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 | def preserve_selection(func): |
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23 | @wraps_dec(func) |
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24 | def wrap(obj, *args, **kw): |
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25 | basesel = obj.get_selection() |
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26 | try: |
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27 | val = func(obj, *args, **kw) |
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28 | finally: |
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29 | obj.set_selection(basesel) |
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30 | return val |
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31 | return wrap |
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32 | |
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33 | def is_scantable(filename): |
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34 | """Is the given file a scantable? |
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35 | |
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36 | Parameters: |
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37 | |
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38 | filename: the name of the file/directory to test |
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39 | |
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40 | """ |
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41 | if ( os.path.isdir(filename) |
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42 | and os.path.exists(filename+'/table.info') |
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43 | and os.path.exists(filename+'/table.dat') ): |
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44 | f=open(filename+'/table.info') |
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45 | l=f.readline() |
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46 | f.close() |
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47 | #if ( l.find('Scantable') != -1 ): |
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48 | if ( l.find('Measurement Set') == -1 ): |
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49 | return True |
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50 | else: |
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51 | return False |
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52 | else: |
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53 | return False |
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54 | ## return (os.path.isdir(filename) |
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55 | ## and not os.path.exists(filename+'/table.f1') |
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56 | ## and os.path.exists(filename+'/table.info')) |
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57 | |
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58 | def is_ms(filename): |
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59 | """Is the given file a MeasurementSet? |
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60 | |
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61 | Parameters: |
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62 | |
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63 | filename: the name of the file/directory to test |
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64 | |
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65 | """ |
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66 | if ( os.path.isdir(filename) |
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67 | and os.path.exists(filename+'/table.info') |
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68 | and os.path.exists(filename+'/table.dat') ): |
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69 | f=open(filename+'/table.info') |
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70 | l=f.readline() |
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71 | f.close() |
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72 | if ( l.find('Measurement Set') != -1 ): |
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73 | return True |
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74 | else: |
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75 | return False |
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76 | else: |
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77 | return False |
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78 | |
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79 | def normalise_edge_param(edge): |
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80 | """\ |
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81 | Convert a given edge value to a one-dimensional array that can be |
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82 | given to baseline-fitting/subtraction functions. |
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83 | The length of the output value will be an even because values for |
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84 | the both sides of spectra are to be contained for each IF. When |
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85 | the length is 2, the values will be applied to all IFs. If the length |
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86 | is larger than 2, it will be 2*ifnos(). |
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87 | Accepted format of edge include: |
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88 | * an integer - will be used for both sides of spectra of all IFs. |
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89 | e.g. 10 is converted to [10,10] |
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90 | * an empty list/tuple [] - converted to [0, 0] and used for all IFs. |
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91 | * a list/tuple containing an integer - same as the above case. |
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92 | e.g. [10] is converted to [10,10] |
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93 | * a list/tuple containing two integers - will be used for all IFs. |
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94 | e.g. [5,10] is output as it is. no need to convert. |
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95 | * a list/tuple of lists/tuples containing TWO integers - |
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96 | each element of edge will be used for each IF. |
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97 | e.g. [[5,10],[15,20]] - [5,10] for IF[0] and [15,20] for IF[1]. |
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98 | |
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99 | If an element contains the same integer values, the input 'edge' |
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100 | parameter can be given in a simpler shape in the following cases: |
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101 | ** when len(edge)!=2 |
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102 | any elements containing the same values can be replaced |
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103 | to single integers. |
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104 | e.g. [[15,15]] can be simplified to [15] (or [15,15] or 15 also). |
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105 | e.g. [[1,1],[2,2],[3,3]] can be simplified to [1,2,3]. |
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106 | ** when len(edge)=2 |
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107 | care is needed for this case: ONLY ONE of the |
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108 | elements can be a single integer, |
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109 | e.g. [[5,5],[10,10]] can be simplified to [5,[10,10]] |
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110 | or [[5,5],10], but can NOT be simplified to [5,10]. |
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111 | when [5,10] given, it is interpreted as |
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112 | [[5,10],[5,10],[5,10],...] instead, as shown before. |
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113 | """ |
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114 | from asap import _is_sequence_or_number as _is_valid |
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115 | if isinstance(edge, list) or isinstance(edge, tuple): |
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116 | for edgepar in edge: |
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117 | if not _is_valid(edgepar, int): |
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118 | raise ValueError, "Each element of the 'edge' tuple has \ |
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119 | to be a pair of integers or an integer." |
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120 | if isinstance(edgepar, list) or isinstance(edgepar, tuple): |
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121 | if len(edgepar) != 2: |
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122 | raise ValueError, "Each element of the 'edge' tuple has \ |
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123 | to be a pair of integers or an integer." |
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124 | else: |
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125 | if not _is_valid(edge, int): |
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126 | raise ValueError, "Parameter 'edge' has to be an integer or a \ |
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127 | pair of integers specified as a tuple. \ |
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128 | Nested tuples are allowed \ |
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129 | to make individual selection for different IFs." |
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130 | |
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131 | |
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132 | if isinstance(edge, int): |
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133 | edge = [ edge, edge ] # e.g. 3 => [3,3] |
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134 | elif isinstance(edge, list) or isinstance(edge, tuple): |
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135 | if len(edge) == 0: |
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136 | edge = [0, 0] # e.g. [] => [0,0] |
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137 | elif len(edge) == 1: |
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138 | if isinstance(edge[0], int): |
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139 | edge = [ edge[0], edge[0] ] # e.g. [1] => [1,1] |
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140 | |
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141 | commonedge = True |
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142 | if len(edge) > 2: commonedge = False |
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143 | else: |
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144 | for edgepar in edge: |
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145 | if isinstance(edgepar, list) or isinstance(edgepar, tuple): |
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146 | commonedge = False |
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147 | break |
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148 | |
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149 | if commonedge: |
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150 | if len(edge) > 1: |
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151 | norm_edge = edge |
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152 | else: |
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153 | norm_edge = edge + edge |
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154 | else: |
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155 | norm_edge = [] |
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156 | for edgepar in edge: |
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157 | if isinstance(edgepar, int): |
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158 | norm_edge += [edgepar, edgepar] |
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159 | else: |
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160 | norm_edge += edgepar |
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161 | |
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162 | return norm_edge |
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163 | |
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164 | def raise_fitting_failure_exception(e): |
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165 | msg = "The fit failed, possibly because it didn't converge." |
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166 | if rcParams["verbose"]: |
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167 | asaplog.push(str(e)) |
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168 | asaplog.push(str(msg)) |
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169 | else: |
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170 | raise RuntimeError(str(e)+'\n'+msg) |
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171 | |
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172 | def pack_progress_params(showprogress, minnrow): |
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173 | return str(showprogress).lower() + ',' + str(minnrow) |
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174 | |
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175 | class scantable(Scantable): |
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176 | """\ |
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177 | The ASAP container for scans (single-dish data). |
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178 | """ |
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179 | |
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180 | @asaplog_post_dec |
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181 | def __init__(self, filename, average=None, unit=None, parallactify=None, |
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182 | **args): |
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183 | """\ |
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184 | Create a scantable from a saved one or make a reference |
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185 | |
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186 | Parameters: |
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187 | |
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188 | filename: the name of an asap table on disk |
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189 | or |
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190 | the name of a rpfits/sdfits/ms file |
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191 | (integrations within scans are auto averaged |
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192 | and the whole file is read) or |
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193 | [advanced] a reference to an existing scantable |
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194 | |
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195 | average: average all integrations withinb a scan on read. |
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196 | The default (True) is taken from .asaprc. |
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197 | |
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198 | unit: brightness unit; must be consistent with K or Jy. |
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199 | Over-rides the default selected by the filler |
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200 | (input rpfits/sdfits/ms) or replaces the value |
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201 | in existing scantables |
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202 | |
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203 | antenna: for MeasurementSet input data only: |
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204 | Antenna selection. integer (id) or string |
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205 | (name or id). |
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206 | |
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207 | parallactify: Indicate that the data had been parallactified. |
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208 | Default (false) is taken from rc file. |
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209 | |
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210 | """ |
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211 | if average is None: |
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212 | average = rcParams['scantable.autoaverage'] |
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213 | parallactify = parallactify or rcParams['scantable.parallactify'] |
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214 | varlist = vars() |
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215 | from asap._asap import stmath |
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216 | self._math = stmath( rcParams['insitu'] ) |
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217 | if isinstance(filename, Scantable): |
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218 | Scantable.__init__(self, filename) |
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219 | else: |
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220 | if isinstance(filename, str): |
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221 | filename = os.path.expandvars(filename) |
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222 | filename = os.path.expanduser(filename) |
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223 | if not os.path.exists(filename): |
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224 | s = "File '%s' not found." % (filename) |
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225 | raise IOError(s) |
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226 | if is_scantable(filename): |
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227 | ondisk = rcParams['scantable.storage'] == 'disk' |
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228 | Scantable.__init__(self, filename, ondisk) |
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229 | if unit is not None: |
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230 | self.set_fluxunit(unit) |
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231 | if average: |
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232 | self._assign( self.average_time( scanav=True ) ) |
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233 | # do not reset to the default freqframe |
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234 | #self.set_freqframe(rcParams['scantable.freqframe']) |
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235 | elif is_ms(filename): |
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236 | # Measurement Set |
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237 | opts={'ms': {}} |
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238 | mskeys=['getpt','antenna'] |
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239 | for key in mskeys: |
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240 | if key in args.keys(): |
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241 | opts['ms'][key] = args[key] |
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242 | self._fill([filename], unit, average, opts) |
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243 | elif os.path.isfile(filename): |
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244 | self._fill([filename], unit, average) |
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245 | # only apply to new data not "copy constructor" |
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246 | self.parallactify(parallactify) |
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247 | else: |
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248 | msg = "The given file '%s'is not a valid " \ |
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249 | "asap table." % (filename) |
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250 | raise IOError(msg) |
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251 | elif (isinstance(filename, list) or isinstance(filename, tuple)) \ |
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252 | and isinstance(filename[-1], str): |
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253 | self._fill(filename, unit, average) |
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254 | self.parallactify(parallactify) |
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255 | self._add_history("scantable", varlist) |
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256 | |
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257 | @asaplog_post_dec |
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258 | def save(self, name=None, format=None, overwrite=False): |
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259 | """\ |
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260 | Store the scantable on disk. This can be an asap (aips++) Table, |
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261 | SDFITS or MS2 format. |
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262 | |
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263 | Parameters: |
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264 | |
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265 | name: the name of the outputfile. For format 'ASCII' |
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266 | this is the root file name (data in 'name'.txt |
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267 | and header in 'name'_header.txt) |
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268 | |
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269 | format: an optional file format. Default is ASAP. |
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270 | Allowed are: |
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271 | |
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272 | * 'ASAP' (save as ASAP [aips++] Table), |
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273 | * 'SDFITS' (save as SDFITS file) |
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274 | * 'ASCII' (saves as ascii text file) |
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275 | * 'MS2' (saves as an casacore MeasurementSet V2) |
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276 | * 'FITS' (save as image FITS - not readable by |
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277 | class) |
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278 | * 'CLASS' (save as FITS readable by CLASS) |
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279 | |
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280 | overwrite: If the file should be overwritten if it exists. |
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281 | The default False is to return with warning |
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282 | without writing the output. USE WITH CARE. |
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283 | |
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284 | Example:: |
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285 | |
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286 | scan.save('myscan.asap') |
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287 | scan.save('myscan.sdfits', 'SDFITS') |
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288 | |
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289 | """ |
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290 | from os import path |
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291 | format = format or rcParams['scantable.save'] |
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292 | suffix = '.'+format.lower() |
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293 | if name is None or name == "": |
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294 | name = 'scantable'+suffix |
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295 | msg = "No filename given. Using default name %s..." % name |
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296 | asaplog.push(msg) |
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297 | name = path.expandvars(name) |
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298 | if path.isfile(name) or path.isdir(name): |
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299 | if not overwrite: |
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300 | msg = "File %s exists." % name |
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301 | raise IOError(msg) |
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302 | format2 = format.upper() |
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303 | if format2 == 'ASAP': |
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304 | self._save(name) |
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305 | elif format2 == 'MS2': |
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306 | msopt = {'ms': {'overwrite': overwrite } } |
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307 | from asap._asap import mswriter |
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308 | writer = mswriter( self ) |
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309 | writer.write( name, msopt ) |
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310 | else: |
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311 | from asap._asap import stwriter as stw |
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312 | writer = stw(format2) |
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313 | writer.write(self, name) |
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314 | return |
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315 | |
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316 | def copy(self): |
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317 | """Return a copy of this scantable. |
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318 | |
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319 | *Note*: |
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320 | |
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321 | This makes a full (deep) copy. scan2 = scan1 makes a reference. |
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322 | |
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323 | Example:: |
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324 | |
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325 | copiedscan = scan.copy() |
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326 | |
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327 | """ |
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328 | sd = scantable(Scantable._copy(self)) |
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329 | return sd |
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330 | |
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331 | def drop_scan(self, scanid=None): |
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332 | """\ |
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333 | Return a new scantable where the specified scan number(s) has(have) |
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334 | been dropped. |
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335 | |
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336 | Parameters: |
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337 | |
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338 | scanid: a (list of) scan number(s) |
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339 | |
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340 | """ |
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341 | from asap import _is_sequence_or_number as _is_valid |
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342 | from asap import _to_list |
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343 | from asap import unique |
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344 | if not _is_valid(scanid): |
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345 | raise RuntimeError( 'Please specify a scanno to drop from the' |
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346 | ' scantable' ) |
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347 | scanid = _to_list(scanid) |
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348 | allscans = unique([ self.getscan(i) for i in range(self.nrow())]) |
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349 | for sid in scanid: allscans.remove(sid) |
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350 | if len(allscans) == 0: |
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351 | raise ValueError("Can't remove all scans") |
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352 | sel = selector(scans=allscans) |
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353 | return self._select_copy(sel) |
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354 | |
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355 | def _select_copy(self, selection): |
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356 | orig = self.get_selection() |
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357 | self.set_selection(orig+selection) |
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358 | cp = self.copy() |
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359 | self.set_selection(orig) |
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360 | return cp |
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361 | |
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362 | def get_scan(self, scanid=None): |
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363 | """\ |
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364 | Return a specific scan (by scanno) or collection of scans (by |
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365 | source name) in a new scantable. |
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366 | |
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367 | *Note*: |
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368 | |
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369 | See scantable.drop_scan() for the inverse operation. |
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370 | |
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371 | Parameters: |
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372 | |
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373 | scanid: a (list of) scanno or a source name, unix-style |
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374 | patterns are accepted for source name matching, e.g. |
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375 | '*_R' gets all 'ref scans |
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376 | |
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377 | Example:: |
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378 | |
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379 | # get all scans containing the source '323p459' |
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380 | newscan = scan.get_scan('323p459') |
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381 | # get all 'off' scans |
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382 | refscans = scan.get_scan('*_R') |
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383 | # get a susbset of scans by scanno (as listed in scan.summary()) |
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384 | newscan = scan.get_scan([0, 2, 7, 10]) |
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385 | |
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386 | """ |
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387 | if scanid is None: |
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388 | raise RuntimeError( 'Please specify a scan no or name to ' |
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389 | 'retrieve from the scantable' ) |
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390 | try: |
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391 | bsel = self.get_selection() |
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392 | sel = selector() |
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393 | if type(scanid) is str: |
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394 | sel.set_name(scanid) |
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395 | return self._select_copy(sel) |
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396 | elif type(scanid) is int: |
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397 | sel.set_scans([scanid]) |
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398 | return self._select_copy(sel) |
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399 | elif type(scanid) is list: |
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400 | sel.set_scans(scanid) |
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401 | return self._select_copy(sel) |
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402 | else: |
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403 | msg = "Illegal scanid type, use 'int' or 'list' if ints." |
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404 | raise TypeError(msg) |
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405 | except RuntimeError: |
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406 | raise |
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407 | |
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408 | def __str__(self): |
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409 | tempFile = tempfile.NamedTemporaryFile() |
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410 | Scantable._summary(self, tempFile.name) |
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411 | tempFile.seek(0) |
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412 | asaplog.clear() |
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413 | return tempFile.file.read() |
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414 | |
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415 | @asaplog_post_dec |
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416 | def summary(self, filename=None): |
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417 | """\ |
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418 | Print a summary of the contents of this scantable. |
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419 | |
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420 | Parameters: |
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421 | |
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422 | filename: the name of a file to write the putput to |
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423 | Default - no file output |
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424 | |
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425 | """ |
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426 | if filename is not None: |
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427 | if filename is "": |
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428 | filename = 'scantable_summary.txt' |
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429 | from os.path import expandvars, isdir |
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430 | filename = expandvars(filename) |
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431 | if isdir(filename): |
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432 | msg = "Illegal file name '%s'." % (filename) |
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433 | raise IOError(msg) |
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434 | else: |
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435 | filename = "" |
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436 | Scantable._summary(self, filename) |
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437 | |
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438 | def get_spectrum(self, rowno): |
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439 | """Return the spectrum for the current row in the scantable as a list. |
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440 | |
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441 | Parameters: |
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442 | |
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443 | rowno: the row number to retrieve the spectrum from |
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444 | |
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445 | """ |
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446 | return self._getspectrum(rowno) |
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447 | |
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448 | def get_mask(self, rowno): |
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449 | """Return the mask for the current row in the scantable as a list. |
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450 | |
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451 | Parameters: |
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452 | |
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453 | rowno: the row number to retrieve the mask from |
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454 | |
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455 | """ |
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456 | return self._getmask(rowno) |
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457 | |
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458 | def set_spectrum(self, spec, rowno): |
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459 | """Set the spectrum for the current row in the scantable. |
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460 | |
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461 | Parameters: |
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462 | |
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463 | spec: the new spectrum |
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464 | |
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465 | rowno: the row number to set the spectrum for |
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466 | |
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467 | """ |
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468 | assert(len(spec) == self.nchan(self.getif(rowno))) |
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469 | return self._setspectrum(spec, rowno) |
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470 | |
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471 | def get_coordinate(self, rowno): |
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472 | """Return the (spectral) coordinate for a a given 'rowno'. |
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473 | |
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474 | *Note*: |
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475 | |
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476 | * This coordinate is only valid until a scantable method modifies |
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477 | the frequency axis. |
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478 | * This coordinate does contain the original frequency set-up |
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479 | NOT the new frame. The conversions however are done using the user |
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480 | specified frame (e.g. LSRK/TOPO). To get the 'real' coordinate, |
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481 | use scantable.freq_align first. Without it there is no closure, |
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482 | i.e.:: |
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483 | |
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484 | c = myscan.get_coordinate(0) |
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485 | c.to_frequency(c.get_reference_pixel()) != c.get_reference_value() |
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486 | |
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487 | Parameters: |
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488 | |
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489 | rowno: the row number for the spectral coordinate |
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490 | |
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491 | """ |
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492 | return coordinate(Scantable.get_coordinate(self, rowno)) |
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493 | |
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494 | def get_selection(self): |
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495 | """\ |
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496 | Get the selection object currently set on this scantable. |
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497 | |
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498 | Example:: |
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499 | |
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500 | sel = scan.get_selection() |
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501 | sel.set_ifs(0) # select IF 0 |
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502 | scan.set_selection(sel) # apply modified selection |
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503 | |
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504 | """ |
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505 | return selector(self._getselection()) |
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506 | |
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507 | def set_selection(self, selection=None, **kw): |
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508 | """\ |
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509 | Select a subset of the data. All following operations on this scantable |
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510 | are only applied to thi selection. |
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511 | |
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512 | Parameters: |
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513 | |
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514 | selection: a selector object (default unset the selection), or |
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515 | any combination of 'pols', 'ifs', 'beams', 'scans', |
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516 | 'cycles', 'name', 'query' |
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517 | |
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518 | Examples:: |
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519 | |
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520 | sel = selector() # create a selection object |
---|
521 | self.set_scans([0, 3]) # select SCANNO 0 and 3 |
---|
522 | scan.set_selection(sel) # set the selection |
---|
523 | scan.summary() # will only print summary of scanno 0 an 3 |
---|
524 | scan.set_selection() # unset the selection |
---|
525 | # or the equivalent |
---|
526 | scan.set_selection(scans=[0,3]) |
---|
527 | scan.summary() # will only print summary of scanno 0 an 3 |
---|
528 | scan.set_selection() # unset the selection |
---|
529 | |
---|
530 | """ |
---|
531 | if selection is None: |
---|
532 | # reset |
---|
533 | if len(kw) == 0: |
---|
534 | selection = selector() |
---|
535 | else: |
---|
536 | # try keywords |
---|
537 | for k in kw: |
---|
538 | if k not in selector.fields: |
---|
539 | raise KeyError("Invalid selection key '%s', " |
---|
540 | "valid keys are %s" % (k, |
---|
541 | selector.fields)) |
---|
542 | selection = selector(**kw) |
---|
543 | self._setselection(selection) |
---|
544 | |
---|
545 | def get_row(self, row=0, insitu=None): |
---|
546 | """\ |
---|
547 | Select a row in the scantable. |
---|
548 | Return a scantable with single row. |
---|
549 | |
---|
550 | Parameters: |
---|
551 | |
---|
552 | row: row no of integration, default is 0. |
---|
553 | insitu: if False a new scantable is returned. Otherwise, the |
---|
554 | scaling is done in-situ. The default is taken from .asaprc |
---|
555 | (False) |
---|
556 | |
---|
557 | """ |
---|
558 | if insitu is None: |
---|
559 | insitu = rcParams['insitu'] |
---|
560 | if not insitu: |
---|
561 | workscan = self.copy() |
---|
562 | else: |
---|
563 | workscan = self |
---|
564 | # Select a row |
---|
565 | sel = selector() |
---|
566 | sel.set_rows([row]) |
---|
567 | workscan.set_selection(sel) |
---|
568 | if not workscan.nrow() == 1: |
---|
569 | msg = "Could not identify single row. %d rows selected." \ |
---|
570 | % (workscan.nrow()) |
---|
571 | raise RuntimeError(msg) |
---|
572 | if insitu: |
---|
573 | self._assign(workscan) |
---|
574 | else: |
---|
575 | return workscan |
---|
576 | |
---|
577 | @asaplog_post_dec |
---|
578 | def stats(self, stat='stddev', mask=None, form='3.3f', row=None): |
---|
579 | """\ |
---|
580 | Determine the specified statistic of the current beam/if/pol |
---|
581 | Takes a 'mask' as an optional parameter to specify which |
---|
582 | channels should be excluded. |
---|
583 | |
---|
584 | Parameters: |
---|
585 | |
---|
586 | stat: 'min', 'max', 'min_abc', 'max_abc', 'sumsq', 'sum', |
---|
587 | 'mean', 'var', 'stddev', 'avdev', 'rms', 'median' |
---|
588 | |
---|
589 | mask: an optional mask specifying where the statistic |
---|
590 | should be determined. |
---|
591 | |
---|
592 | form: format string to print statistic values |
---|
593 | |
---|
594 | row: row number of spectrum to process. |
---|
595 | (default is None: for all rows) |
---|
596 | |
---|
597 | Example: |
---|
598 | scan.set_unit('channel') |
---|
599 | msk = scan.create_mask([100, 200], [500, 600]) |
---|
600 | scan.stats(stat='mean', mask=m) |
---|
601 | |
---|
602 | """ |
---|
603 | mask = mask or [] |
---|
604 | if not self._check_ifs(): |
---|
605 | raise ValueError("Cannot apply mask as the IFs have different " |
---|
606 | "number of channels. Please use setselection() " |
---|
607 | "to select individual IFs") |
---|
608 | rtnabc = False |
---|
609 | if stat.lower().endswith('_abc'): rtnabc = True |
---|
610 | getchan = False |
---|
611 | if stat.lower().startswith('min') or stat.lower().startswith('max'): |
---|
612 | chan = self._math._minmaxchan(self, mask, stat) |
---|
613 | getchan = True |
---|
614 | statvals = [] |
---|
615 | if not rtnabc: |
---|
616 | if row == None: |
---|
617 | statvals = self._math._stats(self, mask, stat) |
---|
618 | else: |
---|
619 | statvals = self._math._statsrow(self, mask, stat, int(row)) |
---|
620 | |
---|
621 | #def cb(i): |
---|
622 | # return statvals[i] |
---|
623 | |
---|
624 | #return self._row_callback(cb, stat) |
---|
625 | |
---|
626 | label=stat |
---|
627 | #callback=cb |
---|
628 | out = "" |
---|
629 | #outvec = [] |
---|
630 | sep = '-'*50 |
---|
631 | |
---|
632 | if row == None: |
---|
633 | rows = xrange(self.nrow()) |
---|
634 | elif isinstance(row, int): |
---|
635 | rows = [ row ] |
---|
636 | |
---|
637 | for i in rows: |
---|
638 | refstr = '' |
---|
639 | statunit= '' |
---|
640 | if getchan: |
---|
641 | qx, qy = self.chan2data(rowno=i, chan=chan[i]) |
---|
642 | if rtnabc: |
---|
643 | statvals.append(qx['value']) |
---|
644 | refstr = ('(value: %'+form) % (qy['value'])+' ['+qy['unit']+'])' |
---|
645 | statunit= '['+qx['unit']+']' |
---|
646 | else: |
---|
647 | refstr = ('(@ %'+form) % (qx['value'])+' ['+qx['unit']+'])' |
---|
648 | |
---|
649 | tm = self._gettime(i) |
---|
650 | src = self._getsourcename(i) |
---|
651 | out += 'Scan[%d] (%s) ' % (self.getscan(i), src) |
---|
652 | out += 'Time[%s]:\n' % (tm) |
---|
653 | if self.nbeam(-1) > 1: out += ' Beam[%d] ' % (self.getbeam(i)) |
---|
654 | if self.nif(-1) > 1: out += ' IF[%d] ' % (self.getif(i)) |
---|
655 | if self.npol(-1) > 1: out += ' Pol[%d] ' % (self.getpol(i)) |
---|
656 | #outvec.append(callback(i)) |
---|
657 | if len(rows) > 1: |
---|
658 | # out += ('= %'+form) % (outvec[i]) +' '+refstr+'\n' |
---|
659 | out += ('= %'+form) % (statvals[i]) +' '+refstr+'\n' |
---|
660 | else: |
---|
661 | # out += ('= %'+form) % (outvec[0]) +' '+refstr+'\n' |
---|
662 | out += ('= %'+form) % (statvals[0]) +' '+refstr+'\n' |
---|
663 | out += sep+"\n" |
---|
664 | |
---|
665 | import os |
---|
666 | if os.environ.has_key( 'USER' ): |
---|
667 | usr = os.environ['USER'] |
---|
668 | else: |
---|
669 | import commands |
---|
670 | usr = commands.getoutput( 'whoami' ) |
---|
671 | tmpfile = '/tmp/tmp_'+usr+'_casapy_asap_scantable_stats' |
---|
672 | f = open(tmpfile,'w') |
---|
673 | print >> f, sep |
---|
674 | print >> f, ' %s %s' % (label, statunit) |
---|
675 | print >> f, sep |
---|
676 | print >> f, out |
---|
677 | f.close() |
---|
678 | f = open(tmpfile,'r') |
---|
679 | x = f.readlines() |
---|
680 | f.close() |
---|
681 | asaplog.push(''.join(x), False) |
---|
682 | |
---|
683 | return statvals |
---|
684 | |
---|
685 | def chan2data(self, rowno=0, chan=0): |
---|
686 | """\ |
---|
687 | Returns channel/frequency/velocity and spectral value |
---|
688 | at an arbitrary row and channel in the scantable. |
---|
689 | |
---|
690 | Parameters: |
---|
691 | |
---|
692 | rowno: a row number in the scantable. Default is the |
---|
693 | first row, i.e. rowno=0 |
---|
694 | |
---|
695 | chan: a channel in the scantable. Default is the first |
---|
696 | channel, i.e. pos=0 |
---|
697 | |
---|
698 | """ |
---|
699 | if isinstance(rowno, int) and isinstance(chan, int): |
---|
700 | qx = {'unit': self.get_unit(), |
---|
701 | 'value': self._getabcissa(rowno)[chan]} |
---|
702 | qy = {'unit': self.get_fluxunit(), |
---|
703 | 'value': self._getspectrum(rowno)[chan]} |
---|
704 | return qx, qy |
---|
705 | |
---|
706 | def stddev(self, mask=None): |
---|
707 | """\ |
---|
708 | Determine the standard deviation of the current beam/if/pol |
---|
709 | Takes a 'mask' as an optional parameter to specify which |
---|
710 | channels should be excluded. |
---|
711 | |
---|
712 | Parameters: |
---|
713 | |
---|
714 | mask: an optional mask specifying where the standard |
---|
715 | deviation should be determined. |
---|
716 | |
---|
717 | Example:: |
---|
718 | |
---|
719 | scan.set_unit('channel') |
---|
720 | msk = scan.create_mask([100, 200], [500, 600]) |
---|
721 | scan.stddev(mask=m) |
---|
722 | |
---|
723 | """ |
---|
724 | return self.stats(stat='stddev', mask=mask); |
---|
725 | |
---|
726 | |
---|
727 | def get_column_names(self): |
---|
728 | """\ |
---|
729 | Return a list of column names, which can be used for selection. |
---|
730 | """ |
---|
731 | return list(Scantable.get_column_names(self)) |
---|
732 | |
---|
733 | def get_tsys(self, row=-1): |
---|
734 | """\ |
---|
735 | Return the System temperatures. |
---|
736 | |
---|
737 | Parameters: |
---|
738 | |
---|
739 | row: the rowno to get the information for. (default all rows) |
---|
740 | |
---|
741 | Returns: |
---|
742 | |
---|
743 | a list of Tsys values for the current selection |
---|
744 | |
---|
745 | """ |
---|
746 | if row > -1: |
---|
747 | return self._get_column(self._gettsys, row) |
---|
748 | return self._row_callback(self._gettsys, "Tsys") |
---|
749 | |
---|
750 | def get_tsysspectrum(self, row=-1): |
---|
751 | """\ |
---|
752 | Return the channel dependent system temperatures. |
---|
753 | |
---|
754 | Parameters: |
---|
755 | |
---|
756 | row: the rowno to get the information for. (default all rows) |
---|
757 | |
---|
758 | Returns: |
---|
759 | |
---|
760 | a list of Tsys values for the current selection |
---|
761 | |
---|
762 | """ |
---|
763 | return self._get_column( self._gettsysspectrum, row ) |
---|
764 | |
---|
765 | def get_weather(self, row=-1): |
---|
766 | """\ |
---|
767 | Return the weather informations. |
---|
768 | |
---|
769 | Parameters: |
---|
770 | |
---|
771 | row: the rowno to get the information for. (default all rows) |
---|
772 | |
---|
773 | Returns: |
---|
774 | |
---|
775 | a dict or list of of dicts of values for the current selection |
---|
776 | |
---|
777 | """ |
---|
778 | |
---|
779 | values = self._get_column(self._get_weather, row) |
---|
780 | if row > -1: |
---|
781 | return {'temperature': values[0], |
---|
782 | 'pressure': values[1], 'humidity' : values[2], |
---|
783 | 'windspeed' : values[3], 'windaz' : values[4] |
---|
784 | } |
---|
785 | else: |
---|
786 | out = [] |
---|
787 | for r in values: |
---|
788 | |
---|
789 | out.append({'temperature': r[0], |
---|
790 | 'pressure': r[1], 'humidity' : r[2], |
---|
791 | 'windspeed' : r[3], 'windaz' : r[4] |
---|
792 | }) |
---|
793 | return out |
---|
794 | |
---|
795 | def _row_callback(self, callback, label): |
---|
796 | out = "" |
---|
797 | outvec = [] |
---|
798 | sep = '-'*50 |
---|
799 | for i in range(self.nrow()): |
---|
800 | tm = self._gettime(i) |
---|
801 | src = self._getsourcename(i) |
---|
802 | out += 'Scan[%d] (%s) ' % (self.getscan(i), src) |
---|
803 | out += 'Time[%s]:\n' % (tm) |
---|
804 | if self.nbeam(-1) > 1: |
---|
805 | out += ' Beam[%d] ' % (self.getbeam(i)) |
---|
806 | if self.nif(-1) > 1: out += ' IF[%d] ' % (self.getif(i)) |
---|
807 | if self.npol(-1) > 1: out += ' Pol[%d] ' % (self.getpol(i)) |
---|
808 | outvec.append(callback(i)) |
---|
809 | out += '= %3.3f\n' % (outvec[i]) |
---|
810 | out += sep+'\n' |
---|
811 | |
---|
812 | asaplog.push(sep) |
---|
813 | asaplog.push(" %s" % (label)) |
---|
814 | asaplog.push(sep) |
---|
815 | asaplog.push(out) |
---|
816 | asaplog.post() |
---|
817 | return outvec |
---|
818 | |
---|
819 | def _get_column(self, callback, row=-1, *args): |
---|
820 | """ |
---|
821 | """ |
---|
822 | if row == -1: |
---|
823 | return [callback(i, *args) for i in range(self.nrow())] |
---|
824 | else: |
---|
825 | if 0 <= row < self.nrow(): |
---|
826 | return callback(row, *args) |
---|
827 | |
---|
828 | |
---|
829 | def get_time(self, row=-1, asdatetime=False, prec=-1): |
---|
830 | """\ |
---|
831 | Get a list of time stamps for the observations. |
---|
832 | Return a datetime object or a string (default) for each |
---|
833 | integration time stamp in the scantable. |
---|
834 | |
---|
835 | Parameters: |
---|
836 | |
---|
837 | row: row no of integration. Default -1 return all rows |
---|
838 | |
---|
839 | asdatetime: return values as datetime objects rather than strings |
---|
840 | |
---|
841 | prec: number of digits shown. Default -1 to automatic |
---|
842 | calculation. |
---|
843 | Note this number is equals to the digits of MVTime, |
---|
844 | i.e., 0<prec<3: dates with hh:: only, |
---|
845 | <5: with hh:mm:, <7 or 0: with hh:mm:ss, |
---|
846 | and 6> : with hh:mm:ss.tt... (prec-6 t's added) |
---|
847 | |
---|
848 | """ |
---|
849 | from datetime import datetime |
---|
850 | if prec < 0: |
---|
851 | # automagically set necessary precision +1 |
---|
852 | prec = 7 - \ |
---|
853 | numpy.floor(numpy.log10(numpy.min(self.get_inttime(row)))) |
---|
854 | prec = max(6, int(prec)) |
---|
855 | else: |
---|
856 | prec = max(0, prec) |
---|
857 | if asdatetime: |
---|
858 | #precision can be 1 millisecond at max |
---|
859 | prec = min(12, prec) |
---|
860 | |
---|
861 | times = self._get_column(self._gettime, row, prec) |
---|
862 | if not asdatetime: |
---|
863 | return times |
---|
864 | format = "%Y/%m/%d/%H:%M:%S.%f" |
---|
865 | if prec < 7: |
---|
866 | nsub = 1 + (((6-prec)/2) % 3) |
---|
867 | substr = [".%f","%S","%M"] |
---|
868 | for i in range(nsub): |
---|
869 | format = format.replace(substr[i],"") |
---|
870 | if isinstance(times, list): |
---|
871 | return [datetime.strptime(i, format) for i in times] |
---|
872 | else: |
---|
873 | return datetime.strptime(times, format) |
---|
874 | |
---|
875 | |
---|
876 | def get_inttime(self, row=-1): |
---|
877 | """\ |
---|
878 | Get a list of integration times for the observations. |
---|
879 | Return a time in seconds for each integration in the scantable. |
---|
880 | |
---|
881 | Parameters: |
---|
882 | |
---|
883 | row: row no of integration. Default -1 return all rows. |
---|
884 | |
---|
885 | """ |
---|
886 | return self._get_column(self._getinttime, row) |
---|
887 | |
---|
888 | |
---|
889 | def get_sourcename(self, row=-1): |
---|
890 | """\ |
---|
891 | Get a list source names for the observations. |
---|
892 | Return a string for each integration in the scantable. |
---|
893 | Parameters: |
---|
894 | |
---|
895 | row: row no of integration. Default -1 return all rows. |
---|
896 | |
---|
897 | """ |
---|
898 | return self._get_column(self._getsourcename, row) |
---|
899 | |
---|
900 | def get_elevation(self, row=-1): |
---|
901 | """\ |
---|
902 | Get a list of elevations for the observations. |
---|
903 | Return a float for each integration in the scantable. |
---|
904 | |
---|
905 | Parameters: |
---|
906 | |
---|
907 | row: row no of integration. Default -1 return all rows. |
---|
908 | |
---|
909 | """ |
---|
910 | return self._get_column(self._getelevation, row) |
---|
911 | |
---|
912 | def get_azimuth(self, row=-1): |
---|
913 | """\ |
---|
914 | Get a list of azimuths for the observations. |
---|
915 | Return a float for each integration in the scantable. |
---|
916 | |
---|
917 | Parameters: |
---|
918 | row: row no of integration. Default -1 return all rows. |
---|
919 | |
---|
920 | """ |
---|
921 | return self._get_column(self._getazimuth, row) |
---|
922 | |
---|
923 | def get_parangle(self, row=-1): |
---|
924 | """\ |
---|
925 | Get a list of parallactic angles for the observations. |
---|
926 | Return a float for each integration in the scantable. |
---|
927 | |
---|
928 | Parameters: |
---|
929 | |
---|
930 | row: row no of integration. Default -1 return all rows. |
---|
931 | |
---|
932 | """ |
---|
933 | return self._get_column(self._getparangle, row) |
---|
934 | |
---|
935 | def get_direction(self, row=-1): |
---|
936 | """ |
---|
937 | Get a list of Positions on the sky (direction) for the observations. |
---|
938 | Return a string for each integration in the scantable. |
---|
939 | |
---|
940 | Parameters: |
---|
941 | |
---|
942 | row: row no of integration. Default -1 return all rows |
---|
943 | |
---|
944 | """ |
---|
945 | return self._get_column(self._getdirection, row) |
---|
946 | |
---|
947 | def get_directionval(self, row=-1): |
---|
948 | """\ |
---|
949 | Get a list of Positions on the sky (direction) for the observations. |
---|
950 | Return a float for each integration in the scantable. |
---|
951 | |
---|
952 | Parameters: |
---|
953 | |
---|
954 | row: row no of integration. Default -1 return all rows |
---|
955 | |
---|
956 | """ |
---|
957 | return self._get_column(self._getdirectionvec, row) |
---|
958 | |
---|
959 | @asaplog_post_dec |
---|
960 | def set_unit(self, unit='channel'): |
---|
961 | """\ |
---|
962 | Set the unit for all following operations on this scantable |
---|
963 | |
---|
964 | Parameters: |
---|
965 | |
---|
966 | unit: optional unit, default is 'channel'. Use one of '*Hz', |
---|
967 | 'km/s', 'channel' or equivalent '' |
---|
968 | |
---|
969 | """ |
---|
970 | varlist = vars() |
---|
971 | if unit in ['', 'pixel', 'channel']: |
---|
972 | unit = '' |
---|
973 | inf = list(self._getcoordinfo()) |
---|
974 | inf[0] = unit |
---|
975 | self._setcoordinfo(inf) |
---|
976 | self._add_history("set_unit", varlist) |
---|
977 | |
---|
978 | @asaplog_post_dec |
---|
979 | def set_instrument(self, instr): |
---|
980 | """\ |
---|
981 | Set the instrument for subsequent processing. |
---|
982 | |
---|
983 | Parameters: |
---|
984 | |
---|
985 | instr: Select from 'ATPKSMB', 'ATPKSHOH', 'ATMOPRA', |
---|
986 | 'DSS-43' (Tid), 'CEDUNA', and 'HOBART' |
---|
987 | |
---|
988 | """ |
---|
989 | self._setInstrument(instr) |
---|
990 | self._add_history("set_instument", vars()) |
---|
991 | |
---|
992 | @asaplog_post_dec |
---|
993 | def set_feedtype(self, feedtype): |
---|
994 | """\ |
---|
995 | Overwrite the feed type, which might not be set correctly. |
---|
996 | |
---|
997 | Parameters: |
---|
998 | |
---|
999 | feedtype: 'linear' or 'circular' |
---|
1000 | |
---|
1001 | """ |
---|
1002 | self._setfeedtype(feedtype) |
---|
1003 | self._add_history("set_feedtype", vars()) |
---|
1004 | |
---|
1005 | @asaplog_post_dec |
---|
1006 | def set_doppler(self, doppler='RADIO'): |
---|
1007 | """\ |
---|
1008 | Set the doppler for all following operations on this scantable. |
---|
1009 | |
---|
1010 | Parameters: |
---|
1011 | |
---|
1012 | doppler: One of 'RADIO', 'OPTICAL', 'Z', 'BETA', 'GAMMA' |
---|
1013 | |
---|
1014 | """ |
---|
1015 | varlist = vars() |
---|
1016 | inf = list(self._getcoordinfo()) |
---|
1017 | inf[2] = doppler |
---|
1018 | self._setcoordinfo(inf) |
---|
1019 | self._add_history("set_doppler", vars()) |
---|
1020 | |
---|
1021 | @asaplog_post_dec |
---|
1022 | def set_freqframe(self, frame=None): |
---|
1023 | """\ |
---|
1024 | Set the frame type of the Spectral Axis. |
---|
1025 | |
---|
1026 | Parameters: |
---|
1027 | |
---|
1028 | frame: an optional frame type, default 'LSRK'. Valid frames are: |
---|
1029 | 'TOPO', 'LSRD', 'LSRK', 'BARY', |
---|
1030 | 'GEO', 'GALACTO', 'LGROUP', 'CMB' |
---|
1031 | |
---|
1032 | Example:: |
---|
1033 | |
---|
1034 | scan.set_freqframe('BARY') |
---|
1035 | |
---|
1036 | """ |
---|
1037 | frame = frame or rcParams['scantable.freqframe'] |
---|
1038 | varlist = vars() |
---|
1039 | # "REST" is not implemented in casacore |
---|
1040 | #valid = ['REST', 'TOPO', 'LSRD', 'LSRK', 'BARY', \ |
---|
1041 | # 'GEO', 'GALACTO', 'LGROUP', 'CMB'] |
---|
1042 | valid = ['TOPO', 'LSRD', 'LSRK', 'BARY', \ |
---|
1043 | 'GEO', 'GALACTO', 'LGROUP', 'CMB'] |
---|
1044 | |
---|
1045 | if frame in valid: |
---|
1046 | inf = list(self._getcoordinfo()) |
---|
1047 | inf[1] = frame |
---|
1048 | self._setcoordinfo(inf) |
---|
1049 | self._add_history("set_freqframe", varlist) |
---|
1050 | else: |
---|
1051 | msg = "Please specify a valid freq type. Valid types are:\n", valid |
---|
1052 | raise TypeError(msg) |
---|
1053 | |
---|
1054 | @asaplog_post_dec |
---|
1055 | def set_dirframe(self, frame=""): |
---|
1056 | """\ |
---|
1057 | Set the frame type of the Direction on the sky. |
---|
1058 | |
---|
1059 | Parameters: |
---|
1060 | |
---|
1061 | frame: an optional frame type, default ''. Valid frames are: |
---|
1062 | 'J2000', 'B1950', 'GALACTIC' |
---|
1063 | |
---|
1064 | Example: |
---|
1065 | |
---|
1066 | scan.set_dirframe('GALACTIC') |
---|
1067 | |
---|
1068 | """ |
---|
1069 | varlist = vars() |
---|
1070 | Scantable.set_dirframe(self, frame) |
---|
1071 | self._add_history("set_dirframe", varlist) |
---|
1072 | |
---|
1073 | def get_unit(self): |
---|
1074 | """\ |
---|
1075 | Get the default unit set in this scantable |
---|
1076 | |
---|
1077 | Returns: |
---|
1078 | |
---|
1079 | A unit string |
---|
1080 | |
---|
1081 | """ |
---|
1082 | inf = self._getcoordinfo() |
---|
1083 | unit = inf[0] |
---|
1084 | if unit == '': unit = 'channel' |
---|
1085 | return unit |
---|
1086 | |
---|
1087 | @asaplog_post_dec |
---|
1088 | def get_abcissa(self, rowno=0): |
---|
1089 | """\ |
---|
1090 | Get the abcissa in the current coordinate setup for the currently |
---|
1091 | selected Beam/IF/Pol |
---|
1092 | |
---|
1093 | Parameters: |
---|
1094 | |
---|
1095 | rowno: an optional row number in the scantable. Default is the |
---|
1096 | first row, i.e. rowno=0 |
---|
1097 | |
---|
1098 | Returns: |
---|
1099 | |
---|
1100 | The abcissa values and the format string (as a dictionary) |
---|
1101 | |
---|
1102 | """ |
---|
1103 | abc = self._getabcissa(rowno) |
---|
1104 | lbl = self._getabcissalabel(rowno) |
---|
1105 | return abc, lbl |
---|
1106 | |
---|
1107 | @asaplog_post_dec |
---|
1108 | def flag(self, mask=None, unflag=False, row=-1): |
---|
1109 | """\ |
---|
1110 | Flag the selected data using an optional channel mask. |
---|
1111 | |
---|
1112 | Parameters: |
---|
1113 | |
---|
1114 | mask: an optional channel mask, created with create_mask. Default |
---|
1115 | (no mask) is all channels. |
---|
1116 | |
---|
1117 | unflag: if True, unflag the data |
---|
1118 | |
---|
1119 | row: an optional row number in the scantable. |
---|
1120 | Default -1 flags all rows |
---|
1121 | |
---|
1122 | """ |
---|
1123 | varlist = vars() |
---|
1124 | mask = mask or [] |
---|
1125 | self._flag(row, mask, unflag) |
---|
1126 | self._add_history("flag", varlist) |
---|
1127 | |
---|
1128 | @asaplog_post_dec |
---|
1129 | def flag_row(self, rows=None, unflag=False): |
---|
1130 | """\ |
---|
1131 | Flag the selected data in row-based manner. |
---|
1132 | |
---|
1133 | Parameters: |
---|
1134 | |
---|
1135 | rows: list of row numbers to be flagged. Default is no row |
---|
1136 | (must be explicitly specified to execute row-based |
---|
1137 | flagging). |
---|
1138 | |
---|
1139 | unflag: if True, unflag the data. |
---|
1140 | |
---|
1141 | """ |
---|
1142 | varlist = vars() |
---|
1143 | if rows is None: |
---|
1144 | rows = [] |
---|
1145 | self._flag_row(rows, unflag) |
---|
1146 | self._add_history("flag_row", varlist) |
---|
1147 | |
---|
1148 | @asaplog_post_dec |
---|
1149 | def clip(self, uthres=None, dthres=None, clipoutside=True, unflag=False): |
---|
1150 | """\ |
---|
1151 | Flag the selected data outside a specified range (in channel-base) |
---|
1152 | |
---|
1153 | Parameters: |
---|
1154 | |
---|
1155 | uthres: upper threshold. |
---|
1156 | |
---|
1157 | dthres: lower threshold |
---|
1158 | |
---|
1159 | clipoutside: True for flagging data outside the range |
---|
1160 | [dthres:uthres]. |
---|
1161 | False for flagging data inside the range. |
---|
1162 | |
---|
1163 | unflag: if True, unflag the data. |
---|
1164 | |
---|
1165 | """ |
---|
1166 | varlist = vars() |
---|
1167 | self._clip(uthres, dthres, clipoutside, unflag) |
---|
1168 | self._add_history("clip", varlist) |
---|
1169 | |
---|
1170 | @asaplog_post_dec |
---|
1171 | def lag_flag(self, start, end, unit="MHz", insitu=None): |
---|
1172 | """\ |
---|
1173 | Flag the data in 'lag' space by providing a frequency to remove. |
---|
1174 | Flagged data in the scantable get interpolated over the region. |
---|
1175 | No taper is applied. |
---|
1176 | |
---|
1177 | Parameters: |
---|
1178 | |
---|
1179 | start: the start frequency (really a period within the |
---|
1180 | bandwidth) or period to remove |
---|
1181 | |
---|
1182 | end: the end frequency or period to remove |
---|
1183 | |
---|
1184 | unit: the frequency unit (default 'MHz') or '' for |
---|
1185 | explicit lag channels |
---|
1186 | |
---|
1187 | *Notes*: |
---|
1188 | |
---|
1189 | It is recommended to flag edges of the band or strong |
---|
1190 | signals beforehand. |
---|
1191 | |
---|
1192 | """ |
---|
1193 | if insitu is None: insitu = rcParams['insitu'] |
---|
1194 | self._math._setinsitu(insitu) |
---|
1195 | varlist = vars() |
---|
1196 | base = { "GHz": 1000000000., "MHz": 1000000., "kHz": 1000., "Hz": 1.} |
---|
1197 | if not (unit == "" or base.has_key(unit)): |
---|
1198 | raise ValueError("%s is not a valid unit." % unit) |
---|
1199 | if unit == "": |
---|
1200 | s = scantable(self._math._lag_flag(self, start, end, "lags")) |
---|
1201 | else: |
---|
1202 | s = scantable(self._math._lag_flag(self, start*base[unit], |
---|
1203 | end*base[unit], "frequency")) |
---|
1204 | s._add_history("lag_flag", varlist) |
---|
1205 | if insitu: |
---|
1206 | self._assign(s) |
---|
1207 | else: |
---|
1208 | return s |
---|
1209 | |
---|
1210 | @asaplog_post_dec |
---|
1211 | def fft(self, rowno=None, mask=None, getrealimag=False): |
---|
1212 | """\ |
---|
1213 | Apply FFT to the spectra. |
---|
1214 | Flagged data in the scantable get interpolated over the region. |
---|
1215 | |
---|
1216 | Parameters: |
---|
1217 | |
---|
1218 | rowno: The row number(s) to be processed. int, list |
---|
1219 | and tuple are accepted. By default (None), FFT |
---|
1220 | is applied to the whole data. |
---|
1221 | |
---|
1222 | mask: Auxiliary channel mask(s). Given as a boolean |
---|
1223 | list, it is applied to all specified rows. |
---|
1224 | A list of boolean lists can also be used to |
---|
1225 | apply different masks. In the latter case, the |
---|
1226 | length of 'mask' must be the same as that of |
---|
1227 | 'rowno'. The default is None. |
---|
1228 | |
---|
1229 | getrealimag: If True, returns the real and imaginary part |
---|
1230 | values of the complex results. |
---|
1231 | If False (the default), returns the amplitude |
---|
1232 | (absolute value) normalised with Ndata/2 and |
---|
1233 | phase (argument, in unit of radian). |
---|
1234 | |
---|
1235 | Returns: |
---|
1236 | |
---|
1237 | A list of dictionaries containing the results for each spectrum. |
---|
1238 | Each dictionary contains two values, the real and the imaginary |
---|
1239 | parts when getrealimag = True, or the amplitude(absolute value) |
---|
1240 | and the phase(argument) when getrealimag = False. The key for |
---|
1241 | these values are 'real' and 'imag', or 'ampl' and 'phase', |
---|
1242 | respectively. |
---|
1243 | """ |
---|
1244 | if rowno is None: |
---|
1245 | rowno = [] |
---|
1246 | if isinstance(rowno, int): |
---|
1247 | rowno = [rowno] |
---|
1248 | elif not (isinstance(rowno, list) or isinstance(rowno, tuple)): |
---|
1249 | raise TypeError("The row number(s) must be int, list or tuple.") |
---|
1250 | if len(rowno) == 0: rowno = [i for i in xrange(self.nrow())] |
---|
1251 | |
---|
1252 | usecommonmask = True |
---|
1253 | |
---|
1254 | if mask is None: |
---|
1255 | mask = [] |
---|
1256 | if isinstance(mask, list) or isinstance(mask, tuple): |
---|
1257 | if len(mask) == 0: |
---|
1258 | mask = [[]] |
---|
1259 | else: |
---|
1260 | if isinstance(mask[0], bool): |
---|
1261 | if len(mask) != self.nchan(self.getif(rowno[0])): |
---|
1262 | raise ValueError("The spectra and the mask have " |
---|
1263 | "different length.") |
---|
1264 | mask = [mask] |
---|
1265 | elif isinstance(mask[0], list) or isinstance(mask[0], tuple): |
---|
1266 | usecommonmask = False |
---|
1267 | if len(mask) != len(rowno): |
---|
1268 | raise ValueError("When specifying masks for each " |
---|
1269 | "spectrum, the numbers of them " |
---|
1270 | "must be identical.") |
---|
1271 | for i in xrange(mask): |
---|
1272 | if len(mask[i]) != self.nchan(self.getif(rowno[i])): |
---|
1273 | raise ValueError("The spectra and the mask have " |
---|
1274 | "different length.") |
---|
1275 | else: |
---|
1276 | raise TypeError("The mask must be a boolean list or " |
---|
1277 | "a list of boolean list.") |
---|
1278 | else: |
---|
1279 | raise TypeError("The mask must be a boolean list or a list of " |
---|
1280 | "boolean list.") |
---|
1281 | |
---|
1282 | res = [] |
---|
1283 | |
---|
1284 | imask = 0 |
---|
1285 | for whichrow in rowno: |
---|
1286 | fspec = self._fft(whichrow, mask[imask], getrealimag) |
---|
1287 | nspec = len(fspec) |
---|
1288 | |
---|
1289 | i = 0 |
---|
1290 | v1 = [] |
---|
1291 | v2 = [] |
---|
1292 | reselem = {"real":[],"imag":[]} if getrealimag \ |
---|
1293 | else {"ampl":[],"phase":[]} |
---|
1294 | |
---|
1295 | while (i < nspec): |
---|
1296 | v1.append(fspec[i]) |
---|
1297 | v2.append(fspec[i+1]) |
---|
1298 | i += 2 |
---|
1299 | |
---|
1300 | if getrealimag: |
---|
1301 | reselem["real"] += v1 |
---|
1302 | reselem["imag"] += v2 |
---|
1303 | else: |
---|
1304 | reselem["ampl"] += v1 |
---|
1305 | reselem["phase"] += v2 |
---|
1306 | |
---|
1307 | res.append(reselem) |
---|
1308 | |
---|
1309 | if not usecommonmask: |
---|
1310 | imask += 1 |
---|
1311 | |
---|
1312 | return res |
---|
1313 | |
---|
1314 | @asaplog_post_dec |
---|
1315 | def create_mask(self, *args, **kwargs): |
---|
1316 | """\ |
---|
1317 | Compute and return a mask based on [min, max] windows. |
---|
1318 | The specified windows are to be INCLUDED, when the mask is |
---|
1319 | applied. |
---|
1320 | |
---|
1321 | Parameters: |
---|
1322 | |
---|
1323 | [min, max], [min2, max2], ... |
---|
1324 | Pairs of start/end points (inclusive)specifying the regions |
---|
1325 | to be masked |
---|
1326 | |
---|
1327 | invert: optional argument. If specified as True, |
---|
1328 | return an inverted mask, i.e. the regions |
---|
1329 | specified are EXCLUDED |
---|
1330 | |
---|
1331 | row: create the mask using the specified row for |
---|
1332 | unit conversions, default is row=0 |
---|
1333 | only necessary if frequency varies over rows. |
---|
1334 | |
---|
1335 | Examples:: |
---|
1336 | |
---|
1337 | scan.set_unit('channel') |
---|
1338 | # a) |
---|
1339 | msk = scan.create_mask([400, 500], [800, 900]) |
---|
1340 | # masks everything outside 400 and 500 |
---|
1341 | # and 800 and 900 in the unit 'channel' |
---|
1342 | |
---|
1343 | # b) |
---|
1344 | msk = scan.create_mask([400, 500], [800, 900], invert=True) |
---|
1345 | # masks the regions between 400 and 500 |
---|
1346 | # and 800 and 900 in the unit 'channel' |
---|
1347 | |
---|
1348 | # c) |
---|
1349 | #mask only channel 400 |
---|
1350 | msk = scan.create_mask([400]) |
---|
1351 | |
---|
1352 | """ |
---|
1353 | row = kwargs.get("row", 0) |
---|
1354 | data = self._getabcissa(row) |
---|
1355 | u = self._getcoordinfo()[0] |
---|
1356 | if u == "": |
---|
1357 | u = "channel" |
---|
1358 | msg = "The current mask window unit is %s" % u |
---|
1359 | i = self._check_ifs() |
---|
1360 | if not i: |
---|
1361 | msg += "\nThis mask is only valid for IF=%d" % (self.getif(i)) |
---|
1362 | asaplog.push(msg) |
---|
1363 | n = len(data) |
---|
1364 | msk = _n_bools(n, False) |
---|
1365 | # test if args is a 'list' or a 'normal *args - UGLY!!! |
---|
1366 | |
---|
1367 | ws = (isinstance(args[-1][-1], int) |
---|
1368 | or isinstance(args[-1][-1], float)) and args or args[0] |
---|
1369 | for window in ws: |
---|
1370 | if len(window) == 1: |
---|
1371 | window = [window[0], window[0]] |
---|
1372 | if len(window) == 0 or len(window) > 2: |
---|
1373 | raise ValueError("A window needs to be defined as " |
---|
1374 | "[start(, end)]") |
---|
1375 | if window[0] > window[1]: |
---|
1376 | tmp = window[0] |
---|
1377 | window[0] = window[1] |
---|
1378 | window[1] = tmp |
---|
1379 | for i in range(n): |
---|
1380 | if data[i] >= window[0] and data[i] <= window[1]: |
---|
1381 | msk[i] = True |
---|
1382 | if kwargs.has_key('invert'): |
---|
1383 | if kwargs.get('invert'): |
---|
1384 | msk = mask_not(msk) |
---|
1385 | return msk |
---|
1386 | |
---|
1387 | def get_masklist(self, mask=None, row=0, silent=False): |
---|
1388 | """\ |
---|
1389 | Compute and return a list of mask windows, [min, max]. |
---|
1390 | |
---|
1391 | Parameters: |
---|
1392 | |
---|
1393 | mask: channel mask, created with create_mask. |
---|
1394 | |
---|
1395 | row: calcutate the masklist using the specified row |
---|
1396 | for unit conversions, default is row=0 |
---|
1397 | only necessary if frequency varies over rows. |
---|
1398 | |
---|
1399 | Returns: |
---|
1400 | |
---|
1401 | [min, max], [min2, max2], ... |
---|
1402 | Pairs of start/end points (inclusive)specifying |
---|
1403 | the masked regions |
---|
1404 | |
---|
1405 | """ |
---|
1406 | if not (isinstance(mask,list) or isinstance(mask, tuple)): |
---|
1407 | raise TypeError("The mask should be list or tuple.") |
---|
1408 | if len(mask) <= 0: |
---|
1409 | raise TypeError("The mask elements should be > 0") |
---|
1410 | data = self._getabcissa(row) |
---|
1411 | if len(data) != len(mask): |
---|
1412 | msg = "Number of channels in scantable != number of mask elements" |
---|
1413 | raise TypeError(msg) |
---|
1414 | u = self._getcoordinfo()[0] |
---|
1415 | if u == "": |
---|
1416 | u = "channel" |
---|
1417 | msg = "The current mask window unit is %s" % u |
---|
1418 | i = self._check_ifs() |
---|
1419 | if not i: |
---|
1420 | msg += "\nThis mask is only valid for IF=%d" % (self.getif(i)) |
---|
1421 | if not silent: |
---|
1422 | asaplog.push(msg) |
---|
1423 | masklist = [] |
---|
1424 | ist, ien = None, None |
---|
1425 | ist, ien=self.get_mask_indices(mask) |
---|
1426 | if ist is not None and ien is not None: |
---|
1427 | for i in xrange(len(ist)): |
---|
1428 | range=[data[ist[i]],data[ien[i]]] |
---|
1429 | range.sort() |
---|
1430 | masklist.append([range[0],range[1]]) |
---|
1431 | return masklist |
---|
1432 | |
---|
1433 | def get_mask_indices(self, mask=None): |
---|
1434 | """\ |
---|
1435 | Compute and Return lists of mask start indices and mask end indices. |
---|
1436 | |
---|
1437 | Parameters: |
---|
1438 | |
---|
1439 | mask: channel mask, created with create_mask. |
---|
1440 | |
---|
1441 | Returns: |
---|
1442 | |
---|
1443 | List of mask start indices and that of mask end indices, |
---|
1444 | i.e., [istart1,istart2,....], [iend1,iend2,....]. |
---|
1445 | |
---|
1446 | """ |
---|
1447 | if not (isinstance(mask,list) or isinstance(mask, tuple)): |
---|
1448 | raise TypeError("The mask should be list or tuple.") |
---|
1449 | if len(mask) <= 0: |
---|
1450 | raise TypeError("The mask elements should be > 0") |
---|
1451 | istart = [] |
---|
1452 | iend = [] |
---|
1453 | if mask[0]: |
---|
1454 | istart.append(0) |
---|
1455 | for i in range(len(mask)-1): |
---|
1456 | if not mask[i] and mask[i+1]: |
---|
1457 | istart.append(i+1) |
---|
1458 | elif mask[i] and not mask[i+1]: |
---|
1459 | iend.append(i) |
---|
1460 | if mask[len(mask)-1]: |
---|
1461 | iend.append(len(mask)-1) |
---|
1462 | if len(istart) != len(iend): |
---|
1463 | raise RuntimeError("Numbers of mask start != mask end.") |
---|
1464 | for i in range(len(istart)): |
---|
1465 | if istart[i] > iend[i]: |
---|
1466 | raise RuntimeError("Mask start index > mask end index") |
---|
1467 | break |
---|
1468 | return istart,iend |
---|
1469 | |
---|
1470 | @asaplog_post_dec |
---|
1471 | def parse_maskexpr(self, maskstring): |
---|
1472 | """ |
---|
1473 | Parse CASA type mask selection syntax (IF dependent). |
---|
1474 | |
---|
1475 | Parameters: |
---|
1476 | maskstring : A string mask selection expression. |
---|
1477 | A comma separated selections mean different IF - |
---|
1478 | channel combinations. IFs and channel selections |
---|
1479 | are partitioned by a colon, ':'. |
---|
1480 | examples: |
---|
1481 | '' = all IFs (all channels) |
---|
1482 | '<2,4~6,9' = IFs 0,1,4,5,6,9 (all channels) |
---|
1483 | '3:3~45;60' = channels 3 to 45 and 60 in IF 3 |
---|
1484 | '0~1:2~6,8' = channels 2 to 6 in IFs 0,1, and |
---|
1485 | all channels in IF8 |
---|
1486 | Returns: |
---|
1487 | A dictionary of selected (valid) IF and masklist pairs, |
---|
1488 | e.g. {'0': [[50,250],[350,462]], '2': [[100,400],[550,974]]} |
---|
1489 | """ |
---|
1490 | if not isinstance(maskstring,str): |
---|
1491 | asaplog.post() |
---|
1492 | asaplog.push("Mask expression should be a string.") |
---|
1493 | asaplog.post("ERROR") |
---|
1494 | |
---|
1495 | valid_ifs = self.getifnos() |
---|
1496 | frequnit = self.get_unit() |
---|
1497 | seldict = {} |
---|
1498 | if maskstring == "": |
---|
1499 | maskstring = str(valid_ifs)[1:-1] |
---|
1500 | ## split each selection "IF range[:CHAN range]" |
---|
1501 | sellist = maskstring.split(',') |
---|
1502 | for currselstr in sellist: |
---|
1503 | selset = currselstr.split(':') |
---|
1504 | # spw and mask string (may include ~, < or >) |
---|
1505 | spwmasklist = self._parse_selection(selset[0], typestr='integer', |
---|
1506 | minval=min(valid_ifs), |
---|
1507 | maxval=max(valid_ifs)) |
---|
1508 | for spwlist in spwmasklist: |
---|
1509 | selspws = [] |
---|
1510 | for ispw in range(spwlist[0],spwlist[1]+1): |
---|
1511 | # Put into the list only if ispw exists |
---|
1512 | if valid_ifs.count(ispw): |
---|
1513 | selspws.append(ispw) |
---|
1514 | del spwmasklist, spwlist |
---|
1515 | |
---|
1516 | # parse frequency mask list |
---|
1517 | if len(selset) > 1: |
---|
1518 | freqmasklist = self._parse_selection(selset[1], typestr='float', |
---|
1519 | offset=0.) |
---|
1520 | else: |
---|
1521 | # want to select the whole spectrum |
---|
1522 | freqmasklist = [None] |
---|
1523 | |
---|
1524 | ## define a dictionary of spw - masklist combination |
---|
1525 | for ispw in selspws: |
---|
1526 | #print "working on", ispw |
---|
1527 | spwstr = str(ispw) |
---|
1528 | if len(selspws) == 0: |
---|
1529 | # empty spw |
---|
1530 | continue |
---|
1531 | else: |
---|
1532 | ## want to get min and max of the spw and |
---|
1533 | ## offset to set for '<' and '>' |
---|
1534 | if frequnit == 'channel': |
---|
1535 | minfreq = 0 |
---|
1536 | maxfreq = self.nchan(ifno=ispw) |
---|
1537 | offset = 0.5 |
---|
1538 | else: |
---|
1539 | ## This is ugly part. need improvement |
---|
1540 | for ifrow in xrange(self.nrow()): |
---|
1541 | if self.getif(ifrow) == ispw: |
---|
1542 | #print "IF",ispw,"found in row =",ifrow |
---|
1543 | break |
---|
1544 | freqcoord = self.get_coordinate(ifrow) |
---|
1545 | freqs = self._getabcissa(ifrow) |
---|
1546 | minfreq = min(freqs) |
---|
1547 | maxfreq = max(freqs) |
---|
1548 | if len(freqs) == 1: |
---|
1549 | offset = 0.5 |
---|
1550 | elif frequnit.find('Hz') > 0: |
---|
1551 | offset = abs(freqcoord.to_frequency(1, |
---|
1552 | unit=frequnit) |
---|
1553 | -freqcoord.to_frequency(0, |
---|
1554 | unit=frequnit) |
---|
1555 | )*0.5 |
---|
1556 | elif frequnit.find('m/s') > 0: |
---|
1557 | offset = abs(freqcoord.to_velocity(1, |
---|
1558 | unit=frequnit) |
---|
1559 | -freqcoord.to_velocity(0, |
---|
1560 | unit=frequnit) |
---|
1561 | )*0.5 |
---|
1562 | else: |
---|
1563 | asaplog.post() |
---|
1564 | asaplog.push("Invalid frequency unit") |
---|
1565 | asaplog.post("ERROR") |
---|
1566 | del freqs, freqcoord, ifrow |
---|
1567 | for freq in freqmasklist: |
---|
1568 | selmask = freq or [minfreq, maxfreq] |
---|
1569 | if selmask[0] == None: |
---|
1570 | ## selection was "<freq[1]". |
---|
1571 | if selmask[1] < minfreq: |
---|
1572 | ## avoid adding region selection |
---|
1573 | selmask = None |
---|
1574 | else: |
---|
1575 | selmask = [minfreq,selmask[1]-offset] |
---|
1576 | elif selmask[1] == None: |
---|
1577 | ## selection was ">freq[0]" |
---|
1578 | if selmask[0] > maxfreq: |
---|
1579 | ## avoid adding region selection |
---|
1580 | selmask = None |
---|
1581 | else: |
---|
1582 | selmask = [selmask[0]+offset,maxfreq] |
---|
1583 | if selmask: |
---|
1584 | if not seldict.has_key(spwstr): |
---|
1585 | # new spw selection |
---|
1586 | seldict[spwstr] = [] |
---|
1587 | seldict[spwstr] += [selmask] |
---|
1588 | del minfreq,maxfreq,offset,freq,selmask |
---|
1589 | del spwstr |
---|
1590 | del freqmasklist |
---|
1591 | del valid_ifs |
---|
1592 | if len(seldict) == 0: |
---|
1593 | asaplog.post() |
---|
1594 | asaplog.push("No valid selection in the mask expression: " |
---|
1595 | +maskstring) |
---|
1596 | asaplog.post("WARN") |
---|
1597 | return None |
---|
1598 | msg = "Selected masklist:\n" |
---|
1599 | for sif, lmask in seldict.iteritems(): |
---|
1600 | msg += " IF"+sif+" - "+str(lmask)+"\n" |
---|
1601 | asaplog.push(msg) |
---|
1602 | return seldict |
---|
1603 | |
---|
1604 | @asaplog_post_dec |
---|
1605 | def parse_idx_selection(self, mode, selexpr): |
---|
1606 | """ |
---|
1607 | Parse CASA type mask selection syntax of SCANNO, IFNO, POLNO, |
---|
1608 | BEAMNO, and row number |
---|
1609 | |
---|
1610 | Parameters: |
---|
1611 | mode : which column to select. |
---|
1612 | ['scan',|'if'|'pol'|'beam'|'row'] |
---|
1613 | selexpr : A comma separated selection expression. |
---|
1614 | examples: |
---|
1615 | '' = all (returns []) |
---|
1616 | '<2,4~6,9' = indices less than 2, 4 to 6 and 9 |
---|
1617 | (returns [0,1,4,5,6,9]) |
---|
1618 | Returns: |
---|
1619 | A List of selected indices |
---|
1620 | """ |
---|
1621 | if selexpr == "": |
---|
1622 | return [] |
---|
1623 | valid_modes = {'s': 'scan', 'i': 'if', 'p': 'pol', |
---|
1624 | 'b': 'beam', 'r': 'row'} |
---|
1625 | smode = mode.lower()[0] |
---|
1626 | if not (smode in valid_modes.keys()): |
---|
1627 | msg = "Invalid mode '%s'. Valid modes are %s" %\ |
---|
1628 | (mode, str(valid_modes.values())) |
---|
1629 | asaplog.post() |
---|
1630 | asaplog.push(msg) |
---|
1631 | asaplog.post("ERROR") |
---|
1632 | mode = valid_modes[smode] |
---|
1633 | minidx = None |
---|
1634 | maxidx = None |
---|
1635 | if smode == 'r': |
---|
1636 | minidx = 0 |
---|
1637 | maxidx = self.nrow() |
---|
1638 | else: |
---|
1639 | idx = getattr(self,"get"+mode+"nos")() |
---|
1640 | minidx = min(idx) |
---|
1641 | maxidx = max(idx) |
---|
1642 | del idx |
---|
1643 | sellist = selexpr.split(',') |
---|
1644 | idxlist = [] |
---|
1645 | for currselstr in sellist: |
---|
1646 | # single range (may include ~, < or >) |
---|
1647 | currlist = self._parse_selection(currselstr, typestr='integer', |
---|
1648 | minval=minidx,maxval=maxidx) |
---|
1649 | for thelist in currlist: |
---|
1650 | idxlist += range(thelist[0],thelist[1]+1) |
---|
1651 | msg = "Selected %s: %s" % (mode.upper()+"NO", str(idxlist)) |
---|
1652 | asaplog.push(msg) |
---|
1653 | return idxlist |
---|
1654 | |
---|
1655 | def _parse_selection(self, selstr, typestr='float', offset=0., |
---|
1656 | minval=None, maxval=None): |
---|
1657 | """ |
---|
1658 | Parameters: |
---|
1659 | selstr : The Selection string, e.g., '<3;5~7;100~103;9' |
---|
1660 | typestr : The type of the values in returned list |
---|
1661 | ('integer' or 'float') |
---|
1662 | offset : The offset value to subtract from or add to |
---|
1663 | the boundary value if the selection string |
---|
1664 | includes '<' or '>' [Valid only for typestr='float'] |
---|
1665 | minval, maxval : The minimum/maximum values to set if the |
---|
1666 | selection string includes '<' or '>'. |
---|
1667 | The list element is filled with None by default. |
---|
1668 | Returns: |
---|
1669 | A list of min/max pair of selections. |
---|
1670 | Example: |
---|
1671 | _parse_selection('<3;5~7;9',typestr='int',minval=0) |
---|
1672 | --> returns [[0,2],[5,7],[9,9]] |
---|
1673 | _parse_selection('<3;5~7;9',typestr='float',offset=0.5,minval=0) |
---|
1674 | --> returns [[0.,2.5],[5.0,7.0],[9.,9.]] |
---|
1675 | """ |
---|
1676 | selgroups = selstr.split(';') |
---|
1677 | sellists = [] |
---|
1678 | if typestr.lower().startswith('int'): |
---|
1679 | formatfunc = int |
---|
1680 | offset = 1 |
---|
1681 | else: |
---|
1682 | formatfunc = float |
---|
1683 | |
---|
1684 | for currsel in selgroups: |
---|
1685 | if currsel.find('~') > 0: |
---|
1686 | # val0 <= x <= val1 |
---|
1687 | minsel = formatfunc(currsel.split('~')[0].strip()) |
---|
1688 | maxsel = formatfunc(currsel.split('~')[1].strip()) |
---|
1689 | elif currsel.strip().find('<=') > -1: |
---|
1690 | bound = currsel.split('<=') |
---|
1691 | try: # try "x <= val" |
---|
1692 | minsel = minval |
---|
1693 | maxsel = formatfunc(bound[1].strip()) |
---|
1694 | except ValueError: # now "val <= x" |
---|
1695 | minsel = formatfunc(bound[0].strip()) |
---|
1696 | maxsel = maxval |
---|
1697 | elif currsel.strip().find('>=') > -1: |
---|
1698 | bound = currsel.split('>=') |
---|
1699 | try: # try "x >= val" |
---|
1700 | minsel = formatfunc(bound[1].strip()) |
---|
1701 | maxsel = maxval |
---|
1702 | except ValueError: # now "val >= x" |
---|
1703 | minsel = minval |
---|
1704 | maxsel = formatfunc(bound[0].strip()) |
---|
1705 | elif currsel.strip().find('<') > -1: |
---|
1706 | bound = currsel.split('<') |
---|
1707 | try: # try "x < val" |
---|
1708 | minsel = minval |
---|
1709 | maxsel = formatfunc(bound[1].strip()) \ |
---|
1710 | - formatfunc(offset) |
---|
1711 | except ValueError: # now "val < x" |
---|
1712 | minsel = formatfunc(bound[0].strip()) \ |
---|
1713 | + formatfunc(offset) |
---|
1714 | maxsel = maxval |
---|
1715 | elif currsel.strip().find('>') > -1: |
---|
1716 | bound = currsel.split('>') |
---|
1717 | try: # try "x > val" |
---|
1718 | minsel = formatfunc(bound[1].strip()) \ |
---|
1719 | + formatfunc(offset) |
---|
1720 | maxsel = maxval |
---|
1721 | except ValueError: # now "val > x" |
---|
1722 | minsel = minval |
---|
1723 | maxsel = formatfunc(bound[0].strip()) \ |
---|
1724 | - formatfunc(offset) |
---|
1725 | else: |
---|
1726 | minsel = formatfunc(currsel) |
---|
1727 | maxsel = formatfunc(currsel) |
---|
1728 | sellists.append([minsel,maxsel]) |
---|
1729 | return sellists |
---|
1730 | |
---|
1731 | # def get_restfreqs(self): |
---|
1732 | # """ |
---|
1733 | # Get the restfrequency(s) stored in this scantable. |
---|
1734 | # The return value(s) are always of unit 'Hz' |
---|
1735 | # Parameters: |
---|
1736 | # none |
---|
1737 | # Returns: |
---|
1738 | # a list of doubles |
---|
1739 | # """ |
---|
1740 | # return list(self._getrestfreqs()) |
---|
1741 | |
---|
1742 | def get_restfreqs(self, ids=None): |
---|
1743 | """\ |
---|
1744 | Get the restfrequency(s) stored in this scantable. |
---|
1745 | The return value(s) are always of unit 'Hz' |
---|
1746 | |
---|
1747 | Parameters: |
---|
1748 | |
---|
1749 | ids: (optional) a list of MOLECULE_ID for that restfrequency(s) to |
---|
1750 | be retrieved |
---|
1751 | |
---|
1752 | Returns: |
---|
1753 | |
---|
1754 | dictionary containing ids and a list of doubles for each id |
---|
1755 | |
---|
1756 | """ |
---|
1757 | if ids is None: |
---|
1758 | rfreqs = {} |
---|
1759 | idlist = self.getmolnos() |
---|
1760 | for i in idlist: |
---|
1761 | rfreqs[i] = list(self._getrestfreqs(i)) |
---|
1762 | return rfreqs |
---|
1763 | else: |
---|
1764 | if type(ids) == list or type(ids) == tuple: |
---|
1765 | rfreqs = {} |
---|
1766 | for i in ids: |
---|
1767 | rfreqs[i] = list(self._getrestfreqs(i)) |
---|
1768 | return rfreqs |
---|
1769 | else: |
---|
1770 | return list(self._getrestfreqs(ids)) |
---|
1771 | |
---|
1772 | @asaplog_post_dec |
---|
1773 | def set_restfreqs(self, freqs=None, unit='Hz'): |
---|
1774 | """\ |
---|
1775 | Set or replace the restfrequency specified and |
---|
1776 | if the 'freqs' argument holds a scalar, |
---|
1777 | then that rest frequency will be applied to all the selected |
---|
1778 | data. If the 'freqs' argument holds |
---|
1779 | a vector, then it MUST be of equal or smaller length than |
---|
1780 | the number of IFs (and the available restfrequencies will be |
---|
1781 | replaced by this vector). In this case, *all* data have |
---|
1782 | the restfrequency set per IF according |
---|
1783 | to the corresponding value you give in the 'freqs' vector. |
---|
1784 | E.g. 'freqs=[1e9, 2e9]' would mean IF 0 gets restfreq 1e9 and |
---|
1785 | IF 1 gets restfreq 2e9. |
---|
1786 | |
---|
1787 | You can also specify the frequencies via a linecatalog. |
---|
1788 | |
---|
1789 | Parameters: |
---|
1790 | |
---|
1791 | freqs: list of rest frequency values or string idenitfiers |
---|
1792 | |
---|
1793 | unit: unit for rest frequency (default 'Hz') |
---|
1794 | |
---|
1795 | |
---|
1796 | Example:: |
---|
1797 | |
---|
1798 | # set the given restfrequency for the all currently selected IFs |
---|
1799 | scan.set_restfreqs(freqs=1.4e9) |
---|
1800 | # set restfrequencies for the n IFs (n > 1) in the order of the |
---|
1801 | # list, i.e |
---|
1802 | # IF0 -> 1.4e9, IF1 -> 1.41e9, IF3 -> 1.42e9 |
---|
1803 | # len(list_of_restfreqs) == nIF |
---|
1804 | # for nIF == 1 the following will set multiple restfrequency for |
---|
1805 | # that IF |
---|
1806 | scan.set_restfreqs(freqs=[1.4e9, 1.41e9, 1.42e9]) |
---|
1807 | # set multiple restfrequencies per IF. as a list of lists where |
---|
1808 | # the outer list has nIF elements, the inner s arbitrary |
---|
1809 | scan.set_restfreqs(freqs=[[1.4e9, 1.41e9], [1.67e9]]) |
---|
1810 | |
---|
1811 | *Note*: |
---|
1812 | |
---|
1813 | To do more sophisticate Restfrequency setting, e.g. on a |
---|
1814 | source and IF basis, use scantable.set_selection() before using |
---|
1815 | this function:: |
---|
1816 | |
---|
1817 | # provided your scantable is called scan |
---|
1818 | selection = selector() |
---|
1819 | selection.set_name('ORION*') |
---|
1820 | selection.set_ifs([1]) |
---|
1821 | scan.set_selection(selection) |
---|
1822 | scan.set_restfreqs(freqs=86.6e9) |
---|
1823 | |
---|
1824 | """ |
---|
1825 | varlist = vars() |
---|
1826 | from asap import linecatalog |
---|
1827 | # simple value |
---|
1828 | if isinstance(freqs, int) or isinstance(freqs, float): |
---|
1829 | self._setrestfreqs([freqs], [""], unit) |
---|
1830 | # list of values |
---|
1831 | elif isinstance(freqs, list) or isinstance(freqs, tuple): |
---|
1832 | # list values are scalars |
---|
1833 | if isinstance(freqs[-1], int) or isinstance(freqs[-1], float): |
---|
1834 | if len(freqs) == 1: |
---|
1835 | self._setrestfreqs(freqs, [""], unit) |
---|
1836 | else: |
---|
1837 | # allow the 'old' mode of setting mulitple IFs |
---|
1838 | savesel = self._getselection() |
---|
1839 | sel = self.get_selection() |
---|
1840 | iflist = self.getifnos() |
---|
1841 | if len(freqs)>len(iflist): |
---|
1842 | raise ValueError("number of elements in list of list " |
---|
1843 | "exeeds the current IF selections") |
---|
1844 | iflist = self.getifnos() |
---|
1845 | for i, fval in enumerate(freqs): |
---|
1846 | sel.set_ifs(iflist[i]) |
---|
1847 | self._setselection(sel) |
---|
1848 | self._setrestfreqs([fval], [""], unit) |
---|
1849 | self._setselection(savesel) |
---|
1850 | |
---|
1851 | # list values are dict, {'value'=, 'name'=) |
---|
1852 | elif isinstance(freqs[-1], dict): |
---|
1853 | values = [] |
---|
1854 | names = [] |
---|
1855 | for d in freqs: |
---|
1856 | values.append(d["value"]) |
---|
1857 | names.append(d["name"]) |
---|
1858 | self._setrestfreqs(values, names, unit) |
---|
1859 | elif isinstance(freqs[-1], list) or isinstance(freqs[-1], tuple): |
---|
1860 | savesel = self._getselection() |
---|
1861 | sel = self.get_selection() |
---|
1862 | iflist = self.getifnos() |
---|
1863 | if len(freqs)>len(iflist): |
---|
1864 | raise ValueError("number of elements in list of list exeeds" |
---|
1865 | " the current IF selections") |
---|
1866 | for i, fval in enumerate(freqs): |
---|
1867 | sel.set_ifs(iflist[i]) |
---|
1868 | self._setselection(sel) |
---|
1869 | self._setrestfreqs(fval, [""], unit) |
---|
1870 | self._setselection(savesel) |
---|
1871 | # freqs are to be taken from a linecatalog |
---|
1872 | elif isinstance(freqs, linecatalog): |
---|
1873 | savesel = self._getselection() |
---|
1874 | sel = self.get_selection() |
---|
1875 | for i in xrange(freqs.nrow()): |
---|
1876 | sel.set_ifs(iflist[i]) |
---|
1877 | self._setselection(sel) |
---|
1878 | self._setrestfreqs([freqs.get_frequency(i)], |
---|
1879 | [freqs.get_name(i)], "MHz") |
---|
1880 | # ensure that we are not iterating past nIF |
---|
1881 | if i == self.nif()-1: break |
---|
1882 | self._setselection(savesel) |
---|
1883 | else: |
---|
1884 | return |
---|
1885 | self._add_history("set_restfreqs", varlist) |
---|
1886 | |
---|
1887 | @asaplog_post_dec |
---|
1888 | def shift_refpix(self, delta): |
---|
1889 | """\ |
---|
1890 | Shift the reference pixel of the Spectra Coordinate by an |
---|
1891 | integer amount. |
---|
1892 | |
---|
1893 | Parameters: |
---|
1894 | |
---|
1895 | delta: the amount to shift by |
---|
1896 | |
---|
1897 | *Note*: |
---|
1898 | |
---|
1899 | Be careful using this with broadband data. |
---|
1900 | |
---|
1901 | """ |
---|
1902 | varlist = vars() |
---|
1903 | Scantable.shift_refpix(self, delta) |
---|
1904 | s._add_history("shift_refpix", varlist) |
---|
1905 | |
---|
1906 | @asaplog_post_dec |
---|
1907 | def history(self, filename=None): |
---|
1908 | """\ |
---|
1909 | Print the history. Optionally to a file. |
---|
1910 | |
---|
1911 | Parameters: |
---|
1912 | |
---|
1913 | filename: The name of the file to save the history to. |
---|
1914 | |
---|
1915 | """ |
---|
1916 | hist = list(self._gethistory()) |
---|
1917 | out = "-"*80 |
---|
1918 | for h in hist: |
---|
1919 | if h.startswith("---"): |
---|
1920 | out = "\n".join([out, h]) |
---|
1921 | else: |
---|
1922 | items = h.split("##") |
---|
1923 | date = items[0] |
---|
1924 | func = items[1] |
---|
1925 | items = items[2:] |
---|
1926 | out += "\n"+date+"\n" |
---|
1927 | out += "Function: %s\n Parameters:" % (func) |
---|
1928 | for i in items: |
---|
1929 | if i == '': |
---|
1930 | continue |
---|
1931 | s = i.split("=") |
---|
1932 | out += "\n %s = %s" % (s[0], s[1]) |
---|
1933 | out = "\n".join([out, "-"*80]) |
---|
1934 | if filename is not None: |
---|
1935 | if filename is "": |
---|
1936 | filename = 'scantable_history.txt' |
---|
1937 | import os |
---|
1938 | filename = os.path.expandvars(os.path.expanduser(filename)) |
---|
1939 | if not os.path.isdir(filename): |
---|
1940 | data = open(filename, 'w') |
---|
1941 | data.write(out) |
---|
1942 | data.close() |
---|
1943 | else: |
---|
1944 | msg = "Illegal file name '%s'." % (filename) |
---|
1945 | raise IOError(msg) |
---|
1946 | return page(out) |
---|
1947 | |
---|
1948 | # |
---|
1949 | # Maths business |
---|
1950 | # |
---|
1951 | @asaplog_post_dec |
---|
1952 | def average_time(self, mask=None, scanav=False, weight='tint', align=False): |
---|
1953 | """\ |
---|
1954 | Return the (time) weighted average of a scan. Scans will be averaged |
---|
1955 | only if the source direction (RA/DEC) is within 1' otherwise |
---|
1956 | |
---|
1957 | *Note*: |
---|
1958 | |
---|
1959 | in channels only - align if necessary |
---|
1960 | |
---|
1961 | Parameters: |
---|
1962 | |
---|
1963 | mask: an optional mask (only used for 'var' and 'tsys' |
---|
1964 | weighting) |
---|
1965 | |
---|
1966 | scanav: True averages each scan separately |
---|
1967 | False (default) averages all scans together, |
---|
1968 | |
---|
1969 | weight: Weighting scheme. |
---|
1970 | 'none' (mean no weight) |
---|
1971 | 'var' (1/var(spec) weighted) |
---|
1972 | 'tsys' (1/Tsys**2 weighted) |
---|
1973 | 'tint' (integration time weighted) |
---|
1974 | 'tintsys' (Tint/Tsys**2) |
---|
1975 | 'median' ( median averaging) |
---|
1976 | The default is 'tint' |
---|
1977 | |
---|
1978 | align: align the spectra in velocity before averaging. It takes |
---|
1979 | the time of the first spectrum as reference time. |
---|
1980 | |
---|
1981 | Example:: |
---|
1982 | |
---|
1983 | # time average the scantable without using a mask |
---|
1984 | newscan = scan.average_time() |
---|
1985 | |
---|
1986 | """ |
---|
1987 | varlist = vars() |
---|
1988 | weight = weight or 'TINT' |
---|
1989 | mask = mask or () |
---|
1990 | scanav = (scanav and 'SCAN') or 'NONE' |
---|
1991 | scan = (self, ) |
---|
1992 | |
---|
1993 | if align: |
---|
1994 | scan = (self.freq_align(insitu=False), ) |
---|
1995 | s = None |
---|
1996 | if weight.upper() == 'MEDIAN': |
---|
1997 | s = scantable(self._math._averagechannel(scan[0], 'MEDIAN', |
---|
1998 | scanav)) |
---|
1999 | else: |
---|
2000 | s = scantable(self._math._average(scan, mask, weight.upper(), |
---|
2001 | scanav)) |
---|
2002 | s._add_history("average_time", varlist) |
---|
2003 | return s |
---|
2004 | |
---|
2005 | @asaplog_post_dec |
---|
2006 | def convert_flux(self, jyperk=None, eta=None, d=None, insitu=None): |
---|
2007 | """\ |
---|
2008 | Return a scan where all spectra are converted to either |
---|
2009 | Jansky or Kelvin depending upon the flux units of the scan table. |
---|
2010 | By default the function tries to look the values up internally. |
---|
2011 | If it can't find them (or if you want to over-ride), you must |
---|
2012 | specify EITHER jyperk OR eta (and D which it will try to look up |
---|
2013 | also if you don't set it). jyperk takes precedence if you set both. |
---|
2014 | |
---|
2015 | Parameters: |
---|
2016 | |
---|
2017 | jyperk: the Jy / K conversion factor |
---|
2018 | |
---|
2019 | eta: the aperture efficiency |
---|
2020 | |
---|
2021 | d: the geometric diameter (metres) |
---|
2022 | |
---|
2023 | insitu: if False a new scantable is returned. |
---|
2024 | Otherwise, the scaling is done in-situ |
---|
2025 | The default is taken from .asaprc (False) |
---|
2026 | |
---|
2027 | """ |
---|
2028 | if insitu is None: insitu = rcParams['insitu'] |
---|
2029 | self._math._setinsitu(insitu) |
---|
2030 | varlist = vars() |
---|
2031 | jyperk = jyperk or -1.0 |
---|
2032 | d = d or -1.0 |
---|
2033 | eta = eta or -1.0 |
---|
2034 | s = scantable(self._math._convertflux(self, d, eta, jyperk)) |
---|
2035 | s._add_history("convert_flux", varlist) |
---|
2036 | if insitu: self._assign(s) |
---|
2037 | else: return s |
---|
2038 | |
---|
2039 | @asaplog_post_dec |
---|
2040 | def gain_el(self, poly=None, filename="", method="linear", insitu=None): |
---|
2041 | """\ |
---|
2042 | Return a scan after applying a gain-elevation correction. |
---|
2043 | The correction can be made via either a polynomial or a |
---|
2044 | table-based interpolation (and extrapolation if necessary). |
---|
2045 | You specify polynomial coefficients, an ascii table or neither. |
---|
2046 | If you specify neither, then a polynomial correction will be made |
---|
2047 | with built in coefficients known for certain telescopes (an error |
---|
2048 | will occur if the instrument is not known). |
---|
2049 | The data and Tsys are *divided* by the scaling factors. |
---|
2050 | |
---|
2051 | Parameters: |
---|
2052 | |
---|
2053 | poly: Polynomial coefficients (default None) to compute a |
---|
2054 | gain-elevation correction as a function of |
---|
2055 | elevation (in degrees). |
---|
2056 | |
---|
2057 | filename: The name of an ascii file holding correction factors. |
---|
2058 | The first row of the ascii file must give the column |
---|
2059 | names and these MUST include columns |
---|
2060 | 'ELEVATION' (degrees) and 'FACTOR' (multiply data |
---|
2061 | by this) somewhere. |
---|
2062 | The second row must give the data type of the |
---|
2063 | column. Use 'R' for Real and 'I' for Integer. |
---|
2064 | An example file would be |
---|
2065 | (actual factors are arbitrary) : |
---|
2066 | |
---|
2067 | TIME ELEVATION FACTOR |
---|
2068 | R R R |
---|
2069 | 0.1 0 0.8 |
---|
2070 | 0.2 20 0.85 |
---|
2071 | 0.3 40 0.9 |
---|
2072 | 0.4 60 0.85 |
---|
2073 | 0.5 80 0.8 |
---|
2074 | 0.6 90 0.75 |
---|
2075 | |
---|
2076 | method: Interpolation method when correcting from a table. |
---|
2077 | Values are 'nearest', 'linear' (default), 'cubic' |
---|
2078 | and 'spline' |
---|
2079 | |
---|
2080 | insitu: if False a new scantable is returned. |
---|
2081 | Otherwise, the scaling is done in-situ |
---|
2082 | The default is taken from .asaprc (False) |
---|
2083 | |
---|
2084 | """ |
---|
2085 | |
---|
2086 | if insitu is None: insitu = rcParams['insitu'] |
---|
2087 | self._math._setinsitu(insitu) |
---|
2088 | varlist = vars() |
---|
2089 | poly = poly or () |
---|
2090 | from os.path import expandvars |
---|
2091 | filename = expandvars(filename) |
---|
2092 | s = scantable(self._math._gainel(self, poly, filename, method)) |
---|
2093 | s._add_history("gain_el", varlist) |
---|
2094 | if insitu: |
---|
2095 | self._assign(s) |
---|
2096 | else: |
---|
2097 | return s |
---|
2098 | |
---|
2099 | @asaplog_post_dec |
---|
2100 | def freq_align(self, reftime=None, method='cubic', insitu=None): |
---|
2101 | """\ |
---|
2102 | Return a scan where all rows have been aligned in frequency/velocity. |
---|
2103 | The alignment frequency frame (e.g. LSRK) is that set by function |
---|
2104 | set_freqframe. |
---|
2105 | |
---|
2106 | Parameters: |
---|
2107 | |
---|
2108 | reftime: reference time to align at. By default, the time of |
---|
2109 | the first row of data is used. |
---|
2110 | |
---|
2111 | method: Interpolation method for regridding the spectra. |
---|
2112 | Choose from 'nearest', 'linear', 'cubic' (default) |
---|
2113 | and 'spline' |
---|
2114 | |
---|
2115 | insitu: if False a new scantable is returned. |
---|
2116 | Otherwise, the scaling is done in-situ |
---|
2117 | The default is taken from .asaprc (False) |
---|
2118 | |
---|
2119 | """ |
---|
2120 | if insitu is None: insitu = rcParams["insitu"] |
---|
2121 | oldInsitu = self._math._insitu() |
---|
2122 | self._math._setinsitu(insitu) |
---|
2123 | varlist = vars() |
---|
2124 | reftime = reftime or "" |
---|
2125 | s = scantable(self._math._freq_align(self, reftime, method)) |
---|
2126 | s._add_history("freq_align", varlist) |
---|
2127 | self._math._setinsitu(oldInsitu) |
---|
2128 | if insitu: |
---|
2129 | self._assign(s) |
---|
2130 | else: |
---|
2131 | return s |
---|
2132 | |
---|
2133 | @asaplog_post_dec |
---|
2134 | def opacity(self, tau=None, insitu=None): |
---|
2135 | """\ |
---|
2136 | Apply an opacity correction. The data |
---|
2137 | and Tsys are multiplied by the correction factor. |
---|
2138 | |
---|
2139 | Parameters: |
---|
2140 | |
---|
2141 | tau: (list of) opacity from which the correction factor is |
---|
2142 | exp(tau*ZD) |
---|
2143 | where ZD is the zenith-distance. |
---|
2144 | If a list is provided, it has to be of length nIF, |
---|
2145 | nIF*nPol or 1 and in order of IF/POL, e.g. |
---|
2146 | [opif0pol0, opif0pol1, opif1pol0 ...] |
---|
2147 | if tau is `None` the opacities are determined from a |
---|
2148 | model. |
---|
2149 | |
---|
2150 | insitu: if False a new scantable is returned. |
---|
2151 | Otherwise, the scaling is done in-situ |
---|
2152 | The default is taken from .asaprc (False) |
---|
2153 | |
---|
2154 | """ |
---|
2155 | if insitu is None: |
---|
2156 | insitu = rcParams['insitu'] |
---|
2157 | self._math._setinsitu(insitu) |
---|
2158 | varlist = vars() |
---|
2159 | if not hasattr(tau, "__len__"): |
---|
2160 | tau = [tau] |
---|
2161 | s = scantable(self._math._opacity(self, tau)) |
---|
2162 | s._add_history("opacity", varlist) |
---|
2163 | if insitu: |
---|
2164 | self._assign(s) |
---|
2165 | else: |
---|
2166 | return s |
---|
2167 | |
---|
2168 | @asaplog_post_dec |
---|
2169 | def bin(self, width=5, insitu=None): |
---|
2170 | """\ |
---|
2171 | Return a scan where all spectra have been binned up. |
---|
2172 | |
---|
2173 | Parameters: |
---|
2174 | |
---|
2175 | width: The bin width (default=5) in pixels |
---|
2176 | |
---|
2177 | insitu: if False a new scantable is returned. |
---|
2178 | Otherwise, the scaling is done in-situ |
---|
2179 | The default is taken from .asaprc (False) |
---|
2180 | |
---|
2181 | """ |
---|
2182 | if insitu is None: |
---|
2183 | insitu = rcParams['insitu'] |
---|
2184 | self._math._setinsitu(insitu) |
---|
2185 | varlist = vars() |
---|
2186 | s = scantable(self._math._bin(self, width)) |
---|
2187 | s._add_history("bin", varlist) |
---|
2188 | if insitu: |
---|
2189 | self._assign(s) |
---|
2190 | else: |
---|
2191 | return s |
---|
2192 | |
---|
2193 | @asaplog_post_dec |
---|
2194 | def resample(self, width=5, method='cubic', insitu=None): |
---|
2195 | """\ |
---|
2196 | Return a scan where all spectra have been binned up. |
---|
2197 | |
---|
2198 | Parameters: |
---|
2199 | |
---|
2200 | width: The bin width (default=5) in pixels |
---|
2201 | |
---|
2202 | method: Interpolation method when correcting from a table. |
---|
2203 | Values are 'nearest', 'linear', 'cubic' (default) |
---|
2204 | and 'spline' |
---|
2205 | |
---|
2206 | insitu: if False a new scantable is returned. |
---|
2207 | Otherwise, the scaling is done in-situ |
---|
2208 | The default is taken from .asaprc (False) |
---|
2209 | |
---|
2210 | """ |
---|
2211 | if insitu is None: |
---|
2212 | insitu = rcParams['insitu'] |
---|
2213 | self._math._setinsitu(insitu) |
---|
2214 | varlist = vars() |
---|
2215 | s = scantable(self._math._resample(self, method, width)) |
---|
2216 | s._add_history("resample", varlist) |
---|
2217 | if insitu: |
---|
2218 | self._assign(s) |
---|
2219 | else: |
---|
2220 | return s |
---|
2221 | |
---|
2222 | @asaplog_post_dec |
---|
2223 | def average_pol(self, mask=None, weight='none'): |
---|
2224 | """\ |
---|
2225 | Average the Polarisations together. |
---|
2226 | |
---|
2227 | Parameters: |
---|
2228 | |
---|
2229 | mask: An optional mask defining the region, where the |
---|
2230 | averaging will be applied. The output will have all |
---|
2231 | specified points masked. |
---|
2232 | |
---|
2233 | weight: Weighting scheme. 'none' (default), 'var' (1/var(spec) |
---|
2234 | weighted), or 'tsys' (1/Tsys**2 weighted) |
---|
2235 | |
---|
2236 | """ |
---|
2237 | varlist = vars() |
---|
2238 | mask = mask or () |
---|
2239 | s = scantable(self._math._averagepol(self, mask, weight.upper())) |
---|
2240 | s._add_history("average_pol", varlist) |
---|
2241 | return s |
---|
2242 | |
---|
2243 | @asaplog_post_dec |
---|
2244 | def average_beam(self, mask=None, weight='none'): |
---|
2245 | """\ |
---|
2246 | Average the Beams together. |
---|
2247 | |
---|
2248 | Parameters: |
---|
2249 | mask: An optional mask defining the region, where the |
---|
2250 | averaging will be applied. The output will have all |
---|
2251 | specified points masked. |
---|
2252 | |
---|
2253 | weight: Weighting scheme. 'none' (default), 'var' (1/var(spec) |
---|
2254 | weighted), or 'tsys' (1/Tsys**2 weighted) |
---|
2255 | |
---|
2256 | """ |
---|
2257 | varlist = vars() |
---|
2258 | mask = mask or () |
---|
2259 | s = scantable(self._math._averagebeams(self, mask, weight.upper())) |
---|
2260 | s._add_history("average_beam", varlist) |
---|
2261 | return s |
---|
2262 | |
---|
2263 | def parallactify(self, pflag): |
---|
2264 | """\ |
---|
2265 | Set a flag to indicate whether this data should be treated as having |
---|
2266 | been 'parallactified' (total phase == 0.0) |
---|
2267 | |
---|
2268 | Parameters: |
---|
2269 | |
---|
2270 | pflag: Bool indicating whether to turn this on (True) or |
---|
2271 | off (False) |
---|
2272 | |
---|
2273 | """ |
---|
2274 | varlist = vars() |
---|
2275 | self._parallactify(pflag) |
---|
2276 | self._add_history("parallactify", varlist) |
---|
2277 | |
---|
2278 | @asaplog_post_dec |
---|
2279 | def convert_pol(self, poltype=None): |
---|
2280 | """\ |
---|
2281 | Convert the data to a different polarisation type. |
---|
2282 | Note that you will need cross-polarisation terms for most conversions. |
---|
2283 | |
---|
2284 | Parameters: |
---|
2285 | |
---|
2286 | poltype: The new polarisation type. Valid types are: |
---|
2287 | 'linear', 'circular', 'stokes' and 'linpol' |
---|
2288 | |
---|
2289 | """ |
---|
2290 | varlist = vars() |
---|
2291 | s = scantable(self._math._convertpol(self, poltype)) |
---|
2292 | s._add_history("convert_pol", varlist) |
---|
2293 | return s |
---|
2294 | |
---|
2295 | @asaplog_post_dec |
---|
2296 | def smooth(self, kernel="hanning", width=5.0, order=2, plot=False, |
---|
2297 | insitu=None): |
---|
2298 | """\ |
---|
2299 | Smooth the spectrum by the specified kernel (conserving flux). |
---|
2300 | |
---|
2301 | Parameters: |
---|
2302 | |
---|
2303 | kernel: The type of smoothing kernel. Select from |
---|
2304 | 'hanning' (default), 'gaussian', 'boxcar', 'rmedian' |
---|
2305 | or 'poly' |
---|
2306 | |
---|
2307 | width: The width of the kernel in pixels. For hanning this is |
---|
2308 | ignored otherwise it defauls to 5 pixels. |
---|
2309 | For 'gaussian' it is the Full Width Half |
---|
2310 | Maximum. For 'boxcar' it is the full width. |
---|
2311 | For 'rmedian' and 'poly' it is the half width. |
---|
2312 | |
---|
2313 | order: Optional parameter for 'poly' kernel (default is 2), to |
---|
2314 | specify the order of the polnomial. Ignored by all other |
---|
2315 | kernels. |
---|
2316 | |
---|
2317 | plot: plot the original and the smoothed spectra. |
---|
2318 | In this each indivual fit has to be approved, by |
---|
2319 | typing 'y' or 'n' |
---|
2320 | |
---|
2321 | insitu: if False a new scantable is returned. |
---|
2322 | Otherwise, the scaling is done in-situ |
---|
2323 | The default is taken from .asaprc (False) |
---|
2324 | |
---|
2325 | """ |
---|
2326 | if insitu is None: insitu = rcParams['insitu'] |
---|
2327 | self._math._setinsitu(insitu) |
---|
2328 | varlist = vars() |
---|
2329 | |
---|
2330 | if plot: orgscan = self.copy() |
---|
2331 | |
---|
2332 | s = scantable(self._math._smooth(self, kernel.lower(), width, order)) |
---|
2333 | s._add_history("smooth", varlist) |
---|
2334 | |
---|
2335 | action = 'H' |
---|
2336 | if plot: |
---|
2337 | from asap.asapplotter import new_asaplot |
---|
2338 | theplot = new_asaplot(rcParams['plotter.gui']) |
---|
2339 | from matplotlib import rc as rcp |
---|
2340 | rcp('lines', linewidth=1) |
---|
2341 | theplot.set_panels() |
---|
2342 | ylab=s._get_ordinate_label() |
---|
2343 | #theplot.palette(0,["#777777","red"]) |
---|
2344 | for r in xrange(s.nrow()): |
---|
2345 | xsm=s._getabcissa(r) |
---|
2346 | ysm=s._getspectrum(r) |
---|
2347 | xorg=orgscan._getabcissa(r) |
---|
2348 | yorg=orgscan._getspectrum(r) |
---|
2349 | if action != "N": #skip plotting if rejecting all |
---|
2350 | theplot.clear() |
---|
2351 | theplot.hold() |
---|
2352 | theplot.set_axes('ylabel',ylab) |
---|
2353 | theplot.set_axes('xlabel',s._getabcissalabel(r)) |
---|
2354 | theplot.set_axes('title',s._getsourcename(r)) |
---|
2355 | theplot.set_line(label='Original',color="#777777") |
---|
2356 | theplot.plot(xorg,yorg) |
---|
2357 | theplot.set_line(label='Smoothed',color="red") |
---|
2358 | theplot.plot(xsm,ysm) |
---|
2359 | ### Ugly part for legend |
---|
2360 | for i in [0,1]: |
---|
2361 | theplot.subplots[0]['lines'].append( |
---|
2362 | [theplot.subplots[0]['axes'].lines[i]] |
---|
2363 | ) |
---|
2364 | theplot.release() |
---|
2365 | ### Ugly part for legend |
---|
2366 | theplot.subplots[0]['lines']=[] |
---|
2367 | res = self._get_verify_action("Accept smoothing?",action) |
---|
2368 | #print "IF%d, POL%d: got result = %s" %(s.getif(r),s.getpol(r),res) |
---|
2369 | if r == 0: action = None |
---|
2370 | #res = raw_input("Accept smoothing ([y]/n): ") |
---|
2371 | if res.upper() == 'N': |
---|
2372 | # reject for the current rows |
---|
2373 | s._setspectrum(yorg, r) |
---|
2374 | elif res.upper() == 'R': |
---|
2375 | # reject all the following rows |
---|
2376 | action = "N" |
---|
2377 | s._setspectrum(yorg, r) |
---|
2378 | elif res.upper() == 'A': |
---|
2379 | # accept all the following rows |
---|
2380 | break |
---|
2381 | theplot.quit() |
---|
2382 | del theplot |
---|
2383 | del orgscan |
---|
2384 | |
---|
2385 | if insitu: self._assign(s) |
---|
2386 | else: return s |
---|
2387 | |
---|
2388 | @asaplog_post_dec |
---|
2389 | def regrid_channel(self, width=5, plot=False, insitu=None): |
---|
2390 | """\ |
---|
2391 | Regrid the spectra by the specified channel width |
---|
2392 | |
---|
2393 | Parameters: |
---|
2394 | |
---|
2395 | width: The channel width (float) of regridded spectra |
---|
2396 | in the current spectral unit. |
---|
2397 | |
---|
2398 | plot: [NOT IMPLEMENTED YET] |
---|
2399 | plot the original and the regridded spectra. |
---|
2400 | In this each indivual fit has to be approved, by |
---|
2401 | typing 'y' or 'n' |
---|
2402 | |
---|
2403 | insitu: if False a new scantable is returned. |
---|
2404 | Otherwise, the scaling is done in-situ |
---|
2405 | The default is taken from .asaprc (False) |
---|
2406 | |
---|
2407 | """ |
---|
2408 | if insitu is None: insitu = rcParams['insitu'] |
---|
2409 | varlist = vars() |
---|
2410 | |
---|
2411 | if plot: |
---|
2412 | asaplog.post() |
---|
2413 | asaplog.push("Verification plot is not implemtnetd yet.") |
---|
2414 | asaplog.post("WARN") |
---|
2415 | |
---|
2416 | s = self.copy() |
---|
2417 | s._regrid_specchan(width) |
---|
2418 | |
---|
2419 | s._add_history("regrid_channel", varlist) |
---|
2420 | |
---|
2421 | # if plot: |
---|
2422 | # from asap.asapplotter import new_asaplot |
---|
2423 | # theplot = new_asaplot(rcParams['plotter.gui']) |
---|
2424 | # from matplotlib import rc as rcp |
---|
2425 | # rcp('lines', linewidth=1) |
---|
2426 | # theplot.set_panels() |
---|
2427 | # ylab=s._get_ordinate_label() |
---|
2428 | # #theplot.palette(0,["#777777","red"]) |
---|
2429 | # for r in xrange(s.nrow()): |
---|
2430 | # xsm=s._getabcissa(r) |
---|
2431 | # ysm=s._getspectrum(r) |
---|
2432 | # xorg=orgscan._getabcissa(r) |
---|
2433 | # yorg=orgscan._getspectrum(r) |
---|
2434 | # theplot.clear() |
---|
2435 | # theplot.hold() |
---|
2436 | # theplot.set_axes('ylabel',ylab) |
---|
2437 | # theplot.set_axes('xlabel',s._getabcissalabel(r)) |
---|
2438 | # theplot.set_axes('title',s._getsourcename(r)) |
---|
2439 | # theplot.set_line(label='Original',color="#777777") |
---|
2440 | # theplot.plot(xorg,yorg) |
---|
2441 | # theplot.set_line(label='Smoothed',color="red") |
---|
2442 | # theplot.plot(xsm,ysm) |
---|
2443 | # ### Ugly part for legend |
---|
2444 | # for i in [0,1]: |
---|
2445 | # theplot.subplots[0]['lines'].append( |
---|
2446 | # [theplot.subplots[0]['axes'].lines[i]] |
---|
2447 | # ) |
---|
2448 | # theplot.release() |
---|
2449 | # ### Ugly part for legend |
---|
2450 | # theplot.subplots[0]['lines']=[] |
---|
2451 | # res = raw_input("Accept smoothing ([y]/n): ") |
---|
2452 | # if res.upper() == 'N': |
---|
2453 | # s._setspectrum(yorg, r) |
---|
2454 | # theplot.quit() |
---|
2455 | # del theplot |
---|
2456 | # del orgscan |
---|
2457 | |
---|
2458 | if insitu: self._assign(s) |
---|
2459 | else: return s |
---|
2460 | |
---|
2461 | @asaplog_post_dec |
---|
2462 | def _parse_wn(self, wn): |
---|
2463 | if isinstance(wn, list) or isinstance(wn, tuple): |
---|
2464 | return wn |
---|
2465 | elif isinstance(wn, int): |
---|
2466 | return [ wn ] |
---|
2467 | elif isinstance(wn, str): |
---|
2468 | if '-' in wn: # case 'a-b' : return [a,a+1,...,b-1,b] |
---|
2469 | val = wn.split('-') |
---|
2470 | val = [int(val[0]), int(val[1])] |
---|
2471 | val.sort() |
---|
2472 | res = [i for i in xrange(val[0], val[1]+1)] |
---|
2473 | elif wn[:2] == '<=' or wn[:2] == '=<': # cases '<=a','=<a' : return [0,1,...,a-1,a] |
---|
2474 | val = int(wn[2:])+1 |
---|
2475 | res = [i for i in xrange(val)] |
---|
2476 | elif wn[-2:] == '>=' or wn[-2:] == '=>': # cases 'a>=','a=>' : return [0,1,...,a-1,a] |
---|
2477 | val = int(wn[:-2])+1 |
---|
2478 | res = [i for i in xrange(val)] |
---|
2479 | elif wn[0] == '<': # case '<a' : return [0,1,...,a-2,a-1] |
---|
2480 | val = int(wn[1:]) |
---|
2481 | res = [i for i in xrange(val)] |
---|
2482 | elif wn[-1] == '>': # case 'a>' : return [0,1,...,a-2,a-1] |
---|
2483 | val = int(wn[:-1]) |
---|
2484 | res = [i for i in xrange(val)] |
---|
2485 | elif wn[:2] == '>=' or wn[:2] == '=>': # cases '>=a','=>a' : return [a,-999], which is |
---|
2486 | # then interpreted in C++ |
---|
2487 | # side as [a,a+1,...,a_nyq] |
---|
2488 | # (CAS-3759) |
---|
2489 | val = int(wn[2:]) |
---|
2490 | res = [val, -999] |
---|
2491 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2492 | elif wn[-2:] == '<=' or wn[-2:] == '=<': # cases 'a<=','a=<' : return [a,-999], which is |
---|
2493 | # then interpreted in C++ |
---|
2494 | # side as [a,a+1,...,a_nyq] |
---|
2495 | # (CAS-3759) |
---|
2496 | val = int(wn[:-2]) |
---|
2497 | res = [val, -999] |
---|
2498 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2499 | elif wn[0] == '>': # case '>a' : return [a+1,-999], which is |
---|
2500 | # then interpreted in C++ |
---|
2501 | # side as [a+1,a+2,...,a_nyq] |
---|
2502 | # (CAS-3759) |
---|
2503 | val = int(wn[1:])+1 |
---|
2504 | res = [val, -999] |
---|
2505 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2506 | elif wn[-1] == '<': # case 'a<' : return [a+1,-999], which is |
---|
2507 | # then interpreted in C++ |
---|
2508 | # side as [a+1,a+2,...,a_nyq] |
---|
2509 | # (CAS-3759) |
---|
2510 | val = int(wn[:-1])+1 |
---|
2511 | res = [val, -999] |
---|
2512 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2513 | |
---|
2514 | return res |
---|
2515 | else: |
---|
2516 | msg = 'wrong value given for addwn/rejwn' |
---|
2517 | raise RuntimeError(msg) |
---|
2518 | |
---|
2519 | |
---|
2520 | @asaplog_post_dec |
---|
2521 | def sinusoid_baseline(self, insitu=None, mask=None, applyfft=None, |
---|
2522 | fftmethod=None, fftthresh=None, |
---|
2523 | addwn=None, rejwn=None, clipthresh=None, |
---|
2524 | clipniter=None, plot=None, |
---|
2525 | getresidual=None, showprogress=None, |
---|
2526 | minnrow=None, outlog=None, blfile=None, csvformat=None): |
---|
2527 | """\ |
---|
2528 | Return a scan which has been baselined (all rows) with sinusoidal |
---|
2529 | functions. |
---|
2530 | |
---|
2531 | Parameters: |
---|
2532 | insitu: if False a new scantable is returned. |
---|
2533 | Otherwise, the scaling is done in-situ |
---|
2534 | The default is taken from .asaprc (False) |
---|
2535 | mask: an optional mask |
---|
2536 | applyfft: if True use some method, such as FFT, to find |
---|
2537 | strongest sinusoidal components in the wavenumber |
---|
2538 | domain to be used for baseline fitting. |
---|
2539 | default is True. |
---|
2540 | fftmethod: method to find the strong sinusoidal components. |
---|
2541 | now only 'fft' is available and it is the default. |
---|
2542 | fftthresh: the threshold to select wave numbers to be used for |
---|
2543 | fitting from the distribution of amplitudes in the |
---|
2544 | wavenumber domain. |
---|
2545 | both float and string values accepted. |
---|
2546 | given a float value, the unit is set to sigma. |
---|
2547 | for string values, allowed formats include: |
---|
2548 | 'xsigma' or 'x' (= x-sigma level. e.g., |
---|
2549 | '3sigma'), or |
---|
2550 | 'topx' (= the x strongest ones, e.g. 'top5'). |
---|
2551 | default is 3.0 (unit: sigma). |
---|
2552 | addwn: the additional wave numbers to be used for fitting. |
---|
2553 | list or integer value is accepted to specify every |
---|
2554 | wave numbers. also string value can be used in case |
---|
2555 | you need to specify wave numbers in a certain range, |
---|
2556 | e.g., 'a-b' (= a, a+1, a+2, ..., b-1, b), |
---|
2557 | '<a' (= 0,1,...,a-2,a-1), |
---|
2558 | '>=a' (= a, a+1, ... up to the maximum wave |
---|
2559 | number corresponding to the Nyquist |
---|
2560 | frequency for the case of FFT). |
---|
2561 | default is [0]. |
---|
2562 | rejwn: the wave numbers NOT to be used for fitting. |
---|
2563 | can be set just as addwn but has higher priority: |
---|
2564 | wave numbers which are specified both in addwn |
---|
2565 | and rejwn will NOT be used. default is []. |
---|
2566 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2567 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2568 | clipping (default is 0) |
---|
2569 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2570 | plot the fit and the residual. In this each |
---|
2571 | indivual fit has to be approved, by typing 'y' |
---|
2572 | or 'n' |
---|
2573 | getresidual: if False, returns best-fit values instead of |
---|
2574 | residual. (default is True) |
---|
2575 | showprogress: show progress status for large data. |
---|
2576 | default is True. |
---|
2577 | minnrow: minimum number of input spectra to show. |
---|
2578 | default is 1000. |
---|
2579 | outlog: Output the coefficients of the best-fit |
---|
2580 | function to logger (default is False) |
---|
2581 | blfile: Name of a text file in which the best-fit |
---|
2582 | parameter values to be written |
---|
2583 | (default is '': no file/logger output) |
---|
2584 | csvformat: if True blfile is csv-formatted, default is False. |
---|
2585 | |
---|
2586 | Example: |
---|
2587 | # return a scan baselined by a combination of sinusoidal curves |
---|
2588 | # having wave numbers in spectral window up to 10, |
---|
2589 | # also with 3-sigma clipping, iteration up to 4 times |
---|
2590 | bscan = scan.sinusoid_baseline(addwn='<=10',clipthresh=3.0,clipniter=4) |
---|
2591 | |
---|
2592 | Note: |
---|
2593 | The best-fit parameter values output in logger and/or blfile are now |
---|
2594 | based on specunit of 'channel'. |
---|
2595 | """ |
---|
2596 | |
---|
2597 | try: |
---|
2598 | varlist = vars() |
---|
2599 | |
---|
2600 | if insitu is None: insitu = rcParams['insitu'] |
---|
2601 | if insitu: |
---|
2602 | workscan = self |
---|
2603 | else: |
---|
2604 | workscan = self.copy() |
---|
2605 | |
---|
2606 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2607 | if mask is None: mask = [] |
---|
2608 | if applyfft is None: applyfft = True |
---|
2609 | if fftmethod is None: fftmethod = 'fft' |
---|
2610 | if fftthresh is None: fftthresh = 3.0 |
---|
2611 | if addwn is None: addwn = [0] |
---|
2612 | if rejwn is None: rejwn = [] |
---|
2613 | if clipthresh is None: clipthresh = 3.0 |
---|
2614 | if clipniter is None: clipniter = 0 |
---|
2615 | if plot is None: plot = False |
---|
2616 | if getresidual is None: getresidual = True |
---|
2617 | if showprogress is None: showprogress = True |
---|
2618 | if minnrow is None: minnrow = 1000 |
---|
2619 | if outlog is None: outlog = False |
---|
2620 | if blfile is None: blfile = '' |
---|
2621 | if csvformat is None: csvformat = False |
---|
2622 | |
---|
2623 | if csvformat: |
---|
2624 | scsvformat = "T" |
---|
2625 | else: |
---|
2626 | scsvformat = "F" |
---|
2627 | |
---|
2628 | #CURRENTLY, PLOT=true is UNAVAILABLE UNTIL sinusoidal fitting is implemented as a fitter method. |
---|
2629 | workscan._sinusoid_baseline(mask, applyfft, fftmethod.lower(), |
---|
2630 | str(fftthresh).lower(), |
---|
2631 | workscan._parse_wn(addwn), |
---|
2632 | workscan._parse_wn(rejwn), |
---|
2633 | clipthresh, clipniter, |
---|
2634 | getresidual, |
---|
2635 | pack_progress_params(showprogress, |
---|
2636 | minnrow), |
---|
2637 | outlog, scsvformat+blfile) |
---|
2638 | workscan._add_history('sinusoid_baseline', varlist) |
---|
2639 | |
---|
2640 | if insitu: |
---|
2641 | self._assign(workscan) |
---|
2642 | else: |
---|
2643 | return workscan |
---|
2644 | |
---|
2645 | except RuntimeError, e: |
---|
2646 | raise_fitting_failure_exception(e) |
---|
2647 | |
---|
2648 | |
---|
2649 | @asaplog_post_dec |
---|
2650 | def auto_sinusoid_baseline(self, insitu=None, mask=None, applyfft=None, |
---|
2651 | fftmethod=None, fftthresh=None, |
---|
2652 | addwn=None, rejwn=None, clipthresh=None, |
---|
2653 | clipniter=None, edge=None, threshold=None, |
---|
2654 | chan_avg_limit=None, plot=None, |
---|
2655 | getresidual=None, showprogress=None, |
---|
2656 | minnrow=None, outlog=None, blfile=None, csvformat=None): |
---|
2657 | """\ |
---|
2658 | Return a scan which has been baselined (all rows) with sinusoidal |
---|
2659 | functions. |
---|
2660 | Spectral lines are detected first using linefinder and masked out |
---|
2661 | to avoid them affecting the baseline solution. |
---|
2662 | |
---|
2663 | Parameters: |
---|
2664 | insitu: if False a new scantable is returned. |
---|
2665 | Otherwise, the scaling is done in-situ |
---|
2666 | The default is taken from .asaprc (False) |
---|
2667 | mask: an optional mask retreived from scantable |
---|
2668 | applyfft: if True use some method, such as FFT, to find |
---|
2669 | strongest sinusoidal components in the wavenumber |
---|
2670 | domain to be used for baseline fitting. |
---|
2671 | default is True. |
---|
2672 | fftmethod: method to find the strong sinusoidal components. |
---|
2673 | now only 'fft' is available and it is the default. |
---|
2674 | fftthresh: the threshold to select wave numbers to be used for |
---|
2675 | fitting from the distribution of amplitudes in the |
---|
2676 | wavenumber domain. |
---|
2677 | both float and string values accepted. |
---|
2678 | given a float value, the unit is set to sigma. |
---|
2679 | for string values, allowed formats include: |
---|
2680 | 'xsigma' or 'x' (= x-sigma level. e.g., |
---|
2681 | '3sigma'), or |
---|
2682 | 'topx' (= the x strongest ones, e.g. 'top5'). |
---|
2683 | default is 3.0 (unit: sigma). |
---|
2684 | addwn: the additional wave numbers to be used for fitting. |
---|
2685 | list or integer value is accepted to specify every |
---|
2686 | wave numbers. also string value can be used in case |
---|
2687 | you need to specify wave numbers in a certain range, |
---|
2688 | e.g., 'a-b' (= a, a+1, a+2, ..., b-1, b), |
---|
2689 | '<a' (= 0,1,...,a-2,a-1), |
---|
2690 | '>=a' (= a, a+1, ... up to the maximum wave |
---|
2691 | number corresponding to the Nyquist |
---|
2692 | frequency for the case of FFT). |
---|
2693 | default is [0]. |
---|
2694 | rejwn: the wave numbers NOT to be used for fitting. |
---|
2695 | can be set just as addwn but has higher priority: |
---|
2696 | wave numbers which are specified both in addwn |
---|
2697 | and rejwn will NOT be used. default is []. |
---|
2698 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2699 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2700 | clipping (default is 0) |
---|
2701 | edge: an optional number of channel to drop at |
---|
2702 | the edge of spectrum. If only one value is |
---|
2703 | specified, the same number will be dropped |
---|
2704 | from both sides of the spectrum. Default |
---|
2705 | is to keep all channels. Nested tuples |
---|
2706 | represent individual edge selection for |
---|
2707 | different IFs (a number of spectral channels |
---|
2708 | can be different) |
---|
2709 | threshold: the threshold used by line finder. It is |
---|
2710 | better to keep it large as only strong lines |
---|
2711 | affect the baseline solution. |
---|
2712 | chan_avg_limit: a maximum number of consequtive spectral |
---|
2713 | channels to average during the search of |
---|
2714 | weak and broad lines. The default is no |
---|
2715 | averaging (and no search for weak lines). |
---|
2716 | If such lines can affect the fitted baseline |
---|
2717 | (e.g. a high order polynomial is fitted), |
---|
2718 | increase this parameter (usually values up |
---|
2719 | to 8 are reasonable). Most users of this |
---|
2720 | method should find the default value sufficient. |
---|
2721 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2722 | plot the fit and the residual. In this each |
---|
2723 | indivual fit has to be approved, by typing 'y' |
---|
2724 | or 'n' |
---|
2725 | getresidual: if False, returns best-fit values instead of |
---|
2726 | residual. (default is True) |
---|
2727 | showprogress: show progress status for large data. |
---|
2728 | default is True. |
---|
2729 | minnrow: minimum number of input spectra to show. |
---|
2730 | default is 1000. |
---|
2731 | outlog: Output the coefficients of the best-fit |
---|
2732 | function to logger (default is False) |
---|
2733 | blfile: Name of a text file in which the best-fit |
---|
2734 | parameter values to be written |
---|
2735 | (default is "": no file/logger output) |
---|
2736 | csvformat: if True blfile is csv-formatted, default is False. |
---|
2737 | |
---|
2738 | Example: |
---|
2739 | bscan = scan.auto_sinusoid_baseline(addwn='<=10', insitu=False) |
---|
2740 | |
---|
2741 | Note: |
---|
2742 | The best-fit parameter values output in logger and/or blfile are now |
---|
2743 | based on specunit of 'channel'. |
---|
2744 | """ |
---|
2745 | |
---|
2746 | try: |
---|
2747 | varlist = vars() |
---|
2748 | |
---|
2749 | if insitu is None: insitu = rcParams['insitu'] |
---|
2750 | if insitu: |
---|
2751 | workscan = self |
---|
2752 | else: |
---|
2753 | workscan = self.copy() |
---|
2754 | |
---|
2755 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2756 | if mask is None: mask = [] |
---|
2757 | if applyfft is None: applyfft = True |
---|
2758 | if fftmethod is None: fftmethod = 'fft' |
---|
2759 | if fftthresh is None: fftthresh = 3.0 |
---|
2760 | if addwn is None: addwn = [0] |
---|
2761 | if rejwn is None: rejwn = [] |
---|
2762 | if clipthresh is None: clipthresh = 3.0 |
---|
2763 | if clipniter is None: clipniter = 0 |
---|
2764 | if edge is None: edge = (0,0) |
---|
2765 | if threshold is None: threshold = 3 |
---|
2766 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
2767 | if plot is None: plot = False |
---|
2768 | if getresidual is None: getresidual = True |
---|
2769 | if showprogress is None: showprogress = True |
---|
2770 | if minnrow is None: minnrow = 1000 |
---|
2771 | if outlog is None: outlog = False |
---|
2772 | if blfile is None: blfile = '' |
---|
2773 | if csvformat is None: csvformat = False |
---|
2774 | |
---|
2775 | if csvformat: |
---|
2776 | scsvformat = "T" |
---|
2777 | else: |
---|
2778 | scsvformat = "F" |
---|
2779 | |
---|
2780 | #CURRENTLY, PLOT=true is UNAVAILABLE UNTIL sinusoidal fitting is implemented as a fitter method. |
---|
2781 | workscan._auto_sinusoid_baseline(mask, applyfft, |
---|
2782 | fftmethod.lower(), |
---|
2783 | str(fftthresh).lower(), |
---|
2784 | workscan._parse_wn(addwn), |
---|
2785 | workscan._parse_wn(rejwn), |
---|
2786 | clipthresh, clipniter, |
---|
2787 | normalise_edge_param(edge), |
---|
2788 | threshold, chan_avg_limit, |
---|
2789 | getresidual, |
---|
2790 | pack_progress_params(showprogress, |
---|
2791 | minnrow), |
---|
2792 | outlog, scsvformat+blfile) |
---|
2793 | workscan._add_history("auto_sinusoid_baseline", varlist) |
---|
2794 | |
---|
2795 | if insitu: |
---|
2796 | self._assign(workscan) |
---|
2797 | else: |
---|
2798 | return workscan |
---|
2799 | |
---|
2800 | except RuntimeError, e: |
---|
2801 | raise_fitting_failure_exception(e) |
---|
2802 | |
---|
2803 | @asaplog_post_dec |
---|
2804 | def cspline_baseline(self, insitu=None, mask=None, npiece=None, |
---|
2805 | clipthresh=None, clipniter=None, plot=None, |
---|
2806 | getresidual=None, showprogress=None, minnrow=None, |
---|
2807 | outlog=None, blfile=None, csvformat=None): |
---|
2808 | """\ |
---|
2809 | Return a scan which has been baselined (all rows) by cubic spline |
---|
2810 | function (piecewise cubic polynomial). |
---|
2811 | |
---|
2812 | Parameters: |
---|
2813 | insitu: If False a new scantable is returned. |
---|
2814 | Otherwise, the scaling is done in-situ |
---|
2815 | The default is taken from .asaprc (False) |
---|
2816 | mask: An optional mask |
---|
2817 | npiece: Number of pieces. (default is 2) |
---|
2818 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2819 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2820 | clipping (default is 0) |
---|
2821 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2822 | plot the fit and the residual. In this each |
---|
2823 | indivual fit has to be approved, by typing 'y' |
---|
2824 | or 'n' |
---|
2825 | getresidual: if False, returns best-fit values instead of |
---|
2826 | residual. (default is True) |
---|
2827 | showprogress: show progress status for large data. |
---|
2828 | default is True. |
---|
2829 | minnrow: minimum number of input spectra to show. |
---|
2830 | default is 1000. |
---|
2831 | outlog: Output the coefficients of the best-fit |
---|
2832 | function to logger (default is False) |
---|
2833 | blfile: Name of a text file in which the best-fit |
---|
2834 | parameter values to be written |
---|
2835 | (default is "": no file/logger output) |
---|
2836 | csvformat: if True blfile is csv-formatted, default is False. |
---|
2837 | |
---|
2838 | Example: |
---|
2839 | # return a scan baselined by a cubic spline consisting of 2 pieces |
---|
2840 | # (i.e., 1 internal knot), |
---|
2841 | # also with 3-sigma clipping, iteration up to 4 times |
---|
2842 | bscan = scan.cspline_baseline(npiece=2,clipthresh=3.0,clipniter=4) |
---|
2843 | |
---|
2844 | Note: |
---|
2845 | The best-fit parameter values output in logger and/or blfile are now |
---|
2846 | based on specunit of 'channel'. |
---|
2847 | """ |
---|
2848 | |
---|
2849 | try: |
---|
2850 | varlist = vars() |
---|
2851 | |
---|
2852 | if insitu is None: insitu = rcParams['insitu'] |
---|
2853 | if insitu: |
---|
2854 | workscan = self |
---|
2855 | else: |
---|
2856 | workscan = self.copy() |
---|
2857 | |
---|
2858 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2859 | if mask is None: mask = [] |
---|
2860 | if npiece is None: npiece = 2 |
---|
2861 | if clipthresh is None: clipthresh = 3.0 |
---|
2862 | if clipniter is None: clipniter = 0 |
---|
2863 | if plot is None: plot = False |
---|
2864 | if getresidual is None: getresidual = True |
---|
2865 | if showprogress is None: showprogress = True |
---|
2866 | if minnrow is None: minnrow = 1000 |
---|
2867 | if outlog is None: outlog = False |
---|
2868 | if blfile is None: blfile = '' |
---|
2869 | if csvformat is None: csvformat = False |
---|
2870 | |
---|
2871 | if csvformat: |
---|
2872 | scsvformat = "T" |
---|
2873 | else: |
---|
2874 | scsvformat = "F" |
---|
2875 | |
---|
2876 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
2877 | workscan._cspline_baseline(mask, npiece, clipthresh, clipniter, |
---|
2878 | getresidual, |
---|
2879 | pack_progress_params(showprogress, |
---|
2880 | minnrow), |
---|
2881 | outlog, scsvformat+blfile) |
---|
2882 | workscan._add_history("cspline_baseline", varlist) |
---|
2883 | |
---|
2884 | if insitu: |
---|
2885 | self._assign(workscan) |
---|
2886 | else: |
---|
2887 | return workscan |
---|
2888 | |
---|
2889 | except RuntimeError, e: |
---|
2890 | raise_fitting_failure_exception(e) |
---|
2891 | |
---|
2892 | @asaplog_post_dec |
---|
2893 | def auto_cspline_baseline(self, insitu=None, mask=None, npiece=None, |
---|
2894 | clipthresh=None, clipniter=None, |
---|
2895 | edge=None, threshold=None, chan_avg_limit=None, |
---|
2896 | getresidual=None, plot=None, |
---|
2897 | showprogress=None, minnrow=None, outlog=None, |
---|
2898 | blfile=None, csvformat=None): |
---|
2899 | """\ |
---|
2900 | Return a scan which has been baselined (all rows) by cubic spline |
---|
2901 | function (piecewise cubic polynomial). |
---|
2902 | Spectral lines are detected first using linefinder and masked out |
---|
2903 | to avoid them affecting the baseline solution. |
---|
2904 | |
---|
2905 | Parameters: |
---|
2906 | insitu: if False a new scantable is returned. |
---|
2907 | Otherwise, the scaling is done in-situ |
---|
2908 | The default is taken from .asaprc (False) |
---|
2909 | mask: an optional mask retreived from scantable |
---|
2910 | npiece: Number of pieces. (default is 2) |
---|
2911 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2912 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2913 | clipping (default is 0) |
---|
2914 | edge: an optional number of channel to drop at |
---|
2915 | the edge of spectrum. If only one value is |
---|
2916 | specified, the same number will be dropped |
---|
2917 | from both sides of the spectrum. Default |
---|
2918 | is to keep all channels. Nested tuples |
---|
2919 | represent individual edge selection for |
---|
2920 | different IFs (a number of spectral channels |
---|
2921 | can be different) |
---|
2922 | threshold: the threshold used by line finder. It is |
---|
2923 | better to keep it large as only strong lines |
---|
2924 | affect the baseline solution. |
---|
2925 | chan_avg_limit: a maximum number of consequtive spectral |
---|
2926 | channels to average during the search of |
---|
2927 | weak and broad lines. The default is no |
---|
2928 | averaging (and no search for weak lines). |
---|
2929 | If such lines can affect the fitted baseline |
---|
2930 | (e.g. a high order polynomial is fitted), |
---|
2931 | increase this parameter (usually values up |
---|
2932 | to 8 are reasonable). Most users of this |
---|
2933 | method should find the default value sufficient. |
---|
2934 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2935 | plot the fit and the residual. In this each |
---|
2936 | indivual fit has to be approved, by typing 'y' |
---|
2937 | or 'n' |
---|
2938 | getresidual: if False, returns best-fit values instead of |
---|
2939 | residual. (default is True) |
---|
2940 | showprogress: show progress status for large data. |
---|
2941 | default is True. |
---|
2942 | minnrow: minimum number of input spectra to show. |
---|
2943 | default is 1000. |
---|
2944 | outlog: Output the coefficients of the best-fit |
---|
2945 | function to logger (default is False) |
---|
2946 | blfile: Name of a text file in which the best-fit |
---|
2947 | parameter values to be written |
---|
2948 | (default is "": no file/logger output) |
---|
2949 | csvformat: if True blfile is csv-formatted, default is False. |
---|
2950 | |
---|
2951 | Example: |
---|
2952 | bscan = scan.auto_cspline_baseline(npiece=3, insitu=False) |
---|
2953 | |
---|
2954 | Note: |
---|
2955 | The best-fit parameter values output in logger and/or blfile are now |
---|
2956 | based on specunit of 'channel'. |
---|
2957 | """ |
---|
2958 | |
---|
2959 | try: |
---|
2960 | varlist = vars() |
---|
2961 | |
---|
2962 | if insitu is None: insitu = rcParams['insitu'] |
---|
2963 | if insitu: |
---|
2964 | workscan = self |
---|
2965 | else: |
---|
2966 | workscan = self.copy() |
---|
2967 | |
---|
2968 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2969 | if mask is None: mask = [] |
---|
2970 | if npiece is None: npiece = 2 |
---|
2971 | if clipthresh is None: clipthresh = 3.0 |
---|
2972 | if clipniter is None: clipniter = 0 |
---|
2973 | if edge is None: edge = (0, 0) |
---|
2974 | if threshold is None: threshold = 3 |
---|
2975 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
2976 | if plot is None: plot = False |
---|
2977 | if getresidual is None: getresidual = True |
---|
2978 | if showprogress is None: showprogress = True |
---|
2979 | if minnrow is None: minnrow = 1000 |
---|
2980 | if outlog is None: outlog = False |
---|
2981 | if blfile is None: blfile = '' |
---|
2982 | if csvformat is None: csvformat = False |
---|
2983 | |
---|
2984 | if csvformat: |
---|
2985 | scsvformat = "T" |
---|
2986 | else: |
---|
2987 | scsvformat = "F" |
---|
2988 | |
---|
2989 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
2990 | workscan._auto_cspline_baseline(mask, npiece, clipthresh, |
---|
2991 | clipniter, |
---|
2992 | normalise_edge_param(edge), |
---|
2993 | threshold, |
---|
2994 | chan_avg_limit, getresidual, |
---|
2995 | pack_progress_params(showprogress, |
---|
2996 | minnrow), |
---|
2997 | outlog, scsvformat+blfile) |
---|
2998 | workscan._add_history("auto_cspline_baseline", varlist) |
---|
2999 | |
---|
3000 | if insitu: |
---|
3001 | self._assign(workscan) |
---|
3002 | else: |
---|
3003 | return workscan |
---|
3004 | |
---|
3005 | except RuntimeError, e: |
---|
3006 | raise_fitting_failure_exception(e) |
---|
3007 | |
---|
3008 | @asaplog_post_dec |
---|
3009 | def chebyshev_baseline(self, insitu=None, mask=None, order=None, |
---|
3010 | clipthresh=None, clipniter=None, plot=None, |
---|
3011 | getresidual=None, showprogress=None, minnrow=None, |
---|
3012 | outlog=None, blfile=None, csvformat=None): |
---|
3013 | """\ |
---|
3014 | Return a scan which has been baselined (all rows) by Chebyshev polynomials. |
---|
3015 | |
---|
3016 | Parameters: |
---|
3017 | insitu: If False a new scantable is returned. |
---|
3018 | Otherwise, the scaling is done in-situ |
---|
3019 | The default is taken from .asaprc (False) |
---|
3020 | mask: An optional mask |
---|
3021 | order: the maximum order of Chebyshev polynomial (default is 5) |
---|
3022 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
3023 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
3024 | clipping (default is 0) |
---|
3025 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
3026 | plot the fit and the residual. In this each |
---|
3027 | indivual fit has to be approved, by typing 'y' |
---|
3028 | or 'n' |
---|
3029 | getresidual: if False, returns best-fit values instead of |
---|
3030 | residual. (default is True) |
---|
3031 | showprogress: show progress status for large data. |
---|
3032 | default is True. |
---|
3033 | minnrow: minimum number of input spectra to show. |
---|
3034 | default is 1000. |
---|
3035 | outlog: Output the coefficients of the best-fit |
---|
3036 | function to logger (default is False) |
---|
3037 | blfile: Name of a text file in which the best-fit |
---|
3038 | parameter values to be written |
---|
3039 | (default is "": no file/logger output) |
---|
3040 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3041 | |
---|
3042 | Example: |
---|
3043 | # return a scan baselined by a cubic spline consisting of 2 pieces |
---|
3044 | # (i.e., 1 internal knot), |
---|
3045 | # also with 3-sigma clipping, iteration up to 4 times |
---|
3046 | bscan = scan.cspline_baseline(npiece=2,clipthresh=3.0,clipniter=4) |
---|
3047 | |
---|
3048 | Note: |
---|
3049 | The best-fit parameter values output in logger and/or blfile are now |
---|
3050 | based on specunit of 'channel'. |
---|
3051 | """ |
---|
3052 | |
---|
3053 | try: |
---|
3054 | varlist = vars() |
---|
3055 | |
---|
3056 | if insitu is None: insitu = rcParams['insitu'] |
---|
3057 | if insitu: |
---|
3058 | workscan = self |
---|
3059 | else: |
---|
3060 | workscan = self.copy() |
---|
3061 | |
---|
3062 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
3063 | if mask is None: mask = [] |
---|
3064 | if order is None: order = 5 |
---|
3065 | if clipthresh is None: clipthresh = 3.0 |
---|
3066 | if clipniter is None: clipniter = 0 |
---|
3067 | if plot is None: plot = False |
---|
3068 | if getresidual is None: getresidual = True |
---|
3069 | if showprogress is None: showprogress = True |
---|
3070 | if minnrow is None: minnrow = 1000 |
---|
3071 | if outlog is None: outlog = False |
---|
3072 | if blfile is None: blfile = '' |
---|
3073 | if csvformat is None: csvformat = False |
---|
3074 | |
---|
3075 | if csvformat: |
---|
3076 | scsvformat = "T" |
---|
3077 | else: |
---|
3078 | scsvformat = "F" |
---|
3079 | |
---|
3080 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
3081 | workscan._chebyshev_baseline(mask, order, clipthresh, clipniter, |
---|
3082 | getresidual, |
---|
3083 | pack_progress_params(showprogress, |
---|
3084 | minnrow), |
---|
3085 | outlog, scsvformat+blfile) |
---|
3086 | workscan._add_history("chebyshev_baseline", varlist) |
---|
3087 | |
---|
3088 | if insitu: |
---|
3089 | self._assign(workscan) |
---|
3090 | else: |
---|
3091 | return workscan |
---|
3092 | |
---|
3093 | except RuntimeError, e: |
---|
3094 | raise_fitting_failure_exception(e) |
---|
3095 | |
---|
3096 | @asaplog_post_dec |
---|
3097 | def auto_chebyshev_baseline(self, insitu=None, mask=None, order=None, |
---|
3098 | clipthresh=None, clipniter=None, |
---|
3099 | edge=None, threshold=None, chan_avg_limit=None, |
---|
3100 | getresidual=None, plot=None, |
---|
3101 | showprogress=None, minnrow=None, outlog=None, |
---|
3102 | blfile=None, csvformat=None): |
---|
3103 | """\ |
---|
3104 | Return a scan which has been baselined (all rows) by Chebyshev polynomials. |
---|
3105 | Spectral lines are detected first using linefinder and masked out |
---|
3106 | to avoid them affecting the baseline solution. |
---|
3107 | |
---|
3108 | Parameters: |
---|
3109 | insitu: if False a new scantable is returned. |
---|
3110 | Otherwise, the scaling is done in-situ |
---|
3111 | The default is taken from .asaprc (False) |
---|
3112 | mask: an optional mask retreived from scantable |
---|
3113 | order: the maximum order of Chebyshev polynomial (default is 5) |
---|
3114 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
3115 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
3116 | clipping (default is 0) |
---|
3117 | edge: an optional number of channel to drop at |
---|
3118 | the edge of spectrum. If only one value is |
---|
3119 | specified, the same number will be dropped |
---|
3120 | from both sides of the spectrum. Default |
---|
3121 | is to keep all channels. Nested tuples |
---|
3122 | represent individual edge selection for |
---|
3123 | different IFs (a number of spectral channels |
---|
3124 | can be different) |
---|
3125 | threshold: the threshold used by line finder. It is |
---|
3126 | better to keep it large as only strong lines |
---|
3127 | affect the baseline solution. |
---|
3128 | chan_avg_limit: a maximum number of consequtive spectral |
---|
3129 | channels to average during the search of |
---|
3130 | weak and broad lines. The default is no |
---|
3131 | averaging (and no search for weak lines). |
---|
3132 | If such lines can affect the fitted baseline |
---|
3133 | (e.g. a high order polynomial is fitted), |
---|
3134 | increase this parameter (usually values up |
---|
3135 | to 8 are reasonable). Most users of this |
---|
3136 | method should find the default value sufficient. |
---|
3137 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
3138 | plot the fit and the residual. In this each |
---|
3139 | indivual fit has to be approved, by typing 'y' |
---|
3140 | or 'n' |
---|
3141 | getresidual: if False, returns best-fit values instead of |
---|
3142 | residual. (default is True) |
---|
3143 | showprogress: show progress status for large data. |
---|
3144 | default is True. |
---|
3145 | minnrow: minimum number of input spectra to show. |
---|
3146 | default is 1000. |
---|
3147 | outlog: Output the coefficients of the best-fit |
---|
3148 | function to logger (default is False) |
---|
3149 | blfile: Name of a text file in which the best-fit |
---|
3150 | parameter values to be written |
---|
3151 | (default is "": no file/logger output) |
---|
3152 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3153 | |
---|
3154 | Example: |
---|
3155 | bscan = scan.auto_cspline_baseline(npiece=3, insitu=False) |
---|
3156 | |
---|
3157 | Note: |
---|
3158 | The best-fit parameter values output in logger and/or blfile are now |
---|
3159 | based on specunit of 'channel'. |
---|
3160 | """ |
---|
3161 | |
---|
3162 | try: |
---|
3163 | varlist = vars() |
---|
3164 | |
---|
3165 | if insitu is None: insitu = rcParams['insitu'] |
---|
3166 | if insitu: |
---|
3167 | workscan = self |
---|
3168 | else: |
---|
3169 | workscan = self.copy() |
---|
3170 | |
---|
3171 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
3172 | if mask is None: mask = [] |
---|
3173 | if order is None: order = 5 |
---|
3174 | if clipthresh is None: clipthresh = 3.0 |
---|
3175 | if clipniter is None: clipniter = 0 |
---|
3176 | if edge is None: edge = (0, 0) |
---|
3177 | if threshold is None: threshold = 3 |
---|
3178 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
3179 | if plot is None: plot = False |
---|
3180 | if getresidual is None: getresidual = True |
---|
3181 | if showprogress is None: showprogress = True |
---|
3182 | if minnrow is None: minnrow = 1000 |
---|
3183 | if outlog is None: outlog = False |
---|
3184 | if blfile is None: blfile = '' |
---|
3185 | if csvformat is None: csvformat = False |
---|
3186 | |
---|
3187 | if csvformat: |
---|
3188 | scsvformat = "T" |
---|
3189 | else: |
---|
3190 | scsvformat = "F" |
---|
3191 | |
---|
3192 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
3193 | workscan._auto_chebyshev_baseline(mask, order, clipthresh, |
---|
3194 | clipniter, |
---|
3195 | normalise_edge_param(edge), |
---|
3196 | threshold, |
---|
3197 | chan_avg_limit, getresidual, |
---|
3198 | pack_progress_params(showprogress, |
---|
3199 | minnrow), |
---|
3200 | outlog, scsvformat+blfile) |
---|
3201 | workscan._add_history("auto_chebyshev_baseline", varlist) |
---|
3202 | |
---|
3203 | if insitu: |
---|
3204 | self._assign(workscan) |
---|
3205 | else: |
---|
3206 | return workscan |
---|
3207 | |
---|
3208 | except RuntimeError, e: |
---|
3209 | raise_fitting_failure_exception(e) |
---|
3210 | |
---|
3211 | @asaplog_post_dec |
---|
3212 | def poly_baseline(self, mask=None, order=None, insitu=None, plot=None, |
---|
3213 | getresidual=None, showprogress=None, minnrow=None, |
---|
3214 | outlog=None, blfile=None, csvformat=None): |
---|
3215 | """\ |
---|
3216 | Return a scan which has been baselined (all rows) by a polynomial. |
---|
3217 | Parameters: |
---|
3218 | insitu: if False a new scantable is returned. |
---|
3219 | Otherwise, the scaling is done in-situ |
---|
3220 | The default is taken from .asaprc (False) |
---|
3221 | mask: an optional mask |
---|
3222 | order: the order of the polynomial (default is 0) |
---|
3223 | plot: plot the fit and the residual. In this each |
---|
3224 | indivual fit has to be approved, by typing 'y' |
---|
3225 | or 'n' |
---|
3226 | getresidual: if False, returns best-fit values instead of |
---|
3227 | residual. (default is True) |
---|
3228 | showprogress: show progress status for large data. |
---|
3229 | default is True. |
---|
3230 | minnrow: minimum number of input spectra to show. |
---|
3231 | default is 1000. |
---|
3232 | outlog: Output the coefficients of the best-fit |
---|
3233 | function to logger (default is False) |
---|
3234 | blfile: Name of a text file in which the best-fit |
---|
3235 | parameter values to be written |
---|
3236 | (default is "": no file/logger output) |
---|
3237 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3238 | |
---|
3239 | Example: |
---|
3240 | # return a scan baselined by a third order polynomial, |
---|
3241 | # not using a mask |
---|
3242 | bscan = scan.poly_baseline(order=3) |
---|
3243 | """ |
---|
3244 | |
---|
3245 | try: |
---|
3246 | varlist = vars() |
---|
3247 | |
---|
3248 | if insitu is None: |
---|
3249 | insitu = rcParams["insitu"] |
---|
3250 | if insitu: |
---|
3251 | workscan = self |
---|
3252 | else: |
---|
3253 | workscan = self.copy() |
---|
3254 | |
---|
3255 | #if mask is None: mask = [True for i in \ |
---|
3256 | # xrange(workscan.nchan())] |
---|
3257 | if mask is None: mask = [] |
---|
3258 | if order is None: order = 0 |
---|
3259 | if plot is None: plot = False |
---|
3260 | if getresidual is None: getresidual = True |
---|
3261 | if showprogress is None: showprogress = True |
---|
3262 | if minnrow is None: minnrow = 1000 |
---|
3263 | if outlog is None: outlog = False |
---|
3264 | if blfile is None: blfile = "" |
---|
3265 | if csvformat is None: csvformat = False |
---|
3266 | |
---|
3267 | if csvformat: |
---|
3268 | scsvformat = "T" |
---|
3269 | else: |
---|
3270 | scsvformat = "F" |
---|
3271 | |
---|
3272 | if plot: |
---|
3273 | outblfile = (blfile != "") and \ |
---|
3274 | os.path.exists(os.path.expanduser( |
---|
3275 | os.path.expandvars(blfile)) |
---|
3276 | ) |
---|
3277 | if outblfile: |
---|
3278 | blf = open(blfile, "a") |
---|
3279 | |
---|
3280 | f = fitter() |
---|
3281 | f.set_function(lpoly=order) |
---|
3282 | |
---|
3283 | rows = xrange(workscan.nrow()) |
---|
3284 | #if len(rows) > 0: workscan._init_blinfo() |
---|
3285 | |
---|
3286 | action = "H" |
---|
3287 | for r in rows: |
---|
3288 | f.x = workscan._getabcissa(r) |
---|
3289 | f.y = workscan._getspectrum(r) |
---|
3290 | if mask: |
---|
3291 | f.mask = mask_and(mask, workscan._getmask(r)) # (CAS-1434) |
---|
3292 | else: # mask=None |
---|
3293 | f.mask = workscan._getmask(r) |
---|
3294 | |
---|
3295 | f.data = None |
---|
3296 | f.fit() |
---|
3297 | |
---|
3298 | if action != "Y": # skip plotting when accepting all |
---|
3299 | f.plot(residual=True) |
---|
3300 | #accept_fit = raw_input("Accept fit ( [y]/n ): ") |
---|
3301 | #if accept_fit.upper() == "N": |
---|
3302 | # #workscan._append_blinfo(None, None, None) |
---|
3303 | # continue |
---|
3304 | accept_fit = self._get_verify_action("Accept fit?",action) |
---|
3305 | if r == 0: action = None |
---|
3306 | if accept_fit.upper() == "N": |
---|
3307 | continue |
---|
3308 | elif accept_fit.upper() == "R": |
---|
3309 | break |
---|
3310 | elif accept_fit.upper() == "A": |
---|
3311 | action = "Y" |
---|
3312 | |
---|
3313 | blpars = f.get_parameters() |
---|
3314 | masklist = workscan.get_masklist(f.mask, row=r, silent=True) |
---|
3315 | #workscan._append_blinfo(blpars, masklist, f.mask) |
---|
3316 | workscan._setspectrum((f.fitter.getresidual() |
---|
3317 | if getresidual else f.fitter.getfit()), r) |
---|
3318 | |
---|
3319 | if outblfile: |
---|
3320 | rms = workscan.get_rms(f.mask, r) |
---|
3321 | dataout = \ |
---|
3322 | workscan.format_blparams_row(blpars["params"], |
---|
3323 | blpars["fixed"], |
---|
3324 | rms, str(masklist), |
---|
3325 | r, True, csvformat) |
---|
3326 | blf.write(dataout) |
---|
3327 | |
---|
3328 | f._p.unmap() |
---|
3329 | f._p = None |
---|
3330 | |
---|
3331 | if outblfile: |
---|
3332 | blf.close() |
---|
3333 | else: |
---|
3334 | workscan._poly_baseline(mask, order, getresidual, |
---|
3335 | pack_progress_params(showprogress, |
---|
3336 | minnrow), |
---|
3337 | outlog, scsvformat+blfile) |
---|
3338 | |
---|
3339 | workscan._add_history("poly_baseline", varlist) |
---|
3340 | |
---|
3341 | if insitu: |
---|
3342 | self._assign(workscan) |
---|
3343 | else: |
---|
3344 | return workscan |
---|
3345 | |
---|
3346 | except RuntimeError, e: |
---|
3347 | raise_fitting_failure_exception(e) |
---|
3348 | |
---|
3349 | @asaplog_post_dec |
---|
3350 | def auto_poly_baseline(self, mask=None, order=None, edge=None, |
---|
3351 | threshold=None, chan_avg_limit=None, |
---|
3352 | plot=None, insitu=None, |
---|
3353 | getresidual=None, showprogress=None, |
---|
3354 | minnrow=None, outlog=None, blfile=None, csvformat=None): |
---|
3355 | """\ |
---|
3356 | Return a scan which has been baselined (all rows) by a polynomial. |
---|
3357 | Spectral lines are detected first using linefinder and masked out |
---|
3358 | to avoid them affecting the baseline solution. |
---|
3359 | |
---|
3360 | Parameters: |
---|
3361 | insitu: if False a new scantable is returned. |
---|
3362 | Otherwise, the scaling is done in-situ |
---|
3363 | The default is taken from .asaprc (False) |
---|
3364 | mask: an optional mask retreived from scantable |
---|
3365 | order: the order of the polynomial (default is 0) |
---|
3366 | edge: an optional number of channel to drop at |
---|
3367 | the edge of spectrum. If only one value is |
---|
3368 | specified, the same number will be dropped |
---|
3369 | from both sides of the spectrum. Default |
---|
3370 | is to keep all channels. Nested tuples |
---|
3371 | represent individual edge selection for |
---|
3372 | different IFs (a number of spectral channels |
---|
3373 | can be different) |
---|
3374 | threshold: the threshold used by line finder. It is |
---|
3375 | better to keep it large as only strong lines |
---|
3376 | affect the baseline solution. |
---|
3377 | chan_avg_limit: a maximum number of consequtive spectral |
---|
3378 | channels to average during the search of |
---|
3379 | weak and broad lines. The default is no |
---|
3380 | averaging (and no search for weak lines). |
---|
3381 | If such lines can affect the fitted baseline |
---|
3382 | (e.g. a high order polynomial is fitted), |
---|
3383 | increase this parameter (usually values up |
---|
3384 | to 8 are reasonable). Most users of this |
---|
3385 | method should find the default value sufficient. |
---|
3386 | plot: plot the fit and the residual. In this each |
---|
3387 | indivual fit has to be approved, by typing 'y' |
---|
3388 | or 'n' |
---|
3389 | getresidual: if False, returns best-fit values instead of |
---|
3390 | residual. (default is True) |
---|
3391 | showprogress: show progress status for large data. |
---|
3392 | default is True. |
---|
3393 | minnrow: minimum number of input spectra to show. |
---|
3394 | default is 1000. |
---|
3395 | outlog: Output the coefficients of the best-fit |
---|
3396 | function to logger (default is False) |
---|
3397 | blfile: Name of a text file in which the best-fit |
---|
3398 | parameter values to be written |
---|
3399 | (default is "": no file/logger output) |
---|
3400 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3401 | |
---|
3402 | Example: |
---|
3403 | bscan = scan.auto_poly_baseline(order=7, insitu=False) |
---|
3404 | """ |
---|
3405 | |
---|
3406 | try: |
---|
3407 | varlist = vars() |
---|
3408 | |
---|
3409 | if insitu is None: |
---|
3410 | insitu = rcParams['insitu'] |
---|
3411 | if insitu: |
---|
3412 | workscan = self |
---|
3413 | else: |
---|
3414 | workscan = self.copy() |
---|
3415 | |
---|
3416 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
3417 | if mask is None: mask = [] |
---|
3418 | if order is None: order = 0 |
---|
3419 | if edge is None: edge = (0, 0) |
---|
3420 | if threshold is None: threshold = 3 |
---|
3421 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
3422 | if plot is None: plot = False |
---|
3423 | if getresidual is None: getresidual = True |
---|
3424 | if showprogress is None: showprogress = True |
---|
3425 | if minnrow is None: minnrow = 1000 |
---|
3426 | if outlog is None: outlog = False |
---|
3427 | if blfile is None: blfile = '' |
---|
3428 | if csvformat is None: csvformat = False |
---|
3429 | |
---|
3430 | if csvformat: |
---|
3431 | scsvformat = "T" |
---|
3432 | else: |
---|
3433 | scsvformat = "F" |
---|
3434 | |
---|
3435 | edge = normalise_edge_param(edge) |
---|
3436 | |
---|
3437 | if plot: |
---|
3438 | outblfile = (blfile != "") and \ |
---|
3439 | os.path.exists(os.path.expanduser(os.path.expandvars(blfile))) |
---|
3440 | if outblfile: blf = open(blfile, "a") |
---|
3441 | |
---|
3442 | from asap.asaplinefind import linefinder |
---|
3443 | fl = linefinder() |
---|
3444 | fl.set_options(threshold=threshold, avg_limit=chan_avg_limit) |
---|
3445 | fl.set_scan(workscan) |
---|
3446 | |
---|
3447 | f = fitter() |
---|
3448 | f.set_function(lpoly=order) |
---|
3449 | |
---|
3450 | rows = xrange(workscan.nrow()) |
---|
3451 | #if len(rows) > 0: workscan._init_blinfo() |
---|
3452 | |
---|
3453 | action = "H" |
---|
3454 | for r in rows: |
---|
3455 | idx = 2*workscan.getif(r) |
---|
3456 | if mask: |
---|
3457 | msk = mask_and(mask, workscan._getmask(r)) # (CAS-1434) |
---|
3458 | else: # mask=None |
---|
3459 | msk = workscan._getmask(r) |
---|
3460 | fl.find_lines(r, msk, edge[idx:idx+2]) |
---|
3461 | |
---|
3462 | f.x = workscan._getabcissa(r) |
---|
3463 | f.y = workscan._getspectrum(r) |
---|
3464 | f.mask = fl.get_mask() |
---|
3465 | f.data = None |
---|
3466 | f.fit() |
---|
3467 | |
---|
3468 | if action != "Y": # skip plotting when accepting all |
---|
3469 | f.plot(residual=True) |
---|
3470 | #accept_fit = raw_input("Accept fit ( [y]/n ): ") |
---|
3471 | accept_fit = self._get_verify_action("Accept fit?",action) |
---|
3472 | if r == 0: action = None |
---|
3473 | if accept_fit.upper() == "N": |
---|
3474 | #workscan._append_blinfo(None, None, None) |
---|
3475 | continue |
---|
3476 | elif accept_fit.upper() == "R": |
---|
3477 | break |
---|
3478 | elif accept_fit.upper() == "A": |
---|
3479 | action = "Y" |
---|
3480 | |
---|
3481 | blpars = f.get_parameters() |
---|
3482 | masklist = workscan.get_masklist(f.mask, row=r, silent=True) |
---|
3483 | #workscan._append_blinfo(blpars, masklist, f.mask) |
---|
3484 | workscan._setspectrum( |
---|
3485 | (f.fitter.getresidual() if getresidual |
---|
3486 | else f.fitter.getfit()), r |
---|
3487 | ) |
---|
3488 | |
---|
3489 | if outblfile: |
---|
3490 | rms = workscan.get_rms(f.mask, r) |
---|
3491 | dataout = \ |
---|
3492 | workscan.format_blparams_row(blpars["params"], |
---|
3493 | blpars["fixed"], |
---|
3494 | rms, str(masklist), |
---|
3495 | r, True, csvformat) |
---|
3496 | blf.write(dataout) |
---|
3497 | |
---|
3498 | f._p.unmap() |
---|
3499 | f._p = None |
---|
3500 | |
---|
3501 | if outblfile: blf.close() |
---|
3502 | else: |
---|
3503 | workscan._auto_poly_baseline(mask, order, edge, threshold, |
---|
3504 | chan_avg_limit, getresidual, |
---|
3505 | pack_progress_params(showprogress, |
---|
3506 | minnrow), |
---|
3507 | outlog, scsvformat+blfile) |
---|
3508 | |
---|
3509 | workscan._add_history("auto_poly_baseline", varlist) |
---|
3510 | |
---|
3511 | if insitu: |
---|
3512 | self._assign(workscan) |
---|
3513 | else: |
---|
3514 | return workscan |
---|
3515 | |
---|
3516 | except RuntimeError, e: |
---|
3517 | raise_fitting_failure_exception(e) |
---|
3518 | |
---|
3519 | def _init_blinfo(self): |
---|
3520 | """\ |
---|
3521 | Initialise the following three auxiliary members: |
---|
3522 | blpars : parameters of the best-fit baseline, |
---|
3523 | masklists : mask data (edge positions of masked channels) and |
---|
3524 | actualmask : mask data (in boolean list), |
---|
3525 | to keep for use later (including output to logger/text files). |
---|
3526 | Used by poly_baseline() and auto_poly_baseline() in case of |
---|
3527 | 'plot=True'. |
---|
3528 | """ |
---|
3529 | self.blpars = [] |
---|
3530 | self.masklists = [] |
---|
3531 | self.actualmask = [] |
---|
3532 | return |
---|
3533 | |
---|
3534 | def _append_blinfo(self, data_blpars, data_masklists, data_actualmask): |
---|
3535 | """\ |
---|
3536 | Append baseline-fitting related info to blpars, masklist and |
---|
3537 | actualmask. |
---|
3538 | """ |
---|
3539 | self.blpars.append(data_blpars) |
---|
3540 | self.masklists.append(data_masklists) |
---|
3541 | self.actualmask.append(data_actualmask) |
---|
3542 | return |
---|
3543 | |
---|
3544 | @asaplog_post_dec |
---|
3545 | def rotate_linpolphase(self, angle): |
---|
3546 | """\ |
---|
3547 | Rotate the phase of the complex polarization O=Q+iU correlation. |
---|
3548 | This is always done in situ in the raw data. So if you call this |
---|
3549 | function more than once then each call rotates the phase further. |
---|
3550 | |
---|
3551 | Parameters: |
---|
3552 | |
---|
3553 | angle: The angle (degrees) to rotate (add) by. |
---|
3554 | |
---|
3555 | Example:: |
---|
3556 | |
---|
3557 | scan.rotate_linpolphase(2.3) |
---|
3558 | |
---|
3559 | """ |
---|
3560 | varlist = vars() |
---|
3561 | self._math._rotate_linpolphase(self, angle) |
---|
3562 | self._add_history("rotate_linpolphase", varlist) |
---|
3563 | return |
---|
3564 | |
---|
3565 | @asaplog_post_dec |
---|
3566 | def rotate_xyphase(self, angle): |
---|
3567 | """\ |
---|
3568 | Rotate the phase of the XY correlation. This is always done in situ |
---|
3569 | in the data. So if you call this function more than once |
---|
3570 | then each call rotates the phase further. |
---|
3571 | |
---|
3572 | Parameters: |
---|
3573 | |
---|
3574 | angle: The angle (degrees) to rotate (add) by. |
---|
3575 | |
---|
3576 | Example:: |
---|
3577 | |
---|
3578 | scan.rotate_xyphase(2.3) |
---|
3579 | |
---|
3580 | """ |
---|
3581 | varlist = vars() |
---|
3582 | self._math._rotate_xyphase(self, angle) |
---|
3583 | self._add_history("rotate_xyphase", varlist) |
---|
3584 | return |
---|
3585 | |
---|
3586 | @asaplog_post_dec |
---|
3587 | def swap_linears(self): |
---|
3588 | """\ |
---|
3589 | Swap the linear polarisations XX and YY, or better the first two |
---|
3590 | polarisations as this also works for ciculars. |
---|
3591 | """ |
---|
3592 | varlist = vars() |
---|
3593 | self._math._swap_linears(self) |
---|
3594 | self._add_history("swap_linears", varlist) |
---|
3595 | return |
---|
3596 | |
---|
3597 | @asaplog_post_dec |
---|
3598 | def invert_phase(self): |
---|
3599 | """\ |
---|
3600 | Invert the phase of the complex polarisation |
---|
3601 | """ |
---|
3602 | varlist = vars() |
---|
3603 | self._math._invert_phase(self) |
---|
3604 | self._add_history("invert_phase", varlist) |
---|
3605 | return |
---|
3606 | |
---|
3607 | @asaplog_post_dec |
---|
3608 | def add(self, offset, insitu=None): |
---|
3609 | """\ |
---|
3610 | Return a scan where all spectra have the offset added |
---|
3611 | |
---|
3612 | Parameters: |
---|
3613 | |
---|
3614 | offset: the offset |
---|
3615 | |
---|
3616 | insitu: if False a new scantable is returned. |
---|
3617 | Otherwise, the scaling is done in-situ |
---|
3618 | The default is taken from .asaprc (False) |
---|
3619 | |
---|
3620 | """ |
---|
3621 | if insitu is None: insitu = rcParams['insitu'] |
---|
3622 | self._math._setinsitu(insitu) |
---|
3623 | varlist = vars() |
---|
3624 | s = scantable(self._math._unaryop(self, offset, "ADD", False)) |
---|
3625 | s._add_history("add", varlist) |
---|
3626 | if insitu: |
---|
3627 | self._assign(s) |
---|
3628 | else: |
---|
3629 | return s |
---|
3630 | |
---|
3631 | @asaplog_post_dec |
---|
3632 | def scale(self, factor, tsys=True, insitu=None): |
---|
3633 | """\ |
---|
3634 | |
---|
3635 | Return a scan where all spectra are scaled by the given 'factor' |
---|
3636 | |
---|
3637 | Parameters: |
---|
3638 | |
---|
3639 | factor: the scaling factor (float or 1D float list) |
---|
3640 | |
---|
3641 | insitu: if False a new scantable is returned. |
---|
3642 | Otherwise, the scaling is done in-situ |
---|
3643 | The default is taken from .asaprc (False) |
---|
3644 | |
---|
3645 | tsys: if True (default) then apply the operation to Tsys |
---|
3646 | as well as the data |
---|
3647 | |
---|
3648 | """ |
---|
3649 | if insitu is None: insitu = rcParams['insitu'] |
---|
3650 | self._math._setinsitu(insitu) |
---|
3651 | varlist = vars() |
---|
3652 | s = None |
---|
3653 | import numpy |
---|
3654 | if isinstance(factor, list) or isinstance(factor, numpy.ndarray): |
---|
3655 | if isinstance(factor[0], list) or isinstance(factor[0], |
---|
3656 | numpy.ndarray): |
---|
3657 | from asapmath import _array2dOp |
---|
3658 | s = _array2dOp( self, factor, "MUL", tsys, insitu ) |
---|
3659 | else: |
---|
3660 | s = scantable( self._math._arrayop( self, factor, |
---|
3661 | "MUL", tsys ) ) |
---|
3662 | else: |
---|
3663 | s = scantable(self._math._unaryop(self, factor, "MUL", tsys)) |
---|
3664 | s._add_history("scale", varlist) |
---|
3665 | if insitu: |
---|
3666 | self._assign(s) |
---|
3667 | else: |
---|
3668 | return s |
---|
3669 | |
---|
3670 | @preserve_selection |
---|
3671 | def set_sourcetype(self, match, matchtype="pattern", |
---|
3672 | sourcetype="reference"): |
---|
3673 | """\ |
---|
3674 | Set the type of the source to be an source or reference scan |
---|
3675 | using the provided pattern. |
---|
3676 | |
---|
3677 | Parameters: |
---|
3678 | |
---|
3679 | match: a Unix style pattern, regular expression or selector |
---|
3680 | |
---|
3681 | matchtype: 'pattern' (default) UNIX style pattern or |
---|
3682 | 'regex' regular expression |
---|
3683 | |
---|
3684 | sourcetype: the type of the source to use (source/reference) |
---|
3685 | |
---|
3686 | """ |
---|
3687 | varlist = vars() |
---|
3688 | stype = -1 |
---|
3689 | if sourcetype.lower().startswith("r") or sourcetype.lower() == "off": |
---|
3690 | stype = 1 |
---|
3691 | elif sourcetype.lower().startswith("s") or sourcetype.lower() == "on": |
---|
3692 | stype = 0 |
---|
3693 | else: |
---|
3694 | raise ValueError("Illegal sourcetype use s(ource)/on or r(eference)/off") |
---|
3695 | if matchtype.lower().startswith("p"): |
---|
3696 | matchtype = "pattern" |
---|
3697 | elif matchtype.lower().startswith("r"): |
---|
3698 | matchtype = "regex" |
---|
3699 | else: |
---|
3700 | raise ValueError("Illegal matchtype, use p(attern) or r(egex)") |
---|
3701 | sel = selector() |
---|
3702 | if isinstance(match, selector): |
---|
3703 | sel = match |
---|
3704 | else: |
---|
3705 | sel.set_query("SRCNAME=%s('%s')" % (matchtype, match)) |
---|
3706 | self.set_selection(sel) |
---|
3707 | self._setsourcetype(stype) |
---|
3708 | self._add_history("set_sourcetype", varlist) |
---|
3709 | |
---|
3710 | @asaplog_post_dec |
---|
3711 | @preserve_selection |
---|
3712 | def auto_quotient(self, preserve=True, mode='paired', verify=False): |
---|
3713 | """\ |
---|
3714 | This function allows to build quotients automatically. |
---|
3715 | It assumes the observation to have the same number of |
---|
3716 | "ons" and "offs" |
---|
3717 | |
---|
3718 | Parameters: |
---|
3719 | |
---|
3720 | preserve: you can preserve (default) the continuum or |
---|
3721 | remove it. The equations used are |
---|
3722 | |
---|
3723 | preserve: Output = Toff * (on/off) - Toff |
---|
3724 | |
---|
3725 | remove: Output = Toff * (on/off) - Ton |
---|
3726 | |
---|
3727 | mode: the on/off detection mode |
---|
3728 | 'paired' (default) |
---|
3729 | identifies 'off' scans by the |
---|
3730 | trailing '_R' (Mopra/Parkes) or |
---|
3731 | '_e'/'_w' (Tid) and matches |
---|
3732 | on/off pairs from the observing pattern |
---|
3733 | 'time' |
---|
3734 | finds the closest off in time |
---|
3735 | |
---|
3736 | .. todo:: verify argument is not implemented |
---|
3737 | |
---|
3738 | """ |
---|
3739 | varlist = vars() |
---|
3740 | modes = ["time", "paired"] |
---|
3741 | if not mode in modes: |
---|
3742 | msg = "please provide valid mode. Valid modes are %s" % (modes) |
---|
3743 | raise ValueError(msg) |
---|
3744 | s = None |
---|
3745 | if mode.lower() == "paired": |
---|
3746 | sel = self.get_selection() |
---|
3747 | sel.set_query("SRCTYPE==psoff") |
---|
3748 | self.set_selection(sel) |
---|
3749 | offs = self.copy() |
---|
3750 | sel.set_query("SRCTYPE==pson") |
---|
3751 | self.set_selection(sel) |
---|
3752 | ons = self.copy() |
---|
3753 | s = scantable(self._math._quotient(ons, offs, preserve)) |
---|
3754 | elif mode.lower() == "time": |
---|
3755 | s = scantable(self._math._auto_quotient(self, mode, preserve)) |
---|
3756 | s._add_history("auto_quotient", varlist) |
---|
3757 | return s |
---|
3758 | |
---|
3759 | @asaplog_post_dec |
---|
3760 | def mx_quotient(self, mask = None, weight='median', preserve=True): |
---|
3761 | """\ |
---|
3762 | Form a quotient using "off" beams when observing in "MX" mode. |
---|
3763 | |
---|
3764 | Parameters: |
---|
3765 | |
---|
3766 | mask: an optional mask to be used when weight == 'stddev' |
---|
3767 | |
---|
3768 | weight: How to average the off beams. Default is 'median'. |
---|
3769 | |
---|
3770 | preserve: you can preserve (default) the continuum or |
---|
3771 | remove it. The equations used are: |
---|
3772 | |
---|
3773 | preserve: Output = Toff * (on/off) - Toff |
---|
3774 | |
---|
3775 | remove: Output = Toff * (on/off) - Ton |
---|
3776 | |
---|
3777 | """ |
---|
3778 | mask = mask or () |
---|
3779 | varlist = vars() |
---|
3780 | on = scantable(self._math._mx_extract(self, 'on')) |
---|
3781 | preoff = scantable(self._math._mx_extract(self, 'off')) |
---|
3782 | off = preoff.average_time(mask=mask, weight=weight, scanav=False) |
---|
3783 | from asapmath import quotient |
---|
3784 | q = quotient(on, off, preserve) |
---|
3785 | q._add_history("mx_quotient", varlist) |
---|
3786 | return q |
---|
3787 | |
---|
3788 | @asaplog_post_dec |
---|
3789 | def freq_switch(self, insitu=None): |
---|
3790 | """\ |
---|
3791 | Apply frequency switching to the data. |
---|
3792 | |
---|
3793 | Parameters: |
---|
3794 | |
---|
3795 | insitu: if False a new scantable is returned. |
---|
3796 | Otherwise, the swictching is done in-situ |
---|
3797 | The default is taken from .asaprc (False) |
---|
3798 | |
---|
3799 | """ |
---|
3800 | if insitu is None: insitu = rcParams['insitu'] |
---|
3801 | self._math._setinsitu(insitu) |
---|
3802 | varlist = vars() |
---|
3803 | s = scantable(self._math._freqswitch(self)) |
---|
3804 | s._add_history("freq_switch", varlist) |
---|
3805 | if insitu: |
---|
3806 | self._assign(s) |
---|
3807 | else: |
---|
3808 | return s |
---|
3809 | |
---|
3810 | @asaplog_post_dec |
---|
3811 | def recalc_azel(self): |
---|
3812 | """Recalculate the azimuth and elevation for each position.""" |
---|
3813 | varlist = vars() |
---|
3814 | self._recalcazel() |
---|
3815 | self._add_history("recalc_azel", varlist) |
---|
3816 | return |
---|
3817 | |
---|
3818 | @asaplog_post_dec |
---|
3819 | def __add__(self, other): |
---|
3820 | """ |
---|
3821 | implicit on all axes and on Tsys |
---|
3822 | """ |
---|
3823 | varlist = vars() |
---|
3824 | s = self.__op( other, "ADD" ) |
---|
3825 | s._add_history("operator +", varlist) |
---|
3826 | return s |
---|
3827 | |
---|
3828 | @asaplog_post_dec |
---|
3829 | def __sub__(self, other): |
---|
3830 | """ |
---|
3831 | implicit on all axes and on Tsys |
---|
3832 | """ |
---|
3833 | varlist = vars() |
---|
3834 | s = self.__op( other, "SUB" ) |
---|
3835 | s._add_history("operator -", varlist) |
---|
3836 | return s |
---|
3837 | |
---|
3838 | @asaplog_post_dec |
---|
3839 | def __mul__(self, other): |
---|
3840 | """ |
---|
3841 | implicit on all axes and on Tsys |
---|
3842 | """ |
---|
3843 | varlist = vars() |
---|
3844 | s = self.__op( other, "MUL" ) ; |
---|
3845 | s._add_history("operator *", varlist) |
---|
3846 | return s |
---|
3847 | |
---|
3848 | |
---|
3849 | @asaplog_post_dec |
---|
3850 | def __div__(self, other): |
---|
3851 | """ |
---|
3852 | implicit on all axes and on Tsys |
---|
3853 | """ |
---|
3854 | varlist = vars() |
---|
3855 | s = self.__op( other, "DIV" ) |
---|
3856 | s._add_history("operator /", varlist) |
---|
3857 | return s |
---|
3858 | |
---|
3859 | @asaplog_post_dec |
---|
3860 | def __op( self, other, mode ): |
---|
3861 | s = None |
---|
3862 | if isinstance(other, scantable): |
---|
3863 | s = scantable(self._math._binaryop(self, other, mode)) |
---|
3864 | elif isinstance(other, float): |
---|
3865 | if other == 0.0: |
---|
3866 | raise ZeroDivisionError("Dividing by zero is not recommended") |
---|
3867 | s = scantable(self._math._unaryop(self, other, mode, False)) |
---|
3868 | elif isinstance(other, list) or isinstance(other, numpy.ndarray): |
---|
3869 | if isinstance(other[0], list) \ |
---|
3870 | or isinstance(other[0], numpy.ndarray): |
---|
3871 | from asapmath import _array2dOp |
---|
3872 | s = _array2dOp( self, other, mode, False ) |
---|
3873 | else: |
---|
3874 | s = scantable( self._math._arrayop( self, other, |
---|
3875 | mode, False ) ) |
---|
3876 | else: |
---|
3877 | raise TypeError("Other input is not a scantable or float value") |
---|
3878 | return s |
---|
3879 | |
---|
3880 | @asaplog_post_dec |
---|
3881 | def get_fit(self, row=0): |
---|
3882 | """\ |
---|
3883 | Print or return the stored fits for a row in the scantable |
---|
3884 | |
---|
3885 | Parameters: |
---|
3886 | |
---|
3887 | row: the row which the fit has been applied to. |
---|
3888 | |
---|
3889 | """ |
---|
3890 | if row > self.nrow(): |
---|
3891 | return |
---|
3892 | from asap.asapfit import asapfit |
---|
3893 | fit = asapfit(self._getfit(row)) |
---|
3894 | asaplog.push( '%s' %(fit) ) |
---|
3895 | return fit.as_dict() |
---|
3896 | |
---|
3897 | @preserve_selection |
---|
3898 | def flag_nans(self): |
---|
3899 | """\ |
---|
3900 | Utility function to flag NaN values in the scantable. |
---|
3901 | """ |
---|
3902 | import numpy |
---|
3903 | basesel = self.get_selection() |
---|
3904 | for i in range(self.nrow()): |
---|
3905 | sel = self.get_row_selector(i) |
---|
3906 | self.set_selection(basesel+sel) |
---|
3907 | nans = numpy.isnan(self._getspectrum(0)) |
---|
3908 | if numpy.any(nans): |
---|
3909 | bnans = [ bool(v) for v in nans] |
---|
3910 | self.flag(bnans) |
---|
3911 | |
---|
3912 | def get_row_selector(self, rowno): |
---|
3913 | return selector(rows=[rowno]) |
---|
3914 | |
---|
3915 | def _add_history(self, funcname, parameters): |
---|
3916 | if not rcParams['scantable.history']: |
---|
3917 | return |
---|
3918 | # create date |
---|
3919 | sep = "##" |
---|
3920 | from datetime import datetime |
---|
3921 | dstr = datetime.now().strftime('%Y/%m/%d %H:%M:%S') |
---|
3922 | hist = dstr+sep |
---|
3923 | hist += funcname+sep#cdate+sep |
---|
3924 | if parameters.has_key('self'): |
---|
3925 | del parameters['self'] |
---|
3926 | for k, v in parameters.iteritems(): |
---|
3927 | if type(v) is dict: |
---|
3928 | for k2, v2 in v.iteritems(): |
---|
3929 | hist += k2 |
---|
3930 | hist += "=" |
---|
3931 | if isinstance(v2, scantable): |
---|
3932 | hist += 'scantable' |
---|
3933 | elif k2 == 'mask': |
---|
3934 | if isinstance(v2, list) or isinstance(v2, tuple): |
---|
3935 | hist += str(self._zip_mask(v2)) |
---|
3936 | else: |
---|
3937 | hist += str(v2) |
---|
3938 | else: |
---|
3939 | hist += str(v2) |
---|
3940 | else: |
---|
3941 | hist += k |
---|
3942 | hist += "=" |
---|
3943 | if isinstance(v, scantable): |
---|
3944 | hist += 'scantable' |
---|
3945 | elif k == 'mask': |
---|
3946 | if isinstance(v, list) or isinstance(v, tuple): |
---|
3947 | hist += str(self._zip_mask(v)) |
---|
3948 | else: |
---|
3949 | hist += str(v) |
---|
3950 | else: |
---|
3951 | hist += str(v) |
---|
3952 | hist += sep |
---|
3953 | hist = hist[:-2] # remove trailing '##' |
---|
3954 | self._addhistory(hist) |
---|
3955 | |
---|
3956 | |
---|
3957 | def _zip_mask(self, mask): |
---|
3958 | mask = list(mask) |
---|
3959 | i = 0 |
---|
3960 | segments = [] |
---|
3961 | while mask[i:].count(1): |
---|
3962 | i += mask[i:].index(1) |
---|
3963 | if mask[i:].count(0): |
---|
3964 | j = i + mask[i:].index(0) |
---|
3965 | else: |
---|
3966 | j = len(mask) |
---|
3967 | segments.append([i, j]) |
---|
3968 | i = j |
---|
3969 | return segments |
---|
3970 | |
---|
3971 | def _get_ordinate_label(self): |
---|
3972 | fu = "("+self.get_fluxunit()+")" |
---|
3973 | import re |
---|
3974 | lbl = "Intensity" |
---|
3975 | if re.match(".K.", fu): |
---|
3976 | lbl = "Brightness Temperature "+ fu |
---|
3977 | elif re.match(".Jy.", fu): |
---|
3978 | lbl = "Flux density "+ fu |
---|
3979 | return lbl |
---|
3980 | |
---|
3981 | def _check_ifs(self): |
---|
3982 | # return len(set([self.nchan(i) for i in self.getifnos()])) == 1 |
---|
3983 | nchans = [self.nchan(i) for i in self.getifnos()] |
---|
3984 | nchans = filter(lambda t: t > 0, nchans) |
---|
3985 | return (sum(nchans)/len(nchans) == nchans[0]) |
---|
3986 | |
---|
3987 | @asaplog_post_dec |
---|
3988 | def _fill(self, names, unit, average, opts={}): |
---|
3989 | first = True |
---|
3990 | fullnames = [] |
---|
3991 | for name in names: |
---|
3992 | name = os.path.expandvars(name) |
---|
3993 | name = os.path.expanduser(name) |
---|
3994 | if not os.path.exists(name): |
---|
3995 | msg = "File '%s' does not exists" % (name) |
---|
3996 | raise IOError(msg) |
---|
3997 | fullnames.append(name) |
---|
3998 | if average: |
---|
3999 | asaplog.push('Auto averaging integrations') |
---|
4000 | stype = int(rcParams['scantable.storage'].lower() == 'disk') |
---|
4001 | for name in fullnames: |
---|
4002 | tbl = Scantable(stype) |
---|
4003 | if is_ms( name ): |
---|
4004 | r = msfiller( tbl ) |
---|
4005 | else: |
---|
4006 | r = filler( tbl ) |
---|
4007 | msg = "Importing %s..." % (name) |
---|
4008 | asaplog.push(msg, False) |
---|
4009 | r.open(name, opts) |
---|
4010 | rx = rcParams['scantable.reference'] |
---|
4011 | r.setreferenceexpr(rx) |
---|
4012 | r.fill() |
---|
4013 | if average: |
---|
4014 | tbl = self._math._average((tbl, ), (), 'NONE', 'SCAN') |
---|
4015 | if not first: |
---|
4016 | tbl = self._math._merge([self, tbl]) |
---|
4017 | Scantable.__init__(self, tbl) |
---|
4018 | r.close() |
---|
4019 | del r, tbl |
---|
4020 | first = False |
---|
4021 | #flush log |
---|
4022 | asaplog.post() |
---|
4023 | if unit is not None: |
---|
4024 | self.set_fluxunit(unit) |
---|
4025 | if not is_casapy(): |
---|
4026 | self.set_freqframe(rcParams['scantable.freqframe']) |
---|
4027 | |
---|
4028 | def _get_verify_action( self, msg, action=None ): |
---|
4029 | valid_act = ['Y', 'N', 'A', 'R'] |
---|
4030 | if not action or not isinstance(action, str): |
---|
4031 | action = raw_input("%s [Y/n/a/r] (h for help): " % msg) |
---|
4032 | if action == '': |
---|
4033 | return "Y" |
---|
4034 | elif (action.upper()[0] in valid_act): |
---|
4035 | return action.upper()[0] |
---|
4036 | elif (action.upper()[0] in ['H','?']): |
---|
4037 | print "Available actions of verification [Y|n|a|r]" |
---|
4038 | print " Y : Yes for current data (default)" |
---|
4039 | print " N : No for current data" |
---|
4040 | print " A : Accept all in the following and exit from verification" |
---|
4041 | print " R : Reject all in the following and exit from verification" |
---|
4042 | print " H or ?: help (show this message)" |
---|
4043 | return self._get_verify_action(msg) |
---|
4044 | else: |
---|
4045 | return 'Y' |
---|
4046 | |
---|
4047 | def __getitem__(self, key): |
---|
4048 | if key < 0: |
---|
4049 | key += self.nrow() |
---|
4050 | if key >= self.nrow(): |
---|
4051 | raise IndexError("Row index out of range.") |
---|
4052 | return self._getspectrum(key) |
---|
4053 | |
---|
4054 | def __setitem__(self, key, value): |
---|
4055 | if key < 0: |
---|
4056 | key += self.nrow() |
---|
4057 | if key >= self.nrow(): |
---|
4058 | raise IndexError("Row index out of range.") |
---|
4059 | if not hasattr(value, "__len__") or \ |
---|
4060 | len(value) > self.nchan(self.getif(key)): |
---|
4061 | raise ValueError("Spectrum length doesn't match.") |
---|
4062 | return self._setspectrum(value, key) |
---|
4063 | |
---|
4064 | def __len__(self): |
---|
4065 | return self.nrow() |
---|
4066 | |
---|
4067 | def __iter__(self): |
---|
4068 | for i in range(len(self)): |
---|
4069 | yield self[i] |
---|