO', 'LGROUP', 'CMB' |
---|
1026 | |
---|
1027 | Example:: |
---|
1028 | |
---|
1029 | scan.set_freqframe('BARY') |
---|
1030 | |
---|
1031 | """ |
---|
1032 | frame = frame or rcParams['scantable.freqframe'] |
---|
1033 | varlist = vars() |
---|
1034 | # "REST" is not implemented in casacore |
---|
1035 | #valid = ['REST', 'TOPO', 'LSRD', 'LSRK', 'BARY', \ |
---|
1036 | # 'GEO', 'GALACTO', 'LGROUP', 'CMB'] |
---|
1037 | valid = ['TOPO', 'LSRD', 'LSRK', 'BARY', \ |
---|
1038 | 'GEO', 'GALACTO', 'LGROUP', 'CMB'] |
---|
1039 | |
---|
1040 | if frame in valid: |
---|
1041 | inf = list(self._getcoordinfo()) |
---|
1042 | inf[1] = frame |
---|
1043 | self._setcoordinfo(inf) |
---|
1044 | self._add_history("set_freqframe", varlist) |
---|
1045 | else: |
---|
1046 | msg = "Please specify a valid freq type. Valid types are:\n", valid |
---|
1047 | raise TypeError(msg) |
---|
1048 | |
---|
1049 | @asaplog_post_dec |
---|
1050 | def set_dirframe(self, frame=""): |
---|
1051 | """\ |
---|
1052 | Set the frame type of the Direction on the sky. |
---|
1053 | |
---|
1054 | Parameters: |
---|
1055 | |
---|
1056 | frame: an optional frame type, default ''. Valid frames are: |
---|
1057 | 'J2000', 'B1950', 'GALACTIC' |
---|
1058 | |
---|
1059 | Example: |
---|
1060 | |
---|
1061 | scan.set_dirframe('GALACTIC') |
---|
1062 | |
---|
1063 | """ |
---|
1064 | varlist = vars() |
---|
1065 | Scantable.set_dirframe(self, frame) |
---|
1066 | self._add_history("set_dirframe", varlist) |
---|
1067 | |
---|
1068 | def get_unit(self): |
---|
1069 | """\ |
---|
1070 | Get the default unit set in this scantable |
---|
1071 | |
---|
1072 | Returns: |
---|
1073 | |
---|
1074 | A unit string |
---|
1075 | |
---|
1076 | """ |
---|
1077 | inf = self._getcoordinfo() |
---|
1078 | unit = inf[0] |
---|
1079 | if unit == '': unit = 'channel' |
---|
1080 | return unit |
---|
1081 | |
---|
1082 | @asaplog_post_dec |
---|
1083 | def get_abcissa(self, rowno=0): |
---|
1084 | """\ |
---|
1085 | Get the abcissa in the current coordinate setup for the currently |
---|
1086 | selected Beam/IF/Pol |
---|
1087 | |
---|
1088 | Parameters: |
---|
1089 | |
---|
1090 | rowno: an optional row number in the scantable. Default is the |
---|
1091 | first row, i.e. rowno=0 |
---|
1092 | |
---|
1093 | Returns: |
---|
1094 | |
---|
1095 | The abcissa values and the format string (as a dictionary) |
---|
1096 | |
---|
1097 | """ |
---|
1098 | abc = self._getabcissa(rowno) |
---|
1099 | lbl = self._getabcissalabel(rowno) |
---|
1100 | return abc, lbl |
---|
1101 | |
---|
1102 | @asaplog_post_dec |
---|
1103 | def flag(self, mask=None, unflag=False, row=-1): |
---|
1104 | """\ |
---|
1105 | Flag the selected data using an optional channel mask. |
---|
1106 | |
---|
1107 | Parameters: |
---|
1108 | |
---|
1109 | mask: an optional channel mask, created with create_mask. Default |
---|
1110 | (no mask) is all channels. |
---|
1111 | |
---|
1112 | unflag: if True, unflag the data |
---|
1113 | |
---|
1114 | row: an optional row number in the scantable. |
---|
1115 | Default -1 flags all rows |
---|
1116 | |
---|
1117 | """ |
---|
1118 | varlist = vars() |
---|
1119 | mask = mask or [] |
---|
1120 | self._flag(row, mask, unflag) |
---|
1121 | self._add_history("flag", varlist) |
---|
1122 | |
---|
1123 | @asaplog_post_dec |
---|
1124 | def flag_row(self, rows=None, unflag=False): |
---|
1125 | """\ |
---|
1126 | Flag the selected data in row-based manner. |
---|
1127 | |
---|
1128 | Parameters: |
---|
1129 | |
---|
1130 | rows: list of row numbers to be flagged. Default is no row |
---|
1131 | (must be explicitly specified to execute row-based |
---|
1132 | flagging). |
---|
1133 | |
---|
1134 | unflag: if True, unflag the data. |
---|
1135 | |
---|
1136 | """ |
---|
1137 | varlist = vars() |
---|
1138 | if rows is None: |
---|
1139 | rows = [] |
---|
1140 | self._flag_row(rows, unflag) |
---|
1141 | self._add_history("flag_row", varlist) |
---|
1142 | |
---|
1143 | @asaplog_post_dec |
---|
1144 | def clip(self, uthres=None, dthres=None, clipoutside=True, unflag=False): |
---|
1145 | """\ |
---|
1146 | Flag the selected data outside a specified range (in channel-base) |
---|
1147 | |
---|
1148 | Parameters: |
---|
1149 | |
---|
1150 | uthres: upper threshold. |
---|
1151 | |
---|
1152 | dthres: lower threshold |
---|
1153 | |
---|
1154 | clipoutside: True for flagging data outside the range |
---|
1155 | [dthres:uthres]. |
---|
1156 | False for flagging data inside the range. |
---|
1157 | |
---|
1158 | unflag: if True, unflag the data. |
---|
1159 | |
---|
1160 | """ |
---|
1161 | varlist = vars() |
---|
1162 | self._clip(uthres, dthres, clipoutside, unflag) |
---|
1163 | self._add_history("clip", varlist) |
---|
1164 | |
---|
1165 | @asaplog_post_dec |
---|
1166 | def lag_flag(self, start, end, unit="MHz", insitu=None): |
---|
1167 | """\ |
---|
1168 | Flag the data in 'lag' space by providing a frequency to remove. |
---|
1169 | Flagged data in the scantable get interpolated over the region. |
---|
1170 | No taper is applied. |
---|
1171 | |
---|
1172 | Parameters: |
---|
1173 | |
---|
1174 | start: the start frequency (really a period within the |
---|
1175 | bandwidth) or period to remove |
---|
1176 | |
---|
1177 | end: the end frequency or period to remove |
---|
1178 | |
---|
1179 | unit: the frequency unit (default "MHz") or "" for |
---|
1180 | explicit lag channels |
---|
1181 | |
---|
1182 | *Notes*: |
---|
1183 | |
---|
1184 | It is recommended to flag edges of the band or strong |
---|
1185 | signals beforehand. |
---|
1186 | |
---|
1187 | """ |
---|
1188 | if insitu is None: insitu = rcParams['insitu'] |
---|
1189 | self._math._setinsitu(insitu) |
---|
1190 | varlist = vars() |
---|
1191 | base = { "GHz": 1000000000., "MHz": 1000000., "kHz": 1000., "Hz": 1.} |
---|
1192 | if not (unit == "" or base.has_key(unit)): |
---|
1193 | raise ValueError("%s is not a valid unit." % unit) |
---|
1194 | if unit == "": |
---|
1195 | s = scantable(self._math._lag_flag(self, start, end, "lags")) |
---|
1196 | else: |
---|
1197 | s = scantable(self._math._lag_flag(self, start*base[unit], |
---|
1198 | end*base[unit], "frequency")) |
---|
1199 | s._add_history("lag_flag", varlist) |
---|
1200 | if insitu: |
---|
1201 | self._assign(s) |
---|
1202 | else: |
---|
1203 | return s |
---|
1204 | |
---|
1205 | @asaplog_post_dec |
---|
1206 | def fft(self, rowno=None, mask=None, getrealimag=False): |
---|
1207 | """\ |
---|
1208 | Apply FFT to the spectra. |
---|
1209 | Flagged data in the scantable get interpolated over the region. |
---|
1210 | |
---|
1211 | Parameters: |
---|
1212 | |
---|
1213 | rowno: The row number(s) to be processed. int, list |
---|
1214 | and tuple are accepted. By default (None), FFT |
---|
1215 | is applied to the whole data. |
---|
1216 | |
---|
1217 | mask: Auxiliary channel mask(s). Given as a boolean |
---|
1218 | list, it is applied to all specified rows. |
---|
1219 | A list of boolean lists can also be used to |
---|
1220 | apply different masks. In the latter case, the |
---|
1221 | length of 'mask' must be the same as that of |
---|
1222 | 'rowno'. The default is None. |
---|
1223 | |
---|
1224 | getrealimag: If True, returns the real and imaginary part |
---|
1225 | values of the complex results. |
---|
1226 | If False (the default), returns the amplitude |
---|
1227 | (absolute value) normalised with Ndata/2 and |
---|
1228 | phase (argument, in unit of radian). |
---|
1229 | |
---|
1230 | Returns: |
---|
1231 | |
---|
1232 | A list of dictionaries containing the results for each spectrum. |
---|
1233 | Each dictionary contains two values, the real and the imaginary |
---|
1234 | parts when getrealimag = True, or the amplitude(absolute value) |
---|
1235 | and the phase(argument) when getrealimag = False. The key for |
---|
1236 | these values are 'real' and 'imag', or 'ampl' and 'phase', |
---|
1237 | respectively. |
---|
1238 | """ |
---|
1239 | if rowno is None: |
---|
1240 | rowno = [] |
---|
1241 | if mask is None: |
---|
1242 | mask = [] |
---|
1243 | if isinstance(rowno, int): |
---|
1244 | rowno = [rowno] |
---|
1245 | elif not (isinstance(rowno, list) or isinstance(rowno, tuple)): |
---|
1246 | raise TypeError("The row number(s) must be int, list or tuple.") |
---|
1247 | |
---|
1248 | if len(rowno) == 0: rowno = [i for i in xrange(self.nrow())] |
---|
1249 | |
---|
1250 | if not (isinstance(mask, list) or isinstance(mask, tuple)): |
---|
1251 | raise TypeError("The mask must be a boolean list or a list of " |
---|
1252 | "boolean list.") |
---|
1253 | if len(mask) == 0: mask = [True for i in xrange(self.nchan())] |
---|
1254 | if isinstance(mask[0], bool): mask = [mask] |
---|
1255 | elif not (isinstance(mask[0], list) or isinstance(mask[0], tuple)): |
---|
1256 | raise TypeError("The mask must be a boolean list or a list of " |
---|
1257 | "boolean list.") |
---|
1258 | |
---|
1259 | usecommonmask = (len(mask) == 1) |
---|
1260 | if not usecommonmask: |
---|
1261 | if len(mask) != len(rowno): |
---|
1262 | raise ValueError("When specifying masks for each spectrum, " |
---|
1263 | "the numbers of them must be identical.") |
---|
1264 | for amask in mask: |
---|
1265 | if len(amask) != self.nchan(): |
---|
1266 | raise ValueError("The spectra and the mask have different " |
---|
1267 | "length.") |
---|
1268 | |
---|
1269 | res = [] |
---|
1270 | |
---|
1271 | imask = 0 |
---|
1272 | for whichrow in rowno: |
---|
1273 | fspec = self._fft(whichrow, mask[imask], getrealimag) |
---|
1274 | nspec = len(fspec) |
---|
1275 | |
---|
1276 | i = 0 |
---|
1277 | v1 = [] |
---|
1278 | v2 = [] |
---|
1279 | reselem = {"real":[],"imag":[]} if getrealimag \ |
---|
1280 | else {"ampl":[],"phase":[]} |
---|
1281 | |
---|
1282 | while (i < nspec): |
---|
1283 | v1.append(fspec[i]) |
---|
1284 | v2.append(fspec[i+1]) |
---|
1285 | i += 2 |
---|
1286 | |
---|
1287 | if getrealimag: |
---|
1288 | reselem["real"] += v1 |
---|
1289 | reselem["imag"] += v2 |
---|
1290 | else: |
---|
1291 | reselem["ampl"] += v1 |
---|
1292 | reselem["phase"] += v2 |
---|
1293 | |
---|
1294 | res.append(reselem) |
---|
1295 | |
---|
1296 | if not usecommonmask: |
---|
1297 | imask += 1 |
---|
1298 | |
---|
1299 | return res |
---|
1300 | |
---|
1301 | @asaplog_post_dec |
---|
1302 | def create_mask(self, *args, **kwargs): |
---|
1303 | """\ |
---|
1304 | Compute and return a mask based on [min, max] windows. |
---|
1305 | The specified windows are to be INCLUDED, when the mask is |
---|
1306 | applied. |
---|
1307 | |
---|
1308 | Parameters: |
---|
1309 | |
---|
1310 | [min, max], [min2, max2], ... |
---|
1311 | Pairs of start/end points (inclusive)specifying the regions |
---|
1312 | to be masked |
---|
1313 | |
---|
1314 | invert: optional argument. If specified as True, |
---|
1315 | return an inverted mask, i.e. the regions |
---|
1316 | specified are EXCLUDED |
---|
1317 | |
---|
1318 | row: create the mask using the specified row for |
---|
1319 | unit conversions, default is row=0 |
---|
1320 | only necessary if frequency varies over rows. |
---|
1321 | |
---|
1322 | Examples:: |
---|
1323 | |
---|
1324 | scan.set_unit('channel') |
---|
1325 | # a) |
---|
1326 | msk = scan.create_mask([400, 500], [800, 900]) |
---|
1327 | # masks everything outside 400 and 500 |
---|
1328 | # and 800 and 900 in the unit 'channel' |
---|
1329 | |
---|
1330 | # b) |
---|
1331 | msk = scan.create_mask([400, 500], [800, 900], invert=True) |
---|
1332 | # masks the regions between 400 and 500 |
---|
1333 | # and 800 and 900 in the unit 'channel' |
---|
1334 | |
---|
1335 | # c) |
---|
1336 | #mask only channel 400 |
---|
1337 | msk = scan.create_mask([400]) |
---|
1338 | |
---|
1339 | """ |
---|
1340 | row = kwargs.get("row", 0) |
---|
1341 | data = self._getabcissa(row) |
---|
1342 | u = self._getcoordinfo()[0] |
---|
1343 | if u == "": |
---|
1344 | u = "channel" |
---|
1345 | msg = "The current mask window unit is %s" % u |
---|
1346 | i = self._check_ifs() |
---|
1347 | if not i: |
---|
1348 | msg += "\nThis mask is only valid for IF=%d" % (self.getif(i)) |
---|
1349 | asaplog.push(msg) |
---|
1350 | n = len(data) |
---|
1351 | msk = _n_bools(n, False) |
---|
1352 | # test if args is a 'list' or a 'normal *args - UGLY!!! |
---|
1353 | |
---|
1354 | ws = (isinstance(args[-1][-1], int) |
---|
1355 | or isinstance(args[-1][-1], float)) and args or args[0] |
---|
1356 | for window in ws: |
---|
1357 | if len(window) == 1: |
---|
1358 | window = [window[0], window[0]] |
---|
1359 | if len(window) == 0 or len(window) > 2: |
---|
1360 | raise ValueError("A window needs to be defined as " |
---|
1361 | "[start(, end)]") |
---|
1362 | if window[0] > window[1]: |
---|
1363 | tmp = window[0] |
---|
1364 | window[0] = window[1] |
---|
1365 | window[1] = tmp |
---|
1366 | for i in range(n): |
---|
1367 | if data[i] >= window[0] and data[i] <= window[1]: |
---|
1368 | msk[i] = True |
---|
1369 | if kwargs.has_key('invert'): |
---|
1370 | if kwargs.get('invert'): |
---|
1371 | msk = mask_not(msk) |
---|
1372 | return msk |
---|
1373 | |
---|
1374 | def get_masklist(self, mask=None, row=0, silent=False): |
---|
1375 | """\ |
---|
1376 | Compute and return a list of mask windows, [min, max]. |
---|
1377 | |
---|
1378 | Parameters: |
---|
1379 | |
---|
1380 | mask: channel mask, created with create_mask. |
---|
1381 | |
---|
1382 | row: calcutate the masklist using the specified row |
---|
1383 | for unit conversions, default is row=0 |
---|
1384 | only necessary if frequency varies over rows. |
---|
1385 | |
---|
1386 | Returns: |
---|
1387 | |
---|
1388 | [min, max], [min2, max2], ... |
---|
1389 | Pairs of start/end points (inclusive)specifying |
---|
1390 | the masked regions |
---|
1391 | |
---|
1392 | """ |
---|
1393 | if not (isinstance(mask,list) or isinstance(mask, tuple)): |
---|
1394 | raise TypeError("The mask should be list or tuple.") |
---|
1395 | if len(mask) < 2: |
---|
1396 | raise TypeError("The mask elements should be > 1") |
---|
1397 | data = self._getabcissa(row) |
---|
1398 | if len(data) != len(mask): |
---|
1399 | msg = "Number of channels in scantable != number of mask elements" |
---|
1400 | raise TypeError(msg) |
---|
1401 | u = self._getcoordinfo()[0] |
---|
1402 | if u == "": |
---|
1403 | u = "channel" |
---|
1404 | msg = "The current mask window unit is %s" % u |
---|
1405 | i = self._check_ifs() |
---|
1406 | if not i: |
---|
1407 | msg += "\nThis mask is only valid for IF=%d" % (self.getif(i)) |
---|
1408 | if not silent: |
---|
1409 | asaplog.push(msg) |
---|
1410 | masklist = [] |
---|
1411 | ist, ien = None, None |
---|
1412 | ist, ien=self.get_mask_indices(mask) |
---|
1413 | if ist is not None and ien is not None: |
---|
1414 | for i in xrange(len(ist)): |
---|
1415 | range=[data[ist[i]],data[ien[i]]] |
---|
1416 | range.sort() |
---|
1417 | masklist.append([range[0],range[1]]) |
---|
1418 | return masklist |
---|
1419 | |
---|
1420 | def get_mask_indices(self, mask=None): |
---|
1421 | """\ |
---|
1422 | Compute and Return lists of mask start indices and mask end indices. |
---|
1423 | |
---|
1424 | Parameters: |
---|
1425 | |
---|
1426 | mask: channel mask, created with create_mask. |
---|
1427 | |
---|
1428 | Returns: |
---|
1429 | |
---|
1430 | List of mask start indices and that of mask end indices, |
---|
1431 | i.e., [istart1,istart2,....], [iend1,iend2,....]. |
---|
1432 | |
---|
1433 | """ |
---|
1434 | if not (isinstance(mask,list) or isinstance(mask, tuple)): |
---|
1435 | raise TypeError("The mask should be list or tuple.") |
---|
1436 | if len(mask) < 2: |
---|
1437 | raise TypeError("The mask elements should be > 1") |
---|
1438 | istart = [] |
---|
1439 | iend = [] |
---|
1440 | if mask[0]: |
---|
1441 | istart.append(0) |
---|
1442 | for i in range(len(mask)-1): |
---|
1443 | if not mask[i] and mask[i+1]: |
---|
1444 | istart.append(i+1) |
---|
1445 | elif mask[i] and not mask[i+1]: |
---|
1446 | iend.append(i) |
---|
1447 | if mask[len(mask)-1]: |
---|
1448 | iend.append(len(mask)-1) |
---|
1449 | if len(istart) != len(iend): |
---|
1450 | raise RuntimeError("Numbers of mask start != mask end.") |
---|
1451 | for i in range(len(istart)): |
---|
1452 | if istart[i] > iend[i]: |
---|
1453 | raise RuntimeError("Mask start index > mask end index") |
---|
1454 | break |
---|
1455 | return istart,iend |
---|
1456 | |
---|
1457 | @asaplog_post_dec |
---|
1458 | def parse_maskexpr(self, maskstring): |
---|
1459 | """ |
---|
1460 | Parse CASA type mask selection syntax (IF dependent). |
---|
1461 | |
---|
1462 | Parameters: |
---|
1463 | maskstring : A string mask selection expression. |
---|
1464 | A comma separated selections mean different IF - |
---|
1465 | channel combinations. IFs and channel selections |
---|
1466 | are partitioned by a colon, ':'. |
---|
1467 | examples: |
---|
1468 | '' = all IFs (all channels) |
---|
1469 | '<2,4~6,9' = IFs 0,1,4,5,6,9 (all channels) |
---|
1470 | '3:3~45;60' = channels 3 to 45 and 60 in IF 3 |
---|
1471 | '0~1:2~6,8' = channels 2 to 6 in IFs 0,1, and |
---|
1472 | all channels in IF8 |
---|
1473 | Returns: |
---|
1474 | A dictionary of selected (valid) IF and masklist pairs, |
---|
1475 | e.g. {'0': [[50,250],[350,462]], '2': [[100,400],[550,974]]} |
---|
1476 | """ |
---|
1477 | if not isinstance(maskstring,str): |
---|
1478 | asaplog.post() |
---|
1479 | asaplog.push("Invalid mask expression") |
---|
1480 | asaplog.post("ERROR") |
---|
1481 | |
---|
1482 | valid_ifs = self.getifnos() |
---|
1483 | frequnit = self.get_unit() |
---|
1484 | seldict = {} |
---|
1485 | if maskstring == "": |
---|
1486 | maskstring = str(valid_ifs)[1:-1] |
---|
1487 | ## split each selection |
---|
1488 | sellist = maskstring.split(',') |
---|
1489 | for currselstr in sellist: |
---|
1490 | selset = currselstr.split(':') |
---|
1491 | # spw and mask string (may include ~, < or >) |
---|
1492 | spwmasklist = self._parse_selection(selset[0], typestr='integer', |
---|
1493 | offset=1, minval=min(valid_ifs), |
---|
1494 | maxval=max(valid_ifs)) |
---|
1495 | for spwlist in spwmasklist: |
---|
1496 | selspws = [] |
---|
1497 | for ispw in range(spwlist[0],spwlist[1]+1): |
---|
1498 | # Put into the list only if ispw exists |
---|
1499 | if valid_ifs.count(ispw): |
---|
1500 | selspws.append(ispw) |
---|
1501 | del spwmasklist, spwlist |
---|
1502 | |
---|
1503 | # parse frequency mask list |
---|
1504 | if len(selset) > 1: |
---|
1505 | freqmasklist = self._parse_selection(selset[1], typestr='float', |
---|
1506 | offset=0.) |
---|
1507 | else: |
---|
1508 | # want to select the whole spectrum |
---|
1509 | freqmasklist = [None] |
---|
1510 | |
---|
1511 | ## define a dictionary of spw - masklist combination |
---|
1512 | for ispw in selspws: |
---|
1513 | #print "working on", ispw |
---|
1514 | spwstr = str(ispw) |
---|
1515 | if len(selspws) == 0: |
---|
1516 | # empty spw |
---|
1517 | continue |
---|
1518 | else: |
---|
1519 | ## want to get min and max of the spw and |
---|
1520 | ## offset to set for '<' and '>' |
---|
1521 | if frequnit == 'channel': |
---|
1522 | minfreq = 0 |
---|
1523 | maxfreq = self.nchan(ifno=ispw) |
---|
1524 | offset = 0.5 |
---|
1525 | else: |
---|
1526 | ## This is ugly part. need improvement |
---|
1527 | for ifrow in xrange(self.nrow()): |
---|
1528 | if self.getif(ifrow) == ispw: |
---|
1529 | #print "IF",ispw,"found in row =",ifrow |
---|
1530 | break |
---|
1531 | freqcoord = self.get_coordinate(ifrow) |
---|
1532 | freqs = self._getabcissa(ifrow) |
---|
1533 | minfreq = min(freqs) |
---|
1534 | maxfreq = max(freqs) |
---|
1535 | if len(freqs) == 1: |
---|
1536 | offset = 0.5 |
---|
1537 | elif frequnit.find('Hz') > 0: |
---|
1538 | offset = abs(freqcoord.to_frequency(1, |
---|
1539 | unit=frequnit) |
---|
1540 | -freqcoord.to_frequency(0, |
---|
1541 | unit=frequnit) |
---|
1542 | )*0.5 |
---|
1543 | elif frequnit.find('m/s') > 0: |
---|
1544 | offset = abs(freqcoord.to_velocity(1, |
---|
1545 | unit=frequnit) |
---|
1546 | -freqcoord.to_velocity(0, |
---|
1547 | unit=frequnit) |
---|
1548 | )*0.5 |
---|
1549 | else: |
---|
1550 | asaplog.post() |
---|
1551 | asaplog.push("Invalid frequency unit") |
---|
1552 | asaplog.post("ERROR") |
---|
1553 | del freqs, freqcoord, ifrow |
---|
1554 | for freq in freqmasklist: |
---|
1555 | selmask = freq or [minfreq, maxfreq] |
---|
1556 | if selmask[0] == None: |
---|
1557 | ## selection was "<freq[1]". |
---|
1558 | if selmask[1] < minfreq: |
---|
1559 | ## avoid adding region selection |
---|
1560 | selmask = None |
---|
1561 | else: |
---|
1562 | selmask = [minfreq,selmask[1]-offset] |
---|
1563 | elif selmask[1] == None: |
---|
1564 | ## selection was ">freq[0]" |
---|
1565 | if selmask[0] > maxfreq: |
---|
1566 | ## avoid adding region selection |
---|
1567 | selmask = None |
---|
1568 | else: |
---|
1569 | selmask = [selmask[0]+offset,maxfreq] |
---|
1570 | if selmask: |
---|
1571 | if not seldict.has_key(spwstr): |
---|
1572 | # new spw selection |
---|
1573 | seldict[spwstr] = [] |
---|
1574 | seldict[spwstr] += [selmask] |
---|
1575 | del minfreq,maxfreq,offset,freq,selmask |
---|
1576 | del spwstr |
---|
1577 | del freqmasklist |
---|
1578 | del valid_ifs |
---|
1579 | if len(seldict) == 0: |
---|
1580 | asaplog.post() |
---|
1581 | asaplog.push("No valid selection in the mask expression: " |
---|
1582 | +maskstring) |
---|
1583 | asaplog.post("WARN") |
---|
1584 | return None |
---|
1585 | msg = "Selected masklist:\n" |
---|
1586 | for sif, lmask in seldict.iteritems(): |
---|
1587 | msg += " IF"+sif+" - "+str(lmask)+"\n" |
---|
1588 | asaplog.push(msg) |
---|
1589 | return seldict |
---|
1590 | |
---|
1591 | def _parse_selection(self, selstr, typestr='float', offset=0., |
---|
1592 | minval=None, maxval=None): |
---|
1593 | """ |
---|
1594 | Parameters: |
---|
1595 | selstr : The Selection string, e.g., '<3;5~7;100~103;9' |
---|
1596 | typestr : The type of the values in returned list |
---|
1597 | ('integer' or 'float') |
---|
1598 | offset : The offset value to subtract from or add to |
---|
1599 | the boundary value if the selection string |
---|
1600 | includes '<' or '>' |
---|
1601 | minval, maxval : The minimum/maximum values to set if the |
---|
1602 | selection string includes '<' or '>'. |
---|
1603 | The list element is filled with None by default. |
---|
1604 | Returns: |
---|
1605 | A list of min/max pair of selections. |
---|
1606 | Example: |
---|
1607 | _parseSelection('<3;5~7;9',typestr='int',offset=1,minval=0) |
---|
1608 | returns [[0,2],[5,7],[9,9]] |
---|
1609 | """ |
---|
1610 | selgroups = selstr.split(';') |
---|
1611 | sellists = [] |
---|
1612 | if typestr.lower().startswith('int'): |
---|
1613 | formatfunc = int |
---|
1614 | else: |
---|
1615 | formatfunc = float |
---|
1616 | |
---|
1617 | for currsel in selgroups: |
---|
1618 | if currsel.find('~') > 0: |
---|
1619 | minsel = formatfunc(currsel.split('~')[0].strip()) |
---|
1620 | maxsel = formatfunc(currsel.split('~')[1].strip()) |
---|
1621 | elif currsel.strip().startswith('<'): |
---|
1622 | minsel = minval |
---|
1623 | maxsel = formatfunc(currsel.split('<')[1].strip()) \ |
---|
1624 | - formatfunc(offset) |
---|
1625 | elif currsel.strip().startswith('>'): |
---|
1626 | minsel = formatfunc(currsel.split('>')[1].strip()) \ |
---|
1627 | + formatfunc(offset) |
---|
1628 | maxsel = maxval |
---|
1629 | else: |
---|
1630 | minsel = formatfunc(currsel) |
---|
1631 | maxsel = formatfunc(currsel) |
---|
1632 | sellists.append([minsel,maxsel]) |
---|
1633 | return sellists |
---|
1634 | |
---|
1635 | # def get_restfreqs(self): |
---|
1636 | # """ |
---|
1637 | # Get the restfrequency(s) stored in this scantable. |
---|
1638 | # The return value(s) are always of unit 'Hz' |
---|
1639 | # Parameters: |
---|
1640 | # none |
---|
1641 | # Returns: |
---|
1642 | # a list of doubles |
---|
1643 | # """ |
---|
1644 | # return list(self._getrestfreqs()) |
---|
1645 | |
---|
1646 | def get_restfreqs(self, ids=None): |
---|
1647 | """\ |
---|
1648 | Get the restfrequency(s) stored in this scantable. |
---|
1649 | The return value(s) are always of unit 'Hz' |
---|
1650 | |
---|
1651 | Parameters: |
---|
1652 | |
---|
1653 | ids: (optional) a list of MOLECULE_ID for that restfrequency(s) to |
---|
1654 | be retrieved |
---|
1655 | |
---|
1656 | Returns: |
---|
1657 | |
---|
1658 | dictionary containing ids and a list of doubles for each id |
---|
1659 | |
---|
1660 | """ |
---|
1661 | if ids is None: |
---|
1662 | rfreqs = {} |
---|
1663 | idlist = self.getmolnos() |
---|
1664 | for i in idlist: |
---|
1665 | rfreqs[i] = list(self._getrestfreqs(i)) |
---|
1666 | return rfreqs |
---|
1667 | else: |
---|
1668 | if type(ids) == list or type(ids) == tuple: |
---|
1669 | rfreqs = {} |
---|
1670 | for i in ids: |
---|
1671 | rfreqs[i] = list(self._getrestfreqs(i)) |
---|
1672 | return rfreqs |
---|
1673 | else: |
---|
1674 | return list(self._getrestfreqs(ids)) |
---|
1675 | |
---|
1676 | @asaplog_post_dec |
---|
1677 | def set_restfreqs(self, freqs=None, unit='Hz'): |
---|
1678 | """\ |
---|
1679 | Set or replace the restfrequency specified and |
---|
1680 | if the 'freqs' argument holds a scalar, |
---|
1681 | then that rest frequency will be applied to all the selected |
---|
1682 | data. If the 'freqs' argument holds |
---|
1683 | a vector, then it MUST be of equal or smaller length than |
---|
1684 | the number of IFs (and the available restfrequencies will be |
---|
1685 | replaced by this vector). In this case, *all* data have |
---|
1686 | the restfrequency set per IF according |
---|
1687 | to the corresponding value you give in the 'freqs' vector. |
---|
1688 | E.g. 'freqs=[1e9, 2e9]' would mean IF 0 gets restfreq 1e9 and |
---|
1689 | IF 1 gets restfreq 2e9. |
---|
1690 | |
---|
1691 | You can also specify the frequencies via a linecatalog. |
---|
1692 | |
---|
1693 | Parameters: |
---|
1694 | |
---|
1695 | freqs: list of rest frequency values or string idenitfiers |
---|
1696 | |
---|
1697 | unit: unit for rest frequency (default 'Hz') |
---|
1698 | |
---|
1699 | |
---|
1700 | Example:: |
---|
1701 | |
---|
1702 | # set the given restfrequency for the all currently selected IFs |
---|
1703 | scan.set_restfreqs(freqs=1.4e9) |
---|
1704 | # set restfrequencies for the n IFs (n > 1) in the order of the |
---|
1705 | # list, i.e |
---|
1706 | # IF0 -> 1.4e9, IF1 -> 1.41e9, IF3 -> 1.42e9 |
---|
1707 | # len(list_of_restfreqs) == nIF |
---|
1708 | # for nIF == 1 the following will set multiple restfrequency for |
---|
1709 | # that IF |
---|
1710 | scan.set_restfreqs(freqs=[1.4e9, 1.41e9, 1.42e9]) |
---|
1711 | # set multiple restfrequencies per IF. as a list of lists where |
---|
1712 | # the outer list has nIF elements, the inner s arbitrary |
---|
1713 | scan.set_restfreqs(freqs=[[1.4e9, 1.41e9], [1.67e9]]) |
---|
1714 | |
---|
1715 | *Note*: |
---|
1716 | |
---|
1717 | To do more sophisticate Restfrequency setting, e.g. on a |
---|
1718 | source and IF basis, use scantable.set_selection() before using |
---|
1719 | this function:: |
---|
1720 | |
---|
1721 | # provided your scantable is called scan |
---|
1722 | selection = selector() |
---|
1723 | selection.set_name("ORION*") |
---|
1724 | selection.set_ifs([1]) |
---|
1725 | scan.set_selection(selection) |
---|
1726 | scan.set_restfreqs(freqs=86.6e9) |
---|
1727 | |
---|
1728 | """ |
---|
1729 | varlist = vars() |
---|
1730 | from asap import linecatalog |
---|
1731 | # simple value |
---|
1732 | if isinstance(freqs, int) or isinstance(freqs, float): |
---|
1733 | self._setrestfreqs([freqs], [""], unit) |
---|
1734 | # list of values |
---|
1735 | elif isinstance(freqs, list) or isinstance(freqs, tuple): |
---|
1736 | # list values are scalars |
---|
1737 | if isinstance(freqs[-1], int) or isinstance(freqs[-1], float): |
---|
1738 | if len(freqs) == 1: |
---|
1739 | self._setrestfreqs(freqs, [""], unit) |
---|
1740 | else: |
---|
1741 | # allow the 'old' mode of setting mulitple IFs |
---|
1742 | sel = selector() |
---|
1743 | savesel = self._getselection() |
---|
1744 | iflist = self.getifnos() |
---|
1745 | if len(freqs)>len(iflist): |
---|
1746 | raise ValueError("number of elements in list of list " |
---|
1747 | "exeeds the current IF selections") |
---|
1748 | iflist = self.getifnos() |
---|
1749 | for i, fval in enumerate(freqs): |
---|
1750 | sel.set_ifs(iflist[i]) |
---|
1751 | self._setselection(sel) |
---|
1752 | self._setrestfreqs([fval], [""], unit) |
---|
1753 | self._setselection(savesel) |
---|
1754 | |
---|
1755 | # list values are dict, {'value'=, 'name'=) |
---|
1756 | elif isinstance(freqs[-1], dict): |
---|
1757 | values = [] |
---|
1758 | names = [] |
---|
1759 | for d in freqs: |
---|
1760 | values.append(d["value"]) |
---|
1761 | names.append(d["name"]) |
---|
1762 | self._setrestfreqs(values, names, unit) |
---|
1763 | elif isinstance(freqs[-1], list) or isinstance(freqs[-1], tuple): |
---|
1764 | sel = selector() |
---|
1765 | savesel = self._getselection() |
---|
1766 | iflist = self.getifnos() |
---|
1767 | if len(freqs)>len(iflist): |
---|
1768 | raise ValueError("number of elements in list of list exeeds" |
---|
1769 | " the current IF selections") |
---|
1770 | for i, fval in enumerate(freqs): |
---|
1771 | sel.set_ifs(iflist[i]) |
---|
1772 | self._setselection(sel) |
---|
1773 | self._setrestfreqs(fval, [""], unit) |
---|
1774 | self._setselection(savesel) |
---|
1775 | # freqs are to be taken from a linecatalog |
---|
1776 | elif isinstance(freqs, linecatalog): |
---|
1777 | sel = selector() |
---|
1778 | savesel = self._getselection() |
---|
1779 | for i in xrange(freqs.nrow()): |
---|
1780 | sel.set_ifs(iflist[i]) |
---|
1781 | self._setselection(sel) |
---|
1782 | self._setrestfreqs([freqs.get_frequency(i)], |
---|
1783 | [freqs.get_name(i)], "MHz") |
---|
1784 | # ensure that we are not iterating past nIF |
---|
1785 | if i == self.nif()-1: break |
---|
1786 | self._setselection(savesel) |
---|
1787 | else: |
---|
1788 | return |
---|
1789 | self._add_history("set_restfreqs", varlist) |
---|
1790 | |
---|
1791 | @asaplog_post_dec |
---|
1792 | def shift_refpix(self, delta): |
---|
1793 | """\ |
---|
1794 | Shift the reference pixel of the Spectra Coordinate by an |
---|
1795 | integer amount. |
---|
1796 | |
---|
1797 | Parameters: |
---|
1798 | |
---|
1799 | delta: the amount to shift by |
---|
1800 | |
---|
1801 | *Note*: |
---|
1802 | |
---|
1803 | Be careful using this with broadband data. |
---|
1804 | |
---|
1805 | """ |
---|
1806 | varlist = vars() |
---|
1807 | Scantable.shift_refpix(self, delta) |
---|
1808 | s._add_history("shift_refpix", varlist) |
---|
1809 | |
---|
1810 | @asaplog_post_dec |
---|
1811 | def history(self, filename=None): |
---|
1812 | """\ |
---|
1813 | Print the history. Optionally to a file. |
---|
1814 | |
---|
1815 | Parameters: |
---|
1816 | |
---|
1817 | filename: The name of the file to save the history to. |
---|
1818 | |
---|
1819 | """ |
---|
1820 | hist = list(self._gethistory()) |
---|
1821 | out = "-"*80 |
---|
1822 | for h in hist: |
---|
1823 | if h.startswith("---"): |
---|
1824 | out = "\n".join([out, h]) |
---|
1825 | else: |
---|
1826 | items = h.split("##") |
---|
1827 | date = items[0] |
---|
1828 | func = items[1] |
---|
1829 | items = items[2:] |
---|
1830 | out += "\n"+date+"\n" |
---|
1831 | out += "Function: %s\n Parameters:" % (func) |
---|
1832 | for i in items: |
---|
1833 | if i == '': |
---|
1834 | continue |
---|
1835 | s = i.split("=") |
---|
1836 | out += "\n %s = %s" % (s[0], s[1]) |
---|
1837 | out = "\n".join([out, "-"*80]) |
---|
1838 | if filename is not None: |
---|
1839 | if filename is "": |
---|
1840 | filename = 'scantable_history.txt' |
---|
1841 | import os |
---|
1842 | filename = os.path.expandvars(os.path.expanduser(filename)) |
---|
1843 | if not os.path.isdir(filename): |
---|
1844 | data = open(filename, 'w') |
---|
1845 | data.write(out) |
---|
1846 | data.close() |
---|
1847 | else: |
---|
1848 | msg = "Illegal file name '%s'." % (filename) |
---|
1849 | raise IOError(msg) |
---|
1850 | return page(out) |
---|
1851 | |
---|
1852 | # |
---|
1853 | # Maths business |
---|
1854 | # |
---|
1855 | @asaplog_post_dec |
---|
1856 | def average_time(self, mask=None, scanav=False, weight='tint', align=False): |
---|
1857 | """\ |
---|
1858 | Return the (time) weighted average of a scan. Scans will be averaged |
---|
1859 | only if the source direction (RA/DEC) is within 1' otherwise |
---|
1860 | |
---|
1861 | *Note*: |
---|
1862 | |
---|
1863 | in channels only - align if necessary |
---|
1864 | |
---|
1865 | Parameters: |
---|
1866 | |
---|
1867 | mask: an optional mask (only used for 'var' and 'tsys' |
---|
1868 | weighting) |
---|
1869 | |
---|
1870 | scanav: True averages each scan separately |
---|
1871 | False (default) averages all scans together, |
---|
1872 | |
---|
1873 | weight: Weighting scheme. |
---|
1874 | 'none' (mean no weight) |
---|
1875 | 'var' (1/var(spec) weighted) |
---|
1876 | 'tsys' (1/Tsys**2 weighted) |
---|
1877 | 'tint' (integration time weighted) |
---|
1878 | 'tintsys' (Tint/Tsys**2) |
---|
1879 | 'median' ( median averaging) |
---|
1880 | The default is 'tint' |
---|
1881 | |
---|
1882 | align: align the spectra in velocity before averaging. It takes |
---|
1883 | the time of the first spectrum as reference time. |
---|
1884 | |
---|
1885 | Example:: |
---|
1886 | |
---|
1887 | # time average the scantable without using a mask |
---|
1888 | newscan = scan.average_time() |
---|
1889 | |
---|
1890 | """ |
---|
1891 | varlist = vars() |
---|
1892 | weight = weight or 'TINT' |
---|
1893 | mask = mask or () |
---|
1894 | scanav = (scanav and 'SCAN') or 'NONE' |
---|
1895 | scan = (self, ) |
---|
1896 | |
---|
1897 | if align: |
---|
1898 | scan = (self.freq_align(insitu=False), ) |
---|
1899 | s = None |
---|
1900 | if weight.upper() == 'MEDIAN': |
---|
1901 | s = scantable(self._math._averagechannel(scan[0], 'MEDIAN', |
---|
1902 | scanav)) |
---|
1903 | else: |
---|
1904 | s = scantable(self._math._average(scan, mask, weight.upper(), |
---|
1905 | scanav)) |
---|
1906 | s._add_history("average_time", varlist) |
---|
1907 | return s |
---|
1908 | |
---|
1909 | @asaplog_post_dec |
---|
1910 | def convert_flux(self, jyperk=None, eta=None, d=None, insitu=None): |
---|
1911 | """\ |
---|
1912 | Return a scan where all spectra are converted to either |
---|
1913 | Jansky or Kelvin depending upon the flux units of the scan table. |
---|
1914 | By default the function tries to look the values up internally. |
---|
1915 | If it can't find them (or if you want to over-ride), you must |
---|
1916 | specify EITHER jyperk OR eta (and D which it will try to look up |
---|
1917 | also if you don't set it). jyperk takes precedence if you set both. |
---|
1918 | |
---|
1919 | Parameters: |
---|
1920 | |
---|
1921 | jyperk: the Jy / K conversion factor |
---|
1922 | |
---|
1923 | eta: the aperture efficiency |
---|
1924 | |
---|
1925 | d: the geometric diameter (metres) |
---|
1926 | |
---|
1927 | insitu: if False a new scantable is returned. |
---|
1928 | Otherwise, the scaling is done in-situ |
---|
1929 | The default is taken from .asaprc (False) |
---|
1930 | |
---|
1931 | """ |
---|
1932 | if insitu is None: insitu = rcParams['insitu'] |
---|
1933 | self._math._setinsitu(insitu) |
---|
1934 | varlist = vars() |
---|
1935 | jyperk = jyperk or -1.0 |
---|
1936 | d = d or -1.0 |
---|
1937 | eta = eta or -1.0 |
---|
1938 | s = scantable(self._math._convertflux(self, d, eta, jyperk)) |
---|
1939 | s._add_history("convert_flux", varlist) |
---|
1940 | if insitu: self._assign(s) |
---|
1941 | else: return s |
---|
1942 | |
---|
1943 | @asaplog_post_dec |
---|
1944 | def gain_el(self, poly=None, filename="", method="linear", insitu=None): |
---|
1945 | """\ |
---|
1946 | Return a scan after applying a gain-elevation correction. |
---|
1947 | The correction can be made via either a polynomial or a |
---|
1948 | table-based interpolation (and extrapolation if necessary). |
---|
1949 | You specify polynomial coefficients, an ascii table or neither. |
---|
1950 | If you specify neither, then a polynomial correction will be made |
---|
1951 | with built in coefficients known for certain telescopes (an error |
---|
1952 | will occur if the instrument is not known). |
---|
1953 | The data and Tsys are *divided* by the scaling factors. |
---|
1954 | |
---|
1955 | Parameters: |
---|
1956 | |
---|
1957 | poly: Polynomial coefficients (default None) to compute a |
---|
1958 | gain-elevation correction as a function of |
---|
1959 | elevation (in degrees). |
---|
1960 | |
---|
1961 | filename: The name of an ascii file holding correction factors. |
---|
1962 | The first row of the ascii file must give the column |
---|
1963 | names and these MUST include columns |
---|
1964 | "ELEVATION" (degrees) and "FACTOR" (multiply data |
---|
1965 | by this) somewhere. |
---|
1966 | The second row must give the data type of the |
---|
1967 | column. Use 'R' for Real and 'I' for Integer. |
---|
1968 | An example file would be |
---|
1969 | (actual factors are arbitrary) : |
---|
1970 | |
---|
1971 | TIME ELEVATION FACTOR |
---|
1972 | R R R |
---|
1973 | 0.1 0 0.8 |
---|
1974 | 0.2 20 0.85 |
---|
1975 | 0.3 40 0.9 |
---|
1976 | 0.4 60 0.85 |
---|
1977 | 0.5 80 0.8 |
---|
1978 | 0.6 90 0.75 |
---|
1979 | |
---|
1980 | method: Interpolation method when correcting from a table. |
---|
1981 | Values are "nearest", "linear" (default), "cubic" |
---|
1982 | and "spline" |
---|
1983 | |
---|
1984 | insitu: if False a new scantable is returned. |
---|
1985 | Otherwise, the scaling is done in-situ |
---|
1986 | The default is taken from .asaprc (False) |
---|
1987 | |
---|
1988 | """ |
---|
1989 | |
---|
1990 | if insitu is None: insitu = rcParams['insitu'] |
---|
1991 | self._math._setinsitu(insitu) |
---|
1992 | varlist = vars() |
---|
1993 | poly = poly or () |
---|
1994 | from os.path import expandvars |
---|
1995 | filename = expandvars(filename) |
---|
1996 | s = scantable(self._math._gainel(self, poly, filename, method)) |
---|
1997 | s._add_history("gain_el", varlist) |
---|
1998 | if insitu: |
---|
1999 | self._assign(s) |
---|
2000 | else: |
---|
2001 | return s |
---|
2002 | |
---|
2003 | @asaplog_post_dec |
---|
2004 | def freq_align(self, reftime=None, method='cubic', insitu=None): |
---|
2005 | """\ |
---|
2006 | Return a scan where all rows have been aligned in frequency/velocity. |
---|
2007 | The alignment frequency frame (e.g. LSRK) is that set by function |
---|
2008 | set_freqframe. |
---|
2009 | |
---|
2010 | Parameters: |
---|
2011 | |
---|
2012 | reftime: reference time to align at. By default, the time of |
---|
2013 | the first row of data is used. |
---|
2014 | |
---|
2015 | method: Interpolation method for regridding the spectra. |
---|
2016 | Choose from "nearest", "linear", "cubic" (default) |
---|
2017 | and "spline" |
---|
2018 | |
---|
2019 | insitu: if False a new scantable is returned. |
---|
2020 | Otherwise, the scaling is done in-situ |
---|
2021 | The default is taken from .asaprc (False) |
---|
2022 | |
---|
2023 | """ |
---|
2024 | if insitu is None: insitu = rcParams["insitu"] |
---|
2025 | self._math._setinsitu(insitu) |
---|
2026 | varlist = vars() |
---|
2027 | reftime = reftime or "" |
---|
2028 | s = scantable(self._math._freq_align(self, reftime, method)) |
---|
2029 | s._add_history("freq_align", varlist) |
---|
2030 | if insitu: |
---|
2031 | self._assign(s) |
---|
2032 | else: |
---|
2033 | return s |
---|
2034 | |
---|
2035 | @asaplog_post_dec |
---|
2036 | def opacity(self, tau=None, insitu=None): |
---|
2037 | """\ |
---|
2038 | Apply an opacity correction. The data |
---|
2039 | and Tsys are multiplied by the correction factor. |
---|
2040 | |
---|
2041 | Parameters: |
---|
2042 | |
---|
2043 | tau: (list of) opacity from which the correction factor is |
---|
2044 | exp(tau*ZD) |
---|
2045 | where ZD is the zenith-distance. |
---|
2046 | If a list is provided, it has to be of length nIF, |
---|
2047 | nIF*nPol or 1 and in order of IF/POL, e.g. |
---|
2048 | [opif0pol0, opif0pol1, opif1pol0 ...] |
---|
2049 | if tau is `None` the opacities are determined from a |
---|
2050 | model. |
---|
2051 | |
---|
2052 | insitu: if False a new scantable is returned. |
---|
2053 | Otherwise, the scaling is done in-situ |
---|
2054 | The default is taken from .asaprc (False) |
---|
2055 | |
---|
2056 | """ |
---|
2057 | if insitu is None: |
---|
2058 | insitu = rcParams['insitu'] |
---|
2059 | self._math._setinsitu(insitu) |
---|
2060 | varlist = vars() |
---|
2061 | if not hasattr(tau, "__len__"): |
---|
2062 | tau = [tau] |
---|
2063 | s = scantable(self._math._opacity(self, tau)) |
---|
2064 | s._add_history("opacity", varlist) |
---|
2065 | if insitu: |
---|
2066 | self._assign(s) |
---|
2067 | else: |
---|
2068 | return s |
---|
2069 | |
---|
2070 | @asaplog_post_dec |
---|
2071 | def bin(self, width=5, insitu=None): |
---|
2072 | """\ |
---|
2073 | Return a scan where all spectra have been binned up. |
---|
2074 | |
---|
2075 | Parameters: |
---|
2076 | |
---|
2077 | width: The bin width (default=5) in pixels |
---|
2078 | |
---|
2079 | insitu: if False a new scantable is returned. |
---|
2080 | Otherwise, the scaling is done in-situ |
---|
2081 | The default is taken from .asaprc (False) |
---|
2082 | |
---|
2083 | """ |
---|
2084 | if insitu is None: |
---|
2085 | insitu = rcParams['insitu'] |
---|
2086 | self._math._setinsitu(insitu) |
---|
2087 | varlist = vars() |
---|
2088 | s = scantable(self._math._bin(self, width)) |
---|
2089 | s._add_history("bin", varlist) |
---|
2090 | if insitu: |
---|
2091 | self._assign(s) |
---|
2092 | else: |
---|
2093 | return s |
---|
2094 | |
---|
2095 | @asaplog_post_dec |
---|
2096 | def resample(self, width=5, method='cubic', insitu=None): |
---|
2097 | """\ |
---|
2098 | Return a scan where all spectra have been binned up. |
---|
2099 | |
---|
2100 | Parameters: |
---|
2101 | |
---|
2102 | width: The bin width (default=5) in pixels |
---|
2103 | |
---|
2104 | method: Interpolation method when correcting from a table. |
---|
2105 | Values are "nearest", "linear", "cubic" (default) |
---|
2106 | and "spline" |
---|
2107 | |
---|
2108 | insitu: if False a new scantable is returned. |
---|
2109 | Otherwise, the scaling is done in-situ |
---|
2110 | The default is taken from .asaprc (False) |
---|
2111 | |
---|
2112 | """ |
---|
2113 | if insitu is None: |
---|
2114 | insitu = rcParams['insitu'] |
---|
2115 | self._math._setinsitu(insitu) |
---|
2116 | varlist = vars() |
---|
2117 | s = scantable(self._math._resample(self, method, width)) |
---|
2118 | s._add_history("resample", varlist) |
---|
2119 | if insitu: |
---|
2120 | self._assign(s) |
---|
2121 | else: |
---|
2122 | return s |
---|
2123 | |
---|
2124 | @asaplog_post_dec |
---|
2125 | def average_pol(self, mask=None, weight='none'): |
---|
2126 | """\ |
---|
2127 | Average the Polarisations together. |
---|
2128 | |
---|
2129 | Parameters: |
---|
2130 | |
---|
2131 | mask: An optional mask defining the region, where the |
---|
2132 | averaging will be applied. The output will have all |
---|
2133 | specified points masked. |
---|
2134 | |
---|
2135 | weight: Weighting scheme. 'none' (default), 'var' (1/var(spec) |
---|
2136 | weighted), or 'tsys' (1/Tsys**2 weighted) |
---|
2137 | |
---|
2138 | """ |
---|
2139 | varlist = vars() |
---|
2140 | mask = mask or () |
---|
2141 | s = scantable(self._math._averagepol(self, mask, weight.upper())) |
---|
2142 | s._add_history("average_pol", varlist) |
---|
2143 | return s |
---|
2144 | |
---|
2145 | @asaplog_post_dec |
---|
2146 | def average_beam(self, mask=None, weight='none'): |
---|
2147 | """\ |
---|
2148 | Average the Beams together. |
---|
2149 | |
---|
2150 | Parameters: |
---|
2151 | mask: An optional mask defining the region, where the |
---|
2152 | averaging will be applied. The output will have all |
---|
2153 | specified points masked. |
---|
2154 | |
---|
2155 | weight: Weighting scheme. 'none' (default), 'var' (1/var(spec) |
---|
2156 | weighted), or 'tsys' (1/Tsys**2 weighted) |
---|
2157 | |
---|
2158 | """ |
---|
2159 | varlist = vars() |
---|
2160 | mask = mask or () |
---|
2161 | s = scantable(self._math._averagebeams(self, mask, weight.upper())) |
---|
2162 | s._add_history("average_beam", varlist) |
---|
2163 | return s |
---|
2164 | |
---|
2165 | def parallactify(self, pflag): |
---|
2166 | """\ |
---|
2167 | Set a flag to indicate whether this data should be treated as having |
---|
2168 | been 'parallactified' (total phase == 0.0) |
---|
2169 | |
---|
2170 | Parameters: |
---|
2171 | |
---|
2172 | pflag: Bool indicating whether to turn this on (True) or |
---|
2173 | off (False) |
---|
2174 | |
---|
2175 | """ |
---|
2176 | varlist = vars() |
---|
2177 | self._parallactify(pflag) |
---|
2178 | self._add_history("parallactify", varlist) |
---|
2179 | |
---|
2180 | @asaplog_post_dec |
---|
2181 | def convert_pol(self, poltype=None): |
---|
2182 | """\ |
---|
2183 | Convert the data to a different polarisation type. |
---|
2184 | Note that you will need cross-polarisation terms for most conversions. |
---|
2185 | |
---|
2186 | Parameters: |
---|
2187 | |
---|
2188 | poltype: The new polarisation type. Valid types are: |
---|
2189 | "linear", "circular", "stokes" and "linpol" |
---|
2190 | |
---|
2191 | """ |
---|
2192 | varlist = vars() |
---|
2193 | s = scantable(self._math._convertpol(self, poltype)) |
---|
2194 | s._add_history("convert_pol", varlist) |
---|
2195 | return s |
---|
2196 | |
---|
2197 | @asaplog_post_dec |
---|
2198 | def smooth(self, kernel="hanning", width=5.0, order=2, plot=False, |
---|
2199 | insitu=None): |
---|
2200 | """\ |
---|
2201 | Smooth the spectrum by the specified kernel (conserving flux). |
---|
2202 | |
---|
2203 | Parameters: |
---|
2204 | |
---|
2205 | kernel: The type of smoothing kernel. Select from |
---|
2206 | 'hanning' (default), 'gaussian', 'boxcar', 'rmedian' |
---|
2207 | or 'poly' |
---|
2208 | |
---|
2209 | width: The width of the kernel in pixels. For hanning this is |
---|
2210 | ignored otherwise it defauls to 5 pixels. |
---|
2211 | For 'gaussian' it is the Full Width Half |
---|
2212 | Maximum. For 'boxcar' it is the full width. |
---|
2213 | For 'rmedian' and 'poly' it is the half width. |
---|
2214 | |
---|
2215 | order: Optional parameter for 'poly' kernel (default is 2), to |
---|
2216 | specify the order of the polnomial. Ignored by all other |
---|
2217 | kernels. |
---|
2218 | |
---|
2219 | plot: plot the original and the smoothed spectra. |
---|
2220 | In this each indivual fit has to be approved, by |
---|
2221 | typing 'y' or 'n' |
---|
2222 | |
---|
2223 | insitu: if False a new scantable is returned. |
---|
2224 | Otherwise, the scaling is done in-situ |
---|
2225 | The default is taken from .asaprc (False) |
---|
2226 | |
---|
2227 | """ |
---|
2228 | if insitu is None: insitu = rcParams['insitu'] |
---|
2229 | self._math._setinsitu(insitu) |
---|
2230 | varlist = vars() |
---|
2231 | |
---|
2232 | if plot: orgscan = self.copy() |
---|
2233 | |
---|
2234 | s = scantable(self._math._smooth(self, kernel.lower(), width, order)) |
---|
2235 | s._add_history("smooth", varlist) |
---|
2236 | |
---|
2237 | if plot: |
---|
2238 | from asap.asapplotter import new_asaplot |
---|
2239 | theplot = new_asaplot(rcParams['plotter.gui']) |
---|
2240 | theplot.set_panels() |
---|
2241 | ylab=s._get_ordinate_label() |
---|
2242 | #theplot.palette(0,["#777777","red"]) |
---|
2243 | for r in xrange(s.nrow()): |
---|
2244 | xsm=s._getabcissa(r) |
---|
2245 | ysm=s._getspectrum(r) |
---|
2246 | xorg=orgscan._getabcissa(r) |
---|
2247 | yorg=orgscan._getspectrum(r) |
---|
2248 | theplot.clear() |
---|
2249 | theplot.hold() |
---|
2250 | theplot.set_axes('ylabel',ylab) |
---|
2251 | theplot.set_axes('xlabel',s._getabcissalabel(r)) |
---|
2252 | theplot.set_axes('title',s._getsourcename(r)) |
---|
2253 | theplot.set_line(label='Original',color="#777777") |
---|
2254 | theplot.plot(xorg,yorg) |
---|
2255 | theplot.set_line(label='Smoothed',color="red") |
---|
2256 | theplot.plot(xsm,ysm) |
---|
2257 | ### Ugly part for legend |
---|
2258 | for i in [0,1]: |
---|
2259 | theplot.subplots[0]['lines'].append( |
---|
2260 | [theplot.subplots[0]['axes'].lines[i]] |
---|
2261 | ) |
---|
2262 | theplot.release() |
---|
2263 | ### Ugly part for legend |
---|
2264 | theplot.subplots[0]['lines']=[] |
---|
2265 | res = raw_input("Accept smoothing ([y]/n): ") |
---|
2266 | if res.upper() == 'N': |
---|
2267 | s._setspectrum(yorg, r) |
---|
2268 | theplot.quit() |
---|
2269 | del theplot |
---|
2270 | del orgscan |
---|
2271 | |
---|
2272 | if insitu: self._assign(s) |
---|
2273 | else: return s |
---|
2274 | |
---|
2275 | @asaplog_post_dec |
---|
2276 | def _parse_wn(self, wn): |
---|
2277 | if isinstance(wn, list) or isinstance(wn, tuple): |
---|
2278 | return wn |
---|
2279 | elif isinstance(wn, int): |
---|
2280 | return [ wn ] |
---|
2281 | elif isinstance(wn, str): |
---|
2282 | if '-' in wn: # case 'a-b' : return [a,a+1,...,b-1,b] |
---|
2283 | val = wn.split('-') |
---|
2284 | val = [int(val[0]), int(val[1])] |
---|
2285 | val.sort() |
---|
2286 | res = [i for i in xrange(val[0], val[1]+1)] |
---|
2287 | elif wn[:2] == '<=' or wn[:2] == '=<': # cases '<=a','=<a' : return [0,1,...,a-1,a] |
---|
2288 | val = int(wn[2:])+1 |
---|
2289 | res = [i for i in xrange(val)] |
---|
2290 | elif wn[-2:] == '>=' or wn[-2:] == '=>': # cases 'a>=','a=>' : return [0,1,...,a-1,a] |
---|
2291 | val = int(wn[:-2])+1 |
---|
2292 | res = [i for i in xrange(val)] |
---|
2293 | elif wn[0] == '<': # case '<a' : return [0,1,...,a-2,a-1] |
---|
2294 | val = int(wn[1:]) |
---|
2295 | res = [i for i in xrange(val)] |
---|
2296 | elif wn[-1] == '>': # case 'a>' : return [0,1,...,a-2,a-1] |
---|
2297 | val = int(wn[:-1]) |
---|
2298 | res = [i for i in xrange(val)] |
---|
2299 | elif wn[:2] == '>=' or wn[:2] == '=>': # cases '>=a','=>a' : return [a,a+1,...,a_nyq] |
---|
2300 | val = int(wn[2:]) |
---|
2301 | res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2302 | elif wn[-2:] == '<=' or wn[-2:] == '=<': # cases 'a<=','a=<' : return [a,a+1,...,a_nyq] |
---|
2303 | val = int(wn[:-2]) |
---|
2304 | res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2305 | elif wn[0] == '>': # case '>a' : return [a+1,a+2,...,a_nyq] |
---|
2306 | val = int(wn[1:])+1 |
---|
2307 | res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2308 | elif wn[-1] == '<': # case 'a<' : return [a+1,a+2,...,a_nyq] |
---|
2309 | val = int(wn[:-1])+1 |
---|
2310 | res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2311 | |
---|
2312 | return res |
---|
2313 | else: |
---|
2314 | msg = 'wrong value given for addwn/rejwn' |
---|
2315 | raise RuntimeError(msg) |
---|
2316 | |
---|
2317 | |
---|
2318 | @asaplog_post_dec |
---|
2319 | def sinusoid_baseline(self, insitu=None, mask=None, applyfft=None, |
---|
2320 | fftmethod=None, fftthresh=None, |
---|
2321 | addwn=None, rejwn=None, clipthresh=None, |
---|
2322 | clipniter=None, plot=None, |
---|
2323 | getresidual=None, showprogress=None, |
---|
2324 | minnrow=None, outlog=None, blfile=None): |
---|
2325 | """\ |
---|
2326 | Return a scan which has been baselined (all rows) with sinusoidal |
---|
2327 | functions. |
---|
2328 | |
---|
2329 | Parameters: |
---|
2330 | insitu: if False a new scantable is returned. |
---|
2331 | Otherwise, the scaling is done in-situ |
---|
2332 | The default is taken from .asaprc (False) |
---|
2333 | mask: an optional mask |
---|
2334 | applyfft: if True use some method, such as FFT, to find |
---|
2335 | strongest sinusoidal components in the wavenumber |
---|
2336 | domain to be used for baseline fitting. |
---|
2337 | default is True. |
---|
2338 | fftmethod: method to find the strong sinusoidal components. |
---|
2339 | now only 'fft' is available and it is the default. |
---|
2340 | fftthresh: the threshold to select wave numbers to be used for |
---|
2341 | fitting from the distribution of amplitudes in the |
---|
2342 | wavenumber domain. |
---|
2343 | both float and string values accepted. |
---|
2344 | given a float value, the unit is set to sigma. |
---|
2345 | for string values, allowed formats include: |
---|
2346 | 'xsigma' or 'x' (= x-sigma level. e.g., |
---|
2347 | '3sigma'), or |
---|
2348 | 'topx' (= the x strongest ones, e.g. 'top5'). |
---|
2349 | default is 3.0 (unit: sigma). |
---|
2350 | addwn: the additional wave numbers to be used for fitting. |
---|
2351 | list or integer value is accepted to specify every |
---|
2352 | wave numbers. also string value can be used in case |
---|
2353 | you need to specify wave numbers in a certain range, |
---|
2354 | e.g., 'a-b' (= a, a+1, a+2, ..., b-1, b), |
---|
2355 | '<a' (= 0,1,...,a-2,a-1), |
---|
2356 | '>=a' (= a, a+1, ... up to the maximum wave |
---|
2357 | number corresponding to the Nyquist |
---|
2358 | frequency for the case of FFT). |
---|
2359 | default is []. |
---|
2360 | rejwn: the wave numbers NOT to be used for fitting. |
---|
2361 | can be set just as addwn but has higher priority: |
---|
2362 | wave numbers which are specified both in addwn |
---|
2363 | and rejwn will NOT be used. default is []. |
---|
2364 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2365 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2366 | clipping (default is 0) |
---|
2367 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2368 | plot the fit and the residual. In this each |
---|
2369 | indivual fit has to be approved, by typing 'y' |
---|
2370 | or 'n' |
---|
2371 | getresidual: if False, returns best-fit values instead of |
---|
2372 | residual. (default is True) |
---|
2373 | showprogress: show progress status for large data. |
---|
2374 | default is True. |
---|
2375 | minnrow: minimum number of input spectra to show. |
---|
2376 | default is 1000. |
---|
2377 | outlog: Output the coefficients of the best-fit |
---|
2378 | function to logger (default is False) |
---|
2379 | blfile: Name of a text file in which the best-fit |
---|
2380 | parameter values to be written |
---|
2381 | (default is '': no file/logger output) |
---|
2382 | |
---|
2383 | Example: |
---|
2384 | # return a scan baselined by a combination of sinusoidal curves |
---|
2385 | # having wave numbers in spectral window up to 10, |
---|
2386 | # also with 3-sigma clipping, iteration up to 4 times |
---|
2387 | bscan = scan.sinusoid_baseline(addwn='<=10',clipthresh=3.0,clipniter=4) |
---|
2388 | |
---|
2389 | Note: |
---|
2390 | The best-fit parameter values output in logger and/or blfile are now |
---|
2391 | based on specunit of 'channel'. |
---|
2392 | """ |
---|
2393 | |
---|
2394 | try: |
---|
2395 | varlist = vars() |
---|
2396 | |
---|
2397 | if insitu is None: insitu = rcParams['insitu'] |
---|
2398 | if insitu: |
---|
2399 | workscan = self |
---|
2400 | else: |
---|
2401 | workscan = self.copy() |
---|
2402 | |
---|
2403 | if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2404 | if applyfft is None: applyfft = True |
---|
2405 | if fftmethod is None: fftmethod = 'fft' |
---|
2406 | if fftthresh is None: fftthresh = 3.0 |
---|
2407 | if addwn is None: addwn = [] |
---|
2408 | if rejwn is None: rejwn = [] |
---|
2409 | if clipthresh is None: clipthresh = 3.0 |
---|
2410 | if clipniter is None: clipniter = 0 |
---|
2411 | if plot is None: plot = False |
---|
2412 | if getresidual is None: getresidual = True |
---|
2413 | if showprogress is None: showprogress = True |
---|
2414 | if minnrow is None: minnrow = 1000 |
---|
2415 | if outlog is None: outlog = False |
---|
2416 | if blfile is None: blfile = '' |
---|
2417 | |
---|
2418 | #CURRENTLY, PLOT=true is UNAVAILABLE UNTIL sinusoidal fitting is implemented as a fitter method. |
---|
2419 | workscan._sinusoid_baseline(mask, applyfft, fftmethod.lower(), |
---|
2420 | str(fftthresh).lower(), |
---|
2421 | workscan._parse_wn(addwn), |
---|
2422 | workscan._parse_wn(rejwn), clipthresh, |
---|
2423 | clipniter, getresidual, |
---|
2424 | pack_progress_params(showprogress, |
---|
2425 | minnrow), outlog, |
---|
2426 | blfile) |
---|
2427 | workscan._add_history('sinusoid_baseline', varlist) |
---|
2428 | |
---|
2429 | if insitu: |
---|
2430 | self._assign(workscan) |
---|
2431 | else: |
---|
2432 | return workscan |
---|
2433 | |
---|
2434 | except RuntimeError, e: |
---|
2435 | raise_fitting_failure_exception(e) |
---|
2436 | |
---|
2437 | |
---|
2438 | @asaplog_post_dec |
---|
2439 | def auto_sinusoid_baseline(self, insitu=None, mask=None, applyfft=None, |
---|
2440 | fftmethod=None, fftthresh=None, |
---|
2441 | addwn=None, rejwn=None, clipthresh=None, |
---|
2442 | clipniter=None, edge=None, threshold=None, |
---|
2443 | chan_avg_limit=None, plot=None, |
---|
2444 | getresidual=None, showprogress=None, |
---|
2445 | minnrow=None, outlog=None, blfile=None): |
---|
2446 | """\ |
---|
2447 | Return a scan which has been baselined (all rows) with sinusoidal |
---|
2448 | functions. |
---|
2449 | Spectral lines are detected first using linefinder and masked out |
---|
2450 | to avoid them affecting the baseline solution. |
---|
2451 | |
---|
2452 | Parameters: |
---|
2453 | insitu: if False a new scantable is returned. |
---|
2454 | Otherwise, the scaling is done in-situ |
---|
2455 | The default is taken from .asaprc (False) |
---|
2456 | mask: an optional mask retreived from scantable |
---|
2457 | applyfft: if True use some method, such as FFT, to find |
---|
2458 | strongest sinusoidal components in the wavenumber |
---|
2459 | domain to be used for baseline fitting. |
---|
2460 | default is True. |
---|
2461 | fftmethod: method to find the strong sinusoidal components. |
---|
2462 | now only 'fft' is available and it is the default. |
---|
2463 | fftthresh: the threshold to select wave numbers to be used for |
---|
2464 | fitting from the distribution of amplitudes in the |
---|
2465 | wavenumber domain. |
---|
2466 | both float and string values accepted. |
---|
2467 | given a float value, the unit is set to sigma. |
---|
2468 | for string values, allowed formats include: |
---|
2469 | 'xsigma' or 'x' (= x-sigma level. e.g., |
---|
2470 | '3sigma'), or |
---|
2471 | 'topx' (= the x strongest ones, e.g. 'top5'). |
---|
2472 | default is 3.0 (unit: sigma). |
---|
2473 | addwn: the additional wave numbers to be used for fitting. |
---|
2474 | list or integer value is accepted to specify every |
---|
2475 | wave numbers. also string value can be used in case |
---|
2476 | you need to specify wave numbers in a certain range, |
---|
2477 | e.g., 'a-b' (= a, a+1, a+2, ..., b-1, b), |
---|
2478 | '<a' (= 0,1,...,a-2,a-1), |
---|
2479 | '>=a' (= a, a+1, ... up to the maximum wave |
---|
2480 | number corresponding to the Nyquist |
---|
2481 | frequency for the case of FFT). |
---|
2482 | default is []. |
---|
2483 | rejwn: the wave numbers NOT to be used for fitting. |
---|
2484 | can be set just as addwn but has higher priority: |
---|
2485 | wave numbers which are specified both in addwn |
---|
2486 | and rejwn will NOT be used. default is []. |
---|
2487 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2488 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2489 | clipping (default is 0) |
---|
2490 | edge: an optional number of channel to drop at |
---|
2491 | the edge of spectrum. If only one value is |
---|
2492 | specified, the same number will be dropped |
---|
2493 | from both sides of the spectrum. Default |
---|
2494 | is to keep all channels. Nested tuples |
---|
2495 | represent individual edge selection for |
---|
2496 | different IFs (a number of spectral channels |
---|
2497 | can be different) |
---|
2498 | threshold: the threshold used by line finder. It is |
---|
2499 | better to keep it large as only strong lines |
---|
2500 | affect the baseline solution. |
---|
2501 | chan_avg_limit: a maximum number of consequtive spectral |
---|
2502 | channels to average during the search of |
---|
2503 | weak and broad lines. The default is no |
---|
2504 | averaging (and no search for weak lines). |
---|
2505 | If such lines can affect the fitted baseline |
---|
2506 | (e.g. a high order polynomial is fitted), |
---|
2507 | increase this parameter (usually values up |
---|
2508 | to 8 are reasonable). Most users of this |
---|
2509 | method should find the default value sufficient. |
---|
2510 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2511 | plot the fit and the residual. In this each |
---|
2512 | indivual fit has to be approved, by typing 'y' |
---|
2513 | or 'n' |
---|
2514 | getresidual: if False, returns best-fit values instead of |
---|
2515 | residual. (default is True) |
---|
2516 | showprogress: show progress status for large data. |
---|
2517 | default is True. |
---|
2518 | minnrow: minimum number of input spectra to show. |
---|
2519 | default is 1000. |
---|
2520 | outlog: Output the coefficients of the best-fit |
---|
2521 | function to logger (default is False) |
---|
2522 | blfile: Name of a text file in which the best-fit |
---|
2523 | parameter values to be written |
---|
2524 | (default is "": no file/logger output) |
---|
2525 | |
---|
2526 | Example: |
---|
2527 | bscan = scan.auto_sinusoid_baseline(addwn='<=10', insitu=False) |
---|
2528 | |
---|
2529 | Note: |
---|
2530 | The best-fit parameter values output in logger and/or blfile are now |
---|
2531 | based on specunit of 'channel'. |
---|
2532 | """ |
---|
2533 | |
---|
2534 | try: |
---|
2535 | varlist = vars() |
---|
2536 | |
---|
2537 | if insitu is None: insitu = rcParams['insitu'] |
---|
2538 | if insitu: |
---|
2539 | workscan = self |
---|
2540 | else: |
---|
2541 | workscan = self.copy() |
---|
2542 | |
---|
2543 | if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2544 | if applyfft is None: applyfft = True |
---|
2545 | if fftmethod is None: fftmethod = 'fft' |
---|
2546 | if fftthresh is None: fftthresh = 3.0 |
---|
2547 | if addwn is None: addwn = [] |
---|
2548 | if rejwn is None: rejwn = [] |
---|
2549 | if clipthresh is None: clipthresh = 3.0 |
---|
2550 | if clipniter is None: clipniter = 0 |
---|
2551 | if edge is None: edge = (0,0) |
---|
2552 | if threshold is None: threshold = 3 |
---|
2553 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
2554 | if plot is None: plot = False |
---|
2555 | if getresidual is None: getresidual = True |
---|
2556 | if showprogress is None: showprogress = True |
---|
2557 | if minnrow is None: minnrow = 1000 |
---|
2558 | if outlog is None: outlog = False |
---|
2559 | if blfile is None: blfile = '' |
---|
2560 | |
---|
2561 | #CURRENTLY, PLOT=true is UNAVAILABLE UNTIL sinusoidal fitting is implemented as a fitter method. |
---|
2562 | workscan._auto_sinusoid_baseline(mask, applyfft, |
---|
2563 | fftmethod.lower(), |
---|
2564 | str(fftthresh).lower(), |
---|
2565 | workscan._parse_wn(addwn), |
---|
2566 | workscan._parse_wn(rejwn), |
---|
2567 | clipthresh, clipniter, |
---|
2568 | normalise_edge_param(edge), |
---|
2569 | threshold, chan_avg_limit, |
---|
2570 | getresidual, |
---|
2571 | pack_progress_params(showprogress, |
---|
2572 | minnrow), |
---|
2573 | outlog, blfile) |
---|
2574 | workscan._add_history("auto_sinusoid_baseline", varlist) |
---|
2575 | |
---|
2576 | if insitu: |
---|
2577 | self._assign(workscan) |
---|
2578 | else: |
---|
2579 | return workscan |
---|
2580 | |
---|
2581 | except RuntimeError, e: |
---|
2582 | raise_fitting_failure_exception(e) |
---|
2583 | |
---|
2584 | @asaplog_post_dec |
---|
2585 | def cspline_baseline(self, insitu=None, mask=None, npiece=None, |
---|
2586 | clipthresh=None, clipniter=None, plot=None, |
---|
2587 | getresidual=None, showprogress=None, minnrow=None, |
---|
2588 | outlog=None, blfile=None): |
---|
2589 | """\ |
---|
2590 | Return a scan which has been baselined (all rows) by cubic spline |
---|
2591 | function (piecewise cubic polynomial). |
---|
2592 | |
---|
2593 | Parameters: |
---|
2594 | insitu: If False a new scantable is returned. |
---|
2595 | Otherwise, the scaling is done in-situ |
---|
2596 | The default is taken from .asaprc (False) |
---|
2597 | mask: An optional mask |
---|
2598 | npiece: Number of pieces. (default is 2) |
---|
2599 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2600 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2601 | clipping (default is 0) |
---|
2602 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2603 | plot the fit and the residual. In this each |
---|
2604 | indivual fit has to be approved, by typing 'y' |
---|
2605 | or 'n' |
---|
2606 | getresidual: if False, returns best-fit values instead of |
---|
2607 | residual. (default is True) |
---|
2608 | showprogress: show progress status for large data. |
---|
2609 | default is True. |
---|
2610 | minnrow: minimum number of input spectra to show. |
---|
2611 | default is 1000. |
---|
2612 | outlog: Output the coefficients of the best-fit |
---|
2613 | function to logger (default is False) |
---|
2614 | blfile: Name of a text file in which the best-fit |
---|
2615 | parameter values to be written |
---|
2616 | (default is "": no file/logger output) |
---|
2617 | |
---|
2618 | Example: |
---|
2619 | # return a scan baselined by a cubic spline consisting of 2 pieces |
---|
2620 | # (i.e., 1 internal knot), |
---|
2621 | # also with 3-sigma clipping, iteration up to 4 times |
---|
2622 | bscan = scan.cspline_baseline(npiece=2,clipthresh=3.0,clipniter=4) |
---|
2623 | |
---|
2624 | Note: |
---|
2625 | The best-fit parameter values output in logger and/or blfile are now |
---|
2626 | based on specunit of 'channel'. |
---|
2627 | """ |
---|
2628 | |
---|
2629 | try: |
---|
2630 | varlist = vars() |
---|
2631 | |
---|
2632 | if insitu is None: insitu = rcParams['insitu'] |
---|
2633 | if insitu: |
---|
2634 | workscan = self |
---|
2635 | else: |
---|
2636 | workscan = self.copy() |
---|
2637 | |
---|
2638 | if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2639 | if npiece is None: npiece = 2 |
---|
2640 | if clipthresh is None: clipthresh = 3.0 |
---|
2641 | if clipniter is None: clipniter = 0 |
---|
2642 | if plot is None: plot = False |
---|
2643 | if getresidual is None: getresidual = True |
---|
2644 | if showprogress is None: showprogress = True |
---|
2645 | if minnrow is None: minnrow = 1000 |
---|
2646 | if outlog is None: outlog = False |
---|
2647 | if blfile is None: blfile = '' |
---|
2648 | |
---|
2649 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
2650 | workscan._cspline_baseline(mask, npiece, clipthresh, clipniter, |
---|
2651 | getresidual, |
---|
2652 | pack_progress_params(showprogress, |
---|
2653 | minnrow), outlog, |
---|
2654 | blfile) |
---|
2655 | workscan._add_history("cspline_baseline", varlist) |
---|
2656 | |
---|
2657 | if insitu: |
---|
2658 | self._assign(workscan) |
---|
2659 | else: |
---|
2660 | return workscan |
---|
2661 | |
---|
2662 | except RuntimeError, e: |
---|
2663 | raise_fitting_failure_exception(e) |
---|
2664 | |
---|
2665 | @asaplog_post_dec |
---|
2666 | def auto_cspline_baseline(self, insitu=None, mask=None, npiece=None, |
---|
2667 | clipthresh=None, clipniter=None, |
---|
2668 | edge=None, threshold=None, chan_avg_limit=None, |
---|
2669 | getresidual=None, plot=None, |
---|
2670 | showprogress=None, minnrow=None, outlog=None, |
---|
2671 | blfile=None): |
---|
2672 | """\ |
---|
2673 | Return a scan which has been baselined (all rows) by cubic spline |
---|
2674 | function (piecewise cubic polynomial). |
---|
2675 | Spectral lines are detected first using linefinder and masked out |
---|
2676 | to avoid them affecting the baseline solution. |
---|
2677 | |
---|
2678 | Parameters: |
---|
2679 | insitu: if False a new scantable is returned. |
---|
2680 | Otherwise, the scaling is done in-situ |
---|
2681 | The default is taken from .asaprc (False) |
---|
2682 | mask: an optional mask retreived from scantable |
---|
2683 | npiece: Number of pieces. (default is 2) |
---|
2684 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2685 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2686 | clipping (default is 0) |
---|
2687 | edge: an optional number of channel to drop at |
---|
2688 | the edge of spectrum. If only one value is |
---|
2689 | specified, the same number will be dropped |
---|
2690 | from both sides of the spectrum. Default |
---|
2691 | is to keep all channels. Nested tuples |
---|
2692 | represent individual edge selection for |
---|
2693 | different IFs (a number of spectral channels |
---|
2694 | can be different) |
---|
2695 | threshold: the threshold used by line finder. It is |
---|
2696 | better to keep it large as only strong lines |
---|
2697 | affect the baseline solution. |
---|
2698 | chan_avg_limit: a maximum number of consequtive spectral |
---|
2699 | channels to average during the search of |
---|
2700 | weak and broad lines. The default is no |
---|
2701 | averaging (and no search for weak lines). |
---|
2702 | If such lines can affect the fitted baseline |
---|
2703 | (e.g. a high order polynomial is fitted), |
---|
2704 | increase this parameter (usually values up |
---|
2705 | to 8 are reasonable). Most users of this |
---|
2706 | method should find the default value sufficient. |
---|
2707 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2708 | plot the fit and the residual. In this each |
---|
2709 | indivual fit has to be approved, by typing 'y' |
---|
2710 | or 'n' |
---|
2711 | getresidual: if False, returns best-fit values instead of |
---|
2712 | residual. (default is True) |
---|
2713 | showprogress: show progress status for large data. |
---|
2714 | default is True. |
---|
2715 | minnrow: minimum number of input spectra to show. |
---|
2716 | default is 1000. |
---|
2717 | outlog: Output the coefficients of the best-fit |
---|
2718 | function to logger (default is False) |
---|
2719 | blfile: Name of a text file in which the best-fit |
---|
2720 | parameter values to be written |
---|
2721 | (default is "": no file/logger output) |
---|
2722 | |
---|
2723 | Example: |
---|
2724 | bscan = scan.auto_cspline_baseline(npiece=3, insitu=False) |
---|
2725 | |
---|
2726 | Note: |
---|
2727 | The best-fit parameter values output in logger and/or blfile are now |
---|
2728 | based on specunit of 'channel'. |
---|
2729 | """ |
---|
2730 | |
---|
2731 | try: |
---|
2732 | varlist = vars() |
---|
2733 | |
---|
2734 | if insitu is None: insitu = rcParams['insitu'] |
---|
2735 | if insitu: |
---|
2736 | workscan = self |
---|
2737 | else: |
---|
2738 | workscan = self.copy() |
---|
2739 | |
---|
2740 | if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2741 | if npiece is None: npiece = 2 |
---|
2742 | if clipthresh is None: clipthresh = 3.0 |
---|
2743 | if clipniter is None: clipniter = 0 |
---|
2744 | if edge is None: edge = (0, 0) |
---|
2745 | if threshold is None: threshold = 3 |
---|
2746 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
2747 | if plot is None: plot = False |
---|
2748 | if getresidual is None: getresidual = True |
---|
2749 | if showprogress is None: showprogress = True |
---|
2750 | if minnrow is None: minnrow = 1000 |
---|
2751 | if outlog is None: outlog = False |
---|
2752 | if blfile is None: blfile = '' |
---|
2753 | |
---|
2754 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
2755 | workscan._auto_cspline_baseline(mask, npiece, clipthresh, |
---|
2756 | clipniter, |
---|
2757 | normalise_edge_param(edge), |
---|
2758 | threshold, |
---|
2759 | chan_avg_limit, getresidual, |
---|
2760 | pack_progress_params(showprogress, |
---|
2761 | minnrow), |
---|
2762 | outlog, blfile) |
---|
2763 | workscan._add_history("auto_cspline_baseline", varlist) |
---|
2764 | |
---|
2765 | if insitu: |
---|
2766 | self._assign(workscan) |
---|
2767 | else: |
---|
2768 | return workscan |
---|
2769 | |
---|
2770 | except RuntimeError, e: |
---|
2771 | raise_fitting_failure_exception(e) |
---|
2772 | |
---|
2773 | @asaplog_post_dec |
---|
2774 | def poly_baseline(self, mask=None, order=None, insitu=None, plot=None, |
---|
2775 | getresidual=None, showprogress=None, minnrow=None, |
---|
2776 | outlog=None, blfile=None): |
---|
2777 | """\ |
---|
2778 | Return a scan which has been baselined (all rows) by a polynomial. |
---|
2779 | Parameters: |
---|
2780 | insitu: if False a new scantable is returned. |
---|
2781 | Otherwise, the scaling is done in-situ |
---|
2782 | The default is taken from .asaprc (False) |
---|
2783 | mask: an optional mask |
---|
2784 | order: the order of the polynomial (default is 0) |
---|
2785 | plot: plot the fit and the residual. In this each |
---|
2786 | indivual fit has to be approved, by typing 'y' |
---|
2787 | or 'n' |
---|
2788 | getresidual: if False, returns best-fit values instead of |
---|
2789 | residual. (default is True) |
---|
2790 | showprogress: show progress status for large data. |
---|
2791 | default is True. |
---|
2792 | minnrow: minimum number of input spectra to show. |
---|
2793 | default is 1000. |
---|
2794 | outlog: Output the coefficients of the best-fit |
---|
2795 | function to logger (default is False) |
---|
2796 | blfile: Name of a text file in which the best-fit |
---|
2797 | parameter values to be written |
---|
2798 | (default is "": no file/logger output) |
---|
2799 | |
---|
2800 | Example: |
---|
2801 | # return a scan baselined by a third order polynomial, |
---|
2802 | # not using a mask |
---|
2803 | bscan = scan.poly_baseline(order=3) |
---|
2804 | """ |
---|
2805 | |
---|
2806 | try: |
---|
2807 | varlist = vars() |
---|
2808 | |
---|
2809 | if insitu is None: |
---|
2810 | insitu = rcParams["insitu"] |
---|
2811 | if insitu: |
---|
2812 | workscan = self |
---|
2813 | else: |
---|
2814 | workscan = self.copy() |
---|
2815 | |
---|
2816 | if mask is None: mask = [True for i in \ |
---|
2817 | xrange(workscan.nchan())] |
---|
2818 | if order is None: order = 0 |
---|
2819 | if plot is None: plot = False |
---|
2820 | if getresidual is None: getresidual = True |
---|
2821 | if showprogress is None: showprogress = True |
---|
2822 | if minnrow is None: minnrow = 1000 |
---|
2823 | if outlog is None: outlog = False |
---|
2824 | if blfile is None: blfile = "" |
---|
2825 | |
---|
2826 | if plot: |
---|
2827 | outblfile = (blfile != "") and \ |
---|
2828 | os.path.exists(os.path.expanduser( |
---|
2829 | os.path.expandvars(blfile)) |
---|
2830 | ) |
---|
2831 | if outblfile: |
---|
2832 | blf = open(blfile, "a") |
---|
2833 | |
---|
2834 | f = fitter() |
---|
2835 | f.set_function(lpoly=order) |
---|
2836 | |
---|
2837 | rows = xrange(workscan.nrow()) |
---|
2838 | #if len(rows) > 0: workscan._init_blinfo() |
---|
2839 | |
---|
2840 | for r in rows: |
---|
2841 | f.x = workscan._getabcissa(r) |
---|
2842 | f.y = workscan._getspectrum(r) |
---|
2843 | f.mask = mask_and(mask, workscan._getmask(r)) # (CAS-1434) |
---|
2844 | f.data = None |
---|
2845 | f.fit() |
---|
2846 | |
---|
2847 | f.plot(residual=True) |
---|
2848 | accept_fit = raw_input("Accept fit ( [y]/n ): ") |
---|
2849 | if accept_fit.upper() == "N": |
---|
2850 | #workscan._append_blinfo(None, None, None) |
---|
2851 | continue |
---|
2852 | |
---|
2853 | blpars = f.get_parameters() |
---|
2854 | masklist = workscan.get_masklist(f.mask, row=r, silent=True) |
---|
2855 | #workscan._append_blinfo(blpars, masklist, f.mask) |
---|
2856 | workscan._setspectrum((f.fitter.getresidual() |
---|
2857 | if getresidual else f.fitter.getfit()), r) |
---|
2858 | |
---|
2859 | if outblfile: |
---|
2860 | rms = workscan.get_rms(f.mask, r) |
---|
2861 | dataout = \ |
---|
2862 | workscan.format_blparams_row(blpars["params"], |
---|
2863 | blpars["fixed"], |
---|
2864 | rms, str(masklist), |
---|
2865 | r, True) |
---|
2866 | blf.write(dataout) |
---|
2867 | |
---|
2868 | f._p.unmap() |
---|
2869 | f._p = None |
---|
2870 | |
---|
2871 | if outblfile: |
---|
2872 | blf.close() |
---|
2873 | else: |
---|
2874 | workscan._poly_baseline(mask, order, getresidual, |
---|
2875 | pack_progress_params(showprogress, |
---|
2876 | minnrow), |
---|
2877 | outlog, blfile) |
---|
2878 | |
---|
2879 | workscan._add_history("poly_baseline", varlist) |
---|
2880 | |
---|
2881 | if insitu: |
---|
2882 | self._assign(workscan) |
---|
2883 | else: |
---|
2884 | return workscan |
---|
2885 | |
---|
2886 | except RuntimeError, e: |
---|
2887 | raise_fitting_failure_exception(e) |
---|
2888 | |
---|
2889 | @asaplog_post_dec |
---|
2890 | def auto_poly_baseline(self, mask=None, order=None, edge=None, |
---|
2891 | threshold=None, chan_avg_limit=None, |
---|
2892 | plot=None, insitu=None, |
---|
2893 | getresidual=None, showprogress=None, |
---|
2894 | minnrow=None, outlog=None, blfile=None): |
---|
2895 | """\ |
---|
2896 | Return a scan which has been baselined (all rows) by a polynomial. |
---|
2897 | Spectral lines are detected first using linefinder and masked out |
---|
2898 | to avoid them affecting the baseline solution. |
---|
2899 | |
---|
2900 | Parameters: |
---|
2901 | insitu: if False a new scantable is returned. |
---|
2902 | Otherwise, the scaling is done in-situ |
---|
2903 | The default is taken from .asaprc (False) |
---|
2904 | mask: an optional mask retreived from scantable |
---|
2905 | order: the order of the polynomial (default is 0) |
---|
2906 | edge: an optional number of channel to drop at |
---|
2907 | the edge of spectrum. If only one value is |
---|
2908 | specified, the same number will be dropped |
---|
2909 | from both sides of the spectrum. Default |
---|
2910 | is to keep all channels. Nested tuples |
---|
2911 | represent individual edge selection for |
---|
2912 | different IFs (a number of spectral channels |
---|
2913 | can be different) |
---|
2914 | threshold: the threshold used by line finder. It is |
---|
2915 | better to keep it large as only strong lines |
---|
2916 | affect the baseline solution. |
---|
2917 | chan_avg_limit: a maximum number of consequtive spectral |
---|
2918 | channels to average during the search of |
---|
2919 | weak and broad lines. The default is no |
---|
2920 | averaging (and no search for weak lines). |
---|
2921 | If such lines can affect the fitted baseline |
---|
2922 | (e.g. a high order polynomial is fitted), |
---|
2923 | increase this parameter (usually values up |
---|
2924 | to 8 are reasonable). Most users of this |
---|
2925 | method should find the default value sufficient. |
---|
2926 | plot: plot the fit and the residual. In this each |
---|
2927 | indivual fit has to be approved, by typing 'y' |
---|
2928 | or 'n' |
---|
2929 | getresidual: if False, returns best-fit values instead of |
---|
2930 | residual. (default is True) |
---|
2931 | showprogress: show progress status for large data. |
---|
2932 | default is True. |
---|
2933 | minnrow: minimum number of input spectra to show. |
---|
2934 | default is 1000. |
---|
2935 | outlog: Output the coefficients of the best-fit |
---|
2936 | function to logger (default is False) |
---|
2937 | blfile: Name of a text file in which the best-fit |
---|
2938 | parameter values to be written |
---|
2939 | (default is "": no file/logger output) |
---|
2940 | |
---|
2941 | Example: |
---|
2942 | bscan = scan.auto_poly_baseline(order=7, insitu=False) |
---|
2943 | """ |
---|
2944 | |
---|
2945 | try: |
---|
2946 | varlist = vars() |
---|
2947 | |
---|
2948 | if insitu is None: |
---|
2949 | insitu = rcParams['insitu'] |
---|
2950 | if insitu: |
---|
2951 | workscan = self |
---|
2952 | else: |
---|
2953 | workscan = self.copy() |
---|
2954 | |
---|
2955 | if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2956 | if order is None: order = 0 |
---|
2957 | if edge is None: edge = (0, 0) |
---|
2958 | if threshold is None: threshold = 3 |
---|
2959 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
2960 | if plot is None: plot = False |
---|
2961 | if getresidual is None: getresidual = True |
---|
2962 | if showprogress is None: showprogress = True |
---|
2963 | if minnrow is None: minnrow = 1000 |
---|
2964 | if outlog is None: outlog = False |
---|
2965 | if blfile is None: blfile = '' |
---|
2966 | |
---|
2967 | edge = normalise_edge_param(edge) |
---|
2968 | |
---|
2969 | if plot: |
---|
2970 | outblfile = (blfile != "") and \ |
---|
2971 | os.path.exists(os.path.expanduser(os.path.expandvars(blfile))) |
---|
2972 | if outblfile: blf = open(blfile, "a") |
---|
2973 | |
---|
2974 | from asap.asaplinefind import linefinder |
---|
2975 | fl = linefinder() |
---|
2976 | fl.set_options(threshold=threshold, avg_limit=chan_avg_limit) |
---|
2977 | fl.set_scan(workscan) |
---|
2978 | |
---|
2979 | f = fitter() |
---|
2980 | f.set_function(lpoly=order) |
---|
2981 | |
---|
2982 | rows = xrange(workscan.nrow()) |
---|
2983 | #if len(rows) > 0: workscan._init_blinfo() |
---|
2984 | |
---|
2985 | for r in rows: |
---|
2986 | idx = 2*workscan.getif(r) |
---|
2987 | fl.find_lines(r, mask_and(mask, workscan._getmask(r)), |
---|
2988 | edge[idx:idx+2]) # (CAS-1434) |
---|
2989 | |
---|
2990 | f.x = workscan._getabcissa(r) |
---|
2991 | f.y = workscan._getspectrum(r) |
---|
2992 | f.mask = fl.get_mask() |
---|
2993 | f.data = None |
---|
2994 | f.fit() |
---|
2995 | |
---|
2996 | f.plot(residual=True) |
---|
2997 | accept_fit = raw_input("Accept fit ( [y]/n ): ") |
---|
2998 | if accept_fit.upper() == "N": |
---|
2999 | #workscan._append_blinfo(None, None, None) |
---|
3000 | continue |
---|
3001 | |
---|
3002 | blpars = f.get_parameters() |
---|
3003 | masklist = workscan.get_masklist(f.mask, row=r, silent=True) |
---|
3004 | #workscan._append_blinfo(blpars, masklist, f.mask) |
---|
3005 | workscan._setspectrum( |
---|
3006 | (f.fitter.getresidual() if getresidual |
---|
3007 | else f.fitter.getfit()), r |
---|
3008 | ) |
---|
3009 | |
---|
3010 | if outblfile: |
---|
3011 | rms = workscan.get_rms(f.mask, r) |
---|
3012 | dataout = \ |
---|
3013 | workscan.format_blparams_row(blpars["params"], |
---|
3014 | blpars["fixed"], |
---|
3015 | rms, str(masklist), |
---|
3016 | r, True) |
---|
3017 | blf.write(dataout) |
---|
3018 | |
---|
3019 | f._p.unmap() |
---|
3020 | f._p = None |
---|
3021 | |
---|
3022 | if outblfile: blf.close() |
---|
3023 | else: |
---|
3024 | workscan._auto_poly_baseline(mask, order, edge, threshold, |
---|
3025 | chan_avg_limit, getresidual, |
---|
3026 | pack_progress_params(showprogress, |
---|
3027 | minnrow), |
---|
3028 | outlog, blfile) |
---|
3029 | |
---|
3030 | workscan._add_history("auto_poly_baseline", varlist) |
---|
3031 | |
---|
3032 | if insitu: |
---|
3033 | self._assign(workscan) |
---|
3034 | else: |
---|
3035 | return workscan |
---|
3036 | |
---|
3037 | except RuntimeError, e: |
---|
3038 | raise_fitting_failure_exception(e) |
---|
3039 | |
---|
3040 | def _init_blinfo(self): |
---|
3041 | """\ |
---|
3042 | Initialise the following three auxiliary members: |
---|
3043 | blpars : parameters of the best-fit baseline, |
---|
3044 | masklists : mask data (edge positions of masked channels) and |
---|
3045 | actualmask : mask data (in boolean list), |
---|
3046 | to keep for use later (including output to logger/text files). |
---|
3047 | Used by poly_baseline() and auto_poly_baseline() in case of |
---|
3048 | 'plot=True'. |
---|
3049 | """ |
---|
3050 | self.blpars = [] |
---|
3051 | self.masklists = [] |
---|
3052 | self.actualmask = [] |
---|
3053 | return |
---|
3054 | |
---|
3055 | def _append_blinfo(self, data_blpars, data_masklists, data_actualmask): |
---|
3056 | """\ |
---|
3057 | Append baseline-fitting related info to blpars, masklist and |
---|
3058 | actualmask. |
---|
3059 | """ |
---|
3060 | self.blpars.append(data_blpars) |
---|
3061 | self.masklists.append(data_masklists) |
---|
3062 | self.actualmask.append(data_actualmask) |
---|
3063 | return |
---|
3064 | |
---|
3065 | @asaplog_post_dec |
---|
3066 | def rotate_linpolphase(self, angle): |
---|
3067 | """\ |
---|
3068 | Rotate the phase of the complex polarization O=Q+iU correlation. |
---|
3069 | This is always done in situ in the raw data. So if you call this |
---|
3070 | function more than once then each call rotates the phase further. |
---|
3071 | |
---|
3072 | Parameters: |
---|
3073 | |
---|
3074 | angle: The angle (degrees) to rotate (add) by. |
---|
3075 | |
---|
3076 | Example:: |
---|
3077 | |
---|
3078 | scan.rotate_linpolphase(2.3) |
---|
3079 | |
---|
3080 | """ |
---|
3081 | varlist = vars() |
---|
3082 | self._math._rotate_linpolphase(self, angle) |
---|
3083 | self._add_history("rotate_linpolphase", varlist) |
---|
3084 | return |
---|
3085 | |
---|
3086 | @asaplog_post_dec |
---|
3087 | def rotate_xyphase(self, angle): |
---|
3088 | """\ |
---|
3089 | Rotate the phase of the XY correlation. This is always done in situ |
---|
3090 | in the data. So if you call this function more than once |
---|
3091 | then each call rotates the phase further. |
---|
3092 | |
---|
3093 | Parameters: |
---|
3094 | |
---|
3095 | angle: The angle (degrees) to rotate (add) by. |
---|
3096 | |
---|
3097 | Example:: |
---|
3098 | |
---|
3099 | scan.rotate_xyphase(2.3) |
---|
3100 | |
---|
3101 | """ |
---|
3102 | varlist = vars() |
---|
3103 | self._math._rotate_xyphase(self, angle) |
---|
3104 | self._add_history("rotate_xyphase", varlist) |
---|
3105 | return |
---|
3106 | |
---|
3107 | @asaplog_post_dec |
---|
3108 | def swap_linears(self): |
---|
3109 | """\ |
---|
3110 | Swap the linear polarisations XX and YY, or better the first two |
---|
3111 | polarisations as this also works for ciculars. |
---|
3112 | """ |
---|
3113 | varlist = vars() |
---|
3114 | self._math._swap_linears(self) |
---|
3115 | self._add_history("swap_linears", varlist) |
---|
3116 | return |
---|
3117 | |
---|
3118 | @asaplog_post_dec |
---|
3119 | def invert_phase(self): |
---|
3120 | """\ |
---|
3121 | Invert the phase of the complex polarisation |
---|
3122 | """ |
---|
3123 | varlist = vars() |
---|
3124 | self._math._invert_phase(self) |
---|
3125 | self._add_history("invert_phase", varlist) |
---|
3126 | return |
---|
3127 | |
---|
3128 | @asaplog_post_dec |
---|
3129 | def add(self, offset, insitu=None): |
---|
3130 | """\ |
---|
3131 | Return a scan where all spectra have the offset added |
---|
3132 | |
---|
3133 | Parameters: |
---|
3134 | |
---|
3135 | offset: the offset |
---|
3136 | |
---|
3137 | insitu: if False a new scantable is returned. |
---|
3138 | Otherwise, the scaling is done in-situ |
---|
3139 | The default is taken from .asaprc (False) |
---|
3140 | |
---|
3141 | """ |
---|
3142 | if insitu is None: insitu = rcParams['insitu'] |
---|
3143 | self._math._setinsitu(insitu) |
---|
3144 | varlist = vars() |
---|
3145 | s = scantable(self._math._unaryop(self, offset, "ADD", False)) |
---|
3146 | s._add_history("add", varlist) |
---|
3147 | if insitu: |
---|
3148 | self._assign(s) |
---|
3149 | else: |
---|
3150 | return s |
---|
3151 | |
---|
3152 | @asaplog_post_dec |
---|
3153 | def scale(self, factor, tsys=True, insitu=None): |
---|
3154 | """\ |
---|
3155 | |
---|
3156 | Return a scan where all spectra are scaled by the given 'factor' |
---|
3157 | |
---|
3158 | Parameters: |
---|
3159 | |
---|
3160 | factor: the scaling factor (float or 1D float list) |
---|
3161 | |
---|
3162 | insitu: if False a new scantable is returned. |
---|
3163 | Otherwise, the scaling is done in-situ |
---|
3164 | The default is taken from .asaprc (False) |
---|
3165 | |
---|
3166 | tsys: if True (default) then apply the operation to Tsys |
---|
3167 | as well as the data |
---|
3168 | |
---|
3169 | """ |
---|
3170 | if insitu is None: insitu = rcParams['insitu'] |
---|
3171 | self._math._setinsitu(insitu) |
---|
3172 | varlist = vars() |
---|
3173 | s = None |
---|
3174 | import numpy |
---|
3175 | if isinstance(factor, list) or isinstance(factor, numpy.ndarray): |
---|
3176 | if isinstance(factor[0], list) or isinstance(factor[0], |
---|
3177 | numpy.ndarray): |
---|
3178 | from asapmath import _array2dOp |
---|
3179 | s = _array2dOp( self, factor, "MUL", tsys, insitu ) |
---|
3180 | else: |
---|
3181 | s = scantable( self._math._arrayop( self, factor, |
---|
3182 | "MUL", tsys ) ) |
---|
3183 | else: |
---|
3184 | s = scantable(self._math._unaryop(self, factor, "MUL", tsys)) |
---|
3185 | s._add_history("scale", varlist) |
---|
3186 | if insitu: |
---|
3187 | self._assign(s) |
---|
3188 | else: |
---|
3189 | return s |
---|
3190 | |
---|
3191 | @preserve_selection |
---|
3192 | def set_sourcetype(self, match, matchtype="pattern", |
---|
3193 | sourcetype="reference"): |
---|
3194 | """\ |
---|
3195 | Set the type of the source to be an source or reference scan |
---|
3196 | using the provided pattern. |
---|
3197 | |
---|
3198 | Parameters: |
---|
3199 | |
---|
3200 | match: a Unix style pattern, regular expression or selector |
---|
3201 | |
---|
3202 | matchtype: 'pattern' (default) UNIX style pattern or |
---|
3203 | 'regex' regular expression |
---|
3204 | |
---|
3205 | sourcetype: the type of the source to use (source/reference) |
---|
3206 | |
---|
3207 | """ |
---|
3208 | varlist = vars() |
---|
3209 | basesel = self.get_selection() |
---|
3210 | stype = -1 |
---|
3211 | if sourcetype.lower().startswith("r"): |
---|
3212 | stype = 1 |
---|
3213 | elif sourcetype.lower().startswith("s"): |
---|
3214 | stype = 0 |
---|
3215 | else: |
---|
3216 | raise ValueError("Illegal sourcetype use s(ource) or r(eference)") |
---|
3217 | if matchtype.lower().startswith("p"): |
---|
3218 | matchtype = "pattern" |
---|
3219 | elif matchtype.lower().startswith("r"): |
---|
3220 | matchtype = "regex" |
---|
3221 | else: |
---|
3222 | raise ValueError("Illegal matchtype, use p(attern) or r(egex)") |
---|
3223 | sel = selector() |
---|
3224 | if isinstance(match, selector): |
---|
3225 | sel = match |
---|
3226 | else: |
---|
3227 | sel.set_query("SRCNAME == %s('%s')" % (matchtype, match)) |
---|
3228 | self._setsourcetype(stype) |
---|
3229 | self._add_history("set_sourcetype", varlist) |
---|
3230 | |
---|
3231 | @asaplog_post_dec |
---|
3232 | @preserve_selection |
---|
3233 | def auto_quotient(self, preserve=True, mode='paired', verify=False): |
---|
3234 | """\ |
---|
3235 | This function allows to build quotients automatically. |
---|
3236 | It assumes the observation to have the same number of |
---|
3237 | "ons" and "offs" |
---|
3238 | |
---|
3239 | Parameters: |
---|
3240 | |
---|
3241 | preserve: you can preserve (default) the continuum or |
---|
3242 | remove it. The equations used are |
---|
3243 | |
---|
3244 | preserve: Output = Toff * (on/off) - Toff |
---|
3245 | |
---|
3246 | remove: Output = Toff * (on/off) - Ton |
---|
3247 | |
---|
3248 | mode: the on/off detection mode |
---|
3249 | 'paired' (default) |
---|
3250 | identifies 'off' scans by the |
---|
3251 | trailing '_R' (Mopra/Parkes) or |
---|
3252 | '_e'/'_w' (Tid) and matches |
---|
3253 | on/off pairs from the observing pattern |
---|
3254 | 'time' |
---|
3255 | finds the closest off in time |
---|
3256 | |
---|
3257 | .. todo:: verify argument is not implemented |
---|
3258 | |
---|
3259 | """ |
---|
3260 | varlist = vars() |
---|
3261 | modes = ["time", "paired"] |
---|
3262 | if not mode in modes: |
---|
3263 | msg = "please provide valid mode. Valid modes are %s" % (modes) |
---|
3264 | raise ValueError(msg) |
---|
3265 | s = None |
---|
3266 | if mode.lower() == "paired": |
---|
3267 | sel = self.get_selection() |
---|
3268 | sel.set_query("SRCTYPE==psoff") |
---|
3269 | self.set_selection(sel) |
---|
3270 | offs = self.copy() |
---|
3271 | sel.set_query("SRCTYPE==pson") |
---|
3272 | self.set_selection(sel) |
---|
3273 | ons = self.copy() |
---|
3274 | s = scantable(self._math._quotient(ons, offs, preserve)) |
---|
3275 | elif mode.lower() == "time": |
---|
3276 | s = scantable(self._math._auto_quotient(self, mode, preserve)) |
---|
3277 | s._add_history("auto_quotient", varlist) |
---|
3278 | return s |
---|
3279 | |
---|
3280 | @asaplog_post_dec |
---|
3281 | def mx_quotient(self, mask = None, weight='median', preserve=True): |
---|
3282 | """\ |
---|
3283 | Form a quotient using "off" beams when observing in "MX" mode. |
---|
3284 | |
---|
3285 | Parameters: |
---|
3286 | |
---|
3287 | mask: an optional mask to be used when weight == 'stddev' |
---|
3288 | |
---|
3289 | weight: How to average the off beams. Default is 'median'. |
---|
3290 | |
---|
3291 | preserve: you can preserve (default) the continuum or |
---|
3292 | remove it. The equations used are: |
---|
3293 | |
---|
3294 | preserve: Output = Toff * (on/off) - Toff |
---|
3295 | |
---|
3296 | remove: Output = Toff * (on/off) - Ton |
---|
3297 | |
---|
3298 | """ |
---|
3299 | mask = mask or () |
---|
3300 | varlist = vars() |
---|
3301 | on = scantable(self._math._mx_extract(self, 'on')) |
---|
3302 | preoff = scantable(self._math._mx_extract(self, 'off')) |
---|
3303 | off = preoff.average_time(mask=mask, weight=weight, scanav=False) |
---|
3304 | from asapmath import quotient |
---|
3305 | q = quotient(on, off, preserve) |
---|
3306 | q._add_history("mx_quotient", varlist) |
---|
3307 | return q |
---|
3308 | |
---|
3309 | @asaplog_post_dec |
---|
3310 | def freq_switch(self, insitu=None): |
---|
3311 | """\ |
---|
3312 | Apply frequency switching to the data. |
---|
3313 | |
---|
3314 | Parameters: |
---|
3315 | |
---|
3316 | insitu: if False a new scantable is returned. |
---|
3317 | Otherwise, the swictching is done in-situ |
---|
3318 | The default is taken from .asaprc (False) |
---|
3319 | |
---|
3320 | """ |
---|
3321 | if insitu is None: insitu = rcParams['insitu'] |
---|
3322 | self._math._setinsitu(insitu) |
---|
3323 | varlist = vars() |
---|
3324 | s = scantable(self._math._freqswitch(self)) |
---|
3325 | s._add_history("freq_switch", varlist) |
---|
3326 | if insitu: |
---|
3327 | self._assign(s) |
---|
3328 | else: |
---|
3329 | return s |
---|
3330 | |
---|
3331 | @asaplog_post_dec |
---|
3332 | def recalc_azel(self): |
---|
3333 | """Recalculate the azimuth and elevation for each position.""" |
---|
3334 | varlist = vars() |
---|
3335 | self._recalcazel() |
---|
3336 | self._add_history("recalc_azel", varlist) |
---|
3337 | return |
---|
3338 | |
---|
3339 | @asaplog_post_dec |
---|
3340 | def __add__(self, other): |
---|
3341 | varlist = vars() |
---|
3342 | s = None |
---|
3343 | if isinstance(other, scantable): |
---|
3344 | s = scantable(self._math._binaryop(self, other, "ADD")) |
---|
3345 | elif isinstance(other, float): |
---|
3346 | s = scantable(self._math._unaryop(self, other, "ADD", False)) |
---|
3347 | elif isinstance(other, list) or isinstance(other, numpy.ndarray): |
---|
3348 | if isinstance(other[0], list) \ |
---|
3349 | or isinstance(other[0], numpy.ndarray): |
---|
3350 | from asapmath import _array2dOp |
---|
3351 | s = _array2dOp( self.copy(), other, "ADD", False ) |
---|
3352 | else: |
---|
3353 | s = scantable( self._math._arrayop( self.copy(), other, |
---|
3354 | "ADD", False ) ) |
---|
3355 | else: |
---|
3356 | raise TypeError("Other input is not a scantable or float value") |
---|
3357 | s._add_history("operator +", varlist) |
---|
3358 | return s |
---|
3359 | |
---|
3360 | @asaplog_post_dec |
---|
3361 | def __sub__(self, other): |
---|
3362 | """ |
---|
3363 | implicit on all axes and on Tsys |
---|
3364 | """ |
---|
3365 | varlist = vars() |
---|
3366 | s = None |
---|
3367 | if isinstance(other, scantable): |
---|
3368 | s = scantable(self._math._binaryop(self, other, "SUB")) |
---|
3369 | elif isinstance(other, float): |
---|
3370 | s = scantable(self._math._unaryop(self, other, "SUB", False)) |
---|
3371 | elif isinstance(other, list) or isinstance(other, numpy.ndarray): |
---|
3372 | if isinstance(other[0], list) \ |
---|
3373 | or isinstance(other[0], numpy.ndarray): |
---|
3374 | from asapmath import _array2dOp |
---|
3375 | s = _array2dOp( self.copy(), other, "SUB", False ) |
---|
3376 | else: |
---|
3377 | s = scantable( self._math._arrayop( self.copy(), other, |
---|
3378 | "SUB", False ) ) |
---|
3379 | else: |
---|
3380 | raise TypeError("Other input is not a scantable or float value") |
---|
3381 | s._add_history("operator -", varlist) |
---|
3382 | return s |
---|
3383 | |
---|
3384 | @asaplog_post_dec |
---|
3385 | def __mul__(self, other): |
---|
3386 | """ |
---|
3387 | implicit on all axes and on Tsys |
---|
3388 | """ |
---|
3389 | varlist = vars() |
---|
3390 | s = None |
---|
3391 | if isinstance(other, scantable): |
---|
3392 | s = scantable(self._math._binaryop(self, other, "MUL")) |
---|
3393 | elif isinstance(other, float): |
---|
3394 | s = scantable(self._math._unaryop(self, other, "MUL", False)) |
---|
3395 | elif isinstance(other, list) or isinstance(other, numpy.ndarray): |
---|
3396 | if isinstance(other[0], list) \ |
---|
3397 | or isinstance(other[0], numpy.ndarray): |
---|
3398 | from asapmath import _array2dOp |
---|
3399 | s = _array2dOp( self.copy(), other, "MUL", False ) |
---|
3400 | else: |
---|
3401 | s = scantable( self._math._arrayop( self.copy(), other, |
---|
3402 | "MUL", False ) ) |
---|
3403 | else: |
---|
3404 | raise TypeError("Other input is not a scantable or float value") |
---|
3405 | s._add_history("operator *", varlist) |
---|
3406 | return s |
---|
3407 | |
---|
3408 | |
---|
3409 | @asaplog_post_dec |
---|
3410 | def __div__(self, other): |
---|
3411 | """ |
---|
3412 | implicit on all axes and on Tsys |
---|
3413 | """ |
---|
3414 | varlist = vars() |
---|
3415 | s = None |
---|
3416 | if isinstance(other, scantable): |
---|
3417 | s = scantable(self._math._binaryop(self, other, "DIV")) |
---|
3418 | elif isinstance(other, float): |
---|
3419 | if other == 0.0: |
---|
3420 | raise ZeroDivisionError("Dividing by zero is not recommended") |
---|
3421 | s = scantable(self._math._unaryop(self, other, "DIV", False)) |
---|
3422 | elif isinstance(other, list) or isinstance(other, numpy.ndarray): |
---|
3423 | if isinstance(other[0], list) \ |
---|
3424 | or isinstance(other[0], numpy.ndarray): |
---|
3425 | from asapmath import _array2dOp |
---|
3426 | s = _array2dOp( self.copy(), other, "DIV", False ) |
---|
3427 | else: |
---|
3428 | s = scantable( self._math._arrayop( self.copy(), other, |
---|
3429 | "DIV", False ) ) |
---|
3430 | else: |
---|
3431 | raise TypeError("Other input is not a scantable or float value") |
---|
3432 | s._add_history("operator /", varlist) |
---|
3433 | return s |
---|
3434 | |
---|
3435 | @asaplog_post_dec |
---|
3436 | def get_fit(self, row=0): |
---|
3437 | """\ |
---|
3438 | Print or return the stored fits for a row in the scantable |
---|
3439 | |
---|
3440 | Parameters: |
---|
3441 | |
---|
3442 | row: the row which the fit has been applied to. |
---|
3443 | |
---|
3444 | """ |
---|
3445 | if row > self.nrow(): |
---|
3446 | return |
---|
3447 | from asap.asapfit import asapfit |
---|
3448 | fit = asapfit(self._getfit(row)) |
---|
3449 | asaplog.push( '%s' %(fit) ) |
---|
3450 | return fit.as_dict() |
---|
3451 | |
---|
3452 | @preserve_selection |
---|
3453 | def flag_nans(self): |
---|
3454 | """\ |
---|
3455 | Utility function to flag NaN values in the scantable. |
---|
3456 | """ |
---|
3457 | import numpy |
---|
3458 | basesel = self.get_selection() |
---|
3459 | for i in range(self.nrow()): |
---|
3460 | sel = self.get_row_selector(i) |
---|
3461 | self.set_selection(basesel+sel) |
---|
3462 | nans = numpy.isnan(self._getspectrum(0)) |
---|
3463 | if numpy.any(nans): |
---|
3464 | bnans = [ bool(v) for v in nans] |
---|
3465 | self.flag(bnans) |
---|
3466 | |
---|
3467 | def get_row_selector(self, rowno): |
---|
3468 | return selector(rows=[rowno]) |
---|
3469 | |
---|
3470 | def _add_history(self, funcname, parameters): |
---|
3471 | if not rcParams['scantable.history']: |
---|
3472 | return |
---|
3473 | # create date |
---|
3474 | sep = "##" |
---|
3475 | from datetime import datetime |
---|
3476 | dstr = datetime.now().strftime('%Y/%m/%d %H:%M:%S') |
---|
3477 | hist = dstr+sep |
---|
3478 | hist += funcname+sep#cdate+sep |
---|
3479 | if parameters.has_key('self'): |
---|
3480 | del parameters['self'] |
---|
3481 | for k, v in parameters.iteritems(): |
---|
3482 | if type(v) is dict: |
---|
3483 | for k2, v2 in v.iteritems(): |
---|
3484 | hist += k2 |
---|
3485 | hist += "=" |
---|
3486 | if isinstance(v2, scantable): |
---|
3487 | hist += 'scantable' |
---|
3488 | elif k2 == 'mask': |
---|
3489 | if isinstance(v2, list) or isinstance(v2, tuple): |
---|
3490 | hist += str(self._zip_mask(v2)) |
---|
3491 | else: |
---|
3492 | hist += str(v2) |
---|
3493 | else: |
---|
3494 | hist += str(v2) |
---|
3495 | else: |
---|
3496 | hist += k |
---|
3497 | hist += "=" |
---|
3498 | if isinstance(v, scantable): |
---|
3499 | hist += 'scantable' |
---|
3500 | elif k == 'mask': |
---|
3501 | if isinstance(v, list) or isinstance(v, tuple): |
---|
3502 | hist += str(self._zip_mask(v)) |
---|
3503 | else: |
---|
3504 | hist += str(v) |
---|
3505 | else: |
---|
3506 | hist += str(v) |
---|
3507 | hist += sep |
---|
3508 | hist = hist[:-2] # remove trailing '##' |
---|
3509 | self._addhistory(hist) |
---|
3510 | |
---|
3511 | |
---|
3512 | def _zip_mask(self, mask): |
---|
3513 | mask = list(mask) |
---|
3514 | i = 0 |
---|
3515 | segments = [] |
---|
3516 | while mask[i:].count(1): |
---|
3517 | i += mask[i:].index(1) |
---|
3518 | if mask[i:].count(0): |
---|
3519 | j = i + mask[i:].index(0) |
---|
3520 | else: |
---|
3521 | j = len(mask) |
---|
3522 | segments.append([i, j]) |
---|
3523 | i = j |
---|
3524 | return segments |
---|
3525 | |
---|
3526 | def _get_ordinate_label(self): |
---|
3527 | fu = "("+self.get_fluxunit()+")" |
---|
3528 | import re |
---|
3529 | lbl = "Intensity" |
---|
3530 | if re.match(".K.", fu): |
---|
3531 | lbl = "Brightness Temperature "+ fu |
---|
3532 | elif re.match(".Jy.", fu): |
---|
3533 | lbl = "Flux density "+ fu |
---|
3534 | return lbl |
---|
3535 | |
---|
3536 | def _check_ifs(self): |
---|
3537 | # return len(set([self.nchan(i) for i in self.getifnos()])) == 1 |
---|
3538 | nchans = [self.nchan(i) for i in self.getifnos()] |
---|
3539 | nchans = filter(lambda t: t > 0, nchans) |
---|
3540 | return (sum(nchans)/len(nchans) == nchans[0]) |
---|
3541 | |
---|
3542 | @asaplog_post_dec |
---|
3543 | def _fill(self, names, unit, average, opts={}): |
---|
3544 | first = True |
---|
3545 | fullnames = [] |
---|
3546 | for name in names: |
---|
3547 | name = os.path.expandvars(name) |
---|
3548 | name = os.path.expanduser(name) |
---|
3549 | if not os.path.exists(name): |
---|
3550 | msg = "File '%s' does not exists" % (name) |
---|
3551 | raise IOError(msg) |
---|
3552 | fullnames.append(name) |
---|
3553 | if average: |
---|
3554 | asaplog.push('Auto averaging integrations') |
---|
3555 | stype = int(rcParams['scantable.storage'].lower() == 'disk') |
---|
3556 | for name in fullnames: |
---|
3557 | tbl = Scantable(stype) |
---|
3558 | if is_ms( name ): |
---|
3559 | r = msfiller( tbl ) |
---|
3560 | else: |
---|
3561 | r = filler( tbl ) |
---|
3562 | rx = rcParams['scantable.reference'] |
---|
3563 | r.setreferenceexpr(rx) |
---|
3564 | msg = "Importing %s..." % (name) |
---|
3565 | asaplog.push(msg, False) |
---|
3566 | r.open(name, opts) |
---|
3567 | r.fill() |
---|
3568 | if average: |
---|
3569 | tbl = self._math._average((tbl, ), (), 'NONE', 'SCAN') |
---|
3570 | if not first: |
---|
3571 | tbl = self._math._merge([self, tbl]) |
---|
3572 | Scantable.__init__(self, tbl) |
---|
3573 | r.close() |
---|
3574 | del r, tbl |
---|
3575 | first = False |
---|
3576 | #flush log |
---|
3577 | asaplog.post() |
---|
3578 | if unit is not None: |
---|
3579 | self.set_fluxunit(unit) |
---|
3580 | if not is_casapy(): |
---|
3581 | self.set_freqframe(rcParams['scantable.freqframe']) |
---|
3582 | |
---|
3583 | |
---|
3584 | def __getitem__(self, key): |
---|
3585 | if key < 0: |
---|
3586 | key += self.nrow() |
---|
3587 | if key >= self.nrow(): |
---|
3588 | raise IndexError("Row index out of range.") |
---|
3589 | return self._getspectrum(key) |
---|
3590 | |
---|
3591 | def __setitem__(self, key, value): |
---|
3592 | if key < 0: |
---|
3593 | key += self.nrow() |
---|
3594 | if key >= self.nrow(): |
---|
3595 | raise IndexError("Row index out of range.") |
---|
3596 | if not hasattr(value, "__len__") or \ |
---|
3597 | len(value) > self.nchan(self.getif(key)): |
---|
3598 | raise ValueError("Spectrum length doesn't match.") |
---|
3599 | return self._setspectrum(value, key) |
---|
3600 | |
---|
3601 | def __len__(self): |
---|
3602 | return self.nrow() |
---|
3603 | |
---|
3604 | def __iter__(self): |
---|
3605 | for i in range(len(self)): |
---|
3606 | yield self[i] |
---|