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1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
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2 | 2 | # All rights reserved. |
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3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
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5 | 5 | """API to create signal chain projects |
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6 | 6 | |
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7 | 7 | The API is provide through class: Project |
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8 | 8 | """ |
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9 | 9 | |
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10 | 10 | import re |
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11 | 11 | import sys |
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12 | 12 | import ast |
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13 | 13 | import datetime |
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14 | 14 | import traceback |
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15 | 15 | import time |
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16 | 16 | import multiprocessing |
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17 | 17 | from multiprocessing import Process, Queue |
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18 | 18 | from threading import Thread |
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19 | 19 | from xml.etree.ElementTree import ElementTree, Element, SubElement |
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20 | 20 | |
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21 | 21 | from schainpy.admin import Alarm, SchainWarning |
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22 | 22 | from schainpy.model import * |
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23 | 23 | from schainpy.utils import log |
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24 | 24 | |
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25 | 25 | if 'darwin' in sys.platform and sys.version_info[0] == 3 and sys.version_info[1] > 7: |
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26 | 26 | multiprocessing.set_start_method('fork') |
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27 | 27 | |
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28 | 28 | class ConfBase(): |
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29 | 29 | |
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30 | 30 | def __init__(self): |
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31 | 31 | |
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32 | 32 | self.id = '0' |
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33 | 33 | self.name = None |
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34 | 34 | self.priority = None |
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35 | 35 | self.parameters = {} |
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36 | 36 | self.object = None |
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37 | 37 | self.operations = [] |
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38 | 38 | |
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39 | 39 | def getId(self): |
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40 | 40 | |
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41 | 41 | return self.id |
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42 | 42 | |
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43 | 43 | def getNewId(self): |
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44 | 44 | |
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45 | 45 | return int(self.id) * 10 + len(self.operations) + 1 |
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46 | 46 | |
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47 | 47 | def updateId(self, new_id): |
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48 | 48 | |
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49 | 49 | self.id = str(new_id) |
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50 | 50 | |
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51 | 51 | n = 1 |
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52 | 52 | for conf in self.operations: |
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53 | 53 | conf_id = str(int(new_id) * 10 + n) |
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54 | 54 | conf.updateId(conf_id) |
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55 | 55 | n += 1 |
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56 | 56 | |
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57 | 57 | def getKwargs(self): |
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58 | 58 | |
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59 | 59 | params = {} |
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60 | 60 | |
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61 | 61 | for key, value in self.parameters.items(): |
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62 | 62 | if value not in (None, '', ' '): |
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63 | 63 | params[key] = value |
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64 | 64 | |
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65 | 65 | return params |
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66 | 66 | |
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67 | 67 | def update(self, **kwargs): |
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68 | 68 | |
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69 | 69 | if 'format' not in kwargs: |
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70 | 70 | kwargs['format'] = None |
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71 | 71 | for key, value, fmt in kwargs.items(): |
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72 | 72 | self.addParameter(name=key, value=value, format=fmt) |
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73 | 73 | |
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74 | 74 | def addParameter(self, name, value, format=None): |
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75 | 75 | ''' |
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76 | 76 | ''' |
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77 | 77 | if format is not None: |
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78 | 78 | self.parameters[name] = eval(format)(value) |
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79 | 79 | elif isinstance(value, str) and re.search(r'(\d+/\d+/\d+)', value): |
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80 | 80 | self.parameters[name] = datetime.date(*[int(x) for x in value.split('/')]) |
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81 | 81 | elif isinstance(value, str) and re.search(r'(\d+:\d+:\d+)', value): |
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82 | 82 | self.parameters[name] = datetime.time(*[int(x) for x in value.split(':')]) |
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83 | 83 | else: |
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84 | 84 | try: |
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85 | 85 | self.parameters[name] = ast.literal_eval(value) |
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86 | 86 | except: |
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87 | 87 | if isinstance(value, str) and ',' in value: |
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88 | 88 | self.parameters[name] = value.split(',') |
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89 | 89 | else: |
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90 | 90 | self.parameters[name] = value |
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91 | 91 | |
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92 | 92 | def getParameters(self): |
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93 | 93 | |
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94 | 94 | params = {} |
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95 | 95 | for key, value in self.parameters.items(): |
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96 | 96 | s = type(value).__name__ |
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97 | 97 | if s == 'date': |
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98 | 98 | params[key] = value.strftime('%Y/%m/%d') |
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99 | 99 | elif s == 'time': |
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100 | 100 | params[key] = value.strftime('%H:%M:%S') |
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101 | 101 | else: |
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102 | 102 | params[key] = str(value) |
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103 | 103 | |
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104 | 104 | return params |
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105 | 105 | |
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106 | 106 | def makeXml(self, element): |
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107 | 107 | |
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108 | 108 | xml = SubElement(element, self.ELEMENTNAME) |
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109 | 109 | for label in self.xml_labels: |
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110 | 110 | xml.set(label, str(getattr(self, label))) |
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111 | 111 | |
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112 | 112 | for key, value in self.getParameters().items(): |
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113 | 113 | xml_param = SubElement(xml, 'Parameter') |
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114 | 114 | xml_param.set('name', key) |
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115 | 115 | xml_param.set('value', value) |
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116 | 116 | |
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117 | 117 | for conf in self.operations: |
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118 | 118 | conf.makeXml(xml) |
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119 | 119 | |
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120 | 120 | def __str__(self): |
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121 | 121 | |
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122 | 122 | if self.ELEMENTNAME == 'Operation': |
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123 | 123 | s = ' {}[id={}]\n'.format(self.name, self.id) |
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124 | 124 | else: |
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125 | 125 | s = '{}[id={}, inputId={}]\n'.format(self.name, self.id, self.inputId) |
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126 | 126 | |
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127 | 127 | for key, value in self.parameters.items(): |
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128 | 128 | if self.ELEMENTNAME == 'Operation': |
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129 | 129 | s += ' {}: {}\n'.format(key, value) |
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130 | 130 | else: |
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131 | 131 | s += ' {}: {}\n'.format(key, value) |
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132 | 132 | |
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133 | 133 | for conf in self.operations: |
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134 | 134 | s += str(conf) |
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135 | 135 | |
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136 | 136 | return s |
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137 | 137 | |
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138 | 138 | class OperationConf(ConfBase): |
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139 | 139 | |
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140 | 140 | ELEMENTNAME = 'Operation' |
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141 | 141 | xml_labels = ['id', 'name'] |
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142 | 142 | |
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143 | 143 | def setup(self, id, name, priority, project_id, err_queue): |
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144 | 144 | |
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145 | 145 | self.id = str(id) |
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146 | 146 | self.project_id = project_id |
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147 | 147 | self.name = name |
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148 | 148 | self.type = 'other' |
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149 | 149 | self.err_queue = err_queue |
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150 | 150 | |
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151 | 151 | def readXml(self, element, project_id, err_queue): |
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152 | 152 | |
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153 | 153 | self.id = element.get('id') |
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154 | 154 | self.name = element.get('name') |
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155 | 155 | self.type = 'other' |
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156 | 156 | self.project_id = str(project_id) |
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157 | 157 | self.err_queue = err_queue |
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158 | 158 | |
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159 | 159 | for elm in element.iter('Parameter'): |
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160 | 160 | self.addParameter(elm.get('name'), elm.get('value')) |
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161 | 161 | |
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162 | 162 | def createObject(self): |
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163 | 163 | |
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164 | 164 | className = eval(self.name) |
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165 | 165 | |
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166 | 166 | if 'Plot' in self.name or 'Writer' in self.name or 'Send' in self.name or 'print' in self.name: |
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167 | 167 | kwargs = self.getKwargs() |
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168 | 168 | opObj = className(self.id, self.id, self.project_id, self.err_queue, **kwargs) |
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169 | 169 | opObj.start() |
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170 | 170 | self.type = 'external' |
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171 | 171 | else: |
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172 | 172 | opObj = className() |
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173 | 173 | |
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174 | 174 | self.object = opObj |
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175 | 175 | return opObj |
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176 | 176 | |
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177 | 177 | class ProcUnitConf(ConfBase): |
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178 | 178 | |
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179 | 179 | ELEMENTNAME = 'ProcUnit' |
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180 | 180 | xml_labels = ['id', 'inputId', 'name'] |
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181 | 181 | |
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182 | 182 | def setup(self, project_id, id, name, datatype, inputId, err_queue): |
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183 | 183 | ''' |
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184 | 184 | ''' |
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185 | 185 | |
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186 | 186 | if datatype == None and name == None: |
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187 | 187 | raise ValueError('datatype or name should be defined') |
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188 | 188 | |
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189 | 189 | if name == None: |
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190 | 190 | if 'Proc' in datatype: |
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191 | 191 | name = datatype |
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192 | 192 | else: |
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193 | 193 | name = '%sProc' % (datatype) |
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194 | 194 | |
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195 | 195 | if datatype == None: |
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196 | 196 | datatype = name.replace('Proc', '') |
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197 | 197 | |
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198 | 198 | self.id = str(id) |
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199 | 199 | self.project_id = project_id |
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200 | 200 | self.name = name |
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201 | 201 | self.datatype = datatype |
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202 | 202 | self.inputId = inputId |
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203 | 203 | self.err_queue = err_queue |
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204 | 204 | self.operations = [] |
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205 | 205 | self.parameters = {} |
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206 | 206 | |
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207 | 207 | def removeOperation(self, id): |
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208 | 208 | |
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209 | 209 | i = [1 if x.id==id else 0 for x in self.operations] |
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210 | 210 | self.operations.pop(i.index(1)) |
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211 | 211 | |
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212 | 212 | def getOperation(self, id): |
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213 | 213 | |
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214 | 214 | for conf in self.operations: |
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215 | 215 | if conf.id == id: |
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216 | 216 | return conf |
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217 | 217 | |
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218 | 218 | def addOperation(self, name, optype='self'): |
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219 | 219 | ''' |
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220 | 220 | ''' |
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221 | 221 | |
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222 | 222 | id = self.getNewId() |
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223 | 223 | conf = OperationConf() |
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224 | 224 | conf.setup(id, name=name, priority='0', project_id=self.project_id, err_queue=self.err_queue) |
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225 | 225 | self.operations.append(conf) |
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226 | 226 | |
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227 | 227 | return conf |
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228 | 228 | |
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229 | 229 | def readXml(self, element, project_id, err_queue): |
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230 | 230 | |
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231 | 231 | self.id = element.get('id') |
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232 | 232 | self.name = element.get('name') |
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233 | 233 | self.inputId = None if element.get('inputId') == 'None' else element.get('inputId') |
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234 | 234 | self.datatype = element.get('datatype', self.name.replace(self.ELEMENTNAME.replace('Unit', ''), '')) |
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235 | 235 | self.project_id = str(project_id) |
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236 | 236 | self.err_queue = err_queue |
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237 | 237 | self.operations = [] |
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238 | 238 | self.parameters = {} |
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239 | 239 | |
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240 | 240 | for elm in element: |
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241 | 241 | if elm.tag == 'Parameter': |
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242 | 242 | self.addParameter(elm.get('name'), elm.get('value')) |
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243 | 243 | elif elm.tag == 'Operation': |
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244 | 244 | conf = OperationConf() |
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245 | 245 | conf.readXml(elm, project_id, err_queue) |
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246 | 246 | self.operations.append(conf) |
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247 | 247 | |
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248 | 248 | def createObjects(self): |
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249 | 249 | ''' |
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250 | 250 | Instancia de unidades de procesamiento. |
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251 | 251 | ''' |
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252 | 252 | |
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253 | 253 | className = eval(self.name) |
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254 | 254 | kwargs = self.getKwargs() |
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255 | 255 | procUnitObj = className() |
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256 | 256 | procUnitObj.name = self.name |
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257 | 257 | log.success('creating process...', self.name) |
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258 | 258 | |
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259 | 259 | for conf in self.operations: |
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260 | 260 | |
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261 | 261 | opObj = conf.createObject() |
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262 | 262 | |
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263 | 263 | log.success('adding operation: {}, type:{}'.format( |
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264 | 264 | conf.name, |
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265 | 265 | conf.type), self.name) |
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266 | 266 | |
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267 | 267 | procUnitObj.addOperation(conf, opObj) |
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268 | 268 | |
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269 | 269 | self.object = procUnitObj |
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270 | 270 | |
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271 | 271 | def run(self): |
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272 | 272 | ''' |
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273 | 273 | ''' |
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274 | 274 | |
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275 | 275 | return self.object.call(**self.getKwargs()) |
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276 | 276 | |
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277 | 277 | |
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278 | 278 | class ReadUnitConf(ProcUnitConf): |
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279 | 279 | |
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280 | 280 | ELEMENTNAME = 'ReadUnit' |
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281 | 281 | |
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282 | 282 | def __init__(self): |
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283 | 283 | |
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284 | 284 | self.id = None |
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285 | 285 | self.datatype = None |
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286 | 286 | self.name = None |
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287 | 287 | self.inputId = None |
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288 | 288 | self.operations = [] |
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289 | 289 | self.parameters = {} |
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290 | 290 | |
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291 | 291 | def setup(self, project_id, id, name, datatype, err_queue, path='', startDate='', endDate='', |
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292 | 292 | startTime='', endTime='', server=None, **kwargs): |
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293 | 293 | |
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294 | 294 | if datatype == None and name == None: |
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295 | 295 | raise ValueError('datatype or name should be defined') |
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296 | 296 | if name == None: |
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297 | 297 | if 'Reader' in datatype: |
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298 | 298 | name = datatype |
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299 | 299 | datatype = name.replace('Reader','') |
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300 | 300 | else: |
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301 | 301 | name = '{}Reader'.format(datatype) |
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302 | 302 | if datatype == None: |
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303 | 303 | if 'Reader' in name: |
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304 | 304 | datatype = name.replace('Reader','') |
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305 | 305 | else: |
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306 | 306 | datatype = name |
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307 | 307 | name = '{}Reader'.format(name) |
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308 | 308 | |
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309 | 309 | self.id = id |
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310 | 310 | self.project_id = project_id |
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311 | 311 | self.name = name |
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312 | 312 | self.datatype = datatype |
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313 | 313 | self.err_queue = err_queue |
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314 | 314 | |
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315 | 315 | self.addParameter(name='path', value=path, format='str') |
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316 | 316 | self.addParameter(name='startDate', value=startDate) |
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317 | 317 | self.addParameter(name='endDate', value=endDate) |
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318 | 318 | self.addParameter(name='startTime', value=startTime) |
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319 | 319 | self.addParameter(name='endTime', value=endTime) |
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320 | 320 | |
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321 | 321 | for key, value in kwargs.items(): |
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322 | 322 | self.addParameter(name=key, value=value) |
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323 | 323 | |
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324 | 324 | |
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325 | 325 | class Project(Process): |
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326 | 326 | """API to create signal chain projects""" |
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327 | 327 | |
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328 | 328 | ELEMENTNAME = 'Project' |
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329 | 329 | |
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330 | 330 | def __init__(self, name=''): |
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331 | 331 | |
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332 | 332 | Process.__init__(self) |
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333 | 333 | self.id = '1' |
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334 | 334 | if name: |
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335 | 335 | self.name = '{} ({})'.format(Process.__name__, name) |
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336 | 336 | self.filename = None |
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337 | 337 | self.description = None |
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338 | 338 | self.email = None |
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339 | 339 | self.alarm = [] |
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340 | 340 | self.configurations = {} |
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341 | 341 | # self.err_queue = Queue() |
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342 | 342 | self.err_queue = None |
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343 | 343 | self.started = False |
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344 | 344 | |
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345 | 345 | def getNewId(self): |
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346 | 346 | |
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347 | 347 | idList = list(self.configurations.keys()) |
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348 | 348 | id = int(self.id) * 10 |
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349 | 349 | |
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350 | 350 | while True: |
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351 | 351 | id += 1 |
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352 | 352 | |
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353 | 353 | if str(id) in idList: |
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354 | 354 | continue |
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355 | 355 | |
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356 | 356 | break |
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357 | 357 | |
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358 | 358 | return str(id) |
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359 | 359 | |
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360 | 360 | def updateId(self, new_id): |
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361 | 361 | |
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362 | 362 | self.id = str(new_id) |
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363 | 363 | |
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364 | 364 | keyList = list(self.configurations.keys()) |
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365 | 365 | keyList.sort() |
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366 | 366 | |
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367 | 367 | n = 1 |
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368 | 368 | new_confs = {} |
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369 | 369 | |
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370 | 370 | for procKey in keyList: |
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371 | 371 | |
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372 | 372 | conf = self.configurations[procKey] |
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373 | 373 | idProcUnit = str(int(self.id) * 10 + n) |
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374 | 374 | conf.updateId(idProcUnit) |
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375 | 375 | new_confs[idProcUnit] = conf |
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376 | 376 | n += 1 |
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377 | 377 | |
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378 | 378 | self.configurations = new_confs |
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379 | 379 | |
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380 | 380 | def setup(self, id=1, name='', description='', email=None, alarm=[]): |
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381 | 381 | |
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382 | 382 | self.id = str(id) |
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383 | 383 | self.description = description |
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384 | 384 | self.email = email |
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385 | 385 | self.alarm = alarm |
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386 | 386 | if name: |
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387 | 387 | self.name = '{} ({})'.format(Process.__name__, name) |
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388 | 388 | |
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389 | 389 | def update(self, **kwargs): |
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390 | 390 | |
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391 | 391 | for key, value in kwargs.items(): |
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392 | 392 | setattr(self, key, value) |
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393 | 393 | |
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394 | 394 | def clone(self): |
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395 | 395 | |
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396 | 396 | p = Project() |
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397 | 397 | p.id = self.id |
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398 | 398 | p.name = self.name |
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399 | 399 | p.description = self.description |
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400 | 400 | p.configurations = self.configurations.copy() |
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401 | 401 | |
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402 | 402 | return p |
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403 | 403 | |
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404 | 404 | def addReadUnit(self, id=None, datatype=None, name=None, **kwargs): |
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405 | 405 | |
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406 | 406 | ''' |
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407 | 407 | ''' |
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408 | 408 | |
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409 | 409 | if id is None: |
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410 | 410 | idReadUnit = self.getNewId() |
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411 | 411 | else: |
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412 | 412 | idReadUnit = str(id) |
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413 | 413 | |
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414 | 414 | conf = ReadUnitConf() |
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415 | 415 | conf.setup(self.id, idReadUnit, name, datatype, self.err_queue, **kwargs) |
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416 | 416 | self.configurations[conf.id] = conf |
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417 | 417 | |
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418 | 418 | return conf |
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419 | 419 | |
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420 | 420 | def addProcUnit(self, id=None, inputId='0', datatype=None, name=None): |
|
|
421 | 421 | |
|
|
422 | 422 | ''' |
|
|
423 | 423 | ''' |
|
|
424 | 424 | |
|
|
425 | 425 | if id is None: |
|
|
426 | 426 | idProcUnit = self.getNewId() |
|
|
427 | 427 | else: |
|
|
428 | 428 | idProcUnit = id |
|
|
429 | 429 | |
|
|
430 | 430 | conf = ProcUnitConf() |
|
|
431 | 431 | conf.setup(self.id, idProcUnit, name, datatype, inputId, self.err_queue) |
|
|
432 | 432 | self.configurations[conf.id] = conf |
|
|
433 | 433 | |
|
|
434 | 434 | return conf |
|
|
435 | 435 | |
|
|
436 | 436 | def removeProcUnit(self, id): |
|
|
437 | 437 | |
|
|
438 | 438 | if id in self.configurations: |
|
|
439 | 439 | self.configurations.pop(id) |
|
|
440 | 440 | |
|
|
441 | 441 | def getReadUnit(self): |
|
|
442 | 442 | |
|
|
443 | 443 | for obj in list(self.configurations.values()): |
|
|
444 | 444 | if obj.ELEMENTNAME == 'ReadUnit': |
|
|
445 | 445 | return obj |
|
|
446 | 446 | |
|
|
447 | 447 | return None |
|
|
448 | 448 | |
|
|
449 | 449 | def getProcUnit(self, id): |
|
|
450 | 450 | |
|
|
451 | 451 | return self.configurations[id] |
|
|
452 | 452 | |
|
|
453 | 453 | def getUnits(self): |
|
|
454 | 454 | |
|
|
455 | 455 | keys = list(self.configurations) |
|
|
456 | 456 | keys.sort() |
|
|
457 | 457 | |
|
|
458 | 458 | for key in keys: |
|
|
459 | 459 | yield self.configurations[key] |
|
|
460 | 460 | |
|
|
461 | 461 | def updateUnit(self, id, **kwargs): |
|
|
462 | 462 | |
|
|
463 | 463 | conf = self.configurations[id].update(**kwargs) |
|
|
464 | 464 | |
|
|
465 | 465 | def makeXml(self): |
|
|
466 | 466 | |
|
|
467 | 467 | xml = Element('Project') |
|
|
468 | 468 | xml.set('id', str(self.id)) |
|
|
469 | 469 | xml.set('name', self.name) |
|
|
470 | 470 | xml.set('description', self.description) |
|
|
471 | 471 | |
|
|
472 | 472 | for conf in self.configurations.values(): |
|
|
473 | 473 | conf.makeXml(xml) |
|
|
474 | 474 | |
|
|
475 | 475 | self.xml = xml |
|
|
476 | 476 | |
|
|
477 | 477 | def writeXml(self, filename=None): |
|
|
478 | 478 | |
|
|
479 | 479 | if filename == None: |
|
|
480 | 480 | if self.filename: |
|
|
481 | 481 | filename = self.filename |
|
|
482 | 482 | else: |
|
|
483 | 483 | filename = 'schain.xml' |
|
|
484 | 484 | |
|
|
485 | 485 | if not filename: |
|
|
486 | 486 | print('filename has not been defined. Use setFilename(filename) for do it.') |
|
|
487 | 487 | return 0 |
|
|
488 | 488 | |
|
|
489 | 489 | abs_file = os.path.abspath(filename) |
|
|
490 | 490 | |
|
|
491 | 491 | if not os.access(os.path.dirname(abs_file), os.W_OK): |
|
|
492 | 492 | print('No write permission on %s' % os.path.dirname(abs_file)) |
|
|
493 | 493 | return 0 |
|
|
494 | 494 | |
|
|
495 | 495 | if os.path.isfile(abs_file) and not(os.access(abs_file, os.W_OK)): |
|
|
496 | 496 | print('File %s already exists and it could not be overwriten' % abs_file) |
|
|
497 | 497 | return 0 |
|
|
498 | 498 | |
|
|
499 | 499 | self.makeXml() |
|
|
500 | 500 | |
|
|
501 | 501 | ElementTree(self.xml).write(abs_file, method='xml') |
|
|
502 | 502 | |
|
|
503 | 503 | self.filename = abs_file |
|
|
504 | 504 | |
|
|
505 | 505 | return 1 |
|
|
506 | 506 | |
|
|
507 | 507 | def readXml(self, filename): |
|
|
508 | 508 | |
|
|
509 | 509 | abs_file = os.path.abspath(filename) |
|
|
510 | 510 | |
|
|
511 | 511 | self.configurations = {} |
|
|
512 | 512 | |
|
|
513 | 513 | try: |
|
|
514 | 514 | self.xml = ElementTree().parse(abs_file) |
|
|
515 | 515 | except: |
|
|
516 | 516 | log.error('Error reading %s, verify file format' % filename) |
|
|
517 | 517 | return 0 |
|
|
518 | 518 | |
|
|
519 | 519 | self.id = self.xml.get('id') |
|
|
520 | 520 | self.name = self.xml.get('name') |
|
|
521 | 521 | self.description = self.xml.get('description') |
|
|
522 | 522 | |
|
|
523 | 523 | for element in self.xml: |
|
|
524 | 524 | if element.tag == 'ReadUnit': |
|
|
525 | 525 | conf = ReadUnitConf() |
|
|
526 | 526 | conf.readXml(element, self.id, self.err_queue) |
|
|
527 | 527 | self.configurations[conf.id] = conf |
|
|
528 | 528 | elif element.tag == 'ProcUnit': |
|
|
529 | 529 | conf = ProcUnitConf() |
|
|
530 | 530 | input_proc = self.configurations[element.get('inputId')] |
|
|
531 | 531 | conf.readXml(element, self.id, self.err_queue) |
|
|
532 | 532 | self.configurations[conf.id] = conf |
|
|
533 | 533 | |
|
|
534 | 534 | self.filename = abs_file |
|
|
535 | 535 | |
|
|
536 | 536 | return 1 |
|
|
537 | 537 | |
|
|
538 | 538 | def __str__(self): |
|
|
539 | 539 | |
|
|
540 | 540 | text = '\nProject[id=%s, name=%s, description=%s]\n\n' % ( |
|
|
541 | 541 | self.id, |
|
|
542 | 542 | self.name, |
|
|
543 | 543 | self.description, |
|
|
544 | 544 | ) |
|
|
545 | 545 | |
|
|
546 | 546 | for conf in self.configurations.values(): |
|
|
547 | 547 | text += '{}'.format(conf) |
|
|
548 | 548 | |
|
|
549 | 549 | return text |
|
|
550 | 550 | |
|
|
551 | 551 | def createObjects(self): |
|
|
552 | 552 | |
|
|
553 | 553 | keys = list(self.configurations.keys()) |
|
|
554 | 554 | keys.sort() |
|
|
555 | 555 | for key in keys: |
|
|
556 | 556 | conf = self.configurations[key] |
|
|
557 | 557 | conf.createObjects() |
|
|
558 | 558 | if conf.inputId is not None: |
|
|
559 | conf.object.setInput(self.configurations[conf.inputId].object) | |
|
|
559 | if isinstance(conf.inputId, list): | |
|
|
560 | conf.object.setInput([self.configurations[x].object for x in conf.inputId]) | |
|
|
561 | else: | |
|
|
562 | conf.object.setInput([self.configurations[conf.inputId].object]) | |
|
|
560 | 563 | |
|
|
561 | 564 | def monitor(self): |
|
|
562 | 565 | |
|
|
563 | 566 | t = Thread(target=self._monitor, args=(self.err_queue, self.ctx)) |
|
|
564 | 567 | t.start() |
|
|
565 | 568 | |
|
|
566 | 569 | def _monitor(self, queue, ctx): |
|
|
567 | 570 | |
|
|
568 | 571 | import socket |
|
|
569 | 572 | |
|
|
570 | 573 | procs = 0 |
|
|
571 | 574 | err_msg = '' |
|
|
572 | 575 | |
|
|
573 | 576 | while True: |
|
|
574 | 577 | msg = queue.get() |
|
|
575 | 578 | if '#_start_#' in msg: |
|
|
576 | 579 | procs += 1 |
|
|
577 | 580 | elif '#_end_#' in msg: |
|
|
578 | 581 | procs -=1 |
|
|
579 | 582 | else: |
|
|
580 | 583 | err_msg = msg |
|
|
581 | 584 | |
|
|
582 | 585 | if procs == 0 or 'Traceback' in err_msg: |
|
|
583 | 586 | break |
|
|
584 | 587 | time.sleep(0.1) |
|
|
585 | 588 | |
|
|
586 | 589 | if '|' in err_msg: |
|
|
587 | 590 | name, err = err_msg.split('|') |
|
|
588 | 591 | if 'SchainWarning' in err: |
|
|
589 | 592 | log.warning(err.split('SchainWarning:')[-1].split('\n')[0].strip(), name) |
|
|
590 | 593 | elif 'SchainError' in err: |
|
|
591 | 594 | log.error(err.split('SchainError:')[-1].split('\n')[0].strip(), name) |
|
|
592 | 595 | else: |
|
|
593 | 596 | log.error(err, name) |
|
|
594 | 597 | else: |
|
|
595 | 598 | name, err = self.name, err_msg |
|
|
596 | 599 | |
|
|
597 | 600 | time.sleep(1) |
|
|
598 | 601 | |
|
|
599 | 602 | ctx.term() |
|
|
600 | 603 | |
|
|
601 | 604 | message = ''.join(err) |
|
|
602 | 605 | |
|
|
603 | 606 | if err_msg: |
|
|
604 | 607 | subject = 'SChain v%s: Error running %s\n' % ( |
|
|
605 | 608 | schainpy.__version__, self.name) |
|
|
606 | 609 | |
|
|
607 | 610 | subtitle = 'Hostname: %s\n' % socket.gethostbyname( |
|
|
608 | 611 | socket.gethostname()) |
|
|
609 | 612 | subtitle += 'Working directory: %s\n' % os.path.abspath('./') |
|
|
610 | 613 | subtitle += 'Configuration file: %s\n' % self.filename |
|
|
611 | 614 | subtitle += 'Time: %s\n' % str(datetime.datetime.now()) |
|
|
612 | 615 | |
|
|
613 | 616 | readUnitConfObj = self.getReadUnit() |
|
|
614 | 617 | if readUnitConfObj: |
|
|
615 | 618 | subtitle += '\nInput parameters:\n' |
|
|
616 | 619 | subtitle += '[Data path = %s]\n' % readUnitConfObj.parameters['path'] |
|
|
617 | 620 | subtitle += '[Start date = %s]\n' % readUnitConfObj.parameters['startDate'] |
|
|
618 | 621 | subtitle += '[End date = %s]\n' % readUnitConfObj.parameters['endDate'] |
|
|
619 | 622 | subtitle += '[Start time = %s]\n' % readUnitConfObj.parameters['startTime'] |
|
|
620 | 623 | subtitle += '[End time = %s]\n' % readUnitConfObj.parameters['endTime'] |
|
|
621 | 624 | |
|
|
622 | 625 | a = Alarm( |
|
|
623 | 626 | modes=self.alarm, |
|
|
624 | 627 | email=self.email, |
|
|
625 | 628 | message=message, |
|
|
626 | 629 | subject=subject, |
|
|
627 | 630 | subtitle=subtitle, |
|
|
628 | 631 | filename=self.filename |
|
|
629 | 632 | ) |
|
|
630 | 633 | |
|
|
631 | 634 | a.start() |
|
|
632 | 635 | |
|
|
633 | 636 | def setFilename(self, filename): |
|
|
634 | 637 | |
|
|
635 | 638 | self.filename = filename |
|
|
636 | 639 | |
|
|
637 | 640 | def runProcs(self): |
|
|
638 | 641 | |
|
|
639 | 642 | err = False |
|
|
640 | 643 | n = len(self.configurations) |
|
|
641 | 644 | |
|
|
642 | 645 | while not err: |
|
|
643 | 646 | for conf in self.getUnits(): |
|
|
644 | 647 | ok = conf.run() |
|
|
645 | 648 | if ok == 'Error': |
|
|
646 | 649 | n -= 1 |
|
|
647 | 650 | continue |
|
|
648 | 651 | elif not ok: |
|
|
649 | 652 | break |
|
|
650 | 653 | if n == 0: |
|
|
651 | 654 | err = True |
|
|
652 | 655 | |
|
|
653 | 656 | def run(self): |
|
|
654 | 657 | |
|
|
655 | 658 | log.success('\nStarting Project {} [id={}]'.format(self.name, self.id), tag='') |
|
|
656 | 659 | self.started = True |
|
|
657 | 660 | self.start_time = time.time() |
|
|
658 | 661 | self.createObjects() |
|
|
659 | 662 | self.runProcs() |
|
|
660 | 663 | log.success('{} Done (Time: {:4.2f}s)'.format( |
|
|
661 | 664 | self.name, |
|
|
662 | 665 | time.time()-self.start_time), '') |
| @@ -1,1788 +1,1788 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import numpy |
|
|
4 | 4 | from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter |
|
|
5 | 5 | |
|
|
6 | 6 | from schainpy.model.graphics.jroplot_base import Plot, plt |
|
|
7 | 7 | from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot |
|
|
8 | 8 | from schainpy.utils import log |
|
|
9 | 9 | # libreria wradlib |
|
|
10 | 10 | import wradlib as wrl |
|
|
11 | 11 | |
|
|
12 | 12 | EARTH_RADIUS = 6.3710e3 |
|
|
13 | 13 | |
|
|
14 | 14 | |
|
|
15 | 15 | def ll2xy(lat1, lon1, lat2, lon2): |
|
|
16 | 16 | |
|
|
17 | 17 | p = 0.017453292519943295 |
|
|
18 | 18 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
|
|
19 | 19 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
|
|
20 | 20 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
|
|
21 | 21 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
|
|
22 | 22 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
|
|
23 | 23 | theta = -theta + numpy.pi/2 |
|
|
24 | 24 | return r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
25 | 25 | |
|
|
26 | 26 | |
|
|
27 | 27 | def km2deg(km): |
|
|
28 | 28 | ''' |
|
|
29 | 29 | Convert distance in km to degrees |
|
|
30 | 30 | ''' |
|
|
31 | 31 | |
|
|
32 | 32 | return numpy.rad2deg(km/EARTH_RADIUS) |
|
|
33 | 33 | |
|
|
34 | 34 | |
|
|
35 | 35 | |
|
|
36 | 36 | class SpectralMomentsPlot(SpectraPlot): |
|
|
37 | 37 | ''' |
|
|
38 | 38 | Plot for Spectral Moments |
|
|
39 | 39 | ''' |
|
|
40 | 40 | CODE = 'spc_moments' |
|
|
41 | 41 | # colormap = 'jet' |
|
|
42 | 42 | # plot_type = 'pcolor' |
|
|
43 | 43 | |
|
|
44 | 44 | class DobleGaussianPlot(SpectraPlot): |
|
|
45 | 45 | ''' |
|
|
46 | 46 | Plot for Double Gaussian Plot |
|
|
47 | 47 | ''' |
|
|
48 | 48 | CODE = 'gaussian_fit' |
|
|
49 | 49 | # colormap = 'jet' |
|
|
50 | 50 | # plot_type = 'pcolor' |
|
|
51 | 51 | |
|
|
52 | 52 | class DoubleGaussianSpectraCutPlot(SpectraCutPlot): |
|
|
53 | 53 | ''' |
|
|
54 | 54 | Plot SpectraCut with Double Gaussian Fit |
|
|
55 | 55 | ''' |
|
|
56 | 56 | CODE = 'cut_gaussian_fit' |
|
|
57 | 57 | |
|
|
58 | 58 | class SnrPlot(RTIPlot): |
|
|
59 | 59 | ''' |
|
|
60 | 60 | Plot for SNR Data |
|
|
61 | 61 | ''' |
|
|
62 | 62 | |
|
|
63 | 63 | CODE = 'snr' |
|
|
64 | 64 | colormap = 'jet' |
|
|
65 | 65 | |
|
|
66 | 66 | def update(self, dataOut): |
|
|
67 | 67 | |
|
|
68 | 68 | data = { |
|
|
69 | 69 | 'snr': 10*numpy.log10(dataOut.data_snr) |
|
|
70 | 70 | } |
|
|
71 | 71 | |
|
|
72 | 72 | return data, {} |
|
|
73 | 73 | |
|
|
74 | 74 | class DopplerPlot(RTIPlot): |
|
|
75 | 75 | ''' |
|
|
76 | 76 | Plot for DOPPLER Data (1st moment) |
|
|
77 | 77 | ''' |
|
|
78 | 78 | |
|
|
79 | 79 | CODE = 'dop' |
|
|
80 | 80 | colormap = 'jet' |
|
|
81 | 81 | |
|
|
82 | 82 | def update(self, dataOut): |
|
|
83 | 83 | |
|
|
84 | 84 | data = { |
|
|
85 | 85 | 'dop': 10*numpy.log10(dataOut.data_dop) |
|
|
86 | 86 | } |
|
|
87 | 87 | |
|
|
88 | 88 | return data, {} |
|
|
89 | 89 | |
|
|
90 | 90 | class PowerPlot(RTIPlot): |
|
|
91 | 91 | ''' |
|
|
92 | 92 | Plot for Power Data (0 moment) |
|
|
93 | 93 | ''' |
|
|
94 | 94 | |
|
|
95 | 95 | CODE = 'pow' |
|
|
96 | 96 | colormap = 'jet' |
|
|
97 | 97 | |
|
|
98 | 98 | def update(self, dataOut): |
|
|
99 | 99 | data = { |
|
|
100 | 100 | 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor) |
|
|
101 | 101 | } |
|
|
102 | 102 | return data, {} |
|
|
103 | 103 | |
|
|
104 | 104 | class SpectralWidthPlot(RTIPlot): |
|
|
105 | 105 | ''' |
|
|
106 | 106 | Plot for Spectral Width Data (2nd moment) |
|
|
107 | 107 | ''' |
|
|
108 | 108 | |
|
|
109 | 109 | CODE = 'width' |
|
|
110 | 110 | colormap = 'jet' |
|
|
111 | 111 | |
|
|
112 | 112 | def update(self, dataOut): |
|
|
113 | 113 | |
|
|
114 | 114 | data = { |
|
|
115 | 115 | 'width': dataOut.data_width |
|
|
116 | 116 | } |
|
|
117 | 117 | |
|
|
118 | 118 | return data, {} |
|
|
119 | 119 | |
|
|
120 | 120 | class SkyMapPlot(Plot): |
|
|
121 | 121 | ''' |
|
|
122 | 122 | Plot for meteors detection data |
|
|
123 | 123 | ''' |
|
|
124 | 124 | |
|
|
125 | 125 | CODE = 'param' |
|
|
126 | 126 | |
|
|
127 | 127 | def setup(self): |
|
|
128 | 128 | |
|
|
129 | 129 | self.ncols = 1 |
|
|
130 | 130 | self.nrows = 1 |
|
|
131 | 131 | self.width = 7.2 |
|
|
132 | 132 | self.height = 7.2 |
|
|
133 | 133 | self.nplots = 1 |
|
|
134 | 134 | self.xlabel = 'Zonal Zenith Angle (deg)' |
|
|
135 | 135 | self.ylabel = 'Meridional Zenith Angle (deg)' |
|
|
136 | 136 | self.polar = True |
|
|
137 | 137 | self.ymin = -180 |
|
|
138 | 138 | self.ymax = 180 |
|
|
139 | 139 | self.colorbar = False |
|
|
140 | 140 | |
|
|
141 | 141 | def plot(self): |
|
|
142 | 142 | |
|
|
143 | 143 | arrayParameters = numpy.concatenate(self.data['param']) |
|
|
144 | 144 | error = arrayParameters[:, -1] |
|
|
145 | 145 | indValid = numpy.where(error == 0)[0] |
|
|
146 | 146 | finalMeteor = arrayParameters[indValid, :] |
|
|
147 | 147 | finalAzimuth = finalMeteor[:, 3] |
|
|
148 | 148 | finalZenith = finalMeteor[:, 4] |
|
|
149 | 149 | |
|
|
150 | 150 | x = finalAzimuth * numpy.pi / 180 |
|
|
151 | 151 | y = finalZenith |
|
|
152 | 152 | |
|
|
153 | 153 | ax = self.axes[0] |
|
|
154 | 154 | |
|
|
155 | 155 | if ax.firsttime: |
|
|
156 | 156 | ax.plot = ax.plot(x, y, 'bo', markersize=5)[0] |
|
|
157 | 157 | else: |
|
|
158 | 158 | ax.plot.set_data(x, y) |
|
|
159 | 159 | |
|
|
160 | 160 | dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
161 | 161 | dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
162 | 162 | title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, |
|
|
163 | 163 | dt2, |
|
|
164 | 164 | len(x)) |
|
|
165 | 165 | self.titles[0] = title |
|
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166 | 166 | |
|
|
167 | 167 | |
|
|
168 | 168 | class GenericRTIPlot(Plot): |
|
|
169 | 169 | ''' |
|
|
170 | 170 | Plot for data_xxxx object |
|
|
171 | 171 | ''' |
|
|
172 | 172 | |
|
|
173 | 173 | CODE = 'param' |
|
|
174 | 174 | colormap = 'viridis' |
|
|
175 | 175 | plot_type = 'pcolorbuffer' |
|
|
176 | 176 | |
|
|
177 | 177 | def setup(self): |
|
|
178 | 178 | self.xaxis = 'time' |
|
|
179 | 179 | self.ncols = 1 |
|
|
180 | 180 | self.nrows = self.data.shape('param')[0] |
|
|
181 | 181 | self.nplots = self.nrows |
|
|
182 | 182 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95}) |
|
|
183 | 183 | |
|
|
184 | 184 | if not self.xlabel: |
|
|
185 | 185 | self.xlabel = 'Time' |
|
|
186 | 186 | |
|
|
187 | 187 | self.ylabel = 'Range [km]' |
|
|
188 | 188 | if not self.titles: |
|
|
189 | 189 | self.titles = ['Param {}'.format(x) for x in range(self.nrows)] |
|
|
190 | 190 | |
|
|
191 | 191 | def update(self, dataOut): |
|
|
192 | 192 | |
|
|
193 | 193 | data = { |
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|
194 | 194 | 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0) |
|
|
195 | 195 | } |
|
|
196 | 196 | |
|
|
197 | 197 | meta = {} |
|
|
198 | 198 | |
|
|
199 | 199 | return data, meta |
|
|
200 | 200 | |
|
|
201 | 201 | def plot(self): |
|
|
202 | 202 | # self.data.normalize_heights() |
|
|
203 | 203 | self.x = self.data.times |
|
|
204 | 204 | self.y = self.data.yrange |
|
|
205 | 205 | self.z = self.data['param'] |
|
|
206 | 206 | self.z = 10*numpy.log10(self.z) |
|
|
207 | 207 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
208 | 208 | |
|
|
209 | 209 | if self.decimation is None: |
|
|
210 | 210 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
211 | 211 | else: |
|
|
212 | 212 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
213 | 213 | |
|
|
214 | 214 | for n, ax in enumerate(self.axes): |
|
|
215 | 215 | |
|
|
216 | 216 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
|
|
217 | 217 | self.z[n]) |
|
|
218 | 218 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
|
|
219 | 219 | self.z[n]) |
|
|
220 | 220 | |
|
|
221 | 221 | if ax.firsttime: |
|
|
222 | 222 | if self.zlimits is not None: |
|
|
223 | 223 | self.zmin, self.zmax = self.zlimits[n] |
|
|
224 | 224 | |
|
|
225 | 225 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
226 | 226 | vmin=self.zmin, |
|
|
227 | 227 | vmax=self.zmax, |
|
|
228 | 228 | cmap=self.cmaps[n] |
|
|
229 | 229 | ) |
|
|
230 | 230 | else: |
|
|
231 | 231 | if self.zlimits is not None: |
|
|
232 | 232 | self.zmin, self.zmax = self.zlimits[n] |
|
|
233 | 233 | ax.collections.remove(ax.collections[0]) |
|
|
234 | 234 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
235 | 235 | vmin=self.zmin, |
|
|
236 | 236 | vmax=self.zmax, |
|
|
237 | 237 | cmap=self.cmaps[n] |
|
|
238 | 238 | ) |
|
|
239 | 239 | |
|
|
240 | 240 | |
|
|
241 | 241 | class PolarMapPlot(Plot): |
|
|
242 | 242 | ''' |
|
|
243 | 243 | Plot for weather radar |
|
|
244 | 244 | ''' |
|
|
245 | 245 | |
|
|
246 | 246 | CODE = 'param' |
|
|
247 | 247 | colormap = 'seismic' |
|
|
248 | 248 | |
|
|
249 | 249 | def setup(self): |
|
|
250 | 250 | self.ncols = 1 |
|
|
251 | 251 | self.nrows = 1 |
|
|
252 | 252 | self.width = 9 |
|
|
253 | 253 | self.height = 8 |
|
|
254 | 254 | self.mode = self.data.meta['mode'] |
|
|
255 | 255 | if self.channels is not None: |
|
|
256 | 256 | self.nplots = len(self.channels) |
|
|
257 | 257 | self.nrows = len(self.channels) |
|
|
258 | 258 | else: |
|
|
259 | 259 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
260 | 260 | self.nrows = self.nplots |
|
|
261 | 261 | self.channels = list(range(self.nplots)) |
|
|
262 | 262 | if self.mode == 'E': |
|
|
263 | 263 | self.xlabel = 'Longitude' |
|
|
264 | 264 | self.ylabel = 'Latitude' |
|
|
265 | 265 | else: |
|
|
266 | 266 | self.xlabel = 'Range (km)' |
|
|
267 | 267 | self.ylabel = 'Height (km)' |
|
|
268 | 268 | self.bgcolor = 'white' |
|
|
269 | 269 | self.cb_labels = self.data.meta['units'] |
|
|
270 | 270 | self.lat = self.data.meta['latitude'] |
|
|
271 | 271 | self.lon = self.data.meta['longitude'] |
|
|
272 | 272 | self.xmin, self.xmax = float( |
|
|
273 | 273 | km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon) |
|
|
274 | 274 | self.ymin, self.ymax = float( |
|
|
275 | 275 | km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat) |
|
|
276 | 276 | # self.polar = True |
|
|
277 | 277 | |
|
|
278 | 278 | def plot(self): |
|
|
279 | 279 | |
|
|
280 | 280 | for n, ax in enumerate(self.axes): |
|
|
281 | 281 | data = self.data['param'][self.channels[n]] |
|
|
282 | 282 | |
|
|
283 | 283 | zeniths = numpy.linspace( |
|
|
284 | 284 | 0, self.data.meta['max_range'], data.shape[1]) |
|
|
285 | 285 | if self.mode == 'E': |
|
|
286 | 286 | azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2 |
|
|
287 | 287 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
288 | 288 | x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin( |
|
|
289 | 289 | theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])) |
|
|
290 | 290 | x = km2deg(x) + self.lon |
|
|
291 | 291 | y = km2deg(y) + self.lat |
|
|
292 | 292 | else: |
|
|
293 | 293 | azimuths = numpy.radians(self.data.yrange) |
|
|
294 | 294 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
295 | 295 | x, y = r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
296 | 296 | self.y = zeniths |
|
|
297 | 297 | |
|
|
298 | 298 | if ax.firsttime: |
|
|
299 | 299 | if self.zlimits is not None: |
|
|
300 | 300 | self.zmin, self.zmax = self.zlimits[n] |
|
|
301 | 301 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
302 | 302 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
303 | 303 | vmin=self.zmin, |
|
|
304 | 304 | vmax=self.zmax, |
|
|
305 | 305 | cmap=self.cmaps[n]) |
|
|
306 | 306 | else: |
|
|
307 | 307 | if self.zlimits is not None: |
|
|
308 | 308 | self.zmin, self.zmax = self.zlimits[n] |
|
|
309 | 309 | ax.collections.remove(ax.collections[0]) |
|
|
310 | 310 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
311 | 311 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
312 | 312 | vmin=self.zmin, |
|
|
313 | 313 | vmax=self.zmax, |
|
|
314 | 314 | cmap=self.cmaps[n]) |
|
|
315 | 315 | |
|
|
316 | 316 | if self.mode == 'A': |
|
|
317 | 317 | continue |
|
|
318 | 318 | |
|
|
319 | 319 | # plot district names |
|
|
320 | 320 | f = open('/data/workspace/schain_scripts/distrito.csv') |
|
|
321 | 321 | for line in f: |
|
|
322 | 322 | label, lon, lat = [s.strip() for s in line.split(',') if s] |
|
|
323 | 323 | lat = float(lat) |
|
|
324 | 324 | lon = float(lon) |
|
|
325 | 325 | # ax.plot(lon, lat, '.b', ms=2) |
|
|
326 | 326 | ax.text(lon, lat, label.decode('utf8'), ha='center', |
|
|
327 | 327 | va='bottom', size='8', color='black') |
|
|
328 | 328 | |
|
|
329 | 329 | # plot limites |
|
|
330 | 330 | limites = [] |
|
|
331 | 331 | tmp = [] |
|
|
332 | 332 | for line in open('/data/workspace/schain_scripts/lima.csv'): |
|
|
333 | 333 | if '#' in line: |
|
|
334 | 334 | if tmp: |
|
|
335 | 335 | limites.append(tmp) |
|
|
336 | 336 | tmp = [] |
|
|
337 | 337 | continue |
|
|
338 | 338 | values = line.strip().split(',') |
|
|
339 | 339 | tmp.append((float(values[0]), float(values[1]))) |
|
|
340 | 340 | for points in limites: |
|
|
341 | 341 | ax.add_patch( |
|
|
342 | 342 | Polygon(points, ec='k', fc='none', ls='--', lw=0.5)) |
|
|
343 | 343 | |
|
|
344 | 344 | # plot Cuencas |
|
|
345 | 345 | for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'): |
|
|
346 | 346 | f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca)) |
|
|
347 | 347 | values = [line.strip().split(',') for line in f] |
|
|
348 | 348 | points = [(float(s[0]), float(s[1])) for s in values] |
|
|
349 | 349 | ax.add_patch(Polygon(points, ec='b', fc='none')) |
|
|
350 | 350 | |
|
|
351 | 351 | # plot grid |
|
|
352 | 352 | for r in (15, 30, 45, 60): |
|
|
353 | 353 | ax.add_artist(plt.Circle((self.lon, self.lat), |
|
|
354 | 354 | km2deg(r), color='0.6', fill=False, lw=0.2)) |
|
|
355 | 355 | ax.text( |
|
|
356 | 356 | self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180), |
|
|
357 | 357 | self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180), |
|
|
358 | 358 | '{}km'.format(r), |
|
|
359 | 359 | ha='center', va='bottom', size='8', color='0.6', weight='heavy') |
|
|
360 | 360 | |
|
|
361 | 361 | if self.mode == 'E': |
|
|
362 | 362 | title = 'El={}$^\circ$'.format(self.data.meta['elevation']) |
|
|
363 | 363 | label = 'E{:02d}'.format(int(self.data.meta['elevation'])) |
|
|
364 | 364 | else: |
|
|
365 | 365 | title = 'Az={}$^\circ$'.format(self.data.meta['azimuth']) |
|
|
366 | 366 | label = 'A{:02d}'.format(int(self.data.meta['azimuth'])) |
|
|
367 | 367 | |
|
|
368 | 368 | self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels] |
|
|
369 | 369 | self.titles = ['{} {}'.format( |
|
|
370 | 370 | self.data.parameters[x], title) for x in self.channels] |
|
|
371 | 371 | |
|
|
372 | 372 | class WeatherPlot(Plot): |
|
|
373 | 373 | CODE = 'weather' |
|
|
374 | 374 | plot_name = 'weather' |
|
|
375 | 375 | plot_type = 'ppistyle' |
|
|
376 | 376 | buffering = False |
|
|
377 | 377 | |
|
|
378 | 378 | def setup(self): |
|
|
379 | 379 | self.ncols = 1 |
|
|
380 | 380 | self.nrows = 1 |
|
|
381 | 381 | self.width =8 |
|
|
382 | 382 | self.height =8 |
|
|
383 | 383 | self.nplots= 1 |
|
|
384 | 384 | self.ylabel= 'Range [Km]' |
|
|
385 | 385 | self.titles= ['Weather'] |
|
|
386 | 386 | self.colorbar=False |
|
|
387 | 387 | self.ini =0 |
|
|
388 | 388 | self.len_azi =0 |
|
|
389 | 389 | self.buffer_ini = None |
|
|
390 | 390 | self.buffer_azi = None |
|
|
391 | 391 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
|
|
392 | 392 | self.flag =0 |
|
|
393 | 393 | self.indicador= 0 |
|
|
394 | 394 | self.last_data_azi = None |
|
|
395 | 395 | self.val_mean = None |
|
|
396 | 396 | |
|
|
397 | 397 | def update(self, dataOut): |
|
|
398 | 398 | |
|
|
399 | 399 | data = {} |
|
|
400 | 400 | meta = {} |
|
|
401 | 401 | if hasattr(dataOut, 'dataPP_POWER'): |
|
|
402 | 402 | factor = 1 |
|
|
403 | 403 | if hasattr(dataOut, 'nFFTPoints'): |
|
|
404 | 404 | factor = dataOut.normFactor |
|
|
405 | 405 | #print("DIME EL SHAPE PORFAVOR",dataOut.data_360.shape) |
|
|
406 | 406 | data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor)) |
|
|
407 | 407 | data['azi'] = dataOut.data_azi |
|
|
408 | 408 | data['ele'] = dataOut.data_ele |
|
|
409 | 409 | return data, meta |
|
|
410 | 410 | |
|
|
411 | 411 | def get2List(self,angulos): |
|
|
412 | 412 | list1=[] |
|
|
413 | 413 | list2=[] |
|
|
414 | 414 | for i in reversed(range(len(angulos))): |
|
|
415 | 415 | diff_ = angulos[i]-angulos[i-1] |
|
|
416 | 416 | if diff_ >1.5: |
|
|
417 | 417 | list1.append(i-1) |
|
|
418 | 418 | list2.append(diff_) |
|
|
419 | 419 | return list(reversed(list1)),list(reversed(list2)) |
|
|
420 | 420 | |
|
|
421 | 421 | def fixData360(self,list_,ang_): |
|
|
422 | 422 | if list_[0]==-1: |
|
|
423 | 423 | vec = numpy.where(ang_<ang_[0]) |
|
|
424 | 424 | ang_[vec] = ang_[vec]+360 |
|
|
425 | 425 | return ang_ |
|
|
426 | 426 | return ang_ |
|
|
427 | 427 | |
|
|
428 | 428 | def fixData360HL(self,angulos): |
|
|
429 | 429 | vec = numpy.where(angulos>=360) |
|
|
430 | 430 | angulos[vec]=angulos[vec]-360 |
|
|
431 | 431 | return angulos |
|
|
432 | 432 | |
|
|
433 | 433 | def search_pos(self,pos,list_): |
|
|
434 | 434 | for i in range(len(list_)): |
|
|
435 | 435 | if pos == list_[i]: |
|
|
436 | 436 | return True,i |
|
|
437 | 437 | i=None |
|
|
438 | 438 | return False,i |
|
|
439 | 439 | |
|
|
440 | 440 | def fixDataComp(self,ang_,list1_,list2_): |
|
|
441 | 441 | size = len(ang_) |
|
|
442 | 442 | size2 = 0 |
|
|
443 | 443 | for i in range(len(list2_)): |
|
|
444 | 444 | size2=size2+round(list2_[i])-1 |
|
|
445 | 445 | new_size= size+size2 |
|
|
446 | 446 | ang_new = numpy.zeros(new_size) |
|
|
447 | 447 | ang_new2 = numpy.zeros(new_size) |
|
|
448 | 448 | |
|
|
449 | 449 | tmp = 0 |
|
|
450 | 450 | c = 0 |
|
|
451 | 451 | for i in range(len(ang_)): |
|
|
452 | 452 | ang_new[tmp +c] = ang_[i] |
|
|
453 | 453 | ang_new2[tmp+c] = ang_[i] |
|
|
454 | 454 | condition , value = self.search_pos(i,list1_) |
|
|
455 | 455 | if condition: |
|
|
456 | 456 | pos = tmp + c + 1 |
|
|
457 | 457 | for k in range(round(list2_[value])-1): |
|
|
458 | 458 | ang_new[pos+k] = ang_new[pos+k-1]+1 |
|
|
459 | 459 | ang_new2[pos+k] = numpy.nan |
|
|
460 | 460 | tmp = pos +k |
|
|
461 | 461 | c = 0 |
|
|
462 | 462 | c=c+1 |
|
|
463 | 463 | return ang_new,ang_new2 |
|
|
464 | 464 | |
|
|
465 | 465 | def globalCheckPED(self,angulos): |
|
|
466 | 466 | l1,l2 = self.get2List(angulos) |
|
|
467 | 467 | if len(l1)>0: |
|
|
468 | 468 | angulos2 = self.fixData360(list_=l1,ang_=angulos) |
|
|
469 | 469 | l1,l2 = self.get2List(angulos2) |
|
|
470 | 470 | |
|
|
471 | 471 | ang1_,ang2_ = self.fixDataComp(ang_=angulos2,list1_=l1,list2_=l2) |
|
|
472 | 472 | ang1_ = self.fixData360HL(ang1_) |
|
|
473 | 473 | ang2_ = self.fixData360HL(ang2_) |
|
|
474 | 474 | else: |
|
|
475 | 475 | ang1_= angulos |
|
|
476 | 476 | ang2_= angulos |
|
|
477 | 477 | return ang1_,ang2_ |
|
|
478 | 478 | |
|
|
479 | 479 | def analizeDATA(self,data_azi): |
|
|
480 | 480 | list1 = [] |
|
|
481 | 481 | list2 = [] |
|
|
482 | 482 | dat = data_azi |
|
|
483 | 483 | for i in reversed(range(1,len(dat))): |
|
|
484 | 484 | if dat[i]>dat[i-1]: |
|
|
485 | 485 | diff = int(dat[i])-int(dat[i-1]) |
|
|
486 | 486 | else: |
|
|
487 | 487 | diff = 360+int(dat[i])-int(dat[i-1]) |
|
|
488 | 488 | if diff > 1: |
|
|
489 | 489 | list1.append(i-1) |
|
|
490 | 490 | list2.append(diff-1) |
|
|
491 | 491 | return list1,list2 |
|
|
492 | 492 | |
|
|
493 | 493 | def fixDATANEW(self,data_azi,data_weather): |
|
|
494 | 494 | list1,list2 = self.analizeDATA(data_azi) |
|
|
495 | 495 | if len(list1)== 0: |
|
|
496 | 496 | return data_azi,data_weather |
|
|
497 | 497 | else: |
|
|
498 | 498 | resize = 0 |
|
|
499 | 499 | for i in range(len(list2)): |
|
|
500 | 500 | resize= resize + list2[i] |
|
|
501 | 501 | new_data_azi = numpy.resize(data_azi,resize) |
|
|
502 | 502 | new_data_weather= numpy.resize(date_weather,resize) |
|
|
503 | 503 | |
|
|
504 | 504 | for i in range(len(list2)): |
|
|
505 | 505 | j=0 |
|
|
506 | 506 | position=list1[i]+1 |
|
|
507 | 507 | for j in range(list2[i]): |
|
|
508 | 508 | new_data_azi[position+j]=new_data_azi[position+j-1]+1 |
|
|
509 | 509 | return new_data_azi |
|
|
510 | 510 | |
|
|
511 | 511 | def fixDATA(self,data_azi): |
|
|
512 | 512 | data=data_azi |
|
|
513 | 513 | for i in range(len(data)): |
|
|
514 | 514 | if numpy.isnan(data[i]): |
|
|
515 | 515 | data[i]=data[i-1]+1 |
|
|
516 | 516 | return data |
|
|
517 | 517 | |
|
|
518 | 518 | def replaceNAN(self,data_weather,data_azi,val): |
|
|
519 | 519 | data= data_azi |
|
|
520 | 520 | data_T= data_weather |
|
|
521 | 521 | if data.shape[0]> data_T.shape[0]: |
|
|
522 | 522 | data_N = numpy.ones( [data.shape[0],data_T.shape[1]]) |
|
|
523 | 523 | c = 0 |
|
|
524 | 524 | for i in range(len(data)): |
|
|
525 | 525 | if numpy.isnan(data[i]): |
|
|
526 | 526 | data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan |
|
|
527 | 527 | else: |
|
|
528 | 528 | data_N[i,:]=data_T[c,:] |
|
|
529 | 529 | c=c+1 |
|
|
530 | 530 | return data_N |
|
|
531 | 531 | else: |
|
|
532 | 532 | for i in range(len(data)): |
|
|
533 | 533 | if numpy.isnan(data[i]): |
|
|
534 | 534 | data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan |
|
|
535 | 535 | return data_T |
|
|
536 | 536 | |
|
|
537 | 537 | def const_ploteo(self,data_weather,data_azi,step,res): |
|
|
538 | 538 | if self.ini==0: |
|
|
539 | 539 | #------- |
|
|
540 | 540 | n = (360/res)-len(data_azi) |
|
|
541 | 541 | #--------------------- new ------------------------- |
|
|
542 | 542 | data_azi_new ,data_azi_old= self.globalCheckPED(data_azi) |
|
|
543 | 543 | #------------------------ |
|
|
544 | 544 | start = data_azi_new[-1] + res |
|
|
545 | 545 | end = data_azi_new[0] - res |
|
|
546 | 546 | #------ new |
|
|
547 | 547 | self.last_data_azi = end |
|
|
548 | 548 | if start>end: |
|
|
549 | 549 | end = end + 360 |
|
|
550 | 550 | azi_vacia = numpy.linspace(start,end,int(n)) |
|
|
551 | 551 | azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia) |
|
|
552 | 552 | data_azi = numpy.hstack((data_azi_new,azi_vacia)) |
|
|
553 | 553 | # RADAR |
|
|
554 | 554 | val_mean = numpy.mean(data_weather[:,-1]) |
|
|
555 | 555 | self.val_mean = val_mean |
|
|
556 | 556 | data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean |
|
|
557 | 557 | data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean) |
|
|
558 | 558 | data_weather = numpy.vstack((data_weather,data_weather_cmp)) |
|
|
559 | 559 | else: |
|
|
560 | 560 | # azimuth |
|
|
561 | 561 | flag=0 |
|
|
562 | 562 | start_azi = self.res_azi[0] |
|
|
563 | 563 | #-----------new------------ |
|
|
564 | 564 | data_azi ,data_azi_old= self.globalCheckPED(data_azi) |
|
|
565 | 565 | data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean) |
|
|
566 | 566 | #-------------------------- |
|
|
567 | 567 | start = data_azi[0] |
|
|
568 | 568 | end = data_azi[-1] |
|
|
569 | 569 | self.last_data_azi= end |
|
|
570 | 570 | if start< start_azi: |
|
|
571 | 571 | start = start +360 |
|
|
572 | 572 | if end <start_azi: |
|
|
573 | 573 | end = end +360 |
|
|
574 | 574 | |
|
|
575 | 575 | pos_ini = int((start-start_azi)/res) |
|
|
576 | 576 | len_azi = len(data_azi) |
|
|
577 | 577 | if (360-pos_ini)<len_azi: |
|
|
578 | 578 | if pos_ini+1==360: |
|
|
579 | 579 | pos_ini=0 |
|
|
580 | 580 | else: |
|
|
581 | 581 | flag=1 |
|
|
582 | 582 | dif= 360-pos_ini |
|
|
583 | 583 | comp= len_azi-dif |
|
|
584 | 584 | #----------------- |
|
|
585 | 585 | if flag==0: |
|
|
586 | 586 | # AZIMUTH |
|
|
587 | 587 | self.res_azi[pos_ini:pos_ini+len_azi] = data_azi |
|
|
588 | 588 | # RADAR |
|
|
589 | 589 | self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather |
|
|
590 | 590 | else: |
|
|
591 | 591 | # AZIMUTH |
|
|
592 | 592 | self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif] |
|
|
593 | 593 | self.res_azi[0:comp] = data_azi[dif:] |
|
|
594 | 594 | # RADAR |
|
|
595 | 595 | self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:] |
|
|
596 | 596 | self.res_weather[0:comp,:] = data_weather[dif:,:] |
|
|
597 | 597 | flag=0 |
|
|
598 | 598 | data_azi = self.res_azi |
|
|
599 | 599 | data_weather = self.res_weather |
|
|
600 | 600 | |
|
|
601 | 601 | return data_weather,data_azi |
|
|
602 | 602 | |
|
|
603 | 603 | def plot(self): |
|
|
604 | 604 | thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S') |
|
|
605 | 605 | data = self.data[-1] |
|
|
606 | 606 | r = self.data.yrange |
|
|
607 | 607 | delta_height = r[1]-r[0] |
|
|
608 | 608 | r_mask = numpy.where(r>=0)[0] |
|
|
609 | 609 | r = numpy.arange(len(r_mask))*delta_height |
|
|
610 | 610 | self.y = 2*r |
|
|
611 | 611 | # RADAR |
|
|
612 | 612 | #data_weather = data['weather'] |
|
|
613 | 613 | # PEDESTAL |
|
|
614 | 614 | #data_azi = data['azi'] |
|
|
615 | 615 | res = 1 |
|
|
616 | 616 | # STEP |
|
|
617 | 617 | step = (360/(res*data['weather'].shape[0])) |
|
|
618 | 618 | |
|
|
619 | 619 | self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res) |
|
|
620 | 620 | self.res_ele = numpy.mean(data['ele']) |
|
|
621 | 621 | ################# PLOTEO ################### |
|
|
622 | 622 | for i,ax in enumerate(self.axes): |
|
|
623 | 623 | self.zmin = self.zmin if self.zmin else 20 |
|
|
624 | 624 | self.zmax = self.zmax if self.zmax else 80 |
|
|
625 | 625 | if ax.firsttime: |
|
|
626 | 626 | plt.clf() |
|
|
627 | 627 | cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=self.zmin, vmax=self.zmax) |
|
|
628 | 628 | else: |
|
|
629 | 629 | plt.clf() |
|
|
630 | 630 | cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=self.zmin, vmax=self.zmax) |
|
|
631 | 631 | caax = cgax.parasites[0] |
|
|
632 | 632 | paax = cgax.parasites[1] |
|
|
633 | 633 | cbar = plt.gcf().colorbar(pm, pad=0.075) |
|
|
634 | 634 | caax.set_xlabel('x_range [km]') |
|
|
635 | 635 | caax.set_ylabel('y_range [km]') |
|
|
636 | 636 | plt.text(1.0, 1.05, 'Azimuth '+str(thisDatetime)+" Step "+str(self.ini)+ " EL: "+str(round(self.res_ele, 1)), transform=caax.transAxes, va='bottom',ha='right') |
|
|
637 | 637 | |
|
|
638 | 638 | self.ini= self.ini+1 |
|
|
639 | 639 | |
|
|
640 | 640 | |
|
|
641 | 641 | class WeatherRHIPlot(Plot): |
|
|
642 | 642 | CODE = 'weather' |
|
|
643 | 643 | plot_name = 'weather' |
|
|
644 | 644 | plot_type = 'rhistyle' |
|
|
645 | 645 | buffering = False |
|
|
646 | 646 | data_ele_tmp = None |
|
|
647 | 647 | |
|
|
648 | 648 | def setup(self): |
|
|
649 | 649 | print("********************") |
|
|
650 | 650 | print("********************") |
|
|
651 | 651 | print("********************") |
|
|
652 | 652 | print("SETUP WEATHER PLOT") |
|
|
653 | 653 | self.ncols = 1 |
|
|
654 | 654 | self.nrows = 1 |
|
|
655 | 655 | self.nplots= 1 |
|
|
656 | 656 | self.ylabel= 'Range [Km]' |
|
|
657 | 657 | self.titles= ['Weather'] |
|
|
658 | 658 | if self.channels is not None: |
|
|
659 | 659 | self.nplots = len(self.channels) |
|
|
660 | 660 | self.nrows = len(self.channels) |
|
|
661 | 661 | else: |
|
|
662 | 662 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
663 | 663 | self.nrows = self.nplots |
|
|
664 | 664 | self.channels = list(range(self.nplots)) |
|
|
665 | 665 | print("channels",self.channels) |
|
|
666 | 666 | print("que saldra", self.data.shape(self.CODE)[0]) |
|
|
667 | 667 | self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
668 | 668 | print("self.titles",self.titles) |
|
|
669 | 669 | self.colorbar=False |
|
|
670 | 670 | self.width =12 |
|
|
671 | 671 | self.height =8 |
|
|
672 | 672 | self.ini =0 |
|
|
673 | 673 | self.len_azi =0 |
|
|
674 | 674 | self.buffer_ini = None |
|
|
675 | 675 | self.buffer_ele = None |
|
|
676 | 676 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
|
|
677 | 677 | self.flag =0 |
|
|
678 | 678 | self.indicador= 0 |
|
|
679 | 679 | self.last_data_ele = None |
|
|
680 | 680 | self.val_mean = None |
|
|
681 | 681 | |
|
|
682 | 682 | def update(self, dataOut): |
|
|
683 | 683 | |
|
|
684 | 684 | data = {} |
|
|
685 | 685 | meta = {} |
|
|
686 | 686 | if hasattr(dataOut, 'dataPP_POWER'): |
|
|
687 | 687 | factor = 1 |
|
|
688 | 688 | if hasattr(dataOut, 'nFFTPoints'): |
|
|
689 | 689 | factor = dataOut.normFactor |
|
|
690 | 690 | print("dataOut",dataOut.data_360.shape) |
|
|
691 | 691 | # |
|
|
692 | 692 | data['weather'] = 10*numpy.log10(dataOut.data_360/(factor)) |
|
|
693 | 693 | # |
|
|
694 | 694 | #data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor)) |
|
|
695 | 695 | data['azi'] = dataOut.data_azi |
|
|
696 | 696 | data['ele'] = dataOut.data_ele |
|
|
697 | 697 | #print("UPDATE") |
|
|
698 | 698 | #print("data[weather]",data['weather'].shape) |
|
|
699 | 699 | #print("data[azi]",data['azi']) |
|
|
700 | 700 | return data, meta |
|
|
701 | 701 | |
|
|
702 | 702 | def get2List(self,angulos): |
|
|
703 | 703 | list1=[] |
|
|
704 | 704 | list2=[] |
|
|
705 | 705 | for i in reversed(range(len(angulos))): |
|
|
706 | 706 | if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante |
|
|
707 | 707 | diff_ = angulos[i]-angulos[i-1] |
|
|
708 | 708 | if abs(diff_) >1.5: |
|
|
709 | 709 | list1.append(i-1) |
|
|
710 | 710 | list2.append(diff_) |
|
|
711 | 711 | return list(reversed(list1)),list(reversed(list2)) |
|
|
712 | 712 | |
|
|
713 | 713 | def fixData90(self,list_,ang_): |
|
|
714 | 714 | if list_[0]==-1: |
|
|
715 | 715 | vec = numpy.where(ang_<ang_[0]) |
|
|
716 | 716 | ang_[vec] = ang_[vec]+90 |
|
|
717 | 717 | return ang_ |
|
|
718 | 718 | return ang_ |
|
|
719 | 719 | |
|
|
720 | 720 | def fixData90HL(self,angulos): |
|
|
721 | 721 | vec = numpy.where(angulos>=90) |
|
|
722 | 722 | angulos[vec]=angulos[vec]-90 |
|
|
723 | 723 | return angulos |
|
|
724 | 724 | |
|
|
725 | 725 | |
|
|
726 | 726 | def search_pos(self,pos,list_): |
|
|
727 | 727 | for i in range(len(list_)): |
|
|
728 | 728 | if pos == list_[i]: |
|
|
729 | 729 | return True,i |
|
|
730 | 730 | i=None |
|
|
731 | 731 | return False,i |
|
|
732 | 732 | |
|
|
733 | 733 | def fixDataComp(self,ang_,list1_,list2_,tipo_case): |
|
|
734 | 734 | size = len(ang_) |
|
|
735 | 735 | size2 = 0 |
|
|
736 | 736 | for i in range(len(list2_)): |
|
|
737 | 737 | size2=size2+round(abs(list2_[i]))-1 |
|
|
738 | 738 | new_size= size+size2 |
|
|
739 | 739 | ang_new = numpy.zeros(new_size) |
|
|
740 | 740 | ang_new2 = numpy.zeros(new_size) |
|
|
741 | 741 | |
|
|
742 | 742 | tmp = 0 |
|
|
743 | 743 | c = 0 |
|
|
744 | 744 | for i in range(len(ang_)): |
|
|
745 | 745 | ang_new[tmp +c] = ang_[i] |
|
|
746 | 746 | ang_new2[tmp+c] = ang_[i] |
|
|
747 | 747 | condition , value = self.search_pos(i,list1_) |
|
|
748 | 748 | if condition: |
|
|
749 | 749 | pos = tmp + c + 1 |
|
|
750 | 750 | for k in range(round(abs(list2_[value]))-1): |
|
|
751 | 751 | if tipo_case==0 or tipo_case==3:#subida |
|
|
752 | 752 | ang_new[pos+k] = ang_new[pos+k-1]+1 |
|
|
753 | 753 | ang_new2[pos+k] = numpy.nan |
|
|
754 | 754 | elif tipo_case==1 or tipo_case==2:#bajada |
|
|
755 | 755 | ang_new[pos+k] = ang_new[pos+k-1]-1 |
|
|
756 | 756 | ang_new2[pos+k] = numpy.nan |
|
|
757 | 757 | |
|
|
758 | 758 | tmp = pos +k |
|
|
759 | 759 | c = 0 |
|
|
760 | 760 | c=c+1 |
|
|
761 | 761 | return ang_new,ang_new2 |
|
|
762 | 762 | |
|
|
763 | 763 | def globalCheckPED(self,angulos,tipo_case): |
|
|
764 | 764 | l1,l2 = self.get2List(angulos) |
|
|
765 | 765 | ##print("l1",l1) |
|
|
766 | 766 | ##print("l2",l2) |
|
|
767 | 767 | if len(l1)>0: |
|
|
768 | 768 | #angulos2 = self.fixData90(list_=l1,ang_=angulos) |
|
|
769 | 769 | #l1,l2 = self.get2List(angulos2) |
|
|
770 | 770 | ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case) |
|
|
771 | 771 | #ang1_ = self.fixData90HL(ang1_) |
|
|
772 | 772 | #ang2_ = self.fixData90HL(ang2_) |
|
|
773 | 773 | else: |
|
|
774 | 774 | ang1_= angulos |
|
|
775 | 775 | ang2_= angulos |
|
|
776 | 776 | return ang1_,ang2_ |
|
|
777 | 777 | |
|
|
778 | 778 | |
|
|
779 | 779 | def replaceNAN(self,data_weather,data_ele,val): |
|
|
780 | 780 | data= data_ele |
|
|
781 | 781 | data_T= data_weather |
|
|
782 | 782 | if data.shape[0]> data_T.shape[0]: |
|
|
783 | 783 | data_N = numpy.ones( [data.shape[0],data_T.shape[1]]) |
|
|
784 | 784 | c = 0 |
|
|
785 | 785 | for i in range(len(data)): |
|
|
786 | 786 | if numpy.isnan(data[i]): |
|
|
787 | 787 | data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan |
|
|
788 | 788 | else: |
|
|
789 | 789 | data_N[i,:]=data_T[c,:] |
|
|
790 | 790 | c=c+1 |
|
|
791 | 791 | return data_N |
|
|
792 | 792 | else: |
|
|
793 | 793 | for i in range(len(data)): |
|
|
794 | 794 | if numpy.isnan(data[i]): |
|
|
795 | 795 | data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan |
|
|
796 | 796 | return data_T |
|
|
797 | 797 | |
|
|
798 | 798 | def check_case(self,data_ele,ang_max,ang_min): |
|
|
799 | 799 | start = data_ele[0] |
|
|
800 | 800 | end = data_ele[-1] |
|
|
801 | 801 | number = (end-start) |
|
|
802 | 802 | len_ang=len(data_ele) |
|
|
803 | 803 | print("start",start) |
|
|
804 | 804 | print("end",end) |
|
|
805 | 805 | print("number",number) |
|
|
806 | 806 | |
|
|
807 | 807 | print("len_ang",len_ang) |
|
|
808 | 808 | |
|
|
809 | 809 | #exit(1) |
|
|
810 | 810 | |
|
|
811 | 811 | if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida |
|
|
812 | 812 | return 0 |
|
|
813 | 813 | #elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada |
|
|
814 | 814 | # return 1 |
|
|
815 | 815 | elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada |
|
|
816 | 816 | return 1 |
|
|
817 | 817 | elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX |
|
|
818 | 818 | return 2 |
|
|
819 | 819 | elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN |
|
|
820 | 820 | return 3 |
|
|
821 | 821 | |
|
|
822 | 822 | |
|
|
823 | 823 | def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min): |
|
|
824 | 824 | ang_max= ang_max |
|
|
825 | 825 | ang_min= ang_min |
|
|
826 | 826 | data_weather=data_weather |
|
|
827 | 827 | val_ch=val_ch |
|
|
828 | 828 | ##print("*********************DATA WEATHER**************************************") |
|
|
829 | 829 | ##print(data_weather) |
|
|
830 | 830 | if self.ini==0: |
|
|
831 | 831 | ''' |
|
|
832 | 832 | print("**********************************************") |
|
|
833 | 833 | print("**********************************************") |
|
|
834 | 834 | print("***************ini**************") |
|
|
835 | 835 | print("**********************************************") |
|
|
836 | 836 | print("**********************************************") |
|
|
837 | 837 | ''' |
|
|
838 | 838 | #print("data_ele",data_ele) |
|
|
839 | 839 | #---------------------------------------------------------- |
|
|
840 | 840 | tipo_case = self.check_case(data_ele,ang_max,ang_min) |
|
|
841 | 841 | print("check_case",tipo_case) |
|
|
842 | 842 | #exit(1) |
|
|
843 | 843 | #--------------------- new ------------------------- |
|
|
844 | 844 | data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case) |
|
|
845 | 845 | |
|
|
846 | 846 | #-------------------------CAMBIOS RHI--------------------------------- |
|
|
847 | 847 | start= ang_min |
|
|
848 | 848 | end = ang_max |
|
|
849 | 849 | n= (ang_max-ang_min)/res |
|
|
850 | 850 | #------ new |
|
|
851 | 851 | self.start_data_ele = data_ele_new[0] |
|
|
852 | 852 | self.end_data_ele = data_ele_new[-1] |
|
|
853 | 853 | if tipo_case==0 or tipo_case==3: # SUBIDA |
|
|
854 | 854 | n1= round(self.start_data_ele)- start |
|
|
855 | 855 | n2= end - round(self.end_data_ele) |
|
|
856 | 856 | print(self.start_data_ele) |
|
|
857 | 857 | print(self.end_data_ele) |
|
|
858 | 858 | if n1>0: |
|
|
859 | 859 | ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1) |
|
|
860 | 860 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
861 | 861 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
862 | 862 | print("ele1_nan",ele1_nan.shape) |
|
|
863 | 863 | print("data_ele_old",data_ele_old.shape) |
|
|
864 | 864 | data_ele_old = numpy.hstack((ele1_nan,data_ele_old)) |
|
|
865 | 865 | if n2>0: |
|
|
866 | 866 | ele2= numpy.linspace(self.end_data_ele+1,end,n2) |
|
|
867 | 867 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
868 | 868 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
869 | 869 | print("ele2_nan",ele2_nan.shape) |
|
|
870 | 870 | print("data_ele_old",data_ele_old.shape) |
|
|
871 | 871 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
872 | 872 | |
|
|
873 | 873 | if tipo_case==1 or tipo_case==2: # BAJADA |
|
|
874 | 874 | data_ele_new = data_ele_new[::-1] # reversa |
|
|
875 | 875 | data_ele_old = data_ele_old[::-1]# reversa |
|
|
876 | 876 | data_weather = data_weather[::-1,:]# reversa |
|
|
877 | 877 | vec= numpy.where(data_ele_new<ang_max) |
|
|
878 | 878 | data_ele_new = data_ele_new[vec] |
|
|
879 | 879 | data_ele_old = data_ele_old[vec] |
|
|
880 | 880 | data_weather = data_weather[vec[0]] |
|
|
881 | 881 | vec2= numpy.where(0<data_ele_new) |
|
|
882 | 882 | data_ele_new = data_ele_new[vec2] |
|
|
883 | 883 | data_ele_old = data_ele_old[vec2] |
|
|
884 | 884 | data_weather = data_weather[vec2[0]] |
|
|
885 | 885 | self.start_data_ele = data_ele_new[0] |
|
|
886 | 886 | self.end_data_ele = data_ele_new[-1] |
|
|
887 | 887 | |
|
|
888 | 888 | n1= round(self.start_data_ele)- start |
|
|
889 | 889 | n2= end - round(self.end_data_ele)-1 |
|
|
890 | 890 | print(self.start_data_ele) |
|
|
891 | 891 | print(self.end_data_ele) |
|
|
892 | 892 | if n1>0: |
|
|
893 | 893 | ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1) |
|
|
894 | 894 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
895 | 895 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
896 | 896 | data_ele_old = numpy.hstack((ele1_nan,data_ele_old)) |
|
|
897 | 897 | if n2>0: |
|
|
898 | 898 | ele2= numpy.linspace(self.end_data_ele+1,end,n2) |
|
|
899 | 899 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
900 | 900 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
901 | 901 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
902 | 902 | # RADAR |
|
|
903 | 903 | # NOTA data_ele y data_weather es la variable que retorna |
|
|
904 | 904 | val_mean = numpy.mean(data_weather[:,-1]) |
|
|
905 | 905 | self.val_mean = val_mean |
|
|
906 | 906 | data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
907 | 907 | self.data_ele_tmp[val_ch]= data_ele_old |
|
|
908 | 908 | else: |
|
|
909 | 909 | #print("**********************************************") |
|
|
910 | 910 | #print("****************VARIABLE**********************") |
|
|
911 | 911 | #-------------------------CAMBIOS RHI--------------------------------- |
|
|
912 | 912 | #--------------------------------------------------------------------- |
|
|
913 | 913 | ##print("INPUT data_ele",data_ele) |
|
|
914 | 914 | flag=0 |
|
|
915 | 915 | start_ele = self.res_ele[0] |
|
|
916 | 916 | tipo_case = self.check_case(data_ele,ang_max,ang_min) |
|
|
917 | 917 | #print("TIPO DE DATA",tipo_case) |
|
|
918 | 918 | #-----------new------------ |
|
|
919 | 919 | data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case) |
|
|
920 | 920 | data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
921 | 921 | |
|
|
922 | 922 | #-------------------------------NEW RHI ITERATIVO------------------------- |
|
|
923 | 923 | |
|
|
924 | 924 | if tipo_case==0 : # SUBIDA |
|
|
925 | 925 | vec = numpy.where(data_ele<ang_max) |
|
|
926 | 926 | data_ele = data_ele[vec] |
|
|
927 | 927 | data_ele_old = data_ele_old[vec] |
|
|
928 | 928 | data_weather = data_weather[vec[0]] |
|
|
929 | 929 | |
|
|
930 | 930 | vec2 = numpy.where(0<data_ele) |
|
|
931 | 931 | data_ele= data_ele[vec2] |
|
|
932 | 932 | data_ele_old= data_ele_old[vec2] |
|
|
933 | 933 | ##print(data_ele_new) |
|
|
934 | 934 | data_weather= data_weather[vec2[0]] |
|
|
935 | 935 | |
|
|
936 | 936 | new_i_ele = int(round(data_ele[0])) |
|
|
937 | 937 | new_f_ele = int(round(data_ele[-1])) |
|
|
938 | 938 | #print(new_i_ele) |
|
|
939 | 939 | #print(new_f_ele) |
|
|
940 | 940 | #print(data_ele,len(data_ele)) |
|
|
941 | 941 | #print(data_ele_old,len(data_ele_old)) |
|
|
942 | 942 | if new_i_ele< 2: |
|
|
943 | 943 | self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan |
|
|
944 | 944 | self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean) |
|
|
945 | 945 | self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old |
|
|
946 | 946 | self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele |
|
|
947 | 947 | self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather |
|
|
948 | 948 | data_ele = self.res_ele |
|
|
949 | 949 | data_weather = self.res_weather[val_ch] |
|
|
950 | 950 | |
|
|
951 | 951 | elif tipo_case==1 : #BAJADA |
|
|
952 | 952 | data_ele = data_ele[::-1] # reversa |
|
|
953 | 953 | data_ele_old = data_ele_old[::-1]# reversa |
|
|
954 | 954 | data_weather = data_weather[::-1,:]# reversa |
|
|
955 | 955 | vec= numpy.where(data_ele<ang_max) |
|
|
956 | 956 | data_ele = data_ele[vec] |
|
|
957 | 957 | data_ele_old = data_ele_old[vec] |
|
|
958 | 958 | data_weather = data_weather[vec[0]] |
|
|
959 | 959 | vec2= numpy.where(0<data_ele) |
|
|
960 | 960 | data_ele = data_ele[vec2] |
|
|
961 | 961 | data_ele_old = data_ele_old[vec2] |
|
|
962 | 962 | data_weather = data_weather[vec2[0]] |
|
|
963 | 963 | |
|
|
964 | 964 | |
|
|
965 | 965 | new_i_ele = int(round(data_ele[0])) |
|
|
966 | 966 | new_f_ele = int(round(data_ele[-1])) |
|
|
967 | 967 | #print(data_ele) |
|
|
968 | 968 | #print(ang_max) |
|
|
969 | 969 | #print(data_ele_old) |
|
|
970 | 970 | if new_i_ele <= 1: |
|
|
971 | 971 | new_i_ele = 1 |
|
|
972 | 972 | if round(data_ele[-1])>=ang_max-1: |
|
|
973 | 973 | self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan |
|
|
974 | 974 | self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean) |
|
|
975 | 975 | self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old |
|
|
976 | 976 | self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele |
|
|
977 | 977 | self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather |
|
|
978 | 978 | data_ele = self.res_ele |
|
|
979 | 979 | data_weather = self.res_weather[val_ch] |
|
|
980 | 980 | |
|
|
981 | 981 | elif tipo_case==2: #bajada |
|
|
982 | 982 | vec = numpy.where(data_ele<ang_max) |
|
|
983 | 983 | data_ele = data_ele[vec] |
|
|
984 | 984 | data_weather= data_weather[vec[0]] |
|
|
985 | 985 | |
|
|
986 | 986 | len_vec = len(vec) |
|
|
987 | 987 | data_ele_new = data_ele[::-1] # reversa |
|
|
988 | 988 | data_weather = data_weather[::-1,:] |
|
|
989 | 989 | new_i_ele = int(data_ele_new[0]) |
|
|
990 | 990 | new_f_ele = int(data_ele_new[-1]) |
|
|
991 | 991 | |
|
|
992 | 992 | n1= new_i_ele- ang_min |
|
|
993 | 993 | n2= ang_max - new_f_ele-1 |
|
|
994 | 994 | if n1>0: |
|
|
995 | 995 | ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1) |
|
|
996 | 996 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
997 | 997 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
998 | 998 | data_ele_old = numpy.hstack((ele1_nan,data_ele_new)) |
|
|
999 | 999 | if n2>0: |
|
|
1000 | 1000 | ele2= numpy.linspace(new_f_ele+1,ang_max,n2) |
|
|
1001 | 1001 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
1002 | 1002 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
1003 | 1003 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
1004 | 1004 | |
|
|
1005 | 1005 | self.data_ele_tmp[val_ch] = data_ele_old |
|
|
1006 | 1006 | self.res_ele = data_ele |
|
|
1007 | 1007 | self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
1008 | 1008 | data_ele = self.res_ele |
|
|
1009 | 1009 | data_weather = self.res_weather[val_ch] |
|
|
1010 | 1010 | |
|
|
1011 | 1011 | elif tipo_case==3:#subida |
|
|
1012 | 1012 | vec = numpy.where(0<data_ele) |
|
|
1013 | 1013 | data_ele= data_ele[vec] |
|
|
1014 | 1014 | data_ele_new = data_ele |
|
|
1015 | 1015 | data_ele_old= data_ele_old[vec] |
|
|
1016 | 1016 | data_weather= data_weather[vec[0]] |
|
|
1017 | 1017 | pos_ini = numpy.argmin(data_ele) |
|
|
1018 | 1018 | if pos_ini>0: |
|
|
1019 | 1019 | len_vec= len(data_ele) |
|
|
1020 | 1020 | vec3 = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int) |
|
|
1021 | 1021 | #print(vec3) |
|
|
1022 | 1022 | data_ele= data_ele[vec3] |
|
|
1023 | 1023 | data_ele_new = data_ele |
|
|
1024 | 1024 | data_ele_old= data_ele_old[vec3] |
|
|
1025 | 1025 | data_weather= data_weather[vec3] |
|
|
1026 | 1026 | |
|
|
1027 | 1027 | new_i_ele = int(data_ele_new[0]) |
|
|
1028 | 1028 | new_f_ele = int(data_ele_new[-1]) |
|
|
1029 | 1029 | n1= new_i_ele- ang_min |
|
|
1030 | 1030 | n2= ang_max - new_f_ele-1 |
|
|
1031 | 1031 | if n1>0: |
|
|
1032 | 1032 | ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1) |
|
|
1033 | 1033 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
1034 | 1034 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
1035 | 1035 | data_ele_old = numpy.hstack((ele1_nan,data_ele_new)) |
|
|
1036 | 1036 | if n2>0: |
|
|
1037 | 1037 | ele2= numpy.linspace(new_f_ele+1,ang_max,n2) |
|
|
1038 | 1038 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
1039 | 1039 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
1040 | 1040 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
1041 | 1041 | |
|
|
1042 | 1042 | self.data_ele_tmp[val_ch] = data_ele_old |
|
|
1043 | 1043 | self.res_ele = data_ele |
|
|
1044 | 1044 | self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
1045 | 1045 | data_ele = self.res_ele |
|
|
1046 | 1046 | data_weather = self.res_weather[val_ch] |
|
|
1047 | 1047 | #print("self.data_ele_tmp",self.data_ele_tmp) |
|
|
1048 | 1048 | return data_weather,data_ele |
|
|
1049 | 1049 | |
|
|
1050 | 1050 | |
|
|
1051 | 1051 | def plot(self): |
|
|
1052 | 1052 | thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S') |
|
|
1053 | 1053 | data = self.data[-1] |
|
|
1054 | 1054 | r = self.data.yrange |
|
|
1055 | 1055 | delta_height = r[1]-r[0] |
|
|
1056 | 1056 | r_mask = numpy.where(r>=0)[0] |
|
|
1057 | 1057 | ##print("delta_height",delta_height) |
|
|
1058 | 1058 | #print("r_mask",r_mask,len(r_mask)) |
|
|
1059 | 1059 | r = numpy.arange(len(r_mask))*delta_height |
|
|
1060 | 1060 | self.y = 2*r |
|
|
1061 | 1061 | res = 1 |
|
|
1062 | 1062 | ###print("data['weather'].shape[0]",data['weather'].shape[0]) |
|
|
1063 | 1063 | ang_max = self.ang_max |
|
|
1064 | 1064 | ang_min = self.ang_min |
|
|
1065 | 1065 | var_ang =ang_max - ang_min |
|
|
1066 | 1066 | step = (int(var_ang)/(res*data['weather'].shape[0])) |
|
|
1067 | 1067 | ###print("step",step) |
|
|
1068 | 1068 | #-------------------------------------------------------- |
|
|
1069 | 1069 | ##print('weather',data['weather'].shape) |
|
|
1070 | 1070 | ##print('ele',data['ele'].shape) |
|
|
1071 | 1071 | |
|
|
1072 | 1072 | ###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min) |
|
|
1073 | 1073 | ###self.res_azi = numpy.mean(data['azi']) |
|
|
1074 | 1074 | ###print("self.res_ele",self.res_ele) |
|
|
1075 | 1075 | plt.clf() |
|
|
1076 | 1076 | subplots = [121, 122] |
|
|
1077 | 1077 | cg={'angular_spacing': 20.} |
|
|
1078 | 1078 | if self.ini==0: |
|
|
1079 | 1079 | self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan |
|
|
1080 | 1080 | self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan |
|
|
1081 | 1081 | print("SHAPE",self.data_ele_tmp.shape) |
|
|
1082 | 1082 | |
|
|
1083 | 1083 | for i,ax in enumerate(self.axes): |
|
|
1084 | 1084 | self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min) |
|
|
1085 | 1085 | self.res_azi = numpy.mean(data['azi']) |
|
|
1086 | 1086 | if i==0: |
|
|
1087 | 1087 | print("*****************************************************************************to plot**************************",self.res_weather[i].shape) |
|
|
1088 | 1088 | self.zmin = self.zmin if self.zmin else 20 |
|
|
1089 | 1089 | self.zmax = self.zmax if self.zmax else 80 |
|
|
1090 | 1090 | if ax.firsttime: |
|
|
1091 | 1091 | #plt.clf() |
|
|
1092 | 1092 | cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj=cg,vmin=self.zmin, vmax=self.zmax) |
|
|
1093 | 1093 | #fig=self.figures[0] |
|
|
1094 | 1094 | else: |
|
|
1095 | 1095 | #plt.clf() |
|
|
1096 | 1096 | if i==0: |
|
|
1097 | 1097 | print(self.res_weather[i]) |
|
|
1098 | 1098 | print(self.res_ele) |
|
|
1099 | 1099 | cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj=cg,vmin=self.zmin, vmax=self.zmax) |
|
|
1100 | 1100 | caax = cgax.parasites[0] |
|
|
1101 | 1101 | paax = cgax.parasites[1] |
|
|
1102 | 1102 | cbar = plt.gcf().colorbar(pm, pad=0.075) |
|
|
1103 | 1103 | caax.set_xlabel('x_range [km]') |
|
|
1104 | 1104 | caax.set_ylabel('y_range [km]') |
|
|
1105 | 1105 | plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right') |
|
|
1106 | 1106 | print("***************************self.ini****************************",self.ini) |
|
|
1107 | 1107 | self.ini= self.ini+1 |
|
|
1108 | 1108 | |
|
|
1109 | 1109 | class Weather_vRF_Plot(Plot): |
|
|
1110 | 1110 | CODE = 'PPI' |
|
|
1111 | 1111 | plot_name = 'PPI' |
|
|
1112 | 1112 | #plot_type = 'ppistyle' |
|
|
1113 | 1113 | buffering = False |
|
|
1114 | 1114 | |
|
|
1115 | 1115 | def setup(self): |
|
|
1116 | 1116 | |
|
|
1117 | 1117 | self.ncols = 1 |
|
|
1118 | 1118 | self.nrows = 1 |
|
|
1119 | 1119 | self.width =8 |
|
|
1120 | 1120 | self.height =8 |
|
|
1121 | 1121 | self.nplots= 1 |
|
|
1122 | 1122 | self.ylabel= 'Range [Km]' |
|
|
1123 | 1123 | self.xlabel= 'Range [Km]' |
|
|
1124 | 1124 | self.titles= ['PPI'] |
|
|
1125 | 1125 | self.polar = True |
|
|
1126 | 1126 | if self.channels is not None: |
|
|
1127 | 1127 | self.nplots = len(self.channels) |
|
|
1128 | 1128 | self.nrows = len(self.channels) |
|
|
1129 | 1129 | else: |
|
|
1130 | 1130 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
1131 | 1131 | self.nrows = self.nplots |
|
|
1132 | 1132 | self.channels = list(range(self.nplots)) |
|
|
1133 | 1133 | |
|
|
1134 | 1134 | if self.CODE == 'POWER': |
|
|
1135 | 1135 | self.cb_label = r'Power (dB)' |
|
|
1136 | 1136 | elif self.CODE == 'DOPPLER': |
|
|
1137 | 1137 | self.cb_label = r'Velocity (m/s)' |
|
|
1138 | 1138 | self.colorbar=True |
|
|
1139 | 1139 | self.width = 9 |
|
|
1140 | 1140 | self.height =8 |
|
|
1141 | 1141 | self.ini =0 |
|
|
1142 | 1142 | self.len_azi =0 |
|
|
1143 | 1143 | self.buffer_ini = None |
|
|
1144 | 1144 | self.buffer_ele = None |
|
|
1145 | 1145 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.15, 'right': 0.9, 'bottom': 0.08}) |
|
|
1146 | 1146 | self.flag =0 |
|
|
1147 | 1147 | self.indicador= 0 |
|
|
1148 | 1148 | self.last_data_ele = None |
|
|
1149 | 1149 | self.val_mean = None |
|
|
1150 | 1150 | |
|
|
1151 | 1151 | def update(self, dataOut): |
|
|
1152 | 1152 | |
|
|
1153 | 1153 | data = {} |
|
|
1154 | 1154 | meta = {} |
|
|
1155 | 1155 | if hasattr(dataOut, 'dataPP_POWER'): |
|
|
1156 | 1156 | factor = 1 |
|
|
1157 | 1157 | if hasattr(dataOut, 'nFFTPoints'): |
|
|
1158 | 1158 | factor = dataOut.normFactor |
|
|
1159 | 1159 | |
|
|
1160 | 1160 | if 'pow' in self.attr_data[0].lower(): |
|
|
1161 | 1161 | data['data'] = 10*numpy.log10(getattr(dataOut, self.attr_data[0])/(factor)) |
|
|
1162 | 1162 | else: |
|
|
1163 | 1163 | data['data'] = getattr(dataOut, self.attr_data[0])/(factor) |
|
|
1164 | 1164 | |
|
|
1165 | 1165 | data['azi'] = dataOut.data_azi |
|
|
1166 | 1166 | data['ele'] = dataOut.data_ele |
|
|
1167 | 1167 | |
|
|
1168 | 1168 | return data, meta |
|
|
1169 | 1169 | |
|
|
1170 | 1170 | def plot(self): |
|
|
1171 | 1171 | data = self.data[-1] |
|
|
1172 | 1172 | r = self.data.yrange |
|
|
1173 | 1173 | delta_height = r[1]-r[0] |
|
|
1174 | 1174 | r_mask = numpy.where(r>=0)[0] |
|
|
1175 | 1175 | self.r_mask = r_mask |
|
|
1176 | 1176 | r = numpy.arange(len(r_mask))*delta_height |
|
|
1177 | 1177 | self.y = 2*r |
|
|
1178 | 1178 | |
|
|
1179 | 1179 | z = data['data'][self.channels[0]][:,r_mask] |
|
|
1180 | 1180 | |
|
|
1181 | 1181 | self.titles = [] |
|
|
1182 | 1182 | |
|
|
1183 | 1183 | self.ymax = self.ymax if self.ymax else numpy.nanmax(r) |
|
|
1184 | 1184 | self.ymin = self.ymin if self.ymin else numpy.nanmin(r) |
|
|
1185 | 1185 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
1186 | 1186 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
|
1187 | 1187 | self.ang_min = self.ang_min if self.ang_min else 0 |
|
|
1188 | 1188 | self.ang_max = self.ang_max if self.ang_max else 360 |
|
|
1189 | 1189 | |
|
|
1190 | 1190 | r, theta = numpy.meshgrid(r, numpy.radians(data['azi']) ) |
|
|
1191 | 1191 | |
|
|
1192 | 1192 | for i,ax in enumerate(self.axes): |
|
|
1193 | 1193 | |
|
|
1194 | 1194 | if ax.firsttime: |
|
|
1195 | 1195 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) |
|
|
1196 | 1196 | ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) |
|
|
1197 | 1197 | |
|
|
1198 | 1198 | else: |
|
|
1199 | 1199 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) |
|
|
1200 | 1200 | ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) |
|
|
1201 | ||
|
|
1201 | ||
|
|
1202 | 1202 | ax.grid(True) |
|
|
1203 | ||
|
|
1203 | ||
|
|
1204 | 1204 | if len(self.channels) !=1: |
|
|
1205 | 1205 | self.titles = ['PPI {} at EL: {} Channel {}'.format(self.self.labels[x], str(round(numpy.mean(data['ele']),1)), x) for x in range(self.nrows)] |
|
|
1206 | 1206 | else: |
|
|
1207 | 1207 | self.titles = ['PPI {} at EL: {} Channel {}'.format(self.labels[0], str(round(numpy.mean(data['ele']),1)), self.channels[0])] |
|
|
1208 | 1208 | |
|
|
1209 | 1209 | class WeatherRHI_vRF2_Plot(Plot): |
|
|
1210 | 1210 | CODE = 'weather' |
|
|
1211 | 1211 | plot_name = 'weather' |
|
|
1212 | 1212 | plot_type = 'rhistyle' |
|
|
1213 | 1213 | buffering = False |
|
|
1214 | 1214 | data_ele_tmp = None |
|
|
1215 | 1215 | |
|
|
1216 | 1216 | def setup(self): |
|
|
1217 | 1217 | print("********************") |
|
|
1218 | 1218 | print("********************") |
|
|
1219 | 1219 | print("********************") |
|
|
1220 | 1220 | print("SETUP WEATHER PLOT") |
|
|
1221 | 1221 | self.ncols = 1 |
|
|
1222 | 1222 | self.nrows = 1 |
|
|
1223 | 1223 | self.nplots= 1 |
|
|
1224 | 1224 | self.ylabel= 'Range [Km]' |
|
|
1225 | 1225 | self.titles= ['Weather'] |
|
|
1226 | 1226 | if self.channels is not None: |
|
|
1227 | 1227 | self.nplots = len(self.channels) |
|
|
1228 | 1228 | self.nrows = len(self.channels) |
|
|
1229 | 1229 | else: |
|
|
1230 | 1230 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
1231 | 1231 | self.nrows = self.nplots |
|
|
1232 | 1232 | self.channels = list(range(self.nplots)) |
|
|
1233 | 1233 | print("channels",self.channels) |
|
|
1234 | 1234 | print("que saldra", self.data.shape(self.CODE)[0]) |
|
|
1235 | 1235 | self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
1236 | 1236 | print("self.titles",self.titles) |
|
|
1237 | 1237 | self.colorbar=False |
|
|
1238 | 1238 | self.width =8 |
|
|
1239 | 1239 | self.height =8 |
|
|
1240 | 1240 | self.ini =0 |
|
|
1241 | 1241 | self.len_azi =0 |
|
|
1242 | 1242 | self.buffer_ini = None |
|
|
1243 | 1243 | self.buffer_ele = None |
|
|
1244 | 1244 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
|
|
1245 | 1245 | self.flag =0 |
|
|
1246 | 1246 | self.indicador= 0 |
|
|
1247 | 1247 | self.last_data_ele = None |
|
|
1248 | 1248 | self.val_mean = None |
|
|
1249 | 1249 | |
|
|
1250 | 1250 | def update(self, dataOut): |
|
|
1251 | 1251 | |
|
|
1252 | 1252 | data = {} |
|
|
1253 | 1253 | meta = {} |
|
|
1254 | 1254 | if hasattr(dataOut, 'dataPP_POWER'): |
|
|
1255 | 1255 | factor = 1 |
|
|
1256 | 1256 | if hasattr(dataOut, 'nFFTPoints'): |
|
|
1257 | 1257 | factor = dataOut.normFactor |
|
|
1258 | 1258 | print("dataOut",dataOut.data_360.shape) |
|
|
1259 | 1259 | # |
|
|
1260 | 1260 | data['weather'] = 10*numpy.log10(dataOut.data_360/(factor)) |
|
|
1261 | 1261 | # |
|
|
1262 | 1262 | #data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor)) |
|
|
1263 | 1263 | data['azi'] = dataOut.data_azi |
|
|
1264 | 1264 | data['ele'] = dataOut.data_ele |
|
|
1265 | 1265 | data['case_flag'] = dataOut.case_flag |
|
|
1266 | 1266 | #print("UPDATE") |
|
|
1267 | 1267 | #print("data[weather]",data['weather'].shape) |
|
|
1268 | 1268 | #print("data[azi]",data['azi']) |
|
|
1269 | 1269 | return data, meta |
|
|
1270 | 1270 | |
|
|
1271 | 1271 | def get2List(self,angulos): |
|
|
1272 | 1272 | list1=[] |
|
|
1273 | 1273 | list2=[] |
|
|
1274 | 1274 | for i in reversed(range(len(angulos))): |
|
|
1275 | 1275 | if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante |
|
|
1276 | 1276 | diff_ = angulos[i]-angulos[i-1] |
|
|
1277 | 1277 | if abs(diff_) >1.5: |
|
|
1278 | 1278 | list1.append(i-1) |
|
|
1279 | 1279 | list2.append(diff_) |
|
|
1280 | 1280 | return list(reversed(list1)),list(reversed(list2)) |
|
|
1281 | 1281 | |
|
|
1282 | 1282 | def fixData90(self,list_,ang_): |
|
|
1283 | 1283 | if list_[0]==-1: |
|
|
1284 | 1284 | vec = numpy.where(ang_<ang_[0]) |
|
|
1285 | 1285 | ang_[vec] = ang_[vec]+90 |
|
|
1286 | 1286 | return ang_ |
|
|
1287 | 1287 | return ang_ |
|
|
1288 | 1288 | |
|
|
1289 | 1289 | def fixData90HL(self,angulos): |
|
|
1290 | 1290 | vec = numpy.where(angulos>=90) |
|
|
1291 | 1291 | angulos[vec]=angulos[vec]-90 |
|
|
1292 | 1292 | return angulos |
|
|
1293 | 1293 | |
|
|
1294 | 1294 | |
|
|
1295 | 1295 | def search_pos(self,pos,list_): |
|
|
1296 | 1296 | for i in range(len(list_)): |
|
|
1297 | 1297 | if pos == list_[i]: |
|
|
1298 | 1298 | return True,i |
|
|
1299 | 1299 | i=None |
|
|
1300 | 1300 | return False,i |
|
|
1301 | 1301 | |
|
|
1302 | 1302 | def fixDataComp(self,ang_,list1_,list2_,tipo_case): |
|
|
1303 | 1303 | size = len(ang_) |
|
|
1304 | 1304 | size2 = 0 |
|
|
1305 | 1305 | for i in range(len(list2_)): |
|
|
1306 | 1306 | size2=size2+round(abs(list2_[i]))-1 |
|
|
1307 | 1307 | new_size= size+size2 |
|
|
1308 | 1308 | ang_new = numpy.zeros(new_size) |
|
|
1309 | 1309 | ang_new2 = numpy.zeros(new_size) |
|
|
1310 | 1310 | |
|
|
1311 | 1311 | tmp = 0 |
|
|
1312 | 1312 | c = 0 |
|
|
1313 | 1313 | for i in range(len(ang_)): |
|
|
1314 | 1314 | ang_new[tmp +c] = ang_[i] |
|
|
1315 | 1315 | ang_new2[tmp+c] = ang_[i] |
|
|
1316 | 1316 | condition , value = self.search_pos(i,list1_) |
|
|
1317 | 1317 | if condition: |
|
|
1318 | 1318 | pos = tmp + c + 1 |
|
|
1319 | 1319 | for k in range(round(abs(list2_[value]))-1): |
|
|
1320 | 1320 | if tipo_case==0 or tipo_case==3:#subida |
|
|
1321 | 1321 | ang_new[pos+k] = ang_new[pos+k-1]+1 |
|
|
1322 | 1322 | ang_new2[pos+k] = numpy.nan |
|
|
1323 | 1323 | elif tipo_case==1 or tipo_case==2:#bajada |
|
|
1324 | 1324 | ang_new[pos+k] = ang_new[pos+k-1]-1 |
|
|
1325 | 1325 | ang_new2[pos+k] = numpy.nan |
|
|
1326 | 1326 | |
|
|
1327 | 1327 | tmp = pos +k |
|
|
1328 | 1328 | c = 0 |
|
|
1329 | 1329 | c=c+1 |
|
|
1330 | 1330 | return ang_new,ang_new2 |
|
|
1331 | 1331 | |
|
|
1332 | 1332 | def globalCheckPED(self,angulos,tipo_case): |
|
|
1333 | 1333 | l1,l2 = self.get2List(angulos) |
|
|
1334 | 1334 | ##print("l1",l1) |
|
|
1335 | 1335 | ##print("l2",l2) |
|
|
1336 | 1336 | if len(l1)>0: |
|
|
1337 | 1337 | #angulos2 = self.fixData90(list_=l1,ang_=angulos) |
|
|
1338 | 1338 | #l1,l2 = self.get2List(angulos2) |
|
|
1339 | 1339 | ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case) |
|
|
1340 | 1340 | #ang1_ = self.fixData90HL(ang1_) |
|
|
1341 | 1341 | #ang2_ = self.fixData90HL(ang2_) |
|
|
1342 | 1342 | else: |
|
|
1343 | 1343 | ang1_= angulos |
|
|
1344 | 1344 | ang2_= angulos |
|
|
1345 | 1345 | return ang1_,ang2_ |
|
|
1346 | 1346 | |
|
|
1347 | 1347 | |
|
|
1348 | 1348 | def replaceNAN(self,data_weather,data_ele,val): |
|
|
1349 | 1349 | data= data_ele |
|
|
1350 | 1350 | data_T= data_weather |
|
|
1351 | 1351 | if data.shape[0]> data_T.shape[0]: |
|
|
1352 | 1352 | data_N = numpy.ones( [data.shape[0],data_T.shape[1]]) |
|
|
1353 | 1353 | c = 0 |
|
|
1354 | 1354 | for i in range(len(data)): |
|
|
1355 | 1355 | if numpy.isnan(data[i]): |
|
|
1356 | 1356 | data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan |
|
|
1357 | 1357 | else: |
|
|
1358 | 1358 | data_N[i,:]=data_T[c,:] |
|
|
1359 | 1359 | c=c+1 |
|
|
1360 | 1360 | return data_N |
|
|
1361 | 1361 | else: |
|
|
1362 | 1362 | for i in range(len(data)): |
|
|
1363 | 1363 | if numpy.isnan(data[i]): |
|
|
1364 | 1364 | data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan |
|
|
1365 | 1365 | return data_T |
|
|
1366 | 1366 | |
|
|
1367 | 1367 | def check_case(self,data_ele,ang_max,ang_min): |
|
|
1368 | 1368 | start = data_ele[0] |
|
|
1369 | 1369 | end = data_ele[-1] |
|
|
1370 | 1370 | number = (end-start) |
|
|
1371 | 1371 | len_ang=len(data_ele) |
|
|
1372 | 1372 | print("start",start) |
|
|
1373 | 1373 | print("end",end) |
|
|
1374 | 1374 | print("number",number) |
|
|
1375 | 1375 | |
|
|
1376 | 1376 | print("len_ang",len_ang) |
|
|
1377 | 1377 | |
|
|
1378 | 1378 | #exit(1) |
|
|
1379 | 1379 | |
|
|
1380 | 1380 | if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida |
|
|
1381 | 1381 | return 0 |
|
|
1382 | 1382 | #elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada |
|
|
1383 | 1383 | # return 1 |
|
|
1384 | 1384 | elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada |
|
|
1385 | 1385 | return 1 |
|
|
1386 | 1386 | elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX |
|
|
1387 | 1387 | return 2 |
|
|
1388 | 1388 | elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN |
|
|
1389 | 1389 | return 3 |
|
|
1390 | 1390 | |
|
|
1391 | 1391 | |
|
|
1392 | 1392 | def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min,case_flag): |
|
|
1393 | 1393 | ang_max= ang_max |
|
|
1394 | 1394 | ang_min= ang_min |
|
|
1395 | 1395 | data_weather=data_weather |
|
|
1396 | 1396 | val_ch=val_ch |
|
|
1397 | 1397 | ##print("*********************DATA WEATHER**************************************") |
|
|
1398 | 1398 | ##print(data_weather) |
|
|
1399 | 1399 | if self.ini==0: |
|
|
1400 | 1400 | ''' |
|
|
1401 | 1401 | print("**********************************************") |
|
|
1402 | 1402 | print("**********************************************") |
|
|
1403 | 1403 | print("***************ini**************") |
|
|
1404 | 1404 | print("**********************************************") |
|
|
1405 | 1405 | print("**********************************************") |
|
|
1406 | 1406 | ''' |
|
|
1407 | 1407 | #print("data_ele",data_ele) |
|
|
1408 | 1408 | #---------------------------------------------------------- |
|
|
1409 | 1409 | tipo_case = case_flag[-1] |
|
|
1410 | 1410 | #tipo_case = self.check_case(data_ele,ang_max,ang_min) |
|
|
1411 | 1411 | print("check_case",tipo_case) |
|
|
1412 | 1412 | #exit(1) |
|
|
1413 | 1413 | #--------------------- new ------------------------- |
|
|
1414 | 1414 | data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case) |
|
|
1415 | 1415 | |
|
|
1416 | 1416 | #-------------------------CAMBIOS RHI--------------------------------- |
|
|
1417 | 1417 | start= ang_min |
|
|
1418 | 1418 | end = ang_max |
|
|
1419 | 1419 | n= (ang_max-ang_min)/res |
|
|
1420 | 1420 | #------ new |
|
|
1421 | 1421 | self.start_data_ele = data_ele_new[0] |
|
|
1422 | 1422 | self.end_data_ele = data_ele_new[-1] |
|
|
1423 | 1423 | if tipo_case==0 or tipo_case==3: # SUBIDA |
|
|
1424 | 1424 | n1= round(self.start_data_ele)- start |
|
|
1425 | 1425 | n2= end - round(self.end_data_ele) |
|
|
1426 | 1426 | print(self.start_data_ele) |
|
|
1427 | 1427 | print(self.end_data_ele) |
|
|
1428 | 1428 | if n1>0: |
|
|
1429 | 1429 | ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1) |
|
|
1430 | 1430 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
1431 | 1431 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
1432 | 1432 | print("ele1_nan",ele1_nan.shape) |
|
|
1433 | 1433 | print("data_ele_old",data_ele_old.shape) |
|
|
1434 | 1434 | data_ele_old = numpy.hstack((ele1_nan,data_ele_old)) |
|
|
1435 | 1435 | if n2>0: |
|
|
1436 | 1436 | ele2= numpy.linspace(self.end_data_ele+1,end,n2) |
|
|
1437 | 1437 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
1438 | 1438 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
1439 | 1439 | print("ele2_nan",ele2_nan.shape) |
|
|
1440 | 1440 | print("data_ele_old",data_ele_old.shape) |
|
|
1441 | 1441 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
1442 | 1442 | |
|
|
1443 | 1443 | if tipo_case==1 or tipo_case==2: # BAJADA |
|
|
1444 | 1444 | data_ele_new = data_ele_new[::-1] # reversa |
|
|
1445 | 1445 | data_ele_old = data_ele_old[::-1]# reversa |
|
|
1446 | 1446 | data_weather = data_weather[::-1,:]# reversa |
|
|
1447 | 1447 | vec= numpy.where(data_ele_new<ang_max) |
|
|
1448 | 1448 | data_ele_new = data_ele_new[vec] |
|
|
1449 | 1449 | data_ele_old = data_ele_old[vec] |
|
|
1450 | 1450 | data_weather = data_weather[vec[0]] |
|
|
1451 | 1451 | vec2= numpy.where(0<data_ele_new) |
|
|
1452 | 1452 | data_ele_new = data_ele_new[vec2] |
|
|
1453 | 1453 | data_ele_old = data_ele_old[vec2] |
|
|
1454 | 1454 | data_weather = data_weather[vec2[0]] |
|
|
1455 | 1455 | self.start_data_ele = data_ele_new[0] |
|
|
1456 | 1456 | self.end_data_ele = data_ele_new[-1] |
|
|
1457 | 1457 | |
|
|
1458 | 1458 | n1= round(self.start_data_ele)- start |
|
|
1459 | 1459 | n2= end - round(self.end_data_ele)-1 |
|
|
1460 | 1460 | print(self.start_data_ele) |
|
|
1461 | 1461 | print(self.end_data_ele) |
|
|
1462 | 1462 | if n1>0: |
|
|
1463 | 1463 | ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1) |
|
|
1464 | 1464 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
1465 | 1465 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
1466 | 1466 | data_ele_old = numpy.hstack((ele1_nan,data_ele_old)) |
|
|
1467 | 1467 | if n2>0: |
|
|
1468 | 1468 | ele2= numpy.linspace(self.end_data_ele+1,end,n2) |
|
|
1469 | 1469 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
1470 | 1470 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
1471 | 1471 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
1472 | 1472 | # RADAR |
|
|
1473 | 1473 | # NOTA data_ele y data_weather es la variable que retorna |
|
|
1474 | 1474 | val_mean = numpy.mean(data_weather[:,-1]) |
|
|
1475 | 1475 | self.val_mean = val_mean |
|
|
1476 | 1476 | data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
1477 | 1477 | print("eleold",data_ele_old) |
|
|
1478 | 1478 | print(self.data_ele_tmp[val_ch]) |
|
|
1479 | 1479 | print(data_ele_old.shape[0]) |
|
|
1480 | 1480 | print(self.data_ele_tmp[val_ch].shape[0]) |
|
|
1481 | 1481 | if (data_ele_old.shape[0]==91 or self.data_ele_tmp[val_ch].shape[0]==91): |
|
|
1482 | 1482 | import sys |
|
|
1483 | 1483 | print("EXIT",self.ini) |
|
|
1484 | 1484 | |
|
|
1485 | 1485 | sys.exit(1) |
|
|
1486 | 1486 | self.data_ele_tmp[val_ch]= data_ele_old |
|
|
1487 | 1487 | else: |
|
|
1488 | 1488 | #print("**********************************************") |
|
|
1489 | 1489 | #print("****************VARIABLE**********************") |
|
|
1490 | 1490 | #-------------------------CAMBIOS RHI--------------------------------- |
|
|
1491 | 1491 | #--------------------------------------------------------------------- |
|
|
1492 | 1492 | ##print("INPUT data_ele",data_ele) |
|
|
1493 | 1493 | flag=0 |
|
|
1494 | 1494 | start_ele = self.res_ele[0] |
|
|
1495 | 1495 | #tipo_case = self.check_case(data_ele,ang_max,ang_min) |
|
|
1496 | 1496 | tipo_case = case_flag[-1] |
|
|
1497 | 1497 | #print("TIPO DE DATA",tipo_case) |
|
|
1498 | 1498 | #-----------new------------ |
|
|
1499 | 1499 | data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case) |
|
|
1500 | 1500 | data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
1501 | 1501 | |
|
|
1502 | 1502 | #-------------------------------NEW RHI ITERATIVO------------------------- |
|
|
1503 | 1503 | |
|
|
1504 | 1504 | if tipo_case==0 : # SUBIDA |
|
|
1505 | 1505 | vec = numpy.where(data_ele<ang_max) |
|
|
1506 | 1506 | data_ele = data_ele[vec] |
|
|
1507 | 1507 | data_ele_old = data_ele_old[vec] |
|
|
1508 | 1508 | data_weather = data_weather[vec[0]] |
|
|
1509 | 1509 | |
|
|
1510 | 1510 | vec2 = numpy.where(0<data_ele) |
|
|
1511 | 1511 | data_ele= data_ele[vec2] |
|
|
1512 | 1512 | data_ele_old= data_ele_old[vec2] |
|
|
1513 | 1513 | ##print(data_ele_new) |
|
|
1514 | 1514 | data_weather= data_weather[vec2[0]] |
|
|
1515 | 1515 | |
|
|
1516 | 1516 | new_i_ele = int(round(data_ele[0])) |
|
|
1517 | 1517 | new_f_ele = int(round(data_ele[-1])) |
|
|
1518 | 1518 | #print(new_i_ele) |
|
|
1519 | 1519 | #print(new_f_ele) |
|
|
1520 | 1520 | #print(data_ele,len(data_ele)) |
|
|
1521 | 1521 | #print(data_ele_old,len(data_ele_old)) |
|
|
1522 | 1522 | if new_i_ele< 2: |
|
|
1523 | 1523 | self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan |
|
|
1524 | 1524 | self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean) |
|
|
1525 | 1525 | self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old |
|
|
1526 | 1526 | self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele |
|
|
1527 | 1527 | self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather |
|
|
1528 | 1528 | data_ele = self.res_ele |
|
|
1529 | 1529 | data_weather = self.res_weather[val_ch] |
|
|
1530 | 1530 | |
|
|
1531 | 1531 | elif tipo_case==1 : #BAJADA |
|
|
1532 | 1532 | data_ele = data_ele[::-1] # reversa |
|
|
1533 | 1533 | data_ele_old = data_ele_old[::-1]# reversa |
|
|
1534 | 1534 | data_weather = data_weather[::-1,:]# reversa |
|
|
1535 | 1535 | vec= numpy.where(data_ele<ang_max) |
|
|
1536 | 1536 | data_ele = data_ele[vec] |
|
|
1537 | 1537 | data_ele_old = data_ele_old[vec] |
|
|
1538 | 1538 | data_weather = data_weather[vec[0]] |
|
|
1539 | 1539 | vec2= numpy.where(0<data_ele) |
|
|
1540 | 1540 | data_ele = data_ele[vec2] |
|
|
1541 | 1541 | data_ele_old = data_ele_old[vec2] |
|
|
1542 | 1542 | data_weather = data_weather[vec2[0]] |
|
|
1543 | 1543 | |
|
|
1544 | 1544 | |
|
|
1545 | 1545 | new_i_ele = int(round(data_ele[0])) |
|
|
1546 | 1546 | new_f_ele = int(round(data_ele[-1])) |
|
|
1547 | 1547 | #print(data_ele) |
|
|
1548 | 1548 | #print(ang_max) |
|
|
1549 | 1549 | #print(data_ele_old) |
|
|
1550 | 1550 | if new_i_ele <= 1: |
|
|
1551 | 1551 | new_i_ele = 1 |
|
|
1552 | 1552 | if round(data_ele[-1])>=ang_max-1: |
|
|
1553 | 1553 | self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan |
|
|
1554 | 1554 | self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean) |
|
|
1555 | 1555 | self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old |
|
|
1556 | 1556 | self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele |
|
|
1557 | 1557 | self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather |
|
|
1558 | 1558 | data_ele = self.res_ele |
|
|
1559 | 1559 | data_weather = self.res_weather[val_ch] |
|
|
1560 | 1560 | |
|
|
1561 | 1561 | elif tipo_case==2: #bajada |
|
|
1562 | 1562 | vec = numpy.where(data_ele<ang_max) |
|
|
1563 | 1563 | data_ele = data_ele[vec] |
|
|
1564 | 1564 | data_weather= data_weather[vec[0]] |
|
|
1565 | 1565 | |
|
|
1566 | 1566 | len_vec = len(vec) |
|
|
1567 | 1567 | data_ele_new = data_ele[::-1] # reversa |
|
|
1568 | 1568 | data_weather = data_weather[::-1,:] |
|
|
1569 | 1569 | new_i_ele = int(data_ele_new[0]) |
|
|
1570 | 1570 | new_f_ele = int(data_ele_new[-1]) |
|
|
1571 | 1571 | |
|
|
1572 | 1572 | n1= new_i_ele- ang_min |
|
|
1573 | 1573 | n2= ang_max - new_f_ele-1 |
|
|
1574 | 1574 | if n1>0: |
|
|
1575 | 1575 | ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1) |
|
|
1576 | 1576 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
1577 | 1577 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
1578 | 1578 | data_ele_old = numpy.hstack((ele1_nan,data_ele_new)) |
|
|
1579 | 1579 | if n2>0: |
|
|
1580 | 1580 | ele2= numpy.linspace(new_f_ele+1,ang_max,n2) |
|
|
1581 | 1581 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
1582 | 1582 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
1583 | 1583 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
1584 | 1584 | |
|
|
1585 | 1585 | self.data_ele_tmp[val_ch] = data_ele_old |
|
|
1586 | 1586 | self.res_ele = data_ele |
|
|
1587 | 1587 | self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
1588 | 1588 | data_ele = self.res_ele |
|
|
1589 | 1589 | data_weather = self.res_weather[val_ch] |
|
|
1590 | 1590 | |
|
|
1591 | 1591 | elif tipo_case==3:#subida |
|
|
1592 | 1592 | vec = numpy.where(0<data_ele) |
|
|
1593 | 1593 | data_ele= data_ele[vec] |
|
|
1594 | 1594 | data_ele_new = data_ele |
|
|
1595 | 1595 | data_ele_old= data_ele_old[vec] |
|
|
1596 | 1596 | data_weather= data_weather[vec[0]] |
|
|
1597 | 1597 | pos_ini = numpy.argmin(data_ele) |
|
|
1598 | 1598 | if pos_ini>0: |
|
|
1599 | 1599 | len_vec= len(data_ele) |
|
|
1600 | 1600 | vec3 = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int) |
|
|
1601 | 1601 | #print(vec3) |
|
|
1602 | 1602 | data_ele= data_ele[vec3] |
|
|
1603 | 1603 | data_ele_new = data_ele |
|
|
1604 | 1604 | data_ele_old= data_ele_old[vec3] |
|
|
1605 | 1605 | data_weather= data_weather[vec3] |
|
|
1606 | 1606 | |
|
|
1607 | 1607 | new_i_ele = int(data_ele_new[0]) |
|
|
1608 | 1608 | new_f_ele = int(data_ele_new[-1]) |
|
|
1609 | 1609 | n1= new_i_ele- ang_min |
|
|
1610 | 1610 | n2= ang_max - new_f_ele-1 |
|
|
1611 | 1611 | if n1>0: |
|
|
1612 | 1612 | ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1) |
|
|
1613 | 1613 | ele1_nan= numpy.ones(n1)*numpy.nan |
|
|
1614 | 1614 | data_ele = numpy.hstack((ele1,data_ele_new)) |
|
|
1615 | 1615 | data_ele_old = numpy.hstack((ele1_nan,data_ele_new)) |
|
|
1616 | 1616 | if n2>0: |
|
|
1617 | 1617 | ele2= numpy.linspace(new_f_ele+1,ang_max,n2) |
|
|
1618 | 1618 | ele2_nan= numpy.ones(n2)*numpy.nan |
|
|
1619 | 1619 | data_ele = numpy.hstack((data_ele,ele2)) |
|
|
1620 | 1620 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
|
|
1621 | 1621 | |
|
|
1622 | 1622 | self.data_ele_tmp[val_ch] = data_ele_old |
|
|
1623 | 1623 | self.res_ele = data_ele |
|
|
1624 | 1624 | self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
|
|
1625 | 1625 | data_ele = self.res_ele |
|
|
1626 | 1626 | data_weather = self.res_weather[val_ch] |
|
|
1627 | 1627 | #print("self.data_ele_tmp",self.data_ele_tmp) |
|
|
1628 | 1628 | return data_weather,data_ele |
|
|
1629 | 1629 | |
|
|
1630 | 1630 | |
|
|
1631 | 1631 | def plot(self): |
|
|
1632 | 1632 | thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S') |
|
|
1633 | 1633 | data = self.data[-1] |
|
|
1634 | 1634 | r = self.data.yrange |
|
|
1635 | 1635 | delta_height = r[1]-r[0] |
|
|
1636 | 1636 | r_mask = numpy.where(r>=0)[0] |
|
|
1637 | 1637 | ##print("delta_height",delta_height) |
|
|
1638 | 1638 | #print("r_mask",r_mask,len(r_mask)) |
|
|
1639 | 1639 | r = numpy.arange(len(r_mask))*delta_height |
|
|
1640 | 1640 | self.y = 2*r |
|
|
1641 | 1641 | res = 1 |
|
|
1642 | 1642 | ###print("data['weather'].shape[0]",data['weather'].shape[0]) |
|
|
1643 | 1643 | ang_max = self.ang_max |
|
|
1644 | 1644 | ang_min = self.ang_min |
|
|
1645 | 1645 | var_ang =ang_max - ang_min |
|
|
1646 | 1646 | step = (int(var_ang)/(res*data['weather'].shape[0])) |
|
|
1647 | 1647 | ###print("step",step) |
|
|
1648 | 1648 | #-------------------------------------------------------- |
|
|
1649 | 1649 | ##print('weather',data['weather'].shape) |
|
|
1650 | 1650 | ##print('ele',data['ele'].shape) |
|
|
1651 | 1651 | |
|
|
1652 | 1652 | ###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min) |
|
|
1653 | 1653 | ###self.res_azi = numpy.mean(data['azi']) |
|
|
1654 | 1654 | ###print("self.res_ele",self.res_ele) |
|
|
1655 | 1655 | plt.clf() |
|
|
1656 | 1656 | subplots = [121, 122] |
|
|
1657 | 1657 | try: |
|
|
1658 | 1658 | if self.data[-2]['ele'].max()<data['ele'].max(): |
|
|
1659 | 1659 | self.ini=0 |
|
|
1660 | 1660 | except: |
|
|
1661 | 1661 | pass |
|
|
1662 | 1662 | if self.ini==0: |
|
|
1663 | 1663 | self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan |
|
|
1664 | 1664 | self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan |
|
|
1665 | 1665 | print("SHAPE",self.data_ele_tmp.shape) |
|
|
1666 | 1666 | |
|
|
1667 | 1667 | for i,ax in enumerate(self.axes): |
|
|
1668 | 1668 | self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min,case_flag=self.data['case_flag']) |
|
|
1669 | 1669 | self.res_azi = numpy.mean(data['azi']) |
|
|
1670 | 1670 | |
|
|
1671 | 1671 | if ax.firsttime: |
|
|
1672 | 1672 | #plt.clf() |
|
|
1673 | 1673 | print("Frist Plot") |
|
|
1674 | 1674 | cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80) |
|
|
1675 | 1675 | #fig=self.figures[0] |
|
|
1676 | 1676 | else: |
|
|
1677 | 1677 | #plt.clf() |
|
|
1678 | 1678 | print("ELSE PLOT") |
|
|
1679 | 1679 | cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80) |
|
|
1680 | 1680 | caax = cgax.parasites[0] |
|
|
1681 | 1681 | paax = cgax.parasites[1] |
|
|
1682 | 1682 | cbar = plt.gcf().colorbar(pm, pad=0.075) |
|
|
1683 | 1683 | caax.set_xlabel('x_range [km]') |
|
|
1684 | 1684 | caax.set_ylabel('y_range [km]') |
|
|
1685 | 1685 | plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right') |
|
|
1686 | 1686 | print("***************************self.ini****************************",self.ini) |
|
|
1687 | 1687 | self.ini= self.ini+1 |
|
|
1688 | 1688 | |
|
|
1689 | 1689 | |
|
|
1690 | 1690 | |
|
|
1691 | 1691 | |
|
|
1692 | 1692 | |
|
|
1693 | 1693 | class WeatherRHI_vRF4_Plot(Plot): |
|
|
1694 | 1694 | CODE = 'RHI' |
|
|
1695 | 1695 | plot_name = 'RHI' |
|
|
1696 | 1696 | #plot_type = 'rhistyle' |
|
|
1697 | 1697 | buffering = False |
|
|
1698 | 1698 | |
|
|
1699 | 1699 | def setup(self): |
|
|
1700 | 1700 | |
|
|
1701 | 1701 | self.ncols = 1 |
|
|
1702 | 1702 | self.nrows = 1 |
|
|
1703 | 1703 | self.nplots= 1 |
|
|
1704 | 1704 | self.ylabel= 'Range [Km]' |
|
|
1705 | 1705 | self.xlabel= 'Range [Km]' |
|
|
1706 | 1706 | self.titles= ['RHI'] |
|
|
1707 | 1707 | self.polar = True |
|
|
1708 | 1708 | self.grid = True |
|
|
1709 | 1709 | if self.channels is not None: |
|
|
1710 | 1710 | self.nplots = len(self.channels) |
|
|
1711 | 1711 | self.nrows = len(self.channels) |
|
|
1712 | 1712 | else: |
|
|
1713 | 1713 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
1714 | 1714 | self.nrows = self.nplots |
|
|
1715 | 1715 | self.channels = list(range(self.nplots)) |
|
|
1716 | 1716 | |
|
|
1717 | 1717 | if self.CODE == 'Power': |
|
|
1718 | 1718 | self.cb_label = r'Power (dB)' |
|
|
1719 | 1719 | elif self.CODE == 'Doppler': |
|
|
1720 | 1720 | self.cb_label = r'Velocity (m/s)' |
|
|
1721 | 1721 | self.colorbar=True |
|
|
1722 | 1722 | self.width =8 |
|
|
1723 | 1723 | self.height =8 |
|
|
1724 | 1724 | self.ini =0 |
|
|
1725 | 1725 | self.len_azi =0 |
|
|
1726 | 1726 | self.buffer_ini = None |
|
|
1727 | 1727 | self.buffer_ele = None |
|
|
1728 | 1728 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
|
|
1729 | 1729 | self.flag =0 |
|
|
1730 | 1730 | self.indicador= 0 |
|
|
1731 | 1731 | self.last_data_ele = None |
|
|
1732 | 1732 | self.val_mean = None |
|
|
1733 | 1733 | |
|
|
1734 | 1734 | def update(self, dataOut): |
|
|
1735 | 1735 | |
|
|
1736 | 1736 | data = {} |
|
|
1737 | 1737 | meta = {} |
|
|
1738 | 1738 | if hasattr(dataOut, 'dataPP_POWER'): |
|
|
1739 | 1739 | factor = 1 |
|
|
1740 | 1740 | if hasattr(dataOut, 'nFFTPoints'): |
|
|
1741 | 1741 | factor = dataOut.normFactor |
|
|
1742 | 1742 | |
|
|
1743 | 1743 | if 'pow' in self.attr_data[0].lower(): |
|
|
1744 | 1744 | data['data'] = 10*numpy.log10(getattr(dataOut, self.attr_data[0])/(factor)) |
|
|
1745 | 1745 | else: |
|
|
1746 | 1746 | data['data'] = getattr(dataOut, self.attr_data[0])/(factor) |
|
|
1747 | 1747 | |
|
|
1748 | 1748 | data['azi'] = dataOut.data_azi |
|
|
1749 | 1749 | data['ele'] = dataOut.data_ele |
|
|
1750 | 1750 | |
|
|
1751 | 1751 | return data, meta |
|
|
1752 | 1752 | |
|
|
1753 | 1753 | def plot(self): |
|
|
1754 | 1754 | data = self.data[-1] |
|
|
1755 | 1755 | r = self.data.yrange |
|
|
1756 | 1756 | delta_height = r[1]-r[0] |
|
|
1757 | 1757 | r_mask = numpy.where(r>=0)[0] |
|
|
1758 | 1758 | self.r_mask =r_mask |
|
|
1759 | 1759 | r = numpy.arange(len(r_mask))*delta_height |
|
|
1760 | 1760 | self.y = 2*r |
|
|
1761 | 1761 | |
|
|
1762 | 1762 | z = data['data'][self.channels[0]][:,r_mask] |
|
|
1763 | 1763 | |
|
|
1764 | 1764 | self.titles = [] |
|
|
1765 | 1765 | |
|
|
1766 | 1766 | self.ymax = self.ymax if self.ymax else numpy.nanmax(r) |
|
|
1767 | 1767 | self.ymin = self.ymin if self.ymin else numpy.nanmin(r) |
|
|
1768 | 1768 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
1769 | 1769 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
|
1770 | 1770 | self.ang_min = self.ang_min if self.ang_min else 0 |
|
|
1771 | 1771 | self.ang_max = self.ang_max if self.ang_max else 90 |
|
|
1772 | 1772 | |
|
|
1773 | 1773 | r, theta = numpy.meshgrid(r, numpy.radians(data['ele']) ) |
|
|
1774 | 1774 | |
|
|
1775 | 1775 | for i,ax in enumerate(self.axes): |
|
|
1776 | 1776 | |
|
|
1777 | 1777 | if ax.firsttime: |
|
|
1778 | 1778 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) |
|
|
1779 | 1779 | ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) |
|
|
1780 | 1780 | |
|
|
1781 | 1781 | else: |
|
|
1782 | 1782 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) |
|
|
1783 | 1783 | ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) |
|
|
1784 | 1784 | ax.grid(True) |
|
|
1785 | 1785 | if len(self.channels) !=1: |
|
|
1786 | 1786 | self.titles = ['RHI {} at AZ: {} Channel {}'.format(self.labels[x], str(round(numpy.mean(data['azi']),1)), x) for x in range(self.nrows)] |
|
|
1787 | 1787 | else: |
|
|
1788 | 1788 | self.titles = ['RHI {} at AZ: {} Channel {}'.format(self.labels[0], str(round(numpy.mean(data['azi']),1)), self.channels[0])] |
| @@ -1,208 +1,226 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Base clases to create Processing units and operations, the MPDecorator |
|
|
3 | 3 | must be used in plotting and writing operations to allow to run as an |
|
|
4 | 4 | external process. |
|
|
5 | 5 | ''' |
|
|
6 | 6 | |
|
|
7 | 7 | import os |
|
|
8 | 8 | import inspect |
|
|
9 | 9 | import zmq |
|
|
10 | 10 | import time |
|
|
11 | 11 | import pickle |
|
|
12 | 12 | import traceback |
|
|
13 | 13 | from threading import Thread |
|
|
14 | 14 | from multiprocessing import Process, Queue |
|
|
15 | 15 | from schainpy.utils import log |
|
|
16 | 16 | |
|
|
17 | 17 | QUEUE_SIZE = int(os.environ.get('QUEUE_MAX_SIZE', '10')) |
|
|
18 | 18 | |
|
|
19 | 19 | class ProcessingUnit(object): |
|
|
20 | 20 | ''' |
|
|
21 | 21 | Base class to create Signal Chain Units |
|
|
22 | 22 | ''' |
|
|
23 | 23 | |
|
|
24 | 24 | proc_type = 'processing' |
|
|
25 | 25 | |
|
|
26 | 26 | def __init__(self): |
|
|
27 | 27 | |
|
|
28 | 28 | self.dataIn = None |
|
|
29 | 29 | self.dataOut = None |
|
|
30 | 30 | self.isConfig = False |
|
|
31 | 31 | self.operations = [] |
|
|
32 | self.name = 'Test' | |
|
|
33 | self.inputs = [] | |
|
|
32 | 34 | |
|
|
33 | 35 | def setInput(self, unit): |
|
|
34 | 36 | |
|
|
35 | self.dataIn = unit.dataOut | |
|
|
37 | attr = 'dataIn' | |
|
|
38 | for i, u in enumerate(unit): | |
|
|
39 | if i==0: | |
|
|
40 | self.dataIn = u.dataOut | |
|
|
41 | self.inputs.append('dataIn') | |
|
|
42 | else: | |
|
|
43 | setattr(self, 'dataIn{}'.format(i), u.dataOut) | |
|
|
44 | self.inputs.append('dataIn{}'.format(i)) | |
|
|
36 | 45 | |
|
|
37 | 46 | def getAllowedArgs(self): |
|
|
38 | 47 | if hasattr(self, '__attrs__'): |
|
|
39 | 48 | return self.__attrs__ |
|
|
40 | 49 | else: |
|
|
41 | 50 | return inspect.getargspec(self.run).args |
|
|
42 | 51 | |
|
|
43 | 52 | def addOperation(self, conf, operation): |
|
|
44 | 53 | ''' |
|
|
45 | 54 | ''' |
|
|
46 | 55 | |
|
|
47 | 56 | self.operations.append((operation, conf.type, conf.getKwargs())) |
|
|
48 | 57 | |
|
|
49 | 58 | def getOperationObj(self, objId): |
|
|
50 | 59 | |
|
|
51 | 60 | if objId not in list(self.operations.keys()): |
|
|
52 | 61 | return None |
|
|
53 | 62 | |
|
|
54 | 63 | return self.operations[objId] |
|
|
55 | 64 | |
|
|
56 | 65 | def call(self, **kwargs): |
|
|
57 | 66 | ''' |
|
|
58 | 67 | ''' |
|
|
59 | 68 | |
|
|
60 | 69 | try: |
|
|
61 | 70 | if self.dataIn is not None and self.dataIn.flagNoData and not self.dataIn.error: |
|
|
62 | 71 | return self.dataIn.isReady() |
|
|
63 | 72 | elif self.dataIn is None or not self.dataIn.error: |
|
|
64 | 73 | self.run(**kwargs) |
|
|
65 | 74 | elif self.dataIn.error: |
|
|
66 | 75 | self.dataOut.error = self.dataIn.error |
|
|
67 | 76 | self.dataOut.flagNoData = True |
|
|
68 | 77 | except: |
|
|
69 | 78 | err = traceback.format_exc() |
|
|
70 | 79 | if 'SchainWarning' in err: |
|
|
71 | 80 | log.warning(err.split('SchainWarning:')[-1].split('\n')[0].strip(), self.name) |
|
|
72 | 81 | elif 'SchainError' in err: |
|
|
73 | 82 | log.error(err.split('SchainError:')[-1].split('\n')[0].strip(), self.name) |
|
|
74 | 83 | else: |
|
|
75 | 84 | log.error(err, self.name) |
|
|
76 | 85 | self.dataOut.error = True |
|
|
77 | 86 | ##### correcion de la declaracion Out |
|
|
78 | 87 | for op, optype, opkwargs in self.operations: |
|
|
79 | 88 | aux = self.dataOut.copy() |
|
|
80 | 89 | if optype == 'other' and not self.dataOut.flagNoData: |
|
|
81 | 90 | self.dataOut = op.run(self.dataOut, **opkwargs) |
|
|
82 | 91 | elif optype == 'external' and not self.dataOut.flagNoData: |
|
|
83 | 92 | #op.queue.put(self.dataOut) |
|
|
84 | 93 | op.queue.put(aux) |
|
|
85 | 94 | elif optype == 'external' and self.dataOut.error: |
|
|
86 | 95 | #op.queue.put(self.dataOut) |
|
|
87 | 96 | op.queue.put(aux) |
|
|
88 | 97 | |
|
|
89 | return 'Error' if self.dataOut.error else self.dataOut.isReady() | |
|
|
98 | try: | |
|
|
99 | if self.dataOut.runNextUnit: | |
|
|
100 | runNextUnit = self.dataOut.runNextUnit | |
|
|
101 | ||
|
|
102 | else: | |
|
|
103 | runNextUnit = self.dataOut.isReady() | |
|
|
104 | except: | |
|
|
105 | runNextUnit = self.dataOut.isReady() | |
|
|
106 | ||
|
|
107 | return 'Error' if self.dataOut.error else runNextUnit | |
|
|
90 | 108 | |
|
|
91 | 109 | def setup(self): |
|
|
92 | 110 | |
|
|
93 | 111 | raise NotImplementedError |
|
|
94 | 112 | |
|
|
95 | 113 | def run(self): |
|
|
96 | 114 | |
|
|
97 | 115 | raise NotImplementedError |
|
|
98 | 116 | |
|
|
99 | 117 | def close(self): |
|
|
100 | 118 | |
|
|
101 | 119 | return |
|
|
102 | 120 | |
|
|
103 | 121 | |
|
|
104 | 122 | class Operation(object): |
|
|
105 | 123 | |
|
|
106 | 124 | ''' |
|
|
107 | 125 | ''' |
|
|
108 | 126 | |
|
|
109 | 127 | proc_type = 'operation' |
|
|
110 | 128 | |
|
|
111 | 129 | def __init__(self): |
|
|
112 | 130 | |
|
|
113 | 131 | self.id = None |
|
|
114 | 132 | self.isConfig = False |
|
|
115 | 133 | |
|
|
116 | 134 | if not hasattr(self, 'name'): |
|
|
117 | 135 | self.name = self.__class__.__name__ |
|
|
118 | 136 | |
|
|
119 | 137 | def getAllowedArgs(self): |
|
|
120 | 138 | if hasattr(self, '__attrs__'): |
|
|
121 | 139 | return self.__attrs__ |
|
|
122 | 140 | else: |
|
|
123 | 141 | return inspect.getargspec(self.run).args |
|
|
124 | 142 | |
|
|
125 | 143 | def setup(self): |
|
|
126 | 144 | |
|
|
127 | 145 | self.isConfig = True |
|
|
128 | 146 | |
|
|
129 | 147 | raise NotImplementedError |
|
|
130 | 148 | |
|
|
131 | 149 | def run(self, dataIn, **kwargs): |
|
|
132 | 150 | """ |
|
|
133 | 151 | Realiza las operaciones necesarias sobre la dataIn.data y actualiza los |
|
|
134 | 152 | atributos del objeto dataIn. |
|
|
135 | 153 | |
|
|
136 | 154 | Input: |
|
|
137 | 155 | |
|
|
138 | 156 | dataIn : objeto del tipo JROData |
|
|
139 | 157 | |
|
|
140 | 158 | Return: |
|
|
141 | 159 | |
|
|
142 | 160 | None |
|
|
143 | 161 | |
|
|
144 | 162 | Affected: |
|
|
145 | 163 | __buffer : buffer de recepcion de datos. |
|
|
146 | 164 | |
|
|
147 | 165 | """ |
|
|
148 | 166 | if not self.isConfig: |
|
|
149 | 167 | self.setup(**kwargs) |
|
|
150 | 168 | |
|
|
151 | 169 | raise NotImplementedError |
|
|
152 | 170 | |
|
|
153 | 171 | def close(self): |
|
|
154 | 172 | |
|
|
155 | 173 | return |
|
|
156 | 174 | |
|
|
157 | 175 | |
|
|
158 | 176 | def MPDecorator(BaseClass): |
|
|
159 | 177 | """ |
|
|
160 | 178 | Multiprocessing class decorator |
|
|
161 | 179 | |
|
|
162 | 180 | This function add multiprocessing features to a BaseClass. |
|
|
163 | 181 | """ |
|
|
164 | 182 | |
|
|
165 | 183 | class MPClass(BaseClass, Process): |
|
|
166 | 184 | |
|
|
167 | 185 | def __init__(self, *args, **kwargs): |
|
|
168 | 186 | super(MPClass, self).__init__() |
|
|
169 | 187 | Process.__init__(self) |
|
|
170 | 188 | |
|
|
171 | 189 | self.args = args |
|
|
172 | 190 | self.kwargs = kwargs |
|
|
173 | 191 | self.t = time.time() |
|
|
174 | 192 | self.op_type = 'external' |
|
|
175 | 193 | self.name = BaseClass.__name__ |
|
|
176 | 194 | self.__doc__ = BaseClass.__doc__ |
|
|
177 | 195 | |
|
|
178 | 196 | if 'plot' in self.name.lower() and not self.name.endswith('_'): |
|
|
179 | 197 | self.name = '{}{}'.format(self.CODE.upper(), 'Plot') |
|
|
180 | 198 | |
|
|
181 | 199 | self.start_time = time.time() |
|
|
182 | 200 | self.err_queue = args[3] |
|
|
183 | 201 | self.queue = Queue(maxsize=QUEUE_SIZE) |
|
|
184 | 202 | self.myrun = BaseClass.run |
|
|
185 | 203 | |
|
|
186 | 204 | def run(self): |
|
|
187 | 205 | |
|
|
188 | 206 | while True: |
|
|
189 | 207 | |
|
|
190 | 208 | dataOut = self.queue.get() |
|
|
191 | 209 | |
|
|
192 | 210 | if not dataOut.error: |
|
|
193 | 211 | try: |
|
|
194 | 212 | BaseClass.run(self, dataOut, **self.kwargs) |
|
|
195 | 213 | except: |
|
|
196 | 214 | err = traceback.format_exc() |
|
|
197 | 215 | log.error(err, self.name) |
|
|
198 | 216 | else: |
|
|
199 | 217 | break |
|
|
200 | 218 | |
|
|
201 | 219 | self.close() |
|
|
202 | 220 | |
|
|
203 | 221 | def close(self): |
|
|
204 | 222 | |
|
|
205 | 223 | BaseClass.close(self) |
|
|
206 | 224 | log.success('Done...(Time:{:4.2f} secs)'.format(time.time()-self.start_time), self.name) |
|
|
207 | 225 | |
|
|
208 | 226 | return MPClass |
| @@ -1,4999 +1,5088 | |||
|
|
1 | 1 | |
|
|
2 | 2 | import os |
|
|
3 | 3 | import time |
|
|
4 | 4 | import math |
|
|
5 | 5 | |
|
|
6 | 6 | import re |
|
|
7 | 7 | import datetime |
|
|
8 | 8 | import copy |
|
|
9 | 9 | import sys |
|
|
10 | 10 | import importlib |
|
|
11 | 11 | import itertools |
|
|
12 | 12 | |
|
|
13 | 13 | from multiprocessing import Pool, TimeoutError |
|
|
14 | 14 | from multiprocessing.pool import ThreadPool |
|
|
15 | 15 | import numpy |
|
|
16 | 16 | import glob |
|
|
17 | 17 | import scipy |
|
|
18 | 18 | import h5py |
|
|
19 | 19 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
|
20 | 20 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
21 | 21 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
22 | 22 | from scipy import asarray as ar,exp |
|
|
23 | 23 | from scipy.optimize import curve_fit |
|
|
24 | 24 | from schainpy.utils import log |
|
|
25 | 25 | import schainpy.admin |
|
|
26 | 26 | import warnings |
|
|
27 | 27 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
28 | 28 | from scipy.optimize.optimize import OptimizeWarning |
|
|
29 | 29 | warnings.filterwarnings('ignore') |
|
|
30 | 30 | |
|
|
31 | 31 | |
|
|
32 | 32 | SPEED_OF_LIGHT = 299792458 |
|
|
33 | 33 | |
|
|
34 | 34 | '''solving pickling issue''' |
|
|
35 | 35 | |
|
|
36 | 36 | def _pickle_method(method): |
|
|
37 | 37 | func_name = method.__func__.__name__ |
|
|
38 | 38 | obj = method.__self__ |
|
|
39 | 39 | cls = method.__self__.__class__ |
|
|
40 | 40 | return _unpickle_method, (func_name, obj, cls) |
|
|
41 | 41 | |
|
|
42 | 42 | def _unpickle_method(func_name, obj, cls): |
|
|
43 | 43 | for cls in cls.mro(): |
|
|
44 | 44 | try: |
|
|
45 | 45 | func = cls.__dict__[func_name] |
|
|
46 | 46 | except KeyError: |
|
|
47 | 47 | pass |
|
|
48 | 48 | else: |
|
|
49 | 49 | break |
|
|
50 | 50 | return func.__get__(obj, cls) |
|
|
51 | 51 | |
|
|
52 | 52 | def isNumber(str): |
|
|
53 | 53 | try: |
|
|
54 | 54 | float(str) |
|
|
55 | 55 | return True |
|
|
56 | 56 | except: |
|
|
57 | 57 | return False |
|
|
58 | 58 | |
|
|
59 | 59 | class ParametersProc(ProcessingUnit): |
|
|
60 | 60 | |
|
|
61 | 61 | METHODS = {} |
|
|
62 | 62 | nSeconds = None |
|
|
63 | 63 | |
|
|
64 | 64 | def __init__(self): |
|
|
65 | 65 | ProcessingUnit.__init__(self) |
|
|
66 | 66 | |
|
|
67 | 67 | # self.objectDict = {} |
|
|
68 | 68 | self.buffer = None |
|
|
69 | 69 | self.firstdatatime = None |
|
|
70 | 70 | self.profIndex = 0 |
|
|
71 | 71 | self.dataOut = Parameters() |
|
|
72 | 72 | self.setupReq = False #Agregar a todas las unidades de proc |
|
|
73 | 73 | |
|
|
74 | 74 | def __updateObjFromInput(self): |
|
|
75 | 75 | |
|
|
76 | 76 | self.dataOut.inputUnit = self.dataIn.type |
|
|
77 | 77 | |
|
|
78 | 78 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
79 | 79 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
80 | 80 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
81 | 81 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
82 | 82 | |
|
|
83 | 83 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
84 | 84 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
85 | 85 | self.dataOut.channelList = self.dataIn.channelList |
|
|
86 | 86 | self.dataOut.heightList = self.dataIn.heightList |
|
|
87 | 87 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
88 | 88 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
89 | 89 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
90 | 90 | # self.dataOut.nBaud = self.dataIn.nBaud |
|
|
91 | 91 | # self.dataOut.nCode = self.dataIn.nCode |
|
|
92 | 92 | # self.dataOut.code = self.dataIn.code |
|
|
93 | 93 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
94 | 94 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
95 | 95 | # self.dataOut.utctime = self.firstdatatime |
|
|
96 | 96 | self.dataOut.utctime = self.dataIn.utctime |
|
|
97 | 97 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
98 | 98 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
99 | 99 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
100 | 100 | # self.dataOut.nIncohInt = 1 |
|
|
101 | 101 | # self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
102 | 102 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
103 | 103 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
104 | 104 | self.dataOut.heightList = self.dataIn.heightList |
|
|
105 | 105 | self.dataOut.frequency = self.dataIn.frequency |
|
|
106 | 106 | # self.dataOut.noise = self.dataIn.noise |
|
|
107 | self.dataOut.runNextUnit = self.dataIn.runNextUnit | |
|
|
107 | 108 | |
|
|
108 | def run(self): | |
|
|
109 | ||
|
|
109 | def run(self, runNextUnit = 0): | |
|
|
110 | 110 | |
|
|
111 | self.dataIn.runNextUnit = runNextUnit | |
|
|
111 | 112 | #print("HOLA MUNDO SOY YO") |
|
|
112 | 113 | #---------------------- Voltage Data --------------------------- |
|
|
113 | 114 | |
|
|
114 | 115 | if self.dataIn.type == "Voltage": |
|
|
115 | 116 | |
|
|
116 | 117 | self.__updateObjFromInput() |
|
|
117 | 118 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
118 | 119 | self.dataOut.flagNoData = False |
|
|
119 | 120 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
120 | 121 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
121 | 122 | |
|
|
122 | 123 | if hasattr(self.dataIn, 'flagDataAsBlock'): |
|
|
123 | 124 | self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock |
|
|
124 | 125 | |
|
|
125 | 126 | if hasattr(self.dataIn, 'profileIndex'): |
|
|
126 | 127 | self.dataOut.profileIndex = self.dataIn.profileIndex |
|
|
127 | 128 | |
|
|
128 | 129 | if hasattr(self.dataIn, 'dataPP_POW'): |
|
|
129 | 130 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW |
|
|
130 | 131 | |
|
|
131 | 132 | if hasattr(self.dataIn, 'dataPP_POWER'): |
|
|
132 | 133 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER |
|
|
133 | 134 | |
|
|
134 | 135 | if hasattr(self.dataIn, 'dataPP_DOP'): |
|
|
135 | 136 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP |
|
|
136 | 137 | |
|
|
137 | 138 | if hasattr(self.dataIn, 'dataPP_SNR'): |
|
|
138 | 139 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR |
|
|
139 | 140 | |
|
|
140 | 141 | if hasattr(self.dataIn, 'dataPP_WIDTH'): |
|
|
141 | 142 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH |
|
|
142 | 143 | return |
|
|
143 | 144 | |
|
|
144 | 145 | #---------------------- Spectra Data --------------------------- |
|
|
145 | 146 | |
|
|
146 | 147 | if self.dataIn.type == "Spectra": |
|
|
147 | 148 | #print("que paso en spectra") |
|
|
148 | 149 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] |
|
|
149 | 150 | self.dataOut.data_spc = self.dataIn.data_spc |
|
|
150 | 151 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
151 | 152 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
152 | 153 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
153 | 154 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
154 | 155 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
155 | 156 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
156 | 157 | self.dataOut.spc_noise = self.dataIn.getNoise() |
|
|
157 | 158 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
|
158 | 159 | # self.dataOut.normFactor = self.dataIn.normFactor |
|
|
159 | 160 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|
160 | 161 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
161 | 162 | self.dataOut.flagNoData = False |
|
|
162 | 163 | |
|
|
163 | 164 | if hasattr(self.dataIn, 'flagDataAsBlock'): |
|
|
164 | 165 | self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock |
|
|
165 | 166 | |
|
|
166 | 167 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
|
167 | 168 | self.dataOut.ChanDist = self.dataIn.ChanDist |
|
|
168 | 169 | else: self.dataOut.ChanDist = None |
|
|
169 | 170 | |
|
|
170 | 171 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
|
171 | 172 | # self.dataOut.VelRange = self.dataIn.VelRange |
|
|
172 | 173 | #else: self.dataOut.VelRange = None |
|
|
173 | 174 | |
|
|
174 | 175 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
|
175 | 176 | self.dataOut.RadarConst = self.dataIn.RadarConst |
|
|
176 | 177 | |
|
|
177 | 178 | if hasattr(self.dataIn, 'NPW'): #NPW |
|
|
178 | 179 | self.dataOut.NPW = self.dataIn.NPW |
|
|
179 | 180 | |
|
|
180 | 181 | if hasattr(self.dataIn, 'COFA'): #COFA |
|
|
181 | 182 | self.dataOut.COFA = self.dataIn.COFA |
|
|
182 | 183 | |
|
|
183 | 184 | |
|
|
184 | 185 | |
|
|
185 | 186 | #---------------------- Correlation Data --------------------------- |
|
|
186 | 187 | |
|
|
187 | 188 | if self.dataIn.type == "Correlation": |
|
|
188 | 189 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
189 | 190 | |
|
|
190 | 191 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
191 | 192 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
192 | 193 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
193 | 194 | |
|
|
194 | 195 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
195 | 196 | self.dataOut.noise = self.dataIn.noise |
|
|
196 | 197 | self.dataOut.data_snr = self.dataIn.SNR |
|
|
197 | 198 | self.dataOut.flagNoData = False |
|
|
198 | 199 | self.dataOut.nAvg = self.dataIn.nAvg |
|
|
199 | 200 | |
|
|
200 | 201 | #---------------------- Parameters Data --------------------------- |
|
|
201 | 202 | |
|
|
202 | 203 | if self.dataIn.type == "Parameters": |
|
|
203 | 204 | self.dataOut.copy(self.dataIn) |
|
|
204 | 205 | self.dataOut.flagNoData = False |
|
|
205 | 206 | #print("yo si entre") |
|
|
206 | 207 | |
|
|
207 | 208 | return True |
|
|
208 | 209 | |
|
|
209 | 210 | self.__updateObjFromInput() |
|
|
210 | 211 | #print("yo si entre2") |
|
|
211 | 212 | |
|
|
212 | 213 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
213 | 214 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
214 | 215 | #print("soy spectra ",self.dataOut.utctimeInit) |
|
|
215 | 216 | return |
|
|
216 | 217 | |
|
|
217 | 218 | |
|
|
218 | 219 | def target(tups): |
|
|
219 | 220 | |
|
|
220 | 221 | obj, args = tups |
|
|
221 | 222 | |
|
|
222 | 223 | return obj.FitGau(args) |
|
|
223 | 224 | |
|
|
224 | 225 | class RemoveWideGC(Operation): |
|
|
225 | 226 | ''' This class remove the wide clutter and replace it with a simple interpolation points |
|
|
226 | 227 | This mainly applies to CLAIRE radar |
|
|
227 | 228 | |
|
|
228 | 229 | ClutterWidth : Width to look for the clutter peak |
|
|
229 | 230 | |
|
|
230 | 231 | Input: |
|
|
231 | 232 | |
|
|
232 | 233 | self.dataOut.data_pre : SPC and CSPC |
|
|
233 | 234 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
|
234 | 235 | |
|
|
235 | 236 | Affected: |
|
|
236 | 237 | |
|
|
237 | 238 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
|
238 | 239 | |
|
|
239 | 240 | Written by D. ScipiΓ³n 25.02.2021 |
|
|
240 | 241 | ''' |
|
|
241 | 242 | def __init__(self): |
|
|
242 | 243 | Operation.__init__(self) |
|
|
243 | 244 | self.i = 0 |
|
|
244 | 245 | self.ich = 0 |
|
|
245 | 246 | self.ir = 0 |
|
|
246 | 247 | |
|
|
247 | 248 | def run(self, dataOut, ClutterWidth=2.5): |
|
|
248 | 249 | # print ('Entering RemoveWideGC ... ') |
|
|
249 | 250 | |
|
|
250 | 251 | self.spc = dataOut.data_pre[0].copy() |
|
|
251 | 252 | self.spc_out = dataOut.data_pre[0].copy() |
|
|
252 | 253 | self.Num_Chn = self.spc.shape[0] |
|
|
253 | 254 | self.Num_Hei = self.spc.shape[2] |
|
|
254 | 255 | VelRange = dataOut.spc_range[2][:-1] |
|
|
255 | 256 | dv = VelRange[1]-VelRange[0] |
|
|
256 | 257 | |
|
|
257 | 258 | # Find the velocities that corresponds to zero |
|
|
258 | 259 | gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth)) |
|
|
259 | 260 | |
|
|
260 | 261 | # Removing novalid data from the spectra |
|
|
261 | 262 | for ich in range(self.Num_Chn) : |
|
|
262 | 263 | for ir in range(self.Num_Hei) : |
|
|
263 | 264 | # Estimate the noise at each range |
|
|
264 | 265 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) |
|
|
265 | 266 | |
|
|
266 | 267 | # Removing the noise floor at each range |
|
|
267 | 268 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) |
|
|
268 | 269 | self.spc[ich,novalid,ir] = HSn |
|
|
269 | 270 | |
|
|
270 | 271 | junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn) |
|
|
271 | 272 | j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0)) |
|
|
272 | 273 | j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0)) |
|
|
273 | 274 | if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) : |
|
|
274 | 275 | continue |
|
|
275 | 276 | junk3 = numpy.squeeze(numpy.diff(j1index)) |
|
|
276 | 277 | junk4 = numpy.squeeze(numpy.diff(j2index)) |
|
|
277 | 278 | |
|
|
278 | 279 | valleyindex = j2index[numpy.where(junk4>1)] |
|
|
279 | 280 | peakindex = j1index[numpy.where(junk3>1)] |
|
|
280 | 281 | |
|
|
281 | 282 | isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv)) |
|
|
282 | 283 | if numpy.size(isvalid) == 0 : |
|
|
283 | 284 | continue |
|
|
284 | 285 | if numpy.size(isvalid) >1 : |
|
|
285 | 286 | vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir]) |
|
|
286 | 287 | isvalid = isvalid[vindex] |
|
|
287 | 288 | |
|
|
288 | 289 | # clutter peak |
|
|
289 | 290 | gcpeak = peakindex[isvalid] |
|
|
290 | 291 | vl = numpy.where(valleyindex < gcpeak) |
|
|
291 | 292 | if numpy.size(vl) == 0: |
|
|
292 | 293 | continue |
|
|
293 | 294 | gcvl = valleyindex[vl[0][-1]] |
|
|
294 | 295 | vr = numpy.where(valleyindex > gcpeak) |
|
|
295 | 296 | if numpy.size(vr) == 0: |
|
|
296 | 297 | continue |
|
|
297 | 298 | gcvr = valleyindex[vr[0][0]] |
|
|
298 | 299 | |
|
|
299 | 300 | # Removing the clutter |
|
|
300 | 301 | interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]]) |
|
|
301 | 302 | gcindex = gc_values[gcvl+1:gcvr-1] |
|
|
302 | 303 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) |
|
|
303 | 304 | |
|
|
304 | 305 | dataOut.data_pre[0] = self.spc_out |
|
|
305 | 306 | #print ('Leaving RemoveWideGC ... ') |
|
|
306 | 307 | return dataOut |
|
|
307 | 308 | |
|
|
308 | 309 | class SpectralFilters(Operation): |
|
|
309 | 310 | ''' This class allows to replace the novalid values with noise for each channel |
|
|
310 | 311 | This applies to CLAIRE RADAR |
|
|
311 | 312 | |
|
|
312 | 313 | PositiveLimit : RightLimit of novalid data |
|
|
313 | 314 | NegativeLimit : LeftLimit of novalid data |
|
|
314 | 315 | |
|
|
315 | 316 | Input: |
|
|
316 | 317 | |
|
|
317 | 318 | self.dataOut.data_pre : SPC and CSPC |
|
|
318 | 319 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
|
319 | 320 | |
|
|
320 | 321 | Affected: |
|
|
321 | 322 | |
|
|
322 | 323 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
|
323 | 324 | |
|
|
324 | 325 | Written by D. ScipiΓ³n 29.01.2021 |
|
|
325 | 326 | ''' |
|
|
326 | 327 | def __init__(self): |
|
|
327 | 328 | Operation.__init__(self) |
|
|
328 | 329 | self.i = 0 |
|
|
329 | 330 | |
|
|
330 | 331 | def run(self, dataOut, ): |
|
|
331 | 332 | |
|
|
332 | 333 | self.spc = dataOut.data_pre[0].copy() |
|
|
333 | 334 | self.Num_Chn = self.spc.shape[0] |
|
|
334 | 335 | VelRange = dataOut.spc_range[2] |
|
|
335 | 336 | |
|
|
336 | 337 | # novalid corresponds to data within the Negative and PositiveLimit |
|
|
337 | 338 | |
|
|
338 | 339 | |
|
|
339 | 340 | # Removing novalid data from the spectra |
|
|
340 | 341 | for i in range(self.Num_Chn): |
|
|
341 | 342 | self.spc[i,novalid,:] = dataOut.noise[i] |
|
|
342 | 343 | dataOut.data_pre[0] = self.spc |
|
|
343 | 344 | return dataOut |
|
|
344 | 345 | |
|
|
345 | 346 | class GaussianFit(Operation): |
|
|
346 | 347 | |
|
|
347 | 348 | ''' |
|
|
348 | 349 | Function that fit of one and two generalized gaussians (gg) based |
|
|
349 | 350 | on the PSD shape across an "power band" identified from a cumsum of |
|
|
350 | 351 | the measured spectrum - noise. |
|
|
351 | 352 | |
|
|
352 | 353 | Input: |
|
|
353 | 354 | self.dataOut.data_pre : SelfSpectra |
|
|
354 | 355 | |
|
|
355 | 356 | Output: |
|
|
356 | 357 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
|
357 | 358 | |
|
|
358 | 359 | ''' |
|
|
359 | 360 | def __init__(self): |
|
|
360 | 361 | Operation.__init__(self) |
|
|
361 | 362 | self.i=0 |
|
|
362 | 363 | |
|
|
363 | 364 | |
|
|
364 | 365 | # def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points |
|
|
365 | 366 | def run(self, dataOut, SNRdBlimit=-9, method='generalized'): |
|
|
366 | 367 | """This routine will find a couple of generalized Gaussians to a power spectrum |
|
|
367 | 368 | methods: generalized, squared |
|
|
368 | 369 | input: spc |
|
|
369 | 370 | output: |
|
|
370 | 371 | noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1 |
|
|
371 | 372 | """ |
|
|
372 | 373 | print ('Entering ',method,' double Gaussian fit') |
|
|
373 | 374 | self.spc = dataOut.data_pre[0].copy() |
|
|
374 | 375 | self.Num_Hei = self.spc.shape[2] |
|
|
375 | 376 | self.Num_Bin = self.spc.shape[1] |
|
|
376 | 377 | self.Num_Chn = self.spc.shape[0] |
|
|
377 | 378 | |
|
|
378 | 379 | start_time = time.time() |
|
|
379 | 380 | |
|
|
380 | 381 | pool = Pool(processes=self.Num_Chn) |
|
|
381 | 382 | args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)] |
|
|
382 | 383 | objs = [self for __ in range(self.Num_Chn)] |
|
|
383 | 384 | attrs = list(zip(objs, args)) |
|
|
384 | 385 | DGauFitParam = pool.map(target, attrs) |
|
|
385 | 386 | # Parameters: |
|
|
386 | 387 | # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power |
|
|
387 | 388 | dataOut.DGauFitParams = numpy.asarray(DGauFitParam) |
|
|
388 | 389 | |
|
|
389 | 390 | # Double Gaussian Curves |
|
|
390 | 391 | gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
391 | 392 | gau0[:] = numpy.NaN |
|
|
392 | 393 | gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
393 | 394 | gau1[:] = numpy.NaN |
|
|
394 | 395 | x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1))) |
|
|
395 | 396 | for iCh in range(self.Num_Chn): |
|
|
396 | 397 | N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin)) |
|
|
397 | 398 | N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin)) |
|
|
398 | 399 | A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin)) |
|
|
399 | 400 | A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin)) |
|
|
400 | 401 | v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin)) |
|
|
401 | 402 | v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin)) |
|
|
402 | 403 | s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin)) |
|
|
403 | 404 | s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin)) |
|
|
404 | 405 | if method == 'genealized': |
|
|
405 | 406 | p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin)) |
|
|
406 | 407 | p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin)) |
|
|
407 | 408 | elif method == 'squared': |
|
|
408 | 409 | p0 = 2. |
|
|
409 | 410 | p1 = 2. |
|
|
410 | 411 | gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0 |
|
|
411 | 412 | gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1 |
|
|
412 | 413 | dataOut.GaussFit0 = gau0 |
|
|
413 | 414 | dataOut.GaussFit1 = gau1 |
|
|
414 | 415 | |
|
|
415 | 416 | print('Leaving ',method ,' double Gaussian fit') |
|
|
416 | 417 | return dataOut |
|
|
417 | 418 | |
|
|
418 | 419 | def FitGau(self, X): |
|
|
419 | 420 | # print('Entering FitGau') |
|
|
420 | 421 | # Assigning the variables |
|
|
421 | 422 | Vrange, ch, wnoise, num_intg, SNRlimit = X |
|
|
422 | 423 | # Noise Limits |
|
|
423 | 424 | noisebl = wnoise * 0.9 |
|
|
424 | 425 | noisebh = wnoise * 1.1 |
|
|
425 | 426 | # Radar Velocity |
|
|
426 | 427 | Va = max(Vrange) |
|
|
427 | 428 | deltav = Vrange[1] - Vrange[0] |
|
|
428 | 429 | x = numpy.arange(self.Num_Bin) |
|
|
429 | 430 | |
|
|
430 | 431 | # print ('stop 0') |
|
|
431 | 432 | |
|
|
432 | 433 | # 5 parameters, 2 Gaussians |
|
|
433 | 434 | DGauFitParam = numpy.zeros([5, self.Num_Hei,2]) |
|
|
434 | 435 | DGauFitParam[:] = numpy.NaN |
|
|
435 | 436 | |
|
|
436 | 437 | # SPCparam = [] |
|
|
437 | 438 | # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
438 | 439 | # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
439 | 440 | # SPC_ch1[:] = 0 #numpy.NaN |
|
|
440 | 441 | # SPC_ch2[:] = 0 #numpy.NaN |
|
|
441 | 442 | # print ('stop 1') |
|
|
442 | 443 | for ht in range(self.Num_Hei): |
|
|
443 | 444 | # print (ht) |
|
|
444 | 445 | # print ('stop 2') |
|
|
445 | 446 | # Spectra at each range |
|
|
446 | 447 | spc = numpy.asarray(self.spc)[ch,:,ht] |
|
|
447 | 448 | snr = ( spc.mean() - wnoise ) / wnoise |
|
|
448 | 449 | snrdB = 10.*numpy.log10(snr) |
|
|
449 | 450 | |
|
|
450 | 451 | #print ('stop 3') |
|
|
451 | 452 | if snrdB < SNRlimit : |
|
|
452 | 453 | # snr = numpy.NaN |
|
|
453 | 454 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
454 | 455 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
455 | 456 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
|
456 | 457 | # print ('SNR less than SNRth') |
|
|
457 | 458 | continue |
|
|
458 | 459 | # wnoise = hildebrand_sekhon(spc,num_intg) |
|
|
459 | 460 | # print ('stop 2.01') |
|
|
460 | 461 | ############################################# |
|
|
461 | 462 | # normalizing spc and noise |
|
|
462 | 463 | # This part differs from gg1 |
|
|
463 | 464 | # spc_norm_max = max(spc) #commented by D. ScipiΓ³n 19.03.2021 |
|
|
464 | 465 | #spc = spc / spc_norm_max |
|
|
465 | 466 | # pnoise = pnoise #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 |
|
|
466 | 467 | ############################################# |
|
|
467 | 468 | |
|
|
468 | 469 | # print ('stop 2.1') |
|
|
469 | 470 | fatspectra=1.0 |
|
|
470 | 471 | # noise per channel.... we might want to use the noise at each range |
|
|
471 | 472 | |
|
|
472 | 473 | # wnoise = noise_ #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 |
|
|
473 | 474 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
|
474 | 475 | #if wnoise>1.1*pnoise: # to be tested later |
|
|
475 | 476 | # wnoise=pnoise |
|
|
476 | 477 | # noisebl = wnoise*0.9 |
|
|
477 | 478 | # noisebh = wnoise*1.1 |
|
|
478 | 479 | spc = spc - wnoise # signal |
|
|
479 | 480 | |
|
|
480 | 481 | # print ('stop 2.2') |
|
|
481 | 482 | minx = numpy.argmin(spc) |
|
|
482 | 483 | #spcs=spc.copy() |
|
|
483 | 484 | spcs = numpy.roll(spc,-minx) |
|
|
484 | 485 | cum = numpy.cumsum(spcs) |
|
|
485 | 486 | # tot_noise = wnoise * self.Num_Bin #64; |
|
|
486 | 487 | |
|
|
487 | 488 | # print ('stop 2.3') |
|
|
488 | 489 | # snr = sum(spcs) / tot_noise |
|
|
489 | 490 | # snrdB = 10.*numpy.log10(snr) |
|
|
490 | 491 | #print ('stop 3') |
|
|
491 | 492 | # if snrdB < SNRlimit : |
|
|
492 | 493 | # snr = numpy.NaN |
|
|
493 | 494 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
494 | 495 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
495 | 496 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
|
496 | 497 | # print ('SNR less than SNRth') |
|
|
497 | 498 | # continue |
|
|
498 | 499 | |
|
|
499 | 500 | |
|
|
500 | 501 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
|
501 | 502 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
|
502 | 503 | # print ('stop 4') |
|
|
503 | 504 | cummax = max(cum) |
|
|
504 | 505 | epsi = 0.08 * fatspectra # cumsum to narrow down the energy region |
|
|
505 | 506 | cumlo = cummax * epsi |
|
|
506 | 507 | cumhi = cummax * (1-epsi) |
|
|
507 | 508 | powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
|
508 | 509 | |
|
|
509 | 510 | # print ('stop 5') |
|
|
510 | 511 | if len(powerindex) < 1:# case for powerindex 0 |
|
|
511 | 512 | # print ('powerindex < 1') |
|
|
512 | 513 | continue |
|
|
513 | 514 | powerlo = powerindex[0] |
|
|
514 | 515 | powerhi = powerindex[-1] |
|
|
515 | 516 | powerwidth = powerhi-powerlo |
|
|
516 | 517 | if powerwidth <= 1: |
|
|
517 | 518 | # print('powerwidth <= 1') |
|
|
518 | 519 | continue |
|
|
519 | 520 | |
|
|
520 | 521 | # print ('stop 6') |
|
|
521 | 522 | firstpeak = powerlo + powerwidth/10.# first gaussian energy location |
|
|
522 | 523 | secondpeak = powerhi - powerwidth/10. #second gaussian energy location |
|
|
523 | 524 | midpeak = (firstpeak + secondpeak)/2. |
|
|
524 | 525 | firstamp = spcs[int(firstpeak)] |
|
|
525 | 526 | secondamp = spcs[int(secondpeak)] |
|
|
526 | 527 | midamp = spcs[int(midpeak)] |
|
|
527 | 528 | |
|
|
528 | 529 | y_data = spc + wnoise |
|
|
529 | 530 | |
|
|
530 | 531 | ''' single Gaussian ''' |
|
|
531 | 532 | shift0 = numpy.mod(midpeak+minx, self.Num_Bin ) |
|
|
532 | 533 | width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
|
533 | 534 | power0 = 2. |
|
|
534 | 535 | amplitude0 = midamp |
|
|
535 | 536 | state0 = [shift0,width0,amplitude0,power0,wnoise] |
|
|
536 | 537 | bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
537 | 538 | lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True) |
|
|
538 | 539 | # print ('stop 7.1') |
|
|
539 | 540 | # print (bnds) |
|
|
540 | 541 | |
|
|
541 | 542 | chiSq1=lsq1[1] |
|
|
542 | 543 | |
|
|
543 | 544 | # print ('stop 8') |
|
|
544 | 545 | if fatspectra<1.0 and powerwidth<4: |
|
|
545 | 546 | choice=0 |
|
|
546 | 547 | Amplitude0=lsq1[0][2] |
|
|
547 | 548 | shift0=lsq1[0][0] |
|
|
548 | 549 | width0=lsq1[0][1] |
|
|
549 | 550 | p0=lsq1[0][3] |
|
|
550 | 551 | Amplitude1=0. |
|
|
551 | 552 | shift1=0. |
|
|
552 | 553 | width1=0. |
|
|
553 | 554 | p1=0. |
|
|
554 | 555 | noise=lsq1[0][4] |
|
|
555 | 556 | #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
|
556 | 557 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
|
557 | 558 | |
|
|
558 | 559 | # print ('stop 9') |
|
|
559 | 560 | ''' two Gaussians ''' |
|
|
560 | 561 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
|
561 | 562 | shift0 = numpy.mod(firstpeak+minx, self.Num_Bin ) |
|
|
562 | 563 | shift1 = numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
|
563 | 564 | width0 = powerwidth/6. |
|
|
564 | 565 | width1 = width0 |
|
|
565 | 566 | power0 = 2. |
|
|
566 | 567 | power1 = power0 |
|
|
567 | 568 | amplitude0 = firstamp |
|
|
568 | 569 | amplitude1 = secondamp |
|
|
569 | 570 | state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
|
570 | 571 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
571 | 572 | bnds=((0,self.Num_Bin-1),(1,powerwidth/2.),(0,None),(0.5,3.),(0,self.Num_Bin-1),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
572 | 573 | #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5)) |
|
|
573 | 574 | |
|
|
574 | 575 | # print ('stop 10') |
|
|
575 | 576 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
|
576 | 577 | |
|
|
577 | 578 | # print ('stop 11') |
|
|
578 | 579 | chiSq2 = lsq2[1] |
|
|
579 | 580 | |
|
|
580 | 581 | # print ('stop 12') |
|
|
581 | 582 | |
|
|
582 | 583 | oneG = (chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10) |
|
|
583 | 584 | |
|
|
584 | 585 | # print ('stop 13') |
|
|
585 | 586 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
|
586 | 587 | if oneG: |
|
|
587 | 588 | choice = 0 |
|
|
588 | 589 | else: |
|
|
589 | 590 | w1 = lsq2[0][1]; w2 = lsq2[0][5] |
|
|
590 | 591 | a1 = lsq2[0][2]; a2 = lsq2[0][6] |
|
|
591 | 592 | p1 = lsq2[0][3]; p2 = lsq2[0][7] |
|
|
592 | 593 | s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 |
|
|
593 | 594 | s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 |
|
|
594 | 595 | gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
|
595 | 596 | |
|
|
596 | 597 | if gp1>gp2: |
|
|
597 | 598 | if a1>0.7*a2: |
|
|
598 | 599 | choice = 1 |
|
|
599 | 600 | else: |
|
|
600 | 601 | choice = 2 |
|
|
601 | 602 | elif gp2>gp1: |
|
|
602 | 603 | if a2>0.7*a1: |
|
|
603 | 604 | choice = 2 |
|
|
604 | 605 | else: |
|
|
605 | 606 | choice = 1 |
|
|
606 | 607 | else: |
|
|
607 | 608 | choice = numpy.argmax([a1,a2])+1 |
|
|
608 | 609 | #else: |
|
|
609 | 610 | #choice=argmin([std2a,std2b])+1 |
|
|
610 | 611 | |
|
|
611 | 612 | else: # with low SNR go to the most energetic peak |
|
|
612 | 613 | choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
|
613 | 614 | |
|
|
614 | 615 | # print ('stop 14') |
|
|
615 | 616 | shift0 = lsq2[0][0] |
|
|
616 | 617 | vel0 = Vrange[0] + shift0 * deltav |
|
|
617 | 618 | shift1 = lsq2[0][4] |
|
|
618 | 619 | # vel1=Vrange[0] + shift1 * deltav |
|
|
619 | 620 | |
|
|
620 | 621 | # max_vel = 1.0 |
|
|
621 | 622 | # Va = max(Vrange) |
|
|
622 | 623 | # deltav = Vrange[1]-Vrange[0] |
|
|
623 | 624 | # print ('stop 15') |
|
|
624 | 625 | #first peak will be 0, second peak will be 1 |
|
|
625 | 626 | # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.ScipiΓ³n 19.03.2021 |
|
|
626 | 627 | if vel0 > -Va and vel0 < Va : #first peak is in the correct range |
|
|
627 | 628 | shift0 = lsq2[0][0] |
|
|
628 | 629 | width0 = lsq2[0][1] |
|
|
629 | 630 | Amplitude0 = lsq2[0][2] |
|
|
630 | 631 | p0 = lsq2[0][3] |
|
|
631 | 632 | |
|
|
632 | 633 | shift1 = lsq2[0][4] |
|
|
633 | 634 | width1 = lsq2[0][5] |
|
|
634 | 635 | Amplitude1 = lsq2[0][6] |
|
|
635 | 636 | p1 = lsq2[0][7] |
|
|
636 | 637 | noise = lsq2[0][8] |
|
|
637 | 638 | else: |
|
|
638 | 639 | shift1 = lsq2[0][0] |
|
|
639 | 640 | width1 = lsq2[0][1] |
|
|
640 | 641 | Amplitude1 = lsq2[0][2] |
|
|
641 | 642 | p1 = lsq2[0][3] |
|
|
642 | 643 | |
|
|
643 | 644 | shift0 = lsq2[0][4] |
|
|
644 | 645 | width0 = lsq2[0][5] |
|
|
645 | 646 | Amplitude0 = lsq2[0][6] |
|
|
646 | 647 | p0 = lsq2[0][7] |
|
|
647 | 648 | noise = lsq2[0][8] |
|
|
648 | 649 | |
|
|
649 | 650 | if Amplitude0<0.05: # in case the peak is noise |
|
|
650 | 651 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] |
|
|
651 | 652 | if Amplitude1<0.05: |
|
|
652 | 653 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] |
|
|
653 | 654 | |
|
|
654 | 655 | # print ('stop 16 ') |
|
|
655 | 656 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0) |
|
|
656 | 657 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1) |
|
|
657 | 658 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
|
658 | 659 | |
|
|
659 | 660 | DGauFitParam[0,ht,0] = noise |
|
|
660 | 661 | DGauFitParam[0,ht,1] = noise |
|
|
661 | 662 | DGauFitParam[1,ht,0] = Amplitude0 |
|
|
662 | 663 | DGauFitParam[1,ht,1] = Amplitude1 |
|
|
663 | 664 | DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav |
|
|
664 | 665 | DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav |
|
|
665 | 666 | DGauFitParam[3,ht,0] = width0 * deltav |
|
|
666 | 667 | DGauFitParam[3,ht,1] = width1 * deltav |
|
|
667 | 668 | DGauFitParam[4,ht,0] = p0 |
|
|
668 | 669 | DGauFitParam[4,ht,1] = p1 |
|
|
669 | 670 | |
|
|
670 | 671 | # print (DGauFitParam.shape) |
|
|
671 | 672 | # print ('Leaving FitGau') |
|
|
672 | 673 | return DGauFitParam |
|
|
673 | 674 | # return SPCparam |
|
|
674 | 675 | # return GauSPC |
|
|
675 | 676 | |
|
|
676 | 677 | def y_model1(self,x,state): |
|
|
677 | 678 | shift0, width0, amplitude0, power0, noise = state |
|
|
678 | 679 | model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0) |
|
|
679 | 680 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
|
680 | 681 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
|
681 | 682 | return model0 + model0u + model0d + noise |
|
|
682 | 683 | |
|
|
683 | 684 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
|
684 | 685 | shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state |
|
|
685 | 686 | model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
686 | 687 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
|
687 | 688 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
|
688 | 689 | |
|
|
689 | 690 | model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1) |
|
|
690 | 691 | model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1) |
|
|
691 | 692 | model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1) |
|
|
692 | 693 | return model0 + model0u + model0d + model1 + model1u + model1d + noise |
|
|
693 | 694 | |
|
|
694 | 695 | def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is. |
|
|
695 | 696 | |
|
|
696 | 697 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
|
697 | 698 | |
|
|
698 | 699 | def misfit2(self,state,y_data,x,num_intg): |
|
|
699 | 700 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
|
700 | 701 | |
|
|
701 | 702 | |
|
|
702 | 703 | |
|
|
703 | 704 | class PrecipitationProc(Operation): |
|
|
704 | 705 | |
|
|
705 | 706 | ''' |
|
|
706 | 707 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
|
707 | 708 | |
|
|
708 | 709 | Input: |
|
|
709 | 710 | self.dataOut.data_pre : SelfSpectra |
|
|
710 | 711 | |
|
|
711 | 712 | Output: |
|
|
712 | 713 | |
|
|
713 | 714 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
|
714 | 715 | |
|
|
715 | 716 | |
|
|
716 | 717 | Parameters affected: |
|
|
717 | 718 | ''' |
|
|
718 | 719 | |
|
|
719 | 720 | def __init__(self): |
|
|
720 | 721 | Operation.__init__(self) |
|
|
721 | 722 | self.i=0 |
|
|
722 | 723 | |
|
|
723 | 724 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
|
724 | 725 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30): |
|
|
725 | 726 | |
|
|
726 | 727 | # print ('Entering PrecepitationProc ... ') |
|
|
727 | 728 | |
|
|
728 | 729 | if radar == "MIRA35C" : |
|
|
729 | 730 | |
|
|
730 | 731 | self.spc = dataOut.data_pre[0].copy() |
|
|
731 | 732 | self.Num_Hei = self.spc.shape[2] |
|
|
732 | 733 | self.Num_Bin = self.spc.shape[1] |
|
|
733 | 734 | self.Num_Chn = self.spc.shape[0] |
|
|
734 | 735 | Ze = self.dBZeMODE2(dataOut) |
|
|
735 | 736 | |
|
|
736 | 737 | else: |
|
|
737 | 738 | |
|
|
738 | 739 | self.spc = dataOut.data_pre[0].copy() |
|
|
739 | 740 | |
|
|
740 | 741 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX |
|
|
741 | 742 | self.spc[:,:,0:7]= numpy.NaN |
|
|
742 | 743 | |
|
|
743 | 744 | self.Num_Hei = self.spc.shape[2] |
|
|
744 | 745 | self.Num_Bin = self.spc.shape[1] |
|
|
745 | 746 | self.Num_Chn = self.spc.shape[0] |
|
|
746 | 747 | |
|
|
747 | 748 | VelRange = dataOut.spc_range[2] |
|
|
748 | 749 | |
|
|
749 | 750 | ''' Se obtiene la constante del RADAR ''' |
|
|
750 | 751 | |
|
|
751 | 752 | self.Pt = Pt |
|
|
752 | 753 | self.Gt = Gt |
|
|
753 | 754 | self.Gr = Gr |
|
|
754 | 755 | self.Lambda = Lambda |
|
|
755 | 756 | self.aL = aL |
|
|
756 | 757 | self.tauW = tauW |
|
|
757 | 758 | self.ThetaT = ThetaT |
|
|
758 | 759 | self.ThetaR = ThetaR |
|
|
759 | 760 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB |
|
|
760 | 761 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB |
|
|
761 | 762 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB |
|
|
762 | 763 | |
|
|
763 | 764 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
764 | 765 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
|
765 | 766 | RadarConstant = 10e-26 * Numerator / Denominator # |
|
|
766 | 767 | ExpConstant = 10**(40/10) #Constante Experimental |
|
|
767 | 768 | |
|
|
768 | 769 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
769 | 770 | for i in range(self.Num_Chn): |
|
|
770 | 771 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] |
|
|
771 | 772 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 |
|
|
772 | 773 | |
|
|
773 | 774 | SPCmean = numpy.mean(SignalPower, 0) |
|
|
774 | 775 | Pr = SPCmean[:,:]/dataOut.normFactor |
|
|
775 | 776 | |
|
|
776 | 777 | # Declaring auxiliary variables |
|
|
777 | 778 | Range = dataOut.heightList*1000. #Range in m |
|
|
778 | 779 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] |
|
|
779 | 780 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) |
|
|
780 | 781 | zMtrx = rMtrx+Altitude |
|
|
781 | 782 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] |
|
|
782 | 783 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) |
|
|
783 | 784 | |
|
|
784 | 785 | # height dependence to air density Foote and Du Toit (1969) |
|
|
785 | 786 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 |
|
|
786 | 787 | VMtrx = VelMtrx / delv_z #Normalized velocity |
|
|
787 | 788 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN |
|
|
788 | 789 | # Diameter is related to the fall speed of falling drops |
|
|
789 | 790 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] |
|
|
790 | 791 | # Only valid for D>= 0.16 mm |
|
|
791 | 792 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN |
|
|
792 | 793 | |
|
|
793 | 794 | #Calculate Radar Reflectivity ETAn |
|
|
794 | 795 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) |
|
|
795 | 796 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z |
|
|
796 | 797 | # Radar Cross Section |
|
|
797 | 798 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 |
|
|
798 | 799 | # Drop Size Distribution |
|
|
799 | 800 | DSD = ETAn / sigmaD |
|
|
800 | 801 | # Equivalente Reflectivy |
|
|
801 | 802 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) |
|
|
802 | 803 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] |
|
|
803 | 804 | # RainFall Rate |
|
|
804 | 805 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr |
|
|
805 | 806 | |
|
|
806 | 807 | # Censoring the data |
|
|
807 | 808 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal |
|
|
808 | 809 | SNRth = 10**(SNRdBlimit/10) #-30dB |
|
|
809 | 810 | novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better |
|
|
810 | 811 | W = numpy.nanmean(dataOut.data_dop,0) |
|
|
811 | 812 | W[novalid] = numpy.NaN |
|
|
812 | 813 | Ze_org[novalid] = numpy.NaN |
|
|
813 | 814 | RR[novalid] = numpy.NaN |
|
|
814 | 815 | |
|
|
815 | 816 | dataOut.data_output = RR[8] |
|
|
816 | 817 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
|
817 | 818 | dataOut.channelList = [0,1,2] |
|
|
818 | 819 | |
|
|
819 | 820 | dataOut.data_param[0]=10*numpy.log10(Ze_org) |
|
|
820 | 821 | dataOut.data_param[1]=-W |
|
|
821 | 822 | dataOut.data_param[2]=RR |
|
|
822 | 823 | |
|
|
823 | 824 | # print ('Leaving PrecepitationProc ... ') |
|
|
824 | 825 | return dataOut |
|
|
825 | 826 | |
|
|
826 | 827 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
|
827 | 828 | |
|
|
828 | 829 | NPW = dataOut.NPW |
|
|
829 | 830 | COFA = dataOut.COFA |
|
|
830 | 831 | |
|
|
831 | 832 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
|
832 | 833 | RadarConst = dataOut.RadarConst |
|
|
833 | 834 | #frequency = 34.85*10**9 |
|
|
834 | 835 | |
|
|
835 | 836 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
|
836 | 837 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
|
837 | 838 | |
|
|
838 | 839 | ETA = numpy.sum(SNR,1) |
|
|
839 | 840 | |
|
|
840 | 841 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) |
|
|
841 | 842 | |
|
|
842 | 843 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
|
843 | 844 | |
|
|
844 | 845 | for r in range(self.Num_Hei): |
|
|
845 | 846 | |
|
|
846 | 847 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
|
847 | 848 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
|
848 | 849 | |
|
|
849 | 850 | return Ze |
|
|
850 | 851 | |
|
|
851 | 852 | # def GetRadarConstant(self): |
|
|
852 | 853 | # |
|
|
853 | 854 | # """ |
|
|
854 | 855 | # Constants: |
|
|
855 | 856 | # |
|
|
856 | 857 | # Pt: Transmission Power dB 5kW 5000 |
|
|
857 | 858 | # Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
|
858 | 859 | # Gr: Reception Gain dB 18.5 dB 70.7945 |
|
|
859 | 860 | # Lambda: Wavelenght m 0.6741 m 0.6741 |
|
|
860 | 861 | # aL: Attenuation loses dB 4dB 2.5118 |
|
|
861 | 862 | # tauW: Width of transmission pulse s 4us 4e-6 |
|
|
862 | 863 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
|
863 | 864 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
|
864 | 865 | # |
|
|
865 | 866 | # """ |
|
|
866 | 867 | # |
|
|
867 | 868 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
868 | 869 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
|
869 | 870 | # RadarConstant = Numerator / Denominator |
|
|
870 | 871 | # |
|
|
871 | 872 | # return RadarConstant |
|
|
872 | 873 | |
|
|
873 | 874 | |
|
|
874 | 875 | |
|
|
875 | 876 | class FullSpectralAnalysis(Operation): |
|
|
876 | 877 | |
|
|
877 | 878 | """ |
|
|
878 | 879 | Function that implements Full Spectral Analysis technique. |
|
|
879 | 880 | |
|
|
880 | 881 | Input: |
|
|
881 | 882 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data |
|
|
882 | 883 | self.dataOut.groupList : Pairlist of channels |
|
|
883 | 884 | self.dataOut.ChanDist : Physical distance between receivers |
|
|
884 | 885 | |
|
|
885 | 886 | |
|
|
886 | 887 | Output: |
|
|
887 | 888 | |
|
|
888 | 889 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind |
|
|
889 | 890 | |
|
|
890 | 891 | |
|
|
891 | 892 | Parameters affected: Winds, height range, SNR |
|
|
892 | 893 | |
|
|
893 | 894 | """ |
|
|
894 | 895 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30, |
|
|
895 | 896 | minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None): |
|
|
896 | 897 | |
|
|
897 | 898 | spc = dataOut.data_pre[0].copy() |
|
|
898 | 899 | cspc = dataOut.data_pre[1] |
|
|
899 | 900 | nHeights = spc.shape[2] |
|
|
900 | 901 | |
|
|
901 | 902 | # first_height = 0.75 #km (ref: data header 20170822) |
|
|
902 | 903 | # resolution_height = 0.075 #km |
|
|
903 | 904 | ''' |
|
|
904 | 905 | finding height range. check this when radar parameters are changed! |
|
|
905 | 906 | ''' |
|
|
906 | 907 | if maxheight is not None: |
|
|
907 | 908 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical |
|
|
908 | 909 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better |
|
|
909 | 910 | else: |
|
|
910 | 911 | range_max = nHeights |
|
|
911 | 912 | if minheight is not None: |
|
|
912 | 913 | # range_min = int((minheight - first_height) / resolution_height) # theoretical |
|
|
913 | 914 | range_min = int(13.26 * minheight - 5) # empirical, works better |
|
|
914 | 915 | if range_min < 0: |
|
|
915 | 916 | range_min = 0 |
|
|
916 | 917 | else: |
|
|
917 | 918 | range_min = 0 |
|
|
918 | 919 | |
|
|
919 | 920 | pairsList = dataOut.groupList |
|
|
920 | 921 | if dataOut.ChanDist is not None : |
|
|
921 | 922 | ChanDist = dataOut.ChanDist |
|
|
922 | 923 | else: |
|
|
923 | 924 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
|
924 | 925 | |
|
|
925 | 926 | # 4 variables: zonal, meridional, vertical, and average SNR |
|
|
926 | 927 | data_param = numpy.zeros([4,nHeights]) * numpy.NaN |
|
|
927 | 928 | velocityX = numpy.zeros([nHeights]) * numpy.NaN |
|
|
928 | 929 | velocityY = numpy.zeros([nHeights]) * numpy.NaN |
|
|
929 | 930 | velocityZ = numpy.zeros([nHeights]) * numpy.NaN |
|
|
930 | 931 | |
|
|
931 | 932 | dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0)) |
|
|
932 | 933 | |
|
|
933 | 934 | '''***********************************************WIND ESTIMATION**************************************''' |
|
|
934 | 935 | for Height in range(nHeights): |
|
|
935 | 936 | |
|
|
936 | 937 | if Height >= range_min and Height < range_max: |
|
|
937 | 938 | # error_code will be useful in future analysis |
|
|
938 | 939 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, |
|
|
939 | 940 | ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency) |
|
|
940 | 941 | |
|
|
941 | 942 | if abs(Vzon) < 100. and abs(Vmer) < 100.: |
|
|
942 | 943 | velocityX[Height] = Vzon |
|
|
943 | 944 | velocityY[Height] = -Vmer |
|
|
944 | 945 | velocityZ[Height] = Vver |
|
|
945 | 946 | |
|
|
946 | 947 | # Censoring data with SNR threshold |
|
|
947 | 948 | dbSNR [dbSNR < SNRdBlimit] = numpy.NaN |
|
|
948 | 949 | |
|
|
949 | 950 | data_param[0] = velocityX |
|
|
950 | 951 | data_param[1] = velocityY |
|
|
951 | 952 | data_param[2] = velocityZ |
|
|
952 | 953 | data_param[3] = dbSNR |
|
|
953 | 954 | dataOut.data_param = data_param |
|
|
954 | 955 | return dataOut |
|
|
955 | 956 | |
|
|
956 | 957 | def moving_average(self,x, N=2): |
|
|
957 | 958 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ |
|
|
958 | 959 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
|
959 | 960 | |
|
|
960 | 961 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
961 | 962 | return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2) |
|
|
962 | 963 | |
|
|
963 | 964 | def Moments(self, ySamples, xSamples): |
|
|
964 | 965 | Power = numpy.nanmean(ySamples) # Power, 0th Moment |
|
|
965 | 966 | yNorm = ySamples / numpy.nansum(ySamples) |
|
|
966 | 967 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment |
|
|
967 | 968 | Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment |
|
|
968 | 969 | StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral |
|
|
969 | 970 | return numpy.array([Power,RadVel,StdDev]) |
|
|
970 | 971 | |
|
|
971 | 972 | def StopWindEstimation(self, error_code): |
|
|
972 | 973 | Vzon = numpy.NaN |
|
|
973 | 974 | Vmer = numpy.NaN |
|
|
974 | 975 | Vver = numpy.NaN |
|
|
975 | 976 | return Vzon, Vmer, Vver, error_code |
|
|
976 | 977 | |
|
|
977 | 978 | def AntiAliasing(self, interval, maxstep): |
|
|
978 | 979 | """ |
|
|
979 | 980 | function to prevent errors from aliased values when computing phaseslope |
|
|
980 | 981 | """ |
|
|
981 | 982 | antialiased = numpy.zeros(len(interval)) |
|
|
982 | 983 | copyinterval = interval.copy() |
|
|
983 | 984 | |
|
|
984 | 985 | antialiased[0] = copyinterval[0] |
|
|
985 | 986 | |
|
|
986 | 987 | for i in range(1,len(antialiased)): |
|
|
987 | 988 | step = interval[i] - interval[i-1] |
|
|
988 | 989 | if step > maxstep: |
|
|
989 | 990 | copyinterval -= 2*numpy.pi |
|
|
990 | 991 | antialiased[i] = copyinterval[i] |
|
|
991 | 992 | elif step < maxstep*(-1): |
|
|
992 | 993 | copyinterval += 2*numpy.pi |
|
|
993 | 994 | antialiased[i] = copyinterval[i] |
|
|
994 | 995 | else: |
|
|
995 | 996 | antialiased[i] = copyinterval[i].copy() |
|
|
996 | 997 | |
|
|
997 | 998 | return antialiased |
|
|
998 | 999 | |
|
|
999 | 1000 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq): |
|
|
1000 | 1001 | """ |
|
|
1001 | 1002 | Function that Calculates Zonal, Meridional and Vertical wind velocities. |
|
|
1002 | 1003 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. |
|
|
1003 | 1004 | |
|
|
1004 | 1005 | Input: |
|
|
1005 | 1006 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. |
|
|
1006 | 1007 | pairsList : Pairlist of channels |
|
|
1007 | 1008 | ChanDist : array of xi_ij and eta_ij |
|
|
1008 | 1009 | Height : height at which data is processed |
|
|
1009 | 1010 | noise : noise in [channels] format for specific height |
|
|
1010 | 1011 | Abbsisarange : range of the frequencies or velocities |
|
|
1011 | 1012 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit |
|
|
1012 | 1013 | |
|
|
1013 | 1014 | Output: |
|
|
1014 | 1015 | Vzon, Vmer, Vver : wind velocities |
|
|
1015 | 1016 | error_code : int that states where code is terminated |
|
|
1016 | 1017 | |
|
|
1017 | 1018 | 0 : no error detected |
|
|
1018 | 1019 | 1 : Gaussian of mean spc exceeds widthlimit |
|
|
1019 | 1020 | 2 : no Gaussian of mean spc found |
|
|
1020 | 1021 | 3 : SNR to low or velocity to high -> prec. e.g. |
|
|
1021 | 1022 | 4 : at least one Gaussian of cspc exceeds widthlimit |
|
|
1022 | 1023 | 5 : zero out of three cspc Gaussian fits converged |
|
|
1023 | 1024 | 6 : phase slope fit could not be found |
|
|
1024 | 1025 | 7 : arrays used to fit phase have different length |
|
|
1025 | 1026 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) |
|
|
1026 | 1027 | |
|
|
1027 | 1028 | """ |
|
|
1028 | 1029 | |
|
|
1029 | 1030 | error_code = 0 |
|
|
1030 | 1031 | |
|
|
1031 | 1032 | nChan = spc.shape[0] |
|
|
1032 | 1033 | nProf = spc.shape[1] |
|
|
1033 | 1034 | nPair = cspc.shape[0] |
|
|
1034 | 1035 | |
|
|
1035 | 1036 | SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height |
|
|
1036 | 1037 | CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values |
|
|
1037 | 1038 | phase = numpy.zeros([nPair, nProf]) # phase between channels |
|
|
1038 | 1039 | PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise |
|
|
1039 | 1040 | PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise |
|
|
1040 | 1041 | xFrec = AbbsisaRange[0][:-1] # frequency range |
|
|
1041 | 1042 | xVel = AbbsisaRange[2][:-1] # velocity range |
|
|
1042 | 1043 | xSamples = xFrec # the frequency range is taken |
|
|
1043 | 1044 | delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x |
|
|
1044 | 1045 | |
|
|
1045 | 1046 | # only consider velocities with in NegativeLimit and PositiveLimit |
|
|
1046 | 1047 | if (NegativeLimit is None): |
|
|
1047 | 1048 | NegativeLimit = numpy.min(xVel) |
|
|
1048 | 1049 | if (PositiveLimit is None): |
|
|
1049 | 1050 | PositiveLimit = numpy.max(xVel) |
|
|
1050 | 1051 | xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit)) |
|
|
1051 | 1052 | xSamples_zoom = xSamples[xvalid] |
|
|
1052 | 1053 | |
|
|
1053 | 1054 | '''Getting Eij and Nij''' |
|
|
1054 | 1055 | Xi01, Xi02, Xi12 = ChanDist[:,0] |
|
|
1055 | 1056 | Eta01, Eta02, Eta12 = ChanDist[:,1] |
|
|
1056 | 1057 | |
|
|
1057 | 1058 | # spwd limit - updated by D. ScipiΓ³n 30.03.2021 |
|
|
1058 | 1059 | widthlimit = 10 |
|
|
1059 | 1060 | '''************************* SPC is normalized ********************************''' |
|
|
1060 | 1061 | spc_norm = spc.copy() |
|
|
1061 | 1062 | # For each channel |
|
|
1062 | 1063 | for i in range(nChan): |
|
|
1063 | 1064 | spc_sub = spc_norm[i,:] - noise[i] # only the signal power |
|
|
1064 | 1065 | SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x) |
|
|
1065 | 1066 | |
|
|
1066 | 1067 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' |
|
|
1067 | 1068 | |
|
|
1068 | 1069 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because |
|
|
1069 | 1070 | you only fit the curve and don't need the absolute value of height for calculation, |
|
|
1070 | 1071 | only for estimation of width. for normalization of cross spectra, you need initial, |
|
|
1071 | 1072 | unnormalized self-spectra With noise. |
|
|
1072 | 1073 | |
|
|
1073 | 1074 | Technically, you don't even need to normalize the self-spectra, as you only need the |
|
|
1074 | 1075 | width of the peak. However, it was left this way. Note that the normalization has a flaw: |
|
|
1075 | 1076 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be |
|
|
1076 | 1077 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) |
|
|
1077 | 1078 | """ |
|
|
1078 | 1079 | # initial conditions |
|
|
1079 | 1080 | popt = [1e-10,0,1e-10] |
|
|
1080 | 1081 | # Spectra average |
|
|
1081 | 1082 | SPCMean = numpy.average(SPC_Samples,0) |
|
|
1082 | 1083 | # Moments in frequency |
|
|
1083 | 1084 | SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom) |
|
|
1084 | 1085 | |
|
|
1085 | 1086 | # Gauss Fit SPC in frequency domain |
|
|
1086 | 1087 | if dbSNR > SNRlimit: # only if SNR > SNRth |
|
|
1087 | 1088 | try: |
|
|
1088 | 1089 | popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments) |
|
|
1089 | 1090 | if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION |
|
|
1090 | 1091 | return self.StopWindEstimation(error_code = 1) |
|
|
1091 | 1092 | FitGauss = self.gaus(xSamples_zoom,*popt) |
|
|
1092 | 1093 | except :#RuntimeError: |
|
|
1093 | 1094 | return self.StopWindEstimation(error_code = 2) |
|
|
1094 | 1095 | else: |
|
|
1095 | 1096 | return self.StopWindEstimation(error_code = 3) |
|
|
1096 | 1097 | |
|
|
1097 | 1098 | '''***************************** CSPC Normalization ************************* |
|
|
1098 | 1099 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation |
|
|
1099 | 1100 | influence the norm which is not desired. First, a range is identified where the |
|
|
1100 | 1101 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area |
|
|
1101 | 1102 | around it gets cut off and values replaced by mean determined by the boundary |
|
|
1102 | 1103 | data -> sum_noise (spc is not normalized here, thats why the noise is important) |
|
|
1103 | 1104 | |
|
|
1104 | 1105 | The sums are then added and multiplied by range/datapoints, because you need |
|
|
1105 | 1106 | an integral and not a sum for normalization. |
|
|
1106 | 1107 | |
|
|
1107 | 1108 | A norm is found according to Briggs 92. |
|
|
1108 | 1109 | ''' |
|
|
1109 | 1110 | # for each pair |
|
|
1110 | 1111 | for i in range(nPair): |
|
|
1111 | 1112 | cspc_norm = cspc[i,:].copy() |
|
|
1112 | 1113 | chan_index0 = pairsList[i][0] |
|
|
1113 | 1114 | chan_index1 = pairsList[i][1] |
|
|
1114 | 1115 | CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x) |
|
|
1115 | 1116 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) |
|
|
1116 | 1117 | |
|
|
1117 | 1118 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom), |
|
|
1118 | 1119 | self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom), |
|
|
1119 | 1120 | self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)]) |
|
|
1120 | 1121 | |
|
|
1121 | 1122 | popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10] |
|
|
1122 | 1123 | FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)) |
|
|
1123 | 1124 | |
|
|
1124 | 1125 | '''*******************************FIT GAUSS CSPC************************************''' |
|
|
1125 | 1126 | try: |
|
|
1126 | 1127 | popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0]) |
|
|
1127 | 1128 | if popt01[2] > widthlimit: # CONDITION |
|
|
1128 | 1129 | return self.StopWindEstimation(error_code = 4) |
|
|
1129 | 1130 | popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1]) |
|
|
1130 | 1131 | if popt02[2] > widthlimit: # CONDITION |
|
|
1131 | 1132 | return self.StopWindEstimation(error_code = 4) |
|
|
1132 | 1133 | popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2]) |
|
|
1133 | 1134 | if popt12[2] > widthlimit: # CONDITION |
|
|
1134 | 1135 | return self.StopWindEstimation(error_code = 4) |
|
|
1135 | 1136 | |
|
|
1136 | 1137 | FitGauss01 = self.gaus(xSamples_zoom, *popt01) |
|
|
1137 | 1138 | FitGauss02 = self.gaus(xSamples_zoom, *popt02) |
|
|
1138 | 1139 | FitGauss12 = self.gaus(xSamples_zoom, *popt12) |
|
|
1139 | 1140 | except: |
|
|
1140 | 1141 | return self.StopWindEstimation(error_code = 5) |
|
|
1141 | 1142 | |
|
|
1142 | 1143 | |
|
|
1143 | 1144 | '''************* Getting Fij ***************''' |
|
|
1144 | 1145 | # x-axis point of the gaussian where the center is located from GaussFit of spectra |
|
|
1145 | 1146 | GaussCenter = popt[1] |
|
|
1146 | 1147 | ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()] |
|
|
1147 | 1148 | PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0] |
|
|
1148 | 1149 | |
|
|
1149 | 1150 | # Point where e^-1 is located in the gaussian |
|
|
1150 | 1151 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) |
|
|
1151 | 1152 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss" |
|
|
1152 | 1153 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
|
1153 | 1154 | Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter]) |
|
|
1154 | 1155 | |
|
|
1155 | 1156 | '''********** Taking frequency ranges from mean SPCs **********''' |
|
|
1156 | 1157 | GauWidth = popt[2] * 3/2 # Bandwidth of Gau01 |
|
|
1157 | 1158 | Range = numpy.empty(2) |
|
|
1158 | 1159 | Range[0] = GaussCenter - GauWidth |
|
|
1159 | 1160 | Range[1] = GaussCenter + GauWidth |
|
|
1160 | 1161 | # Point in x-axis where the bandwidth is located (min:max) |
|
|
1161 | 1162 | ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()] |
|
|
1162 | 1163 | ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()] |
|
|
1163 | 1164 | PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0] |
|
|
1164 | 1165 | PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0] |
|
|
1165 | 1166 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) |
|
|
1166 | 1167 | FrecRange = xSamples_zoom[ Range[0] : Range[1] ] |
|
|
1167 | 1168 | |
|
|
1168 | 1169 | '''************************** Getting Phase Slope ***************************''' |
|
|
1169 | 1170 | for i in range(nPair): |
|
|
1170 | 1171 | if len(FrecRange) > 5: |
|
|
1171 | 1172 | PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy() |
|
|
1172 | 1173 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
|
1173 | 1174 | if len(FrecRange) == len(PhaseRange): |
|
|
1174 | 1175 | try: |
|
|
1175 | 1176 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) |
|
|
1176 | 1177 | PhaseSlope[i] = slope |
|
|
1177 | 1178 | PhaseInter[i] = intercept |
|
|
1178 | 1179 | except: |
|
|
1179 | 1180 | return self.StopWindEstimation(error_code = 6) |
|
|
1180 | 1181 | else: |
|
|
1181 | 1182 | return self.StopWindEstimation(error_code = 7) |
|
|
1182 | 1183 | else: |
|
|
1183 | 1184 | return self.StopWindEstimation(error_code = 8) |
|
|
1184 | 1185 | |
|
|
1185 | 1186 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' |
|
|
1186 | 1187 | |
|
|
1187 | 1188 | '''Getting constant C''' |
|
|
1188 | 1189 | cC=(Fij*numpy.pi)**2 |
|
|
1189 | 1190 | |
|
|
1190 | 1191 | '''****** Getting constants F and G ******''' |
|
|
1191 | 1192 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
|
1192 | 1193 | # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]]) |
|
|
1193 | 1194 | # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi) |
|
|
1194 | 1195 | MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi) |
|
|
1195 | 1196 | MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi) |
|
|
1196 | 1197 | # MijResults = numpy.array([MijResult0, MijResult1, MijResult2]) |
|
|
1197 | 1198 | MijResults = numpy.array([MijResult1, MijResult2]) |
|
|
1198 | 1199 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
|
1199 | 1200 | |
|
|
1200 | 1201 | '''****** Getting constants A, B and H ******''' |
|
|
1201 | 1202 | W01 = numpy.nanmax( FitGauss01 ) |
|
|
1202 | 1203 | W02 = numpy.nanmax( FitGauss02 ) |
|
|
1203 | 1204 | W12 = numpy.nanmax( FitGauss12 ) |
|
|
1204 | 1205 | |
|
|
1205 | 1206 | WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) |
|
|
1206 | 1207 | WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) |
|
|
1207 | 1208 | WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) |
|
|
1208 | 1209 | WijResults = numpy.array([WijResult01, WijResult02, WijResult12]) |
|
|
1209 | 1210 | |
|
|
1210 | 1211 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
|
1211 | 1212 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
|
1212 | 1213 | |
|
|
1213 | 1214 | VxVy = numpy.array([[cA,cH],[cH,cB]]) |
|
|
1214 | 1215 | VxVyResults = numpy.array([-cF,-cG]) |
|
|
1215 | 1216 | (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults) |
|
|
1216 | 1217 | Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq) |
|
|
1217 | 1218 | error_code = 0 |
|
|
1218 | 1219 | |
|
|
1219 | 1220 | return Vzon, Vmer, Vver, error_code |
|
|
1220 | 1221 | |
|
|
1221 | 1222 | class SpectralMoments(Operation): |
|
|
1222 | 1223 | |
|
|
1223 | 1224 | ''' |
|
|
1224 | 1225 | Function SpectralMoments() |
|
|
1225 | 1226 | |
|
|
1226 | 1227 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
1227 | 1228 | |
|
|
1228 | 1229 | Type of dataIn: Spectra |
|
|
1229 | 1230 | |
|
|
1230 | 1231 | Configuration Parameters: |
|
|
1231 | 1232 | |
|
|
1232 | 1233 | dirCosx : Cosine director in X axis |
|
|
1233 | 1234 | dirCosy : Cosine director in Y axis |
|
|
1234 | 1235 | |
|
|
1235 | 1236 | elevation : |
|
|
1236 | 1237 | azimuth : |
|
|
1237 | 1238 | |
|
|
1238 | 1239 | Input: |
|
|
1239 | 1240 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
1240 | 1241 | self.dataOut.data_pre : Spectral data |
|
|
1241 | 1242 | self.dataOut.abscissaList : List of frequencies |
|
|
1242 | 1243 | self.dataOut.noise : Noise level per channel |
|
|
1243 | 1244 | |
|
|
1244 | 1245 | Affected: |
|
|
1245 | 1246 | self.dataOut.moments : Parameters per channel |
|
|
1246 | 1247 | self.dataOut.data_snr : SNR per channel |
|
|
1247 | 1248 | |
|
|
1248 | 1249 | ''' |
|
|
1249 | 1250 | |
|
|
1250 | 1251 | def run(self, dataOut): |
|
|
1251 | 1252 | |
|
|
1252 | 1253 | data = dataOut.data_pre[0] |
|
|
1253 | 1254 | absc = dataOut.abscissaList[:-1] |
|
|
1254 | 1255 | noise = dataOut.noise |
|
|
1255 | 1256 | nChannel = data.shape[0] |
|
|
1256 | 1257 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
1257 | 1258 | |
|
|
1258 | 1259 | for ind in range(nChannel): |
|
|
1259 | 1260 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) |
|
|
1260 | 1261 | |
|
|
1261 | 1262 | dataOut.moments = data_param[:,1:,:] |
|
|
1262 | 1263 | dataOut.data_snr = data_param[:,0] |
|
|
1263 | 1264 | dataOut.data_pow = data_param[:,1] |
|
|
1264 | 1265 | dataOut.data_dop = data_param[:,2] |
|
|
1265 | 1266 | dataOut.data_width = data_param[:,3] |
|
|
1266 | 1267 | return dataOut |
|
|
1267 | 1268 | |
|
|
1268 | 1269 | def __calculateMoments(self, oldspec, oldfreq, n0, |
|
|
1269 | 1270 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
1270 | 1271 | |
|
|
1271 | 1272 | if (nicoh is None): nicoh = 1 |
|
|
1272 | 1273 | if (graph is None): graph = 0 |
|
|
1273 | 1274 | if (smooth is None): smooth = 0 |
|
|
1274 | 1275 | elif (self.smooth < 3): smooth = 0 |
|
|
1275 | 1276 | |
|
|
1276 | 1277 | if (type1 is None): type1 = 0 |
|
|
1277 | 1278 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
1278 | 1279 | if (snrth is None): snrth = -3 |
|
|
1279 | 1280 | if (dc is None): dc = 0 |
|
|
1280 | 1281 | if (aliasing is None): aliasing = 0 |
|
|
1281 | 1282 | if (oldfd is None): oldfd = 0 |
|
|
1282 | 1283 | if (wwauto is None): wwauto = 0 |
|
|
1283 | 1284 | |
|
|
1284 | 1285 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
1285 | 1286 | |
|
|
1286 | 1287 | freq = oldfreq |
|
|
1287 | 1288 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
1288 | 1289 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
1289 | 1290 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
1290 | 1291 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
1291 | 1292 | |
|
|
1292 | 1293 | # oldspec = numpy.ma.masked_invalid(oldspec) |
|
|
1293 | 1294 | for ind in range(oldspec.shape[1]): |
|
|
1294 | 1295 | |
|
|
1295 | 1296 | spec = oldspec[:,ind] |
|
|
1296 | 1297 | aux = spec*fwindow |
|
|
1297 | 1298 | max_spec = aux.max() |
|
|
1298 | 1299 | m = aux.tolist().index(max_spec) |
|
|
1299 | 1300 | |
|
|
1300 | 1301 | # Smooth |
|
|
1301 | 1302 | if (smooth == 0): |
|
|
1302 | 1303 | spec2 = spec |
|
|
1303 | 1304 | else: |
|
|
1304 | 1305 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
1305 | 1306 | |
|
|
1306 | 1307 | # Moments Estimation |
|
|
1307 | 1308 | bb = spec2[numpy.arange(m,spec2.size)] |
|
|
1308 | 1309 | bb = (bb<n0).nonzero() |
|
|
1309 | 1310 | bb = bb[0] |
|
|
1310 | 1311 | |
|
|
1311 | 1312 | ss = spec2[numpy.arange(0,m + 1)] |
|
|
1312 | 1313 | ss = (ss<n0).nonzero() |
|
|
1313 | 1314 | ss = ss[0] |
|
|
1314 | 1315 | |
|
|
1315 | 1316 | if (bb.size == 0): |
|
|
1316 | 1317 | bb0 = spec.size - 1 - m |
|
|
1317 | 1318 | else: |
|
|
1318 | 1319 | bb0 = bb[0] - 1 |
|
|
1319 | 1320 | if (bb0 < 0): |
|
|
1320 | 1321 | bb0 = 0 |
|
|
1321 | 1322 | |
|
|
1322 | 1323 | if (ss.size == 0): |
|
|
1323 | 1324 | ss1 = 1 |
|
|
1324 | 1325 | else: |
|
|
1325 | 1326 | ss1 = max(ss) + 1 |
|
|
1326 | 1327 | |
|
|
1327 | 1328 | if (ss1 > m): |
|
|
1328 | 1329 | ss1 = m |
|
|
1329 | 1330 | |
|
|
1330 | 1331 | #valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
|
1331 | 1332 | valid = numpy.arange(1,oldspec.shape[0])# valid perfil completo igual pulsepair |
|
|
1332 | 1333 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition |
|
|
1333 | 1334 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition |
|
|
1334 | 1335 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() |
|
|
1335 | 1336 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power |
|
|
1336 | 1337 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) |
|
|
1337 | 1338 | snr = (spec2.mean()-n0)/n0 |
|
|
1338 | 1339 | if (snr < 1.e-20) : |
|
|
1339 | 1340 | snr = 1.e-20 |
|
|
1340 | 1341 | |
|
|
1341 | 1342 | # vec_power[ind] = power #D. ScipiΓ³n replaced with the line below |
|
|
1342 | 1343 | vec_power[ind] = total_power |
|
|
1343 | 1344 | vec_fd[ind] = fd |
|
|
1344 | 1345 | vec_w[ind] = w |
|
|
1345 | 1346 | vec_snr[ind] = snr |
|
|
1346 | 1347 | |
|
|
1347 | 1348 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
1348 | 1349 | |
|
|
1349 | 1350 | #------------------ Get SA Parameters -------------------------- |
|
|
1350 | 1351 | |
|
|
1351 | 1352 | def GetSAParameters(self): |
|
|
1352 | 1353 | #SA en frecuencia |
|
|
1353 | 1354 | pairslist = self.dataOut.groupList |
|
|
1354 | 1355 | num_pairs = len(pairslist) |
|
|
1355 | 1356 | |
|
|
1356 | 1357 | vel = self.dataOut.abscissaList |
|
|
1357 | 1358 | spectra = self.dataOut.data_pre |
|
|
1358 | 1359 | cspectra = self.dataIn.data_cspc |
|
|
1359 | 1360 | delta_v = vel[1] - vel[0] |
|
|
1360 | 1361 | |
|
|
1361 | 1362 | #Calculating the power spectrum |
|
|
1362 | 1363 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
1363 | 1364 | #Normalizing Spectra |
|
|
1364 | 1365 | norm_spectra = spectra/spc_pow |
|
|
1365 | 1366 | #Calculating the norm_spectra at peak |
|
|
1366 | 1367 | max_spectra = numpy.max(norm_spectra, 3) |
|
|
1367 | 1368 | |
|
|
1368 | 1369 | #Normalizing Cross Spectra |
|
|
1369 | 1370 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
|
1370 | 1371 | |
|
|
1371 | 1372 | for i in range(num_chan): |
|
|
1372 | 1373 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
|
1373 | 1374 | |
|
|
1374 | 1375 | max_cspectra = numpy.max(norm_cspectra,2) |
|
|
1375 | 1376 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
1376 | 1377 | |
|
|
1377 | 1378 | for i in range(num_pairs): |
|
|
1378 | 1379 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
1379 | 1380 | #------------------- Get Lags ---------------------------------- |
|
|
1380 | 1381 | |
|
|
1381 | 1382 | class SALags(Operation): |
|
|
1382 | 1383 | ''' |
|
|
1383 | 1384 | Function GetMoments() |
|
|
1384 | 1385 | |
|
|
1385 | 1386 | Input: |
|
|
1386 | 1387 | self.dataOut.data_pre |
|
|
1387 | 1388 | self.dataOut.abscissaList |
|
|
1388 | 1389 | self.dataOut.noise |
|
|
1389 | 1390 | self.dataOut.normFactor |
|
|
1390 | 1391 | self.dataOut.data_snr |
|
|
1391 | 1392 | self.dataOut.groupList |
|
|
1392 | 1393 | self.dataOut.nChannels |
|
|
1393 | 1394 | |
|
|
1394 | 1395 | Affected: |
|
|
1395 | 1396 | self.dataOut.data_param |
|
|
1396 | 1397 | |
|
|
1397 | 1398 | ''' |
|
|
1398 | 1399 | def run(self, dataOut): |
|
|
1399 | 1400 | data_acf = dataOut.data_pre[0] |
|
|
1400 | 1401 | data_ccf = dataOut.data_pre[1] |
|
|
1401 | 1402 | normFactor_acf = dataOut.normFactor[0] |
|
|
1402 | 1403 | normFactor_ccf = dataOut.normFactor[1] |
|
|
1403 | 1404 | pairs_acf = dataOut.groupList[0] |
|
|
1404 | 1405 | pairs_ccf = dataOut.groupList[1] |
|
|
1405 | 1406 | |
|
|
1406 | 1407 | nHeights = dataOut.nHeights |
|
|
1407 | 1408 | absc = dataOut.abscissaList |
|
|
1408 | 1409 | noise = dataOut.noise |
|
|
1409 | 1410 | SNR = dataOut.data_snr |
|
|
1410 | 1411 | nChannels = dataOut.nChannels |
|
|
1411 | 1412 | # pairsList = dataOut.groupList |
|
|
1412 | 1413 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1413 | 1414 | |
|
|
1414 | 1415 | for l in range(len(pairs_acf)): |
|
|
1415 | 1416 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
|
1416 | 1417 | |
|
|
1417 | 1418 | for l in range(len(pairs_ccf)): |
|
|
1418 | 1419 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
|
1419 | 1420 | |
|
|
1420 | 1421 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
|
1421 | 1422 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
1422 | 1423 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
1423 | 1424 | return |
|
|
1424 | 1425 | |
|
|
1425 | 1426 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1426 | 1427 | # |
|
|
1427 | 1428 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1428 | 1429 | # |
|
|
1429 | 1430 | # for l in range(len(pairsList)): |
|
|
1430 | 1431 | # firstChannel = pairsList[l][0] |
|
|
1431 | 1432 | # secondChannel = pairsList[l][1] |
|
|
1432 | 1433 | # |
|
|
1433 | 1434 | # #Obteniendo pares de Autocorrelacion |
|
|
1434 | 1435 | # if firstChannel == secondChannel: |
|
|
1435 | 1436 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1436 | 1437 | # |
|
|
1437 | 1438 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1438 | 1439 | # |
|
|
1439 | 1440 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1440 | 1441 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1441 | 1442 | # |
|
|
1442 | 1443 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1443 | 1444 | |
|
|
1444 | 1445 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
1445 | 1446 | |
|
|
1446 | 1447 | lag0 = data_acf.shape[1]/2 |
|
|
1447 | 1448 | #Funcion de Autocorrelacion |
|
|
1448 | 1449 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
1449 | 1450 | |
|
|
1450 | 1451 | #Obtencion Indice de TauCross |
|
|
1451 | 1452 | ind_ccf = data_ccf.argmax(axis = 1) |
|
|
1452 | 1453 | #Obtencion Indice de TauAuto |
|
|
1453 | 1454 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
1454 | 1455 | ccf_lag0 = data_ccf[:,lag0,:] |
|
|
1455 | 1456 | |
|
|
1456 | 1457 | for i in range(ccf_lag0.shape[0]): |
|
|
1457 | 1458 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
1458 | 1459 | |
|
|
1459 | 1460 | #Obtencion de TauCross y TauAuto |
|
|
1460 | 1461 | tau_ccf = lagRange[ind_ccf] |
|
|
1461 | 1462 | tau_acf = lagRange[ind_acf] |
|
|
1462 | 1463 | |
|
|
1463 | 1464 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
1464 | 1465 | |
|
|
1465 | 1466 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
1466 | 1467 | tau_acf[Nan1,Nan2] = numpy.nan |
|
|
1467 | 1468 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
1468 | 1469 | |
|
|
1469 | 1470 | return tau |
|
|
1470 | 1471 | |
|
|
1471 | 1472 | def __calculateLag1Phase(self, data, lagTRange): |
|
|
1472 | 1473 | data1 = stats.nanmean(data, axis = 0) |
|
|
1473 | 1474 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
1474 | 1475 | |
|
|
1475 | 1476 | phase = numpy.angle(data1[lag1,:]) |
|
|
1476 | 1477 | |
|
|
1477 | 1478 | return phase |
|
|
1478 | 1479 | |
|
|
1479 | 1480 | class SpectralFitting(Operation): |
|
|
1480 | 1481 | ''' |
|
|
1481 | 1482 | Function GetMoments() |
|
|
1482 | 1483 | |
|
|
1483 | 1484 | Input: |
|
|
1484 | 1485 | Output: |
|
|
1485 | 1486 | Variables modified: |
|
|
1486 | 1487 | ''' |
|
|
1487 | 1488 | |
|
|
1488 | 1489 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
1489 | 1490 | |
|
|
1490 | 1491 | |
|
|
1491 | 1492 | if path != None: |
|
|
1492 | 1493 | sys.path.append(path) |
|
|
1493 | 1494 | self.dataOut.library = importlib.import_module(file) |
|
|
1494 | 1495 | |
|
|
1495 | 1496 | #To be inserted as a parameter |
|
|
1496 | 1497 | groupArray = numpy.array(groupList) |
|
|
1497 | 1498 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1498 | 1499 | self.dataOut.groupList = groupArray |
|
|
1499 | 1500 | |
|
|
1500 | 1501 | nGroups = groupArray.shape[0] |
|
|
1501 | 1502 | nChannels = self.dataIn.nChannels |
|
|
1502 | 1503 | nHeights=self.dataIn.heightList.size |
|
|
1503 | 1504 | |
|
|
1504 | 1505 | #Parameters Array |
|
|
1505 | 1506 | self.dataOut.data_param = None |
|
|
1506 | 1507 | |
|
|
1507 | 1508 | #Set constants |
|
|
1508 | 1509 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1509 | 1510 | self.dataOut.constants = constants |
|
|
1510 | 1511 | M = self.dataIn.normFactor |
|
|
1511 | 1512 | N = self.dataIn.nFFTPoints |
|
|
1512 | 1513 | ippSeconds = self.dataIn.ippSeconds |
|
|
1513 | 1514 | K = self.dataIn.nIncohInt |
|
|
1514 | 1515 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1515 | 1516 | |
|
|
1516 | 1517 | #List of possible combinations |
|
|
1517 | 1518 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1518 | 1519 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1519 | 1520 | |
|
|
1520 | 1521 | if getSNR: |
|
|
1521 | 1522 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1522 | 1523 | listChannels.sort() |
|
|
1523 | 1524 | noise = self.dataIn.getNoise() |
|
|
1524 | 1525 | self.dataOut.data_snr = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1525 | 1526 | |
|
|
1526 | 1527 | for i in range(nGroups): |
|
|
1527 | 1528 | coord = groupArray[i,:] |
|
|
1528 | 1529 | |
|
|
1529 | 1530 | #Input data array |
|
|
1530 | 1531 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1531 | 1532 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1532 | 1533 | |
|
|
1533 | 1534 | #Cross Spectra data array for Covariance Matrixes |
|
|
1534 | 1535 | ind = 0 |
|
|
1535 | 1536 | for pairs in listComb: |
|
|
1536 | 1537 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1537 | 1538 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1538 | 1539 | ind += 1 |
|
|
1539 | 1540 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1540 | 1541 | dataCross = dataCross**2/K |
|
|
1541 | 1542 | |
|
|
1542 | 1543 | for h in range(nHeights): |
|
|
1543 | 1544 | |
|
|
1544 | 1545 | #Input |
|
|
1545 | 1546 | d = data[:,h] |
|
|
1546 | 1547 | |
|
|
1547 | 1548 | #Covariance Matrix |
|
|
1548 | 1549 | D = numpy.diag(d**2/K) |
|
|
1549 | 1550 | ind = 0 |
|
|
1550 | 1551 | for pairs in listComb: |
|
|
1551 | 1552 | #Coordinates in Covariance Matrix |
|
|
1552 | 1553 | x = pairs[0] |
|
|
1553 | 1554 | y = pairs[1] |
|
|
1554 | 1555 | #Channel Index |
|
|
1555 | 1556 | S12 = dataCross[ind,:,h] |
|
|
1556 | 1557 | D12 = numpy.diag(S12) |
|
|
1557 | 1558 | #Completing Covariance Matrix with Cross Spectras |
|
|
1558 | 1559 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1559 | 1560 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1560 | 1561 | ind += 1 |
|
|
1561 | 1562 | Dinv=numpy.linalg.inv(D) |
|
|
1562 | 1563 | L=numpy.linalg.cholesky(Dinv) |
|
|
1563 | 1564 | LT=L.T |
|
|
1564 | 1565 | |
|
|
1565 | 1566 | dp = numpy.dot(LT,d) |
|
|
1566 | 1567 | |
|
|
1567 | 1568 | #Initial values |
|
|
1568 | 1569 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1569 | 1570 | |
|
|
1570 | 1571 | if (h>0)and(error1[3]<5): |
|
|
1571 | 1572 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1572 | 1573 | else: |
|
|
1573 | 1574 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1574 | 1575 | |
|
|
1575 | 1576 | try: |
|
|
1576 | 1577 | #Least Squares |
|
|
1577 | 1578 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1578 | 1579 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1579 | 1580 | #Chi square error |
|
|
1580 | 1581 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1581 | 1582 | #Error with Jacobian |
|
|
1582 | 1583 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1583 | 1584 | except: |
|
|
1584 | 1585 | minp = p0*numpy.nan |
|
|
1585 | 1586 | error0 = numpy.nan |
|
|
1586 | 1587 | error1 = p0*numpy.nan |
|
|
1587 | 1588 | |
|
|
1588 | 1589 | #Save |
|
|
1589 | 1590 | if self.dataOut.data_param is None: |
|
|
1590 | 1591 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1591 | 1592 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1592 | 1593 | |
|
|
1593 | 1594 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1594 | 1595 | self.dataOut.data_param[i,:,h] = minp |
|
|
1595 | 1596 | return |
|
|
1596 | 1597 | |
|
|
1597 | 1598 | def __residFunction(self, p, dp, LT, constants): |
|
|
1598 | 1599 | |
|
|
1599 | 1600 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1600 | 1601 | fmp=numpy.dot(LT,fm) |
|
|
1601 | 1602 | |
|
|
1602 | 1603 | return dp-fmp |
|
|
1603 | 1604 | |
|
|
1604 | 1605 | def __getSNR(self, z, noise): |
|
|
1605 | 1606 | |
|
|
1606 | 1607 | avg = numpy.average(z, axis=1) |
|
|
1607 | 1608 | SNR = (avg.T-noise)/noise |
|
|
1608 | 1609 | SNR = SNR.T |
|
|
1609 | 1610 | return SNR |
|
|
1610 | 1611 | |
|
|
1611 | 1612 | def __chisq(p,chindex,hindex): |
|
|
1612 | 1613 | #similar to Resid but calculates CHI**2 |
|
|
1613 | 1614 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1614 | 1615 | dp=numpy.dot(LT,d) |
|
|
1615 | 1616 | fmp=numpy.dot(LT,fm) |
|
|
1616 | 1617 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1617 | 1618 | return chisq |
|
|
1618 | 1619 | |
|
|
1619 | 1620 | class WindProfiler(Operation): |
|
|
1620 | 1621 | |
|
|
1621 | 1622 | __isConfig = False |
|
|
1622 | 1623 | |
|
|
1623 | 1624 | __initime = None |
|
|
1624 | 1625 | __lastdatatime = None |
|
|
1625 | 1626 | __integrationtime = None |
|
|
1626 | 1627 | |
|
|
1627 | 1628 | __buffer = None |
|
|
1628 | 1629 | |
|
|
1629 | 1630 | __dataReady = False |
|
|
1630 | 1631 | |
|
|
1631 | 1632 | __firstdata = None |
|
|
1632 | 1633 | |
|
|
1633 | 1634 | n = None |
|
|
1634 | 1635 | |
|
|
1635 | 1636 | def __init__(self): |
|
|
1636 | 1637 | Operation.__init__(self) |
|
|
1637 | 1638 | |
|
|
1638 | 1639 | def __calculateCosDir(self, elev, azim): |
|
|
1639 | 1640 | zen = (90 - elev)*numpy.pi/180 |
|
|
1640 | 1641 | azim = azim*numpy.pi/180 |
|
|
1641 | 1642 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1642 | 1643 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1643 | 1644 | |
|
|
1644 | 1645 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1645 | 1646 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1646 | 1647 | |
|
|
1647 | 1648 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1648 | 1649 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1649 | 1650 | return cosDirX, cosDirY |
|
|
1650 | 1651 | |
|
|
1651 | 1652 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1652 | 1653 | |
|
|
1653 | 1654 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1654 | 1655 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1655 | 1656 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1656 | 1657 | |
|
|
1657 | 1658 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1658 | 1659 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1659 | 1660 | |
|
|
1660 | 1661 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1661 | 1662 | |
|
|
1662 | 1663 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1663 | 1664 | |
|
|
1664 | 1665 | # |
|
|
1665 | 1666 | if horOnly: |
|
|
1666 | 1667 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1667 | 1668 | else: |
|
|
1668 | 1669 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1669 | 1670 | A = numpy.asmatrix(A) |
|
|
1670 | 1671 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1671 | 1672 | |
|
|
1672 | 1673 | return A1 |
|
|
1673 | 1674 | |
|
|
1674 | 1675 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1675 | 1676 | listPhi = phi.tolist() |
|
|
1676 | 1677 | maxid = listPhi.index(max(listPhi)) |
|
|
1677 | 1678 | minid = listPhi.index(min(listPhi)) |
|
|
1678 | 1679 | |
|
|
1679 | 1680 | rango = list(range(len(phi))) |
|
|
1680 | 1681 | # rango = numpy.delete(rango,maxid) |
|
|
1681 | 1682 | |
|
|
1682 | 1683 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1683 | 1684 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1684 | 1685 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1685 | 1686 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1686 | 1687 | |
|
|
1687 | 1688 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1688 | 1689 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1689 | 1690 | |
|
|
1690 | 1691 | for i in rango: |
|
|
1691 | 1692 | x = heiRang*math.cos(phi[i]) |
|
|
1692 | 1693 | y1 = velRadial[i,:] |
|
|
1693 | 1694 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1694 | 1695 | |
|
|
1695 | 1696 | x1 = heiRang1 |
|
|
1696 | 1697 | y11 = f1(x1) |
|
|
1697 | 1698 | |
|
|
1698 | 1699 | y2 = SNR[i,:] |
|
|
1699 | 1700 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1700 | 1701 | y21 = f2(x1) |
|
|
1701 | 1702 | |
|
|
1702 | 1703 | velRadial1[i,:] = y11 |
|
|
1703 | 1704 | SNR1[i,:] = y21 |
|
|
1704 | 1705 | |
|
|
1705 | 1706 | return heiRang1, velRadial1, SNR1 |
|
|
1706 | 1707 | |
|
|
1707 | 1708 | def __calculateVelUVW(self, A, velRadial): |
|
|
1708 | 1709 | |
|
|
1709 | 1710 | #Operacion Matricial |
|
|
1710 | 1711 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1711 | 1712 | # for ind in range(velRadial.shape[1]): |
|
|
1712 | 1713 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1713 | 1714 | # velUVW = velUVW.transpose() |
|
|
1714 | 1715 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1715 | 1716 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1716 | 1717 | |
|
|
1717 | 1718 | |
|
|
1718 | 1719 | return velUVW |
|
|
1719 | 1720 | |
|
|
1720 | 1721 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1721 | 1722 | |
|
|
1722 | 1723 | def techniqueDBS(self, kwargs): |
|
|
1723 | 1724 | """ |
|
|
1724 | 1725 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1725 | 1726 | |
|
|
1726 | 1727 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1727 | 1728 | Direction correction (if necessary), Ranges and SNR |
|
|
1728 | 1729 | |
|
|
1729 | 1730 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1730 | 1731 | |
|
|
1731 | 1732 | Parameters affected: Winds, height range, SNR |
|
|
1732 | 1733 | """ |
|
|
1733 | 1734 | velRadial0 = kwargs['velRadial'] |
|
|
1734 | 1735 | heiRang = kwargs['heightList'] |
|
|
1735 | 1736 | SNR0 = kwargs['SNR'] |
|
|
1736 | 1737 | |
|
|
1737 | 1738 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
|
1738 | 1739 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1739 | 1740 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1740 | 1741 | else: |
|
|
1741 | 1742 | elev = numpy.array(kwargs['elevation']) |
|
|
1742 | 1743 | azim = numpy.array(kwargs['azimuth']) |
|
|
1743 | 1744 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1744 | 1745 | azimuth = kwargs['correctAzimuth'] |
|
|
1745 | 1746 | if 'horizontalOnly' in kwargs: |
|
|
1746 | 1747 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1747 | 1748 | else: horizontalOnly = False |
|
|
1748 | 1749 | if 'correctFactor' in kwargs: |
|
|
1749 | 1750 | correctFactor = kwargs['correctFactor'] |
|
|
1750 | 1751 | else: correctFactor = 1 |
|
|
1751 | 1752 | if 'channelList' in kwargs: |
|
|
1752 | 1753 | channelList = kwargs['channelList'] |
|
|
1753 | 1754 | if len(channelList) == 2: |
|
|
1754 | 1755 | horizontalOnly = True |
|
|
1755 | 1756 | arrayChannel = numpy.array(channelList) |
|
|
1756 | 1757 | param = param[arrayChannel,:,:] |
|
|
1757 | 1758 | theta_x = theta_x[arrayChannel] |
|
|
1758 | 1759 | theta_y = theta_y[arrayChannel] |
|
|
1759 | 1760 | |
|
|
1760 | 1761 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
1761 | 1762 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
1762 | 1763 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1763 | 1764 | |
|
|
1764 | 1765 | #Calculo de Componentes de la velocidad con DBS |
|
|
1765 | 1766 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1766 | 1767 | |
|
|
1767 | 1768 | return winds, heiRang1, SNR1 |
|
|
1768 | 1769 | |
|
|
1769 | 1770 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
1770 | 1771 | |
|
|
1771 | 1772 | nPairs = len(pairs_ccf) |
|
|
1772 | 1773 | posx = numpy.asarray(posx) |
|
|
1773 | 1774 | posy = numpy.asarray(posy) |
|
|
1774 | 1775 | |
|
|
1775 | 1776 | #Rotacion Inversa para alinear con el azimuth |
|
|
1776 | 1777 | if azimuth!= None: |
|
|
1777 | 1778 | azimuth = azimuth*math.pi/180 |
|
|
1778 | 1779 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1779 | 1780 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1780 | 1781 | else: |
|
|
1781 | 1782 | posx1 = posx |
|
|
1782 | 1783 | posy1 = posy |
|
|
1783 | 1784 | |
|
|
1784 | 1785 | #Calculo de Distancias |
|
|
1785 | 1786 | distx = numpy.zeros(nPairs) |
|
|
1786 | 1787 | disty = numpy.zeros(nPairs) |
|
|
1787 | 1788 | dist = numpy.zeros(nPairs) |
|
|
1788 | 1789 | ang = numpy.zeros(nPairs) |
|
|
1789 | 1790 | |
|
|
1790 | 1791 | for i in range(nPairs): |
|
|
1791 | 1792 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
1792 | 1793 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
1793 | 1794 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1794 | 1795 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1795 | 1796 | |
|
|
1796 | 1797 | return distx, disty, dist, ang |
|
|
1797 | 1798 | #Calculo de Matrices |
|
|
1798 | 1799 | # nPairs = len(pairs) |
|
|
1799 | 1800 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1800 | 1801 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1801 | 1802 | # |
|
|
1802 | 1803 | # for j in range(nPairs): |
|
|
1803 | 1804 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1804 | 1805 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1805 | 1806 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1806 | 1807 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1807 | 1808 | # |
|
|
1808 | 1809 | # return distx,disty, dist1,ang1 |
|
|
1809 | 1810 | |
|
|
1810 | 1811 | |
|
|
1811 | 1812 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1812 | 1813 | |
|
|
1813 | 1814 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1814 | 1815 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1815 | 1816 | |
|
|
1816 | 1817 | return velW |
|
|
1817 | 1818 | |
|
|
1818 | 1819 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1819 | 1820 | nPairs = tau1.shape[0] |
|
|
1820 | 1821 | nHeights = tau1.shape[1] |
|
|
1821 | 1822 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
1822 | 1823 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
1823 | 1824 | |
|
|
1824 | 1825 | angCos = numpy.cos(ang) |
|
|
1825 | 1826 | angSin = numpy.sin(ang) |
|
|
1826 | 1827 | |
|
|
1827 | 1828 | vel0 = dist1*tau1/(2*tau2**2) |
|
|
1828 | 1829 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1829 | 1830 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1830 | 1831 | |
|
|
1831 | 1832 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1832 | 1833 | vel[ind] = numpy.nan |
|
|
1833 | 1834 | |
|
|
1834 | 1835 | return vel |
|
|
1835 | 1836 | |
|
|
1836 | 1837 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1837 | 1838 | # |
|
|
1838 | 1839 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1839 | 1840 | # |
|
|
1840 | 1841 | # for l in range(len(pairsList)): |
|
|
1841 | 1842 | # firstChannel = pairsList[l][0] |
|
|
1842 | 1843 | # secondChannel = pairsList[l][1] |
|
|
1843 | 1844 | # |
|
|
1844 | 1845 | # #Obteniendo pares de Autocorrelacion |
|
|
1845 | 1846 | # if firstChannel == secondChannel: |
|
|
1846 | 1847 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1847 | 1848 | # |
|
|
1848 | 1849 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1849 | 1850 | # |
|
|
1850 | 1851 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1851 | 1852 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1852 | 1853 | # |
|
|
1853 | 1854 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1854 | 1855 | |
|
|
1855 | 1856 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1856 | 1857 | def techniqueSA(self, kwargs): |
|
|
1857 | 1858 | |
|
|
1858 | 1859 | """ |
|
|
1859 | 1860 | Function that implements Spaced Antenna (SA) technique. |
|
|
1860 | 1861 | |
|
|
1861 | 1862 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1862 | 1863 | Direction correction (if necessary), Ranges and SNR |
|
|
1863 | 1864 | |
|
|
1864 | 1865 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1865 | 1866 | |
|
|
1866 | 1867 | Parameters affected: Winds |
|
|
1867 | 1868 | """ |
|
|
1868 | 1869 | position_x = kwargs['positionX'] |
|
|
1869 | 1870 | position_y = kwargs['positionY'] |
|
|
1870 | 1871 | azimuth = kwargs['azimuth'] |
|
|
1871 | 1872 | |
|
|
1872 | 1873 | if 'correctFactor' in kwargs: |
|
|
1873 | 1874 | correctFactor = kwargs['correctFactor'] |
|
|
1874 | 1875 | else: |
|
|
1875 | 1876 | correctFactor = 1 |
|
|
1876 | 1877 | |
|
|
1877 | 1878 | groupList = kwargs['groupList'] |
|
|
1878 | 1879 | pairs_ccf = groupList[1] |
|
|
1879 | 1880 | tau = kwargs['tau'] |
|
|
1880 | 1881 | _lambda = kwargs['_lambda'] |
|
|
1881 | 1882 | |
|
|
1882 | 1883 | #Cross Correlation pairs obtained |
|
|
1883 | 1884 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
1884 | 1885 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1885 | 1886 | # pairsSelArray = numpy.array(pairsSelected) |
|
|
1886 | 1887 | # pairs = [] |
|
|
1887 | 1888 | # |
|
|
1888 | 1889 | # #Wind estimation pairs obtained |
|
|
1889 | 1890 | # for i in range(pairsSelArray.shape[0]/2): |
|
|
1890 | 1891 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1891 | 1892 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1892 | 1893 | # pairs.append((ind1,ind2)) |
|
|
1893 | 1894 | |
|
|
1894 | 1895 | indtau = tau.shape[0]/2 |
|
|
1895 | 1896 | tau1 = tau[:indtau,:] |
|
|
1896 | 1897 | tau2 = tau[indtau:-1,:] |
|
|
1897 | 1898 | # tau1 = tau1[pairs,:] |
|
|
1898 | 1899 | # tau2 = tau2[pairs,:] |
|
|
1899 | 1900 | phase1 = tau[-1,:] |
|
|
1900 | 1901 | |
|
|
1901 | 1902 | #--------------------------------------------------------------------- |
|
|
1902 | 1903 | #Metodo Directo |
|
|
1903 | 1904 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
1904 | 1905 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1905 | 1906 | winds = stats.nanmean(winds, axis=0) |
|
|
1906 | 1907 | #--------------------------------------------------------------------- |
|
|
1907 | 1908 | #Metodo General |
|
|
1908 | 1909 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1909 | 1910 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
1910 | 1911 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1911 | 1912 | # #Calculo de Velocidades |
|
|
1912 | 1913 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1913 | 1914 | |
|
|
1914 | 1915 | #--------------------------------------------------------------------- |
|
|
1915 | 1916 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1916 | 1917 | winds = correctFactor*winds |
|
|
1917 | 1918 | return winds |
|
|
1918 | 1919 | |
|
|
1919 | 1920 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
1920 | 1921 | |
|
|
1921 | 1922 | dataTime = currentTime + paramInterval |
|
|
1922 | 1923 | deltaTime = dataTime - self.__initime |
|
|
1923 | 1924 | |
|
|
1924 | 1925 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
1925 | 1926 | self.__dataReady = True |
|
|
1926 | 1927 | return |
|
|
1927 | 1928 | |
|
|
1928 | 1929 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
1929 | 1930 | ''' |
|
|
1930 | 1931 | Function that implements winds estimation technique with detected meteors. |
|
|
1931 | 1932 | |
|
|
1932 | 1933 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
1933 | 1934 | |
|
|
1934 | 1935 | Output: Winds estimation (Zonal and Meridional) |
|
|
1935 | 1936 | |
|
|
1936 | 1937 | Parameters affected: Winds |
|
|
1937 | 1938 | ''' |
|
|
1938 | 1939 | #Settings |
|
|
1939 | 1940 | nInt = (heightMax - heightMin)/2 |
|
|
1940 | 1941 | nInt = int(nInt) |
|
|
1941 | 1942 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
1942 | 1943 | |
|
|
1943 | 1944 | #Filter errors |
|
|
1944 | 1945 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
1945 | 1946 | finalMeteor = arrayMeteor[error,:] |
|
|
1946 | 1947 | |
|
|
1947 | 1948 | #Meteor Histogram |
|
|
1948 | 1949 | finalHeights = finalMeteor[:,2] |
|
|
1949 | 1950 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
1950 | 1951 | nMeteorsPerI = hist[0] |
|
|
1951 | 1952 | heightPerI = hist[1] |
|
|
1952 | 1953 | |
|
|
1953 | 1954 | #Sort of meteors |
|
|
1954 | 1955 | indSort = finalHeights.argsort() |
|
|
1955 | 1956 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
1956 | 1957 | |
|
|
1957 | 1958 | # Calculating winds |
|
|
1958 | 1959 | ind1 = 0 |
|
|
1959 | 1960 | ind2 = 0 |
|
|
1960 | 1961 | |
|
|
1961 | 1962 | for i in range(nInt): |
|
|
1962 | 1963 | nMet = nMeteorsPerI[i] |
|
|
1963 | 1964 | ind1 = ind2 |
|
|
1964 | 1965 | ind2 = ind1 + nMet |
|
|
1965 | 1966 | |
|
|
1966 | 1967 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
1967 | 1968 | |
|
|
1968 | 1969 | if meteorAux.shape[0] >= meteorThresh: |
|
|
1969 | 1970 | vel = meteorAux[:, 6] |
|
|
1970 | 1971 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
1971 | 1972 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
1972 | 1973 | |
|
|
1973 | 1974 | n = numpy.cos(zen) |
|
|
1974 | 1975 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
1975 | 1976 | # l = m*numpy.tan(azim) |
|
|
1976 | 1977 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
1977 | 1978 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
1978 | 1979 | |
|
|
1979 | 1980 | A = numpy.vstack((l, m)).transpose() |
|
|
1980 | 1981 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
1981 | 1982 | windsAux = numpy.dot(A1, vel) |
|
|
1982 | 1983 | |
|
|
1983 | 1984 | winds[0,i] = windsAux[0] |
|
|
1984 | 1985 | winds[1,i] = windsAux[1] |
|
|
1985 | 1986 | |
|
|
1986 | 1987 | return winds, heightPerI[:-1] |
|
|
1987 | 1988 | |
|
|
1988 | 1989 | def techniqueNSM_SA(self, **kwargs): |
|
|
1989 | 1990 | metArray = kwargs['metArray'] |
|
|
1990 | 1991 | heightList = kwargs['heightList'] |
|
|
1991 | 1992 | timeList = kwargs['timeList'] |
|
|
1992 | 1993 | |
|
|
1993 | 1994 | rx_location = kwargs['rx_location'] |
|
|
1994 | 1995 | groupList = kwargs['groupList'] |
|
|
1995 | 1996 | azimuth = kwargs['azimuth'] |
|
|
1996 | 1997 | dfactor = kwargs['dfactor'] |
|
|
1997 | 1998 | k = kwargs['k'] |
|
|
1998 | 1999 | |
|
|
1999 | 2000 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
2000 | 2001 | d = dist*dfactor |
|
|
2001 | 2002 | #Phase calculation |
|
|
2002 | 2003 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
2003 | 2004 | |
|
|
2004 | 2005 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
2005 | 2006 | |
|
|
2006 | 2007 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2007 | 2008 | azimuth1 = azimuth1*numpy.pi/180 |
|
|
2008 | 2009 | |
|
|
2009 | 2010 | for i in range(heightList.size): |
|
|
2010 | 2011 | h = heightList[i] |
|
|
2011 | 2012 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
2012 | 2013 | metHeight = metArray1[indH,:] |
|
|
2013 | 2014 | if metHeight.shape[0] >= 2: |
|
|
2014 | 2015 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
2015 | 2016 | iazim = metHeight[:,1].astype(int) |
|
|
2016 | 2017 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
2017 | 2018 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
2018 | 2019 | A = numpy.asmatrix(A) |
|
|
2019 | 2020 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
2020 | 2021 | velHor = numpy.dot(A1,velAux) |
|
|
2021 | 2022 | |
|
|
2022 | 2023 | velEst[i,:] = numpy.squeeze(velHor) |
|
|
2023 | 2024 | return velEst |
|
|
2024 | 2025 | |
|
|
2025 | 2026 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
2026 | 2027 | meteorList = [] |
|
|
2027 | 2028 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
2028 | 2029 | #Putting back together the meteor matrix |
|
|
2029 | 2030 | utctime = metArray[:,0] |
|
|
2030 | 2031 | uniqueTime = numpy.unique(utctime) |
|
|
2031 | 2032 | |
|
|
2032 | 2033 | phaseDerThresh = 0.5 |
|
|
2033 | 2034 | ippSeconds = timeList[1] - timeList[0] |
|
|
2034 | 2035 | sec = numpy.where(timeList>1)[0][0] |
|
|
2035 | 2036 | nPairs = metArray.shape[1] - 6 |
|
|
2036 | 2037 | nHeights = len(heightList) |
|
|
2037 | 2038 | |
|
|
2038 | 2039 | for t in uniqueTime: |
|
|
2039 | 2040 | metArray1 = metArray[utctime==t,:] |
|
|
2040 | 2041 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
2041 | 2042 | tmet = metArray1[:,1].astype(int) |
|
|
2042 | 2043 | hmet = metArray1[:,2].astype(int) |
|
|
2043 | 2044 | |
|
|
2044 | 2045 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
2045 | 2046 | metPhase[:,:] = numpy.nan |
|
|
2046 | 2047 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
2047 | 2048 | |
|
|
2048 | 2049 | #Delete short trails |
|
|
2049 | 2050 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
2050 | 2051 | heightVect = numpy.sum(metBool, axis = 1) |
|
|
2051 | 2052 | metBool[heightVect<sec,:] = False |
|
|
2052 | 2053 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
2053 | 2054 | |
|
|
2054 | 2055 | #Derivative |
|
|
2055 | 2056 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
2056 | 2057 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
2057 | 2058 | metPhase[phDerAux] = numpy.nan |
|
|
2058 | 2059 | |
|
|
2059 | 2060 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
|
2060 | 2061 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
2061 | 2062 | |
|
|
2062 | 2063 | for p in numpy.arange(nPairs): |
|
|
2063 | 2064 | phase = metPhase[p,:,:] |
|
|
2064 | 2065 | phDer = metDer[p,:,:] |
|
|
2065 | 2066 | |
|
|
2066 | 2067 | for h in indMet: |
|
|
2067 | 2068 | height = heightList[h] |
|
|
2068 | 2069 | phase1 = phase[h,:] #82 |
|
|
2069 | 2070 | phDer1 = phDer[h,:] |
|
|
2070 | 2071 | |
|
|
2071 | 2072 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
2072 | 2073 | |
|
|
2073 | 2074 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
2074 | 2075 | initMet = indValid[0] |
|
|
2075 | 2076 | endMet = 0 |
|
|
2076 | 2077 | |
|
|
2077 | 2078 | for i in range(len(indValid)-1): |
|
|
2078 | 2079 | |
|
|
2079 | 2080 | #Time difference |
|
|
2080 | 2081 | inow = indValid[i] |
|
|
2081 | 2082 | inext = indValid[i+1] |
|
|
2082 | 2083 | idiff = inext - inow |
|
|
2083 | 2084 | #Phase difference |
|
|
2084 | 2085 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
2085 | 2086 | |
|
|
2086 | 2087 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
2087 | 2088 | sizeTrail = inow - initMet + 1 |
|
|
2088 | 2089 | if sizeTrail>3*sec: #Too short meteors |
|
|
2089 | 2090 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
2090 | 2091 | y = phase1[initMet:inow+1] |
|
|
2091 | 2092 | ynnan = ~numpy.isnan(y) |
|
|
2092 | 2093 | x = x[ynnan] |
|
|
2093 | 2094 | y = y[ynnan] |
|
|
2094 | 2095 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
2095 | 2096 | ylin = x*slope + intercept |
|
|
2096 | 2097 | rsq = r_value**2 |
|
|
2097 | 2098 | if rsq > 0.5: |
|
|
2098 | 2099 | vel = slope#*height*1000/(k*d) |
|
|
2099 | 2100 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
2100 | 2101 | meteorList.append(estAux) |
|
|
2101 | 2102 | initMet = inext |
|
|
2102 | 2103 | metArray2 = numpy.array(meteorList) |
|
|
2103 | 2104 | |
|
|
2104 | 2105 | return metArray2 |
|
|
2105 | 2106 | |
|
|
2106 | 2107 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
2107 | 2108 | |
|
|
2108 | 2109 | azimuth1 = numpy.zeros(len(pairslist)) |
|
|
2109 | 2110 | dist = numpy.zeros(len(pairslist)) |
|
|
2110 | 2111 | |
|
|
2111 | 2112 | for i in range(len(rx_location)): |
|
|
2112 | 2113 | ch0 = pairslist[i][0] |
|
|
2113 | 2114 | ch1 = pairslist[i][1] |
|
|
2114 | 2115 | |
|
|
2115 | 2116 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
2116 | 2117 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
2117 | 2118 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
2118 | 2119 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
2119 | 2120 | |
|
|
2120 | 2121 | azimuth1 -= azimuth0 |
|
|
2121 | 2122 | return azimuth1, dist |
|
|
2122 | 2123 | |
|
|
2123 | 2124 | def techniqueNSM_DBS(self, **kwargs): |
|
|
2124 | 2125 | metArray = kwargs['metArray'] |
|
|
2125 | 2126 | heightList = kwargs['heightList'] |
|
|
2126 | 2127 | timeList = kwargs['timeList'] |
|
|
2127 | 2128 | azimuth = kwargs['azimuth'] |
|
|
2128 | 2129 | theta_x = numpy.array(kwargs['theta_x']) |
|
|
2129 | 2130 | theta_y = numpy.array(kwargs['theta_y']) |
|
|
2130 | 2131 | |
|
|
2131 | 2132 | utctime = metArray[:,0] |
|
|
2132 | 2133 | cmet = metArray[:,1].astype(int) |
|
|
2133 | 2134 | hmet = metArray[:,3].astype(int) |
|
|
2134 | 2135 | SNRmet = metArray[:,4] |
|
|
2135 | 2136 | vmet = metArray[:,5] |
|
|
2136 | 2137 | spcmet = metArray[:,6] |
|
|
2137 | 2138 | |
|
|
2138 | 2139 | nChan = numpy.max(cmet) + 1 |
|
|
2139 | 2140 | nHeights = len(heightList) |
|
|
2140 | 2141 | |
|
|
2141 | 2142 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
2142 | 2143 | hmet = heightList[hmet] |
|
|
2143 | 2144 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
|
2144 | 2145 | |
|
|
2145 | 2146 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2146 | 2147 | |
|
|
2147 | 2148 | for i in range(nHeights - 1): |
|
|
2148 | 2149 | hmin = heightList[i] |
|
|
2149 | 2150 | hmax = heightList[i + 1] |
|
|
2150 | 2151 | |
|
|
2151 | 2152 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
|
2152 | 2153 | indthisH = numpy.where(thisH) |
|
|
2153 | 2154 | |
|
|
2154 | 2155 | if numpy.size(indthisH) > 3: |
|
|
2155 | 2156 | |
|
|
2156 | 2157 | vel_aux = vmet[thisH] |
|
|
2157 | 2158 | chan_aux = cmet[thisH] |
|
|
2158 | 2159 | cosu_aux = dir_cosu[chan_aux] |
|
|
2159 | 2160 | cosv_aux = dir_cosv[chan_aux] |
|
|
2160 | 2161 | cosw_aux = dir_cosw[chan_aux] |
|
|
2161 | 2162 | |
|
|
2162 | 2163 | nch = numpy.size(numpy.unique(chan_aux)) |
|
|
2163 | 2164 | if nch > 1: |
|
|
2164 | 2165 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
|
2165 | 2166 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
|
2166 | 2167 | |
|
|
2167 | 2168 | return velEst |
|
|
2168 | 2169 | |
|
|
2169 | 2170 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
|
2170 | 2171 | |
|
|
2171 | 2172 | param = dataOut.data_param |
|
|
2172 | 2173 | if dataOut.abscissaList != None: |
|
|
2173 | 2174 | absc = dataOut.abscissaList[:-1] |
|
|
2174 | 2175 | # noise = dataOut.noise |
|
|
2175 | 2176 | heightList = dataOut.heightList |
|
|
2176 | 2177 | SNR = dataOut.data_snr |
|
|
2177 | 2178 | |
|
|
2178 | 2179 | if technique == 'DBS': |
|
|
2179 | 2180 | |
|
|
2180 | 2181 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
2181 | 2182 | kwargs['heightList'] = heightList |
|
|
2182 | 2183 | kwargs['SNR'] = SNR |
|
|
2183 | 2184 | |
|
|
2184 | 2185 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function |
|
|
2185 | 2186 | dataOut.utctimeInit = dataOut.utctime |
|
|
2186 | 2187 | dataOut.outputInterval = dataOut.paramInterval |
|
|
2187 | 2188 | |
|
|
2188 | 2189 | elif technique == 'SA': |
|
|
2189 | 2190 | |
|
|
2190 | 2191 | #Parameters |
|
|
2191 | 2192 | # position_x = kwargs['positionX'] |
|
|
2192 | 2193 | # position_y = kwargs['positionY'] |
|
|
2193 | 2194 | # azimuth = kwargs['azimuth'] |
|
|
2194 | 2195 | # |
|
|
2195 | 2196 | # if kwargs.has_key('crosspairsList'): |
|
|
2196 | 2197 | # pairs = kwargs['crosspairsList'] |
|
|
2197 | 2198 | # else: |
|
|
2198 | 2199 | # pairs = None |
|
|
2199 | 2200 | # |
|
|
2200 | 2201 | # if kwargs.has_key('correctFactor'): |
|
|
2201 | 2202 | # correctFactor = kwargs['correctFactor'] |
|
|
2202 | 2203 | # else: |
|
|
2203 | 2204 | # correctFactor = 1 |
|
|
2204 | 2205 | |
|
|
2205 | 2206 | # tau = dataOut.data_param |
|
|
2206 | 2207 | # _lambda = dataOut.C/dataOut.frequency |
|
|
2207 | 2208 | # pairsList = dataOut.groupList |
|
|
2208 | 2209 | # nChannels = dataOut.nChannels |
|
|
2209 | 2210 | |
|
|
2210 | 2211 | kwargs['groupList'] = dataOut.groupList |
|
|
2211 | 2212 | kwargs['tau'] = dataOut.data_param |
|
|
2212 | 2213 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
2213 | 2214 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
2214 | 2215 | dataOut.data_output = self.techniqueSA(kwargs) |
|
|
2215 | 2216 | dataOut.utctimeInit = dataOut.utctime |
|
|
2216 | 2217 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2217 | 2218 | |
|
|
2218 | 2219 | elif technique == 'Meteors': |
|
|
2219 | 2220 | dataOut.flagNoData = True |
|
|
2220 | 2221 | self.__dataReady = False |
|
|
2221 | 2222 | |
|
|
2222 | 2223 | if 'nHours' in kwargs: |
|
|
2223 | 2224 | nHours = kwargs['nHours'] |
|
|
2224 | 2225 | else: |
|
|
2225 | 2226 | nHours = 1 |
|
|
2226 | 2227 | |
|
|
2227 | 2228 | if 'meteorsPerBin' in kwargs: |
|
|
2228 | 2229 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
2229 | 2230 | else: |
|
|
2230 | 2231 | meteorThresh = 6 |
|
|
2231 | 2232 | |
|
|
2232 | 2233 | if 'hmin' in kwargs: |
|
|
2233 | 2234 | hmin = kwargs['hmin'] |
|
|
2234 | 2235 | else: hmin = 70 |
|
|
2235 | 2236 | if 'hmax' in kwargs: |
|
|
2236 | 2237 | hmax = kwargs['hmax'] |
|
|
2237 | 2238 | else: hmax = 110 |
|
|
2238 | 2239 | |
|
|
2239 | 2240 | dataOut.outputInterval = nHours*3600 |
|
|
2240 | 2241 | |
|
|
2241 | 2242 | if self.__isConfig == False: |
|
|
2242 | 2243 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2243 | 2244 | #Get Initial LTC time |
|
|
2244 | 2245 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2245 | 2246 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2246 | 2247 | |
|
|
2247 | 2248 | self.__isConfig = True |
|
|
2248 | 2249 | |
|
|
2249 | 2250 | if self.__buffer is None: |
|
|
2250 | 2251 | self.__buffer = dataOut.data_param |
|
|
2251 | 2252 | self.__firstdata = copy.copy(dataOut) |
|
|
2252 | 2253 | |
|
|
2253 | 2254 | else: |
|
|
2254 | 2255 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2255 | 2256 | |
|
|
2256 | 2257 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2257 | 2258 | |
|
|
2258 | 2259 | if self.__dataReady: |
|
|
2259 | 2260 | dataOut.utctimeInit = self.__initime |
|
|
2260 | 2261 | |
|
|
2261 | 2262 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2262 | 2263 | |
|
|
2263 | 2264 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
2264 | 2265 | dataOut.flagNoData = False |
|
|
2265 | 2266 | self.__buffer = None |
|
|
2266 | 2267 | |
|
|
2267 | 2268 | elif technique == 'Meteors1': |
|
|
2268 | 2269 | dataOut.flagNoData = True |
|
|
2269 | 2270 | self.__dataReady = False |
|
|
2270 | 2271 | |
|
|
2271 | 2272 | if 'nMins' in kwargs: |
|
|
2272 | 2273 | nMins = kwargs['nMins'] |
|
|
2273 | 2274 | else: nMins = 20 |
|
|
2274 | 2275 | if 'rx_location' in kwargs: |
|
|
2275 | 2276 | rx_location = kwargs['rx_location'] |
|
|
2276 | 2277 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
2277 | 2278 | if 'azimuth' in kwargs: |
|
|
2278 | 2279 | azimuth = kwargs['azimuth'] |
|
|
2279 | 2280 | else: azimuth = 51.06 |
|
|
2280 | 2281 | if 'dfactor' in kwargs: |
|
|
2281 | 2282 | dfactor = kwargs['dfactor'] |
|
|
2282 | 2283 | if 'mode' in kwargs: |
|
|
2283 | 2284 | mode = kwargs['mode'] |
|
|
2284 | 2285 | if 'theta_x' in kwargs: |
|
|
2285 | 2286 | theta_x = kwargs['theta_x'] |
|
|
2286 | 2287 | if 'theta_y' in kwargs: |
|
|
2287 | 2288 | theta_y = kwargs['theta_y'] |
|
|
2288 | 2289 | else: mode = 'SA' |
|
|
2289 | 2290 | |
|
|
2290 | 2291 | #Borrar luego esto |
|
|
2291 | 2292 | if dataOut.groupList is None: |
|
|
2292 | 2293 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
2293 | 2294 | groupList = dataOut.groupList |
|
|
2294 | 2295 | C = 3e8 |
|
|
2295 | 2296 | freq = 50e6 |
|
|
2296 | 2297 | lamb = C/freq |
|
|
2297 | 2298 | k = 2*numpy.pi/lamb |
|
|
2298 | 2299 | |
|
|
2299 | 2300 | timeList = dataOut.abscissaList |
|
|
2300 | 2301 | heightList = dataOut.heightList |
|
|
2301 | 2302 | |
|
|
2302 | 2303 | if self.__isConfig == False: |
|
|
2303 | 2304 | dataOut.outputInterval = nMins*60 |
|
|
2304 | 2305 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2305 | 2306 | #Get Initial LTC time |
|
|
2306 | 2307 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2307 | 2308 | minuteAux = initime.minute |
|
|
2308 | 2309 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
2309 | 2310 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2310 | 2311 | |
|
|
2311 | 2312 | self.__isConfig = True |
|
|
2312 | 2313 | |
|
|
2313 | 2314 | if self.__buffer is None: |
|
|
2314 | 2315 | self.__buffer = dataOut.data_param |
|
|
2315 | 2316 | self.__firstdata = copy.copy(dataOut) |
|
|
2316 | 2317 | |
|
|
2317 | 2318 | else: |
|
|
2318 | 2319 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2319 | 2320 | |
|
|
2320 | 2321 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2321 | 2322 | |
|
|
2322 | 2323 | if self.__dataReady: |
|
|
2323 | 2324 | dataOut.utctimeInit = self.__initime |
|
|
2324 | 2325 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2325 | 2326 | |
|
|
2326 | 2327 | metArray = self.__buffer |
|
|
2327 | 2328 | if mode == 'SA': |
|
|
2328 | 2329 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
2329 | 2330 | elif mode == 'DBS': |
|
|
2330 | 2331 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
|
2331 | 2332 | dataOut.data_output = dataOut.data_output.T |
|
|
2332 | 2333 | dataOut.flagNoData = False |
|
|
2333 | 2334 | self.__buffer = None |
|
|
2334 | 2335 | |
|
|
2335 | 2336 | return |
|
|
2336 | 2337 | |
|
|
2337 | 2338 | class EWDriftsEstimation(Operation): |
|
|
2338 | 2339 | |
|
|
2339 | 2340 | def __init__(self): |
|
|
2340 | 2341 | Operation.__init__(self) |
|
|
2341 | 2342 | |
|
|
2342 | 2343 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
2343 | 2344 | listPhi = phi.tolist() |
|
|
2344 | 2345 | maxid = listPhi.index(max(listPhi)) |
|
|
2345 | 2346 | minid = listPhi.index(min(listPhi)) |
|
|
2346 | 2347 | |
|
|
2347 | 2348 | rango = list(range(len(phi))) |
|
|
2348 | 2349 | # rango = numpy.delete(rango,maxid) |
|
|
2349 | 2350 | |
|
|
2350 | 2351 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
2351 | 2352 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
2352 | 2353 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
2353 | 2354 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
2354 | 2355 | |
|
|
2355 | 2356 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2356 | 2357 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2357 | 2358 | |
|
|
2358 | 2359 | for i in rango: |
|
|
2359 | 2360 | x = heiRang*math.cos(phi[i]) |
|
|
2360 | 2361 | y1 = velRadial[i,:] |
|
|
2361 | 2362 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
2362 | 2363 | |
|
|
2363 | 2364 | x1 = heiRang1 |
|
|
2364 | 2365 | y11 = f1(x1) |
|
|
2365 | 2366 | |
|
|
2366 | 2367 | y2 = SNR[i,:] |
|
|
2367 | 2368 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
2368 | 2369 | y21 = f2(x1) |
|
|
2369 | 2370 | |
|
|
2370 | 2371 | velRadial1[i,:] = y11 |
|
|
2371 | 2372 | SNR1[i,:] = y21 |
|
|
2372 | 2373 | |
|
|
2373 | 2374 | return heiRang1, velRadial1, SNR1 |
|
|
2374 | 2375 | |
|
|
2375 | 2376 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
2376 | 2377 | heiRang = dataOut.heightList |
|
|
2377 | 2378 | velRadial = dataOut.data_param[:,3,:] |
|
|
2378 | 2379 | SNR = dataOut.data_snr |
|
|
2379 | 2380 | |
|
|
2380 | 2381 | zenith = numpy.array(zenith) |
|
|
2381 | 2382 | zenith -= zenithCorrection |
|
|
2382 | 2383 | zenith *= numpy.pi/180 |
|
|
2383 | 2384 | |
|
|
2384 | 2385 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
2385 | 2386 | |
|
|
2386 | 2387 | alp = zenith[0] |
|
|
2387 | 2388 | bet = zenith[1] |
|
|
2388 | 2389 | |
|
|
2389 | 2390 | w_w = velRadial1[0,:] |
|
|
2390 | 2391 | w_e = velRadial1[1,:] |
|
|
2391 | 2392 | |
|
|
2392 | 2393 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
2393 | 2394 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
2394 | 2395 | |
|
|
2395 | 2396 | winds = numpy.vstack((u,w)) |
|
|
2396 | 2397 | |
|
|
2397 | 2398 | dataOut.heightList = heiRang1 |
|
|
2398 | 2399 | dataOut.data_output = winds |
|
|
2399 | 2400 | dataOut.data_snr = SNR1 |
|
|
2400 | 2401 | |
|
|
2401 | 2402 | dataOut.utctimeInit = dataOut.utctime |
|
|
2402 | 2403 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2403 | 2404 | return |
|
|
2404 | 2405 | |
|
|
2405 | 2406 | #--------------- Non Specular Meteor ---------------- |
|
|
2406 | 2407 | |
|
|
2407 | 2408 | class NonSpecularMeteorDetection(Operation): |
|
|
2408 | 2409 | |
|
|
2409 | 2410 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
2410 | 2411 | data_acf = dataOut.data_pre[0] |
|
|
2411 | 2412 | data_ccf = dataOut.data_pre[1] |
|
|
2412 | 2413 | pairsList = dataOut.groupList[1] |
|
|
2413 | 2414 | |
|
|
2414 | 2415 | lamb = dataOut.C/dataOut.frequency |
|
|
2415 | 2416 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
|
2416 | 2417 | paramInterval = dataOut.paramInterval |
|
|
2417 | 2418 | |
|
|
2418 | 2419 | nChannels = data_acf.shape[0] |
|
|
2419 | 2420 | nLags = data_acf.shape[1] |
|
|
2420 | 2421 | nProfiles = data_acf.shape[2] |
|
|
2421 | 2422 | nHeights = dataOut.nHeights |
|
|
2422 | 2423 | nCohInt = dataOut.nCohInt |
|
|
2423 | 2424 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
|
2424 | 2425 | heightList = dataOut.heightList |
|
|
2425 | 2426 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
|
2426 | 2427 | utctime = dataOut.utctime |
|
|
2427 | 2428 | |
|
|
2428 | 2429 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
2429 | 2430 | |
|
|
2430 | 2431 | #------------------------ SNR -------------------------------------- |
|
|
2431 | 2432 | power = data_acf[:,0,:,:].real |
|
|
2432 | 2433 | noise = numpy.zeros(nChannels) |
|
|
2433 | 2434 | SNR = numpy.zeros(power.shape) |
|
|
2434 | 2435 | for i in range(nChannels): |
|
|
2435 | 2436 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
2436 | 2437 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
2437 | 2438 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
2438 | 2439 | SNRdB = 10*numpy.log10(SNR) |
|
|
2439 | 2440 | |
|
|
2440 | 2441 | if mode == 'SA': |
|
|
2441 | 2442 | dataOut.groupList = dataOut.groupList[1] |
|
|
2442 | 2443 | nPairs = data_ccf.shape[0] |
|
|
2443 | 2444 | #---------------------- Coherence and Phase -------------------------- |
|
|
2444 | 2445 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2445 | 2446 | # phase1 = numpy.copy(phase) |
|
|
2446 | 2447 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2447 | 2448 | |
|
|
2448 | 2449 | for p in range(nPairs): |
|
|
2449 | 2450 | ch0 = pairsList[p][0] |
|
|
2450 | 2451 | ch1 = pairsList[p][1] |
|
|
2451 | 2452 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
2452 | 2453 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
2453 | 2454 | # phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
2454 | 2455 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
2455 | 2456 | # coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
2456 | 2457 | coh = numpy.nanmax(coh1, axis = 0) |
|
|
2457 | 2458 | # struc = numpy.ones((5,1)) |
|
|
2458 | 2459 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
2459 | 2460 | #---------------------- Radial Velocity ---------------------------- |
|
|
2460 | 2461 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
2461 | 2462 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
2462 | 2463 | |
|
|
2463 | 2464 | if allData: |
|
|
2464 | 2465 | boolMetFin = ~numpy.isnan(SNRm) |
|
|
2465 | 2466 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2466 | 2467 | else: |
|
|
2467 | 2468 | #------------------------ Meteor mask --------------------------------- |
|
|
2468 | 2469 | # #SNR mask |
|
|
2469 | 2470 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
2470 | 2471 | # |
|
|
2471 | 2472 | # #Erase small objects |
|
|
2472 | 2473 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
2473 | 2474 | # |
|
|
2474 | 2475 | # auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
2475 | 2476 | # indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
2476 | 2477 | # indEEJ = numpy.where(indOver)[0] |
|
|
2477 | 2478 | # indNEEJ = numpy.where(~indOver)[0] |
|
|
2478 | 2479 | # |
|
|
2479 | 2480 | # boolMetFin = boolMet1 |
|
|
2480 | 2481 | # |
|
|
2481 | 2482 | # if indEEJ.size > 0: |
|
|
2482 | 2483 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
2483 | 2484 | # |
|
|
2484 | 2485 | # boolMet2 = coh > cohThresh |
|
|
2485 | 2486 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
2486 | 2487 | # |
|
|
2487 | 2488 | # #Final Meteor mask |
|
|
2488 | 2489 | # boolMetFin = boolMet1|boolMet2 |
|
|
2489 | 2490 | |
|
|
2490 | 2491 | #Coherence mask |
|
|
2491 | 2492 | boolMet1 = coh > 0.75 |
|
|
2492 | 2493 | struc = numpy.ones((30,1)) |
|
|
2493 | 2494 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
2494 | 2495 | |
|
|
2495 | 2496 | #Derivative mask |
|
|
2496 | 2497 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2497 | 2498 | boolMet2 = derPhase < 0.2 |
|
|
2498 | 2499 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
2499 | 2500 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
2500 | 2501 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
2501 | 2502 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
2502 | 2503 | # #Final mask |
|
|
2503 | 2504 | # boolMetFin = boolMet2 |
|
|
2504 | 2505 | boolMetFin = boolMet1&boolMet2 |
|
|
2505 | 2506 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
2506 | 2507 | #Creating data_param |
|
|
2507 | 2508 | coordMet = numpy.where(boolMetFin) |
|
|
2508 | 2509 | |
|
|
2509 | 2510 | tmet = coordMet[0] |
|
|
2510 | 2511 | hmet = coordMet[1] |
|
|
2511 | 2512 | |
|
|
2512 | 2513 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
2513 | 2514 | data_param[:,0] = utctime |
|
|
2514 | 2515 | data_param[:,1] = tmet |
|
|
2515 | 2516 | data_param[:,2] = hmet |
|
|
2516 | 2517 | data_param[:,3] = SNRm[tmet,hmet] |
|
|
2517 | 2518 | data_param[:,4] = velRad[tmet,hmet] |
|
|
2518 | 2519 | data_param[:,5] = coh[tmet,hmet] |
|
|
2519 | 2520 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
2520 | 2521 | |
|
|
2521 | 2522 | elif mode == 'DBS': |
|
|
2522 | 2523 | dataOut.groupList = numpy.arange(nChannels) |
|
|
2523 | 2524 | |
|
|
2524 | 2525 | #Radial Velocities |
|
|
2525 | 2526 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
2526 | 2527 | # phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2527 | 2528 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
2528 | 2529 | |
|
|
2529 | 2530 | #Spectral width |
|
|
2530 | 2531 | # acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2531 | 2532 | # acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
2532 | 2533 | acf1 = data_acf[:,1,:,:] |
|
|
2533 | 2534 | acf2 = data_acf[:,2,:,:] |
|
|
2534 | 2535 | |
|
|
2535 | 2536 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
2536 | 2537 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
2537 | 2538 | if allData: |
|
|
2538 | 2539 | boolMetFin = ~numpy.isnan(SNRdB) |
|
|
2539 | 2540 | else: |
|
|
2540 | 2541 | #SNR |
|
|
2541 | 2542 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
2542 | 2543 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
2543 | 2544 | |
|
|
2544 | 2545 | #Radial velocity |
|
|
2545 | 2546 | boolMet2 = numpy.abs(velRad) < 20 |
|
|
2546 | 2547 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
2547 | 2548 | |
|
|
2548 | 2549 | #Spectral Width |
|
|
2549 | 2550 | boolMet3 = spcWidth < 30 |
|
|
2550 | 2551 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
2551 | 2552 | # boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
2552 | 2553 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
2553 | 2554 | |
|
|
2554 | 2555 | #Creating data_param |
|
|
2555 | 2556 | coordMet = numpy.where(boolMetFin) |
|
|
2556 | 2557 | |
|
|
2557 | 2558 | cmet = coordMet[0] |
|
|
2558 | 2559 | tmet = coordMet[1] |
|
|
2559 | 2560 | hmet = coordMet[2] |
|
|
2560 | 2561 | |
|
|
2561 | 2562 | data_param = numpy.zeros((tmet.size, 7)) |
|
|
2562 | 2563 | data_param[:,0] = utctime |
|
|
2563 | 2564 | data_param[:,1] = cmet |
|
|
2564 | 2565 | data_param[:,2] = tmet |
|
|
2565 | 2566 | data_param[:,3] = hmet |
|
|
2566 | 2567 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
2567 | 2568 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
2568 | 2569 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
2569 | 2570 | |
|
|
2570 | 2571 | # self.dataOut.data_param = data_int |
|
|
2571 | 2572 | if len(data_param) == 0: |
|
|
2572 | 2573 | dataOut.flagNoData = True |
|
|
2573 | 2574 | else: |
|
|
2574 | 2575 | dataOut.data_param = data_param |
|
|
2575 | 2576 | |
|
|
2576 | 2577 | def __erase_small(self, binArray, threshX, threshY): |
|
|
2577 | 2578 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
2578 | 2579 | binArray1 = numpy.copy(binArray) |
|
|
2579 | 2580 | |
|
|
2580 | 2581 | for i in range(1,numfeat + 1): |
|
|
2581 | 2582 | auxBin = (labarray==i) |
|
|
2582 | 2583 | auxSize = auxBin.sum() |
|
|
2583 | 2584 | |
|
|
2584 | 2585 | x,y = numpy.where(auxBin) |
|
|
2585 | 2586 | widthX = x.max() - x.min() |
|
|
2586 | 2587 | widthY = y.max() - y.min() |
|
|
2587 | 2588 | |
|
|
2588 | 2589 | #width X: 3 seg -> 12.5*3 |
|
|
2589 | 2590 | #width Y: |
|
|
2590 | 2591 | |
|
|
2591 | 2592 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
2592 | 2593 | binArray1[auxBin] = False |
|
|
2593 | 2594 | |
|
|
2594 | 2595 | return binArray1 |
|
|
2595 | 2596 | |
|
|
2596 | 2597 | #--------------- Specular Meteor ---------------- |
|
|
2597 | 2598 | |
|
|
2598 | 2599 | class SMDetection(Operation): |
|
|
2599 | 2600 | ''' |
|
|
2600 | 2601 | Function DetectMeteors() |
|
|
2601 | 2602 | Project developed with paper: |
|
|
2602 | 2603 | HOLDSWORTH ET AL. 2004 |
|
|
2603 | 2604 | |
|
|
2604 | 2605 | Input: |
|
|
2605 | 2606 | self.dataOut.data_pre |
|
|
2606 | 2607 | |
|
|
2607 | 2608 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
2608 | 2609 | |
|
|
2609 | 2610 | hei_ref: Height reference for the Beacon signal extraction |
|
|
2610 | 2611 | tauindex: |
|
|
2611 | 2612 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
2612 | 2613 | |
|
|
2613 | 2614 | cohDetection: Whether to user Coherent detection or not |
|
|
2614 | 2615 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
2615 | 2616 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
2616 | 2617 | |
|
|
2617 | 2618 | noise_timeStep: Noise calculation time step |
|
|
2618 | 2619 | noise_multiple: Noise multiple to define signal threshold |
|
|
2619 | 2620 | |
|
|
2620 | 2621 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
2621 | 2622 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
2622 | 2623 | |
|
|
2623 | 2624 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
2624 | 2625 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
2625 | 2626 | |
|
|
2626 | 2627 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
2627 | 2628 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
2628 | 2629 | azimuth: Azimuth angle correction |
|
|
2629 | 2630 | |
|
|
2630 | 2631 | Affected: |
|
|
2631 | 2632 | self.dataOut.data_param |
|
|
2632 | 2633 | |
|
|
2633 | 2634 | Rejection Criteria (Errors): |
|
|
2634 | 2635 | 0: No error; analysis OK |
|
|
2635 | 2636 | 1: SNR < SNR threshold |
|
|
2636 | 2637 | 2: angle of arrival (AOA) ambiguously determined |
|
|
2637 | 2638 | 3: AOA estimate not feasible |
|
|
2638 | 2639 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2639 | 2640 | 5: echo at start or end of time series |
|
|
2640 | 2641 | 6: echo less than 5 examples long; too short for analysis |
|
|
2641 | 2642 | 7: echo rise exceeds 0.3s |
|
|
2642 | 2643 | 8: echo decay time less than twice rise time |
|
|
2643 | 2644 | 9: large power level before echo |
|
|
2644 | 2645 | 10: large power level after echo |
|
|
2645 | 2646 | 11: poor fit to amplitude for estimation of decay time |
|
|
2646 | 2647 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
2647 | 2648 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
2648 | 2649 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
2649 | 2650 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2650 | 2651 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
2651 | 2652 | |
|
|
2652 | 2653 | 17: phase difference in meteor Reestimation |
|
|
2653 | 2654 | |
|
|
2654 | 2655 | Data Storage: |
|
|
2655 | 2656 | Meteors for Wind Estimation (8): |
|
|
2656 | 2657 | Utc Time | Range Height |
|
|
2657 | 2658 | Azimuth Zenith errorCosDir |
|
|
2658 | 2659 | VelRad errorVelRad |
|
|
2659 | 2660 | Phase0 Phase1 Phase2 Phase3 |
|
|
2660 | 2661 | TypeError |
|
|
2661 | 2662 | |
|
|
2662 | 2663 | ''' |
|
|
2663 | 2664 | |
|
|
2664 | 2665 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
2665 | 2666 | phaseOffsets = None, |
|
|
2666 | 2667 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
2667 | 2668 | noise_timeStep = 4, noise_multiple = 4, |
|
|
2668 | 2669 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
2669 | 2670 | phaseThresh = 20, SNRThresh = 5, |
|
|
2670 | 2671 | hmin = 50, hmax=150, azimuth = 0, |
|
|
2671 | 2672 | channelPositions = None) : |
|
|
2672 | 2673 | |
|
|
2673 | 2674 | |
|
|
2674 | 2675 | #Getting Pairslist |
|
|
2675 | 2676 | if channelPositions is None: |
|
|
2676 | 2677 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2677 | 2678 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2678 | 2679 | meteorOps = SMOperations() |
|
|
2679 | 2680 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2680 | 2681 | heiRang = dataOut.heightList |
|
|
2681 | 2682 | #Get Beacon signal - No Beacon signal anymore |
|
|
2682 | 2683 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
2683 | 2684 | # |
|
|
2684 | 2685 | # if hei_ref != None: |
|
|
2685 | 2686 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
2686 | 2687 | # |
|
|
2687 | 2688 | |
|
|
2688 | 2689 | |
|
|
2689 | 2690 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
2690 | 2691 | # see if the user put in pre defined phase shifts |
|
|
2691 | 2692 | voltsPShift = dataOut.data_pre.copy() |
|
|
2692 | 2693 | |
|
|
2693 | 2694 | # if predefinedPhaseShifts != None: |
|
|
2694 | 2695 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
2695 | 2696 | # |
|
|
2696 | 2697 | # # elif beaconPhaseShifts: |
|
|
2697 | 2698 | # # #get hardware phase shifts using beacon signal |
|
|
2698 | 2699 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
2699 | 2700 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
2700 | 2701 | # |
|
|
2701 | 2702 | # else: |
|
|
2702 | 2703 | # hardwarePhaseShifts = numpy.zeros(5) |
|
|
2703 | 2704 | # |
|
|
2704 | 2705 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
2705 | 2706 | # for i in range(self.dataOut.data_pre.shape[0]): |
|
|
2706 | 2707 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
2707 | 2708 | |
|
|
2708 | 2709 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
2709 | 2710 | |
|
|
2710 | 2711 | #Remove DC |
|
|
2711 | 2712 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
2712 | 2713 | voltsDC = numpy.mean(voltsDC,1) |
|
|
2713 | 2714 | for i in range(voltsDC.shape[0]): |
|
|
2714 | 2715 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
2715 | 2716 | |
|
|
2716 | 2717 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
2717 | 2718 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
2718 | 2719 | |
|
|
2719 | 2720 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
2720 | 2721 | #Coherent Detection |
|
|
2721 | 2722 | if cohDetection: |
|
|
2722 | 2723 | #use coherent detection to get the net power |
|
|
2723 | 2724 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
2724 | 2725 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
2725 | 2726 | |
|
|
2726 | 2727 | #Non-coherent detection! |
|
|
2727 | 2728 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
2728 | 2729 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
2729 | 2730 | |
|
|
2730 | 2731 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
2731 | 2732 | #Get noise |
|
|
2732 | 2733 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
2733 | 2734 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
2734 | 2735 | #Get signal threshold |
|
|
2735 | 2736 | signalThresh = noise_multiple*noise |
|
|
2736 | 2737 | #Meteor echoes detection |
|
|
2737 | 2738 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
2738 | 2739 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
2739 | 2740 | |
|
|
2740 | 2741 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
2741 | 2742 | #Parameters |
|
|
2742 | 2743 | heiRange = dataOut.heightList |
|
|
2743 | 2744 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
2744 | 2745 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
2745 | 2746 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
2746 | 2747 | #Multiple detection removals |
|
|
2747 | 2748 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
2748 | 2749 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
2749 | 2750 | |
|
|
2750 | 2751 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
2751 | 2752 | #Parameters |
|
|
2752 | 2753 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
2753 | 2754 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
2754 | 2755 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
2755 | 2756 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
2756 | 2757 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
2757 | 2758 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
2758 | 2759 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
2759 | 2760 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
2760 | 2761 | |
|
|
2761 | 2762 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
2762 | 2763 | #Calculating Radial Velocity (Error N 15) |
|
|
2763 | 2764 | radialStdThresh = 10 |
|
|
2764 | 2765 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
2765 | 2766 | |
|
|
2766 | 2767 | if len(listMeteors4) > 0: |
|
|
2767 | 2768 | #Setting New Array |
|
|
2768 | 2769 | date = dataOut.utctime |
|
|
2769 | 2770 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
2770 | 2771 | |
|
|
2771 | 2772 | #Correcting phase offset |
|
|
2772 | 2773 | if phaseOffsets != None: |
|
|
2773 | 2774 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2774 | 2775 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2775 | 2776 | |
|
|
2776 | 2777 | #Second Pairslist |
|
|
2777 | 2778 | pairsList = [] |
|
|
2778 | 2779 | pairx = (0,1) |
|
|
2779 | 2780 | pairy = (2,3) |
|
|
2780 | 2781 | pairsList.append(pairx) |
|
|
2781 | 2782 | pairsList.append(pairy) |
|
|
2782 | 2783 | |
|
|
2783 | 2784 | jph = numpy.array([0,0,0,0]) |
|
|
2784 | 2785 | h = (hmin,hmax) |
|
|
2785 | 2786 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2786 | 2787 | |
|
|
2787 | 2788 | # #Calculate AOA (Error N 3, 4) |
|
|
2788 | 2789 | # #JONES ET AL. 1998 |
|
|
2789 | 2790 | # error = arrayParameters[:,-1] |
|
|
2790 | 2791 | # AOAthresh = numpy.pi/8 |
|
|
2791 | 2792 | # phases = -arrayParameters[:,9:13] |
|
|
2792 | 2793 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
2793 | 2794 | # |
|
|
2794 | 2795 | # #Calculate Heights (Error N 13 and 14) |
|
|
2795 | 2796 | # error = arrayParameters[:,-1] |
|
|
2796 | 2797 | # Ranges = arrayParameters[:,2] |
|
|
2797 | 2798 | # zenith = arrayParameters[:,5] |
|
|
2798 | 2799 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2799 | 2800 | # error = arrayParameters[:,-1] |
|
|
2800 | 2801 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
2801 | 2802 | |
|
|
2802 | 2803 | #***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
2803 | 2804 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
2804 | 2805 | dataOut.data_param = arrayParameters |
|
|
2805 | 2806 | |
|
|
2806 | 2807 | if arrayParameters is None: |
|
|
2807 | 2808 | dataOut.flagNoData = True |
|
|
2808 | 2809 | else: |
|
|
2809 | 2810 | dataOut.flagNoData = True |
|
|
2810 | 2811 | |
|
|
2811 | 2812 | return |
|
|
2812 | 2813 | |
|
|
2813 | 2814 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
2814 | 2815 | |
|
|
2815 | 2816 | minIndex = min(newheis[0]) |
|
|
2816 | 2817 | maxIndex = max(newheis[0]) |
|
|
2817 | 2818 | |
|
|
2818 | 2819 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
2819 | 2820 | nLength = voltage.shape[1]/n |
|
|
2820 | 2821 | nMin = 0 |
|
|
2821 | 2822 | nMax = 0 |
|
|
2822 | 2823 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
2823 | 2824 | |
|
|
2824 | 2825 | for i in range(n): |
|
|
2825 | 2826 | nMax += nLength |
|
|
2826 | 2827 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
2827 | 2828 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
2828 | 2829 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
2829 | 2830 | nMin = nMax |
|
|
2830 | 2831 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
2831 | 2832 | |
|
|
2832 | 2833 | #Remove Outliers |
|
|
2833 | 2834 | factor = 2 |
|
|
2834 | 2835 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
2835 | 2836 | dw = numpy.std(wt,axis = 1) |
|
|
2836 | 2837 | dw = dw.reshape((dw.size,1)) |
|
|
2837 | 2838 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
2838 | 2839 | phaseOffset[ind] = numpy.nan |
|
|
2839 | 2840 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
2840 | 2841 | |
|
|
2841 | 2842 | return phaseOffset |
|
|
2842 | 2843 | |
|
|
2843 | 2844 | def __shiftPhase(self, data, phaseShift): |
|
|
2844 | 2845 | #this will shift the phase of a complex number |
|
|
2845 | 2846 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
2846 | 2847 | return dataShifted |
|
|
2847 | 2848 | |
|
|
2848 | 2849 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
2849 | 2850 | nChannel = array.shape[0] |
|
|
2850 | 2851 | nHeights = array.shape[2] |
|
|
2851 | 2852 | numPairs = len(pairslist) |
|
|
2852 | 2853 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
2853 | 2854 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
2854 | 2855 | |
|
|
2855 | 2856 | #Correct phases |
|
|
2856 | 2857 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
2857 | 2858 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
2858 | 2859 | |
|
|
2859 | 2860 | if indDer[0].shape[0] > 0: |
|
|
2860 | 2861 | for i in range(indDer[0].shape[0]): |
|
|
2861 | 2862 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
2862 | 2863 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
2863 | 2864 | |
|
|
2864 | 2865 | # for j in range(numSides): |
|
|
2865 | 2866 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
2866 | 2867 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
2867 | 2868 | # |
|
|
2868 | 2869 | #Linear |
|
|
2869 | 2870 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
2870 | 2871 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
2871 | 2872 | for j in range(numPairs): |
|
|
2872 | 2873 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
2873 | 2874 | phaseInt[j] = fit[1] |
|
|
2874 | 2875 | #Phase Differences |
|
|
2875 | 2876 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
2876 | 2877 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
2877 | 2878 | |
|
|
2878 | 2879 | #Dealias |
|
|
2879 | 2880 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
2880 | 2881 | # indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
2881 | 2882 | # phaseArrival[indAlias] -= 2*numpy.pi |
|
|
2882 | 2883 | # indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
2883 | 2884 | # phaseArrival[indAlias] += 2*numpy.pi |
|
|
2884 | 2885 | |
|
|
2885 | 2886 | return phaseDiff, phaseArrival |
|
|
2886 | 2887 | |
|
|
2887 | 2888 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
2888 | 2889 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
2889 | 2890 | #find the phase shifts of each channel over 1 second intervals |
|
|
2890 | 2891 | #only look at ranges below the beacon signal |
|
|
2891 | 2892 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
2892 | 2893 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
2893 | 2894 | numHeights = volts.shape[2] |
|
|
2894 | 2895 | nChannel = volts.shape[0] |
|
|
2895 | 2896 | voltsCohDet = volts.copy() |
|
|
2896 | 2897 | |
|
|
2897 | 2898 | pairsarray = numpy.array(pairslist) |
|
|
2898 | 2899 | indSides = pairsarray[:,1] |
|
|
2899 | 2900 | # indSides = numpy.array(range(nChannel)) |
|
|
2900 | 2901 | # indSides = numpy.delete(indSides, indCenter) |
|
|
2901 | 2902 | # |
|
|
2902 | 2903 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
2903 | 2904 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
2904 | 2905 | |
|
|
2905 | 2906 | startInd = 0 |
|
|
2906 | 2907 | endInd = 0 |
|
|
2907 | 2908 | |
|
|
2908 | 2909 | for i in range(numBlocks): |
|
|
2909 | 2910 | startInd = endInd |
|
|
2910 | 2911 | endInd = endInd + listBlocks[i].shape[1] |
|
|
2911 | 2912 | |
|
|
2912 | 2913 | arrayBlock = listBlocks[i] |
|
|
2913 | 2914 | # arrayBlockCenter = listCenter[i] |
|
|
2914 | 2915 | |
|
|
2915 | 2916 | #Estimate the Phase Difference |
|
|
2916 | 2917 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
2917 | 2918 | #Phase Difference RMS |
|
|
2918 | 2919 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
2919 | 2920 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
2920 | 2921 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
2921 | 2922 | #Shifting |
|
|
2922 | 2923 | if indPhase[0].shape[0] > 0: |
|
|
2923 | 2924 | for j in range(indSides.size): |
|
|
2924 | 2925 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
2925 | 2926 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
2926 | 2927 | |
|
|
2927 | 2928 | return voltsCohDet |
|
|
2928 | 2929 | |
|
|
2929 | 2930 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
2930 | 2931 | |
|
|
2931 | 2932 | nHeights = volts.shape[2] |
|
|
2932 | 2933 | nPoints = volts.shape[1] |
|
|
2933 | 2934 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
2934 | 2935 | |
|
|
2935 | 2936 | for i in range(len(pairslist)): |
|
|
2936 | 2937 | volts1 = volts[pairslist[i][0]] |
|
|
2937 | 2938 | volts2 = volts[pairslist[i][1]] |
|
|
2938 | 2939 | |
|
|
2939 | 2940 | for t in range(len(laglist)): |
|
|
2940 | 2941 | idxT = laglist[t] |
|
|
2941 | 2942 | if idxT >= 0: |
|
|
2942 | 2943 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
2943 | 2944 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
2944 | 2945 | else: |
|
|
2945 | 2946 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
2946 | 2947 | volts2[:(nPoints + idxT),:])) |
|
|
2947 | 2948 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
2948 | 2949 | |
|
|
2949 | 2950 | vStacked = None |
|
|
2950 | 2951 | return voltsCCF |
|
|
2951 | 2952 | |
|
|
2952 | 2953 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
2953 | 2954 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
2954 | 2955 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
2955 | 2956 | numHeights = power.shape[1] |
|
|
2956 | 2957 | |
|
|
2957 | 2958 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
2958 | 2959 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
2959 | 2960 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
2960 | 2961 | |
|
|
2961 | 2962 | startInd = 0 |
|
|
2962 | 2963 | endInd = 0 |
|
|
2963 | 2964 | |
|
|
2964 | 2965 | for i in range(numBlocks): #split por canal |
|
|
2965 | 2966 | startInd = endInd |
|
|
2966 | 2967 | endInd = endInd + listPower[i].shape[0] |
|
|
2967 | 2968 | |
|
|
2968 | 2969 | arrayBlock = listPower[i] |
|
|
2969 | 2970 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
2970 | 2971 | # noiseAux = numpy.median(noiseAux) |
|
|
2971 | 2972 | # noiseAux = numpy.mean(arrayBlock) |
|
|
2972 | 2973 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
2973 | 2974 | |
|
|
2974 | 2975 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
2975 | 2976 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
2976 | 2977 | |
|
|
2977 | 2978 | return noise, noise1 |
|
|
2978 | 2979 | |
|
|
2979 | 2980 | def __findMeteors(self, power, thresh): |
|
|
2980 | 2981 | nProf = power.shape[0] |
|
|
2981 | 2982 | nHeights = power.shape[1] |
|
|
2982 | 2983 | listMeteors = [] |
|
|
2983 | 2984 | |
|
|
2984 | 2985 | for i in range(nHeights): |
|
|
2985 | 2986 | powerAux = power[:,i] |
|
|
2986 | 2987 | threshAux = thresh[:,i] |
|
|
2987 | 2988 | |
|
|
2988 | 2989 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
2989 | 2990 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
2990 | 2991 | |
|
|
2991 | 2992 | j = 0 |
|
|
2992 | 2993 | |
|
|
2993 | 2994 | while (j < indUPthresh.size - 2): |
|
|
2994 | 2995 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
2995 | 2996 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
2996 | 2997 | indDNthresh = indDNthresh[indDNAux] |
|
|
2997 | 2998 | |
|
|
2998 | 2999 | if (indDNthresh.size > 0): |
|
|
2999 | 3000 | indEnd = indDNthresh[0] - 1 |
|
|
3000 | 3001 | indInit = indUPthresh[j] |
|
|
3001 | 3002 | |
|
|
3002 | 3003 | meteor = powerAux[indInit:indEnd + 1] |
|
|
3003 | 3004 | indPeak = meteor.argmax() + indInit |
|
|
3004 | 3005 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
3005 | 3006 | |
|
|
3006 | 3007 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
3007 | 3008 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
3008 | 3009 | else: j+=1 |
|
|
3009 | 3010 | else: j+=1 |
|
|
3010 | 3011 | |
|
|
3011 | 3012 | return listMeteors |
|
|
3012 | 3013 | |
|
|
3013 | 3014 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
3014 | 3015 | |
|
|
3015 | 3016 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
3016 | 3017 | listMeteors1 = [] |
|
|
3017 | 3018 | |
|
|
3018 | 3019 | while arrayMeteors.shape[0] > 0: |
|
|
3019 | 3020 | FLAs = arrayMeteors[:,4] |
|
|
3020 | 3021 | maxFLA = FLAs.argmax() |
|
|
3021 | 3022 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
3022 | 3023 | |
|
|
3023 | 3024 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
3024 | 3025 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
3025 | 3026 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
3026 | 3027 | |
|
|
3027 | 3028 | #Check neighborhood |
|
|
3028 | 3029 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
3029 | 3030 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
3030 | 3031 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
3031 | 3032 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
3032 | 3033 | |
|
|
3033 | 3034 | #Check Heights |
|
|
3034 | 3035 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
3035 | 3036 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
3036 | 3037 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
3037 | 3038 | |
|
|
3038 | 3039 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
3039 | 3040 | |
|
|
3040 | 3041 | return listMeteors1 |
|
|
3041 | 3042 | |
|
|
3042 | 3043 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
3043 | 3044 | numHeights = volts.shape[2] |
|
|
3044 | 3045 | nChannel = volts.shape[0] |
|
|
3045 | 3046 | |
|
|
3046 | 3047 | thresholdPhase = thresh[0] |
|
|
3047 | 3048 | thresholdNoise = thresh[1] |
|
|
3048 | 3049 | thresholdDB = float(thresh[2]) |
|
|
3049 | 3050 | |
|
|
3050 | 3051 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
3051 | 3052 | pairsarray = numpy.array(pairslist) |
|
|
3052 | 3053 | indSides = pairsarray[:,1] |
|
|
3053 | 3054 | |
|
|
3054 | 3055 | pairslist1 = list(pairslist) |
|
|
3055 | 3056 | pairslist1.append((0,1)) |
|
|
3056 | 3057 | pairslist1.append((3,4)) |
|
|
3057 | 3058 | |
|
|
3058 | 3059 | listMeteors1 = [] |
|
|
3059 | 3060 | listPowerSeries = [] |
|
|
3060 | 3061 | listVoltageSeries = [] |
|
|
3061 | 3062 | #volts has the war data |
|
|
3062 | 3063 | |
|
|
3063 | 3064 | if frequency == 30e6: |
|
|
3064 | 3065 | timeLag = 45*10**-3 |
|
|
3065 | 3066 | else: |
|
|
3066 | 3067 | timeLag = 15*10**-3 |
|
|
3067 | 3068 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3068 | 3069 | |
|
|
3069 | 3070 | for i in range(len(listMeteors)): |
|
|
3070 | 3071 | |
|
|
3071 | 3072 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
3072 | 3073 | meteorAux = numpy.zeros(16) |
|
|
3073 | 3074 | |
|
|
3074 | 3075 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
3075 | 3076 | mHeight = listMeteors[i][0] |
|
|
3076 | 3077 | mStart = listMeteors[i][1] |
|
|
3077 | 3078 | mPeak = listMeteors[i][2] |
|
|
3078 | 3079 | mEnd = listMeteors[i][3] |
|
|
3079 | 3080 | |
|
|
3080 | 3081 | #get the volt data between the start and end times of the meteor |
|
|
3081 | 3082 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
3082 | 3083 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3083 | 3084 | |
|
|
3084 | 3085 | #3.6. Phase Difference estimation |
|
|
3085 | 3086 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
3086 | 3087 | |
|
|
3087 | 3088 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
3088 | 3089 | #meteorVolts0.- all Channels, all Profiles |
|
|
3089 | 3090 | meteorVolts0 = volts[:,:,mHeight] |
|
|
3090 | 3091 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
3091 | 3092 | meteorNoise = noise[:,mHeight] |
|
|
3092 | 3093 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
3093 | 3094 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
3094 | 3095 | |
|
|
3095 | 3096 | #Times reestimation |
|
|
3096 | 3097 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
3097 | 3098 | if mStart1.size > 0: |
|
|
3098 | 3099 | mStart1 = mStart1[-1] + 1 |
|
|
3099 | 3100 | |
|
|
3100 | 3101 | else: |
|
|
3101 | 3102 | mStart1 = mPeak |
|
|
3102 | 3103 | |
|
|
3103 | 3104 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
3104 | 3105 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
3105 | 3106 | if mEndDecayTime1.size == 0: |
|
|
3106 | 3107 | mEndDecayTime1 = powerNet0.size |
|
|
3107 | 3108 | else: |
|
|
3108 | 3109 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
3109 | 3110 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
3110 | 3111 | |
|
|
3111 | 3112 | #meteorVolts1.- all Channels, from start to end |
|
|
3112 | 3113 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
3113 | 3114 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
3114 | 3115 | if meteorVolts2.shape[1] == 0: |
|
|
3115 | 3116 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
3116 | 3117 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
3117 | 3118 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
3118 | 3119 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
3119 | 3120 | |
|
|
3120 | 3121 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
3121 | 3122 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3122 | 3123 | if meteorVolts2.shape[1] > 0: |
|
|
3123 | 3124 | #Phase Difference re-estimation |
|
|
3124 | 3125 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
3125 | 3126 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
3126 | 3127 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
3127 | 3128 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
3128 | 3129 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
3129 | 3130 | |
|
|
3130 | 3131 | #Phase Difference RMS |
|
|
3131 | 3132 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
3132 | 3133 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
3133 | 3134 | #Data from Meteor |
|
|
3134 | 3135 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
3135 | 3136 | mPeakPower1 = powerNet1.max() |
|
|
3136 | 3137 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
3137 | 3138 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
3138 | 3139 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
3139 | 3140 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
3140 | 3141 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
3141 | 3142 | #Vectorize |
|
|
3142 | 3143 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
3143 | 3144 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
3144 | 3145 | |
|
|
3145 | 3146 | #Rejection Criterions |
|
|
3146 | 3147 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
3147 | 3148 | meteorAux[-1] = 17 |
|
|
3148 | 3149 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
3149 | 3150 | meteorAux[-1] = 1 |
|
|
3150 | 3151 | |
|
|
3151 | 3152 | |
|
|
3152 | 3153 | else: |
|
|
3153 | 3154 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
3154 | 3155 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3155 | 3156 | PowerSeries = 0 |
|
|
3156 | 3157 | |
|
|
3157 | 3158 | listMeteors1.append(meteorAux) |
|
|
3158 | 3159 | listPowerSeries.append(PowerSeries) |
|
|
3159 | 3160 | listVoltageSeries.append(meteorVolts1) |
|
|
3160 | 3161 | |
|
|
3161 | 3162 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
3162 | 3163 | |
|
|
3163 | 3164 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
3164 | 3165 | |
|
|
3165 | 3166 | threshError = 10 |
|
|
3166 | 3167 | #Depending if it is 30 or 50 MHz |
|
|
3167 | 3168 | if frequency == 30e6: |
|
|
3168 | 3169 | timeLag = 45*10**-3 |
|
|
3169 | 3170 | else: |
|
|
3170 | 3171 | timeLag = 15*10**-3 |
|
|
3171 | 3172 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3172 | 3173 | |
|
|
3173 | 3174 | listMeteors1 = [] |
|
|
3174 | 3175 | |
|
|
3175 | 3176 | for i in range(len(listMeteors)): |
|
|
3176 | 3177 | meteorPower = listPower[i] |
|
|
3177 | 3178 | meteorAux = listMeteors[i] |
|
|
3178 | 3179 | |
|
|
3179 | 3180 | if meteorAux[-1] == 0: |
|
|
3180 | 3181 | |
|
|
3181 | 3182 | try: |
|
|
3182 | 3183 | indmax = meteorPower.argmax() |
|
|
3183 | 3184 | indlag = indmax + lag |
|
|
3184 | 3185 | |
|
|
3185 | 3186 | y = meteorPower[indlag:] |
|
|
3186 | 3187 | x = numpy.arange(0, y.size)*timeLag |
|
|
3187 | 3188 | |
|
|
3188 | 3189 | #first guess |
|
|
3189 | 3190 | a = y[0] |
|
|
3190 | 3191 | tau = timeLag |
|
|
3191 | 3192 | #exponential fit |
|
|
3192 | 3193 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
3193 | 3194 | y1 = self.__exponential_function(x, *popt) |
|
|
3194 | 3195 | #error estimation |
|
|
3195 | 3196 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
3196 | 3197 | |
|
|
3197 | 3198 | decayTime = popt[1] |
|
|
3198 | 3199 | riseTime = indmax*timeInterval |
|
|
3199 | 3200 | meteorAux[11:13] = [decayTime, error] |
|
|
3200 | 3201 | |
|
|
3201 | 3202 | #Table items 7, 8 and 11 |
|
|
3202 | 3203 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
3203 | 3204 | meteorAux[-1] = 7 |
|
|
3204 | 3205 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
3205 | 3206 | meteorAux[-1] = 8 |
|
|
3206 | 3207 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
3207 | 3208 | meteorAux[-1] = 11 |
|
|
3208 | 3209 | |
|
|
3209 | 3210 | |
|
|
3210 | 3211 | except: |
|
|
3211 | 3212 | meteorAux[-1] = 11 |
|
|
3212 | 3213 | |
|
|
3213 | 3214 | |
|
|
3214 | 3215 | listMeteors1.append(meteorAux) |
|
|
3215 | 3216 | |
|
|
3216 | 3217 | return listMeteors1 |
|
|
3217 | 3218 | |
|
|
3218 | 3219 | #Exponential Function |
|
|
3219 | 3220 | |
|
|
3220 | 3221 | def __exponential_function(self, x, a, tau): |
|
|
3221 | 3222 | y = a*numpy.exp(-x/tau) |
|
|
3222 | 3223 | return y |
|
|
3223 | 3224 | |
|
|
3224 | 3225 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
3225 | 3226 | |
|
|
3226 | 3227 | pairslist1 = list(pairslist) |
|
|
3227 | 3228 | pairslist1.append((0,1)) |
|
|
3228 | 3229 | pairslist1.append((3,4)) |
|
|
3229 | 3230 | numPairs = len(pairslist1) |
|
|
3230 | 3231 | #Time Lag |
|
|
3231 | 3232 | timeLag = 45*10**-3 |
|
|
3232 | 3233 | c = 3e8 |
|
|
3233 | 3234 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3234 | 3235 | freq = 30e6 |
|
|
3235 | 3236 | |
|
|
3236 | 3237 | listMeteors1 = [] |
|
|
3237 | 3238 | |
|
|
3238 | 3239 | for i in range(len(listMeteors)): |
|
|
3239 | 3240 | meteorAux = listMeteors[i] |
|
|
3240 | 3241 | if meteorAux[-1] == 0: |
|
|
3241 | 3242 | mStart = listMeteors[i][1] |
|
|
3242 | 3243 | mPeak = listMeteors[i][2] |
|
|
3243 | 3244 | mLag = mPeak - mStart + lag |
|
|
3244 | 3245 | |
|
|
3245 | 3246 | #get the volt data between the start and end times of the meteor |
|
|
3246 | 3247 | meteorVolts = listVolts[i] |
|
|
3247 | 3248 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3248 | 3249 | |
|
|
3249 | 3250 | #Get CCF |
|
|
3250 | 3251 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
3251 | 3252 | |
|
|
3252 | 3253 | #Method 2 |
|
|
3253 | 3254 | slopes = numpy.zeros(numPairs) |
|
|
3254 | 3255 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
3255 | 3256 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
3256 | 3257 | |
|
|
3257 | 3258 | #Correct phases |
|
|
3258 | 3259 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
3259 | 3260 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
3260 | 3261 | |
|
|
3261 | 3262 | if indDer[0].shape[0] > 0: |
|
|
3262 | 3263 | for i in range(indDer[0].shape[0]): |
|
|
3263 | 3264 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
3264 | 3265 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
3265 | 3266 | |
|
|
3266 | 3267 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
3267 | 3268 | for j in range(numPairs): |
|
|
3268 | 3269 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
3269 | 3270 | slopes[j] = fit[0] |
|
|
3270 | 3271 | |
|
|
3271 | 3272 | #Remove Outlier |
|
|
3272 | 3273 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3273 | 3274 | # slopes = numpy.delete(slopes,indOut) |
|
|
3274 | 3275 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3275 | 3276 | # slopes = numpy.delete(slopes,indOut) |
|
|
3276 | 3277 | |
|
|
3277 | 3278 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3278 | 3279 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3279 | 3280 | meteorAux[-2] = radialError |
|
|
3280 | 3281 | meteorAux[-3] = radialVelocity |
|
|
3281 | 3282 | |
|
|
3282 | 3283 | #Setting Error |
|
|
3283 | 3284 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
3284 | 3285 | if numpy.abs(radialVelocity) > 200: |
|
|
3285 | 3286 | meteorAux[-1] = 15 |
|
|
3286 | 3287 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
3287 | 3288 | elif radialError > radialStdThresh: |
|
|
3288 | 3289 | meteorAux[-1] = 12 |
|
|
3289 | 3290 | |
|
|
3290 | 3291 | listMeteors1.append(meteorAux) |
|
|
3291 | 3292 | return listMeteors1 |
|
|
3292 | 3293 | |
|
|
3293 | 3294 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
3294 | 3295 | |
|
|
3295 | 3296 | #New arrays |
|
|
3296 | 3297 | arrayMeteors = numpy.array(listMeteors) |
|
|
3297 | 3298 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
3298 | 3299 | |
|
|
3299 | 3300 | #Date inclusion |
|
|
3300 | 3301 | # date = re.findall(r'\((.*?)\)', date) |
|
|
3301 | 3302 | # date = date[0].split(',') |
|
|
3302 | 3303 | # date = map(int, date) |
|
|
3303 | 3304 | # |
|
|
3304 | 3305 | # if len(date)<6: |
|
|
3305 | 3306 | # date.append(0) |
|
|
3306 | 3307 | # |
|
|
3307 | 3308 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
3308 | 3309 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
3309 | 3310 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
3310 | 3311 | |
|
|
3311 | 3312 | #Meteor array |
|
|
3312 | 3313 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
3313 | 3314 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
3314 | 3315 | |
|
|
3315 | 3316 | #Parameters Array |
|
|
3316 | 3317 | arrayParameters[:,0] = arrayDate #Date |
|
|
3317 | 3318 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
3318 | 3319 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
3319 | 3320 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
3320 | 3321 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
3321 | 3322 | |
|
|
3322 | 3323 | |
|
|
3323 | 3324 | return arrayParameters |
|
|
3324 | 3325 | |
|
|
3325 | 3326 | class CorrectSMPhases(Operation): |
|
|
3326 | 3327 | |
|
|
3327 | 3328 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
3328 | 3329 | |
|
|
3329 | 3330 | arrayParameters = dataOut.data_param |
|
|
3330 | 3331 | pairsList = [] |
|
|
3331 | 3332 | pairx = (0,1) |
|
|
3332 | 3333 | pairy = (2,3) |
|
|
3333 | 3334 | pairsList.append(pairx) |
|
|
3334 | 3335 | pairsList.append(pairy) |
|
|
3335 | 3336 | jph = numpy.zeros(4) |
|
|
3336 | 3337 | |
|
|
3337 | 3338 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
3338 | 3339 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
3339 | 3340 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
3340 | 3341 | |
|
|
3341 | 3342 | meteorOps = SMOperations() |
|
|
3342 | 3343 | if channelPositions is None: |
|
|
3343 | 3344 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3344 | 3345 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3345 | 3346 | |
|
|
3346 | 3347 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3347 | 3348 | h = (hmin,hmax) |
|
|
3348 | 3349 | |
|
|
3349 | 3350 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
3350 | 3351 | |
|
|
3351 | 3352 | dataOut.data_param = arrayParameters |
|
|
3352 | 3353 | return |
|
|
3353 | 3354 | |
|
|
3354 | 3355 | class SMPhaseCalibration(Operation): |
|
|
3355 | 3356 | |
|
|
3356 | 3357 | __buffer = None |
|
|
3357 | 3358 | |
|
|
3358 | 3359 | __initime = None |
|
|
3359 | 3360 | |
|
|
3360 | 3361 | __dataReady = False |
|
|
3361 | 3362 | |
|
|
3362 | 3363 | __isConfig = False |
|
|
3363 | 3364 | |
|
|
3364 | 3365 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
3365 | 3366 | |
|
|
3366 | 3367 | dataTime = currentTime + paramInterval |
|
|
3367 | 3368 | deltaTime = dataTime - initTime |
|
|
3368 | 3369 | |
|
|
3369 | 3370 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
3370 | 3371 | return True |
|
|
3371 | 3372 | |
|
|
3372 | 3373 | return False |
|
|
3373 | 3374 | |
|
|
3374 | 3375 | def __getGammas(self, pairs, d, phases): |
|
|
3375 | 3376 | gammas = numpy.zeros(2) |
|
|
3376 | 3377 | |
|
|
3377 | 3378 | for i in range(len(pairs)): |
|
|
3378 | 3379 | |
|
|
3379 | 3380 | pairi = pairs[i] |
|
|
3380 | 3381 | |
|
|
3381 | 3382 | phip3 = phases[:,pairi[0]] |
|
|
3382 | 3383 | d3 = d[pairi[0]] |
|
|
3383 | 3384 | phip2 = phases[:,pairi[1]] |
|
|
3384 | 3385 | d2 = d[pairi[1]] |
|
|
3385 | 3386 | #Calculating gamma |
|
|
3386 | 3387 | # jdcos = alp1/(k*d1) |
|
|
3387 | 3388 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
3388 | 3389 | jgamma = -phip2*d3/d2 - phip3 |
|
|
3389 | 3390 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
3390 | 3391 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
3391 | 3392 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
3392 | 3393 | |
|
|
3393 | 3394 | #Revised distribution |
|
|
3394 | 3395 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
3395 | 3396 | |
|
|
3396 | 3397 | #Histogram |
|
|
3397 | 3398 | nBins = 64 |
|
|
3398 | 3399 | rmin = -0.5*numpy.pi |
|
|
3399 | 3400 | rmax = 0.5*numpy.pi |
|
|
3400 | 3401 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
3401 | 3402 | |
|
|
3402 | 3403 | meteorsY = phaseHisto[0] |
|
|
3403 | 3404 | phasesX = phaseHisto[1][:-1] |
|
|
3404 | 3405 | width = phasesX[1] - phasesX[0] |
|
|
3405 | 3406 | phasesX += width/2 |
|
|
3406 | 3407 | |
|
|
3407 | 3408 | #Gaussian aproximation |
|
|
3408 | 3409 | bpeak = meteorsY.argmax() |
|
|
3409 | 3410 | peak = meteorsY.max() |
|
|
3410 | 3411 | jmin = bpeak - 5 |
|
|
3411 | 3412 | jmax = bpeak + 5 + 1 |
|
|
3412 | 3413 | |
|
|
3413 | 3414 | if jmin<0: |
|
|
3414 | 3415 | jmin = 0 |
|
|
3415 | 3416 | jmax = 6 |
|
|
3416 | 3417 | elif jmax > meteorsY.size: |
|
|
3417 | 3418 | jmin = meteorsY.size - 6 |
|
|
3418 | 3419 | jmax = meteorsY.size |
|
|
3419 | 3420 | |
|
|
3420 | 3421 | x0 = numpy.array([peak,bpeak,50]) |
|
|
3421 | 3422 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
3422 | 3423 | |
|
|
3423 | 3424 | #Gammas |
|
|
3424 | 3425 | gammas[i] = coeff[0][1] |
|
|
3425 | 3426 | |
|
|
3426 | 3427 | return gammas |
|
|
3427 | 3428 | |
|
|
3428 | 3429 | def __residualFunction(self, coeffs, y, t): |
|
|
3429 | 3430 | |
|
|
3430 | 3431 | return y - self.__gauss_function(t, coeffs) |
|
|
3431 | 3432 | |
|
|
3432 | 3433 | def __gauss_function(self, t, coeffs): |
|
|
3433 | 3434 | |
|
|
3434 | 3435 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
3435 | 3436 | |
|
|
3436 | 3437 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
3437 | 3438 | meteorOps = SMOperations() |
|
|
3438 | 3439 | nchan = 4 |
|
|
3439 | 3440 | pairx = pairsList[0] #x es 0 |
|
|
3440 | 3441 | pairy = pairsList[1] #y es 1 |
|
|
3441 | 3442 | center_xangle = 0 |
|
|
3442 | 3443 | center_yangle = 0 |
|
|
3443 | 3444 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
3444 | 3445 | ntimes = len(range_angle) |
|
|
3445 | 3446 | |
|
|
3446 | 3447 | nstepsx = 20 |
|
|
3447 | 3448 | nstepsy = 20 |
|
|
3448 | 3449 | |
|
|
3449 | 3450 | for iz in range(ntimes): |
|
|
3450 | 3451 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
3451 | 3452 | max_xangle = range_angle[iz]/2 + center_xangle |
|
|
3452 | 3453 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
3453 | 3454 | max_yangle = range_angle[iz]/2 + center_yangle |
|
|
3454 | 3455 | |
|
|
3455 | 3456 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
3456 | 3457 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
3457 | 3458 | |
|
|
3458 | 3459 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
3459 | 3460 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
3460 | 3461 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
3461 | 3462 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
3462 | 3463 | jph = numpy.zeros(nchan) |
|
|
3463 | 3464 | |
|
|
3464 | 3465 | # Iterations looking for the offset |
|
|
3465 | 3466 | for iy in range(int(nstepsy)): |
|
|
3466 | 3467 | for ix in range(int(nstepsx)): |
|
|
3467 | 3468 | d3 = d[pairsList[1][0]] |
|
|
3468 | 3469 | d2 = d[pairsList[1][1]] |
|
|
3469 | 3470 | d5 = d[pairsList[0][0]] |
|
|
3470 | 3471 | d4 = d[pairsList[0][1]] |
|
|
3471 | 3472 | |
|
|
3472 | 3473 | alp2 = alpha_y[iy] #gamma 1 |
|
|
3473 | 3474 | alp4 = alpha_x[ix] #gamma 0 |
|
|
3474 | 3475 | |
|
|
3475 | 3476 | alp3 = -alp2*d3/d2 - gammas[1] |
|
|
3476 | 3477 | alp5 = -alp4*d5/d4 - gammas[0] |
|
|
3477 | 3478 | # jph[pairy[1]] = alpha_y[iy] |
|
|
3478 | 3479 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
3479 | 3480 | |
|
|
3480 | 3481 | # jph[pairx[1]] = alpha_x[ix] |
|
|
3481 | 3482 | # jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
3482 | 3483 | jph[pairsList[0][1]] = alp4 |
|
|
3483 | 3484 | jph[pairsList[0][0]] = alp5 |
|
|
3484 | 3485 | jph[pairsList[1][0]] = alp3 |
|
|
3485 | 3486 | jph[pairsList[1][1]] = alp2 |
|
|
3486 | 3487 | jph_array[:,ix,iy] = jph |
|
|
3487 | 3488 | # d = [2.0,2.5,2.5,2.0] |
|
|
3488 | 3489 | #falta chequear si va a leer bien los meteoros |
|
|
3489 | 3490 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
3490 | 3491 | error = meteorsArray1[:,-1] |
|
|
3491 | 3492 | ind1 = numpy.where(error==0)[0] |
|
|
3492 | 3493 | penalty[ix,iy] = ind1.size |
|
|
3493 | 3494 | |
|
|
3494 | 3495 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
3495 | 3496 | phOffset = jph_array[:,i,j] |
|
|
3496 | 3497 | |
|
|
3497 | 3498 | center_xangle = phOffset[pairx[1]] |
|
|
3498 | 3499 | center_yangle = phOffset[pairy[1]] |
|
|
3499 | 3500 | |
|
|
3500 | 3501 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
3501 | 3502 | phOffset = phOffset*180/numpy.pi |
|
|
3502 | 3503 | return phOffset |
|
|
3503 | 3504 | |
|
|
3504 | 3505 | |
|
|
3505 | 3506 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
3506 | 3507 | |
|
|
3507 | 3508 | dataOut.flagNoData = True |
|
|
3508 | 3509 | self.__dataReady = False |
|
|
3509 | 3510 | dataOut.outputInterval = nHours*3600 |
|
|
3510 | 3511 | |
|
|
3511 | 3512 | if self.__isConfig == False: |
|
|
3512 | 3513 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
3513 | 3514 | #Get Initial LTC time |
|
|
3514 | 3515 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
3515 | 3516 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
3516 | 3517 | |
|
|
3517 | 3518 | self.__isConfig = True |
|
|
3518 | 3519 | |
|
|
3519 | 3520 | if self.__buffer is None: |
|
|
3520 | 3521 | self.__buffer = dataOut.data_param.copy() |
|
|
3521 | 3522 | |
|
|
3522 | 3523 | else: |
|
|
3523 | 3524 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
3524 | 3525 | |
|
|
3525 | 3526 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
3526 | 3527 | |
|
|
3527 | 3528 | if self.__dataReady: |
|
|
3528 | 3529 | dataOut.utctimeInit = self.__initime |
|
|
3529 | 3530 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
3530 | 3531 | |
|
|
3531 | 3532 | freq = dataOut.frequency |
|
|
3532 | 3533 | c = dataOut.C #m/s |
|
|
3533 | 3534 | lamb = c/freq |
|
|
3534 | 3535 | k = 2*numpy.pi/lamb |
|
|
3535 | 3536 | azimuth = 0 |
|
|
3536 | 3537 | h = (hmin, hmax) |
|
|
3537 | 3538 | # pairs = ((0,1),(2,3)) #Estrella |
|
|
3538 | 3539 | # pairs = ((1,0),(2,3)) #T |
|
|
3539 | 3540 | |
|
|
3540 | 3541 | if channelPositions is None: |
|
|
3541 | 3542 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3542 | 3543 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3543 | 3544 | meteorOps = SMOperations() |
|
|
3544 | 3545 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3545 | 3546 | |
|
|
3546 | 3547 | #Checking correct order of pairs |
|
|
3547 | 3548 | pairs = [] |
|
|
3548 | 3549 | if distances[1] > distances[0]: |
|
|
3549 | 3550 | pairs.append((1,0)) |
|
|
3550 | 3551 | else: |
|
|
3551 | 3552 | pairs.append((0,1)) |
|
|
3552 | 3553 | |
|
|
3553 | 3554 | if distances[3] > distances[2]: |
|
|
3554 | 3555 | pairs.append((3,2)) |
|
|
3555 | 3556 | else: |
|
|
3556 | 3557 | pairs.append((2,3)) |
|
|
3557 | 3558 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
3558 | 3559 | |
|
|
3559 | 3560 | meteorsArray = self.__buffer |
|
|
3560 | 3561 | error = meteorsArray[:,-1] |
|
|
3561 | 3562 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
3562 | 3563 | ind1 = numpy.where(boolError)[0] |
|
|
3563 | 3564 | meteorsArray = meteorsArray[ind1,:] |
|
|
3564 | 3565 | meteorsArray[:,-1] = 0 |
|
|
3565 | 3566 | phases = meteorsArray[:,8:12] |
|
|
3566 | 3567 | |
|
|
3567 | 3568 | #Calculate Gammas |
|
|
3568 | 3569 | gammas = self.__getGammas(pairs, distances, phases) |
|
|
3569 | 3570 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
3570 | 3571 | #Calculate Phases |
|
|
3571 | 3572 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
3572 | 3573 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
3573 | 3574 | dataOut.data_output = -phasesOff |
|
|
3574 | 3575 | dataOut.flagNoData = False |
|
|
3575 | 3576 | self.__buffer = None |
|
|
3576 | 3577 | |
|
|
3577 | 3578 | |
|
|
3578 | 3579 | return |
|
|
3579 | 3580 | |
|
|
3580 | 3581 | class SMOperations(): |
|
|
3581 | 3582 | |
|
|
3582 | 3583 | def __init__(self): |
|
|
3583 | 3584 | |
|
|
3584 | 3585 | return |
|
|
3585 | 3586 | |
|
|
3586 | 3587 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
3587 | 3588 | |
|
|
3588 | 3589 | arrayParameters = arrayParameters0.copy() |
|
|
3589 | 3590 | hmin = h[0] |
|
|
3590 | 3591 | hmax = h[1] |
|
|
3591 | 3592 | |
|
|
3592 | 3593 | #Calculate AOA (Error N 3, 4) |
|
|
3593 | 3594 | #JONES ET AL. 1998 |
|
|
3594 | 3595 | AOAthresh = numpy.pi/8 |
|
|
3595 | 3596 | error = arrayParameters[:,-1] |
|
|
3596 | 3597 | phases = -arrayParameters[:,8:12] + jph |
|
|
3597 | 3598 | # phases = numpy.unwrap(phases) |
|
|
3598 | 3599 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
3599 | 3600 | |
|
|
3600 | 3601 | #Calculate Heights (Error N 13 and 14) |
|
|
3601 | 3602 | error = arrayParameters[:,-1] |
|
|
3602 | 3603 | Ranges = arrayParameters[:,1] |
|
|
3603 | 3604 | zenith = arrayParameters[:,4] |
|
|
3604 | 3605 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
3605 | 3606 | |
|
|
3606 | 3607 | #----------------------- Get Final data ------------------------------------ |
|
|
3607 | 3608 | # error = arrayParameters[:,-1] |
|
|
3608 | 3609 | # ind1 = numpy.where(error==0)[0] |
|
|
3609 | 3610 | # arrayParameters = arrayParameters[ind1,:] |
|
|
3610 | 3611 | |
|
|
3611 | 3612 | return arrayParameters |
|
|
3612 | 3613 | |
|
|
3613 | 3614 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
3614 | 3615 | |
|
|
3615 | 3616 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3616 | 3617 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
3617 | 3618 | |
|
|
3618 | 3619 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3619 | 3620 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3620 | 3621 | arrayAOA[:,2] = cosDirError |
|
|
3621 | 3622 | |
|
|
3622 | 3623 | azimuthAngle = arrayAOA[:,0] |
|
|
3623 | 3624 | zenithAngle = arrayAOA[:,1] |
|
|
3624 | 3625 | |
|
|
3625 | 3626 | #Setting Error |
|
|
3626 | 3627 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
3627 | 3628 | error[indError] = 0 |
|
|
3628 | 3629 | #Number 3: AOA not fesible |
|
|
3629 | 3630 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3630 | 3631 | error[indInvalid] = 3 |
|
|
3631 | 3632 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3632 | 3633 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3633 | 3634 | error[indInvalid] = 4 |
|
|
3634 | 3635 | return arrayAOA, error |
|
|
3635 | 3636 | |
|
|
3636 | 3637 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
3637 | 3638 | |
|
|
3638 | 3639 | #Initializing some variables |
|
|
3639 | 3640 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3640 | 3641 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3641 | 3642 | |
|
|
3642 | 3643 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3643 | 3644 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3644 | 3645 | |
|
|
3645 | 3646 | |
|
|
3646 | 3647 | for i in range(2): |
|
|
3647 | 3648 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
3648 | 3649 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
3649 | 3650 | d0 = distances[pairsList[i][0]] |
|
|
3650 | 3651 | d1 = distances[pairsList[i][1]] |
|
|
3651 | 3652 | |
|
|
3652 | 3653 | ph0_aux = ph0 + ph1 |
|
|
3653 | 3654 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
3654 | 3655 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
3655 | 3656 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
3656 | 3657 | #First Estimation |
|
|
3657 | 3658 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
3658 | 3659 | |
|
|
3659 | 3660 | #Most-Accurate Second Estimation |
|
|
3660 | 3661 | phi1_aux = ph0 - ph1 |
|
|
3661 | 3662 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3662 | 3663 | #Direction Cosine 1 |
|
|
3663 | 3664 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
3664 | 3665 | |
|
|
3665 | 3666 | #Searching the correct Direction Cosine |
|
|
3666 | 3667 | cosdir0_aux = cosdir0[:,i] |
|
|
3667 | 3668 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3668 | 3669 | #Minimum Distance |
|
|
3669 | 3670 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3670 | 3671 | indcos = cosDiff.argmin(axis = 1) |
|
|
3671 | 3672 | #Saving Value obtained |
|
|
3672 | 3673 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3673 | 3674 | |
|
|
3674 | 3675 | return cosdir0, cosdir |
|
|
3675 | 3676 | |
|
|
3676 | 3677 | def __calculateAOA(self, cosdir, azimuth): |
|
|
3677 | 3678 | cosdirX = cosdir[:,0] |
|
|
3678 | 3679 | cosdirY = cosdir[:,1] |
|
|
3679 | 3680 | |
|
|
3680 | 3681 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3681 | 3682 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
3682 | 3683 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3683 | 3684 | |
|
|
3684 | 3685 | return angles |
|
|
3685 | 3686 | |
|
|
3686 | 3687 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3687 | 3688 | |
|
|
3688 | 3689 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3689 | 3690 | Re = 6371 #Earth Radius |
|
|
3690 | 3691 | heights = numpy.zeros(Ranges.shape) |
|
|
3691 | 3692 | |
|
|
3692 | 3693 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
3693 | 3694 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3694 | 3695 | |
|
|
3695 | 3696 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3696 | 3697 | |
|
|
3697 | 3698 | Ri = Ranges + R_aux |
|
|
3698 | 3699 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3699 | 3700 | |
|
|
3700 | 3701 | #Check if there is a height between 70 and 110 km |
|
|
3701 | 3702 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3702 | 3703 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
3703 | 3704 | |
|
|
3704 | 3705 | hCorr = hi[ind_h, :] |
|
|
3705 | 3706 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3706 | 3707 | |
|
|
3707 | 3708 | hCorr = hi[ind_hCorr][:len(ind_h)] |
|
|
3708 | 3709 | heights[ind_h] = hCorr |
|
|
3709 | 3710 | |
|
|
3710 | 3711 | #Setting Error |
|
|
3711 | 3712 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3712 | 3713 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3713 | 3714 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
3714 | 3715 | error[indError] = 0 |
|
|
3715 | 3716 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3716 | 3717 | error[indInvalid2] = 14 |
|
|
3717 | 3718 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3718 | 3719 | error[indInvalid1] = 13 |
|
|
3719 | 3720 | |
|
|
3720 | 3721 | return heights, error |
|
|
3721 | 3722 | |
|
|
3722 | 3723 | def getPhasePairs(self, channelPositions): |
|
|
3723 | 3724 | chanPos = numpy.array(channelPositions) |
|
|
3724 | 3725 | listOper = list(itertools.combinations(list(range(5)),2)) |
|
|
3725 | 3726 | |
|
|
3726 | 3727 | distances = numpy.zeros(4) |
|
|
3727 | 3728 | axisX = [] |
|
|
3728 | 3729 | axisY = [] |
|
|
3729 | 3730 | distX = numpy.zeros(3) |
|
|
3730 | 3731 | distY = numpy.zeros(3) |
|
|
3731 | 3732 | ix = 0 |
|
|
3732 | 3733 | iy = 0 |
|
|
3733 | 3734 | |
|
|
3734 | 3735 | pairX = numpy.zeros((2,2)) |
|
|
3735 | 3736 | pairY = numpy.zeros((2,2)) |
|
|
3736 | 3737 | |
|
|
3737 | 3738 | for i in range(len(listOper)): |
|
|
3738 | 3739 | pairi = listOper[i] |
|
|
3739 | 3740 | |
|
|
3740 | 3741 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
3741 | 3742 | |
|
|
3742 | 3743 | if posDif[0] == 0: |
|
|
3743 | 3744 | axisY.append(pairi) |
|
|
3744 | 3745 | distY[iy] = posDif[1] |
|
|
3745 | 3746 | iy += 1 |
|
|
3746 | 3747 | elif posDif[1] == 0: |
|
|
3747 | 3748 | axisX.append(pairi) |
|
|
3748 | 3749 | distX[ix] = posDif[0] |
|
|
3749 | 3750 | ix += 1 |
|
|
3750 | 3751 | |
|
|
3751 | 3752 | for i in range(2): |
|
|
3752 | 3753 | if i==0: |
|
|
3753 | 3754 | dist0 = distX |
|
|
3754 | 3755 | axis0 = axisX |
|
|
3755 | 3756 | else: |
|
|
3756 | 3757 | dist0 = distY |
|
|
3757 | 3758 | axis0 = axisY |
|
|
3758 | 3759 | |
|
|
3759 | 3760 | side = numpy.argsort(dist0)[:-1] |
|
|
3760 | 3761 | axis0 = numpy.array(axis0)[side,:] |
|
|
3761 | 3762 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
3762 | 3763 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
3763 | 3764 | side = axis1[axis1 != chanC] |
|
|
3764 | 3765 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
3765 | 3766 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
3766 | 3767 | if diff1<0: |
|
|
3767 | 3768 | chan2 = side[0] |
|
|
3768 | 3769 | d2 = numpy.abs(diff1) |
|
|
3769 | 3770 | chan1 = side[1] |
|
|
3770 | 3771 | d1 = numpy.abs(diff2) |
|
|
3771 | 3772 | else: |
|
|
3772 | 3773 | chan2 = side[1] |
|
|
3773 | 3774 | d2 = numpy.abs(diff2) |
|
|
3774 | 3775 | chan1 = side[0] |
|
|
3775 | 3776 | d1 = numpy.abs(diff1) |
|
|
3776 | 3777 | |
|
|
3777 | 3778 | if i==0: |
|
|
3778 | 3779 | chanCX = chanC |
|
|
3779 | 3780 | chan1X = chan1 |
|
|
3780 | 3781 | chan2X = chan2 |
|
|
3781 | 3782 | distances[0:2] = numpy.array([d1,d2]) |
|
|
3782 | 3783 | else: |
|
|
3783 | 3784 | chanCY = chanC |
|
|
3784 | 3785 | chan1Y = chan1 |
|
|
3785 | 3786 | chan2Y = chan2 |
|
|
3786 | 3787 | distances[2:4] = numpy.array([d1,d2]) |
|
|
3787 | 3788 | # axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
3788 | 3789 | # |
|
|
3789 | 3790 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
3790 | 3791 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
3791 | 3792 | # |
|
|
3792 | 3793 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
3793 | 3794 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
3794 | 3795 | # channel25X = int(pairX[0,ind25X]) |
|
|
3795 | 3796 | # channel20X = int(pairX[1,ind20X]) |
|
|
3796 | 3797 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
3797 | 3798 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
3798 | 3799 | # channel25Y = int(pairY[0,ind25Y]) |
|
|
3799 | 3800 | # channel20Y = int(pairY[1,ind20Y]) |
|
|
3800 | 3801 | |
|
|
3801 | 3802 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
3802 | 3803 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
3803 | 3804 | |
|
|
3804 | 3805 | return pairslist, distances |
|
|
3805 | 3806 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
3806 | 3807 | # |
|
|
3807 | 3808 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3808 | 3809 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
3809 | 3810 | # |
|
|
3810 | 3811 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3811 | 3812 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3812 | 3813 | # arrayAOA[:,2] = cosDirError |
|
|
3813 | 3814 | # |
|
|
3814 | 3815 | # azimuthAngle = arrayAOA[:,0] |
|
|
3815 | 3816 | # zenithAngle = arrayAOA[:,1] |
|
|
3816 | 3817 | # |
|
|
3817 | 3818 | # #Setting Error |
|
|
3818 | 3819 | # #Number 3: AOA not fesible |
|
|
3819 | 3820 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3820 | 3821 | # error[indInvalid] = 3 |
|
|
3821 | 3822 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3822 | 3823 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3823 | 3824 | # error[indInvalid] = 4 |
|
|
3824 | 3825 | # return arrayAOA, error |
|
|
3825 | 3826 | # |
|
|
3826 | 3827 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
3827 | 3828 | # |
|
|
3828 | 3829 | # #Initializing some variables |
|
|
3829 | 3830 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3830 | 3831 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3831 | 3832 | # |
|
|
3832 | 3833 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3833 | 3834 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3834 | 3835 | # |
|
|
3835 | 3836 | # |
|
|
3836 | 3837 | # for i in range(2): |
|
|
3837 | 3838 | # #First Estimation |
|
|
3838 | 3839 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
3839 | 3840 | # #Dealias |
|
|
3840 | 3841 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
3841 | 3842 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
|
3842 | 3843 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
3843 | 3844 | # phi0_aux[indcsi] += 2*numpy.pi |
|
|
3844 | 3845 | # #Direction Cosine 0 |
|
|
3845 | 3846 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
3846 | 3847 | # |
|
|
3847 | 3848 | # #Most-Accurate Second Estimation |
|
|
3848 | 3849 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
3849 | 3850 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3850 | 3851 | # #Direction Cosine 1 |
|
|
3851 | 3852 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
3852 | 3853 | # |
|
|
3853 | 3854 | # #Searching the correct Direction Cosine |
|
|
3854 | 3855 | # cosdir0_aux = cosdir0[:,i] |
|
|
3855 | 3856 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3856 | 3857 | # #Minimum Distance |
|
|
3857 | 3858 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3858 | 3859 | # indcos = cosDiff.argmin(axis = 1) |
|
|
3859 | 3860 | # #Saving Value obtained |
|
|
3860 | 3861 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3861 | 3862 | # |
|
|
3862 | 3863 | # return cosdir0, cosdir |
|
|
3863 | 3864 | # |
|
|
3864 | 3865 | # def __calculateAOA(self, cosdir, azimuth): |
|
|
3865 | 3866 | # cosdirX = cosdir[:,0] |
|
|
3866 | 3867 | # cosdirY = cosdir[:,1] |
|
|
3867 | 3868 | # |
|
|
3868 | 3869 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3869 | 3870 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
3870 | 3871 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3871 | 3872 | # |
|
|
3872 | 3873 | # return angles |
|
|
3873 | 3874 | # |
|
|
3874 | 3875 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3875 | 3876 | # |
|
|
3876 | 3877 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3877 | 3878 | # Re = 6371 #Earth Radius |
|
|
3878 | 3879 | # heights = numpy.zeros(Ranges.shape) |
|
|
3879 | 3880 | # |
|
|
3880 | 3881 | # R_aux = numpy.array([0,1,2])*Ramb |
|
|
3881 | 3882 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3882 | 3883 | # |
|
|
3883 | 3884 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3884 | 3885 | # |
|
|
3885 | 3886 | # Ri = Ranges + R_aux |
|
|
3886 | 3887 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3887 | 3888 | # |
|
|
3888 | 3889 | # #Check if there is a height between 70 and 110 km |
|
|
3889 | 3890 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3890 | 3891 | # ind_h = numpy.where(h_bool == 1)[0] |
|
|
3891 | 3892 | # |
|
|
3892 | 3893 | # hCorr = hi[ind_h, :] |
|
|
3893 | 3894 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3894 | 3895 | # |
|
|
3895 | 3896 | # hCorr = hi[ind_hCorr] |
|
|
3896 | 3897 | # heights[ind_h] = hCorr |
|
|
3897 | 3898 | # |
|
|
3898 | 3899 | # #Setting Error |
|
|
3899 | 3900 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3900 | 3901 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3901 | 3902 | # |
|
|
3902 | 3903 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3903 | 3904 | # error[indInvalid2] = 14 |
|
|
3904 | 3905 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3905 | 3906 | # error[indInvalid1] = 13 |
|
|
3906 | 3907 | # |
|
|
3907 | 3908 | # return heights, error |
|
|
3908 | 3909 | |
|
|
3909 | 3910 | |
|
|
3910 | 3911 | class WeatherRadar(Operation): |
|
|
3911 | 3912 | ''' |
|
|
3912 | 3913 | Function tat implements Weather Radar operations- |
|
|
3913 | 3914 | Input: |
|
|
3914 | 3915 | Output: |
|
|
3915 | 3916 | Parameters affected: |
|
|
3916 | 3917 | ''' |
|
|
3917 | 3918 | isConfig = False |
|
|
3918 | 3919 | variableList = None |
|
|
3919 | 3920 | |
|
|
3920 | 3921 | def __init__(self): |
|
|
3921 | 3922 | Operation.__init__(self) |
|
|
3922 | 3923 | |
|
|
3923 | 3924 | def setup(self,dataOut,variableList= None,Pt=0,Gt=0,Gr=0,lambda_=0, aL=0, |
|
|
3924 | 3925 | tauW= 0,thetaT=0,thetaR=0,Km =0): |
|
|
3925 | 3926 | self.nCh = dataOut.nChannels |
|
|
3926 | 3927 | self.nHeis = dataOut.nHeights |
|
|
3927 | 3928 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
3928 | 3929 | self.Range = numpy.arange(dataOut.nHeights)*deltaHeight + dataOut.heightList[0] |
|
|
3929 | 3930 | self.Range = self.Range.reshape(1,self.nHeis) |
|
|
3930 | 3931 | self.Range = numpy.tile(self.Range,[self.nCh,1]) |
|
|
3931 | 3932 | '''-----------1 Constante del Radar----------''' |
|
|
3932 | 3933 | self.Pt = Pt |
|
|
3933 | 3934 | self.Gt = Gt |
|
|
3934 | 3935 | self.Gr = Gr |
|
|
3935 | 3936 | self.lambda_ = lambda_ |
|
|
3936 | 3937 | self.aL = aL |
|
|
3937 | 3938 | self.tauW = tauW |
|
|
3938 | 3939 | self.thetaT = thetaT |
|
|
3939 | 3940 | self.thetaR = thetaR |
|
|
3940 | 3941 | self.Km = Km |
|
|
3941 | 3942 | Numerator = ((4*numpy.pi)**3 * aL**2 * 16 *numpy.log(2)) |
|
|
3942 | 3943 | Denominator = (Pt * Gt * Gr * lambda_**2 * SPEED_OF_LIGHT * tauW * numpy.pi*thetaT*thetaR) |
|
|
3943 | 3944 | self.RadarConstant = Numerator/Denominator |
|
|
3944 | 3945 | self.variableList= variableList |
|
|
3945 | 3946 | |
|
|
3946 | 3947 | def setMoments(self,dataOut,i): |
|
|
3947 | 3948 | |
|
|
3948 | 3949 | type = dataOut.inputUnit |
|
|
3949 | 3950 | nCh = dataOut.nChannels |
|
|
3950 | 3951 | nHeis = dataOut.nHeights |
|
|
3951 | 3952 | data_param = numpy.zeros((nCh,4,nHeis)) |
|
|
3952 | 3953 | if type == "Voltage": |
|
|
3953 | 3954 | factor = dataOut.normFactor |
|
|
3954 | 3955 | data_param[:,0,:] = dataOut.dataPP_POW/(factor) |
|
|
3955 | 3956 | data_param[:,1,:] = dataOut.dataPP_DOP |
|
|
3956 | 3957 | data_param[:,2,:] = dataOut.dataPP_WIDTH |
|
|
3957 | 3958 | data_param[:,3,:] = dataOut.dataPP_SNR |
|
|
3958 | 3959 | if type == "Spectra": |
|
|
3959 | 3960 | data_param[:,0,:] = dataOut.data_POW |
|
|
3960 | 3961 | data_param[:,1,:] = dataOut.data_DOP |
|
|
3961 | 3962 | data_param[:,2,:] = dataOut.data_WIDTH |
|
|
3962 | 3963 | data_param[:,3,:] = dataOut.data_SNR |
|
|
3963 | 3964 | |
|
|
3964 | 3965 | return data_param[:,i,:] |
|
|
3965 | 3966 | |
|
|
3966 | 3967 | def getCoeficienteCorrelacionROhv_R(self,dataOut): |
|
|
3967 | 3968 | type = dataOut.inputUnit |
|
|
3968 | 3969 | nHeis = dataOut.nHeights |
|
|
3969 | 3970 | data_RhoHV_R = numpy.zeros((nHeis)) |
|
|
3970 | 3971 | if type == "Voltage": |
|
|
3971 | 3972 | powa = dataOut.dataPP_POWER[0] |
|
|
3972 | 3973 | powb = dataOut.dataPP_POWER[1] |
|
|
3973 | 3974 | ccf = dataOut.dataPP_CCF |
|
|
3974 | 3975 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
3975 | 3976 | data_RhoHV_R = numpy.abs(avgcoherenceComplex) |
|
|
3976 | 3977 | if type == "Spectra": |
|
|
3977 | 3978 | data_RhoHV_R = dataOut.getCoherence() |
|
|
3978 | 3979 | |
|
|
3979 | 3980 | return data_RhoHV_R |
|
|
3980 | 3981 | |
|
|
3981 | 3982 | def getFasediferencialPhiD_P(self,dataOut,phase= True): |
|
|
3982 | 3983 | type = dataOut.inputUnit |
|
|
3983 | 3984 | nHeis = dataOut.nHeights |
|
|
3984 | 3985 | data_PhiD_P = numpy.zeros((nHeis)) |
|
|
3985 | 3986 | if type == "Voltage": |
|
|
3986 | 3987 | powa = dataOut.dataPP_POWER[0] |
|
|
3987 | 3988 | powb = dataOut.dataPP_POWER[1] |
|
|
3988 | 3989 | ccf = dataOut.dataPP_CCF |
|
|
3989 | 3990 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
3990 | 3991 | if phase: |
|
|
3991 | 3992 | data_PhiD_P = numpy.arctan2(avgcoherenceComplex.imag, |
|
|
3992 | 3993 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
|
3993 | 3994 | if type == "Spectra": |
|
|
3994 | 3995 | data_PhiD_P = dataOut.getCoherence(phase = phase) |
|
|
3995 | 3996 | |
|
|
3996 | 3997 | return data_PhiD_P |
|
|
3997 | 3998 | |
|
|
3998 | 3999 | def getReflectividad_D(self,dataOut): |
|
|
3999 | 4000 | '''-----------------------------Potencia de Radar -Signal S-----------------------------''' |
|
|
4000 | 4001 | |
|
|
4001 | 4002 | Pr = self.setMoments(dataOut,0) |
|
|
4002 | 4003 | |
|
|
4003 | 4004 | '''-----------2 Reflectividad del Radar y Factor de Reflectividad------''' |
|
|
4004 | 4005 | self.n_radar = numpy.zeros((self.nCh,self.nHeis)) |
|
|
4005 | 4006 | self.Z_radar = numpy.zeros((self.nCh,self.nHeis)) |
|
|
4006 | 4007 | for R in range(self.nHeis): |
|
|
4007 | 4008 | self.n_radar[:,R] = self.RadarConstant*Pr[:,R]* (self.Range[:,R])**2 |
|
|
4008 | 4009 | |
|
|
4009 | 4010 | self.Z_radar[:,R] = self.n_radar[:,R]* self.lambda_**4/( numpy.pi**5 * self.Km**2) |
|
|
4010 | 4011 | |
|
|
4011 | 4012 | '''----------- Factor de Reflectividad Equivalente lamda_ < 10 cm , lamda_= 3.2cm-------''' |
|
|
4012 | 4013 | Zeh = self.Z_radar |
|
|
4013 | 4014 | dBZeh = 10*numpy.log10(Zeh) |
|
|
4014 | 4015 | Zdb_D = dBZeh[0] - dBZeh[1] |
|
|
4015 | 4016 | return Zdb_D |
|
|
4016 | 4017 | |
|
|
4017 | 4018 | def getRadialVelocity_V(self,dataOut): |
|
|
4018 | 4019 | velRadial_V = self.setMoments(dataOut,1) |
|
|
4019 | 4020 | return velRadial_V |
|
|
4020 | 4021 | |
|
|
4021 | 4022 | def getAnchoEspectral_W(self,dataOut): |
|
|
4022 | 4023 | Sigmav_W = self.setMoments(dataOut,2) |
|
|
4023 | 4024 | return Sigmav_W |
|
|
4024 | 4025 | |
|
|
4025 | 4026 | |
|
|
4026 | 4027 | def run(self,dataOut,variableList=None,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118, |
|
|
4027 | 4028 | tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93): |
|
|
4028 | 4029 | |
|
|
4029 | 4030 | if not self.isConfig: |
|
|
4030 | 4031 | self.setup(dataOut= dataOut,variableList=None,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118, |
|
|
4031 | 4032 | tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93) |
|
|
4032 | 4033 | self.isConfig = True |
|
|
4033 | 4034 | |
|
|
4034 | 4035 | for i in range(len(self.variableList)): |
|
|
4035 | 4036 | if self.variableList[i]=='ReflectividadDiferencial': |
|
|
4036 | 4037 | dataOut.Zdb_D =self.getReflectividad_D(dataOut=dataOut) |
|
|
4037 | 4038 | if self.variableList[i]=='FaseDiferencial': |
|
|
4038 | 4039 | dataOut.PhiD_P =self.getFasediferencialPhiD_P(dataOut=dataOut, phase=True) |
|
|
4039 | 4040 | if self.variableList[i] == "CoeficienteCorrelacion": |
|
|
4040 | 4041 | dataOut.RhoHV_R = self.getCoeficienteCorrelacionROhv_R(dataOut) |
|
|
4041 | 4042 | if self.variableList[i] =="VelocidadRadial": |
|
|
4042 | 4043 | dataOut.velRadial_V = self.getRadialVelocity_V(dataOut) |
|
|
4043 | 4044 | if self.variableList[i] =="AnchoEspectral": |
|
|
4044 | 4045 | dataOut.Sigmav_W = self.getAnchoEspectral_W(dataOut) |
|
|
4045 | 4046 | return dataOut |
|
|
4046 | 4047 | |
|
|
4047 | 4048 | class PedestalInformation(Operation): |
|
|
4048 | 4049 | |
|
|
4049 | 4050 | def __init__(self): |
|
|
4050 | 4051 | Operation.__init__(self) |
|
|
4051 | 4052 | self.filename = False |
|
|
4052 | 4053 | self.delay = 30 |
|
|
4053 | 4054 | self.nTries = 3 |
|
|
4054 | 4055 | |
|
|
4055 | 4056 | def find_file(self, timestamp): |
|
|
4056 | 4057 | |
|
|
4057 | 4058 | dt = datetime.datetime.utcfromtimestamp(timestamp) |
|
|
4058 | 4059 | path = os.path.join(self.path, dt.strftime('%Y-%m-%dT%H-00-00')) |
|
|
4059 | 4060 | |
|
|
4060 | 4061 | if not os.path.exists(path): |
|
|
4061 | 4062 | return False, False |
|
|
4062 | 4063 | fileList = glob.glob(os.path.join(path, '*.h5')) |
|
|
4063 | 4064 | fileList.sort() |
|
|
4064 | 4065 | return fileList |
|
|
4065 | 4066 | |
|
|
4066 | 4067 | def find_next_file(self): |
|
|
4067 | 4068 | |
|
|
4068 | 4069 | while True: |
|
|
4069 | 4070 | if self.utctime < self.utcfile: |
|
|
4070 | 4071 | self.flagNoData = True |
|
|
4071 | 4072 | break |
|
|
4072 | 4073 | self.flagNoData = False |
|
|
4073 | 4074 | file_size = len(self.fp['Data']['utc']) |
|
|
4074 | 4075 | if self.utctime < self.utcfile+file_size*self.interval: |
|
|
4075 | 4076 | break |
|
|
4076 | 4077 | dt = datetime.datetime.utcfromtimestamp(self.utcfile) |
|
|
4077 | 4078 | if dt.second > 0: |
|
|
4078 | 4079 | self.utcfile -= dt.second |
|
|
4079 | 4080 | self.utcfile += self.samples*self.interval |
|
|
4080 | 4081 | dt = datetime.datetime.utcfromtimestamp(self.utctime) |
|
|
4081 | 4082 | path = os.path.join(self.path, dt.strftime('%Y-%m-%dT%H-00-00')) |
|
|
4082 | 4083 | self.filename = os.path.join(path, 'pos@{}.000.h5'.format(int(self.utcfile))) |
|
|
4083 | ||
|
|
4084 | ||
|
|
4084 | 4085 | for n in range(self.nTries): |
|
|
4085 | 4086 | ok = False |
|
|
4086 | 4087 | try: |
|
|
4087 | 4088 | if not os.path.exists(self.filename): |
|
|
4088 | 4089 | log.warning('Waiting {}s for position files...'.format(self.delay), self.name) |
|
|
4089 | 4090 | time.sleep(self.delay) |
|
|
4090 | 4091 | continue |
|
|
4091 | 4092 | self.fp.close() |
|
|
4092 | 4093 | self.fp = h5py.File(self.filename, 'r') |
|
|
4093 | 4094 | log.log('Opening file: {}'.format(self.filename), self.name) |
|
|
4094 | 4095 | ok = True |
|
|
4095 | 4096 | break |
|
|
4096 | 4097 | except: |
|
|
4097 | 4098 | log.warning('Waiting {}s for position file to be ready...'.format(self.delay), self.name) |
|
|
4098 | 4099 | time.sleep(self.delay) |
|
|
4099 | 4100 | continue |
|
|
4100 | ||
|
|
4101 | ||
|
|
4101 | 4102 | if not ok: |
|
|
4102 | 4103 | log.error('No new position files found in {}'.format(path)) |
|
|
4103 | 4104 | raise IOError('No new position files found in {}'.format(path)) |
|
|
4104 | 4105 | |
|
|
4105 | 4106 | |
|
|
4106 | 4107 | def get_values(self): |
|
|
4107 | 4108 | |
|
|
4108 | 4109 | if self.flagNoData: |
|
|
4109 | 4110 | return numpy.nan, numpy.nan |
|
|
4110 | 4111 | else: |
|
|
4111 | 4112 | index = int((self.utctime-self.utcfile)/self.interval) |
|
|
4112 | 4113 | return self.fp['Data']['azi_pos'][index], self.fp['Data']['ele_pos'][index] |
|
|
4113 | 4114 | |
|
|
4114 | 4115 | def setup(self, dataOut, path, conf, samples, interval, az_offset): |
|
|
4115 | 4116 | |
|
|
4116 | 4117 | self.path = path |
|
|
4117 | 4118 | self.conf = conf |
|
|
4118 | 4119 | self.samples = samples |
|
|
4119 | 4120 | self.interval = interval |
|
|
4120 | 4121 | filelist = self.find_file(dataOut.utctime) |
|
|
4121 | 4122 | |
|
|
4122 | 4123 | if not filelist: |
|
|
4123 | 4124 | log.error('No position files found in {}'.format(path), self.name) |
|
|
4124 | 4125 | raise IOError('No position files found in {}'.format(path)) |
|
|
4125 | 4126 | else: |
|
|
4126 | 4127 | self.filename = filelist[0] |
|
|
4127 | 4128 | self.utcfile = int(self.filename.split('/')[-1][4:14]) |
|
|
4128 | 4129 | log.log('Opening file: {}'.format(self.filename), self.name) |
|
|
4129 | 4130 | self.fp = h5py.File(self.filename, 'r') |
|
|
4130 | 4131 | |
|
|
4131 | 4132 | def run(self, dataOut, path, conf=None, samples=1500, interval=0.04, az_offset=0, time_offset=0): |
|
|
4132 | 4133 | |
|
|
4133 | 4134 | if not self.isConfig: |
|
|
4134 | 4135 | self.setup(dataOut, path, conf, samples, interval, az_offset) |
|
|
4135 | 4136 | self.isConfig = True |
|
|
4136 | 4137 | |
|
|
4137 | 4138 | self.utctime = dataOut.utctime + time_offset |
|
|
4138 | 4139 | |
|
|
4139 | 4140 | self.find_next_file() |
|
|
4140 | 4141 | |
|
|
4141 | 4142 | az, el = self.get_values() |
|
|
4142 | 4143 | dataOut.flagNoData = False |
|
|
4143 | 4144 | |
|
|
4144 | 4145 | if numpy.isnan(az) or numpy.isnan(el) : |
|
|
4145 | 4146 | dataOut.flagNoData = True |
|
|
4146 | 4147 | return dataOut |
|
|
4147 | 4148 | |
|
|
4148 | 4149 | dataOut.azimuth = az - az_offset |
|
|
4149 | 4150 | if dataOut.azimuth < 0: |
|
|
4150 | 4151 | dataOut.azimuth += 360 |
|
|
4151 | 4152 | dataOut.elevation = el |
|
|
4152 | 4153 | |
|
|
4153 | 4154 | return dataOut |
|
|
4154 | 4155 | |
|
|
4155 | 4156 | class Block360(Operation): |
|
|
4156 | 4157 | ''' |
|
|
4157 | 4158 | ''' |
|
|
4158 | 4159 | isConfig = False |
|
|
4159 | 4160 | __profIndex = 0 |
|
|
4160 | 4161 | __initime = None |
|
|
4161 | 4162 | __lastdatatime = None |
|
|
4162 | 4163 | __buffer = None |
|
|
4163 | 4164 | __dataReady = False |
|
|
4164 | 4165 | n = None |
|
|
4165 | 4166 | __nch = 0 |
|
|
4166 | 4167 | __nHeis = 0 |
|
|
4167 | 4168 | index = 0 |
|
|
4168 | 4169 | mode = 0 |
|
|
4169 | 4170 | |
|
|
4170 | 4171 | def __init__(self,**kwargs): |
|
|
4171 | 4172 | Operation.__init__(self,**kwargs) |
|
|
4172 | 4173 | |
|
|
4173 | 4174 | def setup(self, dataOut, n = None, mode = None): |
|
|
4174 | 4175 | ''' |
|
|
4175 | 4176 | n= Numero de PRF's de entrada |
|
|
4176 | 4177 | ''' |
|
|
4177 | 4178 | self.__initime = None |
|
|
4178 | 4179 | self.__lastdatatime = 0 |
|
|
4179 | 4180 | self.__dataReady = False |
|
|
4180 | 4181 | self.__buffer = 0 |
|
|
4181 | 4182 | self.__buffer_1D = 0 |
|
|
4182 | 4183 | self.__profIndex = 0 |
|
|
4183 | 4184 | self.index = 0 |
|
|
4184 | 4185 | self.__nch = dataOut.nChannels |
|
|
4185 | 4186 | self.__nHeis = dataOut.nHeights |
|
|
4186 | 4187 | ##print("ELVALOR DE n es:", n) |
|
|
4187 | 4188 | if n == None: |
|
|
4188 | 4189 | raise ValueError("n should be specified.") |
|
|
4189 | 4190 | |
|
|
4190 | 4191 | if mode == None: |
|
|
4191 | 4192 | raise ValueError("mode should be specified.") |
|
|
4192 | 4193 | |
|
|
4193 | 4194 | if n != None: |
|
|
4194 | 4195 | if n<1: |
|
|
4195 | 4196 | print("n should be greater than 2") |
|
|
4196 | 4197 | raise ValueError("n should be greater than 2") |
|
|
4197 | 4198 | |
|
|
4198 | 4199 | self.n = n |
|
|
4199 | 4200 | self.mode = mode |
|
|
4200 | 4201 | #print("self.mode",self.mode) |
|
|
4201 | 4202 | #print("nHeights") |
|
|
4202 | 4203 | self.__buffer = numpy.zeros(( dataOut.nChannels,n, dataOut.nHeights)) |
|
|
4203 | 4204 | self.__buffer2 = numpy.zeros(n) |
|
|
4204 | 4205 | self.__buffer3 = numpy.zeros(n) |
|
|
4205 | 4206 | |
|
|
4206 | 4207 | |
|
|
4207 | 4208 | |
|
|
4208 | 4209 | |
|
|
4209 | 4210 | def putData(self,data,mode): |
|
|
4210 | 4211 | ''' |
|
|
4211 | 4212 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
4212 | 4213 | ''' |
|
|
4213 | 4214 | #print("line 4049",data.dataPP_POW.shape,data.dataPP_POW[:10]) |
|
|
4214 | 4215 | #print("line 4049",data.azimuth.shape,data.azimuth) |
|
|
4215 | 4216 | if self.mode==0: |
|
|
4216 | 4217 | self.__buffer[:,self.__profIndex,:]= data.dataPP_POWER# PRIMER MOMENTO |
|
|
4217 | 4218 | if self.mode==1: |
|
|
4218 | 4219 | self.__buffer[:,self.__profIndex,:]= data.data_pow |
|
|
4219 | 4220 | #print("me casi",self.index,data.azimuth[self.index]) |
|
|
4220 | 4221 | #print(self.__profIndex, self.index , data.azimuth[self.index] ) |
|
|
4221 | 4222 | #print("magic",data.profileIndex) |
|
|
4222 | 4223 | #print(data.azimuth[self.index]) |
|
|
4223 | 4224 | #print("index",self.index) |
|
|
4224 | 4225 | |
|
|
4225 | 4226 | #####self.__buffer2[self.__profIndex] = data.azimuth[self.index] |
|
|
4226 | 4227 | self.__buffer2[self.__profIndex] = data.azimuth |
|
|
4227 | 4228 | self.__buffer3[self.__profIndex] = data.elevation |
|
|
4228 | 4229 | #print("q pasa") |
|
|
4229 | 4230 | #####self.index+=1 |
|
|
4230 | 4231 | #print("index",self.index,data.azimuth[:10]) |
|
|
4231 | 4232 | self.__profIndex += 1 |
|
|
4232 | 4233 | return #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Remove DCΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
4233 | 4234 | |
|
|
4234 | 4235 | def pushData(self,data): |
|
|
4235 | 4236 | ''' |
|
|
4236 | 4237 | Return the PULSEPAIR and the profiles used in the operation |
|
|
4237 | 4238 | Affected : self.__profileIndex |
|
|
4238 | 4239 | ''' |
|
|
4239 | 4240 | #print("pushData") |
|
|
4240 | 4241 | |
|
|
4241 | 4242 | data_360 = self.__buffer |
|
|
4242 | 4243 | data_p = self.__buffer2 |
|
|
4243 | 4244 | data_e = self.__buffer3 |
|
|
4244 | 4245 | n = self.__profIndex |
|
|
4245 | 4246 | |
|
|
4246 | 4247 | self.__buffer = numpy.zeros((self.__nch, self.n,self.__nHeis)) |
|
|
4247 | 4248 | self.__buffer2 = numpy.zeros(self.n) |
|
|
4248 | 4249 | self.__buffer3 = numpy.zeros(self.n) |
|
|
4249 | 4250 | self.__profIndex = 0 |
|
|
4250 | 4251 | #print("pushData") |
|
|
4251 | 4252 | return data_360,n,data_p,data_e |
|
|
4252 | 4253 | |
|
|
4253 | 4254 | |
|
|
4254 | 4255 | def byProfiles(self,dataOut): |
|
|
4255 | 4256 | |
|
|
4256 | 4257 | self.__dataReady = False |
|
|
4257 | 4258 | data_360 = None |
|
|
4258 | 4259 | data_p = None |
|
|
4259 | 4260 | data_e = None |
|
|
4260 | 4261 | #print("dataOu",dataOut.dataPP_POW) |
|
|
4261 | 4262 | self.putData(data=dataOut,mode = self.mode) |
|
|
4262 | 4263 | ##### print("profIndex",self.__profIndex) |
|
|
4263 | 4264 | if self.__profIndex == self.n: |
|
|
4264 | 4265 | data_360,n,data_p,data_e = self.pushData(data=dataOut) |
|
|
4265 | 4266 | self.__dataReady = True |
|
|
4266 | 4267 | |
|
|
4267 | 4268 | return data_360,data_p,data_e |
|
|
4268 | 4269 | |
|
|
4269 | 4270 | |
|
|
4270 | 4271 | def blockOp(self, dataOut, datatime= None): |
|
|
4271 | 4272 | if self.__initime == None: |
|
|
4272 | 4273 | self.__initime = datatime |
|
|
4273 | 4274 | data_360,data_p,data_e = self.byProfiles(dataOut) |
|
|
4274 | 4275 | self.__lastdatatime = datatime |
|
|
4275 | 4276 | |
|
|
4276 | 4277 | if data_360 is None: |
|
|
4277 | 4278 | return None, None,None,None |
|
|
4278 | 4279 | |
|
|
4279 | 4280 | |
|
|
4280 | 4281 | avgdatatime = self.__initime |
|
|
4281 | 4282 | if self.n==1: |
|
|
4282 | 4283 | avgdatatime = datatime |
|
|
4283 | 4284 | deltatime = datatime - self.__lastdatatime |
|
|
4284 | 4285 | self.__initime = datatime |
|
|
4285 | 4286 | #print(data_360.shape,avgdatatime,data_p.shape) |
|
|
4286 | 4287 | return data_360,avgdatatime,data_p,data_e |
|
|
4287 | 4288 | |
|
|
4288 | 4289 | def run(self, dataOut,n = None,mode=None,**kwargs): |
|
|
4289 | 4290 | #print("BLOCK 360 HERE WE GO MOMENTOS") |
|
|
4290 | 4291 | print("Block 360") |
|
|
4291 | 4292 | #exit(1) |
|
|
4292 | 4293 | if not self.isConfig: |
|
|
4293 | 4294 | self.setup(dataOut = dataOut, n = n ,mode= mode ,**kwargs) |
|
|
4294 | 4295 | ####self.index = 0 |
|
|
4295 | 4296 | #print("comova",self.isConfig) |
|
|
4296 | 4297 | self.isConfig = True |
|
|
4297 | 4298 | ####if self.index==dataOut.azimuth.shape[0]: |
|
|
4298 | 4299 | #### self.index=0 |
|
|
4299 | 4300 | data_360, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime) |
|
|
4300 | 4301 | dataOut.flagNoData = True |
|
|
4301 | 4302 | |
|
|
4302 | 4303 | if self.__dataReady: |
|
|
4303 | 4304 | dataOut.data_360 = data_360 # S |
|
|
4304 | 4305 | #print("DATA 360") |
|
|
4305 | 4306 | #print(dataOut.data_360) |
|
|
4306 | 4307 | #print("---------------------------------------------------------------------------------") |
|
|
4307 | 4308 | print("---------------------------DATAREADY---------------------------------------------") |
|
|
4308 | 4309 | #print("---------------------------------------------------------------------------------") |
|
|
4309 | 4310 | #print("data_360",dataOut.data_360.shape) |
|
|
4310 | 4311 | dataOut.data_azi = data_p |
|
|
4311 | 4312 | dataOut.data_ele = data_e |
|
|
4312 | 4313 | ###print("azi: ",dataOut.data_azi) |
|
|
4313 | 4314 | #print("ele: ",dataOut.data_ele) |
|
|
4314 | 4315 | #print("jroproc_parameters",data_p[0],data_p[-1])#,data_360.shape,avgdatatime) |
|
|
4315 | 4316 | dataOut.utctime = avgdatatime |
|
|
4316 | 4317 | dataOut.flagNoData = False |
|
|
4317 | 4318 | return dataOut |
|
|
4318 | 4319 | |
|
|
4319 | 4320 | class Block360_vRF(Operation): |
|
|
4320 | 4321 | ''' |
|
|
4321 | 4322 | ''' |
|
|
4322 | 4323 | isConfig = False |
|
|
4323 | 4324 | __profIndex = 0 |
|
|
4324 | 4325 | __initime = None |
|
|
4325 | 4326 | __lastdatatime = None |
|
|
4326 | 4327 | __buffer = None |
|
|
4327 | 4328 | __dataReady = False |
|
|
4328 | 4329 | n = None |
|
|
4329 | 4330 | __nch = 0 |
|
|
4330 | 4331 | __nHeis = 0 |
|
|
4331 | 4332 | index = 0 |
|
|
4332 | 4333 | mode = 0 |
|
|
4333 | 4334 | |
|
|
4334 | 4335 | def __init__(self,**kwargs): |
|
|
4335 | 4336 | Operation.__init__(self,**kwargs) |
|
|
4336 | 4337 | |
|
|
4337 | 4338 | def setup(self, dataOut, n = None, mode = None): |
|
|
4338 | 4339 | ''' |
|
|
4339 | 4340 | n= Numero de PRF's de entrada |
|
|
4340 | 4341 | ''' |
|
|
4341 | 4342 | self.__initime = None |
|
|
4342 | 4343 | self.__lastdatatime = 0 |
|
|
4343 | 4344 | self.__dataReady = False |
|
|
4344 | 4345 | self.__buffer = 0 |
|
|
4345 | 4346 | self.__buffer_1D = 0 |
|
|
4346 | 4347 | self.__profIndex = 0 |
|
|
4347 | 4348 | self.index = 0 |
|
|
4348 | 4349 | self.__nch = dataOut.nChannels |
|
|
4349 | 4350 | self.__nHeis = dataOut.nHeights |
|
|
4350 | 4351 | ##print("ELVALOR DE n es:", n) |
|
|
4351 | 4352 | if n == None: |
|
|
4352 | 4353 | raise ValueError("n should be specified.") |
|
|
4353 | 4354 | |
|
|
4354 | 4355 | if mode == None: |
|
|
4355 | 4356 | raise ValueError("mode should be specified.") |
|
|
4356 | 4357 | |
|
|
4357 | 4358 | if n != None: |
|
|
4358 | 4359 | if n<1: |
|
|
4359 | 4360 | print("n should be greater than 2") |
|
|
4360 | 4361 | raise ValueError("n should be greater than 2") |
|
|
4361 | 4362 | |
|
|
4362 | 4363 | self.n = n |
|
|
4363 | 4364 | self.mode = mode |
|
|
4364 | 4365 | #print("self.mode",self.mode) |
|
|
4365 | 4366 | #print("nHeights") |
|
|
4366 | 4367 | self.__buffer = numpy.zeros(( dataOut.nChannels,n, dataOut.nHeights)) |
|
|
4367 | 4368 | self.__buffer2 = numpy.zeros(n) |
|
|
4368 | 4369 | self.__buffer3 = numpy.zeros(n) |
|
|
4369 | 4370 | |
|
|
4370 | 4371 | |
|
|
4371 | 4372 | |
|
|
4372 | 4373 | |
|
|
4373 | 4374 | def putData(self,data,mode): |
|
|
4374 | 4375 | ''' |
|
|
4375 | 4376 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
4376 | 4377 | ''' |
|
|
4377 | 4378 | #print("line 4049",data.dataPP_POW.shape,data.dataPP_POW[:10]) |
|
|
4378 | 4379 | #print("line 4049",data.azimuth.shape,data.azimuth) |
|
|
4379 | 4380 | if self.mode==0: |
|
|
4380 | 4381 | self.__buffer[:,self.__profIndex,:]= data.dataPP_POWER# PRIMER MOMENTO |
|
|
4381 | 4382 | if self.mode==1: |
|
|
4382 | 4383 | self.__buffer[:,self.__profIndex,:]= data.data_pow |
|
|
4383 | 4384 | #print("me casi",self.index,data.azimuth[self.index]) |
|
|
4384 | 4385 | #print(self.__profIndex, self.index , data.azimuth[self.index] ) |
|
|
4385 | 4386 | #print("magic",data.profileIndex) |
|
|
4386 | 4387 | #print(data.azimuth[self.index]) |
|
|
4387 | 4388 | #print("index",self.index) |
|
|
4388 | 4389 | |
|
|
4389 | 4390 | #####self.__buffer2[self.__profIndex] = data.azimuth[self.index] |
|
|
4390 | 4391 | self.__buffer2[self.__profIndex] = data.azimuth |
|
|
4391 | 4392 | self.__buffer3[self.__profIndex] = data.elevation |
|
|
4392 | 4393 | #print("q pasa") |
|
|
4393 | 4394 | #####self.index+=1 |
|
|
4394 | 4395 | #print("index",self.index,data.azimuth[:10]) |
|
|
4395 | 4396 | self.__profIndex += 1 |
|
|
4396 | 4397 | return #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Remove DCΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
4397 | 4398 | |
|
|
4398 | 4399 | def pushData(self,data): |
|
|
4399 | 4400 | ''' |
|
|
4400 | 4401 | Return the PULSEPAIR and the profiles used in the operation |
|
|
4401 | 4402 | Affected : self.__profileIndex |
|
|
4402 | 4403 | ''' |
|
|
4403 | 4404 | #print("pushData") |
|
|
4404 | 4405 | |
|
|
4405 | 4406 | data_360 = self.__buffer |
|
|
4406 | 4407 | data_p = self.__buffer2 |
|
|
4407 | 4408 | data_e = self.__buffer3 |
|
|
4408 | 4409 | n = self.__profIndex |
|
|
4409 | 4410 | |
|
|
4410 | 4411 | self.__buffer = numpy.zeros((self.__nch, self.n,self.__nHeis)) |
|
|
4411 | 4412 | self.__buffer2 = numpy.zeros(self.n) |
|
|
4412 | 4413 | self.__buffer3 = numpy.zeros(self.n) |
|
|
4413 | 4414 | self.__profIndex = 0 |
|
|
4414 | 4415 | #print("pushData") |
|
|
4415 | 4416 | return data_360,n,data_p,data_e |
|
|
4416 | 4417 | |
|
|
4417 | 4418 | |
|
|
4418 | 4419 | def byProfiles(self,dataOut): |
|
|
4419 | 4420 | |
|
|
4420 | 4421 | self.__dataReady = False |
|
|
4421 | 4422 | data_360 = None |
|
|
4422 | 4423 | data_p = None |
|
|
4423 | 4424 | data_e = None |
|
|
4424 | 4425 | #print("dataOu",dataOut.dataPP_POW) |
|
|
4425 | 4426 | self.putData(data=dataOut,mode = self.mode) |
|
|
4426 | 4427 | ##### print("profIndex",self.__profIndex) |
|
|
4427 | 4428 | if self.__profIndex == self.n: |
|
|
4428 | 4429 | data_360,n,data_p,data_e = self.pushData(data=dataOut) |
|
|
4429 | 4430 | self.__dataReady = True |
|
|
4430 | 4431 | |
|
|
4431 | 4432 | return data_360,data_p,data_e |
|
|
4432 | 4433 | |
|
|
4433 | 4434 | |
|
|
4434 | 4435 | def blockOp(self, dataOut, datatime= None): |
|
|
4435 | 4436 | if self.__initime == None: |
|
|
4436 | 4437 | self.__initime = datatime |
|
|
4437 | 4438 | data_360,data_p,data_e = self.byProfiles(dataOut) |
|
|
4438 | 4439 | self.__lastdatatime = datatime |
|
|
4439 | 4440 | |
|
|
4440 | 4441 | if data_360 is None: |
|
|
4441 | 4442 | return None, None,None,None |
|
|
4442 | 4443 | |
|
|
4443 | 4444 | |
|
|
4444 | 4445 | avgdatatime = self.__initime |
|
|
4445 | 4446 | if self.n==1: |
|
|
4446 | 4447 | avgdatatime = datatime |
|
|
4447 | 4448 | deltatime = datatime - self.__lastdatatime |
|
|
4448 | 4449 | self.__initime = datatime |
|
|
4449 | 4450 | #print(data_360.shape,avgdatatime,data_p.shape) |
|
|
4450 | 4451 | return data_360,avgdatatime,data_p,data_e |
|
|
4451 | 4452 | |
|
|
4452 | 4453 | def checkcase(self,data_ele): |
|
|
4453 | 4454 | start = data_ele[0] |
|
|
4454 | 4455 | end = data_ele[-1] |
|
|
4455 | 4456 | diff_angle = (end-start) |
|
|
4456 | 4457 | len_ang=len(data_ele) |
|
|
4457 | 4458 | print("start",start) |
|
|
4458 | 4459 | print("end",end) |
|
|
4459 | 4460 | print("number",diff_angle) |
|
|
4460 | 4461 | |
|
|
4461 | 4462 | print("len_ang",len_ang) |
|
|
4462 | 4463 | |
|
|
4463 | 4464 | aux = (data_ele<0).any(axis=0) |
|
|
4464 | 4465 | |
|
|
4465 | 4466 | #exit(1) |
|
|
4466 | 4467 | if diff_angle<0 and aux!=1: #Bajada |
|
|
4467 | 4468 | return 1 |
|
|
4468 | 4469 | elif diff_angle<0 and aux==1: #Bajada con angulos negativos |
|
|
4469 | 4470 | return 0 |
|
|
4470 | 4471 | elif diff_angle == 0: # This case happens when the angle reaches the max_angle if n = 2 |
|
|
4471 | 4472 | self.flagEraseFirstData = 1 |
|
|
4472 | 4473 | print("ToDO this case") |
|
|
4473 | 4474 | exit(1) |
|
|
4474 | 4475 | elif diff_angle>0: #Subida |
|
|
4475 | 4476 | return 0 |
|
|
4476 | 4477 | |
|
|
4477 | 4478 | def run(self, dataOut,n = None,mode=None,**kwargs): |
|
|
4478 | 4479 | #print("BLOCK 360 HERE WE GO MOMENTOS") |
|
|
4479 | 4480 | print("Block 360") |
|
|
4480 | 4481 | |
|
|
4481 | 4482 | #exit(1) |
|
|
4482 | 4483 | if not self.isConfig: |
|
|
4483 | 4484 | if n == 1: |
|
|
4484 | 4485 | print("*******************Min Value is 2. Setting n = 2*******************") |
|
|
4485 | 4486 | n = 2 |
|
|
4486 | 4487 | #exit(1) |
|
|
4487 | 4488 | print(n) |
|
|
4488 | 4489 | self.setup(dataOut = dataOut, n = n ,mode= mode ,**kwargs) |
|
|
4489 | 4490 | ####self.index = 0 |
|
|
4490 | 4491 | #print("comova",self.isConfig) |
|
|
4491 | 4492 | self.isConfig = True |
|
|
4492 | 4493 | ####if self.index==dataOut.azimuth.shape[0]: |
|
|
4493 | 4494 | #### self.index=0 |
|
|
4494 | 4495 | data_360, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime) |
|
|
4495 | 4496 | dataOut.flagNoData = True |
|
|
4496 | 4497 | |
|
|
4497 | 4498 | if self.__dataReady: |
|
|
4498 | 4499 | dataOut.data_360 = data_360 # S |
|
|
4499 | 4500 | #print("DATA 360") |
|
|
4500 | 4501 | #print(dataOut.data_360) |
|
|
4501 | 4502 | #print("---------------------------------------------------------------------------------") |
|
|
4502 | 4503 | print("---------------------------DATAREADY---------------------------------------------") |
|
|
4503 | 4504 | #print("---------------------------------------------------------------------------------") |
|
|
4504 | 4505 | #print("data_360",dataOut.data_360.shape) |
|
|
4505 | 4506 | dataOut.data_azi = data_p |
|
|
4506 | 4507 | dataOut.data_ele = data_e |
|
|
4507 | 4508 | ###print("azi: ",dataOut.data_azi) |
|
|
4508 | 4509 | #print("ele: ",dataOut.data_ele) |
|
|
4509 | 4510 | #print("jroproc_parameters",data_p[0],data_p[-1])#,data_360.shape,avgdatatime) |
|
|
4510 | 4511 | dataOut.utctime = avgdatatime |
|
|
4511 | 4512 | |
|
|
4512 | 4513 | dataOut.case_flag = self.checkcase(dataOut.data_ele) |
|
|
4513 | 4514 | if dataOut.case_flag: #Si estΓ‘ de bajada empieza a plotear |
|
|
4514 | 4515 | print("INSIDE CASE FLAG BAJADA") |
|
|
4515 | 4516 | dataOut.flagNoData = False |
|
|
4516 | 4517 | else: |
|
|
4517 | 4518 | print("CASE SUBIDA") |
|
|
4518 | 4519 | dataOut.flagNoData = True |
|
|
4519 | 4520 | |
|
|
4520 | 4521 | #dataOut.flagNoData = False |
|
|
4521 | 4522 | return dataOut |
|
|
4522 | 4523 | |
|
|
4523 | 4524 | class Block360_vRF2(Operation): |
|
|
4524 | 4525 | ''' |
|
|
4525 | 4526 | ''' |
|
|
4526 | 4527 | isConfig = False |
|
|
4527 | 4528 | __profIndex = 0 |
|
|
4528 | 4529 | __initime = None |
|
|
4529 | 4530 | __lastdatatime = None |
|
|
4530 | 4531 | __buffer = None |
|
|
4531 | 4532 | __dataReady = False |
|
|
4532 | 4533 | n = None |
|
|
4533 | 4534 | __nch = 0 |
|
|
4534 | 4535 | __nHeis = 0 |
|
|
4535 | 4536 | index = 0 |
|
|
4536 | 4537 | mode = None |
|
|
4537 | 4538 | |
|
|
4538 | 4539 | def __init__(self,**kwargs): |
|
|
4539 | 4540 | Operation.__init__(self,**kwargs) |
|
|
4540 | 4541 | |
|
|
4541 | 4542 | def setup(self, dataOut, n = None, mode = None): |
|
|
4542 | 4543 | ''' |
|
|
4543 | 4544 | n= Numero de PRF's de entrada |
|
|
4544 | 4545 | ''' |
|
|
4545 | 4546 | self.__initime = None |
|
|
4546 | 4547 | self.__lastdatatime = 0 |
|
|
4547 | 4548 | self.__dataReady = False |
|
|
4548 | 4549 | self.__buffer = 0 |
|
|
4549 | 4550 | self.__buffer_1D = 0 |
|
|
4550 | 4551 | #self.__profIndex = 0 |
|
|
4551 | 4552 | self.index = 0 |
|
|
4552 | 4553 | self.__nch = dataOut.nChannels |
|
|
4553 | 4554 | self.__nHeis = dataOut.nHeights |
|
|
4554 | 4555 | |
|
|
4555 | 4556 | self.mode = mode |
|
|
4556 | 4557 | #print("self.mode",self.mode) |
|
|
4557 | 4558 | #print("nHeights") |
|
|
4558 | 4559 | self.__buffer = [] |
|
|
4559 | 4560 | self.__buffer2 = [] |
|
|
4560 | 4561 | self.__buffer3 = [] |
|
|
4561 | 4562 | self.__buffer4 = [] |
|
|
4562 | 4563 | |
|
|
4563 | 4564 | def putData(self,data,mode): |
|
|
4564 | 4565 | ''' |
|
|
4565 | 4566 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
4566 | 4567 | ''' |
|
|
4567 | 4568 | |
|
|
4568 | 4569 | if self.mode==0: |
|
|
4569 | 4570 | self.__buffer.append(data.dataPP_POWER)# PRIMER MOMENTO |
|
|
4570 | 4571 | if self.mode==1: |
|
|
4571 | 4572 | self.__buffer.append(data.data_pow) |
|
|
4572 | 4573 | |
|
|
4573 | 4574 | self.__buffer4.append(data.dataPP_DOP) |
|
|
4574 | 4575 | |
|
|
4575 | 4576 | self.__buffer2.append(data.azimuth) |
|
|
4576 | 4577 | self.__buffer3.append(data.elevation) |
|
|
4577 | 4578 | self.__profIndex += 1 |
|
|
4578 | 4579 | |
|
|
4579 | 4580 | return numpy.array(self.__buffer3) #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Remove DCΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· |
|
|
4580 | 4581 | |
|
|
4581 | 4582 | def pushData(self,data): |
|
|
4582 | 4583 | ''' |
|
|
4583 | 4584 | Return the PULSEPAIR and the profiles used in the operation |
|
|
4584 | 4585 | Affected : self.__profileIndex |
|
|
4585 | 4586 | ''' |
|
|
4586 | 4587 | |
|
|
4587 | 4588 | data_360_Power = numpy.array(self.__buffer).transpose(1,0,2) |
|
|
4588 | 4589 | data_360_Velocity = numpy.array(self.__buffer4).transpose(1,0,2) |
|
|
4589 | 4590 | data_p = numpy.array(self.__buffer2) |
|
|
4590 | 4591 | data_e = numpy.array(self.__buffer3) |
|
|
4591 | 4592 | n = self.__profIndex |
|
|
4592 | 4593 | |
|
|
4593 | 4594 | self.__buffer = [] |
|
|
4594 | 4595 | self.__buffer4 = [] |
|
|
4595 | 4596 | self.__buffer2 = [] |
|
|
4596 | 4597 | self.__buffer3 = [] |
|
|
4597 | 4598 | self.__profIndex = 0 |
|
|
4598 | 4599 | return data_360_Power,data_360_Velocity,n,data_p,data_e |
|
|
4599 | 4600 | |
|
|
4600 | 4601 | |
|
|
4601 | 4602 | def byProfiles(self,dataOut): |
|
|
4602 | 4603 | |
|
|
4603 | 4604 | self.__dataReady = False |
|
|
4604 | 4605 | data_360_Power = [] |
|
|
4605 | 4606 | data_360_Velocity = [] |
|
|
4606 | 4607 | data_p = None |
|
|
4607 | 4608 | data_e = None |
|
|
4608 | 4609 | |
|
|
4609 | 4610 | elevations = self.putData(data=dataOut,mode = self.mode) |
|
|
4610 | 4611 | |
|
|
4611 | 4612 | if self.__profIndex > 1: |
|
|
4612 | 4613 | case_flag = self.checkcase(elevations) |
|
|
4613 | 4614 | |
|
|
4614 | 4615 | if case_flag == 0: #Subida |
|
|
4615 | 4616 | |
|
|
4616 | 4617 | if len(self.__buffer) == 2: #Cuando estΓ‘ de subida |
|
|
4617 | 4618 | #Se borra el dato anterior para liberar buffer y comparar el dato actual con el siguiente |
|
|
4618 | 4619 | self.__buffer.pop(0) #Erase first data |
|
|
4619 | 4620 | self.__buffer2.pop(0) |
|
|
4620 | 4621 | self.__buffer3.pop(0) |
|
|
4621 | 4622 | self.__buffer4.pop(0) |
|
|
4622 | 4623 | self.__profIndex -= 1 |
|
|
4623 | 4624 | else: #Cuando ha estado de bajada y ha vuelto a subir |
|
|
4624 | 4625 | #Se borra el ΓΊltimo dato |
|
|
4625 | 4626 | self.__buffer.pop() #Erase last data |
|
|
4626 | 4627 | self.__buffer2.pop() |
|
|
4627 | 4628 | self.__buffer3.pop() |
|
|
4628 | 4629 | self.__buffer4.pop() |
|
|
4629 | 4630 | data_360_Power,data_360_Velocity,n,data_p,data_e = self.pushData(data=dataOut) |
|
|
4630 | 4631 | |
|
|
4631 | 4632 | self.__dataReady = True |
|
|
4632 | 4633 | |
|
|
4633 | 4634 | return data_360_Power,data_360_Velocity,data_p,data_e |
|
|
4634 | 4635 | |
|
|
4635 | 4636 | |
|
|
4636 | 4637 | def blockOp(self, dataOut, datatime= None): |
|
|
4637 | 4638 | if self.__initime == None: |
|
|
4638 | 4639 | self.__initime = datatime |
|
|
4639 | 4640 | data_360_Power,data_360_Velocity,data_p,data_e = self.byProfiles(dataOut) |
|
|
4640 | 4641 | self.__lastdatatime = datatime |
|
|
4641 | 4642 | |
|
|
4642 | 4643 | avgdatatime = self.__initime |
|
|
4643 | 4644 | if self.n==1: |
|
|
4644 | 4645 | avgdatatime = datatime |
|
|
4645 | 4646 | deltatime = datatime - self.__lastdatatime |
|
|
4646 | 4647 | self.__initime = datatime |
|
|
4647 | 4648 | return data_360_Power,data_360_Velocity,avgdatatime,data_p,data_e |
|
|
4648 | 4649 | |
|
|
4649 | 4650 | def checkcase(self,data_ele): |
|
|
4650 | 4651 | #print(data_ele) |
|
|
4651 | 4652 | start = data_ele[-2] |
|
|
4652 | 4653 | end = data_ele[-1] |
|
|
4653 | 4654 | diff_angle = (end-start) |
|
|
4654 | 4655 | len_ang=len(data_ele) |
|
|
4655 | 4656 | |
|
|
4656 | 4657 | if diff_angle > 0: #Subida |
|
|
4657 | 4658 | return 0 |
|
|
4658 | 4659 | |
|
|
4659 | 4660 | def run(self, dataOut,mode='Power',**kwargs): |
|
|
4660 | 4661 | #print("BLOCK 360 HERE WE GO MOMENTOS") |
|
|
4661 | 4662 | #print("Block 360") |
|
|
4662 | 4663 | dataOut.mode = mode |
|
|
4663 | 4664 | |
|
|
4664 | 4665 | if not self.isConfig: |
|
|
4665 | 4666 | self.setup(dataOut = dataOut ,mode= mode ,**kwargs) |
|
|
4666 | 4667 | self.isConfig = True |
|
|
4667 | 4668 | |
|
|
4668 | 4669 | |
|
|
4669 | 4670 | data_360_Power, data_360_Velocity, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime) |
|
|
4670 | 4671 | |
|
|
4671 | 4672 | |
|
|
4672 | 4673 | dataOut.flagNoData = True |
|
|
4673 | 4674 | |
|
|
4674 | 4675 | |
|
|
4675 | 4676 | if self.__dataReady: |
|
|
4676 | 4677 | dataOut.data_360_Power = data_360_Power # S |
|
|
4677 | 4678 | dataOut.data_360_Velocity = data_360_Velocity |
|
|
4678 | 4679 | dataOut.data_azi = data_p |
|
|
4679 | 4680 | dataOut.data_ele = data_e |
|
|
4680 | 4681 | dataOut.utctime = avgdatatime |
|
|
4681 | 4682 | dataOut.flagNoData = False |
|
|
4682 | 4683 | |
|
|
4683 | 4684 | return dataOut |
|
|
4684 | 4685 | |
|
|
4685 | 4686 | class Block360_vRF3(Operation): |
|
|
4686 | 4687 | ''' |
|
|
4687 | 4688 | ''' |
|
|
4688 | 4689 | isConfig = False |
|
|
4689 | 4690 | __profIndex = 0 |
|
|
4690 | 4691 | __initime = None |
|
|
4691 | 4692 | __lastdatatime = None |
|
|
4692 | 4693 | __buffer = None |
|
|
4693 | 4694 | __dataReady = False |
|
|
4694 | 4695 | n = None |
|
|
4695 | 4696 | __nch = 0 |
|
|
4696 | 4697 | __nHeis = 0 |
|
|
4697 | 4698 | index = 0 |
|
|
4698 | 4699 | mode = None |
|
|
4699 | 4700 | |
|
|
4700 | 4701 | def __init__(self,**kwargs): |
|
|
4701 | 4702 | Operation.__init__(self,**kwargs) |
|
|
4702 | 4703 | |
|
|
4703 | 4704 | def setup(self, dataOut, attr): |
|
|
4704 | 4705 | ''' |
|
|
4705 | 4706 | n= Numero de PRF's de entrada |
|
|
4706 | 4707 | ''' |
|
|
4707 | 4708 | self.__initime = None |
|
|
4708 | 4709 | self.__lastdatatime = 0 |
|
|
4709 | 4710 | self.__dataReady = False |
|
|
4710 | 4711 | self.__buffer = 0 |
|
|
4711 | 4712 | self.__buffer_1D = 0 |
|
|
4712 | 4713 | self.index = 0 |
|
|
4713 | 4714 | self.__nch = dataOut.nChannels |
|
|
4714 | 4715 | self.__nHeis = dataOut.nHeights |
|
|
4715 | 4716 | |
|
|
4716 | 4717 | self.attr = attr |
|
|
4717 | 4718 | #print("self.mode",self.mode) |
|
|
4718 | 4719 | #print("nHeights") |
|
|
4719 | 4720 | self.__buffer = [] |
|
|
4720 | 4721 | self.__buffer2 = [] |
|
|
4721 | 4722 | self.__buffer3 = [] |
|
|
4722 | 4723 | |
|
|
4723 | 4724 | def putData(self, data, attr): |
|
|
4724 | 4725 | ''' |
|
|
4725 | 4726 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
4726 | 4727 | ''' |
|
|
4727 | 4728 | |
|
|
4728 | 4729 | self.__buffer.append(getattr(data, attr)) |
|
|
4729 | 4730 | self.__buffer2.append(data.azimuth) |
|
|
4730 | 4731 | self.__buffer3.append(data.elevation) |
|
|
4731 | 4732 | self.__profIndex += 1 |
|
|
4732 | 4733 | |
|
|
4733 | 4734 | return numpy.array(self.__buffer3) |
|
|
4734 | 4735 | |
|
|
4735 | 4736 | def pushData(self, data): |
|
|
4736 | 4737 | ''' |
|
|
4737 | 4738 | Return the PULSEPAIR and the profiles used in the operation |
|
|
4738 | 4739 | Affected : self.__profileIndex |
|
|
4739 | 4740 | ''' |
|
|
4740 | 4741 | |
|
|
4741 | 4742 | data_360 = numpy.array(self.__buffer).transpose(1, 0, 2) |
|
|
4742 | 4743 | data_p = numpy.array(self.__buffer2) |
|
|
4743 | 4744 | data_e = numpy.array(self.__buffer3) |
|
|
4744 | 4745 | n = self.__profIndex |
|
|
4745 | 4746 | |
|
|
4746 | 4747 | self.__buffer = [] |
|
|
4747 | 4748 | self.__buffer2 = [] |
|
|
4748 | 4749 | self.__buffer3 = [] |
|
|
4749 | 4750 | self.__profIndex = 0 |
|
|
4750 | 4751 | return data_360, n, data_p, data_e |
|
|
4751 | 4752 | |
|
|
4752 | 4753 | |
|
|
4753 | 4754 | def byProfiles(self,dataOut): |
|
|
4754 | 4755 | |
|
|
4755 | 4756 | self.__dataReady = False |
|
|
4756 | 4757 | data_360 = [] |
|
|
4757 | 4758 | data_p = None |
|
|
4758 | 4759 | data_e = None |
|
|
4759 | 4760 | |
|
|
4760 | 4761 | elevations = self.putData(data=dataOut, attr = self.attr) |
|
|
4761 | 4762 | |
|
|
4762 | 4763 | if self.__profIndex > 1: |
|
|
4763 | 4764 | case_flag = self.checkcase(elevations) |
|
|
4764 | 4765 | |
|
|
4765 | 4766 | if case_flag == 0: #Subida |
|
|
4766 | 4767 | |
|
|
4767 | 4768 | if len(self.__buffer) == 2: #Cuando estΓ‘ de subida |
|
|
4768 | 4769 | #Se borra el dato anterior para liberar buffer y comparar el dato actual con el siguiente |
|
|
4769 | 4770 | self.__buffer.pop(0) #Erase first data |
|
|
4770 | 4771 | self.__buffer2.pop(0) |
|
|
4771 | 4772 | self.__buffer3.pop(0) |
|
|
4772 | 4773 | self.__profIndex -= 1 |
|
|
4773 | 4774 | else: #Cuando ha estado de bajada y ha vuelto a subir |
|
|
4774 | 4775 | #Se borra el ΓΊltimo dato |
|
|
4775 | 4776 | self.__buffer.pop() #Erase last data |
|
|
4776 | 4777 | self.__buffer2.pop() |
|
|
4777 | 4778 | self.__buffer3.pop() |
|
|
4778 | 4779 | data_360, n, data_p, data_e = self.pushData(data=dataOut) |
|
|
4779 | 4780 | |
|
|
4780 | 4781 | self.__dataReady = True |
|
|
4781 | 4782 | |
|
|
4782 | 4783 | return data_360, data_p, data_e |
|
|
4783 | 4784 | |
|
|
4784 | 4785 | |
|
|
4785 | 4786 | def blockOp(self, dataOut, datatime= None): |
|
|
4786 | 4787 | if self.__initime == None: |
|
|
4787 | 4788 | self.__initime = datatime |
|
|
4788 | 4789 | data_360, data_p, data_e = self.byProfiles(dataOut) |
|
|
4789 | 4790 | self.__lastdatatime = datatime |
|
|
4790 | 4791 | |
|
|
4791 | 4792 | avgdatatime = self.__initime |
|
|
4792 | 4793 | if self.n==1: |
|
|
4793 | 4794 | avgdatatime = datatime |
|
|
4794 | 4795 | deltatime = datatime - self.__lastdatatime |
|
|
4795 | 4796 | self.__initime = datatime |
|
|
4796 | 4797 | return data_360, avgdatatime, data_p, data_e |
|
|
4797 | 4798 | |
|
|
4798 | 4799 | def checkcase(self, data_ele): |
|
|
4799 | 4800 | |
|
|
4800 | 4801 | start = data_ele[-2] |
|
|
4801 | 4802 | end = data_ele[-1] |
|
|
4802 | 4803 | diff_angle = (end-start) |
|
|
4803 | 4804 | len_ang=len(data_ele) |
|
|
4804 | 4805 | |
|
|
4805 | 4806 | if diff_angle > 0: #Subida |
|
|
4806 | 4807 | return 0 |
|
|
4807 | 4808 | |
|
|
4808 | 4809 | def run(self, dataOut, attr_data='dataPP_POWER',**kwargs): |
|
|
4809 | 4810 | |
|
|
4810 | 4811 | dataOut.attr_data = attr_data |
|
|
4811 | 4812 | |
|
|
4812 | 4813 | if not self.isConfig: |
|
|
4813 | 4814 | self.setup(dataOut = dataOut, attr = attr_data ,**kwargs) |
|
|
4814 | 4815 | self.isConfig = True |
|
|
4815 | 4816 | |
|
|
4816 | 4817 | data_360, avgdatatime, data_p, data_e = self.blockOp(dataOut, dataOut.utctime) |
|
|
4817 | 4818 | |
|
|
4818 | 4819 | dataOut.flagNoData = True |
|
|
4819 | 4820 | |
|
|
4820 | 4821 | if self.__dataReady: |
|
|
4821 | 4822 | setattr(dataOut, attr_data, data_360 ) |
|
|
4822 | 4823 | dataOut.data_azi = data_p |
|
|
4823 | 4824 | dataOut.data_ele = data_e |
|
|
4824 | 4825 | dataOut.utctime = avgdatatime |
|
|
4825 | 4826 | dataOut.flagNoData = False |
|
|
4826 | 4827 | |
|
|
4827 | 4828 | return dataOut |
|
|
4828 | 4829 | |
|
|
4829 | 4830 | class Block360_vRF4(Operation): |
|
|
4830 | 4831 | ''' |
|
|
4831 | 4832 | ''' |
|
|
4832 | 4833 | isConfig = False |
|
|
4833 | 4834 | __profIndex = 0 |
|
|
4834 | 4835 | __initime = None |
|
|
4835 | 4836 | __lastdatatime = None |
|
|
4836 | 4837 | __buffer = None |
|
|
4837 | 4838 | __dataReady = False |
|
|
4838 | 4839 | n = None |
|
|
4839 | 4840 | __nch = 0 |
|
|
4840 | 4841 | __nHeis = 0 |
|
|
4841 | 4842 | index = 0 |
|
|
4842 | 4843 | mode = None |
|
|
4843 | 4844 | |
|
|
4844 | 4845 | def __init__(self,**kwargs): |
|
|
4845 | 4846 | Operation.__init__(self,**kwargs) |
|
|
4846 | 4847 | |
|
|
4847 | 4848 | def setup(self, dataOut, attr): |
|
|
4848 | 4849 | ''' |
|
|
4849 | 4850 | n= Numero de PRF's de entrada |
|
|
4850 | 4851 | ''' |
|
|
4851 | 4852 | self.__initime = None |
|
|
4852 | 4853 | self.__lastdatatime = 0 |
|
|
4853 | 4854 | self.__dataReady = False |
|
|
4854 | 4855 | self.__buffer = 0 |
|
|
4855 | 4856 | self.__buffer_1D = 0 |
|
|
4856 | 4857 | self.index = 0 |
|
|
4857 | 4858 | self.__nch = dataOut.nChannels |
|
|
4858 | 4859 | self.__nHeis = dataOut.nHeights |
|
|
4859 | 4860 | |
|
|
4860 | 4861 | self.attr = attr |
|
|
4861 | 4862 | |
|
|
4862 | 4863 | self.__buffer = [] |
|
|
4863 | 4864 | self.__buffer2 = [] |
|
|
4864 | 4865 | self.__buffer3 = [] |
|
|
4865 | 4866 | |
|
|
4866 | 4867 | def putData(self, data, attr, flagMode): |
|
|
4867 | 4868 | ''' |
|
|
4868 | 4869 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
4869 | 4870 | ''' |
|
|
4870 | 4871 | |
|
|
4871 | 4872 | self.__buffer.append(getattr(data, attr)) |
|
|
4872 | 4873 | self.__buffer2.append(data.azimuth) |
|
|
4873 | 4874 | self.__buffer3.append(data.elevation) |
|
|
4874 | 4875 | self.__profIndex += 1 |
|
|
4875 | 4876 | |
|
|
4876 | 4877 | if flagMode == 1: #'AZI' |
|
|
4877 | 4878 | return numpy.array(self.__buffer2) |
|
|
4878 | 4879 | elif flagMode == 0: #'ELE' |
|
|
4879 | 4880 | return numpy.array(self.__buffer3) |
|
|
4880 | 4881 | |
|
|
4881 | 4882 | def pushData(self, data,flagMode,case_flag): |
|
|
4882 | 4883 | ''' |
|
|
4883 | 4884 | Return the PULSEPAIR and the profiles used in the operation |
|
|
4884 | 4885 | Affected : self.__profileIndex |
|
|
4885 | 4886 | ''' |
|
|
4886 | 4887 | |
|
|
4887 | 4888 | data_360 = numpy.array(self.__buffer).transpose(1, 0, 2) |
|
|
4888 | 4889 | data_p = numpy.array(self.__buffer2) |
|
|
4889 | 4890 | data_e = numpy.array(self.__buffer3) |
|
|
4890 | 4891 | n = self.__profIndex |
|
|
4891 | 4892 | |
|
|
4892 | 4893 | self.__buffer = [] |
|
|
4893 | 4894 | self.__buffer2 = [] |
|
|
4894 | 4895 | self.__buffer3 = [] |
|
|
4895 | 4896 | self.__profIndex = 0 |
|
|
4896 | 4897 | |
|
|
4897 | 4898 | if flagMode == 1 and case_flag == 0: #'AZI' y ha girado |
|
|
4898 | 4899 | self.putData(data=data, attr = self.attr, flagMode=flagMode) |
|
|
4899 | 4900 | |
|
|
4900 | 4901 | return data_360, n, data_p, data_e |
|
|
4901 | 4902 | |
|
|
4902 | 4903 | |
|
|
4903 | 4904 | def byProfiles(self,dataOut,flagMode): |
|
|
4904 | 4905 | |
|
|
4905 | 4906 | self.__dataReady = False |
|
|
4906 | 4907 | data_360 = [] |
|
|
4907 | 4908 | data_p = None |
|
|
4908 | 4909 | data_e = None |
|
|
4909 | 4910 | |
|
|
4910 | 4911 | angles = self.putData(data=dataOut, attr = self.attr, flagMode=flagMode) |
|
|
4911 | ||
|
|
4912 | #print(angles) | |
|
|
4912 | 4913 | if self.__profIndex > 1: |
|
|
4913 | 4914 | case_flag = self.checkcase(angles,flagMode) |
|
|
4914 | 4915 | |
|
|
4915 | 4916 | if flagMode == 1: #'AZI': |
|
|
4916 | 4917 | if case_flag == 0: #Ya girΓ³ |
|
|
4917 | 4918 | self.__buffer.pop() #Erase last data |
|
|
4918 | 4919 | self.__buffer2.pop() |
|
|
4919 | 4920 | self.__buffer3.pop() |
|
|
4920 | 4921 | data_360,n,data_p,data_e = self.pushData(data=dataOut,flagMode=flagMode,case_flag=case_flag) |
|
|
4921 | 4922 | |
|
|
4922 | 4923 | self.__dataReady = True |
|
|
4923 | 4924 | |
|
|
4924 | 4925 | elif flagMode == 0: #'ELE' |
|
|
4925 | 4926 | |
|
|
4926 | 4927 | if case_flag == 0: #Subida |
|
|
4927 | 4928 | |
|
|
4928 | 4929 | if len(self.__buffer) == 2: #Cuando estΓ‘ de subida |
|
|
4929 | 4930 | #Se borra el dato anterior para liberar buffer y comparar el dato actual con el siguiente |
|
|
4930 | 4931 | self.__buffer.pop(0) #Erase first data |
|
|
4931 | 4932 | self.__buffer2.pop(0) |
|
|
4932 | 4933 | self.__buffer3.pop(0) |
|
|
4933 | 4934 | self.__profIndex -= 1 |
|
|
4934 | 4935 | else: #Cuando ha estado de bajada y ha vuelto a subir |
|
|
4935 | 4936 | #Se borra el ΓΊltimo dato |
|
|
4936 | 4937 | self.__buffer.pop() #Erase last data |
|
|
4937 | 4938 | self.__buffer2.pop() |
|
|
4938 | 4939 | self.__buffer3.pop() |
|
|
4939 | 4940 | data_360, n, data_p, data_e = self.pushData(data=dataOut,flagMode=flagMode,case_flag=case_flag) |
|
|
4940 | 4941 | |
|
|
4941 | 4942 | self.__dataReady = True |
|
|
4942 | 4943 | |
|
|
4943 | 4944 | return data_360, data_p, data_e |
|
|
4944 | 4945 | |
|
|
4945 | 4946 | |
|
|
4946 | 4947 | def blockOp(self, dataOut, flagMode, datatime= None): |
|
|
4947 | 4948 | if self.__initime == None: |
|
|
4948 | 4949 | self.__initime = datatime |
|
|
4949 | 4950 | data_360, data_p, data_e = self.byProfiles(dataOut,flagMode) |
|
|
4950 | 4951 | self.__lastdatatime = datatime |
|
|
4951 | 4952 | |
|
|
4952 | 4953 | avgdatatime = self.__initime |
|
|
4953 | 4954 | if self.n==1: |
|
|
4954 | 4955 | avgdatatime = datatime |
|
|
4955 | 4956 | deltatime = datatime - self.__lastdatatime |
|
|
4956 | 4957 | self.__initime = datatime |
|
|
4957 | 4958 | return data_360, avgdatatime, data_p, data_e |
|
|
4958 | 4959 | |
|
|
4959 | 4960 | def checkcase(self, angles, flagMode): |
|
|
4960 | 4961 | |
|
|
4961 | 4962 | if flagMode == 1: #'AZI' |
|
|
4962 | 4963 | start = angles[-2] |
|
|
4963 | 4964 | end = angles[-1] |
|
|
4964 | 4965 | diff_angle = (end-start) |
|
|
4965 | 4966 | |
|
|
4966 | 4967 | if diff_angle < 0: #Ya girΓ³ |
|
|
4967 | 4968 | return 0 |
|
|
4968 | 4969 | |
|
|
4969 | 4970 | elif flagMode == 0: #'ELE' |
|
|
4970 | 4971 | |
|
|
4971 | 4972 | start = angles[-2] |
|
|
4972 | 4973 | end = angles[-1] |
|
|
4973 | 4974 | diff_angle = (end-start) |
|
|
4974 | 4975 | |
|
|
4975 | 4976 | if diff_angle > 0: #Subida |
|
|
4976 | 4977 | return 0 |
|
|
4977 | 4978 | |
|
|
4978 | 4979 | def run(self, dataOut, attr_data='dataPP_POWER', axis=None,**kwargs): |
|
|
4979 | 4980 | |
|
|
4980 | 4981 | dataOut.attr_data = attr_data |
|
|
4981 | 4982 | |
|
|
4982 | 4983 | dataOut.flagMode = axis[0] #Provisional, deberΓa venir del header |
|
|
4983 | 4984 | |
|
|
4984 | 4985 | if not self.isConfig: |
|
|
4985 | 4986 | self.setup(dataOut = dataOut, attr = attr_data ,**kwargs) |
|
|
4986 | 4987 | self.isConfig = True |
|
|
4987 | 4988 | |
|
|
4988 | 4989 | data_360, avgdatatime, data_p, data_e = self.blockOp(dataOut, dataOut.flagMode, dataOut.utctime) |
|
|
4989 | 4990 | |
|
|
4990 | 4991 | dataOut.flagNoData = True |
|
|
4991 | 4992 | |
|
|
4992 | 4993 | if self.__dataReady: |
|
|
4993 | 4994 | setattr(dataOut, attr_data, data_360 ) |
|
|
4994 | 4995 | dataOut.data_azi = data_p |
|
|
4995 | 4996 | dataOut.data_ele = data_e |
|
|
4996 | 4997 | dataOut.utctime = avgdatatime |
|
|
4997 | 4998 | dataOut.flagNoData = False |
|
|
4999 | #print(data_360.shape) | |
|
|
5000 | #print(dataOut.heightList) | |
|
|
4998 | 5001 | |
|
|
4999 | 5002 | return dataOut |
|
|
5003 | ||
|
|
5004 | class MergeProc(ProcessingUnit): | |
|
|
5005 | ||
|
|
5006 | def __init__(self): | |
|
|
5007 | ProcessingUnit.__init__(self) | |
|
|
5008 | ||
|
|
5009 | def run(self, attr_data, mode=0): | |
|
|
5010 | ||
|
|
5011 | #exit(1) | |
|
|
5012 | self.dataOut = getattr(self, self.inputs[0]) | |
|
|
5013 | data_inputs = [getattr(self, attr) for attr in self.inputs] | |
|
|
5014 | #print(data_inputs) | |
|
|
5015 | #print(numpy.shape([getattr(data, attr_data) for data in data_inputs][1])) | |
|
|
5016 | #exit(1) | |
|
|
5017 | if mode==0: | |
|
|
5018 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) | |
|
|
5019 | setattr(self.dataOut, attr_data, data) | |
|
|
5020 | ||
|
|
5021 | if mode==1: #Hybrid | |
|
|
5022 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) | |
|
|
5023 | #setattr(self.dataOut, attr_data, data) | |
|
|
5024 | setattr(self.dataOut, 'dataLag_spc', [getattr(data, attr_data) for data in data_inputs][0]) | |
|
|
5025 | setattr(self.dataOut, 'dataLag_spc_LP', [getattr(data, attr_data) for data in data_inputs][1]) | |
|
|
5026 | setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) | |
|
|
5027 | setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) | |
|
|
5028 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) | |
|
|
5029 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) | |
|
|
5030 | ''' | |
|
|
5031 | print(self.dataOut.dataLag_spc_LP.shape) | |
|
|
5032 | print(self.dataOut.dataLag_cspc_LP.shape) | |
|
|
5033 | exit(1) | |
|
|
5034 | ''' | |
|
|
5035 | ||
|
|
5036 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) | |
|
|
5037 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) | |
|
|
5038 | ''' | |
|
|
5039 | print("Merge") | |
|
|
5040 | print(numpy.shape(self.dataOut.dataLag_spc)) | |
|
|
5041 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) | |
|
|
5042 | print(numpy.shape(self.dataOut.dataLag_cspc)) | |
|
|
5043 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) | |
|
|
5044 | exit(1) | |
|
|
5045 | ''' | |
|
|
5046 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) | |
|
|
5047 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) | |
|
|
5048 | #exit(1) | |
|
|
5049 | #print(self.dataOut.NDP) | |
|
|
5050 | #print(self.dataOut.nNoiseProfiles) | |
|
|
5051 | ||
|
|
5052 | #self.dataOut.nIncohInt_LP = 128 | |
|
|
5053 | self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP | |
|
|
5054 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt | |
|
|
5055 | self.dataOut.NLAG = 16 | |
|
|
5056 | self.dataOut.NRANGE = 200 | |
|
|
5057 | self.dataOut.NSCAN = 128 | |
|
|
5058 | #print(numpy.shape(self.dataOut.data_spc)) | |
|
|
5059 | ||
|
|
5060 | #exit(1) | |
|
|
5061 | ||
|
|
5062 | if mode==2: #HAE 2022 | |
|
|
5063 | data = numpy.sum([getattr(data, attr_data) for data in data_inputs],axis=0) | |
|
|
5064 | setattr(self.dataOut, attr_data, data) | |
|
|
5065 | ||
|
|
5066 | self.dataOut.nIncohInt *= 2 | |
|
|
5067 | #meta = self.dataOut.getFreqRange(1)/1000. | |
|
|
5068 | self.dataOut.freqRange = self.dataOut.getFreqRange(1)/1000. | |
|
|
5069 | ||
|
|
5070 | #exit(1) | |
|
|
5071 | ||
|
|
5072 | if mode==7: #RM | |
|
|
5073 | ||
|
|
5074 | f = [getattr(data, attr_data) for data in data_inputs][0] | |
|
|
5075 | g = [getattr(data, attr_data) for data in data_inputs][1] | |
|
|
5076 | ||
|
|
5077 | data = numpy.concatenate((f,g),axis=2) | |
|
|
5078 | #print(data) | |
|
|
5079 | setattr(self.dataOut, attr_data, data) | |
|
|
5080 | #print(self.dataOut.dataPP_POWER.shape) | |
|
|
5081 | #CONSTRUIR NUEVA ALTURAS | |
|
|
5082 | #print("hei_merge",self.dataOut.heightList) | |
|
|
5083 | dh = self.dataOut.heightList[1]-self.dataOut.heightList[0] | |
|
|
5084 | heightList_2 = (self.dataOut.heightList[-1]+dh) + numpy.arange(g.shape[-1], dtype=numpy.float) * dh | |
|
|
5085 | ||
|
|
5086 | self.dataOut.heightList = numpy.concatenate((self.dataOut.heightList,heightList_2)) | |
|
|
5087 | #print("hei_merge_total",self.dataOut.heightList) | |
|
|
5088 | #exit(1) | |
| @@ -1,1862 +1,1860 | |||
|
|
1 | 1 | import sys |
|
|
2 | 2 | import numpy,math |
|
|
3 | 3 | from scipy import interpolate |
|
|
4 | 4 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
5 | 5 | from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon |
|
|
6 | 6 | from schainpy.utils import log |
|
|
7 | 7 | from time import time |
|
|
8 | 8 | |
|
|
9 | 9 | |
|
|
10 | 10 | |
|
|
11 | 11 | class VoltageProc(ProcessingUnit): |
|
|
12 | 12 | |
|
|
13 | 13 | def __init__(self): |
|
|
14 | 14 | |
|
|
15 | 15 | ProcessingUnit.__init__(self) |
|
|
16 | 16 | |
|
|
17 | 17 | self.dataOut = Voltage() |
|
|
18 | 18 | self.flip = 1 |
|
|
19 | 19 | self.setupReq = False |
|
|
20 | 20 | |
|
|
21 | 21 | def run(self): |
|
|
22 | 22 | |
|
|
23 | 23 | if self.dataIn.type == 'AMISR': |
|
|
24 | 24 | self.__updateObjFromAmisrInput() |
|
|
25 | 25 | |
|
|
26 | 26 | if self.dataIn.type == 'Voltage': |
|
|
27 | 27 | self.dataOut.copy(self.dataIn) |
|
|
28 | 28 | |
|
|
29 | 29 | def __updateObjFromAmisrInput(self): |
|
|
30 | 30 | |
|
|
31 | 31 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
32 | 32 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
33 | 33 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
34 | 34 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
35 | 35 | |
|
|
36 | 36 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
37 | 37 | self.dataOut.data = self.dataIn.data |
|
|
38 | 38 | self.dataOut.utctime = self.dataIn.utctime |
|
|
39 | 39 | self.dataOut.channelList = self.dataIn.channelList |
|
|
40 | 40 | #self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
41 | 41 | self.dataOut.heightList = self.dataIn.heightList |
|
|
42 | 42 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
43 | 43 | |
|
|
44 | 44 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
45 | 45 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
46 | 46 | self.dataOut.frequency = self.dataIn.frequency |
|
|
47 | 47 | |
|
|
48 | 48 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
49 | 49 | self.dataOut.zenith = self.dataIn.zenith |
|
|
50 | 50 | |
|
|
51 | 51 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
52 | 52 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
53 | 53 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
54 | 54 | |
|
|
55 | 55 | |
|
|
56 | 56 | class selectChannels(Operation): |
|
|
57 | 57 | |
|
|
58 | 58 | def run(self, dataOut, channelList): |
|
|
59 | 59 | |
|
|
60 | 60 | channelIndexList = [] |
|
|
61 | 61 | self.dataOut = dataOut |
|
|
62 | 62 | for channel in channelList: |
|
|
63 | 63 | if channel not in self.dataOut.channelList: |
|
|
64 | 64 | raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList))) |
|
|
65 | 65 | |
|
|
66 | 66 | index = self.dataOut.channelList.index(channel) |
|
|
67 | 67 | channelIndexList.append(index) |
|
|
68 | 68 | self.selectChannelsByIndex(channelIndexList) |
|
|
69 | 69 | return self.dataOut |
|
|
70 | 70 | |
|
|
71 | 71 | def selectChannelsByIndex(self, channelIndexList): |
|
|
72 | 72 | """ |
|
|
73 | 73 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
74 | 74 | |
|
|
75 | 75 | Input: |
|
|
76 | 76 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
77 | 77 | |
|
|
78 | 78 | Affected: |
|
|
79 | 79 | self.dataOut.data |
|
|
80 | 80 | self.dataOut.channelIndexList |
|
|
81 | 81 | self.dataOut.nChannels |
|
|
82 | 82 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
|
83 | 83 | self.dataOut.systemHeaderObj.numChannels |
|
|
84 | 84 | self.dataOut.m_ProcessingHeader.blockSize |
|
|
85 | 85 | |
|
|
86 | 86 | Return: |
|
|
87 | 87 | None |
|
|
88 | 88 | """ |
|
|
89 | 89 | |
|
|
90 | 90 | for channelIndex in channelIndexList: |
|
|
91 | 91 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
92 | 92 | raise ValueError("The value %d in channelIndexList is not valid" %channelIndex) |
|
|
93 | 93 | |
|
|
94 | 94 | if self.dataOut.type == 'Voltage': |
|
|
95 | 95 | if self.dataOut.flagDataAsBlock: |
|
|
96 | 96 | """ |
|
|
97 | 97 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
98 | 98 | """ |
|
|
99 | 99 | data = self.dataOut.data[channelIndexList,:,:] |
|
|
100 | 100 | else: |
|
|
101 | 101 | data = self.dataOut.data[channelIndexList,:] |
|
|
102 | 102 | |
|
|
103 | 103 | self.dataOut.data = data |
|
|
104 | 104 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
105 | 105 | self.dataOut.channelList = range(len(channelIndexList)) |
|
|
106 | 106 | |
|
|
107 | 107 | elif self.dataOut.type == 'Spectra': |
|
|
108 | 108 | data_spc = self.dataOut.data_spc[channelIndexList, :] |
|
|
109 | 109 | data_dc = self.dataOut.data_dc[channelIndexList, :] |
|
|
110 | 110 | |
|
|
111 | 111 | self.dataOut.data_spc = data_spc |
|
|
112 | 112 | self.dataOut.data_dc = data_dc |
|
|
113 | 113 | |
|
|
114 | 114 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
115 | 115 | self.dataOut.channelList = range(len(channelIndexList)) |
|
|
116 | 116 | self.__selectPairsByChannel(channelIndexList) |
|
|
117 | 117 | |
|
|
118 | 118 | return 1 |
|
|
119 | 119 | |
|
|
120 | 120 | def __selectPairsByChannel(self, channelList=None): |
|
|
121 | 121 | |
|
|
122 | 122 | if channelList == None: |
|
|
123 | 123 | return |
|
|
124 | 124 | |
|
|
125 | 125 | pairsIndexListSelected = [] |
|
|
126 | 126 | for pairIndex in self.dataOut.pairsIndexList: |
|
|
127 | 127 | # First pair |
|
|
128 | 128 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
|
129 | 129 | continue |
|
|
130 | 130 | # Second pair |
|
|
131 | 131 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
|
132 | 132 | continue |
|
|
133 | 133 | |
|
|
134 | 134 | pairsIndexListSelected.append(pairIndex) |
|
|
135 | 135 | |
|
|
136 | 136 | if not pairsIndexListSelected: |
|
|
137 | 137 | self.dataOut.data_cspc = None |
|
|
138 | 138 | self.dataOut.pairsList = [] |
|
|
139 | 139 | return |
|
|
140 | 140 | |
|
|
141 | 141 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
142 | 142 | self.dataOut.pairsList = [self.dataOut.pairsList[i] |
|
|
143 | 143 | for i in pairsIndexListSelected] |
|
|
144 | 144 | |
|
|
145 | 145 | return |
|
|
146 | 146 | |
|
|
147 | 147 | class selectHeights(Operation): |
|
|
148 | 148 | |
|
|
149 | 149 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): |
|
|
150 | 150 | """ |
|
|
151 | 151 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
152 | 152 | minHei <= height <= maxHei |
|
|
153 | 153 | |
|
|
154 | 154 | Input: |
|
|
155 | 155 | minHei : valor minimo de altura a considerar |
|
|
156 | 156 | maxHei : valor maximo de altura a considerar |
|
|
157 | 157 | |
|
|
158 | 158 | Affected: |
|
|
159 | 159 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
160 | 160 | |
|
|
161 | 161 | Return: |
|
|
162 | 162 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
163 | 163 | """ |
|
|
164 | 164 | |
|
|
165 | 165 | self.dataOut = dataOut |
|
|
166 | 166 | |
|
|
167 | 167 | if minHei and maxHei: |
|
|
168 | 168 | |
|
|
169 | 169 | if (minHei < self.dataOut.heightList[0]): |
|
|
170 | 170 | minHei = self.dataOut.heightList[0] |
|
|
171 | 171 | |
|
|
172 | 172 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
173 | 173 | maxHei = self.dataOut.heightList[-1] |
|
|
174 | 174 | |
|
|
175 | 175 | minIndex = 0 |
|
|
176 | 176 | maxIndex = 0 |
|
|
177 | 177 | heights = self.dataOut.heightList |
|
|
178 | 178 | |
|
|
179 | 179 | inda = numpy.where(heights >= minHei) |
|
|
180 | 180 | indb = numpy.where(heights <= maxHei) |
|
|
181 | 181 | |
|
|
182 | 182 | try: |
|
|
183 | 183 | minIndex = inda[0][0] |
|
|
184 | 184 | except: |
|
|
185 | 185 | minIndex = 0 |
|
|
186 | 186 | |
|
|
187 | 187 | try: |
|
|
188 | 188 | maxIndex = indb[0][-1] |
|
|
189 | 189 | except: |
|
|
190 | 190 | maxIndex = len(heights) |
|
|
191 | 191 | |
|
|
192 | 192 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
193 | 193 | |
|
|
194 | 194 | return self.dataOut |
|
|
195 | 195 | |
|
|
196 | 196 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
197 | 197 | """ |
|
|
198 | 198 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
199 | 199 | minIndex <= index <= maxIndex |
|
|
200 | 200 | |
|
|
201 | 201 | Input: |
|
|
202 | 202 | minIndex : valor de indice minimo de altura a considerar |
|
|
203 | 203 | maxIndex : valor de indice maximo de altura a considerar |
|
|
204 | 204 | |
|
|
205 | 205 | Affected: |
|
|
206 | 206 | self.dataOut.data |
|
|
207 | 207 | self.dataOut.heightList |
|
|
208 | 208 | |
|
|
209 | 209 | Return: |
|
|
210 | 210 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
211 | 211 | """ |
|
|
212 | 212 | |
|
|
213 | 213 | if self.dataOut.type == 'Voltage': |
|
|
214 | 214 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
215 | 215 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
216 | 216 | |
|
|
217 | 217 | if (maxIndex >= self.dataOut.nHeights): |
|
|
218 | 218 | maxIndex = self.dataOut.nHeights |
|
|
219 | 219 | #print("shapeeee",self.dataOut.data.shape) |
|
|
220 | 220 | #voltage |
|
|
221 | 221 | if self.dataOut.flagDataAsBlock: |
|
|
222 | 222 | """ |
|
|
223 | 223 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
224 | 224 | """ |
|
|
225 | 225 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
226 | 226 | else: |
|
|
227 | 227 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
|
228 | 228 | |
|
|
229 | 229 | # firstHeight = self.dataOut.heightList[minIndex] |
|
|
230 | 230 | |
|
|
231 | 231 | self.dataOut.data = data |
|
|
232 | 232 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
233 | 233 | |
|
|
234 | 234 | if self.dataOut.nHeights <= 1: |
|
|
235 | 235 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)) |
|
|
236 | 236 | elif self.dataOut.type == 'Spectra': |
|
|
237 | 237 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
238 | 238 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
|
|
239 | 239 | minIndex, maxIndex)) |
|
|
240 | 240 | |
|
|
241 | 241 | if (maxIndex >= self.dataOut.nHeights): |
|
|
242 | 242 | maxIndex = self.dataOut.nHeights - 1 |
|
|
243 | 243 | |
|
|
244 | 244 | # Spectra |
|
|
245 | 245 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
|
246 | 246 | |
|
|
247 | 247 | data_cspc = None |
|
|
248 | 248 | if self.dataOut.data_cspc is not None: |
|
|
249 | 249 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
250 | 250 | |
|
|
251 | 251 | data_dc = None |
|
|
252 | 252 | if self.dataOut.data_dc is not None: |
|
|
253 | 253 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
254 | 254 | |
|
|
255 | 255 | self.dataOut.data_spc = data_spc |
|
|
256 | 256 | self.dataOut.data_cspc = data_cspc |
|
|
257 | 257 | self.dataOut.data_dc = data_dc |
|
|
258 | 258 | |
|
|
259 | 259 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
|
260 | 260 | |
|
|
261 | 261 | return 1 |
|
|
262 | 262 | |
|
|
263 | 263 | |
|
|
264 | 264 | class filterByHeights(Operation): |
|
|
265 | 265 | |
|
|
266 | 266 | def run(self, dataOut, window): |
|
|
267 | 267 | |
|
|
268 | 268 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
269 | 269 | |
|
|
270 | 270 | if window == None: |
|
|
271 | 271 | window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
272 | 272 | |
|
|
273 | 273 | newdelta = deltaHeight * window |
|
|
274 | 274 | r = dataOut.nHeights % window |
|
|
275 | 275 | newheights = (dataOut.nHeights-r)/window |
|
|
276 | 276 | |
|
|
277 | 277 | if newheights <= 1: |
|
|
278 | 278 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window)) |
|
|
279 | 279 | |
|
|
280 | 280 | if dataOut.flagDataAsBlock: |
|
|
281 | 281 | """ |
|
|
282 | 282 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
283 | 283 | """ |
|
|
284 | 284 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)] |
|
|
285 | 285 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window) |
|
|
286 | 286 | buffer = numpy.sum(buffer,3) |
|
|
287 | 287 | |
|
|
288 | 288 | else: |
|
|
289 | 289 | buffer = dataOut.data[:,0:int(dataOut.nHeights-r)] |
|
|
290 | 290 | buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window)) |
|
|
291 | 291 | buffer = numpy.sum(buffer,2) |
|
|
292 | 292 | |
|
|
293 | 293 | dataOut.data = buffer |
|
|
294 | 294 | dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
295 | 295 | dataOut.windowOfFilter = window |
|
|
296 | 296 | |
|
|
297 | 297 | return dataOut |
|
|
298 | 298 | |
|
|
299 | 299 | |
|
|
300 | 300 | class setH0(Operation): |
|
|
301 | 301 | |
|
|
302 | 302 | def run(self, dataOut, h0, deltaHeight = None): |
|
|
303 | 303 | |
|
|
304 | 304 | if not deltaHeight: |
|
|
305 | 305 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
306 | 306 | |
|
|
307 | 307 | nHeights = dataOut.nHeights |
|
|
308 | 308 | |
|
|
309 | 309 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
310 | 310 | |
|
|
311 | 311 | dataOut.heightList = newHeiRange |
|
|
312 | 312 | |
|
|
313 | 313 | return dataOut |
|
|
314 | 314 | |
|
|
315 | 315 | |
|
|
316 | 316 | class deFlip(Operation): |
|
|
317 | 317 | |
|
|
318 | 318 | def run(self, dataOut, channelList = []): |
|
|
319 | 319 | |
|
|
320 | 320 | data = dataOut.data.copy() |
|
|
321 | 321 | |
|
|
322 | 322 | if dataOut.flagDataAsBlock: |
|
|
323 | 323 | flip = self.flip |
|
|
324 | 324 | profileList = list(range(dataOut.nProfiles)) |
|
|
325 | 325 | |
|
|
326 | 326 | if not channelList: |
|
|
327 | 327 | for thisProfile in profileList: |
|
|
328 | 328 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
329 | 329 | flip *= -1.0 |
|
|
330 | 330 | else: |
|
|
331 | 331 | for thisChannel in channelList: |
|
|
332 | 332 | if thisChannel not in dataOut.channelList: |
|
|
333 | 333 | continue |
|
|
334 | 334 | |
|
|
335 | 335 | for thisProfile in profileList: |
|
|
336 | 336 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
337 | 337 | flip *= -1.0 |
|
|
338 | 338 | |
|
|
339 | 339 | self.flip = flip |
|
|
340 | 340 | |
|
|
341 | 341 | else: |
|
|
342 | 342 | if not channelList: |
|
|
343 | 343 | data[:,:] = data[:,:]*self.flip |
|
|
344 | 344 | else: |
|
|
345 | 345 | for thisChannel in channelList: |
|
|
346 | 346 | if thisChannel not in dataOut.channelList: |
|
|
347 | 347 | continue |
|
|
348 | 348 | |
|
|
349 | 349 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
350 | 350 | |
|
|
351 | 351 | self.flip *= -1. |
|
|
352 | 352 | |
|
|
353 | 353 | dataOut.data = data |
|
|
354 | 354 | |
|
|
355 | 355 | return dataOut |
|
|
356 | 356 | |
|
|
357 | 357 | |
|
|
358 | 358 | class setAttribute(Operation): |
|
|
359 | 359 | ''' |
|
|
360 | 360 | Set an arbitrary attribute(s) to dataOut |
|
|
361 | 361 | ''' |
|
|
362 | 362 | |
|
|
363 | 363 | def __init__(self): |
|
|
364 | 364 | |
|
|
365 | 365 | Operation.__init__(self) |
|
|
366 | 366 | self._ready = False |
|
|
367 | 367 | |
|
|
368 | 368 | def run(self, dataOut, **kwargs): |
|
|
369 | 369 | |
|
|
370 | 370 | for key, value in kwargs.items(): |
|
|
371 | 371 | setattr(dataOut, key, value) |
|
|
372 | 372 | |
|
|
373 | 373 | return dataOut |
|
|
374 | 374 | |
|
|
375 | 375 | |
|
|
376 | 376 | @MPDecorator |
|
|
377 | 377 | class printAttribute(Operation): |
|
|
378 | 378 | ''' |
|
|
379 | 379 | Print an arbitrary attribute of dataOut |
|
|
380 | 380 | ''' |
|
|
381 | 381 | |
|
|
382 | 382 | def __init__(self): |
|
|
383 | 383 | |
|
|
384 | 384 | Operation.__init__(self) |
|
|
385 | 385 | |
|
|
386 | 386 | def run(self, dataOut, attributes): |
|
|
387 | 387 | |
|
|
388 | 388 | if isinstance(attributes, str): |
|
|
389 | 389 | attributes = [attributes] |
|
|
390 | 390 | for attr in attributes: |
|
|
391 | 391 | if hasattr(dataOut, attr): |
|
|
392 | 392 | log.log(getattr(dataOut, attr), attr) |
|
|
393 | 393 | |
|
|
394 | 394 | |
|
|
395 | 395 | class interpolateHeights(Operation): |
|
|
396 | 396 | |
|
|
397 | 397 | def run(self, dataOut, topLim, botLim): |
|
|
398 | 398 | #69 al 72 para julia |
|
|
399 | 399 | #82-84 para meteoros |
|
|
400 | 400 | if len(numpy.shape(dataOut.data))==2: |
|
|
401 | 401 | sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2 |
|
|
402 | 402 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
403 | 403 | #dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
404 | 404 | dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
405 | 405 | else: |
|
|
406 | 406 | nHeights = dataOut.data.shape[2] |
|
|
407 | 407 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
408 | 408 | y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))] |
|
|
409 | 409 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
410 | 410 | xnew = numpy.arange(botLim,topLim+1) |
|
|
411 | 411 | ynew = f(xnew) |
|
|
412 | 412 | dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
413 | 413 | |
|
|
414 | 414 | return dataOut |
|
|
415 | 415 | |
|
|
416 | 416 | |
|
|
417 | 417 | class CohInt(Operation): |
|
|
418 | 418 | |
|
|
419 | 419 | isConfig = False |
|
|
420 | 420 | __profIndex = 0 |
|
|
421 | 421 | __byTime = False |
|
|
422 | 422 | __initime = None |
|
|
423 | 423 | __lastdatatime = None |
|
|
424 | 424 | __integrationtime = None |
|
|
425 | 425 | __buffer = None |
|
|
426 | 426 | __bufferStride = [] |
|
|
427 | 427 | __dataReady = False |
|
|
428 | 428 | __profIndexStride = 0 |
|
|
429 | 429 | __dataToPutStride = False |
|
|
430 | 430 | n = None |
|
|
431 | 431 | |
|
|
432 | 432 | def __init__(self, **kwargs): |
|
|
433 | 433 | |
|
|
434 | 434 | Operation.__init__(self, **kwargs) |
|
|
435 | 435 | |
|
|
436 | 436 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
|
437 | 437 | """ |
|
|
438 | 438 | Set the parameters of the integration class. |
|
|
439 | 439 | |
|
|
440 | 440 | Inputs: |
|
|
441 | 441 | |
|
|
442 | 442 | n : Number of coherent integrations |
|
|
443 | 443 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
444 | 444 | overlapping : |
|
|
445 | 445 | """ |
|
|
446 | 446 | |
|
|
447 | 447 | self.__initime = None |
|
|
448 | 448 | self.__lastdatatime = 0 |
|
|
449 | 449 | self.__buffer = None |
|
|
450 | 450 | self.__dataReady = False |
|
|
451 | 451 | self.byblock = byblock |
|
|
452 | 452 | self.stride = stride |
|
|
453 | 453 | |
|
|
454 | 454 | if n == None and timeInterval == None: |
|
|
455 | 455 | raise ValueError("n or timeInterval should be specified ...") |
|
|
456 | 456 | |
|
|
457 | 457 | if n != None: |
|
|
458 | 458 | self.n = n |
|
|
459 | 459 | self.__byTime = False |
|
|
460 | 460 | else: |
|
|
461 | 461 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
462 | 462 | self.n = 9999 |
|
|
463 | 463 | self.__byTime = True |
|
|
464 | 464 | |
|
|
465 | 465 | if overlapping: |
|
|
466 | 466 | self.__withOverlapping = True |
|
|
467 | 467 | self.__buffer = None |
|
|
468 | 468 | else: |
|
|
469 | 469 | self.__withOverlapping = False |
|
|
470 | 470 | self.__buffer = 0 |
|
|
471 | 471 | |
|
|
472 | 472 | self.__profIndex = 0 |
|
|
473 | 473 | |
|
|
474 | 474 | def putData(self, data): |
|
|
475 | 475 | |
|
|
476 | 476 | """ |
|
|
477 | 477 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
478 | 478 | |
|
|
479 | 479 | """ |
|
|
480 | 480 | |
|
|
481 | 481 | if not self.__withOverlapping: |
|
|
482 | 482 | self.__buffer += data.copy() |
|
|
483 | 483 | self.__profIndex += 1 |
|
|
484 | 484 | return |
|
|
485 | 485 | |
|
|
486 | 486 | #Overlapping data |
|
|
487 | 487 | nChannels, nHeis = data.shape |
|
|
488 | 488 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
489 | 489 | |
|
|
490 | 490 | #If the buffer is empty then it takes the data value |
|
|
491 | 491 | if self.__buffer is None: |
|
|
492 | 492 | self.__buffer = data |
|
|
493 | 493 | self.__profIndex += 1 |
|
|
494 | 494 | return |
|
|
495 | 495 | |
|
|
496 | 496 | #If the buffer length is lower than n then stakcing the data value |
|
|
497 | 497 | if self.__profIndex < self.n: |
|
|
498 | 498 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
499 | 499 | self.__profIndex += 1 |
|
|
500 | 500 | return |
|
|
501 | 501 | |
|
|
502 | 502 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
503 | 503 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
504 | 504 | self.__buffer[self.n-1] = data |
|
|
505 | 505 | self.__profIndex = self.n |
|
|
506 | 506 | return |
|
|
507 | 507 | |
|
|
508 | 508 | |
|
|
509 | 509 | def pushData(self): |
|
|
510 | 510 | """ |
|
|
511 | 511 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
512 | 512 | |
|
|
513 | 513 | Affected: |
|
|
514 | 514 | |
|
|
515 | 515 | self.__profileIndex |
|
|
516 | 516 | |
|
|
517 | 517 | """ |
|
|
518 | 518 | |
|
|
519 | 519 | if not self.__withOverlapping: |
|
|
520 | 520 | data = self.__buffer |
|
|
521 | 521 | n = self.__profIndex |
|
|
522 | 522 | |
|
|
523 | 523 | self.__buffer = 0 |
|
|
524 | 524 | self.__profIndex = 0 |
|
|
525 | 525 | |
|
|
526 | 526 | return data, n |
|
|
527 | 527 | |
|
|
528 | 528 | #Integration with Overlapping |
|
|
529 | 529 | data = numpy.sum(self.__buffer, axis=0) |
|
|
530 | 530 | # print data |
|
|
531 | 531 | # raise |
|
|
532 | 532 | n = self.__profIndex |
|
|
533 | 533 | |
|
|
534 | 534 | return data, n |
|
|
535 | 535 | |
|
|
536 | 536 | def byProfiles(self, data): |
|
|
537 | 537 | |
|
|
538 | 538 | self.__dataReady = False |
|
|
539 | 539 | avgdata = None |
|
|
540 | 540 | # n = None |
|
|
541 | 541 | # print data |
|
|
542 | 542 | # raise |
|
|
543 | 543 | self.putData(data) |
|
|
544 | 544 | |
|
|
545 | 545 | if self.__profIndex == self.n: |
|
|
546 | 546 | avgdata, n = self.pushData() |
|
|
547 | 547 | self.__dataReady = True |
|
|
548 | 548 | |
|
|
549 | 549 | return avgdata |
|
|
550 | 550 | |
|
|
551 | 551 | def byTime(self, data, datatime): |
|
|
552 | 552 | |
|
|
553 | 553 | self.__dataReady = False |
|
|
554 | 554 | avgdata = None |
|
|
555 | 555 | n = None |
|
|
556 | 556 | |
|
|
557 | 557 | self.putData(data) |
|
|
558 | 558 | |
|
|
559 | 559 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
560 | 560 | avgdata, n = self.pushData() |
|
|
561 | 561 | self.n = n |
|
|
562 | 562 | self.__dataReady = True |
|
|
563 | 563 | |
|
|
564 | 564 | return avgdata |
|
|
565 | 565 | |
|
|
566 | 566 | def integrateByStride(self, data, datatime): |
|
|
567 | 567 | # print data |
|
|
568 | 568 | if self.__profIndex == 0: |
|
|
569 | 569 | self.__buffer = [[data.copy(), datatime]] |
|
|
570 | 570 | else: |
|
|
571 | 571 | self.__buffer.append([data.copy(),datatime]) |
|
|
572 | 572 | self.__profIndex += 1 |
|
|
573 | 573 | self.__dataReady = False |
|
|
574 | 574 | |
|
|
575 | 575 | if self.__profIndex == self.n * self.stride : |
|
|
576 | 576 | self.__dataToPutStride = True |
|
|
577 | 577 | self.__profIndexStride = 0 |
|
|
578 | 578 | self.__profIndex = 0 |
|
|
579 | 579 | self.__bufferStride = [] |
|
|
580 | 580 | for i in range(self.stride): |
|
|
581 | 581 | current = self.__buffer[i::self.stride] |
|
|
582 | 582 | data = numpy.sum([t[0] for t in current], axis=0) |
|
|
583 | 583 | avgdatatime = numpy.average([t[1] for t in current]) |
|
|
584 | 584 | # print data |
|
|
585 | 585 | self.__bufferStride.append((data, avgdatatime)) |
|
|
586 | 586 | |
|
|
587 | 587 | if self.__dataToPutStride: |
|
|
588 | 588 | self.__dataReady = True |
|
|
589 | 589 | self.__profIndexStride += 1 |
|
|
590 | 590 | if self.__profIndexStride == self.stride: |
|
|
591 | 591 | self.__dataToPutStride = False |
|
|
592 | 592 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
|
593 | 593 | # raise |
|
|
594 | 594 | return self.__bufferStride[self.__profIndexStride - 1] |
|
|
595 | 595 | |
|
|
596 | 596 | |
|
|
597 | 597 | return None, None |
|
|
598 | 598 | |
|
|
599 | 599 | def integrate(self, data, datatime=None): |
|
|
600 | 600 | |
|
|
601 | 601 | if self.__initime == None: |
|
|
602 | 602 | self.__initime = datatime |
|
|
603 | 603 | |
|
|
604 | 604 | if self.__byTime: |
|
|
605 | 605 | avgdata = self.byTime(data, datatime) |
|
|
606 | 606 | else: |
|
|
607 | 607 | avgdata = self.byProfiles(data) |
|
|
608 | 608 | |
|
|
609 | 609 | |
|
|
610 | 610 | self.__lastdatatime = datatime |
|
|
611 | 611 | |
|
|
612 | 612 | if avgdata is None: |
|
|
613 | 613 | return None, None |
|
|
614 | 614 | |
|
|
615 | 615 | avgdatatime = self.__initime |
|
|
616 | 616 | |
|
|
617 | 617 | deltatime = datatime - self.__lastdatatime |
|
|
618 | 618 | |
|
|
619 | 619 | if not self.__withOverlapping: |
|
|
620 | 620 | self.__initime = datatime |
|
|
621 | 621 | else: |
|
|
622 | 622 | self.__initime += deltatime |
|
|
623 | 623 | |
|
|
624 | 624 | return avgdata, avgdatatime |
|
|
625 | 625 | |
|
|
626 | 626 | def integrateByBlock(self, dataOut): |
|
|
627 | 627 | |
|
|
628 | 628 | times = int(dataOut.data.shape[1]/self.n) |
|
|
629 | 629 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
630 | 630 | |
|
|
631 | 631 | id_min = 0 |
|
|
632 | 632 | id_max = self.n |
|
|
633 | 633 | |
|
|
634 | 634 | for i in range(times): |
|
|
635 | 635 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
636 | 636 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
637 | 637 | id_min += self.n |
|
|
638 | 638 | id_max += self.n |
|
|
639 | 639 | |
|
|
640 | 640 | timeInterval = dataOut.ippSeconds*self.n |
|
|
641 | 641 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
642 | 642 | self.__dataReady = True |
|
|
643 | 643 | return avgdata, avgdatatime |
|
|
644 | 644 | |
|
|
645 | 645 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
|
646 | 646 | |
|
|
647 | 647 | if not self.isConfig: |
|
|
648 | 648 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
649 | 649 | self.isConfig = True |
|
|
650 | 650 | |
|
|
651 | 651 | if dataOut.flagDataAsBlock: |
|
|
652 | 652 | """ |
|
|
653 | 653 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
654 | 654 | """ |
|
|
655 | 655 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
656 | 656 | dataOut.nProfiles /= self.n |
|
|
657 | 657 | else: |
|
|
658 | 658 | if stride is None: |
|
|
659 | 659 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
660 | 660 | else: |
|
|
661 | 661 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
|
662 | 662 | |
|
|
663 | 663 | |
|
|
664 | 664 | # dataOut.timeInterval *= n |
|
|
665 | 665 | dataOut.flagNoData = True |
|
|
666 | 666 | |
|
|
667 | 667 | if self.__dataReady: |
|
|
668 | 668 | dataOut.data = avgdata |
|
|
669 | 669 | if not dataOut.flagCohInt: |
|
|
670 | 670 | dataOut.nCohInt *= self.n |
|
|
671 | 671 | dataOut.flagCohInt = True |
|
|
672 | 672 | dataOut.utctime = avgdatatime |
|
|
673 | 673 | # print avgdata, avgdatatime |
|
|
674 | 674 | # raise |
|
|
675 | 675 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
676 | 676 | dataOut.flagNoData = False |
|
|
677 | 677 | return dataOut |
|
|
678 | 678 | |
|
|
679 | 679 | class Decoder(Operation): |
|
|
680 | 680 | |
|
|
681 | 681 | isConfig = False |
|
|
682 | 682 | __profIndex = 0 |
|
|
683 | 683 | |
|
|
684 | 684 | code = None |
|
|
685 | 685 | |
|
|
686 | 686 | nCode = None |
|
|
687 | 687 | nBaud = None |
|
|
688 | 688 | |
|
|
689 | 689 | def __init__(self, **kwargs): |
|
|
690 | 690 | |
|
|
691 | 691 | Operation.__init__(self, **kwargs) |
|
|
692 | 692 | |
|
|
693 | 693 | self.times = None |
|
|
694 | 694 | self.osamp = None |
|
|
695 | 695 | # self.__setValues = False |
|
|
696 | 696 | self.isConfig = False |
|
|
697 | 697 | self.setupReq = False |
|
|
698 | 698 | def setup(self, code, osamp, dataOut): |
|
|
699 | 699 | |
|
|
700 | 700 | self.__profIndex = 0 |
|
|
701 | 701 | |
|
|
702 | 702 | self.code = code |
|
|
703 | 703 | |
|
|
704 | 704 | self.nCode = len(code) |
|
|
705 | 705 | self.nBaud = len(code[0]) |
|
|
706 | 706 | |
|
|
707 | 707 | if (osamp != None) and (osamp >1): |
|
|
708 | 708 | self.osamp = osamp |
|
|
709 | 709 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
710 | 710 | self.nBaud = self.nBaud*self.osamp |
|
|
711 | 711 | |
|
|
712 | 712 | self.__nChannels = dataOut.nChannels |
|
|
713 | 713 | self.__nProfiles = dataOut.nProfiles |
|
|
714 | 714 | self.__nHeis = dataOut.nHeights |
|
|
715 | 715 | |
|
|
716 | 716 | if self.__nHeis < self.nBaud: |
|
|
717 | 717 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
|
718 | 718 | |
|
|
719 | 719 | #Frequency |
|
|
720 | 720 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
721 | 721 | |
|
|
722 | 722 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
723 | 723 | |
|
|
724 | 724 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
725 | 725 | |
|
|
726 | 726 | if dataOut.flagDataAsBlock: |
|
|
727 | 727 | |
|
|
728 | 728 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
729 | 729 | |
|
|
730 | 730 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
731 | 731 | |
|
|
732 | 732 | else: |
|
|
733 | 733 | |
|
|
734 | 734 | #Time |
|
|
735 | 735 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
736 | 736 | |
|
|
737 | 737 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
738 | 738 | |
|
|
739 | 739 | def __convolutionInFreq(self, data): |
|
|
740 | 740 | |
|
|
741 | 741 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
742 | 742 | |
|
|
743 | 743 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
744 | 744 | |
|
|
745 | 745 | conv = fft_data*fft_code |
|
|
746 | 746 | |
|
|
747 | 747 | data = numpy.fft.ifft(conv,axis=1) |
|
|
748 | 748 | |
|
|
749 | 749 | return data |
|
|
750 | 750 | |
|
|
751 | 751 | def __convolutionInFreqOpt(self, data): |
|
|
752 | 752 | |
|
|
753 | 753 | raise NotImplementedError |
|
|
754 | 754 | |
|
|
755 | 755 | def __convolutionInTime(self, data): |
|
|
756 | 756 | |
|
|
757 | 757 | code = self.code[self.__profIndex] |
|
|
758 | 758 | for i in range(self.__nChannels): |
|
|
759 | 759 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
760 | 760 | |
|
|
761 | 761 | return self.datadecTime |
|
|
762 | 762 | |
|
|
763 | 763 | def __convolutionByBlockInTime(self, data): |
|
|
764 | 764 | |
|
|
765 | 765 | repetitions = int(self.__nProfiles / self.nCode) |
|
|
766 | 766 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
767 | 767 | junk = junk.flatten() |
|
|
768 | 768 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
769 | 769 | profilesList = range(self.__nProfiles) |
|
|
770 | 770 | |
|
|
771 | 771 | for i in range(self.__nChannels): |
|
|
772 | 772 | for j in profilesList: |
|
|
773 | 773 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
774 | 774 | return self.datadecTime |
|
|
775 | 775 | |
|
|
776 | 776 | def __convolutionByBlockInFreq(self, data): |
|
|
777 | 777 | |
|
|
778 | 778 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
|
779 | 779 | |
|
|
780 | 780 | |
|
|
781 | 781 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
782 | 782 | |
|
|
783 | 783 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
784 | 784 | |
|
|
785 | 785 | conv = fft_data*fft_code |
|
|
786 | 786 | |
|
|
787 | 787 | data = numpy.fft.ifft(conv,axis=2) |
|
|
788 | 788 | |
|
|
789 | 789 | return data |
|
|
790 | 790 | |
|
|
791 | 791 | |
|
|
792 | 792 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
793 | 793 | |
|
|
794 | 794 | if dataOut.flagDecodeData: |
|
|
795 | 795 | print("This data is already decoded, recoding again ...") |
|
|
796 | 796 | |
|
|
797 | 797 | if not self.isConfig: |
|
|
798 | 798 | |
|
|
799 | 799 | if code is None: |
|
|
800 | 800 | if dataOut.code is None: |
|
|
801 | 801 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
|
802 | 802 | |
|
|
803 | 803 | code = dataOut.code |
|
|
804 | 804 | else: |
|
|
805 | 805 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
806 | 806 | self.setup(code, osamp, dataOut) |
|
|
807 | 807 | |
|
|
808 | 808 | self.isConfig = True |
|
|
809 | 809 | |
|
|
810 | 810 | if mode == 3: |
|
|
811 | 811 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
812 | 812 | |
|
|
813 | 813 | if times != None: |
|
|
814 | 814 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
815 | 815 | |
|
|
816 | 816 | if self.code is None: |
|
|
817 | 817 | print("Fail decoding: Code is not defined.") |
|
|
818 | 818 | return |
|
|
819 | 819 | |
|
|
820 | 820 | self.__nProfiles = dataOut.nProfiles |
|
|
821 | 821 | datadec = None |
|
|
822 | 822 | |
|
|
823 | 823 | if mode == 3: |
|
|
824 | 824 | mode = 0 |
|
|
825 | 825 | |
|
|
826 | 826 | if dataOut.flagDataAsBlock: |
|
|
827 | 827 | """ |
|
|
828 | 828 | Decoding when data have been read as block, |
|
|
829 | 829 | """ |
|
|
830 | 830 | |
|
|
831 | 831 | if mode == 0: |
|
|
832 | 832 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
833 | 833 | if mode == 1: |
|
|
834 | 834 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
835 | 835 | else: |
|
|
836 | 836 | """ |
|
|
837 | 837 | Decoding when data have been read profile by profile |
|
|
838 | 838 | """ |
|
|
839 | 839 | if mode == 0: |
|
|
840 | 840 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
841 | 841 | |
|
|
842 | 842 | if mode == 1: |
|
|
843 | 843 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
844 | 844 | |
|
|
845 | 845 | if mode == 2: |
|
|
846 | 846 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
847 | 847 | |
|
|
848 | 848 | if datadec is None: |
|
|
849 | 849 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
|
850 | 850 | |
|
|
851 | 851 | dataOut.code = self.code |
|
|
852 | 852 | dataOut.nCode = self.nCode |
|
|
853 | 853 | dataOut.nBaud = self.nBaud |
|
|
854 | 854 | |
|
|
855 | 855 | dataOut.data = datadec |
|
|
856 | 856 | |
|
|
857 | 857 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
858 | 858 | |
|
|
859 | 859 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
860 | 860 | |
|
|
861 | 861 | if self.__profIndex == self.nCode-1: |
|
|
862 | 862 | self.__profIndex = 0 |
|
|
863 | 863 | return dataOut |
|
|
864 | 864 | |
|
|
865 | 865 | self.__profIndex += 1 |
|
|
866 | 866 | |
|
|
867 | 867 | return dataOut |
|
|
868 | 868 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
869 | 869 | |
|
|
870 | 870 | |
|
|
871 | 871 | class ProfileConcat(Operation): |
|
|
872 | 872 | |
|
|
873 | 873 | isConfig = False |
|
|
874 | 874 | buffer = None |
|
|
875 | 875 | |
|
|
876 | 876 | def __init__(self, **kwargs): |
|
|
877 | 877 | |
|
|
878 | 878 | Operation.__init__(self, **kwargs) |
|
|
879 | 879 | self.profileIndex = 0 |
|
|
880 | 880 | |
|
|
881 | 881 | def reset(self): |
|
|
882 | 882 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
883 | 883 | self.start_index = 0 |
|
|
884 | 884 | self.times = 1 |
|
|
885 | 885 | |
|
|
886 | 886 | def setup(self, data, m, n=1): |
|
|
887 | 887 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
888 | 888 | self.nHeights = data.shape[1]#.nHeights |
|
|
889 | 889 | self.start_index = 0 |
|
|
890 | 890 | self.times = 1 |
|
|
891 | 891 | |
|
|
892 | 892 | def concat(self, data): |
|
|
893 | 893 | |
|
|
894 | 894 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
895 | 895 | self.start_index = self.start_index + self.nHeights |
|
|
896 | 896 | |
|
|
897 | 897 | def run(self, dataOut, m): |
|
|
898 | 898 | dataOut.flagNoData = True |
|
|
899 | 899 | |
|
|
900 | 900 | if not self.isConfig: |
|
|
901 | 901 | self.setup(dataOut.data, m, 1) |
|
|
902 | 902 | self.isConfig = True |
|
|
903 | 903 | |
|
|
904 | 904 | if dataOut.flagDataAsBlock: |
|
|
905 | 905 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
|
906 | 906 | |
|
|
907 | 907 | else: |
|
|
908 | 908 | self.concat(dataOut.data) |
|
|
909 | 909 | self.times += 1 |
|
|
910 | 910 | if self.times > m: |
|
|
911 | 911 | dataOut.data = self.buffer |
|
|
912 | 912 | self.reset() |
|
|
913 | 913 | dataOut.flagNoData = False |
|
|
914 | 914 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
915 | 915 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
916 | 916 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
917 | 917 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
918 | 918 | dataOut.ippSeconds *= m |
|
|
919 | 919 | return dataOut |
|
|
920 | 920 | |
|
|
921 | 921 | class ProfileSelector(Operation): |
|
|
922 | 922 | |
|
|
923 | 923 | profileIndex = None |
|
|
924 | 924 | # Tamanho total de los perfiles |
|
|
925 | 925 | nProfiles = None |
|
|
926 | 926 | |
|
|
927 | 927 | def __init__(self, **kwargs): |
|
|
928 | 928 | |
|
|
929 | 929 | Operation.__init__(self, **kwargs) |
|
|
930 | 930 | self.profileIndex = 0 |
|
|
931 | 931 | |
|
|
932 | 932 | def incProfileIndex(self): |
|
|
933 | 933 | |
|
|
934 | 934 | self.profileIndex += 1 |
|
|
935 | 935 | |
|
|
936 | 936 | if self.profileIndex >= self.nProfiles: |
|
|
937 | 937 | self.profileIndex = 0 |
|
|
938 | 938 | |
|
|
939 | 939 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
940 | 940 | |
|
|
941 | 941 | if profileIndex < minIndex: |
|
|
942 | 942 | return False |
|
|
943 | 943 | |
|
|
944 | 944 | if profileIndex > maxIndex: |
|
|
945 | 945 | return False |
|
|
946 | 946 | |
|
|
947 | 947 | return True |
|
|
948 | 948 | |
|
|
949 | 949 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
950 | 950 | |
|
|
951 | 951 | if profileIndex not in profileList: |
|
|
952 | 952 | return False |
|
|
953 | 953 | |
|
|
954 | 954 | return True |
|
|
955 | 955 | |
|
|
956 | 956 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
957 | ||
|
|
957 | #print("before",dataOut.data.shape) | |
|
|
958 | 958 | """ |
|
|
959 | 959 | ProfileSelector: |
|
|
960 | 960 | |
|
|
961 | 961 | Inputs: |
|
|
962 | 962 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
963 | 963 | |
|
|
964 | 964 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
965 | 965 | |
|
|
966 | 966 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
967 | 967 | |
|
|
968 | 968 | """ |
|
|
969 | 969 | |
|
|
970 | 970 | if rangeList is not None: |
|
|
971 | 971 | if type(rangeList[0]) not in (tuple, list): |
|
|
972 | 972 | rangeList = [rangeList] |
|
|
973 | 973 | |
|
|
974 | 974 | dataOut.flagNoData = True |
|
|
975 | 975 | |
|
|
976 | 976 | if dataOut.flagDataAsBlock: |
|
|
977 | 977 | """ |
|
|
978 | 978 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
979 | 979 | """ |
|
|
980 | 980 | if profileList != None: |
|
|
981 | 981 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
982 | 982 | |
|
|
983 | 983 | if profileRangeList != None: |
|
|
984 | 984 | minIndex = profileRangeList[0] |
|
|
985 | 985 | maxIndex = profileRangeList[1] |
|
|
986 | 986 | profileList = list(range(minIndex, maxIndex+1)) |
|
|
987 | 987 | |
|
|
988 | 988 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
989 | 989 | |
|
|
990 | 990 | if rangeList != None: |
|
|
991 | 991 | |
|
|
992 | 992 | profileList = [] |
|
|
993 | 993 | |
|
|
994 | 994 | for thisRange in rangeList: |
|
|
995 | 995 | minIndex = thisRange[0] |
|
|
996 | 996 | maxIndex = thisRange[1] |
|
|
997 | 997 | |
|
|
998 | 998 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
|
999 | 999 | |
|
|
1000 | 1000 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
1001 | 1001 | |
|
|
1002 | 1002 | dataOut.nProfiles = len(profileList) |
|
|
1003 | 1003 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
1004 | 1004 | dataOut.flagNoData = False |
|
|
1005 | ||
|
|
1005 | #print(dataOut.data.shape) | |
|
|
1006 | 1006 | return dataOut |
|
|
1007 | 1007 | |
|
|
1008 | 1008 | """ |
|
|
1009 | 1009 | data dimension = [nChannels, nHeis] |
|
|
1010 | 1010 | """ |
|
|
1011 | 1011 | |
|
|
1012 | 1012 | if profileList != None: |
|
|
1013 | 1013 | |
|
|
1014 | 1014 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
1015 | 1015 | |
|
|
1016 | 1016 | self.nProfiles = len(profileList) |
|
|
1017 | 1017 | dataOut.nProfiles = self.nProfiles |
|
|
1018 | 1018 | dataOut.profileIndex = self.profileIndex |
|
|
1019 | 1019 | dataOut.flagNoData = False |
|
|
1020 | 1020 | |
|
|
1021 | 1021 | self.incProfileIndex() |
|
|
1022 | 1022 | return dataOut |
|
|
1023 | 1023 | |
|
|
1024 | 1024 | if profileRangeList != None: |
|
|
1025 | 1025 | |
|
|
1026 | 1026 | minIndex = profileRangeList[0] |
|
|
1027 | 1027 | maxIndex = profileRangeList[1] |
|
|
1028 | 1028 | |
|
|
1029 | 1029 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1030 | 1030 | |
|
|
1031 | 1031 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
1032 | 1032 | dataOut.nProfiles = self.nProfiles |
|
|
1033 | 1033 | dataOut.profileIndex = self.profileIndex |
|
|
1034 | 1034 | dataOut.flagNoData = False |
|
|
1035 | 1035 | |
|
|
1036 | 1036 | self.incProfileIndex() |
|
|
1037 | 1037 | return dataOut |
|
|
1038 | 1038 | |
|
|
1039 | 1039 | if rangeList != None: |
|
|
1040 | 1040 | |
|
|
1041 | 1041 | nProfiles = 0 |
|
|
1042 | 1042 | |
|
|
1043 | 1043 | for thisRange in rangeList: |
|
|
1044 | 1044 | minIndex = thisRange[0] |
|
|
1045 | 1045 | maxIndex = thisRange[1] |
|
|
1046 | 1046 | |
|
|
1047 | 1047 | nProfiles += maxIndex - minIndex + 1 |
|
|
1048 | 1048 | |
|
|
1049 | 1049 | for thisRange in rangeList: |
|
|
1050 | 1050 | |
|
|
1051 | 1051 | minIndex = thisRange[0] |
|
|
1052 | 1052 | maxIndex = thisRange[1] |
|
|
1053 | 1053 | |
|
|
1054 | 1054 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | self.nProfiles = nProfiles |
|
|
1057 | 1057 | dataOut.nProfiles = self.nProfiles |
|
|
1058 | 1058 | dataOut.profileIndex = self.profileIndex |
|
|
1059 | 1059 | dataOut.flagNoData = False |
|
|
1060 | 1060 | |
|
|
1061 | 1061 | self.incProfileIndex() |
|
|
1062 | 1062 | |
|
|
1063 | 1063 | break |
|
|
1064 | 1064 | |
|
|
1065 | 1065 | return dataOut |
|
|
1066 | 1066 | |
|
|
1067 | 1067 | |
|
|
1068 | 1068 | if beam != None: #beam is only for AMISR data |
|
|
1069 | 1069 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
1070 | 1070 | dataOut.flagNoData = False |
|
|
1071 | 1071 | dataOut.profileIndex = self.profileIndex |
|
|
1072 | 1072 | |
|
|
1073 | 1073 | self.incProfileIndex() |
|
|
1074 | 1074 | |
|
|
1075 | 1075 | return dataOut |
|
|
1076 | 1076 | |
|
|
1077 | 1077 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
|
1078 | 1078 | |
|
|
1079 | 1079 | |
|
|
1080 | 1080 | class Reshaper(Operation): |
|
|
1081 | 1081 | |
|
|
1082 | 1082 | def __init__(self, **kwargs): |
|
|
1083 | 1083 | |
|
|
1084 | 1084 | Operation.__init__(self, **kwargs) |
|
|
1085 | 1085 | |
|
|
1086 | 1086 | self.__buffer = None |
|
|
1087 | 1087 | self.__nitems = 0 |
|
|
1088 | 1088 | |
|
|
1089 | 1089 | def __appendProfile(self, dataOut, nTxs): |
|
|
1090 | 1090 | |
|
|
1091 | 1091 | if self.__buffer is None: |
|
|
1092 | 1092 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
1093 | 1093 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
1094 | 1094 | |
|
|
1095 | 1095 | ini = dataOut.nHeights * self.__nitems |
|
|
1096 | 1096 | end = ini + dataOut.nHeights |
|
|
1097 | 1097 | |
|
|
1098 | 1098 | self.__buffer[:, ini:end] = dataOut.data |
|
|
1099 | 1099 | |
|
|
1100 | 1100 | self.__nitems += 1 |
|
|
1101 | 1101 | |
|
|
1102 | 1102 | return int(self.__nitems*nTxs) |
|
|
1103 | 1103 | |
|
|
1104 | 1104 | def __getBuffer(self): |
|
|
1105 | 1105 | |
|
|
1106 | 1106 | if self.__nitems == int(1./self.__nTxs): |
|
|
1107 | 1107 | |
|
|
1108 | 1108 | self.__nitems = 0 |
|
|
1109 | 1109 | |
|
|
1110 | 1110 | return self.__buffer.copy() |
|
|
1111 | 1111 | |
|
|
1112 | 1112 | return None |
|
|
1113 | 1113 | |
|
|
1114 | 1114 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | if shape is None and nTxs is None: |
|
|
1117 | 1117 | raise ValueError("Reshaper: shape of factor should be defined") |
|
|
1118 | 1118 | |
|
|
1119 | 1119 | if nTxs: |
|
|
1120 | 1120 | if nTxs < 0: |
|
|
1121 | 1121 | raise ValueError("nTxs should be greater than 0") |
|
|
1122 | 1122 | |
|
|
1123 | 1123 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
1124 | 1124 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
|
1125 | 1125 | |
|
|
1126 | 1126 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
1127 | 1127 | |
|
|
1128 | 1128 | return shape, nTxs |
|
|
1129 | 1129 | |
|
|
1130 | 1130 | if len(shape) != 2 and len(shape) != 3: |
|
|
1131 | 1131 | raise ValueError("shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)) |
|
|
1132 | 1132 | |
|
|
1133 | 1133 | if len(shape) == 2: |
|
|
1134 | 1134 | shape_tuple = [dataOut.nChannels] |
|
|
1135 | 1135 | shape_tuple.extend(shape) |
|
|
1136 | 1136 | else: |
|
|
1137 | 1137 | shape_tuple = list(shape) |
|
|
1138 | 1138 | |
|
|
1139 | 1139 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1140 | 1140 | |
|
|
1141 | 1141 | return shape_tuple, nTxs |
|
|
1142 | 1142 | |
|
|
1143 | 1143 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1144 | 1144 | |
|
|
1145 | 1145 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1146 | 1146 | |
|
|
1147 | 1147 | dataOut.flagNoData = True |
|
|
1148 | 1148 | profileIndex = None |
|
|
1149 | 1149 | |
|
|
1150 | 1150 | if dataOut.flagDataAsBlock: |
|
|
1151 | 1151 | |
|
|
1152 | 1152 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1153 | 1153 | dataOut.flagNoData = False |
|
|
1154 | 1154 | |
|
|
1155 | 1155 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1156 | 1156 | |
|
|
1157 | 1157 | else: |
|
|
1158 | 1158 | |
|
|
1159 | 1159 | if self.__nTxs < 1: |
|
|
1160 | 1160 | |
|
|
1161 | 1161 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1162 | 1162 | new_data = self.__getBuffer() |
|
|
1163 | 1163 | |
|
|
1164 | 1164 | if new_data is not None: |
|
|
1165 | 1165 | dataOut.data = new_data |
|
|
1166 | 1166 | dataOut.flagNoData = False |
|
|
1167 | 1167 | |
|
|
1168 | 1168 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1169 | 1169 | |
|
|
1170 | 1170 | else: |
|
|
1171 | 1171 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
|
1172 | 1172 | |
|
|
1173 | 1173 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1174 | 1174 | |
|
|
1175 | 1175 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1176 | 1176 | |
|
|
1177 | 1177 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1178 | 1178 | |
|
|
1179 | 1179 | dataOut.profileIndex = profileIndex |
|
|
1180 | 1180 | |
|
|
1181 | 1181 | dataOut.ippSeconds /= self.__nTxs |
|
|
1182 | 1182 | |
|
|
1183 | 1183 | return dataOut |
|
|
1184 | 1184 | |
|
|
1185 | 1185 | class SplitProfiles(Operation): |
|
|
1186 | 1186 | |
|
|
1187 | 1187 | def __init__(self, **kwargs): |
|
|
1188 | 1188 | |
|
|
1189 | 1189 | Operation.__init__(self, **kwargs) |
|
|
1190 | 1190 | |
|
|
1191 | 1191 | def run(self, dataOut, n): |
|
|
1192 | 1192 | |
|
|
1193 | 1193 | dataOut.flagNoData = True |
|
|
1194 | 1194 | profileIndex = None |
|
|
1195 | 1195 | |
|
|
1196 | 1196 | if dataOut.flagDataAsBlock: |
|
|
1197 | 1197 | |
|
|
1198 | 1198 | #nchannels, nprofiles, nsamples |
|
|
1199 | 1199 | shape = dataOut.data.shape |
|
|
1200 | 1200 | |
|
|
1201 | 1201 | if shape[2] % n != 0: |
|
|
1202 | 1202 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
|
1203 | 1203 | |
|
|
1204 | 1204 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
|
1205 | 1205 | |
|
|
1206 | 1206 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1207 | 1207 | dataOut.flagNoData = False |
|
|
1208 | 1208 | |
|
|
1209 | 1209 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1210 | 1210 | |
|
|
1211 | 1211 | else: |
|
|
1212 | 1212 | |
|
|
1213 | 1213 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
|
1214 | 1214 | |
|
|
1215 | 1215 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1216 | 1216 | |
|
|
1217 | 1217 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1218 | 1218 | |
|
|
1219 | 1219 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1220 | 1220 | |
|
|
1221 | 1221 | dataOut.profileIndex = profileIndex |
|
|
1222 | 1222 | |
|
|
1223 | 1223 | dataOut.ippSeconds /= n |
|
|
1224 | 1224 | |
|
|
1225 | 1225 | return dataOut |
|
|
1226 | 1226 | |
|
|
1227 | 1227 | class CombineProfiles(Operation): |
|
|
1228 | 1228 | def __init__(self, **kwargs): |
|
|
1229 | 1229 | |
|
|
1230 | 1230 | Operation.__init__(self, **kwargs) |
|
|
1231 | 1231 | |
|
|
1232 | 1232 | self.__remData = None |
|
|
1233 | 1233 | self.__profileIndex = 0 |
|
|
1234 | 1234 | |
|
|
1235 | 1235 | def run(self, dataOut, n): |
|
|
1236 | 1236 | |
|
|
1237 | 1237 | dataOut.flagNoData = True |
|
|
1238 | 1238 | profileIndex = None |
|
|
1239 | 1239 | |
|
|
1240 | 1240 | if dataOut.flagDataAsBlock: |
|
|
1241 | 1241 | |
|
|
1242 | 1242 | #nchannels, nprofiles, nsamples |
|
|
1243 | 1243 | shape = dataOut.data.shape |
|
|
1244 | 1244 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1245 | 1245 | |
|
|
1246 | 1246 | if shape[1] % n != 0: |
|
|
1247 | 1247 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
|
1248 | 1248 | |
|
|
1249 | 1249 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1250 | 1250 | dataOut.flagNoData = False |
|
|
1251 | 1251 | |
|
|
1252 | 1252 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1253 | 1253 | |
|
|
1254 | 1254 | else: |
|
|
1255 | 1255 | |
|
|
1256 | 1256 | #nchannels, nsamples |
|
|
1257 | 1257 | if self.__remData is None: |
|
|
1258 | 1258 | newData = dataOut.data |
|
|
1259 | 1259 | else: |
|
|
1260 | 1260 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1261 | 1261 | |
|
|
1262 | 1262 | self.__profileIndex += 1 |
|
|
1263 | 1263 | |
|
|
1264 | 1264 | if self.__profileIndex < n: |
|
|
1265 | 1265 | self.__remData = newData |
|
|
1266 | 1266 | #continue |
|
|
1267 | 1267 | return |
|
|
1268 | 1268 | |
|
|
1269 | 1269 | self.__profileIndex = 0 |
|
|
1270 | 1270 | self.__remData = None |
|
|
1271 | 1271 | |
|
|
1272 | 1272 | dataOut.data = newData |
|
|
1273 | 1273 | dataOut.flagNoData = False |
|
|
1274 | 1274 | |
|
|
1275 | 1275 | profileIndex = dataOut.profileIndex/n |
|
|
1276 | 1276 | |
|
|
1277 | 1277 | |
|
|
1278 | 1278 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1279 | 1279 | |
|
|
1280 | 1280 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1281 | 1281 | |
|
|
1282 | 1282 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1283 | 1283 | |
|
|
1284 | 1284 | dataOut.profileIndex = profileIndex |
|
|
1285 | 1285 | |
|
|
1286 | 1286 | dataOut.ippSeconds *= n |
|
|
1287 | 1287 | |
|
|
1288 | 1288 | return dataOut |
|
|
1289 | 1289 | |
|
|
1290 | 1290 | class PulsePair(Operation): |
|
|
1291 | 1291 | ''' |
|
|
1292 | 1292 | Function PulsePair(Signal Power, Velocity) |
|
|
1293 | 1293 | The real component of Lag[0] provides Intensity Information |
|
|
1294 | 1294 | The imag component of Lag[1] Phase provides Velocity Information |
|
|
1295 | 1295 | |
|
|
1296 | 1296 | Configuration Parameters: |
|
|
1297 | 1297 | nPRF = Number of Several PRF |
|
|
1298 | 1298 | theta = Degree Azimuth angel Boundaries |
|
|
1299 | 1299 | |
|
|
1300 | 1300 | Input: |
|
|
1301 | 1301 | self.dataOut |
|
|
1302 | 1302 | lag[N] |
|
|
1303 | 1303 | Affected: |
|
|
1304 | 1304 | self.dataOut.spc |
|
|
1305 | 1305 | ''' |
|
|
1306 | 1306 | isConfig = False |
|
|
1307 | 1307 | __profIndex = 0 |
|
|
1308 | 1308 | __initime = None |
|
|
1309 | 1309 | __lastdatatime = None |
|
|
1310 | 1310 | __buffer = None |
|
|
1311 | 1311 | noise = None |
|
|
1312 | 1312 | __dataReady = False |
|
|
1313 | 1313 | n = None |
|
|
1314 | 1314 | __nch = 0 |
|
|
1315 | 1315 | __nHeis = 0 |
|
|
1316 | 1316 | removeDC = False |
|
|
1317 | 1317 | ipp = None |
|
|
1318 | 1318 | lambda_ = 0 |
|
|
1319 | 1319 | |
|
|
1320 | 1320 | def __init__(self,**kwargs): |
|
|
1321 | 1321 | Operation.__init__(self,**kwargs) |
|
|
1322 | 1322 | |
|
|
1323 | 1323 | def setup(self, dataOut, n = None, removeDC=False): |
|
|
1324 | 1324 | ''' |
|
|
1325 | 1325 | n= Numero de PRF's de entrada |
|
|
1326 | 1326 | ''' |
|
|
1327 | 1327 | self.__initime = None |
|
|
1328 | 1328 | ####print("[INICIO]-setup del METODO PULSE PAIR") |
|
|
1329 | 1329 | self.__lastdatatime = 0 |
|
|
1330 | 1330 | self.__dataReady = False |
|
|
1331 | 1331 | self.__buffer = 0 |
|
|
1332 | 1332 | self.__profIndex = 0 |
|
|
1333 | 1333 | self.noise = None |
|
|
1334 | 1334 | self.__nch = dataOut.nChannels |
|
|
1335 | 1335 | self.__nHeis = dataOut.nHeights |
|
|
1336 | 1336 | self.removeDC = removeDC |
|
|
1337 | 1337 | self.lambda_ = 3.0e8/(9345.0e6) |
|
|
1338 | 1338 | self.ippSec = dataOut.ippSeconds |
|
|
1339 | 1339 | self.nCohInt = dataOut.nCohInt |
|
|
1340 | 1340 | ####print("IPPseconds",dataOut.ippSeconds) |
|
|
1341 | 1341 | ####print("ELVALOR DE n es:", n) |
|
|
1342 | 1342 | if n == None: |
|
|
1343 | 1343 | raise ValueError("n should be specified.") |
|
|
1344 | 1344 | |
|
|
1345 | 1345 | if n != None: |
|
|
1346 | 1346 | if n<2: |
|
|
1347 | 1347 | raise ValueError("n should be greater than 2") |
|
|
1348 | 1348 | |
|
|
1349 | 1349 | self.n = n |
|
|
1350 | 1350 | self.__nProf = n |
|
|
1351 | 1351 | |
|
|
1352 | 1352 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
|
1353 | 1353 | n, |
|
|
1354 | 1354 | dataOut.nHeights), |
|
|
1355 | 1355 | dtype='complex') |
|
|
1356 | 1356 | |
|
|
1357 | 1357 | def putData(self,data): |
|
|
1358 | 1358 | ''' |
|
|
1359 | 1359 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
1360 | 1360 | ''' |
|
|
1361 | 1361 | self.__buffer[:,self.__profIndex,:]= data |
|
|
1362 | 1362 | self.__profIndex += 1 |
|
|
1363 | 1363 | return |
|
|
1364 | 1364 | |
|
|
1365 | 1365 | def pushData(self,dataOut): |
|
|
1366 | 1366 | ''' |
|
|
1367 | 1367 | Return the PULSEPAIR and the profiles used in the operation |
|
|
1368 | 1368 | Affected : self.__profileIndex |
|
|
1369 | 1369 | ''' |
|
|
1370 | 1370 | #----------------- Remove DC----------------------------------- |
|
|
1371 | 1371 | if self.removeDC==True: |
|
|
1372 | 1372 | mean = numpy.mean(self.__buffer,1) |
|
|
1373 | 1373 | tmp = mean.reshape(self.__nch,1,self.__nHeis) |
|
|
1374 | 1374 | dc= numpy.tile(tmp,[1,self.__nProf,1]) |
|
|
1375 | 1375 | self.__buffer = self.__buffer - dc |
|
|
1376 | 1376 | #------------------Calculo de Potencia ------------------------ |
|
|
1377 | 1377 | pair0 = self.__buffer*numpy.conj(self.__buffer) |
|
|
1378 | 1378 | pair0 = pair0.real |
|
|
1379 | 1379 | lag_0 = numpy.sum(pair0,1) |
|
|
1380 | 1380 | #-----------------Calculo de Cscp------------------------------ New |
|
|
1381 | 1381 | cspc_pair01 = self.__buffer[0]*self.__buffer[1] |
|
|
1382 | 1382 | #------------------Calculo de Ruido x canal-------------------- |
|
|
1383 | 1383 | self.noise = numpy.zeros(self.__nch) |
|
|
1384 | 1384 | for i in range(self.__nch): |
|
|
1385 | 1385 | daux = numpy.sort(pair0[i,:,:],axis= None) |
|
|
1386 | 1386 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) |
|
|
1387 | 1387 | |
|
|
1388 | 1388 | self.noise = self.noise.reshape(self.__nch,1) |
|
|
1389 | 1389 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) |
|
|
1390 | 1390 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) |
|
|
1391 | 1391 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) |
|
|
1392 | 1392 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
|
1393 | 1393 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
|
1394 | 1394 | #-------------------- Power -------------------------------------------------- |
|
|
1395 | 1395 | data_power = lag_0/(self.n*self.nCohInt) |
|
|
1396 | 1396 | #--------------------CCF------------------------------------------------------ |
|
|
1397 | 1397 | data_ccf =numpy.sum(cspc_pair01,axis=0)/(self.n*self.nCohInt) |
|
|
1398 | 1398 | #------------------ Senal -------------------------------------------------- |
|
|
1399 | 1399 | data_intensity = pair0 - noise_buffer |
|
|
1400 | 1400 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) |
|
|
1401 | 1401 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
|
1402 | 1402 | for i in range(self.__nch): |
|
|
1403 | 1403 | for j in range(self.__nHeis): |
|
|
1404 | 1404 | if data_intensity[i][j] < 0: |
|
|
1405 | 1405 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
|
1406 | 1406 | |
|
|
1407 | 1407 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
|
1408 | 1408 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) |
|
|
1409 | 1409 | lag_1 = numpy.sum(pair1,1) |
|
|
1410 | 1410 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) |
|
|
1411 | 1411 | data_velocity = (self.lambda_/2.0)*data_freq |
|
|
1412 | 1412 | |
|
|
1413 | 1413 | #---------------- Potencia promedio estimada de la Senal----------- |
|
|
1414 | 1414 | lag_0 = lag_0/self.n |
|
|
1415 | 1415 | S = lag_0-self.noise |
|
|
1416 | 1416 | |
|
|
1417 | 1417 | #---------------- Frecuencia Doppler promedio --------------------- |
|
|
1418 | 1418 | lag_1 = lag_1/(self.n-1) |
|
|
1419 | 1419 | R1 = numpy.abs(lag_1) |
|
|
1420 | 1420 | |
|
|
1421 | 1421 | #---------------- Calculo del SNR---------------------------------- |
|
|
1422 | 1422 | data_snrPP = S/self.noise |
|
|
1423 | 1423 | for i in range(self.__nch): |
|
|
1424 | 1424 | for j in range(self.__nHeis): |
|
|
1425 | 1425 | if data_snrPP[i][j] < 1.e-20: |
|
|
1426 | 1426 | data_snrPP[i][j] = 1.e-20 |
|
|
1427 | 1427 | |
|
|
1428 | 1428 | #----------------- Calculo del ancho espectral ---------------------- |
|
|
1429 | 1429 | L = S/R1 |
|
|
1430 | 1430 | L = numpy.where(L<0,1,L) |
|
|
1431 | 1431 | L = numpy.log(L) |
|
|
1432 | 1432 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
|
1433 | 1433 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) |
|
|
1434 | 1434 | n = self.__profIndex |
|
|
1435 | 1435 | |
|
|
1436 | 1436 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') |
|
|
1437 | 1437 | self.__profIndex = 0 |
|
|
1438 | 1438 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,data_ccf,n |
|
|
1439 | 1439 | |
|
|
1440 | 1440 | |
|
|
1441 | 1441 | def pulsePairbyProfiles(self,dataOut): |
|
|
1442 | 1442 | |
|
|
1443 | 1443 | self.__dataReady = False |
|
|
1444 | 1444 | data_power = None |
|
|
1445 | 1445 | data_intensity = None |
|
|
1446 | 1446 | data_velocity = None |
|
|
1447 | 1447 | data_specwidth = None |
|
|
1448 | 1448 | data_snrPP = None |
|
|
1449 | 1449 | data_ccf = None |
|
|
1450 | 1450 | self.putData(data=dataOut.data) |
|
|
1451 | 1451 | if self.__profIndex == self.n: |
|
|
1452 | 1452 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, n = self.pushData(dataOut=dataOut) |
|
|
1453 | 1453 | self.__dataReady = True |
|
|
1454 | 1454 | |
|
|
1455 | 1455 | return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf |
|
|
1456 | 1456 | |
|
|
1457 | 1457 | |
|
|
1458 | 1458 | def pulsePairOp(self, dataOut, datatime= None): |
|
|
1459 | 1459 | |
|
|
1460 | 1460 | if self.__initime == None: |
|
|
1461 | 1461 | self.__initime = datatime |
|
|
1462 | 1462 | data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf = self.pulsePairbyProfiles(dataOut) |
|
|
1463 | 1463 | self.__lastdatatime = datatime |
|
|
1464 | 1464 | |
|
|
1465 | 1465 | if data_power is None: |
|
|
1466 | 1466 | return None, None, None,None,None,None,None |
|
|
1467 | 1467 | |
|
|
1468 | 1468 | avgdatatime = self.__initime |
|
|
1469 | 1469 | deltatime = datatime - self.__lastdatatime |
|
|
1470 | 1470 | self.__initime = datatime |
|
|
1471 | 1471 | |
|
|
1472 | 1472 | return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf, avgdatatime |
|
|
1473 | 1473 | |
|
|
1474 | 1474 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): |
|
|
1475 | 1475 | #print("hey") |
|
|
1476 | 1476 | #print(dataOut.data.shape) |
|
|
1477 | 1477 | #exit(1) |
|
|
1478 | 1478 | #print(self.__profIndex) |
|
|
1479 | 1479 | if not self.isConfig: |
|
|
1480 | 1480 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) |
|
|
1481 | 1481 | self.isConfig = True |
|
|
1482 | 1482 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) |
|
|
1483 | 1483 | dataOut.flagNoData = True |
|
|
1484 | 1484 | |
|
|
1485 | 1485 | if self.__dataReady: |
|
|
1486 | 1486 | ###print("READY ----------------------------------") |
|
|
1487 | 1487 | dataOut.nCohInt *= self.n |
|
|
1488 | 1488 | dataOut.dataPP_POW = data_intensity # S |
|
|
1489 | 1489 | dataOut.dataPP_POWER = data_power # P valor que corresponde a POTENCIA MOMENTO |
|
|
1490 | 1490 | dataOut.dataPP_DOP = data_velocity |
|
|
1491 | 1491 | dataOut.dataPP_SNR = data_snrPP |
|
|
1492 | 1492 | dataOut.dataPP_WIDTH = data_specwidth |
|
|
1493 | 1493 | dataOut.dataPP_CCF = data_ccf |
|
|
1494 | 1494 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
|
1495 | 1495 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1496 | 1496 | dataOut.utctime = avgdatatime |
|
|
1497 | 1497 | dataOut.flagNoData = False |
|
|
1498 | 1498 | return dataOut |
|
|
1499 | 1499 | |
|
|
1500 | 1500 | class PulsePair_vRF(Operation): |
|
|
1501 | 1501 | ''' |
|
|
1502 | 1502 | Function PulsePair(Signal Power, Velocity) |
|
|
1503 | 1503 | The real component of Lag[0] provides Intensity Information |
|
|
1504 | 1504 | The imag component of Lag[1] Phase provides Velocity Information |
|
|
1505 | 1505 | |
|
|
1506 | 1506 | Configuration Parameters: |
|
|
1507 | 1507 | nPRF = Number of Several PRF |
|
|
1508 | 1508 | theta = Degree Azimuth angel Boundaries |
|
|
1509 | 1509 | |
|
|
1510 | 1510 | Input: |
|
|
1511 | 1511 | self.dataOut |
|
|
1512 | 1512 | lag[N] |
|
|
1513 | 1513 | Affected: |
|
|
1514 | 1514 | self.dataOut.spc |
|
|
1515 | 1515 | ''' |
|
|
1516 | 1516 | isConfig = False |
|
|
1517 | 1517 | __profIndex = 0 |
|
|
1518 | 1518 | __initime = None |
|
|
1519 | 1519 | __lastdatatime = None |
|
|
1520 | 1520 | __buffer = None |
|
|
1521 | 1521 | noise = None |
|
|
1522 | 1522 | __dataReady = False |
|
|
1523 | 1523 | n = None |
|
|
1524 | 1524 | __nch = 0 |
|
|
1525 | 1525 | __nHeis = 0 |
|
|
1526 | 1526 | removeDC = False |
|
|
1527 | 1527 | ipp = None |
|
|
1528 | 1528 | lambda_ = 0 |
|
|
1529 | 1529 | |
|
|
1530 | 1530 | def __init__(self,**kwargs): |
|
|
1531 | 1531 | Operation.__init__(self,**kwargs) |
|
|
1532 | 1532 | |
|
|
1533 | 1533 | def setup(self, dataOut, n = None, removeDC=False): |
|
|
1534 | 1534 | ''' |
|
|
1535 | 1535 | n= Numero de PRF's de entrada |
|
|
1536 | 1536 | ''' |
|
|
1537 | 1537 | self.__initime = None |
|
|
1538 | 1538 | ####print("[INICIO]-setup del METODO PULSE PAIR") |
|
|
1539 | 1539 | self.__lastdatatime = 0 |
|
|
1540 | 1540 | self.__dataReady = False |
|
|
1541 | 1541 | self.__buffer = 0 |
|
|
1542 | 1542 | self.__profIndex = 0 |
|
|
1543 | 1543 | self.noise = None |
|
|
1544 | 1544 | self.__nch = dataOut.nChannels |
|
|
1545 | 1545 | self.__nHeis = dataOut.nHeights |
|
|
1546 | 1546 | self.removeDC = removeDC |
|
|
1547 | 1547 | self.lambda_ = 3.0e8/(9345.0e6) |
|
|
1548 | 1548 | self.ippSec = dataOut.ippSeconds |
|
|
1549 | 1549 | self.nCohInt = dataOut.nCohInt |
|
|
1550 | 1550 | ####print("IPPseconds",dataOut.ippSeconds) |
|
|
1551 | 1551 | ####print("ELVALOR DE n es:", n) |
|
|
1552 | 1552 | if n == None: |
|
|
1553 | 1553 | raise ValueError("n should be specified.") |
|
|
1554 | 1554 | |
|
|
1555 | 1555 | if n != None: |
|
|
1556 | 1556 | if n<2: |
|
|
1557 | 1557 | raise ValueError("n should be greater than 2") |
|
|
1558 | 1558 | |
|
|
1559 | 1559 | self.n = n |
|
|
1560 | 1560 | self.__nProf = n |
|
|
1561 | 1561 | |
|
|
1562 | 1562 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
|
1563 | 1563 | n, |
|
|
1564 | 1564 | dataOut.nHeights), |
|
|
1565 | 1565 | dtype='complex') |
|
|
1566 | 1566 | |
|
|
1567 | 1567 | def putData(self,data): |
|
|
1568 | 1568 | ''' |
|
|
1569 | 1569 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
1570 | 1570 | ''' |
|
|
1571 | 1571 | self.__buffer[:,self.__profIndex,:]= data |
|
|
1572 | 1572 | self.__profIndex += 1 |
|
|
1573 | 1573 | return |
|
|
1574 | 1574 | |
|
|
1575 | 1575 | def putDataByBlock(self,data,n): |
|
|
1576 | 1576 | ''' |
|
|
1577 | 1577 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
1578 | 1578 | ''' |
|
|
1579 | 1579 | self.__buffer[:]= data |
|
|
1580 | 1580 | self.__profIndex = n |
|
|
1581 | 1581 | return |
|
|
1582 | 1582 | |
|
|
1583 | 1583 | def pushData(self,dataOut): |
|
|
1584 | 1584 | ''' |
|
|
1585 | 1585 | Return the PULSEPAIR and the profiles used in the operation |
|
|
1586 | 1586 | Affected : self.__profileIndex |
|
|
1587 | 1587 | ''' |
|
|
1588 | 1588 | #----------------- Remove DC----------------------------------- |
|
|
1589 | 1589 | if self.removeDC==True: |
|
|
1590 | 1590 | mean = numpy.mean(self.__buffer,1) |
|
|
1591 | 1591 | tmp = mean.reshape(self.__nch,1,self.__nHeis) |
|
|
1592 | 1592 | dc= numpy.tile(tmp,[1,self.__nProf,1]) |
|
|
1593 | 1593 | self.__buffer = self.__buffer - dc |
|
|
1594 | 1594 | #------------------Calculo de Potencia ------------------------ |
|
|
1595 | 1595 | pair0 = self.__buffer*numpy.conj(self.__buffer) |
|
|
1596 | 1596 | pair0 = pair0.real |
|
|
1597 | 1597 | lag_0 = numpy.sum(pair0,1) |
|
|
1598 | 1598 | #-----------------Calculo de Cscp------------------------------ New |
|
|
1599 | 1599 | cspc_pair01 = self.__buffer[0]*self.__buffer[1] |
|
|
1600 | 1600 | #------------------Calculo de Ruido x canal-------------------- |
|
|
1601 | 1601 | self.noise = numpy.zeros(self.__nch) |
|
|
1602 | 1602 | for i in range(self.__nch): |
|
|
1603 | 1603 | daux = numpy.sort(pair0[i,:,:],axis= None) |
|
|
1604 | 1604 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) |
|
|
1605 | 1605 | |
|
|
1606 | 1606 | self.noise = self.noise.reshape(self.__nch,1) |
|
|
1607 | 1607 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) |
|
|
1608 | 1608 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) |
|
|
1609 | 1609 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) |
|
|
1610 | 1610 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
|
1611 | 1611 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
|
1612 | 1612 | #-------------------- Power -------------------------------------------------- |
|
|
1613 | 1613 | data_power = lag_0/(self.n*self.nCohInt) |
|
|
1614 | 1614 | #--------------------CCF------------------------------------------------------ |
|
|
1615 | 1615 | data_ccf =numpy.sum(cspc_pair01,axis=0)/(self.n*self.nCohInt) |
|
|
1616 | 1616 | #------------------ Senal -------------------------------------------------- |
|
|
1617 | 1617 | data_intensity = pair0 - noise_buffer |
|
|
1618 | 1618 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) |
|
|
1619 | 1619 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
|
1620 | 1620 | for i in range(self.__nch): |
|
|
1621 | 1621 | for j in range(self.__nHeis): |
|
|
1622 | 1622 | if data_intensity[i][j] < 0: |
|
|
1623 | 1623 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
|
1624 | 1624 | |
|
|
1625 | 1625 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
|
1626 | 1626 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) |
|
|
1627 | 1627 | lag_1 = numpy.sum(pair1,1) |
|
|
1628 | 1628 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) |
|
|
1629 | 1629 | data_velocity = (self.lambda_/2.0)*data_freq |
|
|
1630 | 1630 | |
|
|
1631 | 1631 | #---------------- Potencia promedio estimada de la Senal----------- |
|
|
1632 | 1632 | lag_0 = lag_0/self.n |
|
|
1633 | 1633 | S = lag_0-self.noise |
|
|
1634 | 1634 | |
|
|
1635 | 1635 | #---------------- Frecuencia Doppler promedio --------------------- |
|
|
1636 | 1636 | lag_1 = lag_1/(self.n-1) |
|
|
1637 | 1637 | R1 = numpy.abs(lag_1) |
|
|
1638 | 1638 | |
|
|
1639 | 1639 | #---------------- Calculo del SNR---------------------------------- |
|
|
1640 | 1640 | data_snrPP = S/self.noise |
|
|
1641 | 1641 | for i in range(self.__nch): |
|
|
1642 | 1642 | for j in range(self.__nHeis): |
|
|
1643 | 1643 | if data_snrPP[i][j] < 1.e-20: |
|
|
1644 | 1644 | data_snrPP[i][j] = 1.e-20 |
|
|
1645 | 1645 | |
|
|
1646 | 1646 | #----------------- Calculo del ancho espectral ---------------------- |
|
|
1647 | 1647 | L = S/R1 |
|
|
1648 | 1648 | L = numpy.where(L<0,1,L) |
|
|
1649 | 1649 | L = numpy.log(L) |
|
|
1650 | 1650 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
|
1651 | 1651 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) |
|
|
1652 | 1652 | n = self.__profIndex |
|
|
1653 | 1653 | |
|
|
1654 | 1654 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') |
|
|
1655 | 1655 | self.__profIndex = 0 |
|
|
1656 | 1656 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,data_ccf,n |
|
|
1657 | 1657 | |
|
|
1658 | 1658 | |
|
|
1659 | 1659 | def pulsePairbyProfiles(self,dataOut,n): |
|
|
1660 | 1660 | |
|
|
1661 | 1661 | self.__dataReady = False |
|
|
1662 | 1662 | data_power = None |
|
|
1663 | 1663 | data_intensity = None |
|
|
1664 | 1664 | data_velocity = None |
|
|
1665 | 1665 | data_specwidth = None |
|
|
1666 | 1666 | data_snrPP = None |
|
|
1667 | 1667 | data_ccf = None |
|
|
1668 | 1668 | |
|
|
1669 | 1669 | if dataOut.flagDataAsBlock: |
|
|
1670 | 1670 | self.putDataByBlock(data=dataOut.data,n=n) |
|
|
1671 | 1671 | else: |
|
|
1672 | 1672 | self.putData(data=dataOut.data) |
|
|
1673 | 1673 | if self.__profIndex == self.n: |
|
|
1674 | 1674 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, n = self.pushData(dataOut=dataOut) |
|
|
1675 | 1675 | self.__dataReady = True |
|
|
1676 | 1676 | |
|
|
1677 | 1677 | return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf |
|
|
1678 | 1678 | |
|
|
1679 | 1679 | |
|
|
1680 | 1680 | def pulsePairOp(self, dataOut, n, datatime= None): |
|
|
1681 | 1681 | |
|
|
1682 | 1682 | if self.__initime == None: |
|
|
1683 | 1683 | self.__initime = datatime |
|
|
1684 | 1684 | data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf = self.pulsePairbyProfiles(dataOut,n) |
|
|
1685 | 1685 | self.__lastdatatime = datatime |
|
|
1686 | 1686 | |
|
|
1687 | 1687 | if data_power is None: |
|
|
1688 | 1688 | return None, None, None,None,None,None,None |
|
|
1689 | 1689 | |
|
|
1690 | 1690 | avgdatatime = self.__initime |
|
|
1691 | 1691 | deltatime = datatime - self.__lastdatatime |
|
|
1692 | 1692 | self.__initime = datatime |
|
|
1693 | 1693 | |
|
|
1694 | 1694 | return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf, avgdatatime |
|
|
1695 | 1695 | |
|
|
1696 | 1696 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): |
|
|
1697 | #print("hey") | |
|
|
1698 | #print(dataOut.data.shape) | |
|
|
1699 | #exit(1) | |
|
|
1697 | ||
|
|
1700 | 1698 | if dataOut.flagDataAsBlock: |
|
|
1701 |
n = dataOut.nProfile |
|
|
|
1702 | #print(self.__profIndex) | |
|
|
1699 | n = dataOut.nProfiles | |
|
|
1700 | ||
|
|
1703 | 1701 | if not self.isConfig: |
|
|
1704 | 1702 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) |
|
|
1705 | 1703 | self.isConfig = True |
|
|
1706 | 1704 | |
|
|
1707 | 1705 | |
|
|
1708 | 1706 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, avgdatatime = self.pulsePairOp(dataOut, n, dataOut.utctime) |
|
|
1709 | 1707 | |
|
|
1710 | 1708 | |
|
|
1711 | 1709 | dataOut.flagNoData = True |
|
|
1712 | 1710 | |
|
|
1713 | 1711 | if self.__dataReady: |
|
|
1714 | 1712 | ###print("READY ----------------------------------") |
|
|
1715 | 1713 | dataOut.nCohInt *= self.n |
|
|
1716 | 1714 | dataOut.dataPP_POW = data_intensity # S |
|
|
1717 | 1715 | dataOut.dataPP_POWER = data_power # P valor que corresponde a POTENCIA MOMENTO |
|
|
1718 | 1716 | dataOut.dataPP_DOP = data_velocity |
|
|
1719 | 1717 | dataOut.dataPP_SNR = data_snrPP |
|
|
1720 | 1718 | dataOut.dataPP_WIDTH = data_specwidth |
|
|
1721 | 1719 | dataOut.dataPP_CCF = data_ccf |
|
|
1722 | 1720 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
|
1723 | 1721 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1724 | 1722 | dataOut.utctime = avgdatatime |
|
|
1725 | 1723 | dataOut.flagNoData = False |
|
|
1726 | 1724 | return dataOut |
|
|
1727 | 1725 | |
|
|
1728 | 1726 | # import collections |
|
|
1729 | 1727 | # from scipy.stats import mode |
|
|
1730 | 1728 | # |
|
|
1731 | 1729 | # class Synchronize(Operation): |
|
|
1732 | 1730 | # |
|
|
1733 | 1731 | # isConfig = False |
|
|
1734 | 1732 | # __profIndex = 0 |
|
|
1735 | 1733 | # |
|
|
1736 | 1734 | # def __init__(self, **kwargs): |
|
|
1737 | 1735 | # |
|
|
1738 | 1736 | # Operation.__init__(self, **kwargs) |
|
|
1739 | 1737 | # # self.isConfig = False |
|
|
1740 | 1738 | # self.__powBuffer = None |
|
|
1741 | 1739 | # self.__startIndex = 0 |
|
|
1742 | 1740 | # self.__pulseFound = False |
|
|
1743 | 1741 | # |
|
|
1744 | 1742 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1745 | 1743 | # |
|
|
1746 | 1744 | # #Read data |
|
|
1747 | 1745 | # |
|
|
1748 | 1746 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1749 | 1747 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1750 | 1748 | # |
|
|
1751 | 1749 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1752 | 1750 | # |
|
|
1753 | 1751 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1754 | 1752 | # |
|
|
1755 | 1753 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1756 | 1754 | # |
|
|
1757 | 1755 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1758 | 1756 | # |
|
|
1759 | 1757 | # if maxValue < noisedB + 10: |
|
|
1760 | 1758 | # #No se encuentra ningun pulso de transmision |
|
|
1761 | 1759 | # return None |
|
|
1762 | 1760 | # |
|
|
1763 | 1761 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1764 | 1762 | # |
|
|
1765 | 1763 | # if len(maxValuesIndex) < 2: |
|
|
1766 | 1764 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1767 | 1765 | # return None |
|
|
1768 | 1766 | # |
|
|
1769 | 1767 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1770 | 1768 | # |
|
|
1771 | 1769 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1772 | 1770 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1773 | 1771 | # |
|
|
1774 | 1772 | # if len(pulseIndex) < 2: |
|
|
1775 | 1773 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1776 | 1774 | # return None |
|
|
1777 | 1775 | # |
|
|
1778 | 1776 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1779 | 1777 | # |
|
|
1780 | 1778 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1781 | 1779 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1782 | 1780 | # |
|
|
1783 | 1781 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1784 | 1782 | # |
|
|
1785 | 1783 | # if len(realIndex) < 2: |
|
|
1786 | 1784 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1787 | 1785 | # return None |
|
|
1788 | 1786 | # |
|
|
1789 | 1787 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1790 | 1788 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1791 | 1789 | # |
|
|
1792 | 1790 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1793 | 1791 | # |
|
|
1794 | 1792 | # print "IPP = %d samples" %period |
|
|
1795 | 1793 | # |
|
|
1796 | 1794 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1797 | 1795 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1798 | 1796 | # |
|
|
1799 | 1797 | # return 1 |
|
|
1800 | 1798 | # |
|
|
1801 | 1799 | # |
|
|
1802 | 1800 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1803 | 1801 | # |
|
|
1804 | 1802 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1805 | 1803 | # maxlen = buffer_size*nSamples) |
|
|
1806 | 1804 | # |
|
|
1807 | 1805 | # bufferList = [] |
|
|
1808 | 1806 | # |
|
|
1809 | 1807 | # for i in range(nChannels): |
|
|
1810 | 1808 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1811 | 1809 | # maxlen = buffer_size*nSamples) |
|
|
1812 | 1810 | # |
|
|
1813 | 1811 | # bufferList.append(bufferByChannel) |
|
|
1814 | 1812 | # |
|
|
1815 | 1813 | # self.__nSamples = nSamples |
|
|
1816 | 1814 | # self.__nChannels = nChannels |
|
|
1817 | 1815 | # self.__bufferList = bufferList |
|
|
1818 | 1816 | # |
|
|
1819 | 1817 | # def run(self, dataOut, channel = 0): |
|
|
1820 | 1818 | # |
|
|
1821 | 1819 | # if not self.isConfig: |
|
|
1822 | 1820 | # nSamples = dataOut.nHeights |
|
|
1823 | 1821 | # nChannels = dataOut.nChannels |
|
|
1824 | 1822 | # self.setup(nSamples, nChannels) |
|
|
1825 | 1823 | # self.isConfig = True |
|
|
1826 | 1824 | # |
|
|
1827 | 1825 | # #Append new data to internal buffer |
|
|
1828 | 1826 | # for thisChannel in range(self.__nChannels): |
|
|
1829 | 1827 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1830 | 1828 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1831 | 1829 | # |
|
|
1832 | 1830 | # if self.__pulseFound: |
|
|
1833 | 1831 | # self.__startIndex -= self.__nSamples |
|
|
1834 | 1832 | # |
|
|
1835 | 1833 | # #Finding Tx Pulse |
|
|
1836 | 1834 | # if not self.__pulseFound: |
|
|
1837 | 1835 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1838 | 1836 | # |
|
|
1839 | 1837 | # if indexFound == None: |
|
|
1840 | 1838 | # dataOut.flagNoData = True |
|
|
1841 | 1839 | # return |
|
|
1842 | 1840 | # |
|
|
1843 | 1841 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1844 | 1842 | # self.__pulseFound = True |
|
|
1845 | 1843 | # self.__startIndex = indexFound |
|
|
1846 | 1844 | # |
|
|
1847 | 1845 | # #If pulse was found ... |
|
|
1848 | 1846 | # for thisChannel in range(self.__nChannels): |
|
|
1849 | 1847 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1850 | 1848 | # #print self.__startIndex |
|
|
1851 | 1849 | # x = numpy.array(bufferByChannel) |
|
|
1852 | 1850 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1853 | 1851 | # |
|
|
1854 | 1852 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1855 | 1853 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1856 | 1854 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1857 | 1855 | # |
|
|
1858 | 1856 | # dataOut.data = self.__arrayBuffer |
|
|
1859 | 1857 | # |
|
|
1860 | 1858 | # self.__startIndex += self.__newNSamples |
|
|
1861 | 1859 | # |
|
|
1862 | 1860 | # return |
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