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 re |
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5 | import tempfile |
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6 | import numpy |
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7 | try: |
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8 | from functools import wraps as wraps_dec |
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9 | except ImportError: |
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10 | from asap.compatibility import wraps as wraps_dec |
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11 | |
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12 | from asap.env import is_casapy |
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13 | from asap._asap import Scantable |
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14 | from asap._asap import filler, msfiller |
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15 | from asap.parameters import rcParams |
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16 | from asap.logging import asaplog, asaplog_post_dec |
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17 | from asap.selector import selector |
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18 | from asap.linecatalog import linecatalog |
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19 | from asap.coordinate import coordinate |
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20 | from asap.utils import _n_bools, mask_not, mask_and, mask_or, page |
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21 | from asap.asapfitter import fitter |
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22 | |
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23 | def preserve_selection(func): |
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24 | @wraps_dec(func) |
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25 | def wrap(obj, *args, **kw): |
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26 | basesel = obj.get_selection() |
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27 | try: |
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28 | val = func(obj, *args, **kw) |
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29 | finally: |
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30 | obj.set_selection(basesel) |
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31 | return val |
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32 | return wrap |
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33 | |
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34 | def is_scantable(filename): |
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35 | """Is the given file a scantable? |
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36 | |
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37 | Parameters: |
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38 | |
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39 | filename: the name of the file/directory to test |
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40 | |
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41 | """ |
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42 | if ( os.path.isdir(filename) |
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43 | and os.path.exists(filename+'/table.info') |
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44 | and os.path.exists(filename+'/table.dat') ): |
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45 | f=open(filename+'/table.info') |
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46 | l=f.readline() |
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47 | f.close() |
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48 | match_pattern = '^Type = (Scantable)? *$' |
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49 | if re.match(match_pattern,l): |
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50 | return True |
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51 | else: |
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52 | return False |
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53 | else: |
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54 | return False |
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55 | ## return (os.path.isdir(filename) |
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56 | ## and not os.path.exists(filename+'/table.f1') |
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57 | ## and os.path.exists(filename+'/table.info')) |
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58 | |
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59 | def is_ms(filename): |
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60 | """Is the given file a MeasurementSet? |
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61 | |
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62 | Parameters: |
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63 | |
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64 | filename: the name of the file/directory to test |
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65 | |
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66 | """ |
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67 | if ( os.path.isdir(filename) |
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68 | and os.path.exists(filename+'/table.info') |
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69 | and os.path.exists(filename+'/table.dat') ): |
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70 | f=open(filename+'/table.info') |
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71 | l=f.readline() |
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72 | f.close() |
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73 | if ( l.find('Measurement Set') != -1 ): |
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74 | return True |
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75 | else: |
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76 | return False |
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77 | else: |
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78 | return False |
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79 | |
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80 | def normalise_edge_param(edge): |
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81 | """\ |
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82 | Convert a given edge value to a one-dimensional array that can be |
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83 | given to baseline-fitting/subtraction functions. |
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84 | The length of the output value will be an even because values for |
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85 | the both sides of spectra are to be contained for each IF. When |
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86 | the length is 2, the values will be applied to all IFs. If the length |
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87 | is larger than 2, it will be 2*ifnos(). |
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88 | Accepted format of edge include: |
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89 | * an integer - will be used for both sides of spectra of all IFs. |
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90 | e.g. 10 is converted to [10,10] |
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91 | * an empty list/tuple [] - converted to [0, 0] and used for all IFs. |
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92 | * a list/tuple containing an integer - same as the above case. |
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93 | e.g. [10] is converted to [10,10] |
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94 | * a list/tuple containing two integers - will be used for all IFs. |
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95 | e.g. [5,10] is output as it is. no need to convert. |
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96 | * a list/tuple of lists/tuples containing TWO integers - |
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97 | each element of edge will be used for each IF. |
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98 | e.g. [[5,10],[15,20]] - [5,10] for IF[0] and [15,20] for IF[1]. |
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99 | |
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100 | If an element contains the same integer values, the input 'edge' |
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101 | parameter can be given in a simpler shape in the following cases: |
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102 | ** when len(edge)!=2 |
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103 | any elements containing the same values can be replaced |
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104 | to single integers. |
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105 | e.g. [[15,15]] can be simplified to [15] (or [15,15] or 15 also). |
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106 | e.g. [[1,1],[2,2],[3,3]] can be simplified to [1,2,3]. |
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107 | ** when len(edge)=2 |
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108 | care is needed for this case: ONLY ONE of the |
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109 | elements can be a single integer, |
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110 | e.g. [[5,5],[10,10]] can be simplified to [5,[10,10]] |
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111 | or [[5,5],10], but can NOT be simplified to [5,10]. |
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112 | when [5,10] given, it is interpreted as |
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113 | [[5,10],[5,10],[5,10],...] instead, as shown before. |
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114 | """ |
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115 | from asap import _is_sequence_or_number as _is_valid |
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116 | if isinstance(edge, list) or isinstance(edge, tuple): |
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117 | for edgepar in edge: |
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118 | if not _is_valid(edgepar, int): |
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119 | raise ValueError, "Each element of the 'edge' tuple has \ |
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120 | to be a pair of integers or an integer." |
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121 | if isinstance(edgepar, list) or isinstance(edgepar, tuple): |
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122 | if len(edgepar) != 2: |
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123 | raise ValueError, "Each element of the 'edge' tuple has \ |
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124 | to be a pair of integers or an integer." |
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125 | else: |
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126 | if not _is_valid(edge, int): |
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127 | raise ValueError, "Parameter 'edge' has to be an integer or a \ |
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128 | pair of integers specified as a tuple. \ |
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129 | Nested tuples are allowed \ |
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130 | to make individual selection for different IFs." |
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131 | |
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132 | |
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133 | if isinstance(edge, int): |
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134 | edge = [ edge, edge ] # e.g. 3 => [3,3] |
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135 | elif isinstance(edge, list) or isinstance(edge, tuple): |
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136 | if len(edge) == 0: |
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137 | edge = [0, 0] # e.g. [] => [0,0] |
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138 | elif len(edge) == 1: |
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139 | if isinstance(edge[0], int): |
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140 | edge = [ edge[0], edge[0] ] # e.g. [1] => [1,1] |
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141 | |
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142 | commonedge = True |
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143 | if len(edge) > 2: commonedge = False |
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144 | else: |
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145 | for edgepar in edge: |
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146 | if isinstance(edgepar, list) or isinstance(edgepar, tuple): |
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147 | commonedge = False |
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148 | break |
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149 | |
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150 | if commonedge: |
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151 | if len(edge) > 1: |
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152 | norm_edge = edge |
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153 | else: |
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154 | norm_edge = edge + edge |
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155 | else: |
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156 | norm_edge = [] |
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157 | for edgepar in edge: |
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158 | if isinstance(edgepar, int): |
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159 | norm_edge += [edgepar, edgepar] |
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160 | else: |
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161 | norm_edge += edgepar |
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162 | |
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163 | return norm_edge |
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164 | |
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165 | def raise_fitting_failure_exception(e): |
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166 | msg = "The fit failed, possibly because it didn't converge." |
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167 | if rcParams["verbose"]: |
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168 | asaplog.push(str(e)) |
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169 | asaplog.push(str(msg)) |
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170 | else: |
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171 | raise RuntimeError(str(e)+'\n'+msg) |
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172 | |
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173 | def pack_progress_params(showprogress, minnrow): |
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174 | return str(showprogress).lower() + ',' + str(minnrow) |
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175 | |
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176 | class scantable(Scantable): |
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177 | """\ |
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178 | The ASAP container for scans (single-dish data). |
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179 | """ |
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180 | |
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181 | @asaplog_post_dec |
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182 | def __init__(self, filename, average=None, unit=None, parallactify=None, |
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183 | **args): |
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184 | """\ |
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185 | Create a scantable from a saved one or make a reference |
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186 | |
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187 | Parameters: |
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188 | |
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189 | filename: the name of an asap table on disk |
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190 | or |
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191 | the name of a rpfits/sdfits/ms file |
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192 | (integrations within scans are auto averaged |
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193 | and the whole file is read) or |
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194 | [advanced] a reference to an existing scantable |
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195 | |
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196 | average: average all integrations withinb a scan on read. |
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197 | The default (True) is taken from .asaprc. |
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198 | |
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199 | unit: brightness unit; must be consistent with K or Jy. |
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200 | Over-rides the default selected by the filler |
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201 | (input rpfits/sdfits/ms) or replaces the value |
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202 | in existing scantables |
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203 | |
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204 | antenna: for MeasurementSet input data only: |
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205 | Antenna selection. integer (id) or string |
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206 | (name or id). |
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207 | |
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208 | parallactify: Indicate that the data had been parallactified. |
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209 | Default (false) is taken from rc file. |
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210 | |
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211 | getpt: Whether to import direction from MS/POINTING |
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212 | table properly or not. |
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213 | This is effective only when filename is MS. |
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214 | The default (True) is to import direction |
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215 | from MS/POINTING. |
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216 | |
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217 | freq_tolsr: Whether to convert frequency frame information |
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218 | to LSRK or not. |
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219 | This is effective only when filename is MS. |
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220 | The default (False) is to import frequency |
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221 | as is. |
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222 | |
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223 | """ |
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224 | if average is None: |
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225 | average = rcParams['scantable.autoaverage'] |
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226 | parallactify = parallactify or rcParams['scantable.parallactify'] |
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227 | varlist = vars() |
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228 | from asap._asap import stmath |
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229 | self._math = stmath( rcParams['insitu'] ) |
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230 | if isinstance(filename, Scantable): |
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231 | Scantable.__init__(self, filename) |
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232 | else: |
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233 | if isinstance(filename, str): |
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234 | filename = os.path.expandvars(filename) |
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235 | filename = os.path.expanduser(filename) |
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236 | if not os.path.exists(filename): |
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237 | s = "File '%s' not found." % (filename) |
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238 | raise IOError(s) |
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239 | if is_scantable(filename): |
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240 | ondisk = rcParams['scantable.storage'] == 'disk' |
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241 | Scantable.__init__(self, filename, ondisk) |
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242 | if unit is not None: |
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243 | self.set_fluxunit(unit) |
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244 | if average: |
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245 | self._assign( self.average_time( scanav=True ) ) |
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246 | # do not reset to the default freqframe |
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247 | #self.set_freqframe(rcParams['scantable.freqframe']) |
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248 | elif is_ms(filename): |
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249 | # Measurement Set |
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250 | opts={'ms': {}} |
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251 | mskeys=['getpt','antenna','freq_tolsr'] |
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252 | for key in mskeys: |
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253 | if key in args.keys(): |
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254 | opts['ms'][key] = args[key] |
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255 | self._fill([filename], unit, average, opts) |
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256 | elif os.path.isfile(filename): |
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257 | self._fill([filename], unit, average) |
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258 | # only apply to new data not "copy constructor" |
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259 | self.parallactify(parallactify) |
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260 | else: |
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261 | msg = "The given file '%s'is not a valid " \ |
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262 | "asap table." % (filename) |
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263 | raise IOError(msg) |
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264 | elif (isinstance(filename, list) or isinstance(filename, tuple)) \ |
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265 | and isinstance(filename[-1], str): |
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266 | self._fill(filename, unit, average) |
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267 | self.parallactify(parallactify) |
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268 | self._add_history("scantable", varlist) |
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269 | |
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270 | @asaplog_post_dec |
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271 | def save(self, name=None, format=None, overwrite=False): |
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272 | """\ |
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273 | Store the scantable on disk. This can be an asap (aips++) Table, |
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274 | SDFITS or MS2 format. |
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275 | |
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276 | Parameters: |
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277 | |
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278 | name: the name of the outputfile. For format 'ASCII' |
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279 | this is the root file name (data in 'name'.txt |
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280 | and header in 'name'_header.txt) |
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281 | |
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282 | format: an optional file format. Default is ASAP. |
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283 | Allowed are: |
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284 | |
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285 | * 'ASAP' (save as ASAP [aips++] Table), |
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286 | * 'SDFITS' (save as SDFITS file) |
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287 | * 'ASCII' (saves as ascii text file) |
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288 | * 'MS2' (saves as an casacore MeasurementSet V2) |
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289 | * 'FITS' (save as image FITS - not readable by |
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290 | class) |
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291 | * 'CLASS' (save as FITS readable by CLASS) |
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292 | |
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293 | overwrite: If the file should be overwritten if it exists. |
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294 | The default False is to return with warning |
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295 | without writing the output. USE WITH CARE. |
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296 | |
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297 | Example:: |
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298 | |
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299 | scan.save('myscan.asap') |
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300 | scan.save('myscan.sdfits', 'SDFITS') |
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301 | |
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302 | """ |
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303 | from os import path |
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304 | format = format or rcParams['scantable.save'] |
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305 | suffix = '.'+format.lower() |
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306 | if name is None or name == "": |
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307 | name = 'scantable'+suffix |
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308 | msg = "No filename given. Using default name %s..." % name |
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309 | asaplog.push(msg) |
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310 | name = path.expandvars(name) |
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311 | if path.isfile(name) or path.isdir(name): |
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312 | if not overwrite: |
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313 | msg = "File %s exists." % name |
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314 | raise IOError(msg) |
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315 | format2 = format.upper() |
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316 | if format2 == 'ASAP': |
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317 | self._save(name) |
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318 | elif format2 == 'MS2': |
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319 | msopt = {'ms': {'overwrite': overwrite } } |
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320 | from asap._asap import mswriter |
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321 | writer = mswriter( self ) |
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322 | writer.write( name, msopt ) |
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323 | else: |
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324 | from asap._asap import stwriter as stw |
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325 | writer = stw(format2) |
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326 | writer.write(self, name) |
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327 | return |
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328 | |
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329 | def copy(self): |
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330 | """Return a copy of this scantable. |
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331 | |
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332 | *Note*: |
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333 | |
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334 | This makes a full (deep) copy. scan2 = scan1 makes a reference. |
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335 | |
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336 | Example:: |
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337 | |
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338 | copiedscan = scan.copy() |
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339 | |
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340 | """ |
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341 | sd = scantable(Scantable._copy(self)) |
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342 | return sd |
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343 | |
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344 | def drop_scan(self, scanid=None): |
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345 | """\ |
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346 | Return a new scantable where the specified scan number(s) has(have) |
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347 | been dropped. |
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348 | |
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349 | Parameters: |
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350 | |
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351 | scanid: a (list of) scan number(s) |
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352 | |
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353 | """ |
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354 | from asap import _is_sequence_or_number as _is_valid |
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355 | from asap import _to_list |
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356 | from asap import unique |
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357 | if not _is_valid(scanid): |
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358 | raise RuntimeError( 'Please specify a scanno to drop from the' |
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359 | ' scantable' ) |
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360 | scanid = _to_list(scanid) |
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361 | allscans = unique([ self.getscan(i) for i in range(self.nrow())]) |
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362 | for sid in scanid: allscans.remove(sid) |
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363 | if len(allscans) == 0: |
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364 | raise ValueError("Can't remove all scans") |
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365 | sel = selector(scans=allscans) |
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366 | return self._select_copy(sel) |
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367 | |
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368 | def _select_copy(self, selection): |
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369 | orig = self.get_selection() |
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370 | self.set_selection(orig+selection) |
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371 | cp = self.copy() |
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372 | self.set_selection(orig) |
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373 | return cp |
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374 | |
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375 | def get_scan(self, scanid=None): |
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376 | """\ |
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377 | Return a specific scan (by scanno) or collection of scans (by |
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378 | source name) in a new scantable. |
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379 | |
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380 | *Note*: |
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381 | |
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382 | See scantable.drop_scan() for the inverse operation. |
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383 | |
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384 | Parameters: |
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385 | |
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386 | scanid: a (list of) scanno or a source name, unix-style |
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387 | patterns are accepted for source name matching, e.g. |
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388 | '*_R' gets all 'ref scans |
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389 | |
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390 | Example:: |
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391 | |
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392 | # get all scans containing the source '323p459' |
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393 | newscan = scan.get_scan('323p459') |
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394 | # get all 'off' scans |
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395 | refscans = scan.get_scan('*_R') |
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396 | # get a susbset of scans by scanno (as listed in scan.summary()) |
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397 | newscan = scan.get_scan([0, 2, 7, 10]) |
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398 | |
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399 | """ |
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400 | if scanid is None: |
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401 | raise RuntimeError( 'Please specify a scan no or name to ' |
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402 | 'retrieve from the scantable' ) |
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403 | try: |
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404 | bsel = self.get_selection() |
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405 | sel = selector() |
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406 | if type(scanid) is str: |
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407 | sel.set_name(scanid) |
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408 | return self._select_copy(sel) |
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409 | elif type(scanid) is int: |
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410 | sel.set_scans([scanid]) |
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411 | return self._select_copy(sel) |
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412 | elif type(scanid) is list: |
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413 | sel.set_scans(scanid) |
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414 | return self._select_copy(sel) |
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415 | else: |
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416 | msg = "Illegal scanid type, use 'int' or 'list' if ints." |
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417 | raise TypeError(msg) |
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418 | except RuntimeError: |
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419 | raise |
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420 | |
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421 | def __str__(self): |
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422 | tempFile = tempfile.NamedTemporaryFile() |
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423 | Scantable._summary(self, tempFile.name) |
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424 | tempFile.seek(0) |
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425 | asaplog.clear() |
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426 | return tempFile.file.read() |
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427 | |
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428 | @asaplog_post_dec |
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429 | def summary(self, filename=None): |
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430 | """\ |
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431 | Print a summary of the contents of this scantable. |
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432 | |
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433 | Parameters: |
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434 | |
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435 | filename: the name of a file to write the putput to |
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436 | Default - no file output |
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437 | |
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438 | """ |
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439 | if filename is not None: |
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440 | if filename is "": |
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441 | filename = 'scantable_summary.txt' |
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442 | from os.path import expandvars, isdir |
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443 | filename = expandvars(filename) |
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444 | if isdir(filename): |
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445 | msg = "Illegal file name '%s'." % (filename) |
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446 | raise IOError(msg) |
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447 | else: |
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448 | filename = "" |
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449 | Scantable._summary(self, filename) |
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450 | |
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451 | def get_spectrum(self, rowno): |
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452 | """Return the spectrum for the current row in the scantable as a list. |
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453 | |
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454 | Parameters: |
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455 | |
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456 | rowno: the row number to retrieve the spectrum from |
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457 | |
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458 | """ |
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459 | return self._getspectrum(rowno) |
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460 | |
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461 | def get_mask(self, rowno): |
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462 | """Return the mask for the current row in the scantable as a list. |
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463 | |
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464 | Parameters: |
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465 | |
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466 | rowno: the row number to retrieve the mask from |
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467 | |
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468 | """ |
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469 | return self._getmask(rowno) |
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470 | |
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471 | def set_spectrum(self, spec, rowno): |
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472 | """Set the spectrum for the current row in the scantable. |
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473 | |
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474 | Parameters: |
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475 | |
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476 | spec: the new spectrum |
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477 | |
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478 | rowno: the row number to set the spectrum for |
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479 | |
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480 | """ |
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481 | assert(len(spec) == self.nchan(self.getif(rowno))) |
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482 | return self._setspectrum(spec, rowno) |
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483 | |
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484 | def get_coordinate(self, rowno): |
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485 | """Return the (spectral) coordinate for a a given 'rowno'. |
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486 | |
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487 | *Note*: |
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488 | |
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489 | * This coordinate is only valid until a scantable method modifies |
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490 | the frequency axis. |
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491 | * This coordinate does contain the original frequency set-up |
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492 | NOT the new frame. The conversions however are done using the user |
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493 | specified frame (e.g. LSRK/TOPO). To get the 'real' coordinate, |
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494 | use scantable.freq_align first. Without it there is no closure, |
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495 | i.e.:: |
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496 | |
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497 | c = myscan.get_coordinate(0) |
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498 | c.to_frequency(c.get_reference_pixel()) != c.get_reference_value() |
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499 | |
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500 | Parameters: |
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501 | |
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502 | rowno: the row number for the spectral coordinate |
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503 | |
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504 | """ |
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505 | return coordinate(Scantable.get_coordinate(self, rowno)) |
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506 | |
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507 | def get_selection(self): |
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508 | """\ |
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509 | Get the selection object currently set on this scantable. |
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510 | |
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511 | Example:: |
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512 | |
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513 | sel = scan.get_selection() |
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514 | sel.set_ifs(0) # select IF 0 |
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515 | scan.set_selection(sel) # apply modified selection |
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516 | |
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517 | """ |
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518 | return selector(self._getselection()) |
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519 | |
---|
520 | def set_selection(self, selection=None, **kw): |
---|
521 | """\ |
---|
522 | Select a subset of the data. All following operations on this scantable |
---|
523 | are only applied to thi selection. |
---|
524 | |
---|
525 | Parameters: |
---|
526 | |
---|
527 | selection: a selector object (default unset the selection), or |
---|
528 | any combination of 'pols', 'ifs', 'beams', 'scans', |
---|
529 | 'cycles', 'name', 'query' |
---|
530 | |
---|
531 | Examples:: |
---|
532 | |
---|
533 | sel = selector() # create a selection object |
---|
534 | self.set_scans([0, 3]) # select SCANNO 0 and 3 |
---|
535 | scan.set_selection(sel) # set the selection |
---|
536 | scan.summary() # will only print summary of scanno 0 an 3 |
---|
537 | scan.set_selection() # unset the selection |
---|
538 | # or the equivalent |
---|
539 | scan.set_selection(scans=[0,3]) |
---|
540 | scan.summary() # will only print summary of scanno 0 an 3 |
---|
541 | scan.set_selection() # unset the selection |
---|
542 | |
---|
543 | """ |
---|
544 | if selection is None: |
---|
545 | # reset |
---|
546 | if len(kw) == 0: |
---|
547 | selection = selector() |
---|
548 | else: |
---|
549 | # try keywords |
---|
550 | for k in kw: |
---|
551 | if k not in selector.fields: |
---|
552 | raise KeyError("Invalid selection key '%s', " |
---|
553 | "valid keys are %s" % (k, |
---|
554 | selector.fields)) |
---|
555 | selection = selector(**kw) |
---|
556 | self._setselection(selection) |
---|
557 | |
---|
558 | def get_row(self, row=0, insitu=None): |
---|
559 | """\ |
---|
560 | Select a row in the scantable. |
---|
561 | Return a scantable with single row. |
---|
562 | |
---|
563 | Parameters: |
---|
564 | |
---|
565 | row: row no of integration, default is 0. |
---|
566 | insitu: if False a new scantable is returned. Otherwise, the |
---|
567 | scaling is done in-situ. The default is taken from .asaprc |
---|
568 | (False) |
---|
569 | |
---|
570 | """ |
---|
571 | if insitu is None: |
---|
572 | insitu = rcParams['insitu'] |
---|
573 | if not insitu: |
---|
574 | workscan = self.copy() |
---|
575 | else: |
---|
576 | workscan = self |
---|
577 | # Select a row |
---|
578 | sel = selector() |
---|
579 | sel.set_rows([row]) |
---|
580 | workscan.set_selection(sel) |
---|
581 | if not workscan.nrow() == 1: |
---|
582 | msg = "Could not identify single row. %d rows selected." \ |
---|
583 | % (workscan.nrow()) |
---|
584 | raise RuntimeError(msg) |
---|
585 | if insitu: |
---|
586 | self._assign(workscan) |
---|
587 | else: |
---|
588 | return workscan |
---|
589 | |
---|
590 | @asaplog_post_dec |
---|
591 | def stats(self, stat='stddev', mask=None, form='3.3f', row=None): |
---|
592 | """\ |
---|
593 | Determine the specified statistic of the current beam/if/pol |
---|
594 | Takes a 'mask' as an optional parameter to specify which |
---|
595 | channels should be excluded. |
---|
596 | |
---|
597 | Parameters: |
---|
598 | |
---|
599 | stat: 'min', 'max', 'min_abc', 'max_abc', 'sumsq', 'sum', |
---|
600 | 'mean', 'var', 'stddev', 'avdev', 'rms', 'median' |
---|
601 | |
---|
602 | mask: an optional mask specifying where the statistic |
---|
603 | should be determined. |
---|
604 | |
---|
605 | form: format string to print statistic values |
---|
606 | |
---|
607 | row: row number of spectrum to process. |
---|
608 | (default is None: for all rows) |
---|
609 | |
---|
610 | Example: |
---|
611 | scan.set_unit('channel') |
---|
612 | msk = scan.create_mask([100, 200], [500, 600]) |
---|
613 | scan.stats(stat='mean', mask=m) |
---|
614 | |
---|
615 | """ |
---|
616 | mask = mask or [] |
---|
617 | if not self._check_ifs(): |
---|
618 | raise ValueError("Cannot apply mask as the IFs have different " |
---|
619 | "number of channels. Please use setselection() " |
---|
620 | "to select individual IFs") |
---|
621 | rtnabc = False |
---|
622 | if stat.lower().endswith('_abc'): rtnabc = True |
---|
623 | getchan = False |
---|
624 | if stat.lower().startswith('min') or stat.lower().startswith('max'): |
---|
625 | chan = self._math._minmaxchan(self, mask, stat) |
---|
626 | getchan = True |
---|
627 | statvals = [] |
---|
628 | if not rtnabc: |
---|
629 | if row == None: |
---|
630 | statvals = self._math._stats(self, mask, stat) |
---|
631 | else: |
---|
632 | statvals = self._math._statsrow(self, mask, stat, int(row)) |
---|
633 | |
---|
634 | #def cb(i): |
---|
635 | # return statvals[i] |
---|
636 | |
---|
637 | #return self._row_callback(cb, stat) |
---|
638 | |
---|
639 | label=stat |
---|
640 | #callback=cb |
---|
641 | out = "" |
---|
642 | #outvec = [] |
---|
643 | sep = '-'*50 |
---|
644 | |
---|
645 | if row == None: |
---|
646 | rows = xrange(self.nrow()) |
---|
647 | elif isinstance(row, int): |
---|
648 | rows = [ row ] |
---|
649 | |
---|
650 | for i in rows: |
---|
651 | refstr = '' |
---|
652 | statunit= '' |
---|
653 | if getchan: |
---|
654 | qx, qy = self.chan2data(rowno=i, chan=chan[i]) |
---|
655 | if rtnabc: |
---|
656 | statvals.append(qx['value']) |
---|
657 | refstr = ('(value: %'+form) % (qy['value'])+' ['+qy['unit']+'])' |
---|
658 | statunit= '['+qx['unit']+']' |
---|
659 | else: |
---|
660 | refstr = ('(@ %'+form) % (qx['value'])+' ['+qx['unit']+'])' |
---|
661 | |
---|
662 | tm = self._gettime(i) |
---|
663 | src = self._getsourcename(i) |
---|
664 | out += 'Scan[%d] (%s) ' % (self.getscan(i), src) |
---|
665 | out += 'Time[%s]:\n' % (tm) |
---|
666 | if self.nbeam(-1) > 1: out += ' Beam[%d] ' % (self.getbeam(i)) |
---|
667 | if self.nif(-1) > 1: out += ' IF[%d] ' % (self.getif(i)) |
---|
668 | if self.npol(-1) > 1: out += ' Pol[%d] ' % (self.getpol(i)) |
---|
669 | #outvec.append(callback(i)) |
---|
670 | if len(rows) > 1: |
---|
671 | # out += ('= %'+form) % (outvec[i]) +' '+refstr+'\n' |
---|
672 | out += ('= %'+form) % (statvals[i]) +' '+refstr+'\n' |
---|
673 | else: |
---|
674 | # out += ('= %'+form) % (outvec[0]) +' '+refstr+'\n' |
---|
675 | out += ('= %'+form) % (statvals[0]) +' '+refstr+'\n' |
---|
676 | out += sep+"\n" |
---|
677 | |
---|
678 | import os |
---|
679 | if os.environ.has_key( 'USER' ): |
---|
680 | usr = os.environ['USER'] |
---|
681 | else: |
---|
682 | import commands |
---|
683 | usr = commands.getoutput( 'whoami' ) |
---|
684 | tmpfile = '/tmp/tmp_'+usr+'_casapy_asap_scantable_stats' |
---|
685 | f = open(tmpfile,'w') |
---|
686 | print >> f, sep |
---|
687 | print >> f, ' %s %s' % (label, statunit) |
---|
688 | print >> f, sep |
---|
689 | print >> f, out |
---|
690 | f.close() |
---|
691 | f = open(tmpfile,'r') |
---|
692 | x = f.readlines() |
---|
693 | f.close() |
---|
694 | asaplog.push(''.join(x), False) |
---|
695 | |
---|
696 | return statvals |
---|
697 | |
---|
698 | def chan2data(self, rowno=0, chan=0): |
---|
699 | """\ |
---|
700 | Returns channel/frequency/velocity and spectral value |
---|
701 | at an arbitrary row and channel in the scantable. |
---|
702 | |
---|
703 | Parameters: |
---|
704 | |
---|
705 | rowno: a row number in the scantable. Default is the |
---|
706 | first row, i.e. rowno=0 |
---|
707 | |
---|
708 | chan: a channel in the scantable. Default is the first |
---|
709 | channel, i.e. pos=0 |
---|
710 | |
---|
711 | """ |
---|
712 | if isinstance(rowno, int) and isinstance(chan, int): |
---|
713 | qx = {'unit': self.get_unit(), |
---|
714 | 'value': self._getabcissa(rowno)[chan]} |
---|
715 | qy = {'unit': self.get_fluxunit(), |
---|
716 | 'value': self._getspectrum(rowno)[chan]} |
---|
717 | return qx, qy |
---|
718 | |
---|
719 | def stddev(self, mask=None): |
---|
720 | """\ |
---|
721 | Determine the standard deviation of the current beam/if/pol |
---|
722 | Takes a 'mask' as an optional parameter to specify which |
---|
723 | channels should be excluded. |
---|
724 | |
---|
725 | Parameters: |
---|
726 | |
---|
727 | mask: an optional mask specifying where the standard |
---|
728 | deviation should be determined. |
---|
729 | |
---|
730 | Example:: |
---|
731 | |
---|
732 | scan.set_unit('channel') |
---|
733 | msk = scan.create_mask([100, 200], [500, 600]) |
---|
734 | scan.stddev(mask=m) |
---|
735 | |
---|
736 | """ |
---|
737 | return self.stats(stat='stddev', mask=mask); |
---|
738 | |
---|
739 | |
---|
740 | def get_column_names(self): |
---|
741 | """\ |
---|
742 | Return a list of column names, which can be used for selection. |
---|
743 | """ |
---|
744 | return list(Scantable.get_column_names(self)) |
---|
745 | |
---|
746 | def get_tsys(self, row=-1): |
---|
747 | """\ |
---|
748 | Return the System temperatures. |
---|
749 | |
---|
750 | Parameters: |
---|
751 | |
---|
752 | row: the rowno to get the information for. (default all rows) |
---|
753 | |
---|
754 | Returns: |
---|
755 | |
---|
756 | a list of Tsys values for the current selection |
---|
757 | |
---|
758 | """ |
---|
759 | if row > -1: |
---|
760 | return self._get_column(self._gettsys, row) |
---|
761 | return self._row_callback(self._gettsys, "Tsys") |
---|
762 | |
---|
763 | def get_tsysspectrum(self, row=-1): |
---|
764 | """\ |
---|
765 | Return the channel dependent system temperatures. |
---|
766 | |
---|
767 | Parameters: |
---|
768 | |
---|
769 | row: the rowno to get the information for. (default all rows) |
---|
770 | |
---|
771 | Returns: |
---|
772 | |
---|
773 | a list of Tsys values for the current selection |
---|
774 | |
---|
775 | """ |
---|
776 | return self._get_column( self._gettsysspectrum, row ) |
---|
777 | |
---|
778 | def get_weather(self, row=-1): |
---|
779 | """\ |
---|
780 | Return the weather informations. |
---|
781 | |
---|
782 | Parameters: |
---|
783 | |
---|
784 | row: the rowno to get the information for. (default all rows) |
---|
785 | |
---|
786 | Returns: |
---|
787 | |
---|
788 | a dict or list of of dicts of values for the current selection |
---|
789 | |
---|
790 | """ |
---|
791 | |
---|
792 | values = self._get_column(self._get_weather, row) |
---|
793 | if row > -1: |
---|
794 | return {'temperature': values[0], |
---|
795 | 'pressure': values[1], 'humidity' : values[2], |
---|
796 | 'windspeed' : values[3], 'windaz' : values[4] |
---|
797 | } |
---|
798 | else: |
---|
799 | out = [] |
---|
800 | for r in values: |
---|
801 | |
---|
802 | out.append({'temperature': r[0], |
---|
803 | 'pressure': r[1], 'humidity' : r[2], |
---|
804 | 'windspeed' : r[3], 'windaz' : r[4] |
---|
805 | }) |
---|
806 | return out |
---|
807 | |
---|
808 | def _row_callback(self, callback, label): |
---|
809 | out = "" |
---|
810 | outvec = [] |
---|
811 | sep = '-'*50 |
---|
812 | for i in range(self.nrow()): |
---|
813 | tm = self._gettime(i) |
---|
814 | src = self._getsourcename(i) |
---|
815 | out += 'Scan[%d] (%s) ' % (self.getscan(i), src) |
---|
816 | out += 'Time[%s]:\n' % (tm) |
---|
817 | if self.nbeam(-1) > 1: |
---|
818 | out += ' Beam[%d] ' % (self.getbeam(i)) |
---|
819 | if self.nif(-1) > 1: out += ' IF[%d] ' % (self.getif(i)) |
---|
820 | if self.npol(-1) > 1: out += ' Pol[%d] ' % (self.getpol(i)) |
---|
821 | outvec.append(callback(i)) |
---|
822 | out += '= %3.3f\n' % (outvec[i]) |
---|
823 | out += sep+'\n' |
---|
824 | |
---|
825 | asaplog.push(sep) |
---|
826 | asaplog.push(" %s" % (label)) |
---|
827 | asaplog.push(sep) |
---|
828 | asaplog.push(out) |
---|
829 | asaplog.post() |
---|
830 | return outvec |
---|
831 | |
---|
832 | def _get_column(self, callback, row=-1, *args): |
---|
833 | """ |
---|
834 | """ |
---|
835 | if row == -1: |
---|
836 | return [callback(i, *args) for i in range(self.nrow())] |
---|
837 | else: |
---|
838 | if 0 <= row < self.nrow(): |
---|
839 | return callback(row, *args) |
---|
840 | |
---|
841 | |
---|
842 | def get_time(self, row=-1, asdatetime=False, prec=-1): |
---|
843 | """\ |
---|
844 | Get a list of time stamps for the observations. |
---|
845 | Return a datetime object or a string (default) for each |
---|
846 | integration time stamp in the scantable. |
---|
847 | |
---|
848 | Parameters: |
---|
849 | |
---|
850 | row: row no of integration. Default -1 return all rows |
---|
851 | |
---|
852 | asdatetime: return values as datetime objects rather than strings |
---|
853 | |
---|
854 | prec: number of digits shown. Default -1 to automatic |
---|
855 | calculation. |
---|
856 | Note this number is equals to the digits of MVTime, |
---|
857 | i.e., 0<prec<3: dates with hh:: only, |
---|
858 | <5: with hh:mm:, <7 or 0: with hh:mm:ss, |
---|
859 | and 6> : with hh:mm:ss.tt... (prec-6 t's added) |
---|
860 | |
---|
861 | """ |
---|
862 | from datetime import datetime |
---|
863 | if prec < 0: |
---|
864 | # automagically set necessary precision +1 |
---|
865 | prec = 7 - \ |
---|
866 | numpy.floor(numpy.log10(numpy.min(self.get_inttime(row)))) |
---|
867 | prec = max(6, int(prec)) |
---|
868 | else: |
---|
869 | prec = max(0, prec) |
---|
870 | if asdatetime: |
---|
871 | #precision can be 1 millisecond at max |
---|
872 | prec = min(12, prec) |
---|
873 | |
---|
874 | times = self._get_column(self._gettime, row, prec) |
---|
875 | if not asdatetime: |
---|
876 | return times |
---|
877 | format = "%Y/%m/%d/%H:%M:%S.%f" |
---|
878 | if prec < 7: |
---|
879 | nsub = 1 + (((6-prec)/2) % 3) |
---|
880 | substr = [".%f","%S","%M"] |
---|
881 | for i in range(nsub): |
---|
882 | format = format.replace(substr[i],"") |
---|
883 | if isinstance(times, list): |
---|
884 | return [datetime.strptime(i, format) for i in times] |
---|
885 | else: |
---|
886 | return datetime.strptime(times, format) |
---|
887 | |
---|
888 | |
---|
889 | def get_inttime(self, row=-1): |
---|
890 | """\ |
---|
891 | Get a list of integration times for the observations. |
---|
892 | Return a time in seconds for each integration in the scantable. |
---|
893 | |
---|
894 | Parameters: |
---|
895 | |
---|
896 | row: row no of integration. Default -1 return all rows. |
---|
897 | |
---|
898 | """ |
---|
899 | return self._get_column(self._getinttime, row) |
---|
900 | |
---|
901 | |
---|
902 | def get_sourcename(self, row=-1): |
---|
903 | """\ |
---|
904 | Get a list source names for the observations. |
---|
905 | Return a string for each integration in the scantable. |
---|
906 | Parameters: |
---|
907 | |
---|
908 | row: row no of integration. Default -1 return all rows. |
---|
909 | |
---|
910 | """ |
---|
911 | return self._get_column(self._getsourcename, row) |
---|
912 | |
---|
913 | def get_elevation(self, row=-1): |
---|
914 | """\ |
---|
915 | Get a list of elevations for the observations. |
---|
916 | Return a float for each integration in the scantable. |
---|
917 | |
---|
918 | Parameters: |
---|
919 | |
---|
920 | row: row no of integration. Default -1 return all rows. |
---|
921 | |
---|
922 | """ |
---|
923 | return self._get_column(self._getelevation, row) |
---|
924 | |
---|
925 | def get_azimuth(self, row=-1): |
---|
926 | """\ |
---|
927 | Get a list of azimuths for the observations. |
---|
928 | Return a float for each integration in the scantable. |
---|
929 | |
---|
930 | Parameters: |
---|
931 | row: row no of integration. Default -1 return all rows. |
---|
932 | |
---|
933 | """ |
---|
934 | return self._get_column(self._getazimuth, row) |
---|
935 | |
---|
936 | def get_parangle(self, row=-1): |
---|
937 | """\ |
---|
938 | Get a list of parallactic angles for the observations. |
---|
939 | Return a float for each integration in the scantable. |
---|
940 | |
---|
941 | Parameters: |
---|
942 | |
---|
943 | row: row no of integration. Default -1 return all rows. |
---|
944 | |
---|
945 | """ |
---|
946 | return self._get_column(self._getparangle, row) |
---|
947 | |
---|
948 | def get_direction(self, row=-1): |
---|
949 | """ |
---|
950 | Get a list of Positions on the sky (direction) for the observations. |
---|
951 | Return a string for each integration in the scantable. |
---|
952 | |
---|
953 | Parameters: |
---|
954 | |
---|
955 | row: row no of integration. Default -1 return all rows |
---|
956 | |
---|
957 | """ |
---|
958 | return self._get_column(self._getdirection, row) |
---|
959 | |
---|
960 | def get_directionval(self, row=-1): |
---|
961 | """\ |
---|
962 | Get a list of Positions on the sky (direction) for the observations. |
---|
963 | Return a float for each integration in the scantable. |
---|
964 | |
---|
965 | Parameters: |
---|
966 | |
---|
967 | row: row no of integration. Default -1 return all rows |
---|
968 | |
---|
969 | """ |
---|
970 | return self._get_column(self._getdirectionvec, row) |
---|
971 | |
---|
972 | @asaplog_post_dec |
---|
973 | def set_unit(self, unit='channel'): |
---|
974 | """\ |
---|
975 | Set the unit for all following operations on this scantable |
---|
976 | |
---|
977 | Parameters: |
---|
978 | |
---|
979 | unit: optional unit, default is 'channel'. Use one of '*Hz', |
---|
980 | 'km/s', 'channel' or equivalent '' |
---|
981 | |
---|
982 | """ |
---|
983 | varlist = vars() |
---|
984 | if unit in ['', 'pixel', 'channel']: |
---|
985 | unit = '' |
---|
986 | inf = list(self._getcoordinfo()) |
---|
987 | inf[0] = unit |
---|
988 | self._setcoordinfo(inf) |
---|
989 | self._add_history("set_unit", varlist) |
---|
990 | |
---|
991 | @asaplog_post_dec |
---|
992 | def set_instrument(self, instr): |
---|
993 | """\ |
---|
994 | Set the instrument for subsequent processing. |
---|
995 | |
---|
996 | Parameters: |
---|
997 | |
---|
998 | instr: Select from 'ATPKSMB', 'ATPKSHOH', 'ATMOPRA', |
---|
999 | 'DSS-43' (Tid), 'CEDUNA', and 'HOBART' |
---|
1000 | |
---|
1001 | """ |
---|
1002 | self._setInstrument(instr) |
---|
1003 | self._add_history("set_instument", vars()) |
---|
1004 | |
---|
1005 | @asaplog_post_dec |
---|
1006 | def set_feedtype(self, feedtype): |
---|
1007 | """\ |
---|
1008 | Overwrite the feed type, which might not be set correctly. |
---|
1009 | |
---|
1010 | Parameters: |
---|
1011 | |
---|
1012 | feedtype: 'linear' or 'circular' |
---|
1013 | |
---|
1014 | """ |
---|
1015 | self._setfeedtype(feedtype) |
---|
1016 | self._add_history("set_feedtype", vars()) |
---|
1017 | |
---|
1018 | @asaplog_post_dec |
---|
1019 | def set_doppler(self, doppler='RADIO'): |
---|
1020 | """\ |
---|
1021 | Set the doppler for all following operations on this scantable. |
---|
1022 | |
---|
1023 | Parameters: |
---|
1024 | |
---|
1025 | doppler: One of 'RADIO', 'OPTICAL', 'Z', 'BETA', 'GAMMA' |
---|
1026 | |
---|
1027 | """ |
---|
1028 | varlist = vars() |
---|
1029 | inf = list(self._getcoordinfo()) |
---|
1030 | inf[2] = doppler |
---|
1031 | self._setcoordinfo(inf) |
---|
1032 | self._add_history("set_doppler", vars()) |
---|
1033 | |
---|
1034 | @asaplog_post_dec |
---|
1035 | def set_freqframe(self, frame=None): |
---|
1036 | """\ |
---|
1037 | Set the frame type of the Spectral Axis. |
---|
1038 | |
---|
1039 | Parameters: |
---|
1040 | |
---|
1041 | frame: an optional frame type, default 'LSRK'. Valid frames are: |
---|
1042 | 'TOPO', 'LSRD', 'LSRK', 'BARY', |
---|
1043 | 'GEO', 'GALACTO', 'LGROUP', 'CMB' |
---|
1044 | |
---|
1045 | Example:: |
---|
1046 | |
---|
1047 | scan.set_freqframe('BARY') |
---|
1048 | |
---|
1049 | """ |
---|
1050 | frame = frame or rcParams['scantable.freqframe'] |
---|
1051 | varlist = vars() |
---|
1052 | # "REST" is not implemented in casacore |
---|
1053 | #valid = ['REST', 'TOPO', 'LSRD', 'LSRK', 'BARY', \ |
---|
1054 | # 'GEO', 'GALACTO', 'LGROUP', 'CMB'] |
---|
1055 | valid = ['TOPO', 'LSRD', 'LSRK', 'BARY', \ |
---|
1056 | 'GEO', 'GALACTO', 'LGROUP', 'CMB'] |
---|
1057 | |
---|
1058 | if frame in valid: |
---|
1059 | inf = list(self._getcoordinfo()) |
---|
1060 | inf[1] = frame |
---|
1061 | self._setcoordinfo(inf) |
---|
1062 | self._add_history("set_freqframe", varlist) |
---|
1063 | else: |
---|
1064 | msg = "Please specify a valid freq type. Valid types are:\n", valid |
---|
1065 | raise TypeError(msg) |
---|
1066 | |
---|
1067 | @asaplog_post_dec |
---|
1068 | def set_dirframe(self, frame=""): |
---|
1069 | """\ |
---|
1070 | Set the frame type of the Direction on the sky. |
---|
1071 | |
---|
1072 | Parameters: |
---|
1073 | |
---|
1074 | frame: an optional frame type, default ''. Valid frames are: |
---|
1075 | 'J2000', 'B1950', 'GALACTIC' |
---|
1076 | |
---|
1077 | Example: |
---|
1078 | |
---|
1079 | scan.set_dirframe('GALACTIC') |
---|
1080 | |
---|
1081 | """ |
---|
1082 | varlist = vars() |
---|
1083 | Scantable.set_dirframe(self, frame) |
---|
1084 | self._add_history("set_dirframe", varlist) |
---|
1085 | |
---|
1086 | def get_unit(self): |
---|
1087 | """\ |
---|
1088 | Get the default unit set in this scantable |
---|
1089 | |
---|
1090 | Returns: |
---|
1091 | |
---|
1092 | A unit string |
---|
1093 | |
---|
1094 | """ |
---|
1095 | inf = self._getcoordinfo() |
---|
1096 | unit = inf[0] |
---|
1097 | if unit == '': unit = 'channel' |
---|
1098 | return unit |
---|
1099 | |
---|
1100 | @asaplog_post_dec |
---|
1101 | def get_abcissa(self, rowno=0): |
---|
1102 | """\ |
---|
1103 | Get the abcissa in the current coordinate setup for the currently |
---|
1104 | selected Beam/IF/Pol |
---|
1105 | |
---|
1106 | Parameters: |
---|
1107 | |
---|
1108 | rowno: an optional row number in the scantable. Default is the |
---|
1109 | first row, i.e. rowno=0 |
---|
1110 | |
---|
1111 | Returns: |
---|
1112 | |
---|
1113 | The abcissa values and the format string (as a dictionary) |
---|
1114 | |
---|
1115 | """ |
---|
1116 | abc = self._getabcissa(rowno) |
---|
1117 | lbl = self._getabcissalabel(rowno) |
---|
1118 | return abc, lbl |
---|
1119 | |
---|
1120 | @asaplog_post_dec |
---|
1121 | def flag(self, mask=None, unflag=False, row=-1): |
---|
1122 | """\ |
---|
1123 | Flag the selected data using an optional channel mask. |
---|
1124 | |
---|
1125 | Parameters: |
---|
1126 | |
---|
1127 | mask: an optional channel mask, created with create_mask. Default |
---|
1128 | (no mask) is all channels. |
---|
1129 | |
---|
1130 | unflag: if True, unflag the data |
---|
1131 | |
---|
1132 | row: an optional row number in the scantable. |
---|
1133 | Default -1 flags all rows |
---|
1134 | |
---|
1135 | """ |
---|
1136 | varlist = vars() |
---|
1137 | mask = mask or [] |
---|
1138 | self._flag(row, mask, unflag) |
---|
1139 | self._add_history("flag", varlist) |
---|
1140 | |
---|
1141 | @asaplog_post_dec |
---|
1142 | def flag_row(self, rows=None, unflag=False): |
---|
1143 | """\ |
---|
1144 | Flag the selected data in row-based manner. |
---|
1145 | |
---|
1146 | Parameters: |
---|
1147 | |
---|
1148 | rows: list of row numbers to be flagged. Default is no row |
---|
1149 | (must be explicitly specified to execute row-based |
---|
1150 | flagging). |
---|
1151 | |
---|
1152 | unflag: if True, unflag the data. |
---|
1153 | |
---|
1154 | """ |
---|
1155 | varlist = vars() |
---|
1156 | if rows is None: |
---|
1157 | rows = [] |
---|
1158 | self._flag_row(rows, unflag) |
---|
1159 | self._add_history("flag_row", varlist) |
---|
1160 | |
---|
1161 | @asaplog_post_dec |
---|
1162 | def clip(self, uthres=None, dthres=None, clipoutside=True, unflag=False): |
---|
1163 | """\ |
---|
1164 | Flag the selected data outside a specified range (in channel-base) |
---|
1165 | |
---|
1166 | Parameters: |
---|
1167 | |
---|
1168 | uthres: upper threshold. |
---|
1169 | |
---|
1170 | dthres: lower threshold |
---|
1171 | |
---|
1172 | clipoutside: True for flagging data outside the range |
---|
1173 | [dthres:uthres]. |
---|
1174 | False for flagging data inside the range. |
---|
1175 | |
---|
1176 | unflag: if True, unflag the data. |
---|
1177 | |
---|
1178 | """ |
---|
1179 | varlist = vars() |
---|
1180 | self._clip(uthres, dthres, clipoutside, unflag) |
---|
1181 | self._add_history("clip", varlist) |
---|
1182 | |
---|
1183 | @asaplog_post_dec |
---|
1184 | def lag_flag(self, start, end, unit="MHz", insitu=None): |
---|
1185 | """\ |
---|
1186 | Flag the data in 'lag' space by providing a frequency to remove. |
---|
1187 | Flagged data in the scantable get interpolated over the region. |
---|
1188 | No taper is applied. |
---|
1189 | |
---|
1190 | Parameters: |
---|
1191 | |
---|
1192 | start: the start frequency (really a period within the |
---|
1193 | bandwidth) or period to remove |
---|
1194 | |
---|
1195 | end: the end frequency or period to remove |
---|
1196 | |
---|
1197 | unit: the frequency unit (default 'MHz') or '' for |
---|
1198 | explicit lag channels |
---|
1199 | |
---|
1200 | *Notes*: |
---|
1201 | |
---|
1202 | It is recommended to flag edges of the band or strong |
---|
1203 | signals beforehand. |
---|
1204 | |
---|
1205 | """ |
---|
1206 | if insitu is None: insitu = rcParams['insitu'] |
---|
1207 | self._math._setinsitu(insitu) |
---|
1208 | varlist = vars() |
---|
1209 | base = { "GHz": 1000000000., "MHz": 1000000., "kHz": 1000., "Hz": 1.} |
---|
1210 | if not (unit == "" or base.has_key(unit)): |
---|
1211 | raise ValueError("%s is not a valid unit." % unit) |
---|
1212 | if unit == "": |
---|
1213 | s = scantable(self._math._lag_flag(self, start, end, "lags")) |
---|
1214 | else: |
---|
1215 | s = scantable(self._math._lag_flag(self, start*base[unit], |
---|
1216 | end*base[unit], "frequency")) |
---|
1217 | s._add_history("lag_flag", varlist) |
---|
1218 | if insitu: |
---|
1219 | self._assign(s) |
---|
1220 | else: |
---|
1221 | return s |
---|
1222 | |
---|
1223 | @asaplog_post_dec |
---|
1224 | def fft(self, rowno=None, mask=None, getrealimag=False): |
---|
1225 | """\ |
---|
1226 | Apply FFT to the spectra. |
---|
1227 | Flagged data in the scantable get interpolated over the region. |
---|
1228 | |
---|
1229 | Parameters: |
---|
1230 | |
---|
1231 | rowno: The row number(s) to be processed. int, list |
---|
1232 | and tuple are accepted. By default (None), FFT |
---|
1233 | is applied to the whole data. |
---|
1234 | |
---|
1235 | mask: Auxiliary channel mask(s). Given as a boolean |
---|
1236 | list, it is applied to all specified rows. |
---|
1237 | A list of boolean lists can also be used to |
---|
1238 | apply different masks. In the latter case, the |
---|
1239 | length of 'mask' must be the same as that of |
---|
1240 | 'rowno'. The default is None. |
---|
1241 | |
---|
1242 | getrealimag: If True, returns the real and imaginary part |
---|
1243 | values of the complex results. |
---|
1244 | If False (the default), returns the amplitude |
---|
1245 | (absolute value) normalised with Ndata/2 and |
---|
1246 | phase (argument, in unit of radian). |
---|
1247 | |
---|
1248 | Returns: |
---|
1249 | |
---|
1250 | A list of dictionaries containing the results for each spectrum. |
---|
1251 | Each dictionary contains two values, the real and the imaginary |
---|
1252 | parts when getrealimag = True, or the amplitude(absolute value) |
---|
1253 | and the phase(argument) when getrealimag = False. The key for |
---|
1254 | these values are 'real' and 'imag', or 'ampl' and 'phase', |
---|
1255 | respectively. |
---|
1256 | """ |
---|
1257 | if rowno is None: |
---|
1258 | rowno = [] |
---|
1259 | if isinstance(rowno, int): |
---|
1260 | rowno = [rowno] |
---|
1261 | elif not (isinstance(rowno, list) or isinstance(rowno, tuple)): |
---|
1262 | raise TypeError("The row number(s) must be int, list or tuple.") |
---|
1263 | if len(rowno) == 0: rowno = [i for i in xrange(self.nrow())] |
---|
1264 | |
---|
1265 | usecommonmask = True |
---|
1266 | |
---|
1267 | if mask is None: |
---|
1268 | mask = [] |
---|
1269 | if isinstance(mask, list) or isinstance(mask, tuple): |
---|
1270 | if len(mask) == 0: |
---|
1271 | mask = [[]] |
---|
1272 | else: |
---|
1273 | if isinstance(mask[0], bool): |
---|
1274 | if len(mask) != self.nchan(self.getif(rowno[0])): |
---|
1275 | raise ValueError("The spectra and the mask have " |
---|
1276 | "different length.") |
---|
1277 | mask = [mask] |
---|
1278 | elif isinstance(mask[0], list) or isinstance(mask[0], tuple): |
---|
1279 | usecommonmask = False |
---|
1280 | if len(mask) != len(rowno): |
---|
1281 | raise ValueError("When specifying masks for each " |
---|
1282 | "spectrum, the numbers of them " |
---|
1283 | "must be identical.") |
---|
1284 | for i in xrange(mask): |
---|
1285 | if len(mask[i]) != self.nchan(self.getif(rowno[i])): |
---|
1286 | raise ValueError("The spectra and the mask have " |
---|
1287 | "different length.") |
---|
1288 | else: |
---|
1289 | raise TypeError("The mask must be a boolean list or " |
---|
1290 | "a list of boolean list.") |
---|
1291 | else: |
---|
1292 | raise TypeError("The mask must be a boolean list or a list of " |
---|
1293 | "boolean list.") |
---|
1294 | |
---|
1295 | res = [] |
---|
1296 | |
---|
1297 | imask = 0 |
---|
1298 | for whichrow in rowno: |
---|
1299 | fspec = self._fft(whichrow, mask[imask], getrealimag) |
---|
1300 | nspec = len(fspec) |
---|
1301 | |
---|
1302 | i = 0 |
---|
1303 | v1 = [] |
---|
1304 | v2 = [] |
---|
1305 | reselem = {"real":[],"imag":[]} if getrealimag \ |
---|
1306 | else {"ampl":[],"phase":[]} |
---|
1307 | |
---|
1308 | while (i < nspec): |
---|
1309 | v1.append(fspec[i]) |
---|
1310 | v2.append(fspec[i+1]) |
---|
1311 | i += 2 |
---|
1312 | |
---|
1313 | if getrealimag: |
---|
1314 | reselem["real"] += v1 |
---|
1315 | reselem["imag"] += v2 |
---|
1316 | else: |
---|
1317 | reselem["ampl"] += v1 |
---|
1318 | reselem["phase"] += v2 |
---|
1319 | |
---|
1320 | res.append(reselem) |
---|
1321 | |
---|
1322 | if not usecommonmask: |
---|
1323 | imask += 1 |
---|
1324 | |
---|
1325 | return res |
---|
1326 | |
---|
1327 | @asaplog_post_dec |
---|
1328 | def create_mask(self, *args, **kwargs): |
---|
1329 | """\ |
---|
1330 | Compute and return a mask based on [min, max] windows. |
---|
1331 | The specified windows are to be INCLUDED, when the mask is |
---|
1332 | applied. |
---|
1333 | |
---|
1334 | Parameters: |
---|
1335 | |
---|
1336 | [min, max], [min2, max2], ... |
---|
1337 | Pairs of start/end points (inclusive)specifying the regions |
---|
1338 | to be masked |
---|
1339 | |
---|
1340 | invert: optional argument. If specified as True, |
---|
1341 | return an inverted mask, i.e. the regions |
---|
1342 | specified are EXCLUDED |
---|
1343 | |
---|
1344 | row: create the mask using the specified row for |
---|
1345 | unit conversions, default is row=0 |
---|
1346 | only necessary if frequency varies over rows. |
---|
1347 | |
---|
1348 | Examples:: |
---|
1349 | |
---|
1350 | scan.set_unit('channel') |
---|
1351 | # a) |
---|
1352 | msk = scan.create_mask([400, 500], [800, 900]) |
---|
1353 | # masks everything outside 400 and 500 |
---|
1354 | # and 800 and 900 in the unit 'channel' |
---|
1355 | |
---|
1356 | # b) |
---|
1357 | msk = scan.create_mask([400, 500], [800, 900], invert=True) |
---|
1358 | # masks the regions between 400 and 500 |
---|
1359 | # and 800 and 900 in the unit 'channel' |
---|
1360 | |
---|
1361 | # c) |
---|
1362 | #mask only channel 400 |
---|
1363 | msk = scan.create_mask([400]) |
---|
1364 | |
---|
1365 | """ |
---|
1366 | row = kwargs.get("row", 0) |
---|
1367 | data = self._getabcissa(row) |
---|
1368 | u = self._getcoordinfo()[0] |
---|
1369 | if u == "": |
---|
1370 | u = "channel" |
---|
1371 | msg = "The current mask window unit is %s" % u |
---|
1372 | i = self._check_ifs() |
---|
1373 | if not i: |
---|
1374 | msg += "\nThis mask is only valid for IF=%d" % (self.getif(i)) |
---|
1375 | asaplog.push(msg) |
---|
1376 | n = len(data) |
---|
1377 | msk = _n_bools(n, False) |
---|
1378 | # test if args is a 'list' or a 'normal *args - UGLY!!! |
---|
1379 | |
---|
1380 | ws = (isinstance(args[-1][-1], int) |
---|
1381 | or isinstance(args[-1][-1], float)) and args or args[0] |
---|
1382 | for window in ws: |
---|
1383 | if len(window) == 1: |
---|
1384 | window = [window[0], window[0]] |
---|
1385 | if len(window) == 0 or len(window) > 2: |
---|
1386 | raise ValueError("A window needs to be defined as " |
---|
1387 | "[start(, end)]") |
---|
1388 | if window[0] > window[1]: |
---|
1389 | tmp = window[0] |
---|
1390 | window[0] = window[1] |
---|
1391 | window[1] = tmp |
---|
1392 | for i in range(n): |
---|
1393 | if data[i] >= window[0] and data[i] <= window[1]: |
---|
1394 | msk[i] = True |
---|
1395 | if kwargs.has_key('invert'): |
---|
1396 | if kwargs.get('invert'): |
---|
1397 | msk = mask_not(msk) |
---|
1398 | return msk |
---|
1399 | |
---|
1400 | def get_masklist(self, mask=None, row=0, silent=False): |
---|
1401 | """\ |
---|
1402 | Compute and return a list of mask windows, [min, max]. |
---|
1403 | |
---|
1404 | Parameters: |
---|
1405 | |
---|
1406 | mask: channel mask, created with create_mask. |
---|
1407 | |
---|
1408 | row: calcutate the masklist using the specified row |
---|
1409 | for unit conversions, default is row=0 |
---|
1410 | only necessary if frequency varies over rows. |
---|
1411 | |
---|
1412 | Returns: |
---|
1413 | |
---|
1414 | [min, max], [min2, max2], ... |
---|
1415 | Pairs of start/end points (inclusive)specifying |
---|
1416 | the masked regions |
---|
1417 | |
---|
1418 | """ |
---|
1419 | if not (isinstance(mask,list) or isinstance(mask, tuple)): |
---|
1420 | raise TypeError("The mask should be list or tuple.") |
---|
1421 | if len(mask) <= 0: |
---|
1422 | raise TypeError("The mask elements should be > 0") |
---|
1423 | data = self._getabcissa(row) |
---|
1424 | if len(data) != len(mask): |
---|
1425 | msg = "Number of channels in scantable != number of mask elements" |
---|
1426 | raise TypeError(msg) |
---|
1427 | u = self._getcoordinfo()[0] |
---|
1428 | if u == "": |
---|
1429 | u = "channel" |
---|
1430 | msg = "The current mask window unit is %s" % u |
---|
1431 | i = self._check_ifs() |
---|
1432 | if not i: |
---|
1433 | msg += "\nThis mask is only valid for IF=%d" % (self.getif(i)) |
---|
1434 | if not silent: |
---|
1435 | asaplog.push(msg) |
---|
1436 | masklist = [] |
---|
1437 | ist, ien = None, None |
---|
1438 | ist, ien=self.get_mask_indices(mask) |
---|
1439 | if ist is not None and ien is not None: |
---|
1440 | for i in xrange(len(ist)): |
---|
1441 | range=[data[ist[i]],data[ien[i]]] |
---|
1442 | range.sort() |
---|
1443 | masklist.append([range[0],range[1]]) |
---|
1444 | return masklist |
---|
1445 | |
---|
1446 | def get_mask_indices(self, mask=None): |
---|
1447 | """\ |
---|
1448 | Compute and Return lists of mask start indices and mask end indices. |
---|
1449 | |
---|
1450 | Parameters: |
---|
1451 | |
---|
1452 | mask: channel mask, created with create_mask. |
---|
1453 | |
---|
1454 | Returns: |
---|
1455 | |
---|
1456 | List of mask start indices and that of mask end indices, |
---|
1457 | i.e., [istart1,istart2,....], [iend1,iend2,....]. |
---|
1458 | |
---|
1459 | """ |
---|
1460 | if not (isinstance(mask,list) or isinstance(mask, tuple)): |
---|
1461 | raise TypeError("The mask should be list or tuple.") |
---|
1462 | if len(mask) <= 0: |
---|
1463 | raise TypeError("The mask elements should be > 0") |
---|
1464 | istart = [] |
---|
1465 | iend = [] |
---|
1466 | if mask[0]: |
---|
1467 | istart.append(0) |
---|
1468 | for i in range(len(mask)-1): |
---|
1469 | if not mask[i] and mask[i+1]: |
---|
1470 | istart.append(i+1) |
---|
1471 | elif mask[i] and not mask[i+1]: |
---|
1472 | iend.append(i) |
---|
1473 | if mask[len(mask)-1]: |
---|
1474 | iend.append(len(mask)-1) |
---|
1475 | if len(istart) != len(iend): |
---|
1476 | raise RuntimeError("Numbers of mask start != mask end.") |
---|
1477 | for i in range(len(istart)): |
---|
1478 | if istart[i] > iend[i]: |
---|
1479 | raise RuntimeError("Mask start index > mask end index") |
---|
1480 | break |
---|
1481 | return istart,iend |
---|
1482 | |
---|
1483 | @asaplog_post_dec |
---|
1484 | def parse_maskexpr(self, maskstring): |
---|
1485 | """ |
---|
1486 | Parse CASA type mask selection syntax (IF dependent). |
---|
1487 | |
---|
1488 | Parameters: |
---|
1489 | maskstring : A string mask selection expression. |
---|
1490 | A comma separated selections mean different IF - |
---|
1491 | channel combinations. IFs and channel selections |
---|
1492 | are partitioned by a colon, ':'. |
---|
1493 | examples: |
---|
1494 | '' = all IFs (all channels) |
---|
1495 | '<2,4~6,9' = IFs 0,1,4,5,6,9 (all channels) |
---|
1496 | '3:3~45;60' = channels 3 to 45 and 60 in IF 3 |
---|
1497 | '0~1:2~6,8' = channels 2 to 6 in IFs 0,1, and |
---|
1498 | all channels in IF8 |
---|
1499 | Returns: |
---|
1500 | A dictionary of selected (valid) IF and masklist pairs, |
---|
1501 | e.g. {'0': [[50,250],[350,462]], '2': [[100,400],[550,974]]} |
---|
1502 | """ |
---|
1503 | if not isinstance(maskstring,str): |
---|
1504 | asaplog.post() |
---|
1505 | asaplog.push("Mask expression should be a string.") |
---|
1506 | asaplog.post("ERROR") |
---|
1507 | |
---|
1508 | valid_ifs = self.getifnos() |
---|
1509 | frequnit = self.get_unit() |
---|
1510 | seldict = {} |
---|
1511 | if maskstring == "": |
---|
1512 | maskstring = str(valid_ifs)[1:-1] |
---|
1513 | ## split each selection "IF range[:CHAN range]" |
---|
1514 | sellist = maskstring.split(',') |
---|
1515 | for currselstr in sellist: |
---|
1516 | selset = currselstr.split(':') |
---|
1517 | # spw and mask string (may include ~, < or >) |
---|
1518 | spwmasklist = self._parse_selection(selset[0], typestr='integer', |
---|
1519 | minval=min(valid_ifs), |
---|
1520 | maxval=max(valid_ifs)) |
---|
1521 | for spwlist in spwmasklist: |
---|
1522 | selspws = [] |
---|
1523 | for ispw in range(spwlist[0],spwlist[1]+1): |
---|
1524 | # Put into the list only if ispw exists |
---|
1525 | if valid_ifs.count(ispw): |
---|
1526 | selspws.append(ispw) |
---|
1527 | del spwmasklist, spwlist |
---|
1528 | |
---|
1529 | # parse frequency mask list |
---|
1530 | if len(selset) > 1: |
---|
1531 | freqmasklist = self._parse_selection(selset[1], typestr='float', |
---|
1532 | offset=0.) |
---|
1533 | else: |
---|
1534 | # want to select the whole spectrum |
---|
1535 | freqmasklist = [None] |
---|
1536 | |
---|
1537 | ## define a dictionary of spw - masklist combination |
---|
1538 | for ispw in selspws: |
---|
1539 | #print "working on", ispw |
---|
1540 | spwstr = str(ispw) |
---|
1541 | if len(selspws) == 0: |
---|
1542 | # empty spw |
---|
1543 | continue |
---|
1544 | else: |
---|
1545 | ## want to get min and max of the spw and |
---|
1546 | ## offset to set for '<' and '>' |
---|
1547 | if frequnit == 'channel': |
---|
1548 | minfreq = 0 |
---|
1549 | maxfreq = self.nchan(ifno=ispw) |
---|
1550 | offset = 0.5 |
---|
1551 | else: |
---|
1552 | ## This is ugly part. need improvement |
---|
1553 | for ifrow in xrange(self.nrow()): |
---|
1554 | if self.getif(ifrow) == ispw: |
---|
1555 | #print "IF",ispw,"found in row =",ifrow |
---|
1556 | break |
---|
1557 | freqcoord = self.get_coordinate(ifrow) |
---|
1558 | freqs = self._getabcissa(ifrow) |
---|
1559 | minfreq = min(freqs) |
---|
1560 | maxfreq = max(freqs) |
---|
1561 | if len(freqs) == 1: |
---|
1562 | offset = 0.5 |
---|
1563 | elif frequnit.find('Hz') > 0: |
---|
1564 | offset = abs(freqcoord.to_frequency(1, |
---|
1565 | unit=frequnit) |
---|
1566 | -freqcoord.to_frequency(0, |
---|
1567 | unit=frequnit) |
---|
1568 | )*0.5 |
---|
1569 | elif frequnit.find('m/s') > 0: |
---|
1570 | offset = abs(freqcoord.to_velocity(1, |
---|
1571 | unit=frequnit) |
---|
1572 | -freqcoord.to_velocity(0, |
---|
1573 | unit=frequnit) |
---|
1574 | )*0.5 |
---|
1575 | else: |
---|
1576 | asaplog.post() |
---|
1577 | asaplog.push("Invalid frequency unit") |
---|
1578 | asaplog.post("ERROR") |
---|
1579 | del freqs, freqcoord, ifrow |
---|
1580 | for freq in freqmasklist: |
---|
1581 | selmask = freq or [minfreq, maxfreq] |
---|
1582 | if selmask[0] == None: |
---|
1583 | ## selection was "<freq[1]". |
---|
1584 | if selmask[1] < minfreq: |
---|
1585 | ## avoid adding region selection |
---|
1586 | selmask = None |
---|
1587 | else: |
---|
1588 | selmask = [minfreq,selmask[1]-offset] |
---|
1589 | elif selmask[1] == None: |
---|
1590 | ## selection was ">freq[0]" |
---|
1591 | if selmask[0] > maxfreq: |
---|
1592 | ## avoid adding region selection |
---|
1593 | selmask = None |
---|
1594 | else: |
---|
1595 | selmask = [selmask[0]+offset,maxfreq] |
---|
1596 | if selmask: |
---|
1597 | if not seldict.has_key(spwstr): |
---|
1598 | # new spw selection |
---|
1599 | seldict[spwstr] = [] |
---|
1600 | seldict[spwstr] += [selmask] |
---|
1601 | del minfreq,maxfreq,offset,freq,selmask |
---|
1602 | del spwstr |
---|
1603 | del freqmasklist |
---|
1604 | del valid_ifs |
---|
1605 | if len(seldict) == 0: |
---|
1606 | asaplog.post() |
---|
1607 | asaplog.push("No valid selection in the mask expression: " |
---|
1608 | +maskstring) |
---|
1609 | asaplog.post("WARN") |
---|
1610 | return None |
---|
1611 | msg = "Selected masklist:\n" |
---|
1612 | for sif, lmask in seldict.iteritems(): |
---|
1613 | msg += " IF"+sif+" - "+str(lmask)+"\n" |
---|
1614 | asaplog.push(msg) |
---|
1615 | return seldict |
---|
1616 | |
---|
1617 | @asaplog_post_dec |
---|
1618 | def parse_idx_selection(self, mode, selexpr): |
---|
1619 | """ |
---|
1620 | Parse CASA type mask selection syntax of SCANNO, IFNO, POLNO, |
---|
1621 | BEAMNO, and row number |
---|
1622 | |
---|
1623 | Parameters: |
---|
1624 | mode : which column to select. |
---|
1625 | ['scan',|'if'|'pol'|'beam'|'row'] |
---|
1626 | selexpr : A comma separated selection expression. |
---|
1627 | examples: |
---|
1628 | '' = all (returns []) |
---|
1629 | '<2,4~6,9' = indices less than 2, 4 to 6 and 9 |
---|
1630 | (returns [0,1,4,5,6,9]) |
---|
1631 | Returns: |
---|
1632 | A List of selected indices |
---|
1633 | """ |
---|
1634 | if selexpr == "": |
---|
1635 | return [] |
---|
1636 | valid_modes = {'s': 'scan', 'i': 'if', 'p': 'pol', |
---|
1637 | 'b': 'beam', 'r': 'row'} |
---|
1638 | smode = mode.lower()[0] |
---|
1639 | if not (smode in valid_modes.keys()): |
---|
1640 | msg = "Invalid mode '%s'. Valid modes are %s" %\ |
---|
1641 | (mode, str(valid_modes.values())) |
---|
1642 | asaplog.post() |
---|
1643 | asaplog.push(msg) |
---|
1644 | asaplog.post("ERROR") |
---|
1645 | mode = valid_modes[smode] |
---|
1646 | minidx = None |
---|
1647 | maxidx = None |
---|
1648 | if smode == 'r': |
---|
1649 | minidx = 0 |
---|
1650 | maxidx = self.nrow() |
---|
1651 | else: |
---|
1652 | idx = getattr(self,"get"+mode+"nos")() |
---|
1653 | minidx = min(idx) |
---|
1654 | maxidx = max(idx) |
---|
1655 | del idx |
---|
1656 | sellist = selexpr.split(',') |
---|
1657 | idxlist = [] |
---|
1658 | for currselstr in sellist: |
---|
1659 | # single range (may include ~, < or >) |
---|
1660 | currlist = self._parse_selection(currselstr, typestr='integer', |
---|
1661 | minval=minidx,maxval=maxidx) |
---|
1662 | for thelist in currlist: |
---|
1663 | idxlist += range(thelist[0],thelist[1]+1) |
---|
1664 | msg = "Selected %s: %s" % (mode.upper()+"NO", str(idxlist)) |
---|
1665 | asaplog.push(msg) |
---|
1666 | return idxlist |
---|
1667 | |
---|
1668 | def _parse_selection(self, selstr, typestr='float', offset=0., |
---|
1669 | minval=None, maxval=None): |
---|
1670 | """ |
---|
1671 | Parameters: |
---|
1672 | selstr : The Selection string, e.g., '<3;5~7;100~103;9' |
---|
1673 | typestr : The type of the values in returned list |
---|
1674 | ('integer' or 'float') |
---|
1675 | offset : The offset value to subtract from or add to |
---|
1676 | the boundary value if the selection string |
---|
1677 | includes '<' or '>' [Valid only for typestr='float'] |
---|
1678 | minval, maxval : The minimum/maximum values to set if the |
---|
1679 | selection string includes '<' or '>'. |
---|
1680 | The list element is filled with None by default. |
---|
1681 | Returns: |
---|
1682 | A list of min/max pair of selections. |
---|
1683 | Example: |
---|
1684 | _parse_selection('<3;5~7;9',typestr='int',minval=0) |
---|
1685 | --> returns [[0,2],[5,7],[9,9]] |
---|
1686 | _parse_selection('<3;5~7;9',typestr='float',offset=0.5,minval=0) |
---|
1687 | --> returns [[0.,2.5],[5.0,7.0],[9.,9.]] |
---|
1688 | """ |
---|
1689 | selgroups = selstr.split(';') |
---|
1690 | sellists = [] |
---|
1691 | if typestr.lower().startswith('int'): |
---|
1692 | formatfunc = int |
---|
1693 | offset = 1 |
---|
1694 | else: |
---|
1695 | formatfunc = float |
---|
1696 | |
---|
1697 | for currsel in selgroups: |
---|
1698 | if currsel.find('~') > 0: |
---|
1699 | # val0 <= x <= val1 |
---|
1700 | minsel = formatfunc(currsel.split('~')[0].strip()) |
---|
1701 | maxsel = formatfunc(currsel.split('~')[1].strip()) |
---|
1702 | elif currsel.strip().find('<=') > -1: |
---|
1703 | bound = currsel.split('<=') |
---|
1704 | try: # try "x <= val" |
---|
1705 | minsel = minval |
---|
1706 | maxsel = formatfunc(bound[1].strip()) |
---|
1707 | except ValueError: # now "val <= x" |
---|
1708 | minsel = formatfunc(bound[0].strip()) |
---|
1709 | maxsel = maxval |
---|
1710 | elif currsel.strip().find('>=') > -1: |
---|
1711 | bound = currsel.split('>=') |
---|
1712 | try: # try "x >= val" |
---|
1713 | minsel = formatfunc(bound[1].strip()) |
---|
1714 | maxsel = maxval |
---|
1715 | except ValueError: # now "val >= x" |
---|
1716 | minsel = minval |
---|
1717 | maxsel = formatfunc(bound[0].strip()) |
---|
1718 | elif currsel.strip().find('<') > -1: |
---|
1719 | bound = currsel.split('<') |
---|
1720 | try: # try "x < val" |
---|
1721 | minsel = minval |
---|
1722 | maxsel = formatfunc(bound[1].strip()) \ |
---|
1723 | - formatfunc(offset) |
---|
1724 | except ValueError: # now "val < x" |
---|
1725 | minsel = formatfunc(bound[0].strip()) \ |
---|
1726 | + formatfunc(offset) |
---|
1727 | maxsel = maxval |
---|
1728 | elif currsel.strip().find('>') > -1: |
---|
1729 | bound = currsel.split('>') |
---|
1730 | try: # try "x > val" |
---|
1731 | minsel = formatfunc(bound[1].strip()) \ |
---|
1732 | + formatfunc(offset) |
---|
1733 | maxsel = maxval |
---|
1734 | except ValueError: # now "val > x" |
---|
1735 | minsel = minval |
---|
1736 | maxsel = formatfunc(bound[0].strip()) \ |
---|
1737 | - formatfunc(offset) |
---|
1738 | else: |
---|
1739 | minsel = formatfunc(currsel) |
---|
1740 | maxsel = formatfunc(currsel) |
---|
1741 | sellists.append([minsel,maxsel]) |
---|
1742 | return sellists |
---|
1743 | |
---|
1744 | # def get_restfreqs(self): |
---|
1745 | # """ |
---|
1746 | # Get the restfrequency(s) stored in this scantable. |
---|
1747 | # The return value(s) are always of unit 'Hz' |
---|
1748 | # Parameters: |
---|
1749 | # none |
---|
1750 | # Returns: |
---|
1751 | # a list of doubles |
---|
1752 | # """ |
---|
1753 | # return list(self._getrestfreqs()) |
---|
1754 | |
---|
1755 | def get_restfreqs(self, ids=None): |
---|
1756 | """\ |
---|
1757 | Get the restfrequency(s) stored in this scantable. |
---|
1758 | The return value(s) are always of unit 'Hz' |
---|
1759 | |
---|
1760 | Parameters: |
---|
1761 | |
---|
1762 | ids: (optional) a list of MOLECULE_ID for that restfrequency(s) to |
---|
1763 | be retrieved |
---|
1764 | |
---|
1765 | Returns: |
---|
1766 | |
---|
1767 | dictionary containing ids and a list of doubles for each id |
---|
1768 | |
---|
1769 | """ |
---|
1770 | if ids is None: |
---|
1771 | rfreqs = {} |
---|
1772 | idlist = self.getmolnos() |
---|
1773 | for i in idlist: |
---|
1774 | rfreqs[i] = list(self._getrestfreqs(i)) |
---|
1775 | return rfreqs |
---|
1776 | else: |
---|
1777 | if type(ids) == list or type(ids) == tuple: |
---|
1778 | rfreqs = {} |
---|
1779 | for i in ids: |
---|
1780 | rfreqs[i] = list(self._getrestfreqs(i)) |
---|
1781 | return rfreqs |
---|
1782 | else: |
---|
1783 | return list(self._getrestfreqs(ids)) |
---|
1784 | |
---|
1785 | @asaplog_post_dec |
---|
1786 | def set_restfreqs(self, freqs=None, unit='Hz'): |
---|
1787 | """\ |
---|
1788 | Set or replace the restfrequency specified and |
---|
1789 | if the 'freqs' argument holds a scalar, |
---|
1790 | then that rest frequency will be applied to all the selected |
---|
1791 | data. If the 'freqs' argument holds |
---|
1792 | a vector, then it MUST be of equal or smaller length than |
---|
1793 | the number of IFs (and the available restfrequencies will be |
---|
1794 | replaced by this vector). In this case, *all* data have |
---|
1795 | the restfrequency set per IF according |
---|
1796 | to the corresponding value you give in the 'freqs' vector. |
---|
1797 | E.g. 'freqs=[1e9, 2e9]' would mean IF 0 gets restfreq 1e9 and |
---|
1798 | IF 1 gets restfreq 2e9. |
---|
1799 | |
---|
1800 | You can also specify the frequencies via a linecatalog. |
---|
1801 | |
---|
1802 | Parameters: |
---|
1803 | |
---|
1804 | freqs: list of rest frequency values or string idenitfiers |
---|
1805 | |
---|
1806 | unit: unit for rest frequency (default 'Hz') |
---|
1807 | |
---|
1808 | |
---|
1809 | Example:: |
---|
1810 | |
---|
1811 | # set the given restfrequency for the all currently selected IFs |
---|
1812 | scan.set_restfreqs(freqs=1.4e9) |
---|
1813 | # set restfrequencies for the n IFs (n > 1) in the order of the |
---|
1814 | # list, i.e |
---|
1815 | # IF0 -> 1.4e9, IF1 -> 1.41e9, IF3 -> 1.42e9 |
---|
1816 | # len(list_of_restfreqs) == nIF |
---|
1817 | # for nIF == 1 the following will set multiple restfrequency for |
---|
1818 | # that IF |
---|
1819 | scan.set_restfreqs(freqs=[1.4e9, 1.41e9, 1.42e9]) |
---|
1820 | # set multiple restfrequencies per IF. as a list of lists where |
---|
1821 | # the outer list has nIF elements, the inner s arbitrary |
---|
1822 | scan.set_restfreqs(freqs=[[1.4e9, 1.41e9], [1.67e9]]) |
---|
1823 | |
---|
1824 | *Note*: |
---|
1825 | |
---|
1826 | To do more sophisticate Restfrequency setting, e.g. on a |
---|
1827 | source and IF basis, use scantable.set_selection() before using |
---|
1828 | this function:: |
---|
1829 | |
---|
1830 | # provided your scantable is called scan |
---|
1831 | selection = selector() |
---|
1832 | selection.set_name('ORION*') |
---|
1833 | selection.set_ifs([1]) |
---|
1834 | scan.set_selection(selection) |
---|
1835 | scan.set_restfreqs(freqs=86.6e9) |
---|
1836 | |
---|
1837 | """ |
---|
1838 | varlist = vars() |
---|
1839 | from asap import linecatalog |
---|
1840 | # simple value |
---|
1841 | if isinstance(freqs, int) or isinstance(freqs, float): |
---|
1842 | self._setrestfreqs([freqs], [""], unit) |
---|
1843 | # list of values |
---|
1844 | elif isinstance(freqs, list) or isinstance(freqs, tuple): |
---|
1845 | # list values are scalars |
---|
1846 | if isinstance(freqs[-1], int) or isinstance(freqs[-1], float): |
---|
1847 | if len(freqs) == 1: |
---|
1848 | self._setrestfreqs(freqs, [""], unit) |
---|
1849 | else: |
---|
1850 | # allow the 'old' mode of setting mulitple IFs |
---|
1851 | savesel = self._getselection() |
---|
1852 | sel = self.get_selection() |
---|
1853 | iflist = self.getifnos() |
---|
1854 | if len(freqs)>len(iflist): |
---|
1855 | raise ValueError("number of elements in list of list " |
---|
1856 | "exeeds the current IF selections") |
---|
1857 | iflist = self.getifnos() |
---|
1858 | for i, fval in enumerate(freqs): |
---|
1859 | sel.set_ifs(iflist[i]) |
---|
1860 | self._setselection(sel) |
---|
1861 | self._setrestfreqs([fval], [""], unit) |
---|
1862 | self._setselection(savesel) |
---|
1863 | |
---|
1864 | # list values are dict, {'value'=, 'name'=) |
---|
1865 | elif isinstance(freqs[-1], dict): |
---|
1866 | values = [] |
---|
1867 | names = [] |
---|
1868 | for d in freqs: |
---|
1869 | values.append(d["value"]) |
---|
1870 | names.append(d["name"]) |
---|
1871 | self._setrestfreqs(values, names, unit) |
---|
1872 | elif isinstance(freqs[-1], list) or isinstance(freqs[-1], tuple): |
---|
1873 | savesel = self._getselection() |
---|
1874 | sel = self.get_selection() |
---|
1875 | iflist = self.getifnos() |
---|
1876 | if len(freqs)>len(iflist): |
---|
1877 | raise ValueError("number of elements in list of list exeeds" |
---|
1878 | " the current IF selections") |
---|
1879 | for i, fval in enumerate(freqs): |
---|
1880 | sel.set_ifs(iflist[i]) |
---|
1881 | self._setselection(sel) |
---|
1882 | self._setrestfreqs(fval, [""], unit) |
---|
1883 | self._setselection(savesel) |
---|
1884 | # freqs are to be taken from a linecatalog |
---|
1885 | elif isinstance(freqs, linecatalog): |
---|
1886 | savesel = self._getselection() |
---|
1887 | sel = self.get_selection() |
---|
1888 | for i in xrange(freqs.nrow()): |
---|
1889 | sel.set_ifs(iflist[i]) |
---|
1890 | self._setselection(sel) |
---|
1891 | self._setrestfreqs([freqs.get_frequency(i)], |
---|
1892 | [freqs.get_name(i)], "MHz") |
---|
1893 | # ensure that we are not iterating past nIF |
---|
1894 | if i == self.nif()-1: break |
---|
1895 | self._setselection(savesel) |
---|
1896 | else: |
---|
1897 | return |
---|
1898 | self._add_history("set_restfreqs", varlist) |
---|
1899 | |
---|
1900 | @asaplog_post_dec |
---|
1901 | def shift_refpix(self, delta): |
---|
1902 | """\ |
---|
1903 | Shift the reference pixel of the Spectra Coordinate by an |
---|
1904 | integer amount. |
---|
1905 | |
---|
1906 | Parameters: |
---|
1907 | |
---|
1908 | delta: the amount to shift by |
---|
1909 | |
---|
1910 | *Note*: |
---|
1911 | |
---|
1912 | Be careful using this with broadband data. |
---|
1913 | |
---|
1914 | """ |
---|
1915 | varlist = vars() |
---|
1916 | Scantable.shift_refpix(self, delta) |
---|
1917 | s._add_history("shift_refpix", varlist) |
---|
1918 | |
---|
1919 | @asaplog_post_dec |
---|
1920 | def history(self, filename=None): |
---|
1921 | """\ |
---|
1922 | Print the history. Optionally to a file. |
---|
1923 | |
---|
1924 | Parameters: |
---|
1925 | |
---|
1926 | filename: The name of the file to save the history to. |
---|
1927 | |
---|
1928 | """ |
---|
1929 | hist = list(self._gethistory()) |
---|
1930 | out = "-"*80 |
---|
1931 | for h in hist: |
---|
1932 | if h.startswith("---"): |
---|
1933 | out = "\n".join([out, h]) |
---|
1934 | else: |
---|
1935 | items = h.split("##") |
---|
1936 | date = items[0] |
---|
1937 | func = items[1] |
---|
1938 | items = items[2:] |
---|
1939 | out += "\n"+date+"\n" |
---|
1940 | out += "Function: %s\n Parameters:" % (func) |
---|
1941 | for i in items: |
---|
1942 | if i == '': |
---|
1943 | continue |
---|
1944 | s = i.split("=") |
---|
1945 | out += "\n %s = %s" % (s[0], s[1]) |
---|
1946 | out = "\n".join([out, "-"*80]) |
---|
1947 | if filename is not None: |
---|
1948 | if filename is "": |
---|
1949 | filename = 'scantable_history.txt' |
---|
1950 | import os |
---|
1951 | filename = os.path.expandvars(os.path.expanduser(filename)) |
---|
1952 | if not os.path.isdir(filename): |
---|
1953 | data = open(filename, 'w') |
---|
1954 | data.write(out) |
---|
1955 | data.close() |
---|
1956 | else: |
---|
1957 | msg = "Illegal file name '%s'." % (filename) |
---|
1958 | raise IOError(msg) |
---|
1959 | return page(out) |
---|
1960 | |
---|
1961 | # |
---|
1962 | # Maths business |
---|
1963 | # |
---|
1964 | @asaplog_post_dec |
---|
1965 | def average_time(self, mask=None, scanav=False, weight='tint', align=False): |
---|
1966 | """\ |
---|
1967 | Return the (time) weighted average of a scan. Scans will be averaged |
---|
1968 | only if the source direction (RA/DEC) is within 1' otherwise |
---|
1969 | |
---|
1970 | *Note*: |
---|
1971 | |
---|
1972 | in channels only - align if necessary |
---|
1973 | |
---|
1974 | Parameters: |
---|
1975 | |
---|
1976 | mask: an optional mask (only used for 'var' and 'tsys' |
---|
1977 | weighting) |
---|
1978 | |
---|
1979 | scanav: True averages each scan separately |
---|
1980 | False (default) averages all scans together, |
---|
1981 | |
---|
1982 | weight: Weighting scheme. |
---|
1983 | 'none' (mean no weight) |
---|
1984 | 'var' (1/var(spec) weighted) |
---|
1985 | 'tsys' (1/Tsys**2 weighted) |
---|
1986 | 'tint' (integration time weighted) |
---|
1987 | 'tintsys' (Tint/Tsys**2) |
---|
1988 | 'median' ( median averaging) |
---|
1989 | The default is 'tint' |
---|
1990 | |
---|
1991 | align: align the spectra in velocity before averaging. It takes |
---|
1992 | the time of the first spectrum as reference time. |
---|
1993 | |
---|
1994 | Example:: |
---|
1995 | |
---|
1996 | # time average the scantable without using a mask |
---|
1997 | newscan = scan.average_time() |
---|
1998 | |
---|
1999 | """ |
---|
2000 | varlist = vars() |
---|
2001 | weight = weight or 'TINT' |
---|
2002 | mask = mask or () |
---|
2003 | scanav = (scanav and 'SCAN') or 'NONE' |
---|
2004 | scan = (self, ) |
---|
2005 | |
---|
2006 | if align: |
---|
2007 | scan = (self.freq_align(insitu=False), ) |
---|
2008 | s = None |
---|
2009 | if weight.upper() == 'MEDIAN': |
---|
2010 | s = scantable(self._math._averagechannel(scan[0], 'MEDIAN', |
---|
2011 | scanav)) |
---|
2012 | else: |
---|
2013 | s = scantable(self._math._average(scan, mask, weight.upper(), |
---|
2014 | scanav)) |
---|
2015 | s._add_history("average_time", varlist) |
---|
2016 | return s |
---|
2017 | |
---|
2018 | @asaplog_post_dec |
---|
2019 | def convert_flux(self, jyperk=None, eta=None, d=None, insitu=None): |
---|
2020 | """\ |
---|
2021 | Return a scan where all spectra are converted to either |
---|
2022 | Jansky or Kelvin depending upon the flux units of the scan table. |
---|
2023 | By default the function tries to look the values up internally. |
---|
2024 | If it can't find them (or if you want to over-ride), you must |
---|
2025 | specify EITHER jyperk OR eta (and D which it will try to look up |
---|
2026 | also if you don't set it). jyperk takes precedence if you set both. |
---|
2027 | |
---|
2028 | Parameters: |
---|
2029 | |
---|
2030 | jyperk: the Jy / K conversion factor |
---|
2031 | |
---|
2032 | eta: the aperture efficiency |
---|
2033 | |
---|
2034 | d: the geometric diameter (metres) |
---|
2035 | |
---|
2036 | insitu: if False a new scantable is returned. |
---|
2037 | Otherwise, the scaling is done in-situ |
---|
2038 | The default is taken from .asaprc (False) |
---|
2039 | |
---|
2040 | """ |
---|
2041 | if insitu is None: insitu = rcParams['insitu'] |
---|
2042 | self._math._setinsitu(insitu) |
---|
2043 | varlist = vars() |
---|
2044 | jyperk = jyperk or -1.0 |
---|
2045 | d = d or -1.0 |
---|
2046 | eta = eta or -1.0 |
---|
2047 | s = scantable(self._math._convertflux(self, d, eta, jyperk)) |
---|
2048 | s._add_history("convert_flux", varlist) |
---|
2049 | if insitu: self._assign(s) |
---|
2050 | else: return s |
---|
2051 | |
---|
2052 | @asaplog_post_dec |
---|
2053 | def gain_el(self, poly=None, filename="", method="linear", insitu=None): |
---|
2054 | """\ |
---|
2055 | Return a scan after applying a gain-elevation correction. |
---|
2056 | The correction can be made via either a polynomial or a |
---|
2057 | table-based interpolation (and extrapolation if necessary). |
---|
2058 | You specify polynomial coefficients, an ascii table or neither. |
---|
2059 | If you specify neither, then a polynomial correction will be made |
---|
2060 | with built in coefficients known for certain telescopes (an error |
---|
2061 | will occur if the instrument is not known). |
---|
2062 | The data and Tsys are *divided* by the scaling factors. |
---|
2063 | |
---|
2064 | Parameters: |
---|
2065 | |
---|
2066 | poly: Polynomial coefficients (default None) to compute a |
---|
2067 | gain-elevation correction as a function of |
---|
2068 | elevation (in degrees). |
---|
2069 | |
---|
2070 | filename: The name of an ascii file holding correction factors. |
---|
2071 | The first row of the ascii file must give the column |
---|
2072 | names and these MUST include columns |
---|
2073 | 'ELEVATION' (degrees) and 'FACTOR' (multiply data |
---|
2074 | by this) somewhere. |
---|
2075 | The second row must give the data type of the |
---|
2076 | column. Use 'R' for Real and 'I' for Integer. |
---|
2077 | An example file would be |
---|
2078 | (actual factors are arbitrary) : |
---|
2079 | |
---|
2080 | TIME ELEVATION FACTOR |
---|
2081 | R R R |
---|
2082 | 0.1 0 0.8 |
---|
2083 | 0.2 20 0.85 |
---|
2084 | 0.3 40 0.9 |
---|
2085 | 0.4 60 0.85 |
---|
2086 | 0.5 80 0.8 |
---|
2087 | 0.6 90 0.75 |
---|
2088 | |
---|
2089 | method: Interpolation method when correcting from a table. |
---|
2090 | Values are 'nearest', 'linear' (default), 'cubic' |
---|
2091 | and 'spline' |
---|
2092 | |
---|
2093 | insitu: if False a new scantable is returned. |
---|
2094 | Otherwise, the scaling is done in-situ |
---|
2095 | The default is taken from .asaprc (False) |
---|
2096 | |
---|
2097 | """ |
---|
2098 | |
---|
2099 | if insitu is None: insitu = rcParams['insitu'] |
---|
2100 | self._math._setinsitu(insitu) |
---|
2101 | varlist = vars() |
---|
2102 | poly = poly or () |
---|
2103 | from os.path import expandvars |
---|
2104 | filename = expandvars(filename) |
---|
2105 | s = scantable(self._math._gainel(self, poly, filename, method)) |
---|
2106 | s._add_history("gain_el", varlist) |
---|
2107 | if insitu: |
---|
2108 | self._assign(s) |
---|
2109 | else: |
---|
2110 | return s |
---|
2111 | |
---|
2112 | @asaplog_post_dec |
---|
2113 | def freq_align(self, reftime=None, method='cubic', insitu=None): |
---|
2114 | """\ |
---|
2115 | Return a scan where all rows have been aligned in frequency/velocity. |
---|
2116 | The alignment frequency frame (e.g. LSRK) is that set by function |
---|
2117 | set_freqframe. |
---|
2118 | |
---|
2119 | Parameters: |
---|
2120 | |
---|
2121 | reftime: reference time to align at. By default, the time of |
---|
2122 | the first row of data is used. |
---|
2123 | |
---|
2124 | method: Interpolation method for regridding the spectra. |
---|
2125 | Choose from 'nearest', 'linear', 'cubic' (default) |
---|
2126 | and 'spline' |
---|
2127 | |
---|
2128 | insitu: if False a new scantable is returned. |
---|
2129 | Otherwise, the scaling is done in-situ |
---|
2130 | The default is taken from .asaprc (False) |
---|
2131 | |
---|
2132 | """ |
---|
2133 | if insitu is None: insitu = rcParams["insitu"] |
---|
2134 | oldInsitu = self._math._insitu() |
---|
2135 | self._math._setinsitu(insitu) |
---|
2136 | varlist = vars() |
---|
2137 | reftime = reftime or "" |
---|
2138 | s = scantable(self._math._freq_align(self, reftime, method)) |
---|
2139 | s._add_history("freq_align", varlist) |
---|
2140 | self._math._setinsitu(oldInsitu) |
---|
2141 | if insitu: |
---|
2142 | self._assign(s) |
---|
2143 | else: |
---|
2144 | return s |
---|
2145 | |
---|
2146 | @asaplog_post_dec |
---|
2147 | def opacity(self, tau=None, insitu=None): |
---|
2148 | """\ |
---|
2149 | Apply an opacity correction. The data |
---|
2150 | and Tsys are multiplied by the correction factor. |
---|
2151 | |
---|
2152 | Parameters: |
---|
2153 | |
---|
2154 | tau: (list of) opacity from which the correction factor is |
---|
2155 | exp(tau*ZD) |
---|
2156 | where ZD is the zenith-distance. |
---|
2157 | If a list is provided, it has to be of length nIF, |
---|
2158 | nIF*nPol or 1 and in order of IF/POL, e.g. |
---|
2159 | [opif0pol0, opif0pol1, opif1pol0 ...] |
---|
2160 | if tau is `None` the opacities are determined from a |
---|
2161 | model. |
---|
2162 | |
---|
2163 | insitu: if False a new scantable is returned. |
---|
2164 | Otherwise, the scaling is done in-situ |
---|
2165 | The default is taken from .asaprc (False) |
---|
2166 | |
---|
2167 | """ |
---|
2168 | if insitu is None: |
---|
2169 | insitu = rcParams['insitu'] |
---|
2170 | self._math._setinsitu(insitu) |
---|
2171 | varlist = vars() |
---|
2172 | if not hasattr(tau, "__len__"): |
---|
2173 | tau = [tau] |
---|
2174 | s = scantable(self._math._opacity(self, tau)) |
---|
2175 | s._add_history("opacity", varlist) |
---|
2176 | if insitu: |
---|
2177 | self._assign(s) |
---|
2178 | else: |
---|
2179 | return s |
---|
2180 | |
---|
2181 | @asaplog_post_dec |
---|
2182 | def bin(self, width=5, insitu=None): |
---|
2183 | """\ |
---|
2184 | Return a scan where all spectra have been binned up. |
---|
2185 | |
---|
2186 | Parameters: |
---|
2187 | |
---|
2188 | width: The bin width (default=5) in pixels |
---|
2189 | |
---|
2190 | insitu: if False a new scantable is returned. |
---|
2191 | Otherwise, the scaling is done in-situ |
---|
2192 | The default is taken from .asaprc (False) |
---|
2193 | |
---|
2194 | """ |
---|
2195 | if insitu is None: |
---|
2196 | insitu = rcParams['insitu'] |
---|
2197 | self._math._setinsitu(insitu) |
---|
2198 | varlist = vars() |
---|
2199 | s = scantable(self._math._bin(self, width)) |
---|
2200 | s._add_history("bin", varlist) |
---|
2201 | if insitu: |
---|
2202 | self._assign(s) |
---|
2203 | else: |
---|
2204 | return s |
---|
2205 | |
---|
2206 | @asaplog_post_dec |
---|
2207 | def reshape(self, first, last, insitu=None): |
---|
2208 | """Resize the band by providing first and last channel. |
---|
2209 | This will cut off all channels outside [first, last]. |
---|
2210 | """ |
---|
2211 | if insitu is None: |
---|
2212 | insitu = rcParams['insitu'] |
---|
2213 | varlist = vars() |
---|
2214 | if last < 0: |
---|
2215 | last = self.nchan()-1 + last |
---|
2216 | s = None |
---|
2217 | if insitu: |
---|
2218 | s = self |
---|
2219 | else: |
---|
2220 | s = self.copy() |
---|
2221 | s._reshape(first,last) |
---|
2222 | s._add_history("reshape", varlist) |
---|
2223 | if not insitu: |
---|
2224 | return s |
---|
2225 | |
---|
2226 | @asaplog_post_dec |
---|
2227 | def resample(self, width=5, method='cubic', insitu=None): |
---|
2228 | """\ |
---|
2229 | Return a scan where all spectra have been binned up. |
---|
2230 | |
---|
2231 | Parameters: |
---|
2232 | |
---|
2233 | width: The bin width (default=5) in pixels |
---|
2234 | |
---|
2235 | method: Interpolation method when correcting from a table. |
---|
2236 | Values are 'nearest', 'linear', 'cubic' (default) |
---|
2237 | and 'spline' |
---|
2238 | |
---|
2239 | insitu: if False a new scantable is returned. |
---|
2240 | Otherwise, the scaling is done in-situ |
---|
2241 | The default is taken from .asaprc (False) |
---|
2242 | |
---|
2243 | """ |
---|
2244 | if insitu is None: |
---|
2245 | insitu = rcParams['insitu'] |
---|
2246 | self._math._setinsitu(insitu) |
---|
2247 | varlist = vars() |
---|
2248 | s = scantable(self._math._resample(self, method, width)) |
---|
2249 | s._add_history("resample", varlist) |
---|
2250 | if insitu: |
---|
2251 | self._assign(s) |
---|
2252 | else: |
---|
2253 | return s |
---|
2254 | |
---|
2255 | @asaplog_post_dec |
---|
2256 | def average_pol(self, mask=None, weight='none'): |
---|
2257 | """\ |
---|
2258 | Average the Polarisations together. |
---|
2259 | |
---|
2260 | Parameters: |
---|
2261 | |
---|
2262 | mask: An optional mask defining the region, where the |
---|
2263 | averaging will be applied. The output will have all |
---|
2264 | specified points masked. |
---|
2265 | |
---|
2266 | weight: Weighting scheme. 'none' (default), 'var' (1/var(spec) |
---|
2267 | weighted), or 'tsys' (1/Tsys**2 weighted) |
---|
2268 | |
---|
2269 | """ |
---|
2270 | varlist = vars() |
---|
2271 | mask = mask or () |
---|
2272 | s = scantable(self._math._averagepol(self, mask, weight.upper())) |
---|
2273 | s._add_history("average_pol", varlist) |
---|
2274 | return s |
---|
2275 | |
---|
2276 | @asaplog_post_dec |
---|
2277 | def average_beam(self, mask=None, weight='none'): |
---|
2278 | """\ |
---|
2279 | Average the Beams together. |
---|
2280 | |
---|
2281 | Parameters: |
---|
2282 | mask: An optional mask defining the region, where the |
---|
2283 | averaging will be applied. The output will have all |
---|
2284 | specified points masked. |
---|
2285 | |
---|
2286 | weight: Weighting scheme. 'none' (default), 'var' (1/var(spec) |
---|
2287 | weighted), or 'tsys' (1/Tsys**2 weighted) |
---|
2288 | |
---|
2289 | """ |
---|
2290 | varlist = vars() |
---|
2291 | mask = mask or () |
---|
2292 | s = scantable(self._math._averagebeams(self, mask, weight.upper())) |
---|
2293 | s._add_history("average_beam", varlist) |
---|
2294 | return s |
---|
2295 | |
---|
2296 | def parallactify(self, pflag): |
---|
2297 | """\ |
---|
2298 | Set a flag to indicate whether this data should be treated as having |
---|
2299 | been 'parallactified' (total phase == 0.0) |
---|
2300 | |
---|
2301 | Parameters: |
---|
2302 | |
---|
2303 | pflag: Bool indicating whether to turn this on (True) or |
---|
2304 | off (False) |
---|
2305 | |
---|
2306 | """ |
---|
2307 | varlist = vars() |
---|
2308 | self._parallactify(pflag) |
---|
2309 | self._add_history("parallactify", varlist) |
---|
2310 | |
---|
2311 | @asaplog_post_dec |
---|
2312 | def convert_pol(self, poltype=None): |
---|
2313 | """\ |
---|
2314 | Convert the data to a different polarisation type. |
---|
2315 | Note that you will need cross-polarisation terms for most conversions. |
---|
2316 | |
---|
2317 | Parameters: |
---|
2318 | |
---|
2319 | poltype: The new polarisation type. Valid types are: |
---|
2320 | 'linear', 'circular', 'stokes' and 'linpol' |
---|
2321 | |
---|
2322 | """ |
---|
2323 | varlist = vars() |
---|
2324 | s = scantable(self._math._convertpol(self, poltype)) |
---|
2325 | s._add_history("convert_pol", varlist) |
---|
2326 | return s |
---|
2327 | |
---|
2328 | @asaplog_post_dec |
---|
2329 | def smooth(self, kernel="hanning", width=5.0, order=2, plot=False, |
---|
2330 | insitu=None): |
---|
2331 | """\ |
---|
2332 | Smooth the spectrum by the specified kernel (conserving flux). |
---|
2333 | |
---|
2334 | Parameters: |
---|
2335 | |
---|
2336 | kernel: The type of smoothing kernel. Select from |
---|
2337 | 'hanning' (default), 'gaussian', 'boxcar', 'rmedian' |
---|
2338 | or 'poly' |
---|
2339 | |
---|
2340 | width: The width of the kernel in pixels. For hanning this is |
---|
2341 | ignored otherwise it defauls to 5 pixels. |
---|
2342 | For 'gaussian' it is the Full Width Half |
---|
2343 | Maximum. For 'boxcar' it is the full width. |
---|
2344 | For 'rmedian' and 'poly' it is the half width. |
---|
2345 | |
---|
2346 | order: Optional parameter for 'poly' kernel (default is 2), to |
---|
2347 | specify the order of the polnomial. Ignored by all other |
---|
2348 | kernels. |
---|
2349 | |
---|
2350 | plot: plot the original and the smoothed spectra. |
---|
2351 | In this each indivual fit has to be approved, by |
---|
2352 | typing 'y' or 'n' |
---|
2353 | |
---|
2354 | insitu: if False a new scantable is returned. |
---|
2355 | Otherwise, the scaling is done in-situ |
---|
2356 | The default is taken from .asaprc (False) |
---|
2357 | |
---|
2358 | """ |
---|
2359 | if insitu is None: insitu = rcParams['insitu'] |
---|
2360 | self._math._setinsitu(insitu) |
---|
2361 | varlist = vars() |
---|
2362 | |
---|
2363 | if plot: orgscan = self.copy() |
---|
2364 | |
---|
2365 | s = scantable(self._math._smooth(self, kernel.lower(), width, order)) |
---|
2366 | s._add_history("smooth", varlist) |
---|
2367 | |
---|
2368 | action = 'H' |
---|
2369 | if plot: |
---|
2370 | from asap.asapplotter import new_asaplot |
---|
2371 | theplot = new_asaplot(rcParams['plotter.gui']) |
---|
2372 | from matplotlib import rc as rcp |
---|
2373 | rcp('lines', linewidth=1) |
---|
2374 | theplot.set_panels() |
---|
2375 | ylab=s._get_ordinate_label() |
---|
2376 | #theplot.palette(0,["#777777","red"]) |
---|
2377 | for r in xrange(s.nrow()): |
---|
2378 | xsm=s._getabcissa(r) |
---|
2379 | ysm=s._getspectrum(r) |
---|
2380 | xorg=orgscan._getabcissa(r) |
---|
2381 | yorg=orgscan._getspectrum(r) |
---|
2382 | if action != "N": #skip plotting if rejecting all |
---|
2383 | theplot.clear() |
---|
2384 | theplot.hold() |
---|
2385 | theplot.set_axes('ylabel',ylab) |
---|
2386 | theplot.set_axes('xlabel',s._getabcissalabel(r)) |
---|
2387 | theplot.set_axes('title',s._getsourcename(r)) |
---|
2388 | theplot.set_line(label='Original',color="#777777") |
---|
2389 | theplot.plot(xorg,yorg) |
---|
2390 | theplot.set_line(label='Smoothed',color="red") |
---|
2391 | theplot.plot(xsm,ysm) |
---|
2392 | ### Ugly part for legend |
---|
2393 | for i in [0,1]: |
---|
2394 | theplot.subplots[0]['lines'].append( |
---|
2395 | [theplot.subplots[0]['axes'].lines[i]] |
---|
2396 | ) |
---|
2397 | theplot.release() |
---|
2398 | ### Ugly part for legend |
---|
2399 | theplot.subplots[0]['lines']=[] |
---|
2400 | res = self._get_verify_action("Accept smoothing?",action) |
---|
2401 | #print "IF%d, POL%d: got result = %s" %(s.getif(r),s.getpol(r),res) |
---|
2402 | if r == 0: action = None |
---|
2403 | #res = raw_input("Accept smoothing ([y]/n): ") |
---|
2404 | if res.upper() == 'N': |
---|
2405 | # reject for the current rows |
---|
2406 | s._setspectrum(yorg, r) |
---|
2407 | elif res.upper() == 'R': |
---|
2408 | # reject all the following rows |
---|
2409 | action = "N" |
---|
2410 | s._setspectrum(yorg, r) |
---|
2411 | elif res.upper() == 'A': |
---|
2412 | # accept all the following rows |
---|
2413 | break |
---|
2414 | theplot.quit() |
---|
2415 | del theplot |
---|
2416 | del orgscan |
---|
2417 | |
---|
2418 | if insitu: self._assign(s) |
---|
2419 | else: return s |
---|
2420 | |
---|
2421 | @asaplog_post_dec |
---|
2422 | def regrid_channel(self, width=5, plot=False, insitu=None): |
---|
2423 | """\ |
---|
2424 | Regrid the spectra by the specified channel width |
---|
2425 | |
---|
2426 | Parameters: |
---|
2427 | |
---|
2428 | width: The channel width (float) of regridded spectra |
---|
2429 | in the current spectral unit. |
---|
2430 | |
---|
2431 | plot: [NOT IMPLEMENTED YET] |
---|
2432 | plot the original and the regridded spectra. |
---|
2433 | In this each indivual fit has to be approved, by |
---|
2434 | typing 'y' or 'n' |
---|
2435 | |
---|
2436 | insitu: if False a new scantable is returned. |
---|
2437 | Otherwise, the scaling is done in-situ |
---|
2438 | The default is taken from .asaprc (False) |
---|
2439 | |
---|
2440 | """ |
---|
2441 | if insitu is None: insitu = rcParams['insitu'] |
---|
2442 | varlist = vars() |
---|
2443 | |
---|
2444 | if plot: |
---|
2445 | asaplog.post() |
---|
2446 | asaplog.push("Verification plot is not implemtnetd yet.") |
---|
2447 | asaplog.post("WARN") |
---|
2448 | |
---|
2449 | s = self.copy() |
---|
2450 | s._regrid_specchan(width) |
---|
2451 | |
---|
2452 | s._add_history("regrid_channel", varlist) |
---|
2453 | |
---|
2454 | # if plot: |
---|
2455 | # from asap.asapplotter import new_asaplot |
---|
2456 | # theplot = new_asaplot(rcParams['plotter.gui']) |
---|
2457 | # from matplotlib import rc as rcp |
---|
2458 | # rcp('lines', linewidth=1) |
---|
2459 | # theplot.set_panels() |
---|
2460 | # ylab=s._get_ordinate_label() |
---|
2461 | # #theplot.palette(0,["#777777","red"]) |
---|
2462 | # for r in xrange(s.nrow()): |
---|
2463 | # xsm=s._getabcissa(r) |
---|
2464 | # ysm=s._getspectrum(r) |
---|
2465 | # xorg=orgscan._getabcissa(r) |
---|
2466 | # yorg=orgscan._getspectrum(r) |
---|
2467 | # theplot.clear() |
---|
2468 | # theplot.hold() |
---|
2469 | # theplot.set_axes('ylabel',ylab) |
---|
2470 | # theplot.set_axes('xlabel',s._getabcissalabel(r)) |
---|
2471 | # theplot.set_axes('title',s._getsourcename(r)) |
---|
2472 | # theplot.set_line(label='Original',color="#777777") |
---|
2473 | # theplot.plot(xorg,yorg) |
---|
2474 | # theplot.set_line(label='Smoothed',color="red") |
---|
2475 | # theplot.plot(xsm,ysm) |
---|
2476 | # ### Ugly part for legend |
---|
2477 | # for i in [0,1]: |
---|
2478 | # theplot.subplots[0]['lines'].append( |
---|
2479 | # [theplot.subplots[0]['axes'].lines[i]] |
---|
2480 | # ) |
---|
2481 | # theplot.release() |
---|
2482 | # ### Ugly part for legend |
---|
2483 | # theplot.subplots[0]['lines']=[] |
---|
2484 | # res = raw_input("Accept smoothing ([y]/n): ") |
---|
2485 | # if res.upper() == 'N': |
---|
2486 | # s._setspectrum(yorg, r) |
---|
2487 | # theplot.quit() |
---|
2488 | # del theplot |
---|
2489 | # del orgscan |
---|
2490 | |
---|
2491 | if insitu: self._assign(s) |
---|
2492 | else: return s |
---|
2493 | |
---|
2494 | @asaplog_post_dec |
---|
2495 | def _parse_wn(self, wn): |
---|
2496 | if isinstance(wn, list) or isinstance(wn, tuple): |
---|
2497 | return wn |
---|
2498 | elif isinstance(wn, int): |
---|
2499 | return [ wn ] |
---|
2500 | elif isinstance(wn, str): |
---|
2501 | if '-' in wn: # case 'a-b' : return [a,a+1,...,b-1,b] |
---|
2502 | val = wn.split('-') |
---|
2503 | val = [int(val[0]), int(val[1])] |
---|
2504 | val.sort() |
---|
2505 | res = [i for i in xrange(val[0], val[1]+1)] |
---|
2506 | elif wn[:2] == '<=' or wn[:2] == '=<': # cases '<=a','=<a' : return [0,1,...,a-1,a] |
---|
2507 | val = int(wn[2:])+1 |
---|
2508 | res = [i for i in xrange(val)] |
---|
2509 | elif wn[-2:] == '>=' or wn[-2:] == '=>': # cases 'a>=','a=>' : return [0,1,...,a-1,a] |
---|
2510 | val = int(wn[:-2])+1 |
---|
2511 | res = [i for i in xrange(val)] |
---|
2512 | elif wn[0] == '<': # case '<a' : return [0,1,...,a-2,a-1] |
---|
2513 | val = int(wn[1:]) |
---|
2514 | res = [i for i in xrange(val)] |
---|
2515 | elif wn[-1] == '>': # case 'a>' : return [0,1,...,a-2,a-1] |
---|
2516 | val = int(wn[:-1]) |
---|
2517 | res = [i for i in xrange(val)] |
---|
2518 | elif wn[:2] == '>=' or wn[:2] == '=>': # cases '>=a','=>a' : return [a,-999], which is |
---|
2519 | # then interpreted in C++ |
---|
2520 | # side as [a,a+1,...,a_nyq] |
---|
2521 | # (CAS-3759) |
---|
2522 | val = int(wn[2:]) |
---|
2523 | res = [val, -999] |
---|
2524 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2525 | elif wn[-2:] == '<=' or wn[-2:] == '=<': # cases 'a<=','a=<' : return [a,-999], which is |
---|
2526 | # then interpreted in C++ |
---|
2527 | # side as [a,a+1,...,a_nyq] |
---|
2528 | # (CAS-3759) |
---|
2529 | val = int(wn[:-2]) |
---|
2530 | res = [val, -999] |
---|
2531 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2532 | elif wn[0] == '>': # case '>a' : return [a+1,-999], which is |
---|
2533 | # then interpreted in C++ |
---|
2534 | # side as [a+1,a+2,...,a_nyq] |
---|
2535 | # (CAS-3759) |
---|
2536 | val = int(wn[1:])+1 |
---|
2537 | res = [val, -999] |
---|
2538 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2539 | elif wn[-1] == '<': # case 'a<' : return [a+1,-999], which is |
---|
2540 | # then interpreted in C++ |
---|
2541 | # side as [a+1,a+2,...,a_nyq] |
---|
2542 | # (CAS-3759) |
---|
2543 | val = int(wn[:-1])+1 |
---|
2544 | res = [val, -999] |
---|
2545 | #res = [i for i in xrange(val, self.nchan()/2+1)] |
---|
2546 | |
---|
2547 | return res |
---|
2548 | else: |
---|
2549 | msg = 'wrong value given for addwn/rejwn' |
---|
2550 | raise RuntimeError(msg) |
---|
2551 | |
---|
2552 | @asaplog_post_dec |
---|
2553 | def calc_aic(self, value=None, blfunc=None, order=None, mask=None, |
---|
2554 | whichrow=None, uselinefinder=None, edge=None, |
---|
2555 | threshold=None, chan_avg_limit=None): |
---|
2556 | """\ |
---|
2557 | Calculates and returns model selection criteria for a specified |
---|
2558 | baseline model and a given spectrum data. |
---|
2559 | Available values include Akaike Information Criterion (AIC), the |
---|
2560 | corrected Akaike Information Criterion (AICc) by Sugiura(1978), |
---|
2561 | Bayesian Information Criterion (BIC) and the Generalised Cross |
---|
2562 | Validation (GCV). |
---|
2563 | |
---|
2564 | Parameters: |
---|
2565 | value: name of model selection criteria to calculate. |
---|
2566 | available ones include 'aic', 'aicc', 'bic' and |
---|
2567 | 'gcv'. default is 'aicc'. |
---|
2568 | blfunc: baseline function name. available ones include |
---|
2569 | 'chebyshev', 'cspline' and 'sinusoid'. |
---|
2570 | default is 'chebyshev'. |
---|
2571 | order: parameter for basline function. actually stands for |
---|
2572 | order of polynomial (order) for 'chebyshev', |
---|
2573 | number of spline pieces (npiece) for 'cspline' and |
---|
2574 | maximum wave number for 'sinusoid', respectively. |
---|
2575 | default is 5 (which is also the default order value |
---|
2576 | for [auto_]chebyshev_baseline()). |
---|
2577 | mask: an optional mask. default is []. |
---|
2578 | whichrow: row number. default is 0 (the first row) |
---|
2579 | uselinefinder: use sd.linefinder() to flag out line regions |
---|
2580 | default is True. |
---|
2581 | edge: an optional number of channel to drop at |
---|
2582 | the edge of spectrum. If only one value is |
---|
2583 | specified, the same number will be dropped |
---|
2584 | from both sides of the spectrum. Default |
---|
2585 | is to keep all channels. Nested tuples |
---|
2586 | represent individual edge selection for |
---|
2587 | different IFs (a number of spectral channels |
---|
2588 | can be different) |
---|
2589 | default is (0, 0). |
---|
2590 | threshold: the threshold used by line finder. It is |
---|
2591 | better to keep it large as only strong lines |
---|
2592 | affect the baseline solution. |
---|
2593 | default is 3. |
---|
2594 | chan_avg_limit: a maximum number of consequtive spectral |
---|
2595 | channels to average during the search of |
---|
2596 | weak and broad lines. The default is no |
---|
2597 | averaging (and no search for weak lines). |
---|
2598 | If such lines can affect the fitted baseline |
---|
2599 | (e.g. a high order polynomial is fitted), |
---|
2600 | increase this parameter (usually values up |
---|
2601 | to 8 are reasonable). Most users of this |
---|
2602 | method should find the default value sufficient. |
---|
2603 | default is 1. |
---|
2604 | |
---|
2605 | Example: |
---|
2606 | aic = scan.calc_aic(blfunc='chebyshev', order=5, whichrow=0) |
---|
2607 | """ |
---|
2608 | |
---|
2609 | try: |
---|
2610 | varlist = vars() |
---|
2611 | |
---|
2612 | if value is None: value = 'aicc' |
---|
2613 | if blfunc is None: blfunc = 'chebyshev' |
---|
2614 | if order is None: order = 5 |
---|
2615 | if mask is None: mask = [] |
---|
2616 | if whichrow is None: whichrow = 0 |
---|
2617 | if uselinefinder is None: uselinefinder = True |
---|
2618 | if edge is None: edge = (0, 0) |
---|
2619 | if threshold is None: threshold = 3 |
---|
2620 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
2621 | |
---|
2622 | return self._calc_aic(value, blfunc, order, mask, |
---|
2623 | whichrow, uselinefinder, edge, |
---|
2624 | threshold, chan_avg_limit) |
---|
2625 | |
---|
2626 | except RuntimeError, e: |
---|
2627 | raise_fitting_failure_exception(e) |
---|
2628 | |
---|
2629 | @asaplog_post_dec |
---|
2630 | def sinusoid_baseline(self, insitu=None, mask=None, applyfft=None, |
---|
2631 | fftmethod=None, fftthresh=None, |
---|
2632 | addwn=None, rejwn=None, clipthresh=None, |
---|
2633 | clipniter=None, plot=None, |
---|
2634 | getresidual=None, showprogress=None, |
---|
2635 | minnrow=None, outlog=None, blfile=None, csvformat=None): |
---|
2636 | """\ |
---|
2637 | Return a scan which has been baselined (all rows) with sinusoidal |
---|
2638 | functions. |
---|
2639 | |
---|
2640 | Parameters: |
---|
2641 | insitu: if False a new scantable is returned. |
---|
2642 | Otherwise, the scaling is done in-situ |
---|
2643 | The default is taken from .asaprc (False) |
---|
2644 | mask: an optional mask |
---|
2645 | applyfft: if True use some method, such as FFT, to find |
---|
2646 | strongest sinusoidal components in the wavenumber |
---|
2647 | domain to be used for baseline fitting. |
---|
2648 | default is True. |
---|
2649 | fftmethod: method to find the strong sinusoidal components. |
---|
2650 | now only 'fft' is available and it is the default. |
---|
2651 | fftthresh: the threshold to select wave numbers to be used for |
---|
2652 | fitting from the distribution of amplitudes in the |
---|
2653 | wavenumber domain. |
---|
2654 | both float and string values accepted. |
---|
2655 | given a float value, the unit is set to sigma. |
---|
2656 | for string values, allowed formats include: |
---|
2657 | 'xsigma' or 'x' (= x-sigma level. e.g., |
---|
2658 | '3sigma'), or |
---|
2659 | 'topx' (= the x strongest ones, e.g. 'top5'). |
---|
2660 | default is 3.0 (unit: sigma). |
---|
2661 | addwn: the additional wave numbers to be used for fitting. |
---|
2662 | list or integer value is accepted to specify every |
---|
2663 | wave numbers. also string value can be used in case |
---|
2664 | you need to specify wave numbers in a certain range, |
---|
2665 | e.g., 'a-b' (= a, a+1, a+2, ..., b-1, b), |
---|
2666 | '<a' (= 0,1,...,a-2,a-1), |
---|
2667 | '>=a' (= a, a+1, ... up to the maximum wave |
---|
2668 | number corresponding to the Nyquist |
---|
2669 | frequency for the case of FFT). |
---|
2670 | default is [0]. |
---|
2671 | rejwn: the wave numbers NOT to be used for fitting. |
---|
2672 | can be set just as addwn but has higher priority: |
---|
2673 | wave numbers which are specified both in addwn |
---|
2674 | and rejwn will NOT be used. default is []. |
---|
2675 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2676 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2677 | clipping (default is 0) |
---|
2678 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2679 | plot the fit and the residual. In this each |
---|
2680 | indivual fit has to be approved, by typing 'y' |
---|
2681 | or 'n' |
---|
2682 | getresidual: if False, returns best-fit values instead of |
---|
2683 | residual. (default is True) |
---|
2684 | showprogress: show progress status for large data. |
---|
2685 | default is True. |
---|
2686 | minnrow: minimum number of input spectra to show. |
---|
2687 | default is 1000. |
---|
2688 | outlog: Output the coefficients of the best-fit |
---|
2689 | function to logger (default is False) |
---|
2690 | blfile: Name of a text file in which the best-fit |
---|
2691 | parameter values to be written |
---|
2692 | (default is '': no file/logger output) |
---|
2693 | csvformat: if True blfile is csv-formatted, default is False. |
---|
2694 | |
---|
2695 | Example: |
---|
2696 | # return a scan baselined by a combination of sinusoidal curves |
---|
2697 | # having wave numbers in spectral window up to 10, |
---|
2698 | # also with 3-sigma clipping, iteration up to 4 times |
---|
2699 | bscan = scan.sinusoid_baseline(addwn='<=10',clipthresh=3.0,clipniter=4) |
---|
2700 | |
---|
2701 | Note: |
---|
2702 | The best-fit parameter values output in logger and/or blfile are now |
---|
2703 | based on specunit of 'channel'. |
---|
2704 | """ |
---|
2705 | |
---|
2706 | try: |
---|
2707 | varlist = vars() |
---|
2708 | |
---|
2709 | if insitu is None: insitu = rcParams['insitu'] |
---|
2710 | if insitu: |
---|
2711 | workscan = self |
---|
2712 | else: |
---|
2713 | workscan = self.copy() |
---|
2714 | |
---|
2715 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2716 | if mask is None: mask = [] |
---|
2717 | if applyfft is None: applyfft = True |
---|
2718 | if fftmethod is None: fftmethod = 'fft' |
---|
2719 | if fftthresh is None: fftthresh = 3.0 |
---|
2720 | if addwn is None: addwn = [0] |
---|
2721 | if rejwn is None: rejwn = [] |
---|
2722 | if clipthresh is None: clipthresh = 3.0 |
---|
2723 | if clipniter is None: clipniter = 0 |
---|
2724 | if plot is None: plot = False |
---|
2725 | if getresidual is None: getresidual = True |
---|
2726 | if showprogress is None: showprogress = True |
---|
2727 | if minnrow is None: minnrow = 1000 |
---|
2728 | if outlog is None: outlog = False |
---|
2729 | if blfile is None: blfile = '' |
---|
2730 | if csvformat is None: csvformat = False |
---|
2731 | |
---|
2732 | if csvformat: |
---|
2733 | scsvformat = "T" |
---|
2734 | else: |
---|
2735 | scsvformat = "F" |
---|
2736 | |
---|
2737 | #CURRENTLY, PLOT=true is UNAVAILABLE UNTIL sinusoidal fitting is implemented as a fitter method. |
---|
2738 | workscan._sinusoid_baseline(mask, applyfft, fftmethod.lower(), |
---|
2739 | str(fftthresh).lower(), |
---|
2740 | workscan._parse_wn(addwn), |
---|
2741 | workscan._parse_wn(rejwn), |
---|
2742 | clipthresh, clipniter, |
---|
2743 | getresidual, |
---|
2744 | pack_progress_params(showprogress, |
---|
2745 | minnrow), |
---|
2746 | outlog, scsvformat+blfile) |
---|
2747 | workscan._add_history('sinusoid_baseline', varlist) |
---|
2748 | |
---|
2749 | if insitu: |
---|
2750 | self._assign(workscan) |
---|
2751 | else: |
---|
2752 | return workscan |
---|
2753 | |
---|
2754 | except RuntimeError, e: |
---|
2755 | raise_fitting_failure_exception(e) |
---|
2756 | |
---|
2757 | |
---|
2758 | @asaplog_post_dec |
---|
2759 | def auto_sinusoid_baseline(self, insitu=None, mask=None, applyfft=None, |
---|
2760 | fftmethod=None, fftthresh=None, |
---|
2761 | addwn=None, rejwn=None, clipthresh=None, |
---|
2762 | clipniter=None, edge=None, threshold=None, |
---|
2763 | chan_avg_limit=None, plot=None, |
---|
2764 | getresidual=None, showprogress=None, |
---|
2765 | minnrow=None, outlog=None, blfile=None, csvformat=None): |
---|
2766 | """\ |
---|
2767 | Return a scan which has been baselined (all rows) with sinusoidal |
---|
2768 | functions. |
---|
2769 | Spectral lines are detected first using linefinder and masked out |
---|
2770 | to avoid them affecting the baseline solution. |
---|
2771 | |
---|
2772 | Parameters: |
---|
2773 | insitu: if False a new scantable is returned. |
---|
2774 | Otherwise, the scaling is done in-situ |
---|
2775 | The default is taken from .asaprc (False) |
---|
2776 | mask: an optional mask retreived from scantable |
---|
2777 | applyfft: if True use some method, such as FFT, to find |
---|
2778 | strongest sinusoidal components in the wavenumber |
---|
2779 | domain to be used for baseline fitting. |
---|
2780 | default is True. |
---|
2781 | fftmethod: method to find the strong sinusoidal components. |
---|
2782 | now only 'fft' is available and it is the default. |
---|
2783 | fftthresh: the threshold to select wave numbers to be used for |
---|
2784 | fitting from the distribution of amplitudes in the |
---|
2785 | wavenumber domain. |
---|
2786 | both float and string values accepted. |
---|
2787 | given a float value, the unit is set to sigma. |
---|
2788 | for string values, allowed formats include: |
---|
2789 | 'xsigma' or 'x' (= x-sigma level. e.g., |
---|
2790 | '3sigma'), or |
---|
2791 | 'topx' (= the x strongest ones, e.g. 'top5'). |
---|
2792 | default is 3.0 (unit: sigma). |
---|
2793 | addwn: the additional wave numbers to be used for fitting. |
---|
2794 | list or integer value is accepted to specify every |
---|
2795 | wave numbers. also string value can be used in case |
---|
2796 | you need to specify wave numbers in a certain range, |
---|
2797 | e.g., 'a-b' (= a, a+1, a+2, ..., b-1, b), |
---|
2798 | '<a' (= 0,1,...,a-2,a-1), |
---|
2799 | '>=a' (= a, a+1, ... up to the maximum wave |
---|
2800 | number corresponding to the Nyquist |
---|
2801 | frequency for the case of FFT). |
---|
2802 | default is [0]. |
---|
2803 | rejwn: the wave numbers NOT to be used for fitting. |
---|
2804 | can be set just as addwn but has higher priority: |
---|
2805 | wave numbers which are specified both in addwn |
---|
2806 | and rejwn will NOT be used. default is []. |
---|
2807 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2808 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2809 | clipping (default is 0) |
---|
2810 | edge: an optional number of channel to drop at |
---|
2811 | the edge of spectrum. If only one value is |
---|
2812 | specified, the same number will be dropped |
---|
2813 | from both sides of the spectrum. Default |
---|
2814 | is to keep all channels. Nested tuples |
---|
2815 | represent individual edge selection for |
---|
2816 | different IFs (a number of spectral channels |
---|
2817 | can be different) |
---|
2818 | threshold: the threshold used by line finder. It is |
---|
2819 | better to keep it large as only strong lines |
---|
2820 | affect the baseline solution. |
---|
2821 | chan_avg_limit: a maximum number of consequtive spectral |
---|
2822 | channels to average during the search of |
---|
2823 | weak and broad lines. The default is no |
---|
2824 | averaging (and no search for weak lines). |
---|
2825 | If such lines can affect the fitted baseline |
---|
2826 | (e.g. a high order polynomial is fitted), |
---|
2827 | increase this parameter (usually values up |
---|
2828 | to 8 are reasonable). Most users of this |
---|
2829 | method should find the default value sufficient. |
---|
2830 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2831 | plot the fit and the residual. In this each |
---|
2832 | indivual fit has to be approved, by typing 'y' |
---|
2833 | or 'n' |
---|
2834 | getresidual: if False, returns best-fit values instead of |
---|
2835 | residual. (default is True) |
---|
2836 | showprogress: show progress status for large data. |
---|
2837 | default is True. |
---|
2838 | minnrow: minimum number of input spectra to show. |
---|
2839 | default is 1000. |
---|
2840 | outlog: Output the coefficients of the best-fit |
---|
2841 | function to logger (default is False) |
---|
2842 | blfile: Name of a text file in which the best-fit |
---|
2843 | parameter values to be written |
---|
2844 | (default is "": no file/logger output) |
---|
2845 | csvformat: if True blfile is csv-formatted, default is False. |
---|
2846 | |
---|
2847 | Example: |
---|
2848 | bscan = scan.auto_sinusoid_baseline(addwn='<=10', insitu=False) |
---|
2849 | |
---|
2850 | Note: |
---|
2851 | The best-fit parameter values output in logger and/or blfile are now |
---|
2852 | based on specunit of 'channel'. |
---|
2853 | """ |
---|
2854 | |
---|
2855 | try: |
---|
2856 | varlist = vars() |
---|
2857 | |
---|
2858 | if insitu is None: insitu = rcParams['insitu'] |
---|
2859 | if insitu: |
---|
2860 | workscan = self |
---|
2861 | else: |
---|
2862 | workscan = self.copy() |
---|
2863 | |
---|
2864 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2865 | if mask is None: mask = [] |
---|
2866 | if applyfft is None: applyfft = True |
---|
2867 | if fftmethod is None: fftmethod = 'fft' |
---|
2868 | if fftthresh is None: fftthresh = 3.0 |
---|
2869 | if addwn is None: addwn = [0] |
---|
2870 | if rejwn is None: rejwn = [] |
---|
2871 | if clipthresh is None: clipthresh = 3.0 |
---|
2872 | if clipniter is None: clipniter = 0 |
---|
2873 | if edge is None: edge = (0,0) |
---|
2874 | if threshold is None: threshold = 3 |
---|
2875 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
2876 | if plot is None: plot = False |
---|
2877 | if getresidual is None: getresidual = True |
---|
2878 | if showprogress is None: showprogress = True |
---|
2879 | if minnrow is None: minnrow = 1000 |
---|
2880 | if outlog is None: outlog = False |
---|
2881 | if blfile is None: blfile = '' |
---|
2882 | if csvformat is None: csvformat = False |
---|
2883 | |
---|
2884 | if csvformat: |
---|
2885 | scsvformat = "T" |
---|
2886 | else: |
---|
2887 | scsvformat = "F" |
---|
2888 | |
---|
2889 | #CURRENTLY, PLOT=true is UNAVAILABLE UNTIL sinusoidal fitting is implemented as a fitter method. |
---|
2890 | workscan._auto_sinusoid_baseline(mask, applyfft, |
---|
2891 | fftmethod.lower(), |
---|
2892 | str(fftthresh).lower(), |
---|
2893 | workscan._parse_wn(addwn), |
---|
2894 | workscan._parse_wn(rejwn), |
---|
2895 | clipthresh, clipniter, |
---|
2896 | normalise_edge_param(edge), |
---|
2897 | threshold, chan_avg_limit, |
---|
2898 | getresidual, |
---|
2899 | pack_progress_params(showprogress, |
---|
2900 | minnrow), |
---|
2901 | outlog, scsvformat+blfile) |
---|
2902 | workscan._add_history("auto_sinusoid_baseline", varlist) |
---|
2903 | |
---|
2904 | if insitu: |
---|
2905 | self._assign(workscan) |
---|
2906 | else: |
---|
2907 | return workscan |
---|
2908 | |
---|
2909 | except RuntimeError, e: |
---|
2910 | raise_fitting_failure_exception(e) |
---|
2911 | |
---|
2912 | @asaplog_post_dec |
---|
2913 | def cspline_baseline(self, insitu=None, mask=None, npiece=None, |
---|
2914 | clipthresh=None, clipniter=None, plot=None, |
---|
2915 | getresidual=None, showprogress=None, minnrow=None, |
---|
2916 | outlog=None, blfile=None, csvformat=None): |
---|
2917 | """\ |
---|
2918 | Return a scan which has been baselined (all rows) by cubic spline |
---|
2919 | function (piecewise cubic polynomial). |
---|
2920 | |
---|
2921 | Parameters: |
---|
2922 | insitu: If False a new scantable is returned. |
---|
2923 | Otherwise, the scaling is done in-situ |
---|
2924 | The default is taken from .asaprc (False) |
---|
2925 | mask: An optional mask |
---|
2926 | npiece: Number of pieces. (default is 2) |
---|
2927 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
2928 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
2929 | clipping (default is 0) |
---|
2930 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
2931 | plot the fit and the residual. In this each |
---|
2932 | indivual fit has to be approved, by typing 'y' |
---|
2933 | or 'n' |
---|
2934 | getresidual: if False, returns best-fit values instead of |
---|
2935 | residual. (default is True) |
---|
2936 | showprogress: show progress status for large data. |
---|
2937 | default is True. |
---|
2938 | minnrow: minimum number of input spectra to show. |
---|
2939 | default is 1000. |
---|
2940 | outlog: Output the coefficients of the best-fit |
---|
2941 | function to logger (default is False) |
---|
2942 | blfile: Name of a text file in which the best-fit |
---|
2943 | parameter values to be written |
---|
2944 | (default is "": no file/logger output) |
---|
2945 | csvformat: if True blfile is csv-formatted, default is False. |
---|
2946 | |
---|
2947 | Example: |
---|
2948 | # return a scan baselined by a cubic spline consisting of 2 pieces |
---|
2949 | # (i.e., 1 internal knot), |
---|
2950 | # also with 3-sigma clipping, iteration up to 4 times |
---|
2951 | bscan = scan.cspline_baseline(npiece=2,clipthresh=3.0,clipniter=4) |
---|
2952 | |
---|
2953 | Note: |
---|
2954 | The best-fit parameter values output in logger and/or blfile are now |
---|
2955 | based on specunit of 'channel'. |
---|
2956 | """ |
---|
2957 | |
---|
2958 | try: |
---|
2959 | varlist = vars() |
---|
2960 | |
---|
2961 | if insitu is None: insitu = rcParams['insitu'] |
---|
2962 | if insitu: |
---|
2963 | workscan = self |
---|
2964 | else: |
---|
2965 | workscan = self.copy() |
---|
2966 | |
---|
2967 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
2968 | if mask is None: mask = [] |
---|
2969 | if npiece is None: npiece = 2 |
---|
2970 | if clipthresh is None: clipthresh = 3.0 |
---|
2971 | if clipniter is None: clipniter = 0 |
---|
2972 | if plot is None: plot = False |
---|
2973 | if getresidual is None: getresidual = True |
---|
2974 | if showprogress is None: showprogress = True |
---|
2975 | if minnrow is None: minnrow = 1000 |
---|
2976 | if outlog is None: outlog = False |
---|
2977 | if blfile is None: blfile = '' |
---|
2978 | if csvformat is None: csvformat = False |
---|
2979 | |
---|
2980 | if csvformat: |
---|
2981 | scsvformat = "T" |
---|
2982 | else: |
---|
2983 | scsvformat = "F" |
---|
2984 | |
---|
2985 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
2986 | workscan._cspline_baseline(mask, npiece, clipthresh, clipniter, |
---|
2987 | getresidual, |
---|
2988 | pack_progress_params(showprogress, |
---|
2989 | minnrow), |
---|
2990 | outlog, scsvformat+blfile) |
---|
2991 | workscan._add_history("cspline_baseline", varlist) |
---|
2992 | |
---|
2993 | if insitu: |
---|
2994 | self._assign(workscan) |
---|
2995 | else: |
---|
2996 | return workscan |
---|
2997 | |
---|
2998 | except RuntimeError, e: |
---|
2999 | raise_fitting_failure_exception(e) |
---|
3000 | |
---|
3001 | @asaplog_post_dec |
---|
3002 | def auto_cspline_baseline(self, insitu=None, mask=None, npiece=None, |
---|
3003 | clipthresh=None, clipniter=None, |
---|
3004 | edge=None, threshold=None, chan_avg_limit=None, |
---|
3005 | getresidual=None, plot=None, |
---|
3006 | showprogress=None, minnrow=None, outlog=None, |
---|
3007 | blfile=None, csvformat=None): |
---|
3008 | """\ |
---|
3009 | Return a scan which has been baselined (all rows) by cubic spline |
---|
3010 | function (piecewise cubic polynomial). |
---|
3011 | Spectral lines are detected first using linefinder and masked out |
---|
3012 | to avoid them affecting the baseline solution. |
---|
3013 | |
---|
3014 | Parameters: |
---|
3015 | insitu: if False a new scantable is returned. |
---|
3016 | Otherwise, the scaling is done in-situ |
---|
3017 | The default is taken from .asaprc (False) |
---|
3018 | mask: an optional mask retreived from scantable |
---|
3019 | npiece: Number of pieces. (default is 2) |
---|
3020 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
3021 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
3022 | clipping (default is 0) |
---|
3023 | edge: an optional number of channel to drop at |
---|
3024 | the edge of spectrum. If only one value is |
---|
3025 | specified, the same number will be dropped |
---|
3026 | from both sides of the spectrum. Default |
---|
3027 | is to keep all channels. Nested tuples |
---|
3028 | represent individual edge selection for |
---|
3029 | different IFs (a number of spectral channels |
---|
3030 | can be different) |
---|
3031 | threshold: the threshold used by line finder. It is |
---|
3032 | better to keep it large as only strong lines |
---|
3033 | affect the baseline solution. |
---|
3034 | chan_avg_limit: a maximum number of consequtive spectral |
---|
3035 | channels to average during the search of |
---|
3036 | weak and broad lines. The default is no |
---|
3037 | averaging (and no search for weak lines). |
---|
3038 | If such lines can affect the fitted baseline |
---|
3039 | (e.g. a high order polynomial is fitted), |
---|
3040 | increase this parameter (usually values up |
---|
3041 | to 8 are reasonable). Most users of this |
---|
3042 | method should find the default value sufficient. |
---|
3043 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
3044 | plot the fit and the residual. In this each |
---|
3045 | indivual fit has to be approved, by typing 'y' |
---|
3046 | or 'n' |
---|
3047 | getresidual: if False, returns best-fit values instead of |
---|
3048 | residual. (default is True) |
---|
3049 | showprogress: show progress status for large data. |
---|
3050 | default is True. |
---|
3051 | minnrow: minimum number of input spectra to show. |
---|
3052 | default is 1000. |
---|
3053 | outlog: Output the coefficients of the best-fit |
---|
3054 | function to logger (default is False) |
---|
3055 | blfile: Name of a text file in which the best-fit |
---|
3056 | parameter values to be written |
---|
3057 | (default is "": no file/logger output) |
---|
3058 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3059 | |
---|
3060 | Example: |
---|
3061 | bscan = scan.auto_cspline_baseline(npiece=3, insitu=False) |
---|
3062 | |
---|
3063 | Note: |
---|
3064 | The best-fit parameter values output in logger and/or blfile are now |
---|
3065 | based on specunit of 'channel'. |
---|
3066 | """ |
---|
3067 | |
---|
3068 | try: |
---|
3069 | varlist = vars() |
---|
3070 | |
---|
3071 | if insitu is None: insitu = rcParams['insitu'] |
---|
3072 | if insitu: |
---|
3073 | workscan = self |
---|
3074 | else: |
---|
3075 | workscan = self.copy() |
---|
3076 | |
---|
3077 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
3078 | if mask is None: mask = [] |
---|
3079 | if npiece is None: npiece = 2 |
---|
3080 | if clipthresh is None: clipthresh = 3.0 |
---|
3081 | if clipniter is None: clipniter = 0 |
---|
3082 | if edge is None: edge = (0, 0) |
---|
3083 | if threshold is None: threshold = 3 |
---|
3084 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
3085 | if plot is None: plot = False |
---|
3086 | if getresidual is None: getresidual = True |
---|
3087 | if showprogress is None: showprogress = True |
---|
3088 | if minnrow is None: minnrow = 1000 |
---|
3089 | if outlog is None: outlog = False |
---|
3090 | if blfile is None: blfile = '' |
---|
3091 | if csvformat is None: csvformat = False |
---|
3092 | |
---|
3093 | if csvformat: |
---|
3094 | scsvformat = "T" |
---|
3095 | else: |
---|
3096 | scsvformat = "F" |
---|
3097 | |
---|
3098 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
3099 | workscan._auto_cspline_baseline(mask, npiece, clipthresh, |
---|
3100 | clipniter, |
---|
3101 | normalise_edge_param(edge), |
---|
3102 | threshold, |
---|
3103 | chan_avg_limit, getresidual, |
---|
3104 | pack_progress_params(showprogress, |
---|
3105 | minnrow), |
---|
3106 | outlog, scsvformat+blfile) |
---|
3107 | workscan._add_history("auto_cspline_baseline", varlist) |
---|
3108 | |
---|
3109 | if insitu: |
---|
3110 | self._assign(workscan) |
---|
3111 | else: |
---|
3112 | return workscan |
---|
3113 | |
---|
3114 | except RuntimeError, e: |
---|
3115 | raise_fitting_failure_exception(e) |
---|
3116 | |
---|
3117 | @asaplog_post_dec |
---|
3118 | def chebyshev_baseline(self, insitu=None, mask=None, order=None, |
---|
3119 | clipthresh=None, clipniter=None, plot=None, |
---|
3120 | getresidual=None, showprogress=None, minnrow=None, |
---|
3121 | outlog=None, blfile=None, csvformat=None): |
---|
3122 | """\ |
---|
3123 | Return a scan which has been baselined (all rows) by Chebyshev polynomials. |
---|
3124 | |
---|
3125 | Parameters: |
---|
3126 | insitu: If False a new scantable is returned. |
---|
3127 | Otherwise, the scaling is done in-situ |
---|
3128 | The default is taken from .asaprc (False) |
---|
3129 | mask: An optional mask |
---|
3130 | order: the maximum order of Chebyshev polynomial (default is 5) |
---|
3131 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
3132 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
3133 | clipping (default is 0) |
---|
3134 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
3135 | plot the fit and the residual. In this each |
---|
3136 | indivual fit has to be approved, by typing 'y' |
---|
3137 | or 'n' |
---|
3138 | getresidual: if False, returns best-fit values instead of |
---|
3139 | residual. (default is True) |
---|
3140 | showprogress: show progress status for large data. |
---|
3141 | default is True. |
---|
3142 | minnrow: minimum number of input spectra to show. |
---|
3143 | default is 1000. |
---|
3144 | outlog: Output the coefficients of the best-fit |
---|
3145 | function to logger (default is False) |
---|
3146 | blfile: Name of a text file in which the best-fit |
---|
3147 | parameter values to be written |
---|
3148 | (default is "": no file/logger output) |
---|
3149 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3150 | |
---|
3151 | Example: |
---|
3152 | # return a scan baselined by a cubic spline consisting of 2 pieces |
---|
3153 | # (i.e., 1 internal knot), |
---|
3154 | # also with 3-sigma clipping, iteration up to 4 times |
---|
3155 | bscan = scan.cspline_baseline(npiece=2,clipthresh=3.0,clipniter=4) |
---|
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 plot is None: plot = False |
---|
3177 | if getresidual is None: getresidual = True |
---|
3178 | if showprogress is None: showprogress = True |
---|
3179 | if minnrow is None: minnrow = 1000 |
---|
3180 | if outlog is None: outlog = False |
---|
3181 | if blfile is None: blfile = '' |
---|
3182 | if csvformat is None: csvformat = False |
---|
3183 | |
---|
3184 | if csvformat: |
---|
3185 | scsvformat = "T" |
---|
3186 | else: |
---|
3187 | scsvformat = "F" |
---|
3188 | |
---|
3189 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
3190 | workscan._chebyshev_baseline(mask, order, clipthresh, clipniter, |
---|
3191 | getresidual, |
---|
3192 | pack_progress_params(showprogress, |
---|
3193 | minnrow), |
---|
3194 | outlog, scsvformat+blfile) |
---|
3195 | workscan._add_history("chebyshev_baseline", varlist) |
---|
3196 | |
---|
3197 | if insitu: |
---|
3198 | self._assign(workscan) |
---|
3199 | else: |
---|
3200 | return workscan |
---|
3201 | |
---|
3202 | except RuntimeError, e: |
---|
3203 | raise_fitting_failure_exception(e) |
---|
3204 | |
---|
3205 | @asaplog_post_dec |
---|
3206 | def auto_chebyshev_baseline(self, insitu=None, mask=None, order=None, |
---|
3207 | clipthresh=None, clipniter=None, |
---|
3208 | edge=None, threshold=None, chan_avg_limit=None, |
---|
3209 | getresidual=None, plot=None, |
---|
3210 | showprogress=None, minnrow=None, outlog=None, |
---|
3211 | blfile=None, csvformat=None): |
---|
3212 | """\ |
---|
3213 | Return a scan which has been baselined (all rows) by Chebyshev polynomials. |
---|
3214 | Spectral lines are detected first using linefinder and masked out |
---|
3215 | to avoid them affecting the baseline solution. |
---|
3216 | |
---|
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 retreived from scantable |
---|
3222 | order: the maximum order of Chebyshev polynomial (default is 5) |
---|
3223 | clipthresh: Clipping threshold. (default is 3.0, unit: sigma) |
---|
3224 | clipniter: maximum number of iteration of 'clipthresh'-sigma |
---|
3225 | clipping (default is 0) |
---|
3226 | edge: an optional number of channel to drop at |
---|
3227 | the edge of spectrum. If only one value is |
---|
3228 | specified, the same number will be dropped |
---|
3229 | from both sides of the spectrum. Default |
---|
3230 | is to keep all channels. Nested tuples |
---|
3231 | represent individual edge selection for |
---|
3232 | different IFs (a number of spectral channels |
---|
3233 | can be different) |
---|
3234 | threshold: the threshold used by line finder. It is |
---|
3235 | better to keep it large as only strong lines |
---|
3236 | affect the baseline solution. |
---|
3237 | chan_avg_limit: a maximum number of consequtive spectral |
---|
3238 | channels to average during the search of |
---|
3239 | weak and broad lines. The default is no |
---|
3240 | averaging (and no search for weak lines). |
---|
3241 | If such lines can affect the fitted baseline |
---|
3242 | (e.g. a high order polynomial is fitted), |
---|
3243 | increase this parameter (usually values up |
---|
3244 | to 8 are reasonable). Most users of this |
---|
3245 | method should find the default value sufficient. |
---|
3246 | plot: *** CURRENTLY UNAVAILABLE, ALWAYS FALSE *** |
---|
3247 | plot the fit and the residual. In this each |
---|
3248 | indivual fit has to be approved, by typing 'y' |
---|
3249 | or 'n' |
---|
3250 | getresidual: if False, returns best-fit values instead of |
---|
3251 | residual. (default is True) |
---|
3252 | showprogress: show progress status for large data. |
---|
3253 | default is True. |
---|
3254 | minnrow: minimum number of input spectra to show. |
---|
3255 | default is 1000. |
---|
3256 | outlog: Output the coefficients of the best-fit |
---|
3257 | function to logger (default is False) |
---|
3258 | blfile: Name of a text file in which the best-fit |
---|
3259 | parameter values to be written |
---|
3260 | (default is "": no file/logger output) |
---|
3261 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3262 | |
---|
3263 | Example: |
---|
3264 | bscan = scan.auto_cspline_baseline(npiece=3, insitu=False) |
---|
3265 | |
---|
3266 | Note: |
---|
3267 | The best-fit parameter values output in logger and/or blfile are now |
---|
3268 | based on specunit of 'channel'. |
---|
3269 | """ |
---|
3270 | |
---|
3271 | try: |
---|
3272 | varlist = vars() |
---|
3273 | |
---|
3274 | if insitu is None: insitu = rcParams['insitu'] |
---|
3275 | if insitu: |
---|
3276 | workscan = self |
---|
3277 | else: |
---|
3278 | workscan = self.copy() |
---|
3279 | |
---|
3280 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
3281 | if mask is None: mask = [] |
---|
3282 | if order is None: order = 5 |
---|
3283 | if clipthresh is None: clipthresh = 3.0 |
---|
3284 | if clipniter is None: clipniter = 0 |
---|
3285 | if edge is None: edge = (0, 0) |
---|
3286 | if threshold is None: threshold = 3 |
---|
3287 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
3288 | if plot is None: plot = False |
---|
3289 | if getresidual is None: getresidual = True |
---|
3290 | if showprogress is None: showprogress = True |
---|
3291 | if minnrow is None: minnrow = 1000 |
---|
3292 | if outlog is None: outlog = False |
---|
3293 | if blfile is None: blfile = '' |
---|
3294 | if csvformat is None: csvformat = False |
---|
3295 | |
---|
3296 | if csvformat: |
---|
3297 | scsvformat = "T" |
---|
3298 | else: |
---|
3299 | scsvformat = "F" |
---|
3300 | |
---|
3301 | #CURRENTLY, PLOT=true UNAVAILABLE UNTIL cubic spline fitting is implemented as a fitter method. |
---|
3302 | workscan._auto_chebyshev_baseline(mask, order, clipthresh, |
---|
3303 | clipniter, |
---|
3304 | normalise_edge_param(edge), |
---|
3305 | threshold, |
---|
3306 | chan_avg_limit, getresidual, |
---|
3307 | pack_progress_params(showprogress, |
---|
3308 | minnrow), |
---|
3309 | outlog, scsvformat+blfile) |
---|
3310 | workscan._add_history("auto_chebyshev_baseline", varlist) |
---|
3311 | |
---|
3312 | if insitu: |
---|
3313 | self._assign(workscan) |
---|
3314 | else: |
---|
3315 | return workscan |
---|
3316 | |
---|
3317 | except RuntimeError, e: |
---|
3318 | raise_fitting_failure_exception(e) |
---|
3319 | |
---|
3320 | @asaplog_post_dec |
---|
3321 | def poly_baseline(self, mask=None, order=None, insitu=None, plot=None, |
---|
3322 | getresidual=None, showprogress=None, minnrow=None, |
---|
3323 | outlog=None, blfile=None, csvformat=None): |
---|
3324 | """\ |
---|
3325 | Return a scan which has been baselined (all rows) by a polynomial. |
---|
3326 | Parameters: |
---|
3327 | insitu: if False a new scantable is returned. |
---|
3328 | Otherwise, the scaling is done in-situ |
---|
3329 | The default is taken from .asaprc (False) |
---|
3330 | mask: an optional mask |
---|
3331 | order: the order of the polynomial (default is 0) |
---|
3332 | plot: plot the fit and the residual. In this each |
---|
3333 | indivual fit has to be approved, by typing 'y' |
---|
3334 | or 'n' |
---|
3335 | getresidual: if False, returns best-fit values instead of |
---|
3336 | residual. (default is True) |
---|
3337 | showprogress: show progress status for large data. |
---|
3338 | default is True. |
---|
3339 | minnrow: minimum number of input spectra to show. |
---|
3340 | default is 1000. |
---|
3341 | outlog: Output the coefficients of the best-fit |
---|
3342 | function to logger (default is False) |
---|
3343 | blfile: Name of a text file in which the best-fit |
---|
3344 | parameter values to be written |
---|
3345 | (default is "": no file/logger output) |
---|
3346 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3347 | |
---|
3348 | Example: |
---|
3349 | # return a scan baselined by a third order polynomial, |
---|
3350 | # not using a mask |
---|
3351 | bscan = scan.poly_baseline(order=3) |
---|
3352 | """ |
---|
3353 | |
---|
3354 | try: |
---|
3355 | varlist = vars() |
---|
3356 | |
---|
3357 | if insitu is None: |
---|
3358 | insitu = rcParams["insitu"] |
---|
3359 | if insitu: |
---|
3360 | workscan = self |
---|
3361 | else: |
---|
3362 | workscan = self.copy() |
---|
3363 | |
---|
3364 | #if mask is None: mask = [True for i in \ |
---|
3365 | # xrange(workscan.nchan())] |
---|
3366 | if mask is None: mask = [] |
---|
3367 | if order is None: order = 0 |
---|
3368 | if plot is None: plot = False |
---|
3369 | if getresidual is None: getresidual = True |
---|
3370 | if showprogress is None: showprogress = True |
---|
3371 | if minnrow is None: minnrow = 1000 |
---|
3372 | if outlog is None: outlog = False |
---|
3373 | if blfile is None: blfile = "" |
---|
3374 | if csvformat is None: csvformat = False |
---|
3375 | |
---|
3376 | if csvformat: |
---|
3377 | scsvformat = "T" |
---|
3378 | else: |
---|
3379 | scsvformat = "F" |
---|
3380 | |
---|
3381 | if plot: |
---|
3382 | outblfile = (blfile != "") and \ |
---|
3383 | os.path.exists(os.path.expanduser( |
---|
3384 | os.path.expandvars(blfile)) |
---|
3385 | ) |
---|
3386 | if outblfile: |
---|
3387 | blf = open(blfile, "a") |
---|
3388 | |
---|
3389 | f = fitter() |
---|
3390 | f.set_function(lpoly=order) |
---|
3391 | |
---|
3392 | rows = xrange(workscan.nrow()) |
---|
3393 | #if len(rows) > 0: workscan._init_blinfo() |
---|
3394 | |
---|
3395 | action = "H" |
---|
3396 | for r in rows: |
---|
3397 | f.x = workscan._getabcissa(r) |
---|
3398 | f.y = workscan._getspectrum(r) |
---|
3399 | if mask: |
---|
3400 | f.mask = mask_and(mask, workscan._getmask(r)) # (CAS-1434) |
---|
3401 | else: # mask=None |
---|
3402 | f.mask = workscan._getmask(r) |
---|
3403 | |
---|
3404 | f.data = None |
---|
3405 | f.fit() |
---|
3406 | |
---|
3407 | if action != "Y": # skip plotting when accepting all |
---|
3408 | f.plot(residual=True) |
---|
3409 | #accept_fit = raw_input("Accept fit ( [y]/n ): ") |
---|
3410 | #if accept_fit.upper() == "N": |
---|
3411 | # #workscan._append_blinfo(None, None, None) |
---|
3412 | # continue |
---|
3413 | accept_fit = self._get_verify_action("Accept fit?",action) |
---|
3414 | if r == 0: action = None |
---|
3415 | if accept_fit.upper() == "N": |
---|
3416 | continue |
---|
3417 | elif accept_fit.upper() == "R": |
---|
3418 | break |
---|
3419 | elif accept_fit.upper() == "A": |
---|
3420 | action = "Y" |
---|
3421 | |
---|
3422 | blpars = f.get_parameters() |
---|
3423 | masklist = workscan.get_masklist(f.mask, row=r, silent=True) |
---|
3424 | #workscan._append_blinfo(blpars, masklist, f.mask) |
---|
3425 | workscan._setspectrum((f.fitter.getresidual() |
---|
3426 | if getresidual else f.fitter.getfit()), r) |
---|
3427 | |
---|
3428 | if outblfile: |
---|
3429 | rms = workscan.get_rms(f.mask, r) |
---|
3430 | dataout = \ |
---|
3431 | workscan.format_blparams_row(blpars["params"], |
---|
3432 | blpars["fixed"], |
---|
3433 | rms, str(masklist), |
---|
3434 | r, True, csvformat) |
---|
3435 | blf.write(dataout) |
---|
3436 | |
---|
3437 | f._p.unmap() |
---|
3438 | f._p = None |
---|
3439 | |
---|
3440 | if outblfile: |
---|
3441 | blf.close() |
---|
3442 | else: |
---|
3443 | workscan._poly_baseline(mask, order, getresidual, |
---|
3444 | pack_progress_params(showprogress, |
---|
3445 | minnrow), |
---|
3446 | outlog, scsvformat+blfile) |
---|
3447 | |
---|
3448 | workscan._add_history("poly_baseline", varlist) |
---|
3449 | |
---|
3450 | if insitu: |
---|
3451 | self._assign(workscan) |
---|
3452 | else: |
---|
3453 | return workscan |
---|
3454 | |
---|
3455 | except RuntimeError, e: |
---|
3456 | raise_fitting_failure_exception(e) |
---|
3457 | |
---|
3458 | @asaplog_post_dec |
---|
3459 | def auto_poly_baseline(self, mask=None, order=None, edge=None, |
---|
3460 | threshold=None, chan_avg_limit=None, |
---|
3461 | plot=None, insitu=None, |
---|
3462 | getresidual=None, showprogress=None, |
---|
3463 | minnrow=None, outlog=None, blfile=None, csvformat=None): |
---|
3464 | """\ |
---|
3465 | Return a scan which has been baselined (all rows) by a polynomial. |
---|
3466 | Spectral lines are detected first using linefinder and masked out |
---|
3467 | to avoid them affecting the baseline solution. |
---|
3468 | |
---|
3469 | Parameters: |
---|
3470 | insitu: if False a new scantable is returned. |
---|
3471 | Otherwise, the scaling is done in-situ |
---|
3472 | The default is taken from .asaprc (False) |
---|
3473 | mask: an optional mask retreived from scantable |
---|
3474 | order: the order of the polynomial (default is 0) |
---|
3475 | edge: an optional number of channel to drop at |
---|
3476 | the edge of spectrum. If only one value is |
---|
3477 | specified, the same number will be dropped |
---|
3478 | from both sides of the spectrum. Default |
---|
3479 | is to keep all channels. Nested tuples |
---|
3480 | represent individual edge selection for |
---|
3481 | different IFs (a number of spectral channels |
---|
3482 | can be different) |
---|
3483 | threshold: the threshold used by line finder. It is |
---|
3484 | better to keep it large as only strong lines |
---|
3485 | affect the baseline solution. |
---|
3486 | chan_avg_limit: a maximum number of consequtive spectral |
---|
3487 | channels to average during the search of |
---|
3488 | weak and broad lines. The default is no |
---|
3489 | averaging (and no search for weak lines). |
---|
3490 | If such lines can affect the fitted baseline |
---|
3491 | (e.g. a high order polynomial is fitted), |
---|
3492 | increase this parameter (usually values up |
---|
3493 | to 8 are reasonable). Most users of this |
---|
3494 | method should find the default value sufficient. |
---|
3495 | plot: plot the fit and the residual. In this each |
---|
3496 | indivual fit has to be approved, by typing 'y' |
---|
3497 | or 'n' |
---|
3498 | getresidual: if False, returns best-fit values instead of |
---|
3499 | residual. (default is True) |
---|
3500 | showprogress: show progress status for large data. |
---|
3501 | default is True. |
---|
3502 | minnrow: minimum number of input spectra to show. |
---|
3503 | default is 1000. |
---|
3504 | outlog: Output the coefficients of the best-fit |
---|
3505 | function to logger (default is False) |
---|
3506 | blfile: Name of a text file in which the best-fit |
---|
3507 | parameter values to be written |
---|
3508 | (default is "": no file/logger output) |
---|
3509 | csvformat: if True blfile is csv-formatted, default is False. |
---|
3510 | |
---|
3511 | Example: |
---|
3512 | bscan = scan.auto_poly_baseline(order=7, insitu=False) |
---|
3513 | """ |
---|
3514 | |
---|
3515 | try: |
---|
3516 | varlist = vars() |
---|
3517 | |
---|
3518 | if insitu is None: |
---|
3519 | insitu = rcParams['insitu'] |
---|
3520 | if insitu: |
---|
3521 | workscan = self |
---|
3522 | else: |
---|
3523 | workscan = self.copy() |
---|
3524 | |
---|
3525 | #if mask is None: mask = [True for i in xrange(workscan.nchan())] |
---|
3526 | if mask is None: mask = [] |
---|
3527 | if order is None: order = 0 |
---|
3528 | if edge is None: edge = (0, 0) |
---|
3529 | if threshold is None: threshold = 3 |
---|
3530 | if chan_avg_limit is None: chan_avg_limit = 1 |
---|
3531 | if plot is None: plot = False |
---|
3532 | if getresidual is None: getresidual = True |
---|
3533 | if showprogress is None: showprogress = True |
---|
3534 | if minnrow is None: minnrow = 1000 |
---|
3535 | if outlog is None: outlog = False |
---|
3536 | if blfile is None: blfile = '' |
---|
3537 | if csvformat is None: csvformat = False |
---|
3538 | |
---|
3539 | if csvformat: |
---|
3540 | scsvformat = "T" |
---|
3541 | else: |
---|
3542 | scsvformat = "F" |
---|
3543 | |
---|
3544 | edge = normalise_edge_param(edge) |
---|
3545 | |
---|
3546 | if plot: |
---|
3547 | outblfile = (blfile != "") and \ |
---|
3548 | os.path.exists(os.path.expanduser(os.path.expandvars(blfile))) |
---|
3549 | if outblfile: blf = open(blfile, "a") |
---|
3550 | |
---|
3551 | from asap.asaplinefind import linefinder |
---|
3552 | fl = linefinder() |
---|
3553 | fl.set_options(threshold=threshold, avg_limit=chan_avg_limit) |
---|
3554 | fl.set_scan(workscan) |
---|
3555 | |
---|
3556 | f = fitter() |
---|
3557 | f.set_function(lpoly=order) |
---|
3558 | |
---|
3559 | rows = xrange(workscan.nrow()) |
---|
3560 | #if len(rows) > 0: workscan._init_blinfo() |
---|
3561 | |
---|
3562 | action = "H" |
---|
3563 | for r in rows: |
---|
3564 | idx = 2*workscan.getif(r) |
---|
3565 | if mask: |
---|
3566 | msk = mask_and(mask, workscan._getmask(r)) # (CAS-1434) |
---|
3567 | else: # mask=None |
---|
3568 | msk = workscan._getmask(r) |
---|
3569 | fl.find_lines(r, msk, edge[idx:idx+2]) |
---|
3570 | |
---|
3571 | f.x = workscan._getabcissa(r) |
---|
3572 | f.y = workscan._getspectrum(r) |
---|
3573 | f.mask = fl.get_mask() |
---|
3574 | f.data = None |
---|
3575 | f.fit() |
---|
3576 | |
---|
3577 | if action != "Y": # skip plotting when accepting all |
---|
3578 | f.plot(residual=True) |
---|
3579 | #accept_fit = raw_input("Accept fit ( [y]/n ): ") |
---|
3580 | accept_fit = self._get_verify_action("Accept fit?",action) |
---|
3581 | if r == 0: action = None |
---|
3582 | if accept_fit.upper() == "N": |
---|
3583 | #workscan._append_blinfo(None, None, None) |
---|
3584 | continue |
---|
3585 | elif accept_fit.upper() == "R": |
---|
3586 | break |
---|
3587 | elif accept_fit.upper() == "A": |
---|
3588 | action = "Y" |
---|
3589 | |
---|
3590 | blpars = f.get_parameters() |
---|
3591 | masklist = workscan.get_masklist(f.mask, row=r, silent=True) |
---|
3592 | #workscan._append_blinfo(blpars, masklist, f.mask) |
---|
3593 | workscan._setspectrum( |
---|
3594 | (f.fitter.getresidual() if getresidual |
---|
3595 | else f.fitter.getfit()), r |
---|
3596 | ) |
---|
3597 | |
---|
3598 | if outblfile: |
---|
3599 | rms = workscan.get_rms(f.mask, r) |
---|
3600 | dataout = \ |
---|
3601 | workscan.format_blparams_row(blpars["params"], |
---|
3602 | blpars["fixed"], |
---|
3603 | rms, str(masklist), |
---|
3604 | r, True, csvformat) |
---|
3605 | blf.write(dataout) |
---|
3606 | |
---|
3607 | f._p.unmap() |
---|
3608 | f._p = None |
---|
3609 | |
---|
3610 | if outblfile: blf.close() |
---|
3611 | else: |
---|
3612 | workscan._auto_poly_baseline(mask, order, edge, threshold, |
---|
3613 | chan_avg_limit, getresidual, |
---|
3614 | pack_progress_params(showprogress, |
---|
3615 | minnrow), |
---|
3616 | outlog, scsvformat+blfile) |
---|
3617 | |
---|
3618 | workscan._add_history("auto_poly_baseline", varlist) |
---|
3619 | |
---|
3620 | if insitu: |
---|
3621 | self._assign(workscan) |
---|
3622 | else: |
---|
3623 | return workscan |
---|
3624 | |
---|
3625 | except RuntimeError, e: |
---|
3626 | raise_fitting_failure_exception(e) |
---|
3627 | |
---|
3628 | def _init_blinfo(self): |
---|
3629 | """\ |
---|
3630 | Initialise the following three auxiliary members: |
---|
3631 | blpars : parameters of the best-fit baseline, |
---|
3632 | masklists : mask data (edge positions of masked channels) and |
---|
3633 | actualmask : mask data (in boolean list), |
---|
3634 | to keep for use later (including output to logger/text files). |
---|
3635 | Used by poly_baseline() and auto_poly_baseline() in case of |
---|
3636 | 'plot=True'. |
---|
3637 | """ |
---|
3638 | self.blpars = [] |
---|
3639 | self.masklists = [] |
---|
3640 | self.actualmask = [] |
---|
3641 | return |
---|
3642 | |
---|
3643 | def _append_blinfo(self, data_blpars, data_masklists, data_actualmask): |
---|
3644 | """\ |
---|
3645 | Append baseline-fitting related info to blpars, masklist and |
---|
3646 | actualmask. |
---|
3647 | """ |
---|
3648 | self.blpars.append(data_blpars) |
---|
3649 | self.masklists.append(data_masklists) |
---|
3650 | self.actualmask.append(data_actualmask) |
---|
3651 | return |
---|
3652 | |
---|
3653 | @asaplog_post_dec |
---|
3654 | def rotate_linpolphase(self, angle): |
---|
3655 | """\ |
---|
3656 | Rotate the phase of the complex polarization O=Q+iU correlation. |
---|
3657 | This is always done in situ in the raw data. So if you call this |
---|
3658 | function more than once then each call rotates the phase further. |
---|
3659 | |
---|
3660 | Parameters: |
---|
3661 | |
---|
3662 | angle: The angle (degrees) to rotate (add) by. |
---|
3663 | |
---|
3664 | Example:: |
---|
3665 | |
---|
3666 | scan.rotate_linpolphase(2.3) |
---|
3667 | |
---|
3668 | """ |
---|
3669 | varlist = vars() |
---|
3670 | self._math._rotate_linpolphase(self, angle) |
---|
3671 | self._add_history("rotate_linpolphase", varlist) |
---|
3672 | return |
---|
3673 | |
---|
3674 | @asaplog_post_dec |
---|
3675 | def rotate_xyphase(self, angle): |
---|
3676 | """\ |
---|
3677 | Rotate the phase of the XY correlation. This is always done in situ |
---|
3678 | in the data. So if you call this function more than once |
---|
3679 | then each call rotates the phase further. |
---|
3680 | |
---|
3681 | Parameters: |
---|
3682 | |
---|
3683 | angle: The angle (degrees) to rotate (add) by. |
---|
3684 | |
---|
3685 | Example:: |
---|
3686 | |
---|
3687 | scan.rotate_xyphase(2.3) |
---|
3688 | |
---|
3689 | """ |
---|
3690 | varlist = vars() |
---|
3691 | self._math._rotate_xyphase(self, angle) |
---|
3692 | self._add_history("rotate_xyphase", varlist) |
---|
3693 | return |
---|
3694 | |
---|
3695 | @asaplog_post_dec |
---|
3696 | def swap_linears(self): |
---|
3697 | """\ |
---|
3698 | Swap the linear polarisations XX and YY, or better the first two |
---|
3699 | polarisations as this also works for ciculars. |
---|
3700 | """ |
---|
3701 | varlist = vars() |
---|
3702 | self._math._swap_linears(self) |
---|
3703 | self._add_history("swap_linears", varlist) |
---|
3704 | return |
---|
3705 | |
---|
3706 | @asaplog_post_dec |
---|
3707 | def invert_phase(self): |
---|
3708 | """\ |
---|
3709 | Invert the phase of the complex polarisation |
---|
3710 | """ |
---|
3711 | varlist = vars() |
---|
3712 | self._math._invert_phase(self) |
---|
3713 | self._add_history("invert_phase", varlist) |
---|
3714 | return |
---|
3715 | |
---|
3716 | @asaplog_post_dec |
---|
3717 | def add(self, offset, insitu=None): |
---|
3718 | """\ |
---|
3719 | Return a scan where all spectra have the offset added |
---|
3720 | |
---|
3721 | Parameters: |
---|
3722 | |
---|
3723 | offset: the offset |
---|
3724 | |
---|
3725 | insitu: if False a new scantable is returned. |
---|
3726 | Otherwise, the scaling is done in-situ |
---|
3727 | The default is taken from .asaprc (False) |
---|
3728 | |
---|
3729 | """ |
---|
3730 | if insitu is None: insitu = rcParams['insitu'] |
---|
3731 | self._math._setinsitu(insitu) |
---|
3732 | varlist = vars() |
---|
3733 | s = scantable(self._math._unaryop(self, offset, "ADD", False)) |
---|
3734 | s._add_history("add", varlist) |
---|
3735 | if insitu: |
---|
3736 | self._assign(s) |
---|
3737 | else: |
---|
3738 | return s |
---|
3739 | |
---|
3740 | @asaplog_post_dec |
---|
3741 | def scale(self, factor, tsys=True, insitu=None): |
---|
3742 | """\ |
---|
3743 | |
---|
3744 | Return a scan where all spectra are scaled by the given 'factor' |
---|
3745 | |
---|
3746 | Parameters: |
---|
3747 | |
---|
3748 | factor: the scaling factor (float or 1D float list) |
---|
3749 | |
---|
3750 | insitu: if False a new scantable is returned. |
---|
3751 | Otherwise, the scaling is done in-situ |
---|
3752 | The default is taken from .asaprc (False) |
---|
3753 | |
---|
3754 | tsys: if True (default) then apply the operation to Tsys |
---|
3755 | as well as the data |
---|
3756 | |
---|
3757 | """ |
---|
3758 | if insitu is None: insitu = rcParams['insitu'] |
---|
3759 | self._math._setinsitu(insitu) |
---|
3760 | varlist = vars() |
---|
3761 | s = None |
---|
3762 | import numpy |
---|
3763 | if isinstance(factor, list) or isinstance(factor, numpy.ndarray): |
---|
3764 | if isinstance(factor[0], list) or isinstance(factor[0], |
---|
3765 | numpy.ndarray): |
---|
3766 | from asapmath import _array2dOp |
---|
3767 | s = _array2dOp( self, factor, "MUL", tsys, insitu ) |
---|
3768 | else: |
---|
3769 | s = scantable( self._math._arrayop( self, factor, |
---|
3770 | "MUL", tsys ) ) |
---|
3771 | else: |
---|
3772 | s = scantable(self._math._unaryop(self, factor, "MUL", tsys)) |
---|
3773 | s._add_history("scale", varlist) |
---|
3774 | if insitu: |
---|
3775 | self._assign(s) |
---|
3776 | else: |
---|
3777 | return s |
---|
3778 | |
---|
3779 | @preserve_selection |
---|
3780 | def set_sourcetype(self, match, matchtype="pattern", |
---|
3781 | sourcetype="reference"): |
---|
3782 | """\ |
---|
3783 | Set the type of the source to be an source or reference scan |
---|
3784 | using the provided pattern. |
---|
3785 | |
---|
3786 | Parameters: |
---|
3787 | |
---|
3788 | match: a Unix style pattern, regular expression or selector |
---|
3789 | |
---|
3790 | matchtype: 'pattern' (default) UNIX style pattern or |
---|
3791 | 'regex' regular expression |
---|
3792 | |
---|
3793 | sourcetype: the type of the source to use (source/reference) |
---|
3794 | |
---|
3795 | """ |
---|
3796 | varlist = vars() |
---|
3797 | stype = -1 |
---|
3798 | if sourcetype.lower().startswith("r") or sourcetype.lower() == "off": |
---|
3799 | stype = 1 |
---|
3800 | elif sourcetype.lower().startswith("s") or sourcetype.lower() == "on": |
---|
3801 | stype = 0 |
---|
3802 | else: |
---|
3803 | raise ValueError("Illegal sourcetype use s(ource)/on or r(eference)/off") |
---|
3804 | if matchtype.lower().startswith("p"): |
---|
3805 | matchtype = "pattern" |
---|
3806 | elif matchtype.lower().startswith("r"): |
---|
3807 | matchtype = "regex" |
---|
3808 | else: |
---|
3809 | raise ValueError("Illegal matchtype, use p(attern) or r(egex)") |
---|
3810 | sel = selector() |
---|
3811 | if isinstance(match, selector): |
---|
3812 | sel = match |
---|
3813 | else: |
---|
3814 | sel.set_query("SRCNAME=%s('%s')" % (matchtype, match)) |
---|
3815 | self.set_selection(sel) |
---|
3816 | self._setsourcetype(stype) |
---|
3817 | self._add_history("set_sourcetype", varlist) |
---|
3818 | |
---|
3819 | @asaplog_post_dec |
---|
3820 | @preserve_selection |
---|
3821 | def auto_quotient(self, preserve=True, mode='paired', verify=False): |
---|
3822 | """\ |
---|
3823 | This function allows to build quotients automatically. |
---|
3824 | It assumes the observation to have the same number of |
---|
3825 | "ons" and "offs" |
---|
3826 | |
---|
3827 | Parameters: |
---|
3828 | |
---|
3829 | preserve: you can preserve (default) the continuum or |
---|
3830 | remove it. The equations used are |
---|
3831 | |
---|
3832 | preserve: Output = Toff * (on/off) - Toff |
---|
3833 | |
---|
3834 | remove: Output = Toff * (on/off) - Ton |
---|
3835 | |
---|
3836 | mode: the on/off detection mode |
---|
3837 | 'paired' (default) |
---|
3838 | identifies 'off' scans by the |
---|
3839 | trailing '_R' (Mopra/Parkes) or |
---|
3840 | '_e'/'_w' (Tid) and matches |
---|
3841 | on/off pairs from the observing pattern |
---|
3842 | 'time' |
---|
3843 | finds the closest off in time |
---|
3844 | |
---|
3845 | .. todo:: verify argument is not implemented |
---|
3846 | |
---|
3847 | """ |
---|
3848 | varlist = vars() |
---|
3849 | modes = ["time", "paired"] |
---|
3850 | if not mode in modes: |
---|
3851 | msg = "please provide valid mode. Valid modes are %s" % (modes) |
---|
3852 | raise ValueError(msg) |
---|
3853 | s = None |
---|
3854 | if mode.lower() == "paired": |
---|
3855 | sel = self.get_selection() |
---|
3856 | sel.set_query("SRCTYPE==psoff") |
---|
3857 | self.set_selection(sel) |
---|
3858 | offs = self.copy() |
---|
3859 | sel.set_query("SRCTYPE==pson") |
---|
3860 | self.set_selection(sel) |
---|
3861 | ons = self.copy() |
---|
3862 | s = scantable(self._math._quotient(ons, offs, preserve)) |
---|
3863 | elif mode.lower() == "time": |
---|
3864 | s = scantable(self._math._auto_quotient(self, mode, preserve)) |
---|
3865 | s._add_history("auto_quotient", varlist) |
---|
3866 | return s |
---|
3867 | |
---|
3868 | @asaplog_post_dec |
---|
3869 | def mx_quotient(self, mask = None, weight='median', preserve=True): |
---|
3870 | """\ |
---|
3871 | Form a quotient using "off" beams when observing in "MX" mode. |
---|
3872 | |
---|
3873 | Parameters: |
---|
3874 | |
---|
3875 | mask: an optional mask to be used when weight == 'stddev' |
---|
3876 | |
---|
3877 | weight: How to average the off beams. Default is 'median'. |
---|
3878 | |
---|
3879 | preserve: you can preserve (default) the continuum or |
---|
3880 | remove it. The equations used are: |
---|
3881 | |
---|
3882 | preserve: Output = Toff * (on/off) - Toff |
---|
3883 | |
---|
3884 | remove: Output = Toff * (on/off) - Ton |
---|
3885 | |
---|
3886 | """ |
---|
3887 | mask = mask or () |
---|
3888 | varlist = vars() |
---|
3889 | on = scantable(self._math._mx_extract(self, 'on')) |
---|
3890 | preoff = scantable(self._math._mx_extract(self, 'off')) |
---|
3891 | off = preoff.average_time(mask=mask, weight=weight, scanav=False) |
---|
3892 | from asapmath import quotient |
---|
3893 | q = quotient(on, off, preserve) |
---|
3894 | q._add_history("mx_quotient", varlist) |
---|
3895 | return q |
---|
3896 | |
---|
3897 | @asaplog_post_dec |
---|
3898 | def freq_switch(self, insitu=None): |
---|
3899 | """\ |
---|
3900 | Apply frequency switching to the data. |
---|
3901 | |
---|
3902 | Parameters: |
---|
3903 | |
---|
3904 | insitu: if False a new scantable is returned. |
---|
3905 | Otherwise, the swictching is done in-situ |
---|
3906 | The default is taken from .asaprc (False) |
---|
3907 | |
---|
3908 | """ |
---|
3909 | if insitu is None: insitu = rcParams['insitu'] |
---|
3910 | self._math._setinsitu(insitu) |
---|
3911 | varlist = vars() |
---|
3912 | s = scantable(self._math._freqswitch(self)) |
---|
3913 | s._add_history("freq_switch", varlist) |
---|
3914 | if insitu: |
---|
3915 | self._assign(s) |
---|
3916 | else: |
---|
3917 | return s |
---|
3918 | |
---|
3919 | @asaplog_post_dec |
---|
3920 | def recalc_azel(self): |
---|
3921 | """Recalculate the azimuth and elevation for each position.""" |
---|
3922 | varlist = vars() |
---|
3923 | self._recalcazel() |
---|
3924 | self._add_history("recalc_azel", varlist) |
---|
3925 | return |
---|
3926 | |
---|
3927 | @asaplog_post_dec |
---|
3928 | def __add__(self, other): |
---|
3929 | """ |
---|
3930 | implicit on all axes and on Tsys |
---|
3931 | """ |
---|
3932 | varlist = vars() |
---|
3933 | s = self.__op( other, "ADD" ) |
---|
3934 | s._add_history("operator +", varlist) |
---|
3935 | return s |
---|
3936 | |
---|
3937 | @asaplog_post_dec |
---|
3938 | def __sub__(self, other): |
---|
3939 | """ |
---|
3940 | implicit on all axes and on Tsys |
---|
3941 | """ |
---|
3942 | varlist = vars() |
---|
3943 | s = self.__op( other, "SUB" ) |
---|
3944 | s._add_history("operator -", varlist) |
---|
3945 | return s |
---|
3946 | |
---|
3947 | @asaplog_post_dec |
---|
3948 | def __mul__(self, other): |
---|
3949 | """ |
---|
3950 | implicit on all axes and on Tsys |
---|
3951 | """ |
---|
3952 | varlist = vars() |
---|
3953 | s = self.__op( other, "MUL" ) ; |
---|
3954 | s._add_history("operator *", varlist) |
---|
3955 | return s |
---|
3956 | |
---|
3957 | |
---|
3958 | @asaplog_post_dec |
---|
3959 | def __div__(self, other): |
---|
3960 | """ |
---|
3961 | implicit on all axes and on Tsys |
---|
3962 | """ |
---|
3963 | varlist = vars() |
---|
3964 | s = self.__op( other, "DIV" ) |
---|
3965 | s._add_history("operator /", varlist) |
---|
3966 | return s |
---|
3967 | |
---|
3968 | @asaplog_post_dec |
---|
3969 | def __op( self, other, mode ): |
---|
3970 | s = None |
---|
3971 | if isinstance(other, scantable): |
---|
3972 | s = scantable(self._math._binaryop(self, other, mode)) |
---|
3973 | elif isinstance(other, float): |
---|
3974 | if other == 0.0: |
---|
3975 | raise ZeroDivisionError("Dividing by zero is not recommended") |
---|
3976 | s = scantable(self._math._unaryop(self, other, mode, False)) |
---|
3977 | elif isinstance(other, list) or isinstance(other, numpy.ndarray): |
---|
3978 | if isinstance(other[0], list) \ |
---|
3979 | or isinstance(other[0], numpy.ndarray): |
---|
3980 | from asapmath import _array2dOp |
---|
3981 | s = _array2dOp( self, other, mode, False ) |
---|
3982 | else: |
---|
3983 | s = scantable( self._math._arrayop( self, other, |
---|
3984 | mode, False ) ) |
---|
3985 | else: |
---|
3986 | raise TypeError("Other input is not a scantable or float value") |
---|
3987 | return s |
---|
3988 | |
---|
3989 | @asaplog_post_dec |
---|
3990 | def get_fit(self, row=0): |
---|
3991 | """\ |
---|
3992 | Print or return the stored fits for a row in the scantable |
---|
3993 | |
---|
3994 | Parameters: |
---|
3995 | |
---|
3996 | row: the row which the fit has been applied to. |
---|
3997 | |
---|
3998 | """ |
---|
3999 | if row > self.nrow(): |
---|
4000 | return |
---|
4001 | from asap.asapfit import asapfit |
---|
4002 | fit = asapfit(self._getfit(row)) |
---|
4003 | asaplog.push( '%s' %(fit) ) |
---|
4004 | return fit.as_dict() |
---|
4005 | |
---|
4006 | @preserve_selection |
---|
4007 | def flag_nans(self): |
---|
4008 | """\ |
---|
4009 | Utility function to flag NaN values in the scantable. |
---|
4010 | """ |
---|
4011 | import numpy |
---|
4012 | basesel = self.get_selection() |
---|
4013 | for i in range(self.nrow()): |
---|
4014 | sel = self.get_row_selector(i) |
---|
4015 | self.set_selection(basesel+sel) |
---|
4016 | nans = numpy.isnan(self._getspectrum(0)) |
---|
4017 | if numpy.any(nans): |
---|
4018 | bnans = [ bool(v) for v in nans] |
---|
4019 | self.flag(bnans) |
---|
4020 | |
---|
4021 | def get_row_selector(self, rowno): |
---|
4022 | return selector(rows=[rowno]) |
---|
4023 | |
---|
4024 | def _add_history(self, funcname, parameters): |
---|
4025 | if not rcParams['scantable.history']: |
---|
4026 | return |
---|
4027 | # create date |
---|
4028 | sep = "##" |
---|
4029 | from datetime import datetime |
---|
4030 | dstr = datetime.now().strftime('%Y/%m/%d %H:%M:%S') |
---|
4031 | hist = dstr+sep |
---|
4032 | hist += funcname+sep#cdate+sep |
---|
4033 | if parameters.has_key('self'): |
---|
4034 | del parameters['self'] |
---|
4035 | for k, v in parameters.iteritems(): |
---|
4036 | if type(v) is dict: |
---|
4037 | for k2, v2 in v.iteritems(): |
---|
4038 | hist += k2 |
---|
4039 | hist += "=" |
---|
4040 | if isinstance(v2, scantable): |
---|
4041 | hist += 'scantable' |
---|
4042 | elif k2 == 'mask': |
---|
4043 | if isinstance(v2, list) or isinstance(v2, tuple): |
---|
4044 | hist += str(self._zip_mask(v2)) |
---|
4045 | else: |
---|
4046 | hist += str(v2) |
---|
4047 | else: |
---|
4048 | hist += str(v2) |
---|
4049 | else: |
---|
4050 | hist += k |
---|
4051 | hist += "=" |
---|
4052 | if isinstance(v, scantable): |
---|
4053 | hist += 'scantable' |
---|
4054 | elif k == 'mask': |
---|
4055 | if isinstance(v, list) or isinstance(v, tuple): |
---|
4056 | hist += str(self._zip_mask(v)) |
---|
4057 | else: |
---|
4058 | hist += str(v) |
---|
4059 | else: |
---|
4060 | hist += str(v) |
---|
4061 | hist += sep |
---|
4062 | hist = hist[:-2] # remove trailing '##' |
---|
4063 | self._addhistory(hist) |
---|
4064 | |
---|
4065 | |
---|
4066 | def _zip_mask(self, mask): |
---|
4067 | mask = list(mask) |
---|
4068 | i = 0 |
---|
4069 | segments = [] |
---|
4070 | while mask[i:].count(1): |
---|
4071 | i += mask[i:].index(1) |
---|
4072 | if mask[i:].count(0): |
---|
4073 | j = i + mask[i:].index(0) |
---|
4074 | else: |
---|
4075 | j = len(mask) |
---|
4076 | segments.append([i, j]) |
---|
4077 | i = j |
---|
4078 | return segments |
---|
4079 | |
---|
4080 | def _get_ordinate_label(self): |
---|
4081 | fu = "("+self.get_fluxunit()+")" |
---|
4082 | import re |
---|
4083 | lbl = "Intensity" |
---|
4084 | if re.match(".K.", fu): |
---|
4085 | lbl = "Brightness Temperature "+ fu |
---|
4086 | elif re.match(".Jy.", fu): |
---|
4087 | lbl = "Flux density "+ fu |
---|
4088 | return lbl |
---|
4089 | |
---|
4090 | def _check_ifs(self): |
---|
4091 | # return len(set([self.nchan(i) for i in self.getifnos()])) == 1 |
---|
4092 | nchans = [self.nchan(i) for i in self.getifnos()] |
---|
4093 | nchans = filter(lambda t: t > 0, nchans) |
---|
4094 | return (sum(nchans)/len(nchans) == nchans[0]) |
---|
4095 | |
---|
4096 | @asaplog_post_dec |
---|
4097 | def _fill(self, names, unit, average, opts={}): |
---|
4098 | first = True |
---|
4099 | fullnames = [] |
---|
4100 | for name in names: |
---|
4101 | name = os.path.expandvars(name) |
---|
4102 | name = os.path.expanduser(name) |
---|
4103 | if not os.path.exists(name): |
---|
4104 | msg = "File '%s' does not exists" % (name) |
---|
4105 | raise IOError(msg) |
---|
4106 | fullnames.append(name) |
---|
4107 | if average: |
---|
4108 | asaplog.push('Auto averaging integrations') |
---|
4109 | stype = int(rcParams['scantable.storage'].lower() == 'disk') |
---|
4110 | for name in fullnames: |
---|
4111 | tbl = Scantable(stype) |
---|
4112 | if is_ms( name ): |
---|
4113 | r = msfiller( tbl ) |
---|
4114 | else: |
---|
4115 | r = filler( tbl ) |
---|
4116 | msg = "Importing %s..." % (name) |
---|
4117 | asaplog.push(msg, False) |
---|
4118 | r.open(name, opts) |
---|
4119 | rx = rcParams['scantable.reference'] |
---|
4120 | r.setreferenceexpr(rx) |
---|
4121 | r.fill() |
---|
4122 | if average: |
---|
4123 | tbl = self._math._average((tbl, ), (), 'NONE', 'SCAN') |
---|
4124 | if not first: |
---|
4125 | tbl = self._math._merge([self, tbl]) |
---|
4126 | Scantable.__init__(self, tbl) |
---|
4127 | r.close() |
---|
4128 | del r, tbl |
---|
4129 | first = False |
---|
4130 | #flush log |
---|
4131 | asaplog.post() |
---|
4132 | if unit is not None: |
---|
4133 | self.set_fluxunit(unit) |
---|
4134 | if not is_casapy(): |
---|
4135 | self.set_freqframe(rcParams['scantable.freqframe']) |
---|
4136 | |
---|
4137 | def _get_verify_action( self, msg, action=None ): |
---|
4138 | valid_act = ['Y', 'N', 'A', 'R'] |
---|
4139 | if not action or not isinstance(action, str): |
---|
4140 | action = raw_input("%s [Y/n/a/r] (h for help): " % msg) |
---|
4141 | if action == '': |
---|
4142 | return "Y" |
---|
4143 | elif (action.upper()[0] in valid_act): |
---|
4144 | return action.upper()[0] |
---|
4145 | elif (action.upper()[0] in ['H','?']): |
---|
4146 | print "Available actions of verification [Y|n|a|r]" |
---|
4147 | print " Y : Yes for current data (default)" |
---|
4148 | print " N : No for current data" |
---|
4149 | print " A : Accept all in the following and exit from verification" |
---|
4150 | print " R : Reject all in the following and exit from verification" |
---|
4151 | print " H or ?: help (show this message)" |
---|
4152 | return self._get_verify_action(msg) |
---|
4153 | else: |
---|
4154 | return 'Y' |
---|
4155 | |
---|
4156 | def __getitem__(self, key): |
---|
4157 | if key < 0: |
---|
4158 | key += self.nrow() |
---|
4159 | if key >= self.nrow(): |
---|
4160 | raise IndexError("Row index out of range.") |
---|
4161 | return self._getspectrum(key) |
---|
4162 | |
---|
4163 | def __setitem__(self, key, value): |
---|
4164 | if key < 0: |
---|
4165 | key += self.nrow() |
---|
4166 | if key >= self.nrow(): |
---|
4167 | raise IndexError("Row index out of range.") |
---|
4168 | if not hasattr(value, "__len__") or \ |
---|
4169 | len(value) > self.nchan(self.getif(key)): |
---|
4170 | raise ValueError("Spectrum length doesn't match.") |
---|
4171 | return self._setspectrum(value, key) |
---|
4172 | |
---|
4173 | def __len__(self): |
---|
4174 | return self.nrow() |
---|
4175 | |
---|
4176 | def __iter__(self): |
---|
4177 | for i in range(len(self)): |
---|
4178 | yield self[i] |
---|