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1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
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2 | 2 | # All rights reserved. |
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3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
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5 | 5 | """Base class to create plot operations |
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6 | 6 | |
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7 | 7 | """ |
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8 | 8 | |
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9 | 9 | import os |
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10 | 10 | import sys |
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11 | 11 | import zmq |
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12 | 12 | import time |
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13 | 13 | import numpy |
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14 | 14 | import datetime |
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15 | 15 | from collections import deque |
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16 | 16 | from functools import wraps |
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17 | 17 | from threading import Thread |
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18 | 18 | import matplotlib,re |
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19 | 19 | |
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20 | 20 | if 'BACKEND' in os.environ: |
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21 | 21 | matplotlib.use(os.environ['BACKEND']) |
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22 | 22 | elif 'linux' in sys.platform: |
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23 |
matplotlib.use(" |
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23 | matplotlib.use("Agg") | |
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24 | 24 | elif 'darwin' in sys.platform: |
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25 | 25 | matplotlib.use('MacOSX') |
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26 | 26 | else: |
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27 | 27 | from schainpy.utils import log |
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28 | 28 | log.warning('Using default Backend="Agg"', 'INFO') |
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29 | 29 | matplotlib.use('Agg') |
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30 | 30 | |
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31 | 31 | import matplotlib.pyplot as plt |
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32 | 32 | from matplotlib.patches import Polygon |
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33 | 33 | from mpl_toolkits.axes_grid1 import make_axes_locatable |
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34 | 34 | from matplotlib.ticker import FuncFormatter, LinearLocator, MultipleLocator |
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35 | 35 | |
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36 | 36 | from .plotting_codes import * |
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37 | 37 | |
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38 | 38 | from schainpy.model.data.jrodata import PlotterData |
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39 | 39 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
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40 | 40 | from schainpy.utils import log |
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41 | 41 | |
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42 | 42 | for name, cb_table in sophy_cb_tables: |
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43 | 43 | ncmap = matplotlib.colors.ListedColormap(cb_table, name=name) |
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44 | 44 | matplotlib.pyplot.register_cmap(cmap=ncmap) |
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45 | 45 | |
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46 | 46 | EARTH_RADIUS = 6.3710e3 |
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47 | 47 | |
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48 | 48 | def ll2xy(lat1, lon1, lat2, lon2): |
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49 | 49 | |
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50 | 50 | p = 0.017453292519943295 |
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51 | 51 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
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52 | 52 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
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53 | 53 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
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54 | 54 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
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55 | 55 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
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56 | 56 | theta = -theta + numpy.pi/2 |
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57 | 57 | return r*numpy.cos(theta), r*numpy.sin(theta) |
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58 | 58 | |
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59 | 59 | |
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60 | 60 | def km2deg(km): |
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61 | 61 | ''' |
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62 | 62 | Convert distance in km to degrees |
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63 | 63 | ''' |
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64 | 64 | |
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65 | 65 | return numpy.rad2deg(km/EARTH_RADIUS) |
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66 | 66 | |
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67 | 67 | |
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68 | 68 | def figpause(interval): |
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69 | 69 | backend = plt.rcParams['backend'] |
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70 | 70 | if backend in matplotlib.rcsetup.interactive_bk: |
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71 | 71 | figManager = matplotlib._pylab_helpers.Gcf.get_active() |
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72 | 72 | if figManager is not None: |
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73 | 73 | canvas = figManager.canvas |
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74 | 74 | if canvas.figure.stale: |
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75 | 75 | canvas.draw() |
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76 | 76 | try: |
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77 | 77 | canvas.start_event_loop(interval) |
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78 | 78 | except: |
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79 | 79 | pass |
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80 | 80 | return |
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81 | 81 | |
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82 | 82 | def popup(message): |
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83 | 83 | ''' |
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84 | 84 | ''' |
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85 | 85 | |
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86 | 86 | fig = plt.figure(figsize=(12, 8), facecolor='r') |
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87 | 87 | text = '\n'.join([s.strip() for s in message.split(':')]) |
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88 | 88 | fig.text(0.01, 0.5, text, ha='left', va='center', |
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89 | 89 | size='20', weight='heavy', color='w') |
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90 | 90 | fig.show() |
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91 | 91 | figpause(1000) |
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92 | 92 | |
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93 | 93 | |
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94 | 94 | class Throttle(object): |
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95 | 95 | ''' |
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96 | 96 | Decorator that prevents a function from being called more than once every |
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97 | 97 | time period. |
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98 | 98 | To create a function that cannot be called more than once a minute, but |
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99 | 99 | will sleep until it can be called: |
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100 | 100 | @Throttle(minutes=1) |
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101 | 101 | def foo(): |
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102 | 102 | pass |
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103 | 103 | |
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104 | 104 | for i in range(10): |
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105 | 105 | foo() |
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106 | 106 | print "This function has run %s times." % i |
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107 | 107 | ''' |
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108 | 108 | |
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109 | 109 | def __init__(self, seconds=0, minutes=0, hours=0): |
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110 | 110 | self.throttle_period = datetime.timedelta( |
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111 | 111 | seconds=seconds, minutes=minutes, hours=hours |
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112 | 112 | ) |
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113 | 113 | |
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114 | 114 | self.time_of_last_call = datetime.datetime.min |
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115 | 115 | |
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116 | 116 | def __call__(self, fn): |
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117 | 117 | @wraps(fn) |
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118 | 118 | def wrapper(*args, **kwargs): |
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119 | 119 | coerce = kwargs.pop('coerce', None) |
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120 | 120 | if coerce: |
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121 | 121 | self.time_of_last_call = datetime.datetime.now() |
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122 | 122 | return fn(*args, **kwargs) |
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123 | 123 | else: |
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124 | 124 | now = datetime.datetime.now() |
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125 | 125 | time_since_last_call = now - self.time_of_last_call |
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126 | 126 | time_left = self.throttle_period - time_since_last_call |
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127 | 127 | |
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128 | 128 | if time_left > datetime.timedelta(seconds=0): |
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129 | 129 | return |
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130 | 130 | |
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131 | 131 | self.time_of_last_call = datetime.datetime.now() |
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132 | 132 | return fn(*args, **kwargs) |
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133 | 133 | |
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134 | 134 | return wrapper |
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135 | 135 | |
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136 | 136 | def apply_throttle(value): |
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137 | 137 | |
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138 | 138 | @Throttle(seconds=value) |
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139 | 139 | def fnThrottled(fn): |
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140 | 140 | fn() |
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141 | 141 | |
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142 | 142 | return fnThrottled |
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143 | 143 | |
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144 | 144 | |
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145 | 145 | @MPDecorator |
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146 | 146 | class Plot(Operation): |
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147 | 147 | """Base class for Schain plotting operations |
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148 | 148 | |
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149 | 149 | This class should never be use directtly you must subclass a new operation, |
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150 | 150 | children classes must be defined as follow: |
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151 | 151 | |
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152 | 152 | ExamplePlot(Plot): |
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153 | 153 | |
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154 | 154 | CODE = 'code' |
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155 | 155 | colormap = 'jet' |
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156 | 156 | plot_type = 'pcolor' # options are ('pcolor', 'pcolorbuffer', 'scatter', 'scatterbuffer') |
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157 | 157 | |
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158 | 158 | def setup(self): |
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159 | 159 | pass |
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160 | 160 | |
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161 | 161 | def plot(self): |
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162 | 162 | pass |
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163 | 163 | |
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164 | 164 | """ |
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165 | 165 | |
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166 | 166 | CODE = 'Figure' |
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167 | 167 | colormap = 'jet' |
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168 | 168 | bgcolor = 'white' |
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169 | 169 | buffering = True |
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170 | 170 | __missing = 1E30 |
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171 | projection = None | |
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171 | 172 | |
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172 | 173 | __attrs__ = ['show', 'save', 'ymin', 'ymax', 'zmin', 'zmax', 'title', |
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173 | 174 | 'showprofile'] |
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174 | 175 | |
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175 | 176 | def __init__(self): |
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176 | 177 | |
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177 | 178 | Operation.__init__(self) |
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178 | 179 | self.isConfig = False |
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179 | 180 | self.isPlotConfig = False |
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180 | 181 | self.save_time = 0 |
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181 | 182 | self.sender_time = 0 |
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182 | 183 | self.data = None |
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183 | 184 | self.firsttime = True |
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184 | 185 | self.sender_queue = deque(maxlen=10) |
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185 | 186 | self.plots_adjust = {'left': 0.125, 'right': 0.9, 'bottom': 0.15, 'top': 0.9, 'wspace': 0.2, 'hspace': 0.2} |
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186 | 187 | |
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187 | 188 | def __fmtTime(self, x, pos): |
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188 | 189 | ''' |
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189 | 190 | ''' |
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190 | 191 | |
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191 | 192 | return '{}'.format(self.getDateTime(x).strftime('%H:%M')) |
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192 | 193 | |
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193 | 194 | def __setup(self, **kwargs): |
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194 | 195 | ''' |
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195 | 196 | Initialize variables |
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196 | 197 | ''' |
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197 | 198 | |
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198 | 199 | self.figures = [] |
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199 | 200 | self.axes = [] |
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200 | 201 | self.cb_axes = [] |
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201 | 202 | self.localtime = kwargs.pop('localtime', True) |
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202 | 203 | self.show = kwargs.get('show', True) |
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203 | 204 | self.save = kwargs.get('save', False) |
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204 | 205 | self.save_period = kwargs.get('save_period', 0) |
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205 | 206 | self.colormap = kwargs.get('colormap', self.colormap) |
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206 | 207 | self.colormap_coh = kwargs.get('colormap_coh', 'jet') |
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207 | 208 | self.colormap_phase = kwargs.get('colormap_phase', 'RdBu_r') |
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208 | 209 | self.colormaps = kwargs.get('colormaps', None) |
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209 | 210 | self.bgcolor = kwargs.get('bgcolor', self.bgcolor) |
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210 | 211 | self.showprofile = kwargs.get('showprofile', False) |
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211 | 212 | self.title = kwargs.get('wintitle', self.CODE.upper()) |
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212 | 213 | self.cb_label = kwargs.get('cb_label', None) |
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213 | 214 | self.cb_labels = kwargs.get('cb_labels', None) |
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214 | 215 | self.labels = kwargs.get('labels', None) |
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215 | 216 | self.xaxis = kwargs.get('xaxis', 'frequency') |
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216 | 217 | self.zmin = kwargs.get('zmin', None) |
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217 | 218 | self.zmax = kwargs.get('zmax', None) |
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218 | 219 | self.zlimits = kwargs.get('zlimits', None) |
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219 | 220 | self.xmin = kwargs.get('xmin', None) |
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220 | 221 | self.xmax = kwargs.get('xmax', None) |
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221 | 222 | self.xrange = kwargs.get('xrange', 12) |
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222 | 223 | self.xscale = kwargs.get('xscale', None) |
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223 | 224 | self.ymin = kwargs.get('ymin', None) |
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224 | 225 | self.ymax = kwargs.get('ymax', None) |
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225 | 226 | self.yscale = kwargs.get('yscale', None) |
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226 | 227 | self.xlabel = kwargs.get('xlabel', None) |
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227 | 228 | self.attr_time = kwargs.get('attr_time', 'utctime') |
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228 | 229 | self.attr_data = kwargs.get('attr_data', 'data_param') |
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229 | 230 | self.decimation = kwargs.get('decimation', None) |
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230 | 231 | self.oneFigure = kwargs.get('oneFigure', True) |
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231 | 232 | self.width = kwargs.get('width', None) |
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232 | 233 | self.height = kwargs.get('height', None) |
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233 | 234 | self.colorbar = kwargs.get('colorbar', True) |
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234 | 235 | self.factors = kwargs.get('factors', [1, 1, 1, 1, 1, 1, 1, 1]) |
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235 | 236 | self.channels = kwargs.get('channels', None) |
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236 | 237 | self.titles = kwargs.get('titles', []) |
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237 | 238 | self.polar = False |
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238 | 239 | self.type = kwargs.get('type', 'iq') |
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239 | 240 | self.grid = kwargs.get('grid', False) |
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240 | 241 | self.pause = kwargs.get('pause', False) |
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241 | 242 | self.save_code = kwargs.get('save_code', self.CODE) |
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242 | 243 | self.throttle = kwargs.get('throttle', 0) |
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243 | 244 | self.exp_code = kwargs.get('exp_code', None) |
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244 | 245 | self.server = kwargs.get('server', False) |
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245 | 246 | self.sender_period = kwargs.get('sender_period', 60) |
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246 | 247 | self.tag = kwargs.get('tag', '') |
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247 | 248 | self.height_index = kwargs.get('height_index', None) |
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248 | 249 | self.__throttle_plot = apply_throttle(self.throttle) |
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249 | 250 | code = self.attr_data if self.attr_data else self.CODE |
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250 | 251 | self.data = PlotterData(self.CODE, self.exp_code, self.localtime) |
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251 | 252 | self.ang_min = kwargs.get('ang_min', None) |
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252 | 253 | self.ang_max = kwargs.get('ang_max', None) |
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253 | 254 | self.mode = kwargs.get('mode', None) |
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254 | 255 | self.mask = kwargs.get('mask', False) |
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255 | ||
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256 | self.shapes = kwargs.get('shapes', './') | |
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256 | 257 | |
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257 | 258 | if self.server: |
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258 | 259 | if not self.server.startswith('tcp://'): |
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259 | 260 | self.server = 'tcp://{}'.format(self.server) |
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260 | 261 | log.success( |
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261 | 262 | 'Sending to server: {}'.format(self.server), |
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262 | 263 | self.name |
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263 | 264 | ) |
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264 | 265 | |
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265 | 266 | if isinstance(self.attr_data, str): |
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266 | 267 | self.attr_data = [self.attr_data] |
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267 | 268 | |
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268 | 269 | def __setup_plot(self): |
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269 | 270 | ''' |
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270 | 271 | Common setup for all figures, here figures and axes are created |
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271 | 272 | ''' |
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272 | 273 | |
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273 | 274 | self.setup() |
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274 | 275 | |
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275 | 276 | self.time_label = 'LT' if self.localtime else 'UTC' |
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276 | 277 | |
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277 | 278 | if self.width is None: |
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278 | 279 | self.width = 8 |
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279 | 280 | |
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280 | self.figures = [] | |
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281 | self.axes = [] | |
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281 | self.figures = {'PPI':[], 'RHI':[]} | |
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282 | self.axes = {'PPI':[], 'RHI':[]} | |
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282 | 283 | self.cb_axes = [] |
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283 | 284 | self.pf_axes = [] |
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284 | 285 | self.cmaps = [] |
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285 | 286 | |
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286 | 287 | size = '15%' if self.ncols == 1 else '30%' |
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287 | 288 | pad = '4%' if self.ncols == 1 else '8%' |
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288 | 289 | |
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289 | 290 | if self.oneFigure: |
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290 | 291 | if self.height is None: |
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291 | 292 | self.height = 1.4 * self.nrows + 1 |
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292 | fig = plt.figure(figsize=(self.width, self.height), | |
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293 | fig_p = plt.figure(figsize=(self.width, self.height), | |
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293 | 294 | edgecolor='k', |
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294 | 295 | facecolor='w') |
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295 | self.figures.append(fig) | |
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296 | for n in range(self.nplots): | |
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297 | ax = fig.add_subplot(self.nrows, self.ncols, | |
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298 | n + 1, polar=self.polar) | |
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299 | ax.tick_params(labelsize=8) | |
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300 | ax.firsttime = True | |
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301 | ax.index = 0 | |
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302 | ax.press = None | |
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303 | self.axes.append(ax) | |
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296 | fig_r = plt.figure(figsize=(self.width, self.height), | |
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297 | edgecolor='k', | |
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298 | facecolor='w') | |
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299 | self.figures['PPI'].append(fig_p) | |
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300 | self.figures['RHI'].append(fig_r) | |
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301 | for n in range(self.nplots): | |
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302 | ax_p = fig_p.add_subplot(self.nrows, self.ncols, n+1, polar=self.polar, projection=self.projection) | |
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303 | ax_r = fig_r.add_subplot(self.nrows, self.ncols, n+1, polar=self.polar) | |
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304 | ax_p.tick_params(labelsize=8) | |
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305 | ax_p.firsttime = True | |
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306 | ax_p.index = 0 | |
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307 | ax_p.press = None | |
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308 | ax_r.tick_params(labelsize=8) | |
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309 | ax_r.firsttime = True | |
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310 | ax_r.index = 0 | |
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311 | ax_r.press = None | |
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312 | ||
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313 | self.axes['PPI'].append(ax_p) | |
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314 | self.axes['RHI'].append(ax_r) | |
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315 | ||
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304 | 316 | if self.showprofile: |
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305 | 317 | cax = self.__add_axes(ax, size=size, pad=pad) |
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306 | 318 | cax.tick_params(labelsize=8) |
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307 | 319 | self.pf_axes.append(cax) |
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308 | 320 | else: |
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309 | 321 | if self.height is None: |
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310 | 322 | self.height = 3 |
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311 | 323 | for n in range(self.nplots): |
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312 | 324 | fig = plt.figure(figsize=(self.width, self.height), |
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313 | 325 | edgecolor='k', |
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314 | 326 | facecolor='w') |
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315 | ax = fig.add_subplot(1, 1, 1, polar=self.polar) | |
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316 | ax.tick_params(labelsize=8) | |
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317 |
ax. |
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318 |
ax. |
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319 |
ax. |
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327 | ax_p = fig.add_subplot(1, 1, 1, polar=self.polar, projection=self.projection) | |
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328 | ax_r = fig.add_subplot(1, 1, 1, polar=self.polar) | |
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329 | ax_p.tick_params(labelsize=8) | |
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330 | ax_p.firsttime = True | |
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331 | ax_p.index = 0 | |
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332 | ax_p.press = None | |
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333 | ax_r.tick_params(labelsize=8) | |
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334 | ax_r.firsttime = True | |
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335 | ax_r.index = 0 | |
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336 | ax_r.press = None | |
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320 | 337 | self.figures.append(fig) |
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321 | self.axes.append(ax) | |
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338 | self.axes['PPI'].append(ax_p) | |
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339 | self.axes['RHI'].append(ax_r) | |
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322 | 340 | if self.showprofile: |
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323 | 341 | cax = self.__add_axes(ax, size=size, pad=pad) |
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324 | 342 | cax.tick_params(labelsize=8) |
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325 | 343 | self.pf_axes.append(cax) |
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326 | 344 | |
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327 | 345 | for n in range(self.nrows): |
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328 | 346 | if self.colormaps is not None: |
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329 | 347 | cmap = plt.get_cmap(self.colormaps[n]) |
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330 | 348 | else: |
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331 | 349 | cmap = plt.get_cmap(self.colormap) |
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332 | 350 | cmap.set_bad(self.bgcolor, 1.) |
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|
333 | 351 | self.cmaps.append(cmap) |
|
|
334 | 352 | |
|
|
335 | 353 | def __add_axes(self, ax, size='30%', pad='8%'): |
|
|
336 | 354 | ''' |
|
|
337 | 355 | Add new axes to the given figure |
|
|
338 | 356 | ''' |
|
|
339 | 357 | divider = make_axes_locatable(ax) |
|
|
340 | 358 | nax = divider.new_horizontal(size=size, pad=pad) |
|
|
341 | 359 | ax.figure.add_axes(nax) |
|
|
342 | 360 | return nax |
|
|
343 | 361 | |
|
|
344 | 362 | def fill_gaps(self, x_buffer, y_buffer, z_buffer): |
|
|
345 | 363 | ''' |
|
|
346 | 364 | Create a masked array for missing data |
|
|
347 | 365 | ''' |
|
|
348 | 366 | if x_buffer.shape[0] < 2: |
|
|
349 | 367 | return x_buffer, y_buffer, z_buffer |
|
|
350 | 368 | |
|
|
351 | 369 | deltas = x_buffer[1:] - x_buffer[0:-1] |
|
|
352 | 370 | x_median = numpy.median(deltas) |
|
|
353 | 371 | |
|
|
354 | 372 | index = numpy.where(deltas > 5 * x_median) |
|
|
355 | 373 | |
|
|
356 | 374 | if len(index[0]) != 0: |
|
|
357 | 375 | z_buffer[::, index[0], ::] = self.__missing |
|
|
358 | 376 | z_buffer = numpy.ma.masked_inside(z_buffer, |
|
|
359 | 377 | 0.99 * self.__missing, |
|
|
360 | 378 | 1.01 * self.__missing) |
|
|
361 | 379 | |
|
|
362 | 380 | return x_buffer, y_buffer, z_buffer |
|
|
363 | 381 | |
|
|
364 | 382 | def decimate(self): |
|
|
365 | 383 | |
|
|
366 | 384 | # dx = int(len(self.x)/self.__MAXNUMX) + 1 |
|
|
367 | 385 | dy = int(len(self.y) / self.decimation) + 1 |
|
|
368 | 386 | |
|
|
369 | 387 | # x = self.x[::dx] |
|
|
370 | 388 | x = self.x |
|
|
371 | 389 | y = self.y[::dy] |
|
|
372 | 390 | z = self.z[::, ::, ::dy] |
|
|
373 | 391 | |
|
|
374 | 392 | return x, y, z |
|
|
375 | 393 | |
|
|
376 | 394 | def format(self): |
|
|
377 | 395 | ''' |
|
|
378 | 396 | Set min and max values, labels, ticks and titles |
|
|
379 | 397 | ''' |
|
|
380 | 398 | |
|
|
381 | for n, ax in enumerate(self.axes): | |
|
|
399 | for n, ax in enumerate(self.axes[self.mode]): | |
|
|
382 | 400 | if ax.firsttime: |
|
|
383 | 401 | if self.xaxis != 'time': |
|
|
384 | 402 | xmin = self.xmin |
|
|
385 | 403 | xmax = self.xmax |
|
|
386 | 404 | else: |
|
|
387 | 405 | xmin = self.tmin |
|
|
388 | 406 | xmax = self.tmin + self.xrange*60*60 |
|
|
389 | 407 | ax.xaxis.set_major_formatter(FuncFormatter(self.__fmtTime)) |
|
|
390 | 408 | ax.xaxis.set_major_locator(LinearLocator(9)) |
|
|
391 | 409 | ymin = self.ymin if self.ymin is not None else numpy.nanmin(self.y[numpy.isfinite(self.y)]) |
|
|
392 | 410 | ymax = self.ymax if self.ymax is not None else numpy.nanmax(self.y[numpy.isfinite(self.y)]) |
|
|
411 | ||
|
|
393 | 412 | ax.set_facecolor(self.bgcolor) |
|
|
413 | ||
|
|
394 | 414 | if self.xscale: |
|
|
395 | 415 | ax.xaxis.set_major_formatter(FuncFormatter( |
|
|
396 | 416 | lambda x, pos: '{0:g}'.format(x*self.xscale))) |
|
|
397 | 417 | if self.yscale: |
|
|
398 | 418 | ax.yaxis.set_major_formatter(FuncFormatter( |
|
|
399 | 419 | lambda x, pos: '{0:g}'.format(x*self.yscale))) |
|
|
400 | 420 | if self.xlabel is not None: |
|
|
401 | 421 | ax.set_xlabel(self.xlabel) |
|
|
402 | 422 | if self.ylabel is not None: |
|
|
403 | 423 | ax.set_ylabel(self.ylabel) |
|
|
404 | 424 | if self.showprofile: |
|
|
405 | 425 | self.pf_axes[n].set_ylim(ymin, ymax) |
|
|
406 | 426 | self.pf_axes[n].set_xlim(self.zmin, self.zmax) |
|
|
407 | 427 | self.pf_axes[n].set_xlabel('dB') |
|
|
408 | 428 | self.pf_axes[n].grid(b=True, axis='x') |
|
|
409 | 429 | [tick.set_visible(False) |
|
|
410 |
|
|
|
|
430 | for tick in self.pf_axes[n].get_yticklabels()] | |
|
|
411 | 431 | if self.colorbar: |
|
|
412 | 432 | ax.cbar = plt.colorbar( |
|
|
413 | 433 | ax.plt, ax=ax, fraction=0.05, pad=0.06, aspect=10) |
|
|
414 | 434 | ax.cbar.ax.tick_params(labelsize=8) |
|
|
415 | 435 | ax.cbar.ax.press = None |
|
|
416 | 436 | if self.cb_label: |
|
|
417 | 437 | ax.cbar.set_label(self.cb_label, size=8) |
|
|
418 | 438 | elif self.cb_labels: |
|
|
419 | 439 | ax.cbar.set_label(self.cb_labels[n], size=8) |
|
|
420 | 440 | else: |
|
|
421 | 441 | ax.cbar = None |
|
|
422 | ax.set_xlim(xmin, xmax) | |
|
|
423 |
ax.set_ |
|
|
|
442 | if self.mode == 'RHI': | |
|
|
443 | ax.set_xlim(xmin, xmax) | |
|
|
444 | ax.set_ylim(ymin, ymax) | |
|
|
424 | 445 | ax.firsttime = False |
|
|
425 | 446 | if self.grid: |
|
|
426 | 447 | ax.grid(True) |
|
|
427 | 448 | if not self.polar: |
|
|
428 | 449 | ax.set_title('{} {} {}'.format( |
|
|
429 | 450 | self.titles[n], |
|
|
430 | 451 | self.getDateTime(self.data.max_time).strftime( |
|
|
431 | 452 | '%Y-%m-%d %H:%M:%S'), |
|
|
432 | 453 | self.time_label), |
|
|
433 | 454 | size=8) |
|
|
434 | 455 | else: |
|
|
435 | 456 | #ax.set_title('{}'.format(self.titles[n]), size=8) |
|
|
436 | 457 | ax.set_title('{} {} {}'.format( |
|
|
437 | 458 | self.titles[n], |
|
|
438 | 459 | self.getDateTime(self.data.max_time).strftime( |
|
|
439 | 460 | '%Y-%m-%d %H:%M:%S'), |
|
|
440 | 461 | self.time_label), |
|
|
441 | 462 | size=8) |
|
|
442 | 463 | ax.set_ylim(0, self.ymax) |
|
|
443 | 464 | if self.mode == 'PPI': |
|
|
444 | 465 | ax.set_yticks(ax.get_yticks(), labels=ax.get_yticks(), color='white') |
|
|
445 | 466 | ax.yaxis.labelpad = 28 |
|
|
446 | 467 | elif self.mode == 'RHI': |
|
|
447 | 468 | ax.xaxis.labelpad = 16 |
|
|
448 | 469 | |
|
|
449 | 470 | if self.firsttime: |
|
|
450 |
for |
|
|
|
471 | for fig in self.figures['PPI'] + self.figures['RHI']: | |
|
|
451 | 472 | fig.subplots_adjust(**self.plots_adjust) |
|
|
452 | 473 | self.firsttime = False |
|
|
453 | 474 | |
|
|
454 | 475 | def clear_figures(self): |
|
|
455 | 476 | ''' |
|
|
456 | 477 | Reset axes for redraw plots |
|
|
457 | 478 | ''' |
|
|
458 | 479 | |
|
|
459 |
|
|
|
|
480 | axes = self.pf_axes + self.cb_axes + self.axes[self.mode] | |
|
|
481 | ||
|
|
482 | for ax in axes: | |
|
|
460 | 483 | ax.clear() |
|
|
461 | 484 | ax.firsttime = True |
|
|
462 | 485 | if hasattr(ax, 'cbar') and ax.cbar: |
|
|
463 | 486 | ax.cbar.remove() |
|
|
464 | 487 | |
|
|
465 | 488 | def __plot(self): |
|
|
466 | 489 | ''' |
|
|
467 | 490 | Main function to plot, format and save figures |
|
|
468 | 491 | ''' |
|
|
469 | 492 | |
|
|
470 | 493 | self.plot() |
|
|
471 | 494 | self.format() |
|
|
472 | ||
|
|
473 |
for n, fig in enumerate( |
|
|
|
495 | figures = self.figures[self.mode] | |
|
|
496 | for n, fig in enumerate(figures): | |
|
|
474 | 497 | if self.nrows == 0 or self.nplots == 0: |
|
|
475 | 498 | log.warning('No data', self.name) |
|
|
476 | 499 | fig.text(0.5, 0.5, 'No Data', fontsize='large', ha='center') |
|
|
477 | 500 | fig.canvas.manager.set_window_title(self.CODE) |
|
|
478 | 501 | continue |
|
|
479 | 502 | |
|
|
480 | 503 | fig.canvas.manager.set_window_title('{} - {}'.format(self.title, |
|
|
481 | 504 | self.getDateTime(self.data.max_time).strftime('%Y/%m/%d'))) |
|
|
482 | 505 | fig.canvas.draw() |
|
|
483 | 506 | if self.show: |
|
|
484 | 507 | fig.show() |
|
|
485 | 508 | figpause(0.01) |
|
|
486 | 509 | |
|
|
487 | 510 | if self.save: |
|
|
488 | 511 | self.save_figure(n) |
|
|
489 | 512 | |
|
|
490 | 513 | if self.server: |
|
|
491 | 514 | if self.mode and self.mode == 'RHI': |
|
|
492 | 515 | return |
|
|
493 | 516 | self.send_to_server() |
|
|
494 | 517 | |
|
|
495 | 518 | def __update(self, dataOut, timestamp): |
|
|
496 | 519 | ''' |
|
|
497 | 520 | ''' |
|
|
498 | 521 | |
|
|
499 | 522 | metadata = { |
|
|
500 | 523 | 'yrange': dataOut.heightList, |
|
|
501 | 524 | 'interval': dataOut.timeInterval, |
|
|
502 | 525 | 'channels': dataOut.channelList |
|
|
503 | 526 | } |
|
|
504 | 527 | |
|
|
505 | 528 | data, meta = self.update(dataOut) |
|
|
506 | 529 | metadata.update(meta) |
|
|
507 | 530 | self.data.update(data, timestamp, metadata) |
|
|
508 | 531 | |
|
|
509 | 532 | def save_figure(self, n): |
|
|
510 | 533 | ''' |
|
|
511 | 534 | ''' |
|
|
512 | 535 | if self.mode is not None: |
|
|
513 | 536 | ang = 'AZ' if self.mode == 'RHI' else 'EL' |
|
|
514 |
|
|
|
|
537 | folder = '_{}_{}_{}'.format(self.mode, ang, self.mode_value) | |
|
|
538 | label = '{}{}_{}'.format(ang[0], self.mode_value, self.save_code) | |
|
|
515 | 539 | else: |
|
|
540 | folder = '' | |
|
|
516 | 541 | label = '' |
|
|
517 | 542 | |
|
|
518 | 543 | if self.oneFigure: |
|
|
519 | 544 | if (self.data.max_time - self.save_time) <= self.save_period: |
|
|
520 | 545 | return |
|
|
521 | 546 | |
|
|
522 | 547 | self.save_time = self.data.max_time |
|
|
523 | 548 | |
|
|
524 | fig = self.figures[n] | |
|
|
549 | fig = self.figures[self.mode][n] | |
|
|
525 | 550 | |
|
|
526 | 551 | if self.throttle == 0: |
|
|
527 | 552 | if self.oneFigure: |
|
|
528 | 553 | figname = os.path.join( |
|
|
529 | 554 | self.save, |
|
|
530 |
self.save_code + |
|
|
|
531 | '{}_{}.png'.format( | |
|
|
532 |
|
|
|
|
555 | self.save_code + folder, | |
|
|
556 | '{}_{}_{}.png'.format( | |
|
|
557 | 'SOPHY', | |
|
|
533 | 558 | self.getDateTime(self.data.max_time).strftime( |
|
|
534 | 559 | '%Y%m%d_%H%M%S' |
|
|
535 | 560 | ), |
|
|
561 | label | |
|
|
536 | 562 | ) |
|
|
537 | 563 | ) |
|
|
538 | 564 | else: |
|
|
539 | 565 | figname = os.path.join( |
|
|
540 | 566 | self.save, |
|
|
541 | 567 | self.save_code, |
|
|
542 | 568 | '{}_ch{}_{}.png'.format( |
|
|
543 | 569 | self.save_code, n, |
|
|
544 | 570 | self.getDateTime(self.data.max_time).strftime( |
|
|
545 | 571 | '%Y%m%d_%H%M%S' |
|
|
546 | 572 | ), |
|
|
547 | 573 | ) |
|
|
548 | 574 | ) |
|
|
549 | 575 | log.log('Saving figure: {}'.format(figname), self.name) |
|
|
550 | 576 | if not os.path.isdir(os.path.dirname(figname)): |
|
|
551 | 577 | os.makedirs(os.path.dirname(figname)) |
|
|
552 | 578 | fig.savefig(figname) |
|
|
553 | 579 | |
|
|
554 | 580 | figname = os.path.join( |
|
|
555 | 581 | self.save, |
|
|
556 | 582 | '{}_{}.png'.format( |
|
|
557 | 583 | self.save_code, |
|
|
558 | 584 | self.getDateTime(self.data.min_time).strftime( |
|
|
559 | 585 | '%Y%m%d' |
|
|
560 | 586 | ), |
|
|
561 | 587 | ) |
|
|
562 | 588 | ) |
|
|
563 | 589 | |
|
|
564 | 590 | log.log('Saving figure: {}'.format(figname), self.name) |
|
|
565 | 591 | if not os.path.isdir(os.path.dirname(figname)): |
|
|
566 | 592 | os.makedirs(os.path.dirname(figname)) |
|
|
567 | 593 | fig.savefig(figname) |
|
|
568 | 594 | |
|
|
569 | 595 | def send_to_server(self): |
|
|
570 | 596 | ''' |
|
|
571 | 597 | ''' |
|
|
572 | 598 | |
|
|
573 | 599 | if self.exp_code == None: |
|
|
574 | 600 | log.warning('Missing `exp_code` skipping sending to server...') |
|
|
575 | 601 | |
|
|
576 | 602 | last_time = self.data.max_time |
|
|
577 | 603 | interval = last_time - self.sender_time |
|
|
578 | 604 | if interval < self.sender_period: |
|
|
579 | 605 | return |
|
|
580 | 606 | |
|
|
581 | 607 | self.sender_time = last_time |
|
|
582 | 608 | |
|
|
583 | 609 | attrs = ['titles', 'zmin', 'zmax', 'tag', 'ymin', 'ymax'] |
|
|
584 | 610 | for attr in attrs: |
|
|
585 | 611 | value = getattr(self, attr) |
|
|
586 | 612 | if value: |
|
|
587 | 613 | if isinstance(value, (numpy.float32, numpy.float64)): |
|
|
588 | 614 | value = round(float(value), 2) |
|
|
589 | 615 | self.data.meta[attr] = value |
|
|
590 | 616 | if self.colormap == 'jet' or self.colormap == 'sophy_w': |
|
|
591 | 617 | self.data.meta['colormap'] = 'Jet' |
|
|
592 | 618 | elif 'sophy_v' in self.colormap: |
|
|
593 | 619 | self.data.meta['colormap'] = 'RdBu' |
|
|
594 | 620 | else: |
|
|
595 | 621 | self.data.meta['colormap'] = 'Viridis' |
|
|
596 | 622 | self.data.meta['interval'] = int(interval) |
|
|
597 | 623 | |
|
|
598 | 624 | self.sender_queue.append(last_time) |
|
|
599 | 625 | |
|
|
600 | 626 | while True: |
|
|
601 | 627 | try: |
|
|
602 | 628 | tm = self.sender_queue.popleft() |
|
|
603 | 629 | except IndexError: |
|
|
604 | 630 | break |
|
|
605 | 631 | msg = self.data.jsonify(tm, self.save_code, self.plot_type, key='var') |
|
|
606 | 632 | self.socket.send_string(msg) |
|
|
607 | 633 | socks = dict(self.poll.poll(2000)) |
|
|
608 | 634 | if socks.get(self.socket) == zmq.POLLIN: |
|
|
609 | 635 | reply = self.socket.recv_string() |
|
|
610 | 636 | if reply == 'ok': |
|
|
611 | 637 | log.log("Response from server ok", self.name) |
|
|
612 | 638 | time.sleep(0.1) |
|
|
613 | 639 | continue |
|
|
614 | 640 | else: |
|
|
615 | 641 | log.warning( |
|
|
616 | 642 | "Malformed reply from server: {}".format(reply), self.name) |
|
|
617 | 643 | else: |
|
|
618 | 644 | log.warning( |
|
|
619 | 645 | "No response from server, retrying...", self.name) |
|
|
620 | 646 | self.sender_queue.appendleft(tm) |
|
|
621 | 647 | self.socket.setsockopt(zmq.LINGER, 0) |
|
|
622 | 648 | self.socket.close() |
|
|
623 | 649 | self.poll.unregister(self.socket) |
|
|
624 | 650 | self.socket = self.context.socket(zmq.REQ) |
|
|
625 | 651 | self.socket.connect(self.server) |
|
|
626 | 652 | self.poll.register(self.socket, zmq.POLLIN) |
|
|
627 | 653 | break |
|
|
628 | 654 | |
|
|
629 | 655 | def setup(self): |
|
|
630 | 656 | ''' |
|
|
631 | 657 | This method should be implemented in the child class, the following |
|
|
632 | 658 | attributes should be set: |
|
|
633 | 659 | |
|
|
634 | 660 | self.nrows: number of rows |
|
|
635 | 661 | self.ncols: number of cols |
|
|
636 | 662 | self.nplots: number of plots (channels or pairs) |
|
|
637 | 663 | self.ylabel: label for Y axes |
|
|
638 | 664 | self.titles: list of axes title |
|
|
639 | 665 | |
|
|
640 | 666 | ''' |
|
|
641 | 667 | raise NotImplementedError |
|
|
642 | 668 | |
|
|
643 | 669 | def plot(self): |
|
|
644 | 670 | ''' |
|
|
645 | 671 | Must be defined in the child class, the actual plotting method |
|
|
646 | 672 | ''' |
|
|
647 | 673 | raise NotImplementedError |
|
|
648 | 674 | |
|
|
649 | 675 | def update(self, dataOut): |
|
|
650 | 676 | ''' |
|
|
651 | 677 | Must be defined in the child class, update self.data with new data |
|
|
652 | 678 | ''' |
|
|
653 | 679 | |
|
|
654 | 680 | data = { |
|
|
655 | 681 | self.CODE: getattr(dataOut, 'data_{}'.format(self.CODE)) |
|
|
656 | 682 | } |
|
|
657 | 683 | meta = {} |
|
|
658 | 684 | |
|
|
659 | 685 | return data, meta |
|
|
660 | 686 | |
|
|
661 | 687 | def run(self, dataOut, **kwargs): |
|
|
662 | 688 | ''' |
|
|
663 | 689 | Main plotting routine |
|
|
664 | 690 | ''' |
|
|
665 | 691 | |
|
|
666 | 692 | if self.isConfig is False: |
|
|
667 | 693 | self.__setup(**kwargs) |
|
|
668 | 694 | |
|
|
669 | 695 | if self.localtime: |
|
|
670 | 696 | self.getDateTime = datetime.datetime.fromtimestamp |
|
|
671 | 697 | else: |
|
|
672 | 698 | self.getDateTime = datetime.datetime.utcfromtimestamp |
|
|
673 | 699 | |
|
|
674 | 700 | self.data.setup() |
|
|
675 | 701 | self.isConfig = True |
|
|
676 | 702 | if self.server: |
|
|
677 | 703 | self.context = zmq.Context() |
|
|
678 | 704 | self.socket = self.context.socket(zmq.REQ) |
|
|
679 | 705 | self.socket.connect(self.server) |
|
|
680 | 706 | self.poll = zmq.Poller() |
|
|
681 | 707 | self.poll.register(self.socket, zmq.POLLIN) |
|
|
682 | 708 | |
|
|
683 | 709 | tm = getattr(dataOut, self.attr_time) |
|
|
684 | 710 | |
|
|
685 | 711 | if self.data and 'time' in self.xaxis and (tm - self.tmin) >= self.xrange*60*60: |
|
|
686 | 712 | self.save_time = tm |
|
|
687 | 713 | self.__plot() |
|
|
688 | 714 | self.tmin += self.xrange*60*60 |
|
|
689 | 715 | self.data.setup() |
|
|
690 | 716 | self.clear_figures() |
|
|
691 | 717 | |
|
|
692 | 718 | self.__update(dataOut, tm) |
|
|
693 | 719 | |
|
|
694 | 720 | if self.isPlotConfig is False: |
|
|
695 | 721 | self.__setup_plot() |
|
|
696 | 722 | self.isPlotConfig = True |
|
|
697 | 723 | if self.xaxis == 'time': |
|
|
698 | 724 | dt = self.getDateTime(tm) |
|
|
699 | 725 | if self.xmin is None: |
|
|
700 | 726 | self.tmin = tm |
|
|
701 | 727 | self.xmin = dt.hour |
|
|
702 | 728 | minutes = (self.xmin-int(self.xmin)) * 60 |
|
|
703 | 729 | seconds = (minutes - int(minutes)) * 60 |
|
|
704 | 730 | self.tmin = (dt.replace(hour=int(self.xmin), minute=int(minutes), second=int(seconds)) - |
|
|
705 | 731 | datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
706 | 732 | if self.localtime: |
|
|
707 | 733 | self.tmin += time.timezone |
|
|
708 | 734 | |
|
|
709 | 735 | if self.xmin is not None and self.xmax is not None: |
|
|
710 | 736 | self.xrange = self.xmax - self.xmin |
|
|
711 | 737 | |
|
|
712 | 738 | if self.throttle == 0: |
|
|
713 | 739 | self.__plot() |
|
|
714 | 740 | else: |
|
|
715 | 741 | self.__throttle_plot(self.__plot)#, coerce=coerce) |
|
|
716 | 742 | |
|
|
717 | 743 | def close(self): |
|
|
718 | 744 | |
|
|
719 | 745 | if self.data and not self.data.flagNoData: |
|
|
720 | 746 | self.save_time = 0 |
|
|
721 | 747 | self.__plot() |
|
|
722 | 748 | if self.data and not self.data.flagNoData and self.pause: |
|
|
723 | 749 | figpause(10) |
| @@ -1,697 +1,716 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import warnings |
|
|
4 | 4 | import numpy |
|
|
5 | 5 | from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter |
|
|
6 | from matplotlib.patches import Circle | |
|
|
7 | import cartopy.crs as ccrs | |
|
|
8 | from cartopy.feature import ShapelyFeature | |
|
|
9 | import cartopy.io.shapereader as shpreader | |
|
|
6 | 10 | |
|
|
7 | 11 | from schainpy.model.graphics.jroplot_base import Plot, plt |
|
|
8 | 12 | from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot |
|
|
9 | 13 | from schainpy.utils import log |
|
|
10 | 14 | |
|
|
11 | 15 | |
|
|
12 | 16 | EARTH_RADIUS = 6.3710e3 |
|
|
13 | 17 | |
|
|
14 | 18 | |
|
|
15 | 19 | def antenna_to_cartesian(ranges, azimuths, elevations): |
|
|
16 | 20 | """ |
|
|
17 | 21 | Return Cartesian coordinates from antenna coordinates. |
|
|
18 | 22 | |
|
|
19 | 23 | Parameters |
|
|
20 | 24 | ---------- |
|
|
21 | 25 | ranges : array |
|
|
22 | 26 | Distances to the center of the radar gates (bins) in kilometers. |
|
|
23 | 27 | azimuths : array |
|
|
24 | 28 | Azimuth angle of the radar in degrees. |
|
|
25 | 29 | elevations : array |
|
|
26 | 30 | Elevation angle of the radar in degrees. |
|
|
27 | 31 | |
|
|
28 | 32 | Returns |
|
|
29 | 33 | ------- |
|
|
30 | 34 | x, y, z : array |
|
|
31 | 35 | Cartesian coordinates in meters from the radar. |
|
|
32 | 36 | |
|
|
33 | 37 | Notes |
|
|
34 | 38 | ----- |
|
|
35 | 39 | The calculation for Cartesian coordinate is adapted from equations |
|
|
36 | 40 | 2.28(b) and 2.28(c) of Doviak and Zrnic [1]_ assuming a |
|
|
37 | 41 | standard atmosphere (4/3 Earth's radius model). |
|
|
38 | 42 | |
|
|
39 | 43 | .. math:: |
|
|
40 | 44 | |
|
|
41 | 45 | z = \\sqrt{r^2+R^2+2*r*R*sin(\\theta_e)} - R |
|
|
42 | 46 | |
|
|
43 | 47 | s = R * arcsin(\\frac{r*cos(\\theta_e)}{R+z}) |
|
|
44 | 48 | |
|
|
45 | 49 | x = s * sin(\\theta_a) |
|
|
46 | 50 | |
|
|
47 | 51 | y = s * cos(\\theta_a) |
|
|
48 | 52 | |
|
|
49 | 53 | Where r is the distance from the radar to the center of the gate, |
|
|
50 | 54 | :math:`\\theta_a` is the azimuth angle, :math:`\\theta_e` is the |
|
|
51 | 55 | elevation angle, s is the arc length, and R is the effective radius |
|
|
52 | 56 | of the earth, taken to be 4/3 the mean radius of earth (6371 km). |
|
|
53 | 57 | |
|
|
54 | 58 | References |
|
|
55 | 59 | ---------- |
|
|
56 | 60 | .. [1] Doviak and Zrnic, Doppler Radar and Weather Observations, Second |
|
|
57 | 61 | Edition, 1993, p. 21. |
|
|
58 | 62 | |
|
|
59 | 63 | """ |
|
|
60 | 64 | theta_e = numpy.deg2rad(elevations) # elevation angle in radians. |
|
|
61 | 65 | theta_a = numpy.deg2rad(azimuths) # azimuth angle in radians. |
|
|
62 | 66 | R = 6371.0 * 1000.0 * 4.0 / 3.0 # effective radius of earth in meters. |
|
|
63 | 67 | r = ranges * 1000.0 # distances to gates in meters. |
|
|
64 | 68 | |
|
|
65 | 69 | z = (r ** 2 + R ** 2 + 2.0 * r * R * numpy.sin(theta_e)) ** 0.5 - R |
|
|
66 | 70 | s = R * numpy.arcsin(r * numpy.cos(theta_e) / (R + z)) # arc length in m. |
|
|
67 | 71 | x = s * numpy.sin(theta_a) |
|
|
68 | 72 | y = s * numpy.cos(theta_a) |
|
|
69 | 73 | return x, y, z |
|
|
70 | 74 | |
|
|
71 | 75 | def cartesian_to_geographic_aeqd(x, y, lon_0, lat_0, R=EARTH_RADIUS): |
|
|
72 | 76 | """ |
|
|
73 | 77 | Azimuthal equidistant Cartesian to geographic coordinate transform. |
|
|
74 | 78 | |
|
|
75 | 79 | Transform a set of Cartesian/Cartographic coordinates (x, y) to |
|
|
76 | 80 | geographic coordinate system (lat, lon) using a azimuthal equidistant |
|
|
77 | 81 | map projection [1]_. |
|
|
78 | 82 | |
|
|
79 | 83 | .. math:: |
|
|
80 | 84 | |
|
|
81 | 85 | lat = \\arcsin(\\cos(c) * \\sin(lat_0) + |
|
|
82 | 86 | (y * \\sin(c) * \\cos(lat_0) / \\rho)) |
|
|
83 | 87 | |
|
|
84 | 88 | lon = lon_0 + \\arctan2( |
|
|
85 | 89 | x * \\sin(c), |
|
|
86 | 90 | \\rho * \\cos(lat_0) * \\cos(c) - y * \\sin(lat_0) * \\sin(c)) |
|
|
87 | 91 | |
|
|
88 | 92 | \\rho = \\sqrt(x^2 + y^2) |
|
|
89 | 93 | |
|
|
90 | 94 | c = \\rho / R |
|
|
91 | 95 | |
|
|
92 | 96 | Where x, y are the Cartesian position from the center of projection; |
|
|
93 | 97 | lat, lon the corresponding latitude and longitude; lat_0, lon_0 are the |
|
|
94 | 98 | latitude and longitude of the center of the projection; R is the radius of |
|
|
95 | 99 | the earth (defaults to ~6371 km). lon is adjusted to be between -180 and |
|
|
96 | 100 | 180. |
|
|
97 | 101 | |
|
|
98 | 102 | Parameters |
|
|
99 | 103 | ---------- |
|
|
100 | 104 | x, y : array-like |
|
|
101 | 105 | Cartesian coordinates in the same units as R, typically meters. |
|
|
102 | 106 | lon_0, lat_0 : float |
|
|
103 | 107 | Longitude and latitude, in degrees, of the center of the projection. |
|
|
104 | 108 | R : float, optional |
|
|
105 | 109 | Earth radius in the same units as x and y. The default value is in |
|
|
106 | 110 | units of meters. |
|
|
107 | 111 | |
|
|
108 | 112 | Returns |
|
|
109 | 113 | ------- |
|
|
110 | 114 | lon, lat : array |
|
|
111 | 115 | Longitude and latitude of Cartesian coordinates in degrees. |
|
|
112 | 116 | |
|
|
113 | 117 | References |
|
|
114 | 118 | ---------- |
|
|
115 | 119 | .. [1] Snyder, J. P. Map Projections--A Working Manual. U. S. Geological |
|
|
116 | 120 | Survey Professional Paper 1395, 1987, pp. 191-202. |
|
|
117 | 121 | |
|
|
118 | 122 | """ |
|
|
119 | 123 | x = numpy.atleast_1d(numpy.asarray(x)) |
|
|
120 | 124 | y = numpy.atleast_1d(numpy.asarray(y)) |
|
|
121 | 125 | |
|
|
122 | 126 | lat_0_rad = numpy.deg2rad(lat_0) |
|
|
123 | 127 | lon_0_rad = numpy.deg2rad(lon_0) |
|
|
124 | 128 | |
|
|
125 | 129 | rho = numpy.sqrt(x*x + y*y) |
|
|
126 | 130 | c = rho / R |
|
|
127 | 131 | |
|
|
128 | 132 | with warnings.catch_warnings(): |
|
|
129 | 133 | # division by zero may occur here but is properly addressed below so |
|
|
130 | 134 | # the warnings can be ignored |
|
|
131 | 135 | warnings.simplefilter("ignore", RuntimeWarning) |
|
|
132 | 136 | lat_rad = numpy.arcsin(numpy.cos(c) * numpy.sin(lat_0_rad) + |
|
|
133 | 137 | y * numpy.sin(c) * numpy.cos(lat_0_rad) / rho) |
|
|
134 | 138 | lat_deg = numpy.rad2deg(lat_rad) |
|
|
135 | 139 | # fix cases where the distance from the center of the projection is zero |
|
|
136 | 140 | lat_deg[rho == 0] = lat_0 |
|
|
137 | 141 | |
|
|
138 | 142 | x1 = x * numpy.sin(c) |
|
|
139 | 143 | x2 = rho*numpy.cos(lat_0_rad)*numpy.cos(c) - y*numpy.sin(lat_0_rad)*numpy.sin(c) |
|
|
140 | 144 | lon_rad = lon_0_rad + numpy.arctan2(x1, x2) |
|
|
141 | 145 | lon_deg = numpy.rad2deg(lon_rad) |
|
|
142 | 146 | # Longitudes should be from -180 to 180 degrees |
|
|
143 | 147 | lon_deg[lon_deg > 180] -= 360. |
|
|
144 | 148 | lon_deg[lon_deg < -180] += 360. |
|
|
145 | 149 | |
|
|
146 | 150 | return lon_deg, lat_deg |
|
|
147 | 151 | |
|
|
148 | 152 | def antenna_to_geographic(ranges, azimuths, elevations, site): |
|
|
149 | 153 | |
|
|
150 | 154 | x, y, z = antenna_to_cartesian(numpy.array(ranges), numpy.array(azimuths), numpy.array(elevations)) |
|
|
151 | 155 | lon, lat = cartesian_to_geographic_aeqd(x, y, site[0], site[1], R=6370997.) |
|
|
152 | 156 | |
|
|
153 | 157 | return lon, lat |
|
|
154 | 158 | |
|
|
155 | 159 | def ll2xy(lat1, lon1, lat2, lon2): |
|
|
156 | 160 | |
|
|
157 | 161 | p = 0.017453292519943295 |
|
|
158 | 162 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
|
|
159 | 163 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
|
|
160 | 164 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
|
|
161 | 165 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
|
|
162 | 166 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
|
|
163 | 167 | theta = -theta + numpy.pi/2 |
|
|
164 | 168 | return r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
165 | 169 | |
|
|
166 | 170 | |
|
|
167 | 171 | def km2deg(km): |
|
|
168 | 172 | ''' |
|
|
169 | 173 | Convert distance in km to degrees |
|
|
170 | 174 | ''' |
|
|
171 | 175 | |
|
|
172 | 176 | return numpy.rad2deg(km/EARTH_RADIUS) |
|
|
173 | 177 | |
|
|
174 | 178 | |
|
|
175 | 179 | |
|
|
176 | 180 | class SpectralMomentsPlot(SpectraPlot): |
|
|
177 | 181 | ''' |
|
|
178 | 182 | Plot for Spectral Moments |
|
|
179 | 183 | ''' |
|
|
180 | 184 | CODE = 'spc_moments' |
|
|
181 | 185 | # colormap = 'jet' |
|
|
182 | 186 | # plot_type = 'pcolor' |
|
|
183 | 187 | |
|
|
184 | 188 | class DobleGaussianPlot(SpectraPlot): |
|
|
185 | 189 | ''' |
|
|
186 | 190 | Plot for Double Gaussian Plot |
|
|
187 | 191 | ''' |
|
|
188 | 192 | CODE = 'gaussian_fit' |
|
|
189 | 193 | # colormap = 'jet' |
|
|
190 | 194 | # plot_type = 'pcolor' |
|
|
191 | 195 | |
|
|
192 | 196 | class DoubleGaussianSpectraCutPlot(SpectraCutPlot): |
|
|
193 | 197 | ''' |
|
|
194 | 198 | Plot SpectraCut with Double Gaussian Fit |
|
|
195 | 199 | ''' |
|
|
196 | 200 | CODE = 'cut_gaussian_fit' |
|
|
197 | 201 | |
|
|
198 | 202 | class SnrPlot(RTIPlot): |
|
|
199 | 203 | ''' |
|
|
200 | 204 | Plot for SNR Data |
|
|
201 | 205 | ''' |
|
|
202 | 206 | |
|
|
203 | 207 | CODE = 'snr' |
|
|
204 | 208 | colormap = 'jet' |
|
|
205 | 209 | |
|
|
206 | 210 | def update(self, dataOut): |
|
|
207 | 211 | |
|
|
208 | 212 | data = { |
|
|
209 | 213 | 'snr': 10*numpy.log10(dataOut.data_snr) |
|
|
210 | 214 | } |
|
|
211 | 215 | |
|
|
212 | 216 | return data, {} |
|
|
213 | 217 | |
|
|
214 | 218 | class DopplerPlot(RTIPlot): |
|
|
215 | 219 | ''' |
|
|
216 | 220 | Plot for DOPPLER Data (1st moment) |
|
|
217 | 221 | ''' |
|
|
218 | 222 | |
|
|
219 | 223 | CODE = 'dop' |
|
|
220 | 224 | colormap = 'jet' |
|
|
221 | 225 | |
|
|
222 | 226 | def update(self, dataOut): |
|
|
223 | 227 | |
|
|
224 | 228 | data = { |
|
|
225 | 229 | 'dop': 10*numpy.log10(dataOut.data_dop) |
|
|
226 | 230 | } |
|
|
227 | 231 | |
|
|
228 | 232 | return data, {} |
|
|
229 | 233 | |
|
|
230 | 234 | class PowerPlot(RTIPlot): |
|
|
231 | 235 | ''' |
|
|
232 | 236 | Plot for Power Data (0 moment) |
|
|
233 | 237 | ''' |
|
|
234 | 238 | |
|
|
235 | 239 | CODE = 'pow' |
|
|
236 | 240 | colormap = 'jet' |
|
|
237 | 241 | |
|
|
238 | 242 | def update(self, dataOut): |
|
|
239 | 243 | data = { |
|
|
240 | 244 | 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor) |
|
|
241 | 245 | } |
|
|
242 | 246 | return data, {} |
|
|
243 | 247 | |
|
|
244 | 248 | class SpectralWidthPlot(RTIPlot): |
|
|
245 | 249 | ''' |
|
|
246 | 250 | Plot for Spectral Width Data (2nd moment) |
|
|
247 | 251 | ''' |
|
|
248 | 252 | |
|
|
249 | 253 | CODE = 'width' |
|
|
250 | 254 | colormap = 'jet' |
|
|
251 | 255 | |
|
|
252 | 256 | def update(self, dataOut): |
|
|
253 | 257 | |
|
|
254 | 258 | data = { |
|
|
255 | 259 | 'width': dataOut.data_width |
|
|
256 | 260 | } |
|
|
257 | 261 | |
|
|
258 | 262 | return data, {} |
|
|
259 | 263 | |
|
|
260 | 264 | class SkyMapPlot(Plot): |
|
|
261 | 265 | ''' |
|
|
262 | 266 | Plot for meteors detection data |
|
|
263 | 267 | ''' |
|
|
264 | 268 | |
|
|
265 | 269 | CODE = 'param' |
|
|
266 | 270 | |
|
|
267 | 271 | def setup(self): |
|
|
268 | 272 | |
|
|
269 | 273 | self.ncols = 1 |
|
|
270 | 274 | self.nrows = 1 |
|
|
271 | 275 | self.width = 7.2 |
|
|
272 | 276 | self.height = 7.2 |
|
|
273 | 277 | self.nplots = 1 |
|
|
274 | 278 | self.xlabel = 'Zonal Zenith Angle (deg)' |
|
|
275 | 279 | self.ylabel = 'Meridional Zenith Angle (deg)' |
|
|
276 | 280 | self.polar = True |
|
|
277 | 281 | self.ymin = -180 |
|
|
278 | 282 | self.ymax = 180 |
|
|
279 | 283 | self.colorbar = False |
|
|
280 | 284 | |
|
|
281 | 285 | def plot(self): |
|
|
282 | 286 | |
|
|
283 | 287 | arrayParameters = numpy.concatenate(self.data['param']) |
|
|
284 | 288 | error = arrayParameters[:, -1] |
|
|
285 | 289 | indValid = numpy.where(error == 0)[0] |
|
|
286 | 290 | finalMeteor = arrayParameters[indValid, :] |
|
|
287 | 291 | finalAzimuth = finalMeteor[:, 3] |
|
|
288 | 292 | finalZenith = finalMeteor[:, 4] |
|
|
289 | 293 | |
|
|
290 | 294 | x = finalAzimuth * numpy.pi / 180 |
|
|
291 | 295 | y = finalZenith |
|
|
292 | 296 | |
|
|
293 | 297 | ax = self.axes[0] |
|
|
294 | 298 | |
|
|
295 | 299 | if ax.firsttime: |
|
|
296 | 300 | ax.plot = ax.plot(x, y, 'bo', markersize=5)[0] |
|
|
297 | 301 | else: |
|
|
298 | 302 | ax.plot.set_data(x, y) |
|
|
299 | 303 | |
|
|
300 | 304 | dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
301 | 305 | dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
302 | 306 | title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, |
|
|
303 | 307 | dt2, |
|
|
304 | 308 | len(x)) |
|
|
305 | 309 | self.titles[0] = title |
|
|
306 | 310 | |
|
|
307 | 311 | |
|
|
308 | 312 | class GenericRTIPlot(Plot): |
|
|
309 | 313 | ''' |
|
|
310 | 314 | Plot for data_xxxx object |
|
|
311 | 315 | ''' |
|
|
312 | 316 | |
|
|
313 | 317 | CODE = 'param' |
|
|
314 | 318 | colormap = 'viridis' |
|
|
315 | 319 | plot_type = 'pcolorbuffer' |
|
|
316 | 320 | |
|
|
317 | 321 | def setup(self): |
|
|
318 | 322 | self.xaxis = 'time' |
|
|
319 | 323 | self.ncols = 1 |
|
|
320 | 324 | self.nrows = self.data.shape('param')[0] |
|
|
321 | 325 | self.nplots = self.nrows |
|
|
322 | 326 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95}) |
|
|
323 | 327 | |
|
|
324 | 328 | if not self.xlabel: |
|
|
325 | 329 | self.xlabel = 'Time' |
|
|
326 | 330 | |
|
|
327 | 331 | self.ylabel = 'Range [km]' |
|
|
328 | 332 | if not self.titles: |
|
|
329 | 333 | self.titles = ['Param {}'.format(x) for x in range(self.nrows)] |
|
|
330 | 334 | |
|
|
331 | 335 | def update(self, dataOut): |
|
|
332 | 336 | |
|
|
333 | 337 | data = { |
|
|
334 | 338 | 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0) |
|
|
335 | 339 | } |
|
|
336 | 340 | |
|
|
337 | 341 | meta = {} |
|
|
338 | 342 | |
|
|
339 | 343 | return data, meta |
|
|
340 | 344 | |
|
|
341 | 345 | def plot(self): |
|
|
342 | 346 | # self.data.normalize_heights() |
|
|
343 | 347 | self.x = self.data.times |
|
|
344 | 348 | self.y = self.data.yrange |
|
|
345 | 349 | self.z = self.data['param'] |
|
|
346 | 350 | self.z = 10*numpy.log10(self.z) |
|
|
347 | 351 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
348 | 352 | |
|
|
349 | 353 | if self.decimation is None: |
|
|
350 | 354 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
351 | 355 | else: |
|
|
352 | 356 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
353 | 357 | |
|
|
354 | 358 | for n, ax in enumerate(self.axes): |
|
|
355 | 359 | |
|
|
356 | 360 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
|
|
357 | 361 | self.z[n]) |
|
|
358 | 362 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
|
|
359 | 363 | self.z[n]) |
|
|
360 | 364 | |
|
|
361 | 365 | if ax.firsttime: |
|
|
362 | 366 | if self.zlimits is not None: |
|
|
363 | 367 | self.zmin, self.zmax = self.zlimits[n] |
|
|
364 | 368 | |
|
|
365 | 369 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
366 | 370 | vmin=self.zmin, |
|
|
367 | 371 | vmax=self.zmax, |
|
|
368 | 372 | cmap=self.cmaps[n] |
|
|
369 | 373 | ) |
|
|
370 | 374 | else: |
|
|
371 | 375 | if self.zlimits is not None: |
|
|
372 | 376 | self.zmin, self.zmax = self.zlimits[n] |
|
|
373 | 377 | ax.collections.remove(ax.collections[0]) |
|
|
374 | 378 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
375 | 379 | vmin=self.zmin, |
|
|
376 | 380 | vmax=self.zmax, |
|
|
377 | 381 | cmap=self.cmaps[n] |
|
|
378 | 382 | ) |
|
|
379 | 383 | |
|
|
380 | 384 | |
|
|
381 | 385 | class PolarMapPlot(Plot): |
|
|
382 | 386 | ''' |
|
|
383 | 387 | Plot for weather radar |
|
|
384 | 388 | ''' |
|
|
385 | 389 | |
|
|
386 | 390 | CODE = 'param' |
|
|
387 | 391 | colormap = 'seismic' |
|
|
388 | 392 | |
|
|
389 | 393 | def setup(self): |
|
|
390 | 394 | self.ncols = 1 |
|
|
391 | 395 | self.nrows = 1 |
|
|
392 | 396 | self.width = 9 |
|
|
393 | 397 | self.height = 8 |
|
|
394 | 398 | self.mode = self.data.meta['mode'] |
|
|
395 | 399 | if self.channels is not None: |
|
|
396 | 400 | self.nplots = len(self.channels) |
|
|
397 | 401 | self.nrows = len(self.channels) |
|
|
398 | 402 | else: |
|
|
399 | 403 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
400 | 404 | self.nrows = self.nplots |
|
|
401 | 405 | self.channels = list(range(self.nplots)) |
|
|
402 | 406 | if self.mode == 'E': |
|
|
403 | 407 | self.xlabel = 'Longitude' |
|
|
404 | 408 | self.ylabel = 'Latitude' |
|
|
405 | 409 | else: |
|
|
406 | 410 | self.xlabel = 'Range (km)' |
|
|
407 | 411 | self.ylabel = 'Height (km)' |
|
|
408 | 412 | self.bgcolor = 'white' |
|
|
409 | 413 | self.cb_labels = self.data.meta['units'] |
|
|
410 | 414 | self.lat = self.data.meta['latitude'] |
|
|
411 | 415 | self.lon = self.data.meta['longitude'] |
|
|
412 | 416 | self.xmin, self.xmax = float( |
|
|
413 | 417 | km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon) |
|
|
414 | 418 | self.ymin, self.ymax = float( |
|
|
415 | 419 | km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat) |
|
|
416 | 420 | # self.polar = True |
|
|
417 | 421 | |
|
|
418 | 422 | def plot(self): |
|
|
419 | 423 | |
|
|
420 | 424 | for n, ax in enumerate(self.axes): |
|
|
421 | 425 | data = self.data['param'][self.channels[n]] |
|
|
422 | 426 | |
|
|
423 | 427 | zeniths = numpy.linspace( |
|
|
424 | 428 | 0, self.data.meta['max_range'], data.shape[1]) |
|
|
425 | 429 | if self.mode == 'E': |
|
|
426 | 430 | azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2 |
|
|
427 | 431 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
428 | 432 | x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin( |
|
|
429 | 433 | theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])) |
|
|
430 | 434 | x = km2deg(x) + self.lon |
|
|
431 | 435 | y = km2deg(y) + self.lat |
|
|
432 | 436 | else: |
|
|
433 | 437 | azimuths = numpy.radians(self.data.yrange) |
|
|
434 | 438 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
435 | 439 | x, y = r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
436 | 440 | self.y = zeniths |
|
|
437 | 441 | |
|
|
438 | 442 | if ax.firsttime: |
|
|
439 | 443 | if self.zlimits is not None: |
|
|
440 | 444 | self.zmin, self.zmax = self.zlimits[n] |
|
|
441 | 445 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
442 | 446 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
443 | 447 | vmin=self.zmin, |
|
|
444 | 448 | vmax=self.zmax, |
|
|
445 | 449 | cmap=self.cmaps[n]) |
|
|
446 | 450 | else: |
|
|
447 | 451 | if self.zlimits is not None: |
|
|
448 | 452 | self.zmin, self.zmax = self.zlimits[n] |
|
|
449 | 453 | ax.collections.remove(ax.collections[0]) |
|
|
450 | 454 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
451 | 455 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
452 | 456 | vmin=self.zmin, |
|
|
453 | 457 | vmax=self.zmax, |
|
|
454 | 458 | cmap=self.cmaps[n]) |
|
|
455 | 459 | |
|
|
456 | 460 | if self.mode == 'A': |
|
|
457 | 461 | continue |
|
|
458 | 462 | |
|
|
459 | 463 | # plot district names |
|
|
460 | 464 | f = open('/data/workspace/schain_scripts/distrito.csv') |
|
|
461 | 465 | for line in f: |
|
|
462 | 466 | label, lon, lat = [s.strip() for s in line.split(',') if s] |
|
|
463 | 467 | lat = float(lat) |
|
|
464 | 468 | lon = float(lon) |
|
|
465 | 469 | # ax.plot(lon, lat, '.b', ms=2) |
|
|
466 | 470 | ax.text(lon, lat, label.decode('utf8'), ha='center', |
|
|
467 | 471 | va='bottom', size='8', color='black') |
|
|
468 | 472 | |
|
|
469 | 473 | # plot limites |
|
|
470 | 474 | limites = [] |
|
|
471 | 475 | tmp = [] |
|
|
472 | 476 | for line in open('/data/workspace/schain_scripts/lima.csv'): |
|
|
473 | 477 | if '#' in line: |
|
|
474 | 478 | if tmp: |
|
|
475 | 479 | limites.append(tmp) |
|
|
476 | 480 | tmp = [] |
|
|
477 | 481 | continue |
|
|
478 | 482 | values = line.strip().split(',') |
|
|
479 | 483 | tmp.append((float(values[0]), float(values[1]))) |
|
|
480 | 484 | for points in limites: |
|
|
481 | 485 | ax.add_patch( |
|
|
482 | 486 | Polygon(points, ec='k', fc='none', ls='--', lw=0.5)) |
|
|
483 | 487 | |
|
|
484 | 488 | # plot Cuencas |
|
|
485 | 489 | for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'): |
|
|
486 | 490 | f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca)) |
|
|
487 | 491 | values = [line.strip().split(',') for line in f] |
|
|
488 | 492 | points = [(float(s[0]), float(s[1])) for s in values] |
|
|
489 | 493 | ax.add_patch(Polygon(points, ec='b', fc='none')) |
|
|
490 | 494 | |
|
|
491 | 495 | # plot grid |
|
|
492 | 496 | for r in (15, 30, 45, 60): |
|
|
493 | 497 | ax.add_artist(plt.Circle((self.lon, self.lat), |
|
|
494 | 498 | km2deg(r), color='0.6', fill=False, lw=0.2)) |
|
|
495 | 499 | ax.text( |
|
|
496 | 500 | self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180), |
|
|
497 | 501 | self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180), |
|
|
498 | 502 | '{}km'.format(r), |
|
|
499 | 503 | ha='center', va='bottom', size='8', color='0.6', weight='heavy') |
|
|
500 | 504 | |
|
|
501 | 505 | if self.mode == 'E': |
|
|
502 | 506 | title = 'El={}$^\circ$'.format(self.data.meta['elevation']) |
|
|
503 | 507 | label = 'E{:02d}'.format(int(self.data.meta['elevation'])) |
|
|
504 | 508 | else: |
|
|
505 | 509 | title = 'Az={}$^\circ$'.format(self.data.meta['azimuth']) |
|
|
506 | 510 | label = 'A{:02d}'.format(int(self.data.meta['azimuth'])) |
|
|
507 | 511 | |
|
|
508 | 512 | self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels] |
|
|
509 | 513 | self.titles = ['{} {}'.format( |
|
|
510 | 514 | self.data.parameters[x], title) for x in self.channels] |
|
|
511 | 515 | |
|
|
512 | 516 | class WeatherParamsPlot(Plot): |
|
|
513 | 517 | #CODE = 'RHI' |
|
|
514 | 518 | #plot_name = 'RHI' |
|
|
515 | 519 | plot_type = 'scattermap' |
|
|
516 | 520 | buffering = False |
|
|
521 | projection = ccrs.PlateCarree() | |
|
|
517 | 522 | |
|
|
518 | 523 | def setup(self): |
|
|
519 | 524 | |
|
|
520 | 525 | self.ncols = 1 |
|
|
521 | 526 | self.nrows = 1 |
|
|
522 | 527 | self.nplots= 1 |
|
|
523 | 528 | self.ylabel= 'Range [km]' |
|
|
524 | 529 | self.xlabel= 'Range [km]' |
|
|
525 | self.polar = True | |
|
|
526 | self.grid = True | |
|
|
530 | ||
|
|
527 | 531 | if self.channels is not None: |
|
|
528 | 532 | self.nplots = len(self.channels) |
|
|
529 | 533 | self.ncols = len(self.channels) |
|
|
530 | 534 | else: |
|
|
531 | 535 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
532 | 536 | self.ncols = self.nplots |
|
|
533 | 537 | self.channels = list(range(self.nplots)) |
|
|
534 | 538 | |
|
|
535 | 539 | self.colorbar=True |
|
|
536 | 540 | if len(self.channels)>1: |
|
|
537 | 541 | self.width = 12 |
|
|
538 | 542 | else: |
|
|
539 | 543 | self.width =8 |
|
|
540 |
self.height = |
|
|
|
544 | self.height =7 | |
|
|
541 | 545 | self.ini =0 |
|
|
542 | 546 | self.len_azi =0 |
|
|
543 | 547 | self.buffer_ini = None |
|
|
544 | 548 | self.buffer_ele = None |
|
|
545 | 549 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
|
|
546 | 550 | self.flag =0 |
|
|
547 | 551 | self.indicador= 0 |
|
|
548 | 552 | self.last_data_ele = None |
|
|
549 | 553 | self.val_mean = None |
|
|
550 | 554 | |
|
|
551 | 555 | def update(self, dataOut): |
|
|
552 | 556 | |
|
|
553 | 557 | vars = { |
|
|
554 | 558 | 'S' : 0, |
|
|
555 | 559 | 'V' : 1, |
|
|
556 | 560 | 'W' : 2, |
|
|
557 | 561 | 'SNR' : 3, |
|
|
558 | 562 | 'Z' : 4, |
|
|
559 | 563 | 'D' : 5, |
|
|
560 | 564 | 'P' : 6, |
|
|
561 | 565 | 'R' : 7, |
|
|
562 | 566 | } |
|
|
563 | 567 | |
|
|
564 | 568 | data = {} |
|
|
565 | 569 | meta = {} |
|
|
566 | 570 | |
|
|
567 | 571 | if hasattr(dataOut, 'nFFTPoints'): |
|
|
568 | 572 | factor = dataOut.normFactor |
|
|
569 | 573 | else: |
|
|
570 | 574 | factor = 1 |
|
|
571 | 575 | |
|
|
572 | 576 | if hasattr(dataOut, 'dparam'): |
|
|
573 | 577 | tmp = getattr(dataOut, 'data_param') |
|
|
574 | 578 | else: |
|
|
575 | 579 | |
|
|
576 | 580 | if 'S' in self.attr_data[0]: |
|
|
577 | 581 | tmp = 10*numpy.log10(10.0*getattr(dataOut, 'data_param')[:,0,:]/(factor)) |
|
|
578 | 582 | else: |
|
|
579 | 583 | tmp = getattr(dataOut, 'data_param')[:,vars[self.attr_data[0]],:] |
|
|
580 | 584 | |
|
|
581 | 585 | if self.mask: |
|
|
582 | 586 | mask = dataOut.data_param[:,3,:] < self.mask |
|
|
583 | 587 | tmp = numpy.ma.masked_array(tmp, mask=mask) |
|
|
584 | 588 | |
|
|
585 | 589 | r = dataOut.heightList |
|
|
586 | 590 | delta_height = r[1]-r[0] |
|
|
587 | 591 | valid = numpy.where(r>=0)[0] |
|
|
588 | 592 | data['r'] = numpy.arange(len(valid))*delta_height |
|
|
589 | 593 | |
|
|
590 | 594 | data['data'] = [0, 0] |
|
|
591 | 595 | |
|
|
592 |
|
|
|
|
593 | data['data'][0] = tmp[0][:,valid] | |
|
|
594 | data['data'][1] = tmp[1][:,valid] | |
|
|
595 |
|
|
|
|
596 |
|
|
|
|
596 | try: | |
|
|
597 | data['data'][0] = tmp[0][:,valid] | |
|
|
598 | data['data'][1] = tmp[1][:,valid] | |
|
|
599 | except: | |
|
|
600 | data['data'][0] = tmp[0][:,valid] | |
|
|
601 | data['data'][1] = tmp[0][:,valid] | |
|
|
597 | 602 | |
|
|
598 | 603 | if dataOut.mode_op == 'PPI': |
|
|
599 | 604 | self.CODE = 'PPI' |
|
|
600 | 605 | self.title = self.CODE |
|
|
601 | 606 | elif dataOut.mode_op == 'RHI': |
|
|
602 | 607 | self.CODE = 'RHI' |
|
|
603 | 608 | self.title = self.CODE |
|
|
604 | 609 | |
|
|
605 | 610 | data['azi'] = dataOut.data_azi |
|
|
606 | 611 | data['ele'] = dataOut.data_ele |
|
|
607 | 612 | data['mode_op'] = dataOut.mode_op |
|
|
608 | 613 | self.mode = dataOut.mode_op |
|
|
609 | var = data['data'][0].flatten() | |
|
|
610 | r = numpy.tile(data['r'], data['data'][0].shape[0]) | |
|
|
611 | az = numpy.repeat(data['azi'], data['data'][0].shape[1]) | |
|
|
612 | el = numpy.repeat(data['ele'], data['data'][0].shape[1]) | |
|
|
613 | ||
|
|
614 | # lla = georef.spherical_to_proj(r, data['azi'], data['ele'], (-75.295893, -12.040436, 3379.2147)) | |
|
|
615 | ||
|
|
616 | latlon = antenna_to_geographic(r, az, el, (-75.295893, -12.040436)) | |
|
|
617 | ||
|
|
618 | if self.mask: | |
|
|
619 | meta['lat'] = latlon[1][var.mask==False] | |
|
|
620 | meta['lon'] = latlon[0][var.mask==False] | |
|
|
621 | data['var'] = numpy.array([var[var.mask==False]]) | |
|
|
622 | else: | |
|
|
623 | meta['lat'] = latlon[1] | |
|
|
624 | meta['lon'] = latlon[0] | |
|
|
625 | data['var'] = numpy.array([var]) | |
|
|
626 | 614 | |
|
|
627 | 615 | return data, meta |
|
|
628 | 616 | |
|
|
629 | 617 | def plot(self): |
|
|
630 | 618 | data = self.data[-1] |
|
|
631 | 619 | z = data['data'] |
|
|
632 | 620 | r = data['r'] |
|
|
633 | 621 | self.titles = [] |
|
|
634 | 622 | |
|
|
635 | 623 | self.ymax = self.ymax if self.ymax else numpy.nanmax(r) |
|
|
636 | 624 | self.ymin = self.ymin if self.ymin else numpy.nanmin(r) |
|
|
637 | 625 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
638 | 626 | self.zmin = self.zmin if self.zmin is not None else numpy.nanmin(z) |
|
|
639 | 627 | |
|
|
640 | 628 | if isinstance(data['mode_op'], bytes): |
|
|
641 | 629 | data['mode_op'] = data['mode_op'].decode() |
|
|
642 | 630 | |
|
|
643 | 631 | if data['mode_op'] == 'RHI': |
|
|
644 | try: | |
|
|
645 | if self.data['mode_op'][-2] == 'PPI': | |
|
|
646 | self.ang_min = None | |
|
|
647 | self.ang_max = None | |
|
|
648 | except: | |
|
|
649 | pass | |
|
|
650 | self.ang_min = self.ang_min if self.ang_min else 0 | |
|
|
651 | self.ang_max = self.ang_max if self.ang_max else 90 | |
|
|
652 | r, theta = numpy.meshgrid(r, numpy.radians(data['ele']) ) | |
|
|
653 | elif data['mode_op'] == 'PPI': | |
|
|
654 | try: | |
|
|
655 | if self.data['mode_op'][-2] == 'RHI': | |
|
|
656 | self.ang_min = None | |
|
|
657 | self.ang_max = None | |
|
|
658 | except: | |
|
|
659 | pass | |
|
|
660 | self.ang_min = self.ang_min if self.ang_min else 0 | |
|
|
661 | self.ang_max = self.ang_max if self.ang_max else 360 | |
|
|
662 | r, theta = numpy.meshgrid(r, numpy.radians(data['azi']) ) | |
|
|
632 | r, theta = numpy.meshgrid(r, numpy.radians(data['ele'])) | |
|
|
633 | len_aux = int(data['azi'].shape[0]/4) | |
|
|
634 | mean = numpy.mean(data['azi'][len_aux:-len_aux]) | |
|
|
635 | x, y = r*numpy.cos(theta), r*numpy.sin(theta) | |
|
|
636 | elif data['mode_op'] == 'PPI': | |
|
|
637 | r, theta = numpy.meshgrid(r, -numpy.radians(data['azi'])+numpy.pi/2) | |
|
|
638 | len_aux = int(data['ele'].shape[0]/4) | |
|
|
639 | mean = numpy.mean(data['ele'][len_aux:-len_aux]) | |
|
|
640 | x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(mean)), r*numpy.sin( | |
|
|
641 | theta)*numpy.cos(numpy.radians(mean)) | |
|
|
642 | x = km2deg(x) + -75.295893 | |
|
|
643 | y = km2deg(y) + -12.040436 | |
|
|
663 | 644 | |
|
|
664 | 645 | self.clear_figures() |
|
|
665 | 646 | |
|
|
666 | for i,ax in enumerate(self.axes): | |
|
|
667 | ||
|
|
668 | if ax.firsttime: | |
|
|
669 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) | |
|
|
670 | ax.plt = ax.pcolormesh(theta, r, z[i], cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) | |
|
|
671 | if data['mode_op'] == 'PPI': | |
|
|
672 | ax.set_theta_direction(-1) | |
|
|
673 | ax.set_theta_offset(numpy.pi/2) | |
|
|
647 | if data['mode_op'] == 'PPI': | |
|
|
648 | axes = self.axes['PPI'] | |
|
|
649 | else: | |
|
|
650 | axes = self.axes['RHI'] | |
|
|
674 | 651 | |
|
|
675 | else: | |
|
|
676 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) | |
|
|
677 | ax.plt = ax.pcolormesh(theta, r, z[i], cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) | |
|
|
678 | if data['mode_op'] == 'PPI': | |
|
|
679 | ax.set_theta_direction(-1) | |
|
|
680 | ax.set_theta_offset(numpy.pi/2) | |
|
|
652 | for i, ax in enumerate(axes): | |
|
|
653 | if data['mode_op'] == 'PPI': | |
|
|
654 | ax.set_extent([-75.745893, -74.845893, -12.490436, -11.590436]) | |
|
|
655 | ||
|
|
656 | ax.plt = ax.pcolormesh(x, y, z[i], cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) | |
|
|
681 | 657 | |
|
|
682 | ax.grid(True) | |
|
|
683 | 658 | if data['mode_op'] == 'RHI': |
|
|
684 | 659 | len_aux = int(data['azi'].shape[0]/4) |
|
|
685 | 660 | mean = numpy.mean(data['azi'][len_aux:-len_aux]) |
|
|
686 | 661 | if len(self.channels) !=1: |
|
|
687 | 662 | self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in self.channels] |
|
|
688 | 663 | else: |
|
|
689 | 664 | self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])] |
|
|
690 | 665 | elif data['mode_op'] == 'PPI': |
|
|
691 | 666 | len_aux = int(data['ele'].shape[0]/4) |
|
|
692 | 667 | mean = numpy.mean(data['ele'][len_aux:-len_aux]) |
|
|
693 | 668 | if len(self.channels) !=1: |
|
|
694 | 669 | self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in self.channels] |
|
|
695 | 670 | else: |
|
|
696 | 671 | self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])] |
|
|
697 | self.mode_value = round(mean,1) No newline at end of file | |
|
|
672 | self.mode_value = round(mean,1) | |
|
|
673 | ||
|
|
674 | if data['mode_op'] == 'PPI': | |
|
|
675 | gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, | |
|
|
676 | linewidth=1, color='gray', alpha=0.5, linestyle='--') | |
|
|
677 | gl.xlabel_style = {'size': 8} | |
|
|
678 | gl.ylabel_style = {'size': 8} | |
|
|
679 | gl.xlabels_top = False | |
|
|
680 | gl.ylabels_right = False | |
|
|
681 | shape_p = os.path.join(self.shapes,'PER_ADM2/PER_ADM2.shp') | |
|
|
682 | shape_d = os.path.join(self.shapes,'PER_ADM1/PER_ADM1.shp') | |
|
|
683 | capitales = os.path.join(self.shapes,'CAPITALES/cap_provincia.shp') | |
|
|
684 | vias = os.path.join(self.shapes,'Carreteras/VIAS_NACIONAL_250000.shp') | |
|
|
685 | reader_d = shpreader.BasicReader(shape_p, encoding='latin1') | |
|
|
686 | reader_p = shpreader.BasicReader(shape_d, encoding='latin1') | |
|
|
687 | reader_c = shpreader.BasicReader(capitales, encoding='latin1') | |
|
|
688 | reader_v = shpreader.BasicReader(vias, encoding='latin1') | |
|
|
689 | caps = [x for x in reader_c.records() if x.attributes["Departa"] in ("JUNIN", "LIMA", "AYACUCHO", "HUANCAVELICA")] | |
|
|
690 | districts = [x for x in reader_d.records() if x.attributes["Name"] in ("JUNÍN", "CHANCHAMAYO", "CHUPACA", "CONCEPCIÓN", "HUANCAYO", "JAUJA", "SATIPO", "TARMA", "YAUYOS", "HUAROCHIRÍ", "CANTA", "HUANTA", "TAYACAJA")] | |
|
|
691 | provs = [x for x in reader_p.records() if x.attributes["NAME"] in ("Junín", "Lima")] | |
|
|
692 | vias = [x for x in reader_v.records() if x.attributes["DEP"] in ("JUNIN", "LIMA")] | |
|
|
693 | ||
|
|
694 | # Display Kenya's shape | |
|
|
695 | shape_feature = ShapelyFeature([x.geometry for x in districts], ccrs.PlateCarree(), facecolor="none", edgecolor='grey', lw=0.5) | |
|
|
696 | ax.add_feature(shape_feature) | |
|
|
697 | shape_feature = ShapelyFeature([x.geometry for x in provs], ccrs.PlateCarree(), facecolor="none", edgecolor='white', lw=1) | |
|
|
698 | ax.add_feature(shape_feature) | |
|
|
699 | shape_feature = ShapelyFeature([x.geometry for x in vias], ccrs.PlateCarree(), facecolor="none", edgecolor='yellow', lw=1) | |
|
|
700 | ax.add_feature(shape_feature) | |
|
|
701 | ||
|
|
702 | for cap in caps: | |
|
|
703 | if cap.attributes['Nombre'] in ("LA OROYA", "CONCEPCIÓN", "HUANCAYO", "JAUJA", "CHUPACA", "YAUYOS", "HUANTA", "PAMPAS"): | |
|
|
704 | ax.text(cap.attributes['X'], cap.attributes['Y'], cap.attributes['Nombre'].title(), size=7, color='white') | |
|
|
705 | ax.text(-75.052003, -11.915552, 'Huaytapallana', size=7, color='cyan') | |
|
|
706 | ax.plot(-75.052003, -11.915552, '*') | |
|
|
707 | ||
|
|
708 | for R in (10, 20, 30 , 40, 50): | |
|
|
709 | circle = Circle((-75.295893, -12.040436), km2deg(R), facecolor='none', | |
|
|
710 | edgecolor='skyblue', linewidth=1, alpha=0.5) | |
|
|
711 | ax.add_patch(circle) | |
|
|
712 | ax.text(km2deg(R)*numpy.cos(numpy.radians(45))-75.295893, | |
|
|
713 | km2deg(R)*numpy.sin(numpy.radians(45))-12.040436, | |
|
|
714 | '{}km'.format(R), color='skyblue', size=7) | |
|
|
715 | elif data['mode_op'] == 'RHI': | |
|
|
716 | ax.grid(color='grey', alpha=0.5, linestyle='--', linewidth=1) | |
| @@ -1,733 +1,739 | |||
|
|
1 | 1 | from email.utils import localtime |
|
|
2 | 2 | import os |
|
|
3 | 3 | import time |
|
|
4 | 4 | import datetime |
|
|
5 | 5 | |
|
|
6 | 6 | import numpy |
|
|
7 | 7 | import h5py |
|
|
8 | 8 | |
|
|
9 | 9 | import schainpy.admin |
|
|
10 | 10 | from schainpy.model.data.jrodata import * |
|
|
11 | 11 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
12 | 12 | from schainpy.model.io.jroIO_base import * |
|
|
13 | 13 | from schainpy.utils import log |
|
|
14 | 14 | |
|
|
15 | 15 | |
|
|
16 | 16 | class HDFReader(Reader, ProcessingUnit): |
|
|
17 | 17 | """Processing unit to read HDF5 format files |
|
|
18 | 18 | |
|
|
19 | 19 | This unit reads HDF5 files created with `HDFWriter` operation contains |
|
|
20 | 20 | by default two groups Data and Metadata all variables would be saved as `dataOut` |
|
|
21 | 21 | attributes. |
|
|
22 | 22 | It is possible to read any HDF5 file by given the structure in the `description` |
|
|
23 | 23 | parameter, also you can add extra values to metadata with the parameter `extras`. |
|
|
24 | 24 | |
|
|
25 | 25 | Parameters: |
|
|
26 | 26 | ----------- |
|
|
27 | 27 | path : str |
|
|
28 | 28 | Path where files are located. |
|
|
29 | 29 | startDate : date |
|
|
30 | 30 | Start date of the files |
|
|
31 | 31 | endDate : list |
|
|
32 | 32 | End date of the files |
|
|
33 | 33 | startTime : time |
|
|
34 | 34 | Start time of the files |
|
|
35 | 35 | endTime : time |
|
|
36 | 36 | End time of the files |
|
|
37 | 37 | description : dict, optional |
|
|
38 | 38 | Dictionary with the description of the HDF5 file |
|
|
39 | 39 | extras : dict, optional |
|
|
40 | 40 | Dictionary with extra metadata to be be added to `dataOut` |
|
|
41 | 41 | |
|
|
42 | 42 | Examples |
|
|
43 | 43 | -------- |
|
|
44 | 44 | |
|
|
45 | 45 | desc = { |
|
|
46 | 46 | 'Data': { |
|
|
47 | 47 | 'data_output': ['u', 'v', 'w'], |
|
|
48 | 48 | 'utctime': 'timestamps', |
|
|
49 | 49 | } , |
|
|
50 | 50 | 'Metadata': { |
|
|
51 | 51 | 'heightList': 'heights' |
|
|
52 | 52 | } |
|
|
53 | 53 | } |
|
|
54 | 54 | |
|
|
55 | 55 | desc = { |
|
|
56 | 56 | 'Data': { |
|
|
57 | 57 | 'data_output': 'winds', |
|
|
58 | 58 | 'utctime': 'timestamps' |
|
|
59 | 59 | }, |
|
|
60 | 60 | 'Metadata': { |
|
|
61 | 61 | 'heightList': 'heights' |
|
|
62 | 62 | } |
|
|
63 | 63 | } |
|
|
64 | 64 | |
|
|
65 | 65 | extras = { |
|
|
66 | 66 | 'timeZone': 300 |
|
|
67 | 67 | } |
|
|
68 | 68 | |
|
|
69 | 69 | reader = project.addReadUnit( |
|
|
70 | 70 | name='HDFReader', |
|
|
71 | 71 | path='/path/to/files', |
|
|
72 | 72 | startDate='2019/01/01', |
|
|
73 | 73 | endDate='2019/01/31', |
|
|
74 | 74 | startTime='00:00:00', |
|
|
75 | 75 | endTime='23:59:59', |
|
|
76 | 76 | # description=json.dumps(desc), |
|
|
77 | 77 | # extras=json.dumps(extras), |
|
|
78 | 78 | ) |
|
|
79 | 79 | |
|
|
80 | 80 | """ |
|
|
81 | 81 | |
|
|
82 | 82 | __attrs__ = ['path', 'startDate', 'endDate', 'startTime', 'endTime', 'description', 'extras'] |
|
|
83 | 83 | |
|
|
84 | 84 | def __init__(self): |
|
|
85 | 85 | ProcessingUnit.__init__(self) |
|
|
86 | 86 | self.dataOut = Parameters() |
|
|
87 | 87 | self.ext = ".hdf5" |
|
|
88 | 88 | self.optchar = "D" |
|
|
89 | 89 | self.meta = {} |
|
|
90 | 90 | self.data = {} |
|
|
91 | 91 | self.open_file = h5py.File |
|
|
92 | 92 | self.open_mode = 'r' |
|
|
93 | 93 | self.description = {} |
|
|
94 | 94 | self.extras = {} |
|
|
95 | 95 | self.filefmt = "*%Y%j***" |
|
|
96 | 96 | self.folderfmt = "*%Y%j" |
|
|
97 | 97 | self.utcoffset = 0 |
|
|
98 | 98 | self.filter = None |
|
|
99 | 99 | self.dparam = None |
|
|
100 | 100 | |
|
|
101 | 101 | def setup(self, **kwargs): |
|
|
102 | 102 | |
|
|
103 | 103 | self.set_kwargs(**kwargs) |
|
|
104 | 104 | if not self.ext.startswith('.'): |
|
|
105 | 105 | self.ext = '.{}'.format(self.ext) |
|
|
106 | 106 | |
|
|
107 | 107 | if self.online: |
|
|
108 | 108 | log.log("Searching files in online mode...", self.name) |
|
|
109 | 109 | |
|
|
110 | 110 | for nTries in range(self.nTries): |
|
|
111 | 111 | fullpath = self.searchFilesOnLine(self.path, self.startDate, |
|
|
112 | 112 | self.endDate, self.expLabel, self.ext, self.walk, |
|
|
113 | 113 | self.filefmt, self.folderfmt,self.filter) |
|
|
114 | 114 | try: |
|
|
115 | 115 | fullpath = next(fullpath) |
|
|
116 | 116 | except: |
|
|
117 | 117 | fullpath = None |
|
|
118 | 118 | |
|
|
119 | 119 | if fullpath: |
|
|
120 | 120 | break |
|
|
121 | 121 | |
|
|
122 | 122 | log.warning( |
|
|
123 | 123 | 'Waiting {} sec for a valid file in {}: try {} ...'.format( |
|
|
124 | 124 | self.delay, self.path, nTries + 1), |
|
|
125 | 125 | self.name) |
|
|
126 | 126 | time.sleep(self.delay) |
|
|
127 | 127 | |
|
|
128 | 128 | if not(fullpath): |
|
|
129 | 129 | raise schainpy.admin.SchainError( |
|
|
130 | 130 | 'There isn\'t any valid file in {}'.format(self.path)) |
|
|
131 | 131 | |
|
|
132 | 132 | pathname, filename = os.path.split(fullpath) |
|
|
133 | 133 | self.year = int(filename[1:5]) |
|
|
134 | 134 | self.doy = int(filename[5:8]) |
|
|
135 | 135 | self.set = int(filename[8:11]) - 1 |
|
|
136 | 136 | else: |
|
|
137 | 137 | log.log("Searching files in {}".format(self.path), self.name) |
|
|
138 | 138 | self.filenameList = self.searchFilesOffLine(self.path, self.startDate, |
|
|
139 | 139 | self.endDate, self.expLabel, self.ext, self.walk, self.filefmt, self.folderfmt,self.filter) |
|
|
140 | 140 | |
|
|
141 | 141 | self.setNextFile() |
|
|
142 | 142 | |
|
|
143 | 143 | return |
|
|
144 | 144 | |
|
|
145 | 145 | def readFirstHeader(self): |
|
|
146 | 146 | '''Read metadata and data''' |
|
|
147 | 147 | |
|
|
148 | 148 | self.__readMetadata() |
|
|
149 | 149 | self.__readData() |
|
|
150 | 150 | self.__setBlockList() |
|
|
151 | 151 | |
|
|
152 | 152 | if 'type' in self.meta: |
|
|
153 | 153 | self.dataOut = eval(self.meta['type'])() |
|
|
154 | 154 | |
|
|
155 | 155 | if self.dparam: |
|
|
156 | 156 | setattr(self.dataOut, "dparam", 1) |
|
|
157 | 157 | |
|
|
158 | 158 | for attr in self.meta: |
|
|
159 | 159 | setattr(self.dataOut, attr, self.meta[attr]) |
|
|
160 | 160 | |
|
|
161 | 161 | self.blockIndex = 0 |
|
|
162 | 162 | |
|
|
163 | 163 | return |
|
|
164 | 164 | |
|
|
165 | 165 | def __setBlockList(self): |
|
|
166 | 166 | ''' |
|
|
167 | 167 | Selects the data within the times defined |
|
|
168 | 168 | |
|
|
169 | 169 | self.fp |
|
|
170 | 170 | self.startTime |
|
|
171 | 171 | self.endTime |
|
|
172 | 172 | self.blockList |
|
|
173 | 173 | self.blocksPerFile |
|
|
174 | 174 | |
|
|
175 | 175 | ''' |
|
|
176 | 176 | |
|
|
177 | 177 | startTime = self.startTime |
|
|
178 | 178 | endTime = self.endTime |
|
|
179 | 179 | thisUtcTime = self.data['utctime'] + self.utcoffset |
|
|
180 | 180 | try: |
|
|
181 | 181 | self.interval = numpy.min(thisUtcTime[1:] - thisUtcTime[:-1]) |
|
|
182 | 182 | except: |
|
|
183 | 183 | self.interval = 0 |
|
|
184 | 184 | thisDatetime = datetime.datetime.utcfromtimestamp(thisUtcTime[0]) |
|
|
185 | 185 | |
|
|
186 | 186 | thisDate = thisDatetime.date() |
|
|
187 | 187 | thisTime = thisDatetime.time() |
|
|
188 | 188 | |
|
|
189 | 189 | startUtcTime = (datetime.datetime.combine(thisDate, startTime) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
190 | 190 | endUtcTime = (datetime.datetime.combine(thisDate, endTime) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
191 | 191 | |
|
|
192 | 192 | ind = numpy.where(numpy.logical_and(thisUtcTime >= startUtcTime, thisUtcTime < endUtcTime))[0] |
|
|
193 | 193 | |
|
|
194 | 194 | self.blockList = ind |
|
|
195 | 195 | self.blocksPerFile = len(ind) |
|
|
196 | 196 | return |
|
|
197 | 197 | |
|
|
198 | 198 | def __readMetadata(self): |
|
|
199 | 199 | ''' |
|
|
200 | 200 | Reads Metadata |
|
|
201 | 201 | ''' |
|
|
202 | 202 | |
|
|
203 | 203 | meta = {} |
|
|
204 | 204 | |
|
|
205 | 205 | if self.description: |
|
|
206 | 206 | for key, value in self.description['Metadata'].items(): |
|
|
207 | 207 | meta[key] = self.fp[value][()] |
|
|
208 | 208 | else: |
|
|
209 | 209 | grp = self.fp['Metadata'] |
|
|
210 | 210 | for name in grp: |
|
|
211 | 211 | meta[name] = grp[name][()] |
|
|
212 | 212 | |
|
|
213 | 213 | if self.extras: |
|
|
214 | 214 | for key, value in self.extras.items(): |
|
|
215 | 215 | meta[key] = value |
|
|
216 | 216 | self.meta = meta |
|
|
217 | 217 | |
|
|
218 | 218 | return |
|
|
219 | 219 | |
|
|
220 | 220 | def __readData(self): |
|
|
221 | 221 | |
|
|
222 | 222 | data = {} |
|
|
223 | 223 | |
|
|
224 | 224 | if self.description: |
|
|
225 | 225 | for key, value in self.description['Data'].items(): |
|
|
226 | 226 | if isinstance(value, str): |
|
|
227 | 227 | if isinstance(self.fp[value], h5py.Dataset): |
|
|
228 | 228 | data[key] = self.fp[value][()] |
|
|
229 | 229 | elif isinstance(self.fp[value], h5py.Group): |
|
|
230 | 230 | array = [] |
|
|
231 | 231 | for ch in self.fp[value]: |
|
|
232 | 232 | array.append(self.fp[value][ch][()]) |
|
|
233 | 233 | data[key] = numpy.array(array) |
|
|
234 | 234 | elif isinstance(value, list): |
|
|
235 | 235 | array = [] |
|
|
236 | 236 | for ch in value: |
|
|
237 | 237 | array.append(self.fp[ch][()]) |
|
|
238 | 238 | data[key] = numpy.array(array) |
|
|
239 | 239 | else: |
|
|
240 | 240 | grp = self.fp['Data'] |
|
|
241 | 241 | for name in grp: |
|
|
242 | 242 | if isinstance(grp[name], h5py.Dataset): |
|
|
243 | 243 | array = grp[name][()] |
|
|
244 | 244 | elif isinstance(grp[name], h5py.Group): |
|
|
245 | 245 | array = [] |
|
|
246 | 246 | for ch in grp[name]: |
|
|
247 | 247 | array.append(grp[name][ch][()]) |
|
|
248 | 248 | array = numpy.array(array) |
|
|
249 | 249 | else: |
|
|
250 | 250 | log.warning('Unknown type: {}'.format(name)) |
|
|
251 | 251 | |
|
|
252 | 252 | if name in self.description: |
|
|
253 | 253 | key = self.description[name] |
|
|
254 | 254 | else: |
|
|
255 | 255 | key = name |
|
|
256 | 256 | data[key] = array |
|
|
257 | 257 | |
|
|
258 | 258 | self.data = data |
|
|
259 | 259 | return |
|
|
260 | 260 | |
|
|
261 | 261 | def getData(self): |
|
|
262 | 262 | |
|
|
263 | 263 | for attr in self.data: |
|
|
264 | 264 | if self.data[attr].ndim == 1: |
|
|
265 | 265 | setattr(self.dataOut, attr, self.data[attr][self.blockIndex]) |
|
|
266 | 266 | else: |
|
|
267 | 267 | if self.dparam: |
|
|
268 | 268 | setattr(self.dataOut, attr, self.data[attr]) |
|
|
269 | 269 | else: |
|
|
270 | 270 | setattr(self.dataOut, attr, self.data[attr][:, self.blockIndex]) |
|
|
271 | 271 | |
|
|
272 | 272 | self.dataOut.flagNoData = False |
|
|
273 | 273 | self.blockIndex += 1 |
|
|
274 | 274 | |
|
|
275 | 275 | log.log("Block No. {}/{} -> {}".format( |
|
|
276 | 276 | self.blockIndex, |
|
|
277 | 277 | self.blocksPerFile, |
|
|
278 | 278 | self.dataOut.datatime.ctime()), self.name) |
|
|
279 | 279 | |
|
|
280 | 280 | return |
|
|
281 | 281 | |
|
|
282 | 282 | def run(self, **kwargs): |
|
|
283 | 283 | |
|
|
284 | 284 | if not(self.isConfig): |
|
|
285 | 285 | self.setup(**kwargs) |
|
|
286 | 286 | self.isConfig = True |
|
|
287 | 287 | |
|
|
288 | 288 | if self.blockIndex == self.blocksPerFile: |
|
|
289 | 289 | self.setNextFile() |
|
|
290 | 290 | |
|
|
291 | 291 | self.getData() |
|
|
292 | 292 | |
|
|
293 | 293 | return |
|
|
294 | 294 | |
|
|
295 | 295 | @MPDecorator |
|
|
296 | 296 | class HDFWriter(Operation): |
|
|
297 | 297 | """Operation to write HDF5 files. |
|
|
298 | 298 | |
|
|
299 | 299 | The HDF5 file contains by default two groups Data and Metadata where |
|
|
300 | 300 | you can save any `dataOut` attribute specified by `dataList` and `metadataList` |
|
|
301 | 301 | parameters, data attributes are normaly time dependent where the metadata |
|
|
302 | 302 | are not. |
|
|
303 | 303 | It is possible to customize the structure of the HDF5 file with the |
|
|
304 | 304 | optional description parameter see the examples. |
|
|
305 | 305 | |
|
|
306 | 306 | Parameters: |
|
|
307 | 307 | ----------- |
|
|
308 | 308 | path : str |
|
|
309 | 309 | Path where files will be saved. |
|
|
310 | 310 | blocksPerFile : int |
|
|
311 | 311 | Number of blocks per file |
|
|
312 | 312 | metadataList : list |
|
|
313 | 313 | List of the dataOut attributes that will be saved as metadata |
|
|
314 | 314 | dataList : int |
|
|
315 | 315 | List of the dataOut attributes that will be saved as data |
|
|
316 | 316 | setType : bool |
|
|
317 | 317 | If True the name of the files corresponds to the timestamp of the data |
|
|
318 | 318 | description : dict, optional |
|
|
319 | 319 | Dictionary with the desired description of the HDF5 file |
|
|
320 | 320 | |
|
|
321 | 321 | Examples |
|
|
322 | 322 | -------- |
|
|
323 | 323 | |
|
|
324 | 324 | desc = { |
|
|
325 | 325 | 'data_output': {'winds': ['z', 'w', 'v']}, |
|
|
326 | 326 | 'utctime': 'timestamps', |
|
|
327 | 327 | 'heightList': 'heights' |
|
|
328 | 328 | } |
|
|
329 | 329 | desc = { |
|
|
330 | 330 | 'data_output': ['z', 'w', 'v'], |
|
|
331 | 331 | 'utctime': 'timestamps', |
|
|
332 | 332 | 'heightList': 'heights' |
|
|
333 | 333 | } |
|
|
334 | 334 | desc = { |
|
|
335 | 335 | 'Data': { |
|
|
336 | 336 | 'data_output': 'winds', |
|
|
337 | 337 | 'utctime': 'timestamps' |
|
|
338 | 338 | }, |
|
|
339 | 339 | 'Metadata': { |
|
|
340 | 340 | 'heightList': 'heights' |
|
|
341 | 341 | } |
|
|
342 | 342 | } |
|
|
343 | 343 | |
|
|
344 | 344 | writer = proc_unit.addOperation(name='HDFWriter') |
|
|
345 | 345 | writer.addParameter(name='path', value='/path/to/file') |
|
|
346 | 346 | writer.addParameter(name='blocksPerFile', value='32') |
|
|
347 | 347 | writer.addParameter(name='metadataList', value='heightList,timeZone') |
|
|
348 | 348 | writer.addParameter(name='dataList',value='data_output,utctime') |
|
|
349 | 349 | # writer.addParameter(name='description',value=json.dumps(desc)) |
|
|
350 | 350 | |
|
|
351 | 351 | """ |
|
|
352 | 352 | |
|
|
353 | 353 | ext = ".hdf5" |
|
|
354 | 354 | optchar = "D" |
|
|
355 | 355 | filename = None |
|
|
356 | 356 | path = None |
|
|
357 | 357 | setFile = None |
|
|
358 | 358 | fp = None |
|
|
359 | 359 | firsttime = True |
|
|
360 | 360 | #Configurations |
|
|
361 | 361 | blocksPerFile = None |
|
|
362 | 362 | blockIndex = None |
|
|
363 | 363 | dataOut = None |
|
|
364 | 364 | #Data Arrays |
|
|
365 | 365 | dataList = None |
|
|
366 | 366 | metadataList = None |
|
|
367 | 367 | currentDay = None |
|
|
368 | 368 | lastTime = None |
|
|
369 | 369 | last_Azipos = None |
|
|
370 | 370 | last_Elepos = None |
|
|
371 | 371 | mode = None |
|
|
372 | 372 | #----------------------- |
|
|
373 | 373 | Typename = None |
|
|
374 | 374 | mask = False |
|
|
375 | 375 | |
|
|
376 | 376 | def __init__(self): |
|
|
377 | 377 | |
|
|
378 | 378 | Operation.__init__(self) |
|
|
379 | 379 | return |
|
|
380 | 380 | |
|
|
381 | 381 | def set_kwargs(self, **kwargs): |
|
|
382 | 382 | |
|
|
383 | 383 | for key, value in kwargs.items(): |
|
|
384 | 384 | setattr(self, key, value) |
|
|
385 | 385 | |
|
|
386 | 386 | def set_kwargs_obj(self,obj, **kwargs): |
|
|
387 | 387 | |
|
|
388 | 388 | for key, value in kwargs.items(): |
|
|
389 | 389 | setattr(obj, key, value) |
|
|
390 | 390 | |
|
|
391 | 391 | def setup(self, path=None, blocksPerFile=10, metadataList=None, dataList=None, setType=None, description=None,type_data=None, localtime=True, **kwargs): |
|
|
392 | 392 | self.path = path |
|
|
393 | 393 | self.blocksPerFile = blocksPerFile |
|
|
394 | 394 | self.metadataList = metadataList |
|
|
395 | 395 | self.dataList = [s.strip() for s in dataList] |
|
|
396 | 396 | self.setType = setType |
|
|
397 | 397 | if self.setType == "weather": |
|
|
398 | 398 | self.set_kwargs(**kwargs) |
|
|
399 | 399 | self.set_kwargs_obj(self.dataOut,**kwargs) |
|
|
400 | 400 | self.weather_vars = { |
|
|
401 | 401 | 'S' : 0, |
|
|
402 | 402 | 'V' : 1, |
|
|
403 | 403 | 'W' : 2, |
|
|
404 | 404 | 'SNR' : 3, |
|
|
405 | 405 | 'Z' : 4, |
|
|
406 | 406 | 'D' : 5, |
|
|
407 | 407 | 'P' : 6, |
|
|
408 | 408 | 'R' : 7, |
|
|
409 | 409 | } |
|
|
410 | 410 | |
|
|
411 | 411 | if localtime: |
|
|
412 | 412 | self.getDateTime = datetime.datetime.fromtimestamp |
|
|
413 | 413 | else: |
|
|
414 | 414 | self.getDateTime = datetime.datetime.utcfromtimestamp |
|
|
415 | 415 | |
|
|
416 | 416 | self.description = description |
|
|
417 | 417 | self.type_data=type_data |
|
|
418 | 418 | |
|
|
419 | 419 | if self.metadataList is None: |
|
|
420 | 420 | self.metadataList = self.dataOut.metadata_list |
|
|
421 | 421 | |
|
|
422 | 422 | dsList = [] |
|
|
423 | 423 | |
|
|
424 | 424 | for i in range(len(self.dataList)): |
|
|
425 | 425 | dsDict = {} |
|
|
426 | 426 | if hasattr(self.dataOut, self.dataList[i]): |
|
|
427 | 427 | dataAux = getattr(self.dataOut, self.dataList[i]) |
|
|
428 | 428 | if self.setType == 'weather' and self.dataList[i] == 'data_param': |
|
|
429 | 429 | dataAux = dataAux[:,self.weather_vars[self.weather_var],:] |
|
|
430 | 430 | dsDict['variable'] = self.dataList[i] |
|
|
431 | 431 | else: |
|
|
432 | 432 | log.warning('Attribute {} not found in dataOut'.format(self.dataList[i]), self.name) |
|
|
433 | 433 | continue |
|
|
434 | 434 | |
|
|
435 | 435 | if dataAux is None: |
|
|
436 | 436 | continue |
|
|
437 | 437 | elif isinstance(dataAux, (int, float, numpy.integer, numpy.float)): |
|
|
438 | 438 | dsDict['nDim'] = 0 |
|
|
439 | 439 | else: |
|
|
440 | 440 | dsDict['nDim'] = len(dataAux.shape) |
|
|
441 | 441 | dsDict['shape'] = dataAux.shape |
|
|
442 | 442 | dsDict['dsNumber'] = dataAux.shape[0] |
|
|
443 | 443 | dsDict['dtype'] = dataAux.dtype |
|
|
444 | 444 | dsList.append(dsDict) |
|
|
445 | 445 | |
|
|
446 | 446 | self.dsList = dsList |
|
|
447 | 447 | self.currentDay = self.dataOut.datatime.date() |
|
|
448 | 448 | |
|
|
449 | 449 | def timeFlag(self): |
|
|
450 | 450 | currentTime = self.dataOut.utctime |
|
|
451 | 451 | dt = self.getDateTime(currentTime) |
|
|
452 | 452 | |
|
|
453 | 453 | dataDay = int(dt.strftime('%j')) |
|
|
454 | 454 | |
|
|
455 | 455 | if self.lastTime is None: |
|
|
456 | 456 | self.lastTime = currentTime |
|
|
457 | 457 | self.currentDay = dataDay |
|
|
458 | 458 | return False |
|
|
459 | 459 | |
|
|
460 | 460 | timeDiff = currentTime - self.lastTime |
|
|
461 | 461 | |
|
|
462 | 462 | #Si el dia es diferente o si la diferencia entre un dato y otro supera la hora |
|
|
463 | 463 | if dataDay != self.currentDay: |
|
|
464 | 464 | self.currentDay = dataDay |
|
|
465 | 465 | return True |
|
|
466 | 466 | elif timeDiff > 3*60*60: |
|
|
467 | 467 | self.lastTime = currentTime |
|
|
468 | 468 | return True |
|
|
469 | 469 | else: |
|
|
470 | 470 | self.lastTime = currentTime |
|
|
471 | 471 | return False |
|
|
472 | 472 | |
|
|
473 | 473 | def run(self, dataOut, path, blocksPerFile=10, metadataList=None, |
|
|
474 | 474 | dataList=[], setType=None, description={}, mode= None, |
|
|
475 | 475 | type_data=None, Reset = False, localtime=True, **kwargs): |
|
|
476 | 476 | |
|
|
477 | 477 | if Reset: |
|
|
478 | 478 | self.isConfig = False |
|
|
479 | 479 | self.closeFile() |
|
|
480 | 480 | self.lastTime = None |
|
|
481 | 481 | self.blockIndex = 0 |
|
|
482 | 482 | |
|
|
483 | 483 | self.dataOut = dataOut |
|
|
484 | 484 | self.mode = mode |
|
|
485 | 485 | |
|
|
486 | 486 | if not(self.isConfig): |
|
|
487 | 487 | self.setup(path=path, blocksPerFile=blocksPerFile, |
|
|
488 | 488 | metadataList=metadataList, dataList=dataList, |
|
|
489 | 489 | setType=setType, description=description,type_data=type_data, |
|
|
490 | 490 | localtime=localtime, **kwargs) |
|
|
491 | 491 | |
|
|
492 | 492 | self.isConfig = True |
|
|
493 | 493 | self.setNextFile() |
|
|
494 | 494 | |
|
|
495 | 495 | self.putData() |
|
|
496 | 496 | return |
|
|
497 | 497 | |
|
|
498 | 498 | def setNextFile(self): |
|
|
499 | 499 | |
|
|
500 | 500 | ext = self.ext |
|
|
501 | 501 | path = self.path |
|
|
502 | 502 | setFile = self.setFile |
|
|
503 | 503 | |
|
|
504 | 504 | dt = self.getDateTime(self.dataOut.utctime) |
|
|
505 | 505 | |
|
|
506 | 506 | if self.setType == 'weather': |
|
|
507 | 507 | subfolder = dt.strftime('%Y-%m-%dT%H-00-00') |
|
|
508 | subfolder = '' | |
|
|
508 | 509 | else: |
|
|
509 | 510 | subfolder = dt.strftime('d%Y%j') |
|
|
510 | 511 | |
|
|
511 | 512 | fullpath = os.path.join(path, subfolder) |
|
|
512 | 513 | |
|
|
513 | 514 | if os.path.exists(fullpath): |
|
|
514 | 515 | filesList = os.listdir(fullpath) |
|
|
515 | 516 | filesList = [k for k in filesList if k.startswith(self.optchar)] |
|
|
516 | 517 | if len( filesList ) > 0: |
|
|
517 | 518 | filesList = sorted(filesList, key=str.lower) |
|
|
518 | 519 | filen = filesList[-1] |
|
|
519 | 520 | # el filename debera tener el siguiente formato |
|
|
520 | 521 | # 0 1234 567 89A BCDE (hex) |
|
|
521 | 522 | # x YYYY DDD SSS .ext |
|
|
522 | 523 | if isNumber(filen[8:11]): |
|
|
523 | 524 | setFile = int(filen[8:11]) #inicializo mi contador de seteo al seteo del ultimo file |
|
|
524 | 525 | else: |
|
|
525 | 526 | setFile = -1 |
|
|
526 | 527 | else: |
|
|
527 | 528 | setFile = -1 #inicializo mi contador de seteo |
|
|
528 | 529 | else: |
|
|
529 | 530 | os.makedirs(fullpath) |
|
|
530 | 531 | setFile = -1 #inicializo mi contador de seteo |
|
|
531 | 532 | |
|
|
532 | 533 | if self.setType is None: |
|
|
533 | 534 | setFile += 1 |
|
|
534 | 535 | file = '%s%4.4d%3.3d%03d%s' % (self.optchar, |
|
|
535 | 536 | dt.year, |
|
|
536 | 537 | int(dt.strftime('%j')), |
|
|
537 | 538 | setFile, |
|
|
538 | 539 | ext ) |
|
|
539 | 540 | elif self.setType == "weather": |
|
|
540 | 541 | |
|
|
541 | 542 | #SOPHY_20200505_140215_E10.0_Z.h5 |
|
|
542 | 543 | #SOPHY_20200505_140215_A40.0_Z.h5 |
|
|
543 | 544 | if self.dataOut.flagMode == 1: #'AZI' #PPI |
|
|
544 | ang_type = 'E' | |
|
|
545 | ang_type = 'EL' | |
|
|
546 | mode_type = 'PPI' | |
|
|
545 | 547 | len_aux = int(self.dataOut.data_ele.shape[0]/4) |
|
|
546 | 548 | mean = numpy.mean(self.dataOut.data_ele[len_aux:-len_aux]) |
|
|
547 | 549 | ang_ = round(mean,1) |
|
|
548 | 550 | elif self.dataOut.flagMode == 0: #'ELE' #RHI |
|
|
549 | ang_type = 'A' | |
|
|
551 | ang_type = 'AZ' | |
|
|
552 | mode_type = 'RHI' | |
|
|
550 | 553 | len_aux = int(self.dataOut.data_azi.shape[0]/4) |
|
|
551 | 554 | mean = numpy.mean(self.dataOut.data_azi[len_aux:-len_aux]) |
|
|
552 | 555 | ang_ = round(mean,1) |
|
|
553 | 556 | |
|
|
554 | 557 | file = '%s_%2.2d%2.2d%2.2d_%2.2d%2.2d%2.2d_%s%2.1f_%s%s' % ( |
|
|
555 | 558 | 'SOPHY', |
|
|
556 | 559 | dt.year, |
|
|
557 | 560 | dt.month, |
|
|
558 | 561 | dt.day, |
|
|
559 | 562 | dt.hour, |
|
|
560 | 563 | dt.minute, |
|
|
561 | 564 | dt.second, |
|
|
562 | ang_type, | |
|
|
565 | ang_type[0], | |
|
|
563 | 566 | ang_, |
|
|
564 | 567 | self.weather_var, |
|
|
565 | 568 | ext ) |
|
|
566 | ||
|
|
569 | subfolder = '{}_{}_{}_{:2.1f}'.format(self.weather_var, mode_type, ang_type, ang_) | |
|
|
570 | fullpath = os.path.join(path, subfolder) | |
|
|
571 | if not os.path.exists(fullpath): | |
|
|
572 | os.makedirs(fullpath) | |
|
|
567 | 573 | else: |
|
|
568 | 574 | setFile = dt.hour*60+dt.minute |
|
|
569 | 575 | file = '%s%4.4d%3.3d%04d%s' % (self.optchar, |
|
|
570 | 576 | dt.year, |
|
|
571 | 577 | int(dt.strftime('%j')), |
|
|
572 | 578 | setFile, |
|
|
573 | 579 | ext ) |
|
|
574 | 580 | |
|
|
575 | 581 | self.filename = os.path.join( path, subfolder, file ) |
|
|
576 | 582 | |
|
|
577 | 583 | self.fp = h5py.File(self.filename, 'w') |
|
|
578 | 584 | #write metadata |
|
|
579 | 585 | self.writeMetadata(self.fp) |
|
|
580 | 586 | #Write data |
|
|
581 | 587 | self.writeData(self.fp) |
|
|
582 | 588 | |
|
|
583 | 589 | def getLabel(self, name, x=None): |
|
|
584 | 590 | |
|
|
585 | 591 | if x is None: |
|
|
586 | 592 | if 'Data' in self.description: |
|
|
587 | 593 | data = self.description['Data'] |
|
|
588 | 594 | if 'Metadata' in self.description: |
|
|
589 | 595 | data.update(self.description['Metadata']) |
|
|
590 | 596 | else: |
|
|
591 | 597 | data = self.description |
|
|
592 | 598 | if name in data: |
|
|
593 | 599 | if isinstance(data[name], str): |
|
|
594 | 600 | return data[name] |
|
|
595 | 601 | elif isinstance(data[name], list): |
|
|
596 | 602 | return None |
|
|
597 | 603 | elif isinstance(data[name], dict): |
|
|
598 | 604 | for key, value in data[name].items(): |
|
|
599 | 605 | return key |
|
|
600 | 606 | return name |
|
|
601 | 607 | else: |
|
|
602 | 608 | if 'Data' in self.description: |
|
|
603 | 609 | data = self.description['Data'] |
|
|
604 | 610 | if 'Metadata' in self.description: |
|
|
605 | 611 | data.update(self.description['Metadata']) |
|
|
606 | 612 | else: |
|
|
607 | 613 | data = self.description |
|
|
608 | 614 | if name in data: |
|
|
609 | 615 | if isinstance(data[name], list): |
|
|
610 | 616 | return data[name][x] |
|
|
611 | 617 | elif isinstance(data[name], dict): |
|
|
612 | 618 | for key, value in data[name].items(): |
|
|
613 | 619 | return value[x] |
|
|
614 | 620 | if 'cspc' in name: |
|
|
615 | 621 | return 'pair{:02d}'.format(x) |
|
|
616 | 622 | else: |
|
|
617 | 623 | return 'channel{:02d}'.format(x) |
|
|
618 | 624 | |
|
|
619 | 625 | def writeMetadata(self, fp): |
|
|
620 | 626 | |
|
|
621 | 627 | if self.description: |
|
|
622 | 628 | if 'Metadata' in self.description: |
|
|
623 | 629 | grp = fp.create_group('Metadata') |
|
|
624 | 630 | else: |
|
|
625 | 631 | grp = fp |
|
|
626 | 632 | else: |
|
|
627 | 633 | grp = fp.create_group('Metadata') |
|
|
628 | 634 | |
|
|
629 | 635 | for i in range(len(self.metadataList)): |
|
|
630 | 636 | if not hasattr(self.dataOut, self.metadataList[i]): |
|
|
631 | 637 | log.warning('Metadata: `{}` not found'.format(self.metadataList[i]), self.name) |
|
|
632 | 638 | continue |
|
|
633 | 639 | value = getattr(self.dataOut, self.metadataList[i]) |
|
|
634 | 640 | if isinstance(value, bool): |
|
|
635 | 641 | if value is True: |
|
|
636 | 642 | value = 1 |
|
|
637 | 643 | else: |
|
|
638 | 644 | value = 0 |
|
|
639 | 645 | grp.create_dataset(self.getLabel(self.metadataList[i]), data=value) |
|
|
640 | 646 | return |
|
|
641 | 647 | |
|
|
642 | 648 | def writeData(self, fp): |
|
|
643 | 649 | |
|
|
644 | 650 | if self.description: |
|
|
645 | 651 | if 'Data' in self.description: |
|
|
646 | 652 | grp = fp.create_group('Data') |
|
|
647 | 653 | else: |
|
|
648 | 654 | grp = fp |
|
|
649 | 655 | else: |
|
|
650 | 656 | grp = fp.create_group('Data') |
|
|
651 | 657 | |
|
|
652 | 658 | dtsets = [] |
|
|
653 | 659 | data = [] |
|
|
654 | 660 | |
|
|
655 | 661 | for dsInfo in self.dsList: |
|
|
656 | 662 | |
|
|
657 | 663 | if dsInfo['nDim'] == 0: |
|
|
658 | 664 | ds = grp.create_dataset( |
|
|
659 | 665 | self.getLabel(dsInfo['variable']), |
|
|
660 | 666 | (self.blocksPerFile, ), |
|
|
661 | 667 | chunks=True, |
|
|
662 | 668 | dtype=numpy.float64) |
|
|
663 | 669 | dtsets.append(ds) |
|
|
664 | 670 | data.append((dsInfo['variable'], -1)) |
|
|
665 | 671 | else: |
|
|
666 | 672 | label = self.getLabel(dsInfo['variable']) |
|
|
667 | 673 | if label is not None: |
|
|
668 | 674 | sgrp = grp.create_group(label) |
|
|
669 | 675 | else: |
|
|
670 | 676 | sgrp = grp |
|
|
671 | 677 | if self.blocksPerFile == 1: |
|
|
672 | 678 | shape = dsInfo['shape'][1:] |
|
|
673 | 679 | else: |
|
|
674 | 680 | shape = (self.blocksPerFile, ) + dsInfo['shape'][1:] |
|
|
675 | 681 | for i in range(dsInfo['dsNumber']): |
|
|
676 | 682 | ds = sgrp.create_dataset( |
|
|
677 | 683 | self.getLabel(dsInfo['variable'], i), |
|
|
678 | 684 | shape, |
|
|
679 | 685 | chunks=True, |
|
|
680 | 686 | dtype=dsInfo['dtype'], |
|
|
681 | 687 | compression='gzip', |
|
|
682 | 688 | ) |
|
|
683 | 689 | dtsets.append(ds) |
|
|
684 | 690 | data.append((dsInfo['variable'], i)) |
|
|
685 | 691 | fp.flush() |
|
|
686 | 692 | |
|
|
687 | 693 | log.log('Creating file: {}'.format(fp.filename), self.name) |
|
|
688 | 694 | |
|
|
689 | 695 | self.ds = dtsets |
|
|
690 | 696 | self.data = data |
|
|
691 | 697 | self.firsttime = True |
|
|
692 | 698 | self.blockIndex = 0 |
|
|
693 | 699 | return |
|
|
694 | 700 | |
|
|
695 | 701 | def putData(self): |
|
|
696 | 702 | |
|
|
697 | 703 | if (self.blockIndex == self.blocksPerFile) or self.timeFlag(): |
|
|
698 | 704 | self.closeFile() |
|
|
699 | 705 | self.setNextFile() |
|
|
700 | 706 | |
|
|
701 | 707 | for i, ds in enumerate(self.ds): |
|
|
702 | 708 | attr, ch = self.data[i] |
|
|
703 | 709 | if ch == -1: |
|
|
704 | 710 | ds[self.blockIndex] = getattr(self.dataOut, attr) |
|
|
705 | 711 | else: |
|
|
706 | 712 | if self.blocksPerFile == 1: |
|
|
707 | 713 | mask = self.dataOut.data_param[:,3,:][ch] < self.mask |
|
|
708 | 714 | tmp = getattr(self.dataOut, attr)[:,self.weather_vars[self.weather_var],:][ch] |
|
|
709 | 715 | if self.mask: |
|
|
710 | 716 | tmp[mask] = numpy.nan |
|
|
711 | 717 | ds[:] = tmp |
|
|
712 | 718 | else: |
|
|
713 | 719 | ds[self.blockIndex] = getattr(self.dataOut, attr)[ch] |
|
|
714 | 720 | |
|
|
715 | 721 | self.fp.flush() |
|
|
716 | 722 | self.blockIndex += 1 |
|
|
717 | 723 | log.log('Block No. {}/{}'.format(self.blockIndex, self.blocksPerFile), self.name) |
|
|
718 | 724 | |
|
|
719 | 725 | return |
|
|
720 | 726 | |
|
|
721 | 727 | def closeFile(self): |
|
|
722 | 728 | |
|
|
723 | 729 | if self.blockIndex != self.blocksPerFile: |
|
|
724 | 730 | for ds in self.ds: |
|
|
725 | 731 | ds.resize(self.blockIndex, axis=0) |
|
|
726 | 732 | |
|
|
727 | 733 | if self.fp: |
|
|
728 | 734 | self.fp.flush() |
|
|
729 | 735 | self.fp.close() |
|
|
730 | 736 | |
|
|
731 | 737 | def close(self): |
|
|
732 | 738 | |
|
|
733 | 739 | self.closeFile() |
| @@ -1,4531 +1,4549 | |||
|
|
1 | 1 | |
|
|
2 | 2 | import os |
|
|
3 | 3 | import time |
|
|
4 | 4 | import math |
|
|
5 | 5 | |
|
|
6 | 6 | import re |
|
|
7 | 7 | import datetime |
|
|
8 | 8 | import copy |
|
|
9 | 9 | import sys |
|
|
10 | 10 | import importlib |
|
|
11 | 11 | import itertools |
|
|
12 | 12 | |
|
|
13 | 13 | from multiprocessing import Pool, TimeoutError |
|
|
14 | 14 | from multiprocessing.pool import ThreadPool |
|
|
15 | 15 | import numpy |
|
|
16 | 16 | import glob |
|
|
17 | 17 | import scipy |
|
|
18 | 18 | import h5py |
|
|
19 | 19 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
|
20 | 20 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
21 | 21 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
22 | 22 | from scipy import asarray as ar,exp |
|
|
23 | 23 | from scipy.optimize import curve_fit |
|
|
24 | 24 | from schainpy.utils import log |
|
|
25 | 25 | import schainpy.admin |
|
|
26 | 26 | import warnings |
|
|
27 | 27 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
28 | 28 | from scipy.optimize.optimize import OptimizeWarning |
|
|
29 | 29 | warnings.filterwarnings('ignore') |
|
|
30 | 30 | |
|
|
31 | 31 | |
|
|
32 | 32 | SPEED_OF_LIGHT = 299792458 |
|
|
33 | 33 | |
|
|
34 | 34 | '''solving pickling issue''' |
|
|
35 | 35 | |
|
|
36 | 36 | def _pickle_method(method): |
|
|
37 | 37 | func_name = method.__func__.__name__ |
|
|
38 | 38 | obj = method.__self__ |
|
|
39 | 39 | cls = method.__self__.__class__ |
|
|
40 | 40 | return _unpickle_method, (func_name, obj, cls) |
|
|
41 | 41 | |
|
|
42 | 42 | def _unpickle_method(func_name, obj, cls): |
|
|
43 | 43 | for cls in cls.mro(): |
|
|
44 | 44 | try: |
|
|
45 | 45 | func = cls.__dict__[func_name] |
|
|
46 | 46 | except KeyError: |
|
|
47 | 47 | pass |
|
|
48 | 48 | else: |
|
|
49 | 49 | break |
|
|
50 | 50 | return func.__get__(obj, cls) |
|
|
51 | 51 | |
|
|
52 | 52 | def isNumber(str): |
|
|
53 | 53 | try: |
|
|
54 | 54 | float(str) |
|
|
55 | 55 | return True |
|
|
56 | 56 | except: |
|
|
57 | 57 | return False |
|
|
58 | 58 | |
|
|
59 | 59 | class ParametersProc(ProcessingUnit): |
|
|
60 | 60 | |
|
|
61 | 61 | METHODS = {} |
|
|
62 | 62 | nSeconds = None |
|
|
63 | 63 | |
|
|
64 | 64 | def __init__(self): |
|
|
65 | 65 | ProcessingUnit.__init__(self) |
|
|
66 | 66 | |
|
|
67 | 67 | # self.objectDict = {} |
|
|
68 | 68 | self.buffer = None |
|
|
69 | 69 | self.firstdatatime = None |
|
|
70 | 70 | self.profIndex = 0 |
|
|
71 | 71 | self.dataOut = Parameters() |
|
|
72 | 72 | self.setupReq = False #Agregar a todas las unidades de proc |
|
|
73 | 73 | |
|
|
74 | 74 | def __updateObjFromInput(self): |
|
|
75 | 75 | |
|
|
76 | 76 | self.dataOut.inputUnit = self.dataIn.type |
|
|
77 | 77 | |
|
|
78 | 78 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
79 | 79 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
80 | 80 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
81 | 81 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
82 | 82 | |
|
|
83 | 83 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
84 | 84 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
85 | 85 | self.dataOut.channelList = self.dataIn.channelList |
|
|
86 | 86 | self.dataOut.heightList = self.dataIn.heightList |
|
|
87 | 87 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
88 | 88 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
89 | 89 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
90 | 90 | # self.dataOut.nBaud = self.dataIn.nBaud |
|
|
91 | 91 | # self.dataOut.nCode = self.dataIn.nCode |
|
|
92 | 92 | # self.dataOut.code = self.dataIn.code |
|
|
93 | 93 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
94 | 94 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
95 | 95 | # self.dataOut.utctime = self.firstdatatime |
|
|
96 | 96 | self.dataOut.utctime = self.dataIn.utctime |
|
|
97 | 97 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
98 | 98 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
99 | 99 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
100 | 100 | # self.dataOut.nIncohInt = 1 |
|
|
101 | 101 | # self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
102 | 102 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
103 | 103 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
104 | 104 | self.dataOut.heightList = self.dataIn.heightList |
|
|
105 | 105 | self.dataOut.frequency = self.dataIn.frequency |
|
|
106 | 106 | # self.dataOut.noise = self.dataIn.noise |
|
|
107 | 107 | self.dataOut.runNextUnit = self.dataIn.runNextUnit |
|
|
108 | 108 | self.dataOut.h0 = self.dataIn.h0 |
|
|
109 | 109 | |
|
|
110 | 110 | def run(self, runNextUnit = 0): |
|
|
111 | 111 | |
|
|
112 | 112 | self.dataIn.runNextUnit = runNextUnit |
|
|
113 | 113 | #print("HOLA MUNDO SOY YO") |
|
|
114 | 114 | #---------------------- Voltage Data --------------------------- |
|
|
115 | 115 | |
|
|
116 | 116 | if self.dataIn.type == "Voltage": |
|
|
117 | 117 | |
|
|
118 | 118 | self.__updateObjFromInput() |
|
|
119 | 119 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
120 | 120 | self.dataOut.flagNoData = False |
|
|
121 | 121 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
122 | 122 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
123 | 123 | |
|
|
124 | 124 | if hasattr(self.dataIn, 'flagDataAsBlock'): |
|
|
125 | 125 | self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock |
|
|
126 | 126 | |
|
|
127 | 127 | if hasattr(self.dataIn, 'profileIndex'): |
|
|
128 | 128 | self.dataOut.profileIndex = self.dataIn.profileIndex |
|
|
129 | 129 | |
|
|
130 | 130 | if hasattr(self.dataIn, 'dataPP_POW'): |
|
|
131 | 131 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW |
|
|
132 | 132 | |
|
|
133 | 133 | if hasattr(self.dataIn, 'dataPP_POWER'): |
|
|
134 | 134 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER |
|
|
135 | 135 | |
|
|
136 | 136 | if hasattr(self.dataIn, 'dataPP_DOP'): |
|
|
137 | 137 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP |
|
|
138 | 138 | |
|
|
139 | 139 | if hasattr(self.dataIn, 'dataPP_SNR'): |
|
|
140 | 140 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR |
|
|
141 | 141 | |
|
|
142 | 142 | if hasattr(self.dataIn, 'dataPP_WIDTH'): |
|
|
143 | 143 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH |
|
|
144 | 144 | |
|
|
145 | 145 | if hasattr(self.dataIn, 'dataPP_CCF'): |
|
|
146 | 146 | self.dataOut.dataPP_CCF = self.dataIn.dataPP_CCF |
|
|
147 | 147 | |
|
|
148 | 148 | if hasattr(self.dataIn, 'dataPP_NOISE'): |
|
|
149 | 149 | self.dataOut.dataPP_NOISE = self.dataIn.dataPP_NOISE |
|
|
150 | 150 | |
|
|
151 | 151 | if hasattr(self.dataIn, 'flagAskMode'): |
|
|
152 | 152 | self.dataOut.flagAskMode = self.dataIn.flagAskMode |
|
|
153 | 153 | |
|
|
154 | 154 | return |
|
|
155 | 155 | |
|
|
156 | 156 | #---------------------- Spectra Data --------------------------- |
|
|
157 | 157 | |
|
|
158 | 158 | if self.dataIn.type == "Spectra": |
|
|
159 | 159 | #print("que paso en spectra") |
|
|
160 | 160 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] |
|
|
161 | 161 | self.dataOut.data_spc = self.dataIn.data_spc |
|
|
162 | 162 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
163 | 163 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
164 | 164 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
165 | 165 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
166 | 166 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
167 | 167 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
168 | 168 | self.dataOut.spc_noise = self.dataIn.getNoise() |
|
|
169 | 169 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
|
170 | 170 | # self.dataOut.normFactor = self.dataIn.normFactor |
|
|
171 | 171 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|
172 | 172 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
173 | 173 | self.dataOut.flagNoData = False |
|
|
174 | 174 | |
|
|
175 | 175 | if hasattr(self.dataIn, 'flagDataAsBlock'): |
|
|
176 | 176 | self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock |
|
|
177 | 177 | |
|
|
178 | 178 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
|
179 | 179 | self.dataOut.ChanDist = self.dataIn.ChanDist |
|
|
180 | 180 | else: self.dataOut.ChanDist = None |
|
|
181 | 181 | |
|
|
182 | 182 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
|
183 | 183 | # self.dataOut.VelRange = self.dataIn.VelRange |
|
|
184 | 184 | #else: self.dataOut.VelRange = None |
|
|
185 | 185 | |
|
|
186 | 186 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
|
187 | 187 | self.dataOut.RadarConst = self.dataIn.RadarConst |
|
|
188 | 188 | |
|
|
189 | 189 | if hasattr(self.dataIn, 'NPW'): #NPW |
|
|
190 | 190 | self.dataOut.NPW = self.dataIn.NPW |
|
|
191 | 191 | |
|
|
192 | 192 | if hasattr(self.dataIn, 'COFA'): #COFA |
|
|
193 | 193 | self.dataOut.COFA = self.dataIn.COFA |
|
|
194 | 194 | |
|
|
195 | 195 | |
|
|
196 | 196 | |
|
|
197 | 197 | #---------------------- Correlation Data --------------------------- |
|
|
198 | 198 | |
|
|
199 | 199 | if self.dataIn.type == "Correlation": |
|
|
200 | 200 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
201 | 201 | |
|
|
202 | 202 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
203 | 203 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
204 | 204 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
205 | 205 | |
|
|
206 | 206 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
207 | 207 | self.dataOut.noise = self.dataIn.noise |
|
|
208 | 208 | self.dataOut.data_snr = self.dataIn.SNR |
|
|
209 | 209 | self.dataOut.flagNoData = False |
|
|
210 | 210 | self.dataOut.nAvg = self.dataIn.nAvg |
|
|
211 | 211 | |
|
|
212 | 212 | #---------------------- Parameters Data --------------------------- |
|
|
213 | 213 | |
|
|
214 | 214 | if self.dataIn.type == "Parameters": |
|
|
215 | 215 | self.dataOut.copy(self.dataIn) |
|
|
216 | 216 | self.dataOut.flagNoData = False |
|
|
217 | 217 | #print("yo si entre") |
|
|
218 | 218 | |
|
|
219 | 219 | return True |
|
|
220 | 220 | |
|
|
221 | 221 | self.__updateObjFromInput() |
|
|
222 | 222 | #print("yo si entre2") |
|
|
223 | 223 | |
|
|
224 | 224 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
225 | 225 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
226 | 226 | #print("soy spectra ",self.dataOut.utctimeInit) |
|
|
227 | 227 | return |
|
|
228 | 228 | |
|
|
229 | 229 | |
|
|
230 | 230 | def target(tups): |
|
|
231 | 231 | |
|
|
232 | 232 | obj, args = tups |
|
|
233 | 233 | |
|
|
234 | 234 | return obj.FitGau(args) |
|
|
235 | 235 | |
|
|
236 | 236 | class RemoveWideGC(Operation): |
|
|
237 | 237 | ''' This class remove the wide clutter and replace it with a simple interpolation points |
|
|
238 | 238 | This mainly applies to CLAIRE radar |
|
|
239 | 239 | |
|
|
240 | 240 | ClutterWidth : Width to look for the clutter peak |
|
|
241 | 241 | |
|
|
242 | 242 | Input: |
|
|
243 | 243 | |
|
|
244 | 244 | self.dataOut.data_pre : SPC and CSPC |
|
|
245 | 245 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
|
246 | 246 | |
|
|
247 | 247 | Affected: |
|
|
248 | 248 | |
|
|
249 | 249 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
|
250 | 250 | |
|
|
251 | 251 | Written by D. Scipión 25.02.2021 |
|
|
252 | 252 | ''' |
|
|
253 | 253 | def __init__(self): |
|
|
254 | 254 | Operation.__init__(self) |
|
|
255 | 255 | self.i = 0 |
|
|
256 | 256 | self.ich = 0 |
|
|
257 | 257 | self.ir = 0 |
|
|
258 | 258 | |
|
|
259 | 259 | def run(self, dataOut, ClutterWidth=2.5): |
|
|
260 | 260 | # print ('Entering RemoveWideGC ... ') |
|
|
261 | 261 | |
|
|
262 | 262 | self.spc = dataOut.data_pre[0].copy() |
|
|
263 | 263 | self.spc_out = dataOut.data_pre[0].copy() |
|
|
264 | 264 | self.Num_Chn = self.spc.shape[0] |
|
|
265 | 265 | self.Num_Hei = self.spc.shape[2] |
|
|
266 | 266 | VelRange = dataOut.spc_range[2][:-1] |
|
|
267 | 267 | dv = VelRange[1]-VelRange[0] |
|
|
268 | 268 | |
|
|
269 | 269 | # Find the velocities that corresponds to zero |
|
|
270 | 270 | gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth)) |
|
|
271 | 271 | |
|
|
272 | 272 | # Removing novalid data from the spectra |
|
|
273 | 273 | for ich in range(self.Num_Chn) : |
|
|
274 | 274 | for ir in range(self.Num_Hei) : |
|
|
275 | 275 | # Estimate the noise at each range |
|
|
276 | 276 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) |
|
|
277 | 277 | |
|
|
278 | 278 | # Removing the noise floor at each range |
|
|
279 | 279 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) |
|
|
280 | 280 | self.spc[ich,novalid,ir] = HSn |
|
|
281 | 281 | |
|
|
282 | 282 | junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn) |
|
|
283 | 283 | j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0)) |
|
|
284 | 284 | j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0)) |
|
|
285 | 285 | if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) : |
|
|
286 | 286 | continue |
|
|
287 | 287 | junk3 = numpy.squeeze(numpy.diff(j1index)) |
|
|
288 | 288 | junk4 = numpy.squeeze(numpy.diff(j2index)) |
|
|
289 | 289 | |
|
|
290 | 290 | valleyindex = j2index[numpy.where(junk4>1)] |
|
|
291 | 291 | peakindex = j1index[numpy.where(junk3>1)] |
|
|
292 | 292 | |
|
|
293 | 293 | isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv)) |
|
|
294 | 294 | if numpy.size(isvalid) == 0 : |
|
|
295 | 295 | continue |
|
|
296 | 296 | if numpy.size(isvalid) >1 : |
|
|
297 | 297 | vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir]) |
|
|
298 | 298 | isvalid = isvalid[vindex] |
|
|
299 | 299 | |
|
|
300 | 300 | # clutter peak |
|
|
301 | 301 | gcpeak = peakindex[isvalid] |
|
|
302 | 302 | vl = numpy.where(valleyindex < gcpeak) |
|
|
303 | 303 | if numpy.size(vl) == 0: |
|
|
304 | 304 | continue |
|
|
305 | 305 | gcvl = valleyindex[vl[0][-1]] |
|
|
306 | 306 | vr = numpy.where(valleyindex > gcpeak) |
|
|
307 | 307 | if numpy.size(vr) == 0: |
|
|
308 | 308 | continue |
|
|
309 | 309 | gcvr = valleyindex[vr[0][0]] |
|
|
310 | 310 | |
|
|
311 | 311 | # Removing the clutter |
|
|
312 | 312 | interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]]) |
|
|
313 | 313 | gcindex = gc_values[gcvl+1:gcvr-1] |
|
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314 | 314 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) |
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315 | 315 | |
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316 | 316 | dataOut.data_pre[0] = self.spc_out |
|
|
317 | 317 | #print ('Leaving RemoveWideGC ... ') |
|
|
318 | 318 | return dataOut |
|
|
319 | 319 | |
|
|
320 | 320 | class SpectralFilters(Operation): |
|
|
321 | 321 | ''' This class allows to replace the novalid values with noise for each channel |
|
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322 | 322 | This applies to CLAIRE RADAR |
|
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323 | 323 | |
|
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324 | 324 | PositiveLimit : RightLimit of novalid data |
|
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325 | 325 | NegativeLimit : LeftLimit of novalid data |
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326 | 326 | |
|
|
327 | 327 | Input: |
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328 | 328 | |
|
|
329 | 329 | self.dataOut.data_pre : SPC and CSPC |
|
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330 | 330 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
|
331 | 331 | |
|
|
332 | 332 | Affected: |
|
|
333 | 333 | |
|
|
334 | 334 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
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335 | 335 | |
|
|
336 | 336 | Written by D. Scipión 29.01.2021 |
|
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337 | 337 | ''' |
|
|
338 | 338 | def __init__(self): |
|
|
339 | 339 | Operation.__init__(self) |
|
|
340 | 340 | self.i = 0 |
|
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341 | 341 | |
|
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342 | 342 | def run(self, dataOut, ): |
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343 | 343 | |
|
|
344 | 344 | self.spc = dataOut.data_pre[0].copy() |
|
|
345 | 345 | self.Num_Chn = self.spc.shape[0] |
|
|
346 | 346 | VelRange = dataOut.spc_range[2] |
|
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347 | 347 | |
|
|
348 | 348 | # novalid corresponds to data within the Negative and PositiveLimit |
|
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349 | 349 | |
|
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350 | 350 | |
|
|
351 | 351 | # Removing novalid data from the spectra |
|
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352 | 352 | for i in range(self.Num_Chn): |
|
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353 | 353 | self.spc[i,novalid,:] = dataOut.noise[i] |
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354 | 354 | dataOut.data_pre[0] = self.spc |
|
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355 | 355 | return dataOut |
|
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356 | 356 | |
|
|
357 | 357 | class GaussianFit(Operation): |
|
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358 | 358 | |
|
|
359 | 359 | ''' |
|
|
360 | 360 | Function that fit of one and two generalized gaussians (gg) based |
|
|
361 | 361 | on the PSD shape across an "power band" identified from a cumsum of |
|
|
362 | 362 | the measured spectrum - noise. |
|
|
363 | 363 | |
|
|
364 | 364 | Input: |
|
|
365 | 365 | self.dataOut.data_pre : SelfSpectra |
|
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366 | 366 | |
|
|
367 | 367 | Output: |
|
|
368 | 368 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
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369 | 369 | |
|
|
370 | 370 | ''' |
|
|
371 | 371 | def __init__(self): |
|
|
372 | 372 | Operation.__init__(self) |
|
|
373 | 373 | self.i=0 |
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374 | 374 | |
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375 | 375 | |
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376 | 376 | # def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points |
|
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377 | 377 | def run(self, dataOut, SNRdBlimit=-9, method='generalized'): |
|
|
378 | 378 | """This routine will find a couple of generalized Gaussians to a power spectrum |
|
|
379 | 379 | methods: generalized, squared |
|
|
380 | 380 | input: spc |
|
|
381 | 381 | output: |
|
|
382 | 382 | noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1 |
|
|
383 | 383 | """ |
|
|
384 | 384 | print ('Entering ',method,' double Gaussian fit') |
|
|
385 | 385 | self.spc = dataOut.data_pre[0].copy() |
|
|
386 | 386 | self.Num_Hei = self.spc.shape[2] |
|
|
387 | 387 | self.Num_Bin = self.spc.shape[1] |
|
|
388 | 388 | self.Num_Chn = self.spc.shape[0] |
|
|
389 | 389 | |
|
|
390 | 390 | start_time = time.time() |
|
|
391 | 391 | |
|
|
392 | 392 | pool = Pool(processes=self.Num_Chn) |
|
|
393 | 393 | args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)] |
|
|
394 | 394 | objs = [self for __ in range(self.Num_Chn)] |
|
|
395 | 395 | attrs = list(zip(objs, args)) |
|
|
396 | 396 | DGauFitParam = pool.map(target, attrs) |
|
|
397 | 397 | # Parameters: |
|
|
398 | 398 | # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power |
|
|
399 | 399 | dataOut.DGauFitParams = numpy.asarray(DGauFitParam) |
|
|
400 | 400 | |
|
|
401 | 401 | # Double Gaussian Curves |
|
|
402 | 402 | gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
403 | 403 | gau0[:] = numpy.NaN |
|
|
404 | 404 | gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
405 | 405 | gau1[:] = numpy.NaN |
|
|
406 | 406 | x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1))) |
|
|
407 | 407 | for iCh in range(self.Num_Chn): |
|
|
408 | 408 | N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin)) |
|
|
409 | 409 | N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin)) |
|
|
410 | 410 | A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin)) |
|
|
411 | 411 | A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin)) |
|
|
412 | 412 | v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin)) |
|
|
413 | 413 | v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin)) |
|
|
414 | 414 | s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin)) |
|
|
415 | 415 | s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin)) |
|
|
416 | 416 | if method == 'genealized': |
|
|
417 | 417 | p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin)) |
|
|
418 | 418 | p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin)) |
|
|
419 | 419 | elif method == 'squared': |
|
|
420 | 420 | p0 = 2. |
|
|
421 | 421 | p1 = 2. |
|
|
422 | 422 | gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0 |
|
|
423 | 423 | gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1 |
|
|
424 | 424 | dataOut.GaussFit0 = gau0 |
|
|
425 | 425 | dataOut.GaussFit1 = gau1 |
|
|
426 | 426 | |
|
|
427 | 427 | print('Leaving ',method ,' double Gaussian fit') |
|
|
428 | 428 | return dataOut |
|
|
429 | 429 | |
|
|
430 | 430 | def FitGau(self, X): |
|
|
431 | 431 | # print('Entering FitGau') |
|
|
432 | 432 | # Assigning the variables |
|
|
433 | 433 | Vrange, ch, wnoise, num_intg, SNRlimit = X |
|
|
434 | 434 | # Noise Limits |
|
|
435 | 435 | noisebl = wnoise * 0.9 |
|
|
436 | 436 | noisebh = wnoise * 1.1 |
|
|
437 | 437 | # Radar Velocity |
|
|
438 | 438 | Va = max(Vrange) |
|
|
439 | 439 | deltav = Vrange[1] - Vrange[0] |
|
|
440 | 440 | x = numpy.arange(self.Num_Bin) |
|
|
441 | 441 | |
|
|
442 | 442 | # print ('stop 0') |
|
|
443 | 443 | |
|
|
444 | 444 | # 5 parameters, 2 Gaussians |
|
|
445 | 445 | DGauFitParam = numpy.zeros([5, self.Num_Hei,2]) |
|
|
446 | 446 | DGauFitParam[:] = numpy.NaN |
|
|
447 | 447 | |
|
|
448 | 448 | # SPCparam = [] |
|
|
449 | 449 | # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
450 | 450 | # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
451 | 451 | # SPC_ch1[:] = 0 #numpy.NaN |
|
|
452 | 452 | # SPC_ch2[:] = 0 #numpy.NaN |
|
|
453 | 453 | # print ('stop 1') |
|
|
454 | 454 | for ht in range(self.Num_Hei): |
|
|
455 | 455 | # print (ht) |
|
|
456 | 456 | # print ('stop 2') |
|
|
457 | 457 | # Spectra at each range |
|
|
458 | 458 | spc = numpy.asarray(self.spc)[ch,:,ht] |
|
|
459 | 459 | snr = ( spc.mean() - wnoise ) / wnoise |
|
|
460 | 460 | snrdB = 10.*numpy.log10(snr) |
|
|
461 | 461 | |
|
|
462 | 462 | #print ('stop 3') |
|
|
463 | 463 | if snrdB < SNRlimit : |
|
|
464 | 464 | # snr = numpy.NaN |
|
|
465 | 465 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
466 | 466 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
467 | 467 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
|
468 | 468 | # print ('SNR less than SNRth') |
|
|
469 | 469 | continue |
|
|
470 | 470 | # wnoise = hildebrand_sekhon(spc,num_intg) |
|
|
471 | 471 | # print ('stop 2.01') |
|
|
472 | 472 | ############################################# |
|
|
473 | 473 | # normalizing spc and noise |
|
|
474 | 474 | # This part differs from gg1 |
|
|
475 | 475 | # spc_norm_max = max(spc) #commented by D. Scipión 19.03.2021 |
|
|
476 | 476 | #spc = spc / spc_norm_max |
|
|
477 | 477 | # pnoise = pnoise #/ spc_norm_max #commented by D. Scipión 19.03.2021 |
|
|
478 | 478 | ############################################# |
|
|
479 | 479 | |
|
|
480 | 480 | # print ('stop 2.1') |
|
|
481 | 481 | fatspectra=1.0 |
|
|
482 | 482 | # noise per channel.... we might want to use the noise at each range |
|
|
483 | 483 | |
|
|
484 | 484 | # wnoise = noise_ #/ spc_norm_max #commented by D. Scipión 19.03.2021 |
|
|
485 | 485 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
|
486 | 486 | #if wnoise>1.1*pnoise: # to be tested later |
|
|
487 | 487 | # wnoise=pnoise |
|
|
488 | 488 | # noisebl = wnoise*0.9 |
|
|
489 | 489 | # noisebh = wnoise*1.1 |
|
|
490 | 490 | spc = spc - wnoise # signal |
|
|
491 | 491 | |
|
|
492 | 492 | # print ('stop 2.2') |
|
|
493 | 493 | minx = numpy.argmin(spc) |
|
|
494 | 494 | #spcs=spc.copy() |
|
|
495 | 495 | spcs = numpy.roll(spc,-minx) |
|
|
496 | 496 | cum = numpy.cumsum(spcs) |
|
|
497 | 497 | # tot_noise = wnoise * self.Num_Bin #64; |
|
|
498 | 498 | |
|
|
499 | 499 | # print ('stop 2.3') |
|
|
500 | 500 | # snr = sum(spcs) / tot_noise |
|
|
501 | 501 | # snrdB = 10.*numpy.log10(snr) |
|
|
502 | 502 | #print ('stop 3') |
|
|
503 | 503 | # if snrdB < SNRlimit : |
|
|
504 | 504 | # snr = numpy.NaN |
|
|
505 | 505 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
506 | 506 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
507 | 507 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
|
508 | 508 | # print ('SNR less than SNRth') |
|
|
509 | 509 | # continue |
|
|
510 | 510 | |
|
|
511 | 511 | |
|
|
512 | 512 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
|
513 | 513 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
|
514 | 514 | # print ('stop 4') |
|
|
515 | 515 | cummax = max(cum) |
|
|
516 | 516 | epsi = 0.08 * fatspectra # cumsum to narrow down the energy region |
|
|
517 | 517 | cumlo = cummax * epsi |
|
|
518 | 518 | cumhi = cummax * (1-epsi) |
|
|
519 | 519 | powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
|
520 | 520 | |
|
|
521 | 521 | # print ('stop 5') |
|
|
522 | 522 | if len(powerindex) < 1:# case for powerindex 0 |
|
|
523 | 523 | # print ('powerindex < 1') |
|
|
524 | 524 | continue |
|
|
525 | 525 | powerlo = powerindex[0] |
|
|
526 | 526 | powerhi = powerindex[-1] |
|
|
527 | 527 | powerwidth = powerhi-powerlo |
|
|
528 | 528 | if powerwidth <= 1: |
|
|
529 | 529 | # print('powerwidth <= 1') |
|
|
530 | 530 | continue |
|
|
531 | 531 | |
|
|
532 | 532 | # print ('stop 6') |
|
|
533 | 533 | firstpeak = powerlo + powerwidth/10.# first gaussian energy location |
|
|
534 | 534 | secondpeak = powerhi - powerwidth/10. #second gaussian energy location |
|
|
535 | 535 | midpeak = (firstpeak + secondpeak)/2. |
|
|
536 | 536 | firstamp = spcs[int(firstpeak)] |
|
|
537 | 537 | secondamp = spcs[int(secondpeak)] |
|
|
538 | 538 | midamp = spcs[int(midpeak)] |
|
|
539 | 539 | |
|
|
540 | 540 | y_data = spc + wnoise |
|
|
541 | 541 | |
|
|
542 | 542 | ''' single Gaussian ''' |
|
|
543 | 543 | shift0 = numpy.mod(midpeak+minx, self.Num_Bin ) |
|
|
544 | 544 | width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
|
545 | 545 | power0 = 2. |
|
|
546 | 546 | amplitude0 = midamp |
|
|
547 | 547 | state0 = [shift0,width0,amplitude0,power0,wnoise] |
|
|
548 | 548 | bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
549 | 549 | lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True) |
|
|
550 | 550 | # print ('stop 7.1') |
|
|
551 | 551 | # print (bnds) |
|
|
552 | 552 | |
|
|
553 | 553 | chiSq1=lsq1[1] |
|
|
554 | 554 | |
|
|
555 | 555 | # print ('stop 8') |
|
|
556 | 556 | if fatspectra<1.0 and powerwidth<4: |
|
|
557 | 557 | choice=0 |
|
|
558 | 558 | Amplitude0=lsq1[0][2] |
|
|
559 | 559 | shift0=lsq1[0][0] |
|
|
560 | 560 | width0=lsq1[0][1] |
|
|
561 | 561 | p0=lsq1[0][3] |
|
|
562 | 562 | Amplitude1=0. |
|
|
563 | 563 | shift1=0. |
|
|
564 | 564 | width1=0. |
|
|
565 | 565 | p1=0. |
|
|
566 | 566 | noise=lsq1[0][4] |
|
|
567 | 567 | #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
|
568 | 568 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
|
569 | 569 | |
|
|
570 | 570 | # print ('stop 9') |
|
|
571 | 571 | ''' two Gaussians ''' |
|
|
572 | 572 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
|
573 | 573 | shift0 = numpy.mod(firstpeak+minx, self.Num_Bin ) |
|
|
574 | 574 | shift1 = numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
|
575 | 575 | width0 = powerwidth/6. |
|
|
576 | 576 | width1 = width0 |
|
|
577 | 577 | power0 = 2. |
|
|
578 | 578 | power1 = power0 |
|
|
579 | 579 | amplitude0 = firstamp |
|
|
580 | 580 | amplitude1 = secondamp |
|
|
581 | 581 | state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
|
582 | 582 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
583 | 583 | bnds=((0,self.Num_Bin-1),(1,powerwidth/2.),(0,None),(0.5,3.),(0,self.Num_Bin-1),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
584 | 584 | #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5)) |
|
|
585 | 585 | |
|
|
586 | 586 | # print ('stop 10') |
|
|
587 | 587 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
|
588 | 588 | |
|
|
589 | 589 | # print ('stop 11') |
|
|
590 | 590 | chiSq2 = lsq2[1] |
|
|
591 | 591 | |
|
|
592 | 592 | # print ('stop 12') |
|
|
593 | 593 | |
|
|
594 | 594 | oneG = (chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10) |
|
|
595 | 595 | |
|
|
596 | 596 | # print ('stop 13') |
|
|
597 | 597 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
|
598 | 598 | if oneG: |
|
|
599 | 599 | choice = 0 |
|
|
600 | 600 | else: |
|
|
601 | 601 | w1 = lsq2[0][1]; w2 = lsq2[0][5] |
|
|
602 | 602 | a1 = lsq2[0][2]; a2 = lsq2[0][6] |
|
|
603 | 603 | p1 = lsq2[0][3]; p2 = lsq2[0][7] |
|
|
604 | 604 | s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 |
|
|
605 | 605 | s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 |
|
|
606 | 606 | gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
|
607 | 607 | |
|
|
608 | 608 | if gp1>gp2: |
|
|
609 | 609 | if a1>0.7*a2: |
|
|
610 | 610 | choice = 1 |
|
|
611 | 611 | else: |
|
|
612 | 612 | choice = 2 |
|
|
613 | 613 | elif gp2>gp1: |
|
|
614 | 614 | if a2>0.7*a1: |
|
|
615 | 615 | choice = 2 |
|
|
616 | 616 | else: |
|
|
617 | 617 | choice = 1 |
|
|
618 | 618 | else: |
|
|
619 | 619 | choice = numpy.argmax([a1,a2])+1 |
|
|
620 | 620 | #else: |
|
|
621 | 621 | #choice=argmin([std2a,std2b])+1 |
|
|
622 | 622 | |
|
|
623 | 623 | else: # with low SNR go to the most energetic peak |
|
|
624 | 624 | choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
|
625 | 625 | |
|
|
626 | 626 | # print ('stop 14') |
|
|
627 | 627 | shift0 = lsq2[0][0] |
|
|
628 | 628 | vel0 = Vrange[0] + shift0 * deltav |
|
|
629 | 629 | shift1 = lsq2[0][4] |
|
|
630 | 630 | # vel1=Vrange[0] + shift1 * deltav |
|
|
631 | 631 | |
|
|
632 | 632 | # max_vel = 1.0 |
|
|
633 | 633 | # Va = max(Vrange) |
|
|
634 | 634 | # deltav = Vrange[1]-Vrange[0] |
|
|
635 | 635 | # print ('stop 15') |
|
|
636 | 636 | #first peak will be 0, second peak will be 1 |
|
|
637 | 637 | # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.Scipión 19.03.2021 |
|
|
638 | 638 | if vel0 > -Va and vel0 < Va : #first peak is in the correct range |
|
|
639 | 639 | shift0 = lsq2[0][0] |
|
|
640 | 640 | width0 = lsq2[0][1] |
|
|
641 | 641 | Amplitude0 = lsq2[0][2] |
|
|
642 | 642 | p0 = lsq2[0][3] |
|
|
643 | 643 | |
|
|
644 | 644 | shift1 = lsq2[0][4] |
|
|
645 | 645 | width1 = lsq2[0][5] |
|
|
646 | 646 | Amplitude1 = lsq2[0][6] |
|
|
647 | 647 | p1 = lsq2[0][7] |
|
|
648 | 648 | noise = lsq2[0][8] |
|
|
649 | 649 | else: |
|
|
650 | 650 | shift1 = lsq2[0][0] |
|
|
651 | 651 | width1 = lsq2[0][1] |
|
|
652 | 652 | Amplitude1 = lsq2[0][2] |
|
|
653 | 653 | p1 = lsq2[0][3] |
|
|
654 | 654 | |
|
|
655 | 655 | shift0 = lsq2[0][4] |
|
|
656 | 656 | width0 = lsq2[0][5] |
|
|
657 | 657 | Amplitude0 = lsq2[0][6] |
|
|
658 | 658 | p0 = lsq2[0][7] |
|
|
659 | 659 | noise = lsq2[0][8] |
|
|
660 | 660 | |
|
|
661 | 661 | if Amplitude0<0.05: # in case the peak is noise |
|
|
662 | 662 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] |
|
|
663 | 663 | if Amplitude1<0.05: |
|
|
664 | 664 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] |
|
|
665 | 665 | |
|
|
666 | 666 | # print ('stop 16 ') |
|
|
667 | 667 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0) |
|
|
668 | 668 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1) |
|
|
669 | 669 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
|
670 | 670 | |
|
|
671 | 671 | DGauFitParam[0,ht,0] = noise |
|
|
672 | 672 | DGauFitParam[0,ht,1] = noise |
|
|
673 | 673 | DGauFitParam[1,ht,0] = Amplitude0 |
|
|
674 | 674 | DGauFitParam[1,ht,1] = Amplitude1 |
|
|
675 | 675 | DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav |
|
|
676 | 676 | DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav |
|
|
677 | 677 | DGauFitParam[3,ht,0] = width0 * deltav |
|
|
678 | 678 | DGauFitParam[3,ht,1] = width1 * deltav |
|
|
679 | 679 | DGauFitParam[4,ht,0] = p0 |
|
|
680 | 680 | DGauFitParam[4,ht,1] = p1 |
|
|
681 | 681 | |
|
|
682 | 682 | # print (DGauFitParam.shape) |
|
|
683 | 683 | # print ('Leaving FitGau') |
|
|
684 | 684 | return DGauFitParam |
|
|
685 | 685 | # return SPCparam |
|
|
686 | 686 | # return GauSPC |
|
|
687 | 687 | |
|
|
688 | 688 | def y_model1(self,x,state): |
|
|
689 | 689 | shift0, width0, amplitude0, power0, noise = state |
|
|
690 | 690 | model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0) |
|
|
691 | 691 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
|
692 | 692 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
|
693 | 693 | return model0 + model0u + model0d + noise |
|
|
694 | 694 | |
|
|
695 | 695 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
|
696 | 696 | shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state |
|
|
697 | 697 | model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
698 | 698 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
|
699 | 699 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
|
700 | 700 | |
|
|
701 | 701 | model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1) |
|
|
702 | 702 | model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1) |
|
|
703 | 703 | model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1) |
|
|
704 | 704 | return model0 + model0u + model0d + model1 + model1u + model1d + noise |
|
|
705 | 705 | |
|
|
706 | 706 | def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is. |
|
|
707 | 707 | |
|
|
708 | 708 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
|
709 | 709 | |
|
|
710 | 710 | def misfit2(self,state,y_data,x,num_intg): |
|
|
711 | 711 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
|
712 | 712 | |
|
|
713 | 713 | |
|
|
714 | 714 | |
|
|
715 | 715 | class PrecipitationProc(Operation): |
|
|
716 | 716 | |
|
|
717 | 717 | ''' |
|
|
718 | 718 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
|
719 | 719 | |
|
|
720 | 720 | Input: |
|
|
721 | 721 | self.dataOut.data_pre : SelfSpectra |
|
|
722 | 722 | |
|
|
723 | 723 | Output: |
|
|
724 | 724 | |
|
|
725 | 725 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
|
726 | 726 | |
|
|
727 | 727 | |
|
|
728 | 728 | Parameters affected: |
|
|
729 | 729 | ''' |
|
|
730 | 730 | |
|
|
731 | 731 | def __init__(self): |
|
|
732 | 732 | Operation.__init__(self) |
|
|
733 | 733 | self.i=0 |
|
|
734 | 734 | |
|
|
735 | 735 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
|
736 | 736 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30): |
|
|
737 | 737 | |
|
|
738 | 738 | # print ('Entering PrecepitationProc ... ') |
|
|
739 | 739 | |
|
|
740 | 740 | if radar == "MIRA35C" : |
|
|
741 | 741 | |
|
|
742 | 742 | self.spc = dataOut.data_pre[0].copy() |
|
|
743 | 743 | self.Num_Hei = self.spc.shape[2] |
|
|
744 | 744 | self.Num_Bin = self.spc.shape[1] |
|
|
745 | 745 | self.Num_Chn = self.spc.shape[0] |
|
|
746 | 746 | Ze = self.dBZeMODE2(dataOut) |
|
|
747 | 747 | |
|
|
748 | 748 | else: |
|
|
749 | 749 | |
|
|
750 | 750 | self.spc = dataOut.data_pre[0].copy() |
|
|
751 | 751 | |
|
|
752 | 752 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX |
|
|
753 | 753 | self.spc[:,:,0:7]= numpy.NaN |
|
|
754 | 754 | |
|
|
755 | 755 | self.Num_Hei = self.spc.shape[2] |
|
|
756 | 756 | self.Num_Bin = self.spc.shape[1] |
|
|
757 | 757 | self.Num_Chn = self.spc.shape[0] |
|
|
758 | 758 | |
|
|
759 | 759 | VelRange = dataOut.spc_range[2] |
|
|
760 | 760 | |
|
|
761 | 761 | ''' Se obtiene la constante del RADAR ''' |
|
|
762 | 762 | |
|
|
763 | 763 | self.Pt = Pt |
|
|
764 | 764 | self.Gt = Gt |
|
|
765 | 765 | self.Gr = Gr |
|
|
766 | 766 | self.Lambda = Lambda |
|
|
767 | 767 | self.aL = aL |
|
|
768 | 768 | self.tauW = tauW |
|
|
769 | 769 | self.ThetaT = ThetaT |
|
|
770 | 770 | self.ThetaR = ThetaR |
|
|
771 | 771 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB |
|
|
772 | 772 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB |
|
|
773 | 773 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB |
|
|
774 | 774 | |
|
|
775 | 775 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
776 | 776 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
|
777 | 777 | RadarConstant = 10e-26 * Numerator / Denominator # |
|
|
778 | 778 | ExpConstant = 10**(40/10) #Constante Experimental |
|
|
779 | 779 | |
|
|
780 | 780 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
781 | 781 | for i in range(self.Num_Chn): |
|
|
782 | 782 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] |
|
|
783 | 783 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 |
|
|
784 | 784 | |
|
|
785 | 785 | SPCmean = numpy.mean(SignalPower, 0) |
|
|
786 | 786 | Pr = SPCmean[:,:]/dataOut.normFactor |
|
|
787 | 787 | |
|
|
788 | 788 | # Declaring auxiliary variables |
|
|
789 | 789 | Range = dataOut.heightList*1000. #Range in m |
|
|
790 | 790 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] |
|
|
791 | 791 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) |
|
|
792 | 792 | zMtrx = rMtrx+Altitude |
|
|
793 | 793 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] |
|
|
794 | 794 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) |
|
|
795 | 795 | |
|
|
796 | 796 | # height dependence to air density Foote and Du Toit (1969) |
|
|
797 | 797 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 |
|
|
798 | 798 | VMtrx = VelMtrx / delv_z #Normalized velocity |
|
|
799 | 799 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN |
|
|
800 | 800 | # Diameter is related to the fall speed of falling drops |
|
|
801 | 801 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] |
|
|
802 | 802 | # Only valid for D>= 0.16 mm |
|
|
803 | 803 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN |
|
|
804 | 804 | |
|
|
805 | 805 | #Calculate Radar Reflectivity ETAn |
|
|
806 | 806 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) |
|
|
807 | 807 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z |
|
|
808 | 808 | # Radar Cross Section |
|
|
809 | 809 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 |
|
|
810 | 810 | # Drop Size Distribution |
|
|
811 | 811 | DSD = ETAn / sigmaD |
|
|
812 | 812 | # Equivalente Reflectivy |
|
|
813 | 813 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) |
|
|
814 | 814 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] |
|
|
815 | 815 | # RainFall Rate |
|
|
816 | 816 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr |
|
|
817 | 817 | |
|
|
818 | 818 | # Censoring the data |
|
|
819 | 819 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal |
|
|
820 | 820 | SNRth = 10**(SNRdBlimit/10) #-30dB |
|
|
821 | 821 | novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better |
|
|
822 | 822 | W = numpy.nanmean(dataOut.data_dop,0) |
|
|
823 | 823 | W[novalid] = numpy.NaN |
|
|
824 | 824 | Ze_org[novalid] = numpy.NaN |
|
|
825 | 825 | RR[novalid] = numpy.NaN |
|
|
826 | 826 | |
|
|
827 | 827 | dataOut.data_output = RR[8] |
|
|
828 | 828 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
|
829 | 829 | dataOut.channelList = [0,1,2] |
|
|
830 | 830 | |
|
|
831 | 831 | dataOut.data_param[0]=10*numpy.log10(Ze_org) |
|
|
832 | 832 | dataOut.data_param[1]=-W |
|
|
833 | 833 | dataOut.data_param[2]=RR |
|
|
834 | 834 | |
|
|
835 | 835 | # print ('Leaving PrecepitationProc ... ') |
|
|
836 | 836 | return dataOut |
|
|
837 | 837 | |
|
|
838 | 838 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
|
839 | 839 | |
|
|
840 | 840 | NPW = dataOut.NPW |
|
|
841 | 841 | COFA = dataOut.COFA |
|
|
842 | 842 | |
|
|
843 | 843 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
|
844 | 844 | RadarConst = dataOut.RadarConst |
|
|
845 | 845 | #frequency = 34.85*10**9 |
|
|
846 | 846 | |
|
|
847 | 847 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
|
848 | 848 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
|
849 | 849 | |
|
|
850 | 850 | ETA = numpy.sum(SNR,1) |
|
|
851 | 851 | |
|
|
852 | 852 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) |
|
|
853 | 853 | |
|
|
854 | 854 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
|
855 | 855 | |
|
|
856 | 856 | for r in range(self.Num_Hei): |
|
|
857 | 857 | |
|
|
858 | 858 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
|
859 | 859 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
|
860 | 860 | |
|
|
861 | 861 | return Ze |
|
|
862 | 862 | |
|
|
863 | 863 | # def GetRadarConstant(self): |
|
|
864 | 864 | # |
|
|
865 | 865 | # """ |
|
|
866 | 866 | # Constants: |
|
|
867 | 867 | # |
|
|
868 | 868 | # Pt: Transmission Power dB 5kW 5000 |
|
|
869 | 869 | # Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
|
870 | 870 | # Gr: Reception Gain dB 18.5 dB 70.7945 |
|
|
871 | 871 | # Lambda: Wavelenght m 0.6741 m 0.6741 |
|
|
872 | 872 | # aL: Attenuation loses dB 4dB 2.5118 |
|
|
873 | 873 | # tauW: Width of transmission pulse s 4us 4e-6 |
|
|
874 | 874 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
|
875 | 875 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
|
876 | 876 | # |
|
|
877 | 877 | # """ |
|
|
878 | 878 | # |
|
|
879 | 879 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
880 | 880 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
|
881 | 881 | # RadarConstant = Numerator / Denominator |
|
|
882 | 882 | # |
|
|
883 | 883 | # return RadarConstant |
|
|
884 | 884 | |
|
|
885 | 885 | |
|
|
886 | 886 | |
|
|
887 | 887 | class FullSpectralAnalysis(Operation): |
|
|
888 | 888 | |
|
|
889 | 889 | """ |
|
|
890 | 890 | Function that implements Full Spectral Analysis technique. |
|
|
891 | 891 | |
|
|
892 | 892 | Input: |
|
|
893 | 893 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data |
|
|
894 | 894 | self.dataOut.groupList : Pairlist of channels |
|
|
895 | 895 | self.dataOut.ChanDist : Physical distance between receivers |
|
|
896 | 896 | |
|
|
897 | 897 | |
|
|
898 | 898 | Output: |
|
|
899 | 899 | |
|
|
900 | 900 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind |
|
|
901 | 901 | |
|
|
902 | 902 | |
|
|
903 | 903 | Parameters affected: Winds, height range, SNR |
|
|
904 | 904 | |
|
|
905 | 905 | """ |
|
|
906 | 906 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30, |
|
|
907 | 907 | minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None): |
|
|
908 | 908 | |
|
|
909 | 909 | spc = dataOut.data_pre[0].copy() |
|
|
910 | 910 | cspc = dataOut.data_pre[1] |
|
|
911 | 911 | nHeights = spc.shape[2] |
|
|
912 | 912 | |
|
|
913 | 913 | # first_height = 0.75 #km (ref: data header 20170822) |
|
|
914 | 914 | # resolution_height = 0.075 #km |
|
|
915 | 915 | ''' |
|
|
916 | 916 | finding height range. check this when radar parameters are changed! |
|
|
917 | 917 | ''' |
|
|
918 | 918 | if maxheight is not None: |
|
|
919 | 919 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical |
|
|
920 | 920 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better |
|
|
921 | 921 | else: |
|
|
922 | 922 | range_max = nHeights |
|
|
923 | 923 | if minheight is not None: |
|
|
924 | 924 | # range_min = int((minheight - first_height) / resolution_height) # theoretical |
|
|
925 | 925 | range_min = int(13.26 * minheight - 5) # empirical, works better |
|
|
926 | 926 | if range_min < 0: |
|
|
927 | 927 | range_min = 0 |
|
|
928 | 928 | else: |
|
|
929 | 929 | range_min = 0 |
|
|
930 | 930 | |
|
|
931 | 931 | pairsList = dataOut.groupList |
|
|
932 | 932 | if dataOut.ChanDist is not None : |
|
|
933 | 933 | ChanDist = dataOut.ChanDist |
|
|
934 | 934 | else: |
|
|
935 | 935 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
|
936 | 936 | |
|
|
937 | 937 | # 4 variables: zonal, meridional, vertical, and average SNR |
|
|
938 | 938 | data_param = numpy.zeros([4,nHeights]) * numpy.NaN |
|
|
939 | 939 | velocityX = numpy.zeros([nHeights]) * numpy.NaN |
|
|
940 | 940 | velocityY = numpy.zeros([nHeights]) * numpy.NaN |
|
|
941 | 941 | velocityZ = numpy.zeros([nHeights]) * numpy.NaN |
|
|
942 | 942 | |
|
|
943 | 943 | dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0)) |
|
|
944 | 944 | |
|
|
945 | 945 | '''***********************************************WIND ESTIMATION**************************************''' |
|
|
946 | 946 | for Height in range(nHeights): |
|
|
947 | 947 | |
|
|
948 | 948 | if Height >= range_min and Height < range_max: |
|
|
949 | 949 | # error_code will be useful in future analysis |
|
|
950 | 950 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, |
|
|
951 | 951 | ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency) |
|
|
952 | 952 | |
|
|
953 | 953 | if abs(Vzon) < 100. and abs(Vmer) < 100.: |
|
|
954 | 954 | velocityX[Height] = Vzon |
|
|
955 | 955 | velocityY[Height] = -Vmer |
|
|
956 | 956 | velocityZ[Height] = Vver |
|
|
957 | 957 | |
|
|
958 | 958 | # Censoring data with SNR threshold |
|
|
959 | 959 | dbSNR [dbSNR < SNRdBlimit] = numpy.NaN |
|
|
960 | 960 | |
|
|
961 | 961 | data_param[0] = velocityX |
|
|
962 | 962 | data_param[1] = velocityY |
|
|
963 | 963 | data_param[2] = velocityZ |
|
|
964 | 964 | data_param[3] = dbSNR |
|
|
965 | 965 | dataOut.data_param = data_param |
|
|
966 | 966 | return dataOut |
|
|
967 | 967 | |
|
|
968 | 968 | def moving_average(self,x, N=2): |
|
|
969 | 969 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ |
|
|
970 | 970 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
|
971 | 971 | |
|
|
972 | 972 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
973 | 973 | return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2) |
|
|
974 | 974 | |
|
|
975 | 975 | def Moments(self, ySamples, xSamples): |
|
|
976 | 976 | Power = numpy.nanmean(ySamples) # Power, 0th Moment |
|
|
977 | 977 | yNorm = ySamples / numpy.nansum(ySamples) |
|
|
978 | 978 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment |
|
|
979 | 979 | Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment |
|
|
980 | 980 | StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral |
|
|
981 | 981 | return numpy.array([Power,RadVel,StdDev]) |
|
|
982 | 982 | |
|
|
983 | 983 | def StopWindEstimation(self, error_code): |
|
|
984 | 984 | Vzon = numpy.NaN |
|
|
985 | 985 | Vmer = numpy.NaN |
|
|
986 | 986 | Vver = numpy.NaN |
|
|
987 | 987 | return Vzon, Vmer, Vver, error_code |
|
|
988 | 988 | |
|
|
989 | 989 | def AntiAliasing(self, interval, maxstep): |
|
|
990 | 990 | """ |
|
|
991 | 991 | function to prevent errors from aliased values when computing phaseslope |
|
|
992 | 992 | """ |
|
|
993 | 993 | antialiased = numpy.zeros(len(interval)) |
|
|
994 | 994 | copyinterval = interval.copy() |
|
|
995 | 995 | |
|
|
996 | 996 | antialiased[0] = copyinterval[0] |
|
|
997 | 997 | |
|
|
998 | 998 | for i in range(1,len(antialiased)): |
|
|
999 | 999 | step = interval[i] - interval[i-1] |
|
|
1000 | 1000 | if step > maxstep: |
|
|
1001 | 1001 | copyinterval -= 2*numpy.pi |
|
|
1002 | 1002 | antialiased[i] = copyinterval[i] |
|
|
1003 | 1003 | elif step < maxstep*(-1): |
|
|
1004 | 1004 | copyinterval += 2*numpy.pi |
|
|
1005 | 1005 | antialiased[i] = copyinterval[i] |
|
|
1006 | 1006 | else: |
|
|
1007 | 1007 | antialiased[i] = copyinterval[i].copy() |
|
|
1008 | 1008 | |
|
|
1009 | 1009 | return antialiased |
|
|
1010 | 1010 | |
|
|
1011 | 1011 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq): |
|
|
1012 | 1012 | """ |
|
|
1013 | 1013 | Function that Calculates Zonal, Meridional and Vertical wind velocities. |
|
|
1014 | 1014 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. |
|
|
1015 | 1015 | |
|
|
1016 | 1016 | Input: |
|
|
1017 | 1017 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. |
|
|
1018 | 1018 | pairsList : Pairlist of channels |
|
|
1019 | 1019 | ChanDist : array of xi_ij and eta_ij |
|
|
1020 | 1020 | Height : height at which data is processed |
|
|
1021 | 1021 | noise : noise in [channels] format for specific height |
|
|
1022 | 1022 | Abbsisarange : range of the frequencies or velocities |
|
|
1023 | 1023 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit |
|
|
1024 | 1024 | |
|
|
1025 | 1025 | Output: |
|
|
1026 | 1026 | Vzon, Vmer, Vver : wind velocities |
|
|
1027 | 1027 | error_code : int that states where code is terminated |
|
|
1028 | 1028 | |
|
|
1029 | 1029 | 0 : no error detected |
|
|
1030 | 1030 | 1 : Gaussian of mean spc exceeds widthlimit |
|
|
1031 | 1031 | 2 : no Gaussian of mean spc found |
|
|
1032 | 1032 | 3 : SNR to low or velocity to high -> prec. e.g. |
|
|
1033 | 1033 | 4 : at least one Gaussian of cspc exceeds widthlimit |
|
|
1034 | 1034 | 5 : zero out of three cspc Gaussian fits converged |
|
|
1035 | 1035 | 6 : phase slope fit could not be found |
|
|
1036 | 1036 | 7 : arrays used to fit phase have different length |
|
|
1037 | 1037 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) |
|
|
1038 | 1038 | |
|
|
1039 | 1039 | """ |
|
|
1040 | 1040 | |
|
|
1041 | 1041 | error_code = 0 |
|
|
1042 | 1042 | |
|
|
1043 | 1043 | nChan = spc.shape[0] |
|
|
1044 | 1044 | nProf = spc.shape[1] |
|
|
1045 | 1045 | nPair = cspc.shape[0] |
|
|
1046 | 1046 | |
|
|
1047 | 1047 | SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height |
|
|
1048 | 1048 | CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values |
|
|
1049 | 1049 | phase = numpy.zeros([nPair, nProf]) # phase between channels |
|
|
1050 | 1050 | PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise |
|
|
1051 | 1051 | PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise |
|
|
1052 | 1052 | xFrec = AbbsisaRange[0][:-1] # frequency range |
|
|
1053 | 1053 | xVel = AbbsisaRange[2][:-1] # velocity range |
|
|
1054 | 1054 | xSamples = xFrec # the frequency range is taken |
|
|
1055 | 1055 | delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x |
|
|
1056 | 1056 | |
|
|
1057 | 1057 | # only consider velocities with in NegativeLimit and PositiveLimit |
|
|
1058 | 1058 | if (NegativeLimit is None): |
|
|
1059 | 1059 | NegativeLimit = numpy.min(xVel) |
|
|
1060 | 1060 | if (PositiveLimit is None): |
|
|
1061 | 1061 | PositiveLimit = numpy.max(xVel) |
|
|
1062 | 1062 | xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit)) |
|
|
1063 | 1063 | xSamples_zoom = xSamples[xvalid] |
|
|
1064 | 1064 | |
|
|
1065 | 1065 | '''Getting Eij and Nij''' |
|
|
1066 | 1066 | Xi01, Xi02, Xi12 = ChanDist[:,0] |
|
|
1067 | 1067 | Eta01, Eta02, Eta12 = ChanDist[:,1] |
|
|
1068 | 1068 | |
|
|
1069 | 1069 | # spwd limit - updated by D. Scipión 30.03.2021 |
|
|
1070 | 1070 | widthlimit = 10 |
|
|
1071 | 1071 | '''************************* SPC is normalized ********************************''' |
|
|
1072 | 1072 | spc_norm = spc.copy() |
|
|
1073 | 1073 | # For each channel |
|
|
1074 | 1074 | for i in range(nChan): |
|
|
1075 | 1075 | spc_sub = spc_norm[i,:] - noise[i] # only the signal power |
|
|
1076 | 1076 | SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x) |
|
|
1077 | 1077 | |
|
|
1078 | 1078 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' |
|
|
1079 | 1079 | |
|
|
1080 | 1080 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because |
|
|
1081 | 1081 | you only fit the curve and don't need the absolute value of height for calculation, |
|
|
1082 | 1082 | only for estimation of width. for normalization of cross spectra, you need initial, |
|
|
1083 | 1083 | unnormalized self-spectra With noise. |
|
|
1084 | 1084 | |
|
|
1085 | 1085 | Technically, you don't even need to normalize the self-spectra, as you only need the |
|
|
1086 | 1086 | width of the peak. However, it was left this way. Note that the normalization has a flaw: |
|
|
1087 | 1087 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be |
|
|
1088 | 1088 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) |
|
|
1089 | 1089 | """ |
|
|
1090 | 1090 | # initial conditions |
|
|
1091 | 1091 | popt = [1e-10,0,1e-10] |
|
|
1092 | 1092 | # Spectra average |
|
|
1093 | 1093 | SPCMean = numpy.average(SPC_Samples,0) |
|
|
1094 | 1094 | # Moments in frequency |
|
|
1095 | 1095 | SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom) |
|
|
1096 | 1096 | |
|
|
1097 | 1097 | # Gauss Fit SPC in frequency domain |
|
|
1098 | 1098 | if dbSNR > SNRlimit: # only if SNR > SNRth |
|
|
1099 | 1099 | try: |
|
|
1100 | 1100 | popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments) |
|
|
1101 | 1101 | if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION |
|
|
1102 | 1102 | return self.StopWindEstimation(error_code = 1) |
|
|
1103 | 1103 | FitGauss = self.gaus(xSamples_zoom,*popt) |
|
|
1104 | 1104 | except :#RuntimeError: |
|
|
1105 | 1105 | return self.StopWindEstimation(error_code = 2) |
|
|
1106 | 1106 | else: |
|
|
1107 | 1107 | return self.StopWindEstimation(error_code = 3) |
|
|
1108 | 1108 | |
|
|
1109 | 1109 | '''***************************** CSPC Normalization ************************* |
|
|
1110 | 1110 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation |
|
|
1111 | 1111 | influence the norm which is not desired. First, a range is identified where the |
|
|
1112 | 1112 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area |
|
|
1113 | 1113 | around it gets cut off and values replaced by mean determined by the boundary |
|
|
1114 | 1114 | data -> sum_noise (spc is not normalized here, thats why the noise is important) |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | The sums are then added and multiplied by range/datapoints, because you need |
|
|
1117 | 1117 | an integral and not a sum for normalization. |
|
|
1118 | 1118 | |
|
|
1119 | 1119 | A norm is found according to Briggs 92. |
|
|
1120 | 1120 | ''' |
|
|
1121 | 1121 | # for each pair |
|
|
1122 | 1122 | for i in range(nPair): |
|
|
1123 | 1123 | cspc_norm = cspc[i,:].copy() |
|
|
1124 | 1124 | chan_index0 = pairsList[i][0] |
|
|
1125 | 1125 | chan_index1 = pairsList[i][1] |
|
|
1126 | 1126 | CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x) |
|
|
1127 | 1127 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) |
|
|
1128 | 1128 | |
|
|
1129 | 1129 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom), |
|
|
1130 | 1130 | self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom), |
|
|
1131 | 1131 | self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)]) |
|
|
1132 | 1132 | |
|
|
1133 | 1133 | popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10] |
|
|
1134 | 1134 | FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)) |
|
|
1135 | 1135 | |
|
|
1136 | 1136 | '''*******************************FIT GAUSS CSPC************************************''' |
|
|
1137 | 1137 | try: |
|
|
1138 | 1138 | popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0]) |
|
|
1139 | 1139 | if popt01[2] > widthlimit: # CONDITION |
|
|
1140 | 1140 | return self.StopWindEstimation(error_code = 4) |
|
|
1141 | 1141 | popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1]) |
|
|
1142 | 1142 | if popt02[2] > widthlimit: # CONDITION |
|
|
1143 | 1143 | return self.StopWindEstimation(error_code = 4) |
|
|
1144 | 1144 | popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2]) |
|
|
1145 | 1145 | if popt12[2] > widthlimit: # CONDITION |
|
|
1146 | 1146 | return self.StopWindEstimation(error_code = 4) |
|
|
1147 | 1147 | |
|
|
1148 | 1148 | FitGauss01 = self.gaus(xSamples_zoom, *popt01) |
|
|
1149 | 1149 | FitGauss02 = self.gaus(xSamples_zoom, *popt02) |
|
|
1150 | 1150 | FitGauss12 = self.gaus(xSamples_zoom, *popt12) |
|
|
1151 | 1151 | except: |
|
|
1152 | 1152 | return self.StopWindEstimation(error_code = 5) |
|
|
1153 | 1153 | |
|
|
1154 | 1154 | |
|
|
1155 | 1155 | '''************* Getting Fij ***************''' |
|
|
1156 | 1156 | # x-axis point of the gaussian where the center is located from GaussFit of spectra |
|
|
1157 | 1157 | GaussCenter = popt[1] |
|
|
1158 | 1158 | ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()] |
|
|
1159 | 1159 | PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0] |
|
|
1160 | 1160 | |
|
|
1161 | 1161 | # Point where e^-1 is located in the gaussian |
|
|
1162 | 1162 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) |
|
|
1163 | 1163 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss" |
|
|
1164 | 1164 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
|
1165 | 1165 | Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter]) |
|
|
1166 | 1166 | |
|
|
1167 | 1167 | '''********** Taking frequency ranges from mean SPCs **********''' |
|
|
1168 | 1168 | GauWidth = popt[2] * 3/2 # Bandwidth of Gau01 |
|
|
1169 | 1169 | Range = numpy.empty(2) |
|
|
1170 | 1170 | Range[0] = GaussCenter - GauWidth |
|
|
1171 | 1171 | Range[1] = GaussCenter + GauWidth |
|
|
1172 | 1172 | # Point in x-axis where the bandwidth is located (min:max) |
|
|
1173 | 1173 | ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()] |
|
|
1174 | 1174 | ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()] |
|
|
1175 | 1175 | PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0] |
|
|
1176 | 1176 | PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0] |
|
|
1177 | 1177 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) |
|
|
1178 | 1178 | FrecRange = xSamples_zoom[ Range[0] : Range[1] ] |
|
|
1179 | 1179 | |
|
|
1180 | 1180 | '''************************** Getting Phase Slope ***************************''' |
|
|
1181 | 1181 | for i in range(nPair): |
|
|
1182 | 1182 | if len(FrecRange) > 5: |
|
|
1183 | 1183 | PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy() |
|
|
1184 | 1184 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
|
1185 | 1185 | if len(FrecRange) == len(PhaseRange): |
|
|
1186 | 1186 | try: |
|
|
1187 | 1187 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) |
|
|
1188 | 1188 | PhaseSlope[i] = slope |
|
|
1189 | 1189 | PhaseInter[i] = intercept |
|
|
1190 | 1190 | except: |
|
|
1191 | 1191 | return self.StopWindEstimation(error_code = 6) |
|
|
1192 | 1192 | else: |
|
|
1193 | 1193 | return self.StopWindEstimation(error_code = 7) |
|
|
1194 | 1194 | else: |
|
|
1195 | 1195 | return self.StopWindEstimation(error_code = 8) |
|
|
1196 | 1196 | |
|
|
1197 | 1197 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' |
|
|
1198 | 1198 | |
|
|
1199 | 1199 | '''Getting constant C''' |
|
|
1200 | 1200 | cC=(Fij*numpy.pi)**2 |
|
|
1201 | 1201 | |
|
|
1202 | 1202 | '''****** Getting constants F and G ******''' |
|
|
1203 | 1203 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
|
1204 | 1204 | # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]]) |
|
|
1205 | 1205 | # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi) |
|
|
1206 | 1206 | MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi) |
|
|
1207 | 1207 | MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi) |
|
|
1208 | 1208 | # MijResults = numpy.array([MijResult0, MijResult1, MijResult2]) |
|
|
1209 | 1209 | MijResults = numpy.array([MijResult1, MijResult2]) |
|
|
1210 | 1210 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
|
1211 | 1211 | |
|
|
1212 | 1212 | '''****** Getting constants A, B and H ******''' |
|
|
1213 | 1213 | W01 = numpy.nanmax( FitGauss01 ) |
|
|
1214 | 1214 | W02 = numpy.nanmax( FitGauss02 ) |
|
|
1215 | 1215 | W12 = numpy.nanmax( FitGauss12 ) |
|
|
1216 | 1216 | |
|
|
1217 | 1217 | WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) |
|
|
1218 | 1218 | WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) |
|
|
1219 | 1219 | WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) |
|
|
1220 | 1220 | WijResults = numpy.array([WijResult01, WijResult02, WijResult12]) |
|
|
1221 | 1221 | |
|
|
1222 | 1222 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
|
1223 | 1223 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
|
1224 | 1224 | |
|
|
1225 | 1225 | VxVy = numpy.array([[cA,cH],[cH,cB]]) |
|
|
1226 | 1226 | VxVyResults = numpy.array([-cF,-cG]) |
|
|
1227 | 1227 | (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults) |
|
|
1228 | 1228 | Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq) |
|
|
1229 | 1229 | error_code = 0 |
|
|
1230 | 1230 | |
|
|
1231 | 1231 | return Vzon, Vmer, Vver, error_code |
|
|
1232 | 1232 | |
|
|
1233 | 1233 | class SpectralMoments(Operation): |
|
|
1234 | 1234 | |
|
|
1235 | 1235 | ''' |
|
|
1236 | 1236 | Function SpectralMoments() |
|
|
1237 | 1237 | |
|
|
1238 | 1238 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
1239 | 1239 | |
|
|
1240 | 1240 | Type of dataIn: Spectra |
|
|
1241 | 1241 | |
|
|
1242 | 1242 | Configuration Parameters: |
|
|
1243 | 1243 | |
|
|
1244 | 1244 | dirCosx : Cosine director in X axis |
|
|
1245 | 1245 | dirCosy : Cosine director in Y axis |
|
|
1246 | 1246 | |
|
|
1247 | 1247 | elevation : |
|
|
1248 | 1248 | azimuth : |
|
|
1249 | 1249 | |
|
|
1250 | 1250 | Input: |
|
|
1251 | 1251 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
1252 | 1252 | self.dataOut.data_pre : Spectral data |
|
|
1253 | 1253 | self.dataOut.abscissaList : List of frequencies |
|
|
1254 | 1254 | self.dataOut.noise : Noise level per channel |
|
|
1255 | 1255 | |
|
|
1256 | 1256 | Affected: |
|
|
1257 | 1257 | self.dataOut.moments : Parameters per channel |
|
|
1258 | 1258 | self.dataOut.data_snr : SNR per channel |
|
|
1259 | 1259 | |
|
|
1260 | 1260 | ''' |
|
|
1261 | 1261 | |
|
|
1262 | 1262 | def run(self, dataOut,wradar=False): |
|
|
1263 | 1263 | |
|
|
1264 | 1264 | data = dataOut.data_pre[0] |
|
|
1265 | 1265 | absc = dataOut.abscissaList[:-1] |
|
|
1266 | 1266 | noise = dataOut.noise |
|
|
1267 | 1267 | nChannel = data.shape[0] |
|
|
1268 | 1268 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
1269 | 1269 | |
|
|
1270 | 1270 | for ind in range(nChannel): |
|
|
1271 | 1271 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind],wradar=wradar ) |
|
|
1272 | 1272 | |
|
|
1273 | 1273 | dataOut.moments = data_param[:,1:,:] |
|
|
1274 | 1274 | dataOut.data_snr = data_param[:,0] |
|
|
1275 | 1275 | dataOut.data_pow = data_param[:,1] |
|
|
1276 | 1276 | dataOut.data_dop = data_param[:,2] |
|
|
1277 | 1277 | dataOut.data_width = data_param[:,3] |
|
|
1278 | 1278 | return dataOut |
|
|
1279 | 1279 | |
|
|
1280 | 1280 | def __calculateMoments(self, oldspec, oldfreq, n0, |
|
|
1281 | 1281 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None,wradar=None): |
|
|
1282 | 1282 | |
|
|
1283 | 1283 | if (nicoh is None): nicoh = 1 |
|
|
1284 | 1284 | if (graph is None): graph = 0 |
|
|
1285 | 1285 | if (smooth is None): smooth = 0 |
|
|
1286 | 1286 | elif (self.smooth < 3): smooth = 0 |
|
|
1287 | 1287 | |
|
|
1288 | 1288 | if (type1 is None): type1 = 0 |
|
|
1289 | 1289 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
1290 | 1290 | if (snrth is None): snrth = -3 |
|
|
1291 | 1291 | if (dc is None): dc = 0 |
|
|
1292 | 1292 | if (aliasing is None): aliasing = 0 |
|
|
1293 | 1293 | if (oldfd is None): oldfd = 0 |
|
|
1294 | 1294 | if (wwauto is None): wwauto = 0 |
|
|
1295 | 1295 | |
|
|
1296 | 1296 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
1297 | 1297 | |
|
|
1298 | 1298 | freq = oldfreq |
|
|
1299 | 1299 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
1300 | 1300 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
1301 | 1301 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
1302 | 1302 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
1303 | 1303 | |
|
|
1304 | 1304 | # oldspec = numpy.ma.masked_invalid(oldspec) |
|
|
1305 | 1305 | for ind in range(oldspec.shape[1]): |
|
|
1306 | 1306 | |
|
|
1307 | 1307 | spec = oldspec[:,ind] |
|
|
1308 | 1308 | aux = spec*fwindow |
|
|
1309 | 1309 | max_spec = aux.max() |
|
|
1310 | 1310 | m = aux.tolist().index(max_spec) |
|
|
1311 | 1311 | |
|
|
1312 | 1312 | # Smooth |
|
|
1313 | 1313 | if (smooth == 0): |
|
|
1314 | 1314 | spec2 = spec |
|
|
1315 | 1315 | else: |
|
|
1316 | 1316 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
1317 | 1317 | |
|
|
1318 | 1318 | # Moments Estimation |
|
|
1319 | 1319 | bb = spec2[numpy.arange(m,spec2.size)] |
|
|
1320 | 1320 | bb = (bb<n0).nonzero() |
|
|
1321 | 1321 | bb = bb[0] |
|
|
1322 | 1322 | |
|
|
1323 | 1323 | ss = spec2[numpy.arange(0,m + 1)] |
|
|
1324 | 1324 | ss = (ss<n0).nonzero() |
|
|
1325 | 1325 | ss = ss[0] |
|
|
1326 | 1326 | |
|
|
1327 | 1327 | if (bb.size == 0): |
|
|
1328 | 1328 | bb0 = spec.size - 1 - m |
|
|
1329 | 1329 | else: |
|
|
1330 | 1330 | bb0 = bb[0] - 1 |
|
|
1331 | 1331 | if (bb0 < 0): |
|
|
1332 | 1332 | bb0 = 0 |
|
|
1333 | 1333 | |
|
|
1334 | 1334 | if (ss.size == 0): |
|
|
1335 | 1335 | ss1 = 1 |
|
|
1336 | 1336 | else: |
|
|
1337 | 1337 | ss1 = max(ss) + 1 |
|
|
1338 | 1338 | |
|
|
1339 | 1339 | if (ss1 > m): |
|
|
1340 | 1340 | ss1 = m |
|
|
1341 | 1341 | |
|
|
1342 | 1342 | #valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
|
1343 | 1343 | valid = numpy.arange(1,oldspec.shape[0])# valid perfil completo igual pulsepair |
|
|
1344 | 1344 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. Scipión added with correct definition |
|
|
1345 | 1345 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. Scipión added with correct definition |
|
|
1346 | 1346 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() |
|
|
1347 | 1347 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power |
|
|
1348 | 1348 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) |
|
|
1349 | 1349 | snr = (spec2.mean()-n0)/n0 |
|
|
1350 | 1350 | if (snr < 1.e-20) : |
|
|
1351 | 1351 | snr = 1.e-20 |
|
|
1352 | 1352 | |
|
|
1353 | 1353 | # vec_power[ind] = power #D. Scipión replaced with the line below |
|
|
1354 | 1354 | if wradar ==False: |
|
|
1355 | 1355 | vec_power[ind] = total_power |
|
|
1356 | 1356 | else: |
|
|
1357 | 1357 | vec_power[ind] = signal_power |
|
|
1358 | 1358 | |
|
|
1359 | 1359 | vec_fd[ind] = fd |
|
|
1360 | 1360 | vec_w[ind] = w |
|
|
1361 | 1361 | vec_snr[ind] = snr |
|
|
1362 | 1362 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
1363 | 1363 | |
|
|
1364 | 1364 | #------------------ Get SA Parameters -------------------------- |
|
|
1365 | 1365 | |
|
|
1366 | 1366 | def GetSAParameters(self): |
|
|
1367 | 1367 | #SA en frecuencia |
|
|
1368 | 1368 | pairslist = self.dataOut.groupList |
|
|
1369 | 1369 | num_pairs = len(pairslist) |
|
|
1370 | 1370 | |
|
|
1371 | 1371 | vel = self.dataOut.abscissaList |
|
|
1372 | 1372 | spectra = self.dataOut.data_pre |
|
|
1373 | 1373 | cspectra = self.dataIn.data_cspc |
|
|
1374 | 1374 | delta_v = vel[1] - vel[0] |
|
|
1375 | 1375 | |
|
|
1376 | 1376 | #Calculating the power spectrum |
|
|
1377 | 1377 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
1378 | 1378 | #Normalizing Spectra |
|
|
1379 | 1379 | norm_spectra = spectra/spc_pow |
|
|
1380 | 1380 | #Calculating the norm_spectra at peak |
|
|
1381 | 1381 | max_spectra = numpy.max(norm_spectra, 3) |
|
|
1382 | 1382 | |
|
|
1383 | 1383 | #Normalizing Cross Spectra |
|
|
1384 | 1384 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
|
1385 | 1385 | |
|
|
1386 | 1386 | for i in range(num_chan): |
|
|
1387 | 1387 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
|
1388 | 1388 | |
|
|
1389 | 1389 | max_cspectra = numpy.max(norm_cspectra,2) |
|
|
1390 | 1390 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
1391 | 1391 | |
|
|
1392 | 1392 | for i in range(num_pairs): |
|
|
1393 | 1393 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
1394 | 1394 | #------------------- Get Lags ---------------------------------- |
|
|
1395 | 1395 | |
|
|
1396 | 1396 | class SALags(Operation): |
|
|
1397 | 1397 | ''' |
|
|
1398 | 1398 | Function GetMoments() |
|
|
1399 | 1399 | |
|
|
1400 | 1400 | Input: |
|
|
1401 | 1401 | self.dataOut.data_pre |
|
|
1402 | 1402 | self.dataOut.abscissaList |
|
|
1403 | 1403 | self.dataOut.noise |
|
|
1404 | 1404 | self.dataOut.normFactor |
|
|
1405 | 1405 | self.dataOut.data_snr |
|
|
1406 | 1406 | self.dataOut.groupList |
|
|
1407 | 1407 | self.dataOut.nChannels |
|
|
1408 | 1408 | |
|
|
1409 | 1409 | Affected: |
|
|
1410 | 1410 | self.dataOut.data_param |
|
|
1411 | 1411 | |
|
|
1412 | 1412 | ''' |
|
|
1413 | 1413 | def run(self, dataOut): |
|
|
1414 | 1414 | data_acf = dataOut.data_pre[0] |
|
|
1415 | 1415 | data_ccf = dataOut.data_pre[1] |
|
|
1416 | 1416 | normFactor_acf = dataOut.normFactor[0] |
|
|
1417 | 1417 | normFactor_ccf = dataOut.normFactor[1] |
|
|
1418 | 1418 | pairs_acf = dataOut.groupList[0] |
|
|
1419 | 1419 | pairs_ccf = dataOut.groupList[1] |
|
|
1420 | 1420 | |
|
|
1421 | 1421 | nHeights = dataOut.nHeights |
|
|
1422 | 1422 | absc = dataOut.abscissaList |
|
|
1423 | 1423 | noise = dataOut.noise |
|
|
1424 | 1424 | SNR = dataOut.data_snr |
|
|
1425 | 1425 | nChannels = dataOut.nChannels |
|
|
1426 | 1426 | # pairsList = dataOut.groupList |
|
|
1427 | 1427 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1428 | 1428 | |
|
|
1429 | 1429 | for l in range(len(pairs_acf)): |
|
|
1430 | 1430 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
|
1431 | 1431 | |
|
|
1432 | 1432 | for l in range(len(pairs_ccf)): |
|
|
1433 | 1433 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
|
1434 | 1434 | |
|
|
1435 | 1435 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
|
1436 | 1436 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
1437 | 1437 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
1438 | 1438 | return |
|
|
1439 | 1439 | |
|
|
1440 | 1440 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1441 | 1441 | # |
|
|
1442 | 1442 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1443 | 1443 | # |
|
|
1444 | 1444 | # for l in range(len(pairsList)): |
|
|
1445 | 1445 | # firstChannel = pairsList[l][0] |
|
|
1446 | 1446 | # secondChannel = pairsList[l][1] |
|
|
1447 | 1447 | # |
|
|
1448 | 1448 | # #Obteniendo pares de Autocorrelacion |
|
|
1449 | 1449 | # if firstChannel == secondChannel: |
|
|
1450 | 1450 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1451 | 1451 | # |
|
|
1452 | 1452 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1453 | 1453 | # |
|
|
1454 | 1454 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1455 | 1455 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1456 | 1456 | # |
|
|
1457 | 1457 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1458 | 1458 | |
|
|
1459 | 1459 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
1460 | 1460 | |
|
|
1461 | 1461 | lag0 = data_acf.shape[1]/2 |
|
|
1462 | 1462 | #Funcion de Autocorrelacion |
|
|
1463 | 1463 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
1464 | 1464 | |
|
|
1465 | 1465 | #Obtencion Indice de TauCross |
|
|
1466 | 1466 | ind_ccf = data_ccf.argmax(axis = 1) |
|
|
1467 | 1467 | #Obtencion Indice de TauAuto |
|
|
1468 | 1468 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
1469 | 1469 | ccf_lag0 = data_ccf[:,lag0,:] |
|
|
1470 | 1470 | |
|
|
1471 | 1471 | for i in range(ccf_lag0.shape[0]): |
|
|
1472 | 1472 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
1473 | 1473 | |
|
|
1474 | 1474 | #Obtencion de TauCross y TauAuto |
|
|
1475 | 1475 | tau_ccf = lagRange[ind_ccf] |
|
|
1476 | 1476 | tau_acf = lagRange[ind_acf] |
|
|
1477 | 1477 | |
|
|
1478 | 1478 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
1479 | 1479 | |
|
|
1480 | 1480 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
1481 | 1481 | tau_acf[Nan1,Nan2] = numpy.nan |
|
|
1482 | 1482 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
1483 | 1483 | |
|
|
1484 | 1484 | return tau |
|
|
1485 | 1485 | |
|
|
1486 | 1486 | def __calculateLag1Phase(self, data, lagTRange): |
|
|
1487 | 1487 | data1 = stats.nanmean(data, axis = 0) |
|
|
1488 | 1488 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
1489 | 1489 | |
|
|
1490 | 1490 | phase = numpy.angle(data1[lag1,:]) |
|
|
1491 | 1491 | |
|
|
1492 | 1492 | return phase |
|
|
1493 | 1493 | |
|
|
1494 | 1494 | class SpectralFitting(Operation): |
|
|
1495 | 1495 | ''' |
|
|
1496 | 1496 | Function GetMoments() |
|
|
1497 | 1497 | |
|
|
1498 | 1498 | Input: |
|
|
1499 | 1499 | Output: |
|
|
1500 | 1500 | Variables modified: |
|
|
1501 | 1501 | ''' |
|
|
1502 | 1502 | |
|
|
1503 | 1503 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
1504 | 1504 | |
|
|
1505 | 1505 | |
|
|
1506 | 1506 | if path != None: |
|
|
1507 | 1507 | sys.path.append(path) |
|
|
1508 | 1508 | self.dataOut.library = importlib.import_module(file) |
|
|
1509 | 1509 | |
|
|
1510 | 1510 | #To be inserted as a parameter |
|
|
1511 | 1511 | groupArray = numpy.array(groupList) |
|
|
1512 | 1512 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1513 | 1513 | self.dataOut.groupList = groupArray |
|
|
1514 | 1514 | |
|
|
1515 | 1515 | nGroups = groupArray.shape[0] |
|
|
1516 | 1516 | nChannels = self.dataIn.nChannels |
|
|
1517 | 1517 | nHeights=self.dataIn.heightList.size |
|
|
1518 | 1518 | |
|
|
1519 | 1519 | #Parameters Array |
|
|
1520 | 1520 | self.dataOut.data_param = None |
|
|
1521 | 1521 | |
|
|
1522 | 1522 | #Set constants |
|
|
1523 | 1523 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1524 | 1524 | self.dataOut.constants = constants |
|
|
1525 | 1525 | M = self.dataIn.normFactor |
|
|
1526 | 1526 | N = self.dataIn.nFFTPoints |
|
|
1527 | 1527 | ippSeconds = self.dataIn.ippSeconds |
|
|
1528 | 1528 | K = self.dataIn.nIncohInt |
|
|
1529 | 1529 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1530 | 1530 | |
|
|
1531 | 1531 | #List of possible combinations |
|
|
1532 | 1532 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1533 | 1533 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1534 | 1534 | |
|
|
1535 | 1535 | if getSNR: |
|
|
1536 | 1536 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1537 | 1537 | listChannels.sort() |
|
|
1538 | 1538 | noise = self.dataIn.getNoise() |
|
|
1539 | 1539 | self.dataOut.data_snr = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1540 | 1540 | |
|
|
1541 | 1541 | for i in range(nGroups): |
|
|
1542 | 1542 | coord = groupArray[i,:] |
|
|
1543 | 1543 | |
|
|
1544 | 1544 | #Input data array |
|
|
1545 | 1545 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1546 | 1546 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1547 | 1547 | |
|
|
1548 | 1548 | #Cross Spectra data array for Covariance Matrixes |
|
|
1549 | 1549 | ind = 0 |
|
|
1550 | 1550 | for pairs in listComb: |
|
|
1551 | 1551 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1552 | 1552 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1553 | 1553 | ind += 1 |
|
|
1554 | 1554 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1555 | 1555 | dataCross = dataCross**2/K |
|
|
1556 | 1556 | |
|
|
1557 | 1557 | for h in range(nHeights): |
|
|
1558 | 1558 | |
|
|
1559 | 1559 | #Input |
|
|
1560 | 1560 | d = data[:,h] |
|
|
1561 | 1561 | |
|
|
1562 | 1562 | #Covariance Matrix |
|
|
1563 | 1563 | D = numpy.diag(d**2/K) |
|
|
1564 | 1564 | ind = 0 |
|
|
1565 | 1565 | for pairs in listComb: |
|
|
1566 | 1566 | #Coordinates in Covariance Matrix |
|
|
1567 | 1567 | x = pairs[0] |
|
|
1568 | 1568 | y = pairs[1] |
|
|
1569 | 1569 | #Channel Index |
|
|
1570 | 1570 | S12 = dataCross[ind,:,h] |
|
|
1571 | 1571 | D12 = numpy.diag(S12) |
|
|
1572 | 1572 | #Completing Covariance Matrix with Cross Spectras |
|
|
1573 | 1573 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1574 | 1574 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1575 | 1575 | ind += 1 |
|
|
1576 | 1576 | Dinv=numpy.linalg.inv(D) |
|
|
1577 | 1577 | L=numpy.linalg.cholesky(Dinv) |
|
|
1578 | 1578 | LT=L.T |
|
|
1579 | 1579 | |
|
|
1580 | 1580 | dp = numpy.dot(LT,d) |
|
|
1581 | 1581 | |
|
|
1582 | 1582 | #Initial values |
|
|
1583 | 1583 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1584 | 1584 | |
|
|
1585 | 1585 | if (h>0)and(error1[3]<5): |
|
|
1586 | 1586 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1587 | 1587 | else: |
|
|
1588 | 1588 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1589 | 1589 | |
|
|
1590 | 1590 | try: |
|
|
1591 | 1591 | #Least Squares |
|
|
1592 | 1592 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1593 | 1593 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1594 | 1594 | #Chi square error |
|
|
1595 | 1595 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1596 | 1596 | #Error with Jacobian |
|
|
1597 | 1597 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1598 | 1598 | except: |
|
|
1599 | 1599 | minp = p0*numpy.nan |
|
|
1600 | 1600 | error0 = numpy.nan |
|
|
1601 | 1601 | error1 = p0*numpy.nan |
|
|
1602 | 1602 | |
|
|
1603 | 1603 | #Save |
|
|
1604 | 1604 | if self.dataOut.data_param is None: |
|
|
1605 | 1605 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1606 | 1606 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1607 | 1607 | |
|
|
1608 | 1608 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1609 | 1609 | self.dataOut.data_param[i,:,h] = minp |
|
|
1610 | 1610 | return |
|
|
1611 | 1611 | |
|
|
1612 | 1612 | def __residFunction(self, p, dp, LT, constants): |
|
|
1613 | 1613 | |
|
|
1614 | 1614 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1615 | 1615 | fmp=numpy.dot(LT,fm) |
|
|
1616 | 1616 | |
|
|
1617 | 1617 | return dp-fmp |
|
|
1618 | 1618 | |
|
|
1619 | 1619 | def __getSNR(self, z, noise): |
|
|
1620 | 1620 | |
|
|
1621 | 1621 | avg = numpy.average(z, axis=1) |
|
|
1622 | 1622 | SNR = (avg.T-noise)/noise |
|
|
1623 | 1623 | SNR = SNR.T |
|
|
1624 | 1624 | return SNR |
|
|
1625 | 1625 | |
|
|
1626 | 1626 | def __chisq(p,chindex,hindex): |
|
|
1627 | 1627 | #similar to Resid but calculates CHI**2 |
|
|
1628 | 1628 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1629 | 1629 | dp=numpy.dot(LT,d) |
|
|
1630 | 1630 | fmp=numpy.dot(LT,fm) |
|
|
1631 | 1631 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1632 | 1632 | return chisq |
|
|
1633 | 1633 | |
|
|
1634 | 1634 | class WindProfiler(Operation): |
|
|
1635 | 1635 | |
|
|
1636 | 1636 | __isConfig = False |
|
|
1637 | 1637 | |
|
|
1638 | 1638 | __initime = None |
|
|
1639 | 1639 | __lastdatatime = None |
|
|
1640 | 1640 | __integrationtime = None |
|
|
1641 | 1641 | |
|
|
1642 | 1642 | __buffer = None |
|
|
1643 | 1643 | |
|
|
1644 | 1644 | __dataReady = False |
|
|
1645 | 1645 | |
|
|
1646 | 1646 | __firstdata = None |
|
|
1647 | 1647 | |
|
|
1648 | 1648 | n = None |
|
|
1649 | 1649 | |
|
|
1650 | 1650 | def __init__(self): |
|
|
1651 | 1651 | Operation.__init__(self) |
|
|
1652 | 1652 | |
|
|
1653 | 1653 | def __calculateCosDir(self, elev, azim): |
|
|
1654 | 1654 | zen = (90 - elev)*numpy.pi/180 |
|
|
1655 | 1655 | azim = azim*numpy.pi/180 |
|
|
1656 | 1656 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1657 | 1657 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1658 | 1658 | |
|
|
1659 | 1659 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1660 | 1660 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1661 | 1661 | |
|
|
1662 | 1662 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1663 | 1663 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1664 | 1664 | return cosDirX, cosDirY |
|
|
1665 | 1665 | |
|
|
1666 | 1666 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1667 | 1667 | |
|
|
1668 | 1668 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1669 | 1669 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1670 | 1670 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1671 | 1671 | |
|
|
1672 | 1672 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1673 | 1673 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1674 | 1674 | |
|
|
1675 | 1675 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1676 | 1676 | |
|
|
1677 | 1677 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1678 | 1678 | |
|
|
1679 | 1679 | # |
|
|
1680 | 1680 | if horOnly: |
|
|
1681 | 1681 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1682 | 1682 | else: |
|
|
1683 | 1683 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1684 | 1684 | A = numpy.asmatrix(A) |
|
|
1685 | 1685 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1686 | 1686 | |
|
|
1687 | 1687 | return A1 |
|
|
1688 | 1688 | |
|
|
1689 | 1689 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1690 | 1690 | listPhi = phi.tolist() |
|
|
1691 | 1691 | maxid = listPhi.index(max(listPhi)) |
|
|
1692 | 1692 | minid = listPhi.index(min(listPhi)) |
|
|
1693 | 1693 | |
|
|
1694 | 1694 | rango = list(range(len(phi))) |
|
|
1695 | 1695 | # rango = numpy.delete(rango,maxid) |
|
|
1696 | 1696 | |
|
|
1697 | 1697 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1698 | 1698 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1699 | 1699 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1700 | 1700 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1701 | 1701 | |
|
|
1702 | 1702 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1703 | 1703 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1704 | 1704 | |
|
|
1705 | 1705 | for i in rango: |
|
|
1706 | 1706 | x = heiRang*math.cos(phi[i]) |
|
|
1707 | 1707 | y1 = velRadial[i,:] |
|
|
1708 | 1708 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1709 | 1709 | |
|
|
1710 | 1710 | x1 = heiRang1 |
|
|
1711 | 1711 | y11 = f1(x1) |
|
|
1712 | 1712 | |
|
|
1713 | 1713 | y2 = SNR[i,:] |
|
|
1714 | 1714 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1715 | 1715 | y21 = f2(x1) |
|
|
1716 | 1716 | |
|
|
1717 | 1717 | velRadial1[i,:] = y11 |
|
|
1718 | 1718 | SNR1[i,:] = y21 |
|
|
1719 | 1719 | |
|
|
1720 | 1720 | return heiRang1, velRadial1, SNR1 |
|
|
1721 | 1721 | |
|
|
1722 | 1722 | def __calculateVelUVW(self, A, velRadial): |
|
|
1723 | 1723 | |
|
|
1724 | 1724 | #Operacion Matricial |
|
|
1725 | 1725 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1726 | 1726 | # for ind in range(velRadial.shape[1]): |
|
|
1727 | 1727 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1728 | 1728 | # velUVW = velUVW.transpose() |
|
|
1729 | 1729 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1730 | 1730 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1731 | 1731 | |
|
|
1732 | 1732 | |
|
|
1733 | 1733 | return velUVW |
|
|
1734 | 1734 | |
|
|
1735 | 1735 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1736 | 1736 | |
|
|
1737 | 1737 | def techniqueDBS(self, kwargs): |
|
|
1738 | 1738 | """ |
|
|
1739 | 1739 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1740 | 1740 | |
|
|
1741 | 1741 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1742 | 1742 | Direction correction (if necessary), Ranges and SNR |
|
|
1743 | 1743 | |
|
|
1744 | 1744 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1745 | 1745 | |
|
|
1746 | 1746 | Parameters affected: Winds, height range, SNR |
|
|
1747 | 1747 | """ |
|
|
1748 | 1748 | velRadial0 = kwargs['velRadial'] |
|
|
1749 | 1749 | heiRang = kwargs['heightList'] |
|
|
1750 | 1750 | SNR0 = kwargs['SNR'] |
|
|
1751 | 1751 | |
|
|
1752 | 1752 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
|
1753 | 1753 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1754 | 1754 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1755 | 1755 | else: |
|
|
1756 | 1756 | elev = numpy.array(kwargs['elevation']) |
|
|
1757 | 1757 | azim = numpy.array(kwargs['azimuth']) |
|
|
1758 | 1758 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1759 | 1759 | azimuth = kwargs['correctAzimuth'] |
|
|
1760 | 1760 | if 'horizontalOnly' in kwargs: |
|
|
1761 | 1761 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1762 | 1762 | else: horizontalOnly = False |
|
|
1763 | 1763 | if 'correctFactor' in kwargs: |
|
|
1764 | 1764 | correctFactor = kwargs['correctFactor'] |
|
|
1765 | 1765 | else: correctFactor = 1 |
|
|
1766 | 1766 | if 'channelList' in kwargs: |
|
|
1767 | 1767 | channelList = kwargs['channelList'] |
|
|
1768 | 1768 | if len(channelList) == 2: |
|
|
1769 | 1769 | horizontalOnly = True |
|
|
1770 | 1770 | arrayChannel = numpy.array(channelList) |
|
|
1771 | 1771 | param = param[arrayChannel,:,:] |
|
|
1772 | 1772 | theta_x = theta_x[arrayChannel] |
|
|
1773 | 1773 | theta_y = theta_y[arrayChannel] |
|
|
1774 | 1774 | |
|
|
1775 | 1775 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
1776 | 1776 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
1777 | 1777 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1778 | 1778 | |
|
|
1779 | 1779 | #Calculo de Componentes de la velocidad con DBS |
|
|
1780 | 1780 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1781 | 1781 | |
|
|
1782 | 1782 | return winds, heiRang1, SNR1 |
|
|
1783 | 1783 | |
|
|
1784 | 1784 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
1785 | 1785 | |
|
|
1786 | 1786 | nPairs = len(pairs_ccf) |
|
|
1787 | 1787 | posx = numpy.asarray(posx) |
|
|
1788 | 1788 | posy = numpy.asarray(posy) |
|
|
1789 | 1789 | |
|
|
1790 | 1790 | #Rotacion Inversa para alinear con el azimuth |
|
|
1791 | 1791 | if azimuth!= None: |
|
|
1792 | 1792 | azimuth = azimuth*math.pi/180 |
|
|
1793 | 1793 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1794 | 1794 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1795 | 1795 | else: |
|
|
1796 | 1796 | posx1 = posx |
|
|
1797 | 1797 | posy1 = posy |
|
|
1798 | 1798 | |
|
|
1799 | 1799 | #Calculo de Distancias |
|
|
1800 | 1800 | distx = numpy.zeros(nPairs) |
|
|
1801 | 1801 | disty = numpy.zeros(nPairs) |
|
|
1802 | 1802 | dist = numpy.zeros(nPairs) |
|
|
1803 | 1803 | ang = numpy.zeros(nPairs) |
|
|
1804 | 1804 | |
|
|
1805 | 1805 | for i in range(nPairs): |
|
|
1806 | 1806 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
1807 | 1807 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
1808 | 1808 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1809 | 1809 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1810 | 1810 | |
|
|
1811 | 1811 | return distx, disty, dist, ang |
|
|
1812 | 1812 | #Calculo de Matrices |
|
|
1813 | 1813 | # nPairs = len(pairs) |
|
|
1814 | 1814 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1815 | 1815 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1816 | 1816 | # |
|
|
1817 | 1817 | # for j in range(nPairs): |
|
|
1818 | 1818 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1819 | 1819 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1820 | 1820 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1821 | 1821 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1822 | 1822 | # |
|
|
1823 | 1823 | # return distx,disty, dist1,ang1 |
|
|
1824 | 1824 | |
|
|
1825 | 1825 | |
|
|
1826 | 1826 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1827 | 1827 | |
|
|
1828 | 1828 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1829 | 1829 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1830 | 1830 | |
|
|
1831 | 1831 | return velW |
|
|
1832 | 1832 | |
|
|
1833 | 1833 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1834 | 1834 | nPairs = tau1.shape[0] |
|
|
1835 | 1835 | nHeights = tau1.shape[1] |
|
|
1836 | 1836 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
1837 | 1837 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
1838 | 1838 | |
|
|
1839 | 1839 | angCos = numpy.cos(ang) |
|
|
1840 | 1840 | angSin = numpy.sin(ang) |
|
|
1841 | 1841 | |
|
|
1842 | 1842 | vel0 = dist1*tau1/(2*tau2**2) |
|
|
1843 | 1843 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1844 | 1844 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1845 | 1845 | |
|
|
1846 | 1846 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1847 | 1847 | vel[ind] = numpy.nan |
|
|
1848 | 1848 | |
|
|
1849 | 1849 | return vel |
|
|
1850 | 1850 | |
|
|
1851 | 1851 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1852 | 1852 | # |
|
|
1853 | 1853 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1854 | 1854 | # |
|
|
1855 | 1855 | # for l in range(len(pairsList)): |
|
|
1856 | 1856 | # firstChannel = pairsList[l][0] |
|
|
1857 | 1857 | # secondChannel = pairsList[l][1] |
|
|
1858 | 1858 | # |
|
|
1859 | 1859 | # #Obteniendo pares de Autocorrelacion |
|
|
1860 | 1860 | # if firstChannel == secondChannel: |
|
|
1861 | 1861 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1862 | 1862 | # |
|
|
1863 | 1863 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1864 | 1864 | # |
|
|
1865 | 1865 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1866 | 1866 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1867 | 1867 | # |
|
|
1868 | 1868 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1869 | 1869 | |
|
|
1870 | 1870 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1871 | 1871 | def techniqueSA(self, kwargs): |
|
|
1872 | 1872 | |
|
|
1873 | 1873 | """ |
|
|
1874 | 1874 | Function that implements Spaced Antenna (SA) technique. |
|
|
1875 | 1875 | |
|
|
1876 | 1876 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1877 | 1877 | Direction correction (if necessary), Ranges and SNR |
|
|
1878 | 1878 | |
|
|
1879 | 1879 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1880 | 1880 | |
|
|
1881 | 1881 | Parameters affected: Winds |
|
|
1882 | 1882 | """ |
|
|
1883 | 1883 | position_x = kwargs['positionX'] |
|
|
1884 | 1884 | position_y = kwargs['positionY'] |
|
|
1885 | 1885 | azimuth = kwargs['azimuth'] |
|
|
1886 | 1886 | |
|
|
1887 | 1887 | if 'correctFactor' in kwargs: |
|
|
1888 | 1888 | correctFactor = kwargs['correctFactor'] |
|
|
1889 | 1889 | else: |
|
|
1890 | 1890 | correctFactor = 1 |
|
|
1891 | 1891 | |
|
|
1892 | 1892 | groupList = kwargs['groupList'] |
|
|
1893 | 1893 | pairs_ccf = groupList[1] |
|
|
1894 | 1894 | tau = kwargs['tau'] |
|
|
1895 | 1895 | _lambda = kwargs['_lambda'] |
|
|
1896 | 1896 | |
|
|
1897 | 1897 | #Cross Correlation pairs obtained |
|
|
1898 | 1898 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
1899 | 1899 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1900 | 1900 | # pairsSelArray = numpy.array(pairsSelected) |
|
|
1901 | 1901 | # pairs = [] |
|
|
1902 | 1902 | # |
|
|
1903 | 1903 | # #Wind estimation pairs obtained |
|
|
1904 | 1904 | # for i in range(pairsSelArray.shape[0]/2): |
|
|
1905 | 1905 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1906 | 1906 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1907 | 1907 | # pairs.append((ind1,ind2)) |
|
|
1908 | 1908 | |
|
|
1909 | 1909 | indtau = tau.shape[0]/2 |
|
|
1910 | 1910 | tau1 = tau[:indtau,:] |
|
|
1911 | 1911 | tau2 = tau[indtau:-1,:] |
|
|
1912 | 1912 | # tau1 = tau1[pairs,:] |
|
|
1913 | 1913 | # tau2 = tau2[pairs,:] |
|
|
1914 | 1914 | phase1 = tau[-1,:] |
|
|
1915 | 1915 | |
|
|
1916 | 1916 | #--------------------------------------------------------------------- |
|
|
1917 | 1917 | #Metodo Directo |
|
|
1918 | 1918 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
1919 | 1919 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1920 | 1920 | winds = stats.nanmean(winds, axis=0) |
|
|
1921 | 1921 | #--------------------------------------------------------------------- |
|
|
1922 | 1922 | #Metodo General |
|
|
1923 | 1923 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1924 | 1924 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
1925 | 1925 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1926 | 1926 | # #Calculo de Velocidades |
|
|
1927 | 1927 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1928 | 1928 | |
|
|
1929 | 1929 | #--------------------------------------------------------------------- |
|
|
1930 | 1930 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1931 | 1931 | winds = correctFactor*winds |
|
|
1932 | 1932 | return winds |
|
|
1933 | 1933 | |
|
|
1934 | 1934 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
1935 | 1935 | |
|
|
1936 | 1936 | dataTime = currentTime + paramInterval |
|
|
1937 | 1937 | deltaTime = dataTime - self.__initime |
|
|
1938 | 1938 | |
|
|
1939 | 1939 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
1940 | 1940 | self.__dataReady = True |
|
|
1941 | 1941 | return |
|
|
1942 | 1942 | |
|
|
1943 | 1943 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
1944 | 1944 | ''' |
|
|
1945 | 1945 | Function that implements winds estimation technique with detected meteors. |
|
|
1946 | 1946 | |
|
|
1947 | 1947 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
1948 | 1948 | |
|
|
1949 | 1949 | Output: Winds estimation (Zonal and Meridional) |
|
|
1950 | 1950 | |
|
|
1951 | 1951 | Parameters affected: Winds |
|
|
1952 | 1952 | ''' |
|
|
1953 | 1953 | #Settings |
|
|
1954 | 1954 | nInt = (heightMax - heightMin)/2 |
|
|
1955 | 1955 | nInt = int(nInt) |
|
|
1956 | 1956 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
1957 | 1957 | |
|
|
1958 | 1958 | #Filter errors |
|
|
1959 | 1959 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
1960 | 1960 | finalMeteor = arrayMeteor[error,:] |
|
|
1961 | 1961 | |
|
|
1962 | 1962 | #Meteor Histogram |
|
|
1963 | 1963 | finalHeights = finalMeteor[:,2] |
|
|
1964 | 1964 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
1965 | 1965 | nMeteorsPerI = hist[0] |
|
|
1966 | 1966 | heightPerI = hist[1] |
|
|
1967 | 1967 | |
|
|
1968 | 1968 | #Sort of meteors |
|
|
1969 | 1969 | indSort = finalHeights.argsort() |
|
|
1970 | 1970 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
1971 | 1971 | |
|
|
1972 | 1972 | # Calculating winds |
|
|
1973 | 1973 | ind1 = 0 |
|
|
1974 | 1974 | ind2 = 0 |
|
|
1975 | 1975 | |
|
|
1976 | 1976 | for i in range(nInt): |
|
|
1977 | 1977 | nMet = nMeteorsPerI[i] |
|
|
1978 | 1978 | ind1 = ind2 |
|
|
1979 | 1979 | ind2 = ind1 + nMet |
|
|
1980 | 1980 | |
|
|
1981 | 1981 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
1982 | 1982 | |
|
|
1983 | 1983 | if meteorAux.shape[0] >= meteorThresh: |
|
|
1984 | 1984 | vel = meteorAux[:, 6] |
|
|
1985 | 1985 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
1986 | 1986 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
1987 | 1987 | |
|
|
1988 | 1988 | n = numpy.cos(zen) |
|
|
1989 | 1989 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
1990 | 1990 | # l = m*numpy.tan(azim) |
|
|
1991 | 1991 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
1992 | 1992 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
1993 | 1993 | |
|
|
1994 | 1994 | A = numpy.vstack((l, m)).transpose() |
|
|
1995 | 1995 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
1996 | 1996 | windsAux = numpy.dot(A1, vel) |
|
|
1997 | 1997 | |
|
|
1998 | 1998 | winds[0,i] = windsAux[0] |
|
|
1999 | 1999 | winds[1,i] = windsAux[1] |
|
|
2000 | 2000 | |
|
|
2001 | 2001 | return winds, heightPerI[:-1] |
|
|
2002 | 2002 | |
|
|
2003 | 2003 | def techniqueNSM_SA(self, **kwargs): |
|
|
2004 | 2004 | metArray = kwargs['metArray'] |
|
|
2005 | 2005 | heightList = kwargs['heightList'] |
|
|
2006 | 2006 | timeList = kwargs['timeList'] |
|
|
2007 | 2007 | |
|
|
2008 | 2008 | rx_location = kwargs['rx_location'] |
|
|
2009 | 2009 | groupList = kwargs['groupList'] |
|
|
2010 | 2010 | azimuth = kwargs['azimuth'] |
|
|
2011 | 2011 | dfactor = kwargs['dfactor'] |
|
|
2012 | 2012 | k = kwargs['k'] |
|
|
2013 | 2013 | |
|
|
2014 | 2014 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
2015 | 2015 | d = dist*dfactor |
|
|
2016 | 2016 | #Phase calculation |
|
|
2017 | 2017 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
2018 | 2018 | |
|
|
2019 | 2019 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
2020 | 2020 | |
|
|
2021 | 2021 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2022 | 2022 | azimuth1 = azimuth1*numpy.pi/180 |
|
|
2023 | 2023 | |
|
|
2024 | 2024 | for i in range(heightList.size): |
|
|
2025 | 2025 | h = heightList[i] |
|
|
2026 | 2026 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
2027 | 2027 | metHeight = metArray1[indH,:] |
|
|
2028 | 2028 | if metHeight.shape[0] >= 2: |
|
|
2029 | 2029 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
2030 | 2030 | iazim = metHeight[:,1].astype(int) |
|
|
2031 | 2031 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
2032 | 2032 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
2033 | 2033 | A = numpy.asmatrix(A) |
|
|
2034 | 2034 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
2035 | 2035 | velHor = numpy.dot(A1,velAux) |
|
|
2036 | 2036 | |
|
|
2037 | 2037 | velEst[i,:] = numpy.squeeze(velHor) |
|
|
2038 | 2038 | return velEst |
|
|
2039 | 2039 | |
|
|
2040 | 2040 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
2041 | 2041 | meteorList = [] |
|
|
2042 | 2042 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
2043 | 2043 | #Putting back together the meteor matrix |
|
|
2044 | 2044 | utctime = metArray[:,0] |
|
|
2045 | 2045 | uniqueTime = numpy.unique(utctime) |
|
|
2046 | 2046 | |
|
|
2047 | 2047 | phaseDerThresh = 0.5 |
|
|
2048 | 2048 | ippSeconds = timeList[1] - timeList[0] |
|
|
2049 | 2049 | sec = numpy.where(timeList>1)[0][0] |
|
|
2050 | 2050 | nPairs = metArray.shape[1] - 6 |
|
|
2051 | 2051 | nHeights = len(heightList) |
|
|
2052 | 2052 | |
|
|
2053 | 2053 | for t in uniqueTime: |
|
|
2054 | 2054 | metArray1 = metArray[utctime==t,:] |
|
|
2055 | 2055 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
2056 | 2056 | tmet = metArray1[:,1].astype(int) |
|
|
2057 | 2057 | hmet = metArray1[:,2].astype(int) |
|
|
2058 | 2058 | |
|
|
2059 | 2059 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
2060 | 2060 | metPhase[:,:] = numpy.nan |
|
|
2061 | 2061 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
2062 | 2062 | |
|
|
2063 | 2063 | #Delete short trails |
|
|
2064 | 2064 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
2065 | 2065 | heightVect = numpy.sum(metBool, axis = 1) |
|
|
2066 | 2066 | metBool[heightVect<sec,:] = False |
|
|
2067 | 2067 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
2068 | 2068 | |
|
|
2069 | 2069 | #Derivative |
|
|
2070 | 2070 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
2071 | 2071 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
2072 | 2072 | metPhase[phDerAux] = numpy.nan |
|
|
2073 | 2073 | |
|
|
2074 | 2074 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
|
2075 | 2075 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
2076 | 2076 | |
|
|
2077 | 2077 | for p in numpy.arange(nPairs): |
|
|
2078 | 2078 | phase = metPhase[p,:,:] |
|
|
2079 | 2079 | phDer = metDer[p,:,:] |
|
|
2080 | 2080 | |
|
|
2081 | 2081 | for h in indMet: |
|
|
2082 | 2082 | height = heightList[h] |
|
|
2083 | 2083 | phase1 = phase[h,:] #82 |
|
|
2084 | 2084 | phDer1 = phDer[h,:] |
|
|
2085 | 2085 | |
|
|
2086 | 2086 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
2087 | 2087 | |
|
|
2088 | 2088 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
2089 | 2089 | initMet = indValid[0] |
|
|
2090 | 2090 | endMet = 0 |
|
|
2091 | 2091 | |
|
|
2092 | 2092 | for i in range(len(indValid)-1): |
|
|
2093 | 2093 | |
|
|
2094 | 2094 | #Time difference |
|
|
2095 | 2095 | inow = indValid[i] |
|
|
2096 | 2096 | inext = indValid[i+1] |
|
|
2097 | 2097 | idiff = inext - inow |
|
|
2098 | 2098 | #Phase difference |
|
|
2099 | 2099 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
2100 | 2100 | |
|
|
2101 | 2101 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
2102 | 2102 | sizeTrail = inow - initMet + 1 |
|
|
2103 | 2103 | if sizeTrail>3*sec: #Too short meteors |
|
|
2104 | 2104 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
2105 | 2105 | y = phase1[initMet:inow+1] |
|
|
2106 | 2106 | ynnan = ~numpy.isnan(y) |
|
|
2107 | 2107 | x = x[ynnan] |
|
|
2108 | 2108 | y = y[ynnan] |
|
|
2109 | 2109 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
2110 | 2110 | ylin = x*slope + intercept |
|
|
2111 | 2111 | rsq = r_value**2 |
|
|
2112 | 2112 | if rsq > 0.5: |
|
|
2113 | 2113 | vel = slope#*height*1000/(k*d) |
|
|
2114 | 2114 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
2115 | 2115 | meteorList.append(estAux) |
|
|
2116 | 2116 | initMet = inext |
|
|
2117 | 2117 | metArray2 = numpy.array(meteorList) |
|
|
2118 | 2118 | |
|
|
2119 | 2119 | return metArray2 |
|
|
2120 | 2120 | |
|
|
2121 | 2121 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
2122 | 2122 | |
|
|
2123 | 2123 | azimuth1 = numpy.zeros(len(pairslist)) |
|
|
2124 | 2124 | dist = numpy.zeros(len(pairslist)) |
|
|
2125 | 2125 | |
|
|
2126 | 2126 | for i in range(len(rx_location)): |
|
|
2127 | 2127 | ch0 = pairslist[i][0] |
|
|
2128 | 2128 | ch1 = pairslist[i][1] |
|
|
2129 | 2129 | |
|
|
2130 | 2130 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
2131 | 2131 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
2132 | 2132 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
2133 | 2133 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
2134 | 2134 | |
|
|
2135 | 2135 | azimuth1 -= azimuth0 |
|
|
2136 | 2136 | return azimuth1, dist |
|
|
2137 | 2137 | |
|
|
2138 | 2138 | def techniqueNSM_DBS(self, **kwargs): |
|
|
2139 | 2139 | metArray = kwargs['metArray'] |
|
|
2140 | 2140 | heightList = kwargs['heightList'] |
|
|
2141 | 2141 | timeList = kwargs['timeList'] |
|
|
2142 | 2142 | azimuth = kwargs['azimuth'] |
|
|
2143 | 2143 | theta_x = numpy.array(kwargs['theta_x']) |
|
|
2144 | 2144 | theta_y = numpy.array(kwargs['theta_y']) |
|
|
2145 | 2145 | |
|
|
2146 | 2146 | utctime = metArray[:,0] |
|
|
2147 | 2147 | cmet = metArray[:,1].astype(int) |
|
|
2148 | 2148 | hmet = metArray[:,3].astype(int) |
|
|
2149 | 2149 | SNRmet = metArray[:,4] |
|
|
2150 | 2150 | vmet = metArray[:,5] |
|
|
2151 | 2151 | spcmet = metArray[:,6] |
|
|
2152 | 2152 | |
|
|
2153 | 2153 | nChan = numpy.max(cmet) + 1 |
|
|
2154 | 2154 | nHeights = len(heightList) |
|
|
2155 | 2155 | |
|
|
2156 | 2156 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
2157 | 2157 | hmet = heightList[hmet] |
|
|
2158 | 2158 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
|
2159 | 2159 | |
|
|
2160 | 2160 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2161 | 2161 | |
|
|
2162 | 2162 | for i in range(nHeights - 1): |
|
|
2163 | 2163 | hmin = heightList[i] |
|
|
2164 | 2164 | hmax = heightList[i + 1] |
|
|
2165 | 2165 | |
|
|
2166 | 2166 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
|
2167 | 2167 | indthisH = numpy.where(thisH) |
|
|
2168 | 2168 | |
|
|
2169 | 2169 | if numpy.size(indthisH) > 3: |
|
|
2170 | 2170 | |
|
|
2171 | 2171 | vel_aux = vmet[thisH] |
|
|
2172 | 2172 | chan_aux = cmet[thisH] |
|
|
2173 | 2173 | cosu_aux = dir_cosu[chan_aux] |
|
|
2174 | 2174 | cosv_aux = dir_cosv[chan_aux] |
|
|
2175 | 2175 | cosw_aux = dir_cosw[chan_aux] |
|
|
2176 | 2176 | |
|
|
2177 | 2177 | nch = numpy.size(numpy.unique(chan_aux)) |
|
|
2178 | 2178 | if nch > 1: |
|
|
2179 | 2179 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
|
2180 | 2180 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
|
2181 | 2181 | |
|
|
2182 | 2182 | return velEst |
|
|
2183 | 2183 | |
|
|
2184 | 2184 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
|
2185 | 2185 | |
|
|
2186 | 2186 | param = dataOut.data_param |
|
|
2187 | 2187 | if dataOut.abscissaList != None: |
|
|
2188 | 2188 | absc = dataOut.abscissaList[:-1] |
|
|
2189 | 2189 | # noise = dataOut.noise |
|
|
2190 | 2190 | heightList = dataOut.heightList |
|
|
2191 | 2191 | SNR = dataOut.data_snr |
|
|
2192 | 2192 | |
|
|
2193 | 2193 | if technique == 'DBS': |
|
|
2194 | 2194 | |
|
|
2195 | 2195 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
2196 | 2196 | kwargs['heightList'] = heightList |
|
|
2197 | 2197 | kwargs['SNR'] = SNR |
|
|
2198 | 2198 | |
|
|
2199 | 2199 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function |
|
|
2200 | 2200 | dataOut.utctimeInit = dataOut.utctime |
|
|
2201 | 2201 | dataOut.outputInterval = dataOut.paramInterval |
|
|
2202 | 2202 | |
|
|
2203 | 2203 | elif technique == 'SA': |
|
|
2204 | 2204 | |
|
|
2205 | 2205 | #Parameters |
|
|
2206 | 2206 | # position_x = kwargs['positionX'] |
|
|
2207 | 2207 | # position_y = kwargs['positionY'] |
|
|
2208 | 2208 | # azimuth = kwargs['azimuth'] |
|
|
2209 | 2209 | # |
|
|
2210 | 2210 | # if kwargs.has_key('crosspairsList'): |
|
|
2211 | 2211 | # pairs = kwargs['crosspairsList'] |
|
|
2212 | 2212 | # else: |
|
|
2213 | 2213 | # pairs = None |
|
|
2214 | 2214 | # |
|
|
2215 | 2215 | # if kwargs.has_key('correctFactor'): |
|
|
2216 | 2216 | # correctFactor = kwargs['correctFactor'] |
|
|
2217 | 2217 | # else: |
|
|
2218 | 2218 | # correctFactor = 1 |
|
|
2219 | 2219 | |
|
|
2220 | 2220 | # tau = dataOut.data_param |
|
|
2221 | 2221 | # _lambda = dataOut.C/dataOut.frequency |
|
|
2222 | 2222 | # pairsList = dataOut.groupList |
|
|
2223 | 2223 | # nChannels = dataOut.nChannels |
|
|
2224 | 2224 | |
|
|
2225 | 2225 | kwargs['groupList'] = dataOut.groupList |
|
|
2226 | 2226 | kwargs['tau'] = dataOut.data_param |
|
|
2227 | 2227 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
2228 | 2228 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
2229 | 2229 | dataOut.data_output = self.techniqueSA(kwargs) |
|
|
2230 | 2230 | dataOut.utctimeInit = dataOut.utctime |
|
|
2231 | 2231 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2232 | 2232 | |
|
|
2233 | 2233 | elif technique == 'Meteors': |
|
|
2234 | 2234 | dataOut.flagNoData = True |
|
|
2235 | 2235 | self.__dataReady = False |
|
|
2236 | 2236 | |
|
|
2237 | 2237 | if 'nHours' in kwargs: |
|
|
2238 | 2238 | nHours = kwargs['nHours'] |
|
|
2239 | 2239 | else: |
|
|
2240 | 2240 | nHours = 1 |
|
|
2241 | 2241 | |
|
|
2242 | 2242 | if 'meteorsPerBin' in kwargs: |
|
|
2243 | 2243 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
2244 | 2244 | else: |
|
|
2245 | 2245 | meteorThresh = 6 |
|
|
2246 | 2246 | |
|
|
2247 | 2247 | if 'hmin' in kwargs: |
|
|
2248 | 2248 | hmin = kwargs['hmin'] |
|
|
2249 | 2249 | else: hmin = 70 |
|
|
2250 | 2250 | if 'hmax' in kwargs: |
|
|
2251 | 2251 | hmax = kwargs['hmax'] |
|
|
2252 | 2252 | else: hmax = 110 |
|
|
2253 | 2253 | |
|
|
2254 | 2254 | dataOut.outputInterval = nHours*3600 |
|
|
2255 | 2255 | |
|
|
2256 | 2256 | if self.__isConfig == False: |
|
|
2257 | 2257 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2258 | 2258 | #Get Initial LTC time |
|
|
2259 | 2259 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2260 | 2260 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2261 | 2261 | |
|
|
2262 | 2262 | self.__isConfig = True |
|
|
2263 | 2263 | |
|
|
2264 | 2264 | if self.__buffer is None: |
|
|
2265 | 2265 | self.__buffer = dataOut.data_param |
|
|
2266 | 2266 | self.__firstdata = copy.copy(dataOut) |
|
|
2267 | 2267 | |
|
|
2268 | 2268 | else: |
|
|
2269 | 2269 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2270 | 2270 | |
|
|
2271 | 2271 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2272 | 2272 | |
|
|
2273 | 2273 | if self.__dataReady: |
|
|
2274 | 2274 | dataOut.utctimeInit = self.__initime |
|
|
2275 | 2275 | |
|
|
2276 | 2276 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2277 | 2277 | |
|
|
2278 | 2278 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
2279 | 2279 | dataOut.flagNoData = False |
|
|
2280 | 2280 | self.__buffer = None |
|
|
2281 | 2281 | |
|
|
2282 | 2282 | elif technique == 'Meteors1': |
|
|
2283 | 2283 | dataOut.flagNoData = True |
|
|
2284 | 2284 | self.__dataReady = False |
|
|
2285 | 2285 | |
|
|
2286 | 2286 | if 'nMins' in kwargs: |
|
|
2287 | 2287 | nMins = kwargs['nMins'] |
|
|
2288 | 2288 | else: nMins = 20 |
|
|
2289 | 2289 | if 'rx_location' in kwargs: |
|
|
2290 | 2290 | rx_location = kwargs['rx_location'] |
|
|
2291 | 2291 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
2292 | 2292 | if 'azimuth' in kwargs: |
|
|
2293 | 2293 | azimuth = kwargs['azimuth'] |
|
|
2294 | 2294 | else: azimuth = 51.06 |
|
|
2295 | 2295 | if 'dfactor' in kwargs: |
|
|
2296 | 2296 | dfactor = kwargs['dfactor'] |
|
|
2297 | 2297 | if 'mode' in kwargs: |
|
|
2298 | 2298 | mode = kwargs['mode'] |
|
|
2299 | 2299 | if 'theta_x' in kwargs: |
|
|
2300 | 2300 | theta_x = kwargs['theta_x'] |
|
|
2301 | 2301 | if 'theta_y' in kwargs: |
|
|
2302 | 2302 | theta_y = kwargs['theta_y'] |
|
|
2303 | 2303 | else: mode = 'SA' |
|
|
2304 | 2304 | |
|
|
2305 | 2305 | #Borrar luego esto |
|
|
2306 | 2306 | if dataOut.groupList is None: |
|
|
2307 | 2307 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
2308 | 2308 | groupList = dataOut.groupList |
|
|
2309 | 2309 | C = 3e8 |
|
|
2310 | 2310 | freq = 50e6 |
|
|
2311 | 2311 | lamb = C/freq |
|
|
2312 | 2312 | k = 2*numpy.pi/lamb |
|
|
2313 | 2313 | |
|
|
2314 | 2314 | timeList = dataOut.abscissaList |
|
|
2315 | 2315 | heightList = dataOut.heightList |
|
|
2316 | 2316 | |
|
|
2317 | 2317 | if self.__isConfig == False: |
|
|
2318 | 2318 | dataOut.outputInterval = nMins*60 |
|
|
2319 | 2319 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2320 | 2320 | #Get Initial LTC time |
|
|
2321 | 2321 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2322 | 2322 | minuteAux = initime.minute |
|
|
2323 | 2323 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
2324 | 2324 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2325 | 2325 | |
|
|
2326 | 2326 | self.__isConfig = True |
|
|
2327 | 2327 | |
|
|
2328 | 2328 | if self.__buffer is None: |
|
|
2329 | 2329 | self.__buffer = dataOut.data_param |
|
|
2330 | 2330 | self.__firstdata = copy.copy(dataOut) |
|
|
2331 | 2331 | |
|
|
2332 | 2332 | else: |
|
|
2333 | 2333 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2334 | 2334 | |
|
|
2335 | 2335 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2336 | 2336 | |
|
|
2337 | 2337 | if self.__dataReady: |
|
|
2338 | 2338 | dataOut.utctimeInit = self.__initime |
|
|
2339 | 2339 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2340 | 2340 | |
|
|
2341 | 2341 | metArray = self.__buffer |
|
|
2342 | 2342 | if mode == 'SA': |
|
|
2343 | 2343 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
2344 | 2344 | elif mode == 'DBS': |
|
|
2345 | 2345 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
|
2346 | 2346 | dataOut.data_output = dataOut.data_output.T |
|
|
2347 | 2347 | dataOut.flagNoData = False |
|
|
2348 | 2348 | self.__buffer = None |
|
|
2349 | 2349 | |
|
|
2350 | 2350 | return |
|
|
2351 | 2351 | |
|
|
2352 | 2352 | class EWDriftsEstimation(Operation): |
|
|
2353 | 2353 | |
|
|
2354 | 2354 | def __init__(self): |
|
|
2355 | 2355 | Operation.__init__(self) |
|
|
2356 | 2356 | |
|
|
2357 | 2357 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
2358 | 2358 | listPhi = phi.tolist() |
|
|
2359 | 2359 | maxid = listPhi.index(max(listPhi)) |
|
|
2360 | 2360 | minid = listPhi.index(min(listPhi)) |
|
|
2361 | 2361 | |
|
|
2362 | 2362 | rango = list(range(len(phi))) |
|
|
2363 | 2363 | # rango = numpy.delete(rango,maxid) |
|
|
2364 | 2364 | |
|
|
2365 | 2365 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
2366 | 2366 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
2367 | 2367 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
2368 | 2368 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
2369 | 2369 | |
|
|
2370 | 2370 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2371 | 2371 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2372 | 2372 | |
|
|
2373 | 2373 | for i in rango: |
|
|
2374 | 2374 | x = heiRang*math.cos(phi[i]) |
|
|
2375 | 2375 | y1 = velRadial[i,:] |
|
|
2376 | 2376 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
2377 | 2377 | |
|
|
2378 | 2378 | x1 = heiRang1 |
|
|
2379 | 2379 | y11 = f1(x1) |
|
|
2380 | 2380 | |
|
|
2381 | 2381 | y2 = SNR[i,:] |
|
|
2382 | 2382 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
2383 | 2383 | y21 = f2(x1) |
|
|
2384 | 2384 | |
|
|
2385 | 2385 | velRadial1[i,:] = y11 |
|
|
2386 | 2386 | SNR1[i,:] = y21 |
|
|
2387 | 2387 | |
|
|
2388 | 2388 | return heiRang1, velRadial1, SNR1 |
|
|
2389 | 2389 | |
|
|
2390 | 2390 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
2391 | 2391 | heiRang = dataOut.heightList |
|
|
2392 | 2392 | velRadial = dataOut.data_param[:,3,:] |
|
|
2393 | 2393 | SNR = dataOut.data_snr |
|
|
2394 | 2394 | |
|
|
2395 | 2395 | zenith = numpy.array(zenith) |
|
|
2396 | 2396 | zenith -= zenithCorrection |
|
|
2397 | 2397 | zenith *= numpy.pi/180 |
|
|
2398 | 2398 | |
|
|
2399 | 2399 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
2400 | 2400 | |
|
|
2401 | 2401 | alp = zenith[0] |
|
|
2402 | 2402 | bet = zenith[1] |
|
|
2403 | 2403 | |
|
|
2404 | 2404 | w_w = velRadial1[0,:] |
|
|
2405 | 2405 | w_e = velRadial1[1,:] |
|
|
2406 | 2406 | |
|
|
2407 | 2407 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
2408 | 2408 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
2409 | 2409 | |
|
|
2410 | 2410 | winds = numpy.vstack((u,w)) |
|
|
2411 | 2411 | |
|
|
2412 | 2412 | dataOut.heightList = heiRang1 |
|
|
2413 | 2413 | dataOut.data_output = winds |
|
|
2414 | 2414 | dataOut.data_snr = SNR1 |
|
|
2415 | 2415 | |
|
|
2416 | 2416 | dataOut.utctimeInit = dataOut.utctime |
|
|
2417 | 2417 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2418 | 2418 | return |
|
|
2419 | 2419 | |
|
|
2420 | 2420 | #--------------- Non Specular Meteor ---------------- |
|
|
2421 | 2421 | |
|
|
2422 | 2422 | class NonSpecularMeteorDetection(Operation): |
|
|
2423 | 2423 | |
|
|
2424 | 2424 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
2425 | 2425 | data_acf = dataOut.data_pre[0] |
|
|
2426 | 2426 | data_ccf = dataOut.data_pre[1] |
|
|
2427 | 2427 | pairsList = dataOut.groupList[1] |
|
|
2428 | 2428 | |
|
|
2429 | 2429 | lamb = dataOut.C/dataOut.frequency |
|
|
2430 | 2430 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
|
2431 | 2431 | paramInterval = dataOut.paramInterval |
|
|
2432 | 2432 | |
|
|
2433 | 2433 | nChannels = data_acf.shape[0] |
|
|
2434 | 2434 | nLags = data_acf.shape[1] |
|
|
2435 | 2435 | nProfiles = data_acf.shape[2] |
|
|
2436 | 2436 | nHeights = dataOut.nHeights |
|
|
2437 | 2437 | nCohInt = dataOut.nCohInt |
|
|
2438 | 2438 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
|
2439 | 2439 | heightList = dataOut.heightList |
|
|
2440 | 2440 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
|
2441 | 2441 | utctime = dataOut.utctime |
|
|
2442 | 2442 | |
|
|
2443 | 2443 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
2444 | 2444 | |
|
|
2445 | 2445 | #------------------------ SNR -------------------------------------- |
|
|
2446 | 2446 | power = data_acf[:,0,:,:].real |
|
|
2447 | 2447 | noise = numpy.zeros(nChannels) |
|
|
2448 | 2448 | SNR = numpy.zeros(power.shape) |
|
|
2449 | 2449 | for i in range(nChannels): |
|
|
2450 | 2450 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
2451 | 2451 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
2452 | 2452 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
2453 | 2453 | SNRdB = 10*numpy.log10(SNR) |
|
|
2454 | 2454 | |
|
|
2455 | 2455 | if mode == 'SA': |
|
|
2456 | 2456 | dataOut.groupList = dataOut.groupList[1] |
|
|
2457 | 2457 | nPairs = data_ccf.shape[0] |
|
|
2458 | 2458 | #---------------------- Coherence and Phase -------------------------- |
|
|
2459 | 2459 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2460 | 2460 | # phase1 = numpy.copy(phase) |
|
|
2461 | 2461 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2462 | 2462 | |
|
|
2463 | 2463 | for p in range(nPairs): |
|
|
2464 | 2464 | ch0 = pairsList[p][0] |
|
|
2465 | 2465 | ch1 = pairsList[p][1] |
|
|
2466 | 2466 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
2467 | 2467 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
2468 | 2468 | # phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
2469 | 2469 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
2470 | 2470 | # coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
2471 | 2471 | coh = numpy.nanmax(coh1, axis = 0) |
|
|
2472 | 2472 | # struc = numpy.ones((5,1)) |
|
|
2473 | 2473 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
2474 | 2474 | #---------------------- Radial Velocity ---------------------------- |
|
|
2475 | 2475 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
2476 | 2476 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
2477 | 2477 | |
|
|
2478 | 2478 | if allData: |
|
|
2479 | 2479 | boolMetFin = ~numpy.isnan(SNRm) |
|
|
2480 | 2480 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2481 | 2481 | else: |
|
|
2482 | 2482 | #------------------------ Meteor mask --------------------------------- |
|
|
2483 | 2483 | # #SNR mask |
|
|
2484 | 2484 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
2485 | 2485 | # |
|
|
2486 | 2486 | # #Erase small objects |
|
|
2487 | 2487 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
2488 | 2488 | # |
|
|
2489 | 2489 | # auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
2490 | 2490 | # indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
2491 | 2491 | # indEEJ = numpy.where(indOver)[0] |
|
|
2492 | 2492 | # indNEEJ = numpy.where(~indOver)[0] |
|
|
2493 | 2493 | # |
|
|
2494 | 2494 | # boolMetFin = boolMet1 |
|
|
2495 | 2495 | # |
|
|
2496 | 2496 | # if indEEJ.size > 0: |
|
|
2497 | 2497 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
2498 | 2498 | # |
|
|
2499 | 2499 | # boolMet2 = coh > cohThresh |
|
|
2500 | 2500 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
2501 | 2501 | # |
|
|
2502 | 2502 | # #Final Meteor mask |
|
|
2503 | 2503 | # boolMetFin = boolMet1|boolMet2 |
|
|
2504 | 2504 | |
|
|
2505 | 2505 | #Coherence mask |
|
|
2506 | 2506 | boolMet1 = coh > 0.75 |
|
|
2507 | 2507 | struc = numpy.ones((30,1)) |
|
|
2508 | 2508 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
2509 | 2509 | |
|
|
2510 | 2510 | #Derivative mask |
|
|
2511 | 2511 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2512 | 2512 | boolMet2 = derPhase < 0.2 |
|
|
2513 | 2513 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
2514 | 2514 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
2515 | 2515 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
2516 | 2516 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
2517 | 2517 | # #Final mask |
|
|
2518 | 2518 | # boolMetFin = boolMet2 |
|
|
2519 | 2519 | boolMetFin = boolMet1&boolMet2 |
|
|
2520 | 2520 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
2521 | 2521 | #Creating data_param |
|
|
2522 | 2522 | coordMet = numpy.where(boolMetFin) |
|
|
2523 | 2523 | |
|
|
2524 | 2524 | tmet = coordMet[0] |
|
|
2525 | 2525 | hmet = coordMet[1] |
|
|
2526 | 2526 | |
|
|
2527 | 2527 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
2528 | 2528 | data_param[:,0] = utctime |
|
|
2529 | 2529 | data_param[:,1] = tmet |
|
|
2530 | 2530 | data_param[:,2] = hmet |
|
|
2531 | 2531 | data_param[:,3] = SNRm[tmet,hmet] |
|
|
2532 | 2532 | data_param[:,4] = velRad[tmet,hmet] |
|
|
2533 | 2533 | data_param[:,5] = coh[tmet,hmet] |
|
|
2534 | 2534 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
2535 | 2535 | |
|
|
2536 | 2536 | elif mode == 'DBS': |
|
|
2537 | 2537 | dataOut.groupList = numpy.arange(nChannels) |
|
|
2538 | 2538 | |
|
|
2539 | 2539 | #Radial Velocities |
|
|
2540 | 2540 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
2541 | 2541 | # phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2542 | 2542 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
2543 | 2543 | |
|
|
2544 | 2544 | #Spectral width |
|
|
2545 | 2545 | # acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2546 | 2546 | # acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
2547 | 2547 | acf1 = data_acf[:,1,:,:] |
|
|
2548 | 2548 | acf2 = data_acf[:,2,:,:] |
|
|
2549 | 2549 | |
|
|
2550 | 2550 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
2551 | 2551 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
2552 | 2552 | if allData: |
|
|
2553 | 2553 | boolMetFin = ~numpy.isnan(SNRdB) |
|
|
2554 | 2554 | else: |
|
|
2555 | 2555 | #SNR |
|
|
2556 | 2556 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
2557 | 2557 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
2558 | 2558 | |
|
|
2559 | 2559 | #Radial velocity |
|
|
2560 | 2560 | boolMet2 = numpy.abs(velRad) < 20 |
|
|
2561 | 2561 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
2562 | 2562 | |
|
|
2563 | 2563 | #Spectral Width |
|
|
2564 | 2564 | boolMet3 = spcWidth < 30 |
|
|
2565 | 2565 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
2566 | 2566 | # boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
2567 | 2567 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
2568 | 2568 | |
|
|
2569 | 2569 | #Creating data_param |
|
|
2570 | 2570 | coordMet = numpy.where(boolMetFin) |
|
|
2571 | 2571 | |
|
|
2572 | 2572 | cmet = coordMet[0] |
|
|
2573 | 2573 | tmet = coordMet[1] |
|
|
2574 | 2574 | hmet = coordMet[2] |
|
|
2575 | 2575 | |
|
|
2576 | 2576 | data_param = numpy.zeros((tmet.size, 7)) |
|
|
2577 | 2577 | data_param[:,0] = utctime |
|
|
2578 | 2578 | data_param[:,1] = cmet |
|
|
2579 | 2579 | data_param[:,2] = tmet |
|
|
2580 | 2580 | data_param[:,3] = hmet |
|
|
2581 | 2581 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
2582 | 2582 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
2583 | 2583 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
2584 | 2584 | |
|
|
2585 | 2585 | # self.dataOut.data_param = data_int |
|
|
2586 | 2586 | if len(data_param) == 0: |
|
|
2587 | 2587 | dataOut.flagNoData = True |
|
|
2588 | 2588 | else: |
|
|
2589 | 2589 | dataOut.data_param = data_param |
|
|
2590 | 2590 | |
|
|
2591 | 2591 | def __erase_small(self, binArray, threshX, threshY): |
|
|
2592 | 2592 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
2593 | 2593 | binArray1 = numpy.copy(binArray) |
|
|
2594 | 2594 | |
|
|
2595 | 2595 | for i in range(1,numfeat + 1): |
|
|
2596 | 2596 | auxBin = (labarray==i) |
|
|
2597 | 2597 | auxSize = auxBin.sum() |
|
|
2598 | 2598 | |
|
|
2599 | 2599 | x,y = numpy.where(auxBin) |
|
|
2600 | 2600 | widthX = x.max() - x.min() |
|
|
2601 | 2601 | widthY = y.max() - y.min() |
|
|
2602 | 2602 | |
|
|
2603 | 2603 | #width X: 3 seg -> 12.5*3 |
|
|
2604 | 2604 | #width Y: |
|
|
2605 | 2605 | |
|
|
2606 | 2606 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
2607 | 2607 | binArray1[auxBin] = False |
|
|
2608 | 2608 | |
|
|
2609 | 2609 | return binArray1 |
|
|
2610 | 2610 | |
|
|
2611 | 2611 | #--------------- Specular Meteor ---------------- |
|
|
2612 | 2612 | |
|
|
2613 | 2613 | class SMDetection(Operation): |
|
|
2614 | 2614 | ''' |
|
|
2615 | 2615 | Function DetectMeteors() |
|
|
2616 | 2616 | Project developed with paper: |
|
|
2617 | 2617 | HOLDSWORTH ET AL. 2004 |
|
|
2618 | 2618 | |
|
|
2619 | 2619 | Input: |
|
|
2620 | 2620 | self.dataOut.data_pre |
|
|
2621 | 2621 | |
|
|
2622 | 2622 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
2623 | 2623 | |
|
|
2624 | 2624 | hei_ref: Height reference for the Beacon signal extraction |
|
|
2625 | 2625 | tauindex: |
|
|
2626 | 2626 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
2627 | 2627 | |
|
|
2628 | 2628 | cohDetection: Whether to user Coherent detection or not |
|
|
2629 | 2629 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
2630 | 2630 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
2631 | 2631 | |
|
|
2632 | 2632 | noise_timeStep: Noise calculation time step |
|
|
2633 | 2633 | noise_multiple: Noise multiple to define signal threshold |
|
|
2634 | 2634 | |
|
|
2635 | 2635 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
2636 | 2636 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
2637 | 2637 | |
|
|
2638 | 2638 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
2639 | 2639 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
2640 | 2640 | |
|
|
2641 | 2641 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
2642 | 2642 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
2643 | 2643 | azimuth: Azimuth angle correction |
|
|
2644 | 2644 | |
|
|
2645 | 2645 | Affected: |
|
|
2646 | 2646 | self.dataOut.data_param |
|
|
2647 | 2647 | |
|
|
2648 | 2648 | Rejection Criteria (Errors): |
|
|
2649 | 2649 | 0: No error; analysis OK |
|
|
2650 | 2650 | 1: SNR < SNR threshold |
|
|
2651 | 2651 | 2: angle of arrival (AOA) ambiguously determined |
|
|
2652 | 2652 | 3: AOA estimate not feasible |
|
|
2653 | 2653 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2654 | 2654 | 5: echo at start or end of time series |
|
|
2655 | 2655 | 6: echo less than 5 examples long; too short for analysis |
|
|
2656 | 2656 | 7: echo rise exceeds 0.3s |
|
|
2657 | 2657 | 8: echo decay time less than twice rise time |
|
|
2658 | 2658 | 9: large power level before echo |
|
|
2659 | 2659 | 10: large power level after echo |
|
|
2660 | 2660 | 11: poor fit to amplitude for estimation of decay time |
|
|
2661 | 2661 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
2662 | 2662 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
2663 | 2663 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
2664 | 2664 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2665 | 2665 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
2666 | 2666 | |
|
|
2667 | 2667 | 17: phase difference in meteor Reestimation |
|
|
2668 | 2668 | |
|
|
2669 | 2669 | Data Storage: |
|
|
2670 | 2670 | Meteors for Wind Estimation (8): |
|
|
2671 | 2671 | Utc Time | Range Height |
|
|
2672 | 2672 | Azimuth Zenith errorCosDir |
|
|
2673 | 2673 | VelRad errorVelRad |
|
|
2674 | 2674 | Phase0 Phase1 Phase2 Phase3 |
|
|
2675 | 2675 | TypeError |
|
|
2676 | 2676 | |
|
|
2677 | 2677 | ''' |
|
|
2678 | 2678 | |
|
|
2679 | 2679 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
2680 | 2680 | phaseOffsets = None, |
|
|
2681 | 2681 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
2682 | 2682 | noise_timeStep = 4, noise_multiple = 4, |
|
|
2683 | 2683 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
2684 | 2684 | phaseThresh = 20, SNRThresh = 5, |
|
|
2685 | 2685 | hmin = 50, hmax=150, azimuth = 0, |
|
|
2686 | 2686 | channelPositions = None) : |
|
|
2687 | 2687 | |
|
|
2688 | 2688 | |
|
|
2689 | 2689 | #Getting Pairslist |
|
|
2690 | 2690 | if channelPositions is None: |
|
|
2691 | 2691 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2692 | 2692 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2693 | 2693 | meteorOps = SMOperations() |
|
|
2694 | 2694 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2695 | 2695 | heiRang = dataOut.heightList |
|
|
2696 | 2696 | #Get Beacon signal - No Beacon signal anymore |
|
|
2697 | 2697 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
2698 | 2698 | # |
|
|
2699 | 2699 | # if hei_ref != None: |
|
|
2700 | 2700 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
2701 | 2701 | # |
|
|
2702 | 2702 | |
|
|
2703 | 2703 | |
|
|
2704 | 2704 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
2705 | 2705 | # see if the user put in pre defined phase shifts |
|
|
2706 | 2706 | voltsPShift = dataOut.data_pre.copy() |
|
|
2707 | 2707 | |
|
|
2708 | 2708 | # if predefinedPhaseShifts != None: |
|
|
2709 | 2709 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
2710 | 2710 | # |
|
|
2711 | 2711 | # # elif beaconPhaseShifts: |
|
|
2712 | 2712 | # # #get hardware phase shifts using beacon signal |
|
|
2713 | 2713 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
2714 | 2714 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
2715 | 2715 | # |
|
|
2716 | 2716 | # else: |
|
|
2717 | 2717 | # hardwarePhaseShifts = numpy.zeros(5) |
|
|
2718 | 2718 | # |
|
|
2719 | 2719 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
2720 | 2720 | # for i in range(self.dataOut.data_pre.shape[0]): |
|
|
2721 | 2721 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
2722 | 2722 | |
|
|
2723 | 2723 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
2724 | 2724 | |
|
|
2725 | 2725 | #Remove DC |
|
|
2726 | 2726 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
2727 | 2727 | voltsDC = numpy.mean(voltsDC,1) |
|
|
2728 | 2728 | for i in range(voltsDC.shape[0]): |
|
|
2729 | 2729 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
2730 | 2730 | |
|
|
2731 | 2731 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
2732 | 2732 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
2733 | 2733 | |
|
|
2734 | 2734 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
2735 | 2735 | #Coherent Detection |
|
|
2736 | 2736 | if cohDetection: |
|
|
2737 | 2737 | #use coherent detection to get the net power |
|
|
2738 | 2738 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
2739 | 2739 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
2740 | 2740 | |
|
|
2741 | 2741 | #Non-coherent detection! |
|
|
2742 | 2742 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
2743 | 2743 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
2744 | 2744 | |
|
|
2745 | 2745 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
2746 | 2746 | #Get noise |
|
|
2747 | 2747 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
2748 | 2748 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
2749 | 2749 | #Get signal threshold |
|
|
2750 | 2750 | signalThresh = noise_multiple*noise |
|
|
2751 | 2751 | #Meteor echoes detection |
|
|
2752 | 2752 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
2753 | 2753 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
2754 | 2754 | |
|
|
2755 | 2755 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
2756 | 2756 | #Parameters |
|
|
2757 | 2757 | heiRange = dataOut.heightList |
|
|
2758 | 2758 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
2759 | 2759 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
2760 | 2760 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
2761 | 2761 | #Multiple detection removals |
|
|
2762 | 2762 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
2763 | 2763 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
2764 | 2764 | |
|
|
2765 | 2765 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
2766 | 2766 | #Parameters |
|
|
2767 | 2767 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
2768 | 2768 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
2769 | 2769 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
2770 | 2770 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
2771 | 2771 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
2772 | 2772 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
2773 | 2773 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
2774 | 2774 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
2775 | 2775 | |
|
|
2776 | 2776 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
2777 | 2777 | #Calculating Radial Velocity (Error N 15) |
|
|
2778 | 2778 | radialStdThresh = 10 |
|
|
2779 | 2779 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
2780 | 2780 | |
|
|
2781 | 2781 | if len(listMeteors4) > 0: |
|
|
2782 | 2782 | #Setting New Array |
|
|
2783 | 2783 | date = dataOut.utctime |
|
|
2784 | 2784 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
2785 | 2785 | |
|
|
2786 | 2786 | #Correcting phase offset |
|
|
2787 | 2787 | if phaseOffsets != None: |
|
|
2788 | 2788 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2789 | 2789 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2790 | 2790 | |
|
|
2791 | 2791 | #Second Pairslist |
|
|
2792 | 2792 | pairsList = [] |
|
|
2793 | 2793 | pairx = (0,1) |
|
|
2794 | 2794 | pairy = (2,3) |
|
|
2795 | 2795 | pairsList.append(pairx) |
|
|
2796 | 2796 | pairsList.append(pairy) |
|
|
2797 | 2797 | |
|
|
2798 | 2798 | jph = numpy.array([0,0,0,0]) |
|
|
2799 | 2799 | h = (hmin,hmax) |
|
|
2800 | 2800 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2801 | 2801 | |
|
|
2802 | 2802 | # #Calculate AOA (Error N 3, 4) |
|
|
2803 | 2803 | # #JONES ET AL. 1998 |
|
|
2804 | 2804 | # error = arrayParameters[:,-1] |
|
|
2805 | 2805 | # AOAthresh = numpy.pi/8 |
|
|
2806 | 2806 | # phases = -arrayParameters[:,9:13] |
|
|
2807 | 2807 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
2808 | 2808 | # |
|
|
2809 | 2809 | # #Calculate Heights (Error N 13 and 14) |
|
|
2810 | 2810 | # error = arrayParameters[:,-1] |
|
|
2811 | 2811 | # Ranges = arrayParameters[:,2] |
|
|
2812 | 2812 | # zenith = arrayParameters[:,5] |
|
|
2813 | 2813 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2814 | 2814 | # error = arrayParameters[:,-1] |
|
|
2815 | 2815 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
2816 | 2816 | |
|
|
2817 | 2817 | #***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
2818 | 2818 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
2819 | 2819 | dataOut.data_param = arrayParameters |
|
|
2820 | 2820 | |
|
|
2821 | 2821 | if arrayParameters is None: |
|
|
2822 | 2822 | dataOut.flagNoData = True |
|
|
2823 | 2823 | else: |
|
|
2824 | 2824 | dataOut.flagNoData = True |
|
|
2825 | 2825 | |
|
|
2826 | 2826 | return |
|
|
2827 | 2827 | |
|
|
2828 | 2828 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
2829 | 2829 | |
|
|
2830 | 2830 | minIndex = min(newheis[0]) |
|
|
2831 | 2831 | maxIndex = max(newheis[0]) |
|
|
2832 | 2832 | |
|
|
2833 | 2833 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
2834 | 2834 | nLength = voltage.shape[1]/n |
|
|
2835 | 2835 | nMin = 0 |
|
|
2836 | 2836 | nMax = 0 |
|
|
2837 | 2837 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
2838 | 2838 | |
|
|
2839 | 2839 | for i in range(n): |
|
|
2840 | 2840 | nMax += nLength |
|
|
2841 | 2841 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
2842 | 2842 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
2843 | 2843 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
2844 | 2844 | nMin = nMax |
|
|
2845 | 2845 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
2846 | 2846 | |
|
|
2847 | 2847 | #Remove Outliers |
|
|
2848 | 2848 | factor = 2 |
|
|
2849 | 2849 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
2850 | 2850 | dw = numpy.std(wt,axis = 1) |
|
|
2851 | 2851 | dw = dw.reshape((dw.size,1)) |
|
|
2852 | 2852 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
2853 | 2853 | phaseOffset[ind] = numpy.nan |
|
|
2854 | 2854 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
2855 | 2855 | |
|
|
2856 | 2856 | return phaseOffset |
|
|
2857 | 2857 | |
|
|
2858 | 2858 | def __shiftPhase(self, data, phaseShift): |
|
|
2859 | 2859 | #this will shift the phase of a complex number |
|
|
2860 | 2860 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
2861 | 2861 | return dataShifted |
|
|
2862 | 2862 | |
|
|
2863 | 2863 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
2864 | 2864 | nChannel = array.shape[0] |
|
|
2865 | 2865 | nHeights = array.shape[2] |
|
|
2866 | 2866 | numPairs = len(pairslist) |
|
|
2867 | 2867 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
2868 | 2868 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
2869 | 2869 | |
|
|
2870 | 2870 | #Correct phases |
|
|
2871 | 2871 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
2872 | 2872 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
2873 | 2873 | |
|
|
2874 | 2874 | if indDer[0].shape[0] > 0: |
|
|
2875 | 2875 | for i in range(indDer[0].shape[0]): |
|
|
2876 | 2876 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
2877 | 2877 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
2878 | 2878 | |
|
|
2879 | 2879 | # for j in range(numSides): |
|
|
2880 | 2880 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
2881 | 2881 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
2882 | 2882 | # |
|
|
2883 | 2883 | #Linear |
|
|
2884 | 2884 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
2885 | 2885 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
2886 | 2886 | for j in range(numPairs): |
|
|
2887 | 2887 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
2888 | 2888 | phaseInt[j] = fit[1] |
|
|
2889 | 2889 | #Phase Differences |
|
|
2890 | 2890 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
2891 | 2891 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
2892 | 2892 | |
|
|
2893 | 2893 | #Dealias |
|
|
2894 | 2894 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
2895 | 2895 | # indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
2896 | 2896 | # phaseArrival[indAlias] -= 2*numpy.pi |
|
|
2897 | 2897 | # indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
2898 | 2898 | # phaseArrival[indAlias] += 2*numpy.pi |
|
|
2899 | 2899 | |
|
|
2900 | 2900 | return phaseDiff, phaseArrival |
|
|
2901 | 2901 | |
|
|
2902 | 2902 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
2903 | 2903 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
2904 | 2904 | #find the phase shifts of each channel over 1 second intervals |
|
|
2905 | 2905 | #only look at ranges below the beacon signal |
|
|
2906 | 2906 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
2907 | 2907 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
2908 | 2908 | numHeights = volts.shape[2] |
|
|
2909 | 2909 | nChannel = volts.shape[0] |
|
|
2910 | 2910 | voltsCohDet = volts.copy() |
|
|
2911 | 2911 | |
|
|
2912 | 2912 | pairsarray = numpy.array(pairslist) |
|
|
2913 | 2913 | indSides = pairsarray[:,1] |
|
|
2914 | 2914 | # indSides = numpy.array(range(nChannel)) |
|
|
2915 | 2915 | # indSides = numpy.delete(indSides, indCenter) |
|
|
2916 | 2916 | # |
|
|
2917 | 2917 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
2918 | 2918 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
2919 | 2919 | |
|
|
2920 | 2920 | startInd = 0 |
|
|
2921 | 2921 | endInd = 0 |
|
|
2922 | 2922 | |
|
|
2923 | 2923 | for i in range(numBlocks): |
|
|
2924 | 2924 | startInd = endInd |
|
|
2925 | 2925 | endInd = endInd + listBlocks[i].shape[1] |
|
|
2926 | 2926 | |
|
|
2927 | 2927 | arrayBlock = listBlocks[i] |
|
|
2928 | 2928 | # arrayBlockCenter = listCenter[i] |
|
|
2929 | 2929 | |
|
|
2930 | 2930 | #Estimate the Phase Difference |
|
|
2931 | 2931 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
2932 | 2932 | #Phase Difference RMS |
|
|
2933 | 2933 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
2934 | 2934 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
2935 | 2935 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
2936 | 2936 | #Shifting |
|
|
2937 | 2937 | if indPhase[0].shape[0] > 0: |
|
|
2938 | 2938 | for j in range(indSides.size): |
|
|
2939 | 2939 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
2940 | 2940 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
2941 | 2941 | |
|
|
2942 | 2942 | return voltsCohDet |
|
|
2943 | 2943 | |
|
|
2944 | 2944 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
2945 | 2945 | |
|
|
2946 | 2946 | nHeights = volts.shape[2] |
|
|
2947 | 2947 | nPoints = volts.shape[1] |
|
|
2948 | 2948 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
2949 | 2949 | |
|
|
2950 | 2950 | for i in range(len(pairslist)): |
|
|
2951 | 2951 | volts1 = volts[pairslist[i][0]] |
|
|
2952 | 2952 | volts2 = volts[pairslist[i][1]] |
|
|
2953 | 2953 | |
|
|
2954 | 2954 | for t in range(len(laglist)): |
|
|
2955 | 2955 | idxT = laglist[t] |
|
|
2956 | 2956 | if idxT >= 0: |
|
|
2957 | 2957 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
2958 | 2958 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
2959 | 2959 | else: |
|
|
2960 | 2960 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
2961 | 2961 | volts2[:(nPoints + idxT),:])) |
|
|
2962 | 2962 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
2963 | 2963 | |
|
|
2964 | 2964 | vStacked = None |
|
|
2965 | 2965 | return voltsCCF |
|
|
2966 | 2966 | |
|
|
2967 | 2967 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
2968 | 2968 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
2969 | 2969 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
2970 | 2970 | numHeights = power.shape[1] |
|
|
2971 | 2971 | |
|
|
2972 | 2972 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
2973 | 2973 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
2974 | 2974 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
2975 | 2975 | |
|
|
2976 | 2976 | startInd = 0 |
|
|
2977 | 2977 | endInd = 0 |
|
|
2978 | 2978 | |
|
|
2979 | 2979 | for i in range(numBlocks): #split por canal |
|
|
2980 | 2980 | startInd = endInd |
|
|
2981 | 2981 | endInd = endInd + listPower[i].shape[0] |
|
|
2982 | 2982 | |
|
|
2983 | 2983 | arrayBlock = listPower[i] |
|
|
2984 | 2984 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
2985 | 2985 | # noiseAux = numpy.median(noiseAux) |
|
|
2986 | 2986 | # noiseAux = numpy.mean(arrayBlock) |
|
|
2987 | 2987 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
2988 | 2988 | |
|
|
2989 | 2989 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
2990 | 2990 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
2991 | 2991 | |
|
|
2992 | 2992 | return noise, noise1 |
|
|
2993 | 2993 | |
|
|
2994 | 2994 | def __findMeteors(self, power, thresh): |
|
|
2995 | 2995 | nProf = power.shape[0] |
|
|
2996 | 2996 | nHeights = power.shape[1] |
|
|
2997 | 2997 | listMeteors = [] |
|
|
2998 | 2998 | |
|
|
2999 | 2999 | for i in range(nHeights): |
|
|
3000 | 3000 | powerAux = power[:,i] |
|
|
3001 | 3001 | threshAux = thresh[:,i] |
|
|
3002 | 3002 | |
|
|
3003 | 3003 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
3004 | 3004 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
3005 | 3005 | |
|
|
3006 | 3006 | j = 0 |
|
|
3007 | 3007 | |
|
|
3008 | 3008 | while (j < indUPthresh.size - 2): |
|
|
3009 | 3009 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
3010 | 3010 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
3011 | 3011 | indDNthresh = indDNthresh[indDNAux] |
|
|
3012 | 3012 | |
|
|
3013 | 3013 | if (indDNthresh.size > 0): |
|
|
3014 | 3014 | indEnd = indDNthresh[0] - 1 |
|
|
3015 | 3015 | indInit = indUPthresh[j] |
|
|
3016 | 3016 | |
|
|
3017 | 3017 | meteor = powerAux[indInit:indEnd + 1] |
|
|
3018 | 3018 | indPeak = meteor.argmax() + indInit |
|
|
3019 | 3019 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
3020 | 3020 | |
|
|
3021 | 3021 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
3022 | 3022 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
3023 | 3023 | else: j+=1 |
|
|
3024 | 3024 | else: j+=1 |
|
|
3025 | 3025 | |
|
|
3026 | 3026 | return listMeteors |
|
|
3027 | 3027 | |
|
|
3028 | 3028 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
3029 | 3029 | |
|
|
3030 | 3030 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
3031 | 3031 | listMeteors1 = [] |
|
|
3032 | 3032 | |
|
|
3033 | 3033 | while arrayMeteors.shape[0] > 0: |
|
|
3034 | 3034 | FLAs = arrayMeteors[:,4] |
|
|
3035 | 3035 | maxFLA = FLAs.argmax() |
|
|
3036 | 3036 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
3037 | 3037 | |
|
|
3038 | 3038 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
3039 | 3039 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
3040 | 3040 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
3041 | 3041 | |
|
|
3042 | 3042 | #Check neighborhood |
|
|
3043 | 3043 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
3044 | 3044 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
3045 | 3045 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
3046 | 3046 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
3047 | 3047 | |
|
|
3048 | 3048 | #Check Heights |
|
|
3049 | 3049 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
3050 | 3050 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
3051 | 3051 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
3052 | 3052 | |
|
|
3053 | 3053 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
3054 | 3054 | |
|
|
3055 | 3055 | return listMeteors1 |
|
|
3056 | 3056 | |
|
|
3057 | 3057 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
3058 | 3058 | numHeights = volts.shape[2] |
|
|
3059 | 3059 | nChannel = volts.shape[0] |
|
|
3060 | 3060 | |
|
|
3061 | 3061 | thresholdPhase = thresh[0] |
|
|
3062 | 3062 | thresholdNoise = thresh[1] |
|
|
3063 | 3063 | thresholdDB = float(thresh[2]) |
|
|
3064 | 3064 | |
|
|
3065 | 3065 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
3066 | 3066 | pairsarray = numpy.array(pairslist) |
|
|
3067 | 3067 | indSides = pairsarray[:,1] |
|
|
3068 | 3068 | |
|
|
3069 | 3069 | pairslist1 = list(pairslist) |
|
|
3070 | 3070 | pairslist1.append((0,1)) |
|
|
3071 | 3071 | pairslist1.append((3,4)) |
|
|
3072 | 3072 | |
|
|
3073 | 3073 | listMeteors1 = [] |
|
|
3074 | 3074 | listPowerSeries = [] |
|
|
3075 | 3075 | listVoltageSeries = [] |
|
|
3076 | 3076 | #volts has the war data |
|
|
3077 | 3077 | |
|
|
3078 | 3078 | if frequency == 30e6: |
|
|
3079 | 3079 | timeLag = 45*10**-3 |
|
|
3080 | 3080 | else: |
|
|
3081 | 3081 | timeLag = 15*10**-3 |
|
|
3082 | 3082 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3083 | 3083 | |
|
|
3084 | 3084 | for i in range(len(listMeteors)): |
|
|
3085 | 3085 | |
|
|
3086 | 3086 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
3087 | 3087 | meteorAux = numpy.zeros(16) |
|
|
3088 | 3088 | |
|
|
3089 | 3089 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
3090 | 3090 | mHeight = listMeteors[i][0] |
|
|
3091 | 3091 | mStart = listMeteors[i][1] |
|
|
3092 | 3092 | mPeak = listMeteors[i][2] |
|
|
3093 | 3093 | mEnd = listMeteors[i][3] |
|
|
3094 | 3094 | |
|
|
3095 | 3095 | #get the volt data between the start and end times of the meteor |
|
|
3096 | 3096 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
3097 | 3097 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3098 | 3098 | |
|
|
3099 | 3099 | #3.6. Phase Difference estimation |
|
|
3100 | 3100 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
3101 | 3101 | |
|
|
3102 | 3102 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
3103 | 3103 | #meteorVolts0.- all Channels, all Profiles |
|
|
3104 | 3104 | meteorVolts0 = volts[:,:,mHeight] |
|
|
3105 | 3105 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
3106 | 3106 | meteorNoise = noise[:,mHeight] |
|
|
3107 | 3107 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
3108 | 3108 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
3109 | 3109 | |
|
|
3110 | 3110 | #Times reestimation |
|
|
3111 | 3111 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
3112 | 3112 | if mStart1.size > 0: |
|
|
3113 | 3113 | mStart1 = mStart1[-1] + 1 |
|
|
3114 | 3114 | |
|
|
3115 | 3115 | else: |
|
|
3116 | 3116 | mStart1 = mPeak |
|
|
3117 | 3117 | |
|
|
3118 | 3118 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
3119 | 3119 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
3120 | 3120 | if mEndDecayTime1.size == 0: |
|
|
3121 | 3121 | mEndDecayTime1 = powerNet0.size |
|
|
3122 | 3122 | else: |
|
|
3123 | 3123 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
3124 | 3124 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
3125 | 3125 | |
|
|
3126 | 3126 | #meteorVolts1.- all Channels, from start to end |
|
|
3127 | 3127 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
3128 | 3128 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
3129 | 3129 | if meteorVolts2.shape[1] == 0: |
|
|
3130 | 3130 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
3131 | 3131 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
3132 | 3132 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
3133 | 3133 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
3134 | 3134 | |
|
|
3135 | 3135 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
3136 | 3136 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3137 | 3137 | if meteorVolts2.shape[1] > 0: |
|
|
3138 | 3138 | #Phase Difference re-estimation |
|
|
3139 | 3139 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
3140 | 3140 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
3141 | 3141 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
3142 | 3142 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
3143 | 3143 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
3144 | 3144 | |
|
|
3145 | 3145 | #Phase Difference RMS |
|
|
3146 | 3146 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
3147 | 3147 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
3148 | 3148 | #Data from Meteor |
|
|
3149 | 3149 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
3150 | 3150 | mPeakPower1 = powerNet1.max() |
|
|
3151 | 3151 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
3152 | 3152 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
3153 | 3153 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
3154 | 3154 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
3155 | 3155 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
3156 | 3156 | #Vectorize |
|
|
3157 | 3157 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
3158 | 3158 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
3159 | 3159 | |
|
|
3160 | 3160 | #Rejection Criterions |
|
|
3161 | 3161 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
3162 | 3162 | meteorAux[-1] = 17 |
|
|
3163 | 3163 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
3164 | 3164 | meteorAux[-1] = 1 |
|
|
3165 | 3165 | |
|
|
3166 | 3166 | |
|
|
3167 | 3167 | else: |
|
|
3168 | 3168 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
3169 | 3169 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3170 | 3170 | PowerSeries = 0 |
|
|
3171 | 3171 | |
|
|
3172 | 3172 | listMeteors1.append(meteorAux) |
|
|
3173 | 3173 | listPowerSeries.append(PowerSeries) |
|
|
3174 | 3174 | listVoltageSeries.append(meteorVolts1) |
|
|
3175 | 3175 | |
|
|
3176 | 3176 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
3177 | 3177 | |
|
|
3178 | 3178 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
3179 | 3179 | |
|
|
3180 | 3180 | threshError = 10 |
|
|
3181 | 3181 | #Depending if it is 30 or 50 MHz |
|
|
3182 | 3182 | if frequency == 30e6: |
|
|
3183 | 3183 | timeLag = 45*10**-3 |
|
|
3184 | 3184 | else: |
|
|
3185 | 3185 | timeLag = 15*10**-3 |
|
|
3186 | 3186 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3187 | 3187 | |
|
|
3188 | 3188 | listMeteors1 = [] |
|
|
3189 | 3189 | |
|
|
3190 | 3190 | for i in range(len(listMeteors)): |
|
|
3191 | 3191 | meteorPower = listPower[i] |
|
|
3192 | 3192 | meteorAux = listMeteors[i] |
|
|
3193 | 3193 | |
|
|
3194 | 3194 | if meteorAux[-1] == 0: |
|
|
3195 | 3195 | |
|
|
3196 | 3196 | try: |
|
|
3197 | 3197 | indmax = meteorPower.argmax() |
|
|
3198 | 3198 | indlag = indmax + lag |
|
|
3199 | 3199 | |
|
|
3200 | 3200 | y = meteorPower[indlag:] |
|
|
3201 | 3201 | x = numpy.arange(0, y.size)*timeLag |
|
|
3202 | 3202 | |
|
|
3203 | 3203 | #first guess |
|
|
3204 | 3204 | a = y[0] |
|
|
3205 | 3205 | tau = timeLag |
|
|
3206 | 3206 | #exponential fit |
|
|
3207 | 3207 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
3208 | 3208 | y1 = self.__exponential_function(x, *popt) |
|
|
3209 | 3209 | #error estimation |
|
|
3210 | 3210 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
3211 | 3211 | |
|
|
3212 | 3212 | decayTime = popt[1] |
|
|
3213 | 3213 | riseTime = indmax*timeInterval |
|
|
3214 | 3214 | meteorAux[11:13] = [decayTime, error] |
|
|
3215 | 3215 | |
|
|
3216 | 3216 | #Table items 7, 8 and 11 |
|
|
3217 | 3217 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
3218 | 3218 | meteorAux[-1] = 7 |
|
|
3219 | 3219 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
3220 | 3220 | meteorAux[-1] = 8 |
|
|
3221 | 3221 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
3222 | 3222 | meteorAux[-1] = 11 |
|
|
3223 | 3223 | |
|
|
3224 | 3224 | |
|
|
3225 | 3225 | except: |
|
|
3226 | 3226 | meteorAux[-1] = 11 |
|
|
3227 | 3227 | |
|
|
3228 | 3228 | |
|
|
3229 | 3229 | listMeteors1.append(meteorAux) |
|
|
3230 | 3230 | |
|
|
3231 | 3231 | return listMeteors1 |
|
|
3232 | 3232 | |
|
|
3233 | 3233 | #Exponential Function |
|
|
3234 | 3234 | |
|
|
3235 | 3235 | def __exponential_function(self, x, a, tau): |
|
|
3236 | 3236 | y = a*numpy.exp(-x/tau) |
|
|
3237 | 3237 | return y |
|
|
3238 | 3238 | |
|
|
3239 | 3239 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
3240 | 3240 | |
|
|
3241 | 3241 | pairslist1 = list(pairslist) |
|
|
3242 | 3242 | pairslist1.append((0,1)) |
|
|
3243 | 3243 | pairslist1.append((3,4)) |
|
|
3244 | 3244 | numPairs = len(pairslist1) |
|
|
3245 | 3245 | #Time Lag |
|
|
3246 | 3246 | timeLag = 45*10**-3 |
|
|
3247 | 3247 | c = 3e8 |
|
|
3248 | 3248 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3249 | 3249 | freq = 30e6 |
|
|
3250 | 3250 | |
|
|
3251 | 3251 | listMeteors1 = [] |
|
|
3252 | 3252 | |
|
|
3253 | 3253 | for i in range(len(listMeteors)): |
|
|
3254 | 3254 | meteorAux = listMeteors[i] |
|
|
3255 | 3255 | if meteorAux[-1] == 0: |
|
|
3256 | 3256 | mStart = listMeteors[i][1] |
|
|
3257 | 3257 | mPeak = listMeteors[i][2] |
|
|
3258 | 3258 | mLag = mPeak - mStart + lag |
|
|
3259 | 3259 | |
|
|
3260 | 3260 | #get the volt data between the start and end times of the meteor |
|
|
3261 | 3261 | meteorVolts = listVolts[i] |
|
|
3262 | 3262 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3263 | 3263 | |
|
|
3264 | 3264 | #Get CCF |
|
|
3265 | 3265 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
3266 | 3266 | |
|
|
3267 | 3267 | #Method 2 |
|
|
3268 | 3268 | slopes = numpy.zeros(numPairs) |
|
|
3269 | 3269 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
3270 | 3270 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
3271 | 3271 | |
|
|
3272 | 3272 | #Correct phases |
|
|
3273 | 3273 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
3274 | 3274 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
3275 | 3275 | |
|
|
3276 | 3276 | if indDer[0].shape[0] > 0: |
|
|
3277 | 3277 | for i in range(indDer[0].shape[0]): |
|
|
3278 | 3278 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
3279 | 3279 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
3280 | 3280 | |
|
|
3281 | 3281 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
3282 | 3282 | for j in range(numPairs): |
|
|
3283 | 3283 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
3284 | 3284 | slopes[j] = fit[0] |
|
|
3285 | 3285 | |
|
|
3286 | 3286 | #Remove Outlier |
|
|
3287 | 3287 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3288 | 3288 | # slopes = numpy.delete(slopes,indOut) |
|
|
3289 | 3289 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3290 | 3290 | # slopes = numpy.delete(slopes,indOut) |
|
|
3291 | 3291 | |
|
|
3292 | 3292 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3293 | 3293 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3294 | 3294 | meteorAux[-2] = radialError |
|
|
3295 | 3295 | meteorAux[-3] = radialVelocity |
|
|
3296 | 3296 | |
|
|
3297 | 3297 | #Setting Error |
|
|
3298 | 3298 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
3299 | 3299 | if numpy.abs(radialVelocity) > 200: |
|
|
3300 | 3300 | meteorAux[-1] = 15 |
|
|
3301 | 3301 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
3302 | 3302 | elif radialError > radialStdThresh: |
|
|
3303 | 3303 | meteorAux[-1] = 12 |
|
|
3304 | 3304 | |
|
|
3305 | 3305 | listMeteors1.append(meteorAux) |
|
|
3306 | 3306 | return listMeteors1 |
|
|
3307 | 3307 | |
|
|
3308 | 3308 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
3309 | 3309 | |
|
|
3310 | 3310 | #New arrays |
|
|
3311 | 3311 | arrayMeteors = numpy.array(listMeteors) |
|
|
3312 | 3312 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
3313 | 3313 | |
|
|
3314 | 3314 | #Date inclusion |
|
|
3315 | 3315 | # date = re.findall(r'\((.*?)\)', date) |
|
|
3316 | 3316 | # date = date[0].split(',') |
|
|
3317 | 3317 | # date = map(int, date) |
|
|
3318 | 3318 | # |
|
|
3319 | 3319 | # if len(date)<6: |
|
|
3320 | 3320 | # date.append(0) |
|
|
3321 | 3321 | # |
|
|
3322 | 3322 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
3323 | 3323 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
3324 | 3324 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
3325 | 3325 | |
|
|
3326 | 3326 | #Meteor array |
|
|
3327 | 3327 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
3328 | 3328 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
3329 | 3329 | |
|
|
3330 | 3330 | #Parameters Array |
|
|
3331 | 3331 | arrayParameters[:,0] = arrayDate #Date |
|
|
3332 | 3332 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
3333 | 3333 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
3334 | 3334 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
3335 | 3335 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
3336 | 3336 | |
|
|
3337 | 3337 | |
|
|
3338 | 3338 | return arrayParameters |
|
|
3339 | 3339 | |
|
|
3340 | 3340 | class CorrectSMPhases(Operation): |
|
|
3341 | 3341 | |
|
|
3342 | 3342 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
3343 | 3343 | |
|
|
3344 | 3344 | arrayParameters = dataOut.data_param |
|
|
3345 | 3345 | pairsList = [] |
|
|
3346 | 3346 | pairx = (0,1) |
|
|
3347 | 3347 | pairy = (2,3) |
|
|
3348 | 3348 | pairsList.append(pairx) |
|
|
3349 | 3349 | pairsList.append(pairy) |
|
|
3350 | 3350 | jph = numpy.zeros(4) |
|
|
3351 | 3351 | |
|
|
3352 | 3352 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
3353 | 3353 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
3354 | 3354 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
3355 | 3355 | |
|
|
3356 | 3356 | meteorOps = SMOperations() |
|
|
3357 | 3357 | if channelPositions is None: |
|
|
3358 | 3358 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3359 | 3359 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3360 | 3360 | |
|
|
3361 | 3361 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3362 | 3362 | h = (hmin,hmax) |
|
|
3363 | 3363 | |
|
|
3364 | 3364 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
3365 | 3365 | |
|
|
3366 | 3366 | dataOut.data_param = arrayParameters |
|
|
3367 | 3367 | return |
|
|
3368 | 3368 | |
|
|
3369 | 3369 | class SMPhaseCalibration(Operation): |
|
|
3370 | 3370 | |
|
|
3371 | 3371 | __buffer = None |
|
|
3372 | 3372 | |
|
|
3373 | 3373 | __initime = None |
|
|
3374 | 3374 | |
|
|
3375 | 3375 | __dataReady = False |
|
|
3376 | 3376 | |
|
|
3377 | 3377 | __isConfig = False |
|
|
3378 | 3378 | |
|
|
3379 | 3379 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
3380 | 3380 | |
|
|
3381 | 3381 | dataTime = currentTime + paramInterval |
|
|
3382 | 3382 | deltaTime = dataTime - initTime |
|
|
3383 | 3383 | |
|
|
3384 | 3384 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
3385 | 3385 | return True |
|
|
3386 | 3386 | |
|
|
3387 | 3387 | return False |
|
|
3388 | 3388 | |
|
|
3389 | 3389 | def __getGammas(self, pairs, d, phases): |
|
|
3390 | 3390 | gammas = numpy.zeros(2) |
|
|
3391 | 3391 | |
|
|
3392 | 3392 | for i in range(len(pairs)): |
|
|
3393 | 3393 | |
|
|
3394 | 3394 | pairi = pairs[i] |
|
|
3395 | 3395 | |
|
|
3396 | 3396 | phip3 = phases[:,pairi[0]] |
|
|
3397 | 3397 | d3 = d[pairi[0]] |
|
|
3398 | 3398 | phip2 = phases[:,pairi[1]] |
|
|
3399 | 3399 | d2 = d[pairi[1]] |
|
|
3400 | 3400 | #Calculating gamma |
|
|
3401 | 3401 | # jdcos = alp1/(k*d1) |
|
|
3402 | 3402 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
3403 | 3403 | jgamma = -phip2*d3/d2 - phip3 |
|
|
3404 | 3404 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
3405 | 3405 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
3406 | 3406 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
3407 | 3407 | |
|
|
3408 | 3408 | #Revised distribution |
|
|
3409 | 3409 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
3410 | 3410 | |
|
|
3411 | 3411 | #Histogram |
|
|
3412 | 3412 | nBins = 64 |
|
|
3413 | 3413 | rmin = -0.5*numpy.pi |
|
|
3414 | 3414 | rmax = 0.5*numpy.pi |
|
|
3415 | 3415 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
3416 | 3416 | |
|
|
3417 | 3417 | meteorsY = phaseHisto[0] |
|
|
3418 | 3418 | phasesX = phaseHisto[1][:-1] |
|
|
3419 | 3419 | width = phasesX[1] - phasesX[0] |
|
|
3420 | 3420 | phasesX += width/2 |
|
|
3421 | 3421 | |
|
|
3422 | 3422 | #Gaussian aproximation |
|
|
3423 | 3423 | bpeak = meteorsY.argmax() |
|
|
3424 | 3424 | peak = meteorsY.max() |
|
|
3425 | 3425 | jmin = bpeak - 5 |
|
|
3426 | 3426 | jmax = bpeak + 5 + 1 |
|
|
3427 | 3427 | |
|
|
3428 | 3428 | if jmin<0: |
|
|
3429 | 3429 | jmin = 0 |
|
|
3430 | 3430 | jmax = 6 |
|
|
3431 | 3431 | elif jmax > meteorsY.size: |
|
|
3432 | 3432 | jmin = meteorsY.size - 6 |
|
|
3433 | 3433 | jmax = meteorsY.size |
|
|
3434 | 3434 | |
|
|
3435 | 3435 | x0 = numpy.array([peak,bpeak,50]) |
|
|
3436 | 3436 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
3437 | 3437 | |
|
|
3438 | 3438 | #Gammas |
|
|
3439 | 3439 | gammas[i] = coeff[0][1] |
|
|
3440 | 3440 | |
|
|
3441 | 3441 | return gammas |
|
|
3442 | 3442 | |
|
|
3443 | 3443 | def __residualFunction(self, coeffs, y, t): |
|
|
3444 | 3444 | |
|
|
3445 | 3445 | return y - self.__gauss_function(t, coeffs) |
|
|
3446 | 3446 | |
|
|
3447 | 3447 | def __gauss_function(self, t, coeffs): |
|
|
3448 | 3448 | |
|
|
3449 | 3449 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
3450 | 3450 | |
|
|
3451 | 3451 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
3452 | 3452 | meteorOps = SMOperations() |
|
|
3453 | 3453 | nchan = 4 |
|
|
3454 | 3454 | pairx = pairsList[0] #x es 0 |
|
|
3455 | 3455 | pairy = pairsList[1] #y es 1 |
|
|
3456 | 3456 | center_xangle = 0 |
|
|
3457 | 3457 | center_yangle = 0 |
|
|
3458 | 3458 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
3459 | 3459 | ntimes = len(range_angle) |
|
|
3460 | 3460 | |
|
|
3461 | 3461 | nstepsx = 20 |
|
|
3462 | 3462 | nstepsy = 20 |
|
|
3463 | 3463 | |
|
|
3464 | 3464 | for iz in range(ntimes): |
|
|
3465 | 3465 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
3466 | 3466 | max_xangle = range_angle[iz]/2 + center_xangle |
|
|
3467 | 3467 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
3468 | 3468 | max_yangle = range_angle[iz]/2 + center_yangle |
|
|
3469 | 3469 | |
|
|
3470 | 3470 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
3471 | 3471 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
3472 | 3472 | |
|
|
3473 | 3473 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
3474 | 3474 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
3475 | 3475 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
3476 | 3476 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
3477 | 3477 | jph = numpy.zeros(nchan) |
|
|
3478 | 3478 | |
|
|
3479 | 3479 | # Iterations looking for the offset |
|
|
3480 | 3480 | for iy in range(int(nstepsy)): |
|
|
3481 | 3481 | for ix in range(int(nstepsx)): |
|
|
3482 | 3482 | d3 = d[pairsList[1][0]] |
|
|
3483 | 3483 | d2 = d[pairsList[1][1]] |
|
|
3484 | 3484 | d5 = d[pairsList[0][0]] |
|
|
3485 | 3485 | d4 = d[pairsList[0][1]] |
|
|
3486 | 3486 | |
|
|
3487 | 3487 | alp2 = alpha_y[iy] #gamma 1 |
|
|
3488 | 3488 | alp4 = alpha_x[ix] #gamma 0 |
|
|
3489 | 3489 | |
|
|
3490 | 3490 | alp3 = -alp2*d3/d2 - gammas[1] |
|
|
3491 | 3491 | alp5 = -alp4*d5/d4 - gammas[0] |
|
|
3492 | 3492 | # jph[pairy[1]] = alpha_y[iy] |
|
|
3493 | 3493 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
3494 | 3494 | |
|
|
3495 | 3495 | # jph[pairx[1]] = alpha_x[ix] |
|
|
3496 | 3496 | # jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
3497 | 3497 | jph[pairsList[0][1]] = alp4 |
|
|
3498 | 3498 | jph[pairsList[0][0]] = alp5 |
|
|
3499 | 3499 | jph[pairsList[1][0]] = alp3 |
|
|
3500 | 3500 | jph[pairsList[1][1]] = alp2 |
|
|
3501 | 3501 | jph_array[:,ix,iy] = jph |
|
|
3502 | 3502 | # d = [2.0,2.5,2.5,2.0] |
|
|
3503 | 3503 | #falta chequear si va a leer bien los meteoros |
|
|
3504 | 3504 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
3505 | 3505 | error = meteorsArray1[:,-1] |
|
|
3506 | 3506 | ind1 = numpy.where(error==0)[0] |
|
|
3507 | 3507 | penalty[ix,iy] = ind1.size |
|
|
3508 | 3508 | |
|
|
3509 | 3509 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
3510 | 3510 | phOffset = jph_array[:,i,j] |
|
|
3511 | 3511 | |
|
|
3512 | 3512 | center_xangle = phOffset[pairx[1]] |
|
|
3513 | 3513 | center_yangle = phOffset[pairy[1]] |
|
|
3514 | 3514 | |
|
|
3515 | 3515 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
3516 | 3516 | phOffset = phOffset*180/numpy.pi |
|
|
3517 | 3517 | return phOffset |
|
|
3518 | 3518 | |
|
|
3519 | 3519 | |
|
|
3520 | 3520 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
3521 | 3521 | |
|
|
3522 | 3522 | dataOut.flagNoData = True |
|
|
3523 | 3523 | self.__dataReady = False |
|
|
3524 | 3524 | dataOut.outputInterval = nHours*3600 |
|
|
3525 | 3525 | |
|
|
3526 | 3526 | if self.__isConfig == False: |
|
|
3527 | 3527 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
3528 | 3528 | #Get Initial LTC time |
|
|
3529 | 3529 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
3530 | 3530 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
3531 | 3531 | |
|
|
3532 | 3532 | self.__isConfig = True |
|
|
3533 | 3533 | |
|
|
3534 | 3534 | if self.__buffer is None: |
|
|
3535 | 3535 | self.__buffer = dataOut.data_param.copy() |
|
|
3536 | 3536 | |
|
|
3537 | 3537 | else: |
|
|
3538 | 3538 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
3539 | 3539 | |
|
|
3540 | 3540 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
3541 | 3541 | |
|
|
3542 | 3542 | if self.__dataReady: |
|
|
3543 | 3543 | dataOut.utctimeInit = self.__initime |
|
|
3544 | 3544 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
3545 | 3545 | |
|
|
3546 | 3546 | freq = dataOut.frequency |
|
|
3547 | 3547 | c = dataOut.C #m/s |
|
|
3548 | 3548 | lamb = c/freq |
|
|
3549 | 3549 | k = 2*numpy.pi/lamb |
|
|
3550 | 3550 | azimuth = 0 |
|
|
3551 | 3551 | h = (hmin, hmax) |
|
|
3552 | 3552 | # pairs = ((0,1),(2,3)) #Estrella |
|
|
3553 | 3553 | # pairs = ((1,0),(2,3)) #T |
|
|
3554 | 3554 | |
|
|
3555 | 3555 | if channelPositions is None: |
|
|
3556 | 3556 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3557 | 3557 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3558 | 3558 | meteorOps = SMOperations() |
|
|
3559 | 3559 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3560 | 3560 | |
|
|
3561 | 3561 | #Checking correct order of pairs |
|
|
3562 | 3562 | pairs = [] |
|
|
3563 | 3563 | if distances[1] > distances[0]: |
|
|
3564 | 3564 | pairs.append((1,0)) |
|
|
3565 | 3565 | else: |
|
|
3566 | 3566 | pairs.append((0,1)) |
|
|
3567 | 3567 | |
|
|
3568 | 3568 | if distances[3] > distances[2]: |
|
|
3569 | 3569 | pairs.append((3,2)) |
|
|
3570 | 3570 | else: |
|
|
3571 | 3571 | pairs.append((2,3)) |
|
|
3572 | 3572 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
3573 | 3573 | |
|
|
3574 | 3574 | meteorsArray = self.__buffer |
|
|
3575 | 3575 | error = meteorsArray[:,-1] |
|
|
3576 | 3576 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
3577 | 3577 | ind1 = numpy.where(boolError)[0] |
|
|
3578 | 3578 | meteorsArray = meteorsArray[ind1,:] |
|
|
3579 | 3579 | meteorsArray[:,-1] = 0 |
|
|
3580 | 3580 | phases = meteorsArray[:,8:12] |
|
|
3581 | 3581 | |
|
|
3582 | 3582 | #Calculate Gammas |
|
|
3583 | 3583 | gammas = self.__getGammas(pairs, distances, phases) |
|
|
3584 | 3584 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
3585 | 3585 | #Calculate Phases |
|
|
3586 | 3586 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
3587 | 3587 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
3588 | 3588 | dataOut.data_output = -phasesOff |
|
|
3589 | 3589 | dataOut.flagNoData = False |
|
|
3590 | 3590 | self.__buffer = None |
|
|
3591 | 3591 | |
|
|
3592 | 3592 | |
|
|
3593 | 3593 | return |
|
|
3594 | 3594 | |
|
|
3595 | 3595 | class SMOperations(): |
|
|
3596 | 3596 | |
|
|
3597 | 3597 | def __init__(self): |
|
|
3598 | 3598 | |
|
|
3599 | 3599 | return |
|
|
3600 | 3600 | |
|
|
3601 | 3601 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
3602 | 3602 | |
|
|
3603 | 3603 | arrayParameters = arrayParameters0.copy() |
|
|
3604 | 3604 | hmin = h[0] |
|
|
3605 | 3605 | hmax = h[1] |
|
|
3606 | 3606 | |
|
|
3607 | 3607 | #Calculate AOA (Error N 3, 4) |
|
|
3608 | 3608 | #JONES ET AL. 1998 |
|
|
3609 | 3609 | AOAthresh = numpy.pi/8 |
|
|
3610 | 3610 | error = arrayParameters[:,-1] |
|
|
3611 | 3611 | phases = -arrayParameters[:,8:12] + jph |
|
|
3612 | 3612 | # phases = numpy.unwrap(phases) |
|
|
3613 | 3613 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
3614 | 3614 | |
|
|
3615 | 3615 | #Calculate Heights (Error N 13 and 14) |
|
|
3616 | 3616 | error = arrayParameters[:,-1] |
|
|
3617 | 3617 | Ranges = arrayParameters[:,1] |
|
|
3618 | 3618 | zenith = arrayParameters[:,4] |
|
|
3619 | 3619 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
3620 | 3620 | |
|
|
3621 | 3621 | #----------------------- Get Final data ------------------------------------ |
|
|
3622 | 3622 | # error = arrayParameters[:,-1] |
|
|
3623 | 3623 | # ind1 = numpy.where(error==0)[0] |
|
|
3624 | 3624 | # arrayParameters = arrayParameters[ind1,:] |
|
|
3625 | 3625 | |
|
|
3626 | 3626 | return arrayParameters |
|
|
3627 | 3627 | |
|
|
3628 | 3628 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
3629 | 3629 | |
|
|
3630 | 3630 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3631 | 3631 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
3632 | 3632 | |
|
|
3633 | 3633 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3634 | 3634 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3635 | 3635 | arrayAOA[:,2] = cosDirError |
|
|
3636 | 3636 | |
|
|
3637 | 3637 | azimuthAngle = arrayAOA[:,0] |
|
|
3638 | 3638 | zenithAngle = arrayAOA[:,1] |
|
|
3639 | 3639 | |
|
|
3640 | 3640 | #Setting Error |
|
|
3641 | 3641 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
3642 | 3642 | error[indError] = 0 |
|
|
3643 | 3643 | #Number 3: AOA not fesible |
|
|
3644 | 3644 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3645 | 3645 | error[indInvalid] = 3 |
|
|
3646 | 3646 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3647 | 3647 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3648 | 3648 | error[indInvalid] = 4 |
|
|
3649 | 3649 | return arrayAOA, error |
|
|
3650 | 3650 | |
|
|
3651 | 3651 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
3652 | 3652 | |
|
|
3653 | 3653 | #Initializing some variables |
|
|
3654 | 3654 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3655 | 3655 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3656 | 3656 | |
|
|
3657 | 3657 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3658 | 3658 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3659 | 3659 | |
|
|
3660 | 3660 | |
|
|
3661 | 3661 | for i in range(2): |
|
|
3662 | 3662 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
3663 | 3663 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
3664 | 3664 | d0 = distances[pairsList[i][0]] |
|
|
3665 | 3665 | d1 = distances[pairsList[i][1]] |
|
|
3666 | 3666 | |
|
|
3667 | 3667 | ph0_aux = ph0 + ph1 |
|
|
3668 | 3668 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
3669 | 3669 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
3670 | 3670 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
3671 | 3671 | #First Estimation |
|
|
3672 | 3672 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
3673 | 3673 | |
|
|
3674 | 3674 | #Most-Accurate Second Estimation |
|
|
3675 | 3675 | phi1_aux = ph0 - ph1 |
|
|
3676 | 3676 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3677 | 3677 | #Direction Cosine 1 |
|
|
3678 | 3678 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
3679 | 3679 | |
|
|
3680 | 3680 | #Searching the correct Direction Cosine |
|
|
3681 | 3681 | cosdir0_aux = cosdir0[:,i] |
|
|
3682 | 3682 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3683 | 3683 | #Minimum Distance |
|
|
3684 | 3684 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3685 | 3685 | indcos = cosDiff.argmin(axis = 1) |
|
|
3686 | 3686 | #Saving Value obtained |
|
|
3687 | 3687 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3688 | 3688 | |
|
|
3689 | 3689 | return cosdir0, cosdir |
|
|
3690 | 3690 | |
|
|
3691 | 3691 | def __calculateAOA(self, cosdir, azimuth): |
|
|
3692 | 3692 | cosdirX = cosdir[:,0] |
|
|
3693 | 3693 | cosdirY = cosdir[:,1] |
|
|
3694 | 3694 | |
|
|
3695 | 3695 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3696 | 3696 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
3697 | 3697 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3698 | 3698 | |
|
|
3699 | 3699 | return angles |
|
|
3700 | 3700 | |
|
|
3701 | 3701 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3702 | 3702 | |
|
|
3703 | 3703 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3704 | 3704 | Re = 6371 #Earth Radius |
|
|
3705 | 3705 | heights = numpy.zeros(Ranges.shape) |
|
|
3706 | 3706 | |
|
|
3707 | 3707 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
3708 | 3708 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3709 | 3709 | |
|
|
3710 | 3710 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3711 | 3711 | |
|
|
3712 | 3712 | Ri = Ranges + R_aux |
|
|
3713 | 3713 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3714 | 3714 | |
|
|
3715 | 3715 | #Check if there is a height between 70 and 110 km |
|
|
3716 | 3716 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3717 | 3717 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
3718 | 3718 | |
|
|
3719 | 3719 | hCorr = hi[ind_h, :] |
|
|
3720 | 3720 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3721 | 3721 | |
|
|
3722 | 3722 | hCorr = hi[ind_hCorr][:len(ind_h)] |
|
|
3723 | 3723 | heights[ind_h] = hCorr |
|
|
3724 | 3724 | |
|
|
3725 | 3725 | #Setting Error |
|
|
3726 | 3726 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3727 | 3727 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3728 | 3728 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
3729 | 3729 | error[indError] = 0 |
|
|
3730 | 3730 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3731 | 3731 | error[indInvalid2] = 14 |
|
|
3732 | 3732 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3733 | 3733 | error[indInvalid1] = 13 |
|
|
3734 | 3734 | |
|
|
3735 | 3735 | return heights, error |
|
|
3736 | 3736 | |
|
|
3737 | 3737 | def getPhasePairs(self, channelPositions): |
|
|
3738 | 3738 | chanPos = numpy.array(channelPositions) |
|
|
3739 | 3739 | listOper = list(itertools.combinations(list(range(5)),2)) |
|
|
3740 | 3740 | |
|
|
3741 | 3741 | distances = numpy.zeros(4) |
|
|
3742 | 3742 | axisX = [] |
|
|
3743 | 3743 | axisY = [] |
|
|
3744 | 3744 | distX = numpy.zeros(3) |
|
|
3745 | 3745 | distY = numpy.zeros(3) |
|
|
3746 | 3746 | ix = 0 |
|
|
3747 | 3747 | iy = 0 |
|
|
3748 | 3748 | |
|
|
3749 | 3749 | pairX = numpy.zeros((2,2)) |
|
|
3750 | 3750 | pairY = numpy.zeros((2,2)) |
|
|
3751 | 3751 | |
|
|
3752 | 3752 | for i in range(len(listOper)): |
|
|
3753 | 3753 | pairi = listOper[i] |
|
|
3754 | 3754 | |
|
|
3755 | 3755 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
3756 | 3756 | |
|
|
3757 | 3757 | if posDif[0] == 0: |
|
|
3758 | 3758 | axisY.append(pairi) |
|
|
3759 | 3759 | distY[iy] = posDif[1] |
|
|
3760 | 3760 | iy += 1 |
|
|
3761 | 3761 | elif posDif[1] == 0: |
|
|
3762 | 3762 | axisX.append(pairi) |
|
|
3763 | 3763 | distX[ix] = posDif[0] |
|
|
3764 | 3764 | ix += 1 |
|
|
3765 | 3765 | |
|
|
3766 | 3766 | for i in range(2): |
|
|
3767 | 3767 | if i==0: |
|
|
3768 | 3768 | dist0 = distX |
|
|
3769 | 3769 | axis0 = axisX |
|
|
3770 | 3770 | else: |
|
|
3771 | 3771 | dist0 = distY |
|
|
3772 | 3772 | axis0 = axisY |
|
|
3773 | 3773 | |
|
|
3774 | 3774 | side = numpy.argsort(dist0)[:-1] |
|
|
3775 | 3775 | axis0 = numpy.array(axis0)[side,:] |
|
|
3776 | 3776 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
3777 | 3777 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
3778 | 3778 | side = axis1[axis1 != chanC] |
|
|
3779 | 3779 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
3780 | 3780 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
3781 | 3781 | if diff1<0: |
|
|
3782 | 3782 | chan2 = side[0] |
|
|
3783 | 3783 | d2 = numpy.abs(diff1) |
|
|
3784 | 3784 | chan1 = side[1] |
|
|
3785 | 3785 | d1 = numpy.abs(diff2) |
|
|
3786 | 3786 | else: |
|
|
3787 | 3787 | chan2 = side[1] |
|
|
3788 | 3788 | d2 = numpy.abs(diff2) |
|
|
3789 | 3789 | chan1 = side[0] |
|
|
3790 | 3790 | d1 = numpy.abs(diff1) |
|
|
3791 | 3791 | |
|
|
3792 | 3792 | if i==0: |
|
|
3793 | 3793 | chanCX = chanC |
|
|
3794 | 3794 | chan1X = chan1 |
|
|
3795 | 3795 | chan2X = chan2 |
|
|
3796 | 3796 | distances[0:2] = numpy.array([d1,d2]) |
|
|
3797 | 3797 | else: |
|
|
3798 | 3798 | chanCY = chanC |
|
|
3799 | 3799 | chan1Y = chan1 |
|
|
3800 | 3800 | chan2Y = chan2 |
|
|
3801 | 3801 | distances[2:4] = numpy.array([d1,d2]) |
|
|
3802 | 3802 | # axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
3803 | 3803 | # |
|
|
3804 | 3804 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
3805 | 3805 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
3806 | 3806 | # |
|
|
3807 | 3807 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
3808 | 3808 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
3809 | 3809 | # channel25X = int(pairX[0,ind25X]) |
|
|
3810 | 3810 | # channel20X = int(pairX[1,ind20X]) |
|
|
3811 | 3811 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
3812 | 3812 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
3813 | 3813 | # channel25Y = int(pairY[0,ind25Y]) |
|
|
3814 | 3814 | # channel20Y = int(pairY[1,ind20Y]) |
|
|
3815 | 3815 | |
|
|
3816 | 3816 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
3817 | 3817 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
3818 | 3818 | |
|
|
3819 | 3819 | return pairslist, distances |
|
|
3820 | 3820 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
3821 | 3821 | # |
|
|
3822 | 3822 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3823 | 3823 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
3824 | 3824 | # |
|
|
3825 | 3825 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3826 | 3826 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3827 | 3827 | # arrayAOA[:,2] = cosDirError |
|
|
3828 | 3828 | # |
|
|
3829 | 3829 | # azimuthAngle = arrayAOA[:,0] |
|
|
3830 | 3830 | # zenithAngle = arrayAOA[:,1] |
|
|
3831 | 3831 | # |
|
|
3832 | 3832 | # #Setting Error |
|
|
3833 | 3833 | # #Number 3: AOA not fesible |
|
|
3834 | 3834 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3835 | 3835 | # error[indInvalid] = 3 |
|
|
3836 | 3836 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3837 | 3837 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3838 | 3838 | # error[indInvalid] = 4 |
|
|
3839 | 3839 | # return arrayAOA, error |
|
|
3840 | 3840 | # |
|
|
3841 | 3841 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
3842 | 3842 | # |
|
|
3843 | 3843 | # #Initializing some variables |
|
|
3844 | 3844 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3845 | 3845 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3846 | 3846 | # |
|
|
3847 | 3847 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3848 | 3848 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3849 | 3849 | # |
|
|
3850 | 3850 | # |
|
|
3851 | 3851 | # for i in range(2): |
|
|
3852 | 3852 | # #First Estimation |
|
|
3853 | 3853 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
3854 | 3854 | # #Dealias |
|
|
3855 | 3855 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
3856 | 3856 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
|
3857 | 3857 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
3858 | 3858 | # phi0_aux[indcsi] += 2*numpy.pi |
|
|
3859 | 3859 | # #Direction Cosine 0 |
|
|
3860 | 3860 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
3861 | 3861 | # |
|
|
3862 | 3862 | # #Most-Accurate Second Estimation |
|
|
3863 | 3863 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
3864 | 3864 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3865 | 3865 | # #Direction Cosine 1 |
|
|
3866 | 3866 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
3867 | 3867 | # |
|
|
3868 | 3868 | # #Searching the correct Direction Cosine |
|
|
3869 | 3869 | # cosdir0_aux = cosdir0[:,i] |
|
|
3870 | 3870 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3871 | 3871 | # #Minimum Distance |
|
|
3872 | 3872 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3873 | 3873 | # indcos = cosDiff.argmin(axis = 1) |
|
|
3874 | 3874 | # #Saving Value obtained |
|
|
3875 | 3875 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3876 | 3876 | # |
|
|
3877 | 3877 | # return cosdir0, cosdir |
|
|
3878 | 3878 | # |
|
|
3879 | 3879 | # def __calculateAOA(self, cosdir, azimuth): |
|
|
3880 | 3880 | # cosdirX = cosdir[:,0] |
|
|
3881 | 3881 | # cosdirY = cosdir[:,1] |
|
|
3882 | 3882 | # |
|
|
3883 | 3883 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3884 | 3884 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
3885 | 3885 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3886 | 3886 | # |
|
|
3887 | 3887 | # return angles |
|
|
3888 | 3888 | # |
|
|
3889 | 3889 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3890 | 3890 | # |
|
|
3891 | 3891 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3892 | 3892 | # Re = 6371 #Earth Radius |
|
|
3893 | 3893 | # heights = numpy.zeros(Ranges.shape) |
|
|
3894 | 3894 | # |
|
|
3895 | 3895 | # R_aux = numpy.array([0,1,2])*Ramb |
|
|
3896 | 3896 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3897 | 3897 | # |
|
|
3898 | 3898 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3899 | 3899 | # |
|
|
3900 | 3900 | # Ri = Ranges + R_aux |
|
|
3901 | 3901 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3902 | 3902 | # |
|
|
3903 | 3903 | # #Check if there is a height between 70 and 110 km |
|
|
3904 | 3904 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3905 | 3905 | # ind_h = numpy.where(h_bool == 1)[0] |
|
|
3906 | 3906 | # |
|
|
3907 | 3907 | # hCorr = hi[ind_h, :] |
|
|
3908 | 3908 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3909 | 3909 | # |
|
|
3910 | 3910 | # hCorr = hi[ind_hCorr] |
|
|
3911 | 3911 | # heights[ind_h] = hCorr |
|
|
3912 | 3912 | # |
|
|
3913 | 3913 | # #Setting Error |
|
|
3914 | 3914 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3915 | 3915 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3916 | 3916 | # |
|
|
3917 | 3917 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3918 | 3918 | # error[indInvalid2] = 14 |
|
|
3919 | 3919 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3920 | 3920 | # error[indInvalid1] = 13 |
|
|
3921 | 3921 | # |
|
|
3922 | 3922 | # return heights, error |
|
|
3923 | 3923 | |
|
|
3924 | 3924 | |
|
|
3925 | 3925 | class WeatherRadar(Operation): |
|
|
3926 | 3926 | ''' |
|
|
3927 | 3927 | Function tat implements Weather Radar operations- |
|
|
3928 | 3928 | Input: |
|
|
3929 | 3929 | Output: |
|
|
3930 | 3930 | Parameters affected: |
|
|
3931 | 3931 | |
|
|
3932 | 3932 | Conversion Watt |
|
|
3933 | 3933 | Referencia |
|
|
3934 | 3934 | https://www.tek.com/en/blog/calculating-rf-power-iq-samples |
|
|
3935 | 3935 | |
|
|
3936 | 3936 | data_param = (nCh, 8, nHeis) |
|
|
3937 | 3937 | S, V, W, SNR, Z, D, P, R |
|
|
3938 | 3938 | Power, Velocity, Spectral width, SNR, Reflectivity, Differential reflectivity, PHI DP, RHO HV |
|
|
3939 | 3939 | ''' |
|
|
3940 | 3940 | isConfig = False |
|
|
3941 | 3941 | variableList = None |
|
|
3942 | 3942 | |
|
|
3943 | 3943 | def __init__(self): |
|
|
3944 | 3944 | Operation.__init__(self) |
|
|
3945 | 3945 | |
|
|
3946 | 3946 | def setup(self,dataOut,variableList= None,Pt=0,Gt=0,Gr=0,Glna=0,lambda_=0, aL=0, |
|
|
3947 | 3947 | tauW= 0,thetaT=0,thetaR=0,Km =0): |
|
|
3948 | 3948 | |
|
|
3949 | 3949 | self.nCh = dataOut.nChannels |
|
|
3950 | 3950 | self.nHeis = dataOut.nHeights |
|
|
3951 | 3951 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
3952 | 3952 | self.Range = numpy.arange(dataOut.nHeights)*deltaHeight + dataOut.heightList[0] |
|
|
3953 | 3953 | self.Range = self.Range.reshape(1,self.nHeis) |
|
|
3954 | 3954 | self.Range = numpy.tile(self.Range,[self.nCh,1]) |
|
|
3955 | 3955 | '''-----------1 Constante del Radar----------''' |
|
|
3956 | 3956 | self.Pt = Pt # Pmax =200 W x DC=(0.2 useg/400useg) |
|
|
3957 | 3957 | self.Gt = Gt # 38 db |
|
|
3958 | 3958 | self.Gr = Gr # 38 dB |
|
|
3959 | 3959 | self.Glna = Glna # 60 dB |
|
|
3960 | 3960 | self.lambda_ = lambda_ # 3.2 cm 0.032 m. |
|
|
3961 | 3961 | self.aL = aL # Perdidas |
|
|
3962 | 3962 | self.tauW = tauW #ancho de pulso 0.2useg pulso corto. |
|
|
3963 | 3963 | self.thetaT = thetaT # 1.8º -- 0.0314 rad |
|
|
3964 | 3964 | self.thetaR = thetaR # 1.8ª --0.0314 rad |
|
|
3965 | 3965 | self.Km = Km |
|
|
3966 | 3966 | Numerator = ((4*numpy.pi)**3 * aL**2 * 16 *numpy.log(2)*(10**18)) |
|
|
3967 | 3967 | Denominator = (Pt *(10**(Gt/10.0))*(10**(Gr/10.0))*(10**(Glna/10.0))* lambda_**2 * SPEED_OF_LIGHT * tauW * numpy.pi*thetaT*thetaR) |
|
|
3968 | 3968 | self.RadarConstant = Numerator/Denominator |
|
|
3969 | 3969 | self.variableList = variableList |
|
|
3970 | 3970 | if self.variableList== None: |
|
|
3971 | 3971 | self.variableList= ['Z','D','R','P'] |
|
|
3972 | 3972 | |
|
|
3973 | 3973 | def setMoments(self, dataOut): |
|
|
3974 | 3974 | # S, V, W, SNR, Z, D, P, R |
|
|
3975 | 3975 | type = dataOut.inputUnit |
|
|
3976 | 3976 | nCh = dataOut.nChannels |
|
|
3977 | 3977 | nHeis = dataOut.nHeights |
|
|
3978 | 3978 | data_param = numpy.zeros((nCh, 8, nHeis)) |
|
|
3979 | 3979 | if type == "Voltage": |
|
|
3980 | 3980 | factor = 1 |
|
|
3981 | 3981 | data_param[:,0,:] = dataOut.dataPP_POW/(factor)#dataOut.dataPP_POWER/(factor) |
|
|
3982 | 3982 | data_param[:,1,:] = dataOut.dataPP_DOP |
|
|
3983 | 3983 | data_param[:,2,:] = dataOut.dataPP_WIDTH |
|
|
3984 | 3984 | data_param[:,3,:] = dataOut.dataPP_SNR |
|
|
3985 | 3985 | if type == "Spectra": |
|
|
3986 | 3986 | factor = dataOut.normFactor |
|
|
3987 | 3987 | data_param[:,0,:] = dataOut.data_pow/(factor) |
|
|
3988 | 3988 | data_param[:,1,:] = dataOut.data_dop |
|
|
3989 | 3989 | data_param[:,2,:] = dataOut.data_width |
|
|
3990 | 3990 | data_param[:,3,:] = dataOut.data_snr |
|
|
3991 | 3991 | return data_param |
|
|
3992 | 3992 | |
|
|
3993 | 3993 | def getCoeficienteCorrelacionROhv_R(self,dataOut): |
|
|
3994 | 3994 | type = dataOut.inputUnit |
|
|
3995 | 3995 | nHeis = dataOut.nHeights |
|
|
3996 | 3996 | data_RhoHV_R = numpy.zeros((nHeis)) |
|
|
3997 | 3997 | if type == "Voltage": |
|
|
3998 | 3998 | powa = dataOut.data_param[0,0,:] |
|
|
3999 | 3999 | powb = dataOut.data_param[1,0,:] |
|
|
4000 | 4000 | ccf = dataOut.dataPP_CCF |
|
|
4001 | 4001 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
4002 | 4002 | data_RhoHV_R = numpy.abs(avgcoherenceComplex) |
|
|
4003 | 4003 | if type == "Spectra": |
|
|
4004 | 4004 | data_RhoHV_R = dataOut.getCoherence() |
|
|
4005 | 4005 | |
|
|
4006 | 4006 | return data_RhoHV_R |
|
|
4007 | 4007 | |
|
|
4008 | 4008 | def getFasediferencialPhiD_P(self,dataOut,phase= True): |
|
|
4009 | 4009 | type = dataOut.inputUnit |
|
|
4010 | 4010 | nHeis = dataOut.nHeights |
|
|
4011 | 4011 | data_PhiD_P = numpy.zeros((nHeis)) |
|
|
4012 | 4012 | if type == "Voltage": |
|
|
4013 | 4013 | powa = dataOut.data_param[0,0,:] |
|
|
4014 | 4014 | powb = dataOut.data_param[1,0,:] |
|
|
4015 | 4015 | ccf = dataOut.dataPP_CCF |
|
|
4016 | 4016 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
4017 | 4017 | if phase: |
|
|
4018 | 4018 | data_PhiD_P = numpy.arctan2(avgcoherenceComplex.imag, |
|
|
4019 | 4019 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
|
4020 | 4020 | if type == "Spectra": |
|
|
4021 | 4021 | data_PhiD_P = dataOut.getCoherence(phase = phase) |
|
|
4022 | 4022 | |
|
|
4023 | 4023 | return data_PhiD_P |
|
|
4024 | 4024 | |
|
|
4025 | 4025 | def getReflectividad_D(self,dataOut,type): |
|
|
4026 | 4026 | '''-----------------------------Potencia de Radar -Signal S-----------------------------''' |
|
|
4027 | 4027 | |
|
|
4028 | 4028 | Pr = dataOut.data_param[:,0,:] |
|
|
4029 | 4029 | '''---------------------------- Calculo de Noise y threshold para Reflectividad---------''' |
|
|
4030 | 4030 | # noise = numpy.zeros(self.nCh) |
|
|
4031 | 4031 | # for i in range(self.nCh): |
|
|
4032 | 4032 | # noise[i] = hildebrand_sekhon(Pr[i,:], 1) |
|
|
4033 | 4033 | # window = numpy.where(Pr[i,:]<1.3*noise[i]) |
|
|
4034 | 4034 | # Pr[i,window]= 1e-10 |
|
|
4035 | 4035 | Pr = Pr/1000.0 # Conversion Watt |
|
|
4036 | 4036 | '''-----------2 Reflectividad del Radar y Factor de Reflectividad------''' |
|
|
4037 | 4037 | self.n_radar = numpy.zeros((self.nCh,self.nHeis)) |
|
|
4038 | 4038 | self.Z_radar = numpy.zeros((self.nCh,self.nHeis)) |
|
|
4039 | 4039 | |
|
|
4040 | 4040 | for R in range(self.nHeis): |
|
|
4041 | 4041 | self.n_radar[:,R] = self.RadarConstant*Pr[:,R]* (self.Range[:,R]*(10**3))**2 |
|
|
4042 | 4042 | |
|
|
4043 | 4043 | self.Z_radar[:,R] = self.n_radar[:,R]* self.lambda_**4/( numpy.pi**5 * self.Km**2) |
|
|
4044 | 4044 | |
|
|
4045 | 4045 | '''----------- Factor de Reflectividad Equivalente lamda_ < 10 cm , lamda_= 3.2cm-------''' |
|
|
4046 | 4046 | Zeh = self.Z_radar |
|
|
4047 | 4047 | #print("---------------------------------------------------------------------") |
|
|
4048 | 4048 | #print("RangedBz",10*numpy.log10((self.Range[0,-10:]*(10**3))**2)) |
|
|
4049 | 4049 | #print("CTE",10*numpy.log10(self.RadarConstant)) |
|
|
4050 | 4050 | #print("Pr first10",10*numpy.log10(Pr[0,:20])) |
|
|
4051 | 4051 | #print("Pr last10",10*numpy.log10(Pr[0,-20:])) |
|
|
4052 | 4052 | #print("LCTE",10*numpy.log10(self.lambda_**4/( numpy.pi**5 * self.Km**2))) |
|
|
4053 | 4053 | if self.Pt<0.3: |
|
|
4054 | 4054 | factor=10.072 |
|
|
4055 | 4055 | else: |
|
|
4056 | 4056 | factor=23.072 |
|
|
4057 | 4057 | |
|
|
4058 | 4058 | dBZeh = 10*numpy.log10(Zeh) + factor |
|
|
4059 | 4059 | if type=='N': |
|
|
4060 | 4060 | return dBZeh |
|
|
4061 | 4061 | elif type=='D': |
|
|
4062 | 4062 | Zdb_D = dBZeh[0] - dBZeh[1] |
|
|
4063 | 4063 | return Zdb_D |
|
|
4064 | 4064 | |
|
|
4065 | 4065 | def getRadialVelocity_V(self,dataOut): |
|
|
4066 | 4066 | velRadial_V = dataOut.data_param[:,1,:] |
|
|
4067 | 4067 | return velRadial_V |
|
|
4068 | 4068 | |
|
|
4069 | 4069 | def getAnchoEspectral_W(self,dataOut): |
|
|
4070 | 4070 | Sigmav_W = dataOut.data_param[:,2,:] |
|
|
4071 | 4071 | return Sigmav_W |
|
|
4072 | 4072 | |
|
|
4073 | 4073 | |
|
|
4074 | 4074 | def run(self,dataOut,variableList=None,Pt=1.58,Gt=38.5,Gr=38.5,Glna=70.0,lambda_=0.032, aL=1, |
|
|
4075 | 4075 | tauW= 0.2,thetaT=0.0314,thetaR=0.0314,Km =0.93): |
|
|
4076 | 4076 | |
|
|
4077 | 4077 | if not self.isConfig: |
|
|
4078 | 4078 | self.setup(dataOut= dataOut, variableList=variableList,Pt=Pt,Gt=Gt,Gr=Gr,Glna=Glna,lambda_=lambda_, aL=aL, |
|
|
4079 | 4079 | tauW= tauW,thetaT=thetaT,thetaR=thetaR,Km =Km) |
|
|
4080 | 4080 | self.isConfig = True |
|
|
4081 | 4081 | |
|
|
4082 | 4082 | dataOut.data_param = self.setMoments(dataOut) |
|
|
4083 | 4083 | |
|
|
4084 | 4084 | for i in range(len(self.variableList)): |
|
|
4085 | 4085 | if self.variableList[i] == 'Z': |
|
|
4086 | 4086 | dataOut.data_param[:,4,:] =self.getReflectividad_D(dataOut=dataOut,type='N') |
|
|
4087 | 4087 | if self.variableList[i] == 'D' and dataOut.nChannels>1: |
|
|
4088 | 4088 | dataOut.data_param[:,5,:] =self.getReflectividad_D(dataOut=dataOut,type='D') |
|
|
4089 | 4089 | if self.variableList[i] == 'P' and dataOut.nChannels>1: |
|
|
4090 | 4090 | dataOut.data_param[:,6,:] =self.getFasediferencialPhiD_P(dataOut=dataOut, phase=True) |
|
|
4091 | 4091 | if self.variableList[i] == 'R' and dataOut.nChannels>1: |
|
|
4092 | 4092 | dataOut.data_param[:,7,:] = self.getCoeficienteCorrelacionROhv_R(dataOut) |
|
|
4093 | 4093 | |
|
|
4094 | 4094 | return dataOut |
|
|
4095 | 4095 | |
|
|
4096 | 4096 | class PedestalInformation(Operation): |
|
|
4097 | 4097 | |
|
|
4098 | 4098 | def __init__(self): |
|
|
4099 | 4099 | Operation.__init__(self) |
|
|
4100 | 4100 | self.filename = False |
|
|
4101 | 4101 | self.delay = 32 |
|
|
4102 | 4102 | self.nTries = 3 |
|
|
4103 | 4103 | self.nFiles = 5 |
|
|
4104 | 4104 | self.flagAskMode = False |
|
|
4105 | 4105 | |
|
|
4106 | 4106 | def find_file(self, timestamp): |
|
|
4107 | 4107 | |
|
|
4108 | 4108 | dt = datetime.datetime.utcfromtimestamp(timestamp) |
|
|
4109 | 4109 | path = os.path.join(self.path, dt.strftime('%Y-%m-%dT%H-00-00')) |
|
|
4110 | 4110 | |
|
|
4111 | 4111 | if not os.path.exists(path): |
|
|
4112 | 4112 | return False |
|
|
4113 | 4113 | fileList = glob.glob(os.path.join(path, '*.h5')) |
|
|
4114 | 4114 | fileList.sort() |
|
|
4115 | 4115 | return fileList |
|
|
4116 | 4116 | |
|
|
4117 | 4117 | def find_next_file(self): |
|
|
4118 | 4118 | |
|
|
4119 | 4119 | while True: |
|
|
4120 | 4120 | if self.utctime < self.utcfile: |
|
|
4121 | 4121 | self.flagNoData = True |
|
|
4122 | 4122 | break |
|
|
4123 | 4123 | self.flagNoData = False |
|
|
4124 | 4124 | file_size = len(self.fp['Data']['utc']) |
|
|
4125 | 4125 | if self.utctime < self.utcfile+file_size*self.interval: |
|
|
4126 | 4126 | break |
|
|
4127 | 4127 | dt = datetime.datetime.utcfromtimestamp(self.utcfile) |
|
|
4128 | 4128 | if dt.second > 0: |
|
|
4129 | 4129 | self.utcfile -= dt.second |
|
|
4130 | 4130 | self.utcfile += self.samples*self.interval |
|
|
4131 | 4131 | dt = datetime.datetime.utcfromtimestamp(self.utcfile) |
|
|
4132 | 4132 | path = os.path.join(self.path, dt.strftime('%Y-%m-%dT%H-00-00')) |
|
|
4133 | 4133 | self.filename = os.path.join(path, 'pos@{}.000.h5'.format(int(self.utcfile))) |
|
|
4134 | 4134 | |
|
|
4135 | 4135 | for i in range(self.nFiles): |
|
|
4136 | 4136 | ok = False |
|
|
4137 | 4137 | for j in range(self.nTries): |
|
|
4138 | 4138 | ok = False |
|
|
4139 | 4139 | try: |
|
|
4140 | 4140 | if not os.path.exists(self.filename): |
|
|
4141 | 4141 | log.warning('Waiting {}s for position files...'.format(self.delay), self.name) |
|
|
4142 | 4142 | time.sleep(1) |
|
|
4143 | 4143 | continue |
|
|
4144 | 4144 | self.fp.close() |
|
|
4145 | 4145 | self.fp = h5py.File(self.filename, 'r') |
|
|
4146 | self.ele = self.fp['Data']['ele_pos'][:] | |
|
|
4147 | self.azi = self.fp['Data']['azi_pos'][:] + 26.27 | |
|
|
4148 | self.azi[self.azi>360] = self.azi[self.azi>360] - 360 | |
|
|
4146 | 4149 | log.log('Opening file: {}'.format(self.filename), self.name) |
|
|
4147 | 4150 | ok = True |
|
|
4148 | 4151 | break |
|
|
4149 | 4152 | except Exception as e: |
|
|
4150 | 4153 | log.warning('Waiting {}s for position file to be ready...'.format(self.delay), self.name) |
|
|
4151 | 4154 | time.sleep(self.delay) |
|
|
4152 | 4155 | continue |
|
|
4153 | 4156 | if ok: |
|
|
4154 | 4157 | break |
|
|
4155 | 4158 | log.warning('Trying next file...', self.name) |
|
|
4156 | 4159 | self.utcfile += self.samples*self.interval |
|
|
4157 | 4160 | dt = datetime.datetime.utcfromtimestamp(self.utcfile) |
|
|
4158 | 4161 | path = os.path.join(self.path, dt.strftime('%Y-%m-%dT%H-00-00')) |
|
|
4159 | 4162 | self.filename = os.path.join(path, 'pos@{}.000.h5'.format(int(self.utcfile))) |
|
|
4160 | 4163 | if not ok: |
|
|
4161 | 4164 | log.error('No new position files found in {}'.format(path)) |
|
|
4162 | 4165 | raise IOError('No new position files found in {}'.format(path)) |
|
|
4163 | 4166 | |
|
|
4164 | 4167 | |
|
|
4165 | def find_mode(self,index): | |
|
|
4168 | def find_mode(self, index): | |
|
|
4166 | 4169 | sample_max = 20 |
|
|
4167 | 4170 | start = index |
|
|
4168 | flag_mode = None | |
|
|
4169 | azi = self.fp['Data']['azi_pos'][:] | |
|
|
4170 | ele = self.fp['Data']['ele_pos'][:] | |
|
|
4171 | flag_mode = None | |
|
|
4171 | 4172 | |
|
|
4172 | 4173 | while True: |
|
|
4173 |
|
|
|
|
4174 | print("CANNOT KNOW IF MODE IS PPI OR RHI, ANALIZE NEXT FILE") | |
|
|
4175 | print("ele",ele[start-sample_max:start+sample_max]) | |
|
|
4176 | print("azi",azi[start-sample_max:start+sample_max]) | |
|
|
4174 | print(start, sample_max, numpy.shape(self.ele)) | |
|
|
4175 | if start+sample_max > numpy.shape(self.ele)[0]: | |
|
|
4177 | 4176 | if sample_max == 10: |
|
|
4177 | print("CANNOT KNOW IF MODE IS PPI OR RHI, ANALIZE NEXT FILE") | |
|
|
4178 | 4178 | break |
|
|
4179 | 4179 | else: |
|
|
4180 | 4180 | sample_max = 10 |
|
|
4181 | 4181 | continue |
|
|
4182 | sigma_ele = numpy.nanstd(ele[start:start+sample_max]) | |
|
|
4183 | sigma_azi = numpy.nanstd(azi[start:start+sample_max]) | |
|
|
4182 | sigma_ele = numpy.nanstd(self.ele[start:start+sample_max]) | |
|
|
4183 | sigma_azi = numpy.nanstd(self.azi[start:start+sample_max]) | |
|
|
4184 | print("ele",self.ele[start-sample_max:start+sample_max]) | |
|
|
4185 | print("azi",self.azi[start-sample_max:start+sample_max]) | |
|
|
4186 | print(sigma_azi, sigma_ele) | |
|
|
4184 | 4187 | |
|
|
4185 | 4188 | if sigma_ele<.5 and sigma_azi<.5: |
|
|
4186 | 4189 | if sigma_ele<sigma_azi: |
|
|
4187 | 4190 | flag_mode = 'PPI' |
|
|
4188 | 4191 | break |
|
|
4189 | 4192 | else: |
|
|
4190 | 4193 | flag_mode = 'RHI' |
|
|
4191 | 4194 | break |
|
|
4192 | 4195 | elif sigma_ele < .5: |
|
|
4193 | 4196 | flag_mode = 'PPI' |
|
|
4194 | 4197 | break |
|
|
4195 | 4198 | elif sigma_azi < .5: |
|
|
4196 | 4199 | flag_mode = 'RHI' |
|
|
4197 | 4200 | break |
|
|
4198 | 4201 | |
|
|
4199 | 4202 | start += sample_max |
|
|
4200 | 4203 | |
|
|
4201 | 4204 | return flag_mode |
|
|
4202 | 4205 | |
|
|
4203 | 4206 | def get_values(self): |
|
|
4204 | 4207 | |
|
|
4205 | 4208 | if self.flagNoData: |
|
|
4206 | 4209 | return numpy.nan, numpy.nan, numpy.nan #Should be self.mode? |
|
|
4207 | 4210 | else: |
|
|
4208 | 4211 | index = int((self.utctime-self.utcfile)/self.interval) |
|
|
4212 | try: | |
|
|
4213 | #print( self.azi[index], self.ele[index], None) | |
|
|
4214 | return self.azi[index], self.ele[index], None | |
|
|
4215 | except: | |
|
|
4216 | return numpy.nan, numpy.nan, numpy.nan | |
|
|
4209 | 4217 | |
|
|
4210 | 4218 | if self.flagAskMode: |
|
|
4211 | 4219 | mode = self.find_mode(index) |
|
|
4220 | print('MODE: ', mode) | |
|
|
4212 | 4221 | else: |
|
|
4213 | 4222 | mode = self.mode |
|
|
4214 | 4223 | |
|
|
4215 | 4224 | if mode is not None: |
|
|
4216 |
return self. |
|
|
|
4225 | return self.azi[index], self.ele[index], mode | |
|
|
4217 | 4226 | else: |
|
|
4218 | 4227 | return numpy.nan, numpy.nan, numpy.nan |
|
|
4219 | 4228 | |
|
|
4220 | 4229 | def setup(self, dataOut, path, conf, samples, interval, mode): |
|
|
4221 | 4230 | |
|
|
4222 | 4231 | self.path = path |
|
|
4223 | 4232 | self.conf = conf |
|
|
4224 | 4233 | self.samples = samples |
|
|
4225 | 4234 | self.interval = interval |
|
|
4226 | 4235 | self.mode = mode |
|
|
4227 | 4236 | if mode is None: |
|
|
4228 | 4237 | self.flagAskMode = True |
|
|
4229 | 4238 | N = 0 |
|
|
4230 | 4239 | while True: |
|
|
4231 | 4240 | if N == self.nTries+1: |
|
|
4232 | 4241 | log.error('No position files found in {}'.format(path), self.name) |
|
|
4233 | 4242 | raise IOError('No position files found in {}'.format(path)) |
|
|
4234 | 4243 | filelist = self.find_file(dataOut.utctime) |
|
|
4235 | 4244 | |
|
|
4236 | 4245 | if filelist == 0: |
|
|
4237 | 4246 | N += 1 |
|
|
4238 | 4247 | log.warning('Waiting {}s for position files...'.format(self.delay), self.name) |
|
|
4239 | 4248 | time.sleep(self.delay) |
|
|
4240 | 4249 | continue |
|
|
4241 | 4250 | self.filename = filelist[0] |
|
|
4242 | 4251 | try: |
|
|
4243 | 4252 | self.fp = h5py.File(self.filename, 'r') |
|
|
4244 | 4253 | self.utcfile = int(self.filename.split('/')[-1][4:14]) |
|
|
4254 | ||
|
|
4255 | self.ele = self.fp['Data']['ele_pos'][:] | |
|
|
4256 | self.azi = self.fp['Data']['azi_pos'][:] + 26.27 | |
|
|
4257 | self.azi[self.azi>360] = self.azi[self.azi>360] - 360 | |
|
|
4245 | 4258 | break |
|
|
4246 | 4259 | except: |
|
|
4247 | 4260 | log.warning('Waiting {}s for position file to be ready...'.format(self.delay), self.name) |
|
|
4248 | 4261 | time.sleep(self.delay) |
|
|
4249 | 4262 | |
|
|
4250 | 4263 | def run(self, dataOut, path, conf=None, samples=1500, interval=0.04, time_offset=0, mode=None): |
|
|
4251 | 4264 | |
|
|
4252 | 4265 | if not self.isConfig: |
|
|
4253 | 4266 | self.setup(dataOut, path, conf, samples, interval, mode) |
|
|
4254 | 4267 | self.isConfig = True |
|
|
4255 | 4268 | |
|
|
4256 | 4269 | self.utctime = dataOut.utctime + time_offset |
|
|
4257 | 4270 | |
|
|
4258 | 4271 | self.find_next_file() |
|
|
4259 | 4272 | |
|
|
4260 | 4273 | az, el, scan = self.get_values() |
|
|
4274 | ||
|
|
4261 | 4275 | dataOut.flagNoData = False |
|
|
4262 | 4276 | if numpy.isnan(az) or numpy.isnan(el) : |
|
|
4263 | 4277 | dataOut.flagNoData = True |
|
|
4264 | 4278 | return dataOut |
|
|
4265 | ||
|
|
4266 | dataOut.azimuth = round(az, 2) | |
|
|
4279 | ||
|
|
4280 | dataOut.azimuth = round(az, 2) | |
|
|
4267 | 4281 | dataOut.elevation = round(el, 2) |
|
|
4268 | 4282 | dataOut.mode_op = scan |
|
|
4269 | 4283 | |
|
|
4270 | 4284 | return dataOut |
|
|
4271 | 4285 | |
|
|
4272 | 4286 | |
|
|
4273 | 4287 | class Block360(Operation): |
|
|
4274 | 4288 | ''' |
|
|
4275 | 4289 | ''' |
|
|
4276 | 4290 | isConfig = False |
|
|
4277 | 4291 | __profIndex = 0 |
|
|
4278 | 4292 | __initime = None |
|
|
4279 | 4293 | __lastdatatime = None |
|
|
4280 | 4294 | __buffer = None |
|
|
4281 | 4295 | __dataReady = False |
|
|
4282 | 4296 | n = None |
|
|
4283 | 4297 | __nch = 0 |
|
|
4284 | 4298 | __nHeis = 0 |
|
|
4285 | 4299 | index = 0 |
|
|
4286 | 4300 | mode = None |
|
|
4287 | 4301 | |
|
|
4288 | 4302 | def __init__(self,**kwargs): |
|
|
4289 | 4303 | Operation.__init__(self,**kwargs) |
|
|
4290 | 4304 | |
|
|
4291 | 4305 | def setup(self, dataOut, attr): |
|
|
4292 | 4306 | ''' |
|
|
4293 | 4307 | n= Numero de PRF's de entrada |
|
|
4294 | 4308 | ''' |
|
|
4295 | 4309 | self.__initime = None |
|
|
4296 | 4310 | self.__lastdatatime = 0 |
|
|
4297 | 4311 | self.__dataReady = False |
|
|
4298 | 4312 | self.__buffer = 0 |
|
|
4299 | 4313 | self.index = 0 |
|
|
4300 | 4314 | self.__nch = dataOut.nChannels |
|
|
4301 | 4315 | self.__nHeis = dataOut.nHeights |
|
|
4302 | 4316 | |
|
|
4303 | 4317 | self.attr = attr |
|
|
4304 | 4318 | |
|
|
4305 | 4319 | self.__buffer = [] |
|
|
4306 |
self. |
|
|
|
4307 |
self. |
|
|
|
4308 | self.__buffer4 = [] | |
|
|
4320 | self.azi = [] | |
|
|
4321 | self.ele = [] | |
|
|
4309 | 4322 | |
|
|
4310 |
def putData(self, data, attr |
|
|
|
4323 | def putData(self, data, attr): | |
|
|
4311 | 4324 | ''' |
|
|
4312 | 4325 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
4313 | 4326 | ''' |
|
|
4314 | 4327 | tmp= getattr(data, attr) |
|
|
4315 | 4328 | self.__buffer.append(tmp) |
|
|
4316 |
self. |
|
|
|
4317 |
self. |
|
|
|
4329 | self.azi.append(data.azimuth) | |
|
|
4330 | self.ele.append(data.elevation) | |
|
|
4318 | 4331 | self.__profIndex += 1 |
|
|
4319 | 4332 | |
|
|
4320 | if flagMode == 1: #'AZI' | |
|
|
4321 | return numpy.array(self.__buffer2) | |
|
|
4322 | elif flagMode == 0: #'ELE' | |
|
|
4323 | return numpy.array(self.__buffer3) | |
|
|
4324 | ||
|
|
4325 | def pushData(self, data,flagMode,case_flag): | |
|
|
4333 | def pushData(self, data, case_flag): | |
|
|
4326 | 4334 | ''' |
|
|
4327 | Return the PULSEPAIR and the profiles used in the operation | |
|
|
4328 | Affected : self.__profileIndex | |
|
|
4329 | 4335 | ''' |
|
|
4330 | 4336 | |
|
|
4331 | 4337 | data_360 = numpy.array(self.__buffer).transpose(1, 2, 0, 3) |
|
|
4332 |
data_p = numpy.array(self. |
|
|
|
4333 |
data_e = numpy.array(self. |
|
|
|
4338 | data_p = numpy.array(self.azi) | |
|
|
4339 | data_e = numpy.array(self.ele) | |
|
|
4334 | 4340 | n = self.__profIndex |
|
|
4335 | 4341 | |
|
|
4336 | 4342 | self.__buffer = [] |
|
|
4337 |
self. |
|
|
|
4338 |
self. |
|
|
|
4339 | self.__buffer4 = [] | |
|
|
4343 | self.azi = [] | |
|
|
4344 | self.ele = [] | |
|
|
4340 | 4345 | self.__profIndex = 0 |
|
|
4341 | 4346 | |
|
|
4342 | if flagMode == 1 and case_flag == 0: #'AZI' y ha girado | |
|
|
4343 |
self.putData(data=data, attr = self.attr |
|
|
|
4347 | if case_flag in (0, 1, -1): | |
|
|
4348 | self.putData(data=data, attr = self.attr) | |
|
|
4344 | 4349 | |
|
|
4345 | 4350 | return data_360, n, data_p, data_e |
|
|
4346 | 4351 | |
|
|
4347 |
def byProfiles(self,dataOut |
|
|
|
4352 | def byProfiles(self, dataOut): | |
|
|
4348 | 4353 | |
|
|
4349 |
self.__dataReady |
|
|
|
4354 | self.__dataReady = False | |
|
|
4350 | 4355 | data_360 = [] |
|
|
4351 |
data_p |
|
|
|
4352 |
data_e |
|
|
|
4356 | data_p = None | |
|
|
4357 | data_e = None | |
|
|
4353 | 4358 | |
|
|
4354 |
|
|
|
|
4355 | if self.__profIndex > 1: | |
|
|
4356 | case_flag = self.checkcase(angles,flagMode) | |
|
|
4359 | self.putData(data=dataOut, attr = self.attr) | |
|
|
4360 | ||
|
|
4361 | if self.__profIndex > 5: | |
|
|
4362 | case_flag = self.checkcase() | |
|
|
4357 | 4363 | |
|
|
4358 | if flagMode == 1: #'AZI': | |
|
|
4364 | if self.flagMode == 1: #'AZI': | |
|
|
4359 | 4365 | if case_flag == 0: #Ya giró |
|
|
4360 | 4366 | self.__buffer.pop() #Erase last data |
|
|
4361 |
self. |
|
|
|
4362 |
self. |
|
|
|
4363 |
data_360 ,n,data_p,data_e = self.pushData( |
|
|
|
4364 |
|
|
|
|
4365 | ||
|
|
4366 | elif flagMode == 0: #'ELE' | |
|
|
4367 | ||
|
|
4368 | if case_flag == 0: #Subida | |
|
|
4369 | ||
|
|
4370 | if len(self.__buffer) == 2: #Cuando está de subida | |
|
|
4371 | #Se borra el dato anterior para liberar buffer y comparar el dato actual con el siguiente | |
|
|
4372 | self.__buffer.pop(0) #Erase first data | |
|
|
4373 | self.__buffer2.pop(0) | |
|
|
4374 | self.__buffer3.pop(0) | |
|
|
4375 | self.__profIndex -= 1 | |
|
|
4376 | else: #Cuando ha estado de bajada y ha vuelto a subir | |
|
|
4377 | #Se borra el último dato | |
|
|
4378 | self.__buffer.pop() #Erase last data | |
|
|
4379 | self.__buffer2.pop() | |
|
|
4380 | self.__buffer3.pop() | |
|
|
4381 | data_360, n, data_p, data_e = self.pushData(data=dataOut,flagMode=flagMode,case_flag=case_flag) | |
|
|
4367 | self.azi.pop() | |
|
|
4368 | self.ele.pop() | |
|
|
4369 | data_360 ,n,data_p,data_e = self.pushData(dataOut, case_flag) | |
|
|
4370 | if len(data_p)>350: | |
|
|
4382 | 4371 | self.__dataReady = True |
|
|
4372 | elif self.flagMode == 0: #'ELE' | |
|
|
4373 | if case_flag == 1: #Bajada | |
|
|
4374 | self.__buffer.pop() #Erase last data | |
|
|
4375 | self.azi.pop() | |
|
|
4376 | self.ele.pop() | |
|
|
4377 | data_360, n, data_p, data_e = self.pushData(dataOut, case_flag) | |
|
|
4378 | self.__dataReady = True | |
|
|
4379 | if case_flag == -1: #Subida | |
|
|
4380 | self.__buffer.pop() #Erase last data | |
|
|
4381 | self.azi.pop() | |
|
|
4382 | self.ele.pop() | |
|
|
4383 | data_360, n, data_p, data_e = self.pushData(dataOut, case_flag) | |
|
|
4384 | #self.__dataReady = True | |
|
|
4383 | 4385 | |
|
|
4384 | 4386 | return data_360, data_p, data_e |
|
|
4385 | 4387 | |
|
|
4386 | 4388 | |
|
|
4387 |
def blockOp(self, dataOut, |
|
|
|
4389 | def blockOp(self, dataOut, datatime= None): | |
|
|
4388 | 4390 | if self.__initime == None: |
|
|
4389 | 4391 | self.__initime = datatime |
|
|
4390 |
data_360, data_p, data_e = self.byProfiles(dataOut |
|
|
|
4391 |
self.__lastdatatime |
|
|
|
4392 | data_360, data_p, data_e = self.byProfiles(dataOut) | |
|
|
4393 | self.__lastdatatime = datatime | |
|
|
4392 | 4394 | |
|
|
4393 |
avgdatatime |
|
|
|
4395 | avgdatatime = self.__initime | |
|
|
4394 | 4396 | if self.n==1: |
|
|
4395 | 4397 | avgdatatime = datatime |
|
|
4396 | deltatime = datatime - self.__lastdatatime | |
|
|
4398 | ||
|
|
4397 | 4399 | self.__initime = datatime |
|
|
4398 | 4400 | return data_360, avgdatatime, data_p, data_e |
|
|
4399 | 4401 | |
|
|
4400 |
def checkcase(self |
|
|
|
4402 | def checkcase(self): | |
|
|
4403 | ||
|
|
4404 | sigma_ele = numpy.nanstd(self.ele[-5:]) | |
|
|
4405 | sigma_azi = numpy.nanstd(self.azi[-5:]) | |
|
|
4401 | 4406 | |
|
|
4402 | if flagMode == 1: #'AZI' | |
|
|
4403 | start = angles[-2] | |
|
|
4404 |
|
|
|
|
4407 | if sigma_ele<.5 and sigma_azi<.5: | |
|
|
4408 | if sigma_ele<sigma_azi: | |
|
|
4409 | self.flagMode = 1 | |
|
|
4410 | self.mode_op = 'PPI' | |
|
|
4411 | else: | |
|
|
4412 | self.flagMode = 0 | |
|
|
4413 | self.mode_op = 'RHI' | |
|
|
4414 | elif sigma_ele < .5: | |
|
|
4415 | self.flagMode = 1 | |
|
|
4416 | self.mode_op = 'PPI' | |
|
|
4417 | elif sigma_azi < .5: | |
|
|
4418 | self.flagMode = 0 | |
|
|
4419 | self.mode_op = 'RHI' | |
|
|
4420 | else: | |
|
|
4421 | self.flagMode = None | |
|
|
4422 | self.mode_op = 'None' | |
|
|
4423 | ||
|
|
4424 | if self.flagMode == 1: #'AZI' | |
|
|
4425 | start = self.azi[-2] | |
|
|
4426 | end = self.azi[-1] | |
|
|
4405 | 4427 | diff_angle = (end-start) |
|
|
4406 | 4428 | |
|
|
4407 | 4429 | if diff_angle < 0: #Ya giró |
|
|
4408 | 4430 | return 0 |
|
|
4409 | 4431 | |
|
|
4410 | elif flagMode == 0: #'ELE' | |
|
|
4432 | elif self.flagMode == 0: #'ELE' | |
|
|
4411 | 4433 | |
|
|
4412 |
start = |
|
|
|
4413 |
|
|
|
|
4414 | diff_angle = (end-start) | |
|
|
4434 | start = self.ele[-3] | |
|
|
4435 | middle = self.ele[-2] | |
|
|
4436 | end = self.ele[-1] | |
|
|
4415 | 4437 | |
|
|
4416 |
if d |
|
|
|
4417 |
return |
|
|
|
4438 | if end < 0: | |
|
|
4439 | return 1 | |
|
|
4440 | elif (middle>start and end<middle): | |
|
|
4441 | return -1 | |
|
|
4418 | 4442 | |
|
|
4419 | 4443 | def run(self, dataOut, attr_data='dataPP_POWER', runNextOp = False,**kwargs): |
|
|
4420 | 4444 | |
|
|
4421 | 4445 | dataOut.attr_data = attr_data |
|
|
4422 | 4446 | dataOut.runNextOp = runNextOp |
|
|
4423 | dataOut.flagAskMode = False | |
|
|
4424 | ||
|
|
4425 | if dataOut.mode_op == 'PPI': | |
|
|
4426 | dataOut.flagMode = 1 | |
|
|
4427 | elif dataOut.mode_op == 'RHI': | |
|
|
4428 | dataOut.flagMode = 0 | |
|
|
4429 | 4447 | |
|
|
4430 | 4448 | if not self.isConfig: |
|
|
4431 | 4449 | self.setup(dataOut = dataOut, attr = attr_data ,**kwargs) |
|
|
4432 | 4450 | self.isConfig = True |
|
|
4433 | 4451 | |
|
|
4434 |
data_360, avgdatatime, data_p, data_e = self.blockOp(dataOut, dataOut. |
|
|
|
4452 | data_360, avgdatatime, data_p, data_e = self.blockOp(dataOut, dataOut.utctime) | |
|
|
4435 | 4453 | |
|
|
4436 | 4454 | dataOut.flagNoData = True |
|
|
4437 | 4455 | |
|
|
4438 | 4456 | if self.__dataReady: |
|
|
4439 | 4457 | setattr(dataOut, attr_data, data_360 ) |
|
|
4440 |
dataOut.data_azi = data_p |
|
|
|
4441 | dataOut.data_azi[dataOut.data_azi>360] = dataOut.data_azi[dataOut.data_azi>360] - 360 | |
|
|
4458 | dataOut.data_azi = data_p | |
|
|
4442 | 4459 | dataOut.data_ele = data_e |
|
|
4443 | 4460 | dataOut.utctime = avgdatatime |
|
|
4444 | dataOut.flagNoData = False | |
|
|
4445 |
dataOut.flag |
|
|
|
4461 | dataOut.flagNoData = False | |
|
|
4462 | dataOut.flagMode = self.flagMode | |
|
|
4463 | dataOut.mode_op = self.mode_op | |
|
|
4446 | 4464 | |
|
|
4447 | 4465 | return dataOut |
|
|
4448 | 4466 | |
|
|
4449 | 4467 | class MergeProc(ProcessingUnit): |
|
|
4450 | 4468 | |
|
|
4451 | 4469 | def __init__(self): |
|
|
4452 | 4470 | ProcessingUnit.__init__(self) |
|
|
4453 | 4471 | |
|
|
4454 | 4472 | def run(self, attr_data, mode=0): |
|
|
4455 | 4473 | |
|
|
4456 | 4474 | #exit(1) |
|
|
4457 | 4475 | self.dataOut = getattr(self, self.inputs[0]) |
|
|
4458 | 4476 | data_inputs = [getattr(self, attr) for attr in self.inputs] |
|
|
4459 | 4477 | #print(data_inputs) |
|
|
4460 | 4478 | #print(numpy.shape([getattr(data, attr_data) for data in data_inputs][1])) |
|
|
4461 | 4479 | #exit(1) |
|
|
4462 | 4480 | if mode==0: |
|
|
4463 | 4481 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) |
|
|
4464 | 4482 | setattr(self.dataOut, attr_data, data) |
|
|
4465 | 4483 | |
|
|
4466 | 4484 | if mode==1: #Hybrid |
|
|
4467 | 4485 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) |
|
|
4468 | 4486 | #setattr(self.dataOut, attr_data, data) |
|
|
4469 | 4487 | setattr(self.dataOut, 'dataLag_spc', [getattr(data, attr_data) for data in data_inputs][0]) |
|
|
4470 | 4488 | setattr(self.dataOut, 'dataLag_spc_LP', [getattr(data, attr_data) for data in data_inputs][1]) |
|
|
4471 | 4489 | setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) |
|
|
4472 | 4490 | setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) |
|
|
4473 | 4491 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) |
|
|
4474 | 4492 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) |
|
|
4475 | 4493 | ''' |
|
|
4476 | 4494 | print(self.dataOut.dataLag_spc_LP.shape) |
|
|
4477 | 4495 | print(self.dataOut.dataLag_cspc_LP.shape) |
|
|
4478 | 4496 | exit(1) |
|
|
4479 | 4497 | ''' |
|
|
4480 | 4498 | |
|
|
4481 | 4499 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) |
|
|
4482 | 4500 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) |
|
|
4483 | 4501 | ''' |
|
|
4484 | 4502 | print("Merge") |
|
|
4485 | 4503 | print(numpy.shape(self.dataOut.dataLag_spc)) |
|
|
4486 | 4504 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) |
|
|
4487 | 4505 | print(numpy.shape(self.dataOut.dataLag_cspc)) |
|
|
4488 | 4506 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) |
|
|
4489 | 4507 | exit(1) |
|
|
4490 | 4508 | ''' |
|
|
4491 | 4509 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) |
|
|
4492 | 4510 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) |
|
|
4493 | 4511 | #exit(1) |
|
|
4494 | 4512 | #print(self.dataOut.NDP) |
|
|
4495 | 4513 | #print(self.dataOut.nNoiseProfiles) |
|
|
4496 | 4514 | |
|
|
4497 | 4515 | #self.dataOut.nIncohInt_LP = 128 |
|
|
4498 | 4516 | self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP |
|
|
4499 | 4517 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt |
|
|
4500 | 4518 | self.dataOut.NLAG = 16 |
|
|
4501 | 4519 | self.dataOut.NRANGE = 200 |
|
|
4502 | 4520 | self.dataOut.NSCAN = 128 |
|
|
4503 | 4521 | #print(numpy.shape(self.dataOut.data_spc)) |
|
|
4504 | 4522 | |
|
|
4505 | 4523 | #exit(1) |
|
|
4506 | 4524 | |
|
|
4507 | 4525 | if mode==2: #HAE 2022 |
|
|
4508 | 4526 | data = numpy.sum([getattr(data, attr_data) for data in data_inputs],axis=0) |
|
|
4509 | 4527 | setattr(self.dataOut, attr_data, data) |
|
|
4510 | 4528 | |
|
|
4511 | 4529 | self.dataOut.nIncohInt *= 2 |
|
|
4512 | 4530 | #meta = self.dataOut.getFreqRange(1)/1000. |
|
|
4513 | 4531 | self.dataOut.freqRange = self.dataOut.getFreqRange(1)/1000. |
|
|
4514 | 4532 | |
|
|
4515 | 4533 | #exit(1) |
|
|
4516 | 4534 | |
|
|
4517 | 4535 | if mode==7: #RM |
|
|
4518 | 4536 | |
|
|
4519 | 4537 | f = [getattr(data, attr_data) for data in data_inputs][0] |
|
|
4520 | 4538 | g = [getattr(data, attr_data) for data in data_inputs][1] |
|
|
4521 | 4539 | data = numpy.concatenate((f,g),axis=3) |
|
|
4522 | 4540 | setattr(self.dataOut, attr_data, data) |
|
|
4523 | 4541 | |
|
|
4524 | 4542 | # snr |
|
|
4525 | 4543 | # self.dataOut.data_snr = numpy.concatenate((data_inputs[0].data_snr, data_inputs[1].data_snr), axis=2) |
|
|
4526 | 4544 | |
|
|
4527 | 4545 | # ranges |
|
|
4528 | 4546 | dh = self.dataOut.heightList[1]-self.dataOut.heightList[0] |
|
|
4529 | 4547 | heightList_2 = (self.dataOut.heightList[-1]+dh) + numpy.arange(g.shape[-1], dtype=numpy.float) * dh |
|
|
4530 | 4548 | |
|
|
4531 | 4549 | self.dataOut.heightList = numpy.concatenate((self.dataOut.heightList,heightList_2)) |
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