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1 | 1 | #from schainpy.model.data.jrodata import * |
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2 | 2 | # from schainpy.model.io.jrodataIO import * |
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3 | 3 | # from schainpy.model.proc.jroprocessing import * |
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4 | 4 | # from schainpy.model.graphics.jroplot import * |
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5 | 5 | # from schainpy.model.utils.jroutils import * |
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6 | 6 | # from schainpy.serializer import * |
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7 | 7 | |
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8 | 8 | from data import * |
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9 | 9 | from io import * |
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10 | 10 | from proc import * |
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11 | 11 | from graphics import * |
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12 | 12 | from utils import * |
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13 | ||
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14 | global_type_string = 'string' | |
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15 | global_type_integer = 'int' | |
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16 | global_type_floatList = 'floatList' | |
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17 | global_type_pairsList = 'pairsList' | |
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18 | global_type_boolean = 'bolean' | |
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19 | global_type_float = 'float' | |
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20 | global_type_colormap = 'colormap' | |
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21 | global_type_list = 'list' | |
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22 | global_type_float = 'float' | |
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1 | 1 | import os |
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2 | 2 | import numpy |
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3 | 3 | import time, datetime |
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4 | 4 | import mpldriver |
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5 | 5 | |
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6 | 6 | from schainpy.model.proc.jroproc_base import Operation |
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7 | 7 | |
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8 | 8 | def isTimeInHourRange(datatime, xmin, xmax): |
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9 | 9 | |
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10 | 10 | if xmin == None or xmax == None: |
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11 | 11 | return 1 |
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12 | 12 | hour = datatime.hour + datatime.minute/60.0 |
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13 | 13 | |
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14 | 14 | if xmin < (xmax % 24): |
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15 | 15 | |
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16 | 16 | if hour >= xmin and hour <= xmax: |
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17 | 17 | return 1 |
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18 | 18 | else: |
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19 | 19 | return 0 |
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20 | 20 | |
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21 | 21 | else: |
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22 | 22 | |
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23 | 23 | if hour >= xmin or hour <= (xmax % 24): |
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24 | 24 | return 1 |
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25 | 25 | else: |
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26 | 26 | return 0 |
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27 | 27 | |
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28 | 28 | return 0 |
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29 | 29 | |
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30 | 30 | def isRealtime(utcdatatime): |
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31 | 31 | |
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32 | 32 | utcnow = time.mktime(time.localtime()) |
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33 | 33 | delta = abs(utcnow - utcdatatime) # abs |
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34 | 34 | if delta >= 30.: |
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35 | 35 | return False |
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36 | 36 | return True |
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37 | 37 | |
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38 | 38 | class Figure(Operation): |
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39 | 39 | |
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40 | 40 | __driver = mpldriver |
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41 | 41 | fig = None |
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42 | 42 | |
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43 | 43 | id = None |
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44 | 44 | wintitle = None |
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45 | 45 | width = None |
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46 | 46 | height = None |
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47 | 47 | nplots = None |
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48 | 48 | timerange = None |
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49 | 49 | |
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50 | 50 | axesObjList = [] |
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51 | 51 | |
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52 | 52 | WIDTH = 300 |
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53 | 53 | HEIGHT = 200 |
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54 | 54 | PREFIX = 'fig' |
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55 | 55 | |
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56 | 56 | xmin = None |
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57 | 57 | xmax = None |
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58 | 58 | |
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59 | 59 | counter_imagwr = 0 |
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60 | 60 | |
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61 | 61 | figfile = None |
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62 | 62 | |
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63 | 63 | created = False |
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64 | ||
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64 | parameters = {} | |
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65 | 65 | def __init__(self, **kwargs): |
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66 | 66 | |
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67 | 67 | Operation.__init__(self, **kwargs) |
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68 | 68 | |
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69 | 69 | def __del__(self): |
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70 | 70 | |
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71 | 71 | self.__driver.closeFigure() |
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72 | 72 | |
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73 | 73 | def getFilename(self, name, ext='.png'): |
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74 | 74 | |
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75 | 75 | path = '%s%03d' %(self.PREFIX, self.id) |
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76 | 76 | filename = '%s_%s%s' %(self.PREFIX, name, ext) |
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77 | 77 | return os.path.join(path, filename) |
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78 | 78 | |
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79 | 79 | def getAxesObjList(self): |
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80 | 80 | |
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81 | 81 | return self.axesObjList |
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82 | 82 | |
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83 | 83 | def getSubplots(self): |
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84 | 84 | |
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85 | 85 | raise NotImplementedError |
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86 | 86 | |
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87 | 87 | def getScreenDim(self, widthplot, heightplot): |
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88 | 88 | |
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89 | 89 | nrow, ncol = self.getSubplots() |
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90 | 90 | |
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91 | 91 | widthscreen = widthplot*ncol |
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92 | 92 | heightscreen = heightplot*nrow |
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93 | 93 | |
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94 | 94 | return widthscreen, heightscreen |
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95 | 95 | |
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96 | 96 | def getTimeLim(self, x, xmin=None, xmax=None, timerange=None): |
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97 | 97 | |
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98 | 98 | # if self.xmin != None and self.xmax != None: |
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99 | 99 | # if timerange == None: |
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100 | 100 | # timerange = self.xmax - self.xmin |
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101 | 101 | # xmin = self.xmin + timerange |
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102 | 102 | # xmax = self.xmax + timerange |
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103 | 103 | # |
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104 | 104 | # return xmin, xmax |
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105 | 105 | |
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106 | 106 | if timerange == None and (xmin==None or xmax==None): |
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107 | 107 | timerange = 14400 #seconds |
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108 | 108 | |
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109 | 109 | if timerange != None: |
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110 | 110 | txmin = x[0] #- x[0] % min(timerange/10, 10*60) |
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111 | 111 | else: |
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112 | 112 | txmin = x[0] #- x[0] % 10*60 |
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113 | 113 | |
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114 | 114 | thisdatetime = datetime.datetime.utcfromtimestamp(txmin) |
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115 | 115 | thisdate = datetime.datetime.combine(thisdatetime.date(), datetime.time(0,0,0)) |
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116 | 116 | |
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117 | 117 | if timerange != None: |
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118 | 118 | xmin = (thisdatetime - thisdate).seconds/(60*60.) |
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119 | 119 | xmax = xmin + timerange/(60*60.) |
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120 | 120 | |
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121 | 121 | d1970 = datetime.datetime(1970,1,1) |
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122 | 122 | |
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123 | 123 | mindt = thisdate + datetime.timedelta(hours=xmin) #- datetime.timedelta(seconds=time.timezone) |
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124 | 124 | xmin_sec = (mindt - d1970).total_seconds() #time.mktime(mindt.timetuple()) - time.timezone |
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125 | 125 | |
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126 | 126 | maxdt = thisdate + datetime.timedelta(hours=xmax) #- datetime.timedelta(seconds=time.timezone) |
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127 | 127 | xmax_sec = (maxdt - d1970).total_seconds() #time.mktime(maxdt.timetuple()) - time.timezone |
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128 | 128 | |
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129 | 129 | return xmin_sec, xmax_sec |
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130 | 130 | |
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131 | 131 | def init(self, id, nplots, wintitle): |
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132 | 132 | |
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133 | 133 | raise NotImplementedError, "This method has been replaced by createFigure" |
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134 | 134 | |
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135 | 135 | def createFigure(self, id, wintitle, widthplot=None, heightplot=None, show=True): |
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136 | 136 | |
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137 | 137 | """ |
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138 | 138 | Crea la figura de acuerdo al driver y parametros seleccionados seleccionados. |
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139 | 139 | Las dimensiones de la pantalla es calculada a partir de los atributos self.WIDTH |
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140 | 140 | y self.HEIGHT y el numero de subplots (nrow, ncol) |
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141 | 141 | |
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142 | 142 | Input: |
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143 | 143 | id : Los parametros necesarios son |
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144 | 144 | wintitle : |
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145 | 145 | |
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146 | 146 | """ |
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147 | 147 | |
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148 | 148 | if widthplot == None: |
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149 | 149 | widthplot = self.WIDTH |
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150 | 150 | |
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151 | 151 | if heightplot == None: |
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152 | 152 | heightplot = self.HEIGHT |
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153 | 153 | |
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154 | 154 | self.id = id |
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155 | 155 | |
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156 | 156 | self.wintitle = wintitle |
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157 | 157 | |
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158 | 158 | self.widthscreen, self.heightscreen = self.getScreenDim(widthplot, heightplot) |
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159 | 159 | |
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160 | 160 | # if self.created: |
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161 | 161 | # self.__driver.closeFigure(self.fig) |
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162 | 162 | |
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163 | 163 | if not self.created: |
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164 | 164 | self.fig = self.__driver.createFigure(id=self.id, |
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165 | 165 | wintitle=self.wintitle, |
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166 | 166 | width=self.widthscreen, |
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167 | 167 | height=self.heightscreen, |
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168 | 168 | show=show) |
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169 | 169 | else: |
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170 | 170 | self.__driver.clearFigure(self.fig) |
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171 | 171 | |
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172 | 172 | self.axesObjList = [] |
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173 | 173 | self.counter_imagwr = 0 |
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174 | 174 | |
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175 | 175 | self.created = True |
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176 | 176 | |
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177 | 177 | def setDriver(self, driver=mpldriver): |
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178 | 178 | |
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179 | 179 | self.__driver = driver |
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180 | 180 | |
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181 | 181 | def setTitle(self, title): |
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182 | 182 | |
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183 | 183 | self.__driver.setTitle(self.fig, title) |
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184 | 184 | |
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185 | 185 | def setWinTitle(self, title): |
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186 | 186 | |
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187 | 187 | self.__driver.setWinTitle(self.fig, title=title) |
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188 | 188 | |
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189 | 189 | def setTextFromAxes(self, text): |
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190 | 190 | |
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191 | 191 | raise NotImplementedError, "This method has been replaced with Axes.setText" |
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192 | 192 | |
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193 | 193 | def makeAxes(self, nrow, ncol, xpos, ypos, colspan, rowspan): |
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194 | 194 | |
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195 | 195 | raise NotImplementedError, "This method has been replaced with Axes.addAxes" |
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196 | 196 | |
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197 | 197 | def addAxes(self, *args): |
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198 | 198 | """ |
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199 | 199 | |
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200 | 200 | Input: |
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201 | 201 | *args : Los parametros necesarios son |
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202 | 202 | nrow, ncol, xpos, ypos, colspan, rowspan |
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203 | 203 | """ |
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204 | 204 | |
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205 | 205 | axesObj = Axes(self.fig, *args) |
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206 | 206 | self.axesObjList.append(axesObj) |
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207 | 207 | |
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208 | 208 | def saveFigure(self, figpath, figfile, *args): |
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209 | 209 | |
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210 | 210 | filename = os.path.join(figpath, figfile) |
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211 | 211 | |
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212 | 212 | fullpath = os.path.split(filename)[0] |
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213 | 213 | |
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214 | 214 | if not os.path.exists(fullpath): |
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215 | 215 | subpath = os.path.split(fullpath)[0] |
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216 | 216 | |
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217 | 217 | if not os.path.exists(subpath): |
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218 | 218 | os.mkdir(subpath) |
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219 | 219 | |
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220 | 220 | os.mkdir(fullpath) |
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221 | 221 | |
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222 | 222 | self.__driver.saveFigure(self.fig, filename, *args) |
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223 | 223 | |
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224 | 224 | def save(self, figpath, figfile=None, save=True, ftp=False, wr_period=1, thisDatetime=None, update_figfile=True): |
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225 | 225 | |
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226 | 226 | self.counter_imagwr += 1 |
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227 | 227 | if self.counter_imagwr < wr_period: |
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228 | 228 | return |
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229 | 229 | |
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230 | 230 | self.counter_imagwr = 0 |
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231 | 231 | |
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232 | 232 | if save: |
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233 | 233 | |
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234 | 234 | if not figfile: |
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235 | 235 | |
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236 | 236 | if not thisDatetime: |
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237 | 237 | raise ValueError, "Saving figure: figfile or thisDatetime should be defined" |
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238 | 238 | return |
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239 | 239 | |
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240 | 240 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
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241 | 241 | figfile = self.getFilename(name = str_datetime) |
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242 | 242 | |
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243 | 243 | if self.figfile == None: |
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244 | 244 | self.figfile = figfile |
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245 | 245 | |
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246 | 246 | if update_figfile: |
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247 | 247 | self.figfile = figfile |
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248 | 248 | |
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249 | 249 | # store png plot to local folder |
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250 | 250 | self.saveFigure(figpath, self.figfile) |
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251 | 251 | |
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252 | 252 | |
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253 | 253 | if not ftp: |
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254 | 254 | return |
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255 | 255 | |
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256 | 256 | if not thisDatetime: |
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257 | 257 | return |
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258 | 258 | |
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259 | 259 | # store png plot to FTP server according to RT-Web format |
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260 | 260 | ftp_filename = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) |
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261 | 261 | # ftp_filename = os.path.join(figpath, name) |
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262 | 262 | self.saveFigure(figpath, ftp_filename) |
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263 | 263 | |
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264 | 264 | def getNameToFtp(self, thisDatetime, FTP_WEI, EXP_CODE, SUB_EXP_CODE, PLOT_CODE, PLOT_POS): |
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265 | 265 | YEAR_STR = '%4.4d'%thisDatetime.timetuple().tm_year |
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266 | 266 | DOY_STR = '%3.3d'%thisDatetime.timetuple().tm_yday |
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267 | 267 | FTP_WEI = '%2.2d'%FTP_WEI |
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268 | 268 | EXP_CODE = '%3.3d'%EXP_CODE |
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269 | 269 | SUB_EXP_CODE = '%2.2d'%SUB_EXP_CODE |
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270 | 270 | PLOT_CODE = '%2.2d'%PLOT_CODE |
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271 | 271 | PLOT_POS = '%2.2d'%PLOT_POS |
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272 | 272 | name = YEAR_STR + DOY_STR + FTP_WEI + EXP_CODE + SUB_EXP_CODE + PLOT_CODE + PLOT_POS |
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273 | 273 | return name |
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274 | 274 | |
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275 | 275 | def draw(self): |
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276 | 276 | |
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277 | 277 | self.__driver.draw(self.fig) |
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278 | 278 | |
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279 | 279 | def run(self): |
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280 | 280 | |
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281 | 281 | raise NotImplementedError |
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282 | 282 | |
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283 | 283 | def close(self, show=False): |
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284 | 284 | |
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285 | 285 | self.__driver.closeFigure(show=show, fig=self.fig) |
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286 | 286 | |
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287 | 287 | axesList = property(getAxesObjList) |
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288 | 288 | |
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289 | 289 | |
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290 | 290 | class Axes: |
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291 | 291 | |
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292 | 292 | __driver = mpldriver |
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293 | 293 | fig = None |
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294 | 294 | ax = None |
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295 | 295 | plot = None |
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296 | 296 | __missing = 1E30 |
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297 | 297 | __firsttime = None |
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298 | 298 | |
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299 | 299 | __showprofile = False |
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300 | 300 | |
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301 | 301 | xmin = None |
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302 | 302 | xmax = None |
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303 | 303 | ymin = None |
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304 | 304 | ymax = None |
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305 | 305 | zmin = None |
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306 | 306 | zmax = None |
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307 | 307 | |
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308 | 308 | x_buffer = None |
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309 | 309 | z_buffer = None |
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310 | 310 | |
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311 | 311 | decimationx = None |
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312 | 312 | decimationy = None |
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313 | 313 | |
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314 | 314 | __MAXNUMX = 200 |
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315 | 315 | __MAXNUMY = 400 |
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316 | 316 | |
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317 | 317 | __MAXNUMTIME = 500 |
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318 | 318 | |
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319 | 319 | def __init__(self, *args): |
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320 | 320 | |
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321 | 321 | """ |
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322 | 322 | |
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323 | 323 | Input: |
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324 | 324 | *args : Los parametros necesarios son |
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325 | 325 | fig, nrow, ncol, xpos, ypos, colspan, rowspan |
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326 | 326 | """ |
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327 | 327 | |
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328 | 328 | ax = self.__driver.createAxes(*args) |
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329 | 329 | self.fig = args[0] |
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330 | 330 | self.ax = ax |
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331 | 331 | self.plot = None |
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332 | 332 | |
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333 | 333 | self.__firsttime = True |
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334 | 334 | self.idlineList = [] |
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335 | 335 | |
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336 | 336 | self.x_buffer = numpy.array([]) |
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337 | 337 | self.z_buffer = numpy.array([]) |
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338 | 338 | |
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339 | 339 | def setText(self, text): |
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340 | 340 | |
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341 | 341 | self.__driver.setAxesText(self.ax, text) |
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342 | 342 | |
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343 | 343 | def setXAxisAsTime(self): |
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344 | 344 | pass |
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345 | 345 | |
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346 | 346 | def pline(self, x, y, |
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347 | 347 | xmin=None, xmax=None, |
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348 | 348 | ymin=None, ymax=None, |
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349 | 349 | xlabel='', ylabel='', |
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350 | 350 | title='', |
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351 | 351 | **kwargs): |
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352 | 352 | |
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353 | 353 | """ |
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354 | 354 | |
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355 | 355 | Input: |
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356 | 356 | x : |
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357 | 357 | y : |
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358 | 358 | xmin : |
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359 | 359 | xmax : |
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360 | 360 | ymin : |
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361 | 361 | ymax : |
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362 | 362 | xlabel : |
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363 | 363 | ylabel : |
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364 | 364 | title : |
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365 | 365 | **kwargs : Los parametros aceptados son |
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366 | 366 | |
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367 | 367 | ticksize |
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368 | 368 | ytick_visible |
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369 | 369 | """ |
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370 | 370 | |
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371 | 371 | if self.__firsttime: |
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372 | 372 | |
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373 | 373 | if xmin == None: xmin = numpy.nanmin(x) |
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374 | 374 | if xmax == None: xmax = numpy.nanmax(x) |
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375 | 375 | if ymin == None: ymin = numpy.nanmin(y) |
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376 | 376 | if ymax == None: ymax = numpy.nanmax(y) |
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377 | 377 | |
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378 | 378 | self.plot = self.__driver.createPline(self.ax, x, y, |
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379 | 379 | xmin, xmax, |
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380 | 380 | ymin, ymax, |
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381 | 381 | xlabel=xlabel, |
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382 | 382 | ylabel=ylabel, |
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383 | 383 | title=title, |
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384 | 384 | **kwargs) |
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385 | 385 | |
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386 | 386 | self.idlineList.append(0) |
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387 | 387 | self.__firsttime = False |
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388 | 388 | return |
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389 | 389 | |
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390 | 390 | self.__driver.pline(self.plot, x, y, xlabel=xlabel, |
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391 | 391 | ylabel=ylabel, |
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392 | 392 | title=title) |
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393 | 393 | |
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394 | 394 | # self.__driver.pause() |
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395 | 395 | |
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396 | 396 | def addpline(self, x, y, idline, **kwargs): |
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397 | 397 | lines = self.ax.lines |
|
|
398 | 398 | |
|
|
399 | 399 | if idline in self.idlineList: |
|
|
400 | 400 | self.__driver.set_linedata(self.ax, x, y, idline) |
|
|
401 | 401 | |
|
|
402 | 402 | if idline not in(self.idlineList): |
|
|
403 | 403 | self.__driver.addpline(self.ax, x, y, **kwargs) |
|
|
404 | 404 | self.idlineList.append(idline) |
|
|
405 | 405 | |
|
|
406 | 406 | return |
|
|
407 | 407 | |
|
|
408 | 408 | def pmultiline(self, x, y, |
|
|
409 | 409 | xmin=None, xmax=None, |
|
|
410 | 410 | ymin=None, ymax=None, |
|
|
411 | 411 | xlabel='', ylabel='', |
|
|
412 | 412 | title='', |
|
|
413 | 413 | **kwargs): |
|
|
414 | 414 | |
|
|
415 | 415 | if self.__firsttime: |
|
|
416 | 416 | |
|
|
417 | 417 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
418 | 418 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
419 | 419 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
420 | 420 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
421 | 421 | |
|
|
422 | 422 | self.plot = self.__driver.createPmultiline(self.ax, x, y, |
|
|
423 | 423 | xmin, xmax, |
|
|
424 | 424 | ymin, ymax, |
|
|
425 | 425 | xlabel=xlabel, |
|
|
426 | 426 | ylabel=ylabel, |
|
|
427 | 427 | title=title, |
|
|
428 | 428 | **kwargs) |
|
|
429 | 429 | self.__firsttime = False |
|
|
430 | 430 | return |
|
|
431 | 431 | |
|
|
432 | 432 | self.__driver.pmultiline(self.plot, x, y, xlabel=xlabel, |
|
|
433 | 433 | ylabel=ylabel, |
|
|
434 | 434 | title=title) |
|
|
435 | 435 | |
|
|
436 | 436 | # self.__driver.pause() |
|
|
437 | 437 | |
|
|
438 | 438 | def pmultilineyaxis(self, x, y, |
|
|
439 | 439 | xmin=None, xmax=None, |
|
|
440 | 440 | ymin=None, ymax=None, |
|
|
441 | 441 | xlabel='', ylabel='', |
|
|
442 | 442 | title='', |
|
|
443 | 443 | **kwargs): |
|
|
444 | 444 | |
|
|
445 | 445 | if self.__firsttime: |
|
|
446 | 446 | |
|
|
447 | 447 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
448 | 448 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
449 | 449 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
450 | 450 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
451 | 451 | |
|
|
452 | 452 | self.plot = self.__driver.createPmultilineYAxis(self.ax, x, y, |
|
|
453 | 453 | xmin, xmax, |
|
|
454 | 454 | ymin, ymax, |
|
|
455 | 455 | xlabel=xlabel, |
|
|
456 | 456 | ylabel=ylabel, |
|
|
457 | 457 | title=title, |
|
|
458 | 458 | **kwargs) |
|
|
459 | 459 | if self.xmin == None: self.xmin = xmin |
|
|
460 | 460 | if self.xmax == None: self.xmax = xmax |
|
|
461 | 461 | if self.ymin == None: self.ymin = ymin |
|
|
462 | 462 | if self.ymax == None: self.ymax = ymax |
|
|
463 | 463 | |
|
|
464 | 464 | self.__firsttime = False |
|
|
465 | 465 | return |
|
|
466 | 466 | |
|
|
467 | 467 | self.__driver.pmultilineyaxis(self.plot, x, y, xlabel=xlabel, |
|
|
468 | 468 | ylabel=ylabel, |
|
|
469 | 469 | title=title) |
|
|
470 | 470 | |
|
|
471 | 471 | # self.__driver.pause() |
|
|
472 | 472 | |
|
|
473 | 473 | def pcolor(self, x, y, z, |
|
|
474 | 474 | xmin=None, xmax=None, |
|
|
475 | 475 | ymin=None, ymax=None, |
|
|
476 | 476 | zmin=None, zmax=None, |
|
|
477 | 477 | xlabel='', ylabel='', |
|
|
478 | 478 | title='', colormap='jet', |
|
|
479 | 479 | **kwargs): |
|
|
480 | 480 | |
|
|
481 | 481 | """ |
|
|
482 | 482 | Input: |
|
|
483 | 483 | x : |
|
|
484 | 484 | y : |
|
|
485 | 485 | x : |
|
|
486 | 486 | xmin : |
|
|
487 | 487 | xmax : |
|
|
488 | 488 | ymin : |
|
|
489 | 489 | ymax : |
|
|
490 | 490 | zmin : |
|
|
491 | 491 | zmax : |
|
|
492 | 492 | xlabel : |
|
|
493 | 493 | ylabel : |
|
|
494 | 494 | title : |
|
|
495 | 495 | **kwargs : Los parametros aceptados son |
|
|
496 | 496 | ticksize=9, |
|
|
497 | 497 | cblabel='' |
|
|
498 | 498 | """ |
|
|
499 | 499 | |
|
|
500 | 500 | #Decimating data |
|
|
501 | 501 | xlen = len(x) |
|
|
502 | 502 | ylen = len(y) |
|
|
503 | 503 | |
|
|
504 | 504 | decimationx = int(xlen/self.__MAXNUMX) + 1 |
|
|
505 | 505 | decimationy = int(ylen/self.__MAXNUMY) + 1 |
|
|
506 | 506 | |
|
|
507 | 507 | |
|
|
508 | 508 | x_buffer = x#[::decimationx] |
|
|
509 | 509 | y_buffer = y#[::decimationy] |
|
|
510 | 510 | z_buffer = z#[::decimationx, ::decimationy] |
|
|
511 | 511 | #=================================================== |
|
|
512 | 512 | |
|
|
513 | 513 | if self.__firsttime: |
|
|
514 | 514 | |
|
|
515 | 515 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
516 | 516 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
517 | 517 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
518 | 518 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
519 | 519 | if zmin == None: zmin = numpy.nanmin(z) |
|
|
520 | 520 | if zmax == None: zmax = numpy.nanmax(z) |
|
|
521 | 521 | |
|
|
522 | 522 | |
|
|
523 | 523 | self.plot = self.__driver.createPcolor(self.ax, x_buffer, |
|
|
524 | 524 | y_buffer, |
|
|
525 | 525 | z_buffer, |
|
|
526 | 526 | xmin, xmax, |
|
|
527 | 527 | ymin, ymax, |
|
|
528 | 528 | zmin, zmax, |
|
|
529 | 529 | xlabel=xlabel, |
|
|
530 | 530 | ylabel=ylabel, |
|
|
531 | 531 | title=title, |
|
|
532 | 532 | colormap=colormap, |
|
|
533 | 533 | **kwargs) |
|
|
534 | 534 | |
|
|
535 | 535 | if self.xmin == None: self.xmin = xmin |
|
|
536 | 536 | if self.xmax == None: self.xmax = xmax |
|
|
537 | 537 | if self.ymin == None: self.ymin = ymin |
|
|
538 | 538 | if self.ymax == None: self.ymax = ymax |
|
|
539 | 539 | if self.zmin == None: self.zmin = zmin |
|
|
540 | 540 | if self.zmax == None: self.zmax = zmax |
|
|
541 | 541 | |
|
|
542 | 542 | self.__firsttime = False |
|
|
543 | 543 | return |
|
|
544 | 544 | |
|
|
545 | 545 | self.__driver.pcolor(self.plot, |
|
|
546 | 546 | z_buffer, |
|
|
547 | 547 | xlabel=xlabel, |
|
|
548 | 548 | ylabel=ylabel, |
|
|
549 | 549 | title=title) |
|
|
550 | 550 | |
|
|
551 | 551 | # self.__driver.pause() |
|
|
552 | 552 | |
|
|
553 | 553 | def pcolorbuffer(self, x, y, z, |
|
|
554 | 554 | xmin=None, xmax=None, |
|
|
555 | 555 | ymin=None, ymax=None, |
|
|
556 | 556 | zmin=None, zmax=None, |
|
|
557 | 557 | xlabel='', ylabel='', |
|
|
558 | 558 | title='', rti = True, colormap='jet', |
|
|
559 | 559 | maxNumX = None, maxNumY = None, |
|
|
560 | 560 | **kwargs): |
|
|
561 | 561 | |
|
|
562 | 562 | if maxNumX == None: |
|
|
563 | 563 | maxNumX = self.__MAXNUMTIME |
|
|
564 | 564 | |
|
|
565 | 565 | if maxNumY == None: |
|
|
566 | 566 | maxNumY = self.__MAXNUMY |
|
|
567 | 567 | |
|
|
568 | 568 | if self.__firsttime: |
|
|
569 | 569 | self.z_buffer = z |
|
|
570 | 570 | self.x_buffer = numpy.hstack((self.x_buffer, x)) |
|
|
571 | 571 | |
|
|
572 | 572 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
573 | 573 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
574 | 574 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
575 | 575 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
576 | 576 | if zmin == None: zmin = numpy.nanmin(z) |
|
|
577 | 577 | if zmax == None: zmax = numpy.nanmax(z) |
|
|
578 | 578 | |
|
|
579 | 579 | self.plot = self.__driver.createPcolor(self.ax, self.x_buffer, y, z, |
|
|
580 | 580 | xmin, xmax, |
|
|
581 | 581 | ymin, ymax, |
|
|
582 | 582 | zmin, zmax, |
|
|
583 | 583 | xlabel=xlabel, |
|
|
584 | 584 | ylabel=ylabel, |
|
|
585 | 585 | title=title, |
|
|
586 | 586 | colormap=colormap, |
|
|
587 | 587 | **kwargs) |
|
|
588 | 588 | |
|
|
589 | 589 | if self.xmin == None: self.xmin = xmin |
|
|
590 | 590 | if self.xmax == None: self.xmax = xmax |
|
|
591 | 591 | if self.ymin == None: self.ymin = ymin |
|
|
592 | 592 | if self.ymax == None: self.ymax = ymax |
|
|
593 | 593 | if self.zmin == None: self.zmin = zmin |
|
|
594 | 594 | if self.zmax == None: self.zmax = zmax |
|
|
595 | 595 | |
|
|
596 | 596 | self.__firsttime = False |
|
|
597 | 597 | return |
|
|
598 | 598 | |
|
|
599 | 599 | self.x_buffer = numpy.hstack((self.x_buffer[:-1], x[0], x[-1])) |
|
|
600 | 600 | self.z_buffer = numpy.hstack((self.z_buffer, z)) |
|
|
601 | 601 | z_buffer = self.z_buffer.reshape(-1,len(y)) |
|
|
602 | 602 | |
|
|
603 | 603 | #Decimating data |
|
|
604 | 604 | xlen = len(self.x_buffer) |
|
|
605 | 605 | ylen = len(y) |
|
|
606 | 606 | |
|
|
607 | 607 | decimationx = int(xlen/maxNumX) + 1 |
|
|
608 | 608 | decimationy = int(ylen/maxNumY) + 1 |
|
|
609 | 609 | |
|
|
610 | 610 | x_buffer = self.x_buffer#[::decimationx] |
|
|
611 | 611 | y_buffer = y#[::decimationy] |
|
|
612 | 612 | z_buffer = z_buffer#[::decimationx, ::decimationy] |
|
|
613 | 613 | #=================================================== |
|
|
614 | 614 | |
|
|
615 | 615 | x_buffer, y_buffer, z_buffer = self.__fillGaps(x_buffer, y_buffer, z_buffer) |
|
|
616 | 616 | |
|
|
617 | 617 | self.__driver.addpcolorbuffer(self.ax, x_buffer, y_buffer, z_buffer, self.zmin, self.zmax, |
|
|
618 | 618 | xlabel=xlabel, |
|
|
619 | 619 | ylabel=ylabel, |
|
|
620 | 620 | title=title, |
|
|
621 | 621 | colormap=colormap) |
|
|
622 | 622 | |
|
|
623 | 623 | # self.__driver.pause() |
|
|
624 | 624 | |
|
|
625 | 625 | def polar(self, x, y, |
|
|
626 | 626 | title='', xlabel='',ylabel='',**kwargs): |
|
|
627 | 627 | |
|
|
628 | 628 | if self.__firsttime: |
|
|
629 | 629 | self.plot = self.__driver.createPolar(self.ax, x, y, title = title, xlabel = xlabel, ylabel = ylabel) |
|
|
630 | 630 | self.__firsttime = False |
|
|
631 | 631 | self.x_buffer = x |
|
|
632 | 632 | self.y_buffer = y |
|
|
633 | 633 | return |
|
|
634 | 634 | |
|
|
635 | 635 | self.x_buffer = numpy.hstack((self.x_buffer,x)) |
|
|
636 | 636 | self.y_buffer = numpy.hstack((self.y_buffer,y)) |
|
|
637 | 637 | self.__driver.polar(self.plot, self.x_buffer, self.y_buffer, xlabel=xlabel, |
|
|
638 | 638 | ylabel=ylabel, |
|
|
639 | 639 | title=title) |
|
|
640 | 640 | |
|
|
641 | 641 | # self.__driver.pause() |
|
|
642 | 642 | |
|
|
643 | 643 | def __fillGaps(self, x_buffer, y_buffer, z_buffer): |
|
|
644 | 644 | |
|
|
645 | 645 | if x_buffer.shape[0] < 2: |
|
|
646 | 646 | return x_buffer, y_buffer, z_buffer |
|
|
647 | 647 | |
|
|
648 | 648 | deltas = x_buffer[1:] - x_buffer[0:-1] |
|
|
649 | 649 | x_median = numpy.median(deltas) |
|
|
650 | 650 | |
|
|
651 | 651 | index = numpy.where(deltas > 5*x_median) |
|
|
652 | 652 | |
|
|
653 | 653 | if len(index[0]) != 0: |
|
|
654 | 654 | z_buffer[index[0],::] = self.__missing |
|
|
655 | 655 | z_buffer = numpy.ma.masked_inside(z_buffer,0.99*self.__missing,1.01*self.__missing) |
|
|
656 | 656 | |
|
|
657 | 657 | return x_buffer, y_buffer, z_buffer |
| @@ -1,188 +1,215 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import numpy |
|
|
4 | 4 | import copy |
|
|
5 | ||
|
|
5 | from schainpy.model import * | |
|
|
6 | 6 | from figure import Figure, isRealtime |
|
|
7 | 7 | |
|
|
8 | 8 | class CorrelationPlot(Figure): |
|
|
9 | parameters = { | |
|
|
10 | 'id': global_type_string, | |
|
|
11 | 'wintitle': global_type_string, | |
|
|
12 | 'channelList': global_type_list, | |
|
|
13 | 'showprofile': global_type_string, | |
|
|
14 | 'xmin': global_type_float, | |
|
|
15 | 'xmax': global_type_float, | |
|
|
16 | 'ymin': global_type_float, | |
|
|
17 | 'ymax': global_type_float, | |
|
|
18 | 'zmin': global_type_float, | |
|
|
19 | 'zmax': global_type_float, | |
|
|
20 | 'save': global_type_boolean, | |
|
|
21 | 'figpath': global_type_string, | |
|
|
22 | 'figfile': global_type_string, | |
|
|
23 | 'show': global_type_boolean, | |
|
|
24 | 'ftp': global_type_boolean, | |
|
|
25 | 'wr_period': global_type_integer, | |
|
|
26 | 'server': global_type_string, | |
|
|
27 | 'folder': global_type_string, | |
|
|
28 | 'username': global_type_string, | |
|
|
29 | 'password': global_type_string, | |
|
|
30 | 'ftp_wei': global_type_integer, | |
|
|
31 | 'exp_code': global_type_integer, | |
|
|
32 | 'sub_exp_code': global_type_integer, | |
|
|
33 | 'plot_pos': global_type_integer, | |
|
|
34 | 'realtime': global_type_boolean, | |
|
|
35 | } | |
|
|
9 | 36 | |
|
|
10 | 37 | isConfig = None |
|
|
11 | 38 | __nsubplots = None |
|
|
12 | 39 | |
|
|
13 | 40 | WIDTHPROF = None |
|
|
14 | 41 | HEIGHTPROF = None |
|
|
15 | 42 | PREFIX = 'corr' |
|
|
16 | 43 | |
|
|
17 | 44 | def __init__(self, **kwargs): |
|
|
18 | 45 | Figure.__init__(self, **kwargs) |
|
|
19 | 46 | self.isConfig = False |
|
|
20 | 47 | self.__nsubplots = 1 |
|
|
21 | 48 | |
|
|
22 | 49 | self.WIDTH = 280 |
|
|
23 | 50 | self.HEIGHT = 250 |
|
|
24 | 51 | self.WIDTHPROF = 120 |
|
|
25 | 52 | self.HEIGHTPROF = 0 |
|
|
26 | 53 | self.counter_imagwr = 0 |
|
|
27 | 54 | |
|
|
28 | 55 | self.PLOT_CODE = 1 |
|
|
29 | 56 | self.FTP_WEI = None |
|
|
30 | 57 | self.EXP_CODE = None |
|
|
31 | 58 | self.SUB_EXP_CODE = None |
|
|
32 | 59 | self.PLOT_POS = None |
|
|
33 | 60 | |
|
|
34 | 61 | def getSubplots(self): |
|
|
35 | 62 | |
|
|
36 | 63 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
37 | 64 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
38 | 65 | |
|
|
39 | 66 | return nrow, ncol |
|
|
40 | 67 | |
|
|
41 | 68 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
42 | 69 | |
|
|
43 | 70 | showprofile = False |
|
|
44 | 71 | self.__showprofile = showprofile |
|
|
45 | 72 | self.nplots = nplots |
|
|
46 | 73 | |
|
|
47 | 74 | ncolspan = 1 |
|
|
48 | 75 | colspan = 1 |
|
|
49 | 76 | if showprofile: |
|
|
50 | 77 | ncolspan = 3 |
|
|
51 | 78 | colspan = 2 |
|
|
52 | 79 | self.__nsubplots = 2 |
|
|
53 | 80 | |
|
|
54 | 81 | self.createFigure(id = id, |
|
|
55 | 82 | wintitle = wintitle, |
|
|
56 | 83 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
57 | 84 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
58 | 85 | show=show) |
|
|
59 | 86 | |
|
|
60 | 87 | nrow, ncol = self.getSubplots() |
|
|
61 | 88 | |
|
|
62 | 89 | counter = 0 |
|
|
63 | 90 | for y in range(nrow): |
|
|
64 | 91 | for x in range(ncol): |
|
|
65 | 92 | |
|
|
66 | 93 | if counter >= self.nplots: |
|
|
67 | 94 | break |
|
|
68 | 95 | |
|
|
69 | 96 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
70 | 97 | |
|
|
71 | 98 | if showprofile: |
|
|
72 | 99 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
73 | 100 | |
|
|
74 | 101 | counter += 1 |
|
|
75 | 102 | |
|
|
76 | 103 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
77 | 104 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
78 | 105 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
79 | 106 | server=None, folder=None, username=None, password=None, |
|
|
80 | 107 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
81 | 108 | |
|
|
82 | 109 | """ |
|
|
83 | 110 | |
|
|
84 | 111 | Input: |
|
|
85 | 112 | dataOut : |
|
|
86 | 113 | id : |
|
|
87 | 114 | wintitle : |
|
|
88 | 115 | channelList : |
|
|
89 | 116 | showProfile : |
|
|
90 | 117 | xmin : None, |
|
|
91 | 118 | xmax : None, |
|
|
92 | 119 | ymin : None, |
|
|
93 | 120 | ymax : None, |
|
|
94 | 121 | zmin : None, |
|
|
95 | 122 | zmax : None |
|
|
96 | 123 | """ |
|
|
97 | 124 | |
|
|
98 | 125 | if dataOut.flagNoData: |
|
|
99 | 126 | return None |
|
|
100 | 127 | |
|
|
101 | 128 | if realtime: |
|
|
102 | 129 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
103 | 130 | print 'Skipping this plot function' |
|
|
104 | 131 | return |
|
|
105 | 132 | |
|
|
106 | 133 | if channelList == None: |
|
|
107 | 134 | channelIndexList = dataOut.channelIndexList |
|
|
108 | 135 | else: |
|
|
109 | 136 | channelIndexList = [] |
|
|
110 | 137 | for channel in channelList: |
|
|
111 | 138 | if channel not in dataOut.channelList: |
|
|
112 | 139 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
113 | 140 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
114 | 141 | |
|
|
115 | 142 | factor = dataOut.normFactor |
|
|
116 | 143 | lenfactor = factor.shape[1] |
|
|
117 | 144 | x = dataOut.getLagTRange(1) |
|
|
118 | 145 | y = dataOut.getHeiRange() |
|
|
119 | 146 | |
|
|
120 | 147 | z = copy.copy(dataOut.data_corr[:,:,0,:]) |
|
|
121 | 148 | for i in range(dataOut.data_corr.shape[0]): |
|
|
122 | 149 | z[i,:,:] = z[i,:,:]/factor[i,:] |
|
|
123 | 150 | zdB = numpy.abs(z) |
|
|
124 | 151 | |
|
|
125 | 152 | avg = numpy.average(z, axis=1) |
|
|
126 | 153 | # avg = numpy.nanmean(z, axis=1) |
|
|
127 | 154 | # noise = dataOut.noise/factor |
|
|
128 | 155 | |
|
|
129 | 156 | #thisDatetime = dataOut.datatime |
|
|
130 | 157 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
131 | 158 | title = wintitle + " Correlation" |
|
|
132 | 159 | xlabel = "Lag T (s)" |
|
|
133 | 160 | ylabel = "Range (Km)" |
|
|
134 | 161 | |
|
|
135 | 162 | if not self.isConfig: |
|
|
136 | 163 | |
|
|
137 | 164 | nplots = dataOut.data_corr.shape[0] |
|
|
138 | 165 | |
|
|
139 | 166 | self.setup(id=id, |
|
|
140 | 167 | nplots=nplots, |
|
|
141 | 168 | wintitle=wintitle, |
|
|
142 | 169 | showprofile=showprofile, |
|
|
143 | 170 | show=show) |
|
|
144 | 171 | |
|
|
145 | 172 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
146 | 173 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
147 | 174 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
148 | 175 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
149 | 176 | if zmin == None: zmin = 0 |
|
|
150 | 177 | if zmax == None: zmax = 1 |
|
|
151 | 178 | |
|
|
152 | 179 | self.FTP_WEI = ftp_wei |
|
|
153 | 180 | self.EXP_CODE = exp_code |
|
|
154 | 181 | self.SUB_EXP_CODE = sub_exp_code |
|
|
155 | 182 | self.PLOT_POS = plot_pos |
|
|
156 | 183 | |
|
|
157 | 184 | self.isConfig = True |
|
|
158 | 185 | |
|
|
159 | 186 | self.setWinTitle(title) |
|
|
160 | 187 | |
|
|
161 | 188 | for i in range(self.nplots): |
|
|
162 | 189 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
163 | 190 | title = "Channel %d and %d: : %s" %(dataOut.pairsList[i][0],dataOut.pairsList[i][1] , str_datetime) |
|
|
164 | 191 | axes = self.axesList[i*self.__nsubplots] |
|
|
165 | 192 | axes.pcolor(x, y, zdB[i,:,:], |
|
|
166 | 193 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
167 | 194 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
168 | 195 | ticksize=9, cblabel='') |
|
|
169 | 196 | |
|
|
170 | 197 | # if self.__showprofile: |
|
|
171 | 198 | # axes = self.axesList[i*self.__nsubplots +1] |
|
|
172 | 199 | # axes.pline(avgdB[i], y, |
|
|
173 | 200 | # xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
174 | 201 | # xlabel='dB', ylabel='', title='', |
|
|
175 | 202 | # ytick_visible=False, |
|
|
176 | 203 | # grid='x') |
|
|
177 | 204 | # |
|
|
178 | 205 | # noiseline = numpy.repeat(noisedB[i], len(y)) |
|
|
179 | 206 | # axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
180 | 207 | |
|
|
181 | 208 | self.draw() |
|
|
182 | 209 | |
|
|
183 | 210 | self.save(figpath=figpath, |
|
|
184 | 211 | figfile=figfile, |
|
|
185 | 212 | save=save, |
|
|
186 | 213 | ftp=ftp, |
|
|
187 | 214 | wr_period=wr_period, |
|
|
188 | 215 | thisDatetime=thisDatetime) |
| @@ -1,1945 +1,2077 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import numpy |
|
|
4 | 4 | import inspect |
|
|
5 | 5 | from figure import Figure, isRealtime, isTimeInHourRange |
|
|
6 | 6 | from plotting_codes import * |
|
|
7 | 7 | |
|
|
8 | 8 | |
|
|
9 | 9 | class MomentsPlot(Figure): |
|
|
10 | 10 | |
|
|
11 | 11 | isConfig = None |
|
|
12 | 12 | __nsubplots = None |
|
|
13 | 13 | |
|
|
14 | 14 | WIDTHPROF = None |
|
|
15 | 15 | HEIGHTPROF = None |
|
|
16 | 16 | PREFIX = 'prm' |
|
|
17 | 17 | |
|
|
18 | parameters = { | |
|
|
19 | 'id': global_type_string, | |
|
|
20 | 'wintitle': global_type_string, | |
|
|
21 | 'channelList': global_type_list, | |
|
|
22 | 'showprofile': global_type_boolean, | |
|
|
23 | 'xmin': global_type_float, | |
|
|
24 | 'xmax': global_type_float, | |
|
|
25 | 'ymin': global_type_float, | |
|
|
26 | 'ymax': global_type_float, | |
|
|
27 | 'zmin': global_type_float, | |
|
|
28 | 'zmax': global_type_float, | |
|
|
29 | 'save': global_type_boolean, | |
|
|
30 | 'figpath': global_type_string, | |
|
|
31 | 'figfile': global_type_string, | |
|
|
32 | 'show': global_type_boolean, | |
|
|
33 | 'ftp': global_type_boolean, | |
|
|
34 | 'wr_period': global_type_integer, | |
|
|
35 | 'server': global_type_string, | |
|
|
36 | 'folder': global_type_string, | |
|
|
37 | 'username': global_type_string, | |
|
|
38 | 'password': global_type_string, | |
|
|
39 | 'ftp_wei': global_type_string, | |
|
|
40 | 'exp_code': global_type_integer, | |
|
|
41 | 'sub_exp_code': global_type_integer, | |
|
|
42 | 'plot_pos': global_type_integer, | |
|
|
43 | 'realtime': global_type_boolean, | |
|
|
44 | } | |
|
|
45 | ||
|
|
18 | 46 | def __init__(self, **kwargs): |
|
|
19 | 47 | Figure.__init__(self, **kwargs) |
|
|
20 | 48 | self.isConfig = False |
|
|
21 | 49 | self.__nsubplots = 1 |
|
|
22 | 50 | |
|
|
23 | 51 | self.WIDTH = 280 |
|
|
24 | 52 | self.HEIGHT = 250 |
|
|
25 | 53 | self.WIDTHPROF = 120 |
|
|
26 | 54 | self.HEIGHTPROF = 0 |
|
|
27 | 55 | self.counter_imagwr = 0 |
|
|
28 | 56 | |
|
|
29 | 57 | self.PLOT_CODE = MOMENTS_CODE |
|
|
30 | 58 | |
|
|
31 | 59 | self.FTP_WEI = None |
|
|
32 | 60 | self.EXP_CODE = None |
|
|
33 | 61 | self.SUB_EXP_CODE = None |
|
|
34 | 62 | self.PLOT_POS = None |
|
|
35 | 63 | |
|
|
36 | 64 | def getSubplots(self): |
|
|
37 | 65 | |
|
|
38 | 66 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
39 | 67 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
40 | 68 | |
|
|
41 | 69 | return nrow, ncol |
|
|
42 | 70 | |
|
|
43 | 71 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
44 | 72 | |
|
|
45 | 73 | self.__showprofile = showprofile |
|
|
46 | 74 | self.nplots = nplots |
|
|
47 | 75 | |
|
|
48 | 76 | ncolspan = 1 |
|
|
49 | 77 | colspan = 1 |
|
|
50 | 78 | if showprofile: |
|
|
51 | 79 | ncolspan = 3 |
|
|
52 | 80 | colspan = 2 |
|
|
53 | 81 | self.__nsubplots = 2 |
|
|
54 | 82 | |
|
|
55 | 83 | self.createFigure(id = id, |
|
|
56 | 84 | wintitle = wintitle, |
|
|
57 | 85 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
58 | 86 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
59 | 87 | show=show) |
|
|
60 | 88 | |
|
|
61 | 89 | nrow, ncol = self.getSubplots() |
|
|
62 | 90 | |
|
|
63 | 91 | counter = 0 |
|
|
64 | 92 | for y in range(nrow): |
|
|
65 | 93 | for x in range(ncol): |
|
|
66 | 94 | |
|
|
67 | 95 | if counter >= self.nplots: |
|
|
68 | 96 | break |
|
|
69 | 97 | |
|
|
70 | 98 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
71 | 99 | |
|
|
72 | 100 | if showprofile: |
|
|
73 | 101 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
74 | 102 | |
|
|
75 | 103 | counter += 1 |
|
|
76 | 104 | |
|
|
77 | 105 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
78 | 106 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
79 | 107 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
80 | 108 | server=None, folder=None, username=None, password=None, |
|
|
81 | 109 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
82 | 110 | |
|
|
83 | 111 | """ |
|
|
84 | 112 | |
|
|
85 | 113 | Input: |
|
|
86 | 114 | dataOut : |
|
|
87 | 115 | id : |
|
|
88 | 116 | wintitle : |
|
|
89 | 117 | channelList : |
|
|
90 | 118 | showProfile : |
|
|
91 | 119 | xmin : None, |
|
|
92 | 120 | xmax : None, |
|
|
93 | 121 | ymin : None, |
|
|
94 | 122 | ymax : None, |
|
|
95 | 123 | zmin : None, |
|
|
96 | 124 | zmax : None |
|
|
97 | 125 | """ |
|
|
98 | 126 | |
|
|
99 | 127 | if dataOut.flagNoData: |
|
|
100 | 128 | return None |
|
|
101 | 129 | |
|
|
102 | 130 | if realtime: |
|
|
103 | 131 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
104 | 132 | print 'Skipping this plot function' |
|
|
105 | 133 | return |
|
|
106 | 134 | |
|
|
107 | 135 | if channelList == None: |
|
|
108 | 136 | channelIndexList = dataOut.channelIndexList |
|
|
109 | 137 | else: |
|
|
110 | 138 | channelIndexList = [] |
|
|
111 | 139 | for channel in channelList: |
|
|
112 | 140 | if channel not in dataOut.channelList: |
|
|
113 | 141 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
114 | 142 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
115 | 143 | |
|
|
116 | 144 | factor = dataOut.normFactor |
|
|
117 | 145 | x = dataOut.abscissaList |
|
|
118 | 146 | y = dataOut.heightList |
|
|
119 | 147 | |
|
|
120 | 148 | z = dataOut.data_pre[channelIndexList,:,:]/factor |
|
|
121 | 149 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
122 | 150 | avg = numpy.average(z, axis=1) |
|
|
123 | 151 | noise = dataOut.noise/factor |
|
|
124 | 152 | |
|
|
125 | 153 | zdB = 10*numpy.log10(z) |
|
|
126 | 154 | avgdB = 10*numpy.log10(avg) |
|
|
127 | 155 | noisedB = 10*numpy.log10(noise) |
|
|
128 | 156 | |
|
|
129 | 157 | #thisDatetime = dataOut.datatime |
|
|
130 | 158 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
131 | 159 | title = wintitle + " Parameters" |
|
|
132 | 160 | xlabel = "Velocity (m/s)" |
|
|
133 | 161 | ylabel = "Range (Km)" |
|
|
134 | 162 | |
|
|
135 | 163 | update_figfile = False |
|
|
136 | 164 | |
|
|
137 | 165 | if not self.isConfig: |
|
|
138 | 166 | |
|
|
139 | 167 | nplots = len(channelIndexList) |
|
|
140 | 168 | |
|
|
141 | 169 | self.setup(id=id, |
|
|
142 | 170 | nplots=nplots, |
|
|
143 | 171 | wintitle=wintitle, |
|
|
144 | 172 | showprofile=showprofile, |
|
|
145 | 173 | show=show) |
|
|
146 | 174 | |
|
|
147 | 175 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
148 | 176 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
149 | 177 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
150 | 178 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
151 | 179 | if zmin == None: zmin = numpy.nanmin(avgdB)*0.9 |
|
|
152 | 180 | if zmax == None: zmax = numpy.nanmax(avgdB)*0.9 |
|
|
153 | 181 | |
|
|
154 | 182 | self.FTP_WEI = ftp_wei |
|
|
155 | 183 | self.EXP_CODE = exp_code |
|
|
156 | 184 | self.SUB_EXP_CODE = sub_exp_code |
|
|
157 | 185 | self.PLOT_POS = plot_pos |
|
|
158 | 186 | |
|
|
159 | 187 | self.isConfig = True |
|
|
160 | 188 | update_figfile = True |
|
|
161 | 189 | |
|
|
162 | 190 | self.setWinTitle(title) |
|
|
163 | 191 | |
|
|
164 | 192 | for i in range(self.nplots): |
|
|
165 | 193 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
166 | 194 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[i], noisedB[i], str_datetime) |
|
|
167 | 195 | axes = self.axesList[i*self.__nsubplots] |
|
|
168 | 196 | axes.pcolor(x, y, zdB[i,:,:], |
|
|
169 | 197 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
170 | 198 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
171 | 199 | ticksize=9, cblabel='') |
|
|
172 | 200 | #Mean Line |
|
|
173 | 201 | mean = dataOut.data_param[i, 1, :] |
|
|
174 | 202 | axes.addpline(mean, y, idline=0, color="black", linestyle="solid", lw=1) |
|
|
175 | 203 | |
|
|
176 | 204 | if self.__showprofile: |
|
|
177 | 205 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
178 | 206 | axes.pline(avgdB[i], y, |
|
|
179 | 207 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
180 | 208 | xlabel='dB', ylabel='', title='', |
|
|
181 | 209 | ytick_visible=False, |
|
|
182 | 210 | grid='x') |
|
|
183 | 211 | |
|
|
184 | 212 | noiseline = numpy.repeat(noisedB[i], len(y)) |
|
|
185 | 213 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
186 | 214 | |
|
|
187 | 215 | self.draw() |
|
|
188 | 216 | |
|
|
189 | 217 | self.save(figpath=figpath, |
|
|
190 | 218 | figfile=figfile, |
|
|
191 | 219 | save=save, |
|
|
192 | 220 | ftp=ftp, |
|
|
193 | 221 | wr_period=wr_period, |
|
|
194 | 222 | thisDatetime=thisDatetime) |
|
|
195 | 223 | |
|
|
196 | 224 | |
|
|
197 | 225 | |
|
|
198 | 226 | class SkyMapPlot(Figure): |
|
|
199 | 227 | |
|
|
200 | 228 | __isConfig = None |
|
|
201 | 229 | __nsubplots = None |
|
|
202 | 230 | |
|
|
203 | 231 | WIDTHPROF = None |
|
|
204 | 232 | HEIGHTPROF = None |
|
|
205 | 233 | PREFIX = 'mmap' |
|
|
206 | 234 | |
|
|
207 | 235 | def __init__(self, **kwargs): |
|
|
208 | 236 | Figure.__init__(self, **kwargs) |
|
|
209 | 237 | self.isConfig = False |
|
|
210 | 238 | self.__nsubplots = 1 |
|
|
211 | 239 | |
|
|
212 | 240 | # self.WIDTH = 280 |
|
|
213 | 241 | # self.HEIGHT = 250 |
|
|
214 | 242 | self.WIDTH = 600 |
|
|
215 | 243 | self.HEIGHT = 600 |
|
|
216 | 244 | self.WIDTHPROF = 120 |
|
|
217 | 245 | self.HEIGHTPROF = 0 |
|
|
218 | 246 | self.counter_imagwr = 0 |
|
|
219 | 247 | |
|
|
220 | 248 | self.PLOT_CODE = MSKYMAP_CODE |
|
|
221 | 249 | |
|
|
222 | 250 | self.FTP_WEI = None |
|
|
223 | 251 | self.EXP_CODE = None |
|
|
224 | 252 | self.SUB_EXP_CODE = None |
|
|
225 | 253 | self.PLOT_POS = None |
|
|
226 | 254 | |
|
|
227 | 255 | def getSubplots(self): |
|
|
228 | 256 | |
|
|
229 | 257 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
230 | 258 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
231 | 259 | |
|
|
232 | 260 | return nrow, ncol |
|
|
233 | 261 | |
|
|
234 | 262 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
235 | 263 | |
|
|
236 | 264 | self.__showprofile = showprofile |
|
|
237 | 265 | self.nplots = nplots |
|
|
238 | 266 | |
|
|
239 | 267 | ncolspan = 1 |
|
|
240 | 268 | colspan = 1 |
|
|
241 | 269 | |
|
|
242 | 270 | self.createFigure(id = id, |
|
|
243 | 271 | wintitle = wintitle, |
|
|
244 | 272 | widthplot = self.WIDTH, #+ self.WIDTHPROF, |
|
|
245 | 273 | heightplot = self.HEIGHT,# + self.HEIGHTPROF, |
|
|
246 | 274 | show=show) |
|
|
247 | 275 | |
|
|
248 | 276 | nrow, ncol = 1,1 |
|
|
249 | 277 | counter = 0 |
|
|
250 | 278 | x = 0 |
|
|
251 | 279 | y = 0 |
|
|
252 | 280 | self.addAxes(1, 1, 0, 0, 1, 1, True) |
|
|
253 | 281 | |
|
|
254 | 282 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
255 | 283 | tmin=0, tmax=24, timerange=None, |
|
|
256 | 284 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
257 | 285 | server=None, folder=None, username=None, password=None, |
|
|
258 | 286 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
259 | 287 | |
|
|
260 | 288 | """ |
|
|
261 | 289 | |
|
|
262 | 290 | Input: |
|
|
263 | 291 | dataOut : |
|
|
264 | 292 | id : |
|
|
265 | 293 | wintitle : |
|
|
266 | 294 | channelList : |
|
|
267 | 295 | showProfile : |
|
|
268 | 296 | xmin : None, |
|
|
269 | 297 | xmax : None, |
|
|
270 | 298 | ymin : None, |
|
|
271 | 299 | ymax : None, |
|
|
272 | 300 | zmin : None, |
|
|
273 | 301 | zmax : None |
|
|
274 | 302 | """ |
|
|
275 | 303 | |
|
|
276 | 304 | arrayParameters = dataOut.data_param |
|
|
277 | 305 | error = arrayParameters[:,-1] |
|
|
278 | 306 | indValid = numpy.where(error == 0)[0] |
|
|
279 | 307 | finalMeteor = arrayParameters[indValid,:] |
|
|
280 | 308 | finalAzimuth = finalMeteor[:,3] |
|
|
281 | 309 | finalZenith = finalMeteor[:,4] |
|
|
282 | 310 | |
|
|
283 | 311 | x = finalAzimuth*numpy.pi/180 |
|
|
284 | 312 | y = finalZenith |
|
|
285 | 313 | x1 = [dataOut.ltctime, dataOut.ltctime] |
|
|
286 | 314 | |
|
|
287 | 315 | #thisDatetime = dataOut.datatime |
|
|
288 | 316 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
289 | 317 | title = wintitle + " Parameters" |
|
|
290 | 318 | xlabel = "Zonal Zenith Angle (deg) " |
|
|
291 | 319 | ylabel = "Meridional Zenith Angle (deg)" |
|
|
292 | 320 | update_figfile = False |
|
|
293 | 321 | |
|
|
294 | 322 | if not self.isConfig: |
|
|
295 | 323 | |
|
|
296 | 324 | nplots = 1 |
|
|
297 | 325 | |
|
|
298 | 326 | self.setup(id=id, |
|
|
299 | 327 | nplots=nplots, |
|
|
300 | 328 | wintitle=wintitle, |
|
|
301 | 329 | showprofile=showprofile, |
|
|
302 | 330 | show=show) |
|
|
303 | 331 | |
|
|
304 | 332 | if self.xmin is None and self.xmax is None: |
|
|
305 | 333 | self.xmin, self.xmax = self.getTimeLim(x1, tmin, tmax, timerange) |
|
|
306 | 334 | |
|
|
307 | 335 | if timerange != None: |
|
|
308 | 336 | self.timerange = timerange |
|
|
309 | 337 | else: |
|
|
310 | 338 | self.timerange = self.xmax - self.xmin |
|
|
311 | 339 | |
|
|
312 | 340 | self.FTP_WEI = ftp_wei |
|
|
313 | 341 | self.EXP_CODE = exp_code |
|
|
314 | 342 | self.SUB_EXP_CODE = sub_exp_code |
|
|
315 | 343 | self.PLOT_POS = plot_pos |
|
|
316 | 344 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
317 | 345 | self.firstdate = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
318 | 346 | self.isConfig = True |
|
|
319 | 347 | update_figfile = True |
|
|
320 | 348 | |
|
|
321 | 349 | self.setWinTitle(title) |
|
|
322 | 350 | |
|
|
323 | 351 | i = 0 |
|
|
324 | 352 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
325 | 353 | |
|
|
326 | 354 | axes = self.axesList[i*self.__nsubplots] |
|
|
327 | 355 | nevents = axes.x_buffer.shape[0] + x.shape[0] |
|
|
328 | 356 | title = "Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n" %(self.firstdate,str_datetime,nevents) |
|
|
329 | 357 | axes.polar(x, y, |
|
|
330 | 358 | title=title, xlabel=xlabel, ylabel=ylabel, |
|
|
331 | 359 | ticksize=9, cblabel='') |
|
|
332 | 360 | |
|
|
333 | 361 | self.draw() |
|
|
334 | 362 | |
|
|
335 | 363 | self.save(figpath=figpath, |
|
|
336 | 364 | figfile=figfile, |
|
|
337 | 365 | save=save, |
|
|
338 | 366 | ftp=ftp, |
|
|
339 | 367 | wr_period=wr_period, |
|
|
340 | 368 | thisDatetime=thisDatetime, |
|
|
341 | 369 | update_figfile=update_figfile) |
|
|
342 | 370 | |
|
|
343 | 371 | if dataOut.ltctime >= self.xmax: |
|
|
344 | 372 | self.isConfigmagwr = wr_period |
|
|
345 | 373 | self.isConfig = False |
|
|
346 | 374 | update_figfile = True |
|
|
347 | 375 | axes.__firsttime = True |
|
|
348 | 376 | self.xmin += self.timerange |
|
|
349 | 377 | self.xmax += self.timerange |
|
|
350 | 378 | |
|
|
351 | 379 | |
|
|
352 | 380 | |
|
|
353 | 381 | |
|
|
354 | 382 | class WindProfilerPlot(Figure): |
|
|
355 | 383 | |
|
|
356 | 384 | __isConfig = None |
|
|
357 | 385 | __nsubplots = None |
|
|
358 | 386 | |
|
|
359 | 387 | WIDTHPROF = None |
|
|
360 | 388 | HEIGHTPROF = None |
|
|
361 | 389 | PREFIX = 'wind' |
|
|
362 | 390 | |
|
|
363 | 391 | def __init__(self, **kwargs): |
|
|
364 | 392 | Figure.__init__(self, **kwargs) |
|
|
365 | 393 | self.timerange = None |
|
|
366 | 394 | self.isConfig = False |
|
|
367 | 395 | self.__nsubplots = 1 |
|
|
368 | 396 | |
|
|
369 | 397 | self.WIDTH = 800 |
|
|
370 | 398 | self.HEIGHT = 300 |
|
|
371 | 399 | self.WIDTHPROF = 120 |
|
|
372 | 400 | self.HEIGHTPROF = 0 |
|
|
373 | 401 | self.counter_imagwr = 0 |
|
|
374 | 402 | |
|
|
375 | 403 | self.PLOT_CODE = WIND_CODE |
|
|
376 | 404 | |
|
|
377 | 405 | self.FTP_WEI = None |
|
|
378 | 406 | self.EXP_CODE = None |
|
|
379 | 407 | self.SUB_EXP_CODE = None |
|
|
380 | 408 | self.PLOT_POS = None |
|
|
381 | 409 | self.tmin = None |
|
|
382 | 410 | self.tmax = None |
|
|
383 | 411 | |
|
|
384 | 412 | self.xmin = None |
|
|
385 | 413 | self.xmax = None |
|
|
386 | 414 | |
|
|
387 | 415 | self.figfile = None |
|
|
388 | 416 | |
|
|
389 | 417 | def getSubplots(self): |
|
|
390 | 418 | |
|
|
391 | 419 | ncol = 1 |
|
|
392 | 420 | nrow = self.nplots |
|
|
393 | 421 | |
|
|
394 | 422 | return nrow, ncol |
|
|
395 | 423 | |
|
|
396 | 424 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
397 | 425 | |
|
|
398 | 426 | self.__showprofile = showprofile |
|
|
399 | 427 | self.nplots = nplots |
|
|
400 | 428 | |
|
|
401 | 429 | ncolspan = 1 |
|
|
402 | 430 | colspan = 1 |
|
|
403 | 431 | |
|
|
404 | 432 | self.createFigure(id = id, |
|
|
405 | 433 | wintitle = wintitle, |
|
|
406 | 434 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
407 | 435 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
408 | 436 | show=show) |
|
|
409 | 437 | |
|
|
410 | 438 | nrow, ncol = self.getSubplots() |
|
|
411 | 439 | |
|
|
412 | 440 | counter = 0 |
|
|
413 | 441 | for y in range(nrow): |
|
|
414 | 442 | if counter >= self.nplots: |
|
|
415 | 443 | break |
|
|
416 | 444 | |
|
|
417 | 445 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
418 | 446 | counter += 1 |
|
|
419 | 447 | |
|
|
420 | 448 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='False', |
|
|
421 | 449 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
422 | 450 | zmax_ver = None, zmin_ver = None, SNRmin = None, SNRmax = None, |
|
|
423 | 451 | timerange=None, SNRthresh = None, |
|
|
424 | 452 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
425 | 453 | server=None, folder=None, username=None, password=None, |
|
|
426 | 454 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
427 | 455 | """ |
|
|
428 | 456 | |
|
|
429 | 457 | Input: |
|
|
430 | 458 | dataOut : |
|
|
431 | 459 | id : |
|
|
432 | 460 | wintitle : |
|
|
433 | 461 | channelList : |
|
|
434 | 462 | showProfile : |
|
|
435 | 463 | xmin : None, |
|
|
436 | 464 | xmax : None, |
|
|
437 | 465 | ymin : None, |
|
|
438 | 466 | ymax : None, |
|
|
439 | 467 | zmin : None, |
|
|
440 | 468 | zmax : None |
|
|
441 | 469 | """ |
|
|
442 | 470 | |
|
|
443 | 471 | # if timerange is not None: |
|
|
444 | 472 | # self.timerange = timerange |
|
|
445 | 473 | # |
|
|
446 | 474 | # tmin = None |
|
|
447 | 475 | # tmax = None |
|
|
448 | 476 | |
|
|
449 | 477 | |
|
|
450 | 478 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
|
|
451 | 479 | y = dataOut.heightList |
|
|
452 | 480 | z = dataOut.data_output.copy() |
|
|
453 | 481 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
454 | 482 | nplotsw = nplots |
|
|
455 | 483 | |
|
|
456 | 484 | |
|
|
457 | 485 | #If there is a SNR function defined |
|
|
458 | 486 | if dataOut.data_SNR is not None: |
|
|
459 | 487 | nplots += 1 |
|
|
460 | 488 | SNR = dataOut.data_SNR |
|
|
461 | 489 | SNRavg = numpy.average(SNR, axis=0) |
|
|
462 | 490 | |
|
|
463 | 491 | SNRdB = 10*numpy.log10(SNR) |
|
|
464 | 492 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
465 | 493 | |
|
|
466 | 494 | if SNRthresh == None: SNRthresh = -5.0 |
|
|
467 | 495 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
468 | 496 | |
|
|
469 | 497 | for i in range(nplotsw): |
|
|
470 | 498 | z[i,ind] = numpy.nan |
|
|
471 | 499 | |
|
|
472 | 500 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
473 | 501 | #thisDatetime = datetime.datetime.now() |
|
|
474 | 502 | title = wintitle + "Wind" |
|
|
475 | 503 | xlabel = "" |
|
|
476 | 504 | ylabel = "Height (km)" |
|
|
477 | 505 | update_figfile = False |
|
|
478 | 506 | |
|
|
479 | 507 | if not self.isConfig: |
|
|
480 | 508 | |
|
|
481 | 509 | self.setup(id=id, |
|
|
482 | 510 | nplots=nplots, |
|
|
483 | 511 | wintitle=wintitle, |
|
|
484 | 512 | showprofile=showprofile, |
|
|
485 | 513 | show=show) |
|
|
486 | 514 | |
|
|
487 | 515 | if timerange is not None: |
|
|
488 | 516 | self.timerange = timerange |
|
|
489 | 517 | |
|
|
490 | 518 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
491 | 519 | |
|
|
492 | 520 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
493 | 521 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
494 | 522 | |
|
|
495 | 523 | if zmax == None: zmax = numpy.nanmax(abs(z[range(2),:])) |
|
|
496 | 524 | #if numpy.isnan(zmax): zmax = 50 |
|
|
497 | 525 | if zmin == None: zmin = -zmax |
|
|
498 | 526 | |
|
|
499 | 527 | if nplotsw == 3: |
|
|
500 | 528 | if zmax_ver == None: zmax_ver = numpy.nanmax(abs(z[2,:])) |
|
|
501 | 529 | if zmin_ver == None: zmin_ver = -zmax_ver |
|
|
502 | 530 | |
|
|
503 | 531 | if dataOut.data_SNR is not None: |
|
|
504 | 532 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
505 | 533 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
506 | 534 | |
|
|
507 | 535 | |
|
|
508 | 536 | self.FTP_WEI = ftp_wei |
|
|
509 | 537 | self.EXP_CODE = exp_code |
|
|
510 | 538 | self.SUB_EXP_CODE = sub_exp_code |
|
|
511 | 539 | self.PLOT_POS = plot_pos |
|
|
512 | 540 | |
|
|
513 | 541 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
514 | 542 | self.isConfig = True |
|
|
515 | 543 | self.figfile = figfile |
|
|
516 | 544 | update_figfile = True |
|
|
517 | 545 | |
|
|
518 | 546 | self.setWinTitle(title) |
|
|
519 | 547 | |
|
|
520 | 548 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
521 | 549 | x[1] = self.xmax |
|
|
522 | 550 | |
|
|
523 | 551 | strWind = ['Zonal', 'Meridional', 'Vertical'] |
|
|
524 | 552 | strCb = ['Velocity (m/s)','Velocity (m/s)','Velocity (cm/s)'] |
|
|
525 | 553 | zmaxVector = [zmax, zmax, zmax_ver] |
|
|
526 | 554 | zminVector = [zmin, zmin, zmin_ver] |
|
|
527 | 555 | windFactor = [1,1,100] |
|
|
528 | 556 | |
|
|
529 | 557 | for i in range(nplotsw): |
|
|
530 | 558 | |
|
|
531 | 559 | title = "%s Wind: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
532 | 560 | axes = self.axesList[i*self.__nsubplots] |
|
|
533 | 561 | |
|
|
534 | 562 | z1 = z[i,:].reshape((1,-1))*windFactor[i] |
|
|
535 | 563 | #z1=numpy.ma.masked_where(z1==0.,z1) |
|
|
536 | 564 | |
|
|
537 | 565 | axes.pcolorbuffer(x, y, z1, |
|
|
538 | 566 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
539 | 567 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
540 | 568 | ticksize=9, cblabel=strCb[i], cbsize="1%", colormap="seismic" ) |
|
|
541 | 569 | |
|
|
542 | 570 | if dataOut.data_SNR is not None: |
|
|
543 | 571 | i += 1 |
|
|
544 | 572 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
545 | 573 | axes = self.axesList[i*self.__nsubplots] |
|
|
546 | 574 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
547 | 575 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
548 | 576 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
549 | 577 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
550 | 578 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
551 | 579 | |
|
|
552 | 580 | self.draw() |
|
|
553 | 581 | |
|
|
554 | 582 | self.save(figpath=figpath, |
|
|
555 | 583 | figfile=figfile, |
|
|
556 | 584 | save=save, |
|
|
557 | 585 | ftp=ftp, |
|
|
558 | 586 | wr_period=wr_period, |
|
|
559 | 587 | thisDatetime=thisDatetime, |
|
|
560 | 588 | update_figfile=update_figfile) |
|
|
561 | 589 | |
|
|
562 | 590 | if dataOut.ltctime + dataOut.outputInterval >= self.xmax: |
|
|
563 | 591 | self.counter_imagwr = wr_period |
|
|
564 | 592 | self.isConfig = False |
|
|
565 | 593 | update_figfile = True |
|
|
566 | 594 | |
|
|
567 | 595 | |
|
|
568 | 596 | class ParametersPlot(Figure): |
|
|
569 | 597 | |
|
|
570 | 598 | __isConfig = None |
|
|
571 | 599 | __nsubplots = None |
|
|
572 | 600 | |
|
|
573 | 601 | WIDTHPROF = None |
|
|
574 | 602 | HEIGHTPROF = None |
|
|
575 | 603 | PREFIX = 'param' |
|
|
576 | 604 | |
|
|
577 | 605 | nplots = None |
|
|
578 | 606 | nchan = None |
|
|
579 | 607 | |
|
|
580 | 608 | def __init__(self, **kwargs): |
|
|
581 | 609 | Figure.__init__(self, **kwargs) |
|
|
582 | 610 | self.timerange = None |
|
|
583 | 611 | self.isConfig = False |
|
|
584 | 612 | self.__nsubplots = 1 |
|
|
585 | 613 | |
|
|
586 | 614 | self.WIDTH = 800 |
|
|
587 | 615 | self.HEIGHT = 180 |
|
|
588 | 616 | self.WIDTHPROF = 120 |
|
|
589 | 617 | self.HEIGHTPROF = 0 |
|
|
590 | 618 | self.counter_imagwr = 0 |
|
|
591 | 619 | |
|
|
592 | 620 | self.PLOT_CODE = RTI_CODE |
|
|
593 | 621 | |
|
|
594 | 622 | self.FTP_WEI = None |
|
|
595 | 623 | self.EXP_CODE = None |
|
|
596 | 624 | self.SUB_EXP_CODE = None |
|
|
597 | 625 | self.PLOT_POS = None |
|
|
598 | 626 | self.tmin = None |
|
|
599 | 627 | self.tmax = None |
|
|
600 | 628 | |
|
|
601 | 629 | self.xmin = None |
|
|
602 | 630 | self.xmax = None |
|
|
603 | 631 | |
|
|
604 | 632 | self.figfile = None |
|
|
605 | 633 | |
|
|
606 | 634 | def getSubplots(self): |
|
|
607 | 635 | |
|
|
608 | 636 | ncol = 1 |
|
|
609 | 637 | nrow = self.nplots |
|
|
610 | 638 | |
|
|
611 | 639 | return nrow, ncol |
|
|
612 | 640 | |
|
|
613 | 641 | def setup(self, id, nplots, wintitle, show=True): |
|
|
614 | 642 | |
|
|
615 | 643 | self.nplots = nplots |
|
|
616 | 644 | |
|
|
617 | 645 | ncolspan = 1 |
|
|
618 | 646 | colspan = 1 |
|
|
619 | 647 | |
|
|
620 | 648 | self.createFigure(id = id, |
|
|
621 | 649 | wintitle = wintitle, |
|
|
622 | 650 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
623 | 651 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
624 | 652 | show=show) |
|
|
625 | 653 | |
|
|
626 | 654 | nrow, ncol = self.getSubplots() |
|
|
627 | 655 | |
|
|
628 | 656 | counter = 0 |
|
|
629 | 657 | for y in range(nrow): |
|
|
630 | 658 | for x in range(ncol): |
|
|
631 | 659 | |
|
|
632 | 660 | if counter >= self.nplots: |
|
|
633 | 661 | break |
|
|
634 | 662 | |
|
|
635 | 663 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
636 | 664 | |
|
|
637 | 665 | counter += 1 |
|
|
638 | 666 | |
|
|
639 | 667 | def run(self, dataOut, id, wintitle="", channelList=None, paramIndex = 0, colormap=True, |
|
|
640 | 668 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, timerange=None, |
|
|
641 | 669 | showSNR=False, SNRthresh = -numpy.inf, SNRmin=None, SNRmax=None, |
|
|
642 | 670 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
643 | 671 | server=None, folder=None, username=None, password=None, |
|
|
644 | 672 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
645 | 673 | """ |
|
|
646 | 674 | |
|
|
647 | 675 | Input: |
|
|
648 | 676 | dataOut : |
|
|
649 | 677 | id : |
|
|
650 | 678 | wintitle : |
|
|
651 | 679 | channelList : |
|
|
652 | 680 | showProfile : |
|
|
653 | 681 | xmin : None, |
|
|
654 | 682 | xmax : None, |
|
|
655 | 683 | ymin : None, |
|
|
656 | 684 | ymax : None, |
|
|
657 | 685 | zmin : None, |
|
|
658 | 686 | zmax : None |
|
|
659 | 687 | """ |
|
|
660 | 688 | |
|
|
661 | 689 | if colormap: |
|
|
662 | 690 | colormap="jet" |
|
|
663 | 691 | else: |
|
|
664 | 692 | colormap="RdBu_r" |
|
|
665 | 693 | |
|
|
666 | 694 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
667 | 695 | return |
|
|
668 | 696 | |
|
|
669 | 697 | if channelList == None: |
|
|
670 | 698 | channelIndexList = range(dataOut.data_param.shape[0]) |
|
|
671 | 699 | else: |
|
|
672 | 700 | channelIndexList = [] |
|
|
673 | 701 | for channel in channelList: |
|
|
674 | 702 | if channel not in dataOut.channelList: |
|
|
675 | 703 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
676 | 704 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
677 | 705 | |
|
|
678 | 706 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
679 | 707 | y = dataOut.getHeiRange() |
|
|
680 | 708 | |
|
|
681 | 709 | if dataOut.data_param.ndim == 3: |
|
|
682 | 710 | z = dataOut.data_param[channelIndexList,paramIndex,:] |
|
|
683 | 711 | else: |
|
|
684 | 712 | z = dataOut.data_param[channelIndexList,:] |
|
|
685 | 713 | |
|
|
686 | 714 | if showSNR: |
|
|
687 | 715 | #SNR data |
|
|
688 | 716 | SNRarray = dataOut.data_SNR[channelIndexList,:] |
|
|
689 | 717 | SNRdB = 10*numpy.log10(SNRarray) |
|
|
690 | 718 | ind = numpy.where(SNRdB < SNRthresh) |
|
|
691 | 719 | z[ind] = numpy.nan |
|
|
692 | 720 | |
|
|
693 | 721 | thisDatetime = dataOut.datatime |
|
|
694 | 722 | # thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
695 | 723 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
696 | 724 | xlabel = "" |
|
|
697 | 725 | ylabel = "Range (Km)" |
|
|
698 | 726 | |
|
|
699 | 727 | update_figfile = False |
|
|
700 | 728 | |
|
|
701 | 729 | if not self.isConfig: |
|
|
702 | 730 | |
|
|
703 | 731 | nchan = len(channelIndexList) |
|
|
704 | 732 | self.nchan = nchan |
|
|
705 | 733 | self.plotFact = 1 |
|
|
706 | 734 | nplots = nchan |
|
|
707 | 735 | |
|
|
708 | 736 | if showSNR: |
|
|
709 | 737 | nplots = nchan*2 |
|
|
710 | 738 | self.plotFact = 2 |
|
|
711 | 739 | if SNRmin == None: SNRmin = numpy.nanmin(SNRdB) |
|
|
712 | 740 | if SNRmax == None: SNRmax = numpy.nanmax(SNRdB) |
|
|
713 | 741 | |
|
|
714 | 742 | self.setup(id=id, |
|
|
715 | 743 | nplots=nplots, |
|
|
716 | 744 | wintitle=wintitle, |
|
|
717 | 745 | show=show) |
|
|
718 | 746 | |
|
|
719 | 747 | if timerange != None: |
|
|
720 | 748 | self.timerange = timerange |
|
|
721 | 749 | |
|
|
722 | 750 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
723 | 751 | |
|
|
724 | 752 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
725 | 753 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
726 | 754 | if zmin == None: zmin = numpy.nanmin(z) |
|
|
727 | 755 | if zmax == None: zmax = numpy.nanmax(z) |
|
|
728 | 756 | |
|
|
729 | 757 | self.FTP_WEI = ftp_wei |
|
|
730 | 758 | self.EXP_CODE = exp_code |
|
|
731 | 759 | self.SUB_EXP_CODE = sub_exp_code |
|
|
732 | 760 | self.PLOT_POS = plot_pos |
|
|
733 | 761 | |
|
|
734 | 762 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
735 | 763 | self.isConfig = True |
|
|
736 | 764 | self.figfile = figfile |
|
|
737 | 765 | update_figfile = True |
|
|
738 | 766 | |
|
|
739 | 767 | self.setWinTitle(title) |
|
|
740 | 768 | |
|
|
741 | 769 | for i in range(self.nchan): |
|
|
742 | 770 | index = channelIndexList[i] |
|
|
743 | 771 | title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
744 | 772 | axes = self.axesList[i*self.plotFact] |
|
|
745 | 773 | z1 = z[i,:].reshape((1,-1)) |
|
|
746 | 774 | axes.pcolorbuffer(x, y, z1, |
|
|
747 | 775 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
748 | 776 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
749 | 777 | ticksize=9, cblabel='', cbsize="1%",colormap=colormap) |
|
|
750 | 778 | |
|
|
751 | 779 | if showSNR: |
|
|
752 | 780 | title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
753 | 781 | axes = self.axesList[i*self.plotFact + 1] |
|
|
754 | 782 | SNRdB1 = SNRdB[i,:].reshape((1,-1)) |
|
|
755 | 783 | axes.pcolorbuffer(x, y, SNRdB1, |
|
|
756 | 784 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
757 | 785 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
758 | 786 | ticksize=9, cblabel='', cbsize="1%",colormap='jet') |
|
|
759 | 787 | |
|
|
760 | 788 | |
|
|
761 | 789 | self.draw() |
|
|
762 | 790 | |
|
|
763 | 791 | if dataOut.ltctime >= self.xmax: |
|
|
764 | 792 | self.counter_imagwr = wr_period |
|
|
765 | 793 | self.isConfig = False |
|
|
766 | 794 | update_figfile = True |
|
|
767 | 795 | |
|
|
768 | 796 | self.save(figpath=figpath, |
|
|
769 | 797 | figfile=figfile, |
|
|
770 | 798 | save=save, |
|
|
771 | 799 | ftp=ftp, |
|
|
772 | 800 | wr_period=wr_period, |
|
|
773 | 801 | thisDatetime=thisDatetime, |
|
|
774 | 802 | update_figfile=update_figfile) |
|
|
775 | 803 | |
|
|
776 | 804 | |
|
|
777 | 805 | |
|
|
778 | 806 | class Parameters1Plot(Figure): |
|
|
779 | 807 | |
|
|
780 | 808 | __isConfig = None |
|
|
781 | 809 | __nsubplots = None |
|
|
782 | 810 | |
|
|
783 | 811 | WIDTHPROF = None |
|
|
784 | 812 | HEIGHTPROF = None |
|
|
785 | 813 | PREFIX = 'prm' |
|
|
786 | 814 | |
|
|
787 | 815 | def __init__(self, **kwargs): |
|
|
788 | 816 | Figure.__init__(self, **kwargs) |
|
|
789 | 817 | self.timerange = 2*60*60 |
|
|
790 | 818 | self.isConfig = False |
|
|
791 | 819 | self.__nsubplots = 1 |
|
|
792 | 820 | |
|
|
793 | 821 | self.WIDTH = 800 |
|
|
794 | 822 | self.HEIGHT = 180 |
|
|
795 | 823 | self.WIDTHPROF = 120 |
|
|
796 | 824 | self.HEIGHTPROF = 0 |
|
|
797 | 825 | self.counter_imagwr = 0 |
|
|
798 | 826 | |
|
|
799 | 827 | self.PLOT_CODE = PARMS_CODE |
|
|
800 | 828 | |
|
|
801 | 829 | self.FTP_WEI = None |
|
|
802 | 830 | self.EXP_CODE = None |
|
|
803 | 831 | self.SUB_EXP_CODE = None |
|
|
804 | 832 | self.PLOT_POS = None |
|
|
805 | 833 | self.tmin = None |
|
|
806 | 834 | self.tmax = None |
|
|
807 | 835 | |
|
|
808 | 836 | self.xmin = None |
|
|
809 | 837 | self.xmax = None |
|
|
810 | 838 | |
|
|
811 | 839 | self.figfile = None |
|
|
812 | 840 | |
|
|
813 | 841 | def getSubplots(self): |
|
|
814 | 842 | |
|
|
815 | 843 | ncol = 1 |
|
|
816 | 844 | nrow = self.nplots |
|
|
817 | 845 | |
|
|
818 | 846 | return nrow, ncol |
|
|
819 | 847 | |
|
|
820 | 848 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
821 | 849 | |
|
|
822 | 850 | self.__showprofile = showprofile |
|
|
823 | 851 | self.nplots = nplots |
|
|
824 | 852 | |
|
|
825 | 853 | ncolspan = 1 |
|
|
826 | 854 | colspan = 1 |
|
|
827 | 855 | |
|
|
828 | 856 | self.createFigure(id = id, |
|
|
829 | 857 | wintitle = wintitle, |
|
|
830 | 858 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
831 | 859 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
832 | 860 | show=show) |
|
|
833 | 861 | |
|
|
834 | 862 | nrow, ncol = self.getSubplots() |
|
|
835 | 863 | |
|
|
836 | 864 | counter = 0 |
|
|
837 | 865 | for y in range(nrow): |
|
|
838 | 866 | for x in range(ncol): |
|
|
839 | 867 | |
|
|
840 | 868 | if counter >= self.nplots: |
|
|
841 | 869 | break |
|
|
842 | 870 | |
|
|
843 | 871 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
844 | 872 | |
|
|
845 | 873 | if showprofile: |
|
|
846 | 874 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
847 | 875 | |
|
|
848 | 876 | counter += 1 |
|
|
849 | 877 | |
|
|
850 | 878 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
851 | 879 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None,timerange=None, |
|
|
852 | 880 | parameterIndex = None, onlyPositive = False, |
|
|
853 | 881 | SNRthresh = -numpy.inf, SNR = True, SNRmin = None, SNRmax = None, onlySNR = False, |
|
|
854 | 882 | DOP = True, |
|
|
855 | 883 | zlabel = "", parameterName = "", parameterObject = "data_param", |
|
|
856 | 884 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
857 | 885 | server=None, folder=None, username=None, password=None, |
|
|
858 | 886 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
859 | 887 | #print inspect.getargspec(self.run).args |
|
|
860 | 888 | """ |
|
|
861 | 889 | |
|
|
862 | 890 | Input: |
|
|
863 | 891 | dataOut : |
|
|
864 | 892 | id : |
|
|
865 | 893 | wintitle : |
|
|
866 | 894 | channelList : |
|
|
867 | 895 | showProfile : |
|
|
868 | 896 | xmin : None, |
|
|
869 | 897 | xmax : None, |
|
|
870 | 898 | ymin : None, |
|
|
871 | 899 | ymax : None, |
|
|
872 | 900 | zmin : None, |
|
|
873 | 901 | zmax : None |
|
|
874 | 902 | """ |
|
|
875 | 903 | |
|
|
876 | 904 | data_param = getattr(dataOut, parameterObject) |
|
|
877 | 905 | |
|
|
878 | 906 | if channelList == None: |
|
|
879 | 907 | channelIndexList = numpy.arange(data_param.shape[0]) |
|
|
880 | 908 | else: |
|
|
881 | 909 | channelIndexList = numpy.array(channelList) |
|
|
882 | 910 | |
|
|
883 | 911 | nchan = len(channelIndexList) #Number of channels being plotted |
|
|
884 | 912 | |
|
|
885 | 913 | if nchan < 1: |
|
|
886 | 914 | return |
|
|
887 | 915 | |
|
|
888 | 916 | nGraphsByChannel = 0 |
|
|
889 | 917 | |
|
|
890 | 918 | if SNR: |
|
|
891 | 919 | nGraphsByChannel += 1 |
|
|
892 | 920 | if DOP: |
|
|
893 | 921 | nGraphsByChannel += 1 |
|
|
894 | 922 | |
|
|
895 | 923 | if nGraphsByChannel < 1: |
|
|
896 | 924 | return |
|
|
897 | 925 | |
|
|
898 | 926 | nplots = nGraphsByChannel*nchan |
|
|
899 | 927 | |
|
|
900 | 928 | if timerange is not None: |
|
|
901 | 929 | self.timerange = timerange |
|
|
902 | 930 | |
|
|
903 | 931 | #tmin = None |
|
|
904 | 932 | #tmax = None |
|
|
905 | 933 | if parameterIndex == None: |
|
|
906 | 934 | parameterIndex = 1 |
|
|
907 | 935 | |
|
|
908 | 936 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
909 | 937 | y = dataOut.heightList |
|
|
910 | 938 | z = data_param[channelIndexList,parameterIndex,:].copy() |
|
|
911 | 939 | |
|
|
912 | 940 | zRange = dataOut.abscissaList |
|
|
913 | 941 | # nChannels = z.shape[0] #Number of wind dimensions estimated |
|
|
914 | 942 | # thisDatetime = dataOut.datatime |
|
|
915 | 943 | |
|
|
916 | 944 | if dataOut.data_SNR is not None: |
|
|
917 | 945 | SNRarray = dataOut.data_SNR[channelIndexList,:] |
|
|
918 | 946 | SNRdB = 10*numpy.log10(SNRarray) |
|
|
919 | 947 | # SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
920 | 948 | ind = numpy.where(SNRdB < 10**(SNRthresh/10)) |
|
|
921 | 949 | z[ind] = numpy.nan |
|
|
922 | 950 | |
|
|
923 | 951 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
924 | 952 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
925 | 953 | xlabel = "" |
|
|
926 | 954 | ylabel = "Range (Km)" |
|
|
927 | 955 | |
|
|
928 | 956 | if (SNR and not onlySNR): nplots = 2*nplots |
|
|
929 | 957 | |
|
|
930 | 958 | if onlyPositive: |
|
|
931 | 959 | colormap = "jet" |
|
|
932 | 960 | zmin = 0 |
|
|
933 | 961 | else: colormap = "RdBu_r" |
|
|
934 | 962 | |
|
|
935 | 963 | if not self.isConfig: |
|
|
936 | 964 | |
|
|
937 | 965 | self.setup(id=id, |
|
|
938 | 966 | nplots=nplots, |
|
|
939 | 967 | wintitle=wintitle, |
|
|
940 | 968 | showprofile=showprofile, |
|
|
941 | 969 | show=show) |
|
|
942 | 970 | |
|
|
943 | 971 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
944 | 972 | |
|
|
945 | 973 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
946 | 974 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
947 | 975 | if zmin == None: zmin = numpy.nanmin(zRange) |
|
|
948 | 976 | if zmax == None: zmax = numpy.nanmax(zRange) |
|
|
949 | 977 | |
|
|
950 | 978 | if SNR: |
|
|
951 | 979 | if SNRmin == None: SNRmin = numpy.nanmin(SNRdB) |
|
|
952 | 980 | if SNRmax == None: SNRmax = numpy.nanmax(SNRdB) |
|
|
953 | 981 | |
|
|
954 | 982 | self.FTP_WEI = ftp_wei |
|
|
955 | 983 | self.EXP_CODE = exp_code |
|
|
956 | 984 | self.SUB_EXP_CODE = sub_exp_code |
|
|
957 | 985 | self.PLOT_POS = plot_pos |
|
|
958 | 986 | |
|
|
959 | 987 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
960 | 988 | self.isConfig = True |
|
|
961 | 989 | self.figfile = figfile |
|
|
962 | 990 | |
|
|
963 | 991 | self.setWinTitle(title) |
|
|
964 | 992 | |
|
|
965 | 993 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
966 | 994 | x[1] = self.xmax |
|
|
967 | 995 | |
|
|
968 | 996 | for i in range(nchan): |
|
|
969 | 997 | |
|
|
970 | 998 | if (SNR and not onlySNR): j = 2*i |
|
|
971 | 999 | else: j = i |
|
|
972 | 1000 | |
|
|
973 | 1001 | j = nGraphsByChannel*i |
|
|
974 | 1002 | |
|
|
975 | 1003 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
976 | 1004 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
977 | 1005 | |
|
|
978 | 1006 | if not onlySNR: |
|
|
979 | 1007 | axes = self.axesList[j*self.__nsubplots] |
|
|
980 | 1008 | z1 = z[i,:].reshape((1,-1)) |
|
|
981 | 1009 | axes.pcolorbuffer(x, y, z1, |
|
|
982 | 1010 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
983 | 1011 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
984 | 1012 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
985 | 1013 | |
|
|
986 | 1014 | if DOP: |
|
|
987 | 1015 | title = "%s Channel %d: %s" %(parameterName, channelIndexList[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
988 | 1016 | |
|
|
989 | 1017 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
990 | 1018 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
991 | 1019 | axes = self.axesList[j] |
|
|
992 | 1020 | z1 = z[i,:].reshape((1,-1)) |
|
|
993 | 1021 | axes.pcolorbuffer(x, y, z1, |
|
|
994 | 1022 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
995 | 1023 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
996 | 1024 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
997 | 1025 | |
|
|
998 | 1026 | if SNR: |
|
|
999 | 1027 | title = "Channel %d Signal Noise Ratio (SNR): %s" %(channelIndexList[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1000 | 1028 | axes = self.axesList[(j)*self.__nsubplots] |
|
|
1001 | 1029 | if not onlySNR: |
|
|
1002 | 1030 | axes = self.axesList[(j + 1)*self.__nsubplots] |
|
|
1003 | 1031 | |
|
|
1004 | 1032 | axes = self.axesList[(j + nGraphsByChannel-1)] |
|
|
1005 | 1033 | |
|
|
1006 | 1034 | z1 = SNRdB[i,:].reshape((1,-1)) |
|
|
1007 | 1035 | axes.pcolorbuffer(x, y, z1, |
|
|
1008 | 1036 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
1009 | 1037 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap="jet", |
|
|
1010 | 1038 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
1011 | 1039 | |
|
|
1012 | 1040 | |
|
|
1013 | 1041 | |
|
|
1014 | 1042 | self.draw() |
|
|
1015 | 1043 | |
|
|
1016 | 1044 | if x[1] >= self.axesList[0].xmax: |
|
|
1017 | 1045 | self.counter_imagwr = wr_period |
|
|
1018 | 1046 | self.isConfig = False |
|
|
1019 | 1047 | self.figfile = None |
|
|
1020 | 1048 | |
|
|
1021 | 1049 | self.save(figpath=figpath, |
|
|
1022 | 1050 | figfile=figfile, |
|
|
1023 | 1051 | save=save, |
|
|
1024 | 1052 | ftp=ftp, |
|
|
1025 | 1053 | wr_period=wr_period, |
|
|
1026 | 1054 | thisDatetime=thisDatetime, |
|
|
1027 | 1055 | update_figfile=False) |
|
|
1028 | 1056 | |
|
|
1029 | 1057 | class SpectralFittingPlot(Figure): |
|
|
1030 | 1058 | |
|
|
1031 | 1059 | __isConfig = None |
|
|
1032 | 1060 | __nsubplots = None |
|
|
1033 | 1061 | |
|
|
1034 | 1062 | WIDTHPROF = None |
|
|
1035 | 1063 | HEIGHTPROF = None |
|
|
1036 | 1064 | PREFIX = 'prm' |
|
|
1037 | 1065 | |
|
|
1038 | 1066 | |
|
|
1039 | 1067 | N = None |
|
|
1040 | 1068 | ippSeconds = None |
|
|
1041 | 1069 | |
|
|
1042 | 1070 | def __init__(self, **kwargs): |
|
|
1043 | 1071 | Figure.__init__(self, **kwargs) |
|
|
1044 | 1072 | self.isConfig = False |
|
|
1045 | 1073 | self.__nsubplots = 1 |
|
|
1046 | 1074 | |
|
|
1047 | 1075 | self.PLOT_CODE = SPECFIT_CODE |
|
|
1048 | 1076 | |
|
|
1049 | 1077 | self.WIDTH = 450 |
|
|
1050 | 1078 | self.HEIGHT = 250 |
|
|
1051 | 1079 | self.WIDTHPROF = 0 |
|
|
1052 | 1080 | self.HEIGHTPROF = 0 |
|
|
1053 | 1081 | |
|
|
1054 | 1082 | def getSubplots(self): |
|
|
1055 | 1083 | |
|
|
1056 | 1084 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
1057 | 1085 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
1058 | 1086 | |
|
|
1059 | 1087 | return nrow, ncol |
|
|
1060 | 1088 | |
|
|
1061 | 1089 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
1062 | 1090 | |
|
|
1063 | 1091 | showprofile = False |
|
|
1064 | 1092 | self.__showprofile = showprofile |
|
|
1065 | 1093 | self.nplots = nplots |
|
|
1066 | 1094 | |
|
|
1067 | 1095 | ncolspan = 5 |
|
|
1068 | 1096 | colspan = 4 |
|
|
1069 | 1097 | if showprofile: |
|
|
1070 | 1098 | ncolspan = 5 |
|
|
1071 | 1099 | colspan = 4 |
|
|
1072 | 1100 | self.__nsubplots = 2 |
|
|
1073 | 1101 | |
|
|
1074 | 1102 | self.createFigure(id = id, |
|
|
1075 | 1103 | wintitle = wintitle, |
|
|
1076 | 1104 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1077 | 1105 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1078 | 1106 | show=show) |
|
|
1079 | 1107 | |
|
|
1080 | 1108 | nrow, ncol = self.getSubplots() |
|
|
1081 | 1109 | |
|
|
1082 | 1110 | counter = 0 |
|
|
1083 | 1111 | for y in range(nrow): |
|
|
1084 | 1112 | for x in range(ncol): |
|
|
1085 | 1113 | |
|
|
1086 | 1114 | if counter >= self.nplots: |
|
|
1087 | 1115 | break |
|
|
1088 | 1116 | |
|
|
1089 | 1117 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1090 | 1118 | |
|
|
1091 | 1119 | if showprofile: |
|
|
1092 | 1120 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
1093 | 1121 | |
|
|
1094 | 1122 | counter += 1 |
|
|
1095 | 1123 | |
|
|
1096 | 1124 | def run(self, dataOut, id, cutHeight=None, fit=False, wintitle="", channelList=None, showprofile=True, |
|
|
1097 | 1125 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1098 | 1126 | save=False, figpath='./', figfile=None, show=True): |
|
|
1099 | 1127 | |
|
|
1100 | 1128 | """ |
|
|
1101 | 1129 | |
|
|
1102 | 1130 | Input: |
|
|
1103 | 1131 | dataOut : |
|
|
1104 | 1132 | id : |
|
|
1105 | 1133 | wintitle : |
|
|
1106 | 1134 | channelList : |
|
|
1107 | 1135 | showProfile : |
|
|
1108 | 1136 | xmin : None, |
|
|
1109 | 1137 | xmax : None, |
|
|
1110 | 1138 | zmin : None, |
|
|
1111 | 1139 | zmax : None |
|
|
1112 | 1140 | """ |
|
|
1113 | 1141 | |
|
|
1114 | 1142 | if cutHeight==None: |
|
|
1115 | 1143 | h=270 |
|
|
1116 | 1144 | heightindex = numpy.abs(cutHeight - dataOut.heightList).argmin() |
|
|
1117 | 1145 | cutHeight = dataOut.heightList[heightindex] |
|
|
1118 | 1146 | |
|
|
1119 | 1147 | factor = dataOut.normFactor |
|
|
1120 | 1148 | x = dataOut.abscissaList[:-1] |
|
|
1121 | 1149 | #y = dataOut.getHeiRange() |
|
|
1122 | 1150 | |
|
|
1123 | 1151 | z = dataOut.data_pre[:,:,heightindex]/factor |
|
|
1124 | 1152 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1125 | 1153 | avg = numpy.average(z, axis=1) |
|
|
1126 | 1154 | listChannels = z.shape[0] |
|
|
1127 | 1155 | |
|
|
1128 | 1156 | #Reconstruct Function |
|
|
1129 | 1157 | if fit==True: |
|
|
1130 | 1158 | groupArray = dataOut.groupList |
|
|
1131 | 1159 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1132 | 1160 | listChannels.sort() |
|
|
1133 | 1161 | spcFitLine = numpy.zeros(z.shape) |
|
|
1134 | 1162 | constants = dataOut.constants |
|
|
1135 | 1163 | |
|
|
1136 | 1164 | nGroups = groupArray.shape[0] |
|
|
1137 | 1165 | nChannels = groupArray.shape[1] |
|
|
1138 | 1166 | nProfiles = z.shape[1] |
|
|
1139 | 1167 | |
|
|
1140 | 1168 | for f in range(nGroups): |
|
|
1141 | 1169 | groupChann = groupArray[f,:] |
|
|
1142 | 1170 | p = dataOut.data_param[f,:,heightindex] |
|
|
1143 | 1171 | # p = numpy.array([ 89.343967,0.14036615,0.17086219,18.89835291,1.58388365,1.55099167]) |
|
|
1144 | 1172 | fitLineAux = dataOut.library.modelFunction(p, constants)*nProfiles |
|
|
1145 | 1173 | fitLineAux = fitLineAux.reshape((nChannels,nProfiles)) |
|
|
1146 | 1174 | spcFitLine[groupChann,:] = fitLineAux |
|
|
1147 | 1175 | # spcFitLine = spcFitLine/factor |
|
|
1148 | 1176 | |
|
|
1149 | 1177 | z = z[listChannels,:] |
|
|
1150 | 1178 | spcFitLine = spcFitLine[listChannels,:] |
|
|
1151 | 1179 | spcFitLinedB = 10*numpy.log10(spcFitLine) |
|
|
1152 | 1180 | |
|
|
1153 | 1181 | zdB = 10*numpy.log10(z) |
|
|
1154 | 1182 | #thisDatetime = dataOut.datatime |
|
|
1155 | 1183 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1156 | 1184 | title = wintitle + " Doppler Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1157 | 1185 | xlabel = "Velocity (m/s)" |
|
|
1158 | 1186 | ylabel = "Spectrum" |
|
|
1159 | 1187 | |
|
|
1160 | 1188 | if not self.isConfig: |
|
|
1161 | 1189 | |
|
|
1162 | 1190 | nplots = listChannels.size |
|
|
1163 | 1191 | |
|
|
1164 | 1192 | self.setup(id=id, |
|
|
1165 | 1193 | nplots=nplots, |
|
|
1166 | 1194 | wintitle=wintitle, |
|
|
1167 | 1195 | showprofile=showprofile, |
|
|
1168 | 1196 | show=show) |
|
|
1169 | 1197 | |
|
|
1170 | 1198 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
1171 | 1199 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
1172 | 1200 | if ymin == None: ymin = numpy.nanmin(zdB) |
|
|
1173 | 1201 | if ymax == None: ymax = numpy.nanmax(zdB)+2 |
|
|
1174 | 1202 | |
|
|
1175 | 1203 | self.isConfig = True |
|
|
1176 | 1204 | |
|
|
1177 | 1205 | self.setWinTitle(title) |
|
|
1178 | 1206 | for i in range(self.nplots): |
|
|
1179 | 1207 | # title = "Channel %d: %4.2fdB" %(dataOut.channelList[i]+1, noisedB[i]) |
|
|
1180 | 1208 | title = "Height %4.1f km\nChannel %d:" %(cutHeight, listChannels[i]) |
|
|
1181 | 1209 | axes = self.axesList[i*self.__nsubplots] |
|
|
1182 | 1210 | if fit == False: |
|
|
1183 | 1211 | axes.pline(x, zdB[i,:], |
|
|
1184 | 1212 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1185 | 1213 | xlabel=xlabel, ylabel=ylabel, title=title |
|
|
1186 | 1214 | ) |
|
|
1187 | 1215 | if fit == True: |
|
|
1188 | 1216 | fitline=spcFitLinedB[i,:] |
|
|
1189 | 1217 | y=numpy.vstack([zdB[i,:],fitline] ) |
|
|
1190 | 1218 | legendlabels=['Data','Fitting'] |
|
|
1191 | 1219 | axes.pmultilineyaxis(x, y, |
|
|
1192 | 1220 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1193 | 1221 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
1194 | 1222 | legendlabels=legendlabels, marker=None, |
|
|
1195 | 1223 | linestyle='solid', grid='both') |
|
|
1196 | 1224 | |
|
|
1197 | 1225 | self.draw() |
|
|
1198 | 1226 | |
|
|
1199 | 1227 | self.save(figpath=figpath, |
|
|
1200 | 1228 | figfile=figfile, |
|
|
1201 | 1229 | save=save, |
|
|
1202 | 1230 | ftp=ftp, |
|
|
1203 | 1231 | wr_period=wr_period, |
|
|
1204 | 1232 | thisDatetime=thisDatetime) |
|
|
1205 | 1233 | |
|
|
1206 | 1234 | |
|
|
1207 | 1235 | class EWDriftsPlot(Figure): |
|
|
1208 | 1236 | |
|
|
1209 | 1237 | __isConfig = None |
|
|
1210 | 1238 | __nsubplots = None |
|
|
1211 | 1239 | |
|
|
1212 | 1240 | WIDTHPROF = None |
|
|
1213 | 1241 | HEIGHTPROF = None |
|
|
1214 | 1242 | PREFIX = 'drift' |
|
|
1215 | 1243 | |
|
|
1244 | parameters = { | |
|
|
1245 | 'id': global_type_string, | |
|
|
1246 | 'wintitle': global_type_string, | |
|
|
1247 | 'channelList': global_type_string, | |
|
|
1248 | 'xmin': global_type_float, | |
|
|
1249 | 'xmax': global_type_float, | |
|
|
1250 | 'ymin': global_type_float, | |
|
|
1251 | 'ymax': global_type_float, | |
|
|
1252 | 'zmin': global_type_float, | |
|
|
1253 | 'zmax': global_type_float, | |
|
|
1254 | 'zmaxVertfloat': global_type_float, | |
|
|
1255 | 'zminVertfloat': global_type_float, | |
|
|
1256 | 'zmaxZonafloat': global_type_float, | |
|
|
1257 | 'zminZonafloat': global_type_float, | |
|
|
1258 | 'timerange': global_type_float, | |
|
|
1259 | 'SNRthresh': global_type_float, | |
|
|
1260 | 'SNRmin': global_type_float, | |
|
|
1261 | 'SNRmax': global_type_float, | |
|
|
1262 | 'SNR_1': global_type_boolean, | |
|
|
1263 | 'save': global_type_boolean, | |
|
|
1264 | 'figpath': global_type_string, | |
|
|
1265 | 'lastone': global_type_float, | |
|
|
1266 | 'figfile': global_type_string, | |
|
|
1267 | 'ftp': global_type_string, | |
|
|
1268 | 'wr_period': global_type_integer, | |
|
|
1269 | 'show': global_type_string, | |
|
|
1270 | 'server': global_type_string, | |
|
|
1271 | 'folder': global_type_string, | |
|
|
1272 | 'username': global_type_string, | |
|
|
1273 | 'password': global_type_string, | |
|
|
1274 | 'ftp_wei': global_type_integer, | |
|
|
1275 | 'exp_code': global_type_integer, | |
|
|
1276 | 'sub_exp_code': global_type_integer, | |
|
|
1277 | 'plot_pos': global_type_integer, | |
|
|
1278 | } | |
|
|
1279 | ||
|
|
1216 | 1280 | def __init__(self, **kwargs): |
|
|
1217 | 1281 | Figure.__init__(self, **kwargs) |
|
|
1218 | 1282 | self.timerange = 2*60*60 |
|
|
1219 | 1283 | self.isConfig = False |
|
|
1220 | 1284 | self.__nsubplots = 1 |
|
|
1221 | 1285 | |
|
|
1222 | 1286 | self.WIDTH = 800 |
|
|
1223 | 1287 | self.HEIGHT = 150 |
|
|
1224 | 1288 | self.WIDTHPROF = 120 |
|
|
1225 | 1289 | self.HEIGHTPROF = 0 |
|
|
1226 | 1290 | self.counter_imagwr = 0 |
|
|
1227 | 1291 | |
|
|
1228 | 1292 | self.PLOT_CODE = EWDRIFT_CODE |
|
|
1229 | 1293 | |
|
|
1230 | 1294 | self.FTP_WEI = None |
|
|
1231 | 1295 | self.EXP_CODE = None |
|
|
1232 | 1296 | self.SUB_EXP_CODE = None |
|
|
1233 | 1297 | self.PLOT_POS = None |
|
|
1234 | 1298 | self.tmin = None |
|
|
1235 | 1299 | self.tmax = None |
|
|
1236 | 1300 | |
|
|
1237 | 1301 | self.xmin = None |
|
|
1238 | 1302 | self.xmax = None |
|
|
1239 | 1303 | |
|
|
1240 | 1304 | self.figfile = None |
|
|
1241 | 1305 | |
|
|
1242 | 1306 | def getSubplots(self): |
|
|
1243 | 1307 | |
|
|
1244 | 1308 | ncol = 1 |
|
|
1245 | 1309 | nrow = self.nplots |
|
|
1246 | 1310 | |
|
|
1247 | 1311 | return nrow, ncol |
|
|
1248 | 1312 | |
|
|
1249 | 1313 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1250 | 1314 | |
|
|
1251 | 1315 | self.__showprofile = showprofile |
|
|
1252 | 1316 | self.nplots = nplots |
|
|
1253 | 1317 | |
|
|
1254 | 1318 | ncolspan = 1 |
|
|
1255 | 1319 | colspan = 1 |
|
|
1256 | 1320 | |
|
|
1257 | 1321 | self.createFigure(id = id, |
|
|
1258 | 1322 | wintitle = wintitle, |
|
|
1259 | 1323 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1260 | 1324 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1261 | 1325 | show=show) |
|
|
1262 | 1326 | |
|
|
1263 | 1327 | nrow, ncol = self.getSubplots() |
|
|
1264 | 1328 | |
|
|
1265 | 1329 | counter = 0 |
|
|
1266 | 1330 | for y in range(nrow): |
|
|
1267 | 1331 | if counter >= self.nplots: |
|
|
1268 | 1332 | break |
|
|
1269 | 1333 | |
|
|
1270 | 1334 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
1271 | 1335 | counter += 1 |
|
|
1272 | 1336 | |
|
|
1273 | 1337 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1274 | 1338 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
1275 | 1339 | zmaxVertical = None, zminVertical = None, zmaxZonal = None, zminZonal = None, |
|
|
1276 | 1340 | timerange=None, SNRthresh = -numpy.inf, SNRmin = None, SNRmax = None, SNR_1 = False, |
|
|
1277 | 1341 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
1278 | 1342 | server=None, folder=None, username=None, password=None, |
|
|
1279 | 1343 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1280 | 1344 | """ |
|
|
1281 | 1345 | |
|
|
1282 | 1346 | Input: |
|
|
1283 | 1347 | dataOut : |
|
|
1284 | 1348 | id : |
|
|
1285 | 1349 | wintitle : |
|
|
1286 | 1350 | channelList : |
|
|
1287 | 1351 | showProfile : |
|
|
1288 | 1352 | xmin : None, |
|
|
1289 | 1353 | xmax : None, |
|
|
1290 | 1354 | ymin : None, |
|
|
1291 | 1355 | ymax : None, |
|
|
1292 | 1356 | zmin : None, |
|
|
1293 | 1357 | zmax : None |
|
|
1294 | 1358 | """ |
|
|
1295 | 1359 | |
|
|
1296 | 1360 | if timerange is not None: |
|
|
1297 | 1361 | self.timerange = timerange |
|
|
1298 | 1362 | |
|
|
1299 | 1363 | tmin = None |
|
|
1300 | 1364 | tmax = None |
|
|
1301 | 1365 | |
|
|
1302 | 1366 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
|
|
1303 | 1367 | # y = dataOut.heightList |
|
|
1304 | 1368 | y = dataOut.heightList |
|
|
1305 | 1369 | |
|
|
1306 | 1370 | z = dataOut.data_output |
|
|
1307 | 1371 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
1308 | 1372 | nplotsw = nplots |
|
|
1309 | 1373 | |
|
|
1310 | 1374 | #If there is a SNR function defined |
|
|
1311 | 1375 | if dataOut.data_SNR is not None: |
|
|
1312 | 1376 | nplots += 1 |
|
|
1313 | 1377 | SNR = dataOut.data_SNR |
|
|
1314 | 1378 | |
|
|
1315 | 1379 | if SNR_1: |
|
|
1316 | 1380 | SNR += 1 |
|
|
1317 | 1381 | |
|
|
1318 | 1382 | SNRavg = numpy.average(SNR, axis=0) |
|
|
1319 | 1383 | |
|
|
1320 | 1384 | SNRdB = 10*numpy.log10(SNR) |
|
|
1321 | 1385 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
1322 | 1386 | |
|
|
1323 | 1387 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
1324 | 1388 | |
|
|
1325 | 1389 | for i in range(nplotsw): |
|
|
1326 | 1390 | z[i,ind] = numpy.nan |
|
|
1327 | 1391 | |
|
|
1328 | 1392 | |
|
|
1329 | 1393 | showprofile = False |
|
|
1330 | 1394 | # thisDatetime = dataOut.datatime |
|
|
1331 | 1395 | thisDatetime = datetime.datetime.utcfromtimestamp(x[1]) |
|
|
1332 | 1396 | title = wintitle + " EW Drifts" |
|
|
1333 | 1397 | xlabel = "" |
|
|
1334 | 1398 | ylabel = "Height (Km)" |
|
|
1335 | 1399 | |
|
|
1336 | 1400 | if not self.isConfig: |
|
|
1337 | 1401 | |
|
|
1338 | 1402 | self.setup(id=id, |
|
|
1339 | 1403 | nplots=nplots, |
|
|
1340 | 1404 | wintitle=wintitle, |
|
|
1341 | 1405 | showprofile=showprofile, |
|
|
1342 | 1406 | show=show) |
|
|
1343 | 1407 | |
|
|
1344 | 1408 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1345 | 1409 | |
|
|
1346 | 1410 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
1347 | 1411 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
1348 | 1412 | |
|
|
1349 | 1413 | if zmaxZonal == None: zmaxZonal = numpy.nanmax(abs(z[0,:])) |
|
|
1350 | 1414 | if zminZonal == None: zminZonal = -zmaxZonal |
|
|
1351 | 1415 | if zmaxVertical == None: zmaxVertical = numpy.nanmax(abs(z[1,:])) |
|
|
1352 | 1416 | if zminVertical == None: zminVertical = -zmaxVertical |
|
|
1353 | 1417 | |
|
|
1354 | 1418 | if dataOut.data_SNR is not None: |
|
|
1355 | 1419 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
1356 | 1420 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
1357 | 1421 | |
|
|
1358 | 1422 | self.FTP_WEI = ftp_wei |
|
|
1359 | 1423 | self.EXP_CODE = exp_code |
|
|
1360 | 1424 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1361 | 1425 | self.PLOT_POS = plot_pos |
|
|
1362 | 1426 | |
|
|
1363 | 1427 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1364 | 1428 | self.isConfig = True |
|
|
1365 | 1429 | |
|
|
1366 | 1430 | |
|
|
1367 | 1431 | self.setWinTitle(title) |
|
|
1368 | 1432 | |
|
|
1369 | 1433 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
1370 | 1434 | x[1] = self.xmax |
|
|
1371 | 1435 | |
|
|
1372 | 1436 | strWind = ['Zonal','Vertical'] |
|
|
1373 | 1437 | strCb = 'Velocity (m/s)' |
|
|
1374 | 1438 | zmaxVector = [zmaxZonal, zmaxVertical] |
|
|
1375 | 1439 | zminVector = [zminZonal, zminVertical] |
|
|
1376 | 1440 | |
|
|
1377 | 1441 | for i in range(nplotsw): |
|
|
1378 | 1442 | |
|
|
1379 | 1443 | title = "%s Drifts: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1380 | 1444 | axes = self.axesList[i*self.__nsubplots] |
|
|
1381 | 1445 | |
|
|
1382 | 1446 | z1 = z[i,:].reshape((1,-1)) |
|
|
1383 | 1447 | |
|
|
1384 | 1448 | axes.pcolorbuffer(x, y, z1, |
|
|
1385 | 1449 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
1386 | 1450 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1387 | 1451 | ticksize=9, cblabel=strCb, cbsize="1%", colormap="RdBu_r") |
|
|
1388 | 1452 | |
|
|
1389 | 1453 | if dataOut.data_SNR is not None: |
|
|
1390 | 1454 | i += 1 |
|
|
1391 | 1455 | if SNR_1: |
|
|
1392 | 1456 | title = "Signal Noise Ratio + 1 (SNR+1): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1393 | 1457 | else: |
|
|
1394 | 1458 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1395 | 1459 | axes = self.axesList[i*self.__nsubplots] |
|
|
1396 | 1460 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
1397 | 1461 | |
|
|
1398 | 1462 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
1399 | 1463 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
1400 | 1464 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1401 | 1465 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
1402 | 1466 | |
|
|
1403 | 1467 | self.draw() |
|
|
1404 | 1468 | |
|
|
1405 | 1469 | if x[1] >= self.axesList[0].xmax: |
|
|
1406 | 1470 | self.counter_imagwr = wr_period |
|
|
1407 | 1471 | self.isConfig = False |
|
|
1408 | 1472 | self.figfile = None |
|
|
1409 | 1473 | |
|
|
1410 | 1474 | |
|
|
1411 | 1475 | |
|
|
1412 | 1476 | |
|
|
1413 | 1477 | class PhasePlot(Figure): |
|
|
1414 | 1478 | |
|
|
1415 | 1479 | __isConfig = None |
|
|
1416 | 1480 | __nsubplots = None |
|
|
1417 | 1481 | |
|
|
1418 | 1482 | PREFIX = 'mphase' |
|
|
1419 | 1483 | |
|
|
1420 | 1484 | def __init__(self, **kwargs): |
|
|
1421 | 1485 | Figure.__init__(self, **kwargs) |
|
|
1422 | 1486 | self.timerange = 24*60*60 |
|
|
1423 | 1487 | self.isConfig = False |
|
|
1424 | 1488 | self.__nsubplots = 1 |
|
|
1425 | 1489 | self.counter_imagwr = 0 |
|
|
1426 | 1490 | self.WIDTH = 600 |
|
|
1427 | 1491 | self.HEIGHT = 300 |
|
|
1428 | 1492 | self.WIDTHPROF = 120 |
|
|
1429 | 1493 | self.HEIGHTPROF = 0 |
|
|
1430 | 1494 | self.xdata = None |
|
|
1431 | 1495 | self.ydata = None |
|
|
1432 | 1496 | |
|
|
1433 | 1497 | self.PLOT_CODE = MPHASE_CODE |
|
|
1434 | 1498 | |
|
|
1435 | 1499 | self.FTP_WEI = None |
|
|
1436 | 1500 | self.EXP_CODE = None |
|
|
1437 | 1501 | self.SUB_EXP_CODE = None |
|
|
1438 | 1502 | self.PLOT_POS = None |
|
|
1439 | 1503 | |
|
|
1440 | 1504 | |
|
|
1441 | 1505 | self.filename_phase = None |
|
|
1442 | 1506 | |
|
|
1443 | 1507 | self.figfile = None |
|
|
1444 | 1508 | |
|
|
1445 | 1509 | def getSubplots(self): |
|
|
1446 | 1510 | |
|
|
1447 | 1511 | ncol = 1 |
|
|
1448 | 1512 | nrow = 1 |
|
|
1449 | 1513 | |
|
|
1450 | 1514 | return nrow, ncol |
|
|
1451 | 1515 | |
|
|
1452 | 1516 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1453 | 1517 | |
|
|
1454 | 1518 | self.__showprofile = showprofile |
|
|
1455 | 1519 | self.nplots = nplots |
|
|
1456 | 1520 | |
|
|
1457 | 1521 | ncolspan = 7 |
|
|
1458 | 1522 | colspan = 6 |
|
|
1459 | 1523 | self.__nsubplots = 2 |
|
|
1460 | 1524 | |
|
|
1461 | 1525 | self.createFigure(id = id, |
|
|
1462 | 1526 | wintitle = wintitle, |
|
|
1463 | 1527 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1464 | 1528 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1465 | 1529 | show=show) |
|
|
1466 | 1530 | |
|
|
1467 | 1531 | nrow, ncol = self.getSubplots() |
|
|
1468 | 1532 | |
|
|
1469 | 1533 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1470 | 1534 | |
|
|
1471 | 1535 | |
|
|
1472 | 1536 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
1473 | 1537 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1474 | 1538 | timerange=None, |
|
|
1475 | 1539 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1476 | 1540 | server=None, folder=None, username=None, password=None, |
|
|
1477 | 1541 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1478 | 1542 | |
|
|
1479 | 1543 | |
|
|
1480 | 1544 | tmin = None |
|
|
1481 | 1545 | tmax = None |
|
|
1482 | 1546 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
|
|
1483 | 1547 | y = dataOut.getHeiRange() |
|
|
1484 | 1548 | |
|
|
1485 | 1549 | |
|
|
1486 | 1550 | #thisDatetime = dataOut.datatime |
|
|
1487 | 1551 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
1488 | 1552 | title = wintitle + " Phase of Beacon Signal" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1489 | 1553 | xlabel = "Local Time" |
|
|
1490 | 1554 | ylabel = "Phase" |
|
|
1491 | 1555 | |
|
|
1492 | 1556 | |
|
|
1493 | 1557 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
1494 | 1558 | phase_beacon = dataOut.data_output |
|
|
1495 | 1559 | update_figfile = False |
|
|
1496 | 1560 | |
|
|
1497 | 1561 | if not self.isConfig: |
|
|
1498 | 1562 | |
|
|
1499 | 1563 | self.nplots = phase_beacon.size |
|
|
1500 | 1564 | |
|
|
1501 | 1565 | self.setup(id=id, |
|
|
1502 | 1566 | nplots=self.nplots, |
|
|
1503 | 1567 | wintitle=wintitle, |
|
|
1504 | 1568 | showprofile=showprofile, |
|
|
1505 | 1569 | show=show) |
|
|
1506 | 1570 | |
|
|
1507 | 1571 | if timerange is not None: |
|
|
1508 | 1572 | self.timerange = timerange |
|
|
1509 | 1573 | |
|
|
1510 | 1574 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1511 | 1575 | |
|
|
1512 | 1576 | if ymin == None: ymin = numpy.nanmin(phase_beacon) - 10.0 |
|
|
1513 | 1577 | if ymax == None: ymax = numpy.nanmax(phase_beacon) + 10.0 |
|
|
1514 | 1578 | |
|
|
1515 | 1579 | self.FTP_WEI = ftp_wei |
|
|
1516 | 1580 | self.EXP_CODE = exp_code |
|
|
1517 | 1581 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1518 | 1582 | self.PLOT_POS = plot_pos |
|
|
1519 | 1583 | |
|
|
1520 | 1584 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1521 | 1585 | self.isConfig = True |
|
|
1522 | 1586 | self.figfile = figfile |
|
|
1523 | 1587 | self.xdata = numpy.array([]) |
|
|
1524 | 1588 | self.ydata = numpy.array([]) |
|
|
1525 | 1589 | |
|
|
1526 | 1590 | #open file beacon phase |
|
|
1527 | 1591 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1528 | 1592 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1529 | 1593 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
1530 | 1594 | update_figfile = True |
|
|
1531 | 1595 | |
|
|
1532 | 1596 | |
|
|
1533 | 1597 | #store data beacon phase |
|
|
1534 | 1598 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
1535 | 1599 | |
|
|
1536 | 1600 | self.setWinTitle(title) |
|
|
1537 | 1601 | |
|
|
1538 | 1602 | |
|
|
1539 | 1603 | title = "Phase Offset %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1540 | 1604 | |
|
|
1541 | 1605 | legendlabels = ["phase %d"%(chan) for chan in numpy.arange(self.nplots)] |
|
|
1542 | 1606 | |
|
|
1543 | 1607 | axes = self.axesList[0] |
|
|
1544 | 1608 | |
|
|
1545 | 1609 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1546 | 1610 | |
|
|
1547 | 1611 | if len(self.ydata)==0: |
|
|
1548 | 1612 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
1549 | 1613 | else: |
|
|
1550 | 1614 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
1551 | 1615 | |
|
|
1552 | 1616 | |
|
|
1553 | 1617 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1554 | 1618 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1555 | 1619 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1556 | 1620 | XAxisAsTime=True, grid='both' |
|
|
1557 | 1621 | ) |
|
|
1558 | 1622 | |
|
|
1559 | 1623 | self.draw() |
|
|
1560 | 1624 | |
|
|
1561 | 1625 | self.save(figpath=figpath, |
|
|
1562 | 1626 | figfile=figfile, |
|
|
1563 | 1627 | save=save, |
|
|
1564 | 1628 | ftp=ftp, |
|
|
1565 | 1629 | wr_period=wr_period, |
|
|
1566 | 1630 | thisDatetime=thisDatetime, |
|
|
1567 | 1631 | update_figfile=update_figfile) |
|
|
1568 | 1632 | |
|
|
1569 | 1633 | if dataOut.ltctime + dataOut.outputInterval >= self.xmax: |
|
|
1570 | 1634 | self.counter_imagwr = wr_period |
|
|
1571 | 1635 | self.isConfig = False |
|
|
1572 | 1636 | update_figfile = True |
|
|
1573 | 1637 | |
|
|
1574 | 1638 | |
|
|
1575 | 1639 | |
|
|
1576 | 1640 | class NSMeteorDetection1Plot(Figure): |
|
|
1577 | 1641 | |
|
|
1578 | 1642 | isConfig = None |
|
|
1579 | 1643 | __nsubplots = None |
|
|
1580 | 1644 | |
|
|
1581 | 1645 | WIDTHPROF = None |
|
|
1582 | 1646 | HEIGHTPROF = None |
|
|
1583 | 1647 | PREFIX = 'nsm' |
|
|
1584 | 1648 | |
|
|
1585 | 1649 | zminList = None |
|
|
1586 | 1650 | zmaxList = None |
|
|
1587 | 1651 | cmapList = None |
|
|
1588 | 1652 | titleList = None |
|
|
1589 | 1653 | nPairs = None |
|
|
1590 | 1654 | nChannels = None |
|
|
1591 | 1655 | nParam = None |
|
|
1592 | 1656 | |
|
|
1657 | parameters = { | |
|
|
1658 | 'id': global_type_string, | |
|
|
1659 | 'wintitle': global_type_string, | |
|
|
1660 | 'channelList': global_type_list, | |
|
|
1661 | 'showprofile': global_type_boolean, | |
|
|
1662 | 'xmin': global_type_float, | |
|
|
1663 | 'xmax': global_type_float, | |
|
|
1664 | 'ymin': global_type_float, | |
|
|
1665 | 'ymax': global_type_float, | |
|
|
1666 | 'SNRmin': global_type_float, | |
|
|
1667 | 'SNRmax': global_type_float, | |
|
|
1668 | 'vmin': global_type_float, | |
|
|
1669 | 'vmax': global_type_float, | |
|
|
1670 | 'wmin': global_type_float, | |
|
|
1671 | 'wmax': global_type_float, | |
|
|
1672 | 'mode': global_type_string, | |
|
|
1673 | 'save': global_type_boolean, | |
|
|
1674 | 'figpath': global_type_string, | |
|
|
1675 | 'figfile': global_type_string, | |
|
|
1676 | 'show': global_type_boolean, | |
|
|
1677 | 'ftp': global_type_string, | |
|
|
1678 | 'wr_period': global_type_integer, | |
|
|
1679 | 'server': global_type_string, | |
|
|
1680 | 'folder': global_type_string, | |
|
|
1681 | 'username': global_type_string, | |
|
|
1682 | 'password': global_type_string, | |
|
|
1683 | 'ftp_wei': global_type_integer, | |
|
|
1684 | 'exp_code': global_type_integer, | |
|
|
1685 | 'sub_exp_code': global_type_integer, | |
|
|
1686 | 'plot_pos': global_type_integer, | |
|
|
1687 | 'realtime': global_type_boolean, | |
|
|
1688 | 'xaxis': global_type_string, | |
|
|
1689 | } | |
|
|
1690 | ||
|
|
1593 | 1691 | def __init__(self, **kwargs): |
|
|
1594 | 1692 | Figure.__init__(self, **kwargs) |
|
|
1595 | 1693 | self.isConfig = False |
|
|
1596 | 1694 | self.__nsubplots = 1 |
|
|
1597 | 1695 | |
|
|
1598 | 1696 | self.WIDTH = 750 |
|
|
1599 | 1697 | self.HEIGHT = 250 |
|
|
1600 | 1698 | self.WIDTHPROF = 120 |
|
|
1601 | 1699 | self.HEIGHTPROF = 0 |
|
|
1602 | 1700 | self.counter_imagwr = 0 |
|
|
1603 | 1701 | |
|
|
1604 | 1702 | self.PLOT_CODE = SPEC_CODE |
|
|
1605 | 1703 | |
|
|
1606 | 1704 | self.FTP_WEI = None |
|
|
1607 | 1705 | self.EXP_CODE = None |
|
|
1608 | 1706 | self.SUB_EXP_CODE = None |
|
|
1609 | 1707 | self.PLOT_POS = None |
|
|
1610 | 1708 | |
|
|
1611 | 1709 | self.__xfilter_ena = False |
|
|
1612 | 1710 | self.__yfilter_ena = False |
|
|
1613 | 1711 | |
|
|
1614 | 1712 | def getSubplots(self): |
|
|
1615 | 1713 | |
|
|
1616 | 1714 | ncol = 3 |
|
|
1617 | 1715 | nrow = int(numpy.ceil(self.nplots/3.0)) |
|
|
1618 | 1716 | |
|
|
1619 | 1717 | return nrow, ncol |
|
|
1620 | 1718 | |
|
|
1621 | 1719 | def setup(self, id, nplots, wintitle, show=True): |
|
|
1622 | 1720 | |
|
|
1623 | 1721 | self.nplots = nplots |
|
|
1624 | 1722 | |
|
|
1625 | 1723 | ncolspan = 1 |
|
|
1626 | 1724 | colspan = 1 |
|
|
1627 | 1725 | |
|
|
1628 | 1726 | self.createFigure(id = id, |
|
|
1629 | 1727 | wintitle = wintitle, |
|
|
1630 | 1728 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1631 | 1729 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1632 | 1730 | show=show) |
|
|
1633 | 1731 | |
|
|
1634 | 1732 | nrow, ncol = self.getSubplots() |
|
|
1635 | 1733 | |
|
|
1636 | 1734 | counter = 0 |
|
|
1637 | 1735 | for y in range(nrow): |
|
|
1638 | 1736 | for x in range(ncol): |
|
|
1639 | 1737 | |
|
|
1640 | 1738 | if counter >= self.nplots: |
|
|
1641 | 1739 | break |
|
|
1642 | 1740 | |
|
|
1643 | 1741 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1644 | 1742 | |
|
|
1645 | 1743 | counter += 1 |
|
|
1646 | 1744 | |
|
|
1647 | 1745 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
1648 | 1746 | xmin=None, xmax=None, ymin=None, ymax=None, SNRmin=None, SNRmax=None, |
|
|
1649 | 1747 | vmin=None, vmax=None, wmin=None, wmax=None, mode = 'SA', |
|
|
1650 | 1748 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1651 | 1749 | server=None, folder=None, username=None, password=None, |
|
|
1652 | 1750 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
1653 | 1751 | xaxis="frequency"): |
|
|
1654 | 1752 | |
|
|
1655 | 1753 | """ |
|
|
1656 | 1754 | |
|
|
1657 | 1755 | Input: |
|
|
1658 | 1756 | dataOut : |
|
|
1659 | 1757 | id : |
|
|
1660 | 1758 | wintitle : |
|
|
1661 | 1759 | channelList : |
|
|
1662 | 1760 | showProfile : |
|
|
1663 | 1761 | xmin : None, |
|
|
1664 | 1762 | xmax : None, |
|
|
1665 | 1763 | ymin : None, |
|
|
1666 | 1764 | ymax : None, |
|
|
1667 | 1765 | zmin : None, |
|
|
1668 | 1766 | zmax : None |
|
|
1669 | 1767 | """ |
|
|
1670 | 1768 | #SEPARAR EN DOS PLOTS |
|
|
1671 | 1769 | nParam = dataOut.data_param.shape[1] - 3 |
|
|
1672 | 1770 | |
|
|
1673 | 1771 | utctime = dataOut.data_param[0,0] |
|
|
1674 | 1772 | tmet = dataOut.data_param[:,1].astype(int) |
|
|
1675 | 1773 | hmet = dataOut.data_param[:,2].astype(int) |
|
|
1676 | 1774 | |
|
|
1677 | 1775 | x = dataOut.abscissaList |
|
|
1678 | 1776 | y = dataOut.heightList |
|
|
1679 | 1777 | |
|
|
1680 | 1778 | z = numpy.zeros((nParam, y.size, x.size - 1)) |
|
|
1681 | 1779 | z[:,:] = numpy.nan |
|
|
1682 | 1780 | z[:,hmet,tmet] = dataOut.data_param[:,3:].T |
|
|
1683 | 1781 | z[0,:,:] = 10*numpy.log10(z[0,:,:]) |
|
|
1684 | 1782 | |
|
|
1685 | 1783 | xlabel = "Time (s)" |
|
|
1686 | 1784 | ylabel = "Range (km)" |
|
|
1687 | 1785 | |
|
|
1688 | 1786 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
1689 | 1787 | |
|
|
1690 | 1788 | if not self.isConfig: |
|
|
1691 | 1789 | |
|
|
1692 | 1790 | nplots = nParam |
|
|
1693 | 1791 | |
|
|
1694 | 1792 | self.setup(id=id, |
|
|
1695 | 1793 | nplots=nplots, |
|
|
1696 | 1794 | wintitle=wintitle, |
|
|
1697 | 1795 | show=show) |
|
|
1698 | 1796 | |
|
|
1699 | 1797 | if xmin is None: xmin = numpy.nanmin(x) |
|
|
1700 | 1798 | if xmax is None: xmax = numpy.nanmax(x) |
|
|
1701 | 1799 | if ymin is None: ymin = numpy.nanmin(y) |
|
|
1702 | 1800 | if ymax is None: ymax = numpy.nanmax(y) |
|
|
1703 | 1801 | if SNRmin is None: SNRmin = numpy.nanmin(z[0,:]) |
|
|
1704 | 1802 | if SNRmax is None: SNRmax = numpy.nanmax(z[0,:]) |
|
|
1705 | 1803 | if vmax is None: vmax = numpy.nanmax(numpy.abs(z[1,:])) |
|
|
1706 | 1804 | if vmin is None: vmin = -vmax |
|
|
1707 | 1805 | if wmin is None: wmin = 0 |
|
|
1708 | 1806 | if wmax is None: wmax = 50 |
|
|
1709 | 1807 | |
|
|
1710 | 1808 | pairsList = dataOut.groupList |
|
|
1711 | 1809 | self.nPairs = len(dataOut.groupList) |
|
|
1712 | 1810 | |
|
|
1713 | 1811 | zminList = [SNRmin, vmin, cmin] + [pmin]*self.nPairs |
|
|
1714 | 1812 | zmaxList = [SNRmax, vmax, cmax] + [pmax]*self.nPairs |
|
|
1715 | 1813 | titleList = ["SNR","Radial Velocity","Coherence"] |
|
|
1716 | 1814 | cmapList = ["jet","RdBu_r","jet"] |
|
|
1717 | 1815 | |
|
|
1718 | 1816 | for i in range(self.nPairs): |
|
|
1719 | 1817 | strAux1 = "Phase Difference "+ str(pairsList[i][0]) + str(pairsList[i][1]) |
|
|
1720 | 1818 | titleList = titleList + [strAux1] |
|
|
1721 | 1819 | cmapList = cmapList + ["RdBu_r"] |
|
|
1722 | 1820 | |
|
|
1723 | 1821 | self.zminList = zminList |
|
|
1724 | 1822 | self.zmaxList = zmaxList |
|
|
1725 | 1823 | self.cmapList = cmapList |
|
|
1726 | 1824 | self.titleList = titleList |
|
|
1727 | 1825 | |
|
|
1728 | 1826 | self.FTP_WEI = ftp_wei |
|
|
1729 | 1827 | self.EXP_CODE = exp_code |
|
|
1730 | 1828 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1731 | 1829 | self.PLOT_POS = plot_pos |
|
|
1732 | 1830 | |
|
|
1733 | 1831 | self.isConfig = True |
|
|
1734 | 1832 | |
|
|
1735 | 1833 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
1736 | 1834 | |
|
|
1737 | 1835 | for i in range(nParam): |
|
|
1738 | 1836 | title = self.titleList[i] + ": " +str_datetime |
|
|
1739 | 1837 | axes = self.axesList[i] |
|
|
1740 | 1838 | axes.pcolor(x, y, z[i,:].T, |
|
|
1741 | 1839 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=self.zminList[i], zmax=self.zmaxList[i], |
|
|
1742 | 1840 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=self.cmapList[i],ticksize=9, cblabel='') |
|
|
1743 | 1841 | self.draw() |
|
|
1744 | 1842 | |
|
|
1745 | 1843 | if figfile == None: |
|
|
1746 | 1844 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1747 | 1845 | name = str_datetime |
|
|
1748 | 1846 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1749 | 1847 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
1750 | 1848 | figfile = self.getFilename(name) |
|
|
1751 | 1849 | |
|
|
1752 | 1850 | self.save(figpath=figpath, |
|
|
1753 | 1851 | figfile=figfile, |
|
|
1754 | 1852 | save=save, |
|
|
1755 | 1853 | ftp=ftp, |
|
|
1756 | 1854 | wr_period=wr_period, |
|
|
1757 | 1855 | thisDatetime=thisDatetime) |
|
|
1758 | 1856 | |
|
|
1759 | 1857 | |
|
|
1760 | 1858 | class NSMeteorDetection2Plot(Figure): |
|
|
1761 | 1859 | |
|
|
1762 | 1860 | isConfig = None |
|
|
1763 | 1861 | __nsubplots = None |
|
|
1764 | 1862 | |
|
|
1765 | 1863 | WIDTHPROF = None |
|
|
1766 | 1864 | HEIGHTPROF = None |
|
|
1767 | 1865 | PREFIX = 'nsm' |
|
|
1768 | 1866 | |
|
|
1769 | 1867 | zminList = None |
|
|
1770 | 1868 | zmaxList = None |
|
|
1771 | 1869 | cmapList = None |
|
|
1772 | 1870 | titleList = None |
|
|
1773 | 1871 | nPairs = None |
|
|
1774 | 1872 | nChannels = None |
|
|
1775 | 1873 | nParam = None |
|
|
1776 | 1874 | |
|
|
1875 | parameters = { | |
|
|
1876 | 'id': global_type_string, | |
|
|
1877 | 'wintitle': global_type_string, | |
|
|
1878 | 'channelList': global_type_list, | |
|
|
1879 | 'showprofile': global_type_boolean, | |
|
|
1880 | 'xmin': global_type_float, | |
|
|
1881 | 'xmax': global_type_float, | |
|
|
1882 | 'ymin': global_type_float, | |
|
|
1883 | 'ymax': global_type_float, | |
|
|
1884 | 'SNRmin': global_type_float, | |
|
|
1885 | 'SNRmax': global_type_float, | |
|
|
1886 | 'vmin': global_type_float, | |
|
|
1887 | 'vmax': global_type_float, | |
|
|
1888 | 'wmin': global_type_float, | |
|
|
1889 | 'wmax': global_type_float, | |
|
|
1890 | 'mode': global_type_string, | |
|
|
1891 | 'save': global_type_boolean, | |
|
|
1892 | 'figpath': global_type_string, | |
|
|
1893 | 'figfile': global_type_string, | |
|
|
1894 | 'show': global_type_string, | |
|
|
1895 | 'ftp': global_type_boolean, | |
|
|
1896 | 'wr_period': global_type_integer, | |
|
|
1897 | 'server': global_type_string, | |
|
|
1898 | 'folder': global_type_string, | |
|
|
1899 | 'username': global_type_string, | |
|
|
1900 | 'password': global_type_string, | |
|
|
1901 | 'ftp_wei': global_type_integer, | |
|
|
1902 | 'exp_code': global_type_integer, | |
|
|
1903 | 'sub_exp_code': global_type_integer, | |
|
|
1904 | 'plot_pos': global_type_integer, | |
|
|
1905 | 'realtime': global_type_boolean, | |
|
|
1906 | 'xaxis': global_type_string, | |
|
|
1907 | } | |
|
|
1908 | ||
|
|
1777 | 1909 | def __init__(self, **kwargs): |
|
|
1778 | 1910 | Figure.__init__(self, **kwargs) |
|
|
1779 | 1911 | self.isConfig = False |
|
|
1780 | 1912 | self.__nsubplots = 1 |
|
|
1781 | 1913 | |
|
|
1782 | 1914 | self.WIDTH = 750 |
|
|
1783 | 1915 | self.HEIGHT = 250 |
|
|
1784 | 1916 | self.WIDTHPROF = 120 |
|
|
1785 | 1917 | self.HEIGHTPROF = 0 |
|
|
1786 | 1918 | self.counter_imagwr = 0 |
|
|
1787 | 1919 | |
|
|
1788 | 1920 | self.PLOT_CODE = SPEC_CODE |
|
|
1789 | 1921 | |
|
|
1790 | 1922 | self.FTP_WEI = None |
|
|
1791 | 1923 | self.EXP_CODE = None |
|
|
1792 | 1924 | self.SUB_EXP_CODE = None |
|
|
1793 | 1925 | self.PLOT_POS = None |
|
|
1794 | 1926 | |
|
|
1795 | 1927 | self.__xfilter_ena = False |
|
|
1796 | 1928 | self.__yfilter_ena = False |
|
|
1797 | 1929 | |
|
|
1798 | 1930 | def getSubplots(self): |
|
|
1799 | 1931 | |
|
|
1800 | 1932 | ncol = 3 |
|
|
1801 | 1933 | nrow = int(numpy.ceil(self.nplots/3.0)) |
|
|
1802 | 1934 | |
|
|
1803 | 1935 | return nrow, ncol |
|
|
1804 | 1936 | |
|
|
1805 | 1937 | def setup(self, id, nplots, wintitle, show=True): |
|
|
1806 | 1938 | |
|
|
1807 | 1939 | self.nplots = nplots |
|
|
1808 | 1940 | |
|
|
1809 | 1941 | ncolspan = 1 |
|
|
1810 | 1942 | colspan = 1 |
|
|
1811 | 1943 | |
|
|
1812 | 1944 | self.createFigure(id = id, |
|
|
1813 | 1945 | wintitle = wintitle, |
|
|
1814 | 1946 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1815 | 1947 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1816 | 1948 | show=show) |
|
|
1817 | 1949 | |
|
|
1818 | 1950 | nrow, ncol = self.getSubplots() |
|
|
1819 | 1951 | |
|
|
1820 | 1952 | counter = 0 |
|
|
1821 | 1953 | for y in range(nrow): |
|
|
1822 | 1954 | for x in range(ncol): |
|
|
1823 | 1955 | |
|
|
1824 | 1956 | if counter >= self.nplots: |
|
|
1825 | 1957 | break |
|
|
1826 | 1958 | |
|
|
1827 | 1959 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1828 | 1960 | |
|
|
1829 | 1961 | counter += 1 |
|
|
1830 | 1962 | |
|
|
1831 | 1963 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
1832 | 1964 | xmin=None, xmax=None, ymin=None, ymax=None, SNRmin=None, SNRmax=None, |
|
|
1833 | 1965 | vmin=None, vmax=None, wmin=None, wmax=None, mode = 'SA', |
|
|
1834 | 1966 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1835 | 1967 | server=None, folder=None, username=None, password=None, |
|
|
1836 | 1968 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
1837 | 1969 | xaxis="frequency"): |
|
|
1838 | 1970 | |
|
|
1839 | 1971 | """ |
|
|
1840 | 1972 | |
|
|
1841 | 1973 | Input: |
|
|
1842 | 1974 | dataOut : |
|
|
1843 | 1975 | id : |
|
|
1844 | 1976 | wintitle : |
|
|
1845 | 1977 | channelList : |
|
|
1846 | 1978 | showProfile : |
|
|
1847 | 1979 | xmin : None, |
|
|
1848 | 1980 | xmax : None, |
|
|
1849 | 1981 | ymin : None, |
|
|
1850 | 1982 | ymax : None, |
|
|
1851 | 1983 | zmin : None, |
|
|
1852 | 1984 | zmax : None |
|
|
1853 | 1985 | """ |
|
|
1854 | 1986 | #Rebuild matrix |
|
|
1855 | 1987 | utctime = dataOut.data_param[0,0] |
|
|
1856 | 1988 | cmet = dataOut.data_param[:,1].astype(int) |
|
|
1857 | 1989 | tmet = dataOut.data_param[:,2].astype(int) |
|
|
1858 | 1990 | hmet = dataOut.data_param[:,3].astype(int) |
|
|
1859 | 1991 | |
|
|
1860 | 1992 | nParam = 3 |
|
|
1861 | 1993 | nChan = len(dataOut.groupList) |
|
|
1862 | 1994 | x = dataOut.abscissaList |
|
|
1863 | 1995 | y = dataOut.heightList |
|
|
1864 | 1996 | |
|
|
1865 | 1997 | z = numpy.full((nChan, nParam, y.size, x.size - 1),numpy.nan) |
|
|
1866 | 1998 | z[cmet,:,hmet,tmet] = dataOut.data_param[:,4:] |
|
|
1867 | 1999 | z[:,0,:,:] = 10*numpy.log10(z[:,0,:,:]) #logarithmic scale |
|
|
1868 | 2000 | z = numpy.reshape(z, (nChan*nParam, y.size, x.size-1)) |
|
|
1869 | 2001 | |
|
|
1870 | 2002 | xlabel = "Time (s)" |
|
|
1871 | 2003 | ylabel = "Range (km)" |
|
|
1872 | 2004 | |
|
|
1873 | 2005 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
1874 | 2006 | |
|
|
1875 | 2007 | if not self.isConfig: |
|
|
1876 | 2008 | |
|
|
1877 | 2009 | nplots = nParam*nChan |
|
|
1878 | 2010 | |
|
|
1879 | 2011 | self.setup(id=id, |
|
|
1880 | 2012 | nplots=nplots, |
|
|
1881 | 2013 | wintitle=wintitle, |
|
|
1882 | 2014 | show=show) |
|
|
1883 | 2015 | |
|
|
1884 | 2016 | if xmin is None: xmin = numpy.nanmin(x) |
|
|
1885 | 2017 | if xmax is None: xmax = numpy.nanmax(x) |
|
|
1886 | 2018 | if ymin is None: ymin = numpy.nanmin(y) |
|
|
1887 | 2019 | if ymax is None: ymax = numpy.nanmax(y) |
|
|
1888 | 2020 | if SNRmin is None: SNRmin = numpy.nanmin(z[0,:]) |
|
|
1889 | 2021 | if SNRmax is None: SNRmax = numpy.nanmax(z[0,:]) |
|
|
1890 | 2022 | if vmax is None: vmax = numpy.nanmax(numpy.abs(z[1,:])) |
|
|
1891 | 2023 | if vmin is None: vmin = -vmax |
|
|
1892 | 2024 | if wmin is None: wmin = 0 |
|
|
1893 | 2025 | if wmax is None: wmax = 50 |
|
|
1894 | 2026 | |
|
|
1895 | 2027 | self.nChannels = nChan |
|
|
1896 | 2028 | |
|
|
1897 | 2029 | zminList = [] |
|
|
1898 | 2030 | zmaxList = [] |
|
|
1899 | 2031 | titleList = [] |
|
|
1900 | 2032 | cmapList = [] |
|
|
1901 | 2033 | for i in range(self.nChannels): |
|
|
1902 | 2034 | strAux1 = "SNR Channel "+ str(i) |
|
|
1903 | 2035 | strAux2 = "Radial Velocity Channel "+ str(i) |
|
|
1904 | 2036 | strAux3 = "Spectral Width Channel "+ str(i) |
|
|
1905 | 2037 | |
|
|
1906 | 2038 | titleList = titleList + [strAux1,strAux2,strAux3] |
|
|
1907 | 2039 | cmapList = cmapList + ["jet","RdBu_r","jet"] |
|
|
1908 | 2040 | zminList = zminList + [SNRmin,vmin,wmin] |
|
|
1909 | 2041 | zmaxList = zmaxList + [SNRmax,vmax,wmax] |
|
|
1910 | 2042 | |
|
|
1911 | 2043 | self.zminList = zminList |
|
|
1912 | 2044 | self.zmaxList = zmaxList |
|
|
1913 | 2045 | self.cmapList = cmapList |
|
|
1914 | 2046 | self.titleList = titleList |
|
|
1915 | 2047 | |
|
|
1916 | 2048 | self.FTP_WEI = ftp_wei |
|
|
1917 | 2049 | self.EXP_CODE = exp_code |
|
|
1918 | 2050 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1919 | 2051 | self.PLOT_POS = plot_pos |
|
|
1920 | 2052 | |
|
|
1921 | 2053 | self.isConfig = True |
|
|
1922 | 2054 | |
|
|
1923 | 2055 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
1924 | 2056 | |
|
|
1925 | 2057 | for i in range(self.nplots): |
|
|
1926 | 2058 | title = self.titleList[i] + ": " +str_datetime |
|
|
1927 | 2059 | axes = self.axesList[i] |
|
|
1928 | 2060 | axes.pcolor(x, y, z[i,:].T, |
|
|
1929 | 2061 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=self.zminList[i], zmax=self.zmaxList[i], |
|
|
1930 | 2062 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=self.cmapList[i],ticksize=9, cblabel='') |
|
|
1931 | 2063 | self.draw() |
|
|
1932 | 2064 | |
|
|
1933 | 2065 | if figfile == None: |
|
|
1934 | 2066 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1935 | 2067 | name = str_datetime |
|
|
1936 | 2068 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1937 | 2069 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
1938 | 2070 | figfile = self.getFilename(name) |
|
|
1939 | 2071 | |
|
|
1940 | 2072 | self.save(figpath=figpath, |
|
|
1941 | 2073 | figfile=figfile, |
|
|
1942 | 2074 | save=save, |
|
|
1943 | 2075 | ftp=ftp, |
|
|
1944 | 2076 | wr_period=wr_period, |
|
|
1945 | 2077 | thisDatetime=thisDatetime) |
| @@ -1,1595 +1,1655 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Created on Jul 9, 2014 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 |
|
|
5 | 5 | ''' |
|
|
6 | 6 | import os |
|
|
7 | 7 | import datetime |
|
|
8 | 8 | import numpy |
|
|
9 | 9 | |
|
|
10 | 10 | from figure import Figure, isRealtime, isTimeInHourRange |
|
|
11 | 11 | from plotting_codes import * |
|
|
12 | 12 | |
|
|
13 | 13 | |
|
|
14 | 14 | class SpectraPlot(Figure): |
|
|
15 | 15 | |
|
|
16 | 16 | isConfig = None |
|
|
17 | 17 | __nsubplots = None |
|
|
18 | 18 | |
|
|
19 | 19 | WIDTHPROF = None |
|
|
20 | 20 | HEIGHTPROF = None |
|
|
21 | 21 | PREFIX = 'spc' |
|
|
22 | 22 | |
|
|
23 | 23 | def __init__(self, **kwargs): |
|
|
24 | 24 | Figure.__init__(self, **kwargs) |
|
|
25 | 25 | self.isConfig = False |
|
|
26 | 26 | self.__nsubplots = 1 |
|
|
27 | 27 | |
|
|
28 | 28 | self.WIDTH = 250 |
|
|
29 | 29 | self.HEIGHT = 250 |
|
|
30 | 30 | self.WIDTHPROF = 120 |
|
|
31 | 31 | self.HEIGHTPROF = 0 |
|
|
32 | 32 | self.counter_imagwr = 0 |
|
|
33 | 33 | |
|
|
34 | 34 | self.PLOT_CODE = SPEC_CODE |
|
|
35 | 35 | |
|
|
36 | 36 | self.FTP_WEI = None |
|
|
37 | 37 | self.EXP_CODE = None |
|
|
38 | 38 | self.SUB_EXP_CODE = None |
|
|
39 | 39 | self.PLOT_POS = None |
|
|
40 | 40 | |
|
|
41 | 41 | self.__xfilter_ena = False |
|
|
42 | 42 | self.__yfilter_ena = False |
|
|
43 | 43 | |
|
|
44 | 44 | def getSubplots(self): |
|
|
45 | 45 | |
|
|
46 | 46 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
47 | 47 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
48 | 48 | |
|
|
49 | 49 | return nrow, ncol |
|
|
50 | 50 | |
|
|
51 | 51 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
52 | 52 | |
|
|
53 | 53 | self.__showprofile = showprofile |
|
|
54 | 54 | self.nplots = nplots |
|
|
55 | 55 | |
|
|
56 | 56 | ncolspan = 1 |
|
|
57 | 57 | colspan = 1 |
|
|
58 | 58 | if showprofile: |
|
|
59 | 59 | ncolspan = 3 |
|
|
60 | 60 | colspan = 2 |
|
|
61 | 61 | self.__nsubplots = 2 |
|
|
62 | 62 | |
|
|
63 | 63 | self.createFigure(id = id, |
|
|
64 | 64 | wintitle = wintitle, |
|
|
65 | 65 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
66 | 66 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
67 | 67 | show=show) |
|
|
68 | 68 | |
|
|
69 | 69 | nrow, ncol = self.getSubplots() |
|
|
70 | 70 | |
|
|
71 | 71 | counter = 0 |
|
|
72 | 72 | for y in range(nrow): |
|
|
73 | 73 | for x in range(ncol): |
|
|
74 | 74 | |
|
|
75 | 75 | if counter >= self.nplots: |
|
|
76 | 76 | break |
|
|
77 | 77 | |
|
|
78 | 78 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
79 | 79 | |
|
|
80 | 80 | if showprofile: |
|
|
81 | 81 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
82 | 82 | |
|
|
83 | 83 | counter += 1 |
|
|
84 | 84 | |
|
|
85 | 85 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
86 | 86 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
87 | 87 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
88 | 88 | server=None, folder=None, username=None, password=None, |
|
|
89 | 89 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
90 | 90 | xaxis="velocity", **kwargs): |
|
|
91 | 91 | |
|
|
92 | 92 | """ |
|
|
93 | 93 | |
|
|
94 | 94 | Input: |
|
|
95 | 95 | dataOut : |
|
|
96 | 96 | id : |
|
|
97 | 97 | wintitle : |
|
|
98 | 98 | channelList : |
|
|
99 | 99 | showProfile : |
|
|
100 | 100 | xmin : None, |
|
|
101 | 101 | xmax : None, |
|
|
102 | 102 | ymin : None, |
|
|
103 | 103 | ymax : None, |
|
|
104 | 104 | zmin : None, |
|
|
105 | 105 | zmax : None |
|
|
106 | 106 | """ |
|
|
107 | 107 | |
|
|
108 | 108 | colormap = kwargs.get('colormap','jet') |
|
|
109 | 109 | |
|
|
110 | 110 | if realtime: |
|
|
111 | 111 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
112 | 112 | print 'Skipping this plot function' |
|
|
113 | 113 | return |
|
|
114 | 114 | |
|
|
115 | 115 | if channelList == None: |
|
|
116 | 116 | channelIndexList = dataOut.channelIndexList |
|
|
117 | 117 | else: |
|
|
118 | 118 | channelIndexList = [] |
|
|
119 | 119 | for channel in channelList: |
|
|
120 | 120 | if channel not in dataOut.channelList: |
|
|
121 | 121 | raise ValueError, "Channel %d is not in dataOut.channelList" %channel |
|
|
122 | 122 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
123 | 123 | |
|
|
124 | 124 | factor = dataOut.normFactor |
|
|
125 | 125 | |
|
|
126 | 126 | if xaxis == "frequency": |
|
|
127 | 127 | x = dataOut.getFreqRange(1)/1000. |
|
|
128 | 128 | xlabel = "Frequency (kHz)" |
|
|
129 | 129 | |
|
|
130 | 130 | elif xaxis == "time": |
|
|
131 | 131 | x = dataOut.getAcfRange(1) |
|
|
132 | 132 | xlabel = "Time (ms)" |
|
|
133 | 133 | |
|
|
134 | 134 | else: |
|
|
135 | 135 | x = dataOut.getVelRange(1) |
|
|
136 | 136 | xlabel = "Velocity (m/s)" |
|
|
137 | 137 | |
|
|
138 | 138 | ylabel = "Range (Km)" |
|
|
139 | 139 | |
|
|
140 | 140 | y = dataOut.getHeiRange() |
|
|
141 | 141 | |
|
|
142 | 142 | z = dataOut.data_spc/factor |
|
|
143 | 143 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
144 | 144 | zdB = 10*numpy.log10(z) |
|
|
145 | 145 | |
|
|
146 | 146 | avg = numpy.average(z, axis=1) |
|
|
147 | 147 | avgdB = 10*numpy.log10(avg) |
|
|
148 | 148 | |
|
|
149 | 149 | noise = dataOut.getNoise()/factor |
|
|
150 | 150 | noisedB = 10*numpy.log10(noise) |
|
|
151 | 151 | |
|
|
152 | 152 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
153 | 153 | title = wintitle + " Spectra" |
|
|
154 | 154 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
155 | 155 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
156 | 156 | |
|
|
157 | 157 | if not self.isConfig: |
|
|
158 | 158 | |
|
|
159 | 159 | nplots = len(channelIndexList) |
|
|
160 | 160 | |
|
|
161 | 161 | self.setup(id=id, |
|
|
162 | 162 | nplots=nplots, |
|
|
163 | 163 | wintitle=wintitle, |
|
|
164 | 164 | showprofile=showprofile, |
|
|
165 | 165 | show=show) |
|
|
166 | 166 | |
|
|
167 | 167 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
168 | 168 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
169 | 169 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
170 | 170 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
171 | 171 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
172 | 172 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
173 | 173 | |
|
|
174 | 174 | self.FTP_WEI = ftp_wei |
|
|
175 | 175 | self.EXP_CODE = exp_code |
|
|
176 | 176 | self.SUB_EXP_CODE = sub_exp_code |
|
|
177 | 177 | self.PLOT_POS = plot_pos |
|
|
178 | 178 | |
|
|
179 | 179 | self.isConfig = True |
|
|
180 | 180 | |
|
|
181 | 181 | self.setWinTitle(title) |
|
|
182 | 182 | |
|
|
183 | 183 | for i in range(self.nplots): |
|
|
184 | 184 | index = channelIndexList[i] |
|
|
185 | 185 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
186 | 186 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[index], noisedB[index], str_datetime) |
|
|
187 | 187 | if len(dataOut.beam.codeList) != 0: |
|
|
188 | 188 | title = "Ch%d:%4.2fdB,%2.2f,%2.2f:%s" %(dataOut.channelList[index], noisedB[index], dataOut.beam.azimuthList[index], dataOut.beam.zenithList[index], str_datetime) |
|
|
189 | 189 | |
|
|
190 | 190 | axes = self.axesList[i*self.__nsubplots] |
|
|
191 | 191 | axes.pcolor(x, y, zdB[index,:,:], |
|
|
192 | 192 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
193 | 193 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=colormap, |
|
|
194 | 194 | ticksize=9, cblabel='') |
|
|
195 | 195 | |
|
|
196 | 196 | if self.__showprofile: |
|
|
197 | 197 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
198 | 198 | axes.pline(avgdB[index,:], y, |
|
|
199 | 199 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
200 | 200 | xlabel='dB', ylabel='', title='', |
|
|
201 | 201 | ytick_visible=False, |
|
|
202 | 202 | grid='x') |
|
|
203 | 203 | |
|
|
204 | 204 | noiseline = numpy.repeat(noisedB[index], len(y)) |
|
|
205 | 205 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
206 | 206 | |
|
|
207 | 207 | self.draw() |
|
|
208 | 208 | |
|
|
209 | 209 | if figfile == None: |
|
|
210 | 210 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
211 | 211 | name = str_datetime |
|
|
212 | 212 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
213 | 213 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
214 | 214 | figfile = self.getFilename(name) |
|
|
215 | 215 | |
|
|
216 | 216 | self.save(figpath=figpath, |
|
|
217 | 217 | figfile=figfile, |
|
|
218 | 218 | save=save, |
|
|
219 | 219 | ftp=ftp, |
|
|
220 | 220 | wr_period=wr_period, |
|
|
221 | 221 | thisDatetime=thisDatetime) |
|
|
222 | 222 | |
|
|
223 | 223 | class CrossSpectraPlot(Figure): |
|
|
224 | 224 | |
|
|
225 | 225 | isConfig = None |
|
|
226 | 226 | __nsubplots = None |
|
|
227 | 227 | |
|
|
228 | 228 | WIDTH = None |
|
|
229 | 229 | HEIGHT = None |
|
|
230 | 230 | WIDTHPROF = None |
|
|
231 | 231 | HEIGHTPROF = None |
|
|
232 | 232 | PREFIX = 'cspc' |
|
|
233 | 233 | |
|
|
234 | parameters = { | |
|
|
235 | 'id': global_type_string, | |
|
|
236 | 'wintitle': global_type_string, | |
|
|
237 | 'pairsList': global_type_pairsList, | |
|
|
238 | 'xmin': global_type_float, | |
|
|
239 | 'xmax': global_type_float, | |
|
|
240 | 'ymin': global_type_float, | |
|
|
241 | 'ymax': global_type_float, | |
|
|
242 | 'zmin': global_type_float, | |
|
|
243 | 'zmax': global_type_float, | |
|
|
244 | 'coh_min': global_type_float, | |
|
|
245 | 'coh_max': global_type_float, | |
|
|
246 | 'phase_min': global_type_float, | |
|
|
247 | 'phase_max': global_type_float, | |
|
|
248 | 'save': global_type_boolean, | |
|
|
249 | 'figpath': global_type_string, | |
|
|
250 | 'figfile': global_type_string, | |
|
|
251 | 'ftp': global_type_boolean, | |
|
|
252 | 'wr_period': global_type_integer, | |
|
|
253 | 'power_cmap': global_type_colormap, | |
|
|
254 | 'coherence_cmap': global_type_colormap, | |
|
|
255 | 'phase_cmap': global_type_colormap, | |
|
|
256 | 'show': global_type_boolean, | |
|
|
257 | 'server': global_type_string, | |
|
|
258 | 'folder': global_type_string, | |
|
|
259 | 'username': global_type_string, | |
|
|
260 | 'password': global_type_string, | |
|
|
261 | 'ftp_wei': global_type_integer, | |
|
|
262 | 'exp_code': global_type_integer, | |
|
|
263 | 'sub_exp_code': global_type_integer, | |
|
|
264 | 'plot_pos': global_type_integer, | |
|
|
265 | 'xaxis': global_type_string, | |
|
|
266 | } | |
|
|
267 | ||
|
|
234 | 268 | def __init__(self, **kwargs): |
|
|
235 | 269 | Figure.__init__(self, **kwargs) |
|
|
236 | 270 | self.isConfig = False |
|
|
237 | 271 | self.__nsubplots = 4 |
|
|
238 | 272 | self.counter_imagwr = 0 |
|
|
239 | 273 | self.WIDTH = 250 |
|
|
240 | 274 | self.HEIGHT = 250 |
|
|
241 | 275 | self.WIDTHPROF = 0 |
|
|
242 | 276 | self.HEIGHTPROF = 0 |
|
|
243 | 277 | |
|
|
244 | 278 | self.PLOT_CODE = CROSS_CODE |
|
|
245 | 279 | self.FTP_WEI = None |
|
|
246 | 280 | self.EXP_CODE = None |
|
|
247 | 281 | self.SUB_EXP_CODE = None |
|
|
248 | 282 | self.PLOT_POS = None |
|
|
249 | 283 | |
|
|
250 | 284 | def getSubplots(self): |
|
|
251 | 285 | |
|
|
252 | 286 | ncol = 4 |
|
|
253 | 287 | nrow = self.nplots |
|
|
254 | 288 | |
|
|
255 | 289 | return nrow, ncol |
|
|
256 | 290 | |
|
|
257 | 291 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
258 | 292 | |
|
|
259 | 293 | self.__showprofile = showprofile |
|
|
260 | 294 | self.nplots = nplots |
|
|
261 | 295 | |
|
|
262 | 296 | ncolspan = 1 |
|
|
263 | 297 | colspan = 1 |
|
|
264 | 298 | |
|
|
265 | 299 | self.createFigure(id = id, |
|
|
266 | 300 | wintitle = wintitle, |
|
|
267 | 301 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
268 | 302 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
269 | 303 | show=True) |
|
|
270 | 304 | |
|
|
271 | 305 | nrow, ncol = self.getSubplots() |
|
|
272 | 306 | |
|
|
273 | 307 | counter = 0 |
|
|
274 | 308 | for y in range(nrow): |
|
|
275 | 309 | for x in range(ncol): |
|
|
276 | 310 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
277 | 311 | |
|
|
278 | 312 | counter += 1 |
|
|
279 | 313 | |
|
|
280 | 314 | def run(self, dataOut, id, wintitle="", pairsList=None, |
|
|
281 | 315 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
282 | 316 | coh_min=None, coh_max=None, phase_min=None, phase_max=None, |
|
|
283 | 317 | save=False, figpath='./', figfile=None, ftp=False, wr_period=1, |
|
|
284 | 318 | power_cmap='jet', coherence_cmap='jet', phase_cmap='RdBu_r', show=True, |
|
|
285 | 319 | server=None, folder=None, username=None, password=None, |
|
|
286 | 320 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, |
|
|
287 | 321 | xaxis='frequency'): |
|
|
288 | 322 | |
|
|
289 | 323 | """ |
|
|
290 | 324 | |
|
|
291 | 325 | Input: |
|
|
292 | 326 | dataOut : |
|
|
293 | 327 | id : |
|
|
294 | 328 | wintitle : |
|
|
295 | 329 | channelList : |
|
|
296 | 330 | showProfile : |
|
|
297 | 331 | xmin : None, |
|
|
298 | 332 | xmax : None, |
|
|
299 | 333 | ymin : None, |
|
|
300 | 334 | ymax : None, |
|
|
301 | 335 | zmin : None, |
|
|
302 | 336 | zmax : None |
|
|
303 | 337 | """ |
|
|
304 | 338 | |
|
|
305 | 339 | if pairsList == None: |
|
|
306 | 340 | pairsIndexList = dataOut.pairsIndexList |
|
|
307 | 341 | else: |
|
|
308 | 342 | pairsIndexList = [] |
|
|
309 | 343 | for pair in pairsList: |
|
|
310 | 344 | if pair not in dataOut.pairsList: |
|
|
311 | 345 | raise ValueError, "Pair %s is not in dataOut.pairsList" %str(pair) |
|
|
312 | 346 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
313 | 347 | |
|
|
314 | 348 | if not pairsIndexList: |
|
|
315 | 349 | return |
|
|
316 | 350 | |
|
|
317 | 351 | if len(pairsIndexList) > 4: |
|
|
318 | 352 | pairsIndexList = pairsIndexList[0:4] |
|
|
319 | 353 | |
|
|
320 | 354 | factor = dataOut.normFactor |
|
|
321 | 355 | x = dataOut.getVelRange(1) |
|
|
322 | 356 | y = dataOut.getHeiRange() |
|
|
323 | 357 | z = dataOut.data_spc[:,:,:]/factor |
|
|
324 | 358 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
325 | 359 | |
|
|
326 | 360 | noise = dataOut.noise/factor |
|
|
327 | 361 | |
|
|
328 | 362 | zdB = 10*numpy.log10(z) |
|
|
329 | 363 | noisedB = 10*numpy.log10(noise) |
|
|
330 | 364 | |
|
|
331 | 365 | if coh_min == None: |
|
|
332 | 366 | coh_min = 0.0 |
|
|
333 | 367 | if coh_max == None: |
|
|
334 | 368 | coh_max = 1.0 |
|
|
335 | 369 | |
|
|
336 | 370 | if phase_min == None: |
|
|
337 | 371 | phase_min = -180 |
|
|
338 | 372 | if phase_max == None: |
|
|
339 | 373 | phase_max = 180 |
|
|
340 | 374 | |
|
|
341 | 375 | #thisDatetime = dataOut.datatime |
|
|
342 | 376 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
343 | 377 | title = wintitle + " Cross-Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
344 | 378 | # xlabel = "Velocity (m/s)" |
|
|
345 | 379 | ylabel = "Range (Km)" |
|
|
346 | 380 | |
|
|
347 | 381 | if xaxis == "frequency": |
|
|
348 | 382 | x = dataOut.getFreqRange(1)/1000. |
|
|
349 | 383 | xlabel = "Frequency (kHz)" |
|
|
350 | 384 | |
|
|
351 | 385 | elif xaxis == "time": |
|
|
352 | 386 | x = dataOut.getAcfRange(1) |
|
|
353 | 387 | xlabel = "Time (ms)" |
|
|
354 | 388 | |
|
|
355 | 389 | else: |
|
|
356 | 390 | x = dataOut.getVelRange(1) |
|
|
357 | 391 | xlabel = "Velocity (m/s)" |
|
|
358 | 392 | |
|
|
359 | 393 | if not self.isConfig: |
|
|
360 | 394 | |
|
|
361 | 395 | nplots = len(pairsIndexList) |
|
|
362 | 396 | |
|
|
363 | 397 | self.setup(id=id, |
|
|
364 | 398 | nplots=nplots, |
|
|
365 | 399 | wintitle=wintitle, |
|
|
366 | 400 | showprofile=False, |
|
|
367 | 401 | show=show) |
|
|
368 | 402 | |
|
|
369 | 403 | avg = numpy.abs(numpy.average(z, axis=1)) |
|
|
370 | 404 | avgdB = 10*numpy.log10(avg) |
|
|
371 | 405 | |
|
|
372 | 406 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
373 | 407 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
374 | 408 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
375 | 409 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
376 | 410 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
377 | 411 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
378 | 412 | |
|
|
379 | 413 | self.FTP_WEI = ftp_wei |
|
|
380 | 414 | self.EXP_CODE = exp_code |
|
|
381 | 415 | self.SUB_EXP_CODE = sub_exp_code |
|
|
382 | 416 | self.PLOT_POS = plot_pos |
|
|
383 | 417 | |
|
|
384 | 418 | self.isConfig = True |
|
|
385 | 419 | |
|
|
386 | 420 | self.setWinTitle(title) |
|
|
387 | 421 | |
|
|
388 | 422 | for i in range(self.nplots): |
|
|
389 | 423 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
390 | 424 | |
|
|
391 | 425 | chan_index0 = dataOut.channelList.index(pair[0]) |
|
|
392 | 426 | chan_index1 = dataOut.channelList.index(pair[1]) |
|
|
393 | 427 | |
|
|
394 | 428 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
395 | 429 | title = "Ch%d: %4.2fdB: %s" %(pair[0], noisedB[chan_index0], str_datetime) |
|
|
396 | 430 | zdB = 10.*numpy.log10(dataOut.data_spc[chan_index0,:,:]/factor) |
|
|
397 | 431 | axes0 = self.axesList[i*self.__nsubplots] |
|
|
398 | 432 | axes0.pcolor(x, y, zdB, |
|
|
399 | 433 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
400 | 434 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
401 | 435 | ticksize=9, colormap=power_cmap, cblabel='') |
|
|
402 | 436 | |
|
|
403 | 437 | title = "Ch%d: %4.2fdB: %s" %(pair[1], noisedB[chan_index1], str_datetime) |
|
|
404 | 438 | zdB = 10.*numpy.log10(dataOut.data_spc[chan_index1,:,:]/factor) |
|
|
405 | 439 | axes0 = self.axesList[i*self.__nsubplots+1] |
|
|
406 | 440 | axes0.pcolor(x, y, zdB, |
|
|
407 | 441 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
408 | 442 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
409 | 443 | ticksize=9, colormap=power_cmap, cblabel='') |
|
|
410 | 444 | |
|
|
411 | 445 | coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:]/numpy.sqrt(dataOut.data_spc[chan_index0,:,:]*dataOut.data_spc[chan_index1,:,:]) |
|
|
412 | 446 | coherence = numpy.abs(coherenceComplex) |
|
|
413 | 447 | # phase = numpy.arctan(-1*coherenceComplex.imag/coherenceComplex.real)*180/numpy.pi |
|
|
414 | 448 | phase = numpy.arctan2(coherenceComplex.imag, coherenceComplex.real)*180/numpy.pi |
|
|
415 | 449 | |
|
|
416 | 450 | title = "Coherence Ch%d * Ch%d" %(pair[0], pair[1]) |
|
|
417 | 451 | axes0 = self.axesList[i*self.__nsubplots+2] |
|
|
418 | 452 | axes0.pcolor(x, y, coherence, |
|
|
419 | 453 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=coh_min, zmax=coh_max, |
|
|
420 | 454 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
421 | 455 | ticksize=9, colormap=coherence_cmap, cblabel='') |
|
|
422 | 456 | |
|
|
423 | 457 | title = "Phase Ch%d * Ch%d" %(pair[0], pair[1]) |
|
|
424 | 458 | axes0 = self.axesList[i*self.__nsubplots+3] |
|
|
425 | 459 | axes0.pcolor(x, y, phase, |
|
|
426 | 460 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=phase_min, zmax=phase_max, |
|
|
427 | 461 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
428 | 462 | ticksize=9, colormap=phase_cmap, cblabel='') |
|
|
429 | 463 | |
|
|
430 | 464 | |
|
|
431 | 465 | |
|
|
432 | 466 | self.draw() |
|
|
433 | 467 | |
|
|
434 | 468 | self.save(figpath=figpath, |
|
|
435 | 469 | figfile=figfile, |
|
|
436 | 470 | save=save, |
|
|
437 | 471 | ftp=ftp, |
|
|
438 | 472 | wr_period=wr_period, |
|
|
439 | 473 | thisDatetime=thisDatetime) |
|
|
440 | 474 | |
|
|
441 | 475 | |
|
|
442 | 476 | class RTIPlot(Figure): |
|
|
443 | 477 | |
|
|
444 | 478 | __isConfig = None |
|
|
445 | 479 | __nsubplots = None |
|
|
446 | 480 | |
|
|
447 | 481 | WIDTHPROF = None |
|
|
448 | 482 | HEIGHTPROF = None |
|
|
449 | 483 | PREFIX = 'rti' |
|
|
450 | 484 | |
|
|
451 | 485 | def __init__(self, **kwargs): |
|
|
452 | 486 | |
|
|
453 | 487 | Figure.__init__(self, **kwargs) |
|
|
454 | 488 | self.timerange = None |
|
|
455 | 489 | self.isConfig = False |
|
|
456 | 490 | self.__nsubplots = 1 |
|
|
457 | 491 | |
|
|
458 | 492 | self.WIDTH = 800 |
|
|
459 | 493 | self.HEIGHT = 180 |
|
|
460 | 494 | self.WIDTHPROF = 120 |
|
|
461 | 495 | self.HEIGHTPROF = 0 |
|
|
462 | 496 | self.counter_imagwr = 0 |
|
|
463 | 497 | |
|
|
464 | 498 | self.PLOT_CODE = RTI_CODE |
|
|
465 | 499 | |
|
|
466 | 500 | self.FTP_WEI = None |
|
|
467 | 501 | self.EXP_CODE = None |
|
|
468 | 502 | self.SUB_EXP_CODE = None |
|
|
469 | 503 | self.PLOT_POS = None |
|
|
470 | 504 | self.tmin = None |
|
|
471 | 505 | self.tmax = None |
|
|
472 | 506 | |
|
|
473 | 507 | self.xmin = None |
|
|
474 | 508 | self.xmax = None |
|
|
475 | 509 | |
|
|
476 | 510 | self.figfile = None |
|
|
477 | 511 | |
|
|
478 | 512 | def getSubplots(self): |
|
|
479 | 513 | |
|
|
480 | 514 | ncol = 1 |
|
|
481 | 515 | nrow = self.nplots |
|
|
482 | 516 | |
|
|
483 | 517 | return nrow, ncol |
|
|
484 | 518 | |
|
|
485 | 519 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
486 | 520 | |
|
|
487 | 521 | self.__showprofile = showprofile |
|
|
488 | 522 | self.nplots = nplots |
|
|
489 | 523 | |
|
|
490 | 524 | ncolspan = 1 |
|
|
491 | 525 | colspan = 1 |
|
|
492 | 526 | if showprofile: |
|
|
493 | 527 | ncolspan = 7 |
|
|
494 | 528 | colspan = 6 |
|
|
495 | 529 | self.__nsubplots = 2 |
|
|
496 | 530 | |
|
|
497 | 531 | self.createFigure(id = id, |
|
|
498 | 532 | wintitle = wintitle, |
|
|
499 | 533 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
500 | 534 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
501 | 535 | show=show) |
|
|
502 | 536 | |
|
|
503 | 537 | nrow, ncol = self.getSubplots() |
|
|
504 | 538 | |
|
|
505 | 539 | counter = 0 |
|
|
506 | 540 | for y in range(nrow): |
|
|
507 | 541 | for x in range(ncol): |
|
|
508 | 542 | |
|
|
509 | 543 | if counter >= self.nplots: |
|
|
510 | 544 | break |
|
|
511 | 545 | |
|
|
512 | 546 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
513 | 547 | |
|
|
514 | 548 | if showprofile: |
|
|
515 | 549 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
516 | 550 | |
|
|
517 | 551 | counter += 1 |
|
|
518 | 552 | |
|
|
519 | 553 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='True', |
|
|
520 | 554 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
521 | 555 | timerange=None, |
|
|
522 | 556 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
523 | 557 | server=None, folder=None, username=None, password=None, |
|
|
524 | 558 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, **kwargs): |
|
|
525 | 559 | |
|
|
526 | 560 | """ |
|
|
527 | 561 | |
|
|
528 | 562 | Input: |
|
|
529 | 563 | dataOut : |
|
|
530 | 564 | id : |
|
|
531 | 565 | wintitle : |
|
|
532 | 566 | channelList : |
|
|
533 | 567 | showProfile : |
|
|
534 | 568 | xmin : None, |
|
|
535 | 569 | xmax : None, |
|
|
536 | 570 | ymin : None, |
|
|
537 | 571 | ymax : None, |
|
|
538 | 572 | zmin : None, |
|
|
539 | 573 | zmax : None |
|
|
540 | 574 | """ |
|
|
541 | 575 | |
|
|
542 | 576 | colormap = kwargs.get('colormap', 'jet') |
|
|
543 | 577 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
544 | 578 | return |
|
|
545 | 579 | |
|
|
546 | 580 | if channelList == None: |
|
|
547 | 581 | channelIndexList = dataOut.channelIndexList |
|
|
548 | 582 | else: |
|
|
549 | 583 | channelIndexList = [] |
|
|
550 | 584 | for channel in channelList: |
|
|
551 | 585 | if channel not in dataOut.channelList: |
|
|
552 | 586 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
553 | 587 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
554 | 588 | |
|
|
555 | 589 | if hasattr(dataOut, 'normFactor'): |
|
|
556 | 590 | factor = dataOut.normFactor |
|
|
557 | 591 | else: |
|
|
558 | 592 | factor = 1 |
|
|
559 | 593 | |
|
|
560 | 594 | # factor = dataOut.normFactor |
|
|
561 | 595 | x = dataOut.getTimeRange() |
|
|
562 | 596 | y = dataOut.getHeiRange() |
|
|
563 | 597 | |
|
|
564 | 598 | # z = dataOut.data_spc/factor |
|
|
565 | 599 | # z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
566 | 600 | # avg = numpy.average(z, axis=1) |
|
|
567 | 601 | # avgdB = 10.*numpy.log10(avg) |
|
|
568 | 602 | avgdB = dataOut.getPower() |
|
|
569 | 603 | |
|
|
570 | 604 | thisDatetime = dataOut.datatime |
|
|
571 | 605 | # thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
572 | 606 | title = wintitle + " RTI" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
573 | 607 | xlabel = "" |
|
|
574 | 608 | ylabel = "Range (Km)" |
|
|
575 | 609 | |
|
|
576 | 610 | update_figfile = False |
|
|
577 | 611 | |
|
|
578 | 612 | if dataOut.ltctime >= self.xmax: |
|
|
579 | 613 | self.counter_imagwr = wr_period |
|
|
580 | 614 | self.isConfig = False |
|
|
581 | 615 | update_figfile = True |
|
|
582 | 616 | |
|
|
583 | 617 | if not self.isConfig: |
|
|
584 | 618 | |
|
|
585 | 619 | nplots = len(channelIndexList) |
|
|
586 | 620 | |
|
|
587 | 621 | self.setup(id=id, |
|
|
588 | 622 | nplots=nplots, |
|
|
589 | 623 | wintitle=wintitle, |
|
|
590 | 624 | showprofile=showprofile, |
|
|
591 | 625 | show=show) |
|
|
592 | 626 | |
|
|
593 | 627 | if timerange != None: |
|
|
594 | 628 | self.timerange = timerange |
|
|
595 | 629 | |
|
|
596 | 630 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
597 | 631 | |
|
|
598 | 632 | noise = dataOut.noise/factor |
|
|
599 | 633 | noisedB = 10*numpy.log10(noise) |
|
|
600 | 634 | |
|
|
601 | 635 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
602 | 636 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
603 | 637 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
604 | 638 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
605 | 639 | |
|
|
606 | 640 | self.FTP_WEI = ftp_wei |
|
|
607 | 641 | self.EXP_CODE = exp_code |
|
|
608 | 642 | self.SUB_EXP_CODE = sub_exp_code |
|
|
609 | 643 | self.PLOT_POS = plot_pos |
|
|
610 | 644 | |
|
|
611 | 645 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
612 | 646 | self.isConfig = True |
|
|
613 | 647 | self.figfile = figfile |
|
|
614 | 648 | update_figfile = True |
|
|
615 | 649 | |
|
|
616 | 650 | self.setWinTitle(title) |
|
|
617 | 651 | |
|
|
618 | 652 | for i in range(self.nplots): |
|
|
619 | 653 | index = channelIndexList[i] |
|
|
620 | 654 | title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
621 | 655 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
622 | 656 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
623 | 657 | axes = self.axesList[i*self.__nsubplots] |
|
|
624 | 658 | zdB = avgdB[index].reshape((1,-1)) |
|
|
625 | 659 | axes.pcolorbuffer(x, y, zdB, |
|
|
626 | 660 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
627 | 661 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
628 | 662 | ticksize=9, cblabel='', cbsize="1%", colormap=colormap) |
|
|
629 | 663 | |
|
|
630 | 664 | if self.__showprofile: |
|
|
631 | 665 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
632 | 666 | axes.pline(avgdB[index], y, |
|
|
633 | 667 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
634 | 668 | xlabel='dB', ylabel='', title='', |
|
|
635 | 669 | ytick_visible=False, |
|
|
636 | 670 | grid='x') |
|
|
637 | 671 | |
|
|
638 | 672 | self.draw() |
|
|
639 | 673 | |
|
|
640 | 674 | self.save(figpath=figpath, |
|
|
641 | 675 | figfile=figfile, |
|
|
642 | 676 | save=save, |
|
|
643 | 677 | ftp=ftp, |
|
|
644 | 678 | wr_period=wr_period, |
|
|
645 | 679 | thisDatetime=thisDatetime, |
|
|
646 | 680 | update_figfile=update_figfile) |
|
|
647 | 681 | |
|
|
648 | 682 | class CoherenceMap(Figure): |
|
|
649 | 683 | isConfig = None |
|
|
650 | 684 | __nsubplots = None |
|
|
651 | 685 | |
|
|
652 | 686 | WIDTHPROF = None |
|
|
653 | 687 | HEIGHTPROF = None |
|
|
654 | 688 | PREFIX = 'cmap' |
|
|
655 | 689 | |
|
|
656 | 690 | parameters = { |
|
|
657 | 691 | 'id': 'string', |
|
|
658 | 692 | 'wintitle': 'string', |
|
|
659 | 693 | 'pairsList': 'pairsLists', |
|
|
660 | 694 | 'showprofile': 'boolean', |
|
|
661 | 695 | 'xmin': 'float', |
|
|
662 | 696 | 'xmax': 'float', |
|
|
663 | 697 | 'ymin': 'float', |
|
|
664 | 698 | 'ymax': 'float', |
|
|
665 | 699 | 'zmin': 'float', |
|
|
666 | 700 | 'zmax': 'float', |
|
|
667 | 701 | 'timerange': 'float', |
|
|
668 | 702 | 'phase_min': 'float', |
|
|
669 | 703 | 'phase_max': 'float', |
|
|
670 | 704 | 'save': 'boolean', |
|
|
671 | 705 | 'figpath': 'string', |
|
|
672 | 706 | 'figfile': 'string', |
|
|
673 | 707 | 'ftp': 'boolean', |
|
|
674 | 708 | 'wr_period': 'int', |
|
|
675 | 709 | 'coherence_cmap': 'colormap', |
|
|
676 | 710 | 'phase_cmap': 'colormap', |
|
|
677 | 711 | 'show': 'boolean', |
|
|
678 | 712 | 'server': 'string', |
|
|
679 | 713 | 'folder': 'string', |
|
|
680 | 714 | 'username': 'string', |
|
|
681 | 715 | 'password': 'string', |
|
|
682 | 716 | 'ftp_wei': 'int', |
|
|
683 | 717 | 'exp_code': 'int', |
|
|
684 | 718 | 'sub_exp_code': 'int', |
|
|
685 | 719 | 'plot_pos': 'int', |
|
|
686 | 720 | } |
|
|
687 | 721 | |
|
|
688 | 722 | def __init__(self, **kwargs): |
|
|
689 | 723 | Figure.__init__(self, **kwargs) |
|
|
690 | 724 | self.timerange = 2*60*60 |
|
|
691 | 725 | self.isConfig = False |
|
|
692 | 726 | self.__nsubplots = 1 |
|
|
693 | 727 | |
|
|
694 | 728 | self.WIDTH = 800 |
|
|
695 | 729 | self.HEIGHT = 180 |
|
|
696 | 730 | self.WIDTHPROF = 120 |
|
|
697 | 731 | self.HEIGHTPROF = 0 |
|
|
698 | 732 | self.counter_imagwr = 0 |
|
|
699 | 733 | |
|
|
700 | 734 | self.PLOT_CODE = COH_CODE |
|
|
701 | 735 | |
|
|
702 | 736 | self.FTP_WEI = None |
|
|
703 | 737 | self.EXP_CODE = None |
|
|
704 | 738 | self.SUB_EXP_CODE = None |
|
|
705 | 739 | self.PLOT_POS = None |
|
|
706 | 740 | self.counter_imagwr = 0 |
|
|
707 | 741 | |
|
|
708 | 742 | self.xmin = None |
|
|
709 | 743 | self.xmax = None |
|
|
710 | 744 | |
|
|
711 | 745 | def getSubplots(self): |
|
|
712 | 746 | ncol = 1 |
|
|
713 | 747 | nrow = self.nplots*2 |
|
|
714 | 748 | |
|
|
715 | 749 | return nrow, ncol |
|
|
716 | 750 | |
|
|
717 | 751 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
718 | 752 | self.__showprofile = showprofile |
|
|
719 | 753 | self.nplots = nplots |
|
|
720 | 754 | |
|
|
721 | 755 | ncolspan = 1 |
|
|
722 | 756 | colspan = 1 |
|
|
723 | 757 | if showprofile: |
|
|
724 | 758 | ncolspan = 7 |
|
|
725 | 759 | colspan = 6 |
|
|
726 | 760 | self.__nsubplots = 2 |
|
|
727 | 761 | |
|
|
728 | 762 | self.createFigure(id = id, |
|
|
729 | 763 | wintitle = wintitle, |
|
|
730 | 764 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
731 | 765 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
732 | 766 | show=True) |
|
|
733 | 767 | |
|
|
734 | 768 | nrow, ncol = self.getSubplots() |
|
|
735 | 769 | |
|
|
736 | 770 | for y in range(nrow): |
|
|
737 | 771 | for x in range(ncol): |
|
|
738 | 772 | |
|
|
739 | 773 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
740 | 774 | |
|
|
741 | 775 | if showprofile: |
|
|
742 | 776 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
743 | 777 | |
|
|
744 | 778 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
745 | 779 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
746 | 780 | timerange=None, phase_min=None, phase_max=None, |
|
|
747 | 781 | save=False, figpath='./', figfile=None, ftp=False, wr_period=1, |
|
|
748 | 782 | coherence_cmap='jet', phase_cmap='RdBu_r', show=True, |
|
|
749 | 783 | server=None, folder=None, username=None, password=None, |
|
|
750 | 784 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
751 | 785 | |
|
|
752 | 786 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
753 | 787 | return |
|
|
754 | 788 | |
|
|
755 | 789 | if pairsList == None: |
|
|
756 | 790 | pairsIndexList = dataOut.pairsIndexList |
|
|
757 | 791 | else: |
|
|
758 | 792 | pairsIndexList = [] |
|
|
759 | 793 | for pair in pairsList: |
|
|
760 | 794 | if pair not in dataOut.pairsList: |
|
|
761 | 795 | raise ValueError, "Pair %s is not in dataOut.pairsList" %(pair) |
|
|
762 | 796 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
763 | 797 | |
|
|
764 | 798 | if pairsIndexList == []: |
|
|
765 | 799 | return |
|
|
766 | 800 | |
|
|
767 | 801 | if len(pairsIndexList) > 4: |
|
|
768 | 802 | pairsIndexList = pairsIndexList[0:4] |
|
|
769 | 803 | |
|
|
770 | 804 | if phase_min == None: |
|
|
771 | 805 | phase_min = -180 |
|
|
772 | 806 | if phase_max == None: |
|
|
773 | 807 | phase_max = 180 |
|
|
774 | 808 | |
|
|
775 | 809 | x = dataOut.getTimeRange() |
|
|
776 | 810 | y = dataOut.getHeiRange() |
|
|
777 | 811 | |
|
|
778 | 812 | thisDatetime = dataOut.datatime |
|
|
779 | 813 | |
|
|
780 | 814 | title = wintitle + " CoherenceMap" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
781 | 815 | xlabel = "" |
|
|
782 | 816 | ylabel = "Range (Km)" |
|
|
783 | 817 | update_figfile = False |
|
|
784 | 818 | |
|
|
785 | 819 | if not self.isConfig: |
|
|
786 | 820 | nplots = len(pairsIndexList) |
|
|
787 | 821 | self.setup(id=id, |
|
|
788 | 822 | nplots=nplots, |
|
|
789 | 823 | wintitle=wintitle, |
|
|
790 | 824 | showprofile=showprofile, |
|
|
791 | 825 | show=show) |
|
|
792 | 826 | |
|
|
793 | 827 | if timerange != None: |
|
|
794 | 828 | self.timerange = timerange |
|
|
795 | 829 | |
|
|
796 | 830 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
797 | 831 | |
|
|
798 | 832 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
799 | 833 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
800 | 834 | if zmin == None: zmin = 0. |
|
|
801 | 835 | if zmax == None: zmax = 1. |
|
|
802 | 836 | |
|
|
803 | 837 | self.FTP_WEI = ftp_wei |
|
|
804 | 838 | self.EXP_CODE = exp_code |
|
|
805 | 839 | self.SUB_EXP_CODE = sub_exp_code |
|
|
806 | 840 | self.PLOT_POS = plot_pos |
|
|
807 | 841 | |
|
|
808 | 842 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
809 | 843 | |
|
|
810 | 844 | self.isConfig = True |
|
|
811 | 845 | update_figfile = True |
|
|
812 | 846 | |
|
|
813 | 847 | self.setWinTitle(title) |
|
|
814 | 848 | |
|
|
815 | 849 | for i in range(self.nplots): |
|
|
816 | 850 | |
|
|
817 | 851 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
818 | 852 | |
|
|
819 | 853 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i],:,:],axis=0) |
|
|
820 | 854 | powa = numpy.average(dataOut.data_spc[pair[0],:,:],axis=0) |
|
|
821 | 855 | powb = numpy.average(dataOut.data_spc[pair[1],:,:],axis=0) |
|
|
822 | 856 | |
|
|
823 | 857 | |
|
|
824 | 858 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
825 | 859 | coherence = numpy.abs(avgcoherenceComplex) |
|
|
826 | 860 | |
|
|
827 | 861 | z = coherence.reshape((1,-1)) |
|
|
828 | 862 | |
|
|
829 | 863 | counter = 0 |
|
|
830 | 864 | |
|
|
831 | 865 | title = "Coherence Ch%d * Ch%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
832 | 866 | axes = self.axesList[i*self.__nsubplots*2] |
|
|
833 | 867 | axes.pcolorbuffer(x, y, z, |
|
|
834 | 868 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
835 | 869 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
836 | 870 | ticksize=9, cblabel='', colormap=coherence_cmap, cbsize="1%") |
|
|
837 | 871 | |
|
|
838 | 872 | if self.__showprofile: |
|
|
839 | 873 | counter += 1 |
|
|
840 | 874 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
841 | 875 | axes.pline(coherence, y, |
|
|
842 | 876 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
843 | 877 | xlabel='', ylabel='', title='', ticksize=7, |
|
|
844 | 878 | ytick_visible=False, nxticks=5, |
|
|
845 | 879 | grid='x') |
|
|
846 | 880 | |
|
|
847 | 881 | counter += 1 |
|
|
848 | 882 | |
|
|
849 | 883 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
850 | 884 | |
|
|
851 | 885 | z = phase.reshape((1,-1)) |
|
|
852 | 886 | |
|
|
853 | 887 | title = "Phase Ch%d * Ch%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
854 | 888 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
855 | 889 | axes.pcolorbuffer(x, y, z, |
|
|
856 | 890 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=phase_min, zmax=phase_max, |
|
|
857 | 891 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
858 | 892 | ticksize=9, cblabel='', colormap=phase_cmap, cbsize="1%") |
|
|
859 | 893 | |
|
|
860 | 894 | if self.__showprofile: |
|
|
861 | 895 | counter += 1 |
|
|
862 | 896 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
863 | 897 | axes.pline(phase, y, |
|
|
864 | 898 | xmin=phase_min, xmax=phase_max, ymin=ymin, ymax=ymax, |
|
|
865 | 899 | xlabel='', ylabel='', title='', ticksize=7, |
|
|
866 | 900 | ytick_visible=False, nxticks=4, |
|
|
867 | 901 | grid='x') |
|
|
868 | 902 | |
|
|
869 | 903 | self.draw() |
|
|
870 | 904 | |
|
|
871 | 905 | if dataOut.ltctime >= self.xmax: |
|
|
872 | 906 | self.counter_imagwr = wr_period |
|
|
873 | 907 | self.isConfig = False |
|
|
874 | 908 | update_figfile = True |
|
|
875 | 909 | |
|
|
876 | 910 | self.save(figpath=figpath, |
|
|
877 | 911 | figfile=figfile, |
|
|
878 | 912 | save=save, |
|
|
879 | 913 | ftp=ftp, |
|
|
880 | 914 | wr_period=wr_period, |
|
|
881 | 915 | thisDatetime=thisDatetime, |
|
|
882 | 916 | update_figfile=update_figfile) |
|
|
883 | 917 | |
|
|
884 | 918 | class PowerProfilePlot(Figure): |
|
|
885 | 919 | |
|
|
886 | 920 | isConfig = None |
|
|
887 | 921 | __nsubplots = None |
|
|
888 | 922 | |
|
|
889 | 923 | WIDTHPROF = None |
|
|
890 | 924 | HEIGHTPROF = None |
|
|
891 | 925 | PREFIX = 'spcprofile' |
|
|
892 | 926 | |
|
|
893 | 927 | def __init__(self, **kwargs): |
|
|
894 | 928 | Figure.__init__(self, **kwargs) |
|
|
895 | 929 | self.isConfig = False |
|
|
896 | 930 | self.__nsubplots = 1 |
|
|
897 | 931 | |
|
|
898 | 932 | self.PLOT_CODE = POWER_CODE |
|
|
899 | 933 | |
|
|
900 | 934 | self.WIDTH = 300 |
|
|
901 | 935 | self.HEIGHT = 500 |
|
|
902 | 936 | self.counter_imagwr = 0 |
|
|
903 | 937 | |
|
|
904 | 938 | def getSubplots(self): |
|
|
905 | 939 | ncol = 1 |
|
|
906 | 940 | nrow = 1 |
|
|
907 | 941 | |
|
|
908 | 942 | return nrow, ncol |
|
|
909 | 943 | |
|
|
910 | 944 | def setup(self, id, nplots, wintitle, show): |
|
|
911 | 945 | |
|
|
912 | 946 | self.nplots = nplots |
|
|
913 | 947 | |
|
|
914 | 948 | ncolspan = 1 |
|
|
915 | 949 | colspan = 1 |
|
|
916 | 950 | |
|
|
917 | 951 | self.createFigure(id = id, |
|
|
918 | 952 | wintitle = wintitle, |
|
|
919 | 953 | widthplot = self.WIDTH, |
|
|
920 | 954 | heightplot = self.HEIGHT, |
|
|
921 | 955 | show=show) |
|
|
922 | 956 | |
|
|
923 | 957 | nrow, ncol = self.getSubplots() |
|
|
924 | 958 | |
|
|
925 | 959 | counter = 0 |
|
|
926 | 960 | for y in range(nrow): |
|
|
927 | 961 | for x in range(ncol): |
|
|
928 | 962 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
929 | 963 | |
|
|
930 | 964 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
931 | 965 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
932 | 966 | save=False, figpath='./', figfile=None, show=True, |
|
|
933 | 967 | ftp=False, wr_period=1, server=None, |
|
|
934 | 968 | folder=None, username=None, password=None): |
|
|
935 | 969 | |
|
|
936 | 970 | |
|
|
937 | 971 | if channelList == None: |
|
|
938 | 972 | channelIndexList = dataOut.channelIndexList |
|
|
939 | 973 | channelList = dataOut.channelList |
|
|
940 | 974 | else: |
|
|
941 | 975 | channelIndexList = [] |
|
|
942 | 976 | for channel in channelList: |
|
|
943 | 977 | if channel not in dataOut.channelList: |
|
|
944 | 978 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
945 | 979 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
946 | 980 | |
|
|
947 | 981 | factor = dataOut.normFactor |
|
|
948 | 982 | |
|
|
949 | 983 | y = dataOut.getHeiRange() |
|
|
950 | 984 | |
|
|
951 | 985 | #for voltage |
|
|
952 | 986 | if dataOut.type == 'Voltage': |
|
|
953 | 987 | x = dataOut.data[channelIndexList,:] * numpy.conjugate(dataOut.data[channelIndexList,:]) |
|
|
954 | 988 | x = x.real |
|
|
955 | 989 | x = numpy.where(numpy.isfinite(x), x, numpy.NAN) |
|
|
956 | 990 | |
|
|
957 | 991 | #for spectra |
|
|
958 | 992 | if dataOut.type == 'Spectra': |
|
|
959 | 993 | x = dataOut.data_spc[channelIndexList,:,:]/factor |
|
|
960 | 994 | x = numpy.where(numpy.isfinite(x), x, numpy.NAN) |
|
|
961 | 995 | x = numpy.average(x, axis=1) |
|
|
962 | 996 | |
|
|
963 | 997 | |
|
|
964 | 998 | xdB = 10*numpy.log10(x) |
|
|
965 | 999 | |
|
|
966 | 1000 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
967 | 1001 | title = wintitle + " Power Profile %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
968 | 1002 | xlabel = "dB" |
|
|
969 | 1003 | ylabel = "Range (Km)" |
|
|
970 | 1004 | |
|
|
971 | 1005 | if not self.isConfig: |
|
|
972 | 1006 | |
|
|
973 | 1007 | nplots = 1 |
|
|
974 | 1008 | |
|
|
975 | 1009 | self.setup(id=id, |
|
|
976 | 1010 | nplots=nplots, |
|
|
977 | 1011 | wintitle=wintitle, |
|
|
978 | 1012 | show=show) |
|
|
979 | 1013 | |
|
|
980 | 1014 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
981 | 1015 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
982 | 1016 | if xmin == None: xmin = numpy.nanmin(xdB)*0.9 |
|
|
983 | 1017 | if xmax == None: xmax = numpy.nanmax(xdB)*1.1 |
|
|
984 | 1018 | |
|
|
985 | 1019 | self.isConfig = True |
|
|
986 | 1020 | |
|
|
987 | 1021 | self.setWinTitle(title) |
|
|
988 | 1022 | |
|
|
989 | 1023 | title = "Power Profile: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
990 | 1024 | axes = self.axesList[0] |
|
|
991 | 1025 | |
|
|
992 | 1026 | legendlabels = ["channel %d"%x for x in channelList] |
|
|
993 | 1027 | axes.pmultiline(xdB, y, |
|
|
994 | 1028 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
995 | 1029 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, |
|
|
996 | 1030 | ytick_visible=True, nxticks=5, |
|
|
997 | 1031 | grid='x') |
|
|
998 | 1032 | |
|
|
999 | 1033 | self.draw() |
|
|
1000 | 1034 | |
|
|
1001 | 1035 | self.save(figpath=figpath, |
|
|
1002 | 1036 | figfile=figfile, |
|
|
1003 | 1037 | save=save, |
|
|
1004 | 1038 | ftp=ftp, |
|
|
1005 | 1039 | wr_period=wr_period, |
|
|
1006 | 1040 | thisDatetime=thisDatetime) |
|
|
1007 | 1041 | |
|
|
1008 | 1042 | class SpectraCutPlot(Figure): |
|
|
1009 | 1043 | |
|
|
1010 | 1044 | isConfig = None |
|
|
1011 | 1045 | __nsubplots = None |
|
|
1012 | 1046 | |
|
|
1013 | 1047 | WIDTHPROF = None |
|
|
1014 | 1048 | HEIGHTPROF = None |
|
|
1015 | 1049 | PREFIX = 'spc_cut' |
|
|
1016 | 1050 | |
|
|
1017 | 1051 | def __init__(self, **kwargs): |
|
|
1018 | 1052 | Figure.__init__(self, **kwargs) |
|
|
1019 | 1053 | self.isConfig = False |
|
|
1020 | 1054 | self.__nsubplots = 1 |
|
|
1021 | 1055 | |
|
|
1022 | 1056 | self.PLOT_CODE = POWER_CODE |
|
|
1023 | 1057 | |
|
|
1024 | 1058 | self.WIDTH = 700 |
|
|
1025 | 1059 | self.HEIGHT = 500 |
|
|
1026 | 1060 | self.counter_imagwr = 0 |
|
|
1027 | 1061 | |
|
|
1028 | 1062 | def getSubplots(self): |
|
|
1029 | 1063 | ncol = 1 |
|
|
1030 | 1064 | nrow = 1 |
|
|
1031 | 1065 | |
|
|
1032 | 1066 | return nrow, ncol |
|
|
1033 | 1067 | |
|
|
1034 | 1068 | def setup(self, id, nplots, wintitle, show): |
|
|
1035 | 1069 | |
|
|
1036 | 1070 | self.nplots = nplots |
|
|
1037 | 1071 | |
|
|
1038 | 1072 | ncolspan = 1 |
|
|
1039 | 1073 | colspan = 1 |
|
|
1040 | 1074 | |
|
|
1041 | 1075 | self.createFigure(id = id, |
|
|
1042 | 1076 | wintitle = wintitle, |
|
|
1043 | 1077 | widthplot = self.WIDTH, |
|
|
1044 | 1078 | heightplot = self.HEIGHT, |
|
|
1045 | 1079 | show=show) |
|
|
1046 | 1080 | |
|
|
1047 | 1081 | nrow, ncol = self.getSubplots() |
|
|
1048 | 1082 | |
|
|
1049 | 1083 | counter = 0 |
|
|
1050 | 1084 | for y in range(nrow): |
|
|
1051 | 1085 | for x in range(ncol): |
|
|
1052 | 1086 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1053 | 1087 | |
|
|
1054 | 1088 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1055 | 1089 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1056 | 1090 | save=False, figpath='./', figfile=None, show=True, |
|
|
1057 | 1091 | ftp=False, wr_period=1, server=None, |
|
|
1058 | 1092 | folder=None, username=None, password=None, |
|
|
1059 | 1093 | xaxis="frequency"): |
|
|
1060 | 1094 | |
|
|
1061 | 1095 | |
|
|
1062 | 1096 | if channelList == None: |
|
|
1063 | 1097 | channelIndexList = dataOut.channelIndexList |
|
|
1064 | 1098 | channelList = dataOut.channelList |
|
|
1065 | 1099 | else: |
|
|
1066 | 1100 | channelIndexList = [] |
|
|
1067 | 1101 | for channel in channelList: |
|
|
1068 | 1102 | if channel not in dataOut.channelList: |
|
|
1069 | 1103 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
1070 | 1104 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
1071 | 1105 | |
|
|
1072 | 1106 | factor = dataOut.normFactor |
|
|
1073 | 1107 | |
|
|
1074 | 1108 | y = dataOut.getHeiRange() |
|
|
1075 | 1109 | |
|
|
1076 | 1110 | z = dataOut.data_spc/factor |
|
|
1077 | 1111 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1078 | 1112 | |
|
|
1079 | 1113 | hei_index = numpy.arange(25)*3 + 20 |
|
|
1080 | 1114 | |
|
|
1081 | 1115 | if xaxis == "frequency": |
|
|
1082 | 1116 | x = dataOut.getFreqRange()/1000. |
|
|
1083 | 1117 | zdB = 10*numpy.log10(z[0,:,hei_index]) |
|
|
1084 | 1118 | xlabel = "Frequency (kHz)" |
|
|
1085 | 1119 | ylabel = "Power (dB)" |
|
|
1086 | 1120 | |
|
|
1087 | 1121 | elif xaxis == "time": |
|
|
1088 | 1122 | x = dataOut.getAcfRange() |
|
|
1089 | 1123 | zdB = z[0,:,hei_index] |
|
|
1090 | 1124 | xlabel = "Time (ms)" |
|
|
1091 | 1125 | ylabel = "ACF" |
|
|
1092 | 1126 | |
|
|
1093 | 1127 | else: |
|
|
1094 | 1128 | x = dataOut.getVelRange() |
|
|
1095 | 1129 | zdB = 10*numpy.log10(z[0,:,hei_index]) |
|
|
1096 | 1130 | xlabel = "Velocity (m/s)" |
|
|
1097 | 1131 | ylabel = "Power (dB)" |
|
|
1098 | 1132 | |
|
|
1099 | 1133 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1100 | 1134 | title = wintitle + " Range Cuts %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1101 | 1135 | |
|
|
1102 | 1136 | if not self.isConfig: |
|
|
1103 | 1137 | |
|
|
1104 | 1138 | nplots = 1 |
|
|
1105 | 1139 | |
|
|
1106 | 1140 | self.setup(id=id, |
|
|
1107 | 1141 | nplots=nplots, |
|
|
1108 | 1142 | wintitle=wintitle, |
|
|
1109 | 1143 | show=show) |
|
|
1110 | 1144 | |
|
|
1111 | 1145 | if xmin == None: xmin = numpy.nanmin(x)*0.9 |
|
|
1112 | 1146 | if xmax == None: xmax = numpy.nanmax(x)*1.1 |
|
|
1113 | 1147 | if ymin == None: ymin = numpy.nanmin(zdB) |
|
|
1114 | 1148 | if ymax == None: ymax = numpy.nanmax(zdB) |
|
|
1115 | 1149 | |
|
|
1116 | 1150 | self.isConfig = True |
|
|
1117 | 1151 | |
|
|
1118 | 1152 | self.setWinTitle(title) |
|
|
1119 | 1153 | |
|
|
1120 | 1154 | title = "Spectra Cuts: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1121 | 1155 | axes = self.axesList[0] |
|
|
1122 | 1156 | |
|
|
1123 | 1157 | legendlabels = ["Range = %dKm" %y[i] for i in hei_index] |
|
|
1124 | 1158 | |
|
|
1125 | 1159 | axes.pmultilineyaxis( x, zdB, |
|
|
1126 | 1160 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1127 | 1161 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, |
|
|
1128 | 1162 | ytick_visible=True, nxticks=5, |
|
|
1129 | 1163 | grid='x') |
|
|
1130 | 1164 | |
|
|
1131 | 1165 | self.draw() |
|
|
1132 | 1166 | |
|
|
1133 | 1167 | self.save(figpath=figpath, |
|
|
1134 | 1168 | figfile=figfile, |
|
|
1135 | 1169 | save=save, |
|
|
1136 | 1170 | ftp=ftp, |
|
|
1137 | 1171 | wr_period=wr_period, |
|
|
1138 | 1172 | thisDatetime=thisDatetime) |
|
|
1139 | 1173 | |
|
|
1140 | 1174 | class Noise(Figure): |
|
|
1141 | 1175 | |
|
|
1142 | 1176 | isConfig = None |
|
|
1143 | 1177 | __nsubplots = None |
|
|
1144 | 1178 | |
|
|
1145 | 1179 | PREFIX = 'noise' |
|
|
1180 | ||
|
|
1181 | parameters = { | |
|
|
1182 | 'id': global_type_string, | |
|
|
1183 | 'wintitle': global_type_string, | |
|
|
1184 | 'channelList': global_type_list, | |
|
|
1185 | 'showprofile': global_type_boolean, | |
|
|
1186 | 'xmin': global_type_float, | |
|
|
1187 | 'xmax': global_type_float, | |
|
|
1188 | 'ymin': global_type_float, | |
|
|
1189 | 'ymax': global_type_float, | |
|
|
1190 | 'timerange': global_type_float, | |
|
|
1191 | 'save': global_type_boolean, | |
|
|
1192 | 'figpath': global_type_string, | |
|
|
1193 | 'figfile': global_type_string, | |
|
|
1194 | 'show': global_type_boolean, | |
|
|
1195 | 'ftp': global_type_boolean, | |
|
|
1196 | 'wr_period': global_type_integer, | |
|
|
1197 | 'server': global_type_string, | |
|
|
1198 | 'folder': global_type_string, | |
|
|
1199 | 'username': global_type_string, | |
|
|
1200 | 'password': global_type_string, | |
|
|
1201 | 'ftp_wei': global_type_integer, | |
|
|
1202 | 'exp_code': global_type_integer, | |
|
|
1203 | 'sub_exp_code': global_type_integer, | |
|
|
1204 | 'plot_pos': global_type_integer, | |
|
|
1205 | } | |
|
|
1146 | 1206 | |
|
|
1147 | 1207 | def __init__(self, **kwargs): |
|
|
1148 | 1208 | Figure.__init__(self, **kwargs) |
|
|
1149 | 1209 | self.timerange = 24*60*60 |
|
|
1150 | 1210 | self.isConfig = False |
|
|
1151 | 1211 | self.__nsubplots = 1 |
|
|
1152 | 1212 | self.counter_imagwr = 0 |
|
|
1153 | 1213 | self.WIDTH = 800 |
|
|
1154 | 1214 | self.HEIGHT = 400 |
|
|
1155 | 1215 | self.WIDTHPROF = 120 |
|
|
1156 | 1216 | self.HEIGHTPROF = 0 |
|
|
1157 | 1217 | self.xdata = None |
|
|
1158 | 1218 | self.ydata = None |
|
|
1159 | 1219 | |
|
|
1160 | 1220 | self.PLOT_CODE = NOISE_CODE |
|
|
1161 | 1221 | |
|
|
1162 | 1222 | self.FTP_WEI = None |
|
|
1163 | 1223 | self.EXP_CODE = None |
|
|
1164 | 1224 | self.SUB_EXP_CODE = None |
|
|
1165 | 1225 | self.PLOT_POS = None |
|
|
1166 | 1226 | self.figfile = None |
|
|
1167 | 1227 | |
|
|
1168 | 1228 | self.xmin = None |
|
|
1169 | 1229 | self.xmax = None |
|
|
1170 | 1230 | |
|
|
1171 | 1231 | def getSubplots(self): |
|
|
1172 | 1232 | |
|
|
1173 | 1233 | ncol = 1 |
|
|
1174 | 1234 | nrow = 1 |
|
|
1175 | 1235 | |
|
|
1176 | 1236 | return nrow, ncol |
|
|
1177 | 1237 | |
|
|
1178 | 1238 | def openfile(self, filename): |
|
|
1179 | 1239 | dirname = os.path.dirname(filename) |
|
|
1180 | 1240 | |
|
|
1181 | 1241 | if not os.path.exists(dirname): |
|
|
1182 | 1242 | os.mkdir(dirname) |
|
|
1183 | 1243 | |
|
|
1184 | 1244 | f = open(filename,'w+') |
|
|
1185 | 1245 | f.write('\n\n') |
|
|
1186 | 1246 | f.write('JICAMARCA RADIO OBSERVATORY - Noise \n') |
|
|
1187 | 1247 | f.write('DD MM YYYY HH MM SS Channel0 Channel1 Channel2 Channel3\n\n' ) |
|
|
1188 | 1248 | f.close() |
|
|
1189 | 1249 | |
|
|
1190 | 1250 | def save_data(self, filename_phase, data, data_datetime): |
|
|
1191 | 1251 | |
|
|
1192 | 1252 | f=open(filename_phase,'a') |
|
|
1193 | 1253 | |
|
|
1194 | 1254 | timetuple_data = data_datetime.timetuple() |
|
|
1195 | 1255 | day = str(timetuple_data.tm_mday) |
|
|
1196 | 1256 | month = str(timetuple_data.tm_mon) |
|
|
1197 | 1257 | year = str(timetuple_data.tm_year) |
|
|
1198 | 1258 | hour = str(timetuple_data.tm_hour) |
|
|
1199 | 1259 | minute = str(timetuple_data.tm_min) |
|
|
1200 | 1260 | second = str(timetuple_data.tm_sec) |
|
|
1201 | 1261 | |
|
|
1202 | 1262 | data_msg = '' |
|
|
1203 | 1263 | for i in range(len(data)): |
|
|
1204 | 1264 | data_msg += str(data[i]) + ' ' |
|
|
1205 | 1265 | |
|
|
1206 | 1266 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' ' + data_msg + '\n') |
|
|
1207 | 1267 | f.close() |
|
|
1208 | 1268 | |
|
|
1209 | 1269 | |
|
|
1210 | 1270 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1211 | 1271 | |
|
|
1212 | 1272 | self.__showprofile = showprofile |
|
|
1213 | 1273 | self.nplots = nplots |
|
|
1214 | 1274 | |
|
|
1215 | 1275 | ncolspan = 7 |
|
|
1216 | 1276 | colspan = 6 |
|
|
1217 | 1277 | self.__nsubplots = 2 |
|
|
1218 | 1278 | |
|
|
1219 | 1279 | self.createFigure(id = id, |
|
|
1220 | 1280 | wintitle = wintitle, |
|
|
1221 | 1281 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1222 | 1282 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1223 | 1283 | show=show) |
|
|
1224 | 1284 | |
|
|
1225 | 1285 | nrow, ncol = self.getSubplots() |
|
|
1226 | 1286 | |
|
|
1227 | 1287 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1228 | 1288 | |
|
|
1229 | 1289 | |
|
|
1230 | 1290 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='True', |
|
|
1231 | 1291 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1232 | 1292 | timerange=None, |
|
|
1233 | 1293 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1234 | 1294 | server=None, folder=None, username=None, password=None, |
|
|
1235 | 1295 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1236 | 1296 | |
|
|
1237 | 1297 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
1238 | 1298 | return |
|
|
1239 | 1299 | |
|
|
1240 | 1300 | if channelList == None: |
|
|
1241 | 1301 | channelIndexList = dataOut.channelIndexList |
|
|
1242 | 1302 | channelList = dataOut.channelList |
|
|
1243 | 1303 | else: |
|
|
1244 | 1304 | channelIndexList = [] |
|
|
1245 | 1305 | for channel in channelList: |
|
|
1246 | 1306 | if channel not in dataOut.channelList: |
|
|
1247 | 1307 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
1248 | 1308 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
1249 | 1309 | |
|
|
1250 | 1310 | x = dataOut.getTimeRange() |
|
|
1251 | 1311 | #y = dataOut.getHeiRange() |
|
|
1252 | 1312 | factor = dataOut.normFactor |
|
|
1253 | 1313 | noise = dataOut.noise[channelIndexList]/factor |
|
|
1254 | 1314 | noisedB = 10*numpy.log10(noise) |
|
|
1255 | 1315 | |
|
|
1256 | 1316 | thisDatetime = dataOut.datatime |
|
|
1257 | 1317 | |
|
|
1258 | 1318 | title = wintitle + " Noise" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1259 | 1319 | xlabel = "" |
|
|
1260 | 1320 | ylabel = "Intensity (dB)" |
|
|
1261 | 1321 | update_figfile = False |
|
|
1262 | 1322 | |
|
|
1263 | 1323 | if not self.isConfig: |
|
|
1264 | 1324 | |
|
|
1265 | 1325 | nplots = 1 |
|
|
1266 | 1326 | |
|
|
1267 | 1327 | self.setup(id=id, |
|
|
1268 | 1328 | nplots=nplots, |
|
|
1269 | 1329 | wintitle=wintitle, |
|
|
1270 | 1330 | showprofile=showprofile, |
|
|
1271 | 1331 | show=show) |
|
|
1272 | 1332 | |
|
|
1273 | 1333 | if timerange != None: |
|
|
1274 | 1334 | self.timerange = timerange |
|
|
1275 | 1335 | |
|
|
1276 | 1336 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1277 | 1337 | |
|
|
1278 | 1338 | if ymin == None: ymin = numpy.floor(numpy.nanmin(noisedB)) - 10.0 |
|
|
1279 | 1339 | if ymax == None: ymax = numpy.nanmax(noisedB) + 10.0 |
|
|
1280 | 1340 | |
|
|
1281 | 1341 | self.FTP_WEI = ftp_wei |
|
|
1282 | 1342 | self.EXP_CODE = exp_code |
|
|
1283 | 1343 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1284 | 1344 | self.PLOT_POS = plot_pos |
|
|
1285 | 1345 | |
|
|
1286 | 1346 | |
|
|
1287 | 1347 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1288 | 1348 | self.isConfig = True |
|
|
1289 | 1349 | self.figfile = figfile |
|
|
1290 | 1350 | self.xdata = numpy.array([]) |
|
|
1291 | 1351 | self.ydata = numpy.array([]) |
|
|
1292 | 1352 | |
|
|
1293 | 1353 | update_figfile = True |
|
|
1294 | 1354 | |
|
|
1295 | 1355 | #open file beacon phase |
|
|
1296 | 1356 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1297 | 1357 | noise_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1298 | 1358 | self.filename_noise = os.path.join(figpath,noise_file) |
|
|
1299 | 1359 | |
|
|
1300 | 1360 | self.setWinTitle(title) |
|
|
1301 | 1361 | |
|
|
1302 | 1362 | title = "Noise %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1303 | 1363 | |
|
|
1304 | 1364 | legendlabels = ["channel %d"%(idchannel) for idchannel in channelList] |
|
|
1305 | 1365 | axes = self.axesList[0] |
|
|
1306 | 1366 | |
|
|
1307 | 1367 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1308 | 1368 | |
|
|
1309 | 1369 | if len(self.ydata)==0: |
|
|
1310 | 1370 | self.ydata = noisedB.reshape(-1,1) |
|
|
1311 | 1371 | else: |
|
|
1312 | 1372 | self.ydata = numpy.hstack((self.ydata, noisedB.reshape(-1,1))) |
|
|
1313 | 1373 | |
|
|
1314 | 1374 | |
|
|
1315 | 1375 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1316 | 1376 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1317 | 1377 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1318 | 1378 | XAxisAsTime=True, grid='both' |
|
|
1319 | 1379 | ) |
|
|
1320 | 1380 | |
|
|
1321 | 1381 | self.draw() |
|
|
1322 | 1382 | |
|
|
1323 | 1383 | if dataOut.ltctime >= self.xmax: |
|
|
1324 | 1384 | self.counter_imagwr = wr_period |
|
|
1325 | 1385 | self.isConfig = False |
|
|
1326 | 1386 | update_figfile = True |
|
|
1327 | 1387 | |
|
|
1328 | 1388 | self.save(figpath=figpath, |
|
|
1329 | 1389 | figfile=figfile, |
|
|
1330 | 1390 | save=save, |
|
|
1331 | 1391 | ftp=ftp, |
|
|
1332 | 1392 | wr_period=wr_period, |
|
|
1333 | 1393 | thisDatetime=thisDatetime, |
|
|
1334 | 1394 | update_figfile=update_figfile) |
|
|
1335 | 1395 | |
|
|
1336 | 1396 | #store data beacon phase |
|
|
1337 | 1397 | if save: |
|
|
1338 | 1398 | self.save_data(self.filename_noise, noisedB, thisDatetime) |
|
|
1339 | 1399 | |
|
|
1340 | 1400 | class BeaconPhase(Figure): |
|
|
1341 | 1401 | |
|
|
1342 | 1402 | __isConfig = None |
|
|
1343 | 1403 | __nsubplots = None |
|
|
1344 | 1404 | |
|
|
1345 | 1405 | PREFIX = 'beacon_phase' |
|
|
1346 | 1406 | |
|
|
1347 | 1407 | parameters = { |
|
|
1348 |
'id': |
|
|
|
1349 |
'wintitle': |
|
|
|
1350 |
'pairsList': |
|
|
|
1351 |
'showprofile': |
|
|
|
1352 |
'xmin': |
|
|
|
1353 |
'xmax': |
|
|
|
1354 |
'ymin': |
|
|
|
1355 |
'ymax': |
|
|
|
1356 |
'hmin': |
|
|
|
1357 |
'hmax': |
|
|
|
1358 |
'timerange': |
|
|
|
1359 |
'save': |
|
|
|
1360 |
'figpath': |
|
|
|
1361 |
'figfile': |
|
|
|
1362 |
'show': |
|
|
|
1363 |
'ftp': |
|
|
|
1364 |
'wr_period': |
|
|
|
1365 |
'server': |
|
|
|
1366 |
'folder': |
|
|
|
1367 |
'username': |
|
|
|
1368 |
'password': |
|
|
|
1369 |
'ftp_wei': |
|
|
|
1370 |
'exp_code': |
|
|
|
1371 |
'sub_exp_code': |
|
|
|
1372 |
'plot_pos': |
|
|
|
1408 | 'id': global_type_string, | |
|
|
1409 | 'wintitle': global_type_string, | |
|
|
1410 | 'pairsList': global_type_pairsList, | |
|
|
1411 | 'showprofile': global_type_boolean, | |
|
|
1412 | 'xmin': global_type_float, | |
|
|
1413 | 'xmax': global_type_float, | |
|
|
1414 | 'ymin': global_type_float, | |
|
|
1415 | 'ymax': global_type_float, | |
|
|
1416 | 'hmin': global_type_float, | |
|
|
1417 | 'hmax': global_type_float, | |
|
|
1418 | 'timerange': global_type_float, | |
|
|
1419 | 'save': global_type_boolean, | |
|
|
1420 | 'figpath': global_type_string, | |
|
|
1421 | 'figfile': global_type_string, | |
|
|
1422 | 'show': global_type_boolean, | |
|
|
1423 | 'ftp': global_type_boolean, | |
|
|
1424 | 'wr_period': global_type_integer, | |
|
|
1425 | 'server': global_type_string, | |
|
|
1426 | 'folder': global_type_string, | |
|
|
1427 | 'username': global_type_string, | |
|
|
1428 | 'password': global_type_string, | |
|
|
1429 | 'ftp_wei': global_type_integer, | |
|
|
1430 | 'exp_code': global_type_integer, | |
|
|
1431 | 'sub_exp_code': global_type_integer, | |
|
|
1432 | 'plot_pos': global_type_integer, | |
|
|
1373 | 1433 | } |
|
|
1374 | 1434 | |
|
|
1375 | 1435 | def __init__(self, **kwargs): |
|
|
1376 | 1436 | Figure.__init__(self, **kwargs) |
|
|
1377 | 1437 | self.timerange = 24*60*60 |
|
|
1378 | 1438 | self.isConfig = False |
|
|
1379 | 1439 | self.__nsubplots = 1 |
|
|
1380 | 1440 | self.counter_imagwr = 0 |
|
|
1381 | 1441 | self.WIDTH = 800 |
|
|
1382 | 1442 | self.HEIGHT = 400 |
|
|
1383 | 1443 | self.WIDTHPROF = 120 |
|
|
1384 | 1444 | self.HEIGHTPROF = 0 |
|
|
1385 | 1445 | self.xdata = None |
|
|
1386 | 1446 | self.ydata = None |
|
|
1387 | 1447 | |
|
|
1388 | 1448 | self.PLOT_CODE = BEACON_CODE |
|
|
1389 | 1449 | |
|
|
1390 | 1450 | self.FTP_WEI = None |
|
|
1391 | 1451 | self.EXP_CODE = None |
|
|
1392 | 1452 | self.SUB_EXP_CODE = None |
|
|
1393 | 1453 | self.PLOT_POS = None |
|
|
1394 | 1454 | |
|
|
1395 | 1455 | self.filename_phase = None |
|
|
1396 | 1456 | |
|
|
1397 | 1457 | self.figfile = None |
|
|
1398 | 1458 | |
|
|
1399 | 1459 | self.xmin = None |
|
|
1400 | 1460 | self.xmax = None |
|
|
1401 | 1461 | |
|
|
1402 | 1462 | def getSubplots(self): |
|
|
1403 | 1463 | |
|
|
1404 | 1464 | ncol = 1 |
|
|
1405 | 1465 | nrow = 1 |
|
|
1406 | 1466 | |
|
|
1407 | 1467 | return nrow, ncol |
|
|
1408 | 1468 | |
|
|
1409 | 1469 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1410 | 1470 | |
|
|
1411 | 1471 | self.__showprofile = showprofile |
|
|
1412 | 1472 | self.nplots = nplots |
|
|
1413 | 1473 | |
|
|
1414 | 1474 | ncolspan = 7 |
|
|
1415 | 1475 | colspan = 6 |
|
|
1416 | 1476 | self.__nsubplots = 2 |
|
|
1417 | 1477 | |
|
|
1418 | 1478 | self.createFigure(id = id, |
|
|
1419 | 1479 | wintitle = wintitle, |
|
|
1420 | 1480 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1421 | 1481 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1422 | 1482 | show=show) |
|
|
1423 | 1483 | |
|
|
1424 | 1484 | nrow, ncol = self.getSubplots() |
|
|
1425 | 1485 | |
|
|
1426 | 1486 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1427 | 1487 | |
|
|
1428 | 1488 | def save_phase(self, filename_phase): |
|
|
1429 | 1489 | f = open(filename_phase,'w+') |
|
|
1430 | 1490 | f.write('\n\n') |
|
|
1431 | 1491 | f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') |
|
|
1432 | 1492 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' ) |
|
|
1433 | 1493 | f.close() |
|
|
1434 | 1494 | |
|
|
1435 | 1495 | def save_data(self, filename_phase, data, data_datetime): |
|
|
1436 | 1496 | f=open(filename_phase,'a') |
|
|
1437 | 1497 | timetuple_data = data_datetime.timetuple() |
|
|
1438 | 1498 | day = str(timetuple_data.tm_mday) |
|
|
1439 | 1499 | month = str(timetuple_data.tm_mon) |
|
|
1440 | 1500 | year = str(timetuple_data.tm_year) |
|
|
1441 | 1501 | hour = str(timetuple_data.tm_hour) |
|
|
1442 | 1502 | minute = str(timetuple_data.tm_min) |
|
|
1443 | 1503 | second = str(timetuple_data.tm_sec) |
|
|
1444 | 1504 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' '+str(data[0])+' '+str(data[1])+' '+str(data[2])+' '+str(data[3])+'\n') |
|
|
1445 | 1505 | f.close() |
|
|
1446 | 1506 | |
|
|
1447 | 1507 | |
|
|
1448 | 1508 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
1449 | 1509 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, |
|
|
1450 | 1510 | timerange=None, |
|
|
1451 | 1511 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1452 | 1512 | server=None, folder=None, username=None, password=None, |
|
|
1453 | 1513 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1454 | 1514 | |
|
|
1455 | 1515 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
1456 | 1516 | return |
|
|
1457 | 1517 | |
|
|
1458 | 1518 | if pairsList == None: |
|
|
1459 | 1519 | pairsIndexList = dataOut.pairsIndexList[:10] |
|
|
1460 | 1520 | else: |
|
|
1461 | 1521 | pairsIndexList = [] |
|
|
1462 | 1522 | for pair in pairsList: |
|
|
1463 | 1523 | if pair not in dataOut.pairsList: |
|
|
1464 | 1524 | raise ValueError, "Pair %s is not in dataOut.pairsList" %(pair) |
|
|
1465 | 1525 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
1466 | 1526 | |
|
|
1467 | 1527 | if pairsIndexList == []: |
|
|
1468 | 1528 | return |
|
|
1469 | 1529 | |
|
|
1470 | 1530 | # if len(pairsIndexList) > 4: |
|
|
1471 | 1531 | # pairsIndexList = pairsIndexList[0:4] |
|
|
1472 | 1532 | |
|
|
1473 | 1533 | hmin_index = None |
|
|
1474 | 1534 | hmax_index = None |
|
|
1475 | 1535 | |
|
|
1476 | 1536 | if hmin != None and hmax != None: |
|
|
1477 | 1537 | indexes = numpy.arange(dataOut.nHeights) |
|
|
1478 | 1538 | hmin_list = indexes[dataOut.heightList >= hmin] |
|
|
1479 | 1539 | hmax_list = indexes[dataOut.heightList <= hmax] |
|
|
1480 | 1540 | |
|
|
1481 | 1541 | if hmin_list.any(): |
|
|
1482 | 1542 | hmin_index = hmin_list[0] |
|
|
1483 | 1543 | |
|
|
1484 | 1544 | if hmax_list.any(): |
|
|
1485 | 1545 | hmax_index = hmax_list[-1]+1 |
|
|
1486 | 1546 | |
|
|
1487 | 1547 | x = dataOut.getTimeRange() |
|
|
1488 | 1548 | #y = dataOut.getHeiRange() |
|
|
1489 | 1549 | |
|
|
1490 | 1550 | |
|
|
1491 | 1551 | thisDatetime = dataOut.datatime |
|
|
1492 | 1552 | |
|
|
1493 | 1553 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1494 | 1554 | xlabel = "Local Time" |
|
|
1495 | 1555 | ylabel = "Phase (degrees)" |
|
|
1496 | 1556 | |
|
|
1497 | 1557 | update_figfile = False |
|
|
1498 | 1558 | |
|
|
1499 | 1559 | nplots = len(pairsIndexList) |
|
|
1500 | 1560 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
1501 | 1561 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
1502 | 1562 | for i in range(nplots): |
|
|
1503 | 1563 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
1504 | 1564 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) |
|
|
1505 | 1565 | powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) |
|
|
1506 | 1566 | powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) |
|
|
1507 | 1567 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
1508 | 1568 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
1509 | 1569 | |
|
|
1510 | 1570 | #print "Phase %d%d" %(pair[0], pair[1]) |
|
|
1511 | 1571 | #print phase[dataOut.beacon_heiIndexList] |
|
|
1512 | 1572 | |
|
|
1513 | 1573 | if dataOut.beacon_heiIndexList: |
|
|
1514 | 1574 | phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) |
|
|
1515 | 1575 | else: |
|
|
1516 | 1576 | phase_beacon[i] = numpy.average(phase) |
|
|
1517 | 1577 | |
|
|
1518 | 1578 | if not self.isConfig: |
|
|
1519 | 1579 | |
|
|
1520 | 1580 | nplots = len(pairsIndexList) |
|
|
1521 | 1581 | |
|
|
1522 | 1582 | self.setup(id=id, |
|
|
1523 | 1583 | nplots=nplots, |
|
|
1524 | 1584 | wintitle=wintitle, |
|
|
1525 | 1585 | showprofile=showprofile, |
|
|
1526 | 1586 | show=show) |
|
|
1527 | 1587 | |
|
|
1528 | 1588 | if timerange != None: |
|
|
1529 | 1589 | self.timerange = timerange |
|
|
1530 | 1590 | |
|
|
1531 | 1591 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1532 | 1592 | |
|
|
1533 | 1593 | if ymin == None: ymin = 0 |
|
|
1534 | 1594 | if ymax == None: ymax = 360 |
|
|
1535 | 1595 | |
|
|
1536 | 1596 | self.FTP_WEI = ftp_wei |
|
|
1537 | 1597 | self.EXP_CODE = exp_code |
|
|
1538 | 1598 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1539 | 1599 | self.PLOT_POS = plot_pos |
|
|
1540 | 1600 | |
|
|
1541 | 1601 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1542 | 1602 | self.isConfig = True |
|
|
1543 | 1603 | self.figfile = figfile |
|
|
1544 | 1604 | self.xdata = numpy.array([]) |
|
|
1545 | 1605 | self.ydata = numpy.array([]) |
|
|
1546 | 1606 | |
|
|
1547 | 1607 | update_figfile = True |
|
|
1548 | 1608 | |
|
|
1549 | 1609 | #open file beacon phase |
|
|
1550 | 1610 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1551 | 1611 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1552 | 1612 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
1553 | 1613 | #self.save_phase(self.filename_phase) |
|
|
1554 | 1614 | |
|
|
1555 | 1615 | |
|
|
1556 | 1616 | #store data beacon phase |
|
|
1557 | 1617 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
1558 | 1618 | |
|
|
1559 | 1619 | self.setWinTitle(title) |
|
|
1560 | 1620 | |
|
|
1561 | 1621 | |
|
|
1562 | 1622 | title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1563 | 1623 | |
|
|
1564 | 1624 | legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList] |
|
|
1565 | 1625 | |
|
|
1566 | 1626 | axes = self.axesList[0] |
|
|
1567 | 1627 | |
|
|
1568 | 1628 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1569 | 1629 | |
|
|
1570 | 1630 | if len(self.ydata)==0: |
|
|
1571 | 1631 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
1572 | 1632 | else: |
|
|
1573 | 1633 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
1574 | 1634 | |
|
|
1575 | 1635 | |
|
|
1576 | 1636 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1577 | 1637 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1578 | 1638 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1579 | 1639 | XAxisAsTime=True, grid='both' |
|
|
1580 | 1640 | ) |
|
|
1581 | 1641 | |
|
|
1582 | 1642 | self.draw() |
|
|
1583 | 1643 | |
|
|
1584 | 1644 | if dataOut.ltctime >= self.xmax: |
|
|
1585 | 1645 | self.counter_imagwr = wr_period |
|
|
1586 | 1646 | self.isConfig = False |
|
|
1587 | 1647 | update_figfile = True |
|
|
1588 | 1648 | |
|
|
1589 | 1649 | self.save(figpath=figpath, |
|
|
1590 | 1650 | figfile=figfile, |
|
|
1591 | 1651 | save=save, |
|
|
1592 | 1652 | ftp=ftp, |
|
|
1593 | 1653 | wr_period=wr_period, |
|
|
1594 | 1654 | thisDatetime=thisDatetime, |
|
|
1595 | 1655 | update_figfile=update_figfile) |
| @@ -1,849 +1,853 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Created on Jul 3, 2014 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 |
|
|
5 | 5 | ''' |
|
|
6 | 6 | |
|
|
7 | 7 | import os, sys |
|
|
8 | 8 | import time, datetime |
|
|
9 | 9 | import numpy |
|
|
10 | 10 | import fnmatch |
|
|
11 | 11 | import glob |
|
|
12 | 12 | from time import sleep |
|
|
13 | 13 | |
|
|
14 | 14 | try: |
|
|
15 | 15 | import pyfits |
|
|
16 | 16 | except ImportError, e: |
|
|
17 | 17 | print "Fits data cannot be used. Install pyfits module" |
|
|
18 | 18 | |
|
|
19 | 19 | from xml.etree.ElementTree import ElementTree |
|
|
20 | 20 | |
|
|
21 | 21 | from jroIO_base import isRadarFolder, isNumber |
|
|
22 | 22 | from schainpy.model.data.jrodata import Fits |
|
|
23 | 23 | from schainpy.model.proc.jroproc_base import Operation, ProcessingUnit |
|
|
24 | 24 | |
|
|
25 | 25 | class PyFits(object): |
|
|
26 | 26 | name=None |
|
|
27 | 27 | format=None |
|
|
28 | 28 | array =None |
|
|
29 | 29 | data =None |
|
|
30 | 30 | thdulist=None |
|
|
31 | 31 | prihdr=None |
|
|
32 | 32 | hdu=None |
|
|
33 | 33 | |
|
|
34 | 34 | def __init__(self): |
|
|
35 | 35 | |
|
|
36 | 36 | pass |
|
|
37 | 37 | |
|
|
38 | 38 | def setColF(self,name,format,array): |
|
|
39 | 39 | self.name=name |
|
|
40 | 40 | self.format=format |
|
|
41 | 41 | self.array=array |
|
|
42 | 42 | a1=numpy.array([self.array],dtype=numpy.float32) |
|
|
43 | 43 | self.col1 = pyfits.Column(name=self.name, format=self.format, array=a1) |
|
|
44 | 44 | return self.col1 |
|
|
45 | 45 | |
|
|
46 | 46 | # def setColP(self,name,format,data): |
|
|
47 | 47 | # self.name=name |
|
|
48 | 48 | # self.format=format |
|
|
49 | 49 | # self.data=data |
|
|
50 | 50 | # a2=numpy.array([self.data],dtype=numpy.float32) |
|
|
51 | 51 | # self.col2 = pyfits.Column(name=self.name, format=self.format, array=a2) |
|
|
52 | 52 | # return self.col2 |
|
|
53 | 53 | |
|
|
54 | 54 | |
|
|
55 | 55 | def writeData(self,name,format,data): |
|
|
56 | 56 | self.name=name |
|
|
57 | 57 | self.format=format |
|
|
58 | 58 | self.data=data |
|
|
59 | 59 | a2=numpy.array([self.data],dtype=numpy.float32) |
|
|
60 | 60 | self.col2 = pyfits.Column(name=self.name, format=self.format, array=a2) |
|
|
61 | 61 | return self.col2 |
|
|
62 | 62 | |
|
|
63 | 63 | def cFImage(self,idblock,year,month,day,hour,minute,second): |
|
|
64 | 64 | self.hdu= pyfits.PrimaryHDU(idblock) |
|
|
65 | 65 | self.hdu.header.set("Year",year) |
|
|
66 | 66 | self.hdu.header.set("Month",month) |
|
|
67 | 67 | self.hdu.header.set("Day",day) |
|
|
68 | 68 | self.hdu.header.set("Hour",hour) |
|
|
69 | 69 | self.hdu.header.set("Minute",minute) |
|
|
70 | 70 | self.hdu.header.set("Second",second) |
|
|
71 | 71 | return self.hdu |
|
|
72 | 72 | |
|
|
73 | 73 | |
|
|
74 | 74 | def Ctable(self,colList): |
|
|
75 | 75 | self.cols=pyfits.ColDefs(colList) |
|
|
76 | 76 | self.tbhdu = pyfits.new_table(self.cols) |
|
|
77 | 77 | return self.tbhdu |
|
|
78 | 78 | |
|
|
79 | 79 | |
|
|
80 | 80 | def CFile(self,hdu,tbhdu): |
|
|
81 | 81 | self.thdulist=pyfits.HDUList([hdu,tbhdu]) |
|
|
82 | 82 | |
|
|
83 | 83 | def wFile(self,filename): |
|
|
84 | 84 | if os.path.isfile(filename): |
|
|
85 | 85 | os.remove(filename) |
|
|
86 | 86 | self.thdulist.writeto(filename) |
|
|
87 | 87 | |
|
|
88 | 88 | |
|
|
89 | 89 | class ParameterConf: |
|
|
90 | 90 | ELEMENTNAME = 'Parameter' |
|
|
91 | 91 | def __init__(self): |
|
|
92 | 92 | self.name = '' |
|
|
93 | 93 | self.value = '' |
|
|
94 | 94 | |
|
|
95 | 95 | def readXml(self, parmElement): |
|
|
96 | 96 | self.name = parmElement.get('name') |
|
|
97 | 97 | self.value = parmElement.get('value') |
|
|
98 | 98 | |
|
|
99 | 99 | def getElementName(self): |
|
|
100 | 100 | return self.ELEMENTNAME |
|
|
101 | 101 | |
|
|
102 | 102 | class Metadata(object): |
|
|
103 | 103 | |
|
|
104 | 104 | def __init__(self, filename): |
|
|
105 | 105 | self.parmConfObjList = [] |
|
|
106 | 106 | self.readXml(filename) |
|
|
107 | 107 | |
|
|
108 | 108 | def readXml(self, filename): |
|
|
109 | 109 | self.projectElement = None |
|
|
110 | 110 | self.procUnitConfObjDict = {} |
|
|
111 | 111 | self.projectElement = ElementTree().parse(filename) |
|
|
112 | 112 | self.project = self.projectElement.tag |
|
|
113 | 113 | |
|
|
114 | 114 | parmElementList = self.projectElement.getiterator(ParameterConf().getElementName()) |
|
|
115 | 115 | |
|
|
116 | 116 | for parmElement in parmElementList: |
|
|
117 | 117 | parmConfObj = ParameterConf() |
|
|
118 | 118 | parmConfObj.readXml(parmElement) |
|
|
119 | 119 | self.parmConfObjList.append(parmConfObj) |
|
|
120 | 120 | |
|
|
121 | 121 | class FitsWriter(Operation): |
|
|
122 | ||
|
|
122 | parameters = { | |
|
|
123 | 'path': global_type_string, | |
|
|
124 | 'dataBlocksPerFile': global_type_integer, | |
|
|
125 | 'metadatafile': global_type_string, | |
|
|
126 | } | |
|
|
123 | 127 | def __init__(self, **kwargs): |
|
|
124 | 128 | Operation.__init__(self, **kwargs) |
|
|
125 | 129 | self.isConfig = False |
|
|
126 | 130 | self.dataBlocksPerFile = None |
|
|
127 | 131 | self.blockIndex = 0 |
|
|
128 | 132 | self.flagIsNewFile = 1 |
|
|
129 | 133 | self.fitsObj = None |
|
|
130 | 134 | self.optchar = 'P' |
|
|
131 | 135 | self.ext = '.fits' |
|
|
132 | 136 | self.setFile = 0 |
|
|
133 | 137 | |
|
|
134 | 138 | def setFitsHeader(self, dataOut, metadatafile=None): |
|
|
135 | 139 | |
|
|
136 | 140 | header_data = pyfits.PrimaryHDU() |
|
|
137 | 141 | |
|
|
138 | 142 | header_data.header['EXPNAME'] = "RADAR DATA" |
|
|
139 | 143 | header_data.header['DATATYPE'] = "SPECTRA" |
|
|
140 | 144 | header_data.header['COMMENT'] = "" |
|
|
141 | 145 | |
|
|
142 | 146 | if metadatafile: |
|
|
143 | 147 | |
|
|
144 | 148 | metadata4fits = Metadata(metadatafile) |
|
|
145 | 149 | |
|
|
146 | 150 | for parameter in metadata4fits.parmConfObjList: |
|
|
147 | 151 | parm_name = parameter.name |
|
|
148 | 152 | parm_value = parameter.value |
|
|
149 | 153 | |
|
|
150 | 154 | header_data.header[parm_name] = parm_value |
|
|
151 | 155 | |
|
|
152 | 156 | header_data.header['DATETIME'] = time.strftime("%b %d %Y %H:%M:%S", dataOut.datatime.timetuple()) |
|
|
153 | 157 | header_data.header['CHANNELLIST'] = str(dataOut.channelList) |
|
|
154 | 158 | header_data.header['NCHANNELS'] = dataOut.nChannels |
|
|
155 | 159 | #header_data.header['HEIGHTS'] = dataOut.heightList |
|
|
156 | 160 | header_data.header['NHEIGHTS'] = dataOut.nHeights |
|
|
157 | 161 | |
|
|
158 | 162 | header_data.header['IPPSECONDS'] = dataOut.ippSeconds |
|
|
159 | 163 | header_data.header['NCOHINT'] = dataOut.nCohInt |
|
|
160 | 164 | header_data.header['NINCOHINT'] = dataOut.nIncohInt |
|
|
161 | 165 | header_data.header['TIMEZONE'] = dataOut.timeZone |
|
|
162 | 166 | header_data.header['NBLOCK'] = self.blockIndex |
|
|
163 | 167 | |
|
|
164 | 168 | header_data.writeto(self.filename) |
|
|
165 | 169 | |
|
|
166 | 170 | self.addExtension(dataOut.heightList,'HEIGHTLIST') |
|
|
167 | 171 | |
|
|
168 | 172 | |
|
|
169 | 173 | def setup(self, dataOut, path, dataBlocksPerFile=100, metadatafile=None): |
|
|
170 | 174 | |
|
|
171 | 175 | self.path = path |
|
|
172 | 176 | self.dataOut = dataOut |
|
|
173 | 177 | self.metadatafile = metadatafile |
|
|
174 | 178 | self.dataBlocksPerFile = dataBlocksPerFile |
|
|
175 | 179 | |
|
|
176 | 180 | def open(self): |
|
|
177 | 181 | self.fitsObj = pyfits.open(self.filename, mode='update') |
|
|
178 | 182 | |
|
|
179 | 183 | |
|
|
180 | 184 | def addExtension(self, data, tagname): |
|
|
181 | 185 | self.open() |
|
|
182 | 186 | extension = pyfits.ImageHDU(data=data, name=tagname) |
|
|
183 | 187 | #extension.header['TAG'] = tagname |
|
|
184 | 188 | self.fitsObj.append(extension) |
|
|
185 | 189 | self.write() |
|
|
186 | 190 | |
|
|
187 | 191 | def addData(self, data): |
|
|
188 | 192 | self.open() |
|
|
189 | 193 | extension = pyfits.ImageHDU(data=data, name=self.fitsObj[0].header['DATATYPE']) |
|
|
190 | 194 | extension.header['UTCTIME'] = self.dataOut.utctime |
|
|
191 | 195 | self.fitsObj.append(extension) |
|
|
192 | 196 | self.blockIndex += 1 |
|
|
193 | 197 | self.fitsObj[0].header['NBLOCK'] = self.blockIndex |
|
|
194 | 198 | |
|
|
195 | 199 | self.write() |
|
|
196 | 200 | |
|
|
197 | 201 | def write(self): |
|
|
198 | 202 | |
|
|
199 | 203 | self.fitsObj.flush(verbose=True) |
|
|
200 | 204 | self.fitsObj.close() |
|
|
201 | 205 | |
|
|
202 | 206 | |
|
|
203 | 207 | def setNextFile(self): |
|
|
204 | 208 | |
|
|
205 | 209 | ext = self.ext |
|
|
206 | 210 | path = self.path |
|
|
207 | 211 | |
|
|
208 | 212 | timeTuple = time.localtime( self.dataOut.utctime) |
|
|
209 | 213 | subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday) |
|
|
210 | 214 | |
|
|
211 | 215 | fullpath = os.path.join( path, subfolder ) |
|
|
212 | 216 | if not( os.path.exists(fullpath) ): |
|
|
213 | 217 | os.mkdir(fullpath) |
|
|
214 | 218 | self.setFile = -1 #inicializo mi contador de seteo |
|
|
215 | 219 | else: |
|
|
216 | 220 | filesList = os.listdir( fullpath ) |
|
|
217 | 221 | if len( filesList ) > 0: |
|
|
218 | 222 | filesList = sorted( filesList, key=str.lower ) |
|
|
219 | 223 | filen = filesList[-1] |
|
|
220 | 224 | |
|
|
221 | 225 | if isNumber( filen[8:11] ): |
|
|
222 | 226 | self.setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file |
|
|
223 | 227 | else: |
|
|
224 | 228 | self.setFile = -1 |
|
|
225 | 229 | else: |
|
|
226 | 230 | self.setFile = -1 #inicializo mi contador de seteo |
|
|
227 | 231 | |
|
|
228 | 232 | setFile = self.setFile |
|
|
229 | 233 | setFile += 1 |
|
|
230 | 234 | |
|
|
231 | 235 | thisFile = '%s%4.4d%3.3d%3.3d%s' % (self.optchar, |
|
|
232 | 236 | timeTuple.tm_year, |
|
|
233 | 237 | timeTuple.tm_yday, |
|
|
234 | 238 | setFile, |
|
|
235 | 239 | ext ) |
|
|
236 | 240 | |
|
|
237 | 241 | filename = os.path.join( path, subfolder, thisFile ) |
|
|
238 | 242 | |
|
|
239 | 243 | self.blockIndex = 0 |
|
|
240 | 244 | self.filename = filename |
|
|
241 | 245 | self.setFile = setFile |
|
|
242 | 246 | self.flagIsNewFile = 1 |
|
|
243 | 247 | |
|
|
244 | 248 | print 'Writing the file: %s'%self.filename |
|
|
245 | 249 | |
|
|
246 | 250 | self.setFitsHeader(self.dataOut, self.metadatafile) |
|
|
247 | 251 | |
|
|
248 | 252 | return 1 |
|
|
249 | 253 | |
|
|
250 | 254 | def writeBlock(self): |
|
|
251 | 255 | self.addData(self.dataOut.data_spc) |
|
|
252 | 256 | self.flagIsNewFile = 0 |
|
|
253 | 257 | |
|
|
254 | 258 | |
|
|
255 | 259 | def __setNewBlock(self): |
|
|
256 | 260 | |
|
|
257 | 261 | if self.flagIsNewFile: |
|
|
258 | 262 | return 1 |
|
|
259 | 263 | |
|
|
260 | 264 | if self.blockIndex < self.dataBlocksPerFile: |
|
|
261 | 265 | return 1 |
|
|
262 | 266 | |
|
|
263 | 267 | if not( self.setNextFile() ): |
|
|
264 | 268 | return 0 |
|
|
265 | 269 | |
|
|
266 | 270 | return 1 |
|
|
267 | 271 | |
|
|
268 | 272 | def writeNextBlock(self): |
|
|
269 | 273 | if not( self.__setNewBlock() ): |
|
|
270 | 274 | return 0 |
|
|
271 | 275 | self.writeBlock() |
|
|
272 | 276 | return 1 |
|
|
273 | 277 | |
|
|
274 | 278 | def putData(self): |
|
|
275 | 279 | if self.flagIsNewFile: |
|
|
276 | 280 | self.setNextFile() |
|
|
277 | 281 | self.writeNextBlock() |
|
|
278 | 282 | |
|
|
279 | def run(self, dataOut, **kwargs): | |
|
|
283 | def run(self, dataOut, path, dataBlocksPerFile=100, metadatafile=None, **kwargs): | |
|
|
280 | 284 | if not(self.isConfig): |
|
|
281 | self.setup(dataOut, **kwargs) | |
|
|
285 | self.setup(dataOut, path, dataBlocksPerFile=dataBlocksPerFile, metadatafile=metadatafile, **kwargs) | |
|
|
282 | 286 | self.isConfig = True |
|
|
283 | 287 | self.putData() |
|
|
284 | 288 | |
|
|
285 | 289 | |
|
|
286 | 290 | class FitsReader(ProcessingUnit): |
|
|
287 | 291 | |
|
|
288 | 292 | # __TIMEZONE = time.timezone |
|
|
289 | 293 | |
|
|
290 | 294 | expName = None |
|
|
291 | 295 | datetimestr = None |
|
|
292 | 296 | utc = None |
|
|
293 | 297 | nChannels = None |
|
|
294 | 298 | nSamples = None |
|
|
295 | 299 | dataBlocksPerFile = None |
|
|
296 | 300 | comments = None |
|
|
297 | 301 | lastUTTime = None |
|
|
298 | 302 | header_dict = None |
|
|
299 | 303 | data = None |
|
|
300 | 304 | data_header_dict = None |
|
|
301 | 305 | |
|
|
302 | 306 | def __init__(self, **kwargs): |
|
|
303 | 307 | ProcessingUnit.__init__(self, **kwargs) |
|
|
304 | 308 | self.isConfig = False |
|
|
305 | 309 | self.ext = '.fits' |
|
|
306 | 310 | self.setFile = 0 |
|
|
307 | 311 | self.flagNoMoreFiles = 0 |
|
|
308 | 312 | self.flagIsNewFile = 1 |
|
|
309 | 313 | self.flagDiscontinuousBlock = None |
|
|
310 | 314 | self.fileIndex = None |
|
|
311 | 315 | self.filename = None |
|
|
312 | 316 | self.fileSize = None |
|
|
313 | 317 | self.fitsObj = None |
|
|
314 | 318 | self.timeZone = None |
|
|
315 | 319 | self.nReadBlocks = 0 |
|
|
316 | 320 | self.nTotalBlocks = 0 |
|
|
317 | 321 | self.dataOut = self.createObjByDefault() |
|
|
318 | 322 | self.maxTimeStep = 10# deberia ser definido por el usuario usando el metodo setup() |
|
|
319 | 323 | self.blockIndex = 1 |
|
|
320 | 324 | |
|
|
321 | 325 | def createObjByDefault(self): |
|
|
322 | 326 | |
|
|
323 | 327 | dataObj = Fits() |
|
|
324 | 328 | |
|
|
325 | 329 | return dataObj |
|
|
326 | 330 | |
|
|
327 | 331 | def isFileinThisTime(self, filename, startTime, endTime, useLocalTime=False): |
|
|
328 | 332 | try: |
|
|
329 | 333 | fitsObj = pyfits.open(filename,'readonly') |
|
|
330 | 334 | except: |
|
|
331 | 335 | print "File %s can't be opened" %(filename) |
|
|
332 | 336 | return None |
|
|
333 | 337 | |
|
|
334 | 338 | header = fitsObj[0].header |
|
|
335 | 339 | struct_time = time.strptime(header['DATETIME'], "%b %d %Y %H:%M:%S") |
|
|
336 | 340 | utc = time.mktime(struct_time) - time.timezone #TIMEZONE debe ser un parametro del header FITS |
|
|
337 | 341 | |
|
|
338 | 342 | ltc = utc |
|
|
339 | 343 | if useLocalTime: |
|
|
340 | 344 | ltc -= time.timezone |
|
|
341 | 345 | thisDatetime = datetime.datetime.utcfromtimestamp(ltc) |
|
|
342 | 346 | thisTime = thisDatetime.time() |
|
|
343 | 347 | |
|
|
344 | 348 | if not ((startTime <= thisTime) and (endTime > thisTime)): |
|
|
345 | 349 | return None |
|
|
346 | 350 | |
|
|
347 | 351 | return thisDatetime |
|
|
348 | 352 | |
|
|
349 | 353 | def __setNextFileOnline(self): |
|
|
350 | 354 | raise NotImplementedError |
|
|
351 | 355 | |
|
|
352 | 356 | def __setNextFileOffline(self): |
|
|
353 | 357 | idFile = self.fileIndex |
|
|
354 | 358 | |
|
|
355 | 359 | while (True): |
|
|
356 | 360 | idFile += 1 |
|
|
357 | 361 | if not(idFile < len(self.filenameList)): |
|
|
358 | 362 | self.flagNoMoreFiles = 1 |
|
|
359 | 363 | print "No more Files" |
|
|
360 | 364 | return 0 |
|
|
361 | 365 | |
|
|
362 | 366 | filename = self.filenameList[idFile] |
|
|
363 | 367 | |
|
|
364 | 368 | # if not(self.__verifyFile(filename)): |
|
|
365 | 369 | # continue |
|
|
366 | 370 | |
|
|
367 | 371 | fileSize = os.path.getsize(filename) |
|
|
368 | 372 | fitsObj = pyfits.open(filename,'readonly') |
|
|
369 | 373 | break |
|
|
370 | 374 | |
|
|
371 | 375 | self.flagIsNewFile = 1 |
|
|
372 | 376 | self.fileIndex = idFile |
|
|
373 | 377 | self.filename = filename |
|
|
374 | 378 | self.fileSize = fileSize |
|
|
375 | 379 | self.fitsObj = fitsObj |
|
|
376 | 380 | self.blockIndex = 0 |
|
|
377 | 381 | print "Setting the file: %s"%self.filename |
|
|
378 | 382 | |
|
|
379 | 383 | return 1 |
|
|
380 | 384 | |
|
|
381 | 385 | def __setValuesFromHeader(self): |
|
|
382 | 386 | |
|
|
383 | 387 | self.dataOut.header = self.header_dict |
|
|
384 | 388 | self.dataOut.expName = self.expName |
|
|
385 | 389 | |
|
|
386 | 390 | self.dataOut.timeZone = self.timeZone |
|
|
387 | 391 | self.dataOut.dataBlocksPerFile = self.dataBlocksPerFile |
|
|
388 | 392 | self.dataOut.comments = self.comments |
|
|
389 | 393 | # self.dataOut.timeInterval = self.timeInterval |
|
|
390 | 394 | self.dataOut.channelList = self.channelList |
|
|
391 | 395 | self.dataOut.heightList = self.heightList |
|
|
392 | 396 | |
|
|
393 | 397 | self.dataOut.nCohInt = self.nCohInt |
|
|
394 | 398 | self.dataOut.nIncohInt = self.nIncohInt |
|
|
395 | 399 | |
|
|
396 | 400 | self.dataOut.ippSeconds = self.ippSeconds |
|
|
397 | 401 | |
|
|
398 | 402 | def readHeader(self): |
|
|
399 | 403 | headerObj = self.fitsObj[0] |
|
|
400 | 404 | |
|
|
401 | 405 | self.header_dict = headerObj.header |
|
|
402 | 406 | if 'EXPNAME' in headerObj.header.keys(): |
|
|
403 | 407 | self.expName = headerObj.header['EXPNAME'] |
|
|
404 | 408 | |
|
|
405 | 409 | if 'DATATYPE' in headerObj.header.keys(): |
|
|
406 | 410 | self.dataType = headerObj.header['DATATYPE'] |
|
|
407 | 411 | |
|
|
408 | 412 | self.datetimestr = headerObj.header['DATETIME'] |
|
|
409 | 413 | channelList = headerObj.header['CHANNELLIST'] |
|
|
410 | 414 | channelList = channelList.split('[') |
|
|
411 | 415 | channelList = channelList[1].split(']') |
|
|
412 | 416 | channelList = channelList[0].split(',') |
|
|
413 | 417 | channelList = [int(ch) for ch in channelList] |
|
|
414 | 418 | self.channelList = channelList |
|
|
415 | 419 | self.nChannels = headerObj.header['NCHANNELS'] |
|
|
416 | 420 | self.nHeights = headerObj.header['NHEIGHTS'] |
|
|
417 | 421 | self.ippSeconds = headerObj.header['IPPSECONDS'] |
|
|
418 | 422 | self.nCohInt = headerObj.header['NCOHINT'] |
|
|
419 | 423 | self.nIncohInt = headerObj.header['NINCOHINT'] |
|
|
420 | 424 | self.dataBlocksPerFile = headerObj.header['NBLOCK'] |
|
|
421 | 425 | self.timeZone = headerObj.header['TIMEZONE'] |
|
|
422 | 426 | |
|
|
423 | 427 | # self.timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
424 | 428 | |
|
|
425 | 429 | if 'COMMENT' in headerObj.header.keys(): |
|
|
426 | 430 | self.comments = headerObj.header['COMMENT'] |
|
|
427 | 431 | |
|
|
428 | 432 | self.readHeightList() |
|
|
429 | 433 | |
|
|
430 | 434 | def readHeightList(self): |
|
|
431 | 435 | self.blockIndex = self.blockIndex + 1 |
|
|
432 | 436 | obj = self.fitsObj[self.blockIndex] |
|
|
433 | 437 | self.heightList = obj.data |
|
|
434 | 438 | self.blockIndex = self.blockIndex + 1 |
|
|
435 | 439 | |
|
|
436 | 440 | def readExtension(self): |
|
|
437 | 441 | obj = self.fitsObj[self.blockIndex] |
|
|
438 | 442 | self.heightList = obj.data |
|
|
439 | 443 | self.blockIndex = self.blockIndex + 1 |
|
|
440 | 444 | |
|
|
441 | 445 | def setNextFile(self): |
|
|
442 | 446 | |
|
|
443 | 447 | if self.online: |
|
|
444 | 448 | newFile = self.__setNextFileOnline() |
|
|
445 | 449 | else: |
|
|
446 | 450 | newFile = self.__setNextFileOffline() |
|
|
447 | 451 | |
|
|
448 | 452 | if not(newFile): |
|
|
449 | 453 | return 0 |
|
|
450 | 454 | |
|
|
451 | 455 | self.readHeader() |
|
|
452 | 456 | self.__setValuesFromHeader() |
|
|
453 | 457 | self.nReadBlocks = 0 |
|
|
454 | 458 | # self.blockIndex = 1 |
|
|
455 | 459 | return 1 |
|
|
456 | 460 | |
|
|
457 | 461 | def __searchFilesOffLine(self, |
|
|
458 | 462 | path, |
|
|
459 | 463 | startDate, |
|
|
460 | 464 | endDate, |
|
|
461 | 465 | startTime=datetime.time(0,0,0), |
|
|
462 | 466 | endTime=datetime.time(23,59,59), |
|
|
463 | 467 | set=None, |
|
|
464 | 468 | expLabel='', |
|
|
465 | 469 | ext='.fits', |
|
|
466 | 470 | walk=True): |
|
|
467 | 471 | |
|
|
468 | 472 | pathList = [] |
|
|
469 | 473 | |
|
|
470 | 474 | if not walk: |
|
|
471 | 475 | pathList.append(path) |
|
|
472 | 476 | |
|
|
473 | 477 | else: |
|
|
474 | 478 | dirList = [] |
|
|
475 | 479 | for thisPath in os.listdir(path): |
|
|
476 | 480 | if not os.path.isdir(os.path.join(path,thisPath)): |
|
|
477 | 481 | continue |
|
|
478 | 482 | if not isRadarFolder(thisPath): |
|
|
479 | 483 | continue |
|
|
480 | 484 | |
|
|
481 | 485 | dirList.append(thisPath) |
|
|
482 | 486 | |
|
|
483 | 487 | if not(dirList): |
|
|
484 | 488 | return None, None |
|
|
485 | 489 | |
|
|
486 | 490 | thisDate = startDate |
|
|
487 | 491 | |
|
|
488 | 492 | while(thisDate <= endDate): |
|
|
489 | 493 | year = thisDate.timetuple().tm_year |
|
|
490 | 494 | doy = thisDate.timetuple().tm_yday |
|
|
491 | 495 | |
|
|
492 | 496 | matchlist = fnmatch.filter(dirList, '?' + '%4.4d%3.3d' % (year,doy) + '*') |
|
|
493 | 497 | if len(matchlist) == 0: |
|
|
494 | 498 | thisDate += datetime.timedelta(1) |
|
|
495 | 499 | continue |
|
|
496 | 500 | for match in matchlist: |
|
|
497 | 501 | pathList.append(os.path.join(path,match,expLabel)) |
|
|
498 | 502 | |
|
|
499 | 503 | thisDate += datetime.timedelta(1) |
|
|
500 | 504 | |
|
|
501 | 505 | if pathList == []: |
|
|
502 | 506 | print "Any folder was found for the date range: %s-%s" %(startDate, endDate) |
|
|
503 | 507 | return None, None |
|
|
504 | 508 | |
|
|
505 | 509 | print "%d folder(s) was(were) found for the date range: %s - %s" %(len(pathList), startDate, endDate) |
|
|
506 | 510 | |
|
|
507 | 511 | filenameList = [] |
|
|
508 | 512 | datetimeList = [] |
|
|
509 | 513 | |
|
|
510 | 514 | for i in range(len(pathList)): |
|
|
511 | 515 | |
|
|
512 | 516 | thisPath = pathList[i] |
|
|
513 | 517 | |
|
|
514 | 518 | fileList = glob.glob1(thisPath, "*%s" %ext) |
|
|
515 | 519 | fileList.sort() |
|
|
516 | 520 | |
|
|
517 | 521 | for thisFile in fileList: |
|
|
518 | 522 | |
|
|
519 | 523 | filename = os.path.join(thisPath,thisFile) |
|
|
520 | 524 | thisDatetime = self.isFileinThisTime(filename, startTime, endTime) |
|
|
521 | 525 | |
|
|
522 | 526 | if not(thisDatetime): |
|
|
523 | 527 | continue |
|
|
524 | 528 | |
|
|
525 | 529 | filenameList.append(filename) |
|
|
526 | 530 | datetimeList.append(thisDatetime) |
|
|
527 | 531 | |
|
|
528 | 532 | if not(filenameList): |
|
|
529 | 533 | print "Any file was found for the time range %s - %s" %(startTime, endTime) |
|
|
530 | 534 | return None, None |
|
|
531 | 535 | |
|
|
532 | 536 | print "%d file(s) was(were) found for the time range: %s - %s" %(len(filenameList), startTime, endTime) |
|
|
533 | 537 | |
|
|
534 | 538 | |
|
|
535 | 539 | for i in range(len(filenameList)): |
|
|
536 | 540 | print "%s -> [%s]" %(filenameList[i], datetimeList[i].ctime()) |
|
|
537 | 541 | |
|
|
538 | 542 | self.filenameList = filenameList |
|
|
539 | 543 | self.datetimeList = datetimeList |
|
|
540 | 544 | |
|
|
541 | 545 | return pathList, filenameList |
|
|
542 | 546 | |
|
|
543 | 547 | def setup(self, path=None, |
|
|
544 | 548 | startDate=None, |
|
|
545 | 549 | endDate=None, |
|
|
546 | 550 | startTime=datetime.time(0,0,0), |
|
|
547 | 551 | endTime=datetime.time(23,59,59), |
|
|
548 | 552 | set=0, |
|
|
549 | 553 | expLabel = "", |
|
|
550 | 554 | ext = None, |
|
|
551 | 555 | online = False, |
|
|
552 | 556 | delay = 60, |
|
|
553 | 557 | walk = True): |
|
|
554 | 558 | |
|
|
555 | 559 | if path == None: |
|
|
556 | 560 | raise ValueError, "The path is not valid" |
|
|
557 | 561 | |
|
|
558 | 562 | if ext == None: |
|
|
559 | 563 | ext = self.ext |
|
|
560 | 564 | |
|
|
561 | 565 | if not(online): |
|
|
562 | 566 | print "Searching files in offline mode ..." |
|
|
563 | 567 | pathList, filenameList = self.__searchFilesOffLine(path, startDate=startDate, endDate=endDate, |
|
|
564 | 568 | startTime=startTime, endTime=endTime, |
|
|
565 | 569 | set=set, expLabel=expLabel, ext=ext, |
|
|
566 | 570 | walk=walk) |
|
|
567 | 571 | |
|
|
568 | 572 | if not(pathList): |
|
|
569 | 573 | print "No *%s files into the folder %s \nfor the range: %s - %s"%(ext, path, |
|
|
570 | 574 | datetime.datetime.combine(startDate,startTime).ctime(), |
|
|
571 | 575 | datetime.datetime.combine(endDate,endTime).ctime()) |
|
|
572 | 576 | |
|
|
573 | 577 | sys.exit(-1) |
|
|
574 | 578 | |
|
|
575 | 579 | self.fileIndex = -1 |
|
|
576 | 580 | self.pathList = pathList |
|
|
577 | 581 | self.filenameList = filenameList |
|
|
578 | 582 | |
|
|
579 | 583 | self.online = online |
|
|
580 | 584 | self.delay = delay |
|
|
581 | 585 | ext = ext.lower() |
|
|
582 | 586 | self.ext = ext |
|
|
583 | 587 | |
|
|
584 | 588 | if not(self.setNextFile()): |
|
|
585 | 589 | if (startDate!=None) and (endDate!=None): |
|
|
586 | 590 | print "No files in range: %s - %s" %(datetime.datetime.combine(startDate,startTime).ctime(), datetime.datetime.combine(endDate,endTime).ctime()) |
|
|
587 | 591 | elif startDate != None: |
|
|
588 | 592 | print "No files in range: %s" %(datetime.datetime.combine(startDate,startTime).ctime()) |
|
|
589 | 593 | else: |
|
|
590 | 594 | print "No files" |
|
|
591 | 595 | |
|
|
592 | 596 | sys.exit(-1) |
|
|
593 | 597 | |
|
|
594 | 598 | |
|
|
595 | 599 | |
|
|
596 | 600 | def readBlock(self): |
|
|
597 | 601 | dataObj = self.fitsObj[self.blockIndex] |
|
|
598 | 602 | |
|
|
599 | 603 | self.data = dataObj.data |
|
|
600 | 604 | self.data_header_dict = dataObj.header |
|
|
601 | 605 | self.utc = self.data_header_dict['UTCTIME'] |
|
|
602 | 606 | |
|
|
603 | 607 | self.flagIsNewFile = 0 |
|
|
604 | 608 | self.blockIndex += 1 |
|
|
605 | 609 | self.nTotalBlocks += 1 |
|
|
606 | 610 | self.nReadBlocks += 1 |
|
|
607 | 611 | |
|
|
608 | 612 | return 1 |
|
|
609 | 613 | |
|
|
610 | 614 | def __jumpToLastBlock(self): |
|
|
611 | 615 | raise NotImplementedError |
|
|
612 | 616 | |
|
|
613 | 617 | def __waitNewBlock(self): |
|
|
614 | 618 | """ |
|
|
615 | 619 | Return 1 si se encontro un nuevo bloque de datos, 0 de otra forma. |
|
|
616 | 620 | |
|
|
617 | 621 | Si el modo de lectura es OffLine siempre retorn 0 |
|
|
618 | 622 | """ |
|
|
619 | 623 | if not self.online: |
|
|
620 | 624 | return 0 |
|
|
621 | 625 | |
|
|
622 | 626 | if (self.nReadBlocks >= self.dataBlocksPerFile): |
|
|
623 | 627 | return 0 |
|
|
624 | 628 | |
|
|
625 | 629 | currentPointer = self.fp.tell() |
|
|
626 | 630 | |
|
|
627 | 631 | neededSize = self.processingHeaderObj.blockSize + self.basicHeaderSize |
|
|
628 | 632 | |
|
|
629 | 633 | for nTries in range( self.nTries ): |
|
|
630 | 634 | |
|
|
631 | 635 | self.fp.close() |
|
|
632 | 636 | self.fp = open( self.filename, 'rb' ) |
|
|
633 | 637 | self.fp.seek( currentPointer ) |
|
|
634 | 638 | |
|
|
635 | 639 | self.fileSize = os.path.getsize( self.filename ) |
|
|
636 | 640 | currentSize = self.fileSize - currentPointer |
|
|
637 | 641 | |
|
|
638 | 642 | if ( currentSize >= neededSize ): |
|
|
639 | 643 | self.__rdBasicHeader() |
|
|
640 | 644 | return 1 |
|
|
641 | 645 | |
|
|
642 | 646 | print "\tWaiting %0.2f seconds for the next block, try %03d ..." % (self.delay, nTries+1) |
|
|
643 | 647 | sleep( self.delay ) |
|
|
644 | 648 | |
|
|
645 | 649 | |
|
|
646 | 650 | return 0 |
|
|
647 | 651 | |
|
|
648 | 652 | def __setNewBlock(self): |
|
|
649 | 653 | |
|
|
650 | 654 | if self.online: |
|
|
651 | 655 | self.__jumpToLastBlock() |
|
|
652 | 656 | |
|
|
653 | 657 | if self.flagIsNewFile: |
|
|
654 | 658 | return 1 |
|
|
655 | 659 | |
|
|
656 | 660 | self.lastUTTime = self.utc |
|
|
657 | 661 | |
|
|
658 | 662 | if self.online: |
|
|
659 | 663 | if self.__waitNewBlock(): |
|
|
660 | 664 | return 1 |
|
|
661 | 665 | |
|
|
662 | 666 | if self.nReadBlocks < self.dataBlocksPerFile: |
|
|
663 | 667 | return 1 |
|
|
664 | 668 | |
|
|
665 | 669 | if not(self.setNextFile()): |
|
|
666 | 670 | return 0 |
|
|
667 | 671 | |
|
|
668 | 672 | deltaTime = self.utc - self.lastUTTime |
|
|
669 | 673 | |
|
|
670 | 674 | self.flagDiscontinuousBlock = 0 |
|
|
671 | 675 | |
|
|
672 | 676 | if deltaTime > self.maxTimeStep: |
|
|
673 | 677 | self.flagDiscontinuousBlock = 1 |
|
|
674 | 678 | |
|
|
675 | 679 | return 1 |
|
|
676 | 680 | |
|
|
677 | 681 | |
|
|
678 | 682 | def readNextBlock(self): |
|
|
679 | 683 | if not(self.__setNewBlock()): |
|
|
680 | 684 | return 0 |
|
|
681 | 685 | |
|
|
682 | 686 | if not(self.readBlock()): |
|
|
683 | 687 | return 0 |
|
|
684 | 688 | |
|
|
685 | 689 | return 1 |
|
|
686 | 690 | |
|
|
687 | 691 | def printInfo(self): |
|
|
688 | 692 | |
|
|
689 | 693 | pass |
|
|
690 | 694 | |
|
|
691 | 695 | def getData(self): |
|
|
692 | 696 | |
|
|
693 | 697 | if self.flagNoMoreFiles: |
|
|
694 | 698 | self.dataOut.flagNoData = True |
|
|
695 | 699 | print 'Process finished' |
|
|
696 | 700 | return 0 |
|
|
697 | 701 | |
|
|
698 | 702 | self.flagDiscontinuousBlock = 0 |
|
|
699 | 703 | self.flagIsNewBlock = 0 |
|
|
700 | 704 | |
|
|
701 | 705 | if not(self.readNextBlock()): |
|
|
702 | 706 | return 0 |
|
|
703 | 707 | |
|
|
704 | 708 | if self.data is None: |
|
|
705 | 709 | self.dataOut.flagNoData = True |
|
|
706 | 710 | return 0 |
|
|
707 | 711 | |
|
|
708 | 712 | self.dataOut.data = self.data |
|
|
709 | 713 | self.dataOut.data_header = self.data_header_dict |
|
|
710 | 714 | self.dataOut.utctime = self.utc |
|
|
711 | 715 | |
|
|
712 | 716 | # self.dataOut.header = self.header_dict |
|
|
713 | 717 | # self.dataOut.expName = self.expName |
|
|
714 | 718 | # self.dataOut.nChannels = self.nChannels |
|
|
715 | 719 | # self.dataOut.timeZone = self.timeZone |
|
|
716 | 720 | # self.dataOut.dataBlocksPerFile = self.dataBlocksPerFile |
|
|
717 | 721 | # self.dataOut.comments = self.comments |
|
|
718 | 722 | # # self.dataOut.timeInterval = self.timeInterval |
|
|
719 | 723 | # self.dataOut.channelList = self.channelList |
|
|
720 | 724 | # self.dataOut.heightList = self.heightList |
|
|
721 | 725 | self.dataOut.flagNoData = False |
|
|
722 | 726 | |
|
|
723 | 727 | return self.dataOut.data |
|
|
724 | 728 | |
|
|
725 | 729 | def run(self, **kwargs): |
|
|
726 | 730 | |
|
|
727 | 731 | if not(self.isConfig): |
|
|
728 | 732 | self.setup(**kwargs) |
|
|
729 | 733 | self.isConfig = True |
|
|
730 | 734 | |
|
|
731 | 735 | self.getData() |
|
|
732 | 736 | |
|
|
733 | 737 | class SpectraHeisWriter(Operation): |
|
|
734 | 738 | # set = None |
|
|
735 | 739 | setFile = None |
|
|
736 | 740 | idblock = None |
|
|
737 | 741 | doypath = None |
|
|
738 | 742 | subfolder = None |
|
|
739 | 743 | |
|
|
740 | 744 | def __init__(self, **kwargs): |
|
|
741 | 745 | Operation.__init__(self, **kwargs) |
|
|
742 | 746 | self.wrObj = PyFits() |
|
|
743 | 747 | # self.dataOut = dataOut |
|
|
744 | 748 | self.nTotalBlocks=0 |
|
|
745 | 749 | # self.set = None |
|
|
746 | 750 | self.setFile = None |
|
|
747 | 751 | self.idblock = 0 |
|
|
748 | 752 | self.wrpath = None |
|
|
749 | 753 | self.doypath = None |
|
|
750 | 754 | self.subfolder = None |
|
|
751 | 755 | self.isConfig = False |
|
|
752 | 756 | |
|
|
753 | 757 | def isNumber(str): |
|
|
754 | 758 | """ |
|
|
755 | 759 | Chequea si el conjunto de caracteres que componen un string puede ser convertidos a un numero. |
|
|
756 | 760 | |
|
|
757 | 761 | Excepciones: |
|
|
758 | 762 | Si un determinado string no puede ser convertido a numero |
|
|
759 | 763 | Input: |
|
|
760 | 764 | str, string al cual se le analiza para determinar si convertible a un numero o no |
|
|
761 | 765 | |
|
|
762 | 766 | Return: |
|
|
763 | 767 | True : si el string es uno numerico |
|
|
764 | 768 | False : no es un string numerico |
|
|
765 | 769 | """ |
|
|
766 | 770 | try: |
|
|
767 | 771 | float( str ) |
|
|
768 | 772 | return True |
|
|
769 | 773 | except: |
|
|
770 | 774 | return False |
|
|
771 | 775 | |
|
|
772 | 776 | def setup(self, dataOut, wrpath): |
|
|
773 | 777 | |
|
|
774 | 778 | if not(os.path.exists(wrpath)): |
|
|
775 | 779 | os.mkdir(wrpath) |
|
|
776 | 780 | |
|
|
777 | 781 | self.wrpath = wrpath |
|
|
778 | 782 | # self.setFile = 0 |
|
|
779 | 783 | self.dataOut = dataOut |
|
|
780 | 784 | |
|
|
781 | 785 | def putData(self): |
|
|
782 | 786 | name= time.localtime( self.dataOut.utctime) |
|
|
783 | 787 | ext=".fits" |
|
|
784 | 788 | |
|
|
785 | 789 | if self.doypath == None: |
|
|
786 | 790 | self.subfolder = 'F%4.4d%3.3d_%d' % (name.tm_year,name.tm_yday,time.mktime(datetime.datetime.now().timetuple())) |
|
|
787 | 791 | self.doypath = os.path.join( self.wrpath, self.subfolder ) |
|
|
788 | 792 | os.mkdir(self.doypath) |
|
|
789 | 793 | |
|
|
790 | 794 | if self.setFile == None: |
|
|
791 | 795 | # self.set = self.dataOut.set |
|
|
792 | 796 | self.setFile = 0 |
|
|
793 | 797 | # if self.set != self.dataOut.set: |
|
|
794 | 798 | ## self.set = self.dataOut.set |
|
|
795 | 799 | # self.setFile = 0 |
|
|
796 | 800 | |
|
|
797 | 801 | #make the filename |
|
|
798 | 802 | thisFile = 'D%4.4d%3.3d_%3.3d%s' % (name.tm_year,name.tm_yday,self.setFile,ext) |
|
|
799 | 803 | |
|
|
800 | 804 | filename = os.path.join(self.wrpath,self.subfolder, thisFile) |
|
|
801 | 805 | |
|
|
802 | 806 | idblock = numpy.array([self.idblock],dtype="int64") |
|
|
803 | 807 | header=self.wrObj.cFImage(idblock=idblock, |
|
|
804 | 808 | year=time.gmtime(self.dataOut.utctime).tm_year, |
|
|
805 | 809 | month=time.gmtime(self.dataOut.utctime).tm_mon, |
|
|
806 | 810 | day=time.gmtime(self.dataOut.utctime).tm_mday, |
|
|
807 | 811 | hour=time.gmtime(self.dataOut.utctime).tm_hour, |
|
|
808 | 812 | minute=time.gmtime(self.dataOut.utctime).tm_min, |
|
|
809 | 813 | second=time.gmtime(self.dataOut.utctime).tm_sec) |
|
|
810 | 814 | |
|
|
811 | 815 | c=3E8 |
|
|
812 | 816 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
813 | 817 | freq=numpy.arange(-1*self.dataOut.nHeights/2.,self.dataOut.nHeights/2.)*(c/(2*deltaHeight*1000)) |
|
|
814 | 818 | |
|
|
815 | 819 | colList = [] |
|
|
816 | 820 | |
|
|
817 | 821 | colFreq=self.wrObj.setColF(name="freq", format=str(self.dataOut.nFFTPoints)+'E', array=freq) |
|
|
818 | 822 | |
|
|
819 | 823 | colList.append(colFreq) |
|
|
820 | 824 | |
|
|
821 | 825 | nchannel=self.dataOut.nChannels |
|
|
822 | 826 | |
|
|
823 | 827 | for i in range(nchannel): |
|
|
824 | 828 | col = self.wrObj.writeData(name="PCh"+str(i+1), |
|
|
825 | 829 | format=str(self.dataOut.nFFTPoints)+'E', |
|
|
826 | 830 | data=10*numpy.log10(self.dataOut.data_spc[i,:])) |
|
|
827 | 831 | |
|
|
828 | 832 | colList.append(col) |
|
|
829 | 833 | |
|
|
830 | 834 | data=self.wrObj.Ctable(colList=colList) |
|
|
831 | 835 | |
|
|
832 | 836 | self.wrObj.CFile(header,data) |
|
|
833 | 837 | |
|
|
834 | 838 | self.wrObj.wFile(filename) |
|
|
835 | 839 | |
|
|
836 | 840 | #update the setFile |
|
|
837 | 841 | self.setFile += 1 |
|
|
838 | 842 | self.idblock += 1 |
|
|
839 | 843 | |
|
|
840 | 844 | return 1 |
|
|
841 | 845 | |
|
|
842 | 846 | def run(self, dataOut, **kwargs): |
|
|
843 | 847 | |
|
|
844 | 848 | if not(self.isConfig): |
|
|
845 | 849 | |
|
|
846 | 850 | self.setup(dataOut, **kwargs) |
|
|
847 | 851 | self.isConfig = True |
|
|
848 | 852 | |
|
|
849 | 853 | self.putData() |
| @@ -1,144 +1,144 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | @author: Daniel Suarez |
|
|
3 | 3 | ''' |
|
|
4 | 4 | import numpy |
|
|
5 | 5 | from jroproc_base import ProcessingUnit, Operation |
|
|
6 | 6 | from schainpy.model.data.jroamisr import AMISR |
|
|
7 | 7 | |
|
|
8 | 8 | class AMISRProc(ProcessingUnit): |
|
|
9 | 9 | def __init__(self): |
|
|
10 | 10 | ProcessingUnit.__init__(self) |
|
|
11 | 11 | self.objectDict = {} |
|
|
12 | 12 | self.dataOut = AMISR() |
|
|
13 | 13 | |
|
|
14 | 14 | def run(self): |
|
|
15 | 15 | if self.dataIn.type == 'AMISR': |
|
|
16 | 16 | self.dataOut.copy(self.dataIn) |
|
|
17 | 17 | |
|
|
18 | 18 | |
|
|
19 | 19 | class PrintInfo(Operation): |
|
|
20 | 20 | def __init__(self): |
|
|
21 | 21 | self.__isPrinted = False |
|
|
22 | 22 | |
|
|
23 | 23 | def run(self, dataOut): |
|
|
24 | 24 | |
|
|
25 | 25 | if not self.__isPrinted: |
|
|
26 | 26 | print 'Number of Records by File: %d'%dataOut.nRecords |
|
|
27 | 27 | print 'Number of Pulses: %d'%dataOut.nProfiles |
|
|
28 | 28 | print 'Number of Pulses by Frame: %d'%dataOut.npulseByFrame |
|
|
29 | 29 | print 'Number of Samples by Pulse: %d'%len(dataOut.heightList) |
|
|
30 | 30 | print 'Ipp Seconds: %f'%dataOut.ippSeconds |
|
|
31 | 31 | print 'Number of Beams: %d'%dataOut.nBeams |
|
|
32 | 32 | print 'BeamCodes:' |
|
|
33 | 33 | beamStrList = ['Beam %d -> Code=%d, azimuth=%2.2f, zenith=%2.2f, gain=%2.2f'%(k,v[0],v[1],v[2],v[3]) for k,v in dataOut.beamCodeDict.items()] |
|
|
34 | 34 | for b in beamStrList: |
|
|
35 | 35 | print b |
|
|
36 | 36 | self.__isPrinted = True |
|
|
37 | 37 | |
|
|
38 | 38 | return |
|
|
39 | 39 | |
|
|
40 | 40 | |
|
|
41 | 41 | class BeamSelector(Operation): |
|
|
42 | 42 | profileIndex = None |
|
|
43 | 43 | nProfiles = None |
|
|
44 | 44 | parameters = { |
|
|
45 |
|
|
|
|
45 | 'beam': global_type_string, | |
|
|
46 | 46 | } |
|
|
47 | 47 | |
|
|
48 | 48 | def __init__(self): |
|
|
49 | 49 | |
|
|
50 | 50 | self.profileIndex = 0 |
|
|
51 | 51 | self.__isConfig = False |
|
|
52 | 52 | |
|
|
53 | 53 | def incIndex(self): |
|
|
54 | 54 | self.profileIndex += 1 |
|
|
55 | 55 | |
|
|
56 | 56 | if self.profileIndex >= self.nProfiles: |
|
|
57 | 57 | self.profileIndex = 0 |
|
|
58 | 58 | |
|
|
59 | 59 | def isProfileInRange(self, minIndex, maxIndex): |
|
|
60 | 60 | |
|
|
61 | 61 | if self.profileIndex < minIndex: |
|
|
62 | 62 | return False |
|
|
63 | 63 | |
|
|
64 | 64 | if self.profileIndex > maxIndex: |
|
|
65 | 65 | return False |
|
|
66 | 66 | |
|
|
67 | 67 | return True |
|
|
68 | 68 | |
|
|
69 | 69 | def isProfileInList(self, profileList): |
|
|
70 | 70 | |
|
|
71 | 71 | if self.profileIndex not in profileList: |
|
|
72 | 72 | return False |
|
|
73 | 73 | |
|
|
74 | 74 | return True |
|
|
75 | 75 | |
|
|
76 | 76 | def run(self, dataOut, beam=None): |
|
|
77 | 77 | |
|
|
78 | 78 | dataOut.flagNoData = True |
|
|
79 | 79 | |
|
|
80 | 80 | if not(self.__isConfig): |
|
|
81 | 81 | |
|
|
82 | 82 | self.nProfiles = dataOut.nProfiles |
|
|
83 | 83 | self.profileIndex = dataOut.profileIndex |
|
|
84 | 84 | self.__isConfig = True |
|
|
85 | 85 | |
|
|
86 | 86 | if beam != None: |
|
|
87 | 87 | if self.isProfileInList(dataOut.beamRangeDict[beam]): |
|
|
88 | 88 | beamInfo = dataOut.beamCodeDict[beam] |
|
|
89 | 89 | dataOut.azimuth = beamInfo[1] |
|
|
90 | 90 | dataOut.zenith = beamInfo[2] |
|
|
91 | 91 | dataOut.gain = beamInfo[3] |
|
|
92 | 92 | dataOut.flagNoData = False |
|
|
93 | 93 | |
|
|
94 | 94 | self.incIndex() |
|
|
95 | 95 | return 1 |
|
|
96 | 96 | |
|
|
97 | 97 | else: |
|
|
98 | 98 | raise ValueError, "BeamSelector needs beam value" |
|
|
99 | 99 | |
|
|
100 | 100 | return 0 |
|
|
101 | 101 | |
|
|
102 | 102 | class ProfileToChannels(Operation): |
|
|
103 | 103 | |
|
|
104 | 104 | def __init__(self): |
|
|
105 | 105 | self.__isConfig = False |
|
|
106 | 106 | self.__counter_chan = 0 |
|
|
107 | 107 | self.buffer = None |
|
|
108 | 108 | |
|
|
109 | 109 | def isProfileInList(self, profileList): |
|
|
110 | 110 | |
|
|
111 | 111 | if self.profileIndex not in profileList: |
|
|
112 | 112 | return False |
|
|
113 | 113 | |
|
|
114 | 114 | return True |
|
|
115 | 115 | |
|
|
116 | 116 | def run(self, dataOut): |
|
|
117 | 117 | |
|
|
118 | 118 | dataOut.flagNoData = True |
|
|
119 | 119 | |
|
|
120 | 120 | if not(self.__isConfig): |
|
|
121 | 121 | nchannels = len(dataOut.beamRangeDict.keys()) |
|
|
122 | 122 | nsamples = dataOut.nHeights |
|
|
123 | 123 | self.buffer = numpy.zeros((nchannels, nsamples), dtype = 'complex128') |
|
|
124 | 124 | dataOut.beam.codeList = [dataOut.beamCodeDict[x][0] for x in range(nchannels)] |
|
|
125 | 125 | dataOut.beam.azimuthList = [dataOut.beamCodeDict[x][1] for x in range(nchannels)] |
|
|
126 | 126 | dataOut.beam.zenithList = [dataOut.beamCodeDict[x][2] for x in range(nchannels)] |
|
|
127 | 127 | self.__isConfig = True |
|
|
128 | 128 | |
|
|
129 | 129 | for i in range(self.buffer.shape[0]): |
|
|
130 | 130 | if dataOut.profileIndex in dataOut.beamRangeDict[i]: |
|
|
131 | 131 | self.buffer[i,:] = dataOut.data |
|
|
132 | 132 | break |
|
|
133 | 133 | |
|
|
134 | 134 | |
|
|
135 | 135 | self.__counter_chan += 1 |
|
|
136 | 136 | |
|
|
137 | 137 | if self.__counter_chan >= self.buffer.shape[0]: |
|
|
138 | 138 | self.__counter_chan = 0 |
|
|
139 | 139 | dataOut.data = self.buffer.copy() |
|
|
140 | 140 | dataOut.channelList = range(self.buffer.shape[0]) |
|
|
141 | 141 | self.__isConfig = False |
|
|
142 | 142 | dataOut.flagNoData = False |
|
|
143 | 143 | pass |
|
|
144 | 144 | No newline at end of file |
| @@ -1,345 +1,350 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | |
|
|
3 | 3 | from jroproc_base import ProcessingUnit, Operation |
|
|
4 | 4 | from schainpy.model.data.jrodata import SpectraHeis |
|
|
5 | 5 | |
|
|
6 | 6 | class SpectraHeisProc(ProcessingUnit): |
|
|
7 | 7 | |
|
|
8 | 8 | def __init__(self, **kwargs): |
|
|
9 | 9 | |
|
|
10 | 10 | ProcessingUnit.__init__(self, **kwargs) |
|
|
11 | 11 | |
|
|
12 | 12 | # self.buffer = None |
|
|
13 | 13 | # self.firstdatatime = None |
|
|
14 | 14 | # self.profIndex = 0 |
|
|
15 | 15 | self.dataOut = SpectraHeis() |
|
|
16 | 16 | |
|
|
17 | 17 | def __updateObjFromVoltage(self): |
|
|
18 | 18 | |
|
|
19 | 19 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
20 | 20 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
21 | 21 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
22 | 22 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
23 | 23 | |
|
|
24 | 24 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()# |
|
|
25 | 25 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()# |
|
|
26 | 26 | self.dataOut.channelList = self.dataIn.channelList |
|
|
27 | 27 | self.dataOut.heightList = self.dataIn.heightList |
|
|
28 | 28 | # self.dataOut.dtype = self.dataIn.dtype |
|
|
29 | 29 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
30 | 30 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
31 | 31 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
32 | 32 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
33 | 33 | self.dataOut.nCode = self.dataIn.nCode |
|
|
34 | 34 | self.dataOut.code = self.dataIn.code |
|
|
35 | 35 | # self.dataOut.nProfiles = 1 |
|
|
36 | 36 | self.dataOut.ippFactor = 1 |
|
|
37 | 37 | self.dataOut.noise_estimation = None |
|
|
38 | 38 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
39 | 39 | self.dataOut.nFFTPoints = self.dataIn.nHeights |
|
|
40 | 40 | # self.dataOut.channelIndexList = self.dataIn.channelIndexList |
|
|
41 | 41 | # self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
42 | 42 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
43 | 43 | self.dataOut.utctime = self.dataIn.utctime |
|
|
44 | 44 | # self.dataOut.utctime = self.firstdatatime |
|
|
45 | 45 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
46 | 46 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
47 | 47 | # self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT |
|
|
48 | 48 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
49 | 49 | self.dataOut.nIncohInt = 1 |
|
|
50 | 50 | # self.dataOut.ippSeconds= self.dataIn.ippSeconds |
|
|
51 | 51 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
52 | 52 | |
|
|
53 | 53 | # self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nIncohInt |
|
|
54 | 54 | # self.dataOut.set=self.dataIn.set |
|
|
55 | 55 | # self.dataOut.deltaHeight=self.dataIn.deltaHeight |
|
|
56 | 56 | |
|
|
57 | 57 | |
|
|
58 | 58 | def __updateObjFromFits(self): |
|
|
59 | 59 | |
|
|
60 | 60 | self.dataOut.utctime = self.dataIn.utctime |
|
|
61 | 61 | # self.dataOut.channelIndexList = self.dataIn.channelIndexList |
|
|
62 | 62 | |
|
|
63 | 63 | self.dataOut.channelList = self.dataIn.channelList |
|
|
64 | 64 | self.dataOut.heightList = self.dataIn.heightList |
|
|
65 | 65 | self.dataOut.data_spc = self.dataIn.data |
|
|
66 | 66 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
67 | 67 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
68 | 68 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
69 | 69 | # self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
70 | 70 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
71 | 71 | self.dataOut.useLocalTime = True |
|
|
72 | 72 | # self.dataOut. |
|
|
73 | 73 | # self.dataOut. |
|
|
74 | 74 | |
|
|
75 | 75 | def __getFft(self): |
|
|
76 | 76 | |
|
|
77 | 77 | fft_volt = numpy.fft.fft(self.dataIn.data, axis=1) |
|
|
78 | 78 | fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,)) |
|
|
79 | 79 | spc = numpy.abs(fft_volt * numpy.conjugate(fft_volt))/(self.dataOut.nFFTPoints) |
|
|
80 | 80 | self.dataOut.data_spc = spc |
|
|
81 | 81 | |
|
|
82 | 82 | def run(self): |
|
|
83 | 83 | |
|
|
84 | 84 | self.dataOut.flagNoData = True |
|
|
85 | 85 | |
|
|
86 | 86 | if self.dataIn.type == "Fits": |
|
|
87 | 87 | self.__updateObjFromFits() |
|
|
88 | 88 | self.dataOut.flagNoData = False |
|
|
89 | 89 | return |
|
|
90 | 90 | |
|
|
91 | 91 | if self.dataIn.type == "SpectraHeis": |
|
|
92 | 92 | self.dataOut.copy(self.dataIn) |
|
|
93 | 93 | return |
|
|
94 | 94 | |
|
|
95 | 95 | if self.dataIn.type == "Voltage": |
|
|
96 | 96 | self.__updateObjFromVoltage() |
|
|
97 | 97 | self.__getFft() |
|
|
98 | 98 | self.dataOut.flagNoData = False |
|
|
99 | 99 | |
|
|
100 | 100 | return |
|
|
101 | 101 | |
|
|
102 | 102 | raise ValueError, "The type object %s is not valid"%(self.dataIn.type) |
|
|
103 | 103 | |
|
|
104 | 104 | |
|
|
105 | 105 | def selectChannels(self, channelList): |
|
|
106 | 106 | |
|
|
107 | 107 | channelIndexList = [] |
|
|
108 | 108 | |
|
|
109 | 109 | for channel in channelList: |
|
|
110 | 110 | index = self.dataOut.channelList.index(channel) |
|
|
111 | 111 | channelIndexList.append(index) |
|
|
112 | 112 | |
|
|
113 | 113 | self.selectChannelsByIndex(channelIndexList) |
|
|
114 | 114 | |
|
|
115 | 115 | def selectChannelsByIndex(self, channelIndexList): |
|
|
116 | 116 | """ |
|
|
117 | 117 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
118 | 118 | |
|
|
119 | 119 | Input: |
|
|
120 | 120 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
121 | 121 | |
|
|
122 | 122 | Affected: |
|
|
123 | 123 | self.dataOut.data |
|
|
124 | 124 | self.dataOut.channelIndexList |
|
|
125 | 125 | self.dataOut.nChannels |
|
|
126 | 126 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
|
127 | 127 | self.dataOut.systemHeaderObj.numChannels |
|
|
128 | 128 | self.dataOut.m_ProcessingHeader.blockSize |
|
|
129 | 129 | |
|
|
130 | 130 | Return: |
|
|
131 | 131 | None |
|
|
132 | 132 | """ |
|
|
133 | 133 | |
|
|
134 | 134 | for channelIndex in channelIndexList: |
|
|
135 | 135 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
136 | 136 | print channelIndexList |
|
|
137 | 137 | raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex |
|
|
138 | 138 | |
|
|
139 | 139 | # nChannels = len(channelIndexList) |
|
|
140 | 140 | |
|
|
141 | 141 | data_spc = self.dataOut.data_spc[channelIndexList,:] |
|
|
142 | 142 | |
|
|
143 | 143 | self.dataOut.data_spc = data_spc |
|
|
144 | 144 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
145 | 145 | |
|
|
146 | 146 | return 1 |
|
|
147 | 147 | |
|
|
148 | 148 | class IncohInt4SpectraHeis(Operation): |
|
|
149 | 149 | |
|
|
150 | 150 | isConfig = False |
|
|
151 | 151 | |
|
|
152 | 152 | __profIndex = 0 |
|
|
153 | 153 | __withOverapping = False |
|
|
154 | 154 | |
|
|
155 | 155 | __byTime = False |
|
|
156 | 156 | __initime = None |
|
|
157 | 157 | __lastdatatime = None |
|
|
158 | 158 | __integrationtime = None |
|
|
159 | 159 | |
|
|
160 | 160 | __buffer = None |
|
|
161 | 161 | |
|
|
162 | 162 | __dataReady = False |
|
|
163 | 163 | |
|
|
164 | 164 | n = None |
|
|
165 | parameters = { | |
|
|
166 | 'n': global_type_float, | |
|
|
167 | 'timeInterval': global_type_integer, | |
|
|
168 | 'overlapping': global_type_boolean, | |
|
|
169 | } | |
|
|
165 | 170 | |
|
|
166 | 171 | |
|
|
167 | 172 | def __init__(self, **kwargs): |
|
|
168 | 173 | |
|
|
169 | 174 | Operation.__init__(self, **kwargs) |
|
|
170 | 175 | # self.isConfig = False |
|
|
171 | 176 | |
|
|
172 | 177 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
173 | 178 | """ |
|
|
174 | 179 | Set the parameters of the integration class. |
|
|
175 | 180 | |
|
|
176 | 181 | Inputs: |
|
|
177 | 182 | |
|
|
178 | 183 | n : Number of coherent integrations |
|
|
179 | 184 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
180 | 185 | overlapping : |
|
|
181 | 186 | |
|
|
182 | 187 | """ |
|
|
183 | 188 | |
|
|
184 | 189 | self.__initime = None |
|
|
185 | 190 | self.__lastdatatime = 0 |
|
|
186 | 191 | self.__buffer = None |
|
|
187 | 192 | self.__dataReady = False |
|
|
188 | 193 | |
|
|
189 | 194 | |
|
|
190 | 195 | if n == None and timeInterval == None: |
|
|
191 | 196 | raise ValueError, "n or timeInterval should be specified ..." |
|
|
192 | 197 | |
|
|
193 | 198 | if n != None: |
|
|
194 | 199 | self.n = n |
|
|
195 | 200 | self.__byTime = False |
|
|
196 | 201 | else: |
|
|
197 | 202 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
198 | 203 | self.n = 9999 |
|
|
199 | 204 | self.__byTime = True |
|
|
200 | 205 | |
|
|
201 | 206 | if overlapping: |
|
|
202 | 207 | self.__withOverapping = True |
|
|
203 | 208 | self.__buffer = None |
|
|
204 | 209 | else: |
|
|
205 | 210 | self.__withOverapping = False |
|
|
206 | 211 | self.__buffer = 0 |
|
|
207 | 212 | |
|
|
208 | 213 | self.__profIndex = 0 |
|
|
209 | 214 | |
|
|
210 | 215 | def putData(self, data): |
|
|
211 | 216 | |
|
|
212 | 217 | """ |
|
|
213 | 218 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
214 | 219 | |
|
|
215 | 220 | """ |
|
|
216 | 221 | |
|
|
217 | 222 | if not self.__withOverapping: |
|
|
218 | 223 | self.__buffer += data.copy() |
|
|
219 | 224 | self.__profIndex += 1 |
|
|
220 | 225 | return |
|
|
221 | 226 | |
|
|
222 | 227 | #Overlapping data |
|
|
223 | 228 | nChannels, nHeis = data.shape |
|
|
224 | 229 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
225 | 230 | |
|
|
226 | 231 | #If the buffer is empty then it takes the data value |
|
|
227 | 232 | if self.__buffer is None: |
|
|
228 | 233 | self.__buffer = data |
|
|
229 | 234 | self.__profIndex += 1 |
|
|
230 | 235 | return |
|
|
231 | 236 | |
|
|
232 | 237 | #If the buffer length is lower than n then stakcing the data value |
|
|
233 | 238 | if self.__profIndex < self.n: |
|
|
234 | 239 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
235 | 240 | self.__profIndex += 1 |
|
|
236 | 241 | return |
|
|
237 | 242 | |
|
|
238 | 243 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
239 | 244 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
240 | 245 | self.__buffer[self.n-1] = data |
|
|
241 | 246 | self.__profIndex = self.n |
|
|
242 | 247 | return |
|
|
243 | 248 | |
|
|
244 | 249 | |
|
|
245 | 250 | def pushData(self): |
|
|
246 | 251 | """ |
|
|
247 | 252 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
248 | 253 | |
|
|
249 | 254 | Affected: |
|
|
250 | 255 | |
|
|
251 | 256 | self.__profileIndex |
|
|
252 | 257 | |
|
|
253 | 258 | """ |
|
|
254 | 259 | |
|
|
255 | 260 | if not self.__withOverapping: |
|
|
256 | 261 | data = self.__buffer |
|
|
257 | 262 | n = self.__profIndex |
|
|
258 | 263 | |
|
|
259 | 264 | self.__buffer = 0 |
|
|
260 | 265 | self.__profIndex = 0 |
|
|
261 | 266 | |
|
|
262 | 267 | return data, n |
|
|
263 | 268 | |
|
|
264 | 269 | #Integration with Overlapping |
|
|
265 | 270 | data = numpy.sum(self.__buffer, axis=0) |
|
|
266 | 271 | n = self.__profIndex |
|
|
267 | 272 | |
|
|
268 | 273 | return data, n |
|
|
269 | 274 | |
|
|
270 | 275 | def byProfiles(self, data): |
|
|
271 | 276 | |
|
|
272 | 277 | self.__dataReady = False |
|
|
273 | 278 | avgdata = None |
|
|
274 | 279 | # n = None |
|
|
275 | 280 | |
|
|
276 | 281 | self.putData(data) |
|
|
277 | 282 | |
|
|
278 | 283 | if self.__profIndex == self.n: |
|
|
279 | 284 | |
|
|
280 | 285 | avgdata, n = self.pushData() |
|
|
281 | 286 | self.__dataReady = True |
|
|
282 | 287 | |
|
|
283 | 288 | return avgdata |
|
|
284 | 289 | |
|
|
285 | 290 | def byTime(self, data, datatime): |
|
|
286 | 291 | |
|
|
287 | 292 | self.__dataReady = False |
|
|
288 | 293 | avgdata = None |
|
|
289 | 294 | n = None |
|
|
290 | 295 | |
|
|
291 | 296 | self.putData(data) |
|
|
292 | 297 | |
|
|
293 | 298 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
294 | 299 | avgdata, n = self.pushData() |
|
|
295 | 300 | self.n = n |
|
|
296 | 301 | self.__dataReady = True |
|
|
297 | 302 | |
|
|
298 | 303 | return avgdata |
|
|
299 | 304 | |
|
|
300 | 305 | def integrate(self, data, datatime=None): |
|
|
301 | 306 | |
|
|
302 | 307 | if self.__initime == None: |
|
|
303 | 308 | self.__initime = datatime |
|
|
304 | 309 | |
|
|
305 | 310 | if self.__byTime: |
|
|
306 | 311 | avgdata = self.byTime(data, datatime) |
|
|
307 | 312 | else: |
|
|
308 | 313 | avgdata = self.byProfiles(data) |
|
|
309 | 314 | |
|
|
310 | 315 | |
|
|
311 | 316 | self.__lastdatatime = datatime |
|
|
312 | 317 | |
|
|
313 | 318 | if avgdata is None: |
|
|
314 | 319 | return None, None |
|
|
315 | 320 | |
|
|
316 | 321 | avgdatatime = self.__initime |
|
|
317 | 322 | |
|
|
318 | 323 | deltatime = datatime -self.__lastdatatime |
|
|
319 | 324 | |
|
|
320 | 325 | if not self.__withOverapping: |
|
|
321 | 326 | self.__initime = datatime |
|
|
322 | 327 | else: |
|
|
323 | 328 | self.__initime += deltatime |
|
|
324 | 329 | |
|
|
325 | 330 | return avgdata, avgdatatime |
|
|
326 | 331 | |
|
|
327 | def run(self, dataOut, **kwargs): | |
|
|
332 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False, **kwargs): | |
|
|
328 | 333 | |
|
|
329 | 334 | if not self.isConfig: |
|
|
330 | self.setup(**kwargs) | |
|
|
335 | self.setup(n=n, timeInterval=timeInterval, overlapping=overlapping) | |
|
|
331 | 336 | self.isConfig = True |
|
|
332 | 337 | |
|
|
333 | 338 | avgdata, avgdatatime = self.integrate(dataOut.data_spc, dataOut.utctime) |
|
|
334 | 339 | |
|
|
335 | 340 | # dataOut.timeInterval *= n |
|
|
336 | 341 | dataOut.flagNoData = True |
|
|
337 | 342 | |
|
|
338 | 343 | if self.__dataReady: |
|
|
339 | 344 | dataOut.data_spc = avgdata |
|
|
340 | 345 | dataOut.nIncohInt *= self.n |
|
|
341 | 346 | # dataOut.nCohInt *= self.n |
|
|
342 | 347 | dataOut.utctime = avgdatatime |
|
|
343 | 348 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nIncohInt |
|
|
344 | 349 | # dataOut.timeInterval = self.__timeInterval*self.n |
|
|
345 | 350 | dataOut.flagNoData = False |
| @@ -1,2749 +1,2764 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import math |
|
|
3 | 3 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
4 | 4 | import re |
|
|
5 | 5 | import datetime |
|
|
6 | 6 | import copy |
|
|
7 | 7 | import sys |
|
|
8 | 8 | import importlib |
|
|
9 | 9 | import itertools |
|
|
10 | 10 | |
|
|
11 | 11 | from jroproc_base import ProcessingUnit, Operation |
|
|
12 | 12 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
13 | 13 | |
|
|
14 | 14 | |
|
|
15 | 15 | class ParametersProc(ProcessingUnit): |
|
|
16 | 16 | |
|
|
17 | 17 | nSeconds = None |
|
|
18 | 18 | |
|
|
19 | 19 | def __init__(self): |
|
|
20 | 20 | ProcessingUnit.__init__(self) |
|
|
21 | 21 | |
|
|
22 | 22 | # self.objectDict = {} |
|
|
23 | 23 | self.buffer = None |
|
|
24 | 24 | self.firstdatatime = None |
|
|
25 | 25 | self.profIndex = 0 |
|
|
26 | 26 | self.dataOut = Parameters() |
|
|
27 | 27 | |
|
|
28 | 28 | def __updateObjFromInput(self): |
|
|
29 | 29 | |
|
|
30 | 30 | self.dataOut.inputUnit = self.dataIn.type |
|
|
31 | 31 | |
|
|
32 | 32 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
33 | 33 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
34 | 34 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
35 | 35 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
36 | 36 | |
|
|
37 | 37 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
38 | 38 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
39 | 39 | self.dataOut.channelList = self.dataIn.channelList |
|
|
40 | 40 | self.dataOut.heightList = self.dataIn.heightList |
|
|
41 | 41 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
42 | 42 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
43 | 43 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
44 | 44 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
45 | 45 | self.dataOut.nCode = self.dataIn.nCode |
|
|
46 | 46 | self.dataOut.code = self.dataIn.code |
|
|
47 | 47 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
48 | 48 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
49 | 49 | # self.dataOut.utctime = self.firstdatatime |
|
|
50 | 50 | self.dataOut.utctime = self.dataIn.utctime |
|
|
51 | 51 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
52 | 52 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
53 | 53 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
54 | 54 | # self.dataOut.nIncohInt = 1 |
|
|
55 | 55 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
56 | 56 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
57 | 57 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
58 | 58 | self.dataOut.heightList = self.dataIn.getHeiRange() |
|
|
59 | 59 | self.dataOut.frequency = self.dataIn.frequency |
|
|
60 | 60 | #self.dataOut.noise = self.dataIn.noise |
|
|
61 | 61 | |
|
|
62 | 62 | def run(self): |
|
|
63 | 63 | |
|
|
64 | 64 | #---------------------- Voltage Data --------------------------- |
|
|
65 | 65 | |
|
|
66 | 66 | if self.dataIn.type == "Voltage": |
|
|
67 | 67 | |
|
|
68 | 68 | self.__updateObjFromInput() |
|
|
69 | 69 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
70 | 70 | self.dataOut.flagNoData = False |
|
|
71 | 71 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
72 | 72 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
73 | 73 | return |
|
|
74 | 74 | |
|
|
75 | 75 | #---------------------- Spectra Data --------------------------- |
|
|
76 | 76 | |
|
|
77 | 77 | if self.dataIn.type == "Spectra": |
|
|
78 | 78 | |
|
|
79 | 79 | self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc) |
|
|
80 | 80 | self.dataOut.data_spc = self.dataIn.data_spc |
|
|
81 | 81 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
82 | 82 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
83 | 83 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
84 | 84 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
85 | 85 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
86 | 86 | #self.dataOut.normFactor = self.dataIn.getNormFactor() |
|
|
87 | 87 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|
88 | 88 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
89 | 89 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
90 | 90 | self.dataOut.flagNoData = False |
|
|
91 | 91 | |
|
|
92 | 92 | #---------------------- Correlation Data --------------------------- |
|
|
93 | 93 | |
|
|
94 | 94 | if self.dataIn.type == "Correlation": |
|
|
95 | 95 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
96 | 96 | |
|
|
97 | 97 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
98 | 98 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
99 | 99 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
100 | 100 | |
|
|
101 | 101 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
102 | 102 | self.dataOut.noise = self.dataIn.noise |
|
|
103 | 103 | self.dataOut.data_SNR = self.dataIn.SNR |
|
|
104 | 104 | self.dataOut.flagNoData = False |
|
|
105 | 105 | self.dataOut.nAvg = self.dataIn.nAvg |
|
|
106 | 106 | |
|
|
107 | 107 | #---------------------- Parameters Data --------------------------- |
|
|
108 | 108 | |
|
|
109 | 109 | if self.dataIn.type == "Parameters": |
|
|
110 | 110 | self.dataOut.copy(self.dataIn) |
|
|
111 | 111 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
112 | 112 | self.dataOut.flagNoData = False |
|
|
113 | 113 | |
|
|
114 | 114 | return True |
|
|
115 | 115 | |
|
|
116 | 116 | self.__updateObjFromInput() |
|
|
117 | 117 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
118 | 118 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
119 | 119 | |
|
|
120 | 120 | return |
|
|
121 | 121 | |
|
|
122 | 122 | class SpectralMoments(Operation): |
|
|
123 | 123 | |
|
|
124 | 124 | ''' |
|
|
125 | 125 | Function SpectralMoments() |
|
|
126 | 126 | |
|
|
127 | 127 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
128 | 128 | |
|
|
129 | 129 | Type of dataIn: Spectra |
|
|
130 | 130 | |
|
|
131 | 131 | Configuration Parameters: |
|
|
132 | 132 | |
|
|
133 | 133 | dirCosx : Cosine director in X axis |
|
|
134 | 134 | dirCosy : Cosine director in Y axis |
|
|
135 | 135 | |
|
|
136 | 136 | elevation : |
|
|
137 | 137 | azimuth : |
|
|
138 | 138 | |
|
|
139 | 139 | Input: |
|
|
140 | 140 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
141 | 141 | self.dataOut.data_pre : Spectral data |
|
|
142 | 142 | self.dataOut.abscissaList : List of frequencies |
|
|
143 | 143 | self.dataOut.noise : Noise level per channel |
|
|
144 | 144 | |
|
|
145 | 145 | Affected: |
|
|
146 | 146 | self.dataOut.data_param : Parameters per channel |
|
|
147 | 147 | self.dataOut.data_SNR : SNR per channel |
|
|
148 | 148 | |
|
|
149 | 149 | ''' |
|
|
150 | 150 | |
|
|
151 | 151 | def run(self, dataOut): |
|
|
152 | 152 | |
|
|
153 | 153 | #dataOut.data_pre = dataOut.data_pre[0] |
|
|
154 | 154 | data = dataOut.data_pre[0] |
|
|
155 | 155 | absc = dataOut.abscissaList[:-1] |
|
|
156 | 156 | noise = dataOut.noise |
|
|
157 | 157 | nChannel = data.shape[0] |
|
|
158 | 158 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
159 | 159 | |
|
|
160 | 160 | for ind in range(nChannel): |
|
|
161 | 161 | data_param[ind,:,:] = self.__calculateMoments(data[ind,:,:], absc, noise[ind]) |
|
|
162 | 162 | |
|
|
163 | 163 | dataOut.data_param = data_param[:,1:,:] |
|
|
164 | 164 | dataOut.data_SNR = data_param[:,0] |
|
|
165 | 165 | dataOut.data_DOP = data_param[:,1] |
|
|
166 | 166 | dataOut.data_MEAN = data_param[:,2] |
|
|
167 | 167 | dataOut.data_STD = data_param[:,3] |
|
|
168 | 168 | return |
|
|
169 | 169 | |
|
|
170 | 170 | def __calculateMoments(self, oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
171 | 171 | |
|
|
172 | 172 | if (nicoh is None): nicoh = 1 |
|
|
173 | 173 | if (graph is None): graph = 0 |
|
|
174 | 174 | if (smooth is None): smooth = 0 |
|
|
175 | 175 | elif (self.smooth < 3): smooth = 0 |
|
|
176 | 176 | |
|
|
177 | 177 | if (type1 is None): type1 = 0 |
|
|
178 | 178 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
179 | 179 | if (snrth is None): snrth = -3 |
|
|
180 | 180 | if (dc is None): dc = 0 |
|
|
181 | 181 | if (aliasing is None): aliasing = 0 |
|
|
182 | 182 | if (oldfd is None): oldfd = 0 |
|
|
183 | 183 | if (wwauto is None): wwauto = 0 |
|
|
184 | 184 | |
|
|
185 | 185 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
186 | 186 | |
|
|
187 | 187 | freq = oldfreq |
|
|
188 | 188 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
189 | 189 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
190 | 190 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
191 | 191 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
192 | 192 | |
|
|
193 | 193 | for ind in range(oldspec.shape[1]): |
|
|
194 | 194 | |
|
|
195 | 195 | spec = oldspec[:,ind] |
|
|
196 | 196 | aux = spec*fwindow |
|
|
197 | 197 | max_spec = aux.max() |
|
|
198 | 198 | m = list(aux).index(max_spec) |
|
|
199 | 199 | |
|
|
200 | 200 | #Smooth |
|
|
201 | 201 | if (smooth == 0): spec2 = spec |
|
|
202 | 202 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
203 | 203 | |
|
|
204 | 204 | # Calculo de Momentos |
|
|
205 | 205 | bb = spec2[range(m,spec2.size)] |
|
|
206 | 206 | bb = (bb<n0).nonzero() |
|
|
207 | 207 | bb = bb[0] |
|
|
208 | 208 | |
|
|
209 | 209 | ss = spec2[range(0,m + 1)] |
|
|
210 | 210 | ss = (ss<n0).nonzero() |
|
|
211 | 211 | ss = ss[0] |
|
|
212 | 212 | |
|
|
213 | 213 | if (bb.size == 0): |
|
|
214 | 214 | bb0 = spec.size - 1 - m |
|
|
215 | 215 | else: |
|
|
216 | 216 | bb0 = bb[0] - 1 |
|
|
217 | 217 | if (bb0 < 0): |
|
|
218 | 218 | bb0 = 0 |
|
|
219 | 219 | |
|
|
220 | 220 | if (ss.size == 0): ss1 = 1 |
|
|
221 | 221 | else: ss1 = max(ss) + 1 |
|
|
222 | 222 | |
|
|
223 | 223 | if (ss1 > m): ss1 = m |
|
|
224 | 224 | |
|
|
225 | 225 | valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
|
|
226 | 226 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
|
227 | 227 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
|
228 | 228 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
|
229 | 229 | snr = (spec2.mean()-n0)/n0 |
|
|
230 | 230 | |
|
|
231 | 231 | if (snr < 1.e-20) : |
|
|
232 | 232 | snr = 1.e-20 |
|
|
233 | 233 | |
|
|
234 | 234 | vec_power[ind] = power |
|
|
235 | 235 | vec_fd[ind] = fd |
|
|
236 | 236 | vec_w[ind] = w |
|
|
237 | 237 | vec_snr[ind] = snr |
|
|
238 | 238 | |
|
|
239 | 239 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
240 | 240 | return moments |
|
|
241 | 241 | |
|
|
242 | 242 | #------------------ Get SA Parameters -------------------------- |
|
|
243 | 243 | |
|
|
244 | 244 | def GetSAParameters(self): |
|
|
245 | 245 | #SA en frecuencia |
|
|
246 | 246 | pairslist = self.dataOut.groupList |
|
|
247 | 247 | num_pairs = len(pairslist) |
|
|
248 | 248 | |
|
|
249 | 249 | vel = self.dataOut.abscissaList |
|
|
250 | 250 | spectra = self.dataOut.data_pre[0] |
|
|
251 | 251 | cspectra = self.dataOut.data_pre[1] |
|
|
252 | 252 | delta_v = vel[1] - vel[0] |
|
|
253 | 253 | |
|
|
254 | 254 | #Calculating the power spectrum |
|
|
255 | 255 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
256 | 256 | #Normalizing Spectra |
|
|
257 | 257 | norm_spectra = spectra/spc_pow |
|
|
258 | 258 | #Calculating the norm_spectra at peak |
|
|
259 | 259 | max_spectra = numpy.max(norm_spectra, 3) |
|
|
260 | 260 | |
|
|
261 | 261 | #Normalizing Cross Spectra |
|
|
262 | 262 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
|
263 | 263 | |
|
|
264 | 264 | for i in range(num_chan): |
|
|
265 | 265 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
|
266 | 266 | |
|
|
267 | 267 | max_cspectra = numpy.max(norm_cspectra,2) |
|
|
268 | 268 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
269 | 269 | |
|
|
270 | 270 | for i in range(num_pairs): |
|
|
271 | 271 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
272 | 272 | #------------------- Get Lags ---------------------------------- |
|
|
273 | 273 | |
|
|
274 | 274 | class SALags(Operation): |
|
|
275 | 275 | ''' |
|
|
276 | 276 | Function GetMoments() |
|
|
277 | 277 | |
|
|
278 | 278 | Input: |
|
|
279 | 279 | self.dataOut.data_pre |
|
|
280 | 280 | self.dataOut.abscissaList |
|
|
281 | 281 | self.dataOut.noise |
|
|
282 | 282 | self.dataOut.normFactor |
|
|
283 | 283 | self.dataOut.data_SNR |
|
|
284 | 284 | self.dataOut.groupList |
|
|
285 | 285 | self.dataOut.nChannels |
|
|
286 | 286 | |
|
|
287 | 287 | Affected: |
|
|
288 | 288 | self.dataOut.data_param |
|
|
289 | 289 | |
|
|
290 | 290 | ''' |
|
|
291 | 291 | def run(self, dataOut): |
|
|
292 | 292 | data_acf = dataOut.data_pre[0] |
|
|
293 | 293 | data_ccf = dataOut.data_pre[1] |
|
|
294 | 294 | normFactor_acf = dataOut.normFactor[0] |
|
|
295 | 295 | normFactor_ccf = dataOut.normFactor[1] |
|
|
296 | 296 | pairs_acf = dataOut.groupList[0] |
|
|
297 | 297 | pairs_ccf = dataOut.groupList[1] |
|
|
298 | 298 | |
|
|
299 | 299 | nHeights = dataOut.nHeights |
|
|
300 | 300 | absc = dataOut.abscissaList |
|
|
301 | 301 | noise = dataOut.noise |
|
|
302 | 302 | SNR = dataOut.data_SNR |
|
|
303 | 303 | nChannels = dataOut.nChannels |
|
|
304 | 304 | # pairsList = dataOut.groupList |
|
|
305 | 305 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
306 | 306 | |
|
|
307 | 307 | for l in range(len(pairs_acf)): |
|
|
308 | 308 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
|
309 | 309 | |
|
|
310 | 310 | for l in range(len(pairs_ccf)): |
|
|
311 | 311 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
|
312 | 312 | |
|
|
313 | 313 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
|
314 | 314 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
315 | 315 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
316 | 316 | return |
|
|
317 | 317 | |
|
|
318 | 318 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
319 | 319 | # |
|
|
320 | 320 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
321 | 321 | # |
|
|
322 | 322 | # for l in range(len(pairsList)): |
|
|
323 | 323 | # firstChannel = pairsList[l][0] |
|
|
324 | 324 | # secondChannel = pairsList[l][1] |
|
|
325 | 325 | # |
|
|
326 | 326 | # #Obteniendo pares de Autocorrelacion |
|
|
327 | 327 | # if firstChannel == secondChannel: |
|
|
328 | 328 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
329 | 329 | # |
|
|
330 | 330 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
331 | 331 | # |
|
|
332 | 332 | # pairsCrossCorr = range(len(pairsList)) |
|
|
333 | 333 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
334 | 334 | # |
|
|
335 | 335 | # return pairsAutoCorr, pairsCrossCorr |
|
|
336 | 336 | |
|
|
337 | 337 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
338 | 338 | |
|
|
339 | 339 | lag0 = data_acf.shape[1]/2 |
|
|
340 | 340 | #Funcion de Autocorrelacion |
|
|
341 | 341 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
342 | 342 | |
|
|
343 | 343 | #Obtencion Indice de TauCross |
|
|
344 | 344 | ind_ccf = data_ccf.argmax(axis = 1) |
|
|
345 | 345 | #Obtencion Indice de TauAuto |
|
|
346 | 346 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
347 | 347 | ccf_lag0 = data_ccf[:,lag0,:] |
|
|
348 | 348 | |
|
|
349 | 349 | for i in range(ccf_lag0.shape[0]): |
|
|
350 | 350 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
351 | 351 | |
|
|
352 | 352 | #Obtencion de TauCross y TauAuto |
|
|
353 | 353 | tau_ccf = lagRange[ind_ccf] |
|
|
354 | 354 | tau_acf = lagRange[ind_acf] |
|
|
355 | 355 | |
|
|
356 | 356 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
357 | 357 | |
|
|
358 | 358 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
359 | 359 | tau_acf[Nan1,Nan2] = numpy.nan |
|
|
360 | 360 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
361 | 361 | |
|
|
362 | 362 | return tau |
|
|
363 | 363 | |
|
|
364 | 364 | def __calculateLag1Phase(self, data, lagTRange): |
|
|
365 | 365 | data1 = stats.nanmean(data, axis = 0) |
|
|
366 | 366 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
367 | 367 | |
|
|
368 | 368 | phase = numpy.angle(data1[lag1,:]) |
|
|
369 | 369 | |
|
|
370 | 370 | return phase |
|
|
371 | 371 | |
|
|
372 | 372 | class SpectralFitting(Operation): |
|
|
373 | 373 | ''' |
|
|
374 | 374 | Function GetMoments() |
|
|
375 | 375 | |
|
|
376 | 376 | Input: |
|
|
377 | 377 | Output: |
|
|
378 | 378 | Variables modified: |
|
|
379 | 379 | ''' |
|
|
380 | 380 | |
|
|
381 | 381 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
382 | 382 | |
|
|
383 | 383 | |
|
|
384 | 384 | if path != None: |
|
|
385 | 385 | sys.path.append(path) |
|
|
386 | 386 | self.dataOut.library = importlib.import_module(file) |
|
|
387 | 387 | |
|
|
388 | 388 | #To be inserted as a parameter |
|
|
389 | 389 | groupArray = numpy.array(groupList) |
|
|
390 | 390 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
391 | 391 | self.dataOut.groupList = groupArray |
|
|
392 | 392 | |
|
|
393 | 393 | nGroups = groupArray.shape[0] |
|
|
394 | 394 | nChannels = self.dataIn.nChannels |
|
|
395 | 395 | nHeights=self.dataIn.heightList.size |
|
|
396 | 396 | |
|
|
397 | 397 | #Parameters Array |
|
|
398 | 398 | self.dataOut.data_param = None |
|
|
399 | 399 | |
|
|
400 | 400 | #Set constants |
|
|
401 | 401 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
402 | 402 | self.dataOut.constants = constants |
|
|
403 | 403 | M = self.dataIn.normFactor |
|
|
404 | 404 | N = self.dataIn.nFFTPoints |
|
|
405 | 405 | ippSeconds = self.dataIn.ippSeconds |
|
|
406 | 406 | K = self.dataIn.nIncohInt |
|
|
407 | 407 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
408 | 408 | |
|
|
409 | 409 | #List of possible combinations |
|
|
410 | 410 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
411 | 411 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
412 | 412 | |
|
|
413 | 413 | if getSNR: |
|
|
414 | 414 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
415 | 415 | listChannels.sort() |
|
|
416 | 416 | noise = self.dataIn.getNoise() |
|
|
417 | 417 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
418 | 418 | |
|
|
419 | 419 | for i in range(nGroups): |
|
|
420 | 420 | coord = groupArray[i,:] |
|
|
421 | 421 | |
|
|
422 | 422 | #Input data array |
|
|
423 | 423 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
424 | 424 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
425 | 425 | |
|
|
426 | 426 | #Cross Spectra data array for Covariance Matrixes |
|
|
427 | 427 | ind = 0 |
|
|
428 | 428 | for pairs in listComb: |
|
|
429 | 429 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
430 | 430 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
431 | 431 | ind += 1 |
|
|
432 | 432 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
433 | 433 | dataCross = dataCross**2/K |
|
|
434 | 434 | |
|
|
435 | 435 | for h in range(nHeights): |
|
|
436 | 436 | # print self.dataOut.heightList[h] |
|
|
437 | 437 | |
|
|
438 | 438 | #Input |
|
|
439 | 439 | d = data[:,h] |
|
|
440 | 440 | |
|
|
441 | 441 | #Covariance Matrix |
|
|
442 | 442 | D = numpy.diag(d**2/K) |
|
|
443 | 443 | ind = 0 |
|
|
444 | 444 | for pairs in listComb: |
|
|
445 | 445 | #Coordinates in Covariance Matrix |
|
|
446 | 446 | x = pairs[0] |
|
|
447 | 447 | y = pairs[1] |
|
|
448 | 448 | #Channel Index |
|
|
449 | 449 | S12 = dataCross[ind,:,h] |
|
|
450 | 450 | D12 = numpy.diag(S12) |
|
|
451 | 451 | #Completing Covariance Matrix with Cross Spectras |
|
|
452 | 452 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
453 | 453 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
454 | 454 | ind += 1 |
|
|
455 | 455 | Dinv=numpy.linalg.inv(D) |
|
|
456 | 456 | L=numpy.linalg.cholesky(Dinv) |
|
|
457 | 457 | LT=L.T |
|
|
458 | 458 | |
|
|
459 | 459 | dp = numpy.dot(LT,d) |
|
|
460 | 460 | |
|
|
461 | 461 | #Initial values |
|
|
462 | 462 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
463 | 463 | |
|
|
464 | 464 | if (h>0)and(error1[3]<5): |
|
|
465 | 465 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
466 | 466 | else: |
|
|
467 | 467 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
468 | 468 | |
|
|
469 | 469 | try: |
|
|
470 | 470 | #Least Squares |
|
|
471 | 471 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
472 | 472 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
473 | 473 | #Chi square error |
|
|
474 | 474 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
475 | 475 | #Error with Jacobian |
|
|
476 | 476 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
477 | 477 | except: |
|
|
478 | 478 | minp = p0*numpy.nan |
|
|
479 | 479 | error0 = numpy.nan |
|
|
480 | 480 | error1 = p0*numpy.nan |
|
|
481 | 481 | |
|
|
482 | 482 | #Save |
|
|
483 | 483 | if self.dataOut.data_param is None: |
|
|
484 | 484 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
485 | 485 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
486 | 486 | |
|
|
487 | 487 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
488 | 488 | self.dataOut.data_param[i,:,h] = minp |
|
|
489 | 489 | return |
|
|
490 | 490 | |
|
|
491 | 491 | def __residFunction(self, p, dp, LT, constants): |
|
|
492 | 492 | |
|
|
493 | 493 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
494 | 494 | fmp=numpy.dot(LT,fm) |
|
|
495 | 495 | |
|
|
496 | 496 | return dp-fmp |
|
|
497 | 497 | |
|
|
498 | 498 | def __getSNR(self, z, noise): |
|
|
499 | 499 | |
|
|
500 | 500 | avg = numpy.average(z, axis=1) |
|
|
501 | 501 | SNR = (avg.T-noise)/noise |
|
|
502 | 502 | SNR = SNR.T |
|
|
503 | 503 | return SNR |
|
|
504 | 504 | |
|
|
505 | 505 | def __chisq(p,chindex,hindex): |
|
|
506 | 506 | #similar to Resid but calculates CHI**2 |
|
|
507 | 507 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
508 | 508 | dp=numpy.dot(LT,d) |
|
|
509 | 509 | fmp=numpy.dot(LT,fm) |
|
|
510 | 510 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
511 | 511 | return chisq |
|
|
512 | 512 | |
|
|
513 | 513 | class WindProfiler(Operation): |
|
|
514 | 514 | |
|
|
515 | 515 | __isConfig = False |
|
|
516 | 516 | |
|
|
517 | 517 | __initime = None |
|
|
518 | 518 | __lastdatatime = None |
|
|
519 | 519 | __integrationtime = None |
|
|
520 | 520 | |
|
|
521 | 521 | __buffer = None |
|
|
522 | 522 | |
|
|
523 | 523 | __dataReady = False |
|
|
524 | 524 | |
|
|
525 | 525 | __firstdata = None |
|
|
526 | 526 | |
|
|
527 | 527 | n = None |
|
|
528 | 528 | |
|
|
529 | 529 | def __calculateCosDir(self, elev, azim): |
|
|
530 | 530 | zen = (90 - elev)*numpy.pi/180 |
|
|
531 | 531 | azim = azim*numpy.pi/180 |
|
|
532 | 532 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
533 | 533 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
534 | 534 | |
|
|
535 | 535 | signX = numpy.sign(numpy.cos(azim)) |
|
|
536 | 536 | signY = numpy.sign(numpy.sin(azim)) |
|
|
537 | 537 | |
|
|
538 | 538 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
539 | 539 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
540 | 540 | return cosDirX, cosDirY |
|
|
541 | 541 | |
|
|
542 | 542 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
543 | 543 | |
|
|
544 | 544 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
545 | 545 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
546 | 546 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
547 | 547 | |
|
|
548 | 548 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
549 | 549 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
550 | 550 | |
|
|
551 | 551 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
552 | 552 | |
|
|
553 | 553 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
554 | 554 | |
|
|
555 | 555 | # |
|
|
556 | 556 | if horOnly: |
|
|
557 | 557 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
558 | 558 | else: |
|
|
559 | 559 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
560 | 560 | A = numpy.asmatrix(A) |
|
|
561 | 561 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
562 | 562 | |
|
|
563 | 563 | return A1 |
|
|
564 | 564 | |
|
|
565 | 565 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
566 | 566 | listPhi = phi.tolist() |
|
|
567 | 567 | maxid = listPhi.index(max(listPhi)) |
|
|
568 | 568 | minid = listPhi.index(min(listPhi)) |
|
|
569 | 569 | |
|
|
570 | 570 | rango = range(len(phi)) |
|
|
571 | 571 | # rango = numpy.delete(rango,maxid) |
|
|
572 | 572 | |
|
|
573 | 573 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
574 | 574 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
575 | 575 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
576 | 576 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
577 | 577 | |
|
|
578 | 578 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
579 | 579 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
580 | 580 | |
|
|
581 | 581 | for i in rango: |
|
|
582 | 582 | x = heiRang*math.cos(phi[i]) |
|
|
583 | 583 | y1 = velRadial[i,:] |
|
|
584 | 584 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
585 | 585 | |
|
|
586 | 586 | x1 = heiRang1 |
|
|
587 | 587 | y11 = f1(x1) |
|
|
588 | 588 | |
|
|
589 | 589 | y2 = SNR[i,:] |
|
|
590 | 590 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
591 | 591 | y21 = f2(x1) |
|
|
592 | 592 | |
|
|
593 | 593 | velRadial1[i,:] = y11 |
|
|
594 | 594 | SNR1[i,:] = y21 |
|
|
595 | 595 | |
|
|
596 | 596 | return heiRang1, velRadial1, SNR1 |
|
|
597 | 597 | |
|
|
598 | 598 | def __calculateVelUVW(self, A, velRadial): |
|
|
599 | 599 | |
|
|
600 | 600 | #Operacion Matricial |
|
|
601 | 601 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
602 | 602 | # for ind in range(velRadial.shape[1]): |
|
|
603 | 603 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
604 | 604 | # velUVW = velUVW.transpose() |
|
|
605 | 605 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
606 | 606 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
607 | 607 | |
|
|
608 | 608 | |
|
|
609 | 609 | return velUVW |
|
|
610 | 610 | |
|
|
611 | 611 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
612 | 612 | |
|
|
613 | 613 | def techniqueDBS(self, kwargs): |
|
|
614 | 614 | """ |
|
|
615 | 615 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
616 | 616 | |
|
|
617 | 617 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
618 | 618 | Direction correction (if necessary), Ranges and SNR |
|
|
619 | 619 | |
|
|
620 | 620 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
621 | 621 | |
|
|
622 | 622 | Parameters affected: Winds, height range, SNR |
|
|
623 | 623 | """ |
|
|
624 | 624 | velRadial0 = kwargs['velRadial'] |
|
|
625 | 625 | heiRang = kwargs['heightList'] |
|
|
626 | 626 | SNR0 = kwargs['SNR'] |
|
|
627 | 627 | |
|
|
628 | 628 | if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
629 | 629 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
630 | 630 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
631 | 631 | else: |
|
|
632 | 632 | elev = numpy.array(kwargs['elevation']) |
|
|
633 | 633 | azim = numpy.array(kwargs['azimuth']) |
|
|
634 | 634 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
635 | 635 | azimuth = kwargs['correctAzimuth'] |
|
|
636 | 636 | if kwargs.has_key('horizontalOnly'): |
|
|
637 | 637 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
638 | 638 | else: horizontalOnly = False |
|
|
639 | 639 | if kwargs.has_key('correctFactor'): |
|
|
640 | 640 | correctFactor = kwargs['correctFactor'] |
|
|
641 | 641 | else: correctFactor = 1 |
|
|
642 | 642 | if kwargs.has_key('channelList'): |
|
|
643 | 643 | channelList = kwargs['channelList'] |
|
|
644 | 644 | if len(channelList) == 2: |
|
|
645 | 645 | horizontalOnly = True |
|
|
646 | 646 | arrayChannel = numpy.array(channelList) |
|
|
647 | 647 | param = param[arrayChannel,:,:] |
|
|
648 | 648 | theta_x = theta_x[arrayChannel] |
|
|
649 | 649 | theta_y = theta_y[arrayChannel] |
|
|
650 | 650 | |
|
|
651 | 651 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
652 | 652 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
653 | 653 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
654 | 654 | |
|
|
655 | 655 | #Calculo de Componentes de la velocidad con DBS |
|
|
656 | 656 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
657 | 657 | |
|
|
658 | 658 | return winds, heiRang1, SNR1 |
|
|
659 | 659 | |
|
|
660 | 660 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
661 | 661 | |
|
|
662 | 662 | nPairs = len(pairs_ccf) |
|
|
663 | 663 | posx = numpy.asarray(posx) |
|
|
664 | 664 | posy = numpy.asarray(posy) |
|
|
665 | 665 | |
|
|
666 | 666 | #Rotacion Inversa para alinear con el azimuth |
|
|
667 | 667 | if azimuth!= None: |
|
|
668 | 668 | azimuth = azimuth*math.pi/180 |
|
|
669 | 669 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
670 | 670 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
671 | 671 | else: |
|
|
672 | 672 | posx1 = posx |
|
|
673 | 673 | posy1 = posy |
|
|
674 | 674 | |
|
|
675 | 675 | #Calculo de Distancias |
|
|
676 | 676 | distx = numpy.zeros(nPairs) |
|
|
677 | 677 | disty = numpy.zeros(nPairs) |
|
|
678 | 678 | dist = numpy.zeros(nPairs) |
|
|
679 | 679 | ang = numpy.zeros(nPairs) |
|
|
680 | 680 | |
|
|
681 | 681 | for i in range(nPairs): |
|
|
682 | 682 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
683 | 683 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
684 | 684 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
685 | 685 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
686 | 686 | |
|
|
687 | 687 | return distx, disty, dist, ang |
|
|
688 | 688 | #Calculo de Matrices |
|
|
689 | 689 | # nPairs = len(pairs) |
|
|
690 | 690 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
691 | 691 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
692 | 692 | # |
|
|
693 | 693 | # for j in range(nPairs): |
|
|
694 | 694 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
|
695 | 695 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
|
696 | 696 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
|
697 | 697 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
|
698 | 698 | # |
|
|
699 | 699 | # return distx,disty, dist1,ang1 |
|
|
700 | 700 | |
|
|
701 | 701 | |
|
|
702 | 702 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
703 | 703 | |
|
|
704 | 704 | Ts = lagTRange[1] - lagTRange[0] |
|
|
705 | 705 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
706 | 706 | |
|
|
707 | 707 | return velW |
|
|
708 | 708 | |
|
|
709 | 709 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
710 | 710 | nPairs = tau1.shape[0] |
|
|
711 | 711 | nHeights = tau1.shape[1] |
|
|
712 | 712 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
713 | 713 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
714 | 714 | |
|
|
715 | 715 | angCos = numpy.cos(ang) |
|
|
716 | 716 | angSin = numpy.sin(ang) |
|
|
717 | 717 | |
|
|
718 | 718 | vel0 = dist1*tau1/(2*tau2**2) |
|
|
719 | 719 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
720 | 720 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
721 | 721 | |
|
|
722 | 722 | ind = numpy.where(numpy.isinf(vel)) |
|
|
723 | 723 | vel[ind] = numpy.nan |
|
|
724 | 724 | |
|
|
725 | 725 | return vel |
|
|
726 | 726 | |
|
|
727 | 727 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
728 | 728 | # |
|
|
729 | 729 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
730 | 730 | # |
|
|
731 | 731 | # for l in range(len(pairsList)): |
|
|
732 | 732 | # firstChannel = pairsList[l][0] |
|
|
733 | 733 | # secondChannel = pairsList[l][1] |
|
|
734 | 734 | # |
|
|
735 | 735 | # #Obteniendo pares de Autocorrelacion |
|
|
736 | 736 | # if firstChannel == secondChannel: |
|
|
737 | 737 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
738 | 738 | # |
|
|
739 | 739 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
740 | 740 | # |
|
|
741 | 741 | # pairsCrossCorr = range(len(pairsList)) |
|
|
742 | 742 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
743 | 743 | # |
|
|
744 | 744 | # return pairsAutoCorr, pairsCrossCorr |
|
|
745 | 745 | |
|
|
746 | 746 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
747 | 747 | def techniqueSA(self, kwargs): |
|
|
748 | 748 | |
|
|
749 | 749 | """ |
|
|
750 | 750 | Function that implements Spaced Antenna (SA) technique. |
|
|
751 | 751 | |
|
|
752 | 752 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
753 | 753 | Direction correction (if necessary), Ranges and SNR |
|
|
754 | 754 | |
|
|
755 | 755 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
756 | 756 | |
|
|
757 | 757 | Parameters affected: Winds |
|
|
758 | 758 | """ |
|
|
759 | 759 | position_x = kwargs['positionX'] |
|
|
760 | 760 | position_y = kwargs['positionY'] |
|
|
761 | 761 | azimuth = kwargs['azimuth'] |
|
|
762 | 762 | |
|
|
763 | 763 | if kwargs.has_key('correctFactor'): |
|
|
764 | 764 | correctFactor = kwargs['correctFactor'] |
|
|
765 | 765 | else: |
|
|
766 | 766 | correctFactor = 1 |
|
|
767 | 767 | |
|
|
768 | 768 | groupList = kwargs['groupList'] |
|
|
769 | 769 | pairs_ccf = groupList[1] |
|
|
770 | 770 | tau = kwargs['tau'] |
|
|
771 | 771 | _lambda = kwargs['_lambda'] |
|
|
772 | 772 | |
|
|
773 | 773 | #Cross Correlation pairs obtained |
|
|
774 | 774 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
775 | 775 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
776 | 776 | # pairsSelArray = numpy.array(pairsSelected) |
|
|
777 | 777 | # pairs = [] |
|
|
778 | 778 | # |
|
|
779 | 779 | # #Wind estimation pairs obtained |
|
|
780 | 780 | # for i in range(pairsSelArray.shape[0]/2): |
|
|
781 | 781 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
782 | 782 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
783 | 783 | # pairs.append((ind1,ind2)) |
|
|
784 | 784 | |
|
|
785 | 785 | indtau = tau.shape[0]/2 |
|
|
786 | 786 | tau1 = tau[:indtau,:] |
|
|
787 | 787 | tau2 = tau[indtau:-1,:] |
|
|
788 | 788 | # tau1 = tau1[pairs,:] |
|
|
789 | 789 | # tau2 = tau2[pairs,:] |
|
|
790 | 790 | phase1 = tau[-1,:] |
|
|
791 | 791 | |
|
|
792 | 792 | #--------------------------------------------------------------------- |
|
|
793 | 793 | #Metodo Directo |
|
|
794 | 794 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
795 | 795 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
796 | 796 | winds = stats.nanmean(winds, axis=0) |
|
|
797 | 797 | #--------------------------------------------------------------------- |
|
|
798 | 798 | #Metodo General |
|
|
799 | 799 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
800 | 800 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
801 | 801 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
802 | 802 | # #Calculo de Velocidades |
|
|
803 | 803 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
804 | 804 | |
|
|
805 | 805 | #--------------------------------------------------------------------- |
|
|
806 | 806 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
807 | 807 | winds = correctFactor*winds |
|
|
808 | 808 | return winds |
|
|
809 | 809 | |
|
|
810 | 810 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
811 | 811 | |
|
|
812 | 812 | dataTime = currentTime + paramInterval |
|
|
813 | 813 | deltaTime = dataTime - self.__initime |
|
|
814 | 814 | |
|
|
815 | 815 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
816 | 816 | self.__dataReady = True |
|
|
817 | 817 | return |
|
|
818 | 818 | |
|
|
819 | 819 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax, binkm=2): |
|
|
820 | 820 | ''' |
|
|
821 | 821 | Function that implements winds estimation technique with detected meteors. |
|
|
822 | 822 | |
|
|
823 | 823 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
824 | 824 | |
|
|
825 | 825 | Output: Winds estimation (Zonal and Meridional) |
|
|
826 | 826 | |
|
|
827 | 827 | Parameters affected: Winds |
|
|
828 | 828 | ''' |
|
|
829 | 829 | # print arrayMeteor.shape |
|
|
830 | 830 | #Settings |
|
|
831 | 831 | nInt = (heightMax - heightMin)/binkm |
|
|
832 | 832 | # print nInt |
|
|
833 | 833 | nInt = int(nInt) |
|
|
834 | 834 | # print nInt |
|
|
835 | 835 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
836 | 836 | |
|
|
837 | 837 | #Filter errors |
|
|
838 | 838 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
839 | 839 | finalMeteor = arrayMeteor[error,:] |
|
|
840 | 840 | |
|
|
841 | 841 | #Meteor Histogram |
|
|
842 | 842 | finalHeights = finalMeteor[:,2] |
|
|
843 | 843 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
844 | 844 | nMeteorsPerI = hist[0] |
|
|
845 | 845 | heightPerI = hist[1] |
|
|
846 | 846 | |
|
|
847 | 847 | #Sort of meteors |
|
|
848 | 848 | indSort = finalHeights.argsort() |
|
|
849 | 849 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
850 | 850 | |
|
|
851 | 851 | # Calculating winds |
|
|
852 | 852 | ind1 = 0 |
|
|
853 | 853 | ind2 = 0 |
|
|
854 | 854 | |
|
|
855 | 855 | for i in range(nInt): |
|
|
856 | 856 | nMet = nMeteorsPerI[i] |
|
|
857 | 857 | ind1 = ind2 |
|
|
858 | 858 | ind2 = ind1 + nMet |
|
|
859 | 859 | |
|
|
860 | 860 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
861 | 861 | |
|
|
862 | 862 | if meteorAux.shape[0] >= meteorThresh: |
|
|
863 | 863 | vel = meteorAux[:, 6] |
|
|
864 | 864 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
865 | 865 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
866 | 866 | |
|
|
867 | 867 | n = numpy.cos(zen) |
|
|
868 | 868 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
869 | 869 | # l = m*numpy.tan(azim) |
|
|
870 | 870 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
871 | 871 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
872 | 872 | |
|
|
873 | 873 | A = numpy.vstack((l, m)).transpose() |
|
|
874 | 874 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
875 | 875 | windsAux = numpy.dot(A1, vel) |
|
|
876 | 876 | |
|
|
877 | 877 | winds[0,i] = windsAux[0] |
|
|
878 | 878 | winds[1,i] = windsAux[1] |
|
|
879 | 879 | |
|
|
880 | 880 | return winds, heightPerI[:-1] |
|
|
881 | 881 | |
|
|
882 | 882 | def techniqueNSM_SA(self, **kwargs): |
|
|
883 | 883 | metArray = kwargs['metArray'] |
|
|
884 | 884 | heightList = kwargs['heightList'] |
|
|
885 | 885 | timeList = kwargs['timeList'] |
|
|
886 | 886 | |
|
|
887 | 887 | rx_location = kwargs['rx_location'] |
|
|
888 | 888 | groupList = kwargs['groupList'] |
|
|
889 | 889 | azimuth = kwargs['azimuth'] |
|
|
890 | 890 | dfactor = kwargs['dfactor'] |
|
|
891 | 891 | k = kwargs['k'] |
|
|
892 | 892 | |
|
|
893 | 893 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
894 | 894 | d = dist*dfactor |
|
|
895 | 895 | #Phase calculation |
|
|
896 | 896 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
897 | 897 | |
|
|
898 | 898 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
899 | 899 | |
|
|
900 | 900 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
901 | 901 | azimuth1 = azimuth1*numpy.pi/180 |
|
|
902 | 902 | |
|
|
903 | 903 | for i in range(heightList.size): |
|
|
904 | 904 | h = heightList[i] |
|
|
905 | 905 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
906 | 906 | metHeight = metArray1[indH,:] |
|
|
907 | 907 | if metHeight.shape[0] >= 2: |
|
|
908 | 908 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
909 | 909 | iazim = metHeight[:,1].astype(int) |
|
|
910 | 910 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
911 | 911 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
912 | 912 | A = numpy.asmatrix(A) |
|
|
913 | 913 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
914 | 914 | velHor = numpy.dot(A1,velAux) |
|
|
915 | 915 | |
|
|
916 | 916 | velEst[i,:] = numpy.squeeze(velHor) |
|
|
917 | 917 | return velEst |
|
|
918 | 918 | |
|
|
919 | 919 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
920 | 920 | meteorList = [] |
|
|
921 | 921 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
922 | 922 | #Putting back together the meteor matrix |
|
|
923 | 923 | utctime = metArray[:,0] |
|
|
924 | 924 | uniqueTime = numpy.unique(utctime) |
|
|
925 | 925 | |
|
|
926 | 926 | phaseDerThresh = 0.5 |
|
|
927 | 927 | ippSeconds = timeList[1] - timeList[0] |
|
|
928 | 928 | sec = numpy.where(timeList>1)[0][0] |
|
|
929 | 929 | nPairs = metArray.shape[1] - 6 |
|
|
930 | 930 | nHeights = len(heightList) |
|
|
931 | 931 | |
|
|
932 | 932 | for t in uniqueTime: |
|
|
933 | 933 | metArray1 = metArray[utctime==t,:] |
|
|
934 | 934 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
935 | 935 | tmet = metArray1[:,1].astype(int) |
|
|
936 | 936 | hmet = metArray1[:,2].astype(int) |
|
|
937 | 937 | |
|
|
938 | 938 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
939 | 939 | metPhase[:,:] = numpy.nan |
|
|
940 | 940 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
941 | 941 | |
|
|
942 | 942 | #Delete short trails |
|
|
943 | 943 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
944 | 944 | heightVect = numpy.sum(metBool, axis = 1) |
|
|
945 | 945 | metBool[heightVect<sec,:] = False |
|
|
946 | 946 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
947 | 947 | |
|
|
948 | 948 | #Derivative |
|
|
949 | 949 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
950 | 950 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
951 | 951 | metPhase[phDerAux] = numpy.nan |
|
|
952 | 952 | |
|
|
953 | 953 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
|
954 | 954 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
955 | 955 | |
|
|
956 | 956 | for p in numpy.arange(nPairs): |
|
|
957 | 957 | phase = metPhase[p,:,:] |
|
|
958 | 958 | phDer = metDer[p,:,:] |
|
|
959 | 959 | |
|
|
960 | 960 | for h in indMet: |
|
|
961 | 961 | height = heightList[h] |
|
|
962 | 962 | phase1 = phase[h,:] #82 |
|
|
963 | 963 | phDer1 = phDer[h,:] |
|
|
964 | 964 | |
|
|
965 | 965 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
966 | 966 | |
|
|
967 | 967 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
968 | 968 | initMet = indValid[0] |
|
|
969 | 969 | endMet = 0 |
|
|
970 | 970 | |
|
|
971 | 971 | for i in range(len(indValid)-1): |
|
|
972 | 972 | |
|
|
973 | 973 | #Time difference |
|
|
974 | 974 | inow = indValid[i] |
|
|
975 | 975 | inext = indValid[i+1] |
|
|
976 | 976 | idiff = inext - inow |
|
|
977 | 977 | #Phase difference |
|
|
978 | 978 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
979 | 979 | |
|
|
980 | 980 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
981 | 981 | sizeTrail = inow - initMet + 1 |
|
|
982 | 982 | if sizeTrail>3*sec: #Too short meteors |
|
|
983 | 983 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
984 | 984 | y = phase1[initMet:inow+1] |
|
|
985 | 985 | ynnan = ~numpy.isnan(y) |
|
|
986 | 986 | x = x[ynnan] |
|
|
987 | 987 | y = y[ynnan] |
|
|
988 | 988 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
989 | 989 | ylin = x*slope + intercept |
|
|
990 | 990 | rsq = r_value**2 |
|
|
991 | 991 | if rsq > 0.5: |
|
|
992 | 992 | vel = slope#*height*1000/(k*d) |
|
|
993 | 993 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
994 | 994 | meteorList.append(estAux) |
|
|
995 | 995 | initMet = inext |
|
|
996 | 996 | metArray2 = numpy.array(meteorList) |
|
|
997 | 997 | |
|
|
998 | 998 | return metArray2 |
|
|
999 | 999 | |
|
|
1000 | 1000 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
1001 | 1001 | |
|
|
1002 | 1002 | azimuth1 = numpy.zeros(len(pairslist)) |
|
|
1003 | 1003 | dist = numpy.zeros(len(pairslist)) |
|
|
1004 | 1004 | |
|
|
1005 | 1005 | for i in range(len(rx_location)): |
|
|
1006 | 1006 | ch0 = pairslist[i][0] |
|
|
1007 | 1007 | ch1 = pairslist[i][1] |
|
|
1008 | 1008 | |
|
|
1009 | 1009 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
1010 | 1010 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
1011 | 1011 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
1012 | 1012 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
1013 | 1013 | |
|
|
1014 | 1014 | azimuth1 -= azimuth0 |
|
|
1015 | 1015 | return azimuth1, dist |
|
|
1016 | 1016 | |
|
|
1017 | 1017 | def techniqueNSM_DBS(self, **kwargs): |
|
|
1018 | 1018 | metArray = kwargs['metArray'] |
|
|
1019 | 1019 | heightList = kwargs['heightList'] |
|
|
1020 | 1020 | timeList = kwargs['timeList'] |
|
|
1021 | 1021 | zenithList = kwargs['zenithList'] |
|
|
1022 | 1022 | nChan = numpy.max(cmet) + 1 |
|
|
1023 | 1023 | nHeights = len(heightList) |
|
|
1024 | 1024 | |
|
|
1025 | 1025 | utctime = metArray[:,0] |
|
|
1026 | 1026 | cmet = metArray[:,1] |
|
|
1027 | 1027 | hmet = metArray1[:,3].astype(int) |
|
|
1028 | 1028 | h1met = heightList[hmet]*zenithList[cmet] |
|
|
1029 | 1029 | vmet = metArray1[:,5] |
|
|
1030 | 1030 | |
|
|
1031 | 1031 | for i in range(nHeights - 1): |
|
|
1032 | 1032 | hmin = heightList[i] |
|
|
1033 | 1033 | hmax = heightList[i + 1] |
|
|
1034 | 1034 | |
|
|
1035 | 1035 | vthisH = vmet[(h1met>=hmin) & (h1met<hmax)] |
|
|
1036 | 1036 | |
|
|
1037 | 1037 | |
|
|
1038 | 1038 | |
|
|
1039 | 1039 | return data_output |
|
|
1040 | 1040 | |
|
|
1041 | 1041 | def run(self, dataOut, technique, **kwargs): |
|
|
1042 | 1042 | |
|
|
1043 | 1043 | param = dataOut.data_param |
|
|
1044 | 1044 | if dataOut.abscissaList != None: |
|
|
1045 | 1045 | absc = dataOut.abscissaList[:-1] |
|
|
1046 | 1046 | #noise = dataOut.noise |
|
|
1047 | 1047 | heightList = dataOut.heightList |
|
|
1048 | 1048 | SNR = dataOut.data_SNR |
|
|
1049 | 1049 | |
|
|
1050 | 1050 | if technique == 'DBS': |
|
|
1051 | 1051 | |
|
|
1052 | 1052 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
1053 | 1053 | kwargs['heightList'] = heightList |
|
|
1054 | 1054 | kwargs['SNR'] = SNR |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function |
|
|
1057 | 1057 | dataOut.utctimeInit = dataOut.utctime |
|
|
1058 | 1058 | dataOut.outputInterval = dataOut.paramInterval |
|
|
1059 | 1059 | |
|
|
1060 | 1060 | elif technique == 'SA': |
|
|
1061 | 1061 | |
|
|
1062 | 1062 | #Parameters |
|
|
1063 | 1063 | # position_x = kwargs['positionX'] |
|
|
1064 | 1064 | # position_y = kwargs['positionY'] |
|
|
1065 | 1065 | # azimuth = kwargs['azimuth'] |
|
|
1066 | 1066 | # |
|
|
1067 | 1067 | # if kwargs.has_key('crosspairsList'): |
|
|
1068 | 1068 | # pairs = kwargs['crosspairsList'] |
|
|
1069 | 1069 | # else: |
|
|
1070 | 1070 | # pairs = None |
|
|
1071 | 1071 | # |
|
|
1072 | 1072 | # if kwargs.has_key('correctFactor'): |
|
|
1073 | 1073 | # correctFactor = kwargs['correctFactor'] |
|
|
1074 | 1074 | # else: |
|
|
1075 | 1075 | # correctFactor = 1 |
|
|
1076 | 1076 | |
|
|
1077 | 1077 | # tau = dataOut.data_param |
|
|
1078 | 1078 | # _lambda = dataOut.C/dataOut.frequency |
|
|
1079 | 1079 | # pairsList = dataOut.groupList |
|
|
1080 | 1080 | # nChannels = dataOut.nChannels |
|
|
1081 | 1081 | |
|
|
1082 | 1082 | kwargs['groupList'] = dataOut.groupList |
|
|
1083 | 1083 | kwargs['tau'] = dataOut.data_param |
|
|
1084 | 1084 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
1085 | 1085 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
1086 | 1086 | dataOut.data_output = self.techniqueSA(kwargs) |
|
|
1087 | 1087 | dataOut.utctimeInit = dataOut.utctime |
|
|
1088 | 1088 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1089 | 1089 | |
|
|
1090 | 1090 | elif technique == 'Meteors': |
|
|
1091 | 1091 | dataOut.flagNoData = True |
|
|
1092 | 1092 | self.__dataReady = False |
|
|
1093 | 1093 | |
|
|
1094 | 1094 | if kwargs.has_key('nHours'): |
|
|
1095 | 1095 | nHours = kwargs['nHours'] |
|
|
1096 | 1096 | else: |
|
|
1097 | 1097 | nHours = 1 |
|
|
1098 | 1098 | |
|
|
1099 | 1099 | if kwargs.has_key('meteorsPerBin'): |
|
|
1100 | 1100 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
1101 | 1101 | else: |
|
|
1102 | 1102 | meteorThresh = 6 |
|
|
1103 | 1103 | |
|
|
1104 | 1104 | if kwargs.has_key('hmin'): |
|
|
1105 | 1105 | hmin = kwargs['hmin'] |
|
|
1106 | 1106 | else: hmin = 70 |
|
|
1107 | 1107 | if kwargs.has_key('hmax'): |
|
|
1108 | 1108 | hmax = kwargs['hmax'] |
|
|
1109 | 1109 | else: hmax = 110 |
|
|
1110 | 1110 | |
|
|
1111 | 1111 | if kwargs.has_key('BinKm'): |
|
|
1112 | 1112 | binkm = kwargs['BinKm'] |
|
|
1113 | 1113 | else: |
|
|
1114 | 1114 | binkm = 2 |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | dataOut.outputInterval = nHours*3600 |
|
|
1117 | 1117 | |
|
|
1118 | 1118 | if self.__isConfig == False: |
|
|
1119 | 1119 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1120 | 1120 | #Get Initial LTC time |
|
|
1121 | 1121 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
1122 | 1122 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1123 | 1123 | |
|
|
1124 | 1124 | self.__isConfig = True |
|
|
1125 | 1125 | |
|
|
1126 | 1126 | if self.__buffer is None: |
|
|
1127 | 1127 | self.__buffer = dataOut.data_param |
|
|
1128 | 1128 | self.__firstdata = copy.copy(dataOut) |
|
|
1129 | 1129 | |
|
|
1130 | 1130 | else: |
|
|
1131 | 1131 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
1132 | 1132 | |
|
|
1133 | 1133 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
1134 | 1134 | |
|
|
1135 | 1135 | if self.__dataReady: |
|
|
1136 | 1136 | dataOut.utctimeInit = self.__initime |
|
|
1137 | 1137 | |
|
|
1138 | 1138 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
1139 | 1139 | |
|
|
1140 | 1140 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax, binkm) |
|
|
1141 | 1141 | dataOut.flagNoData = False |
|
|
1142 | 1142 | self.__buffer = None |
|
|
1143 | 1143 | |
|
|
1144 | 1144 | elif technique == 'Meteors1': |
|
|
1145 | 1145 | dataOut.flagNoData = True |
|
|
1146 | 1146 | self.__dataReady = False |
|
|
1147 | 1147 | |
|
|
1148 | 1148 | if kwargs.has_key('nMins'): |
|
|
1149 | 1149 | nMins = kwargs['nMins'] |
|
|
1150 | 1150 | else: nMins = 20 |
|
|
1151 | 1151 | if kwargs.has_key('rx_location'): |
|
|
1152 | 1152 | rx_location = kwargs['rx_location'] |
|
|
1153 | 1153 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
1154 | 1154 | if kwargs.has_key('azimuth'): |
|
|
1155 | 1155 | azimuth = kwargs['azimuth'] |
|
|
1156 | 1156 | else: azimuth = 51 |
|
|
1157 | 1157 | if kwargs.has_key('dfactor'): |
|
|
1158 | 1158 | dfactor = kwargs['dfactor'] |
|
|
1159 | 1159 | if kwargs.has_key('mode'): |
|
|
1160 | 1160 | mode = kwargs['mode'] |
|
|
1161 | 1161 | else: mode = 'SA' |
|
|
1162 | 1162 | |
|
|
1163 | 1163 | #Borrar luego esto |
|
|
1164 | 1164 | if dataOut.groupList is None: |
|
|
1165 | 1165 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
1166 | 1166 | groupList = dataOut.groupList |
|
|
1167 | 1167 | C = 3e8 |
|
|
1168 | 1168 | freq = 50e6 |
|
|
1169 | 1169 | lamb = C/freq |
|
|
1170 | 1170 | k = 2*numpy.pi/lamb |
|
|
1171 | 1171 | |
|
|
1172 | 1172 | timeList = dataOut.abscissaList |
|
|
1173 | 1173 | heightList = dataOut.heightList |
|
|
1174 | 1174 | |
|
|
1175 | 1175 | if self.__isConfig == False: |
|
|
1176 | 1176 | dataOut.outputInterval = nMins*60 |
|
|
1177 | 1177 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1178 | 1178 | #Get Initial LTC time |
|
|
1179 | 1179 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
1180 | 1180 | minuteAux = initime.minute |
|
|
1181 | 1181 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
1182 | 1182 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1183 | 1183 | |
|
|
1184 | 1184 | self.__isConfig = True |
|
|
1185 | 1185 | |
|
|
1186 | 1186 | if self.__buffer is None: |
|
|
1187 | 1187 | self.__buffer = dataOut.data_param |
|
|
1188 | 1188 | self.__firstdata = copy.copy(dataOut) |
|
|
1189 | 1189 | |
|
|
1190 | 1190 | else: |
|
|
1191 | 1191 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
1192 | 1192 | |
|
|
1193 | 1193 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
1194 | 1194 | |
|
|
1195 | 1195 | if self.__dataReady: |
|
|
1196 | 1196 | dataOut.utctimeInit = self.__initime |
|
|
1197 | 1197 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
1198 | 1198 | |
|
|
1199 | 1199 | metArray = self.__buffer |
|
|
1200 | 1200 | if mode == 'SA': |
|
|
1201 | 1201 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
1202 | 1202 | elif mode == 'DBS': |
|
|
1203 | 1203 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList) |
|
|
1204 | 1204 | dataOut.data_output = dataOut.data_output.T |
|
|
1205 | 1205 | dataOut.flagNoData = False |
|
|
1206 | 1206 | self.__buffer = None |
|
|
1207 | 1207 | |
|
|
1208 | 1208 | return |
|
|
1209 | 1209 | |
|
|
1210 | 1210 | class EWDriftsEstimation(Operation): |
|
|
1211 | 1211 | |
|
|
1212 | 1212 | |
|
|
1213 | 1213 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1214 | 1214 | listPhi = phi.tolist() |
|
|
1215 | 1215 | maxid = listPhi.index(max(listPhi)) |
|
|
1216 | 1216 | minid = listPhi.index(min(listPhi)) |
|
|
1217 | 1217 | |
|
|
1218 | 1218 | rango = range(len(phi)) |
|
|
1219 | 1219 | # rango = numpy.delete(rango,maxid) |
|
|
1220 | 1220 | |
|
|
1221 | 1221 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1222 | 1222 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1223 | 1223 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1224 | 1224 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1225 | 1225 | |
|
|
1226 | 1226 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1227 | 1227 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1228 | 1228 | |
|
|
1229 | 1229 | for i in rango: |
|
|
1230 | 1230 | x = heiRang*math.cos(phi[i]) |
|
|
1231 | 1231 | y1 = velRadial[i,:] |
|
|
1232 | 1232 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1233 | 1233 | |
|
|
1234 | 1234 | x1 = heiRang1 |
|
|
1235 | 1235 | y11 = f1(x1) |
|
|
1236 | 1236 | |
|
|
1237 | 1237 | y2 = SNR[i,:] |
|
|
1238 | 1238 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1239 | 1239 | y21 = f2(x1) |
|
|
1240 | 1240 | |
|
|
1241 | 1241 | velRadial1[i,:] = y11 |
|
|
1242 | 1242 | SNR1[i,:] = y21 |
|
|
1243 | 1243 | |
|
|
1244 | 1244 | return heiRang1, velRadial1, SNR1 |
|
|
1245 | 1245 | |
|
|
1246 | 1246 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
1247 | 1247 | heiRang = dataOut.heightList |
|
|
1248 | 1248 | velRadial = dataOut.data_param[:,3,:] |
|
|
1249 | 1249 | SNR = dataOut.data_SNR |
|
|
1250 | 1250 | |
|
|
1251 | 1251 | zenith = numpy.array(zenith) |
|
|
1252 | 1252 | zenith -= zenithCorrection |
|
|
1253 | 1253 | zenith *= numpy.pi/180 |
|
|
1254 | 1254 | |
|
|
1255 | 1255 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
1256 | 1256 | |
|
|
1257 | 1257 | alp = zenith[0] |
|
|
1258 | 1258 | bet = zenith[1] |
|
|
1259 | 1259 | |
|
|
1260 | 1260 | w_w = velRadial1[0,:] |
|
|
1261 | 1261 | w_e = velRadial1[1,:] |
|
|
1262 | 1262 | |
|
|
1263 | 1263 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
1264 | 1264 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
1265 | 1265 | |
|
|
1266 | 1266 | winds = numpy.vstack((u,w)) |
|
|
1267 | 1267 | |
|
|
1268 | 1268 | dataOut.heightList = heiRang1 |
|
|
1269 | 1269 | dataOut.data_output = winds |
|
|
1270 | 1270 | dataOut.data_SNR = SNR1 |
|
|
1271 | 1271 | |
|
|
1272 | 1272 | dataOut.utctimeInit = dataOut.utctime |
|
|
1273 | 1273 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1274 | 1274 | return |
|
|
1275 | 1275 | |
|
|
1276 | 1276 | #--------------- Non Specular Meteor ---------------- |
|
|
1277 | 1277 | |
|
|
1278 | 1278 | class NonSpecularMeteorDetection(Operation): |
|
|
1279 | 1279 | |
|
|
1280 | parameters = { | |
|
|
1281 | 'mode': global_type_string, | |
|
|
1282 | 'SNRthresh': global_type_float, | |
|
|
1283 | 'phaseDerThresh': global_type_float, | |
|
|
1284 | 'cohThresh': global_type_float, | |
|
|
1285 | 'allData': global_type_boolean, | |
|
|
1286 | } | |
|
|
1287 | ||
|
|
1280 | 1288 | def run(self, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
1281 | 1289 | data_acf = self.dataOut.data_pre[0] |
|
|
1282 | 1290 | data_ccf = self.dataOut.data_pre[1] |
|
|
1283 | 1291 | |
|
|
1284 | 1292 | lamb = self.dataOut.C/self.dataOut.frequency |
|
|
1285 | 1293 | tSamp = self.dataOut.ippSeconds*self.dataOut.nCohInt |
|
|
1286 | 1294 | paramInterval = self.dataOut.paramInterval |
|
|
1287 | 1295 | |
|
|
1288 | 1296 | nChannels = data_acf.shape[0] |
|
|
1289 | 1297 | nLags = data_acf.shape[1] |
|
|
1290 | 1298 | nProfiles = data_acf.shape[2] |
|
|
1291 | 1299 | nHeights = self.dataOut.nHeights |
|
|
1292 | 1300 | nCohInt = self.dataOut.nCohInt |
|
|
1293 | 1301 | sec = numpy.round(nProfiles/self.dataOut.paramInterval) |
|
|
1294 | 1302 | heightList = self.dataOut.heightList |
|
|
1295 | 1303 | ippSeconds = self.dataOut.ippSeconds*self.dataOut.nCohInt*self.dataOut.nAvg |
|
|
1296 | 1304 | utctime = self.dataOut.utctime |
|
|
1297 | 1305 | |
|
|
1298 | 1306 | self.dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
1299 | 1307 | |
|
|
1300 | 1308 | #------------------------ SNR -------------------------------------- |
|
|
1301 | 1309 | power = data_acf[:,0,:,:].real |
|
|
1302 | 1310 | noise = numpy.zeros(nChannels) |
|
|
1303 | 1311 | SNR = numpy.zeros(power.shape) |
|
|
1304 | 1312 | for i in range(nChannels): |
|
|
1305 | 1313 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
1306 | 1314 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
1307 | 1315 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
1308 | 1316 | SNRdB = 10*numpy.log10(SNR) |
|
|
1309 | 1317 | |
|
|
1310 | 1318 | if mode == 'SA': |
|
|
1311 | 1319 | nPairs = data_ccf.shape[0] |
|
|
1312 | 1320 | #---------------------- Coherence and Phase -------------------------- |
|
|
1313 | 1321 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
1314 | 1322 | # phase1 = numpy.copy(phase) |
|
|
1315 | 1323 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
1316 | 1324 | |
|
|
1317 | 1325 | for p in range(nPairs): |
|
|
1318 | 1326 | ch0 = self.dataOut.groupList[p][0] |
|
|
1319 | 1327 | ch1 = self.dataOut.groupList[p][1] |
|
|
1320 | 1328 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
1321 | 1329 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
1322 | 1330 | # phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
1323 | 1331 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
1324 | 1332 | # coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
1325 | 1333 | coh = numpy.nanmax(coh1, axis = 0) |
|
|
1326 | 1334 | # struc = numpy.ones((5,1)) |
|
|
1327 | 1335 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
1328 | 1336 | #---------------------- Radial Velocity ---------------------------- |
|
|
1329 | 1337 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
1330 | 1338 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
1331 | 1339 | |
|
|
1332 | 1340 | if allData: |
|
|
1333 | 1341 | boolMetFin = ~numpy.isnan(SNRm) |
|
|
1334 | 1342 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
1335 | 1343 | else: |
|
|
1336 | 1344 | #------------------------ Meteor mask --------------------------------- |
|
|
1337 | 1345 | # #SNR mask |
|
|
1338 | 1346 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
1339 | 1347 | # |
|
|
1340 | 1348 | # #Erase small objects |
|
|
1341 | 1349 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
1342 | 1350 | # |
|
|
1343 | 1351 | # auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
1344 | 1352 | # indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
1345 | 1353 | # indEEJ = numpy.where(indOver)[0] |
|
|
1346 | 1354 | # indNEEJ = numpy.where(~indOver)[0] |
|
|
1347 | 1355 | # |
|
|
1348 | 1356 | # boolMetFin = boolMet1 |
|
|
1349 | 1357 | # |
|
|
1350 | 1358 | # if indEEJ.size > 0: |
|
|
1351 | 1359 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
1352 | 1360 | # |
|
|
1353 | 1361 | # boolMet2 = coh > cohThresh |
|
|
1354 | 1362 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
1355 | 1363 | # |
|
|
1356 | 1364 | # #Final Meteor mask |
|
|
1357 | 1365 | # boolMetFin = boolMet1|boolMet2 |
|
|
1358 | 1366 | |
|
|
1359 | 1367 | #Coherence mask |
|
|
1360 | 1368 | boolMet1 = coh > 0.75 |
|
|
1361 | 1369 | struc = numpy.ones((30,1)) |
|
|
1362 | 1370 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
1363 | 1371 | |
|
|
1364 | 1372 | #Derivative mask |
|
|
1365 | 1373 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
1366 | 1374 | boolMet2 = derPhase < 0.2 |
|
|
1367 | 1375 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
1368 | 1376 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
1369 | 1377 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
1370 | 1378 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
1371 | 1379 | # #Final mask |
|
|
1372 | 1380 | # boolMetFin = boolMet2 |
|
|
1373 | 1381 | boolMetFin = boolMet1&boolMet2 |
|
|
1374 | 1382 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
1375 | 1383 | #Creating data_param |
|
|
1376 | 1384 | coordMet = numpy.where(boolMetFin) |
|
|
1377 | 1385 | |
|
|
1378 | 1386 | tmet = coordMet[0] |
|
|
1379 | 1387 | hmet = coordMet[1] |
|
|
1380 | 1388 | |
|
|
1381 | 1389 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
1382 | 1390 | data_param[:,0] = utctime |
|
|
1383 | 1391 | data_param[:,1] = tmet |
|
|
1384 | 1392 | data_param[:,2] = hmet |
|
|
1385 | 1393 | data_param[:,3] = SNRm[tmet,hmet] |
|
|
1386 | 1394 | data_param[:,4] = velRad[tmet,hmet] |
|
|
1387 | 1395 | data_param[:,5] = coh[tmet,hmet] |
|
|
1388 | 1396 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
1389 | 1397 | |
|
|
1390 | 1398 | elif mode == 'DBS': |
|
|
1391 | 1399 | self.dataOut.groupList = numpy.arange(nChannels) |
|
|
1392 | 1400 | |
|
|
1393 | 1401 | #Radial Velocities |
|
|
1394 | 1402 | # phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
1395 | 1403 | phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
1396 | 1404 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
1397 | 1405 | |
|
|
1398 | 1406 | #Spectral width |
|
|
1399 | 1407 | acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
1400 | 1408 | acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
1401 | 1409 | |
|
|
1402 | 1410 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
1403 | 1411 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
1404 | 1412 | if allData: |
|
|
1405 | 1413 | boolMetFin = ~numpy.isnan(SNRdB) |
|
|
1406 | 1414 | else: |
|
|
1407 | 1415 | #SNR |
|
|
1408 | 1416 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
1409 | 1417 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
1410 | 1418 | |
|
|
1411 | 1419 | #Radial velocity |
|
|
1412 | 1420 | boolMet2 = numpy.abs(velRad) < 30 |
|
|
1413 | 1421 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
1414 | 1422 | |
|
|
1415 | 1423 | #Spectral Width |
|
|
1416 | 1424 | boolMet3 = spcWidth < 30 |
|
|
1417 | 1425 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
1418 | 1426 | # boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
1419 | 1427 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
1420 | 1428 | |
|
|
1421 | 1429 | #Creating data_param |
|
|
1422 | 1430 | coordMet = numpy.where(boolMetFin) |
|
|
1423 | 1431 | |
|
|
1424 | 1432 | cmet = coordMet[0] |
|
|
1425 | 1433 | tmet = coordMet[1] |
|
|
1426 | 1434 | hmet = coordMet[2] |
|
|
1427 | 1435 | |
|
|
1428 | 1436 | data_param = numpy.zeros((tmet.size, 7)) |
|
|
1429 | 1437 | data_param[:,0] = utctime |
|
|
1430 | 1438 | data_param[:,1] = cmet |
|
|
1431 | 1439 | data_param[:,2] = tmet |
|
|
1432 | 1440 | data_param[:,3] = hmet |
|
|
1433 | 1441 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
1434 | 1442 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
1435 | 1443 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
1436 | 1444 | |
|
|
1437 | 1445 | # self.dataOut.data_param = data_int |
|
|
1438 | 1446 | if len(data_param) == 0: |
|
|
1439 | 1447 | self.dataOut.flagNoData = True |
|
|
1440 | 1448 | else: |
|
|
1441 | 1449 | self.dataOut.data_param = data_param |
|
|
1442 | 1450 | |
|
|
1443 | 1451 | def __erase_small(self, binArray, threshX, threshY): |
|
|
1444 | 1452 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
1445 | 1453 | binArray1 = numpy.copy(binArray) |
|
|
1446 | 1454 | |
|
|
1447 | 1455 | for i in range(1,numfeat + 1): |
|
|
1448 | 1456 | auxBin = (labarray==i) |
|
|
1449 | 1457 | auxSize = auxBin.sum() |
|
|
1450 | 1458 | |
|
|
1451 | 1459 | x,y = numpy.where(auxBin) |
|
|
1452 | 1460 | widthX = x.max() - x.min() |
|
|
1453 | 1461 | widthY = y.max() - y.min() |
|
|
1454 | 1462 | |
|
|
1455 | 1463 | #width X: 3 seg -> 12.5*3 |
|
|
1456 | 1464 | #width Y: |
|
|
1457 | 1465 | |
|
|
1458 | 1466 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
1459 | 1467 | binArray1[auxBin] = False |
|
|
1460 | 1468 | |
|
|
1461 | 1469 | return binArray1 |
|
|
1462 | 1470 | |
|
|
1463 | 1471 | #--------------- Specular Meteor ---------------- |
|
|
1464 | 1472 | |
|
|
1465 | 1473 | class SMDetection(Operation): |
|
|
1466 | 1474 | ''' |
|
|
1467 | 1475 | Function DetectMeteors() |
|
|
1468 | 1476 | Project developed with paper: |
|
|
1469 | 1477 | HOLDSWORTH ET AL. 2004 |
|
|
1470 | 1478 | |
|
|
1471 | 1479 | Input: |
|
|
1472 | 1480 | self.dataOut.data_pre |
|
|
1473 | 1481 | |
|
|
1474 | 1482 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
1475 | 1483 | |
|
|
1476 | 1484 | hei_ref: Height reference for the Beacon signal extraction |
|
|
1477 | 1485 | tauindex: |
|
|
1478 | 1486 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
1479 | 1487 | |
|
|
1480 | 1488 | cohDetection: Whether to user Coherent detection or not |
|
|
1481 | 1489 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
1482 | 1490 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
1483 | 1491 | |
|
|
1484 | 1492 | noise_timeStep: Noise calculation time step |
|
|
1485 | 1493 | noise_multiple: Noise multiple to define signal threshold |
|
|
1486 | 1494 | |
|
|
1487 | 1495 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
1488 | 1496 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
1489 | 1497 | |
|
|
1490 | 1498 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
1491 | 1499 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
1492 | 1500 | |
|
|
1493 | 1501 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
1494 | 1502 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
1495 | 1503 | azimuth: Azimuth angle correction |
|
|
1496 | 1504 | |
|
|
1497 | 1505 | Affected: |
|
|
1498 | 1506 | self.dataOut.data_param |
|
|
1499 | 1507 | |
|
|
1500 | 1508 | Rejection Criteria (Errors): |
|
|
1501 | 1509 | 0: No error; analysis OK |
|
|
1502 | 1510 | 1: SNR < SNR threshold |
|
|
1503 | 1511 | 2: angle of arrival (AOA) ambiguously determined |
|
|
1504 | 1512 | 3: AOA estimate not feasible |
|
|
1505 | 1513 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
1506 | 1514 | 5: echo at start or end of time series |
|
|
1507 | 1515 | 6: echo less than 5 examples long; too short for analysis |
|
|
1508 | 1516 | 7: echo rise exceeds 0.3s |
|
|
1509 | 1517 | 8: echo decay time less than twice rise time |
|
|
1510 | 1518 | 9: large power level before echo |
|
|
1511 | 1519 | 10: large power level after echo |
|
|
1512 | 1520 | 11: poor fit to amplitude for estimation of decay time |
|
|
1513 | 1521 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
1514 | 1522 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
1515 | 1523 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
1516 | 1524 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
1517 | 1525 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
1518 | 1526 | |
|
|
1519 | 1527 | 17: phase difference in meteor Reestimation |
|
|
1520 | 1528 | |
|
|
1521 | 1529 | Data Storage: |
|
|
1522 | 1530 | Meteors for Wind Estimation (8): |
|
|
1523 | 1531 | Utc Time | Range Height |
|
|
1524 | 1532 | Azimuth Zenith errorCosDir |
|
|
1525 | 1533 | VelRad errorVelRad |
|
|
1526 | 1534 | Phase0 Phase1 Phase2 Phase3 |
|
|
1527 | 1535 | TypeError |
|
|
1528 | 1536 | |
|
|
1529 | 1537 | ''' |
|
|
1530 | 1538 | |
|
|
1531 | 1539 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
1532 | 1540 | phaseOffsets = None, |
|
|
1533 | 1541 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
1534 | 1542 | noise_timeStep = 4, noise_multiple = 4, |
|
|
1535 | 1543 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
1536 | 1544 | phaseThresh = 20, SNRThresh = 5, |
|
|
1537 | 1545 | hmin = 50, hmax=150, azimuth = 0, |
|
|
1538 | 1546 | channelPositions = None) : |
|
|
1539 | 1547 | |
|
|
1540 | 1548 | |
|
|
1541 | 1549 | #Getting Pairslist |
|
|
1542 | 1550 | if channelPositions is None: |
|
|
1543 | 1551 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
1544 | 1552 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
1545 | 1553 | meteorOps = SMOperations() |
|
|
1546 | 1554 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
1547 | 1555 | heiRang = dataOut.getHeiRange() |
|
|
1548 | 1556 | #Get Beacon signal - No Beacon signal anymore |
|
|
1549 | 1557 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
1550 | 1558 | # |
|
|
1551 | 1559 | # if hei_ref != None: |
|
|
1552 | 1560 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
1553 | 1561 | # |
|
|
1554 | 1562 | |
|
|
1555 | 1563 | |
|
|
1556 | 1564 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
1557 | 1565 | # see if the user put in pre defined phase shifts |
|
|
1558 | 1566 | voltsPShift = dataOut.data_pre.copy() |
|
|
1559 | 1567 | |
|
|
1560 | 1568 | # if predefinedPhaseShifts != None: |
|
|
1561 | 1569 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
1562 | 1570 | # |
|
|
1563 | 1571 | # # elif beaconPhaseShifts: |
|
|
1564 | 1572 | # # #get hardware phase shifts using beacon signal |
|
|
1565 | 1573 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
1566 | 1574 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
1567 | 1575 | # |
|
|
1568 | 1576 | # else: |
|
|
1569 | 1577 | # hardwarePhaseShifts = numpy.zeros(5) |
|
|
1570 | 1578 | # |
|
|
1571 | 1579 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
1572 | 1580 | # for i in range(self.dataOut.data_pre.shape[0]): |
|
|
1573 | 1581 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
1574 | 1582 | |
|
|
1575 | 1583 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
1576 | 1584 | |
|
|
1577 | 1585 | #Remove DC |
|
|
1578 | 1586 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
1579 | 1587 | voltsDC = numpy.mean(voltsDC,1) |
|
|
1580 | 1588 | for i in range(voltsDC.shape[0]): |
|
|
1581 | 1589 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
1582 | 1590 | |
|
|
1583 | 1591 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
1584 | 1592 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
1585 | 1593 | |
|
|
1586 | 1594 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
1587 | 1595 | #Coherent Detection |
|
|
1588 | 1596 | if cohDetection: |
|
|
1589 | 1597 | #use coherent detection to get the net power |
|
|
1590 | 1598 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
1591 | 1599 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
1592 | 1600 | |
|
|
1593 | 1601 | #Non-coherent detection! |
|
|
1594 | 1602 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
1595 | 1603 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
1596 | 1604 | |
|
|
1597 | 1605 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
1598 | 1606 | #Get noise |
|
|
1599 | 1607 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
1600 | 1608 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
1601 | 1609 | #Get signal threshold |
|
|
1602 | 1610 | signalThresh = noise_multiple*noise |
|
|
1603 | 1611 | #Meteor echoes detection |
|
|
1604 | 1612 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
1605 | 1613 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
1606 | 1614 | |
|
|
1607 | 1615 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
1608 | 1616 | #Parameters |
|
|
1609 | 1617 | heiRange = dataOut.getHeiRange() |
|
|
1610 | 1618 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
1611 | 1619 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
1612 | 1620 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
1613 | 1621 | #Multiple detection removals |
|
|
1614 | 1622 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
1615 | 1623 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
1616 | 1624 | |
|
|
1617 | 1625 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
1618 | 1626 | #Parameters |
|
|
1619 | 1627 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
1620 | 1628 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
1621 | 1629 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
1622 | 1630 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
1623 | 1631 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
1624 | 1632 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
1625 | 1633 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
1626 | 1634 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
1627 | 1635 | |
|
|
1628 | 1636 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
1629 | 1637 | #Calculating Radial Velocity (Error N 15) |
|
|
1630 | 1638 | radialStdThresh = 10 |
|
|
1631 | 1639 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
1632 | 1640 | |
|
|
1633 | 1641 | if len(listMeteors4) > 0: |
|
|
1634 | 1642 | #Setting New Array |
|
|
1635 | 1643 | date = dataOut.utctime |
|
|
1636 | 1644 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
1637 | 1645 | |
|
|
1638 | 1646 | #Correcting phase offset |
|
|
1639 | 1647 | if phaseOffsets != None: |
|
|
1640 | 1648 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
1641 | 1649 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
1642 | 1650 | |
|
|
1643 | 1651 | #Second Pairslist |
|
|
1644 | 1652 | pairsList = [] |
|
|
1645 | 1653 | pairx = (0,1) |
|
|
1646 | 1654 | pairy = (2,3) |
|
|
1647 | 1655 | pairsList.append(pairx) |
|
|
1648 | 1656 | pairsList.append(pairy) |
|
|
1649 | 1657 | |
|
|
1650 | 1658 | jph = numpy.array([0,0,0,0]) |
|
|
1651 | 1659 | h = (hmin,hmax) |
|
|
1652 | 1660 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
1653 | 1661 | |
|
|
1654 | 1662 | # #Calculate AOA (Error N 3, 4) |
|
|
1655 | 1663 | # #JONES ET AL. 1998 |
|
|
1656 | 1664 | # error = arrayParameters[:,-1] |
|
|
1657 | 1665 | # AOAthresh = numpy.pi/8 |
|
|
1658 | 1666 | # phases = -arrayParameters[:,9:13] |
|
|
1659 | 1667 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
1660 | 1668 | # |
|
|
1661 | 1669 | # #Calculate Heights (Error N 13 and 14) |
|
|
1662 | 1670 | # error = arrayParameters[:,-1] |
|
|
1663 | 1671 | # Ranges = arrayParameters[:,2] |
|
|
1664 | 1672 | # zenith = arrayParameters[:,5] |
|
|
1665 | 1673 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
1666 | 1674 | # error = arrayParameters[:,-1] |
|
|
1667 | 1675 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
1668 | 1676 | |
|
|
1669 | 1677 | #***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
1670 | 1678 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
1671 | 1679 | dataOut.data_param = arrayParameters |
|
|
1672 | 1680 | |
|
|
1673 | 1681 | if arrayParameters is None: |
|
|
1674 | 1682 | dataOut.flagNoData = True |
|
|
1675 | 1683 | else: |
|
|
1676 | 1684 | dataOut.flagNoData = True |
|
|
1677 | 1685 | |
|
|
1678 | 1686 | return |
|
|
1679 | 1687 | |
|
|
1680 | 1688 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
1681 | 1689 | |
|
|
1682 | 1690 | minIndex = min(newheis[0]) |
|
|
1683 | 1691 | maxIndex = max(newheis[0]) |
|
|
1684 | 1692 | |
|
|
1685 | 1693 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
1686 | 1694 | nLength = voltage.shape[1]/n |
|
|
1687 | 1695 | nMin = 0 |
|
|
1688 | 1696 | nMax = 0 |
|
|
1689 | 1697 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
1690 | 1698 | |
|
|
1691 | 1699 | for i in range(n): |
|
|
1692 | 1700 | nMax += nLength |
|
|
1693 | 1701 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
1694 | 1702 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
1695 | 1703 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
1696 | 1704 | nMin = nMax |
|
|
1697 | 1705 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
1698 | 1706 | |
|
|
1699 | 1707 | #Remove Outliers |
|
|
1700 | 1708 | factor = 2 |
|
|
1701 | 1709 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
1702 | 1710 | dw = numpy.std(wt,axis = 1) |
|
|
1703 | 1711 | dw = dw.reshape((dw.size,1)) |
|
|
1704 | 1712 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
1705 | 1713 | phaseOffset[ind] = numpy.nan |
|
|
1706 | 1714 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
1707 | 1715 | |
|
|
1708 | 1716 | return phaseOffset |
|
|
1709 | 1717 | |
|
|
1710 | 1718 | def __shiftPhase(self, data, phaseShift): |
|
|
1711 | 1719 | #this will shift the phase of a complex number |
|
|
1712 | 1720 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
1713 | 1721 | return dataShifted |
|
|
1714 | 1722 | |
|
|
1715 | 1723 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
1716 | 1724 | nChannel = array.shape[0] |
|
|
1717 | 1725 | nHeights = array.shape[2] |
|
|
1718 | 1726 | numPairs = len(pairslist) |
|
|
1719 | 1727 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
1720 | 1728 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
1721 | 1729 | |
|
|
1722 | 1730 | #Correct phases |
|
|
1723 | 1731 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
1724 | 1732 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
1725 | 1733 | |
|
|
1726 | 1734 | if indDer[0].shape[0] > 0: |
|
|
1727 | 1735 | for i in range(indDer[0].shape[0]): |
|
|
1728 | 1736 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
1729 | 1737 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
1730 | 1738 | |
|
|
1731 | 1739 | # for j in range(numSides): |
|
|
1732 | 1740 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
1733 | 1741 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
1734 | 1742 | # |
|
|
1735 | 1743 | #Linear |
|
|
1736 | 1744 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
1737 | 1745 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
1738 | 1746 | for j in range(numPairs): |
|
|
1739 | 1747 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
1740 | 1748 | phaseInt[j] = fit[1] |
|
|
1741 | 1749 | #Phase Differences |
|
|
1742 | 1750 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
1743 | 1751 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
1744 | 1752 | |
|
|
1745 | 1753 | #Dealias |
|
|
1746 | 1754 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
1747 | 1755 | # indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
1748 | 1756 | # phaseArrival[indAlias] -= 2*numpy.pi |
|
|
1749 | 1757 | # indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
1750 | 1758 | # phaseArrival[indAlias] += 2*numpy.pi |
|
|
1751 | 1759 | |
|
|
1752 | 1760 | return phaseDiff, phaseArrival |
|
|
1753 | 1761 | |
|
|
1754 | 1762 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
1755 | 1763 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
1756 | 1764 | #find the phase shifts of each channel over 1 second intervals |
|
|
1757 | 1765 | #only look at ranges below the beacon signal |
|
|
1758 | 1766 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
1759 | 1767 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
1760 | 1768 | numHeights = volts.shape[2] |
|
|
1761 | 1769 | nChannel = volts.shape[0] |
|
|
1762 | 1770 | voltsCohDet = volts.copy() |
|
|
1763 | 1771 | |
|
|
1764 | 1772 | pairsarray = numpy.array(pairslist) |
|
|
1765 | 1773 | indSides = pairsarray[:,1] |
|
|
1766 | 1774 | # indSides = numpy.array(range(nChannel)) |
|
|
1767 | 1775 | # indSides = numpy.delete(indSides, indCenter) |
|
|
1768 | 1776 | # |
|
|
1769 | 1777 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
1770 | 1778 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
1771 | 1779 | |
|
|
1772 | 1780 | startInd = 0 |
|
|
1773 | 1781 | endInd = 0 |
|
|
1774 | 1782 | |
|
|
1775 | 1783 | for i in range(numBlocks): |
|
|
1776 | 1784 | startInd = endInd |
|
|
1777 | 1785 | endInd = endInd + listBlocks[i].shape[1] |
|
|
1778 | 1786 | |
|
|
1779 | 1787 | arrayBlock = listBlocks[i] |
|
|
1780 | 1788 | # arrayBlockCenter = listCenter[i] |
|
|
1781 | 1789 | |
|
|
1782 | 1790 | #Estimate the Phase Difference |
|
|
1783 | 1791 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
1784 | 1792 | #Phase Difference RMS |
|
|
1785 | 1793 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
1786 | 1794 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
1787 | 1795 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
1788 | 1796 | #Shifting |
|
|
1789 | 1797 | if indPhase[0].shape[0] > 0: |
|
|
1790 | 1798 | for j in range(indSides.size): |
|
|
1791 | 1799 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
1792 | 1800 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
1793 | 1801 | |
|
|
1794 | 1802 | return voltsCohDet |
|
|
1795 | 1803 | |
|
|
1796 | 1804 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
1797 | 1805 | |
|
|
1798 | 1806 | nHeights = volts.shape[2] |
|
|
1799 | 1807 | nPoints = volts.shape[1] |
|
|
1800 | 1808 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
1801 | 1809 | |
|
|
1802 | 1810 | for i in range(len(pairslist)): |
|
|
1803 | 1811 | volts1 = volts[pairslist[i][0]] |
|
|
1804 | 1812 | volts2 = volts[pairslist[i][1]] |
|
|
1805 | 1813 | |
|
|
1806 | 1814 | for t in range(len(laglist)): |
|
|
1807 | 1815 | idxT = laglist[t] |
|
|
1808 | 1816 | if idxT >= 0: |
|
|
1809 | 1817 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
1810 | 1818 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
1811 | 1819 | else: |
|
|
1812 | 1820 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
1813 | 1821 | volts2[:(nPoints + idxT),:])) |
|
|
1814 | 1822 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
1815 | 1823 | |
|
|
1816 | 1824 | vStacked = None |
|
|
1817 | 1825 | return voltsCCF |
|
|
1818 | 1826 | |
|
|
1819 | 1827 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
1820 | 1828 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
1821 | 1829 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
1822 | 1830 | numHeights = power.shape[1] |
|
|
1823 | 1831 | |
|
|
1824 | 1832 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
1825 | 1833 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
1826 | 1834 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
1827 | 1835 | |
|
|
1828 | 1836 | startInd = 0 |
|
|
1829 | 1837 | endInd = 0 |
|
|
1830 | 1838 | |
|
|
1831 | 1839 | for i in range(numBlocks): #split por canal |
|
|
1832 | 1840 | startInd = endInd |
|
|
1833 | 1841 | endInd = endInd + listPower[i].shape[0] |
|
|
1834 | 1842 | |
|
|
1835 | 1843 | arrayBlock = listPower[i] |
|
|
1836 | 1844 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
1837 | 1845 | # noiseAux = numpy.median(noiseAux) |
|
|
1838 | 1846 | # noiseAux = numpy.mean(arrayBlock) |
|
|
1839 | 1847 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
1840 | 1848 | |
|
|
1841 | 1849 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
1842 | 1850 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
1843 | 1851 | |
|
|
1844 | 1852 | return noise, noise1 |
|
|
1845 | 1853 | |
|
|
1846 | 1854 | def __findMeteors(self, power, thresh): |
|
|
1847 | 1855 | nProf = power.shape[0] |
|
|
1848 | 1856 | nHeights = power.shape[1] |
|
|
1849 | 1857 | listMeteors = [] |
|
|
1850 | 1858 | |
|
|
1851 | 1859 | for i in range(nHeights): |
|
|
1852 | 1860 | powerAux = power[:,i] |
|
|
1853 | 1861 | threshAux = thresh[:,i] |
|
|
1854 | 1862 | |
|
|
1855 | 1863 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
1856 | 1864 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
1857 | 1865 | |
|
|
1858 | 1866 | j = 0 |
|
|
1859 | 1867 | |
|
|
1860 | 1868 | while (j < indUPthresh.size - 2): |
|
|
1861 | 1869 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
1862 | 1870 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
1863 | 1871 | indDNthresh = indDNthresh[indDNAux] |
|
|
1864 | 1872 | |
|
|
1865 | 1873 | if (indDNthresh.size > 0): |
|
|
1866 | 1874 | indEnd = indDNthresh[0] - 1 |
|
|
1867 | 1875 | indInit = indUPthresh[j] if isinstance(indUPthresh[j], (int, float)) else indUPthresh[j][0] ##CHECK!!!! |
|
|
1868 | 1876 | |
|
|
1869 | 1877 | meteor = powerAux[indInit:indEnd + 1] |
|
|
1870 | 1878 | indPeak = meteor.argmax() + indInit |
|
|
1871 | 1879 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
1872 | 1880 | |
|
|
1873 | 1881 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
1874 | 1882 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
1875 | 1883 | else: j+=1 |
|
|
1876 | 1884 | else: j+=1 |
|
|
1877 | 1885 | |
|
|
1878 | 1886 | return listMeteors |
|
|
1879 | 1887 | |
|
|
1880 | 1888 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
1881 | 1889 | |
|
|
1882 | 1890 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
1883 | 1891 | listMeteors1 = [] |
|
|
1884 | 1892 | |
|
|
1885 | 1893 | while arrayMeteors.shape[0] > 0: |
|
|
1886 | 1894 | FLAs = arrayMeteors[:,4] |
|
|
1887 | 1895 | maxFLA = FLAs.argmax() |
|
|
1888 | 1896 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
1889 | 1897 | |
|
|
1890 | 1898 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
1891 | 1899 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
1892 | 1900 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
1893 | 1901 | |
|
|
1894 | 1902 | #Check neighborhood |
|
|
1895 | 1903 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
1896 | 1904 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
1897 | 1905 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
1898 | 1906 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
1899 | 1907 | |
|
|
1900 | 1908 | #Check Heights |
|
|
1901 | 1909 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
1902 | 1910 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
1903 | 1911 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
1904 | 1912 | |
|
|
1905 | 1913 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
1906 | 1914 | |
|
|
1907 | 1915 | return listMeteors1 |
|
|
1908 | 1916 | |
|
|
1909 | 1917 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
1910 | 1918 | numHeights = volts.shape[2] |
|
|
1911 | 1919 | nChannel = volts.shape[0] |
|
|
1912 | 1920 | |
|
|
1913 | 1921 | thresholdPhase = thresh[0] |
|
|
1914 | 1922 | thresholdNoise = thresh[1] |
|
|
1915 | 1923 | thresholdDB = float(thresh[2]) |
|
|
1916 | 1924 | |
|
|
1917 | 1925 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
1918 | 1926 | pairsarray = numpy.array(pairslist) |
|
|
1919 | 1927 | indSides = pairsarray[:,1] |
|
|
1920 | 1928 | |
|
|
1921 | 1929 | pairslist1 = list(pairslist) |
|
|
1922 | 1930 | pairslist1.append((0,4)) |
|
|
1923 | 1931 | pairslist1.append((1,3)) |
|
|
1924 | 1932 | |
|
|
1925 | 1933 | listMeteors1 = [] |
|
|
1926 | 1934 | listPowerSeries = [] |
|
|
1927 | 1935 | listVoltageSeries = [] |
|
|
1928 | 1936 | #volts has the war data |
|
|
1929 | 1937 | |
|
|
1930 | 1938 | if frequency == 30.175e6: |
|
|
1931 | 1939 | timeLag = 45*10**-3 |
|
|
1932 | 1940 | else: |
|
|
1933 | 1941 | timeLag = 15*10**-3 |
|
|
1934 | 1942 | lag = int(numpy.ceil(timeLag/timeInterval)) |
|
|
1935 | 1943 | |
|
|
1936 | 1944 | for i in range(len(listMeteors)): |
|
|
1937 | 1945 | |
|
|
1938 | 1946 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
1939 | 1947 | meteorAux = numpy.zeros(16) |
|
|
1940 | 1948 | |
|
|
1941 | 1949 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
1942 | 1950 | mHeight = int(listMeteors[i][0]) |
|
|
1943 | 1951 | mStart = int(listMeteors[i][1]) |
|
|
1944 | 1952 | mPeak = int(listMeteors[i][2]) |
|
|
1945 | 1953 | mEnd = int(listMeteors[i][3]) |
|
|
1946 | 1954 | |
|
|
1947 | 1955 | #get the volt data between the start and end times of the meteor |
|
|
1948 | 1956 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
1949 | 1957 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
1950 | 1958 | |
|
|
1951 | 1959 | #3.6. Phase Difference estimation |
|
|
1952 | 1960 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
1953 | 1961 | |
|
|
1954 | 1962 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
1955 | 1963 | #meteorVolts0.- all Channels, all Profiles |
|
|
1956 | 1964 | meteorVolts0 = volts[:,:,mHeight] |
|
|
1957 | 1965 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
1958 | 1966 | meteorNoise = noise[:,mHeight] |
|
|
1959 | 1967 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
1960 | 1968 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
1961 | 1969 | |
|
|
1962 | 1970 | #Times reestimation |
|
|
1963 | 1971 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
1964 | 1972 | if mStart1.size > 0: |
|
|
1965 | 1973 | mStart1 = mStart1[-1] + 1 |
|
|
1966 | 1974 | |
|
|
1967 | 1975 | else: |
|
|
1968 | 1976 | mStart1 = mPeak |
|
|
1969 | 1977 | |
|
|
1970 | 1978 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
1971 | 1979 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
1972 | 1980 | if mEndDecayTime1.size == 0: |
|
|
1973 | 1981 | mEndDecayTime1 = powerNet0.size |
|
|
1974 | 1982 | else: |
|
|
1975 | 1983 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
1976 | 1984 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
1977 | 1985 | |
|
|
1978 | 1986 | #meteorVolts1.- all Channels, from start to end |
|
|
1979 | 1987 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
1980 | 1988 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
1981 | 1989 | if meteorVolts2.shape[1] == 0: |
|
|
1982 | 1990 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
1983 | 1991 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
1984 | 1992 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
1985 | 1993 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
1986 | 1994 | |
|
|
1987 | 1995 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
1988 | 1996 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
1989 | 1997 | if meteorVolts2.shape[1] > 0: |
|
|
1990 | 1998 | #Phase Difference re-estimation |
|
|
1991 | 1999 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
1992 | 2000 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
1993 | 2001 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
1994 | 2002 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
1995 | 2003 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
1996 | 2004 | |
|
|
1997 | 2005 | #Phase Difference RMS |
|
|
1998 | 2006 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
1999 | 2007 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
2000 | 2008 | #Data from Meteor |
|
|
2001 | 2009 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
2002 | 2010 | mPeakPower1 = powerNet1.max() |
|
|
2003 | 2011 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
2004 | 2012 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
2005 | 2013 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
2006 | 2014 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
2007 | 2015 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
2008 | 2016 | #Vectorize |
|
|
2009 | 2017 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
2010 | 2018 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
2011 | 2019 | |
|
|
2012 | 2020 | #Rejection Criterions |
|
|
2013 | 2021 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
2014 | 2022 | meteorAux[-1] = 17 |
|
|
2015 | 2023 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
2016 | 2024 | meteorAux[-1] = 1 |
|
|
2017 | 2025 | |
|
|
2018 | 2026 | |
|
|
2019 | 2027 | else: |
|
|
2020 | 2028 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
2021 | 2029 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
2022 | 2030 | PowerSeries = 0 |
|
|
2023 | 2031 | |
|
|
2024 | 2032 | listMeteors1.append(meteorAux) |
|
|
2025 | 2033 | listPowerSeries.append(PowerSeries) |
|
|
2026 | 2034 | listVoltageSeries.append(meteorVolts1) |
|
|
2027 | 2035 | |
|
|
2028 | 2036 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
2029 | 2037 | |
|
|
2030 | 2038 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
2031 | 2039 | |
|
|
2032 | 2040 | threshError = 10 |
|
|
2033 | 2041 | #Depending if it is 30 or 50 MHz |
|
|
2034 | 2042 | if frequency == 30.175e6: |
|
|
2035 | 2043 | timeLag = 45*10**-3 |
|
|
2036 | 2044 | else: |
|
|
2037 | 2045 | timeLag = 15*10**-3 |
|
|
2038 | 2046 | lag = int(numpy.ceil(timeLag/timeInterval)) |
|
|
2039 | 2047 | |
|
|
2040 | 2048 | listMeteors1 = [] |
|
|
2041 | 2049 | |
|
|
2042 | 2050 | for i in range(len(listMeteors)): |
|
|
2043 | 2051 | meteorPower = listPower[i] |
|
|
2044 | 2052 | meteorAux = listMeteors[i] |
|
|
2045 | 2053 | |
|
|
2046 | 2054 | if meteorAux[-1] == 0: |
|
|
2047 | 2055 | |
|
|
2048 | 2056 | try: |
|
|
2049 | 2057 | indmax = meteorPower.argmax() |
|
|
2050 | 2058 | indlag = indmax + lag |
|
|
2051 | 2059 | |
|
|
2052 | 2060 | y = meteorPower[indlag:] |
|
|
2053 | 2061 | x = numpy.arange(0, y.size)*timeLag |
|
|
2054 | 2062 | |
|
|
2055 | 2063 | #first guess |
|
|
2056 | 2064 | a = y[0] |
|
|
2057 | 2065 | tau = timeLag |
|
|
2058 | 2066 | #exponential fit |
|
|
2059 | 2067 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
2060 | 2068 | y1 = self.__exponential_function(x, *popt) |
|
|
2061 | 2069 | #error estimation |
|
|
2062 | 2070 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
2063 | 2071 | |
|
|
2064 | 2072 | decayTime = popt[1] |
|
|
2065 | 2073 | riseTime = indmax*timeInterval |
|
|
2066 | 2074 | meteorAux[11:13] = [decayTime, error] |
|
|
2067 | 2075 | |
|
|
2068 | 2076 | #Table items 7, 8 and 11 |
|
|
2069 | 2077 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
2070 | 2078 | meteorAux[-1] = 7 |
|
|
2071 | 2079 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
2072 | 2080 | meteorAux[-1] = 8 |
|
|
2073 | 2081 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
2074 | 2082 | meteorAux[-1] = 11 |
|
|
2075 | 2083 | |
|
|
2076 | 2084 | |
|
|
2077 | 2085 | except: |
|
|
2078 | 2086 | meteorAux[-1] = 11 |
|
|
2079 | 2087 | |
|
|
2080 | 2088 | |
|
|
2081 | 2089 | listMeteors1.append(meteorAux) |
|
|
2082 | 2090 | |
|
|
2083 | 2091 | return listMeteors1 |
|
|
2084 | 2092 | |
|
|
2085 | 2093 | #Exponential Function |
|
|
2086 | 2094 | |
|
|
2087 | 2095 | def __exponential_function(self, x, a, tau): |
|
|
2088 | 2096 | y = a*numpy.exp(-x/tau) |
|
|
2089 | 2097 | return y |
|
|
2090 | 2098 | |
|
|
2091 | 2099 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
2092 | 2100 | |
|
|
2093 | 2101 | pairslist1 = list(pairslist) |
|
|
2094 | 2102 | pairslist1.append((0,4)) |
|
|
2095 | 2103 | pairslist1.append((1,3)) |
|
|
2096 | 2104 | numPairs = len(pairslist1) |
|
|
2097 | 2105 | #Time Lag |
|
|
2098 | 2106 | timeLag = 45*10**-3 |
|
|
2099 | 2107 | c = 3e8 |
|
|
2100 | 2108 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
2101 | 2109 | freq = 30.175e6 |
|
|
2102 | 2110 | |
|
|
2103 | 2111 | listMeteors1 = [] |
|
|
2104 | 2112 | |
|
|
2105 | 2113 | for i in range(len(listMeteors)): |
|
|
2106 | 2114 | meteorAux = listMeteors[i] |
|
|
2107 | 2115 | if meteorAux[-1] == 0: |
|
|
2108 | 2116 | mStart = listMeteors[i][1] |
|
|
2109 | 2117 | mPeak = listMeteors[i][2] |
|
|
2110 | 2118 | mLag = mPeak - mStart + lag |
|
|
2111 | 2119 | |
|
|
2112 | 2120 | #get the volt data between the start and end times of the meteor |
|
|
2113 | 2121 | meteorVolts = listVolts[i] |
|
|
2114 | 2122 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
2115 | 2123 | |
|
|
2116 | 2124 | #Get CCF |
|
|
2117 | 2125 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
2118 | 2126 | |
|
|
2119 | 2127 | #Method 2 |
|
|
2120 | 2128 | slopes = numpy.zeros(numPairs) |
|
|
2121 | 2129 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
2122 | 2130 | angAllCCF = numpy.angle(allCCFs[:,[0,4,2,3],0]) |
|
|
2123 | 2131 | |
|
|
2124 | 2132 | #Correct phases |
|
|
2125 | 2133 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
2126 | 2134 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
2127 | 2135 | |
|
|
2128 | 2136 | if indDer[0].shape[0] > 0: |
|
|
2129 | 2137 | for i in range(indDer[0].shape[0]): |
|
|
2130 | 2138 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
2131 | 2139 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
2132 | 2140 | |
|
|
2133 | 2141 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
2134 | 2142 | for j in range(numPairs): |
|
|
2135 | 2143 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
2136 | 2144 | slopes[j] = fit[0] |
|
|
2137 | 2145 | |
|
|
2138 | 2146 | #Remove Outlier |
|
|
2139 | 2147 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
2140 | 2148 | # slopes = numpy.delete(slopes,indOut) |
|
|
2141 | 2149 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
2142 | 2150 | # slopes = numpy.delete(slopes,indOut) |
|
|
2143 | 2151 | |
|
|
2144 | 2152 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
2145 | 2153 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
2146 | 2154 | meteorAux[-2] = radialError |
|
|
2147 | 2155 | meteorAux[-3] = radialVelocity |
|
|
2148 | 2156 | |
|
|
2149 | 2157 | #Setting Error |
|
|
2150 | 2158 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2151 | 2159 | if numpy.abs(radialVelocity) > 200: |
|
|
2152 | 2160 | meteorAux[-1] = 15 |
|
|
2153 | 2161 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
2154 | 2162 | elif radialError > radialStdThresh: |
|
|
2155 | 2163 | meteorAux[-1] = 12 |
|
|
2156 | 2164 | |
|
|
2157 | 2165 | listMeteors1.append(meteorAux) |
|
|
2158 | 2166 | return listMeteors1 |
|
|
2159 | 2167 | |
|
|
2160 | 2168 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
2161 | 2169 | |
|
|
2162 | 2170 | #New arrays |
|
|
2163 | 2171 | arrayMeteors = numpy.array(listMeteors) |
|
|
2164 | 2172 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
2165 | 2173 | |
|
|
2166 | 2174 | #Date inclusion |
|
|
2167 | 2175 | # date = re.findall(r'\((.*?)\)', date) |
|
|
2168 | 2176 | # date = date[0].split(',') |
|
|
2169 | 2177 | # date = map(int, date) |
|
|
2170 | 2178 | # |
|
|
2171 | 2179 | # if len(date)<6: |
|
|
2172 | 2180 | # date.append(0) |
|
|
2173 | 2181 | # |
|
|
2174 | 2182 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
2175 | 2183 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
2176 | 2184 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
2177 | 2185 | |
|
|
2178 | 2186 | #Meteor array |
|
|
2179 | 2187 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
2180 | 2188 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
2181 | 2189 | |
|
|
2182 | 2190 | #Parameters Array |
|
|
2183 | 2191 | arrayParameters[:,0] = arrayDate #Date |
|
|
2184 | 2192 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
2185 | 2193 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
2186 | 2194 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
2187 | 2195 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
2188 | 2196 | |
|
|
2189 | 2197 | |
|
|
2190 | 2198 | return arrayParameters |
|
|
2191 | 2199 | |
|
|
2192 | 2200 | class CorrectSMPhases(Operation): |
|
|
2201 | parameters = { | |
|
|
2202 | 'phaseOffsets': global_type_pairsList, | |
|
|
2203 | 'hmin': global_type_float, | |
|
|
2204 | 'hmax': global_type_float, | |
|
|
2205 | 'azimuth': global_type_float, | |
|
|
2206 | 'channelPositions': global_type_pairsList, | |
|
|
2207 | } | |
|
|
2193 | 2208 | |
|
|
2194 | 2209 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
2195 | 2210 | |
|
|
2196 | 2211 | arrayParameters = dataOut.data_param |
|
|
2197 | 2212 | pairsList = [] |
|
|
2198 | 2213 | pairx = (0,1) |
|
|
2199 | 2214 | pairy = (2,3) |
|
|
2200 | 2215 | pairsList.append(pairx) |
|
|
2201 | 2216 | pairsList.append(pairy) |
|
|
2202 | 2217 | jph = numpy.zeros(4) |
|
|
2203 | 2218 | |
|
|
2204 | 2219 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2205 | 2220 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2206 | 2221 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
2207 | 2222 | |
|
|
2208 | 2223 | meteorOps = SMOperations() |
|
|
2209 | 2224 | if channelPositions is None: |
|
|
2210 | 2225 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2211 | 2226 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2212 | 2227 | |
|
|
2213 | 2228 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2214 | 2229 | h = (hmin,hmax) |
|
|
2215 | 2230 | |
|
|
2216 | 2231 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2217 | 2232 | |
|
|
2218 | 2233 | dataOut.data_param = arrayParameters |
|
|
2219 | 2234 | return |
|
|
2220 | 2235 | |
|
|
2221 | 2236 | class SMPhaseCalibration(Operation): |
|
|
2222 | 2237 | |
|
|
2223 | 2238 | __buffer = None |
|
|
2224 | 2239 | |
|
|
2225 | 2240 | __initime = None |
|
|
2226 | 2241 | |
|
|
2227 | 2242 | __dataReady = False |
|
|
2228 | 2243 | |
|
|
2229 | 2244 | __isConfig = False |
|
|
2230 | 2245 | |
|
|
2231 | 2246 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
2232 | 2247 | |
|
|
2233 | 2248 | dataTime = currentTime + paramInterval |
|
|
2234 | 2249 | deltaTime = dataTime - initTime |
|
|
2235 | 2250 | |
|
|
2236 | 2251 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
2237 | 2252 | return True |
|
|
2238 | 2253 | |
|
|
2239 | 2254 | return False |
|
|
2240 | 2255 | |
|
|
2241 | 2256 | def __getGammas(self, pairs, d, phases): |
|
|
2242 | 2257 | gammas = numpy.zeros(2) |
|
|
2243 | 2258 | |
|
|
2244 | 2259 | for i in range(len(pairs)): |
|
|
2245 | 2260 | |
|
|
2246 | 2261 | pairi = pairs[i] |
|
|
2247 | 2262 | |
|
|
2248 | 2263 | phip3 = phases[:,pairi[1]] |
|
|
2249 | 2264 | d3 = d[pairi[1]] |
|
|
2250 | 2265 | phip2 = phases[:,pairi[0]] |
|
|
2251 | 2266 | d2 = d[pairi[0]] |
|
|
2252 | 2267 | #Calculating gamma |
|
|
2253 | 2268 | # jdcos = alp1/(k*d1) |
|
|
2254 | 2269 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
2255 | 2270 | jgamma = -phip2*d3/d2 - phip3 |
|
|
2256 | 2271 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
2257 | 2272 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
2258 | 2273 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
2259 | 2274 | |
|
|
2260 | 2275 | #Revised distribution |
|
|
2261 | 2276 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
2262 | 2277 | |
|
|
2263 | 2278 | #Histogram |
|
|
2264 | 2279 | nBins = 64.0 |
|
|
2265 | 2280 | rmin = -0.5*numpy.pi |
|
|
2266 | 2281 | rmax = 0.5*numpy.pi |
|
|
2267 | 2282 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
2268 | 2283 | |
|
|
2269 | 2284 | meteorsY = phaseHisto[0] |
|
|
2270 | 2285 | phasesX = phaseHisto[1][:-1] |
|
|
2271 | 2286 | width = phasesX[1] - phasesX[0] |
|
|
2272 | 2287 | phasesX += width/2 |
|
|
2273 | 2288 | |
|
|
2274 | 2289 | #Gaussian aproximation |
|
|
2275 | 2290 | bpeak = meteorsY.argmax() |
|
|
2276 | 2291 | peak = meteorsY.max() |
|
|
2277 | 2292 | jmin = bpeak - 5 |
|
|
2278 | 2293 | jmax = bpeak + 5 + 1 |
|
|
2279 | 2294 | |
|
|
2280 | 2295 | if jmin<0: |
|
|
2281 | 2296 | jmin = 0 |
|
|
2282 | 2297 | jmax = 6 |
|
|
2283 | 2298 | elif jmax > meteorsY.size: |
|
|
2284 | 2299 | jmin = meteorsY.size - 6 |
|
|
2285 | 2300 | jmax = meteorsY.size |
|
|
2286 | 2301 | |
|
|
2287 | 2302 | x0 = numpy.array([peak,bpeak,50]) |
|
|
2288 | 2303 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
2289 | 2304 | |
|
|
2290 | 2305 | #Gammas |
|
|
2291 | 2306 | gammas[i] = coeff[0][1] |
|
|
2292 | 2307 | |
|
|
2293 | 2308 | return gammas |
|
|
2294 | 2309 | |
|
|
2295 | 2310 | def __residualFunction(self, coeffs, y, t): |
|
|
2296 | 2311 | |
|
|
2297 | 2312 | return y - self.__gauss_function(t, coeffs) |
|
|
2298 | 2313 | |
|
|
2299 | 2314 | def __gauss_function(self, t, coeffs): |
|
|
2300 | 2315 | |
|
|
2301 | 2316 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
2302 | 2317 | |
|
|
2303 | 2318 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
2304 | 2319 | meteorOps = SMOperations() |
|
|
2305 | 2320 | nchan = 4 |
|
|
2306 | 2321 | pairx = pairsList[0] |
|
|
2307 | 2322 | pairy = pairsList[1] |
|
|
2308 | 2323 | center_xangle = 0 |
|
|
2309 | 2324 | center_yangle = 0 |
|
|
2310 | 2325 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
2311 | 2326 | ntimes = len(range_angle) |
|
|
2312 | 2327 | |
|
|
2313 | 2328 | nstepsx = 20.0 |
|
|
2314 | 2329 | nstepsy = 20.0 |
|
|
2315 | 2330 | |
|
|
2316 | 2331 | for iz in range(ntimes): |
|
|
2317 | 2332 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
2318 | 2333 | max_xangle = range_angle[iz]/2 + center_xangle |
|
|
2319 | 2334 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
2320 | 2335 | max_yangle = range_angle[iz]/2 + center_yangle |
|
|
2321 | 2336 | |
|
|
2322 | 2337 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
2323 | 2338 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
2324 | 2339 | |
|
|
2325 | 2340 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
2326 | 2341 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
2327 | 2342 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
2328 | 2343 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
2329 | 2344 | jph = numpy.zeros(nchan) |
|
|
2330 | 2345 | |
|
|
2331 | 2346 | # Iterations looking for the offset |
|
|
2332 | 2347 | for iy in range(int(nstepsy)): |
|
|
2333 | 2348 | for ix in range(int(nstepsx)): |
|
|
2334 | 2349 | jph[pairy[1]] = alpha_y[iy] |
|
|
2335 | 2350 | jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
2336 | 2351 | |
|
|
2337 | 2352 | jph[pairx[1]] = alpha_x[ix] |
|
|
2338 | 2353 | jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
2339 | 2354 | |
|
|
2340 | 2355 | jph_array[:,ix,iy] = jph |
|
|
2341 | 2356 | |
|
|
2342 | 2357 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
2343 | 2358 | error = meteorsArray1[:,-1] |
|
|
2344 | 2359 | ind1 = numpy.where(error==0)[0] |
|
|
2345 | 2360 | penalty[ix,iy] = ind1.size |
|
|
2346 | 2361 | |
|
|
2347 | 2362 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
2348 | 2363 | phOffset = jph_array[:,i,j] |
|
|
2349 | 2364 | |
|
|
2350 | 2365 | center_xangle = phOffset[pairx[1]] |
|
|
2351 | 2366 | center_yangle = phOffset[pairy[1]] |
|
|
2352 | 2367 | |
|
|
2353 | 2368 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
2354 | 2369 | phOffset = phOffset*180/numpy.pi |
|
|
2355 | 2370 | return phOffset |
|
|
2356 | 2371 | |
|
|
2357 | 2372 | |
|
|
2358 | 2373 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
2359 | 2374 | |
|
|
2360 | 2375 | dataOut.flagNoData = True |
|
|
2361 | 2376 | self.__dataReady = False |
|
|
2362 | 2377 | dataOut.outputInterval = nHours*3600 |
|
|
2363 | 2378 | |
|
|
2364 | 2379 | if self.__isConfig == False: |
|
|
2365 | 2380 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2366 | 2381 | #Get Initial LTC time |
|
|
2367 | 2382 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2368 | 2383 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2369 | 2384 | |
|
|
2370 | 2385 | self.__isConfig = True |
|
|
2371 | 2386 | |
|
|
2372 | 2387 | if self.__buffer is None: |
|
|
2373 | 2388 | self.__buffer = dataOut.data_param.copy() |
|
|
2374 | 2389 | |
|
|
2375 | 2390 | else: |
|
|
2376 | 2391 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2377 | 2392 | |
|
|
2378 | 2393 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2379 | 2394 | |
|
|
2380 | 2395 | if self.__dataReady: |
|
|
2381 | 2396 | dataOut.utctimeInit = self.__initime |
|
|
2382 | 2397 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2383 | 2398 | |
|
|
2384 | 2399 | freq = dataOut.frequency |
|
|
2385 | 2400 | c = dataOut.C #m/s |
|
|
2386 | 2401 | lamb = c/freq |
|
|
2387 | 2402 | k = 2*numpy.pi/lamb |
|
|
2388 | 2403 | azimuth = 0 |
|
|
2389 | 2404 | h = (hmin, hmax) |
|
|
2390 | 2405 | pairs = ((0,1),(2,3)) |
|
|
2391 | 2406 | |
|
|
2392 | 2407 | if channelPositions is None: |
|
|
2393 | 2408 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2394 | 2409 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2395 | 2410 | meteorOps = SMOperations() |
|
|
2396 | 2411 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2397 | 2412 | |
|
|
2398 | 2413 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
2399 | 2414 | |
|
|
2400 | 2415 | meteorsArray = self.__buffer |
|
|
2401 | 2416 | error = meteorsArray[:,-1] |
|
|
2402 | 2417 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
2403 | 2418 | ind1 = numpy.where(boolError)[0] |
|
|
2404 | 2419 | meteorsArray = meteorsArray[ind1,:] |
|
|
2405 | 2420 | meteorsArray[:,-1] = 0 |
|
|
2406 | 2421 | phases = meteorsArray[:,8:12] |
|
|
2407 | 2422 | |
|
|
2408 | 2423 | #Calculate Gammas |
|
|
2409 | 2424 | gammas = self.__getGammas(pairs, distances, phases) |
|
|
2410 | 2425 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
2411 | 2426 | #Calculate Phases |
|
|
2412 | 2427 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
2413 | 2428 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
2414 | 2429 | dataOut.data_output = -phasesOff |
|
|
2415 | 2430 | dataOut.flagNoData = False |
|
|
2416 | 2431 | dataOut.channelList = pairslist0 |
|
|
2417 | 2432 | self.__buffer = None |
|
|
2418 | 2433 | |
|
|
2419 | 2434 | |
|
|
2420 | 2435 | return |
|
|
2421 | 2436 | |
|
|
2422 | 2437 | class SMOperations(): |
|
|
2423 | 2438 | |
|
|
2424 | 2439 | def __init__(self): |
|
|
2425 | 2440 | |
|
|
2426 | 2441 | return |
|
|
2427 | 2442 | |
|
|
2428 | 2443 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
2429 | 2444 | |
|
|
2430 | 2445 | arrayParameters = arrayParameters0.copy() |
|
|
2431 | 2446 | hmin = h[0] |
|
|
2432 | 2447 | hmax = h[1] |
|
|
2433 | 2448 | |
|
|
2434 | 2449 | #Calculate AOA (Error N 3, 4) |
|
|
2435 | 2450 | #JONES ET AL. 1998 |
|
|
2436 | 2451 | AOAthresh = numpy.pi/8 |
|
|
2437 | 2452 | error = arrayParameters[:,-1] |
|
|
2438 | 2453 | phases = -arrayParameters[:,8:12] + jph |
|
|
2439 | 2454 | # phases = numpy.unwrap(phases) |
|
|
2440 | 2455 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
2441 | 2456 | |
|
|
2442 | 2457 | #Calculate Heights (Error N 13 and 14) |
|
|
2443 | 2458 | error = arrayParameters[:,-1] |
|
|
2444 | 2459 | Ranges = arrayParameters[:,1] |
|
|
2445 | 2460 | zenith = arrayParameters[:,4] |
|
|
2446 | 2461 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2447 | 2462 | |
|
|
2448 | 2463 | #----------------------- Get Final data ------------------------------------ |
|
|
2449 | 2464 | # error = arrayParameters[:,-1] |
|
|
2450 | 2465 | # ind1 = numpy.where(error==0)[0] |
|
|
2451 | 2466 | # arrayParameters = arrayParameters[ind1,:] |
|
|
2452 | 2467 | |
|
|
2453 | 2468 | return arrayParameters |
|
|
2454 | 2469 | |
|
|
2455 | 2470 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
2456 | 2471 | |
|
|
2457 | 2472 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
2458 | 2473 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
2459 | 2474 | |
|
|
2460 | 2475 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
2461 | 2476 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
2462 | 2477 | arrayAOA[:,2] = cosDirError |
|
|
2463 | 2478 | |
|
|
2464 | 2479 | azimuthAngle = arrayAOA[:,0] |
|
|
2465 | 2480 | zenithAngle = arrayAOA[:,1] |
|
|
2466 | 2481 | |
|
|
2467 | 2482 | #Setting Error |
|
|
2468 | 2483 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
2469 | 2484 | error[indError] = 0 |
|
|
2470 | 2485 | #Number 3: AOA not fesible |
|
|
2471 | 2486 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
2472 | 2487 | error[indInvalid] = 3 |
|
|
2473 | 2488 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2474 | 2489 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
2475 | 2490 | error[indInvalid] = 4 |
|
|
2476 | 2491 | return arrayAOA, error |
|
|
2477 | 2492 | |
|
|
2478 | 2493 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
2479 | 2494 | |
|
|
2480 | 2495 | #Initializing some variables |
|
|
2481 | 2496 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
2482 | 2497 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
2483 | 2498 | |
|
|
2484 | 2499 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2485 | 2500 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2486 | 2501 | |
|
|
2487 | 2502 | |
|
|
2488 | 2503 | for i in range(2): |
|
|
2489 | 2504 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
2490 | 2505 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
2491 | 2506 | d0 = distances[pairsList[i][0]] |
|
|
2492 | 2507 | d1 = distances[pairsList[i][1]] |
|
|
2493 | 2508 | |
|
|
2494 | 2509 | ph0_aux = ph0 + ph1 |
|
|
2495 | 2510 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
2496 | 2511 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
2497 | 2512 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
2498 | 2513 | #First Estimation |
|
|
2499 | 2514 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
2500 | 2515 | |
|
|
2501 | 2516 | #Most-Accurate Second Estimation |
|
|
2502 | 2517 | phi1_aux = ph0 - ph1 |
|
|
2503 | 2518 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
2504 | 2519 | #Direction Cosine 1 |
|
|
2505 | 2520 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
2506 | 2521 | |
|
|
2507 | 2522 | #Searching the correct Direction Cosine |
|
|
2508 | 2523 | cosdir0_aux = cosdir0[:,i] |
|
|
2509 | 2524 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
2510 | 2525 | #Minimum Distance |
|
|
2511 | 2526 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
2512 | 2527 | indcos = cosDiff.argmin(axis = 1) |
|
|
2513 | 2528 | #Saving Value obtained |
|
|
2514 | 2529 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
2515 | 2530 | |
|
|
2516 | 2531 | return cosdir0, cosdir |
|
|
2517 | 2532 | |
|
|
2518 | 2533 | def __calculateAOA(self, cosdir, azimuth): |
|
|
2519 | 2534 | cosdirX = cosdir[:,0] |
|
|
2520 | 2535 | cosdirY = cosdir[:,1] |
|
|
2521 | 2536 | |
|
|
2522 | 2537 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
2523 | 2538 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
2524 | 2539 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
2525 | 2540 | |
|
|
2526 | 2541 | return angles |
|
|
2527 | 2542 | |
|
|
2528 | 2543 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
2529 | 2544 | |
|
|
2530 | 2545 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
2531 | 2546 | Re = 6371 #Earth Radius |
|
|
2532 | 2547 | heights = numpy.zeros(Ranges.shape) |
|
|
2533 | 2548 | |
|
|
2534 | 2549 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
2535 | 2550 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
2536 | 2551 | |
|
|
2537 | 2552 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
2538 | 2553 | |
|
|
2539 | 2554 | Ri = Ranges + R_aux |
|
|
2540 | 2555 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
2541 | 2556 | |
|
|
2542 | 2557 | #Check if there is a height between 70 and 110 km |
|
|
2543 | 2558 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
2544 | 2559 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
2545 | 2560 | |
|
|
2546 | 2561 | hCorr = hi[ind_h, :] |
|
|
2547 | 2562 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
2548 | 2563 | |
|
|
2549 | 2564 | hCorr = hi[ind_hCorr] |
|
|
2550 | 2565 | heights[ind_h] = hCorr |
|
|
2551 | 2566 | |
|
|
2552 | 2567 | #Setting Error |
|
|
2553 | 2568 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
2554 | 2569 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
2555 | 2570 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
2556 | 2571 | error[indError] = 0 |
|
|
2557 | 2572 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
2558 | 2573 | error[indInvalid2] = 14 |
|
|
2559 | 2574 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
2560 | 2575 | error[indInvalid1] = 13 |
|
|
2561 | 2576 | |
|
|
2562 | 2577 | return heights, error |
|
|
2563 | 2578 | |
|
|
2564 | 2579 | def getPhasePairs(self, channelPositions): |
|
|
2565 | 2580 | chanPos = numpy.array(channelPositions) |
|
|
2566 | 2581 | listOper = list(itertools.combinations(range(5),2)) |
|
|
2567 | 2582 | |
|
|
2568 | 2583 | distances = numpy.zeros(4) |
|
|
2569 | 2584 | axisX = [] |
|
|
2570 | 2585 | axisY = [] |
|
|
2571 | 2586 | distX = numpy.zeros(3) |
|
|
2572 | 2587 | distY = numpy.zeros(3) |
|
|
2573 | 2588 | ix = 0 |
|
|
2574 | 2589 | iy = 0 |
|
|
2575 | 2590 | |
|
|
2576 | 2591 | pairX = numpy.zeros((2,2)) |
|
|
2577 | 2592 | pairY = numpy.zeros((2,2)) |
|
|
2578 | 2593 | |
|
|
2579 | 2594 | for i in range(len(listOper)): |
|
|
2580 | 2595 | pairi = listOper[i] |
|
|
2581 | 2596 | |
|
|
2582 | 2597 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
2583 | 2598 | |
|
|
2584 | 2599 | if posDif[0] == 0: |
|
|
2585 | 2600 | axisY.append(pairi) |
|
|
2586 | 2601 | distY[iy] = posDif[1] |
|
|
2587 | 2602 | iy += 1 |
|
|
2588 | 2603 | elif posDif[1] == 0: |
|
|
2589 | 2604 | axisX.append(pairi) |
|
|
2590 | 2605 | distX[ix] = posDif[0] |
|
|
2591 | 2606 | ix += 1 |
|
|
2592 | 2607 | |
|
|
2593 | 2608 | for i in range(2): |
|
|
2594 | 2609 | if i==0: |
|
|
2595 | 2610 | dist0 = distX |
|
|
2596 | 2611 | axis0 = axisX |
|
|
2597 | 2612 | else: |
|
|
2598 | 2613 | dist0 = distY |
|
|
2599 | 2614 | axis0 = axisY |
|
|
2600 | 2615 | |
|
|
2601 | 2616 | side = numpy.argsort(dist0)[:-1] |
|
|
2602 | 2617 | axis0 = numpy.array(axis0)[side,:] |
|
|
2603 | 2618 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
2604 | 2619 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
2605 | 2620 | side = axis1[axis1 != chanC] |
|
|
2606 | 2621 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
2607 | 2622 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
2608 | 2623 | if diff1<0: |
|
|
2609 | 2624 | chan2 = side[0] |
|
|
2610 | 2625 | d2 = numpy.abs(diff1) |
|
|
2611 | 2626 | chan1 = side[1] |
|
|
2612 | 2627 | d1 = numpy.abs(diff2) |
|
|
2613 | 2628 | else: |
|
|
2614 | 2629 | chan2 = side[1] |
|
|
2615 | 2630 | d2 = numpy.abs(diff2) |
|
|
2616 | 2631 | chan1 = side[0] |
|
|
2617 | 2632 | d1 = numpy.abs(diff1) |
|
|
2618 | 2633 | |
|
|
2619 | 2634 | if i==0: |
|
|
2620 | 2635 | chanCX = chanC |
|
|
2621 | 2636 | chan1X = chan1 |
|
|
2622 | 2637 | chan2X = chan2 |
|
|
2623 | 2638 | distances[0:2] = numpy.array([d1,d2]) |
|
|
2624 | 2639 | else: |
|
|
2625 | 2640 | chanCY = chanC |
|
|
2626 | 2641 | chan1Y = chan1 |
|
|
2627 | 2642 | chan2Y = chan2 |
|
|
2628 | 2643 | distances[2:4] = numpy.array([d1,d2]) |
|
|
2629 | 2644 | # axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
2630 | 2645 | # |
|
|
2631 | 2646 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
2632 | 2647 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
2633 | 2648 | # |
|
|
2634 | 2649 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
2635 | 2650 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
2636 | 2651 | # channel25X = int(pairX[0,ind25X]) |
|
|
2637 | 2652 | # channel20X = int(pairX[1,ind20X]) |
|
|
2638 | 2653 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
2639 | 2654 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
2640 | 2655 | # channel25Y = int(pairY[0,ind25Y]) |
|
|
2641 | 2656 | # channel20Y = int(pairY[1,ind20Y]) |
|
|
2642 | 2657 | |
|
|
2643 | 2658 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
2644 | 2659 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
2645 | 2660 | |
|
|
2646 | 2661 | return pairslist, distances |
|
|
2647 | 2662 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
2648 | 2663 | # |
|
|
2649 | 2664 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
2650 | 2665 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
2651 | 2666 | # |
|
|
2652 | 2667 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
2653 | 2668 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
2654 | 2669 | # arrayAOA[:,2] = cosDirError |
|
|
2655 | 2670 | # |
|
|
2656 | 2671 | # azimuthAngle = arrayAOA[:,0] |
|
|
2657 | 2672 | # zenithAngle = arrayAOA[:,1] |
|
|
2658 | 2673 | # |
|
|
2659 | 2674 | # #Setting Error |
|
|
2660 | 2675 | # #Number 3: AOA not fesible |
|
|
2661 | 2676 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
2662 | 2677 | # error[indInvalid] = 3 |
|
|
2663 | 2678 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2664 | 2679 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
2665 | 2680 | # error[indInvalid] = 4 |
|
|
2666 | 2681 | # return arrayAOA, error |
|
|
2667 | 2682 | # |
|
|
2668 | 2683 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
2669 | 2684 | # |
|
|
2670 | 2685 | # #Initializing some variables |
|
|
2671 | 2686 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
2672 | 2687 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
2673 | 2688 | # |
|
|
2674 | 2689 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2675 | 2690 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2676 | 2691 | # |
|
|
2677 | 2692 | # |
|
|
2678 | 2693 | # for i in range(2): |
|
|
2679 | 2694 | # #First Estimation |
|
|
2680 | 2695 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
2681 | 2696 | # #Dealias |
|
|
2682 | 2697 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
2683 | 2698 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
|
2684 | 2699 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
2685 | 2700 | # phi0_aux[indcsi] += 2*numpy.pi |
|
|
2686 | 2701 | # #Direction Cosine 0 |
|
|
2687 | 2702 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
2688 | 2703 | # |
|
|
2689 | 2704 | # #Most-Accurate Second Estimation |
|
|
2690 | 2705 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
2691 | 2706 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
2692 | 2707 | # #Direction Cosine 1 |
|
|
2693 | 2708 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
2694 | 2709 | # |
|
|
2695 | 2710 | # #Searching the correct Direction Cosine |
|
|
2696 | 2711 | # cosdir0_aux = cosdir0[:,i] |
|
|
2697 | 2712 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
2698 | 2713 | # #Minimum Distance |
|
|
2699 | 2714 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
2700 | 2715 | # indcos = cosDiff.argmin(axis = 1) |
|
|
2701 | 2716 | # #Saving Value obtained |
|
|
2702 | 2717 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
2703 | 2718 | # |
|
|
2704 | 2719 | # return cosdir0, cosdir |
|
|
2705 | 2720 | # |
|
|
2706 | 2721 | # def __calculateAOA(self, cosdir, azimuth): |
|
|
2707 | 2722 | # cosdirX = cosdir[:,0] |
|
|
2708 | 2723 | # cosdirY = cosdir[:,1] |
|
|
2709 | 2724 | # |
|
|
2710 | 2725 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
2711 | 2726 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
2712 | 2727 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
2713 | 2728 | # |
|
|
2714 | 2729 | # return angles |
|
|
2715 | 2730 | # |
|
|
2716 | 2731 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
2717 | 2732 | # |
|
|
2718 | 2733 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
|
2719 | 2734 | # Re = 6371 #Earth Radius |
|
|
2720 | 2735 | # heights = numpy.zeros(Ranges.shape) |
|
|
2721 | 2736 | # |
|
|
2722 | 2737 | # R_aux = numpy.array([0,1,2])*Ramb |
|
|
2723 | 2738 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
|
2724 | 2739 | # |
|
|
2725 | 2740 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
|
2726 | 2741 | # |
|
|
2727 | 2742 | # Ri = Ranges + R_aux |
|
|
2728 | 2743 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
2729 | 2744 | # |
|
|
2730 | 2745 | # #Check if there is a height between 70 and 110 km |
|
|
2731 | 2746 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
2732 | 2747 | # ind_h = numpy.where(h_bool == 1)[0] |
|
|
2733 | 2748 | # |
|
|
2734 | 2749 | # hCorr = hi[ind_h, :] |
|
|
2735 | 2750 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
2736 | 2751 | # |
|
|
2737 | 2752 | # hCorr = hi[ind_hCorr] |
|
|
2738 | 2753 | # heights[ind_h] = hCorr |
|
|
2739 | 2754 | # |
|
|
2740 | 2755 | # #Setting Error |
|
|
2741 | 2756 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
2742 | 2757 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
2743 | 2758 | # |
|
|
2744 | 2759 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
2745 | 2760 | # error[indInvalid2] = 14 |
|
|
2746 | 2761 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
2747 | 2762 | # error[indInvalid1] = 13 |
|
|
2748 | 2763 | # |
|
|
2749 | 2764 | # return heights, error |
| @@ -1,904 +1,910 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | |
|
|
3 | 3 | from jroproc_base import ProcessingUnit, Operation |
|
|
4 | 4 | from schainpy.model.data.jrodata import Spectra |
|
|
5 | 5 | from schainpy.model.data.jrodata import hildebrand_sekhon |
|
|
6 | 6 | |
|
|
7 | 7 | class SpectraProc(ProcessingUnit): |
|
|
8 | 8 | |
|
|
9 | 9 | def __init__(self, **kwargs): |
|
|
10 | 10 | |
|
|
11 | 11 | ProcessingUnit.__init__(self, **kwargs) |
|
|
12 | 12 | |
|
|
13 | 13 | self.buffer = None |
|
|
14 | 14 | self.firstdatatime = None |
|
|
15 | 15 | self.profIndex = 0 |
|
|
16 | 16 | self.dataOut = Spectra() |
|
|
17 | 17 | self.id_min = None |
|
|
18 | 18 | self.id_max = None |
|
|
19 | 19 | |
|
|
20 | 20 | def __updateSpecFromVoltage(self): |
|
|
21 | 21 | |
|
|
22 | 22 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
23 | 23 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
24 | 24 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
25 | 25 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
26 | 26 | |
|
|
27 | 27 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
28 | 28 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
29 | 29 | self.dataOut.channelList = self.dataIn.channelList |
|
|
30 | 30 | self.dataOut.heightList = self.dataIn.heightList |
|
|
31 | 31 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
32 | 32 | |
|
|
33 | 33 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
34 | 34 | self.dataOut.nCode = self.dataIn.nCode |
|
|
35 | 35 | self.dataOut.code = self.dataIn.code |
|
|
36 | 36 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
37 | 37 | |
|
|
38 | 38 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
39 | 39 | self.dataOut.utctime = self.firstdatatime |
|
|
40 | 40 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
41 | 41 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
42 | 42 | self.dataOut.flagShiftFFT = False |
|
|
43 | 43 | |
|
|
44 | 44 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
45 | 45 | self.dataOut.nIncohInt = 1 |
|
|
46 | 46 | |
|
|
47 | 47 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
48 | 48 | |
|
|
49 | 49 | self.dataOut.frequency = self.dataIn.frequency |
|
|
50 | 50 | self.dataOut.realtime = self.dataIn.realtime |
|
|
51 | 51 | |
|
|
52 | 52 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
53 | 53 | self.dataOut.zenith = self.dataIn.zenith |
|
|
54 | 54 | |
|
|
55 | 55 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
56 | 56 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
57 | 57 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
58 | 58 | |
|
|
59 | 59 | def __getFft(self): |
|
|
60 | 60 | """ |
|
|
61 | 61 | Convierte valores de Voltaje a Spectra |
|
|
62 | 62 | |
|
|
63 | 63 | Affected: |
|
|
64 | 64 | self.dataOut.data_spc |
|
|
65 | 65 | self.dataOut.data_cspc |
|
|
66 | 66 | self.dataOut.data_dc |
|
|
67 | 67 | self.dataOut.heightList |
|
|
68 | 68 | self.profIndex |
|
|
69 | 69 | self.buffer |
|
|
70 | 70 | self.dataOut.flagNoData |
|
|
71 | 71 | """ |
|
|
72 | 72 | fft_volt = numpy.fft.fft(self.buffer,n=self.dataOut.nFFTPoints,axis=1) |
|
|
73 | 73 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
|
|
74 | 74 | dc = fft_volt[:,0,:] |
|
|
75 | 75 | |
|
|
76 | 76 | #calculo de self-spectra |
|
|
77 | 77 | fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,)) |
|
|
78 | 78 | spc = fft_volt * numpy.conjugate(fft_volt) |
|
|
79 | 79 | spc = spc.real |
|
|
80 | 80 | |
|
|
81 | 81 | blocksize = 0 |
|
|
82 | 82 | blocksize += dc.size |
|
|
83 | 83 | blocksize += spc.size |
|
|
84 | 84 | |
|
|
85 | 85 | cspc = None |
|
|
86 | 86 | pairIndex = 0 |
|
|
87 | 87 | if self.dataOut.pairsList != None: |
|
|
88 | 88 | #calculo de cross-spectra |
|
|
89 | 89 | cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
|
|
90 | 90 | for pair in self.dataOut.pairsList: |
|
|
91 | 91 | if pair[0] not in self.dataOut.channelList: |
|
|
92 | 92 | raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
|
|
93 | 93 | if pair[1] not in self.dataOut.channelList: |
|
|
94 | 94 | raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
|
|
95 | 95 | |
|
|
96 | 96 | cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:]) |
|
|
97 | 97 | pairIndex += 1 |
|
|
98 | 98 | blocksize += cspc.size |
|
|
99 | 99 | |
|
|
100 | 100 | self.dataOut.data_spc = spc |
|
|
101 | 101 | self.dataOut.data_cspc = cspc |
|
|
102 | 102 | self.dataOut.data_dc = dc |
|
|
103 | 103 | self.dataOut.blockSize = blocksize |
|
|
104 | 104 | self.dataOut.flagShiftFFT = True |
|
|
105 | 105 | |
|
|
106 | 106 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None): |
|
|
107 | 107 | |
|
|
108 | 108 | self.dataOut.flagNoData = True |
|
|
109 | 109 | |
|
|
110 | 110 | if self.dataIn.type == "Spectra": |
|
|
111 | 111 | self.dataOut.copy(self.dataIn) |
|
|
112 | 112 | # self.__selectPairs(pairsList) |
|
|
113 | 113 | return True |
|
|
114 | 114 | |
|
|
115 | 115 | if self.dataIn.type == "Voltage": |
|
|
116 | 116 | |
|
|
117 | 117 | if nFFTPoints == None: |
|
|
118 | 118 | raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" |
|
|
119 | 119 | |
|
|
120 | 120 | if nProfiles == None: |
|
|
121 | 121 | nProfiles = nFFTPoints |
|
|
122 | 122 | |
|
|
123 | 123 | if ippFactor == None: |
|
|
124 | 124 | ippFactor = 1 |
|
|
125 | 125 | |
|
|
126 | 126 | self.dataOut.ippFactor = ippFactor |
|
|
127 | 127 | |
|
|
128 | 128 | self.dataOut.nFFTPoints = nFFTPoints |
|
|
129 | 129 | self.dataOut.pairsList = pairsList |
|
|
130 | 130 | |
|
|
131 | 131 | if self.buffer is None: |
|
|
132 | 132 | self.buffer = numpy.zeros( (self.dataIn.nChannels, |
|
|
133 | 133 | nProfiles, |
|
|
134 | 134 | self.dataIn.nHeights), |
|
|
135 | 135 | dtype='complex') |
|
|
136 | 136 | |
|
|
137 | 137 | if self.dataIn.flagDataAsBlock: |
|
|
138 | 138 | #data dimension: [nChannels, nProfiles, nSamples] |
|
|
139 | 139 | nVoltProfiles = self.dataIn.data.shape[1] |
|
|
140 | 140 | # nVoltProfiles = self.dataIn.nProfiles |
|
|
141 | 141 | |
|
|
142 | 142 | if nVoltProfiles == nProfiles: |
|
|
143 | 143 | self.buffer = self.dataIn.data.copy() |
|
|
144 | 144 | self.profIndex = nVoltProfiles |
|
|
145 | 145 | |
|
|
146 | 146 | elif nVoltProfiles < nProfiles: |
|
|
147 | 147 | |
|
|
148 | 148 | if self.profIndex == 0: |
|
|
149 | 149 | self.id_min = 0 |
|
|
150 | 150 | self.id_max = nVoltProfiles |
|
|
151 | 151 | |
|
|
152 | 152 | self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data |
|
|
153 | 153 | self.profIndex += nVoltProfiles |
|
|
154 | 154 | self.id_min += nVoltProfiles |
|
|
155 | 155 | self.id_max += nVoltProfiles |
|
|
156 | 156 | else: |
|
|
157 | 157 | raise ValueError, "The type object %s has %d profiles, it should just has %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles) |
|
|
158 | 158 | self.dataOut.flagNoData = True |
|
|
159 | 159 | return 0 |
|
|
160 | 160 | else: |
|
|
161 | 161 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() |
|
|
162 | 162 | self.profIndex += 1 |
|
|
163 | 163 | |
|
|
164 | 164 | if self.firstdatatime == None: |
|
|
165 | 165 | self.firstdatatime = self.dataIn.utctime |
|
|
166 | 166 | |
|
|
167 | 167 | if self.profIndex == nProfiles: |
|
|
168 | 168 | self.__updateSpecFromVoltage() |
|
|
169 | 169 | self.__getFft() |
|
|
170 | 170 | |
|
|
171 | 171 | self.dataOut.flagNoData = False |
|
|
172 | 172 | self.firstdatatime = None |
|
|
173 | 173 | self.profIndex = 0 |
|
|
174 | 174 | |
|
|
175 | 175 | return True |
|
|
176 | 176 | |
|
|
177 | 177 | raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type) |
|
|
178 | 178 | |
|
|
179 | 179 | def __selectPairs(self, pairsList): |
|
|
180 | 180 | |
|
|
181 | 181 | if channelList == None: |
|
|
182 | 182 | return |
|
|
183 | 183 | |
|
|
184 | 184 | pairsIndexListSelected = [] |
|
|
185 | 185 | |
|
|
186 | 186 | for thisPair in pairsList: |
|
|
187 | 187 | |
|
|
188 | 188 | if thisPair not in self.dataOut.pairsList: |
|
|
189 | 189 | continue |
|
|
190 | 190 | |
|
|
191 | 191 | pairIndex = self.dataOut.pairsList.index(thisPair) |
|
|
192 | 192 | |
|
|
193 | 193 | pairsIndexListSelected.append(pairIndex) |
|
|
194 | 194 | |
|
|
195 | 195 | if not pairsIndexListSelected: |
|
|
196 | 196 | self.dataOut.data_cspc = None |
|
|
197 | 197 | self.dataOut.pairsList = [] |
|
|
198 | 198 | return |
|
|
199 | 199 | |
|
|
200 | 200 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
201 | 201 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
|
|
202 | 202 | |
|
|
203 | 203 | return |
|
|
204 | 204 | |
|
|
205 | 205 | def __selectPairsByChannel(self, channelList=None): |
|
|
206 | 206 | |
|
|
207 | 207 | if channelList == None: |
|
|
208 | 208 | return |
|
|
209 | 209 | |
|
|
210 | 210 | pairsIndexListSelected = [] |
|
|
211 | 211 | for pairIndex in self.dataOut.pairsIndexList: |
|
|
212 | 212 | #First pair |
|
|
213 | 213 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
|
214 | 214 | continue |
|
|
215 | 215 | #Second pair |
|
|
216 | 216 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
|
217 | 217 | continue |
|
|
218 | 218 | |
|
|
219 | 219 | pairsIndexListSelected.append(pairIndex) |
|
|
220 | 220 | |
|
|
221 | 221 | if not pairsIndexListSelected: |
|
|
222 | 222 | self.dataOut.data_cspc = None |
|
|
223 | 223 | self.dataOut.pairsList = [] |
|
|
224 | 224 | return |
|
|
225 | 225 | |
|
|
226 | 226 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
227 | 227 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
|
|
228 | 228 | |
|
|
229 | 229 | return |
|
|
230 | 230 | |
|
|
231 | 231 | def selectChannels(self, channelList): |
|
|
232 | 232 | |
|
|
233 | 233 | channelIndexList = [] |
|
|
234 | 234 | |
|
|
235 | 235 | for channel in channelList: |
|
|
236 | 236 | if channel not in self.dataOut.channelList: |
|
|
237 | 237 | raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList)) |
|
|
238 | 238 | |
|
|
239 | 239 | index = self.dataOut.channelList.index(channel) |
|
|
240 | 240 | channelIndexList.append(index) |
|
|
241 | 241 | |
|
|
242 | 242 | self.selectChannelsByIndex(channelIndexList) |
|
|
243 | 243 | |
|
|
244 | 244 | def selectChannelsByIndex(self, channelIndexList): |
|
|
245 | 245 | """ |
|
|
246 | 246 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
247 | 247 | |
|
|
248 | 248 | Input: |
|
|
249 | 249 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
250 | 250 | |
|
|
251 | 251 | Affected: |
|
|
252 | 252 | self.dataOut.data_spc |
|
|
253 | 253 | self.dataOut.channelIndexList |
|
|
254 | 254 | self.dataOut.nChannels |
|
|
255 | 255 | |
|
|
256 | 256 | Return: |
|
|
257 | 257 | None |
|
|
258 | 258 | """ |
|
|
259 | 259 | |
|
|
260 | 260 | for channelIndex in channelIndexList: |
|
|
261 | 261 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
262 | 262 | raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList) |
|
|
263 | 263 | |
|
|
264 | 264 | # nChannels = len(channelIndexList) |
|
|
265 | 265 | |
|
|
266 | 266 | data_spc = self.dataOut.data_spc[channelIndexList,:] |
|
|
267 | 267 | data_dc = self.dataOut.data_dc[channelIndexList,:] |
|
|
268 | 268 | |
|
|
269 | 269 | self.dataOut.data_spc = data_spc |
|
|
270 | 270 | self.dataOut.data_dc = data_dc |
|
|
271 | 271 | |
|
|
272 | 272 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
273 | 273 | # self.dataOut.nChannels = nChannels |
|
|
274 | 274 | |
|
|
275 | 275 | self.__selectPairsByChannel(self.dataOut.channelList) |
|
|
276 | 276 | |
|
|
277 | 277 | return 1 |
|
|
278 | 278 | |
|
|
279 | 279 | def selectHeights(self, minHei, maxHei): |
|
|
280 | 280 | """ |
|
|
281 | 281 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
282 | 282 | minHei <= height <= maxHei |
|
|
283 | 283 | |
|
|
284 | 284 | Input: |
|
|
285 | 285 | minHei : valor minimo de altura a considerar |
|
|
286 | 286 | maxHei : valor maximo de altura a considerar |
|
|
287 | 287 | |
|
|
288 | 288 | Affected: |
|
|
289 | 289 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
290 | 290 | |
|
|
291 | 291 | Return: |
|
|
292 | 292 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
293 | 293 | """ |
|
|
294 | 294 | |
|
|
295 | 295 | if (minHei > maxHei): |
|
|
296 | 296 | raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei) |
|
|
297 | 297 | |
|
|
298 | 298 | if (minHei < self.dataOut.heightList[0]): |
|
|
299 | 299 | minHei = self.dataOut.heightList[0] |
|
|
300 | 300 | |
|
|
301 | 301 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
302 | 302 | maxHei = self.dataOut.heightList[-1] |
|
|
303 | 303 | |
|
|
304 | 304 | minIndex = 0 |
|
|
305 | 305 | maxIndex = 0 |
|
|
306 | 306 | heights = self.dataOut.heightList |
|
|
307 | 307 | |
|
|
308 | 308 | inda = numpy.where(heights >= minHei) |
|
|
309 | 309 | indb = numpy.where(heights <= maxHei) |
|
|
310 | 310 | |
|
|
311 | 311 | try: |
|
|
312 | 312 | minIndex = inda[0][0] |
|
|
313 | 313 | except: |
|
|
314 | 314 | minIndex = 0 |
|
|
315 | 315 | |
|
|
316 | 316 | try: |
|
|
317 | 317 | maxIndex = indb[0][-1] |
|
|
318 | 318 | except: |
|
|
319 | 319 | maxIndex = len(heights) |
|
|
320 | 320 | |
|
|
321 | 321 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
322 | 322 | |
|
|
323 | 323 | return 1 |
|
|
324 | 324 | |
|
|
325 | 325 | def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None): |
|
|
326 | 326 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
327 | 327 | |
|
|
328 | 328 | if hei_ref != None: |
|
|
329 | 329 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
330 | 330 | |
|
|
331 | 331 | minIndex = min(newheis[0]) |
|
|
332 | 332 | maxIndex = max(newheis[0]) |
|
|
333 | 333 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
334 | 334 | heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
335 | 335 | |
|
|
336 | 336 | # determina indices |
|
|
337 | 337 | nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0])) |
|
|
338 | 338 | avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0)) |
|
|
339 | 339 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
|
|
340 | 340 | beacon_heiIndexList = [] |
|
|
341 | 341 | for val in avg_dB.tolist(): |
|
|
342 | 342 | if val >= beacon_dB[0]: |
|
|
343 | 343 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
|
|
344 | 344 | |
|
|
345 | 345 | #data_spc = data_spc[:,:,beacon_heiIndexList] |
|
|
346 | 346 | data_cspc = None |
|
|
347 | 347 | if self.dataOut.data_cspc is not None: |
|
|
348 | 348 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
349 | 349 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] |
|
|
350 | 350 | |
|
|
351 | 351 | data_dc = None |
|
|
352 | 352 | if self.dataOut.data_dc is not None: |
|
|
353 | 353 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
354 | 354 | #data_dc = data_dc[:,beacon_heiIndexList] |
|
|
355 | 355 | |
|
|
356 | 356 | self.dataOut.data_spc = data_spc |
|
|
357 | 357 | self.dataOut.data_cspc = data_cspc |
|
|
358 | 358 | self.dataOut.data_dc = data_dc |
|
|
359 | 359 | self.dataOut.heightList = heightList |
|
|
360 | 360 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
|
361 | 361 | |
|
|
362 | 362 | return 1 |
|
|
363 | 363 | |
|
|
364 | 364 | |
|
|
365 | 365 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
366 | 366 | """ |
|
|
367 | 367 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
368 | 368 | minIndex <= index <= maxIndex |
|
|
369 | 369 | |
|
|
370 | 370 | Input: |
|
|
371 | 371 | minIndex : valor de indice minimo de altura a considerar |
|
|
372 | 372 | maxIndex : valor de indice maximo de altura a considerar |
|
|
373 | 373 | |
|
|
374 | 374 | Affected: |
|
|
375 | 375 | self.dataOut.data_spc |
|
|
376 | 376 | self.dataOut.data_cspc |
|
|
377 | 377 | self.dataOut.data_dc |
|
|
378 | 378 | self.dataOut.heightList |
|
|
379 | 379 | |
|
|
380 | 380 | Return: |
|
|
381 | 381 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
382 | 382 | """ |
|
|
383 | 383 | |
|
|
384 | 384 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
385 | 385 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
386 | 386 | |
|
|
387 | 387 | if (maxIndex >= self.dataOut.nHeights): |
|
|
388 | 388 | maxIndex = self.dataOut.nHeights-1 |
|
|
389 | 389 | |
|
|
390 | 390 | #Spectra |
|
|
391 | 391 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
392 | 392 | |
|
|
393 | 393 | data_cspc = None |
|
|
394 | 394 | if self.dataOut.data_cspc is not None: |
|
|
395 | 395 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
396 | 396 | |
|
|
397 | 397 | data_dc = None |
|
|
398 | 398 | if self.dataOut.data_dc is not None: |
|
|
399 | 399 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
400 | 400 | |
|
|
401 | 401 | self.dataOut.data_spc = data_spc |
|
|
402 | 402 | self.dataOut.data_cspc = data_cspc |
|
|
403 | 403 | self.dataOut.data_dc = data_dc |
|
|
404 | 404 | |
|
|
405 | 405 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
406 | 406 | |
|
|
407 | 407 | return 1 |
|
|
408 | 408 | |
|
|
409 | 409 | def removeDC(self, mode = 2): |
|
|
410 | 410 | jspectra = self.dataOut.data_spc |
|
|
411 | 411 | jcspectra = self.dataOut.data_cspc |
|
|
412 | 412 | |
|
|
413 | 413 | |
|
|
414 | 414 | num_chan = jspectra.shape[0] |
|
|
415 | 415 | num_hei = jspectra.shape[2] |
|
|
416 | 416 | |
|
|
417 | 417 | if jcspectra is not None: |
|
|
418 | 418 | jcspectraExist = True |
|
|
419 | 419 | num_pairs = jcspectra.shape[0] |
|
|
420 | 420 | else: jcspectraExist = False |
|
|
421 | 421 | |
|
|
422 | 422 | freq_dc = jspectra.shape[1]/2 |
|
|
423 | 423 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc |
|
|
424 | 424 | |
|
|
425 | 425 | if ind_vel[0]<0: |
|
|
426 | 426 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof |
|
|
427 | 427 | |
|
|
428 | 428 | if mode == 1: |
|
|
429 | 429 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION |
|
|
430 | 430 | |
|
|
431 | 431 | if jcspectraExist: |
|
|
432 | 432 | jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 |
|
|
433 | 433 | |
|
|
434 | 434 | if mode == 2: |
|
|
435 | 435 | |
|
|
436 | 436 | vel = numpy.array([-2,-1,1,2]) |
|
|
437 | 437 | xx = numpy.zeros([4,4]) |
|
|
438 | 438 | |
|
|
439 | 439 | for fil in range(4): |
|
|
440 | 440 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) |
|
|
441 | 441 | |
|
|
442 | 442 | xx_inv = numpy.linalg.inv(xx) |
|
|
443 | 443 | xx_aux = xx_inv[0,:] |
|
|
444 | 444 | |
|
|
445 | 445 | for ich in range(num_chan): |
|
|
446 | 446 | yy = jspectra[ich,ind_vel,:] |
|
|
447 | 447 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
448 | 448 | |
|
|
449 | 449 | junkid = jspectra[ich,freq_dc,:]<=0 |
|
|
450 | 450 | cjunkid = sum(junkid) |
|
|
451 | 451 | |
|
|
452 | 452 | if cjunkid.any(): |
|
|
453 | 453 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 |
|
|
454 | 454 | |
|
|
455 | 455 | if jcspectraExist: |
|
|
456 | 456 | for ip in range(num_pairs): |
|
|
457 | 457 | yy = jcspectra[ip,ind_vel,:] |
|
|
458 | 458 | jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
459 | 459 | |
|
|
460 | 460 | |
|
|
461 | 461 | self.dataOut.data_spc = jspectra |
|
|
462 | 462 | self.dataOut.data_cspc = jcspectra |
|
|
463 | 463 | |
|
|
464 | 464 | return 1 |
|
|
465 | 465 | |
|
|
466 | 466 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): |
|
|
467 | 467 | |
|
|
468 | 468 | jspectra = self.dataOut.data_spc |
|
|
469 | 469 | jcspectra = self.dataOut.data_cspc |
|
|
470 | 470 | jnoise = self.dataOut.getNoise() |
|
|
471 | 471 | num_incoh = self.dataOut.nIncohInt |
|
|
472 | 472 | |
|
|
473 | 473 | num_channel = jspectra.shape[0] |
|
|
474 | 474 | num_prof = jspectra.shape[1] |
|
|
475 | 475 | num_hei = jspectra.shape[2] |
|
|
476 | 476 | |
|
|
477 | 477 | #hei_interf |
|
|
478 | 478 | if hei_interf is None: |
|
|
479 | 479 | count_hei = num_hei/2 #Como es entero no importa |
|
|
480 | 480 | hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei |
|
|
481 | 481 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
482 | 482 | #nhei_interf |
|
|
483 | 483 | if (nhei_interf == None): |
|
|
484 | 484 | nhei_interf = 5 |
|
|
485 | 485 | if (nhei_interf < 1): |
|
|
486 | 486 | nhei_interf = 1 |
|
|
487 | 487 | if (nhei_interf > count_hei): |
|
|
488 | 488 | nhei_interf = count_hei |
|
|
489 | 489 | if (offhei_interf == None): |
|
|
490 | 490 | offhei_interf = 0 |
|
|
491 | 491 | |
|
|
492 | 492 | ind_hei = range(num_hei) |
|
|
493 | 493 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
494 | 494 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
495 | 495 | mask_prof = numpy.asarray(range(num_prof)) |
|
|
496 | 496 | num_mask_prof = mask_prof.size |
|
|
497 | 497 | comp_mask_prof = [0, num_prof/2] |
|
|
498 | 498 | |
|
|
499 | 499 | |
|
|
500 | 500 | #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
501 | 501 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
502 | 502 | jnoise = numpy.nan |
|
|
503 | 503 | noise_exist = jnoise[0] < numpy.Inf |
|
|
504 | 504 | |
|
|
505 | 505 | #Subrutina de Remocion de la Interferencia |
|
|
506 | 506 | for ich in range(num_channel): |
|
|
507 | 507 | #Se ordena los espectros segun su potencia (menor a mayor) |
|
|
508 | 508 | power = jspectra[ich,mask_prof,:] |
|
|
509 | 509 | power = power[:,hei_interf] |
|
|
510 | 510 | power = power.sum(axis = 0) |
|
|
511 | 511 | psort = power.ravel().argsort() |
|
|
512 | 512 | |
|
|
513 | 513 | #Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
514 | 514 | junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
515 | 515 | |
|
|
516 | 516 | if noise_exist: |
|
|
517 | 517 | # tmp_noise = jnoise[ich] / num_prof |
|
|
518 | 518 | tmp_noise = jnoise[ich] |
|
|
519 | 519 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
520 | 520 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
521 | 521 | |
|
|
522 | 522 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf |
|
|
523 | 523 | jspc_interf = jspc_interf.transpose() |
|
|
524 | 524 | #Calculando el espectro de interferencia promedio |
|
|
525 | 525 | noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
526 | 526 | noiseid = noiseid[0] |
|
|
527 | 527 | cnoiseid = noiseid.size |
|
|
528 | 528 | interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
529 | 529 | interfid = interfid[0] |
|
|
530 | 530 | cinterfid = interfid.size |
|
|
531 | 531 | |
|
|
532 | 532 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 |
|
|
533 | 533 | |
|
|
534 | 534 | #Expandiendo los perfiles a limpiar |
|
|
535 | 535 | if (cinterfid > 0): |
|
|
536 | 536 | new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof |
|
|
537 | 537 | new_interfid = numpy.asarray(new_interfid) |
|
|
538 | 538 | new_interfid = {x for x in new_interfid} |
|
|
539 | 539 | new_interfid = numpy.array(list(new_interfid)) |
|
|
540 | 540 | new_cinterfid = new_interfid.size |
|
|
541 | 541 | else: new_cinterfid = 0 |
|
|
542 | 542 | |
|
|
543 | 543 | for ip in range(new_cinterfid): |
|
|
544 | 544 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() |
|
|
545 | 545 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] |
|
|
546 | 546 | |
|
|
547 | 547 | |
|
|
548 | 548 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices |
|
|
549 | 549 | |
|
|
550 | 550 | #Removiendo la interferencia del punto de mayor interferencia |
|
|
551 | 551 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
552 | 552 | maxid = ListAux.index(max(ListAux)) |
|
|
553 | 553 | |
|
|
554 | 554 | |
|
|
555 | 555 | if cinterfid > 0: |
|
|
556 | 556 | for ip in range(cinterfid*(interf == 2) - 1): |
|
|
557 | 557 | ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() |
|
|
558 | 558 | cind = len(ind) |
|
|
559 | 559 | |
|
|
560 | 560 | if (cind > 0): |
|
|
561 | 561 | jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) |
|
|
562 | 562 | |
|
|
563 | 563 | ind = numpy.array([-2,-1,1,2]) |
|
|
564 | 564 | xx = numpy.zeros([4,4]) |
|
|
565 | 565 | |
|
|
566 | 566 | for id1 in range(4): |
|
|
567 | 567 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
568 | 568 | |
|
|
569 | 569 | xx_inv = numpy.linalg.inv(xx) |
|
|
570 | 570 | xx = xx_inv[:,0] |
|
|
571 | 571 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
572 | 572 | yy = jspectra[ich,mask_prof[ind],:] |
|
|
573 | 573 | jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
574 | 574 | |
|
|
575 | 575 | |
|
|
576 | 576 | indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero() |
|
|
577 | 577 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) |
|
|
578 | 578 | |
|
|
579 | 579 | #Remocion de Interferencia en el Cross Spectra |
|
|
580 | 580 | if jcspectra is None: return jspectra, jcspectra |
|
|
581 | 581 | num_pairs = jcspectra.size/(num_prof*num_hei) |
|
|
582 | 582 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
583 | 583 | |
|
|
584 | 584 | for ip in range(num_pairs): |
|
|
585 | 585 | |
|
|
586 | 586 | #------------------------------------------- |
|
|
587 | 587 | |
|
|
588 | 588 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) |
|
|
589 | 589 | cspower = cspower[:,hei_interf] |
|
|
590 | 590 | cspower = cspower.sum(axis = 0) |
|
|
591 | 591 | |
|
|
592 | 592 | cspsort = cspower.ravel().argsort() |
|
|
593 | 593 | junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
594 | 594 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
595 | 595 | jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf |
|
|
596 | 596 | |
|
|
597 | 597 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
598 | 598 | |
|
|
599 | 599 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
600 | 600 | median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
601 | 601 | junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) |
|
|
602 | 602 | |
|
|
603 | 603 | for iprof in range(num_prof): |
|
|
604 | 604 | ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort() |
|
|
605 | 605 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]] |
|
|
606 | 606 | |
|
|
607 | 607 | #Removiendo la Interferencia |
|
|
608 | 608 | jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf |
|
|
609 | 609 | |
|
|
610 | 610 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
611 | 611 | maxid = ListAux.index(max(ListAux)) |
|
|
612 | 612 | |
|
|
613 | 613 | ind = numpy.array([-2,-1,1,2]) |
|
|
614 | 614 | xx = numpy.zeros([4,4]) |
|
|
615 | 615 | |
|
|
616 | 616 | for id1 in range(4): |
|
|
617 | 617 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
618 | 618 | |
|
|
619 | 619 | xx_inv = numpy.linalg.inv(xx) |
|
|
620 | 620 | xx = xx_inv[:,0] |
|
|
621 | 621 | |
|
|
622 | 622 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
623 | 623 | yy = jcspectra[ip,mask_prof[ind],:] |
|
|
624 | 624 | jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
625 | 625 | |
|
|
626 | 626 | #Guardar Resultados |
|
|
627 | 627 | self.dataOut.data_spc = jspectra |
|
|
628 | 628 | self.dataOut.data_cspc = jcspectra |
|
|
629 | 629 | |
|
|
630 | 630 | return 1 |
|
|
631 | 631 | |
|
|
632 | 632 | def setRadarFrequency(self, frequency=None): |
|
|
633 | 633 | |
|
|
634 | 634 | if frequency != None: |
|
|
635 | 635 | self.dataOut.frequency = frequency |
|
|
636 | 636 | |
|
|
637 | 637 | return 1 |
|
|
638 | 638 | |
|
|
639 | 639 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
640 | 640 | #validacion de rango |
|
|
641 | 641 | if minHei == None: |
|
|
642 | 642 | minHei = self.dataOut.heightList[0] |
|
|
643 | 643 | |
|
|
644 | 644 | if maxHei == None: |
|
|
645 | 645 | maxHei = self.dataOut.heightList[-1] |
|
|
646 | 646 | |
|
|
647 | 647 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
648 | 648 | print 'minHei: %.2f is out of the heights range'%(minHei) |
|
|
649 | 649 | print 'minHei is setting to %.2f'%(self.dataOut.heightList[0]) |
|
|
650 | 650 | minHei = self.dataOut.heightList[0] |
|
|
651 | 651 | |
|
|
652 | 652 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
653 | 653 | print 'maxHei: %.2f is out of the heights range'%(maxHei) |
|
|
654 | 654 | print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1]) |
|
|
655 | 655 | maxHei = self.dataOut.heightList[-1] |
|
|
656 | 656 | |
|
|
657 | 657 | # validacion de velocidades |
|
|
658 | 658 | velrange = self.dataOut.getVelRange(1) |
|
|
659 | 659 | |
|
|
660 | 660 | if minVel == None: |
|
|
661 | 661 | minVel = velrange[0] |
|
|
662 | 662 | |
|
|
663 | 663 | if maxVel == None: |
|
|
664 | 664 | maxVel = velrange[-1] |
|
|
665 | 665 | |
|
|
666 | 666 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
667 | 667 | print 'minVel: %.2f is out of the velocity range'%(minVel) |
|
|
668 | 668 | print 'minVel is setting to %.2f'%(velrange[0]) |
|
|
669 | 669 | minVel = velrange[0] |
|
|
670 | 670 | |
|
|
671 | 671 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
672 | 672 | print 'maxVel: %.2f is out of the velocity range'%(maxVel) |
|
|
673 | 673 | print 'maxVel is setting to %.2f'%(velrange[-1]) |
|
|
674 | 674 | maxVel = velrange[-1] |
|
|
675 | 675 | |
|
|
676 | 676 | # seleccion de indices para rango |
|
|
677 | 677 | minIndex = 0 |
|
|
678 | 678 | maxIndex = 0 |
|
|
679 | 679 | heights = self.dataOut.heightList |
|
|
680 | 680 | |
|
|
681 | 681 | inda = numpy.where(heights >= minHei) |
|
|
682 | 682 | indb = numpy.where(heights <= maxHei) |
|
|
683 | 683 | |
|
|
684 | 684 | try: |
|
|
685 | 685 | minIndex = inda[0][0] |
|
|
686 | 686 | except: |
|
|
687 | 687 | minIndex = 0 |
|
|
688 | 688 | |
|
|
689 | 689 | try: |
|
|
690 | 690 | maxIndex = indb[0][-1] |
|
|
691 | 691 | except: |
|
|
692 | 692 | maxIndex = len(heights) |
|
|
693 | 693 | |
|
|
694 | 694 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
695 | 695 | raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
696 | 696 | |
|
|
697 | 697 | if (maxIndex >= self.dataOut.nHeights): |
|
|
698 | 698 | maxIndex = self.dataOut.nHeights-1 |
|
|
699 | 699 | |
|
|
700 | 700 | # seleccion de indices para velocidades |
|
|
701 | 701 | indminvel = numpy.where(velrange >= minVel) |
|
|
702 | 702 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
703 | 703 | try: |
|
|
704 | 704 | minIndexVel = indminvel[0][0] |
|
|
705 | 705 | except: |
|
|
706 | 706 | minIndexVel = 0 |
|
|
707 | 707 | |
|
|
708 | 708 | try: |
|
|
709 | 709 | maxIndexVel = indmaxvel[0][-1] |
|
|
710 | 710 | except: |
|
|
711 | 711 | maxIndexVel = len(velrange) |
|
|
712 | 712 | |
|
|
713 | 713 | #seleccion del espectro |
|
|
714 | 714 | data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1] |
|
|
715 | 715 | #estimacion de ruido |
|
|
716 | 716 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
717 | 717 | |
|
|
718 | 718 | for channel in range(self.dataOut.nChannels): |
|
|
719 | 719 | daux = data_spc[channel,:,:] |
|
|
720 | 720 | noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) |
|
|
721 | 721 | |
|
|
722 | 722 | self.dataOut.noise_estimation = noise.copy() |
|
|
723 | 723 | |
|
|
724 | 724 | return 1 |
|
|
725 | 725 | |
|
|
726 | 726 | class IncohInt(Operation): |
|
|
727 | 727 | |
|
|
728 | 728 | |
|
|
729 | 729 | __profIndex = 0 |
|
|
730 | 730 | __withOverapping = False |
|
|
731 | 731 | |
|
|
732 | 732 | __byTime = False |
|
|
733 | 733 | __initime = None |
|
|
734 | 734 | __lastdatatime = None |
|
|
735 | 735 | __integrationtime = None |
|
|
736 | 736 | |
|
|
737 | 737 | __buffer_spc = None |
|
|
738 | 738 | __buffer_cspc = None |
|
|
739 | 739 | __buffer_dc = None |
|
|
740 | 740 | |
|
|
741 | 741 | __dataReady = False |
|
|
742 | 742 | |
|
|
743 | 743 | __timeInterval = None |
|
|
744 | 744 | |
|
|
745 | parameters = { | |
|
|
746 | 'n': global_type_float, | |
|
|
747 | 'timeInterval': global_type_integer, | |
|
|
748 | 'overlapping': global_type_boolean, | |
|
|
749 | } | |
|
|
750 | ||
|
|
745 | 751 | n = None |
|
|
746 | 752 | |
|
|
747 | 753 | |
|
|
748 | 754 | |
|
|
749 | 755 | def __init__(self, **kwargs): |
|
|
750 | 756 | |
|
|
751 | 757 | Operation.__init__(self, **kwargs) |
|
|
752 | 758 | # self.isConfig = False |
|
|
753 | 759 | |
|
|
754 | 760 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
755 | 761 | """ |
|
|
756 | 762 | Set the parameters of the integration class. |
|
|
757 | 763 | |
|
|
758 | 764 | Inputs: |
|
|
759 | 765 | |
|
|
760 | 766 | n : Number of coherent integrations |
|
|
761 | 767 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
762 | 768 | overlapping : |
|
|
763 | 769 | |
|
|
764 | 770 | """ |
|
|
765 | 771 | |
|
|
766 | 772 | self.__initime = None |
|
|
767 | 773 | self.__lastdatatime = 0 |
|
|
768 | 774 | |
|
|
769 | 775 | self.__buffer_spc = 0 |
|
|
770 | 776 | self.__buffer_cspc = 0 |
|
|
771 | 777 | self.__buffer_dc = 0 |
|
|
772 | 778 | |
|
|
773 | 779 | self.__profIndex = 0 |
|
|
774 | 780 | self.__dataReady = False |
|
|
775 | 781 | self.__byTime = False |
|
|
776 | 782 | |
|
|
777 | 783 | if n is None and timeInterval is None: |
|
|
778 | 784 | raise ValueError, "n or timeInterval should be specified ..." |
|
|
779 | 785 | |
|
|
780 | 786 | if n is not None: |
|
|
781 | 787 | self.n = int(n) |
|
|
782 | 788 | else: |
|
|
783 | 789 | self.__integrationtime = int(timeInterval) #if (type(timeInterval)!=integer) -> change this line |
|
|
784 | 790 | self.n = None |
|
|
785 | 791 | self.__byTime = True |
|
|
786 | 792 | |
|
|
787 | 793 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
788 | 794 | |
|
|
789 | 795 | """ |
|
|
790 | 796 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
791 | 797 | |
|
|
792 | 798 | """ |
|
|
793 | 799 | |
|
|
794 | 800 | self.__buffer_spc += data_spc |
|
|
795 | 801 | |
|
|
796 | 802 | if data_cspc is None: |
|
|
797 | 803 | self.__buffer_cspc = None |
|
|
798 | 804 | else: |
|
|
799 | 805 | self.__buffer_cspc += data_cspc |
|
|
800 | 806 | |
|
|
801 | 807 | if data_dc is None: |
|
|
802 | 808 | self.__buffer_dc = None |
|
|
803 | 809 | else: |
|
|
804 | 810 | self.__buffer_dc += data_dc |
|
|
805 | 811 | |
|
|
806 | 812 | self.__profIndex += 1 |
|
|
807 | 813 | |
|
|
808 | 814 | return |
|
|
809 | 815 | |
|
|
810 | 816 | def pushData(self): |
|
|
811 | 817 | """ |
|
|
812 | 818 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
813 | 819 | |
|
|
814 | 820 | Affected: |
|
|
815 | 821 | |
|
|
816 | 822 | self.__profileIndex |
|
|
817 | 823 | |
|
|
818 | 824 | """ |
|
|
819 | 825 | |
|
|
820 | 826 | data_spc = self.__buffer_spc |
|
|
821 | 827 | data_cspc = self.__buffer_cspc |
|
|
822 | 828 | data_dc = self.__buffer_dc |
|
|
823 | 829 | n = self.__profIndex |
|
|
824 | 830 | |
|
|
825 | 831 | self.__buffer_spc = 0 |
|
|
826 | 832 | self.__buffer_cspc = 0 |
|
|
827 | 833 | self.__buffer_dc = 0 |
|
|
828 | 834 | self.__profIndex = 0 |
|
|
829 | 835 | |
|
|
830 | 836 | return data_spc, data_cspc, data_dc, n |
|
|
831 | 837 | |
|
|
832 | 838 | def byProfiles(self, *args): |
|
|
833 | 839 | |
|
|
834 | 840 | self.__dataReady = False |
|
|
835 | 841 | avgdata_spc = None |
|
|
836 | 842 | avgdata_cspc = None |
|
|
837 | 843 | avgdata_dc = None |
|
|
838 | 844 | |
|
|
839 | 845 | self.putData(*args) |
|
|
840 | 846 | |
|
|
841 | 847 | if self.__profIndex == self.n: |
|
|
842 | 848 | |
|
|
843 | 849 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
844 | 850 | self.n = n |
|
|
845 | 851 | self.__dataReady = True |
|
|
846 | 852 | |
|
|
847 | 853 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
848 | 854 | |
|
|
849 | 855 | def byTime(self, datatime, *args): |
|
|
850 | 856 | |
|
|
851 | 857 | self.__dataReady = False |
|
|
852 | 858 | avgdata_spc = None |
|
|
853 | 859 | avgdata_cspc = None |
|
|
854 | 860 | avgdata_dc = None |
|
|
855 | 861 | |
|
|
856 | 862 | self.putData(*args) |
|
|
857 | 863 | |
|
|
858 | 864 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
859 | 865 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
860 | 866 | self.n = n |
|
|
861 | 867 | self.__dataReady = True |
|
|
862 | 868 | |
|
|
863 | 869 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
864 | 870 | |
|
|
865 | 871 | def integrate(self, datatime, *args): |
|
|
866 | 872 | |
|
|
867 | 873 | if self.__profIndex == 0: |
|
|
868 | 874 | self.__initime = datatime |
|
|
869 | 875 | |
|
|
870 | 876 | if self.__byTime: |
|
|
871 | 877 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args) |
|
|
872 | 878 | else: |
|
|
873 | 879 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
874 | 880 | |
|
|
875 | 881 | if not self.__dataReady: |
|
|
876 | 882 | return None, None, None, None |
|
|
877 | 883 | |
|
|
878 | 884 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
879 | 885 | |
|
|
880 | 886 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
|
881 | 887 | |
|
|
882 | 888 | if n==1: |
|
|
883 | 889 | return |
|
|
884 | 890 | |
|
|
885 | 891 | dataOut.flagNoData = True |
|
|
886 | 892 | |
|
|
887 | 893 | if not self.isConfig: |
|
|
888 | 894 | self.setup(n, timeInterval, overlapping) |
|
|
889 | 895 | self.isConfig = True |
|
|
890 | 896 | |
|
|
891 | 897 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
|
892 | 898 | dataOut.data_spc, |
|
|
893 | 899 | dataOut.data_cspc, |
|
|
894 | 900 | dataOut.data_dc) |
|
|
895 | 901 | |
|
|
896 | 902 | if self.__dataReady: |
|
|
897 | 903 | |
|
|
898 | 904 | dataOut.data_spc = avgdata_spc |
|
|
899 | 905 | dataOut.data_cspc = avgdata_cspc |
|
|
900 | 906 | dataOut.data_dc = avgdata_dc |
|
|
901 | 907 | |
|
|
902 | 908 | dataOut.nIncohInt *= self.n |
|
|
903 | 909 | dataOut.utctime = avgdatatime |
|
|
904 | 910 | dataOut.flagNoData = False |
| @@ -1,1290 +1,1322 | |||
|
|
1 | 1 | import sys |
|
|
2 | 2 | import numpy |
|
|
3 | 3 | from scipy import interpolate |
|
|
4 | 4 | |
|
|
5 | 5 | from jroproc_base import ProcessingUnit, Operation |
|
|
6 | 6 | from schainpy.model.data.jrodata import Voltage |
|
|
7 | 7 | |
|
|
8 | 8 | class VoltageProc(ProcessingUnit): |
|
|
9 | 9 | |
|
|
10 | 10 | |
|
|
11 | 11 | def __init__(self, **kwargs): |
|
|
12 | 12 | |
|
|
13 | 13 | ProcessingUnit.__init__(self, **kwargs) |
|
|
14 | 14 | |
|
|
15 | 15 | # self.objectDict = {} |
|
|
16 | 16 | self.dataOut = Voltage() |
|
|
17 | 17 | self.flip = 1 |
|
|
18 | 18 | |
|
|
19 | 19 | def run(self): |
|
|
20 | 20 | if self.dataIn.type == 'AMISR': |
|
|
21 | 21 | self.__updateObjFromAmisrInput() |
|
|
22 | 22 | |
|
|
23 | 23 | if self.dataIn.type == 'Voltage': |
|
|
24 | 24 | self.dataOut.copy(self.dataIn) |
|
|
25 | 25 | |
|
|
26 | 26 | # self.dataOut.copy(self.dataIn) |
|
|
27 | 27 | |
|
|
28 | 28 | def __updateObjFromAmisrInput(self): |
|
|
29 | 29 | |
|
|
30 | 30 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
31 | 31 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
32 | 32 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
33 | 33 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
34 | 34 | |
|
|
35 | 35 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
36 | 36 | self.dataOut.data = self.dataIn.data |
|
|
37 | 37 | self.dataOut.utctime = self.dataIn.utctime |
|
|
38 | 38 | self.dataOut.channelList = self.dataIn.channelList |
|
|
39 | 39 | # self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
40 | 40 | self.dataOut.heightList = self.dataIn.heightList |
|
|
41 | 41 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
42 | 42 | |
|
|
43 | 43 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
44 | 44 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
45 | 45 | self.dataOut.frequency = self.dataIn.frequency |
|
|
46 | 46 | |
|
|
47 | 47 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
48 | 48 | self.dataOut.zenith = self.dataIn.zenith |
|
|
49 | 49 | |
|
|
50 | 50 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
51 | 51 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
52 | 52 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
53 | 53 | # |
|
|
54 | 54 | # pass# |
|
|
55 | 55 | # |
|
|
56 | 56 | # def init(self): |
|
|
57 | 57 | # |
|
|
58 | 58 | # |
|
|
59 | 59 | # if self.dataIn.type == 'AMISR': |
|
|
60 | 60 | # self.__updateObjFromAmisrInput() |
|
|
61 | 61 | # |
|
|
62 | 62 | # if self.dataIn.type == 'Voltage': |
|
|
63 | 63 | # self.dataOut.copy(self.dataIn) |
|
|
64 | 64 | # # No necesita copiar en cada init() los atributos de dataIn |
|
|
65 | 65 | # # la copia deberia hacerse por cada nuevo bloque de datos |
|
|
66 | 66 | |
|
|
67 | 67 | def selectChannels(self, channelList): |
|
|
68 | 68 | |
|
|
69 | 69 | channelIndexList = [] |
|
|
70 | 70 | |
|
|
71 | 71 | for channel in channelList: |
|
|
72 | 72 | if channel not in self.dataOut.channelList: |
|
|
73 | 73 | raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList)) |
|
|
74 | 74 | |
|
|
75 | 75 | index = self.dataOut.channelList.index(channel) |
|
|
76 | 76 | channelIndexList.append(index) |
|
|
77 | 77 | |
|
|
78 | 78 | self.selectChannelsByIndex(channelIndexList) |
|
|
79 | 79 | |
|
|
80 | 80 | def selectChannelsByIndex(self, channelIndexList): |
|
|
81 | 81 | """ |
|
|
82 | 82 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
83 | 83 | |
|
|
84 | 84 | Input: |
|
|
85 | 85 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
86 | 86 | |
|
|
87 | 87 | Affected: |
|
|
88 | 88 | self.dataOut.data |
|
|
89 | 89 | self.dataOut.channelIndexList |
|
|
90 | 90 | self.dataOut.nChannels |
|
|
91 | 91 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
|
92 | 92 | self.dataOut.systemHeaderObj.numChannels |
|
|
93 | 93 | self.dataOut.m_ProcessingHeader.blockSize |
|
|
94 | 94 | |
|
|
95 | 95 | Return: |
|
|
96 | 96 | None |
|
|
97 | 97 | """ |
|
|
98 | 98 | |
|
|
99 | 99 | for channelIndex in channelIndexList: |
|
|
100 | 100 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
101 | 101 | print channelIndexList |
|
|
102 | 102 | raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex |
|
|
103 | 103 | |
|
|
104 | 104 | if self.dataOut.flagDataAsBlock: |
|
|
105 | 105 | """ |
|
|
106 | 106 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
107 | 107 | """ |
|
|
108 | 108 | data = self.dataOut.data[channelIndexList,:,:] |
|
|
109 | 109 | else: |
|
|
110 | 110 | data = self.dataOut.data[channelIndexList,:] |
|
|
111 | 111 | |
|
|
112 | 112 | self.dataOut.data = data |
|
|
113 | 113 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
114 | 114 | # self.dataOut.nChannels = nChannels |
|
|
115 | 115 | |
|
|
116 | 116 | return 1 |
|
|
117 | 117 | |
|
|
118 | 118 | def selectHeights(self, minHei=None, maxHei=None): |
|
|
119 | 119 | """ |
|
|
120 | 120 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
121 | 121 | minHei <= height <= maxHei |
|
|
122 | 122 | |
|
|
123 | 123 | Input: |
|
|
124 | 124 | minHei : valor minimo de altura a considerar |
|
|
125 | 125 | maxHei : valor maximo de altura a considerar |
|
|
126 | 126 | |
|
|
127 | 127 | Affected: |
|
|
128 | 128 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
129 | 129 | |
|
|
130 | 130 | Return: |
|
|
131 | 131 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
132 | 132 | """ |
|
|
133 | 133 | |
|
|
134 | 134 | if minHei == None: |
|
|
135 | 135 | minHei = self.dataOut.heightList[0] |
|
|
136 | 136 | |
|
|
137 | 137 | if maxHei == None: |
|
|
138 | 138 | maxHei = self.dataOut.heightList[-1] |
|
|
139 | 139 | |
|
|
140 | 140 | if (minHei < self.dataOut.heightList[0]): |
|
|
141 | 141 | minHei = self.dataOut.heightList[0] |
|
|
142 | 142 | |
|
|
143 | 143 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
144 | 144 | maxHei = self.dataOut.heightList[-1] |
|
|
145 | 145 | |
|
|
146 | 146 | minIndex = 0 |
|
|
147 | 147 | maxIndex = 0 |
|
|
148 | 148 | heights = self.dataOut.heightList |
|
|
149 | 149 | |
|
|
150 | 150 | inda = numpy.where(heights >= minHei) |
|
|
151 | 151 | indb = numpy.where(heights <= maxHei) |
|
|
152 | 152 | |
|
|
153 | 153 | try: |
|
|
154 | 154 | minIndex = inda[0][0] |
|
|
155 | 155 | except: |
|
|
156 | 156 | minIndex = 0 |
|
|
157 | 157 | |
|
|
158 | 158 | try: |
|
|
159 | 159 | maxIndex = indb[0][-1] |
|
|
160 | 160 | except: |
|
|
161 | 161 | maxIndex = len(heights) |
|
|
162 | 162 | |
|
|
163 | 163 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
164 | 164 | |
|
|
165 | 165 | return 1 |
|
|
166 | 166 | |
|
|
167 | 167 | |
|
|
168 | 168 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
169 | 169 | """ |
|
|
170 | 170 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
171 | 171 | minIndex <= index <= maxIndex |
|
|
172 | 172 | |
|
|
173 | 173 | Input: |
|
|
174 | 174 | minIndex : valor de indice minimo de altura a considerar |
|
|
175 | 175 | maxIndex : valor de indice maximo de altura a considerar |
|
|
176 | 176 | |
|
|
177 | 177 | Affected: |
|
|
178 | 178 | self.dataOut.data |
|
|
179 | 179 | self.dataOut.heightList |
|
|
180 | 180 | |
|
|
181 | 181 | Return: |
|
|
182 | 182 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
183 | 183 | """ |
|
|
184 | 184 | |
|
|
185 | 185 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
186 | 186 | raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
187 | 187 | |
|
|
188 | 188 | if (maxIndex >= self.dataOut.nHeights): |
|
|
189 | 189 | maxIndex = self.dataOut.nHeights |
|
|
190 | 190 | |
|
|
191 | 191 | #voltage |
|
|
192 | 192 | if self.dataOut.flagDataAsBlock: |
|
|
193 | 193 | """ |
|
|
194 | 194 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
195 | 195 | """ |
|
|
196 | 196 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
197 | 197 | else: |
|
|
198 | 198 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
|
199 | 199 | |
|
|
200 | 200 | # firstHeight = self.dataOut.heightList[minIndex] |
|
|
201 | 201 | |
|
|
202 | 202 | self.dataOut.data = data |
|
|
203 | 203 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
204 | 204 | |
|
|
205 | 205 | if self.dataOut.nHeights <= 1: |
|
|
206 | 206 | raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights) |
|
|
207 | 207 | |
|
|
208 | 208 | return 1 |
|
|
209 | 209 | |
|
|
210 | 210 | |
|
|
211 | 211 | def filterByHeights(self, window): |
|
|
212 | 212 | |
|
|
213 | 213 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
214 | 214 | |
|
|
215 | 215 | if window == None: |
|
|
216 | 216 | window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
217 | 217 | |
|
|
218 | 218 | newdelta = deltaHeight * window |
|
|
219 | 219 | r = self.dataOut.nHeights % window |
|
|
220 | 220 | newheights = (self.dataOut.nHeights-r)/window |
|
|
221 | 221 | |
|
|
222 | 222 | if newheights <= 1: |
|
|
223 | 223 | raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window) |
|
|
224 | 224 | |
|
|
225 | 225 | if self.dataOut.flagDataAsBlock: |
|
|
226 | 226 | """ |
|
|
227 | 227 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
228 | 228 | """ |
|
|
229 | 229 | buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r] |
|
|
230 | 230 | buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window) |
|
|
231 | 231 | buffer = numpy.sum(buffer,3) |
|
|
232 | 232 | |
|
|
233 | 233 | else: |
|
|
234 | 234 | buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r] |
|
|
235 | 235 | buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window) |
|
|
236 | 236 | buffer = numpy.sum(buffer,2) |
|
|
237 | 237 | |
|
|
238 | 238 | self.dataOut.data = buffer |
|
|
239 | 239 | self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
240 | 240 | self.dataOut.windowOfFilter = window |
|
|
241 | 241 | |
|
|
242 | 242 | def setH0(self, h0, deltaHeight = None): |
|
|
243 | 243 | |
|
|
244 | 244 | if not deltaHeight: |
|
|
245 | 245 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
246 | 246 | |
|
|
247 | 247 | nHeights = self.dataOut.nHeights |
|
|
248 | 248 | |
|
|
249 | 249 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
250 | 250 | |
|
|
251 | 251 | self.dataOut.heightList = newHeiRange |
|
|
252 | 252 | |
|
|
253 | 253 | def deFlip(self, channelList = []): |
|
|
254 | 254 | |
|
|
255 | 255 | data = self.dataOut.data.copy() |
|
|
256 | 256 | |
|
|
257 | 257 | if self.dataOut.flagDataAsBlock: |
|
|
258 | 258 | flip = self.flip |
|
|
259 | 259 | profileList = range(self.dataOut.nProfiles) |
|
|
260 | 260 | |
|
|
261 | 261 | if not channelList: |
|
|
262 | 262 | for thisProfile in profileList: |
|
|
263 | 263 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
264 | 264 | flip *= -1.0 |
|
|
265 | 265 | else: |
|
|
266 | 266 | for thisChannel in channelList: |
|
|
267 | 267 | if thisChannel not in self.dataOut.channelList: |
|
|
268 | 268 | continue |
|
|
269 | 269 | |
|
|
270 | 270 | for thisProfile in profileList: |
|
|
271 | 271 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
272 | 272 | flip *= -1.0 |
|
|
273 | 273 | |
|
|
274 | 274 | self.flip = flip |
|
|
275 | 275 | |
|
|
276 | 276 | else: |
|
|
277 | 277 | if not channelList: |
|
|
278 | 278 | data[:,:] = data[:,:]*self.flip |
|
|
279 | 279 | else: |
|
|
280 | 280 | for thisChannel in channelList: |
|
|
281 | 281 | if thisChannel not in self.dataOut.channelList: |
|
|
282 | 282 | continue |
|
|
283 | 283 | |
|
|
284 | 284 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
285 | 285 | |
|
|
286 | 286 | self.flip *= -1. |
|
|
287 | 287 | |
|
|
288 | 288 | self.dataOut.data = data |
|
|
289 | 289 | |
|
|
290 | 290 | def setRadarFrequency(self, frequency=None): |
|
|
291 | 291 | |
|
|
292 | 292 | if frequency != None: |
|
|
293 | 293 | self.dataOut.frequency = frequency |
|
|
294 | 294 | |
|
|
295 | 295 | return 1 |
|
|
296 | 296 | |
|
|
297 | 297 | def interpolateHeights(self, topLim, botLim): |
|
|
298 | 298 | #69 al 72 para julia |
|
|
299 | 299 | #82-84 para meteoros |
|
|
300 | 300 | if len(numpy.shape(self.dataOut.data))==2: |
|
|
301 | 301 | sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2 |
|
|
302 | 302 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
303 | 303 | #self.dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
304 | 304 | self.dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
305 | 305 | else: |
|
|
306 | 306 | nHeights = self.dataOut.data.shape[2] |
|
|
307 | 307 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
308 | 308 | y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)] |
|
|
309 | 309 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
310 | 310 | xnew = numpy.arange(botLim,topLim+1) |
|
|
311 | 311 | ynew = f(xnew) |
|
|
312 | 312 | |
|
|
313 | 313 | self.dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
314 | 314 | |
|
|
315 | 315 | # import collections |
|
|
316 | 316 | |
|
|
317 | 317 | class CohInt(Operation): |
|
|
318 | 318 | |
|
|
319 | 319 | isConfig = False |
|
|
320 | 320 | |
|
|
321 | 321 | __profIndex = 0 |
|
|
322 | 322 | __withOverapping = False |
|
|
323 | 323 | |
|
|
324 | 324 | __byTime = False |
|
|
325 | 325 | __initime = None |
|
|
326 | 326 | __lastdatatime = None |
|
|
327 | 327 | __integrationtime = None |
|
|
328 | 328 | |
|
|
329 | 329 | __buffer = None |
|
|
330 | 330 | |
|
|
331 | 331 | __dataReady = False |
|
|
332 | 332 | |
|
|
333 | 333 | n = None |
|
|
334 | 334 | |
|
|
335 | 335 | parameters = { |
|
|
336 | 'n': 'int', | |
|
|
337 | 'timeInterval':'float', | |
|
|
338 | 'overlapping': 'boolean', | |
|
|
339 | 'byblock': 'boolean' | |
|
|
336 | 'id': global_type_string, | |
|
|
337 | 'wintitle': global_type_string, | |
|
|
338 | 'pairsList': global_type_pairsList, | |
|
|
339 | 'showprofile': global_type_boolean, | |
|
|
340 | 'xmin': global_type_float, | |
|
|
341 | 'xmax': global_type_float, | |
|
|
342 | 'ymin': global_type_float, | |
|
|
343 | 'ymax': global_type_float, | |
|
|
344 | 'zmin': global_type_float, | |
|
|
345 | 'zmax': global_type_float, | |
|
|
346 | 'timerange': global_type_float, | |
|
|
347 | 'phase_min': global_type_float, | |
|
|
348 | 'phase_max': global_type_float, | |
|
|
349 | 'save': global_type_boolean, | |
|
|
350 | 'figpath': global_type_string, | |
|
|
351 | 'figfile': global_type_string, | |
|
|
352 | 'ftp': global_type_boolean, | |
|
|
353 | 'wr_period': global_type_integer, | |
|
|
354 | 'coherence_cmap': global_type_colormap, | |
|
|
355 | 'phase_cmap': global_type_colormap, | |
|
|
356 | 'show': global_type_boolean, | |
|
|
357 | 'server': global_type_string, | |
|
|
358 | 'folder': global_type_string, | |
|
|
359 | 'username': global_type_string, | |
|
|
360 | 'password': global_type_string, | |
|
|
361 | 'ftp_wei': global_type_integer, | |
|
|
362 | 'exp_code': global_type_integer, | |
|
|
363 | 'sub_exp_code': global_type_integer, | |
|
|
364 | 'plot_pos': global_type_integer, | |
|
|
340 | 365 | } |
|
|
341 | 366 | |
|
|
342 | 367 | def __init__(self, **kwargs): |
|
|
343 | 368 | |
|
|
344 | 369 | Operation.__init__(self, **kwargs) |
|
|
345 | 370 | |
|
|
346 | 371 | # self.isConfig = False |
|
|
347 | 372 | |
|
|
348 | 373 | def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False): |
|
|
349 | 374 | """ |
|
|
350 | 375 | Set the parameters of the integration class. |
|
|
351 | 376 | |
|
|
352 | 377 | Inputs: |
|
|
353 | 378 | |
|
|
354 | 379 | n : Number of coherent integrations |
|
|
355 | 380 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
356 | 381 | overlapping : |
|
|
357 | 382 | """ |
|
|
358 | 383 | |
|
|
359 | 384 | self.__initime = None |
|
|
360 | 385 | self.__lastdatatime = 0 |
|
|
361 | 386 | self.__buffer = None |
|
|
362 | 387 | self.__dataReady = False |
|
|
363 | 388 | self.byblock = byblock |
|
|
364 | 389 | |
|
|
365 | 390 | if n == None and timeInterval == None: |
|
|
366 | 391 | raise ValueError, "n or timeInterval should be specified ..." |
|
|
367 | 392 | |
|
|
368 | 393 | if n != None: |
|
|
369 | 394 | self.n = n |
|
|
370 | 395 | self.__byTime = False |
|
|
371 | 396 | else: |
|
|
372 | 397 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
373 | 398 | self.n = 9999 |
|
|
374 | 399 | self.__byTime = True |
|
|
375 | 400 | |
|
|
376 | 401 | if overlapping: |
|
|
377 | 402 | self.__withOverapping = True |
|
|
378 | 403 | self.__buffer = None |
|
|
379 | 404 | else: |
|
|
380 | 405 | self.__withOverapping = False |
|
|
381 | 406 | self.__buffer = 0 |
|
|
382 | 407 | |
|
|
383 | 408 | self.__profIndex = 0 |
|
|
384 | 409 | |
|
|
385 | 410 | def putData(self, data): |
|
|
386 | 411 | |
|
|
387 | 412 | """ |
|
|
388 | 413 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
389 | 414 | |
|
|
390 | 415 | """ |
|
|
391 | 416 | |
|
|
392 | 417 | if not self.__withOverapping: |
|
|
393 | 418 | self.__buffer += data.copy() |
|
|
394 | 419 | self.__profIndex += 1 |
|
|
395 | 420 | return |
|
|
396 | 421 | |
|
|
397 | 422 | #Overlapping data |
|
|
398 | 423 | nChannels, nHeis = data.shape |
|
|
399 | 424 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
400 | 425 | |
|
|
401 | 426 | #If the buffer is empty then it takes the data value |
|
|
402 | 427 | if self.__buffer is None: |
|
|
403 | 428 | self.__buffer = data |
|
|
404 | 429 | self.__profIndex += 1 |
|
|
405 | 430 | return |
|
|
406 | 431 | |
|
|
407 | 432 | #If the buffer length is lower than n then stakcing the data value |
|
|
408 | 433 | if self.__profIndex < self.n: |
|
|
409 | 434 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
410 | 435 | self.__profIndex += 1 |
|
|
411 | 436 | return |
|
|
412 | 437 | |
|
|
413 | 438 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
414 | 439 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
415 | 440 | self.__buffer[self.n-1] = data |
|
|
416 | 441 | self.__profIndex = self.n |
|
|
417 | 442 | return |
|
|
418 | 443 | |
|
|
419 | 444 | |
|
|
420 | 445 | def pushData(self): |
|
|
421 | 446 | """ |
|
|
422 | 447 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
423 | 448 | |
|
|
424 | 449 | Affected: |
|
|
425 | 450 | |
|
|
426 | 451 | self.__profileIndex |
|
|
427 | 452 | |
|
|
428 | 453 | """ |
|
|
429 | 454 | |
|
|
430 | 455 | if not self.__withOverapping: |
|
|
431 | 456 | data = self.__buffer |
|
|
432 | 457 | n = self.__profIndex |
|
|
433 | 458 | |
|
|
434 | 459 | self.__buffer = 0 |
|
|
435 | 460 | self.__profIndex = 0 |
|
|
436 | 461 | |
|
|
437 | 462 | return data, n |
|
|
438 | 463 | |
|
|
439 | 464 | #Integration with Overlapping |
|
|
440 | 465 | data = numpy.sum(self.__buffer, axis=0) |
|
|
441 | 466 | n = self.__profIndex |
|
|
442 | 467 | |
|
|
443 | 468 | return data, n |
|
|
444 | 469 | |
|
|
445 | 470 | def byProfiles(self, data): |
|
|
446 | 471 | |
|
|
447 | 472 | self.__dataReady = False |
|
|
448 | 473 | avgdata = None |
|
|
449 | 474 | # n = None |
|
|
450 | 475 | |
|
|
451 | 476 | self.putData(data) |
|
|
452 | 477 | |
|
|
453 | 478 | if self.__profIndex == self.n: |
|
|
454 | 479 | |
|
|
455 | 480 | avgdata, n = self.pushData() |
|
|
456 | 481 | self.__dataReady = True |
|
|
457 | 482 | |
|
|
458 | 483 | return avgdata |
|
|
459 | 484 | |
|
|
460 | 485 | def byTime(self, data, datatime): |
|
|
461 | 486 | |
|
|
462 | 487 | self.__dataReady = False |
|
|
463 | 488 | avgdata = None |
|
|
464 | 489 | n = None |
|
|
465 | 490 | |
|
|
466 | 491 | self.putData(data) |
|
|
467 | 492 | |
|
|
468 | 493 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
469 | 494 | avgdata, n = self.pushData() |
|
|
470 | 495 | self.n = n |
|
|
471 | 496 | self.__dataReady = True |
|
|
472 | 497 | |
|
|
473 | 498 | return avgdata |
|
|
474 | 499 | |
|
|
475 | 500 | def integrate(self, data, datatime=None): |
|
|
476 | 501 | |
|
|
477 | 502 | if self.__initime == None: |
|
|
478 | 503 | self.__initime = datatime |
|
|
479 | 504 | |
|
|
480 | 505 | if self.__byTime: |
|
|
481 | 506 | avgdata = self.byTime(data, datatime) |
|
|
482 | 507 | else: |
|
|
483 | 508 | avgdata = self.byProfiles(data) |
|
|
484 | 509 | |
|
|
485 | 510 | |
|
|
486 | 511 | self.__lastdatatime = datatime |
|
|
487 | 512 | |
|
|
488 | 513 | if avgdata is None: |
|
|
489 | 514 | return None, None |
|
|
490 | 515 | |
|
|
491 | 516 | avgdatatime = self.__initime |
|
|
492 | 517 | |
|
|
493 | 518 | deltatime = datatime -self.__lastdatatime |
|
|
494 | 519 | |
|
|
495 | 520 | if not self.__withOverapping: |
|
|
496 | 521 | self.__initime = datatime |
|
|
497 | 522 | else: |
|
|
498 | 523 | self.__initime += deltatime |
|
|
499 | 524 | |
|
|
500 | 525 | return avgdata, avgdatatime |
|
|
501 | 526 | |
|
|
502 | 527 | def integrateByBlock(self, dataOut): |
|
|
503 | 528 | |
|
|
504 | 529 | times = int(dataOut.data.shape[1]/self.n) |
|
|
505 | 530 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
506 | 531 | |
|
|
507 | 532 | id_min = 0 |
|
|
508 | 533 | id_max = self.n |
|
|
509 | 534 | |
|
|
510 | 535 | for i in range(times): |
|
|
511 | 536 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
512 | 537 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
513 | 538 | id_min += self.n |
|
|
514 | 539 | id_max += self.n |
|
|
515 | 540 | |
|
|
516 | 541 | timeInterval = dataOut.ippSeconds*self.n |
|
|
517 | 542 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
518 | 543 | self.__dataReady = True |
|
|
519 | 544 | return avgdata, avgdatatime |
|
|
520 | 545 | |
|
|
521 | 546 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False, byblock=False): |
|
|
522 | 547 | |
|
|
523 | 548 | if not self.isConfig: |
|
|
524 | 549 | self.setup(n=n, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock) |
|
|
525 | 550 | self.isConfig = True |
|
|
526 | 551 | |
|
|
527 | 552 | if dataOut.flagDataAsBlock: |
|
|
528 | 553 | """ |
|
|
529 | 554 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
530 | 555 | """ |
|
|
531 | 556 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
532 | 557 | dataOut.nProfiles /= self.n |
|
|
533 | 558 | else: |
|
|
534 | 559 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
535 | 560 | |
|
|
536 | 561 | # dataOut.timeInterval *= n |
|
|
537 | 562 | dataOut.flagNoData = True |
|
|
538 | 563 | |
|
|
539 | 564 | if self.__dataReady: |
|
|
540 | 565 | dataOut.data = avgdata |
|
|
541 | 566 | dataOut.nCohInt *= self.n |
|
|
542 | 567 | dataOut.utctime = avgdatatime |
|
|
543 | 568 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
544 | 569 | dataOut.flagNoData = False |
|
|
545 | 570 | |
|
|
546 | 571 | class Decoder(Operation): |
|
|
547 | 572 | |
|
|
548 | 573 | isConfig = False |
|
|
549 | 574 | __profIndex = 0 |
|
|
550 | 575 | |
|
|
551 | 576 | code = None |
|
|
552 | 577 | |
|
|
553 | 578 | nCode = None |
|
|
554 | 579 | nBaud = None |
|
|
555 | ||
|
|
580 | ||
|
|
581 | parameters = { | |
|
|
582 | 'code': global_type_list, | |
|
|
583 | 'nCode': global_type_integer, | |
|
|
584 | 'nBaud': global_type_integer, | |
|
|
585 | 'mode': global_type_integer, | |
|
|
586 | 'osamp': global_type_float, | |
|
|
587 | } | |
|
|
556 | 588 | |
|
|
557 | 589 | def __init__(self, **kwargs): |
|
|
558 | 590 | |
|
|
559 | 591 | Operation.__init__(self, **kwargs) |
|
|
560 | 592 | |
|
|
561 | 593 | self.times = None |
|
|
562 | 594 | self.osamp = None |
|
|
563 | 595 | # self.__setValues = False |
|
|
564 | 596 | self.isConfig = False |
|
|
565 | 597 | |
|
|
566 | 598 | def setup(self, code, osamp, dataOut): |
|
|
567 | 599 | |
|
|
568 | 600 | self.__profIndex = 0 |
|
|
569 | 601 | |
|
|
570 | 602 | self.code = code |
|
|
571 | 603 | |
|
|
572 | 604 | self.nCode = len(code) |
|
|
573 | 605 | self.nBaud = len(code[0]) |
|
|
574 | 606 | |
|
|
575 | 607 | if (osamp != None) and (osamp >1): |
|
|
576 | 608 | self.osamp = osamp |
|
|
577 | 609 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
578 | 610 | self.nBaud = self.nBaud*self.osamp |
|
|
579 | 611 | |
|
|
580 | 612 | self.__nChannels = dataOut.nChannels |
|
|
581 | 613 | self.__nProfiles = dataOut.nProfiles |
|
|
582 | 614 | self.__nHeis = dataOut.nHeights |
|
|
583 | 615 | |
|
|
584 | 616 | if self.__nHeis < self.nBaud: |
|
|
585 | 617 | raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud) |
|
|
586 | 618 | |
|
|
587 | 619 | #Frequency |
|
|
588 | 620 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
589 | 621 | |
|
|
590 | 622 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
591 | 623 | |
|
|
592 | 624 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
593 | 625 | |
|
|
594 | 626 | if dataOut.flagDataAsBlock: |
|
|
595 | 627 | |
|
|
596 | 628 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
597 | 629 | |
|
|
598 | 630 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
599 | 631 | |
|
|
600 | 632 | else: |
|
|
601 | 633 | |
|
|
602 | 634 | #Time |
|
|
603 | 635 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
604 | 636 | |
|
|
605 | 637 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
606 | 638 | |
|
|
607 | 639 | def __convolutionInFreq(self, data): |
|
|
608 | 640 | |
|
|
609 | 641 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
610 | 642 | |
|
|
611 | 643 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
612 | 644 | |
|
|
613 | 645 | conv = fft_data*fft_code |
|
|
614 | 646 | |
|
|
615 | 647 | data = numpy.fft.ifft(conv,axis=1) |
|
|
616 | 648 | |
|
|
617 | 649 | return data |
|
|
618 | 650 | |
|
|
619 | 651 | def __convolutionInFreqOpt(self, data): |
|
|
620 | 652 | |
|
|
621 | 653 | raise NotImplementedError |
|
|
622 | 654 | |
|
|
623 | 655 | def __convolutionInTime(self, data): |
|
|
624 | 656 | |
|
|
625 | 657 | code = self.code[self.__profIndex] |
|
|
626 | 658 | |
|
|
627 | 659 | for i in range(self.__nChannels): |
|
|
628 | 660 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
629 | 661 | |
|
|
630 | 662 | return self.datadecTime |
|
|
631 | 663 | |
|
|
632 | 664 | def __convolutionByBlockInTime(self, data): |
|
|
633 | 665 | |
|
|
634 | 666 | repetitions = self.__nProfiles / self.nCode |
|
|
635 | 667 | |
|
|
636 | 668 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
637 | 669 | junk = junk.flatten() |
|
|
638 | 670 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
639 | 671 | |
|
|
640 | 672 | for i in range(self.__nChannels): |
|
|
641 | 673 | for j in range(self.__nProfiles): |
|
|
642 | 674 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
643 | 675 | |
|
|
644 | 676 | return self.datadecTime |
|
|
645 | 677 | |
|
|
646 | 678 | def __convolutionByBlockInFreq(self, data): |
|
|
647 | 679 | |
|
|
648 | 680 | raise NotImplementedError, "Decoder by frequency fro Blocks not implemented" |
|
|
649 | 681 | |
|
|
650 | 682 | |
|
|
651 | 683 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
652 | 684 | |
|
|
653 | 685 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
654 | 686 | |
|
|
655 | 687 | conv = fft_data*fft_code |
|
|
656 | 688 | |
|
|
657 | 689 | data = numpy.fft.ifft(conv,axis=2) |
|
|
658 | 690 | |
|
|
659 | 691 | return data |
|
|
660 | 692 | |
|
|
661 | 693 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
662 | 694 | |
|
|
663 | 695 | if dataOut.flagDecodeData: |
|
|
664 | 696 | print "This data is already decoded, recoding again ..." |
|
|
665 | 697 | |
|
|
666 | 698 | if not self.isConfig: |
|
|
667 | 699 | |
|
|
668 | 700 | if code is None: |
|
|
669 | 701 | if dataOut.code is None: |
|
|
670 | 702 | raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type |
|
|
671 | 703 | |
|
|
672 | 704 | code = dataOut.code |
|
|
673 | 705 | else: |
|
|
674 | 706 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
675 | 707 | |
|
|
676 | 708 | self.setup(code, osamp, dataOut) |
|
|
677 | 709 | |
|
|
678 | 710 | self.isConfig = True |
|
|
679 | 711 | |
|
|
680 | 712 | if mode == 3: |
|
|
681 | 713 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
682 | 714 | |
|
|
683 | 715 | if times != None: |
|
|
684 | 716 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
685 | 717 | |
|
|
686 | 718 | if self.code is None: |
|
|
687 | 719 | print "Fail decoding: Code is not defined." |
|
|
688 | 720 | return |
|
|
689 | 721 | |
|
|
690 | 722 | datadec = None |
|
|
691 | 723 | if mode == 3: |
|
|
692 | 724 | mode = 0 |
|
|
693 | 725 | |
|
|
694 | 726 | if dataOut.flagDataAsBlock: |
|
|
695 | 727 | """ |
|
|
696 | 728 | Decoding when data have been read as block, |
|
|
697 | 729 | """ |
|
|
698 | 730 | |
|
|
699 | 731 | if mode == 0: |
|
|
700 | 732 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
701 | 733 | if mode == 1: |
|
|
702 | 734 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
703 | 735 | else: |
|
|
704 | 736 | """ |
|
|
705 | 737 | Decoding when data have been read profile by profile |
|
|
706 | 738 | """ |
|
|
707 | 739 | if mode == 0: |
|
|
708 | 740 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
709 | 741 | |
|
|
710 | 742 | if mode == 1: |
|
|
711 | 743 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
712 | 744 | |
|
|
713 | 745 | if mode == 2: |
|
|
714 | 746 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
715 | 747 | |
|
|
716 | 748 | if datadec is None: |
|
|
717 | 749 | raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode |
|
|
718 | 750 | |
|
|
719 | 751 | dataOut.code = self.code |
|
|
720 | 752 | dataOut.nCode = self.nCode |
|
|
721 | 753 | dataOut.nBaud = self.nBaud |
|
|
722 | 754 | |
|
|
723 | 755 | dataOut.data = datadec |
|
|
724 | 756 | |
|
|
725 | 757 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
726 | 758 | |
|
|
727 | 759 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
728 | 760 | |
|
|
729 | 761 | if self.__profIndex == self.nCode-1: |
|
|
730 | 762 | self.__profIndex = 0 |
|
|
731 | 763 | return 1 |
|
|
732 | 764 | |
|
|
733 | 765 | self.__profIndex += 1 |
|
|
734 | 766 | |
|
|
735 | 767 | return 1 |
|
|
736 | 768 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
737 | 769 | |
|
|
738 | 770 | |
|
|
739 | 771 | class ProfileConcat(Operation): |
|
|
740 | 772 | |
|
|
741 | 773 | isConfig = False |
|
|
742 | 774 | buffer = None |
|
|
743 | 775 | |
|
|
744 | 776 | def __init__(self, **kwargs): |
|
|
745 | 777 | |
|
|
746 | 778 | Operation.__init__(self, **kwargs) |
|
|
747 | 779 | self.profileIndex = 0 |
|
|
748 | 780 | |
|
|
749 | 781 | def reset(self): |
|
|
750 | 782 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
751 | 783 | self.start_index = 0 |
|
|
752 | 784 | self.times = 1 |
|
|
753 | 785 | |
|
|
754 | 786 | def setup(self, data, m, n=1): |
|
|
755 | 787 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
756 | 788 | self.nHeights = data.shape[1]#.nHeights |
|
|
757 | 789 | self.start_index = 0 |
|
|
758 | 790 | self.times = 1 |
|
|
759 | 791 | |
|
|
760 | 792 | def concat(self, data): |
|
|
761 | 793 | |
|
|
762 | 794 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
763 | 795 | self.start_index = self.start_index + self.nHeights |
|
|
764 | 796 | |
|
|
765 | 797 | def run(self, dataOut, m): |
|
|
766 | 798 | |
|
|
767 | 799 | dataOut.flagNoData = True |
|
|
768 | 800 | |
|
|
769 | 801 | if not self.isConfig: |
|
|
770 | 802 | self.setup(dataOut.data, m, 1) |
|
|
771 | 803 | self.isConfig = True |
|
|
772 | 804 | |
|
|
773 | 805 | if dataOut.flagDataAsBlock: |
|
|
774 | 806 | raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False" |
|
|
775 | 807 | |
|
|
776 | 808 | else: |
|
|
777 | 809 | self.concat(dataOut.data) |
|
|
778 | 810 | self.times += 1 |
|
|
779 | 811 | if self.times > m: |
|
|
780 | 812 | dataOut.data = self.buffer |
|
|
781 | 813 | self.reset() |
|
|
782 | 814 | dataOut.flagNoData = False |
|
|
783 | 815 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
784 | 816 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
785 | 817 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
786 | 818 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
787 | 819 | dataOut.ippSeconds *= m |
|
|
788 | 820 | |
|
|
789 | 821 | class ProfileSelector(Operation): |
|
|
790 | 822 | |
|
|
791 | 823 | profileIndex = None |
|
|
792 | 824 | # Tamanho total de los perfiles |
|
|
793 | 825 | nProfiles = None |
|
|
794 | 826 | |
|
|
795 | 827 | def __init__(self, **kwargs): |
|
|
796 | 828 | |
|
|
797 | 829 | Operation.__init__(self, **kwargs) |
|
|
798 | 830 | self.profileIndex = 0 |
|
|
799 | 831 | |
|
|
800 | 832 | def incProfileIndex(self): |
|
|
801 | 833 | |
|
|
802 | 834 | self.profileIndex += 1 |
|
|
803 | 835 | |
|
|
804 | 836 | if self.profileIndex >= self.nProfiles: |
|
|
805 | 837 | self.profileIndex = 0 |
|
|
806 | 838 | |
|
|
807 | 839 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
808 | 840 | |
|
|
809 | 841 | if profileIndex < minIndex: |
|
|
810 | 842 | return False |
|
|
811 | 843 | |
|
|
812 | 844 | if profileIndex > maxIndex: |
|
|
813 | 845 | return False |
|
|
814 | 846 | |
|
|
815 | 847 | return True |
|
|
816 | 848 | |
|
|
817 | 849 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
818 | 850 | |
|
|
819 | 851 | if profileIndex not in profileList: |
|
|
820 | 852 | return False |
|
|
821 | 853 | |
|
|
822 | 854 | return True |
|
|
823 | 855 | |
|
|
824 | 856 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
825 | 857 | |
|
|
826 | 858 | """ |
|
|
827 | 859 | ProfileSelector: |
|
|
828 | 860 | |
|
|
829 | 861 | Inputs: |
|
|
830 | 862 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
831 | 863 | |
|
|
832 | 864 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
833 | 865 | |
|
|
834 | 866 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
835 | 867 | |
|
|
836 | 868 | """ |
|
|
837 | 869 | |
|
|
838 | 870 | if rangeList is not None: |
|
|
839 | 871 | if type(rangeList[0]) not in (tuple, list): |
|
|
840 | 872 | rangeList = [rangeList] |
|
|
841 | 873 | |
|
|
842 | 874 | dataOut.flagNoData = True |
|
|
843 | 875 | |
|
|
844 | 876 | if dataOut.flagDataAsBlock: |
|
|
845 | 877 | """ |
|
|
846 | 878 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
847 | 879 | """ |
|
|
848 | 880 | if profileList != None: |
|
|
849 | 881 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
850 | 882 | |
|
|
851 | 883 | if profileRangeList != None: |
|
|
852 | 884 | minIndex = profileRangeList[0] |
|
|
853 | 885 | maxIndex = profileRangeList[1] |
|
|
854 | 886 | profileList = range(minIndex, maxIndex+1) |
|
|
855 | 887 | |
|
|
856 | 888 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
857 | 889 | |
|
|
858 | 890 | if rangeList != None: |
|
|
859 | 891 | |
|
|
860 | 892 | profileList = [] |
|
|
861 | 893 | |
|
|
862 | 894 | for thisRange in rangeList: |
|
|
863 | 895 | minIndex = thisRange[0] |
|
|
864 | 896 | maxIndex = thisRange[1] |
|
|
865 | 897 | |
|
|
866 | 898 | profileList.extend(range(minIndex, maxIndex+1)) |
|
|
867 | 899 | |
|
|
868 | 900 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
869 | 901 | |
|
|
870 | 902 | dataOut.nProfiles = len(profileList) |
|
|
871 | 903 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
872 | 904 | dataOut.flagNoData = False |
|
|
873 | 905 | |
|
|
874 | 906 | return True |
|
|
875 | 907 | |
|
|
876 | 908 | """ |
|
|
877 | 909 | data dimension = [nChannels, nHeis] |
|
|
878 | 910 | """ |
|
|
879 | 911 | |
|
|
880 | 912 | if profileList != None: |
|
|
881 | 913 | |
|
|
882 | 914 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
883 | 915 | |
|
|
884 | 916 | self.nProfiles = len(profileList) |
|
|
885 | 917 | dataOut.nProfiles = self.nProfiles |
|
|
886 | 918 | dataOut.profileIndex = self.profileIndex |
|
|
887 | 919 | dataOut.flagNoData = False |
|
|
888 | 920 | |
|
|
889 | 921 | self.incProfileIndex() |
|
|
890 | 922 | return True |
|
|
891 | 923 | |
|
|
892 | 924 | if profileRangeList != None: |
|
|
893 | 925 | |
|
|
894 | 926 | minIndex = profileRangeList[0] |
|
|
895 | 927 | maxIndex = profileRangeList[1] |
|
|
896 | 928 | |
|
|
897 | 929 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
898 | 930 | |
|
|
899 | 931 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
900 | 932 | dataOut.nProfiles = self.nProfiles |
|
|
901 | 933 | dataOut.profileIndex = self.profileIndex |
|
|
902 | 934 | dataOut.flagNoData = False |
|
|
903 | 935 | |
|
|
904 | 936 | self.incProfileIndex() |
|
|
905 | 937 | return True |
|
|
906 | 938 | |
|
|
907 | 939 | if rangeList != None: |
|
|
908 | 940 | |
|
|
909 | 941 | nProfiles = 0 |
|
|
910 | 942 | |
|
|
911 | 943 | for thisRange in rangeList: |
|
|
912 | 944 | minIndex = thisRange[0] |
|
|
913 | 945 | maxIndex = thisRange[1] |
|
|
914 | 946 | |
|
|
915 | 947 | nProfiles += maxIndex - minIndex + 1 |
|
|
916 | 948 | |
|
|
917 | 949 | for thisRange in rangeList: |
|
|
918 | 950 | |
|
|
919 | 951 | minIndex = thisRange[0] |
|
|
920 | 952 | maxIndex = thisRange[1] |
|
|
921 | 953 | |
|
|
922 | 954 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
923 | 955 | |
|
|
924 | 956 | self.nProfiles = nProfiles |
|
|
925 | 957 | dataOut.nProfiles = self.nProfiles |
|
|
926 | 958 | dataOut.profileIndex = self.profileIndex |
|
|
927 | 959 | dataOut.flagNoData = False |
|
|
928 | 960 | |
|
|
929 | 961 | self.incProfileIndex() |
|
|
930 | 962 | |
|
|
931 | 963 | break |
|
|
932 | 964 | |
|
|
933 | 965 | return True |
|
|
934 | 966 | |
|
|
935 | 967 | |
|
|
936 | 968 | if beam != None: #beam is only for AMISR data |
|
|
937 | 969 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
938 | 970 | dataOut.flagNoData = False |
|
|
939 | 971 | dataOut.profileIndex = self.profileIndex |
|
|
940 | 972 | |
|
|
941 | 973 | self.incProfileIndex() |
|
|
942 | 974 | |
|
|
943 | 975 | return True |
|
|
944 | 976 | |
|
|
945 | 977 | raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter" |
|
|
946 | 978 | |
|
|
947 | 979 | return False |
|
|
948 | 980 | |
|
|
949 | 981 | class Reshaper(Operation): |
|
|
950 | 982 | |
|
|
951 | 983 | def __init__(self, **kwargs): |
|
|
952 | 984 | |
|
|
953 | 985 | Operation.__init__(self, **kwargs) |
|
|
954 | 986 | |
|
|
955 | 987 | self.__buffer = None |
|
|
956 | 988 | self.__nitems = 0 |
|
|
957 | 989 | |
|
|
958 | 990 | def __appendProfile(self, dataOut, nTxs): |
|
|
959 | 991 | |
|
|
960 | 992 | if self.__buffer is None: |
|
|
961 | 993 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
962 | 994 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
963 | 995 | |
|
|
964 | 996 | ini = dataOut.nHeights * self.__nitems |
|
|
965 | 997 | end = ini + dataOut.nHeights |
|
|
966 | 998 | |
|
|
967 | 999 | self.__buffer[:, ini:end] = dataOut.data |
|
|
968 | 1000 | |
|
|
969 | 1001 | self.__nitems += 1 |
|
|
970 | 1002 | |
|
|
971 | 1003 | return int(self.__nitems*nTxs) |
|
|
972 | 1004 | |
|
|
973 | 1005 | def __getBuffer(self): |
|
|
974 | 1006 | |
|
|
975 | 1007 | if self.__nitems == int(1./self.__nTxs): |
|
|
976 | 1008 | |
|
|
977 | 1009 | self.__nitems = 0 |
|
|
978 | 1010 | |
|
|
979 | 1011 | return self.__buffer.copy() |
|
|
980 | 1012 | |
|
|
981 | 1013 | return None |
|
|
982 | 1014 | |
|
|
983 | 1015 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
984 | 1016 | |
|
|
985 | 1017 | if shape is None and nTxs is None: |
|
|
986 | 1018 | raise ValueError, "Reshaper: shape of factor should be defined" |
|
|
987 | 1019 | |
|
|
988 | 1020 | if nTxs: |
|
|
989 | 1021 | if nTxs < 0: |
|
|
990 | 1022 | raise ValueError, "nTxs should be greater than 0" |
|
|
991 | 1023 | |
|
|
992 | 1024 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
993 | 1025 | raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs)) |
|
|
994 | 1026 | |
|
|
995 | 1027 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
996 | 1028 | |
|
|
997 | 1029 | return shape, nTxs |
|
|
998 | 1030 | |
|
|
999 | 1031 | if len(shape) != 2 and len(shape) != 3: |
|
|
1000 | 1032 | 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) |
|
|
1001 | 1033 | |
|
|
1002 | 1034 | if len(shape) == 2: |
|
|
1003 | 1035 | shape_tuple = [dataOut.nChannels] |
|
|
1004 | 1036 | shape_tuple.extend(shape) |
|
|
1005 | 1037 | else: |
|
|
1006 | 1038 | shape_tuple = list(shape) |
|
|
1007 | 1039 | |
|
|
1008 | 1040 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1009 | 1041 | |
|
|
1010 | 1042 | return shape_tuple, nTxs |
|
|
1011 | 1043 | |
|
|
1012 | 1044 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1013 | 1045 | |
|
|
1014 | 1046 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1015 | 1047 | |
|
|
1016 | 1048 | dataOut.flagNoData = True |
|
|
1017 | 1049 | profileIndex = None |
|
|
1018 | 1050 | |
|
|
1019 | 1051 | if dataOut.flagDataAsBlock: |
|
|
1020 | 1052 | |
|
|
1021 | 1053 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1022 | 1054 | dataOut.flagNoData = False |
|
|
1023 | 1055 | |
|
|
1024 | 1056 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1025 | 1057 | |
|
|
1026 | 1058 | else: |
|
|
1027 | 1059 | |
|
|
1028 | 1060 | if self.__nTxs < 1: |
|
|
1029 | 1061 | |
|
|
1030 | 1062 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1031 | 1063 | new_data = self.__getBuffer() |
|
|
1032 | 1064 | |
|
|
1033 | 1065 | if new_data is not None: |
|
|
1034 | 1066 | dataOut.data = new_data |
|
|
1035 | 1067 | dataOut.flagNoData = False |
|
|
1036 | 1068 | |
|
|
1037 | 1069 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1038 | 1070 | |
|
|
1039 | 1071 | else: |
|
|
1040 | 1072 | raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)" |
|
|
1041 | 1073 | |
|
|
1042 | 1074 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1043 | 1075 | |
|
|
1044 | 1076 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1045 | 1077 | |
|
|
1046 | 1078 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1047 | 1079 | |
|
|
1048 | 1080 | dataOut.profileIndex = profileIndex |
|
|
1049 | 1081 | |
|
|
1050 | 1082 | dataOut.ippSeconds /= self.__nTxs |
|
|
1051 | 1083 | |
|
|
1052 | 1084 | class SplitProfiles(Operation): |
|
|
1053 | 1085 | |
|
|
1054 | 1086 | def __init__(self, **kwargs): |
|
|
1055 | 1087 | |
|
|
1056 | 1088 | Operation.__init__(self, **kwargs) |
|
|
1057 | 1089 | |
|
|
1058 | 1090 | def run(self, dataOut, n): |
|
|
1059 | 1091 | |
|
|
1060 | 1092 | dataOut.flagNoData = True |
|
|
1061 | 1093 | profileIndex = None |
|
|
1062 | 1094 | |
|
|
1063 | 1095 | if dataOut.flagDataAsBlock: |
|
|
1064 | 1096 | |
|
|
1065 | 1097 | #nchannels, nprofiles, nsamples |
|
|
1066 | 1098 | shape = dataOut.data.shape |
|
|
1067 | 1099 | |
|
|
1068 | 1100 | if shape[2] % n != 0: |
|
|
1069 | 1101 | raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2]) |
|
|
1070 | 1102 | |
|
|
1071 | 1103 | new_shape = shape[0], shape[1]*n, shape[2]/n |
|
|
1072 | 1104 | |
|
|
1073 | 1105 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1074 | 1106 | dataOut.flagNoData = False |
|
|
1075 | 1107 | |
|
|
1076 | 1108 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1077 | 1109 | |
|
|
1078 | 1110 | else: |
|
|
1079 | 1111 | |
|
|
1080 | 1112 | raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)" |
|
|
1081 | 1113 | |
|
|
1082 | 1114 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1083 | 1115 | |
|
|
1084 | 1116 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1085 | 1117 | |
|
|
1086 | 1118 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1087 | 1119 | |
|
|
1088 | 1120 | dataOut.profileIndex = profileIndex |
|
|
1089 | 1121 | |
|
|
1090 | 1122 | dataOut.ippSeconds /= n |
|
|
1091 | 1123 | |
|
|
1092 | 1124 | class CombineProfiles(Operation): |
|
|
1093 | 1125 | parameters = { |
|
|
1094 |
'n': |
|
|
|
1126 | 'n': global_type_integer, | |
|
|
1095 | 1127 | } |
|
|
1096 | 1128 | def __init__(self, **kwargs): |
|
|
1097 | 1129 | |
|
|
1098 | 1130 | Operation.__init__(self, **kwargs) |
|
|
1099 | 1131 | |
|
|
1100 | 1132 | self.__remData = None |
|
|
1101 | 1133 | self.__profileIndex = 0 |
|
|
1102 | 1134 | |
|
|
1103 | 1135 | def run(self, dataOut, n): |
|
|
1104 | 1136 | |
|
|
1105 | 1137 | dataOut.flagNoData = True |
|
|
1106 | 1138 | profileIndex = None |
|
|
1107 | 1139 | |
|
|
1108 | 1140 | if dataOut.flagDataAsBlock: |
|
|
1109 | 1141 | |
|
|
1110 | 1142 | #nchannels, nprofiles, nsamples |
|
|
1111 | 1143 | shape = dataOut.data.shape |
|
|
1112 | 1144 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1113 | 1145 | |
|
|
1114 | 1146 | if shape[1] % n != 0: |
|
|
1115 | 1147 | raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1]) |
|
|
1116 | 1148 | |
|
|
1117 | 1149 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1118 | 1150 | dataOut.flagNoData = False |
|
|
1119 | 1151 | |
|
|
1120 | 1152 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1121 | 1153 | |
|
|
1122 | 1154 | else: |
|
|
1123 | 1155 | |
|
|
1124 | 1156 | #nchannels, nsamples |
|
|
1125 | 1157 | if self.__remData is None: |
|
|
1126 | 1158 | newData = dataOut.data |
|
|
1127 | 1159 | else: |
|
|
1128 | 1160 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1129 | 1161 | |
|
|
1130 | 1162 | self.__profileIndex += 1 |
|
|
1131 | 1163 | |
|
|
1132 | 1164 | if self.__profileIndex < n: |
|
|
1133 | 1165 | self.__remData = newData |
|
|
1134 | 1166 | #continue |
|
|
1135 | 1167 | return |
|
|
1136 | 1168 | |
|
|
1137 | 1169 | self.__profileIndex = 0 |
|
|
1138 | 1170 | self.__remData = None |
|
|
1139 | 1171 | |
|
|
1140 | 1172 | dataOut.data = newData |
|
|
1141 | 1173 | dataOut.flagNoData = False |
|
|
1142 | 1174 | |
|
|
1143 | 1175 | profileIndex = dataOut.profileIndex/n |
|
|
1144 | 1176 | |
|
|
1145 | 1177 | |
|
|
1146 | 1178 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1147 | 1179 | |
|
|
1148 | 1180 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1149 | 1181 | |
|
|
1150 | 1182 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1151 | 1183 | |
|
|
1152 | 1184 | dataOut.profileIndex = profileIndex |
|
|
1153 | 1185 | |
|
|
1154 | 1186 | dataOut.ippSeconds *= n |
|
|
1155 | 1187 | |
|
|
1156 | 1188 | # import collections |
|
|
1157 | 1189 | # from scipy.stats import mode |
|
|
1158 | 1190 | # |
|
|
1159 | 1191 | # class Synchronize(Operation): |
|
|
1160 | 1192 | # |
|
|
1161 | 1193 | # isConfig = False |
|
|
1162 | 1194 | # __profIndex = 0 |
|
|
1163 | 1195 | # |
|
|
1164 | 1196 | # def __init__(self, **kwargs): |
|
|
1165 | 1197 | # |
|
|
1166 | 1198 | # Operation.__init__(self, **kwargs) |
|
|
1167 | 1199 | # # self.isConfig = False |
|
|
1168 | 1200 | # self.__powBuffer = None |
|
|
1169 | 1201 | # self.__startIndex = 0 |
|
|
1170 | 1202 | # self.__pulseFound = False |
|
|
1171 | 1203 | # |
|
|
1172 | 1204 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1173 | 1205 | # |
|
|
1174 | 1206 | # #Read data |
|
|
1175 | 1207 | # |
|
|
1176 | 1208 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1177 | 1209 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1178 | 1210 | # |
|
|
1179 | 1211 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1180 | 1212 | # |
|
|
1181 | 1213 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1182 | 1214 | # |
|
|
1183 | 1215 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1184 | 1216 | # |
|
|
1185 | 1217 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1186 | 1218 | # |
|
|
1187 | 1219 | # if maxValue < noisedB + 10: |
|
|
1188 | 1220 | # #No se encuentra ningun pulso de transmision |
|
|
1189 | 1221 | # return None |
|
|
1190 | 1222 | # |
|
|
1191 | 1223 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1192 | 1224 | # |
|
|
1193 | 1225 | # if len(maxValuesIndex) < 2: |
|
|
1194 | 1226 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1195 | 1227 | # return None |
|
|
1196 | 1228 | # |
|
|
1197 | 1229 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1198 | 1230 | # |
|
|
1199 | 1231 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1200 | 1232 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1201 | 1233 | # |
|
|
1202 | 1234 | # if len(pulseIndex) < 2: |
|
|
1203 | 1235 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1204 | 1236 | # return None |
|
|
1205 | 1237 | # |
|
|
1206 | 1238 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1207 | 1239 | # |
|
|
1208 | 1240 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1209 | 1241 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1210 | 1242 | # |
|
|
1211 | 1243 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1212 | 1244 | # |
|
|
1213 | 1245 | # if len(realIndex) < 2: |
|
|
1214 | 1246 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1215 | 1247 | # return None |
|
|
1216 | 1248 | # |
|
|
1217 | 1249 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1218 | 1250 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1219 | 1251 | # |
|
|
1220 | 1252 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1221 | 1253 | # |
|
|
1222 | 1254 | # print "IPP = %d samples" %period |
|
|
1223 | 1255 | # |
|
|
1224 | 1256 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1225 | 1257 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1226 | 1258 | # |
|
|
1227 | 1259 | # return 1 |
|
|
1228 | 1260 | # |
|
|
1229 | 1261 | # |
|
|
1230 | 1262 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1231 | 1263 | # |
|
|
1232 | 1264 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1233 | 1265 | # maxlen = buffer_size*nSamples) |
|
|
1234 | 1266 | # |
|
|
1235 | 1267 | # bufferList = [] |
|
|
1236 | 1268 | # |
|
|
1237 | 1269 | # for i in range(nChannels): |
|
|
1238 | 1270 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1239 | 1271 | # maxlen = buffer_size*nSamples) |
|
|
1240 | 1272 | # |
|
|
1241 | 1273 | # bufferList.append(bufferByChannel) |
|
|
1242 | 1274 | # |
|
|
1243 | 1275 | # self.__nSamples = nSamples |
|
|
1244 | 1276 | # self.__nChannels = nChannels |
|
|
1245 | 1277 | # self.__bufferList = bufferList |
|
|
1246 | 1278 | # |
|
|
1247 | 1279 | # def run(self, dataOut, channel = 0): |
|
|
1248 | 1280 | # |
|
|
1249 | 1281 | # if not self.isConfig: |
|
|
1250 | 1282 | # nSamples = dataOut.nHeights |
|
|
1251 | 1283 | # nChannels = dataOut.nChannels |
|
|
1252 | 1284 | # self.setup(nSamples, nChannels) |
|
|
1253 | 1285 | # self.isConfig = True |
|
|
1254 | 1286 | # |
|
|
1255 | 1287 | # #Append new data to internal buffer |
|
|
1256 | 1288 | # for thisChannel in range(self.__nChannels): |
|
|
1257 | 1289 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1258 | 1290 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1259 | 1291 | # |
|
|
1260 | 1292 | # if self.__pulseFound: |
|
|
1261 | 1293 | # self.__startIndex -= self.__nSamples |
|
|
1262 | 1294 | # |
|
|
1263 | 1295 | # #Finding Tx Pulse |
|
|
1264 | 1296 | # if not self.__pulseFound: |
|
|
1265 | 1297 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1266 | 1298 | # |
|
|
1267 | 1299 | # if indexFound == None: |
|
|
1268 | 1300 | # dataOut.flagNoData = True |
|
|
1269 | 1301 | # return |
|
|
1270 | 1302 | # |
|
|
1271 | 1303 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1272 | 1304 | # self.__pulseFound = True |
|
|
1273 | 1305 | # self.__startIndex = indexFound |
|
|
1274 | 1306 | # |
|
|
1275 | 1307 | # #If pulse was found ... |
|
|
1276 | 1308 | # for thisChannel in range(self.__nChannels): |
|
|
1277 | 1309 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1278 | 1310 | # #print self.__startIndex |
|
|
1279 | 1311 | # x = numpy.array(bufferByChannel) |
|
|
1280 | 1312 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1281 | 1313 | # |
|
|
1282 | 1314 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1283 | 1315 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1284 | 1316 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1285 | 1317 | # |
|
|
1286 | 1318 | # dataOut.data = self.__arrayBuffer |
|
|
1287 | 1319 | # |
|
|
1288 | 1320 | # self.__startIndex += self.__newNSamples |
|
|
1289 | 1321 | # |
|
|
1290 | 1322 | # return |
| @@ -1,94 +1,94 | |||
|
|
1 | 1 | import argparse |
|
|
2 | 2 | |
|
|
3 | 3 | from schainpy.controller import Project, multiSchain |
|
|
4 | 4 | |
|
|
5 | 5 | desc = "HF_EXAMPLE" |
|
|
6 | 6 | |
|
|
7 | 7 | def fiber(cursor, skip, q, dt): |
|
|
8 | 8 | |
|
|
9 | 9 | controllerObj = Project() |
|
|
10 | 10 | |
|
|
11 | 11 | controllerObj.setup(id='191', name='test01', description=desc) |
|
|
12 | 12 | |
|
|
13 | 13 | readUnitConfObj = controllerObj.addReadUnit(datatype='SpectraReader', |
|
|
14 |
|
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|
|
15 |
|
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16 |
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17 |
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18 |
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19 |
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20 |
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21 |
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22 |
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23 |
|
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24 |
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25 |
|
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|
26 |
|
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|
27 |
|
|
|
|
14 | path='/home/nanosat/data/julia', | |
|
|
15 | startDate=dt, | |
|
|
16 | endDate=dt, | |
|
|
17 | startTime="00:00:00", | |
|
|
18 | endTime="23:59:59", | |
|
|
19 | online=0, | |
|
|
20 | #set=1426485881, | |
|
|
21 | delay=10, | |
|
|
22 | walk=1, | |
|
|
23 | queue=q, | |
|
|
24 | cursor=cursor, | |
|
|
25 | skip=skip, | |
|
|
26 | #timezone=-5*3600 | |
|
|
27 | ) | |
|
|
28 | 28 | |
|
|
29 | 29 | # #opObj11 = readUnitConfObj.addOperation(name='printNumberOfBlock') |
|
|
30 | 30 | # |
|
|
31 | 31 | procUnitConfObj2 = controllerObj.addProcUnit(datatype='Spectra', inputId=readUnitConfObj.getId()) |
|
|
32 | 32 | # procUnitConfObj2.addParameter(name='nipp', value='5', format='int') |
|
|
33 | 33 | |
|
|
34 | 34 | # procUnitConfObj3 = controllerObj.addProcUnit(datatype='ParametersProc', inputId=readUnitConfObj.getId()) |
|
|
35 | 35 | # opObj11 = procUnitConfObj3.addOperation(name='SpectralMoments', optype='other') |
|
|
36 | 36 | |
|
|
37 | 37 | # |
|
|
38 | 38 | opObj11 = procUnitConfObj2.addOperation(name='RTIPlot', optype='other') |
|
|
39 | 39 | opObj11.addParameter(name='id', value='1000', format='int') |
|
|
40 | 40 | opObj11.addParameter(name='wintitle', value='HF_Jicamarca_Spc', format='str') |
|
|
41 | 41 | opObj11.addParameter(name='xmin', value='0', format='int') |
|
|
42 | 42 | opObj11.addParameter(name='xmax', value='24', format='int') |
|
|
43 | 43 | |
|
|
44 | 44 | # opObj11 = procUnitConfObj3.addOperation(name='Parameters1Plot', optype='other') |
|
|
45 | 45 | # opObj11.addParameter(name='channelList', value='0', format='intList') |
|
|
46 | 46 | # |
|
|
47 | 47 | # opObj11.addParameter(name='id', value='2000', format='int') |
|
|
48 | 48 | # # opObj11.addParameter(name='colormap', value='0', format='bool') |
|
|
49 | 49 | # opObj11.addParameter(name='onlySNR', value='1', format='bool') |
|
|
50 | 50 | # opObj11.addParameter(name='DOP', value='0', format='bool') |
|
|
51 | 51 | # opObj11.addParameter(name='showSNR', value='1', format='bool') |
|
|
52 | 52 | # opObj11.addParameter(name='SNRthresh', value='0', format='int') |
|
|
53 | 53 | # opObj11.addParameter(name='SNRmin', value='-10', format='int') |
|
|
54 | 54 | # opObj11.addParameter(name='SNRmax', value='30', format='int') |
|
|
55 | 55 | |
|
|
56 | 56 | # opObj11.addParameter(name='showSNR', value='1', format='int') |
|
|
57 | 57 | # # opObj11.addParameter(name='channelList', value='0', format='intlist') |
|
|
58 | 58 | # # opObj11.addParameter(name='xmin', value='0', format='float') |
|
|
59 | 59 | # opObj11.addParameter(name='xmin', value='0', format='float') |
|
|
60 | 60 | # opObj11.addParameter(name='xmax', value='24', format='float') |
|
|
61 | 61 | |
|
|
62 | 62 | # opObj11.addParameter(name='zmin', value='-110', format='float') |
|
|
63 | 63 | # opObj11.addParameter(name='zmax', value='-70', format='float') |
|
|
64 | 64 | # opObj11.addParameter(name='save', value='0', format='int') |
|
|
65 | 65 | # # opObj11.addParameter(name='figpath', value='/tmp/', format='str') |
|
|
66 | 66 | # |
|
|
67 | 67 | # opObj12 = procUnitConfObj2.addOperation(name='PublishData', optype='other') |
|
|
68 | 68 | # opObj12.addParameter(name='zeromq', value=1, format='int') |
|
|
69 | 69 | # opObj12.addParameter(name='server', value='tcp://10.10.10.82:7000', format='str') |
|
|
70 | 70 | |
|
|
71 | 71 | |
|
|
72 | 72 | # opObj13 = procUnitConfObj3.addOperation(name='PublishData', optype='other') |
|
|
73 | 73 | # opObj13.addParameter(name='zeromq', value=1, format='int') |
|
|
74 | 74 | # opObj13.addParameter(name='server', value="juanca", format='str') |
|
|
75 | 75 | |
|
|
76 | 76 | # opObj12.addParameter(name='delay', value=1, format='int') |
|
|
77 | 77 | |
|
|
78 | 78 | |
|
|
79 | 79 | # print "Escribiendo el archivo XML" |
|
|
80 | 80 | # controllerObj.writeXml(filename) |
|
|
81 | 81 | # print "Leyendo el archivo XML" |
|
|
82 | 82 | # controllerObj.readXml(filename) |
|
|
83 | 83 | |
|
|
84 | 84 | |
|
|
85 | 85 | # timeit.timeit('controllerObj.run()', number=2) |
|
|
86 | 86 | |
|
|
87 | 87 | controllerObj.start() |
|
|
88 | 88 | |
|
|
89 | 89 | |
|
|
90 | 90 | if __name__ == '__main__': |
|
|
91 | 91 | parser = argparse.ArgumentParser(description='Set number of parallel processes') |
|
|
92 | 92 | parser.add_argument('--nProcess', default=1, type=int) |
|
|
93 | 93 | args = parser.parse_args() |
|
|
94 | 94 | multiSchain(fiber, nProcess=args.nProcess, startDate='2016/08/19', endDate='2016/08/19') |
| This diff has been collapsed as it changes many lines, (1668 lines changed) Show them Hide them | |||
| @@ -1,1160 +1,1172 | |||
|
|
1 | You should install "digital_rf_hdf5" module if you want to read USRP data | |
|
|
2 | BeaconPhase | |
|
|
3 | parameters = { | |
|
|
4 | 'id': 'string', | |
|
|
5 | 'wintitle': 'string', | |
|
|
6 | 'pairsList': 'pairsList', | |
|
|
7 | 'showprofile': 'boolean', | |
|
|
8 | 'xmin': 'float', | |
|
|
9 | 'xmax': 'float', | |
|
|
10 | 'ymin': 'float', | |
|
|
11 | 'ymax': 'float', | |
|
|
12 | 'hmin': 'float', | |
|
|
13 | 'hmax': 'float', | |
|
|
14 | 'timerange': 'float', | |
|
|
15 | 'save': 'boolean', | |
|
|
16 | 'figpath': 'string', | |
|
|
17 | 'figfile': 'string', | |
|
|
18 | 'show': 'boolean', | |
|
|
19 | 'ftp': 'boolean', | |
|
|
20 | 'wr_period': 'int', | |
|
|
21 | 'server': 'string', | |
|
|
22 | 'folder': 'string', | |
|
|
23 | 'username': 'string', | |
|
|
24 | 'password': 'string', | |
|
|
25 | 'ftp_wei': 'int', | |
|
|
26 | 'exp_code': 'int', | |
|
|
27 | 'sub_exp_code': 'int', | |
|
|
28 | 'plot_pos': 'int', | |
|
|
29 | } | |
|
|
30 | ||
|
|
31 | ||
|
|
32 | BeamSelector | |
|
|
33 | parameters = { | |
|
|
34 | 'beam': 'string', | |
|
|
35 | } | |
|
|
36 | ||
|
|
37 | ||
|
|
38 | CohInt | |
|
|
39 | parameters = { | |
|
|
40 | 'n': 'int', | |
|
|
41 | 'timeInterval': 'float', | |
|
|
42 | 'overlapping': 'boolean', | |
|
|
43 | 'byblock': 'boolean' | |
|
|
1 | ||
|
|
2 | global_type_string = 'string' | |
|
|
3 | global_type_integer = 'int' | |
|
|
4 | global_type_floatList = 'floatList' | |
|
|
5 | global_type_pairsList = 'pairsList' | |
|
|
6 | global_type_boolean = 'bolean' | |
|
|
7 | global_type_float = 'float' | |
|
|
8 | global_type_colormap = 'colormap' | |
|
|
9 | global_type_list = 'list' | |
|
|
10 | ||
|
|
11 | #BeaconPhase | |
|
|
12 | parameters = { | |
|
|
13 | 'id': global_type_string, | |
|
|
14 | 'wintitle': global_type_string, | |
|
|
15 | 'pairsList': global_type_pairsList, | |
|
|
16 | 'showprofile': global_type_boolean, | |
|
|
17 | 'xmin': global_type_float, | |
|
|
18 | 'xmax': global_type_float, | |
|
|
19 | 'ymin': global_type_float, | |
|
|
20 | 'ymax': global_type_float, | |
|
|
21 | 'hmin': global_type_float, | |
|
|
22 | 'hmax': global_type_float, | |
|
|
23 | 'timerange': global_type_float, | |
|
|
24 | 'save': global_type_boolean, | |
|
|
25 | 'figpath': global_type_string, | |
|
|
26 | 'figfile': global_type_string, | |
|
|
27 | 'show': global_type_boolean, | |
|
|
28 | 'ftp': global_type_boolean, | |
|
|
29 | 'wr_period': global_type_integer, | |
|
|
30 | 'server': global_type_string, | |
|
|
31 | 'folder': global_type_string, | |
|
|
32 | 'username': global_type_string, | |
|
|
33 | 'password': global_type_string, | |
|
|
34 | 'ftp_wei': global_type_integer, | |
|
|
35 | 'exp_code': global_type_integer, | |
|
|
36 | 'sub_exp_code': global_type_integer, | |
|
|
37 | 'plot_pos': global_type_integer, | |
|
|
38 | } | |
|
|
39 | ||
|
|
40 | ||
|
|
41 | #BeamSelector | |
|
|
42 | parameters = { | |
|
|
43 | 'beam': global_type_string, | |
|
|
44 | } | |
|
|
45 | ||
|
|
46 | ||
|
|
47 | #CohInt | |
|
|
48 | parameters = { | |
|
|
49 | 'n': global_type_integer, | |
|
|
50 | 'timeInterval': global_type_float, | |
|
|
51 | 'overlapping': global_type_boolean, | |
|
|
52 | 'byblock': global_type_boolean | |
|
|
53 | } | |
|
|
54 | ||
|
|
55 | ||
|
|
56 | #CoherenceMap | |
|
|
57 | parameters = { | |
|
|
58 | 'id': global_type_string, | |
|
|
59 | 'wintitle': global_type_string, | |
|
|
60 | 'pairsList': global_type_pairsList, | |
|
|
61 | 'showprofile': global_type_boolean, | |
|
|
62 | 'xmin': global_type_float, | |
|
|
63 | 'xmax': global_type_float, | |
|
|
64 | 'ymin': global_type_float, | |
|
|
65 | 'ymax': global_type_float, | |
|
|
66 | 'zmin': global_type_float, | |
|
|
67 | 'zmax': global_type_float, | |
|
|
68 | 'timerange': global_type_float, | |
|
|
69 | 'phase_min': global_type_float, | |
|
|
70 | 'phase_max': global_type_float, | |
|
|
71 | 'save': global_type_boolean, | |
|
|
72 | 'figpath': global_type_string, | |
|
|
73 | 'figfile': global_type_string, | |
|
|
74 | 'ftp': global_type_boolean, | |
|
|
75 | 'wr_period': global_type_integer, | |
|
|
76 | 'coherence_cmap': global_type_colormap, | |
|
|
77 | 'phase_cmap': global_type_colormap, | |
|
|
78 | 'show': global_type_boolean, | |
|
|
79 | 'server': global_type_string, | |
|
|
80 | 'folder': global_type_string, | |
|
|
81 | 'username': global_type_string, | |
|
|
82 | 'password': global_type_string, | |
|
|
83 | 'ftp_wei': global_type_integer, | |
|
|
84 | 'exp_code': global_type_integer, | |
|
|
85 | 'sub_exp_code': global_type_integer, | |
|
|
86 | 'plot_pos': global_type_integer, | |
|
|
87 | } | |
|
|
88 | ||
|
|
89 | ||
|
|
90 | #CombineProfiles | |
|
|
91 | parameters = { | |
|
|
92 | 'n': global_type_integer, | |
|
|
44 | 93 | } |
|
|
45 | 94 | |
|
|
46 | 95 | |
|
|
47 | CoherenceMap | |
|
|
96 | #CorrectSMPhases | |
|
|
48 | 97 | parameters = { |
|
|
49 | 'id': 'string', | |
|
|
50 | 'wintitle': 'string', | |
|
|
51 | 'pairsList': 'pairsLists', | |
|
|
52 | 'showprofile': 'boolean', | |
|
|
53 | 'xmin': 'float', | |
|
|
54 | 'xmax': 'float', | |
|
|
55 | 'ymin': 'float', | |
|
|
56 | 'ymax': 'float', | |
|
|
57 | 'zmin': 'float', | |
|
|
58 | 'zmax': 'float', | |
|
|
59 | 'timerange': 'float', | |
|
|
60 | 'phase_min': 'float', | |
|
|
61 | 'phase_max': 'float', | |
|
|
62 | 'save': 'boolean', | |
|
|
63 | 'figpath': 'string', | |
|
|
64 | 'figfile': 'string', | |
|
|
65 | 'ftp': 'boolean', | |
|
|
66 | 'wr_period': 'int', | |
|
|
67 | 'coherence_cmap': 'colormap', | |
|
|
68 | 'phase_cmap': 'colormap', | |
|
|
69 | 'show': 'boolean', | |
|
|
70 | 'server': 'string', | |
|
|
71 | 'folder': 'string', | |
|
|
72 | 'username': 'string', | |
|
|
73 | 'password': 'string', | |
|
|
74 | 'ftp_wei': 'int', | |
|
|
75 | 'exp_code': 'int', | |
|
|
76 | 'sub_exp_code': 'int', | |
|
|
77 | 'plot_pos': 'int', | |
|
|
78 | } | |
|
|
79 | ||
|
|
80 | ||
|
|
81 | CombineProfiles | |
|
|
82 | parameters = { | |
|
|
83 | 'n': 'int', | |
|
|
84 | } | |
|
|
85 | ||
|
|
86 | ||
|
|
87 | CorrectSMPhases | |
|
|
88 | parameters = { | |
|
|
89 | 'phaseOffsets': 'pairsLists', | |
|
|
90 | 'hmin': 'float', | |
|
|
91 | 'hmax': 'float', | |
|
|
92 | 'azimuth': 'string', | |
|
|
93 | 'channelPositions': 'string', | |
|
|
94 | } | |
|
|
95 | ||
|
|
96 | ||
|
|
97 | CorrelationPlot | |
|
|
98 | parameters = { | |
|
|
99 | 'id': 'string', | |
|
|
100 | 'wintitle': 'string', | |
|
|
101 | 'channelList': 'string', | |
|
|
102 | 'showprofile': 'string', | |
|
|
103 | 'xmin': 'float', | |
|
|
104 | 'xmax': 'float', | |
|
|
105 | 'ymin': 'float', | |
|
|
106 | 'ymax': 'float', | |
|
|
107 | 'zmin': 'float', | |
|
|
108 | 'zmax': 'float', | |
|
|
109 | 'save': 'boolean', | |
|
|
110 | 'figpath': 'string', | |
|
|
111 | 'figfile': 'string', | |
|
|
112 | 'show': 'boolean', | |
|
|
113 | 'ftp': 'boolean', | |
|
|
114 | 'wr_period': 'int', | |
|
|
115 | 'server': 'string', | |
|
|
116 | 'folder': 'string', | |
|
|
117 | 'username': 'string', | |
|
|
118 | 'password': 'string', | |
|
|
119 | 'ftp_wei': 'string', | |
|
|
120 | 'exp_code': 'int', | |
|
|
121 | 'sub_exp_code': 'int', | |
|
|
122 | 'plot_pos': 'int', | |
|
|
123 | 'realtime': 'string', | |
|
|
124 | } | |
|
|
125 | ||
|
|
126 | ||
|
|
127 | CrossSpectraPlot | |
|
|
128 | parameters = { | |
|
|
129 | 'id': 'string', | |
|
|
130 | 'wintitle': 'string', | |
|
|
131 | 'pairsList': 'pairsLists', | |
|
|
132 | 'xmin': 'float', | |
|
|
133 | 'xmax': 'float', | |
|
|
134 | 'ymin': 'float', | |
|
|
135 | 'ymax': 'float', | |
|
|
136 | 'zmin': 'float', | |
|
|
137 | 'zmax': 'float', | |
|
|
138 | 'coh_min': 'string', | |
|
|
139 | 'coh_max': 'string', | |
|
|
140 | 'phase_min': 'string', | |
|
|
141 | 'phase_max': 'string', | |
|
|
142 | 'save': 'boolean', | |
|
|
143 | 'figpath': 'string', | |
|
|
144 | 'figfile': 'string', | |
|
|
145 | 'ftp': 'boolean', | |
|
|
146 | 'wr_period': 'int', | |
|
|
147 | 'power_cmap': 'string', | |
|
|
148 | 'coherence_cmap': 'string', | |
|
|
149 | 'phase_cmap': 'string', | |
|
|
150 | 'show': 'string', | |
|
|
151 | 'server': 'string', | |
|
|
152 | 'folder': 'string', | |
|
|
153 | 'username': 'string', | |
|
|
154 | 'password': 'string', | |
|
|
155 | 'ftp_wei': 'string', | |
|
|
156 | 'exp_code': 'int', | |
|
|
157 | 'sub_exp_code': 'int', | |
|
|
158 | 'plot_pos': 'int', | |
|
|
159 | 'xaxis': 'string', | |
|
|
160 | } | |
|
|
161 | ||
|
|
162 | ||
|
|
163 | Decoder | |
|
|
164 | parameters = { | |
|
|
165 | 'code': 'string', | |
|
|
166 | 'nCode': 'string', | |
|
|
167 | 'nBaud': 'string', | |
|
|
168 |
' |
|
|
|
169 | 'osamp': 'string', | |
|
|
170 | 'times': 'string', | |
|
|
171 | } | |
|
|
172 | ||
|
|
173 | ||
|
|
174 | EWDriftsEstimation | |
|
|
175 | parameters = { | |
|
|
176 | 'zenith': 'string', | |
|
|
177 | 'zenithCorrection': 'string', | |
|
|
178 | } | |
|
|
179 | ||
|
|
180 | ||
|
|
181 | EWDriftsPlot | |
|
|
182 | parameters = { | |
|
|
183 | 'id': 'string', | |
|
|
184 | 'wintitle': 'string', | |
|
|
185 | 'channelList': 'string', | |
|
|
186 | 'xmin': 'float', | |
|
|
187 | 'xmax': 'float', | |
|
|
188 | 'ymin': 'float', | |
|
|
189 | 'ymax': 'float', | |
|
|
190 | 'zmin': 'float', | |
|
|
191 | 'zmax': 'float', | |
|
|
192 | 'zmaxVertfloat 'string', | |
|
|
193 | 'zminVertfloat 'string', | |
|
|
194 | 'zmaxZonafloattring', | |
|
|
195 | 'zminZonafloattring', | |
|
|
196 | 'timerange': 'string', | |
|
|
197 | 'SNRthresh': 'string', | |
|
|
198 | 'SNRmin': 'string', | |
|
|
199 | 'SNRmax': 'string', | |
|
|
200 | 'SNR_1': 'string', | |
|
|
201 | 'save': 'boolean', | |
|
|
202 | 'figpath': 'string', | |
|
|
203 | 'lastone': 'string', | |
|
|
204 | 'figfile': 'string', | |
|
|
205 | 'ftp': 'string', | |
|
|
206 | 'wr_period': 'int', | |
|
|
207 | 'show': 'string', | |
|
|
208 | 'server': 'string', | |
|
|
209 | 'folder': 'string', | |
|
|
210 | 'username': 'string', | |
|
|
211 | 'password': 'string', | |
|
|
212 | 'ftp_wei': 'string', | |
|
|
213 | 'exp_code': 'int', | |
|
|
214 | 'sub_exp_code': 'int', | |
|
|
215 | 'plot_pos': 'int', | |
|
|
98 | 'phaseOffsets': global_type_pairsList, | |
|
|
99 | 'hmin': global_type_float, | |
|
|
100 | 'hmax': global_type_float, | |
|
|
101 | 'azimuth': global_type_float, | |
|
|
102 | 'channelPositions': global_type_pairsList, | |
|
|
103 | } | |
|
|
104 | ||
|
|
105 | ||
|
|
106 | #CorrelationPlot | |
|
|
107 | parameters = { | |
|
|
108 | 'id': global_type_string, | |
|
|
109 | 'wintitle': global_type_string, | |
|
|
110 | 'channelList': global_type_list, | |
|
|
111 | 'showprofile': global_type_boolean, | |
|
|
112 | 'xmin': global_type_float, | |
|
|
113 | 'xmax': global_type_float, | |
|
|
114 | 'ymin': global_type_float, | |
|
|
115 | 'ymax': global_type_float, | |
|
|
116 | 'zmin': global_type_float, | |
|
|
117 | 'zmax': global_type_float, | |
|
|
118 | 'save': global_type_boolean, | |
|
|
119 | 'figpath': global_type_string, | |
|
|
120 | 'figfile': global_type_string, | |
|
|
121 | 'show': global_type_boolean, | |
|
|
122 | 'ftp': global_type_boolean, | |
|
|
123 | 'wr_period': global_type_integer, | |
|
|
124 | 'server': global_type_string, | |
|
|
125 | 'folder': global_type_string, | |
|
|
126 | 'username': global_type_string, | |
|
|
127 | 'password': global_type_string, | |
|
|
128 | 'ftp_wei': global_type_integer, | |
|
|
129 | 'exp_code': global_type_integer, | |
|
|
130 | 'sub_exp_code': global_type_integer, | |
|
|
131 | 'plot_pos': global_type_integer, | |
|
|
132 | 'realtime': global_type_boolean, | |
|
|
133 | } | |
|
|
134 | ||
|
|
135 | ||
|
|
136 | #CrossSpectraPlot | |
|
|
137 | parameters = { | |
|
|
138 | 'id': global_type_string, | |
|
|
139 | 'wintitle': global_type_string, | |
|
|
140 | 'pairsList': global_type_pairsList, | |
|
|
141 | 'xmin': global_type_float, | |
|
|
142 | 'xmax': global_type_float, | |
|
|
143 | 'ymin': global_type_float, | |
|
|
144 | 'ymax': global_type_float, | |
|
|
145 | 'zmin': global_type_float, | |
|
|
146 | 'zmax': global_type_float, | |
|
|
147 | 'coh_min': global_type_float, | |
|
|
148 | 'coh_max': global_type_float, | |
|
|
149 | 'phase_min': global_type_float, | |
|
|
150 | 'phase_max': global_type_float, | |
|
|
151 | 'save': global_type_boolean, | |
|
|
152 | 'figpath': global_type_string, | |
|
|
153 | 'figfile': global_type_string, | |
|
|
154 | 'ftp': global_type_boolean, | |
|
|
155 | 'wr_period': global_type_integer, | |
|
|
156 | 'power_cmap': global_type_colormap, | |
|
|
157 | 'coherence_cmap': global_type_colormap, | |
|
|
158 | 'phase_cmap': global_type_colormap, | |
|
|
159 | 'show': global_type_boolean, | |
|
|
160 | 'server': global_type_string, | |
|
|
161 | 'folder': global_type_string, | |
|
|
162 | 'username': global_type_string, | |
|
|
163 | 'password': global_type_string, | |
|
|
164 | 'ftp_wei': global_type_integer, | |
|
|
165 | 'exp_code': global_type_integer, | |
|
|
166 | 'sub_exp_code': global_type_integer, | |
|
|
167 | 'plot_pos': global_type_integer, | |
|
|
168 | 'xaxis': global_type_string, | |
|
|
169 | } | |
|
|
170 | ||
|
|
171 | ||
|
|
172 | #Decoder | |
|
|
173 | parameters = { | |
|
|
174 | 'code': global_type_list, | |
|
|
175 | 'nCode': global_type_integer, | |
|
|
176 | 'nBaud': global_type_integer, | |
|
|
177 | 'mode': global_type_integer, | |
|
|
178 | 'osamp': global_type_float, | |
|
|
179 | } | |
|
|
180 | ||
|
|
181 | ||
|
|
182 | #EWDriftsEstimation | |
|
|
183 | parameters = { | |
|
|
184 | 'zenith': global_type_list, | |
|
|
185 | 'zenithCorrection': global_type_float, | |
|
|
186 | } | |
|
|
187 | ||
|
|
188 | ||
|
|
189 | #EWDriftsPlot | |
|
|
190 | parameters = { | |
|
|
191 | 'id': global_type_string, | |
|
|
192 | 'wintitle': global_type_string, | |
|
|
193 | 'channelList': global_type_list, | |
|
|
194 | 'xmin': global_type_float, | |
|
|
195 | 'xmax': global_type_float, | |
|
|
196 | 'ymin': global_type_float, | |
|
|
197 | 'ymax': global_type_float, | |
|
|
198 | 'zmin': global_type_float, | |
|
|
199 | 'zmax': global_type_float, | |
|
|
200 | 'zmaxVertfloat': global_type_float, | |
|
|
201 | 'zminVertfloat': global_type_float, | |
|
|
202 | 'zmaxZonafloat': global_type_float, | |
|
|
203 | 'zminZonafloat': global_type_float, | |
|
|
204 | 'timerange': global_type_float, | |
|
|
205 | 'SNRthresh': global_type_float, | |
|
|
206 | 'SNRmin': global_type_float, | |
|
|
207 | 'SNRmax': global_type_float, | |
|
|
208 | 'SNR_1': global_type_boolean, | |
|
|
209 | 'save': global_type_boolean, | |
|
|
210 | 'figpath': global_type_string, | |
|
|
211 | 'lastone': global_type_float, | |
|
|
212 | 'figfile': global_type_string, | |
|
|
213 | 'ftp': global_type_string, | |
|
|
214 | 'wr_period': global_type_integer, | |
|
|
215 | 'show': global_type_string, | |
|
|
216 | 'server': global_type_string, | |
|
|
217 | 'folder': global_type_string, | |
|
|
218 | 'username': global_type_string, | |
|
|
219 | 'password': global_type_string, | |
|
|
220 | 'ftp_wei': global_type_integer, | |
|
|
221 | 'exp_code': global_type_integer, | |
|
|
222 | 'sub_exp_code': global_type_integer, | |
|
|
223 | 'plot_pos': global_type_integer, | |
|
|
216 | 224 | } |
|
|
217 | 225 | |
|
|
218 | 226 | |
|
|
219 | 227 | Figure |
|
|
228 | # parameters = { | |
|
|
229 | # : global_type_string, | |
|
|
230 | # } | |
|
|
231 | ||
|
|
232 | ||
|
|
233 | #FitsWriter | |
|
|
220 | 234 | parameters = { |
|
|
221 | : 'string', | |
|
|
222 | } | |
|
|
223 | ||
|
|
224 | ||
|
|
225 | FitsWriter | |
|
|
226 | parameters = { | |
|
|
227 | : 'string', | |
|
|
228 | } | |
|
|
229 | ||
|
|
230 | ||
|
|
231 | IncohInt | |
|
|
232 | parameters = { | |
|
|
233 | 'n': 'string', | |
|
|
234 | 'timeInterval': 'string', | |
|
|
235 | 'overlapping': 'string', | |
|
|
236 | } | |
|
|
237 | ||
|
|
238 | ||
|
|
239 | IncohInt4SpectraHeis | |
|
|
240 | parameters = { | |
|
|
241 | : 'string', | |
|
|
242 | } | |
|
|
243 | ||
|
|
244 | ||
|
|
245 | MomentsPlot | |
|
|
246 | parameters = { | |
|
|
247 | 'id': 'string', | |
|
|
248 | 'wintitle': 'string', | |
|
|
249 | 'channelList': 'string', | |
|
|
250 | 'showprofile': 'string', | |
|
|
251 |
' |
|
|
|
252 |
' |
|
|
|
253 |
' |
|
|
|
254 |
' |
|
|
|
255 | 'zmin': 'float', | |
|
|
256 | 'zmax': 'float', | |
|
|
257 | 'save': 'boolean', | |
|
|
258 | 'figpath': 'string', | |
|
|
259 | 'figfile': 'string', | |
|
|
260 | 'show': 'string', | |
|
|
261 | 'ftp': 'string', | |
|
|
262 | 'wr_period': 'int', | |
|
|
263 |
' |
|
|
|
264 | 'folder': 'string', | |
|
|
265 | 'username': 'string', | |
|
|
266 | 'password': 'string', | |
|
|
267 | 'ftp_wei': 'string', | |
|
|
268 | 'exp_code': 'int', | |
|
|
269 | 'sub_exp_code': 'int', | |
|
|
270 | 'plot_pos': 'int', | |
|
|
271 | 'realtime': 'string', | |
|
|
272 | } | |
|
|
273 | ||
|
|
274 | ||
|
|
275 | NSMeteorDetection1Plot | |
|
|
276 | parameters = { | |
|
|
277 | 'id': 'string', | |
|
|
278 | 'wintitle': 'string', | |
|
|
279 | 'channelList': 'string', | |
|
|
280 | 'showprofile': 'string', | |
|
|
281 |
' |
|
|
|
282 |
' |
|
|
|
283 |
' |
|
|
|
284 |
' |
|
|
|
285 | 'SNRmin': 'string', | |
|
|
286 | 'SNRmax': 'string', | |
|
|
287 | 'vmin': 'string', | |
|
|
288 | 'vmax': 'string', | |
|
|
289 | 'wmin': 'string', | |
|
|
290 | 'wmax': 'string', | |
|
|
291 | 'mode': 'string', | |
|
|
292 | 'save': 'boolean', | |
|
|
293 | 'figpath': 'string', | |
|
|
294 | 'figfile': 'string', | |
|
|
295 | 'show': 'string', | |
|
|
296 | 'ftp': 'string', | |
|
|
297 | 'wr_period': 'int', | |
|
|
298 |
' |
|
|
|
299 | 'folder': 'string', | |
|
|
300 | 'username': 'string', | |
|
|
301 | 'password': 'string', | |
|
|
302 | 'ftp_wei': 'string', | |
|
|
303 | 'exp_code': 'int', | |
|
|
304 | 'sub_exp_code': 'int', | |
|
|
305 | 'plot_pos': 'int', | |
|
|
306 | 'realtime': 'string', | |
|
|
307 | 'xaxis': 'string', | |
|
|
308 | } | |
|
|
309 | ||
|
|
310 | ||
|
|
311 | NSMeteorDetection2Plot | |
|
|
312 | parameters = { | |
|
|
313 | 'id': 'string', | |
|
|
314 | 'wintitle': 'string', | |
|
|
315 | 'channelList': 'string', | |
|
|
316 | 'showprofile': 'string', | |
|
|
317 |
' |
|
|
|
318 |
' |
|
|
|
319 |
' |
|
|
|
320 |
' |
|
|
|
321 | 'SNRmin': 'string', | |
|
|
322 | 'SNRmax': 'string', | |
|
|
323 | 'vmin': 'string', | |
|
|
324 | 'vmax': 'string', | |
|
|
325 | 'wmin': 'string', | |
|
|
326 | 'wmax': 'string', | |
|
|
327 | 'mode': 'string', | |
|
|
328 | 'save': 'boolean', | |
|
|
329 | 'figpath': 'string', | |
|
|
330 | 'figfile': 'string', | |
|
|
331 | 'show': 'string', | |
|
|
332 | 'ftp': 'string', | |
|
|
333 | 'wr_period': 'int', | |
|
|
334 |
' |
|
|
|
335 | 'folder': 'string', | |
|
|
336 | 'username': 'string', | |
|
|
337 | 'password': 'string', | |
|
|
338 | 'ftp_wei': 'string', | |
|
|
339 | 'exp_code': 'int', | |
|
|
340 | 'sub_exp_code': 'int', | |
|
|
341 | 'plot_pos': 'int', | |
|
|
342 | 'realtime': 'string', | |
|
|
343 | 'xaxis': 'string', | |
|
|
344 | } | |
|
|
345 | ||
|
|
346 | ||
|
|
347 | Noise | |
|
|
348 | parameters = { | |
|
|
349 | 'id': 'string', | |
|
|
350 | 'wintitle': 'string', | |
|
|
351 | 'channelList': 'string', | |
|
|
352 | 'showprofile': 'string', | |
|
|
353 |
' |
|
|
|
354 |
' |
|
|
|
355 | 'ymin': 'float', | |
|
|
356 | 'ymax': 'float', | |
|
|
357 | 'timerange': 'string', | |
|
|
358 | 'save': 'boolean', | |
|
|
359 | 'figpath': 'string', | |
|
|
360 | 'figfile': 'string', | |
|
|
361 | 'show': 'string', | |
|
|
362 | 'ftp': 'string', | |
|
|
363 | 'wr_period': 'int', | |
|
|
364 |
' |
|
|
|
365 | 'folder': 'string', | |
|
|
366 | 'username': 'string', | |
|
|
367 | 'password': 'string', | |
|
|
368 | 'ftp_wei': 'string', | |
|
|
369 | 'exp_code': 'int', | |
|
|
370 | 'sub_exp_code': 'int', | |
|
|
371 | 'plot_pos': 'int', | |
|
|
372 | } | |
|
|
373 | ||
|
|
374 | ||
|
|
375 | NonSpecularMeteorDetection | |
|
|
376 | parameters = { | |
|
|
377 | 'mode': 'string', | |
|
|
378 | 'SNRthresh': 'string', | |
|
|
379 | 'phaseDerThresh': 'string', | |
|
|
380 | 'cohThresh': 'string', | |
|
|
381 | 'allData': 'string', | |
|
|
235 | 'path': global_type_string, | |
|
|
236 | 'dataBlocksPerFile': global_type_integer, | |
|
|
237 | 'metadatafile': global_type_string, | |
|
|
238 | } | |
|
|
239 | ||
|
|
240 | ||
|
|
241 | #IncohInt | |
|
|
242 | parameters = { | |
|
|
243 | 'n': global_type_float, | |
|
|
244 | 'timeInterval': global_type_integer, | |
|
|
245 | 'overlapping': global_type_boolean, | |
|
|
246 | } | |
|
|
247 | ||
|
|
248 | ||
|
|
249 | #IncohInt4SpectraHeis | |
|
|
250 | parameters = { | |
|
|
251 | 'n': global_type_float, | |
|
|
252 | 'timeInterval': global_type_integer, | |
|
|
253 | 'overlapping': global_type_boolean, | |
|
|
254 | } | |
|
|
255 | ||
|
|
256 | ||
|
|
257 | #MomentsPlot | |
|
|
258 | parameters = { | |
|
|
259 | 'id': global_type_string, | |
|
|
260 | 'wintitle': global_type_string, | |
|
|
261 | 'channelList': global_type_list, | |
|
|
262 | 'showprofile': global_type_boolean, | |
|
|
263 | 'xmin': global_type_float, | |
|
|
264 | 'xmax': global_type_float, | |
|
|
265 | 'ymin': global_type_float, | |
|
|
266 | 'ymax': global_type_float, | |
|
|
267 | 'zmin': global_type_float, | |
|
|
268 | 'zmax': global_type_float, | |
|
|
269 | 'save': global_type_boolean, | |
|
|
270 | 'figpath': global_type_string, | |
|
|
271 | 'figfile': global_type_string, | |
|
|
272 | 'show': global_type_boolean, | |
|
|
273 | 'ftp': global_type_boolean, | |
|
|
274 | 'wr_period': global_type_integer, | |
|
|
275 | 'server': global_type_string, | |
|
|
276 | 'folder': global_type_string, | |
|
|
277 | 'username': global_type_string, | |
|
|
278 | 'password': global_type_string, | |
|
|
279 | 'ftp_wei': global_type_string, | |
|
|
280 | 'exp_code': global_type_integer, | |
|
|
281 | 'sub_exp_code': global_type_integer, | |
|
|
282 | 'plot_pos': global_type_integer, | |
|
|
283 | 'realtime': global_type_boolean, | |
|
|
284 | } | |
|
|
285 | ||
|
|
286 | ||
|
|
287 | #NSMeteorDetection1Plot | |
|
|
288 | parameters = { | |
|
|
289 | 'id': global_type_string, | |
|
|
290 | 'wintitle': global_type_string, | |
|
|
291 | 'channelList': global_type_list, | |
|
|
292 | 'showprofile': global_type_boolean, | |
|
|
293 | 'xmin': global_type_float, | |
|
|
294 | 'xmax': global_type_float, | |
|
|
295 | 'ymin': global_type_float, | |
|
|
296 | 'ymax': global_type_float, | |
|
|
297 | 'SNRmin': global_type_float, | |
|
|
298 | 'SNRmax': global_type_float, | |
|
|
299 | 'vmin': global_type_float, | |
|
|
300 | 'vmax': global_type_float, | |
|
|
301 | 'wmin': global_type_float, | |
|
|
302 | 'wmax': global_type_float, | |
|
|
303 | 'mode': global_type_string, | |
|
|
304 | 'save': global_type_boolean, | |
|
|
305 | 'figpath': global_type_string, | |
|
|
306 | 'figfile': global_type_string, | |
|
|
307 | 'show': global_type_boolean, | |
|
|
308 | 'ftp': global_type_string, | |
|
|
309 | 'wr_period': global_type_integer, | |
|
|
310 | 'server': global_type_string, | |
|
|
311 | 'folder': global_type_string, | |
|
|
312 | 'username': global_type_string, | |
|
|
313 | 'password': global_type_string, | |
|
|
314 | 'ftp_wei': global_type_integer, | |
|
|
315 | 'exp_code': global_type_integer, | |
|
|
316 | 'sub_exp_code': global_type_integer, | |
|
|
317 | 'plot_pos': global_type_integer, | |
|
|
318 | 'realtime': global_type_boolean, | |
|
|
319 | 'xaxis': global_type_string, | |
|
|
320 | } | |
|
|
321 | ||
|
|
322 | ||
|
|
323 | #NSMeteorDetection2Plot | |
|
|
324 | parameters = { | |
|
|
325 | 'id': global_type_string, | |
|
|
326 | 'wintitle': global_type_string, | |
|
|
327 | 'channelList': global_type_list, | |
|
|
328 | 'showprofile': global_type_boolean, | |
|
|
329 | 'xmin': global_type_float, | |
|
|
330 | 'xmax': global_type_float, | |
|
|
331 | 'ymin': global_type_float, | |
|
|
332 | 'ymax': global_type_float, | |
|
|
333 | 'SNRmin': global_type_float, | |
|
|
334 | 'SNRmax': global_type_float, | |
|
|
335 | 'vmin': global_type_float, | |
|
|
336 | 'vmax': global_type_float, | |
|
|
337 | 'wmin': global_type_float, | |
|
|
338 | 'wmax': global_type_float, | |
|
|
339 | 'mode': global_type_string, | |
|
|
340 | 'save': global_type_boolean, | |
|
|
341 | 'figpath': global_type_string, | |
|
|
342 | 'figfile': global_type_string, | |
|
|
343 | 'show': global_type_string, | |
|
|
344 | 'ftp': global_type_boolean, | |
|
|
345 | 'wr_period': global_type_integer, | |
|
|
346 | 'server': global_type_string, | |
|
|
347 | 'folder': global_type_string, | |
|
|
348 | 'username': global_type_string, | |
|
|
349 | 'password': global_type_string, | |
|
|
350 | 'ftp_wei': global_type_integer, | |
|
|
351 | 'exp_code': global_type_integer, | |
|
|
352 | 'sub_exp_code': global_type_integer, | |
|
|
353 | 'plot_pos': global_type_integer, | |
|
|
354 | 'realtime': global_type_boolean, | |
|
|
355 | 'xaxis': global_type_string, | |
|
|
356 | } | |
|
|
357 | ||
|
|
358 | ||
|
|
359 | #Noise | |
|
|
360 | parameters = { | |
|
|
361 | 'id': global_type_string, | |
|
|
362 | 'wintitle': global_type_string, | |
|
|
363 | 'channelList': global_type_list, | |
|
|
364 | 'showprofile': global_type_boolean, | |
|
|
365 | 'xmin': global_type_float, | |
|
|
366 | 'xmax': global_type_float, | |
|
|
367 | 'ymin': global_type_float, | |
|
|
368 | 'ymax': global_type_float, | |
|
|
369 | 'timerange': global_type_float, | |
|
|
370 | 'save': global_type_boolean, | |
|
|
371 | 'figpath': global_type_string, | |
|
|
372 | 'figfile': global_type_string, | |
|
|
373 | 'show': global_type_boolean, | |
|
|
374 | 'ftp': global_type_boolean, | |
|
|
375 | 'wr_period': global_type_integer, | |
|
|
376 | 'server': global_type_string, | |
|
|
377 | 'folder': global_type_string, | |
|
|
378 | 'username': global_type_string, | |
|
|
379 | 'password': global_type_string, | |
|
|
380 | 'ftp_wei': global_type_integer, | |
|
|
381 | 'exp_code': global_type_integer, | |
|
|
382 | 'sub_exp_code': global_type_integer, | |
|
|
383 | 'plot_pos': global_type_integer, | |
|
|
384 | } | |
|
|
385 | ||
|
|
386 | ||
|
|
387 | #NonSpecularMeteorDetection | |
|
|
388 | parameters = { | |
|
|
389 | 'mode': global_type_string, | |
|
|
390 | 'SNRthresh': global_type_float, | |
|
|
391 | 'phaseDerThresh': global_type_float, | |
|
|
392 | 'cohThresh': global_type_float, | |
|
|
393 | 'allData': global_type_boolean, | |
|
|
382 | 394 | } |
|
|
383 | 395 | |
|
|
384 | 396 | |
|
|
385 | 397 | Operation |
|
|
386 | 398 | parameters = { |
|
|
387 |
'dataIn': |
|
|
|
399 | 'dataIn': global_type_string, | |
|
|
388 | 400 | } |
|
|
389 | 401 | |
|
|
390 | 402 | |
|
|
391 | 403 | ParamWriter |
|
|
392 | 404 | parameters = { |
|
|
393 | : 'string', | |
|
|
405 | : global_type_string, | |
|
|
394 | 406 | } |
|
|
395 | 407 | |
|
|
396 | 408 | |
|
|
397 | 409 | Parameters1Plot |
|
|
398 | 410 | parameters = { |
|
|
399 |
'id': |
|
|
|
400 |
'wintitle': |
|
|
|
401 |
'channelList': |
|
|
|
402 |
'showprofile': |
|
|
|
403 |
'xmin': |
|
|
|
404 |
'xmax': |
|
|
|
405 |
'ymin': |
|
|
|
406 |
'ymax': |
|
|
|
407 |
'zmin': |
|
|
|
408 |
'zmax': |
|
|
|
409 | 'timerange': 'string', | |
|
|
410 |
'parameterIndex': |
|
|
|
411 |
'onlyPositive': |
|
|
|
412 |
'SNRthresh': |
|
|
|
413 |
'SNR': |
|
|
|
414 | 'SNRmin': 'string', | |
|
|
415 | 'SNRmax': 'string', | |
|
|
416 |
'onlySNR': |
|
|
|
417 |
'DOP': |
|
|
|
418 |
'zlabel': |
|
|
|
419 |
'parameterName': |
|
|
|
420 |
'parameterObject': |
|
|
|
421 |
'save': |
|
|
|
422 |
'figpath': |
|
|
|
423 |
'lastone': |
|
|
|
424 |
'figfile': |
|
|
|
425 |
'ftp': |
|
|
|
426 |
'wr_period': |
|
|
|
427 |
'show': |
|
|
|
428 |
'server': |
|
|
|
429 |
'folder': |
|
|
|
430 |
'username': |
|
|
|
431 |
'password': |
|
|
|
432 |
'ftp_wei': |
|
|
|
433 |
'exp_code': |
|
|
|
434 |
'sub_exp_code': |
|
|
|
435 |
'plot_pos': |
|
|
|
411 | 'id': global_type_string, | |
|
|
412 | 'wintitle': global_type_string, | |
|
|
413 | 'channelList': global_type_list, | |
|
|
414 | 'showprofile': global_type_boolean, | |
|
|
415 | 'xmin': global_type_float, | |
|
|
416 | 'xmax': global_type_float, | |
|
|
417 | 'ymin': global_type_float, | |
|
|
418 | 'ymax': global_type_float, | |
|
|
419 | 'zmin': global_type_float, | |
|
|
420 | 'zmax': global_type_float, | |
|
|
421 | 'timerange': global_type_float, | |
|
|
422 | 'parameterIndex': global_type_string, | |
|
|
423 | 'onlyPositive': global_type_string, | |
|
|
424 | 'SNRthresh': global_type_string, | |
|
|
425 | 'SNR': global_type_string, | |
|
|
426 | 'SNRmin': global_type_float, | |
|
|
427 | 'SNRmax': global_type_float, | |
|
|
428 | 'onlySNR': global_type_string, | |
|
|
429 | 'DOP': global_type_string, | |
|
|
430 | 'zlabel': global_type_string, | |
|
|
431 | 'parameterName': global_type_string, | |
|
|
432 | 'parameterObject': global_type_string, | |
|
|
433 | 'save': global_type_boolean, | |
|
|
434 | 'figpath': global_type_string, | |
|
|
435 | 'lastone': global_type_string, | |
|
|
436 | 'figfile': global_type_string, | |
|
|
437 | 'ftp': global_type_string, | |
|
|
438 | 'wr_period': global_type_integer, | |
|
|
439 | 'show': global_type_string, | |
|
|
440 | 'server': global_type_string, | |
|
|
441 | 'folder': global_type_string, | |
|
|
442 | 'username': global_type_string, | |
|
|
443 | 'password': global_type_string, | |
|
|
444 | 'ftp_wei': global_type_integer, | |
|
|
445 | 'exp_code': global_type_integer, | |
|
|
446 | 'sub_exp_code': global_type_integer, | |
|
|
447 | 'plot_pos': global_type_integer, | |
|
|
436 | 448 | } |
|
|
437 | 449 | |
|
|
438 | 450 | |
|
|
439 | 451 | ParametersPlot |
|
|
440 | 452 | parameters = { |
|
|
441 |
'id': |
|
|
|
442 |
'wintitle': |
|
|
|
443 |
'channelList': |
|
|
|
444 |
'paramIndex': |
|
|
|
445 |
'colormap': |
|
|
|
446 |
'xmin': |
|
|
|
447 |
'xmax': |
|
|
|
448 |
'ymin': |
|
|
|
449 |
'ymax': |
|
|
|
450 |
'zmin': |
|
|
|
451 |
'zmax': |
|
|
|
452 | 'timerange': 'string', | |
|
|
453 |
'showSNR': |
|
|
|
454 |
'SNRthresh': |
|
|
|
455 | 'SNRmin': 'string', | |
|
|
456 | 'SNRmax': 'string', | |
|
|
457 |
'save': |
|
|
|
458 |
'figpath': |
|
|
|
459 |
'lastone': |
|
|
|
460 |
'figfile': |
|
|
|
461 |
'ftp': |
|
|
|
462 |
'wr_period': |
|
|
|
463 |
'show': |
|
|
|
464 |
'server': |
|
|
|
465 |
'folder': |
|
|
|
466 |
'username': |
|
|
|
467 |
'password': |
|
|
|
468 |
'ftp_wei': |
|
|
|
469 |
'exp_code': |
|
|
|
470 |
'sub_exp_code': |
|
|
|
471 |
'plot_pos': |
|
|
|
453 | 'id': global_type_string, | |
|
|
454 | 'wintitle': global_type_string, | |
|
|
455 | 'channelList': global_type_list, | |
|
|
456 | 'paramIndex': global_type_string, | |
|
|
457 | 'colormap': global_type_string, | |
|
|
458 | 'xmin': global_type_float, | |
|
|
459 | 'xmax': global_type_float, | |
|
|
460 | 'ymin': global_type_float, | |
|
|
461 | 'ymax': global_type_float, | |
|
|
462 | 'zmin': global_type_float, | |
|
|
463 | 'zmax': global_type_float, | |
|
|
464 | 'timerange': global_type_float, | |
|
|
465 | 'showSNR': global_type_string, | |
|
|
466 | 'SNRthresh': global_type_string, | |
|
|
467 | 'SNRmin': global_type_float, | |
|
|
468 | 'SNRmax': global_type_float, | |
|
|
469 | 'save': global_type_boolean, | |
|
|
470 | 'figpath': global_type_string, | |
|
|
471 | 'lastone': global_type_string, | |
|
|
472 | 'figfile': global_type_string, | |
|
|
473 | 'ftp': global_type_string, | |
|
|
474 | 'wr_period': global_type_integer, | |
|
|
475 | 'show': global_type_string, | |
|
|
476 | 'server': global_type_string, | |
|
|
477 | 'folder': global_type_string, | |
|
|
478 | 'username': global_type_string, | |
|
|
479 | 'password': global_type_string, | |
|
|
480 | 'ftp_wei': global_type_integer, | |
|
|
481 | 'exp_code': global_type_integer, | |
|
|
482 | 'sub_exp_code': global_type_integer, | |
|
|
483 | 'plot_pos': global_type_integer, | |
|
|
472 | 484 | } |
|
|
473 | 485 | |
|
|
474 | 486 | |
|
|
475 | 487 | PhasePlot |
|
|
476 | 488 | parameters = { |
|
|
477 |
'id': |
|
|
|
478 |
'wintitle': |
|
|
|
489 | 'id': global_type_string, | |
|
|
490 | 'wintitle': global_type_string, | |
|
|
479 | 491 | 'pairsList': 'pairsLists', |
|
|
480 |
'showprofile': |
|
|
|
481 |
'xmin': |
|
|
|
482 |
'xmax': |
|
|
|
483 |
'ymin': |
|
|
|
484 |
'ymax': |
|
|
|
485 | 'timerange': 'string', | |
|
|
486 |
'save': |
|
|
|
487 |
'figpath': |
|
|
|
488 |
'figfile': |
|
|
|
489 |
'show': |
|
|
|
490 |
'ftp': |
|
|
|
491 |
'wr_period': |
|
|
|
492 |
'server': |
|
|
|
493 |
'folder': |
|
|
|
494 |
'username': |
|
|
|
495 |
'password': |
|
|
|
496 |
'ftp_wei': |
|
|
|
497 |
'exp_code': |
|
|
|
498 |
'sub_exp_code': |
|
|
|
499 |
'plot_pos': |
|
|
|
492 | 'showprofile': global_type_boolean, | |
|
|
493 | 'xmin': global_type_float, | |
|
|
494 | 'xmax': global_type_float, | |
|
|
495 | 'ymin': global_type_float, | |
|
|
496 | 'ymax': global_type_float, | |
|
|
497 | 'timerange': global_type_float, | |
|
|
498 | 'save': global_type_boolean, | |
|
|
499 | 'figpath': global_type_string, | |
|
|
500 | 'figfile': global_type_string, | |
|
|
501 | 'show': global_type_string, | |
|
|
502 | 'ftp': global_type_string, | |
|
|
503 | 'wr_period': global_type_integer, | |
|
|
504 | 'server': global_type_string, | |
|
|
505 | 'folder': global_type_string, | |
|
|
506 | 'username': global_type_string, | |
|
|
507 | 'password': global_type_string, | |
|
|
508 | 'ftp_wei': global_type_integer, | |
|
|
509 | 'exp_code': global_type_integer, | |
|
|
510 | 'sub_exp_code': global_type_integer, | |
|
|
511 | 'plot_pos': global_type_integer, | |
|
|
500 | 512 | } |
|
|
501 | 513 | |
|
|
502 | 514 | |
|
|
503 | 515 | PlotCOHData |
|
|
504 | 516 | parameters = { |
|
|
505 | : 'string', | |
|
|
517 | : global_type_string, | |
|
|
506 | 518 | } |
|
|
507 | 519 | |
|
|
508 | 520 | |
|
|
509 | 521 | PlotCrossSpectraData |
|
|
510 | 522 | parameters = { |
|
|
511 | : 'string', | |
|
|
523 | : global_type_string, | |
|
|
512 | 524 | } |
|
|
513 | 525 | |
|
|
514 | 526 | |
|
|
515 | 527 | PlotDOPData |
|
|
516 | 528 | parameters = { |
|
|
517 | : 'string', | |
|
|
529 | : global_type_string, | |
|
|
518 | 530 | } |
|
|
519 | 531 | |
|
|
520 | 532 | |
|
|
521 | 533 | PlotData |
|
|
522 | 534 | parameters = { |
|
|
523 | : 'string', | |
|
|
535 | : global_type_string, | |
|
|
524 | 536 | } |
|
|
525 | 537 | |
|
|
526 | 538 | |
|
|
527 | 539 | PlotNoiseData |
|
|
528 | 540 | parameters = { |
|
|
529 | : 'string', | |
|
|
541 | : global_type_string, | |
|
|
530 | 542 | } |
|
|
531 | 543 | |
|
|
532 | 544 | |
|
|
533 | 545 | PlotPHASEData |
|
|
534 | 546 | parameters = { |
|
|
535 | : 'string', | |
|
|
547 | : global_type_string, | |
|
|
536 | 548 | } |
|
|
537 | 549 | |
|
|
538 | 550 | |
|
|
539 | 551 | PlotRTIData |
|
|
540 | 552 | parameters = { |
|
|
541 | : 'string', | |
|
|
553 | : global_type_string, | |
|
|
542 | 554 | } |
|
|
543 | 555 | |
|
|
544 | 556 | |
|
|
545 | 557 | PlotSNRData |
|
|
546 | 558 | parameters = { |
|
|
547 | : 'string', | |
|
|
559 | : global_type_string, | |
|
|
548 | 560 | } |
|
|
549 | 561 | |
|
|
550 | 562 | |
|
|
551 | 563 | PlotSpectraData |
|
|
552 | 564 | parameters = { |
|
|
553 | : 'string', | |
|
|
565 | : global_type_string, | |
|
|
554 | 566 | } |
|
|
555 | 567 | |
|
|
556 | 568 | |
|
|
557 | 569 | PlotSpectraMeanData |
|
|
558 | 570 | parameters = { |
|
|
559 | : 'string', | |
|
|
571 | : global_type_string, | |
|
|
560 | 572 | } |
|
|
561 | 573 | |
|
|
562 | 574 | |
|
|
563 | 575 | PlotWindProfilerData |
|
|
564 | 576 | parameters = { |
|
|
565 | : 'string', | |
|
|
577 | : global_type_string, | |
|
|
566 | 578 | } |
|
|
567 | 579 | |
|
|
568 | 580 | |
|
|
569 | 581 | PowerProfilePlot |
|
|
570 | 582 | parameters = { |
|
|
571 |
'id': |
|
|
|
572 |
'wintitle': |
|
|
|
573 |
'channelList': |
|
|
|
574 |
'xmin': |
|
|
|
575 |
'xmax': |
|
|
|
576 |
'ymin': |
|
|
|
577 |
'ymax': |
|
|
|
578 |
'save': |
|
|
|
579 |
'figpath': |
|
|
|
580 |
'figfile': |
|
|
|
581 |
'show': |
|
|
|
582 |
'ftp': |
|
|
|
583 |
'wr_period': |
|
|
|
584 |
'server': |
|
|
|
585 |
'folder': |
|
|
|
586 |
'username': |
|
|
|
587 |
'password': |
|
|
|
583 | 'id': global_type_string, | |
|
|
584 | 'wintitle': global_type_string, | |
|
|
585 | 'channelList': global_type_list, | |
|
|
586 | 'xmin': global_type_float, | |
|
|
587 | 'xmax': global_type_float, | |
|
|
588 | 'ymin': global_type_float, | |
|
|
589 | 'ymax': global_type_float, | |
|
|
590 | 'save': global_type_boolean, | |
|
|
591 | 'figpath': global_type_string, | |
|
|
592 | 'figfile': global_type_string, | |
|
|
593 | 'show': global_type_string, | |
|
|
594 | 'ftp': global_type_string, | |
|
|
595 | 'wr_period': global_type_integer, | |
|
|
596 | 'server': global_type_string, | |
|
|
597 | 'folder': global_type_string, | |
|
|
598 | 'username': global_type_string, | |
|
|
599 | 'password': global_type_string, | |
|
|
588 | 600 | } |
|
|
589 | 601 | |
|
|
590 | 602 | |
|
|
591 | 603 | PrintInfo |
|
|
592 | 604 | parameters = { |
|
|
593 | : 'string', | |
|
|
605 | : global_type_string, | |
|
|
594 | 606 | } |
|
|
595 | 607 | |
|
|
596 | 608 | |
|
|
597 | 609 | ProfileConcat |
|
|
598 | 610 | parameters = { |
|
|
599 |
'm': |
|
|
|
611 | 'm': global_type_string, | |
|
|
600 | 612 | } |
|
|
601 | 613 | |
|
|
602 | 614 | |
|
|
603 | 615 | ProfileSelector |
|
|
604 | 616 | parameters = { |
|
|
605 |
'profileList': |
|
|
|
606 |
'profileRangeList': |
|
|
|
607 |
'beam': |
|
|
|
608 |
'byblock': |
|
|
|
609 |
'rangeList': |
|
|
|
610 |
'nProfiles': |
|
|
|
617 | 'profileList': global_type_string, | |
|
|
618 | 'profileRangeList': global_type_string, | |
|
|
619 | 'beam': global_type_string, | |
|
|
620 | 'byblock': global_type_string, | |
|
|
621 | 'rangeList': global_type_string, | |
|
|
622 | 'nProfiles': global_type_string, | |
|
|
611 | 623 | } |
|
|
612 | 624 | |
|
|
613 | 625 | |
|
|
614 | 626 | ProfileToChannels |
|
|
615 | 627 | parameters = { |
|
|
616 | : 'string', | |
|
|
628 | : global_type_string, | |
|
|
617 | 629 | } |
|
|
618 | 630 | |
|
|
619 | 631 | |
|
|
620 | 632 | PublishData |
|
|
621 | 633 | parameters = { |
|
|
622 | : 'string', | |
|
|
634 | : global_type_string, | |
|
|
623 | 635 | } |
|
|
624 | 636 | |
|
|
625 | 637 | |
|
|
626 | 638 | RTIPlot |
|
|
627 | 639 | parameters = { |
|
|
628 |
'id': |
|
|
|
629 |
'wintitle': |
|
|
|
630 |
'channelList': |
|
|
|
631 |
'showprofile': |
|
|
|
632 |
'xmin': |
|
|
|
633 |
'xmax': |
|
|
|
634 |
'ymin': |
|
|
|
635 |
'ymax': |
|
|
|
636 |
'zmin': |
|
|
|
637 |
'zmax': |
|
|
|
638 | 'timerange': 'string', | |
|
|
639 |
'save': |
|
|
|
640 |
'figpath': |
|
|
|
641 |
'lastone': |
|
|
|
642 |
'figfile': |
|
|
|
643 |
'ftp': |
|
|
|
644 |
'wr_period': |
|
|
|
645 |
'show': |
|
|
|
646 |
'server': |
|
|
|
647 |
'folder': |
|
|
|
648 |
'username': |
|
|
|
649 |
'password': |
|
|
|
650 |
'ftp_wei': |
|
|
|
651 |
'exp_code': |
|
|
|
652 |
'sub_exp_code': |
|
|
|
653 |
'plot_pos': |
|
|
|
640 | 'id': global_type_string, | |
|
|
641 | 'wintitle': global_type_string, | |
|
|
642 | 'channelList': global_type_list, | |
|
|
643 | 'showprofile': global_type_boolean, | |
|
|
644 | 'xmin': global_type_float, | |
|
|
645 | 'xmax': global_type_float, | |
|
|
646 | 'ymin': global_type_float, | |
|
|
647 | 'ymax': global_type_float, | |
|
|
648 | 'zmin': global_type_float, | |
|
|
649 | 'zmax': global_type_float, | |
|
|
650 | 'timerange': global_type_float, | |
|
|
651 | 'save': global_type_boolean, | |
|
|
652 | 'figpath': global_type_string, | |
|
|
653 | 'lastone': global_type_string, | |
|
|
654 | 'figfile': global_type_string, | |
|
|
655 | 'ftp': global_type_string, | |
|
|
656 | 'wr_period': global_type_integer, | |
|
|
657 | 'show': global_type_string, | |
|
|
658 | 'server': global_type_string, | |
|
|
659 | 'folder': global_type_string, | |
|
|
660 | 'username': global_type_string, | |
|
|
661 | 'password': global_type_string, | |
|
|
662 | 'ftp_wei': global_type_integer, | |
|
|
663 | 'exp_code': global_type_integer, | |
|
|
664 | 'sub_exp_code': global_type_integer, | |
|
|
665 | 'plot_pos': global_type_integer, | |
|
|
654 | 666 | } |
|
|
655 | 667 | |
|
|
656 | 668 | |
|
|
657 | 669 | RTIfromSpectraHeis |
|
|
658 | 670 | parameters = { |
|
|
659 |
'id': |
|
|
|
660 |
'wintitle': |
|
|
|
661 |
'channelList': |
|
|
|
662 |
'showprofile': |
|
|
|
663 |
'xmin': |
|
|
|
664 |
'xmax': |
|
|
|
665 |
'ymin': |
|
|
|
666 |
'ymax': |
|
|
|
667 | 'timerange': 'string', | |
|
|
668 |
'save': |
|
|
|
669 |
'figpath': |
|
|
|
670 |
'figfile': |
|
|
|
671 |
'ftp': |
|
|
|
672 |
'wr_period': |
|
|
|
673 |
'show': |
|
|
|
674 |
'server': |
|
|
|
675 |
'folder': |
|
|
|
676 |
'username': |
|
|
|
677 |
'password': |
|
|
|
678 |
'ftp_wei': |
|
|
|
679 |
'exp_code': |
|
|
|
680 |
'sub_exp_code': |
|
|
|
681 |
'plot_pos': |
|
|
|
671 | 'id': global_type_string, | |
|
|
672 | 'wintitle': global_type_string, | |
|
|
673 | 'channelList': global_type_list, | |
|
|
674 | 'showprofile': global_type_boolean, | |
|
|
675 | 'xmin': global_type_float, | |
|
|
676 | 'xmax': global_type_float, | |
|
|
677 | 'ymin': global_type_float, | |
|
|
678 | 'ymax': global_type_float, | |
|
|
679 | 'timerange': global_type_float, | |
|
|
680 | 'save': global_type_boolean, | |
|
|
681 | 'figpath': global_type_string, | |
|
|
682 | 'figfile': global_type_string, | |
|
|
683 | 'ftp': global_type_string, | |
|
|
684 | 'wr_period': global_type_integer, | |
|
|
685 | 'show': global_type_string, | |
|
|
686 | 'server': global_type_string, | |
|
|
687 | 'folder': global_type_string, | |
|
|
688 | 'username': global_type_string, | |
|
|
689 | 'password': global_type_string, | |
|
|
690 | 'ftp_wei': global_type_integer, | |
|
|
691 | 'exp_code': global_type_integer, | |
|
|
692 | 'sub_exp_code': global_type_integer, | |
|
|
693 | 'plot_pos': global_type_integer, | |
|
|
682 | 694 | } |
|
|
683 | 695 | |
|
|
684 | 696 | |
|
|
685 | 697 | Reshaper |
|
|
686 | 698 | parameters = { |
|
|
687 |
'shape': |
|
|
|
688 |
'nTxs': |
|
|
|
699 | 'shape': global_type_string, | |
|
|
700 | 'nTxs': global_type_string, | |
|
|
689 | 701 | } |
|
|
690 | 702 | |
|
|
691 | 703 | |
|
|
692 | 704 | SALags |
|
|
693 | 705 | parameters = { |
|
|
694 | : 'string', | |
|
|
706 | : global_type_string, | |
|
|
695 | 707 | } |
|
|
696 | 708 | |
|
|
697 | 709 | |
|
|
698 | 710 | SMDetection |
|
|
699 | 711 | parameters = { |
|
|
700 |
'hei_ref': |
|
|
|
701 |
'tauindex': |
|
|
|
702 |
'phaseOffsets': |
|
|
|
703 |
'cohDetection': |
|
|
|
704 |
'cohDet_timeStep': |
|
|
|
705 |
'cohDet_thresh': |
|
|
|
706 |
'noise_timeStep': |
|
|
|
707 |
'noise_multiple': |
|
|
|
708 |
'multDet_timeLimit': |
|
|
|
709 |
'multDet_rangeLimit': |
|
|
|
710 |
'phaseThresh': |
|
|
|
711 |
'SNRThresh': |
|
|
|
712 |
'hmin': |
|
|
|
713 |
'hmax': |
|
|
|
714 |
'azimuth': |
|
|
|
715 |
'channelPositions': |
|
|
|
712 | 'hei_ref': global_type_string, | |
|
|
713 | 'tauindex': global_type_string, | |
|
|
714 | 'phaseOffsets': global_type_string, | |
|
|
715 | 'cohDetection': global_type_string, | |
|
|
716 | 'cohDet_timeStep': global_type_string, | |
|
|
717 | 'cohDet_thresh': global_type_string, | |
|
|
718 | 'noise_timeStep': global_type_string, | |
|
|
719 | 'noise_multiple': global_type_string, | |
|
|
720 | 'multDet_timeLimit': global_type_string, | |
|
|
721 | 'multDet_rangeLimit': global_type_string, | |
|
|
722 | 'phaseThresh': global_type_string, | |
|
|
723 | 'SNRThresh': global_type_string, | |
|
|
724 | 'hmin': global_type_string, | |
|
|
725 | 'hmax': global_type_string, | |
|
|
726 | 'azimuth': global_type_string, | |
|
|
727 | 'channelPositions': global_type_string, | |
|
|
716 | 728 | } |
|
|
717 | 729 | |
|
|
718 | 730 | |
|
|
719 | 731 | SMPhaseCalibration |
|
|
720 | 732 | parameters = { |
|
|
721 |
'hmin': |
|
|
|
722 |
'hmax': |
|
|
|
723 |
'channelPositions': |
|
|
|
724 |
'nHours': |
|
|
|
733 | 'hmin': global_type_string, | |
|
|
734 | 'hmax': global_type_string, | |
|
|
735 | 'channelPositions': global_type_string, | |
|
|
736 | 'nHours': global_type_string, | |
|
|
725 | 737 | } |
|
|
726 | 738 | |
|
|
727 | 739 | |
|
|
728 | 740 | Scope |
|
|
729 | 741 | parameters = { |
|
|
730 |
'id': |
|
|
|
731 |
'wintitle': |
|
|
|
732 |
'channelList': |
|
|
|
733 |
'xmin': |
|
|
|
734 |
'xmax': |
|
|
|
735 |
'ymin': |
|
|
|
736 |
'ymax': |
|
|
|
737 |
'save': |
|
|
|
738 |
'figpath': |
|
|
|
739 |
'figfile': |
|
|
|
740 |
'show': |
|
|
|
741 |
'wr_period': |
|
|
|
742 |
'ftp': |
|
|
|
743 |
'server': |
|
|
|
744 |
'folder': |
|
|
|
745 |
'username': |
|
|
|
746 |
'password': |
|
|
|
747 |
'type': |
|
|
|
742 | 'id': global_type_string, | |
|
|
743 | 'wintitle': global_type_string, | |
|
|
744 | 'channelList': global_type_list, | |
|
|
745 | 'xmin': global_type_float, | |
|
|
746 | 'xmax': global_type_float, | |
|
|
747 | 'ymin': global_type_float, | |
|
|
748 | 'ymax': global_type_float, | |
|
|
749 | 'save': global_type_boolean, | |
|
|
750 | 'figpath': global_type_string, | |
|
|
751 | 'figfile': global_type_string, | |
|
|
752 | 'show': global_type_string, | |
|
|
753 | 'wr_period': global_type_integer, | |
|
|
754 | 'ftp': global_type_string, | |
|
|
755 | 'server': global_type_string, | |
|
|
756 | 'folder': global_type_string, | |
|
|
757 | 'username': global_type_string, | |
|
|
758 | 'password': global_type_string, | |
|
|
759 | 'type': global_type_string, | |
|
|
748 | 760 | } |
|
|
749 | 761 | |
|
|
750 | 762 | |
|
|
751 | 763 | SendByFTP |
|
|
752 | 764 | parameters = { |
|
|
753 |
'ext': |
|
|
|
754 |
'localfolder': |
|
|
|
755 |
'remotefolder': |
|
|
|
756 |
'server': |
|
|
|
757 |
'username': |
|
|
|
758 |
'password': |
|
|
|
759 |
'period': |
|
|
|
765 | 'ext': global_type_string, | |
|
|
766 | 'localfolder': global_type_string, | |
|
|
767 | 'remotefolder': global_type_string, | |
|
|
768 | 'server': global_type_string, | |
|
|
769 | 'username': global_type_string, | |
|
|
770 | 'password': global_type_string, | |
|
|
771 | 'period': global_type_string, | |
|
|
760 | 772 | } |
|
|
761 | 773 | |
|
|
762 | 774 | |
|
|
763 | 775 | SkyMapPlot |
|
|
764 | 776 | parameters = { |
|
|
765 |
'id': |
|
|
|
766 |
'wintitle': |
|
|
|
767 |
'channelList': |
|
|
|
768 |
'showprofile': |
|
|
|
769 |
'tmin': |
|
|
|
770 |
'tmax': |
|
|
|
771 | 'timerange': 'string', | |
|
|
772 |
'save': |
|
|
|
773 |
'figpath': |
|
|
|
774 |
'figfile': |
|
|
|
775 |
'show': |
|
|
|
776 |
'ftp': |
|
|
|
777 |
'wr_period': |
|
|
|
778 |
'server': |
|
|
|
779 |
'folder': |
|
|
|
780 |
'username': |
|
|
|
781 |
'password': |
|
|
|
782 |
'ftp_wei': |
|
|
|
783 |
'exp_code': |
|
|
|
784 |
'sub_exp_code': |
|
|
|
785 |
'plot_pos': |
|
|
|
786 | 'realtime': 'string', | |
|
|
777 | 'id': global_type_string, | |
|
|
778 | 'wintitle': global_type_string, | |
|
|
779 | 'channelList': global_type_list, | |
|
|
780 | 'showprofile': global_type_boolean, | |
|
|
781 | 'tmin': global_type_string, | |
|
|
782 | 'tmax': global_type_string, | |
|
|
783 | 'timerange': global_type_float, | |
|
|
784 | 'save': global_type_boolean, | |
|
|
785 | 'figpath': global_type_string, | |
|
|
786 | 'figfile': global_type_string, | |
|
|
787 | 'show': global_type_string, | |
|
|
788 | 'ftp': global_type_string, | |
|
|
789 | 'wr_period': global_type_integer, | |
|
|
790 | 'server': global_type_string, | |
|
|
791 | 'folder': global_type_string, | |
|
|
792 | 'username': global_type_string, | |
|
|
793 | 'password': global_type_string, | |
|
|
794 | 'ftp_wei': global_type_integer, | |
|
|
795 | 'exp_code': global_type_integer, | |
|
|
796 | 'sub_exp_code': global_type_integer, | |
|
|
797 | 'plot_pos': global_type_integer, | |
|
|
798 | 'realtime': global_type_boolean, | |
|
|
787 | 799 | } |
|
|
788 | 800 | |
|
|
789 | 801 | |
|
|
790 | 802 | SpectraCutPlot |
|
|
791 | 803 | parameters = { |
|
|
792 |
'id': |
|
|
|
793 |
'wintitle': |
|
|
|
794 |
'channelList': |
|
|
|
795 |
'xmin': |
|
|
|
796 |
'xmax': |
|
|
|
797 |
'ymin': |
|
|
|
798 |
'ymax': |
|
|
|
799 |
'save': |
|
|
|
800 |
'figpath': |
|
|
|
801 |
'figfile': |
|
|
|
802 |
'show': |
|
|
|
803 |
'ftp': |
|
|
|
804 |
'wr_period': |
|
|
|
805 |
'server': |
|
|
|
806 |
'folder': |
|
|
|
807 |
'username': |
|
|
|
808 |
'password': |
|
|
|
809 |
'xaxis': |
|
|
|
804 | 'id': global_type_string, | |
|
|
805 | 'wintitle': global_type_string, | |
|
|
806 | 'channelList': global_type_list, | |
|
|
807 | 'xmin': global_type_float, | |
|
|
808 | 'xmax': global_type_float, | |
|
|
809 | 'ymin': global_type_float, | |
|
|
810 | 'ymax': global_type_float, | |
|
|
811 | 'save': global_type_boolean, | |
|
|
812 | 'figpath': global_type_string, | |
|
|
813 | 'figfile': global_type_string, | |
|
|
814 | 'show': global_type_string, | |
|
|
815 | 'ftp': global_type_string, | |
|
|
816 | 'wr_period': global_type_integer, | |
|
|
817 | 'server': global_type_string, | |
|
|
818 | 'folder': global_type_string, | |
|
|
819 | 'username': global_type_string, | |
|
|
820 | 'password': global_type_string, | |
|
|
821 | 'xaxis': global_type_string, | |
|
|
810 | 822 | } |
|
|
811 | 823 | |
|
|
812 | 824 | |
|
|
813 | 825 | SpectraHeisScope |
|
|
814 | 826 | parameters = { |
|
|
815 |
'id': |
|
|
|
816 |
'wintitle': |
|
|
|
817 |
'channelList': |
|
|
|
818 |
'xmin': |
|
|
|
819 |
'xmax': |
|
|
|
820 |
'ymin': |
|
|
|
821 |
'ymax': |
|
|
|
822 |
'save': |
|
|
|
823 |
'figpath': |
|
|
|
824 |
'figfile': |
|
|
|
825 |
'ftp': |
|
|
|
826 |
'wr_period': |
|
|
|
827 |
'show': |
|
|
|
828 |
'server': |
|
|
|
829 |
'folder': |
|
|
|
830 |
'username': |
|
|
|
831 |
'password': |
|
|
|
832 |
'ftp_wei': |
|
|
|
833 |
'exp_code': |
|
|
|
834 |
'sub_exp_code': |
|
|
|
835 |
'plot_pos': |
|
|
|
827 | 'id': global_type_string, | |
|
|
828 | 'wintitle': global_type_string, | |
|
|
829 | 'channelList': global_type_list, | |
|
|
830 | 'xmin': global_type_float, | |
|
|
831 | 'xmax': global_type_float, | |
|
|
832 | 'ymin': global_type_float, | |
|
|
833 | 'ymax': global_type_float, | |
|
|
834 | 'save': global_type_boolean, | |
|
|
835 | 'figpath': global_type_string, | |
|
|
836 | 'figfile': global_type_string, | |
|
|
837 | 'ftp': global_type_string, | |
|
|
838 | 'wr_period': global_type_integer, | |
|
|
839 | 'show': global_type_string, | |
|
|
840 | 'server': global_type_string, | |
|
|
841 | 'folder': global_type_string, | |
|
|
842 | 'username': global_type_string, | |
|
|
843 | 'password': global_type_string, | |
|
|
844 | 'ftp_wei': global_type_integer, | |
|
|
845 | 'exp_code': global_type_integer, | |
|
|
846 | 'sub_exp_code': global_type_integer, | |
|
|
847 | 'plot_pos': global_type_integer, | |
|
|
836 | 848 | } |
|
|
837 | 849 | |
|
|
838 | 850 | |
|
|
839 | 851 | SpectraHeisWriter |
|
|
840 | 852 | parameters = { |
|
|
841 | : 'string', | |
|
|
853 | : global_type_string, | |
|
|
842 | 854 | } |
|
|
843 | 855 | |
|
|
844 | 856 | |
|
|
845 | 857 | SpectraPlot |
|
|
846 | 858 | parameters = { |
|
|
847 |
'id': |
|
|
|
848 |
'wintitle': |
|
|
|
849 |
'channelList': |
|
|
|
850 |
'showprofile': |
|
|
|
851 |
'xmin': |
|
|
|
852 |
'xmax': |
|
|
|
853 |
'ymin': |
|
|
|
854 |
'ymax': |
|
|
|
855 |
'zmin': |
|
|
|
856 |
'zmax': |
|
|
|
857 |
'save': |
|
|
|
858 |
'figpath': |
|
|
|
859 |
'figfile': |
|
|
|
860 |
'show': |
|
|
|
861 |
'ftp': |
|
|
|
862 |
'wr_period': |
|
|
|
863 |
'server': |
|
|
|
864 |
'folder': |
|
|
|
865 |
'username': |
|
|
|
866 |
'password': |
|
|
|
867 |
'ftp_wei': |
|
|
|
868 |
'exp_code': |
|
|
|
869 |
'sub_exp_code': |
|
|
|
870 |
'plot_pos': |
|
|
|
871 | 'realtime': 'string', | |
|
|
872 |
'xaxis': |
|
|
|
859 | 'id': global_type_string, | |
|
|
860 | 'wintitle': global_type_string, | |
|
|
861 | 'channelList': global_type_list, | |
|
|
862 | 'showprofile': global_type_boolean, | |
|
|
863 | 'xmin': global_type_float, | |
|
|
864 | 'xmax': global_type_float, | |
|
|
865 | 'ymin': global_type_float, | |
|
|
866 | 'ymax': global_type_float, | |
|
|
867 | 'zmin': global_type_float, | |
|
|
868 | 'zmax': global_type_float, | |
|
|
869 | 'save': global_type_boolean, | |
|
|
870 | 'figpath': global_type_string, | |
|
|
871 | 'figfile': global_type_string, | |
|
|
872 | 'show': global_type_string, | |
|
|
873 | 'ftp': global_type_string, | |
|
|
874 | 'wr_period': global_type_integer, | |
|
|
875 | 'server': global_type_string, | |
|
|
876 | 'folder': global_type_string, | |
|
|
877 | 'username': global_type_string, | |
|
|
878 | 'password': global_type_string, | |
|
|
879 | 'ftp_wei': global_type_integer, | |
|
|
880 | 'exp_code': global_type_integer, | |
|
|
881 | 'sub_exp_code': global_type_integer, | |
|
|
882 | 'plot_pos': global_type_integer, | |
|
|
883 | 'realtime': global_type_boolean, | |
|
|
884 | 'xaxis': global_type_string, | |
|
|
873 | 885 | } |
|
|
874 | 886 | |
|
|
875 | 887 | |
|
|
876 | 888 | SpectraWriter |
|
|
877 | 889 | parameters = { |
|
|
878 |
'path': |
|
|
|
879 |
'blocksPerFile': |
|
|
|
880 |
'profilesPerBlock': |
|
|
|
881 |
'set': |
|
|
|
882 |
'ext': |
|
|
|
883 |
'datatype': |
|
|
|
890 | 'path': global_type_string, | |
|
|
891 | 'blocksPerFile': global_type_string, | |
|
|
892 | 'profilesPerBlock': global_type_string, | |
|
|
893 | 'set': global_type_string, | |
|
|
894 | 'ext': global_type_string, | |
|
|
895 | 'datatype': global_type_string, | |
|
|
884 | 896 | } |
|
|
885 | 897 | |
|
|
886 | 898 | |
|
|
887 | 899 | SpectralFitting |
|
|
888 | 900 | parameters = { |
|
|
889 |
'getSNR': |
|
|
|
890 |
'path': |
|
|
|
891 |
'file': |
|
|
|
892 |
'groupList': |
|
|
|
901 | 'getSNR': global_type_string, | |
|
|
902 | 'path': global_type_string, | |
|
|
903 | 'file': global_type_string, | |
|
|
904 | 'groupList': global_type_string, | |
|
|
893 | 905 | } |
|
|
894 | 906 | |
|
|
895 | 907 | |
|
|
896 | 908 | SpectralFittingPlot |
|
|
897 | 909 | parameters = { |
|
|
898 |
'id': |
|
|
|
899 |
'cutHeight': |
|
|
|
900 |
'fit': |
|
|
|
901 |
'wintitle': |
|
|
|
902 |
'channelList': |
|
|
|
903 |
'showprofile': |
|
|
|
904 |
'xmin': |
|
|
|
905 |
'xmax': |
|
|
|
906 |
'ymin': |
|
|
|
907 |
'ymax': |
|
|
|
908 |
'save': |
|
|
|
909 |
'figpath': |
|
|
|
910 |
'figfile': |
|
|
|
911 |
'show': |
|
|
|
910 | 'id': global_type_string, | |
|
|
911 | 'cutHeight': global_type_string, | |
|
|
912 | 'fit': global_type_string, | |
|
|
913 | 'wintitle': global_type_string, | |
|
|
914 | 'channelList': global_type_list, | |
|
|
915 | 'showprofile': global_type_boolean, | |
|
|
916 | 'xmin': global_type_float, | |
|
|
917 | 'xmax': global_type_float, | |
|
|
918 | 'ymin': global_type_float, | |
|
|
919 | 'ymax': global_type_float, | |
|
|
920 | 'save': global_type_boolean, | |
|
|
921 | 'figpath': global_type_string, | |
|
|
922 | 'figfile': global_type_string, | |
|
|
923 | 'show': global_type_string, | |
|
|
912 | 924 | } |
|
|
913 | 925 | |
|
|
914 | 926 | |
|
|
915 | 927 | SpectralMoments |
|
|
916 | 928 | parameters = { |
|
|
917 | : 'string', | |
|
|
929 | : global_type_string, | |
|
|
918 | 930 | } |
|
|
919 | 931 | |
|
|
920 | 932 | |
|
|
921 | 933 | SplitProfiles |
|
|
922 | 934 | parameters = { |
|
|
923 |
'n': |
|
|
|
935 | 'n': global_type_string, | |
|
|
924 | 936 | } |
|
|
925 | 937 | |
|
|
926 | 938 | |
|
|
927 | 939 | USRPWriter |
|
|
928 | 940 | parameters = { |
|
|
929 |
'dataIn': |
|
|
|
941 | 'dataIn': global_type_string, | |
|
|
930 | 942 | } |
|
|
931 | 943 | |
|
|
932 | 944 | |
|
|
933 | 945 | VoltageWriter |
|
|
934 | 946 | parameters = { |
|
|
935 |
'path': |
|
|
|
936 |
'blocksPerFile': |
|
|
|
937 |
'profilesPerBlock': |
|
|
|
938 |
'set': |
|
|
|
939 |
'ext': |
|
|
|
940 |
'datatype': |
|
|
|
947 | 'path': global_type_string, | |
|
|
948 | 'blocksPerFile': global_type_string, | |
|
|
949 | 'profilesPerBlock': global_type_string, | |
|
|
950 | 'set': global_type_string, | |
|
|
951 | 'ext': global_type_string, | |
|
|
952 | 'datatype': global_type_string, | |
|
|
941 | 953 | } |
|
|
942 | 954 | |
|
|
943 | 955 | |
|
|
944 | 956 | WindProfiler |
|
|
945 | 957 | parameters = { |
|
|
946 |
'technique': |
|
|
|
958 | 'technique': global_type_string, | |
|
|
947 | 959 | } |
|
|
948 | 960 | |
|
|
949 | 961 | |
|
|
950 | 962 | WindProfilerPlot |
|
|
951 | 963 | parameters = { |
|
|
952 |
'id': |
|
|
|
953 |
'wintitle': |
|
|
|
954 |
'channelList': |
|
|
|
955 |
'showprofile': |
|
|
|
956 |
'xmin': |
|
|
|
957 |
'xmax': |
|
|
|
958 |
'ymin': |
|
|
|
959 |
'ymax': |
|
|
|
960 |
'zmin': |
|
|
|
961 |
'zmax': |
|
|
|
962 |
'zmax_ver': |
|
|
|
963 |
'zmin_ver': |
|
|
|
964 | 'SNRmin': 'string', | |
|
|
965 | 'SNRmax': 'string', | |
|
|
966 | 'timerange': 'string', | |
|
|
967 |
'SNRthresh': |
|
|
|
968 |
'save': |
|
|
|
969 |
'figpath': |
|
|
|
970 |
'lastone': |
|
|
|
971 |
'figfile': |
|
|
|
972 |
'ftp': |
|
|
|
973 |
'wr_period': |
|
|
|
974 |
'show': |
|
|
|
975 |
'server': |
|
|
|
976 |
'folder': |
|
|
|
977 |
'username': |
|
|
|
978 |
'password': |
|
|
|
979 |
'ftp_wei': |
|
|
|
980 |
'exp_code': |
|
|
|
981 |
'sub_exp_code': |
|
|
|
982 |
'plot_pos': |
|
|
|
964 | 'id': global_type_string, | |
|
|
965 | 'wintitle': global_type_string, | |
|
|
966 | 'channelList': global_type_list, | |
|
|
967 | 'showprofile': global_type_boolean, | |
|
|
968 | 'xmin': global_type_float, | |
|
|
969 | 'xmax': global_type_float, | |
|
|
970 | 'ymin': global_type_float, | |
|
|
971 | 'ymax': global_type_float, | |
|
|
972 | 'zmin': global_type_float, | |
|
|
973 | 'zmax': global_type_float, | |
|
|
974 | 'zmax_ver': global_type_string, | |
|
|
975 | 'zmin_ver': global_type_string, | |
|
|
976 | 'SNRmin': global_type_float, | |
|
|
977 | 'SNRmax': global_type_float, | |
|
|
978 | 'timerange': global_type_float, | |
|
|
979 | 'SNRthresh': global_type_string, | |
|
|
980 | 'save': global_type_boolean, | |
|
|
981 | 'figpath': global_type_string, | |
|
|
982 | 'lastone': global_type_string, | |
|
|
983 | 'figfile': global_type_string, | |
|
|
984 | 'ftp': global_type_string, | |
|
|
985 | 'wr_period': global_type_integer, | |
|
|
986 | 'show': global_type_string, | |
|
|
987 | 'server': global_type_string, | |
|
|
988 | 'folder': global_type_string, | |
|
|
989 | 'username': global_type_string, | |
|
|
990 | 'password': global_type_string, | |
|
|
991 | 'ftp_wei': global_type_integer, | |
|
|
992 | 'exp_code': global_type_integer, | |
|
|
993 | 'sub_exp_code': global_type_integer, | |
|
|
994 | 'plot_pos': global_type_integer, | |
|
|
983 | 995 | } |
|
|
984 | 996 | |
|
|
985 | 997 | |
|
|
986 | 998 | Writer |
|
|
987 | 999 | parameters = { |
|
|
988 |
'dataIn': |
|
|
|
1000 | 'dataIn': global_type_string, | |
|
|
989 | 1001 | } |
|
|
990 | 1002 | |
|
|
991 | 1003 | |
|
|
992 | 1004 | AMISRProc |
|
|
993 | 1005 | parameters = { |
|
|
994 | : 'string', | |
|
|
1006 | : global_type_string, | |
|
|
995 | 1007 | } |
|
|
996 | 1008 | |
|
|
997 | 1009 | |
|
|
998 | 1010 | AMISRReader |
|
|
999 | 1011 | parameters = { |
|
|
1000 | : 'string', | |
|
|
1012 | : global_type_string, | |
|
|
1001 | 1013 | } |
|
|
1002 | 1014 | |
|
|
1003 | 1015 | |
|
|
1004 | 1016 | CorrelationProc |
|
|
1005 | 1017 | parameters = { |
|
|
1006 |
'lags': |
|
|
|
1007 |
'mode': |
|
|
|
1018 | 'lags': global_type_string, | |
|
|
1019 | 'mode': global_type_string, | |
|
|
1008 | 1020 | 'pairsList': 'pairsLists', |
|
|
1009 |
'fullBuffer': |
|
|
|
1010 |
'nAvg': |
|
|
|
1011 |
'removeDC': |
|
|
|
1012 |
'splitCF': |
|
|
|
1021 | 'fullBuffer': global_type_string, | |
|
|
1022 | 'nAvg': global_type_string, | |
|
|
1023 | 'removeDC': global_type_string, | |
|
|
1024 | 'splitCF': global_type_string, | |
|
|
1013 | 1025 | } |
|
|
1014 | 1026 | |
|
|
1015 | 1027 | |
|
|
1016 | 1028 | FitsReader |
|
|
1017 | 1029 | parameters = { |
|
|
1018 | : 'string', | |
|
|
1030 | : global_type_string, | |
|
|
1019 | 1031 | } |
|
|
1020 | 1032 | |
|
|
1021 | 1033 | |
|
|
1022 | 1034 | HFReader |
|
|
1023 | 1035 | parameters = { |
|
|
1024 | : 'string', | |
|
|
1036 | : global_type_string, | |
|
|
1025 | 1037 | } |
|
|
1026 | 1038 | |
|
|
1027 | 1039 | |
|
|
1028 | 1040 | ParamReader |
|
|
1029 | 1041 | parameters = { |
|
|
1030 | : 'string', | |
|
|
1042 | : global_type_string, | |
|
|
1031 | 1043 | } |
|
|
1032 | 1044 | |
|
|
1033 | 1045 | |
|
|
1034 | 1046 | ParametersProc |
|
|
1035 | 1047 | parameters = { |
|
|
1036 | : 'string', | |
|
|
1048 | : global_type_string, | |
|
|
1037 | 1049 | } |
|
|
1038 | 1050 | |
|
|
1039 | 1051 | |
|
|
1040 | 1052 | ProcessingUnit |
|
|
1041 | 1053 | parameters = { |
|
|
1042 | : 'string', | |
|
|
1054 | : global_type_string, | |
|
|
1043 | 1055 | } |
|
|
1044 | 1056 | |
|
|
1045 | 1057 | |
|
|
1046 | 1058 | ReceiverData |
|
|
1047 | 1059 | parameters = { |
|
|
1048 | : 'string', | |
|
|
1060 | : global_type_string, | |
|
|
1049 | 1061 | } |
|
|
1050 | 1062 | |
|
|
1051 | 1063 | |
|
|
1052 | 1064 | SendToServer |
|
|
1053 | 1065 | parameters = { |
|
|
1054 | : 'string', | |
|
|
1066 | : global_type_string, | |
|
|
1055 | 1067 | } |
|
|
1056 | 1068 | |
|
|
1057 | 1069 | |
|
|
1058 | 1070 | SpectraAFCProc |
|
|
1059 | 1071 | parameters = { |
|
|
1060 |
'nProfiles': |
|
|
|
1061 |
'nFFTPoints': |
|
|
|
1072 | 'nProfiles': global_type_string, | |
|
|
1073 | 'nFFTPoints': global_type_string, | |
|
|
1062 | 1074 | 'pairsList': 'pairsLists', |
|
|
1063 |
'code': |
|
|
|
1064 |
'nCode': |
|
|
|
1065 |
'nBaud': |
|
|
|
1075 | 'code': global_type_string, | |
|
|
1076 | 'nCode': global_type_string, | |
|
|
1077 | 'nBaud': global_type_string, | |
|
|
1066 | 1078 | } |
|
|
1067 | 1079 | |
|
|
1068 | 1080 | |
|
|
1069 | 1081 | SpectraHeisProc |
|
|
1070 | 1082 | parameters = { |
|
|
1071 | : 'string', | |
|
|
1083 | : global_type_string, | |
|
|
1072 | 1084 | } |
|
|
1073 | 1085 | |
|
|
1074 | 1086 | |
|
|
1075 | 1087 | SpectraLagsProc |
|
|
1076 | 1088 | parameters = { |
|
|
1077 |
'nProfiles': |
|
|
|
1078 |
'nFFTPoints': |
|
|
|
1089 | 'nProfiles': global_type_string, | |
|
|
1090 | 'nFFTPoints': global_type_string, | |
|
|
1079 | 1091 | 'pairsList': 'pairsLists', |
|
|
1080 |
'code': |
|
|
|
1081 |
'nCode': |
|
|
|
1082 |
'nBaud': |
|
|
|
1083 |
'codeFromHeader': |
|
|
|
1084 |
'pulseIndex': |
|
|
|
1092 | 'code': global_type_string, | |
|
|
1093 | 'nCode': global_type_string, | |
|
|
1094 | 'nBaud': global_type_string, | |
|
|
1095 | 'codeFromHeader': global_type_string, | |
|
|
1096 | 'pulseIndex': global_type_string, | |
|
|
1085 | 1097 | } |
|
|
1086 | 1098 | |
|
|
1087 | 1099 | |
|
|
1088 | 1100 | SpectraProc |
|
|
1089 | 1101 | parameters = { |
|
|
1090 |
'nProfiles': |
|
|
|
1091 |
'nFFTPoints': |
|
|
|
1102 | 'nProfiles': global_type_string, | |
|
|
1103 | 'nFFTPoints': global_type_string, | |
|
|
1092 | 1104 | 'pairsList': 'pairsLists', |
|
|
1093 |
'ippFactor': |
|
|
|
1105 | 'ippFactor': global_type_string, | |
|
|
1094 | 1106 | } |
|
|
1095 | 1107 | |
|
|
1096 | 1108 | |
|
|
1097 | 1109 | SpectraReader |
|
|
1098 | 1110 | parameters = { |
|
|
1099 |
'path': |
|
|
|
1100 |
'startDate': |
|
|
|
1101 |
'endDate': |
|
|
|
1102 |
'startTime': |
|
|
|
1103 |
'endTime': |
|
|
|
1104 |
'set': |
|
|
|
1105 |
'expLabel': |
|
|
|
1106 |
'ext': |
|
|
|
1107 |
'online': |
|
|
|
1108 |
'delay': |
|
|
|
1109 |
'walk': |
|
|
|
1110 |
'getblock': |
|
|
|
1111 |
'nTxs': |
|
|
|
1112 | 'realtime': 'string', | |
|
|
1113 |
'blocksize': |
|
|
|
1114 |
'blocktime': |
|
|
|
1115 |
'queue': |
|
|
|
1116 |
'skip': |
|
|
|
1117 |
'cursor': |
|
|
|
1118 |
'warnings': |
|
|
|
1119 |
'verbose': |
|
|
|
1111 | 'path': global_type_string, | |
|
|
1112 | 'startDate': global_type_string, | |
|
|
1113 | 'endDate': global_type_string, | |
|
|
1114 | 'startTime': global_type_string, | |
|
|
1115 | 'endTime': global_type_string, | |
|
|
1116 | 'set': global_type_string, | |
|
|
1117 | 'expLabel': global_type_string, | |
|
|
1118 | 'ext': global_type_string, | |
|
|
1119 | 'online': global_type_string, | |
|
|
1120 | 'delay': global_type_string, | |
|
|
1121 | 'walk': global_type_string, | |
|
|
1122 | 'getblock': global_type_string, | |
|
|
1123 | 'nTxs': global_type_string, | |
|
|
1124 | 'realtime': global_type_boolean, | |
|
|
1125 | 'blocksize': global_type_string, | |
|
|
1126 | 'blocktime': global_type_string, | |
|
|
1127 | 'queue': global_type_string, | |
|
|
1128 | 'skip': global_type_string, | |
|
|
1129 | 'cursor': global_type_string, | |
|
|
1130 | 'warnings': global_type_string, | |
|
|
1131 | 'verbose': global_type_string, | |
|
|
1120 | 1132 | } |
|
|
1121 | 1133 | |
|
|
1122 | 1134 | |
|
|
1123 | 1135 | USRPReader |
|
|
1124 | 1136 | parameters = { |
|
|
1125 | : 'string', | |
|
|
1137 | : global_type_string, | |
|
|
1126 | 1138 | } |
|
|
1127 | 1139 | |
|
|
1128 | 1140 | |
|
|
1129 | 1141 | VoltageProc |
|
|
1130 | 1142 | parameters = { |
|
|
1131 | : 'string', | |
|
|
1143 | : global_type_string, | |
|
|
1132 | 1144 | } |
|
|
1133 | 1145 | |
|
|
1134 | 1146 | |
|
|
1135 | 1147 | VoltageReader |
|
|
1136 | 1148 | parameters = { |
|
|
1137 |
'path': |
|
|
|
1138 |
'startDate': |
|
|
|
1139 |
'endDate': |
|
|
|
1140 |
'startTime': |
|
|
|
1141 |
'endTime': |
|
|
|
1142 |
'set': |
|
|
|
1143 |
'expLabel': |
|
|
|
1144 |
'ext': |
|
|
|
1145 |
'online': |
|
|
|
1146 |
'delay': |
|
|
|
1147 |
'walk': |
|
|
|
1148 |
'getblock': |
|
|
|
1149 |
'nTxs': |
|
|
|
1150 | 'realtime': 'string', | |
|
|
1151 |
'blocksize': |
|
|
|
1152 |
'blocktime': |
|
|
|
1153 |
'queue': |
|
|
|
1154 |
'skip': |
|
|
|
1155 |
'cursor': |
|
|
|
1156 |
'warnings': |
|
|
|
1157 |
'verbose': |
|
|
|
1149 | 'path': global_type_string, | |
|
|
1150 | 'startDate': global_type_string, | |
|
|
1151 | 'endDate': global_type_string, | |
|
|
1152 | 'startTime': global_type_string, | |
|
|
1153 | 'endTime': global_type_string, | |
|
|
1154 | 'set': global_type_string, | |
|
|
1155 | 'expLabel': global_type_string, | |
|
|
1156 | 'ext': global_type_string, | |
|
|
1157 | 'online': global_type_string, | |
|
|
1158 | 'delay': global_type_string, | |
|
|
1159 | 'walk': global_type_string, | |
|
|
1160 | 'getblock': global_type_string, | |
|
|
1161 | 'nTxs': global_type_string, | |
|
|
1162 | 'realtime': global_type_boolean, | |
|
|
1163 | 'blocksize': global_type_string, | |
|
|
1164 | 'blocktime': global_type_string, | |
|
|
1165 | 'queue': global_type_string, | |
|
|
1166 | 'skip': global_type_string, | |
|
|
1167 | 'cursor': global_type_string, | |
|
|
1168 | 'warnings': global_type_string, | |
|
|
1169 | 'verbose': global_type_string, | |
|
|
1158 | 1170 | } |
|
|
1159 | 1171 | |
|
|
1160 | 1172 | |
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