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1 | 1 | ''' |
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2 | 2 | Created on Feb 7, 2012 |
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3 | 3 | |
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4 | 4 | @author $Author$ |
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5 | 5 | @version $Id$ |
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6 | 6 | ''' |
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7 |
__version__ = |
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7 | __version__ = '2.3' | |
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1 | 1 | |
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2 | 2 | import os |
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3 | 3 | import time |
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4 | 4 | import glob |
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5 | 5 | import datetime |
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6 | 6 | from multiprocessing import Process |
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7 | 7 | |
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8 | 8 | import zmq |
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9 | 9 | import numpy |
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10 | 10 | import matplotlib |
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11 | 11 | import matplotlib.pyplot as plt |
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12 | 12 | from mpl_toolkits.axes_grid1 import make_axes_locatable |
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13 | 13 | from matplotlib.ticker import FuncFormatter, LinearLocator, MultipleLocator |
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14 | 14 | |
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15 | 15 | from schainpy.model.proc.jroproc_base import Operation |
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16 | 16 | from schainpy.utils import log |
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17 | 17 | |
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18 | 18 | func = lambda x, pos: ('%s') %(datetime.datetime.fromtimestamp(x).strftime('%H:%M')) |
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19 | 19 | |
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20 | 20 | d1970 = datetime.datetime(1970, 1, 1) |
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21 | 21 | |
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22 | 22 | |
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23 | 23 | class PlotData(Operation, Process): |
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24 | 24 | ''' |
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25 | 25 | Base class for Schain plotting operations |
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26 | 26 | ''' |
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27 | 27 | |
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28 | 28 | CODE = 'Figure' |
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29 | 29 | colormap = 'jro' |
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30 | 30 | bgcolor = 'white' |
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31 | 31 | CONFLATE = False |
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32 | 32 | __MAXNUMX = 80 |
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33 | 33 | __missing = 1E30 |
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34 | 34 | |
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35 | 35 | def __init__(self, **kwargs): |
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36 | 36 | |
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37 | 37 | Operation.__init__(self, plot=True, **kwargs) |
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38 | 38 | Process.__init__(self) |
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39 | 39 | self.kwargs['code'] = self.CODE |
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40 | 40 | self.mp = False |
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41 | 41 | self.data = None |
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42 | 42 | self.isConfig = False |
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43 | 43 | self.figures = [] |
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44 | 44 | self.axes = [] |
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45 | 45 | self.cb_axes = [] |
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46 | 46 | self.localtime = kwargs.pop('localtime', True) |
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47 | 47 | self.show = kwargs.get('show', True) |
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48 | 48 | self.save = kwargs.get('save', False) |
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49 | 49 | self.colormap = kwargs.get('colormap', self.colormap) |
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50 | 50 | self.colormap_coh = kwargs.get('colormap_coh', 'jet') |
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51 | 51 | self.colormap_phase = kwargs.get('colormap_phase', 'RdBu_r') |
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52 | 52 | self.colormaps = kwargs.get('colormaps', None) |
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53 | 53 | self.bgcolor = kwargs.get('bgcolor', self.bgcolor) |
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54 | 54 | self.showprofile = kwargs.get('showprofile', False) |
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55 | 55 | self.title = kwargs.get('wintitle', self.CODE.upper()) |
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56 | 56 | self.cb_label = kwargs.get('cb_label', None) |
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57 | 57 | self.cb_labels = kwargs.get('cb_labels', None) |
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58 | 58 | self.xaxis = kwargs.get('xaxis', 'frequency') |
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59 | 59 | self.zmin = kwargs.get('zmin', None) |
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60 | 60 | self.zmax = kwargs.get('zmax', None) |
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61 | 61 | self.zlimits = kwargs.get('zlimits', None) |
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62 | 62 | self.xmin = kwargs.get('xmin', None) |
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63 | 63 | if self.xmin is not None: |
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64 | 64 | self.xmin += 5 |
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65 | 65 | self.xmax = kwargs.get('xmax', None) |
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66 | 66 | self.xrange = kwargs.get('xrange', 24) |
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67 | 67 | self.ymin = kwargs.get('ymin', None) |
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68 | 68 | self.ymax = kwargs.get('ymax', None) |
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69 | 69 | self.xlabel = kwargs.get('xlabel', None) |
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70 | 70 | self.__MAXNUMY = kwargs.get('decimation', 100) |
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71 | 71 | self.showSNR = kwargs.get('showSNR', False) |
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72 | 72 | self.oneFigure = kwargs.get('oneFigure', True) |
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73 | 73 | self.width = kwargs.get('width', None) |
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74 | 74 | self.height = kwargs.get('height', None) |
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75 | 75 | self.colorbar = kwargs.get('colorbar', True) |
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76 | 76 | self.factors = kwargs.get('factors', [1, 1, 1, 1, 1, 1, 1, 1]) |
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77 | 77 | self.titles = ['' for __ in range(16)] |
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78 | 78 | |
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79 | 79 | def __setup(self): |
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80 | 80 | ''' |
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81 | 81 | Common setup for all figures, here figures and axes are created |
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82 | 82 | ''' |
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83 | 83 | |
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84 | 84 | self.setup() |
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85 | 85 | |
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86 | 86 | if self.width is None: |
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87 | 87 | self.width = 8 |
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88 | 88 | |
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89 | 89 | self.figures = [] |
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90 | 90 | self.axes = [] |
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91 | 91 | self.cb_axes = [] |
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92 | 92 | self.pf_axes = [] |
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93 | 93 | self.cmaps = [] |
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94 | 94 | |
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95 | 95 | size = '15%' if self.ncols==1 else '30%' |
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96 | 96 | pad = '4%' if self.ncols==1 else '8%' |
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97 | 97 | |
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98 | 98 | if self.oneFigure: |
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99 | 99 | if self.height is None: |
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100 | 100 | self.height = 1.4*self.nrows + 1 |
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101 | 101 | fig = plt.figure(figsize=(self.width, self.height), |
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102 | 102 | edgecolor='k', |
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103 | 103 | facecolor='w') |
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104 | 104 | self.figures.append(fig) |
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105 | 105 | for n in range(self.nplots): |
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106 | 106 | ax = fig.add_subplot(self.nrows, self.ncols, n+1) |
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107 | 107 | ax.tick_params(labelsize=8) |
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108 | 108 | ax.firsttime = True |
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109 | 109 | self.axes.append(ax) |
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110 | 110 | if self.showprofile: |
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111 | 111 | cax = self.__add_axes(ax, size=size, pad=pad) |
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112 | 112 | cax.tick_params(labelsize=8) |
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113 | 113 | self.pf_axes.append(cax) |
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114 | 114 | else: |
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115 | 115 | if self.height is None: |
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116 | 116 | self.height = 3 |
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117 | 117 | for n in range(self.nplots): |
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118 | 118 | fig = plt.figure(figsize=(self.width, self.height), |
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119 | 119 | edgecolor='k', |
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120 | 120 | facecolor='w') |
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121 | 121 | ax = fig.add_subplot(1, 1, 1) |
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122 | 122 | ax.tick_params(labelsize=8) |
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123 | 123 | ax.firsttime = True |
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124 | 124 | self.figures.append(fig) |
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125 | 125 | self.axes.append(ax) |
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126 | 126 | if self.showprofile: |
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127 | 127 | cax = self.__add_axes(ax, size=size, pad=pad) |
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128 | 128 | cax.tick_params(labelsize=8) |
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129 | 129 | self.pf_axes.append(cax) |
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130 | 130 | |
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131 | 131 | for n in range(self.nrows): |
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132 | 132 | if self.colormaps is not None: |
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133 | 133 | cmap = plt.get_cmap(self.colormaps[n]) |
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134 | 134 | else: |
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135 | 135 | cmap = plt.get_cmap(self.colormap) |
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136 | 136 | cmap.set_bad(self.bgcolor, 1.) |
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137 | 137 | self.cmaps.append(cmap) |
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138 | 138 | |
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139 | 139 | def __add_axes(self, ax, size='30%', pad='8%'): |
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140 | 140 | ''' |
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141 | 141 | Add new axes to the given figure |
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142 | 142 | ''' |
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143 | 143 | divider = make_axes_locatable(ax) |
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144 | 144 | nax = divider.new_horizontal(size=size, pad=pad) |
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145 | 145 | ax.figure.add_axes(nax) |
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146 | 146 | return nax |
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147 | 147 | |
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148 | self.setup() | |
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148 | 149 | |
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149 | 150 | def setup(self): |
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150 | 151 | ''' |
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151 | 152 | This method should be implemented in the child class, the following |
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152 | 153 | attributes should be set: |
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153 | 154 | |
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154 | 155 | self.nrows: number of rows |
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155 | 156 | self.ncols: number of cols |
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156 | 157 | self.nplots: number of plots (channels or pairs) |
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157 | 158 | self.ylabel: label for Y axes |
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158 | 159 | self.titles: list of axes title |
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159 | 160 | |
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160 | 161 | ''' |
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161 | 162 | raise(NotImplementedError, 'Implement this method in child class') |
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162 | 163 | |
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163 | 164 | def fill_gaps(self, x_buffer, y_buffer, z_buffer): |
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164 | 165 | ''' |
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165 | 166 | Create a masked array for missing data |
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166 | 167 | ''' |
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167 | 168 | if x_buffer.shape[0] < 2: |
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168 | 169 | return x_buffer, y_buffer, z_buffer |
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169 | 170 | |
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170 | 171 | deltas = x_buffer[1:] - x_buffer[0:-1] |
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171 | 172 | x_median = numpy.median(deltas) |
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172 | 173 | |
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173 | 174 | index = numpy.where(deltas > 5*x_median) |
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174 | 175 | |
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175 | 176 | if len(index[0]) != 0: |
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176 | 177 | z_buffer[::, index[0], ::] = self.__missing |
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177 | 178 | z_buffer = numpy.ma.masked_inside(z_buffer, |
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178 | 179 | 0.99*self.__missing, |
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179 | 180 | 1.01*self.__missing) |
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180 | 181 | |
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181 | 182 | return x_buffer, y_buffer, z_buffer |
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182 | 183 | |
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183 | 184 | def decimate(self): |
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184 | 185 | |
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185 | 186 | # dx = int(len(self.x)/self.__MAXNUMX) + 1 |
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186 | 187 | dy = int(len(self.y)/self.__MAXNUMY) + 1 |
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187 | 188 | |
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188 | 189 | # x = self.x[::dx] |
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189 | 190 | x = self.x |
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190 | 191 | y = self.y[::dy] |
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191 | 192 | z = self.z[::, ::, ::dy] |
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192 | 193 | |
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193 | 194 | return x, y, z |
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194 | 195 | |
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195 | 196 | def format(self): |
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196 | 197 | ''' |
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197 | 198 | Set min and max values, labels, ticks and titles |
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198 | 199 | ''' |
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199 | 200 | |
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200 | 201 | if self.xmin is None: |
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201 | 202 | xmin = self.min_time |
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202 | 203 | else: |
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203 | 204 | if self.xaxis is 'time': |
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204 | 205 | dt = datetime.datetime.fromtimestamp(self.min_time) |
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205 | 206 | xmin = (datetime.datetime.combine(dt.date(), |
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206 | 207 | datetime.time(int(self.xmin), 0, 0))-d1970).total_seconds() |
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207 | 208 | else: |
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208 | 209 | xmin = self.xmin |
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209 | 210 | |
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210 | 211 | if self.xmax is None: |
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211 | 212 | xmax = xmin+self.xrange*60*60 |
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212 | 213 | else: |
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213 | 214 | if self.xaxis is 'time': |
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214 | 215 | dt = datetime.datetime.fromtimestamp(self.min_time) |
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215 | 216 | xmax = (datetime.datetime.combine(dt.date(), |
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216 | 217 | datetime.time(int(self.xmax), 0, 0))-d1970).total_seconds() |
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217 | 218 | else: |
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218 | 219 | xmax = self.xmax |
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219 | 220 | |
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220 | 221 | ymin = self.ymin if self.ymin else numpy.nanmin(self.y) |
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221 | 222 | ymax = self.ymax if self.ymax else numpy.nanmax(self.y) |
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222 | 223 | |
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223 | 224 | ystep = 200 if ymax>= 800 else 100 if ymax>=400 else 50 if ymax>=200 else 20 |
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224 | 225 | |
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225 | 226 | for n, ax in enumerate(self.axes): |
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226 | 227 | if ax.firsttime: |
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227 | 228 | ax.set_facecolor(self.bgcolor) |
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228 | 229 | ax.yaxis.set_major_locator(MultipleLocator(ystep)) |
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229 | 230 | if self.xaxis is 'time': |
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230 | 231 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
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231 | 232 | ax.xaxis.set_major_locator(LinearLocator(9)) |
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232 | 233 | if self.xlabel is not None: |
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233 | 234 | ax.set_xlabel(self.xlabel) |
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234 | 235 | ax.set_ylabel(self.ylabel) |
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235 | 236 | ax.firsttime = False |
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236 | 237 | if self.showprofile: |
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237 | 238 | self.pf_axes[n].set_ylim(ymin, ymax) |
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238 | 239 | self.pf_axes[n].set_xlim(self.zmin, self.zmax) |
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239 | 240 | self.pf_axes[n].set_xlabel('dB') |
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240 | 241 | self.pf_axes[n].grid(b=True, axis='x') |
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241 | 242 | [tick.set_visible(False) for tick in self.pf_axes[n].get_yticklabels()] |
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242 | 243 | if self.colorbar: |
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243 | 244 | cb = plt.colorbar(ax.plt, ax=ax, pad=0.02) |
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244 | 245 | cb.ax.tick_params(labelsize=8) |
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245 | 246 | if self.cb_label: |
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246 | 247 | cb.set_label(self.cb_label, size=8) |
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247 | 248 | elif self.cb_labels: |
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248 | 249 | cb.set_label(self.cb_labels[n], size=8) |
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249 | 250 | |
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250 | 251 | ax.set_title('{} - {} UTC'.format( |
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251 | 252 | self.titles[n], |
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252 | 253 | datetime.datetime.fromtimestamp(self.max_time).strftime('%H:%M:%S')), |
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253 | 254 | size=8) |
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254 | 255 | ax.set_xlim(xmin, xmax) |
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255 | 256 | ax.set_ylim(ymin, ymax) |
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256 | 257 | |
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257 | 258 | |
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258 | 259 | def __plot(self): |
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259 | 260 | ''' |
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260 | 261 | ''' |
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261 | 262 | log.success('Plotting', self.name) |
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262 | 263 | |
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263 | 264 | self.plot() |
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264 | 265 | self.format() |
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265 | 266 | |
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266 | 267 | for n, fig in enumerate(self.figures): |
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267 | 268 | if self.nrows == 0 or self.nplots == 0: |
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268 | 269 | log.warning('No data', self.name) |
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269 | 270 | continue |
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270 | 271 | if self.show: |
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271 | 272 | fig.show() |
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272 | 273 | |
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273 | 274 | fig.tight_layout() |
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274 | 275 | fig.canvas.manager.set_window_title('{} - {}'.format(self.title, |
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275 | 276 | datetime.datetime.fromtimestamp(self.max_time).strftime('%Y/%m/%d'))) |
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276 | 277 | # fig.canvas.draw() |
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277 | 278 | |
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278 | 279 | if self.save and self.data.ended: |
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279 | 280 | channels = range(self.nrows) |
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280 | 281 | if self.oneFigure: |
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281 | 282 | label = '' |
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282 | 283 | else: |
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283 | 284 | label = '_{}'.format(channels[n]) |
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284 | 285 | figname = os.path.join( |
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285 | 286 | self.save, |
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286 | 287 | '{}{}_{}.png'.format( |
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287 | 288 | self.CODE, |
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288 | 289 | label, |
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289 | 290 | datetime.datetime.fromtimestamp(self.saveTime).strftime('%y%m%d_%H%M%S') |
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290 | 291 | ) |
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291 | 292 | ) |
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292 | 293 | print 'Saving figure: {}'.format(figname) |
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293 | 294 | fig.savefig(figname) |
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294 | 295 | |
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295 | 296 | def plot(self): |
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296 | 297 | ''' |
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297 | 298 | ''' |
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298 | 299 | raise(NotImplementedError, 'Implement this method in child class') |
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299 | 300 | |
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300 | 301 | def run(self): |
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301 | 302 | |
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302 | 303 | log.success('Starting', self.name) |
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303 | 304 | |
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304 | 305 | context = zmq.Context() |
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305 | 306 | receiver = context.socket(zmq.SUB) |
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306 | 307 | receiver.setsockopt(zmq.SUBSCRIBE, '') |
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307 | 308 | receiver.setsockopt(zmq.CONFLATE, self.CONFLATE) |
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308 | 309 | |
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309 | 310 | if 'server' in self.kwargs['parent']: |
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310 | 311 | receiver.connect('ipc:///tmp/{}.plots'.format(self.kwargs['parent']['server'])) |
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311 | 312 | else: |
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312 | 313 | receiver.connect("ipc:///tmp/zmq.plots") |
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313 | 314 | |
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314 | 315 | while True: |
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315 | 316 | try: |
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316 | 317 | self.data = receiver.recv_pyobj(flags=zmq.NOBLOCK) |
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317 | 318 | |
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318 | 319 | self.min_time = self.data.times[0] |
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319 | 320 | self.max_time = self.data.times[-1] |
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320 | 321 | |
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321 | 322 | if self.isConfig is False: |
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322 | 323 | self.__setup() |
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323 | 324 | self.isConfig = True |
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324 | 325 | |
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325 | 326 | self.__plot() |
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326 | 327 | |
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327 | 328 | except zmq.Again as e: |
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328 | 329 | log.log('Waiting for data...') |
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329 | 330 | if self.data: |
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330 | 331 | plt.pause(self.data.throttle) |
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331 | 332 | else: |
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332 | 333 | time.sleep(2) |
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333 | 334 | |
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334 | 335 | def close(self): |
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335 | 336 | if self.data: |
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336 | 337 | self.__plot() |
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337 | 338 | |
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338 | ||
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339 | 339 | class PlotSpectraData(PlotData): |
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340 | 340 | ''' |
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341 | 341 | Plot for Spectra data |
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342 | 342 | ''' |
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343 | 343 | |
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344 | 344 | CODE = 'spc' |
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345 | 345 | colormap = 'jro' |
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346 | 346 | |
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347 | 347 | def setup(self): |
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348 | 348 | self.nplots = len(self.data.channels) |
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349 | 349 | self.ncols = int(numpy.sqrt(self.nplots)+ 0.9) |
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350 | 350 | self.nrows = int((1.0*self.nplots/self.ncols) + 0.9) |
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351 | 351 | self.width = 3.4*self.ncols |
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352 | 352 | self.height = 3*self.nrows |
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353 | 353 | self.cb_label = 'dB' |
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354 | 354 | if self.showprofile: |
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355 | 355 | self.width += 0.8*self.ncols |
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356 | 356 | |
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357 | 357 | self.ylabel = 'Range [Km]' |
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358 | 358 | |
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359 | 359 | def plot(self): |
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360 | 360 | if self.xaxis == "frequency": |
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361 | 361 | x = self.data.xrange[0] |
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362 | 362 | self.xlabel = "Frequency (kHz)" |
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363 | 363 | elif self.xaxis == "time": |
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364 | 364 | x = self.data.xrange[1] |
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365 | 365 | self.xlabel = "Time (ms)" |
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366 | 366 | else: |
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367 | 367 | x = self.data.xrange[2] |
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368 | 368 | self.xlabel = "Velocity (m/s)" |
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369 | 369 | |
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370 | 370 | if self.CODE == 'spc_mean': |
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371 | 371 | x = self.data.xrange[2] |
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372 | 372 | self.xlabel = "Velocity (m/s)" |
|
|
373 | 373 | |
|
|
374 | 374 | self.titles = [] |
|
|
375 | 375 | |
|
|
376 | 376 | y = self.data.heights |
|
|
377 | 377 | self.y = y |
|
|
378 | 378 | z = self.data['spc'] |
|
|
379 | 379 | |
|
|
380 | 380 | for n, ax in enumerate(self.axes): |
|
|
381 | 381 | noise = self.data['noise'][n][-1] |
|
|
382 | 382 | if self.CODE == 'spc_mean': |
|
|
383 | 383 | mean = self.data['mean'][n][-1] |
|
|
384 | 384 | if ax.firsttime: |
|
|
385 | 385 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
386 | 386 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
387 | 387 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
|
388 | 388 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
389 | 389 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
390 | 390 | vmin=self.zmin, |
|
|
391 | 391 | vmax=self.zmax, |
|
|
392 | 392 | cmap=plt.get_cmap(self.colormap) |
|
|
393 | 393 | ) |
|
|
394 | 394 | |
|
|
395 | 395 | if self.showprofile: |
|
|
396 | 396 | ax.plt_profile= self.pf_axes[n].plot(self.data['rti'][n][-1], y)[0] |
|
|
397 | 397 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, |
|
|
398 | 398 | color="k", linestyle="dashed", lw=1)[0] |
|
|
399 | 399 | if self.CODE == 'spc_mean': |
|
|
400 | 400 | ax.plt_mean = ax.plot(mean, y, color='k')[0] |
|
|
401 | 401 | else: |
|
|
402 | 402 | ax.plt.set_array(z[n].T.ravel()) |
|
|
403 | 403 | if self.showprofile: |
|
|
404 | 404 | ax.plt_profile.set_data(self.data['rti'][n][-1], y) |
|
|
405 | 405 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) |
|
|
406 | 406 | if self.CODE == 'spc_mean': |
|
|
407 | 407 | ax.plt_mean.set_data(mean, y) |
|
|
408 | 408 | |
|
|
409 | 409 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
|
|
410 | 410 | self.saveTime = self.max_time |
|
|
411 | 411 | |
|
|
412 | 412 | |
|
|
413 | 413 | class PlotCrossSpectraData(PlotData): |
|
|
414 | 414 | |
|
|
415 | 415 | CODE = 'cspc' |
|
|
416 | 416 | zmin_coh = None |
|
|
417 | 417 | zmax_coh = None |
|
|
418 | 418 | zmin_phase = None |
|
|
419 | 419 | zmax_phase = None |
|
|
420 | 420 | |
|
|
421 | 421 | def setup(self): |
|
|
422 | 422 | |
|
|
423 | 423 | self.ncols = 4 |
|
|
424 | 424 | self.nrows = len(self.data.pairs) |
|
|
425 | 425 | self.nplots = self.nrows*4 |
|
|
426 | 426 | self.width = 3.4*self.ncols |
|
|
427 | 427 | self.height = 3*self.nrows |
|
|
428 | 428 | self.ylabel = 'Range [Km]' |
|
|
429 | 429 | self.showprofile = False |
|
|
430 | 430 | |
|
|
431 | 431 | def plot(self): |
|
|
432 | 432 | |
|
|
433 | 433 | if self.xaxis == "frequency": |
|
|
434 | 434 | x = self.data.xrange[0] |
|
|
435 | 435 | self.xlabel = "Frequency (kHz)" |
|
|
436 | 436 | elif self.xaxis == "time": |
|
|
437 | 437 | x = self.data.xrange[1] |
|
|
438 | 438 | self.xlabel = "Time (ms)" |
|
|
439 | 439 | else: |
|
|
440 | 440 | x = self.data.xrange[2] |
|
|
441 | 441 | self.xlabel = "Velocity (m/s)" |
|
|
442 | 442 | |
|
|
443 | 443 | self.titles = [] |
|
|
444 | 444 | |
|
|
445 | 445 | y = self.data.heights |
|
|
446 | 446 | self.y = y |
|
|
447 | 447 | spc = self.data['spc'] |
|
|
448 | 448 | cspc = self.data['cspc'] |
|
|
449 | 449 | |
|
|
450 | 450 | for n in range(self.nrows): |
|
|
451 | 451 | noise = self.data['noise'][n][-1] |
|
|
452 | 452 | pair = self.data.pairs[n] |
|
|
453 | 453 | ax = self.axes[4*n] |
|
|
454 | 454 | ax3 = self.axes[4*n+3] |
|
|
455 | 455 | if ax.firsttime: |
|
|
456 | 456 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
457 | 457 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
458 | 458 | self.zmin = self.zmin if self.zmin else numpy.nanmin(spc) |
|
|
459 | 459 | self.zmax = self.zmax if self.zmax else numpy.nanmax(spc) |
|
|
460 | 460 | ax.plt = ax.pcolormesh(x, y, spc[pair[0]].T, |
|
|
461 | 461 | vmin=self.zmin, |
|
|
462 | 462 | vmax=self.zmax, |
|
|
463 | 463 | cmap=plt.get_cmap(self.colormap) |
|
|
464 | 464 | ) |
|
|
465 | 465 | else: |
|
|
466 | 466 | ax.plt.set_array(spc[pair[0]].T.ravel()) |
|
|
467 | 467 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
|
|
468 | 468 | |
|
|
469 | 469 | ax = self.axes[4*n+1] |
|
|
470 | 470 | if ax.firsttime: |
|
|
471 | 471 | ax.plt = ax.pcolormesh(x, y, spc[pair[1]].T, |
|
|
472 | 472 | vmin=self.zmin, |
|
|
473 | 473 | vmax=self.zmax, |
|
|
474 | 474 | cmap=plt.get_cmap(self.colormap) |
|
|
475 | 475 | ) |
|
|
476 | 476 | else: |
|
|
477 | 477 | ax.plt.set_array(spc[pair[1]].T.ravel()) |
|
|
478 | 478 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
|
|
479 | 479 | |
|
|
480 | 480 | out = cspc[n]/numpy.sqrt(spc[pair[0]]*spc[pair[1]]) |
|
|
481 | 481 | coh = numpy.abs(out) |
|
|
482 | 482 | phase = numpy.arctan2(out.imag, out.real)*180/numpy.pi |
|
|
483 | 483 | |
|
|
484 | 484 | ax = self.axes[4*n+2] |
|
|
485 | 485 | if ax.firsttime: |
|
|
486 | 486 | ax.plt = ax.pcolormesh(x, y, coh.T, |
|
|
487 | 487 | vmin=0, |
|
|
488 | 488 | vmax=1, |
|
|
489 | 489 | cmap=plt.get_cmap(self.colormap_coh) |
|
|
490 | 490 | ) |
|
|
491 | 491 | else: |
|
|
492 | 492 | ax.plt.set_array(coh.T.ravel()) |
|
|
493 | 493 | self.titles.append('Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
494 | 494 | |
|
|
495 | 495 | ax = self.axes[4*n+3] |
|
|
496 | 496 | if ax.firsttime: |
|
|
497 | 497 | ax.plt = ax.pcolormesh(x, y, phase.T, |
|
|
498 | 498 | vmin=-180, |
|
|
499 | 499 | vmax=180, |
|
|
500 | 500 | cmap=plt.get_cmap(self.colormap_phase) |
|
|
501 | 501 | ) |
|
|
502 | 502 | else: |
|
|
503 | 503 | ax.plt.set_array(phase.T.ravel()) |
|
|
504 | 504 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
|
|
505 | 505 | |
|
|
506 | 506 | self.saveTime = self.max_time |
|
|
507 | 507 | |
|
|
508 | 508 | |
|
|
509 | 509 | class PlotSpectraMeanData(PlotSpectraData): |
|
|
510 | 510 | ''' |
|
|
511 | 511 | Plot for Spectra and Mean |
|
|
512 | 512 | ''' |
|
|
513 | 513 | CODE = 'spc_mean' |
|
|
514 | 514 | colormap = 'jro' |
|
|
515 | 515 | |
|
|
516 | 516 | |
|
|
517 | 517 | class PlotRTIData(PlotData): |
|
|
518 | 518 | ''' |
|
|
519 | 519 | Plot for RTI data |
|
|
520 | 520 | ''' |
|
|
521 | 521 | |
|
|
522 | 522 | CODE = 'rti' |
|
|
523 | 523 | colormap = 'jro' |
|
|
524 | 524 | |
|
|
525 | 525 | def setup(self): |
|
|
526 | 526 | self.xaxis = 'time' |
|
|
527 | 527 | self.ncols = 1 |
|
|
528 | 528 | self.nrows = len(self.data.channels) |
|
|
529 | 529 | self.nplots = len(self.data.channels) |
|
|
530 | 530 | self.ylabel = 'Range [Km]' |
|
|
531 | 531 | self.cb_label = 'dB' |
|
|
532 | 532 | self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
533 | 533 | |
|
|
534 | 534 | def plot(self): |
|
|
535 | 535 | self.x = self.data.times |
|
|
536 | 536 | self.y = self.data.heights |
|
|
537 | 537 | self.z = self.data[self.CODE] |
|
|
538 | 538 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
539 | 539 | |
|
|
540 | 540 | for n, ax in enumerate(self.axes): |
|
|
541 | 541 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
542 | 542 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
543 | 543 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
544 | 544 | if ax.firsttime: |
|
|
545 | 545 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
546 | 546 | vmin=self.zmin, |
|
|
547 | 547 | vmax=self.zmax, |
|
|
548 | 548 | cmap=plt.get_cmap(self.colormap) |
|
|
549 | 549 | ) |
|
|
550 | 550 | if self.showprofile: |
|
|
551 | 551 | ax.plot_profile= self.pf_axes[n].plot(self.data['rti'][n][-1], self.y)[0] |
|
|
552 | 552 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(self.data['noise'][n][-1], len(self.y)), self.y, |
|
|
553 | 553 | color="k", linestyle="dashed", lw=1)[0] |
|
|
554 | 554 | else: |
|
|
555 | 555 | ax.collections.remove(ax.collections[0]) |
|
|
556 | 556 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
557 | 557 | vmin=self.zmin, |
|
|
558 | 558 | vmax=self.zmax, |
|
|
559 | 559 | cmap=plt.get_cmap(self.colormap) |
|
|
560 | 560 | ) |
|
|
561 | 561 | if self.showprofile: |
|
|
562 | 562 | ax.plot_profile.set_data(self.data['rti'][n][-1], self.y) |
|
|
563 | 563 | ax.plot_noise.set_data(numpy.repeat(self.data['noise'][n][-1], len(self.y)), self.y) |
|
|
564 | 564 | |
|
|
565 | 565 | self.saveTime = self.min_time |
|
|
566 | 566 | |
|
|
567 | 567 | |
|
|
568 | 568 | class PlotCOHData(PlotRTIData): |
|
|
569 | 569 | ''' |
|
|
570 | 570 | Plot for Coherence data |
|
|
571 | 571 | ''' |
|
|
572 | 572 | |
|
|
573 | 573 | CODE = 'coh' |
|
|
574 | 574 | |
|
|
575 | 575 | def setup(self): |
|
|
576 | 576 | self.xaxis = 'time' |
|
|
577 | 577 | self.ncols = 1 |
|
|
578 | 578 | self.nrows = len(self.data.pairs) |
|
|
579 | 579 | self.nplots = len(self.data.pairs) |
|
|
580 | 580 | self.ylabel = 'Range [Km]' |
|
|
581 | 581 | if self.CODE == 'coh': |
|
|
582 | 582 | self.cb_label = '' |
|
|
583 | 583 | self.titles = ['Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
584 | 584 | else: |
|
|
585 | 585 | self.cb_label = 'Degrees' |
|
|
586 | 586 | self.titles = ['Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
587 | 587 | |
|
|
588 | 588 | |
|
|
589 | 589 | class PlotPHASEData(PlotCOHData): |
|
|
590 | 590 | ''' |
|
|
591 | 591 | Plot for Phase map data |
|
|
592 | 592 | ''' |
|
|
593 | 593 | |
|
|
594 | 594 | CODE = 'phase' |
|
|
595 | 595 | colormap = 'seismic' |
|
|
596 | 596 | |
|
|
597 | 597 | |
|
|
598 | 598 | class PlotNoiseData(PlotData): |
|
|
599 | 599 | ''' |
|
|
600 | 600 | Plot for noise |
|
|
601 | 601 | ''' |
|
|
602 | 602 | |
|
|
603 | 603 | CODE = 'noise' |
|
|
604 | 604 | |
|
|
605 | 605 | def setup(self): |
|
|
606 | 606 | self.xaxis = 'time' |
|
|
607 | 607 | self.ncols = 1 |
|
|
608 | 608 | self.nrows = 1 |
|
|
609 | 609 | self.nplots = 1 |
|
|
610 | 610 | self.ylabel = 'Intensity [dB]' |
|
|
611 | 611 | self.titles = ['Noise'] |
|
|
612 | 612 | self.colorbar = False |
|
|
613 | 613 | |
|
|
614 | 614 | def plot(self): |
|
|
615 | 615 | |
|
|
616 | 616 | x = self.data.times |
|
|
617 | 617 | xmin = self.min_time |
|
|
618 | 618 | xmax = xmin+self.xrange*60*60 |
|
|
619 | 619 | Y = self.data[self.CODE] |
|
|
620 | 620 | |
|
|
621 | 621 | if self.axes[0].firsttime: |
|
|
622 | 622 | for ch in self.data.channels: |
|
|
623 | 623 | y = Y[ch] |
|
|
624 | 624 | self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) |
|
|
625 | 625 | plt.legend() |
|
|
626 | 626 | else: |
|
|
627 | 627 | for ch in self.data.channels: |
|
|
628 | 628 | y = Y[ch] |
|
|
629 | 629 | self.axes[0].lines[ch].set_data(x, y) |
|
|
630 | 630 | |
|
|
631 | 631 | self.ymin = numpy.nanmin(Y) - 5 |
|
|
632 | 632 | self.ymax = numpy.nanmax(Y) + 5 |
|
|
633 | 633 | self.saveTime = self.min_time |
|
|
634 | 634 | |
|
|
635 | 635 | |
|
|
636 | 636 | class PlotSNRData(PlotRTIData): |
|
|
637 | 637 | ''' |
|
|
638 | 638 | Plot for SNR Data |
|
|
639 | 639 | ''' |
|
|
640 | 640 | |
|
|
641 | 641 | CODE = 'snr' |
|
|
642 | 642 | colormap = 'jet' |
|
|
643 | 643 | |
|
|
644 | 644 | |
|
|
645 | 645 | class PlotDOPData(PlotRTIData): |
|
|
646 | 646 | ''' |
|
|
647 | 647 | Plot for DOPPLER Data |
|
|
648 | 648 | ''' |
|
|
649 | 649 | |
|
|
650 | 650 | CODE = 'dop' |
|
|
651 | 651 | colormap = 'jet' |
|
|
652 | 652 | |
|
|
653 | 653 | |
|
|
654 | 654 | class PlotSkyMapData(PlotData): |
|
|
655 | 655 | ''' |
|
|
656 | 656 | Plot for meteors detection data |
|
|
657 | 657 | ''' |
|
|
658 | 658 | |
|
|
659 | 659 | CODE = 'met' |
|
|
660 | 660 | |
|
|
661 | 661 | def setup(self): |
|
|
662 | 662 | |
|
|
663 | 663 | self.ncols = 1 |
|
|
664 | 664 | self.nrows = 1 |
|
|
665 | 665 | self.width = 7.2 |
|
|
666 | 666 | self.height = 7.2 |
|
|
667 | 667 | |
|
|
668 | 668 | self.xlabel = 'Zonal Zenith Angle (deg)' |
|
|
669 | 669 | self.ylabel = 'Meridional Zenith Angle (deg)' |
|
|
670 | 670 | |
|
|
671 | 671 | if self.figure is None: |
|
|
672 | 672 | self.figure = plt.figure(figsize=(self.width, self.height), |
|
|
673 | 673 | edgecolor='k', |
|
|
674 | 674 | facecolor='w') |
|
|
675 | 675 | else: |
|
|
676 | 676 | self.figure.clf() |
|
|
677 | 677 | |
|
|
678 | 678 | self.ax = plt.subplot2grid((self.nrows, self.ncols), (0, 0), 1, 1, polar=True) |
|
|
679 | 679 | self.ax.firsttime = True |
|
|
680 | 680 | |
|
|
681 | 681 | |
|
|
682 | 682 | def plot(self): |
|
|
683 | 683 | |
|
|
684 | 684 | arrayParameters = numpy.concatenate([self.data['param'][t] for t in self.data.times]) |
|
|
685 | 685 | error = arrayParameters[:,-1] |
|
|
686 | 686 | indValid = numpy.where(error == 0)[0] |
|
|
687 | 687 | finalMeteor = arrayParameters[indValid,:] |
|
|
688 | 688 | finalAzimuth = finalMeteor[:,3] |
|
|
689 | 689 | finalZenith = finalMeteor[:,4] |
|
|
690 | 690 | |
|
|
691 | 691 | x = finalAzimuth*numpy.pi/180 |
|
|
692 | 692 | y = finalZenith |
|
|
693 | 693 | |
|
|
694 | 694 | if self.ax.firsttime: |
|
|
695 | 695 | self.ax.plot = self.ax.plot(x, y, 'bo', markersize=5)[0] |
|
|
696 | 696 | self.ax.set_ylim(0,90) |
|
|
697 | 697 | self.ax.set_yticks(numpy.arange(0,90,20)) |
|
|
698 | 698 | self.ax.set_xlabel(self.xlabel) |
|
|
699 | 699 | self.ax.set_ylabel(self.ylabel) |
|
|
700 | 700 | self.ax.yaxis.labelpad = 40 |
|
|
701 | 701 | self.ax.firsttime = False |
|
|
702 | 702 | else: |
|
|
703 | 703 | self.ax.plot.set_data(x, y) |
|
|
704 | 704 | |
|
|
705 | 705 | |
|
|
706 | 706 | dt1 = datetime.datetime.fromtimestamp(self.min_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
707 | 707 | dt2 = datetime.datetime.fromtimestamp(self.max_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
708 | 708 | title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, |
|
|
709 | 709 | dt2, |
|
|
710 | 710 | len(x)) |
|
|
711 | 711 | self.ax.set_title(title, size=8) |
|
|
712 | 712 | |
|
|
713 | 713 | self.saveTime = self.max_time |
|
|
714 | 714 | |
|
|
715 | 715 | class PlotParamData(PlotRTIData): |
|
|
716 | 716 | ''' |
|
|
717 | 717 | Plot for data_param object |
|
|
718 | 718 | ''' |
|
|
719 | 719 | |
|
|
720 | 720 | CODE = 'param' |
|
|
721 | 721 | colormap = 'seismic' |
|
|
722 | 722 | |
|
|
723 | 723 | def setup(self): |
|
|
724 | 724 | self.xaxis = 'time' |
|
|
725 | 725 | self.ncols = 1 |
|
|
726 | 726 | self.nrows = self.data.shape(self.CODE)[0] |
|
|
727 | 727 | self.nplots = self.nrows |
|
|
728 | 728 | if self.showSNR: |
|
|
729 | 729 | self.nrows += 1 |
|
|
730 | self.nplots += 1 | |
|
|
730 | 731 | |
|
|
731 | 732 | self.ylabel = 'Height [Km]' |
|
|
732 | 733 | self.titles = self.data.parameters \ |
|
|
733 | 734 | if self.data.parameters else ['Param {}'.format(x) for x in xrange(self.nrows)] |
|
|
734 | 735 | if self.showSNR: |
|
|
735 | 736 | self.titles.append('SNR') |
|
|
736 | 737 | |
|
|
737 | 738 | def plot(self): |
|
|
738 | 739 | self.data.normalize_heights() |
|
|
739 | 740 | self.x = self.data.times |
|
|
740 | 741 | self.y = self.data.heights |
|
|
741 | 742 | if self.showSNR: |
|
|
742 | 743 | self.z = numpy.concatenate( |
|
|
743 | 744 | (self.data[self.CODE], self.data['snr']) |
|
|
744 | 745 | ) |
|
|
745 | 746 | else: |
|
|
746 | 747 | self.z = self.data[self.CODE] |
|
|
747 | 748 | |
|
|
748 | 749 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
749 | 750 | |
|
|
750 | 751 | for n, ax in enumerate(self.axes): |
|
|
751 | 752 | |
|
|
752 | 753 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
753 | 754 | |
|
|
754 | 755 | if ax.firsttime: |
|
|
755 | 756 | if self.zlimits is not None: |
|
|
756 | 757 | self.zmin, self.zmax = self.zlimits[n] |
|
|
757 | 758 | self.zmax = self.zmax if self.zmax is not None else numpy.nanmax(abs(self.z[:-1, :])) |
|
|
758 | 759 | self.zmin = self.zmin if self.zmin is not None else -self.zmax |
|
|
759 | 760 | ax.plt = ax.pcolormesh(x, y, z[n, :, :].T*self.factors[n], |
|
|
760 | 761 | vmin=self.zmin, |
|
|
761 | 762 | vmax=self.zmax, |
|
|
762 | 763 | cmap=self.cmaps[n] |
|
|
763 | 764 | ) |
|
|
764 | 765 | else: |
|
|
765 | 766 | if self.zlimits is not None: |
|
|
766 | 767 | self.zmin, self.zmax = self.zlimits[n] |
|
|
767 | 768 | ax.collections.remove(ax.collections[0]) |
|
|
768 | 769 | ax.plt = ax.pcolormesh(x, y, z[n, :, :].T*self.factors[n], |
|
|
769 | 770 | vmin=self.zmin, |
|
|
770 | 771 | vmax=self.zmax, |
|
|
771 | 772 | cmap=self.cmaps[n] |
|
|
772 | 773 | ) |
|
|
773 | 774 | |
|
|
774 | 775 | self.saveTime = self.min_time |
|
|
775 | 776 | |
|
|
776 | 777 | class PlotOuputData(PlotParamData): |
|
|
777 | 778 | ''' |
|
|
778 | 779 | Plot data_output object |
|
|
779 | 780 | ''' |
|
|
780 | 781 | |
|
|
781 | 782 | CODE = 'output' |
|
|
782 | colormap = 'seismic' No newline at end of file | |
|
|
783 | colormap = 'seismic' | |
| @@ -1,2154 +1,2151 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import numpy |
|
|
4 | 4 | import inspect |
|
|
5 | 5 | from figure import Figure, isRealtime, isTimeInHourRange |
|
|
6 | 6 | from plotting_codes import * |
|
|
7 | 7 | |
|
|
8 | 8 | |
|
|
9 | 9 | class FitGauPlot(Figure): |
|
|
10 | 10 | |
|
|
11 | 11 | isConfig = None |
|
|
12 | 12 | __nsubplots = None |
|
|
13 | 13 | |
|
|
14 | 14 | WIDTHPROF = None |
|
|
15 | 15 | HEIGHTPROF = None |
|
|
16 | 16 | PREFIX = 'fitgau' |
|
|
17 | 17 | |
|
|
18 | 18 | def __init__(self, **kwargs): |
|
|
19 | 19 | Figure.__init__(self, **kwargs) |
|
|
20 | 20 | self.isConfig = False |
|
|
21 | 21 | self.__nsubplots = 1 |
|
|
22 | 22 | |
|
|
23 | 23 | self.WIDTH = 250 |
|
|
24 | 24 | self.HEIGHT = 250 |
|
|
25 | 25 | self.WIDTHPROF = 120 |
|
|
26 | 26 | self.HEIGHTPROF = 0 |
|
|
27 | 27 | self.counter_imagwr = 0 |
|
|
28 | 28 | |
|
|
29 | 29 | self.PLOT_CODE = SPEC_CODE |
|
|
30 | 30 | |
|
|
31 | 31 | self.FTP_WEI = None |
|
|
32 | 32 | self.EXP_CODE = None |
|
|
33 | 33 | self.SUB_EXP_CODE = None |
|
|
34 | 34 | self.PLOT_POS = None |
|
|
35 | 35 | |
|
|
36 | 36 | self.__xfilter_ena = False |
|
|
37 | 37 | self.__yfilter_ena = False |
|
|
38 | 38 | |
|
|
39 | 39 | def getSubplots(self): |
|
|
40 | 40 | |
|
|
41 | 41 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
42 | 42 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
43 | 43 | |
|
|
44 | 44 | return nrow, ncol |
|
|
45 | 45 | |
|
|
46 | 46 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
47 | 47 | |
|
|
48 | 48 | self.__showprofile = showprofile |
|
|
49 | 49 | self.nplots = nplots |
|
|
50 | 50 | |
|
|
51 | 51 | ncolspan = 1 |
|
|
52 | 52 | colspan = 1 |
|
|
53 | 53 | if showprofile: |
|
|
54 | 54 | ncolspan = 3 |
|
|
55 | 55 | colspan = 2 |
|
|
56 | 56 | self.__nsubplots = 2 |
|
|
57 | 57 | |
|
|
58 | 58 | self.createFigure(id = id, |
|
|
59 | 59 | wintitle = wintitle, |
|
|
60 | 60 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
61 | 61 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
62 | 62 | show=show) |
|
|
63 | 63 | |
|
|
64 | 64 | nrow, ncol = self.getSubplots() |
|
|
65 | 65 | |
|
|
66 | 66 | counter = 0 |
|
|
67 | 67 | for y in range(nrow): |
|
|
68 | 68 | for x in range(ncol): |
|
|
69 | 69 | |
|
|
70 | 70 | if counter >= self.nplots: |
|
|
71 | 71 | break |
|
|
72 | 72 | |
|
|
73 | 73 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
74 | 74 | |
|
|
75 | 75 | if showprofile: |
|
|
76 | 76 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
77 | 77 | |
|
|
78 | 78 | counter += 1 |
|
|
79 | 79 | |
|
|
80 | 80 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
81 | 81 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
82 | 82 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
83 | 83 | server=None, folder=None, username=None, password=None, |
|
|
84 | 84 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
85 | 85 | xaxis="frequency", colormap='jet', normFactor=None , GauSelector = 1): |
|
|
86 | 86 | |
|
|
87 | 87 | """ |
|
|
88 | 88 | |
|
|
89 | 89 | Input: |
|
|
90 | 90 | dataOut : |
|
|
91 | 91 | id : |
|
|
92 | 92 | wintitle : |
|
|
93 | 93 | channelList : |
|
|
94 | 94 | showProfile : |
|
|
95 | 95 | xmin : None, |
|
|
96 | 96 | xmax : None, |
|
|
97 | 97 | ymin : None, |
|
|
98 | 98 | ymax : None, |
|
|
99 | 99 | zmin : None, |
|
|
100 | 100 | zmax : None |
|
|
101 | 101 | """ |
|
|
102 | 102 | if realtime: |
|
|
103 | 103 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
104 | 104 | print 'Skipping this plot function' |
|
|
105 | 105 | return |
|
|
106 | 106 | |
|
|
107 | 107 | if channelList == None: |
|
|
108 | 108 | channelIndexList = dataOut.channelIndexList |
|
|
109 | 109 | else: |
|
|
110 | 110 | channelIndexList = [] |
|
|
111 | 111 | for channel in channelList: |
|
|
112 | 112 | if channel not in dataOut.channelList: |
|
|
113 | 113 | raise ValueError, "Channel %d is not in dataOut.channelList" %channel |
|
|
114 | 114 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
115 | 115 | |
|
|
116 | 116 | # if normFactor is None: |
|
|
117 | 117 | # factor = dataOut.normFactor |
|
|
118 | 118 | # else: |
|
|
119 | 119 | # factor = normFactor |
|
|
120 | 120 | if xaxis == "frequency": |
|
|
121 | 121 | x = dataOut.spc_range[0] |
|
|
122 | 122 | xlabel = "Frequency (kHz)" |
|
|
123 | 123 | |
|
|
124 | 124 | elif xaxis == "time": |
|
|
125 | 125 | x = dataOut.spc_range[1] |
|
|
126 | 126 | xlabel = "Time (ms)" |
|
|
127 | 127 | |
|
|
128 | 128 | else: |
|
|
129 | 129 | x = dataOut.spc_range[2] |
|
|
130 | 130 | xlabel = "Velocity (m/s)" |
|
|
131 | 131 | |
|
|
132 | 132 | ylabel = "Range (Km)" |
|
|
133 | 133 | |
|
|
134 | 134 | y = dataOut.getHeiRange() |
|
|
135 | 135 | |
|
|
136 | 136 | z = dataOut.GauSPC[:,GauSelector,:,:] #GauSelector] #dataOut.data_spc/factor |
|
|
137 | 137 | print 'GausSPC', z[0,32,10:40] |
|
|
138 | 138 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
139 | 139 | zdB = 10*numpy.log10(z) |
|
|
140 | 140 | |
|
|
141 | 141 | avg = numpy.average(z, axis=1) |
|
|
142 | 142 | avgdB = 10*numpy.log10(avg) |
|
|
143 | 143 | |
|
|
144 | 144 | noise = dataOut.spc_noise |
|
|
145 | 145 | noisedB = 10*numpy.log10(noise) |
|
|
146 | 146 | |
|
|
147 | 147 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
148 | 148 | title = wintitle + " Spectra" |
|
|
149 | 149 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
150 | 150 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
151 | 151 | |
|
|
152 | 152 | if not self.isConfig: |
|
|
153 | 153 | |
|
|
154 | 154 | nplots = len(channelIndexList) |
|
|
155 | 155 | |
|
|
156 | 156 | self.setup(id=id, |
|
|
157 | 157 | nplots=nplots, |
|
|
158 | 158 | wintitle=wintitle, |
|
|
159 | 159 | showprofile=showprofile, |
|
|
160 | 160 | show=show) |
|
|
161 | 161 | |
|
|
162 | 162 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
163 | 163 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
164 | 164 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
165 | 165 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
166 | 166 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
167 | 167 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
168 | 168 | |
|
|
169 | 169 | self.FTP_WEI = ftp_wei |
|
|
170 | 170 | self.EXP_CODE = exp_code |
|
|
171 | 171 | self.SUB_EXP_CODE = sub_exp_code |
|
|
172 | 172 | self.PLOT_POS = plot_pos |
|
|
173 | 173 | |
|
|
174 | 174 | self.isConfig = True |
|
|
175 | 175 | |
|
|
176 | 176 | self.setWinTitle(title) |
|
|
177 | 177 | |
|
|
178 | 178 | for i in range(self.nplots): |
|
|
179 | 179 | index = channelIndexList[i] |
|
|
180 | 180 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
181 | 181 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[index], noisedB[index], str_datetime) |
|
|
182 | 182 | if len(dataOut.beam.codeList) != 0: |
|
|
183 | 183 | title = "Ch%d:%4.2fdB,%2.2f,%2.2f:%s" %(dataOut.channelList[index], noisedB[index], dataOut.beam.azimuthList[index], dataOut.beam.zenithList[index], str_datetime) |
|
|
184 | 184 | |
|
|
185 | 185 | axes = self.axesList[i*self.__nsubplots] |
|
|
186 | 186 | axes.pcolor(x, y, zdB[index,:,:], |
|
|
187 | 187 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
188 | 188 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=colormap, |
|
|
189 | 189 | ticksize=9, cblabel='') |
|
|
190 | 190 | |
|
|
191 | 191 | if self.__showprofile: |
|
|
192 | 192 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
193 | 193 | axes.pline(avgdB[index,:], y, |
|
|
194 | 194 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
195 | 195 | xlabel='dB', ylabel='', title='', |
|
|
196 | 196 | ytick_visible=False, |
|
|
197 | 197 | grid='x') |
|
|
198 | 198 | |
|
|
199 | 199 | noiseline = numpy.repeat(noisedB[index], len(y)) |
|
|
200 | 200 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
201 | 201 | |
|
|
202 | 202 | self.draw() |
|
|
203 | 203 | |
|
|
204 | 204 | if figfile == None: |
|
|
205 | 205 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
206 | 206 | name = str_datetime |
|
|
207 | 207 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
208 | 208 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
209 | 209 | figfile = self.getFilename(name) |
|
|
210 | 210 | |
|
|
211 | 211 | self.save(figpath=figpath, |
|
|
212 | 212 | figfile=figfile, |
|
|
213 | 213 | save=save, |
|
|
214 | 214 | ftp=ftp, |
|
|
215 | 215 | wr_period=wr_period, |
|
|
216 | 216 | thisDatetime=thisDatetime) |
|
|
217 | 217 | |
|
|
218 | 218 | |
|
|
219 | 219 | |
|
|
220 | 220 | class MomentsPlot(Figure): |
|
|
221 | 221 | |
|
|
222 | 222 | isConfig = None |
|
|
223 | 223 | __nsubplots = None |
|
|
224 | 224 | |
|
|
225 | 225 | WIDTHPROF = None |
|
|
226 | 226 | HEIGHTPROF = None |
|
|
227 | 227 | PREFIX = 'prm' |
|
|
228 | 228 | def __init__(self, **kwargs): |
|
|
229 | 229 | Figure.__init__(self, **kwargs) |
|
|
230 | 230 | self.isConfig = False |
|
|
231 | 231 | self.__nsubplots = 1 |
|
|
232 | 232 | |
|
|
233 | 233 | self.WIDTH = 280 |
|
|
234 | 234 | self.HEIGHT = 250 |
|
|
235 | 235 | self.WIDTHPROF = 120 |
|
|
236 | 236 | self.HEIGHTPROF = 0 |
|
|
237 | 237 | self.counter_imagwr = 0 |
|
|
238 | 238 | |
|
|
239 | 239 | self.PLOT_CODE = MOMENTS_CODE |
|
|
240 | 240 | |
|
|
241 | 241 | self.FTP_WEI = None |
|
|
242 | 242 | self.EXP_CODE = None |
|
|
243 | 243 | self.SUB_EXP_CODE = None |
|
|
244 | 244 | self.PLOT_POS = None |
|
|
245 | 245 | |
|
|
246 | 246 | def getSubplots(self): |
|
|
247 | 247 | |
|
|
248 | 248 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
249 | 249 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
250 | 250 | |
|
|
251 | 251 | return nrow, ncol |
|
|
252 | 252 | |
|
|
253 | 253 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
254 | 254 | |
|
|
255 | 255 | self.__showprofile = showprofile |
|
|
256 | 256 | self.nplots = nplots |
|
|
257 | 257 | |
|
|
258 | 258 | ncolspan = 1 |
|
|
259 | 259 | colspan = 1 |
|
|
260 | 260 | if showprofile: |
|
|
261 | 261 | ncolspan = 3 |
|
|
262 | 262 | colspan = 2 |
|
|
263 | 263 | self.__nsubplots = 2 |
|
|
264 | 264 | |
|
|
265 | 265 | self.createFigure(id = id, |
|
|
266 | 266 | wintitle = wintitle, |
|
|
267 | 267 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
268 | 268 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
269 | 269 | show=show) |
|
|
270 | 270 | |
|
|
271 | 271 | nrow, ncol = self.getSubplots() |
|
|
272 | 272 | |
|
|
273 | 273 | counter = 0 |
|
|
274 | 274 | for y in range(nrow): |
|
|
275 | 275 | for x in range(ncol): |
|
|
276 | 276 | |
|
|
277 | 277 | if counter >= self.nplots: |
|
|
278 | 278 | break |
|
|
279 | 279 | |
|
|
280 | 280 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
281 | 281 | |
|
|
282 | 282 | if showprofile: |
|
|
283 | 283 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
284 | 284 | |
|
|
285 | 285 | counter += 1 |
|
|
286 | 286 | |
|
|
287 | 287 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
288 | 288 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
289 | 289 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
290 | 290 | server=None, folder=None, username=None, password=None, |
|
|
291 | 291 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
292 | 292 | |
|
|
293 | 293 | """ |
|
|
294 | 294 | |
|
|
295 | 295 | Input: |
|
|
296 | 296 | dataOut : |
|
|
297 | 297 | id : |
|
|
298 | 298 | wintitle : |
|
|
299 | 299 | channelList : |
|
|
300 | 300 | showProfile : |
|
|
301 | 301 | xmin : None, |
|
|
302 | 302 | xmax : None, |
|
|
303 | 303 | ymin : None, |
|
|
304 | 304 | ymax : None, |
|
|
305 | 305 | zmin : None, |
|
|
306 | 306 | zmax : None |
|
|
307 | 307 | """ |
|
|
308 | 308 | |
|
|
309 | 309 | if dataOut.flagNoData: |
|
|
310 | 310 | return None |
|
|
311 | 311 | |
|
|
312 | 312 | if realtime: |
|
|
313 | 313 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
314 | 314 | print 'Skipping this plot function' |
|
|
315 | 315 | return |
|
|
316 | 316 | |
|
|
317 | 317 | if channelList == None: |
|
|
318 | 318 | channelIndexList = dataOut.channelIndexList |
|
|
319 | 319 | else: |
|
|
320 | 320 | channelIndexList = [] |
|
|
321 | 321 | for channel in channelList: |
|
|
322 | 322 | if channel not in dataOut.channelList: |
|
|
323 | 323 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
324 | 324 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
325 | 325 | |
|
|
326 | 326 | factor = dataOut.normFactor |
|
|
327 | 327 | x = dataOut.abscissaList |
|
|
328 | 328 | y = dataOut.heightList |
|
|
329 | 329 | |
|
|
330 | 330 | z = dataOut.data_pre[channelIndexList,:,:]/factor |
|
|
331 | 331 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
332 | 332 | avg = numpy.average(z, axis=1) |
|
|
333 | 333 | noise = dataOut.noise/factor |
|
|
334 | 334 | |
|
|
335 | 335 | zdB = 10*numpy.log10(z) |
|
|
336 | 336 | avgdB = 10*numpy.log10(avg) |
|
|
337 | 337 | noisedB = 10*numpy.log10(noise) |
|
|
338 | 338 | |
|
|
339 | 339 | #thisDatetime = dataOut.datatime |
|
|
340 | 340 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
341 | 341 | title = wintitle + " Parameters" |
|
|
342 | 342 | xlabel = "Velocity (m/s)" |
|
|
343 | 343 | ylabel = "Range (Km)" |
|
|
344 | 344 | |
|
|
345 | 345 | update_figfile = False |
|
|
346 | 346 | |
|
|
347 | 347 | if not self.isConfig: |
|
|
348 | 348 | |
|
|
349 | 349 | nplots = len(channelIndexList) |
|
|
350 | 350 | |
|
|
351 | 351 | self.setup(id=id, |
|
|
352 | 352 | nplots=nplots, |
|
|
353 | 353 | wintitle=wintitle, |
|
|
354 | 354 | showprofile=showprofile, |
|
|
355 | 355 | show=show) |
|
|
356 | 356 | |
|
|
357 | 357 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
358 | 358 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
359 | 359 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
360 | 360 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
361 | 361 | if zmin == None: zmin = numpy.nanmin(avgdB)*0.9 |
|
|
362 | 362 | if zmax == None: zmax = numpy.nanmax(avgdB)*0.9 |
|
|
363 | 363 | |
|
|
364 | 364 | self.FTP_WEI = ftp_wei |
|
|
365 | 365 | self.EXP_CODE = exp_code |
|
|
366 | 366 | self.SUB_EXP_CODE = sub_exp_code |
|
|
367 | 367 | self.PLOT_POS = plot_pos |
|
|
368 | 368 | |
|
|
369 | 369 | self.isConfig = True |
|
|
370 | 370 | update_figfile = True |
|
|
371 | 371 | |
|
|
372 | 372 | self.setWinTitle(title) |
|
|
373 | 373 | |
|
|
374 | 374 | for i in range(self.nplots): |
|
|
375 | 375 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
376 | 376 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[i], noisedB[i], str_datetime) |
|
|
377 | 377 | axes = self.axesList[i*self.__nsubplots] |
|
|
378 | 378 | axes.pcolor(x, y, zdB[i,:,:], |
|
|
379 | 379 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
380 | 380 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
381 | 381 | ticksize=9, cblabel='') |
|
|
382 | 382 | #Mean Line |
|
|
383 | 383 | mean = dataOut.data_param[i, 1, :] |
|
|
384 | 384 | axes.addpline(mean, y, idline=0, color="black", linestyle="solid", lw=1) |
|
|
385 | 385 | |
|
|
386 | 386 | if self.__showprofile: |
|
|
387 | 387 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
388 | 388 | axes.pline(avgdB[i], y, |
|
|
389 | 389 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
390 | 390 | xlabel='dB', ylabel='', title='', |
|
|
391 | 391 | ytick_visible=False, |
|
|
392 | 392 | grid='x') |
|
|
393 | 393 | |
|
|
394 | 394 | noiseline = numpy.repeat(noisedB[i], len(y)) |
|
|
395 | 395 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
396 | 396 | |
|
|
397 | 397 | self.draw() |
|
|
398 | 398 | |
|
|
399 | 399 | self.save(figpath=figpath, |
|
|
400 | 400 | figfile=figfile, |
|
|
401 | 401 | save=save, |
|
|
402 | 402 | ftp=ftp, |
|
|
403 | 403 | wr_period=wr_period, |
|
|
404 | 404 | thisDatetime=thisDatetime) |
|
|
405 | 405 | |
|
|
406 | 406 | |
|
|
407 | 407 | |
|
|
408 | 408 | class SkyMapPlot(Figure): |
|
|
409 | 409 | |
|
|
410 | 410 | __isConfig = None |
|
|
411 | 411 | __nsubplots = None |
|
|
412 | 412 | |
|
|
413 | 413 | WIDTHPROF = None |
|
|
414 | 414 | HEIGHTPROF = None |
|
|
415 | 415 | PREFIX = 'mmap' |
|
|
416 | 416 | |
|
|
417 | 417 | def __init__(self, **kwargs): |
|
|
418 | 418 | Figure.__init__(self, **kwargs) |
|
|
419 | 419 | self.isConfig = False |
|
|
420 | 420 | self.__nsubplots = 1 |
|
|
421 | 421 | |
|
|
422 | 422 | # self.WIDTH = 280 |
|
|
423 | 423 | # self.HEIGHT = 250 |
|
|
424 | 424 | self.WIDTH = 600 |
|
|
425 | 425 | self.HEIGHT = 600 |
|
|
426 | 426 | self.WIDTHPROF = 120 |
|
|
427 | 427 | self.HEIGHTPROF = 0 |
|
|
428 | 428 | self.counter_imagwr = 0 |
|
|
429 | 429 | |
|
|
430 | 430 | self.PLOT_CODE = MSKYMAP_CODE |
|
|
431 | 431 | |
|
|
432 | 432 | self.FTP_WEI = None |
|
|
433 | 433 | self.EXP_CODE = None |
|
|
434 | 434 | self.SUB_EXP_CODE = None |
|
|
435 | 435 | self.PLOT_POS = None |
|
|
436 | 436 | |
|
|
437 | 437 | def getSubplots(self): |
|
|
438 | 438 | |
|
|
439 | 439 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
440 | 440 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
441 | 441 | |
|
|
442 | 442 | return nrow, ncol |
|
|
443 | 443 | |
|
|
444 | 444 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
445 | 445 | |
|
|
446 | 446 | self.__showprofile = showprofile |
|
|
447 | 447 | self.nplots = nplots |
|
|
448 | 448 | |
|
|
449 | 449 | ncolspan = 1 |
|
|
450 | 450 | colspan = 1 |
|
|
451 | 451 | |
|
|
452 | 452 | self.createFigure(id = id, |
|
|
453 | 453 | wintitle = wintitle, |
|
|
454 | 454 | widthplot = self.WIDTH, #+ self.WIDTHPROF, |
|
|
455 | 455 | heightplot = self.HEIGHT,# + self.HEIGHTPROF, |
|
|
456 | 456 | show=show) |
|
|
457 | 457 | |
|
|
458 | 458 | nrow, ncol = 1,1 |
|
|
459 | 459 | counter = 0 |
|
|
460 | 460 | x = 0 |
|
|
461 | 461 | y = 0 |
|
|
462 | 462 | self.addAxes(1, 1, 0, 0, 1, 1, True) |
|
|
463 | 463 | |
|
|
464 | 464 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
465 | 465 | tmin=0, tmax=24, timerange=None, |
|
|
466 | 466 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
467 | 467 | server=None, folder=None, username=None, password=None, |
|
|
468 | 468 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
469 | 469 | |
|
|
470 | 470 | """ |
|
|
471 | 471 | |
|
|
472 | 472 | Input: |
|
|
473 | 473 | dataOut : |
|
|
474 | 474 | id : |
|
|
475 | 475 | wintitle : |
|
|
476 | 476 | channelList : |
|
|
477 | 477 | showProfile : |
|
|
478 | 478 | xmin : None, |
|
|
479 | 479 | xmax : None, |
|
|
480 | 480 | ymin : None, |
|
|
481 | 481 | ymax : None, |
|
|
482 | 482 | zmin : None, |
|
|
483 | 483 | zmax : None |
|
|
484 | 484 | """ |
|
|
485 | 485 | |
|
|
486 | 486 | arrayParameters = dataOut.data_param |
|
|
487 | 487 | error = arrayParameters[:,-1] |
|
|
488 | 488 | indValid = numpy.where(error == 0)[0] |
|
|
489 | 489 | finalMeteor = arrayParameters[indValid,:] |
|
|
490 | 490 | finalAzimuth = finalMeteor[:,3] |
|
|
491 | 491 | finalZenith = finalMeteor[:,4] |
|
|
492 | 492 | |
|
|
493 | 493 | x = finalAzimuth*numpy.pi/180 |
|
|
494 | 494 | y = finalZenith |
|
|
495 | 495 | x1 = [dataOut.ltctime, dataOut.ltctime] |
|
|
496 | 496 | |
|
|
497 | 497 | #thisDatetime = dataOut.datatime |
|
|
498 | 498 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
499 | 499 | title = wintitle + " Parameters" |
|
|
500 | 500 | xlabel = "Zonal Zenith Angle (deg) " |
|
|
501 | 501 | ylabel = "Meridional Zenith Angle (deg)" |
|
|
502 | 502 | update_figfile = False |
|
|
503 | 503 | |
|
|
504 | 504 | if not self.isConfig: |
|
|
505 | 505 | |
|
|
506 | 506 | nplots = 1 |
|
|
507 | 507 | |
|
|
508 | 508 | self.setup(id=id, |
|
|
509 | 509 | nplots=nplots, |
|
|
510 | 510 | wintitle=wintitle, |
|
|
511 | 511 | showprofile=showprofile, |
|
|
512 | 512 | show=show) |
|
|
513 | 513 | |
|
|
514 | 514 | if self.xmin is None and self.xmax is None: |
|
|
515 | 515 | self.xmin, self.xmax = self.getTimeLim(x1, tmin, tmax, timerange) |
|
|
516 | 516 | |
|
|
517 | 517 | if timerange != None: |
|
|
518 | 518 | self.timerange = timerange |
|
|
519 | 519 | else: |
|
|
520 | 520 | self.timerange = self.xmax - self.xmin |
|
|
521 | 521 | |
|
|
522 | 522 | self.FTP_WEI = ftp_wei |
|
|
523 | 523 | self.EXP_CODE = exp_code |
|
|
524 | 524 | self.SUB_EXP_CODE = sub_exp_code |
|
|
525 | 525 | self.PLOT_POS = plot_pos |
|
|
526 | 526 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
527 | 527 | self.firstdate = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
528 | 528 | self.isConfig = True |
|
|
529 | 529 | update_figfile = True |
|
|
530 | 530 | |
|
|
531 | 531 | self.setWinTitle(title) |
|
|
532 | 532 | |
|
|
533 | 533 | i = 0 |
|
|
534 | 534 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
535 | 535 | |
|
|
536 | 536 | axes = self.axesList[i*self.__nsubplots] |
|
|
537 | 537 | nevents = axes.x_buffer.shape[0] + x.shape[0] |
|
|
538 | 538 | title = "Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n" %(self.firstdate,str_datetime,nevents) |
|
|
539 | 539 | axes.polar(x, y, |
|
|
540 | 540 | title=title, xlabel=xlabel, ylabel=ylabel, |
|
|
541 | 541 | ticksize=9, cblabel='') |
|
|
542 | 542 | |
|
|
543 | 543 | self.draw() |
|
|
544 | 544 | |
|
|
545 | 545 | self.save(figpath=figpath, |
|
|
546 | 546 | figfile=figfile, |
|
|
547 | 547 | save=save, |
|
|
548 | 548 | ftp=ftp, |
|
|
549 | 549 | wr_period=wr_period, |
|
|
550 | 550 | thisDatetime=thisDatetime, |
|
|
551 | 551 | update_figfile=update_figfile) |
|
|
552 | 552 | |
|
|
553 | 553 | if dataOut.ltctime >= self.xmax: |
|
|
554 | 554 | self.isConfigmagwr = wr_period |
|
|
555 | 555 | self.isConfig = False |
|
|
556 | 556 | update_figfile = True |
|
|
557 | 557 | axes.__firsttime = True |
|
|
558 | 558 | self.xmin += self.timerange |
|
|
559 | 559 | self.xmax += self.timerange |
|
|
560 | 560 | |
|
|
561 | 561 | |
|
|
562 | 562 | |
|
|
563 | 563 | |
|
|
564 | 564 | class WindProfilerPlot(Figure): |
|
|
565 | 565 | |
|
|
566 | 566 | __isConfig = None |
|
|
567 | 567 | __nsubplots = None |
|
|
568 | 568 | |
|
|
569 | 569 | WIDTHPROF = None |
|
|
570 | 570 | HEIGHTPROF = None |
|
|
571 | 571 | PREFIX = 'wind' |
|
|
572 | 572 | |
|
|
573 | 573 | def __init__(self, **kwargs): |
|
|
574 | 574 | Figure.__init__(self, **kwargs) |
|
|
575 | 575 | self.timerange = None |
|
|
576 | 576 | self.isConfig = False |
|
|
577 | 577 | self.__nsubplots = 1 |
|
|
578 | 578 | |
|
|
579 | 579 | self.WIDTH = 800 |
|
|
580 | 580 | self.HEIGHT = 300 |
|
|
581 | 581 | self.WIDTHPROF = 120 |
|
|
582 | 582 | self.HEIGHTPROF = 0 |
|
|
583 | 583 | self.counter_imagwr = 0 |
|
|
584 | 584 | |
|
|
585 | 585 | self.PLOT_CODE = WIND_CODE |
|
|
586 | 586 | |
|
|
587 | 587 | self.FTP_WEI = None |
|
|
588 | 588 | self.EXP_CODE = None |
|
|
589 | 589 | self.SUB_EXP_CODE = None |
|
|
590 | 590 | self.PLOT_POS = None |
|
|
591 | 591 | self.tmin = None |
|
|
592 | 592 | self.tmax = None |
|
|
593 | 593 | |
|
|
594 | 594 | self.xmin = None |
|
|
595 | 595 | self.xmax = None |
|
|
596 | 596 | |
|
|
597 | 597 | self.figfile = None |
|
|
598 | 598 | |
|
|
599 | 599 | def getSubplots(self): |
|
|
600 | 600 | |
|
|
601 | 601 | ncol = 1 |
|
|
602 | 602 | nrow = self.nplots |
|
|
603 | 603 | |
|
|
604 | 604 | return nrow, ncol |
|
|
605 | 605 | |
|
|
606 | 606 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
607 | 607 | |
|
|
608 | 608 | self.__showprofile = showprofile |
|
|
609 | 609 | self.nplots = nplots |
|
|
610 | 610 | |
|
|
611 | 611 | ncolspan = 1 |
|
|
612 | 612 | colspan = 1 |
|
|
613 | 613 | |
|
|
614 | 614 | self.createFigure(id = id, |
|
|
615 | 615 | wintitle = wintitle, |
|
|
616 | 616 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
617 | 617 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
618 | 618 | show=show) |
|
|
619 | 619 | |
|
|
620 | 620 | nrow, ncol = self.getSubplots() |
|
|
621 | 621 | |
|
|
622 | 622 | counter = 0 |
|
|
623 | 623 | for y in range(nrow): |
|
|
624 | 624 | if counter >= self.nplots: |
|
|
625 | 625 | break |
|
|
626 | 626 | |
|
|
627 | 627 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
628 | 628 | counter += 1 |
|
|
629 | 629 | |
|
|
630 | 630 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='False', |
|
|
631 | 631 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
632 | 632 | zmax_ver = None, zmin_ver = None, SNRmin = None, SNRmax = None, |
|
|
633 | 633 | timerange=None, SNRthresh = None, |
|
|
634 | 634 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
635 | 635 | server=None, folder=None, username=None, password=None, |
|
|
636 | 636 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
637 | 637 | """ |
|
|
638 | 638 | |
|
|
639 | 639 | Input: |
|
|
640 | 640 | dataOut : |
|
|
641 | 641 | id : |
|
|
642 | 642 | wintitle : |
|
|
643 | 643 | channelList : |
|
|
644 | 644 | showProfile : |
|
|
645 | 645 | xmin : None, |
|
|
646 | 646 | xmax : None, |
|
|
647 | 647 | ymin : None, |
|
|
648 | 648 | ymax : None, |
|
|
649 | 649 | zmin : None, |
|
|
650 | 650 | zmax : None |
|
|
651 | 651 | """ |
|
|
652 | 652 | |
|
|
653 | 653 | # if timerange is not None: |
|
|
654 | 654 | # self.timerange = timerange |
|
|
655 | 655 | # |
|
|
656 | 656 | # tmin = None |
|
|
657 | 657 | # tmax = None |
|
|
658 | 658 | |
|
|
659 | 659 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
660 | 660 | y = dataOut.heightList |
|
|
661 | 661 | z = dataOut.data_output.copy() |
|
|
662 | 662 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
663 | 663 | nplotsw = nplots |
|
|
664 | 664 | |
|
|
665 | 665 | |
|
|
666 | 666 | #If there is a SNR function defined |
|
|
667 | 667 | if dataOut.data_SNR is not None: |
|
|
668 | 668 | nplots += 1 |
|
|
669 | 669 | SNR = dataOut.data_SNR |
|
|
670 | 670 | SNRavg = numpy.average(SNR, axis=0) |
|
|
671 | 671 | |
|
|
672 | 672 | SNRdB = 10*numpy.log10(SNR) |
|
|
673 | 673 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
674 | 674 | |
|
|
675 | 675 | if SNRthresh == None: SNRthresh = -5.0 |
|
|
676 | 676 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
677 | 677 | |
|
|
678 | 678 | for i in range(nplotsw): |
|
|
679 | 679 | z[i,ind] = numpy.nan |
|
|
680 | 680 | |
|
|
681 | 681 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
682 | 682 | #thisDatetime = datetime.datetime.now() |
|
|
683 | 683 | title = wintitle + "Wind" |
|
|
684 | 684 | xlabel = "" |
|
|
685 | 685 | ylabel = "Height (km)" |
|
|
686 | 686 | update_figfile = False |
|
|
687 | 687 | |
|
|
688 | 688 | if not self.isConfig: |
|
|
689 | 689 | |
|
|
690 | 690 | self.setup(id=id, |
|
|
691 | 691 | nplots=nplots, |
|
|
692 | 692 | wintitle=wintitle, |
|
|
693 | 693 | showprofile=showprofile, |
|
|
694 | 694 | show=show) |
|
|
695 | 695 | |
|
|
696 | 696 | if timerange is not None: |
|
|
697 | 697 | self.timerange = timerange |
|
|
698 | 698 | |
|
|
699 | 699 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
700 | 700 | |
|
|
701 | 701 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
702 | 702 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
703 | 703 | |
|
|
704 | 704 | if zmax == None: zmax = numpy.nanmax(abs(z[range(2),:])) |
|
|
705 | 705 | #if numpy.isnan(zmax): zmax = 50 |
|
|
706 | 706 | if zmin == None: zmin = -zmax |
|
|
707 | 707 | |
|
|
708 | 708 | if nplotsw == 3: |
|
|
709 | 709 | if zmax_ver == None: zmax_ver = numpy.nanmax(abs(z[2,:])) |
|
|
710 | 710 | if zmin_ver == None: zmin_ver = -zmax_ver |
|
|
711 | 711 | |
|
|
712 | 712 | if dataOut.data_SNR is not None: |
|
|
713 | 713 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
714 | 714 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
715 | 715 | |
|
|
716 | 716 | |
|
|
717 | 717 | self.FTP_WEI = ftp_wei |
|
|
718 | 718 | self.EXP_CODE = exp_code |
|
|
719 | 719 | self.SUB_EXP_CODE = sub_exp_code |
|
|
720 | 720 | self.PLOT_POS = plot_pos |
|
|
721 | 721 | |
|
|
722 | 722 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
723 | 723 | self.isConfig = True |
|
|
724 | 724 | self.figfile = figfile |
|
|
725 | 725 | update_figfile = True |
|
|
726 | 726 | |
|
|
727 | 727 | self.setWinTitle(title) |
|
|
728 | 728 | |
|
|
729 | 729 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
730 | 730 | x[1] = self.xmax |
|
|
731 | 731 | |
|
|
732 | 732 | strWind = ['Zonal', 'Meridional', 'Vertical'] |
|
|
733 | 733 | strCb = ['Velocity (m/s)','Velocity (m/s)','Velocity (cm/s)'] |
|
|
734 | 734 | zmaxVector = [zmax, zmax, zmax_ver] |
|
|
735 | 735 | zminVector = [zmin, zmin, zmin_ver] |
|
|
736 | 736 | windFactor = [1,1,100] |
|
|
737 | 737 | |
|
|
738 | 738 | for i in range(nplotsw): |
|
|
739 | 739 | |
|
|
740 | 740 | title = "%s Wind: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
741 | 741 | axes = self.axesList[i*self.__nsubplots] |
|
|
742 | 742 | |
|
|
743 | 743 | z1 = z[i,:].reshape((1,-1))*windFactor[i] |
|
|
744 | 744 | #z1=numpy.ma.masked_where(z1==0.,z1) |
|
|
745 | 745 | |
|
|
746 | 746 | axes.pcolorbuffer(x, y, z1, |
|
|
747 | 747 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
748 | 748 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
749 | 749 | ticksize=9, cblabel=strCb[i], cbsize="1%", colormap="seismic" ) |
|
|
750 | 750 | |
|
|
751 | 751 | if dataOut.data_SNR is not None: |
|
|
752 | 752 | i += 1 |
|
|
753 | 753 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
754 | 754 | axes = self.axesList[i*self.__nsubplots] |
|
|
755 | 755 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
756 | 756 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
757 | 757 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
758 | 758 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
759 | 759 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
760 | 760 | |
|
|
761 | 761 | self.draw() |
|
|
762 | 762 | |
|
|
763 | 763 | self.save(figpath=figpath, |
|
|
764 | 764 | figfile=figfile, |
|
|
765 | 765 | save=save, |
|
|
766 | 766 | ftp=ftp, |
|
|
767 | 767 | wr_period=wr_period, |
|
|
768 | 768 | thisDatetime=thisDatetime, |
|
|
769 | 769 | update_figfile=update_figfile) |
|
|
770 | 770 | |
|
|
771 | 771 | if dataOut.ltctime + dataOut.paramInterval >= self.xmax: |
|
|
772 | 772 | self.counter_imagwr = wr_period |
|
|
773 | 773 | self.isConfig = False |
|
|
774 | 774 | update_figfile = True |
|
|
775 | 775 | |
|
|
776 | 776 | |
|
|
777 | 777 | class ParametersPlot(Figure): |
|
|
778 | 778 | |
|
|
779 | 779 | __isConfig = None |
|
|
780 | 780 | __nsubplots = None |
|
|
781 | 781 | |
|
|
782 | 782 | WIDTHPROF = None |
|
|
783 | 783 | HEIGHTPROF = None |
|
|
784 | 784 | PREFIX = 'param' |
|
|
785 | 785 | |
|
|
786 | 786 | nplots = None |
|
|
787 | 787 | nchan = None |
|
|
788 | 788 | |
|
|
789 | 789 | def __init__(self, **kwargs): |
|
|
790 | 790 | Figure.__init__(self, **kwargs) |
|
|
791 | 791 | self.timerange = None |
|
|
792 | 792 | self.isConfig = False |
|
|
793 | 793 | self.__nsubplots = 1 |
|
|
794 | 794 | |
|
|
795 | 795 | self.WIDTH = 800 |
|
|
796 | 796 | self.HEIGHT = 180 |
|
|
797 | 797 | self.WIDTHPROF = 120 |
|
|
798 | 798 | self.HEIGHTPROF = 0 |
|
|
799 | 799 | self.counter_imagwr = 0 |
|
|
800 | 800 | |
|
|
801 | 801 | self.PLOT_CODE = RTI_CODE |
|
|
802 | 802 | |
|
|
803 | 803 | self.FTP_WEI = None |
|
|
804 | 804 | self.EXP_CODE = None |
|
|
805 | 805 | self.SUB_EXP_CODE = None |
|
|
806 | 806 | self.PLOT_POS = None |
|
|
807 | 807 | self.tmin = None |
|
|
808 | 808 | self.tmax = None |
|
|
809 | 809 | |
|
|
810 | 810 | self.xmin = None |
|
|
811 | 811 | self.xmax = None |
|
|
812 | 812 | |
|
|
813 | 813 | self.figfile = None |
|
|
814 | 814 | |
|
|
815 | 815 | def getSubplots(self): |
|
|
816 | 816 | |
|
|
817 | 817 | ncol = 1 |
|
|
818 | 818 | nrow = self.nplots |
|
|
819 | 819 | |
|
|
820 | 820 | return nrow, ncol |
|
|
821 | 821 | |
|
|
822 | 822 | def setup(self, id, nplots, wintitle, show=True): |
|
|
823 | 823 | |
|
|
824 | 824 | self.nplots = nplots |
|
|
825 | 825 | |
|
|
826 | 826 | ncolspan = 1 |
|
|
827 | 827 | colspan = 1 |
|
|
828 | 828 | |
|
|
829 | 829 | self.createFigure(id = id, |
|
|
830 | 830 | wintitle = wintitle, |
|
|
831 | 831 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
832 | 832 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
833 | 833 | show=show) |
|
|
834 | 834 | |
|
|
835 | 835 | nrow, ncol = self.getSubplots() |
|
|
836 | 836 | |
|
|
837 | 837 | counter = 0 |
|
|
838 | 838 | for y in range(nrow): |
|
|
839 | 839 | for x in range(ncol): |
|
|
840 | 840 | |
|
|
841 | 841 | if counter >= self.nplots: |
|
|
842 | 842 | break |
|
|
843 | 843 | |
|
|
844 | 844 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
845 | 845 | |
|
|
846 | 846 | counter += 1 |
|
|
847 | 847 | |
|
|
848 | 848 | def run(self, dataOut, id, wintitle="", channelList=None, paramIndex = 0, colormap="jet", |
|
|
849 | 849 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, timerange=None, |
|
|
850 | 850 | showSNR=False, SNRthresh = -numpy.inf, SNRmin=None, SNRmax=None, |
|
|
851 | 851 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
852 | 852 | server=None, folder=None, username=None, password=None, |
|
|
853 | 853 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, HEIGHT=None): |
|
|
854 | 854 | """ |
|
|
855 | 855 | |
|
|
856 | 856 | Input: |
|
|
857 | 857 | dataOut : |
|
|
858 | 858 | id : |
|
|
859 | 859 | wintitle : |
|
|
860 | 860 | channelList : |
|
|
861 | 861 | showProfile : |
|
|
862 | 862 | xmin : None, |
|
|
863 | 863 | xmax : None, |
|
|
864 | 864 | ymin : None, |
|
|
865 | 865 | ymax : None, |
|
|
866 | 866 | zmin : None, |
|
|
867 | 867 | zmax : None |
|
|
868 | 868 | """ |
|
|
869 | 869 | |
|
|
870 | 870 | if HEIGHT is not None: |
|
|
871 | 871 | self.HEIGHT = HEIGHT |
|
|
872 | 872 | |
|
|
873 | 873 | |
|
|
874 | 874 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
875 | 875 | return |
|
|
876 | 876 | |
|
|
877 | 877 | if channelList == None: |
|
|
878 | 878 | channelIndexList = range(dataOut.data_param.shape[0]) |
|
|
879 | 879 | else: |
|
|
880 | 880 | channelIndexList = [] |
|
|
881 | 881 | for channel in channelList: |
|
|
882 | 882 | if channel not in dataOut.channelList: |
|
|
883 | 883 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
884 | 884 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
885 | 885 | |
|
|
886 | 886 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
887 | 887 | y = dataOut.getHeiRange() |
|
|
888 | 888 | |
|
|
889 | 889 | if dataOut.data_param.ndim == 3: |
|
|
890 | 890 | z = dataOut.data_param[channelIndexList,paramIndex,:] |
|
|
891 | 891 | else: |
|
|
892 | 892 | z = dataOut.data_param[channelIndexList,:] |
|
|
893 | 893 | |
|
|
894 | 894 | if showSNR: |
|
|
895 | 895 | #SNR data |
|
|
896 | 896 | SNRarray = dataOut.data_SNR[channelIndexList,:] |
|
|
897 | 897 | SNRdB = 10*numpy.log10(SNRarray) |
|
|
898 | 898 | ind = numpy.where(SNRdB < SNRthresh) |
|
|
899 | 899 | z[ind] = numpy.nan |
|
|
900 | 900 | |
|
|
901 | 901 | thisDatetime = dataOut.datatime |
|
|
902 | 902 | # thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
903 | 903 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
904 | 904 | xlabel = "" |
|
|
905 | 905 | ylabel = "Range (Km)" |
|
|
906 | 906 | |
|
|
907 | 907 | update_figfile = False |
|
|
908 | 908 | |
|
|
909 | 909 | if not self.isConfig: |
|
|
910 | 910 | |
|
|
911 | 911 | nchan = len(channelIndexList) |
|
|
912 | 912 | self.nchan = nchan |
|
|
913 | 913 | self.plotFact = 1 |
|
|
914 | 914 | nplots = nchan |
|
|
915 | 915 | |
|
|
916 | 916 | if showSNR: |
|
|
917 | 917 | nplots = nchan*2 |
|
|
918 | 918 | self.plotFact = 2 |
|
|
919 | 919 | if SNRmin == None: SNRmin = numpy.nanmin(SNRdB) |
|
|
920 | 920 | if SNRmax == None: SNRmax = numpy.nanmax(SNRdB) |
|
|
921 | 921 | |
|
|
922 | 922 | self.setup(id=id, |
|
|
923 | 923 | nplots=nplots, |
|
|
924 | 924 | wintitle=wintitle, |
|
|
925 | 925 | show=show) |
|
|
926 | 926 | |
|
|
927 | 927 | if timerange != None: |
|
|
928 | 928 | self.timerange = timerange |
|
|
929 | 929 | |
|
|
930 | 930 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
931 | 931 | |
|
|
932 | 932 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
933 | 933 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
934 | 934 | if zmin == None: zmin = numpy.nanmin(z) |
|
|
935 | 935 | if zmax == None: zmax = numpy.nanmax(z) |
|
|
936 | 936 | |
|
|
937 | 937 | self.FTP_WEI = ftp_wei |
|
|
938 | 938 | self.EXP_CODE = exp_code |
|
|
939 | 939 | self.SUB_EXP_CODE = sub_exp_code |
|
|
940 | 940 | self.PLOT_POS = plot_pos |
|
|
941 | 941 | |
|
|
942 | 942 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
943 | 943 | self.isConfig = True |
|
|
944 | 944 | self.figfile = figfile |
|
|
945 | 945 | update_figfile = True |
|
|
946 | 946 | |
|
|
947 | 947 | self.setWinTitle(title) |
|
|
948 | 948 | |
|
|
949 | 949 | for i in range(self.nchan): |
|
|
950 | 950 | index = channelIndexList[i] |
|
|
951 | 951 | title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
952 | 952 | axes = self.axesList[i*self.plotFact] |
|
|
953 | 953 | z1 = z[i,:].reshape((1,-1)) |
|
|
954 | 954 | axes.pcolorbuffer(x, y, z1, |
|
|
955 | 955 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
956 | 956 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
957 | 957 | ticksize=9, cblabel='', cbsize="1%",colormap=colormap) |
|
|
958 | 958 | |
|
|
959 | 959 | if showSNR: |
|
|
960 | 960 | title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
961 | 961 | axes = self.axesList[i*self.plotFact + 1] |
|
|
962 | 962 | SNRdB1 = SNRdB[i,:].reshape((1,-1)) |
|
|
963 | 963 | axes.pcolorbuffer(x, y, SNRdB1, |
|
|
964 | 964 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
965 | 965 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
966 | 966 | ticksize=9, cblabel='', cbsize="1%",colormap='jet') |
|
|
967 | 967 | |
|
|
968 | 968 | |
|
|
969 | 969 | self.draw() |
|
|
970 | 970 | |
|
|
971 | 971 | if dataOut.ltctime >= self.xmax: |
|
|
972 | 972 | self.counter_imagwr = wr_period |
|
|
973 | 973 | self.isConfig = False |
|
|
974 | 974 | update_figfile = True |
|
|
975 | 975 | |
|
|
976 | 976 | self.save(figpath=figpath, |
|
|
977 | 977 | figfile=figfile, |
|
|
978 | 978 | save=save, |
|
|
979 | 979 | ftp=ftp, |
|
|
980 | 980 | wr_period=wr_period, |
|
|
981 | 981 | thisDatetime=thisDatetime, |
|
|
982 | 982 | update_figfile=update_figfile) |
|
|
983 | 983 | |
|
|
984 | 984 | |
|
|
985 | 985 | |
|
|
986 | 986 | class Parameters1Plot(Figure): |
|
|
987 | 987 | |
|
|
988 | 988 | __isConfig = None |
|
|
989 | 989 | __nsubplots = None |
|
|
990 | 990 | |
|
|
991 | 991 | WIDTHPROF = None |
|
|
992 | 992 | HEIGHTPROF = None |
|
|
993 | 993 | PREFIX = 'prm' |
|
|
994 | 994 | |
|
|
995 | 995 | def __init__(self, **kwargs): |
|
|
996 | 996 | Figure.__init__(self, **kwargs) |
|
|
997 | 997 | self.timerange = 2*60*60 |
|
|
998 | 998 | self.isConfig = False |
|
|
999 | 999 | self.__nsubplots = 1 |
|
|
1000 | 1000 | |
|
|
1001 | 1001 | self.WIDTH = 800 |
|
|
1002 | 1002 | self.HEIGHT = 180 |
|
|
1003 | 1003 | self.WIDTHPROF = 120 |
|
|
1004 | 1004 | self.HEIGHTPROF = 0 |
|
|
1005 | 1005 | self.counter_imagwr = 0 |
|
|
1006 | 1006 | |
|
|
1007 | 1007 | self.PLOT_CODE = PARMS_CODE |
|
|
1008 | 1008 | |
|
|
1009 | 1009 | self.FTP_WEI = None |
|
|
1010 | 1010 | self.EXP_CODE = None |
|
|
1011 | 1011 | self.SUB_EXP_CODE = None |
|
|
1012 | 1012 | self.PLOT_POS = None |
|
|
1013 | 1013 | self.tmin = None |
|
|
1014 | 1014 | self.tmax = None |
|
|
1015 | 1015 | |
|
|
1016 | 1016 | self.xmin = None |
|
|
1017 | 1017 | self.xmax = None |
|
|
1018 | 1018 | |
|
|
1019 | 1019 | self.figfile = None |
|
|
1020 | 1020 | |
|
|
1021 | 1021 | def getSubplots(self): |
|
|
1022 | 1022 | |
|
|
1023 | 1023 | ncol = 1 |
|
|
1024 | 1024 | nrow = self.nplots |
|
|
1025 | 1025 | |
|
|
1026 | 1026 | return nrow, ncol |
|
|
1027 | 1027 | |
|
|
1028 | 1028 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1029 | 1029 | |
|
|
1030 | 1030 | self.__showprofile = showprofile |
|
|
1031 | 1031 | self.nplots = nplots |
|
|
1032 | 1032 | |
|
|
1033 | 1033 | ncolspan = 1 |
|
|
1034 | 1034 | colspan = 1 |
|
|
1035 | 1035 | |
|
|
1036 | 1036 | self.createFigure(id = id, |
|
|
1037 | 1037 | wintitle = wintitle, |
|
|
1038 | 1038 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1039 | 1039 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1040 | 1040 | show=show) |
|
|
1041 | 1041 | |
|
|
1042 | 1042 | nrow, ncol = self.getSubplots() |
|
|
1043 | 1043 | |
|
|
1044 | 1044 | counter = 0 |
|
|
1045 | 1045 | for y in range(nrow): |
|
|
1046 | 1046 | for x in range(ncol): |
|
|
1047 | 1047 | |
|
|
1048 | 1048 | if counter >= self.nplots: |
|
|
1049 | 1049 | break |
|
|
1050 | 1050 | |
|
|
1051 | 1051 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1052 | 1052 | |
|
|
1053 | 1053 | if showprofile: |
|
|
1054 | 1054 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | counter += 1 |
|
|
1057 | 1057 | |
|
|
1058 | 1058 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
1059 | 1059 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None,timerange=None, |
|
|
1060 | 1060 | parameterIndex = None, onlyPositive = False, |
|
|
1061 | 1061 | SNRthresh = -numpy.inf, SNR = True, SNRmin = None, SNRmax = None, onlySNR = False, |
|
|
1062 | 1062 | DOP = True, |
|
|
1063 | 1063 | zlabel = "", parameterName = "", parameterObject = "data_param", |
|
|
1064 | 1064 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
1065 | 1065 | server=None, folder=None, username=None, password=None, |
|
|
1066 | 1066 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1067 | 1067 | #print inspect.getargspec(self.run).args |
|
|
1068 | 1068 | """ |
|
|
1069 | 1069 | |
|
|
1070 | 1070 | Input: |
|
|
1071 | 1071 | dataOut : |
|
|
1072 | 1072 | id : |
|
|
1073 | 1073 | wintitle : |
|
|
1074 | 1074 | channelList : |
|
|
1075 | 1075 | showProfile : |
|
|
1076 | 1076 | xmin : None, |
|
|
1077 | 1077 | xmax : None, |
|
|
1078 | 1078 | ymin : None, |
|
|
1079 | 1079 | ymax : None, |
|
|
1080 | 1080 | zmin : None, |
|
|
1081 | 1081 | zmax : None |
|
|
1082 | 1082 | """ |
|
|
1083 | 1083 | |
|
|
1084 | 1084 | data_param = getattr(dataOut, parameterObject) |
|
|
1085 | 1085 | |
|
|
1086 | 1086 | if channelList == None: |
|
|
1087 | 1087 | channelIndexList = numpy.arange(data_param.shape[0]) |
|
|
1088 | 1088 | else: |
|
|
1089 | 1089 | channelIndexList = numpy.array(channelList) |
|
|
1090 | 1090 | |
|
|
1091 | 1091 | nchan = len(channelIndexList) #Number of channels being plotted |
|
|
1092 | 1092 | |
|
|
1093 | 1093 | if nchan < 1: |
|
|
1094 | 1094 | return |
|
|
1095 | 1095 | |
|
|
1096 | 1096 | nGraphsByChannel = 0 |
|
|
1097 | 1097 | |
|
|
1098 | 1098 | if SNR: |
|
|
1099 | 1099 | nGraphsByChannel += 1 |
|
|
1100 | 1100 | if DOP: |
|
|
1101 | 1101 | nGraphsByChannel += 1 |
|
|
1102 | 1102 | |
|
|
1103 | 1103 | if nGraphsByChannel < 1: |
|
|
1104 | 1104 | return |
|
|
1105 | 1105 | |
|
|
1106 | 1106 | nplots = nGraphsByChannel*nchan |
|
|
1107 | 1107 | |
|
|
1108 | 1108 | if timerange is not None: |
|
|
1109 | 1109 | self.timerange = timerange |
|
|
1110 | 1110 | |
|
|
1111 | 1111 | #tmin = None |
|
|
1112 | 1112 | #tmax = None |
|
|
1113 | 1113 | if parameterIndex == None: |
|
|
1114 | 1114 | parameterIndex = 1 |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
1117 | 1117 | y = dataOut.heightList |
|
|
1118 | z = data_param[channelIndexList,parameterIndex,:].copy() | |
|
|
1119 | 1118 | |
|
|
1120 | zRange = dataOut.abscissaList | |
|
|
1121 | # nChannels = z.shape[0] #Number of wind dimensions estimated | |
|
|
1122 | # thisDatetime = dataOut.datatime | |
|
|
1119 | if dataOut.data_param.ndim == 3: | |
|
|
1120 | z = dataOut.data_param[channelIndexList,parameterIndex,:] | |
|
|
1121 | else: | |
|
|
1122 | z = dataOut.data_param[channelIndexList,:] | |
|
|
1123 | 1123 | |
|
|
1124 | 1124 | if dataOut.data_SNR is not None: |
|
|
1125 |
|
|
|
|
1126 | SNRdB = 10*numpy.log10(SNRarray) | |
|
|
1127 | # SNRavgdB = 10*numpy.log10(SNRavg) | |
|
|
1128 | ind = numpy.where(SNRdB < 10**(SNRthresh/10)) | |
|
|
1129 | z[ind] = numpy.nan | |
|
|
1125 | if dataOut.data_SNR.ndim == 2: | |
|
|
1126 | SNRavg = numpy.average(dataOut.data_SNR, axis=0) | |
|
|
1127 | else: | |
|
|
1128 | SNRavg = dataOut.data_SNR | |
|
|
1129 | SNRdB = 10*numpy.log10(SNRavg) | |
|
|
1130 | 1130 | |
|
|
1131 | 1131 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1132 | 1132 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1133 | 1133 | xlabel = "" |
|
|
1134 | ylabel = "Range (Km)" | |
|
|
1135 | ||
|
|
1136 | if (SNR and not onlySNR): nplots = 2*nplots | |
|
|
1134 | ylabel = "Range (Km)" | |
|
|
1137 | 1135 | |
|
|
1138 | 1136 | if onlyPositive: |
|
|
1139 | 1137 | colormap = "jet" |
|
|
1140 | 1138 | zmin = 0 |
|
|
1141 | 1139 | else: colormap = "RdBu_r" |
|
|
1142 | 1140 | |
|
|
1143 | 1141 | if not self.isConfig: |
|
|
1144 | 1142 | |
|
|
1145 | 1143 | self.setup(id=id, |
|
|
1146 | 1144 | nplots=nplots, |
|
|
1147 | 1145 | wintitle=wintitle, |
|
|
1148 | 1146 | showprofile=showprofile, |
|
|
1149 | 1147 | show=show) |
|
|
1150 | 1148 | |
|
|
1151 | 1149 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1152 | 1150 | |
|
|
1153 | 1151 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
1154 | 1152 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
1155 |
if zmin == None: zmin = numpy.nanmin(z |
|
|
|
1156 |
if zmax == None: zmax = numpy.nanmax(z |
|
|
|
1153 | if zmin == None: zmin = numpy.nanmin(z) | |
|
|
1154 | if zmax == None: zmax = numpy.nanmax(z) | |
|
|
1157 | 1155 | |
|
|
1158 | 1156 | if SNR: |
|
|
1159 | 1157 | if SNRmin == None: SNRmin = numpy.nanmin(SNRdB) |
|
|
1160 | 1158 | if SNRmax == None: SNRmax = numpy.nanmax(SNRdB) |
|
|
1161 | 1159 | |
|
|
1162 | 1160 | self.FTP_WEI = ftp_wei |
|
|
1163 | 1161 | self.EXP_CODE = exp_code |
|
|
1164 | 1162 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1165 | 1163 | self.PLOT_POS = plot_pos |
|
|
1166 | 1164 | |
|
|
1167 | 1165 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1168 | 1166 | self.isConfig = True |
|
|
1169 | 1167 | self.figfile = figfile |
|
|
1170 | 1168 | |
|
|
1171 | 1169 | self.setWinTitle(title) |
|
|
1172 | 1170 | |
|
|
1173 | 1171 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
1174 | 1172 | x[1] = self.xmax |
|
|
1175 | 1173 | |
|
|
1176 | 1174 | for i in range(nchan): |
|
|
1177 | 1175 | |
|
|
1178 | 1176 | if (SNR and not onlySNR): j = 2*i |
|
|
1179 | 1177 | else: j = i |
|
|
1180 | 1178 | |
|
|
1181 | 1179 | j = nGraphsByChannel*i |
|
|
1182 | 1180 | |
|
|
1183 | 1181 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1184 | 1182 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
1185 | 1183 | |
|
|
1186 | 1184 | if not onlySNR: |
|
|
1187 | 1185 | axes = self.axesList[j*self.__nsubplots] |
|
|
1188 | 1186 | z1 = z[i,:].reshape((1,-1)) |
|
|
1189 | 1187 | axes.pcolorbuffer(x, y, z1, |
|
|
1190 | 1188 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
1191 | 1189 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
1192 | 1190 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
1193 | 1191 | |
|
|
1194 | 1192 | if DOP: |
|
|
1195 | 1193 | title = "%s Channel %d: %s" %(parameterName, channelIndexList[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1196 | 1194 | |
|
|
1197 | 1195 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1198 | 1196 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
1199 | 1197 | axes = self.axesList[j] |
|
|
1200 | 1198 | z1 = z[i,:].reshape((1,-1)) |
|
|
1201 | 1199 | axes.pcolorbuffer(x, y, z1, |
|
|
1202 | 1200 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
1203 | 1201 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
1204 | 1202 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
1205 | 1203 | |
|
|
1206 |
|
|
|
|
1207 |
|
|
|
|
1208 |
|
|
|
|
1209 |
|
|
|
|
1210 |
|
|
|
|
1211 | ||
|
|
1212 | axes = self.axesList[(j + nGraphsByChannel-1)] | |
|
|
1204 | if SNR: | |
|
|
1205 | title = "Channel %d Signal Noise Ratio (SNR): %s" %(channelIndexList[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1206 | axes = self.axesList[(j)*self.__nsubplots] | |
|
|
1207 | if not onlySNR: | |
|
|
1208 | axes = self.axesList[(j + 1)*self.__nsubplots] | |
|
|
1213 | 1209 | |
|
|
1214 | z1 = SNRdB[i,:].reshape((1,-1)) | |
|
|
1215 | axes.pcolorbuffer(x, y, z1, | |
|
|
1216 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
1217 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap="jet", | |
|
|
1218 | ticksize=9, cblabel=zlabel, cbsize="1%") | |
|
|
1210 | axes = self.axesList[(j + nGraphsByChannel-1)] | |
|
|
1211 | z1 = SNRdB.reshape((1,-1)) | |
|
|
1212 | axes.pcolorbuffer(x, y, z1, | |
|
|
1213 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
1214 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap="jet", | |
|
|
1215 | ticksize=9, cblabel=zlabel, cbsize="1%") | |
|
|
1219 | 1216 | |
|
|
1220 | 1217 | |
|
|
1221 | 1218 | |
|
|
1222 | 1219 | self.draw() |
|
|
1223 | 1220 | |
|
|
1224 | 1221 | if x[1] >= self.axesList[0].xmax: |
|
|
1225 | 1222 | self.counter_imagwr = wr_period |
|
|
1226 | 1223 | self.isConfig = False |
|
|
1227 | 1224 | self.figfile = None |
|
|
1228 | 1225 | |
|
|
1229 | 1226 | self.save(figpath=figpath, |
|
|
1230 | 1227 | figfile=figfile, |
|
|
1231 | 1228 | save=save, |
|
|
1232 | 1229 | ftp=ftp, |
|
|
1233 | 1230 | wr_period=wr_period, |
|
|
1234 | 1231 | thisDatetime=thisDatetime, |
|
|
1235 | 1232 | update_figfile=False) |
|
|
1236 | 1233 | |
|
|
1237 | 1234 | class SpectralFittingPlot(Figure): |
|
|
1238 | 1235 | |
|
|
1239 | 1236 | __isConfig = None |
|
|
1240 | 1237 | __nsubplots = None |
|
|
1241 | 1238 | |
|
|
1242 | 1239 | WIDTHPROF = None |
|
|
1243 | 1240 | HEIGHTPROF = None |
|
|
1244 | 1241 | PREFIX = 'prm' |
|
|
1245 | 1242 | |
|
|
1246 | 1243 | |
|
|
1247 | 1244 | N = None |
|
|
1248 | 1245 | ippSeconds = None |
|
|
1249 | 1246 | |
|
|
1250 | 1247 | def __init__(self, **kwargs): |
|
|
1251 | 1248 | Figure.__init__(self, **kwargs) |
|
|
1252 | 1249 | self.isConfig = False |
|
|
1253 | 1250 | self.__nsubplots = 1 |
|
|
1254 | 1251 | |
|
|
1255 | 1252 | self.PLOT_CODE = SPECFIT_CODE |
|
|
1256 | 1253 | |
|
|
1257 | 1254 | self.WIDTH = 450 |
|
|
1258 | 1255 | self.HEIGHT = 250 |
|
|
1259 | 1256 | self.WIDTHPROF = 0 |
|
|
1260 | 1257 | self.HEIGHTPROF = 0 |
|
|
1261 | 1258 | |
|
|
1262 | 1259 | def getSubplots(self): |
|
|
1263 | 1260 | |
|
|
1264 | 1261 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
1265 | 1262 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
1266 | 1263 | |
|
|
1267 | 1264 | return nrow, ncol |
|
|
1268 | 1265 | |
|
|
1269 | 1266 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
1270 | 1267 | |
|
|
1271 | 1268 | showprofile = False |
|
|
1272 | 1269 | self.__showprofile = showprofile |
|
|
1273 | 1270 | self.nplots = nplots |
|
|
1274 | 1271 | |
|
|
1275 | 1272 | ncolspan = 5 |
|
|
1276 | 1273 | colspan = 4 |
|
|
1277 | 1274 | if showprofile: |
|
|
1278 | 1275 | ncolspan = 5 |
|
|
1279 | 1276 | colspan = 4 |
|
|
1280 | 1277 | self.__nsubplots = 2 |
|
|
1281 | 1278 | |
|
|
1282 | 1279 | self.createFigure(id = id, |
|
|
1283 | 1280 | wintitle = wintitle, |
|
|
1284 | 1281 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1285 | 1282 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1286 | 1283 | show=show) |
|
|
1287 | 1284 | |
|
|
1288 | 1285 | nrow, ncol = self.getSubplots() |
|
|
1289 | 1286 | |
|
|
1290 | 1287 | counter = 0 |
|
|
1291 | 1288 | for y in range(nrow): |
|
|
1292 | 1289 | for x in range(ncol): |
|
|
1293 | 1290 | |
|
|
1294 | 1291 | if counter >= self.nplots: |
|
|
1295 | 1292 | break |
|
|
1296 | 1293 | |
|
|
1297 | 1294 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1298 | 1295 | |
|
|
1299 | 1296 | if showprofile: |
|
|
1300 | 1297 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
1301 | 1298 | |
|
|
1302 | 1299 | counter += 1 |
|
|
1303 | 1300 | |
|
|
1304 | 1301 | def run(self, dataOut, id, cutHeight=None, fit=False, wintitle="", channelList=None, showprofile=True, |
|
|
1305 | 1302 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1306 | 1303 | save=False, figpath='./', figfile=None, show=True): |
|
|
1307 | 1304 | |
|
|
1308 | 1305 | """ |
|
|
1309 | 1306 | |
|
|
1310 | 1307 | Input: |
|
|
1311 | 1308 | dataOut : |
|
|
1312 | 1309 | id : |
|
|
1313 | 1310 | wintitle : |
|
|
1314 | 1311 | channelList : |
|
|
1315 | 1312 | showProfile : |
|
|
1316 | 1313 | xmin : None, |
|
|
1317 | 1314 | xmax : None, |
|
|
1318 | 1315 | zmin : None, |
|
|
1319 | 1316 | zmax : None |
|
|
1320 | 1317 | """ |
|
|
1321 | 1318 | |
|
|
1322 | 1319 | if cutHeight==None: |
|
|
1323 | 1320 | h=270 |
|
|
1324 | 1321 | heightindex = numpy.abs(cutHeight - dataOut.heightList).argmin() |
|
|
1325 | 1322 | cutHeight = dataOut.heightList[heightindex] |
|
|
1326 | 1323 | |
|
|
1327 | 1324 | factor = dataOut.normFactor |
|
|
1328 | 1325 | x = dataOut.abscissaList[:-1] |
|
|
1329 | 1326 | #y = dataOut.getHeiRange() |
|
|
1330 | 1327 | |
|
|
1331 | 1328 | z = dataOut.data_pre[:,:,heightindex]/factor |
|
|
1332 | 1329 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1333 | 1330 | avg = numpy.average(z, axis=1) |
|
|
1334 | 1331 | listChannels = z.shape[0] |
|
|
1335 | 1332 | |
|
|
1336 | 1333 | #Reconstruct Function |
|
|
1337 | 1334 | if fit==True: |
|
|
1338 | 1335 | groupArray = dataOut.groupList |
|
|
1339 | 1336 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1340 | 1337 | listChannels.sort() |
|
|
1341 | 1338 | spcFitLine = numpy.zeros(z.shape) |
|
|
1342 | 1339 | constants = dataOut.constants |
|
|
1343 | 1340 | |
|
|
1344 | 1341 | nGroups = groupArray.shape[0] |
|
|
1345 | 1342 | nChannels = groupArray.shape[1] |
|
|
1346 | 1343 | nProfiles = z.shape[1] |
|
|
1347 | 1344 | |
|
|
1348 | 1345 | for f in range(nGroups): |
|
|
1349 | 1346 | groupChann = groupArray[f,:] |
|
|
1350 | 1347 | p = dataOut.data_param[f,:,heightindex] |
|
|
1351 | 1348 | # p = numpy.array([ 89.343967,0.14036615,0.17086219,18.89835291,1.58388365,1.55099167]) |
|
|
1352 | 1349 | fitLineAux = dataOut.library.modelFunction(p, constants)*nProfiles |
|
|
1353 | 1350 | fitLineAux = fitLineAux.reshape((nChannels,nProfiles)) |
|
|
1354 | 1351 | spcFitLine[groupChann,:] = fitLineAux |
|
|
1355 | 1352 | # spcFitLine = spcFitLine/factor |
|
|
1356 | 1353 | |
|
|
1357 | 1354 | z = z[listChannels,:] |
|
|
1358 | 1355 | spcFitLine = spcFitLine[listChannels,:] |
|
|
1359 | 1356 | spcFitLinedB = 10*numpy.log10(spcFitLine) |
|
|
1360 | 1357 | |
|
|
1361 | 1358 | zdB = 10*numpy.log10(z) |
|
|
1362 | 1359 | #thisDatetime = dataOut.datatime |
|
|
1363 | 1360 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1364 | 1361 | title = wintitle + " Doppler Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1365 | 1362 | xlabel = "Velocity (m/s)" |
|
|
1366 | 1363 | ylabel = "Spectrum" |
|
|
1367 | 1364 | |
|
|
1368 | 1365 | if not self.isConfig: |
|
|
1369 | 1366 | |
|
|
1370 | 1367 | nplots = listChannels.size |
|
|
1371 | 1368 | |
|
|
1372 | 1369 | self.setup(id=id, |
|
|
1373 | 1370 | nplots=nplots, |
|
|
1374 | 1371 | wintitle=wintitle, |
|
|
1375 | 1372 | showprofile=showprofile, |
|
|
1376 | 1373 | show=show) |
|
|
1377 | 1374 | |
|
|
1378 | 1375 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
1379 | 1376 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
1380 | 1377 | if ymin == None: ymin = numpy.nanmin(zdB) |
|
|
1381 | 1378 | if ymax == None: ymax = numpy.nanmax(zdB)+2 |
|
|
1382 | 1379 | |
|
|
1383 | 1380 | self.isConfig = True |
|
|
1384 | 1381 | |
|
|
1385 | 1382 | self.setWinTitle(title) |
|
|
1386 | 1383 | for i in range(self.nplots): |
|
|
1387 | 1384 | # title = "Channel %d: %4.2fdB" %(dataOut.channelList[i]+1, noisedB[i]) |
|
|
1388 | 1385 | title = "Height %4.1f km\nChannel %d:" %(cutHeight, listChannels[i]) |
|
|
1389 | 1386 | axes = self.axesList[i*self.__nsubplots] |
|
|
1390 | 1387 | if fit == False: |
|
|
1391 | 1388 | axes.pline(x, zdB[i,:], |
|
|
1392 | 1389 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1393 | 1390 | xlabel=xlabel, ylabel=ylabel, title=title |
|
|
1394 | 1391 | ) |
|
|
1395 | 1392 | if fit == True: |
|
|
1396 | 1393 | fitline=spcFitLinedB[i,:] |
|
|
1397 | 1394 | y=numpy.vstack([zdB[i,:],fitline] ) |
|
|
1398 | 1395 | legendlabels=['Data','Fitting'] |
|
|
1399 | 1396 | axes.pmultilineyaxis(x, y, |
|
|
1400 | 1397 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1401 | 1398 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
1402 | 1399 | legendlabels=legendlabels, marker=None, |
|
|
1403 | 1400 | linestyle='solid', grid='both') |
|
|
1404 | 1401 | |
|
|
1405 | 1402 | self.draw() |
|
|
1406 | 1403 | |
|
|
1407 | 1404 | self.save(figpath=figpath, |
|
|
1408 | 1405 | figfile=figfile, |
|
|
1409 | 1406 | save=save, |
|
|
1410 | 1407 | ftp=ftp, |
|
|
1411 | 1408 | wr_period=wr_period, |
|
|
1412 | 1409 | thisDatetime=thisDatetime) |
|
|
1413 | 1410 | |
|
|
1414 | 1411 | |
|
|
1415 | 1412 | class EWDriftsPlot(Figure): |
|
|
1416 | 1413 | |
|
|
1417 | 1414 | __isConfig = None |
|
|
1418 | 1415 | __nsubplots = None |
|
|
1419 | 1416 | |
|
|
1420 | 1417 | WIDTHPROF = None |
|
|
1421 | 1418 | HEIGHTPROF = None |
|
|
1422 | 1419 | PREFIX = 'drift' |
|
|
1423 | 1420 | |
|
|
1424 | 1421 | def __init__(self, **kwargs): |
|
|
1425 | 1422 | Figure.__init__(self, **kwargs) |
|
|
1426 | 1423 | self.timerange = 2*60*60 |
|
|
1427 | 1424 | self.isConfig = False |
|
|
1428 | 1425 | self.__nsubplots = 1 |
|
|
1429 | 1426 | |
|
|
1430 | 1427 | self.WIDTH = 800 |
|
|
1431 | 1428 | self.HEIGHT = 150 |
|
|
1432 | 1429 | self.WIDTHPROF = 120 |
|
|
1433 | 1430 | self.HEIGHTPROF = 0 |
|
|
1434 | 1431 | self.counter_imagwr = 0 |
|
|
1435 | 1432 | |
|
|
1436 | 1433 | self.PLOT_CODE = EWDRIFT_CODE |
|
|
1437 | 1434 | |
|
|
1438 | 1435 | self.FTP_WEI = None |
|
|
1439 | 1436 | self.EXP_CODE = None |
|
|
1440 | 1437 | self.SUB_EXP_CODE = None |
|
|
1441 | 1438 | self.PLOT_POS = None |
|
|
1442 | 1439 | self.tmin = None |
|
|
1443 | 1440 | self.tmax = None |
|
|
1444 | 1441 | |
|
|
1445 | 1442 | self.xmin = None |
|
|
1446 | 1443 | self.xmax = None |
|
|
1447 | 1444 | |
|
|
1448 | 1445 | self.figfile = None |
|
|
1449 | 1446 | |
|
|
1450 | 1447 | def getSubplots(self): |
|
|
1451 | 1448 | |
|
|
1452 | 1449 | ncol = 1 |
|
|
1453 | 1450 | nrow = self.nplots |
|
|
1454 | 1451 | |
|
|
1455 | 1452 | return nrow, ncol |
|
|
1456 | 1453 | |
|
|
1457 | 1454 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1458 | 1455 | |
|
|
1459 | 1456 | self.__showprofile = showprofile |
|
|
1460 | 1457 | self.nplots = nplots |
|
|
1461 | 1458 | |
|
|
1462 | 1459 | ncolspan = 1 |
|
|
1463 | 1460 | colspan = 1 |
|
|
1464 | 1461 | |
|
|
1465 | 1462 | self.createFigure(id = id, |
|
|
1466 | 1463 | wintitle = wintitle, |
|
|
1467 | 1464 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1468 | 1465 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1469 | 1466 | show=show) |
|
|
1470 | 1467 | |
|
|
1471 | 1468 | nrow, ncol = self.getSubplots() |
|
|
1472 | 1469 | |
|
|
1473 | 1470 | counter = 0 |
|
|
1474 | 1471 | for y in range(nrow): |
|
|
1475 | 1472 | if counter >= self.nplots: |
|
|
1476 | 1473 | break |
|
|
1477 | 1474 | |
|
|
1478 | 1475 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
1479 | 1476 | counter += 1 |
|
|
1480 | 1477 | |
|
|
1481 | 1478 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1482 | 1479 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
1483 | 1480 | zmaxVertical = None, zminVertical = None, zmaxZonal = None, zminZonal = None, |
|
|
1484 | 1481 | timerange=None, SNRthresh = -numpy.inf, SNRmin = None, SNRmax = None, SNR_1 = False, |
|
|
1485 | 1482 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
1486 | 1483 | server=None, folder=None, username=None, password=None, |
|
|
1487 | 1484 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1488 | 1485 | """ |
|
|
1489 | 1486 | |
|
|
1490 | 1487 | Input: |
|
|
1491 | 1488 | dataOut : |
|
|
1492 | 1489 | id : |
|
|
1493 | 1490 | wintitle : |
|
|
1494 | 1491 | channelList : |
|
|
1495 | 1492 | showProfile : |
|
|
1496 | 1493 | xmin : None, |
|
|
1497 | 1494 | xmax : None, |
|
|
1498 | 1495 | ymin : None, |
|
|
1499 | 1496 | ymax : None, |
|
|
1500 | 1497 | zmin : None, |
|
|
1501 | 1498 | zmax : None |
|
|
1502 | 1499 | """ |
|
|
1503 | 1500 | |
|
|
1504 | 1501 | if timerange is not None: |
|
|
1505 | 1502 | self.timerange = timerange |
|
|
1506 | 1503 | |
|
|
1507 | 1504 | tmin = None |
|
|
1508 | 1505 | tmax = None |
|
|
1509 | 1506 | |
|
|
1510 | 1507 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
|
|
1511 | 1508 | # y = dataOut.heightList |
|
|
1512 | 1509 | y = dataOut.heightList |
|
|
1513 | 1510 | |
|
|
1514 | 1511 | z = dataOut.data_output |
|
|
1515 | 1512 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
1516 | 1513 | nplotsw = nplots |
|
|
1517 | 1514 | |
|
|
1518 | 1515 | #If there is a SNR function defined |
|
|
1519 | 1516 | if dataOut.data_SNR is not None: |
|
|
1520 | 1517 | nplots += 1 |
|
|
1521 | 1518 | SNR = dataOut.data_SNR |
|
|
1522 | 1519 | |
|
|
1523 | 1520 | if SNR_1: |
|
|
1524 | 1521 | SNR += 1 |
|
|
1525 | 1522 | |
|
|
1526 | 1523 | SNRavg = numpy.average(SNR, axis=0) |
|
|
1527 | 1524 | |
|
|
1528 | 1525 | SNRdB = 10*numpy.log10(SNR) |
|
|
1529 | 1526 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
1530 | 1527 | |
|
|
1531 | 1528 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
1532 | 1529 | |
|
|
1533 | 1530 | for i in range(nplotsw): |
|
|
1534 | 1531 | z[i,ind] = numpy.nan |
|
|
1535 | 1532 | |
|
|
1536 | 1533 | |
|
|
1537 | 1534 | showprofile = False |
|
|
1538 | 1535 | # thisDatetime = dataOut.datatime |
|
|
1539 | 1536 | thisDatetime = datetime.datetime.utcfromtimestamp(x[1]) |
|
|
1540 | 1537 | title = wintitle + " EW Drifts" |
|
|
1541 | 1538 | xlabel = "" |
|
|
1542 | 1539 | ylabel = "Height (Km)" |
|
|
1543 | 1540 | |
|
|
1544 | 1541 | if not self.isConfig: |
|
|
1545 | 1542 | |
|
|
1546 | 1543 | self.setup(id=id, |
|
|
1547 | 1544 | nplots=nplots, |
|
|
1548 | 1545 | wintitle=wintitle, |
|
|
1549 | 1546 | showprofile=showprofile, |
|
|
1550 | 1547 | show=show) |
|
|
1551 | 1548 | |
|
|
1552 | 1549 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1553 | 1550 | |
|
|
1554 | 1551 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
1555 | 1552 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
1556 | 1553 | |
|
|
1557 | 1554 | if zmaxZonal == None: zmaxZonal = numpy.nanmax(abs(z[0,:])) |
|
|
1558 | 1555 | if zminZonal == None: zminZonal = -zmaxZonal |
|
|
1559 | 1556 | if zmaxVertical == None: zmaxVertical = numpy.nanmax(abs(z[1,:])) |
|
|
1560 | 1557 | if zminVertical == None: zminVertical = -zmaxVertical |
|
|
1561 | 1558 | |
|
|
1562 | 1559 | if dataOut.data_SNR is not None: |
|
|
1563 | 1560 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
1564 | 1561 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
1565 | 1562 | |
|
|
1566 | 1563 | self.FTP_WEI = ftp_wei |
|
|
1567 | 1564 | self.EXP_CODE = exp_code |
|
|
1568 | 1565 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1569 | 1566 | self.PLOT_POS = plot_pos |
|
|
1570 | 1567 | |
|
|
1571 | 1568 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1572 | 1569 | self.isConfig = True |
|
|
1573 | 1570 | |
|
|
1574 | 1571 | |
|
|
1575 | 1572 | self.setWinTitle(title) |
|
|
1576 | 1573 | |
|
|
1577 | 1574 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
1578 | 1575 | x[1] = self.xmax |
|
|
1579 | 1576 | |
|
|
1580 | 1577 | strWind = ['Zonal','Vertical'] |
|
|
1581 | 1578 | strCb = 'Velocity (m/s)' |
|
|
1582 | 1579 | zmaxVector = [zmaxZonal, zmaxVertical] |
|
|
1583 | 1580 | zminVector = [zminZonal, zminVertical] |
|
|
1584 | 1581 | |
|
|
1585 | 1582 | for i in range(nplotsw): |
|
|
1586 | 1583 | |
|
|
1587 | 1584 | title = "%s Drifts: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1588 | 1585 | axes = self.axesList[i*self.__nsubplots] |
|
|
1589 | 1586 | |
|
|
1590 | 1587 | z1 = z[i,:].reshape((1,-1)) |
|
|
1591 | 1588 | |
|
|
1592 | 1589 | axes.pcolorbuffer(x, y, z1, |
|
|
1593 | 1590 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
1594 | 1591 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1595 | 1592 | ticksize=9, cblabel=strCb, cbsize="1%", colormap="RdBu_r") |
|
|
1596 | 1593 | |
|
|
1597 | 1594 | if dataOut.data_SNR is not None: |
|
|
1598 | 1595 | i += 1 |
|
|
1599 | 1596 | if SNR_1: |
|
|
1600 | 1597 | title = "Signal Noise Ratio + 1 (SNR+1): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1601 | 1598 | else: |
|
|
1602 | 1599 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1603 | 1600 | axes = self.axesList[i*self.__nsubplots] |
|
|
1604 | 1601 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
1605 | 1602 | |
|
|
1606 | 1603 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
1607 | 1604 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
1608 | 1605 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1609 | 1606 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
1610 | 1607 | |
|
|
1611 | 1608 | self.draw() |
|
|
1612 | 1609 | |
|
|
1613 | 1610 | if x[1] >= self.axesList[0].xmax: |
|
|
1614 | 1611 | self.counter_imagwr = wr_period |
|
|
1615 | 1612 | self.isConfig = False |
|
|
1616 | 1613 | self.figfile = None |
|
|
1617 | 1614 | |
|
|
1618 | 1615 | |
|
|
1619 | 1616 | |
|
|
1620 | 1617 | |
|
|
1621 | 1618 | class PhasePlot(Figure): |
|
|
1622 | 1619 | |
|
|
1623 | 1620 | __isConfig = None |
|
|
1624 | 1621 | __nsubplots = None |
|
|
1625 | 1622 | |
|
|
1626 | 1623 | PREFIX = 'mphase' |
|
|
1627 | 1624 | |
|
|
1628 | 1625 | |
|
|
1629 | 1626 | def __init__(self, **kwargs): |
|
|
1630 | 1627 | Figure.__init__(self, **kwargs) |
|
|
1631 | 1628 | self.timerange = 24*60*60 |
|
|
1632 | 1629 | self.isConfig = False |
|
|
1633 | 1630 | self.__nsubplots = 1 |
|
|
1634 | 1631 | self.counter_imagwr = 0 |
|
|
1635 | 1632 | self.WIDTH = 600 |
|
|
1636 | 1633 | self.HEIGHT = 300 |
|
|
1637 | 1634 | self.WIDTHPROF = 120 |
|
|
1638 | 1635 | self.HEIGHTPROF = 0 |
|
|
1639 | 1636 | self.xdata = None |
|
|
1640 | 1637 | self.ydata = None |
|
|
1641 | 1638 | |
|
|
1642 | 1639 | self.PLOT_CODE = MPHASE_CODE |
|
|
1643 | 1640 | |
|
|
1644 | 1641 | self.FTP_WEI = None |
|
|
1645 | 1642 | self.EXP_CODE = None |
|
|
1646 | 1643 | self.SUB_EXP_CODE = None |
|
|
1647 | 1644 | self.PLOT_POS = None |
|
|
1648 | 1645 | |
|
|
1649 | 1646 | |
|
|
1650 | 1647 | self.filename_phase = None |
|
|
1651 | 1648 | |
|
|
1652 | 1649 | self.figfile = None |
|
|
1653 | 1650 | |
|
|
1654 | 1651 | def getSubplots(self): |
|
|
1655 | 1652 | |
|
|
1656 | 1653 | ncol = 1 |
|
|
1657 | 1654 | nrow = 1 |
|
|
1658 | 1655 | |
|
|
1659 | 1656 | return nrow, ncol |
|
|
1660 | 1657 | |
|
|
1661 | 1658 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1662 | 1659 | |
|
|
1663 | 1660 | self.__showprofile = showprofile |
|
|
1664 | 1661 | self.nplots = nplots |
|
|
1665 | 1662 | |
|
|
1666 | 1663 | ncolspan = 7 |
|
|
1667 | 1664 | colspan = 6 |
|
|
1668 | 1665 | self.__nsubplots = 2 |
|
|
1669 | 1666 | |
|
|
1670 | 1667 | self.createFigure(id = id, |
|
|
1671 | 1668 | wintitle = wintitle, |
|
|
1672 | 1669 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1673 | 1670 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1674 | 1671 | show=show) |
|
|
1675 | 1672 | |
|
|
1676 | 1673 | nrow, ncol = self.getSubplots() |
|
|
1677 | 1674 | |
|
|
1678 | 1675 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1679 | 1676 | |
|
|
1680 | 1677 | |
|
|
1681 | 1678 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
1682 | 1679 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1683 | 1680 | timerange=None, |
|
|
1684 | 1681 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1685 | 1682 | server=None, folder=None, username=None, password=None, |
|
|
1686 | 1683 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1687 | 1684 | |
|
|
1688 | 1685 | |
|
|
1689 | 1686 | tmin = None |
|
|
1690 | 1687 | tmax = None |
|
|
1691 | 1688 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
|
|
1692 | 1689 | y = dataOut.getHeiRange() |
|
|
1693 | 1690 | |
|
|
1694 | 1691 | |
|
|
1695 | 1692 | #thisDatetime = dataOut.datatime |
|
|
1696 | 1693 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
1697 | 1694 | title = wintitle + " Phase of Beacon Signal" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1698 | 1695 | xlabel = "Local Time" |
|
|
1699 | 1696 | ylabel = "Phase" |
|
|
1700 | 1697 | |
|
|
1701 | 1698 | |
|
|
1702 | 1699 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
1703 | 1700 | phase_beacon = dataOut.data_output |
|
|
1704 | 1701 | update_figfile = False |
|
|
1705 | 1702 | |
|
|
1706 | 1703 | if not self.isConfig: |
|
|
1707 | 1704 | |
|
|
1708 | 1705 | self.nplots = phase_beacon.size |
|
|
1709 | 1706 | |
|
|
1710 | 1707 | self.setup(id=id, |
|
|
1711 | 1708 | nplots=self.nplots, |
|
|
1712 | 1709 | wintitle=wintitle, |
|
|
1713 | 1710 | showprofile=showprofile, |
|
|
1714 | 1711 | show=show) |
|
|
1715 | 1712 | |
|
|
1716 | 1713 | if timerange is not None: |
|
|
1717 | 1714 | self.timerange = timerange |
|
|
1718 | 1715 | |
|
|
1719 | 1716 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1720 | 1717 | |
|
|
1721 | 1718 | if ymin == None: ymin = numpy.nanmin(phase_beacon) - 10.0 |
|
|
1722 | 1719 | if ymax == None: ymax = numpy.nanmax(phase_beacon) + 10.0 |
|
|
1723 | 1720 | |
|
|
1724 | 1721 | self.FTP_WEI = ftp_wei |
|
|
1725 | 1722 | self.EXP_CODE = exp_code |
|
|
1726 | 1723 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1727 | 1724 | self.PLOT_POS = plot_pos |
|
|
1728 | 1725 | |
|
|
1729 | 1726 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1730 | 1727 | self.isConfig = True |
|
|
1731 | 1728 | self.figfile = figfile |
|
|
1732 | 1729 | self.xdata = numpy.array([]) |
|
|
1733 | 1730 | self.ydata = numpy.array([]) |
|
|
1734 | 1731 | |
|
|
1735 | 1732 | #open file beacon phase |
|
|
1736 | 1733 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1737 | 1734 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1738 | 1735 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
1739 | 1736 | update_figfile = True |
|
|
1740 | 1737 | |
|
|
1741 | 1738 | |
|
|
1742 | 1739 | #store data beacon phase |
|
|
1743 | 1740 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
1744 | 1741 | |
|
|
1745 | 1742 | self.setWinTitle(title) |
|
|
1746 | 1743 | |
|
|
1747 | 1744 | |
|
|
1748 | 1745 | title = "Phase Offset %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1749 | 1746 | |
|
|
1750 | 1747 | legendlabels = ["phase %d"%(chan) for chan in numpy.arange(self.nplots)] |
|
|
1751 | 1748 | |
|
|
1752 | 1749 | axes = self.axesList[0] |
|
|
1753 | 1750 | |
|
|
1754 | 1751 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1755 | 1752 | |
|
|
1756 | 1753 | if len(self.ydata)==0: |
|
|
1757 | 1754 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
1758 | 1755 | else: |
|
|
1759 | 1756 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
1760 | 1757 | |
|
|
1761 | 1758 | |
|
|
1762 | 1759 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1763 | 1760 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1764 | 1761 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1765 | 1762 | XAxisAsTime=True, grid='both' |
|
|
1766 | 1763 | ) |
|
|
1767 | 1764 | |
|
|
1768 | 1765 | self.draw() |
|
|
1769 | 1766 | |
|
|
1770 | 1767 | self.save(figpath=figpath, |
|
|
1771 | 1768 | figfile=figfile, |
|
|
1772 | 1769 | save=save, |
|
|
1773 | 1770 | ftp=ftp, |
|
|
1774 | 1771 | wr_period=wr_period, |
|
|
1775 | 1772 | thisDatetime=thisDatetime, |
|
|
1776 | 1773 | update_figfile=update_figfile) |
|
|
1777 | 1774 | |
|
|
1778 | 1775 | if dataOut.ltctime + dataOut.outputInterval >= self.xmax: |
|
|
1779 | 1776 | self.counter_imagwr = wr_period |
|
|
1780 | 1777 | self.isConfig = False |
|
|
1781 | 1778 | update_figfile = True |
|
|
1782 | 1779 | |
|
|
1783 | 1780 | |
|
|
1784 | 1781 | |
|
|
1785 | 1782 | class NSMeteorDetection1Plot(Figure): |
|
|
1786 | 1783 | |
|
|
1787 | 1784 | isConfig = None |
|
|
1788 | 1785 | __nsubplots = None |
|
|
1789 | 1786 | |
|
|
1790 | 1787 | WIDTHPROF = None |
|
|
1791 | 1788 | HEIGHTPROF = None |
|
|
1792 | 1789 | PREFIX = 'nsm' |
|
|
1793 | 1790 | |
|
|
1794 | 1791 | zminList = None |
|
|
1795 | 1792 | zmaxList = None |
|
|
1796 | 1793 | cmapList = None |
|
|
1797 | 1794 | titleList = None |
|
|
1798 | 1795 | nPairs = None |
|
|
1799 | 1796 | nChannels = None |
|
|
1800 | 1797 | nParam = None |
|
|
1801 | 1798 | |
|
|
1802 | 1799 | def __init__(self, **kwargs): |
|
|
1803 | 1800 | Figure.__init__(self, **kwargs) |
|
|
1804 | 1801 | self.isConfig = False |
|
|
1805 | 1802 | self.__nsubplots = 1 |
|
|
1806 | 1803 | |
|
|
1807 | 1804 | self.WIDTH = 750 |
|
|
1808 | 1805 | self.HEIGHT = 250 |
|
|
1809 | 1806 | self.WIDTHPROF = 120 |
|
|
1810 | 1807 | self.HEIGHTPROF = 0 |
|
|
1811 | 1808 | self.counter_imagwr = 0 |
|
|
1812 | 1809 | |
|
|
1813 | 1810 | self.PLOT_CODE = SPEC_CODE |
|
|
1814 | 1811 | |
|
|
1815 | 1812 | self.FTP_WEI = None |
|
|
1816 | 1813 | self.EXP_CODE = None |
|
|
1817 | 1814 | self.SUB_EXP_CODE = None |
|
|
1818 | 1815 | self.PLOT_POS = None |
|
|
1819 | 1816 | |
|
|
1820 | 1817 | self.__xfilter_ena = False |
|
|
1821 | 1818 | self.__yfilter_ena = False |
|
|
1822 | 1819 | |
|
|
1823 | 1820 | def getSubplots(self): |
|
|
1824 | 1821 | |
|
|
1825 | 1822 | ncol = 3 |
|
|
1826 | 1823 | nrow = int(numpy.ceil(self.nplots/3.0)) |
|
|
1827 | 1824 | |
|
|
1828 | 1825 | return nrow, ncol |
|
|
1829 | 1826 | |
|
|
1830 | 1827 | def setup(self, id, nplots, wintitle, show=True): |
|
|
1831 | 1828 | |
|
|
1832 | 1829 | self.nplots = nplots |
|
|
1833 | 1830 | |
|
|
1834 | 1831 | ncolspan = 1 |
|
|
1835 | 1832 | colspan = 1 |
|
|
1836 | 1833 | |
|
|
1837 | 1834 | self.createFigure(id = id, |
|
|
1838 | 1835 | wintitle = wintitle, |
|
|
1839 | 1836 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1840 | 1837 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1841 | 1838 | show=show) |
|
|
1842 | 1839 | |
|
|
1843 | 1840 | nrow, ncol = self.getSubplots() |
|
|
1844 | 1841 | |
|
|
1845 | 1842 | counter = 0 |
|
|
1846 | 1843 | for y in range(nrow): |
|
|
1847 | 1844 | for x in range(ncol): |
|
|
1848 | 1845 | |
|
|
1849 | 1846 | if counter >= self.nplots: |
|
|
1850 | 1847 | break |
|
|
1851 | 1848 | |
|
|
1852 | 1849 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1853 | 1850 | |
|
|
1854 | 1851 | counter += 1 |
|
|
1855 | 1852 | |
|
|
1856 | 1853 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
1857 | 1854 | xmin=None, xmax=None, ymin=None, ymax=None, SNRmin=None, SNRmax=None, |
|
|
1858 | 1855 | vmin=None, vmax=None, wmin=None, wmax=None, mode = 'SA', |
|
|
1859 | 1856 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1860 | 1857 | server=None, folder=None, username=None, password=None, |
|
|
1861 | 1858 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
1862 | 1859 | xaxis="frequency"): |
|
|
1863 | 1860 | |
|
|
1864 | 1861 | """ |
|
|
1865 | 1862 | |
|
|
1866 | 1863 | Input: |
|
|
1867 | 1864 | dataOut : |
|
|
1868 | 1865 | id : |
|
|
1869 | 1866 | wintitle : |
|
|
1870 | 1867 | channelList : |
|
|
1871 | 1868 | showProfile : |
|
|
1872 | 1869 | xmin : None, |
|
|
1873 | 1870 | xmax : None, |
|
|
1874 | 1871 | ymin : None, |
|
|
1875 | 1872 | ymax : None, |
|
|
1876 | 1873 | zmin : None, |
|
|
1877 | 1874 | zmax : None |
|
|
1878 | 1875 | """ |
|
|
1879 | 1876 | #SEPARAR EN DOS PLOTS |
|
|
1880 | 1877 | nParam = dataOut.data_param.shape[1] - 3 |
|
|
1881 | 1878 | |
|
|
1882 | 1879 | utctime = dataOut.data_param[0,0] |
|
|
1883 | 1880 | tmet = dataOut.data_param[:,1].astype(int) |
|
|
1884 | 1881 | hmet = dataOut.data_param[:,2].astype(int) |
|
|
1885 | 1882 | |
|
|
1886 | 1883 | x = dataOut.abscissaList |
|
|
1887 | 1884 | y = dataOut.heightList |
|
|
1888 | 1885 | |
|
|
1889 | 1886 | z = numpy.zeros((nParam, y.size, x.size - 1)) |
|
|
1890 | 1887 | z[:,:] = numpy.nan |
|
|
1891 | 1888 | z[:,hmet,tmet] = dataOut.data_param[:,3:].T |
|
|
1892 | 1889 | z[0,:,:] = 10*numpy.log10(z[0,:,:]) |
|
|
1893 | 1890 | |
|
|
1894 | 1891 | xlabel = "Time (s)" |
|
|
1895 | 1892 | ylabel = "Range (km)" |
|
|
1896 | 1893 | |
|
|
1897 | 1894 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
1898 | 1895 | |
|
|
1899 | 1896 | if not self.isConfig: |
|
|
1900 | 1897 | |
|
|
1901 | 1898 | nplots = nParam |
|
|
1902 | 1899 | |
|
|
1903 | 1900 | self.setup(id=id, |
|
|
1904 | 1901 | nplots=nplots, |
|
|
1905 | 1902 | wintitle=wintitle, |
|
|
1906 | 1903 | show=show) |
|
|
1907 | 1904 | |
|
|
1908 | 1905 | if xmin is None: xmin = numpy.nanmin(x) |
|
|
1909 | 1906 | if xmax is None: xmax = numpy.nanmax(x) |
|
|
1910 | 1907 | if ymin is None: ymin = numpy.nanmin(y) |
|
|
1911 | 1908 | if ymax is None: ymax = numpy.nanmax(y) |
|
|
1912 | 1909 | if SNRmin is None: SNRmin = numpy.nanmin(z[0,:]) |
|
|
1913 | 1910 | if SNRmax is None: SNRmax = numpy.nanmax(z[0,:]) |
|
|
1914 | 1911 | if vmax is None: vmax = numpy.nanmax(numpy.abs(z[1,:])) |
|
|
1915 | 1912 | if vmin is None: vmin = -vmax |
|
|
1916 | 1913 | if wmin is None: wmin = 0 |
|
|
1917 | 1914 | if wmax is None: wmax = 50 |
|
|
1918 | 1915 | |
|
|
1919 | 1916 | pairsList = dataOut.groupList |
|
|
1920 | 1917 | self.nPairs = len(dataOut.groupList) |
|
|
1921 | 1918 | |
|
|
1922 | 1919 | zminList = [SNRmin, vmin, cmin] + [pmin]*self.nPairs |
|
|
1923 | 1920 | zmaxList = [SNRmax, vmax, cmax] + [pmax]*self.nPairs |
|
|
1924 | 1921 | titleList = ["SNR","Radial Velocity","Coherence"] |
|
|
1925 | 1922 | cmapList = ["jet","RdBu_r","jet"] |
|
|
1926 | 1923 | |
|
|
1927 | 1924 | for i in range(self.nPairs): |
|
|
1928 | 1925 | strAux1 = "Phase Difference "+ str(pairsList[i][0]) + str(pairsList[i][1]) |
|
|
1929 | 1926 | titleList = titleList + [strAux1] |
|
|
1930 | 1927 | cmapList = cmapList + ["RdBu_r"] |
|
|
1931 | 1928 | |
|
|
1932 | 1929 | self.zminList = zminList |
|
|
1933 | 1930 | self.zmaxList = zmaxList |
|
|
1934 | 1931 | self.cmapList = cmapList |
|
|
1935 | 1932 | self.titleList = titleList |
|
|
1936 | 1933 | |
|
|
1937 | 1934 | self.FTP_WEI = ftp_wei |
|
|
1938 | 1935 | self.EXP_CODE = exp_code |
|
|
1939 | 1936 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1940 | 1937 | self.PLOT_POS = plot_pos |
|
|
1941 | 1938 | |
|
|
1942 | 1939 | self.isConfig = True |
|
|
1943 | 1940 | |
|
|
1944 | 1941 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
1945 | 1942 | |
|
|
1946 | 1943 | for i in range(nParam): |
|
|
1947 | 1944 | title = self.titleList[i] + ": " +str_datetime |
|
|
1948 | 1945 | axes = self.axesList[i] |
|
|
1949 | 1946 | axes.pcolor(x, y, z[i,:].T, |
|
|
1950 | 1947 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=self.zminList[i], zmax=self.zmaxList[i], |
|
|
1951 | 1948 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=self.cmapList[i],ticksize=9, cblabel='') |
|
|
1952 | 1949 | self.draw() |
|
|
1953 | 1950 | |
|
|
1954 | 1951 | if figfile == None: |
|
|
1955 | 1952 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1956 | 1953 | name = str_datetime |
|
|
1957 | 1954 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1958 | 1955 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
1959 | 1956 | figfile = self.getFilename(name) |
|
|
1960 | 1957 | |
|
|
1961 | 1958 | self.save(figpath=figpath, |
|
|
1962 | 1959 | figfile=figfile, |
|
|
1963 | 1960 | save=save, |
|
|
1964 | 1961 | ftp=ftp, |
|
|
1965 | 1962 | wr_period=wr_period, |
|
|
1966 | 1963 | thisDatetime=thisDatetime) |
|
|
1967 | 1964 | |
|
|
1968 | 1965 | |
|
|
1969 | 1966 | class NSMeteorDetection2Plot(Figure): |
|
|
1970 | 1967 | |
|
|
1971 | 1968 | isConfig = None |
|
|
1972 | 1969 | __nsubplots = None |
|
|
1973 | 1970 | |
|
|
1974 | 1971 | WIDTHPROF = None |
|
|
1975 | 1972 | HEIGHTPROF = None |
|
|
1976 | 1973 | PREFIX = 'nsm' |
|
|
1977 | 1974 | |
|
|
1978 | 1975 | zminList = None |
|
|
1979 | 1976 | zmaxList = None |
|
|
1980 | 1977 | cmapList = None |
|
|
1981 | 1978 | titleList = None |
|
|
1982 | 1979 | nPairs = None |
|
|
1983 | 1980 | nChannels = None |
|
|
1984 | 1981 | nParam = None |
|
|
1985 | 1982 | |
|
|
1986 | 1983 | def __init__(self, **kwargs): |
|
|
1987 | 1984 | Figure.__init__(self, **kwargs) |
|
|
1988 | 1985 | self.isConfig = False |
|
|
1989 | 1986 | self.__nsubplots = 1 |
|
|
1990 | 1987 | |
|
|
1991 | 1988 | self.WIDTH = 750 |
|
|
1992 | 1989 | self.HEIGHT = 250 |
|
|
1993 | 1990 | self.WIDTHPROF = 120 |
|
|
1994 | 1991 | self.HEIGHTPROF = 0 |
|
|
1995 | 1992 | self.counter_imagwr = 0 |
|
|
1996 | 1993 | |
|
|
1997 | 1994 | self.PLOT_CODE = SPEC_CODE |
|
|
1998 | 1995 | |
|
|
1999 | 1996 | self.FTP_WEI = None |
|
|
2000 | 1997 | self.EXP_CODE = None |
|
|
2001 | 1998 | self.SUB_EXP_CODE = None |
|
|
2002 | 1999 | self.PLOT_POS = None |
|
|
2003 | 2000 | |
|
|
2004 | 2001 | self.__xfilter_ena = False |
|
|
2005 | 2002 | self.__yfilter_ena = False |
|
|
2006 | 2003 | |
|
|
2007 | 2004 | def getSubplots(self): |
|
|
2008 | 2005 | |
|
|
2009 | 2006 | ncol = 3 |
|
|
2010 | 2007 | nrow = int(numpy.ceil(self.nplots/3.0)) |
|
|
2011 | 2008 | |
|
|
2012 | 2009 | return nrow, ncol |
|
|
2013 | 2010 | |
|
|
2014 | 2011 | def setup(self, id, nplots, wintitle, show=True): |
|
|
2015 | 2012 | |
|
|
2016 | 2013 | self.nplots = nplots |
|
|
2017 | 2014 | |
|
|
2018 | 2015 | ncolspan = 1 |
|
|
2019 | 2016 | colspan = 1 |
|
|
2020 | 2017 | |
|
|
2021 | 2018 | self.createFigure(id = id, |
|
|
2022 | 2019 | wintitle = wintitle, |
|
|
2023 | 2020 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
2024 | 2021 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
2025 | 2022 | show=show) |
|
|
2026 | 2023 | |
|
|
2027 | 2024 | nrow, ncol = self.getSubplots() |
|
|
2028 | 2025 | |
|
|
2029 | 2026 | counter = 0 |
|
|
2030 | 2027 | for y in range(nrow): |
|
|
2031 | 2028 | for x in range(ncol): |
|
|
2032 | 2029 | |
|
|
2033 | 2030 | if counter >= self.nplots: |
|
|
2034 | 2031 | break |
|
|
2035 | 2032 | |
|
|
2036 | 2033 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
2037 | 2034 | |
|
|
2038 | 2035 | counter += 1 |
|
|
2039 | 2036 | |
|
|
2040 | 2037 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
2041 | 2038 | xmin=None, xmax=None, ymin=None, ymax=None, SNRmin=None, SNRmax=None, |
|
|
2042 | 2039 | vmin=None, vmax=None, wmin=None, wmax=None, mode = 'SA', |
|
|
2043 | 2040 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
2044 | 2041 | server=None, folder=None, username=None, password=None, |
|
|
2045 | 2042 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
2046 | 2043 | xaxis="frequency"): |
|
|
2047 | 2044 | |
|
|
2048 | 2045 | """ |
|
|
2049 | 2046 | |
|
|
2050 | 2047 | Input: |
|
|
2051 | 2048 | dataOut : |
|
|
2052 | 2049 | id : |
|
|
2053 | 2050 | wintitle : |
|
|
2054 | 2051 | channelList : |
|
|
2055 | 2052 | showProfile : |
|
|
2056 | 2053 | xmin : None, |
|
|
2057 | 2054 | xmax : None, |
|
|
2058 | 2055 | ymin : None, |
|
|
2059 | 2056 | ymax : None, |
|
|
2060 | 2057 | zmin : None, |
|
|
2061 | 2058 | zmax : None |
|
|
2062 | 2059 | """ |
|
|
2063 | 2060 | #Rebuild matrix |
|
|
2064 | 2061 | utctime = dataOut.data_param[0,0] |
|
|
2065 | 2062 | cmet = dataOut.data_param[:,1].astype(int) |
|
|
2066 | 2063 | tmet = dataOut.data_param[:,2].astype(int) |
|
|
2067 | 2064 | hmet = dataOut.data_param[:,3].astype(int) |
|
|
2068 | 2065 | |
|
|
2069 | 2066 | nParam = 3 |
|
|
2070 | 2067 | nChan = len(dataOut.groupList) |
|
|
2071 | 2068 | x = dataOut.abscissaList |
|
|
2072 | 2069 | y = dataOut.heightList |
|
|
2073 | 2070 | |
|
|
2074 | 2071 | z = numpy.full((nChan, nParam, y.size, x.size - 1),numpy.nan) |
|
|
2075 | 2072 | z[cmet,:,hmet,tmet] = dataOut.data_param[:,4:] |
|
|
2076 | 2073 | z[:,0,:,:] = 10*numpy.log10(z[:,0,:,:]) #logarithmic scale |
|
|
2077 | 2074 | z = numpy.reshape(z, (nChan*nParam, y.size, x.size-1)) |
|
|
2078 | 2075 | |
|
|
2079 | 2076 | xlabel = "Time (s)" |
|
|
2080 | 2077 | ylabel = "Range (km)" |
|
|
2081 | 2078 | |
|
|
2082 | 2079 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
2083 | 2080 | |
|
|
2084 | 2081 | if not self.isConfig: |
|
|
2085 | 2082 | |
|
|
2086 | 2083 | nplots = nParam*nChan |
|
|
2087 | 2084 | |
|
|
2088 | 2085 | self.setup(id=id, |
|
|
2089 | 2086 | nplots=nplots, |
|
|
2090 | 2087 | wintitle=wintitle, |
|
|
2091 | 2088 | show=show) |
|
|
2092 | 2089 | |
|
|
2093 | 2090 | if xmin is None: xmin = numpy.nanmin(x) |
|
|
2094 | 2091 | if xmax is None: xmax = numpy.nanmax(x) |
|
|
2095 | 2092 | if ymin is None: ymin = numpy.nanmin(y) |
|
|
2096 | 2093 | if ymax is None: ymax = numpy.nanmax(y) |
|
|
2097 | 2094 | if SNRmin is None: SNRmin = numpy.nanmin(z[0,:]) |
|
|
2098 | 2095 | if SNRmax is None: SNRmax = numpy.nanmax(z[0,:]) |
|
|
2099 | 2096 | if vmax is None: vmax = numpy.nanmax(numpy.abs(z[1,:])) |
|
|
2100 | 2097 | if vmin is None: vmin = -vmax |
|
|
2101 | 2098 | if wmin is None: wmin = 0 |
|
|
2102 | 2099 | if wmax is None: wmax = 50 |
|
|
2103 | 2100 | |
|
|
2104 | 2101 | self.nChannels = nChan |
|
|
2105 | 2102 | |
|
|
2106 | 2103 | zminList = [] |
|
|
2107 | 2104 | zmaxList = [] |
|
|
2108 | 2105 | titleList = [] |
|
|
2109 | 2106 | cmapList = [] |
|
|
2110 | 2107 | for i in range(self.nChannels): |
|
|
2111 | 2108 | strAux1 = "SNR Channel "+ str(i) |
|
|
2112 | 2109 | strAux2 = "Radial Velocity Channel "+ str(i) |
|
|
2113 | 2110 | strAux3 = "Spectral Width Channel "+ str(i) |
|
|
2114 | 2111 | |
|
|
2115 | 2112 | titleList = titleList + [strAux1,strAux2,strAux3] |
|
|
2116 | 2113 | cmapList = cmapList + ["jet","RdBu_r","jet"] |
|
|
2117 | 2114 | zminList = zminList + [SNRmin,vmin,wmin] |
|
|
2118 | 2115 | zmaxList = zmaxList + [SNRmax,vmax,wmax] |
|
|
2119 | 2116 | |
|
|
2120 | 2117 | self.zminList = zminList |
|
|
2121 | 2118 | self.zmaxList = zmaxList |
|
|
2122 | 2119 | self.cmapList = cmapList |
|
|
2123 | 2120 | self.titleList = titleList |
|
|
2124 | 2121 | |
|
|
2125 | 2122 | self.FTP_WEI = ftp_wei |
|
|
2126 | 2123 | self.EXP_CODE = exp_code |
|
|
2127 | 2124 | self.SUB_EXP_CODE = sub_exp_code |
|
|
2128 | 2125 | self.PLOT_POS = plot_pos |
|
|
2129 | 2126 | |
|
|
2130 | 2127 | self.isConfig = True |
|
|
2131 | 2128 | |
|
|
2132 | 2129 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
2133 | 2130 | |
|
|
2134 | 2131 | for i in range(self.nplots): |
|
|
2135 | 2132 | title = self.titleList[i] + ": " +str_datetime |
|
|
2136 | 2133 | axes = self.axesList[i] |
|
|
2137 | 2134 | axes.pcolor(x, y, z[i,:].T, |
|
|
2138 | 2135 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=self.zminList[i], zmax=self.zmaxList[i], |
|
|
2139 | 2136 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=self.cmapList[i],ticksize=9, cblabel='') |
|
|
2140 | 2137 | self.draw() |
|
|
2141 | 2138 | |
|
|
2142 | 2139 | if figfile == None: |
|
|
2143 | 2140 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
2144 | 2141 | name = str_datetime |
|
|
2145 | 2142 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
2146 | 2143 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
2147 | 2144 | figfile = self.getFilename(name) |
|
|
2148 | 2145 | |
|
|
2149 | 2146 | self.save(figpath=figpath, |
|
|
2150 | 2147 | figfile=figfile, |
|
|
2151 | 2148 | save=save, |
|
|
2152 | 2149 | ftp=ftp, |
|
|
2153 | 2150 | wr_period=wr_period, |
|
|
2154 | 2151 | thisDatetime=thisDatetime) |
| @@ -1,481 +1,481 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import sys |
|
|
4 | 4 | import matplotlib |
|
|
5 | 5 | |
|
|
6 | 6 | if 'linux' in sys.platform: |
|
|
7 | matplotlib.use("TKAgg") | |
|
|
7 | matplotlib.use("GTK3Agg") | |
|
|
8 | 8 | |
|
|
9 | 9 | if 'darwin' in sys.platform: |
|
|
10 | 10 | matplotlib.use('TKAgg') |
|
|
11 | 11 | #Qt4Agg', 'GTK', 'GTKAgg', 'ps', 'agg', 'cairo', 'MacOSX', 'GTKCairo', 'WXAgg', 'template', 'TkAgg', 'GTK3Cairo', 'GTK3Agg', 'svg', 'WebAgg', 'CocoaAgg', 'emf', 'gdk', 'WX' |
|
|
12 | 12 | import matplotlib.pyplot |
|
|
13 | 13 | |
|
|
14 | 14 | from mpl_toolkits.axes_grid1 import make_axes_locatable |
|
|
15 | 15 | from matplotlib.ticker import FuncFormatter, LinearLocator |
|
|
16 | 16 | |
|
|
17 | 17 | ########################################### |
|
|
18 | 18 | #Actualizacion de las funciones del driver |
|
|
19 | 19 | ########################################### |
|
|
20 | 20 | |
|
|
21 | 21 | # create jro colormap |
|
|
22 | 22 | |
|
|
23 | 23 | jet_values = matplotlib.pyplot.get_cmap("jet", 100)(numpy.arange(100))[10:90] |
|
|
24 | 24 | blu_values = matplotlib.pyplot.get_cmap("seismic_r", 20)(numpy.arange(20))[10:15] |
|
|
25 | 25 | ncmap = matplotlib.colors.LinearSegmentedColormap.from_list("jro", numpy.vstack((blu_values, jet_values))) |
|
|
26 | 26 | matplotlib.pyplot.register_cmap(cmap=ncmap) |
|
|
27 | 27 | |
|
|
28 | 28 | def createFigure(id, wintitle, width, height, facecolor="w", show=True, dpi = 80): |
|
|
29 | 29 | |
|
|
30 | 30 | matplotlib.pyplot.ioff() |
|
|
31 | 31 | |
|
|
32 | 32 | fig = matplotlib.pyplot.figure(num=id, facecolor=facecolor, figsize=(1.0*width/dpi, 1.0*height/dpi)) |
|
|
33 | 33 | fig.canvas.manager.set_window_title(wintitle) |
|
|
34 | 34 | # fig.canvas.manager.resize(width, height) |
|
|
35 | 35 | matplotlib.pyplot.ion() |
|
|
36 | 36 | |
|
|
37 | 37 | if show: |
|
|
38 | 38 | matplotlib.pyplot.show() |
|
|
39 | 39 | |
|
|
40 | 40 | return fig |
|
|
41 | 41 | |
|
|
42 | 42 | def closeFigure(show=False, fig=None): |
|
|
43 | 43 | |
|
|
44 | 44 | # matplotlib.pyplot.ioff() |
|
|
45 | 45 | # matplotlib.pyplot.pause(0) |
|
|
46 | 46 | |
|
|
47 | 47 | if show: |
|
|
48 | 48 | matplotlib.pyplot.show() |
|
|
49 | 49 | |
|
|
50 | 50 | if fig != None: |
|
|
51 | 51 | matplotlib.pyplot.close(fig) |
|
|
52 | 52 | # matplotlib.pyplot.pause(0) |
|
|
53 | 53 | # matplotlib.pyplot.ion() |
|
|
54 | 54 | |
|
|
55 | 55 | return |
|
|
56 | 56 | |
|
|
57 | 57 | matplotlib.pyplot.close("all") |
|
|
58 | 58 | # matplotlib.pyplot.pause(0) |
|
|
59 | 59 | # matplotlib.pyplot.ion() |
|
|
60 | 60 | |
|
|
61 | 61 | return |
|
|
62 | 62 | |
|
|
63 | 63 | def saveFigure(fig, filename): |
|
|
64 | 64 | |
|
|
65 | 65 | # matplotlib.pyplot.ioff() |
|
|
66 | 66 | fig.savefig(filename, dpi=matplotlib.pyplot.gcf().dpi) |
|
|
67 | 67 | # matplotlib.pyplot.ion() |
|
|
68 | 68 | |
|
|
69 | 69 | def clearFigure(fig): |
|
|
70 | 70 | |
|
|
71 | 71 | fig.clf() |
|
|
72 | 72 | |
|
|
73 | 73 | def setWinTitle(fig, title): |
|
|
74 | 74 | |
|
|
75 | 75 | fig.canvas.manager.set_window_title(title) |
|
|
76 | 76 | |
|
|
77 | 77 | def setTitle(fig, title): |
|
|
78 | 78 | |
|
|
79 | 79 | fig.suptitle(title) |
|
|
80 | 80 | |
|
|
81 | 81 | def createAxes(fig, nrow, ncol, xpos, ypos, colspan, rowspan, polar=False): |
|
|
82 | 82 | |
|
|
83 | 83 | matplotlib.pyplot.ioff() |
|
|
84 | 84 | matplotlib.pyplot.figure(fig.number) |
|
|
85 | 85 | axes = matplotlib.pyplot.subplot2grid((nrow, ncol), |
|
|
86 | 86 | (xpos, ypos), |
|
|
87 | 87 | colspan=colspan, |
|
|
88 | 88 | rowspan=rowspan, |
|
|
89 | 89 | polar=polar) |
|
|
90 | 90 | |
|
|
91 | 91 | axes.grid(True) |
|
|
92 | 92 | |
|
|
93 | 93 | matplotlib.pyplot.ion() |
|
|
94 | 94 | return axes |
|
|
95 | 95 | |
|
|
96 | 96 | def setAxesText(ax, text): |
|
|
97 | 97 | |
|
|
98 | 98 | ax.annotate(text, |
|
|
99 | 99 | xy = (.1, .99), |
|
|
100 | 100 | xycoords = 'figure fraction', |
|
|
101 | 101 | horizontalalignment = 'left', |
|
|
102 | 102 | verticalalignment = 'top', |
|
|
103 | 103 | fontsize = 10) |
|
|
104 | 104 | |
|
|
105 | 105 | def printLabels(ax, xlabel, ylabel, title): |
|
|
106 | 106 | |
|
|
107 | 107 | ax.set_xlabel(xlabel, size=11) |
|
|
108 | 108 | ax.set_ylabel(ylabel, size=11) |
|
|
109 | 109 | ax.set_title(title, size=8) |
|
|
110 | 110 | |
|
|
111 | 111 | def createPline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', |
|
|
112 | 112 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
113 | 113 | nxticks=4, nyticks=10, |
|
|
114 | 114 | grid=None,color='blue'): |
|
|
115 | 115 | |
|
|
116 | 116 | """ |
|
|
117 | 117 | |
|
|
118 | 118 | Input: |
|
|
119 | 119 | grid : None, 'both', 'x', 'y' |
|
|
120 | 120 | """ |
|
|
121 | 121 | |
|
|
122 | 122 | matplotlib.pyplot.ioff() |
|
|
123 | 123 | |
|
|
124 | 124 | ax.set_xlim([xmin,xmax]) |
|
|
125 | 125 | ax.set_ylim([ymin,ymax]) |
|
|
126 | 126 | |
|
|
127 | 127 | printLabels(ax, xlabel, ylabel, title) |
|
|
128 | 128 | |
|
|
129 | 129 | ###################################################### |
|
|
130 | 130 | if (xmax-xmin)<=1: |
|
|
131 | 131 | xtickspos = numpy.linspace(xmin,xmax,nxticks) |
|
|
132 | 132 | xtickspos = numpy.array([float("%.1f"%i) for i in xtickspos]) |
|
|
133 | 133 | ax.set_xticks(xtickspos) |
|
|
134 | 134 | else: |
|
|
135 | 135 | xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
136 | 136 | # xtickspos = numpy.arange(nxticks)*float(xmax-xmin)/float(nxticks) + int(xmin) |
|
|
137 | 137 | ax.set_xticks(xtickspos) |
|
|
138 | 138 | |
|
|
139 | 139 | for tick in ax.get_xticklabels(): |
|
|
140 | 140 | tick.set_visible(xtick_visible) |
|
|
141 | 141 | |
|
|
142 | 142 | for tick in ax.xaxis.get_major_ticks(): |
|
|
143 | 143 | tick.label.set_fontsize(ticksize) |
|
|
144 | 144 | |
|
|
145 | 145 | ###################################################### |
|
|
146 | 146 | for tick in ax.get_yticklabels(): |
|
|
147 | 147 | tick.set_visible(ytick_visible) |
|
|
148 | 148 | |
|
|
149 | 149 | for tick in ax.yaxis.get_major_ticks(): |
|
|
150 | 150 | tick.label.set_fontsize(ticksize) |
|
|
151 | 151 | |
|
|
152 | 152 | ax.plot(x, y, color=color) |
|
|
153 | 153 | iplot = ax.lines[-1] |
|
|
154 | 154 | |
|
|
155 | 155 | ###################################################### |
|
|
156 | 156 | if '0.' in matplotlib.__version__[0:2]: |
|
|
157 | 157 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
158 | 158 | return iplot |
|
|
159 | 159 | |
|
|
160 | 160 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
161 | 161 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
162 | 162 | return iplot |
|
|
163 | 163 | |
|
|
164 | 164 | if grid != None: |
|
|
165 | 165 | ax.grid(b=True, which='major', axis=grid) |
|
|
166 | 166 | |
|
|
167 | 167 | matplotlib.pyplot.tight_layout() |
|
|
168 | 168 | |
|
|
169 | 169 | matplotlib.pyplot.ion() |
|
|
170 | 170 | |
|
|
171 | 171 | return iplot |
|
|
172 | 172 | |
|
|
173 | 173 | def set_linedata(ax, x, y, idline): |
|
|
174 | 174 | |
|
|
175 | 175 | ax.lines[idline].set_data(x,y) |
|
|
176 | 176 | |
|
|
177 | 177 | def pline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
178 | 178 | |
|
|
179 | 179 | ax = iplot.get_axes() |
|
|
180 | 180 | |
|
|
181 | 181 | printLabels(ax, xlabel, ylabel, title) |
|
|
182 | 182 | |
|
|
183 | 183 | set_linedata(ax, x, y, idline=0) |
|
|
184 | 184 | |
|
|
185 | 185 | def addpline(ax, x, y, color, linestyle, lw): |
|
|
186 | 186 | |
|
|
187 | 187 | ax.plot(x,y,color=color,linestyle=linestyle,lw=lw) |
|
|
188 | 188 | |
|
|
189 | 189 | |
|
|
190 | 190 | def createPcolor(ax, x, y, z, xmin, xmax, ymin, ymax, zmin, zmax, |
|
|
191 | 191 | xlabel='', ylabel='', title='', ticksize = 9, |
|
|
192 | 192 | colormap='jet',cblabel='', cbsize="5%", |
|
|
193 | 193 | XAxisAsTime=False): |
|
|
194 | 194 | |
|
|
195 | 195 | matplotlib.pyplot.ioff() |
|
|
196 | 196 | |
|
|
197 | 197 | divider = make_axes_locatable(ax) |
|
|
198 | 198 | ax_cb = divider.new_horizontal(size=cbsize, pad=0.05) |
|
|
199 | 199 | fig = ax.get_figure() |
|
|
200 | 200 | fig.add_axes(ax_cb) |
|
|
201 | 201 | |
|
|
202 | 202 | ax.set_xlim([xmin,xmax]) |
|
|
203 | 203 | ax.set_ylim([ymin,ymax]) |
|
|
204 | 204 | |
|
|
205 | 205 | printLabels(ax, xlabel, ylabel, title) |
|
|
206 | 206 | |
|
|
207 | 207 | z = numpy.ma.masked_invalid(z) |
|
|
208 | 208 | cmap=matplotlib.pyplot.get_cmap(colormap) |
|
|
209 | 209 | cmap.set_bad('white',1.) |
|
|
210 | 210 | imesh = ax.pcolormesh(x,y,z.T, vmin=zmin, vmax=zmax, cmap=cmap) |
|
|
211 | 211 | cb = matplotlib.pyplot.colorbar(imesh, cax=ax_cb) |
|
|
212 | 212 | cb.set_label(cblabel) |
|
|
213 | 213 | |
|
|
214 | 214 | # for tl in ax_cb.get_yticklabels(): |
|
|
215 | 215 | # tl.set_visible(True) |
|
|
216 | 216 | |
|
|
217 | 217 | for tick in ax.yaxis.get_major_ticks(): |
|
|
218 | 218 | tick.label.set_fontsize(ticksize) |
|
|
219 | 219 | |
|
|
220 | 220 | for tick in ax.xaxis.get_major_ticks(): |
|
|
221 | 221 | tick.label.set_fontsize(ticksize) |
|
|
222 | 222 | |
|
|
223 | 223 | for tick in cb.ax.get_yticklabels(): |
|
|
224 | 224 | tick.set_fontsize(ticksize) |
|
|
225 | 225 | |
|
|
226 | 226 | ax_cb.yaxis.tick_right() |
|
|
227 | 227 | |
|
|
228 | 228 | if '0.' in matplotlib.__version__[0:2]: |
|
|
229 | 229 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
230 | 230 | return imesh |
|
|
231 | 231 | |
|
|
232 | 232 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
233 | 233 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
234 | 234 | return imesh |
|
|
235 | 235 | |
|
|
236 | 236 | matplotlib.pyplot.tight_layout() |
|
|
237 | 237 | |
|
|
238 | 238 | if XAxisAsTime: |
|
|
239 | 239 | |
|
|
240 | 240 | func = lambda x, pos: ('%s') %(datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
241 | 241 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
242 | 242 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
243 | 243 | |
|
|
244 | 244 | ax.grid(True) |
|
|
245 | 245 | matplotlib.pyplot.ion() |
|
|
246 | 246 | return imesh |
|
|
247 | 247 | |
|
|
248 | 248 | def pcolor(imesh, z, xlabel='', ylabel='', title=''): |
|
|
249 | 249 | |
|
|
250 | 250 | z = numpy.ma.masked_invalid(z) |
|
|
251 | 251 | |
|
|
252 | 252 | cmap=matplotlib.pyplot.get_cmap('jet') |
|
|
253 | 253 | cmap.set_bad('white',1.) |
|
|
254 | 254 | |
|
|
255 | 255 | z = z.T |
|
|
256 | 256 | ax = imesh.get_axes() |
|
|
257 | 257 | printLabels(ax, xlabel, ylabel, title) |
|
|
258 | 258 | imesh.set_array(z.ravel()) |
|
|
259 | 259 | ax.grid(True) |
|
|
260 | 260 | |
|
|
261 | 261 | |
|
|
262 | 262 | def addpcolor(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
263 | 263 | |
|
|
264 | 264 | printLabels(ax, xlabel, ylabel, title) |
|
|
265 | 265 | z = numpy.ma.masked_invalid(z) |
|
|
266 | 266 | cmap=matplotlib.pyplot.get_cmap(colormap) |
|
|
267 | 267 | cmap.set_bad('white',1.) |
|
|
268 | 268 | ax.pcolormesh(x,y,z.T,vmin=zmin,vmax=zmax, cmap=matplotlib.pyplot.get_cmap(colormap)) |
|
|
269 | 269 | ax.grid(True) |
|
|
270 | 270 | |
|
|
271 | 271 | def addpcolorbuffer(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
272 | 272 | |
|
|
273 | 273 | printLabels(ax, xlabel, ylabel, title) |
|
|
274 | 274 | |
|
|
275 | 275 | ax.collections.remove(ax.collections[0]) |
|
|
276 | 276 | |
|
|
277 | 277 | z = numpy.ma.masked_invalid(z) |
|
|
278 | 278 | |
|
|
279 | 279 | cmap=matplotlib.pyplot.get_cmap(colormap) |
|
|
280 | 280 | cmap.set_bad('white',1.) |
|
|
281 | 281 | |
|
|
282 | 282 | ax.pcolormesh(x,y,z.T,vmin=zmin,vmax=zmax, cmap=cmap) |
|
|
283 | 283 | ax.grid(True) |
|
|
284 | 284 | |
|
|
285 | 285 | |
|
|
286 | 286 | def createPmultiline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
287 | 287 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
288 | 288 | nxticks=4, nyticks=10, |
|
|
289 | 289 | grid=None): |
|
|
290 | 290 | |
|
|
291 | 291 | """ |
|
|
292 | 292 | |
|
|
293 | 293 | Input: |
|
|
294 | 294 | grid : None, 'both', 'x', 'y' |
|
|
295 | 295 | """ |
|
|
296 | 296 | |
|
|
297 | 297 | matplotlib.pyplot.ioff() |
|
|
298 | 298 | |
|
|
299 | 299 | lines = ax.plot(x.T, y) |
|
|
300 | 300 | leg = ax.legend(lines, legendlabels, loc='upper right') |
|
|
301 | 301 | leg.get_frame().set_alpha(0.5) |
|
|
302 | 302 | ax.set_xlim([xmin,xmax]) |
|
|
303 | 303 | ax.set_ylim([ymin,ymax]) |
|
|
304 | 304 | printLabels(ax, xlabel, ylabel, title) |
|
|
305 | 305 | |
|
|
306 | 306 | xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
307 | 307 | ax.set_xticks(xtickspos) |
|
|
308 | 308 | |
|
|
309 | 309 | for tick in ax.get_xticklabels(): |
|
|
310 | 310 | tick.set_visible(xtick_visible) |
|
|
311 | 311 | |
|
|
312 | 312 | for tick in ax.xaxis.get_major_ticks(): |
|
|
313 | 313 | tick.label.set_fontsize(ticksize) |
|
|
314 | 314 | |
|
|
315 | 315 | for tick in ax.get_yticklabels(): |
|
|
316 | 316 | tick.set_visible(ytick_visible) |
|
|
317 | 317 | |
|
|
318 | 318 | for tick in ax.yaxis.get_major_ticks(): |
|
|
319 | 319 | tick.label.set_fontsize(ticksize) |
|
|
320 | 320 | |
|
|
321 | 321 | iplot = ax.lines[-1] |
|
|
322 | 322 | |
|
|
323 | 323 | if '0.' in matplotlib.__version__[0:2]: |
|
|
324 | 324 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
325 | 325 | return iplot |
|
|
326 | 326 | |
|
|
327 | 327 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
328 | 328 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
329 | 329 | return iplot |
|
|
330 | 330 | |
|
|
331 | 331 | if grid != None: |
|
|
332 | 332 | ax.grid(b=True, which='major', axis=grid) |
|
|
333 | 333 | |
|
|
334 | 334 | matplotlib.pyplot.tight_layout() |
|
|
335 | 335 | |
|
|
336 | 336 | matplotlib.pyplot.ion() |
|
|
337 | 337 | |
|
|
338 | 338 | return iplot |
|
|
339 | 339 | |
|
|
340 | 340 | |
|
|
341 | 341 | def pmultiline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
342 | 342 | |
|
|
343 | 343 | ax = iplot.get_axes() |
|
|
344 | 344 | |
|
|
345 | 345 | printLabels(ax, xlabel, ylabel, title) |
|
|
346 | 346 | |
|
|
347 | 347 | for i in range(len(ax.lines)): |
|
|
348 | 348 | line = ax.lines[i] |
|
|
349 | 349 | line.set_data(x[i,:],y) |
|
|
350 | 350 | |
|
|
351 | 351 | def createPmultilineYAxis(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
352 | 352 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
353 | 353 | nxticks=4, nyticks=10, marker='.', markersize=10, linestyle="None", |
|
|
354 | 354 | grid=None, XAxisAsTime=False): |
|
|
355 | 355 | |
|
|
356 | 356 | """ |
|
|
357 | 357 | |
|
|
358 | 358 | Input: |
|
|
359 | 359 | grid : None, 'both', 'x', 'y' |
|
|
360 | 360 | """ |
|
|
361 | 361 | |
|
|
362 | 362 | matplotlib.pyplot.ioff() |
|
|
363 | 363 | |
|
|
364 | 364 | # lines = ax.plot(x, y.T, marker=marker,markersize=markersize,linestyle=linestyle) |
|
|
365 | 365 | lines = ax.plot(x, y.T) |
|
|
366 | 366 | # leg = ax.legend(lines, legendlabels, loc=2, bbox_to_anchor=(1.01, 1.00), numpoints=1, handlelength=1.5, \ |
|
|
367 | 367 | # handletextpad=0.5, borderpad=0.5, labelspacing=0.5, borderaxespad=0.) |
|
|
368 | 368 | |
|
|
369 | 369 | leg = ax.legend(lines, legendlabels, |
|
|
370 | 370 | loc='upper right', bbox_to_anchor=(1.16, 1), borderaxespad=0) |
|
|
371 | 371 | |
|
|
372 | 372 | for label in leg.get_texts(): label.set_fontsize(9) |
|
|
373 | 373 | |
|
|
374 | 374 | ax.set_xlim([xmin,xmax]) |
|
|
375 | 375 | ax.set_ylim([ymin,ymax]) |
|
|
376 | 376 | printLabels(ax, xlabel, ylabel, title) |
|
|
377 | 377 | |
|
|
378 | 378 | # xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
379 | 379 | # ax.set_xticks(xtickspos) |
|
|
380 | 380 | |
|
|
381 | 381 | for tick in ax.get_xticklabels(): |
|
|
382 | 382 | tick.set_visible(xtick_visible) |
|
|
383 | 383 | |
|
|
384 | 384 | for tick in ax.xaxis.get_major_ticks(): |
|
|
385 | 385 | tick.label.set_fontsize(ticksize) |
|
|
386 | 386 | |
|
|
387 | 387 | for tick in ax.get_yticklabels(): |
|
|
388 | 388 | tick.set_visible(ytick_visible) |
|
|
389 | 389 | |
|
|
390 | 390 | for tick in ax.yaxis.get_major_ticks(): |
|
|
391 | 391 | tick.label.set_fontsize(ticksize) |
|
|
392 | 392 | |
|
|
393 | 393 | iplot = ax.lines[-1] |
|
|
394 | 394 | |
|
|
395 | 395 | if '0.' in matplotlib.__version__[0:2]: |
|
|
396 | 396 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
397 | 397 | return iplot |
|
|
398 | 398 | |
|
|
399 | 399 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
400 | 400 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
401 | 401 | return iplot |
|
|
402 | 402 | |
|
|
403 | 403 | if grid != None: |
|
|
404 | 404 | ax.grid(b=True, which='major', axis=grid) |
|
|
405 | 405 | |
|
|
406 | 406 | matplotlib.pyplot.tight_layout() |
|
|
407 | 407 | |
|
|
408 | 408 | if XAxisAsTime: |
|
|
409 | 409 | |
|
|
410 | 410 | func = lambda x, pos: ('%s') %(datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
411 | 411 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
412 | 412 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
413 | 413 | |
|
|
414 | 414 | matplotlib.pyplot.ion() |
|
|
415 | 415 | |
|
|
416 | 416 | return iplot |
|
|
417 | 417 | |
|
|
418 | 418 | def pmultilineyaxis(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
419 | 419 | |
|
|
420 | 420 | ax = iplot.get_axes() |
|
|
421 | 421 | printLabels(ax, xlabel, ylabel, title) |
|
|
422 | 422 | |
|
|
423 | 423 | for i in range(len(ax.lines)): |
|
|
424 | 424 | line = ax.lines[i] |
|
|
425 | 425 | line.set_data(x,y[i,:]) |
|
|
426 | 426 | |
|
|
427 | 427 | def createPolar(ax, x, y, |
|
|
428 | 428 | xlabel='', ylabel='', title='', ticksize = 9, |
|
|
429 | 429 | colormap='jet',cblabel='', cbsize="5%", |
|
|
430 | 430 | XAxisAsTime=False): |
|
|
431 | 431 | |
|
|
432 | 432 | matplotlib.pyplot.ioff() |
|
|
433 | 433 | |
|
|
434 | 434 | ax.plot(x,y,'bo', markersize=5) |
|
|
435 | 435 | # ax.set_rmax(90) |
|
|
436 | 436 | ax.set_ylim(0,90) |
|
|
437 | 437 | ax.set_yticks(numpy.arange(0,90,20)) |
|
|
438 | 438 | # ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center' ,size='11') |
|
|
439 | 439 | # ax.text(0, 50, ylabel, rotation='vertical', va ='center', ha = 'left' ,size='11') |
|
|
440 | 440 | # ax.text(100, 100, 'example', ha='left', va='center', rotation='vertical') |
|
|
441 | 441 | ax.yaxis.labelpad = 230 |
|
|
442 | 442 | printLabels(ax, xlabel, ylabel, title) |
|
|
443 | 443 | iplot = ax.lines[-1] |
|
|
444 | 444 | |
|
|
445 | 445 | if '0.' in matplotlib.__version__[0:2]: |
|
|
446 | 446 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
447 | 447 | return iplot |
|
|
448 | 448 | |
|
|
449 | 449 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
450 | 450 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
451 | 451 | return iplot |
|
|
452 | 452 | |
|
|
453 | 453 | # if grid != None: |
|
|
454 | 454 | # ax.grid(b=True, which='major', axis=grid) |
|
|
455 | 455 | |
|
|
456 | 456 | matplotlib.pyplot.tight_layout() |
|
|
457 | 457 | |
|
|
458 | 458 | matplotlib.pyplot.ion() |
|
|
459 | 459 | |
|
|
460 | 460 | |
|
|
461 | 461 | return iplot |
|
|
462 | 462 | |
|
|
463 | 463 | def polar(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
464 | 464 | |
|
|
465 | 465 | ax = iplot.get_axes() |
|
|
466 | 466 | |
|
|
467 | 467 | # ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center',size='11') |
|
|
468 | 468 | printLabels(ax, xlabel, ylabel, title) |
|
|
469 | 469 | |
|
|
470 | 470 | set_linedata(ax, x, y, idline=0) |
|
|
471 | 471 | |
|
|
472 | 472 | def draw(fig): |
|
|
473 | 473 | |
|
|
474 | 474 | if type(fig) == 'int': |
|
|
475 | 475 | raise ValueError, "Error drawing: Fig parameter should be a matplotlib figure object figure" |
|
|
476 | 476 | |
|
|
477 | 477 | fig.canvas.draw() |
|
|
478 | 478 | |
|
|
479 | 479 | def pause(interval=0.000001): |
|
|
480 | 480 | |
|
|
481 | 481 | matplotlib.pyplot.pause(interval) |
| @@ -1,21 +1,20 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | |
|
|
3 | 3 | $Author: murco $ |
|
|
4 | 4 | $Id: JRODataIO.py 169 2012-11-19 21:57:03Z murco $ |
|
|
5 | 5 | ''' |
|
|
6 | 6 | |
|
|
7 | 7 | from jroIO_voltage import * |
|
|
8 | 8 | from jroIO_spectra import * |
|
|
9 | 9 | from jroIO_heispectra import * |
|
|
10 | 10 | from jroIO_usrp import * |
|
|
11 | 11 | |
|
|
12 | 12 | from jroIO_kamisr import * |
|
|
13 | 13 | from jroIO_param import * |
|
|
14 | 14 | from jroIO_hf import * |
|
|
15 | 15 | |
|
|
16 | 16 | from jroIO_madrigal import * |
|
|
17 | 17 | |
|
|
18 | 18 | from bltrIO_param import * |
|
|
19 | 19 | from jroIO_bltr import * |
|
|
20 | 20 | from jroIO_mira35c import * |
|
|
21 | ||
| @@ -1,1795 +1,1807 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Created on Jul 2, 2014 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 |
|
|
5 | 5 | ''' |
|
|
6 | 6 | import os |
|
|
7 | 7 | import sys |
|
|
8 | 8 | import glob |
|
|
9 | 9 | import time |
|
|
10 | 10 | import numpy |
|
|
11 | 11 | import fnmatch |
|
|
12 | 12 | import inspect |
|
|
13 | 13 | import time, datetime |
|
|
14 | 14 | import traceback |
|
|
15 | 15 | import zmq |
|
|
16 | 16 | |
|
|
17 | 17 | try: |
|
|
18 | 18 | from gevent import sleep |
|
|
19 | 19 | except: |
|
|
20 | 20 | from time import sleep |
|
|
21 | 21 | |
|
|
22 | 22 | from schainpy.model.data.jroheaderIO import PROCFLAG, BasicHeader, SystemHeader, RadarControllerHeader, ProcessingHeader |
|
|
23 | 23 | from schainpy.model.data.jroheaderIO import get_dtype_index, get_numpy_dtype, get_procflag_dtype, get_dtype_width |
|
|
24 | 24 | |
|
|
25 | 25 | LOCALTIME = True |
|
|
26 | 26 | |
|
|
27 | 27 | def isNumber(cad): |
|
|
28 | 28 | """ |
|
|
29 | 29 | Chequea si el conjunto de caracteres que componen un string puede ser convertidos a un numero. |
|
|
30 | 30 | |
|
|
31 | 31 | Excepciones: |
|
|
32 | 32 | Si un determinado string no puede ser convertido a numero |
|
|
33 | 33 | Input: |
|
|
34 | 34 | str, string al cual se le analiza para determinar si convertible a un numero o no |
|
|
35 | 35 | |
|
|
36 | 36 | Return: |
|
|
37 | 37 | True : si el string es uno numerico |
|
|
38 | 38 | False : no es un string numerico |
|
|
39 | 39 | """ |
|
|
40 | 40 | try: |
|
|
41 | 41 | float( cad ) |
|
|
42 | 42 | return True |
|
|
43 | 43 | except: |
|
|
44 | 44 | return False |
|
|
45 | 45 | |
|
|
46 | 46 | def isFileInEpoch(filename, startUTSeconds, endUTSeconds): |
|
|
47 | 47 | """ |
|
|
48 | 48 | Esta funcion determina si un archivo de datos se encuentra o no dentro del rango de fecha especificado. |
|
|
49 | 49 | |
|
|
50 | 50 | Inputs: |
|
|
51 | 51 | filename : nombre completo del archivo de datos en formato Jicamarca (.r) |
|
|
52 | 52 | |
|
|
53 | 53 | startUTSeconds : fecha inicial del rango seleccionado. La fecha esta dada en |
|
|
54 | 54 | segundos contados desde 01/01/1970. |
|
|
55 | 55 | endUTSeconds : fecha final del rango seleccionado. La fecha esta dada en |
|
|
56 | 56 | segundos contados desde 01/01/1970. |
|
|
57 | 57 | |
|
|
58 | 58 | Return: |
|
|
59 | 59 | Boolean : Retorna True si el archivo de datos contiene datos en el rango de |
|
|
60 | 60 | fecha especificado, de lo contrario retorna False. |
|
|
61 | 61 | |
|
|
62 | 62 | Excepciones: |
|
|
63 | 63 | Si el archivo no existe o no puede ser abierto |
|
|
64 | 64 | Si la cabecera no puede ser leida. |
|
|
65 | 65 | |
|
|
66 | 66 | """ |
|
|
67 | 67 | basicHeaderObj = BasicHeader(LOCALTIME) |
|
|
68 | 68 | |
|
|
69 | 69 | try: |
|
|
70 | 70 | fp = open(filename,'rb') |
|
|
71 | 71 | except IOError: |
|
|
72 | 72 | print "The file %s can't be opened" %(filename) |
|
|
73 | 73 | return 0 |
|
|
74 | 74 | |
|
|
75 | 75 | sts = basicHeaderObj.read(fp) |
|
|
76 | 76 | fp.close() |
|
|
77 | 77 | |
|
|
78 | 78 | if not(sts): |
|
|
79 | 79 | print "Skipping the file %s because it has not a valid header" %(filename) |
|
|
80 | 80 | return 0 |
|
|
81 | 81 | |
|
|
82 | 82 | if not ((startUTSeconds <= basicHeaderObj.utc) and (endUTSeconds > basicHeaderObj.utc)): |
|
|
83 | 83 | return 0 |
|
|
84 | 84 | |
|
|
85 | 85 | return 1 |
|
|
86 | 86 | |
|
|
87 | 87 | def isTimeInRange(thisTime, startTime, endTime): |
|
|
88 | 88 | |
|
|
89 | 89 | if endTime >= startTime: |
|
|
90 | 90 | if (thisTime < startTime) or (thisTime > endTime): |
|
|
91 | 91 | return 0 |
|
|
92 | 92 | |
|
|
93 | 93 | return 1 |
|
|
94 | 94 | else: |
|
|
95 | 95 | if (thisTime < startTime) and (thisTime > endTime): |
|
|
96 | 96 | return 0 |
|
|
97 | 97 | |
|
|
98 | 98 | return 1 |
|
|
99 | 99 | |
|
|
100 | 100 | def isFileInTimeRange(filename, startDate, endDate, startTime, endTime): |
|
|
101 | 101 | """ |
|
|
102 | 102 | Retorna 1 si el archivo de datos se encuentra dentro del rango de horas especificado. |
|
|
103 | 103 | |
|
|
104 | 104 | Inputs: |
|
|
105 | 105 | filename : nombre completo del archivo de datos en formato Jicamarca (.r) |
|
|
106 | 106 | |
|
|
107 | 107 | startDate : fecha inicial del rango seleccionado en formato datetime.date |
|
|
108 | 108 | |
|
|
109 | 109 | endDate : fecha final del rango seleccionado en formato datetime.date |
|
|
110 | 110 | |
|
|
111 | 111 | startTime : tiempo inicial del rango seleccionado en formato datetime.time |
|
|
112 | 112 | |
|
|
113 | 113 | endTime : tiempo final del rango seleccionado en formato datetime.time |
|
|
114 | 114 | |
|
|
115 | 115 | Return: |
|
|
116 | 116 | Boolean : Retorna True si el archivo de datos contiene datos en el rango de |
|
|
117 | 117 | fecha especificado, de lo contrario retorna False. |
|
|
118 | 118 | |
|
|
119 | 119 | Excepciones: |
|
|
120 | 120 | Si el archivo no existe o no puede ser abierto |
|
|
121 | 121 | Si la cabecera no puede ser leida. |
|
|
122 | 122 | |
|
|
123 | 123 | """ |
|
|
124 | 124 | |
|
|
125 | 125 | |
|
|
126 | 126 | try: |
|
|
127 | 127 | fp = open(filename,'rb') |
|
|
128 | 128 | except IOError: |
|
|
129 | 129 | print "The file %s can't be opened" %(filename) |
|
|
130 | 130 | return None |
|
|
131 | 131 | |
|
|
132 | 132 | firstBasicHeaderObj = BasicHeader(LOCALTIME) |
|
|
133 | 133 | systemHeaderObj = SystemHeader() |
|
|
134 | 134 | radarControllerHeaderObj = RadarControllerHeader() |
|
|
135 | 135 | processingHeaderObj = ProcessingHeader() |
|
|
136 | 136 | |
|
|
137 | 137 | lastBasicHeaderObj = BasicHeader(LOCALTIME) |
|
|
138 | 138 | |
|
|
139 | 139 | sts = firstBasicHeaderObj.read(fp) |
|
|
140 | 140 | |
|
|
141 | 141 | if not(sts): |
|
|
142 | 142 | print "[Reading] Skipping the file %s because it has not a valid header" %(filename) |
|
|
143 | 143 | return None |
|
|
144 | 144 | |
|
|
145 | 145 | if not systemHeaderObj.read(fp): |
|
|
146 | 146 | return None |
|
|
147 | 147 | |
|
|
148 | 148 | if not radarControllerHeaderObj.read(fp): |
|
|
149 | 149 | return None |
|
|
150 | 150 | |
|
|
151 | 151 | if not processingHeaderObj.read(fp): |
|
|
152 | 152 | return None |
|
|
153 | 153 | |
|
|
154 | 154 | filesize = os.path.getsize(filename) |
|
|
155 | 155 | |
|
|
156 | 156 | offset = processingHeaderObj.blockSize + 24 #header size |
|
|
157 | 157 | |
|
|
158 | 158 | if filesize <= offset: |
|
|
159 | 159 | print "[Reading] %s: This file has not enough data" %filename |
|
|
160 | 160 | return None |
|
|
161 | 161 | |
|
|
162 | 162 | fp.seek(-offset, 2) |
|
|
163 | 163 | |
|
|
164 | 164 | sts = lastBasicHeaderObj.read(fp) |
|
|
165 | 165 | |
|
|
166 | 166 | fp.close() |
|
|
167 | 167 | |
|
|
168 | 168 | thisDatetime = lastBasicHeaderObj.datatime |
|
|
169 | 169 | thisTime_last_block = thisDatetime.time() |
|
|
170 | 170 | |
|
|
171 | 171 | thisDatetime = firstBasicHeaderObj.datatime |
|
|
172 | 172 | thisDate = thisDatetime.date() |
|
|
173 | 173 | thisTime_first_block = thisDatetime.time() |
|
|
174 | 174 | |
|
|
175 | 175 | #General case |
|
|
176 | 176 | # o>>>>>>>>>>>>>><<<<<<<<<<<<<<o |
|
|
177 | 177 | #-----------o----------------------------o----------- |
|
|
178 | 178 | # startTime endTime |
|
|
179 | 179 | |
|
|
180 | 180 | if endTime >= startTime: |
|
|
181 | 181 | if (thisTime_last_block < startTime) or (thisTime_first_block > endTime): |
|
|
182 | 182 | return None |
|
|
183 | 183 | |
|
|
184 | 184 | return thisDatetime |
|
|
185 | 185 | |
|
|
186 | 186 | #If endTime < startTime then endTime belongs to the next day |
|
|
187 | 187 | |
|
|
188 | 188 | |
|
|
189 | 189 | #<<<<<<<<<<<o o>>>>>>>>>>> |
|
|
190 | 190 | #-----------o----------------------------o----------- |
|
|
191 | 191 | # endTime startTime |
|
|
192 | 192 | |
|
|
193 | 193 | if (thisDate == startDate) and (thisTime_last_block < startTime): |
|
|
194 | 194 | return None |
|
|
195 | 195 | |
|
|
196 | 196 | if (thisDate == endDate) and (thisTime_first_block > endTime): |
|
|
197 | 197 | return None |
|
|
198 | 198 | |
|
|
199 | 199 | if (thisTime_last_block < startTime) and (thisTime_first_block > endTime): |
|
|
200 | 200 | return None |
|
|
201 | 201 | |
|
|
202 | 202 | return thisDatetime |
|
|
203 | 203 | |
|
|
204 | 204 | def isFolderInDateRange(folder, startDate=None, endDate=None): |
|
|
205 | 205 | """ |
|
|
206 | 206 | Retorna 1 si el archivo de datos se encuentra dentro del rango de horas especificado. |
|
|
207 | 207 | |
|
|
208 | 208 | Inputs: |
|
|
209 | 209 | folder : nombre completo del directorio. |
|
|
210 | 210 | Su formato deberia ser "/path_root/?YYYYDDD" |
|
|
211 | 211 | |
|
|
212 | 212 | siendo: |
|
|
213 | 213 | YYYY : Anio (ejemplo 2015) |
|
|
214 | 214 | DDD : Dia del anio (ejemplo 305) |
|
|
215 | 215 | |
|
|
216 | 216 | startDate : fecha inicial del rango seleccionado en formato datetime.date |
|
|
217 | 217 | |
|
|
218 | 218 | endDate : fecha final del rango seleccionado en formato datetime.date |
|
|
219 | 219 | |
|
|
220 | 220 | Return: |
|
|
221 | 221 | Boolean : Retorna True si el archivo de datos contiene datos en el rango de |
|
|
222 | 222 | fecha especificado, de lo contrario retorna False. |
|
|
223 | 223 | Excepciones: |
|
|
224 | 224 | Si el directorio no tiene el formato adecuado |
|
|
225 | 225 | """ |
|
|
226 | 226 | |
|
|
227 | 227 | basename = os.path.basename(folder) |
|
|
228 | 228 | |
|
|
229 | 229 | if not isRadarFolder(basename): |
|
|
230 | 230 | print "The folder %s has not the rigth format" %folder |
|
|
231 | 231 | return 0 |
|
|
232 | 232 | |
|
|
233 | 233 | if startDate and endDate: |
|
|
234 | 234 | thisDate = getDateFromRadarFolder(basename) |
|
|
235 | 235 | |
|
|
236 | 236 | if thisDate < startDate: |
|
|
237 | 237 | return 0 |
|
|
238 | 238 | |
|
|
239 | 239 | if thisDate > endDate: |
|
|
240 | 240 | return 0 |
|
|
241 | 241 | |
|
|
242 | 242 | return 1 |
|
|
243 | 243 | |
|
|
244 | 244 | def isFileInDateRange(filename, startDate=None, endDate=None): |
|
|
245 | 245 | """ |
|
|
246 | 246 | Retorna 1 si el archivo de datos se encuentra dentro del rango de horas especificado. |
|
|
247 | 247 | |
|
|
248 | 248 | Inputs: |
|
|
249 | 249 | filename : nombre completo del archivo de datos en formato Jicamarca (.r) |
|
|
250 | 250 | |
|
|
251 | 251 | Su formato deberia ser "?YYYYDDDsss" |
|
|
252 | 252 | |
|
|
253 | 253 | siendo: |
|
|
254 | 254 | YYYY : Anio (ejemplo 2015) |
|
|
255 | 255 | DDD : Dia del anio (ejemplo 305) |
|
|
256 | 256 | sss : set |
|
|
257 | 257 | |
|
|
258 | 258 | startDate : fecha inicial del rango seleccionado en formato datetime.date |
|
|
259 | 259 | |
|
|
260 | 260 | endDate : fecha final del rango seleccionado en formato datetime.date |
|
|
261 | 261 | |
|
|
262 | 262 | Return: |
|
|
263 | 263 | Boolean : Retorna True si el archivo de datos contiene datos en el rango de |
|
|
264 | 264 | fecha especificado, de lo contrario retorna False. |
|
|
265 | 265 | Excepciones: |
|
|
266 | 266 | Si el archivo no tiene el formato adecuado |
|
|
267 | 267 | """ |
|
|
268 | 268 | |
|
|
269 | 269 | basename = os.path.basename(filename) |
|
|
270 | 270 | |
|
|
271 | 271 | if not isRadarFile(basename): |
|
|
272 | 272 | print "The filename %s has not the rigth format" %filename |
|
|
273 | 273 | return 0 |
|
|
274 | 274 | |
|
|
275 | 275 | if startDate and endDate: |
|
|
276 | 276 | thisDate = getDateFromRadarFile(basename) |
|
|
277 | 277 | |
|
|
278 | 278 | if thisDate < startDate: |
|
|
279 | 279 | return 0 |
|
|
280 | 280 | |
|
|
281 | 281 | if thisDate > endDate: |
|
|
282 | 282 | return 0 |
|
|
283 | 283 | |
|
|
284 | 284 | return 1 |
|
|
285 | 285 | |
|
|
286 | 286 | def getFileFromSet(path, ext, set): |
|
|
287 | 287 | validFilelist = [] |
|
|
288 | 288 | fileList = os.listdir(path) |
|
|
289 | 289 | |
|
|
290 | 290 | # 0 1234 567 89A BCDE |
|
|
291 | 291 | # H YYYY DDD SSS .ext |
|
|
292 | 292 | |
|
|
293 | 293 | for thisFile in fileList: |
|
|
294 | 294 | try: |
|
|
295 | 295 | year = int(thisFile[1:5]) |
|
|
296 | 296 | doy = int(thisFile[5:8]) |
|
|
297 | 297 | except: |
|
|
298 | 298 | continue |
|
|
299 | 299 | |
|
|
300 | 300 | if (os.path.splitext(thisFile)[-1].lower() != ext.lower()): |
|
|
301 | 301 | continue |
|
|
302 | 302 | |
|
|
303 | 303 | validFilelist.append(thisFile) |
|
|
304 | 304 | |
|
|
305 | 305 | myfile = fnmatch.filter(validFilelist,'*%4.4d%3.3d%3.3d*'%(year,doy,set)) |
|
|
306 | 306 | |
|
|
307 | 307 | if len(myfile)!= 0: |
|
|
308 | 308 | return myfile[0] |
|
|
309 | 309 | else: |
|
|
310 | 310 | filename = '*%4.4d%3.3d%3.3d%s'%(year,doy,set,ext.lower()) |
|
|
311 | 311 | print 'the filename %s does not exist'%filename |
|
|
312 | 312 | print '...going to the last file: ' |
|
|
313 | 313 | |
|
|
314 | 314 | if validFilelist: |
|
|
315 | 315 | validFilelist = sorted( validFilelist, key=str.lower ) |
|
|
316 | 316 | return validFilelist[-1] |
|
|
317 | 317 | |
|
|
318 | 318 | return None |
|
|
319 | 319 | |
|
|
320 | 320 | def getlastFileFromPath(path, ext): |
|
|
321 | 321 | """ |
|
|
322 | 322 | Depura el fileList dejando solo los que cumplan el formato de "PYYYYDDDSSS.ext" |
|
|
323 | 323 | al final de la depuracion devuelve el ultimo file de la lista que quedo. |
|
|
324 | 324 | |
|
|
325 | 325 | Input: |
|
|
326 | 326 | fileList : lista conteniendo todos los files (sin path) que componen una determinada carpeta |
|
|
327 | 327 | ext : extension de los files contenidos en una carpeta |
|
|
328 | 328 | |
|
|
329 | 329 | Return: |
|
|
330 | 330 | El ultimo file de una determinada carpeta, no se considera el path. |
|
|
331 | 331 | """ |
|
|
332 | 332 | validFilelist = [] |
|
|
333 | 333 | fileList = os.listdir(path) |
|
|
334 | 334 | |
|
|
335 | 335 | # 0 1234 567 89A BCDE |
|
|
336 | 336 | # H YYYY DDD SSS .ext |
|
|
337 | 337 | |
|
|
338 | 338 | for thisFile in fileList: |
|
|
339 | 339 | |
|
|
340 | 340 | year = thisFile[1:5] |
|
|
341 | 341 | if not isNumber(year): |
|
|
342 | 342 | continue |
|
|
343 | 343 | |
|
|
344 | 344 | doy = thisFile[5:8] |
|
|
345 | 345 | if not isNumber(doy): |
|
|
346 | 346 | continue |
|
|
347 | 347 | |
|
|
348 | 348 | year = int(year) |
|
|
349 | 349 | doy = int(doy) |
|
|
350 | 350 | |
|
|
351 | 351 | if (os.path.splitext(thisFile)[-1].lower() != ext.lower()): |
|
|
352 | 352 | continue |
|
|
353 | 353 | |
|
|
354 | 354 | validFilelist.append(thisFile) |
|
|
355 | 355 | |
|
|
356 | 356 | if validFilelist: |
|
|
357 | 357 | validFilelist = sorted( validFilelist, key=str.lower ) |
|
|
358 | 358 | return validFilelist[-1] |
|
|
359 | 359 | |
|
|
360 | 360 | return None |
|
|
361 | 361 | |
|
|
362 | 362 | def checkForRealPath(path, foldercounter, year, doy, set, ext): |
|
|
363 | 363 | """ |
|
|
364 | 364 | Por ser Linux Case Sensitive entonces checkForRealPath encuentra el nombre correcto de un path, |
|
|
365 | 365 | Prueba por varias combinaciones de nombres entre mayusculas y minusculas para determinar |
|
|
366 | 366 | el path exacto de un determinado file. |
|
|
367 | 367 | |
|
|
368 | 368 | Example : |
|
|
369 | 369 | nombre correcto del file es .../.../D2009307/P2009307367.ext |
|
|
370 | 370 | |
|
|
371 | 371 | Entonces la funcion prueba con las siguientes combinaciones |
|
|
372 | 372 | .../.../y2009307367.ext |
|
|
373 | 373 | .../.../Y2009307367.ext |
|
|
374 | 374 | .../.../x2009307/y2009307367.ext |
|
|
375 | 375 | .../.../x2009307/Y2009307367.ext |
|
|
376 | 376 | .../.../X2009307/y2009307367.ext |
|
|
377 | 377 | .../.../X2009307/Y2009307367.ext |
|
|
378 | 378 | siendo para este caso, la ultima combinacion de letras, identica al file buscado |
|
|
379 | 379 | |
|
|
380 | 380 | Return: |
|
|
381 | 381 | Si encuentra la cobinacion adecuada devuelve el path completo y el nombre del file |
|
|
382 | 382 | caso contrario devuelve None como path y el la ultima combinacion de nombre en mayusculas |
|
|
383 | 383 | para el filename |
|
|
384 | 384 | """ |
|
|
385 | 385 | fullfilename = None |
|
|
386 | 386 | find_flag = False |
|
|
387 | 387 | filename = None |
|
|
388 | 388 | |
|
|
389 | 389 | prefixDirList = [None,'d','D'] |
|
|
390 | 390 | if ext.lower() == ".r": #voltage |
|
|
391 | 391 | prefixFileList = ['d','D'] |
|
|
392 | 392 | elif ext.lower() == ".pdata": #spectra |
|
|
393 | 393 | prefixFileList = ['p','P'] |
|
|
394 | 394 | else: |
|
|
395 | 395 | return None, filename |
|
|
396 | 396 | |
|
|
397 | 397 | #barrido por las combinaciones posibles |
|
|
398 | 398 | for prefixDir in prefixDirList: |
|
|
399 | 399 | thispath = path |
|
|
400 | 400 | if prefixDir != None: |
|
|
401 | 401 | #formo el nombre del directorio xYYYYDDD (x=d o x=D) |
|
|
402 | 402 | if foldercounter == 0: |
|
|
403 | 403 | thispath = os.path.join(path, "%s%04d%03d" % ( prefixDir, year, doy )) |
|
|
404 | 404 | else: |
|
|
405 | 405 | thispath = os.path.join(path, "%s%04d%03d_%02d" % ( prefixDir, year, doy , foldercounter)) |
|
|
406 | 406 | for prefixFile in prefixFileList: #barrido por las dos combinaciones posibles de "D" |
|
|
407 | 407 | filename = "%s%04d%03d%03d%s" % ( prefixFile, year, doy, set, ext ) #formo el nombre del file xYYYYDDDSSS.ext |
|
|
408 | 408 | fullfilename = os.path.join( thispath, filename ) #formo el path completo |
|
|
409 | 409 | |
|
|
410 | 410 | if os.path.exists( fullfilename ): #verifico que exista |
|
|
411 | 411 | find_flag = True |
|
|
412 | 412 | break |
|
|
413 | 413 | if find_flag: |
|
|
414 | 414 | break |
|
|
415 | 415 | |
|
|
416 | 416 | if not(find_flag): |
|
|
417 | 417 | return None, filename |
|
|
418 | 418 | |
|
|
419 | 419 | return fullfilename, filename |
|
|
420 | 420 | |
|
|
421 | 421 | def isRadarFolder(folder): |
|
|
422 | 422 | try: |
|
|
423 | 423 | year = int(folder[1:5]) |
|
|
424 | 424 | doy = int(folder[5:8]) |
|
|
425 | 425 | except: |
|
|
426 | 426 | return 0 |
|
|
427 | 427 | |
|
|
428 | 428 | return 1 |
|
|
429 | 429 | |
|
|
430 | 430 | def isRadarFile(file): |
|
|
431 | 431 | try: |
|
|
432 | 432 | year = int(file[1:5]) |
|
|
433 | 433 | doy = int(file[5:8]) |
|
|
434 | 434 | set = int(file[8:11]) |
|
|
435 | 435 | except: |
|
|
436 | 436 | return 0 |
|
|
437 | 437 | |
|
|
438 | 438 | return 1 |
|
|
439 | 439 | |
|
|
440 | 440 | def getDateFromRadarFile(file): |
|
|
441 | 441 | try: |
|
|
442 | 442 | year = int(file[1:5]) |
|
|
443 | 443 | doy = int(file[5:8]) |
|
|
444 | 444 | set = int(file[8:11]) |
|
|
445 | 445 | except: |
|
|
446 | 446 | return None |
|
|
447 | 447 | |
|
|
448 | 448 | thisDate = datetime.date(year, 1, 1) + datetime.timedelta(doy-1) |
|
|
449 | 449 | return thisDate |
|
|
450 | 450 | |
|
|
451 | 451 | def getDateFromRadarFolder(folder): |
|
|
452 | 452 | try: |
|
|
453 | 453 | year = int(folder[1:5]) |
|
|
454 | 454 | doy = int(folder[5:8]) |
|
|
455 | 455 | except: |
|
|
456 | 456 | return None |
|
|
457 | 457 | |
|
|
458 | 458 | thisDate = datetime.date(year, 1, 1) + datetime.timedelta(doy-1) |
|
|
459 | 459 | return thisDate |
|
|
460 | 460 | |
|
|
461 | 461 | class JRODataIO: |
|
|
462 | 462 | |
|
|
463 | 463 | c = 3E8 |
|
|
464 | 464 | |
|
|
465 | 465 | isConfig = False |
|
|
466 | 466 | |
|
|
467 | 467 | basicHeaderObj = None |
|
|
468 | 468 | |
|
|
469 | 469 | systemHeaderObj = None |
|
|
470 | 470 | |
|
|
471 | 471 | radarControllerHeaderObj = None |
|
|
472 | 472 | |
|
|
473 | 473 | processingHeaderObj = None |
|
|
474 | 474 | |
|
|
475 | 475 | dtype = None |
|
|
476 | 476 | |
|
|
477 | 477 | pathList = [] |
|
|
478 | 478 | |
|
|
479 | 479 | filenameList = [] |
|
|
480 | 480 | |
|
|
481 | 481 | filename = None |
|
|
482 | 482 | |
|
|
483 | 483 | ext = None |
|
|
484 | 484 | |
|
|
485 | 485 | flagIsNewFile = 1 |
|
|
486 | 486 | |
|
|
487 | 487 | flagDiscontinuousBlock = 0 |
|
|
488 | 488 | |
|
|
489 | 489 | flagIsNewBlock = 0 |
|
|
490 | 490 | |
|
|
491 | 491 | fp = None |
|
|
492 | 492 | |
|
|
493 | 493 | firstHeaderSize = 0 |
|
|
494 | 494 | |
|
|
495 | 495 | basicHeaderSize = 24 |
|
|
496 | 496 | |
|
|
497 | 497 | versionFile = 1103 |
|
|
498 | 498 | |
|
|
499 | 499 | fileSize = None |
|
|
500 | 500 | |
|
|
501 | 501 | # ippSeconds = None |
|
|
502 | 502 | |
|
|
503 | 503 | fileSizeByHeader = None |
|
|
504 | 504 | |
|
|
505 | 505 | fileIndex = None |
|
|
506 | 506 | |
|
|
507 | 507 | profileIndex = None |
|
|
508 | 508 | |
|
|
509 | 509 | blockIndex = None |
|
|
510 | 510 | |
|
|
511 | 511 | nTotalBlocks = None |
|
|
512 | 512 | |
|
|
513 | 513 | maxTimeStep = 30 |
|
|
514 | 514 | |
|
|
515 | 515 | lastUTTime = None |
|
|
516 | 516 | |
|
|
517 | 517 | datablock = None |
|
|
518 | 518 | |
|
|
519 | 519 | dataOut = None |
|
|
520 | 520 | |
|
|
521 | 521 | blocksize = None |
|
|
522 | 522 | |
|
|
523 | 523 | getByBlock = False |
|
|
524 | 524 | |
|
|
525 | 525 | def __init__(self): |
|
|
526 | 526 | |
|
|
527 | 527 | raise NotImplementedError |
|
|
528 | 528 | |
|
|
529 | 529 | def run(self): |
|
|
530 | 530 | |
|
|
531 | 531 | raise NotImplementedError |
|
|
532 | 532 | |
|
|
533 | 533 | def getDtypeWidth(self): |
|
|
534 | 534 | |
|
|
535 | 535 | dtype_index = get_dtype_index(self.dtype) |
|
|
536 | 536 | dtype_width = get_dtype_width(dtype_index) |
|
|
537 | 537 | |
|
|
538 | 538 | return dtype_width |
|
|
539 | 539 | |
|
|
540 | 540 | def getAllowedArgs(self): |
|
|
541 | 541 | return inspect.getargspec(self.run).args |
|
|
542 | 542 | |
|
|
543 | 543 | class JRODataReader(JRODataIO): |
|
|
544 | 544 | |
|
|
545 | 545 | online = 0 |
|
|
546 | 546 | |
|
|
547 | 547 | realtime = 0 |
|
|
548 | 548 | |
|
|
549 | 549 | nReadBlocks = 0 |
|
|
550 | 550 | |
|
|
551 | 551 | delay = 10 #number of seconds waiting a new file |
|
|
552 | 552 | |
|
|
553 | 553 | nTries = 3 #quantity tries |
|
|
554 | 554 | |
|
|
555 | 555 | nFiles = 3 #number of files for searching |
|
|
556 | 556 | |
|
|
557 | 557 | path = None |
|
|
558 | 558 | |
|
|
559 | 559 | foldercounter = 0 |
|
|
560 | 560 | |
|
|
561 | 561 | flagNoMoreFiles = 0 |
|
|
562 | 562 | |
|
|
563 | 563 | datetimeList = [] |
|
|
564 | 564 | |
|
|
565 | 565 | __isFirstTimeOnline = 1 |
|
|
566 | 566 | |
|
|
567 | 567 | __printInfo = True |
|
|
568 | 568 | |
|
|
569 | 569 | profileIndex = None |
|
|
570 | 570 | |
|
|
571 | 571 | nTxs = 1 |
|
|
572 | 572 | |
|
|
573 | 573 | txIndex = None |
|
|
574 | 574 | |
|
|
575 | 575 | #Added-------------------- |
|
|
576 | 576 | |
|
|
577 | 577 | selBlocksize = None |
|
|
578 | 578 | |
|
|
579 | 579 | selBlocktime = None |
|
|
580 | 580 | |
|
|
581 | 581 | def __init__(self): |
|
|
582 | 582 | |
|
|
583 | 583 | """ |
|
|
584 | 584 | This class is used to find data files |
|
|
585 | 585 | |
|
|
586 | 586 | Example: |
|
|
587 | 587 | reader = JRODataReader() |
|
|
588 | 588 | fileList = reader.findDataFiles() |
|
|
589 | 589 | |
|
|
590 | 590 | """ |
|
|
591 | 591 | pass |
|
|
592 | 592 | |
|
|
593 | 593 | |
|
|
594 | 594 | def createObjByDefault(self): |
|
|
595 | 595 | """ |
|
|
596 | 596 | |
|
|
597 | 597 | """ |
|
|
598 | 598 | raise NotImplementedError |
|
|
599 | 599 | |
|
|
600 | 600 | def getBlockDimension(self): |
|
|
601 | 601 | |
|
|
602 | 602 | raise NotImplementedError |
|
|
603 | 603 | |
|
|
604 | 604 | def searchFilesOffLine(self, |
|
|
605 | 605 | path, |
|
|
606 | 606 | startDate=None, |
|
|
607 | 607 | endDate=None, |
|
|
608 | 608 | startTime=datetime.time(0,0,0), |
|
|
609 | 609 | endTime=datetime.time(23,59,59), |
|
|
610 | 610 | set=None, |
|
|
611 | 611 | expLabel='', |
|
|
612 | 612 | ext='.r', |
|
|
613 | 613 | cursor=None, |
|
|
614 | 614 | skip=None, |
|
|
615 | 615 | walk=True): |
|
|
616 | 616 | |
|
|
617 | 617 | self.filenameList = [] |
|
|
618 | 618 | self.datetimeList = [] |
|
|
619 | 619 | |
|
|
620 | 620 | pathList = [] |
|
|
621 | 621 | |
|
|
622 | 622 | dateList, pathList = self.findDatafiles(path, startDate, endDate, expLabel, ext, walk, include_path=True) |
|
|
623 | 623 | |
|
|
624 | 624 | if dateList == []: |
|
|
625 | 625 | return [], [] |
|
|
626 | 626 | |
|
|
627 | 627 | if len(dateList) > 1: |
|
|
628 | 628 | print "[Reading] Data found for date range [%s - %s]: total days = %d" %(startDate, endDate, len(dateList)) |
|
|
629 | 629 | else: |
|
|
630 | 630 | print "[Reading] Data found for date range [%s - %s]: date = %s" %(startDate, endDate, dateList[0]) |
|
|
631 | 631 | |
|
|
632 | 632 | filenameList = [] |
|
|
633 | 633 | datetimeList = [] |
|
|
634 | 634 | |
|
|
635 | 635 | for thisPath in pathList: |
|
|
636 | 636 | |
|
|
637 | 637 | fileList = glob.glob1(thisPath, "*%s" %ext) |
|
|
638 | 638 | fileList.sort() |
|
|
639 | 639 | |
|
|
640 | 640 | skippedFileList = [] |
|
|
641 | 641 | |
|
|
642 | 642 | if cursor is not None and skip is not None: |
|
|
643 | 643 | |
|
|
644 | 644 | if skip == 0: |
|
|
645 | 645 | skippedFileList = [] |
|
|
646 | 646 | else: |
|
|
647 | 647 | skippedFileList = fileList[cursor*skip: cursor*skip + skip] |
|
|
648 | 648 | |
|
|
649 | 649 | else: |
|
|
650 | 650 | skippedFileList = fileList |
|
|
651 | 651 | |
|
|
652 | 652 | for file in skippedFileList: |
|
|
653 | 653 | |
|
|
654 | 654 | filename = os.path.join(thisPath,file) |
|
|
655 | 655 | |
|
|
656 | 656 | if not isFileInDateRange(filename, startDate, endDate): |
|
|
657 | 657 | continue |
|
|
658 | 658 | |
|
|
659 | 659 | thisDatetime = isFileInTimeRange(filename, startDate, endDate, startTime, endTime) |
|
|
660 | 660 | |
|
|
661 | 661 | if not(thisDatetime): |
|
|
662 | 662 | continue |
|
|
663 | 663 | |
|
|
664 | 664 | filenameList.append(filename) |
|
|
665 | 665 | datetimeList.append(thisDatetime) |
|
|
666 | 666 | |
|
|
667 | 667 | if not(filenameList): |
|
|
668 | 668 | print "[Reading] Time range selected invalid [%s - %s]: No *%s files in %s)" %(startTime, endTime, ext, path) |
|
|
669 | 669 | return [], [] |
|
|
670 | 670 | |
|
|
671 | 671 | print "[Reading] %d file(s) was(were) found in time range: %s - %s" %(len(filenameList), startTime, endTime) |
|
|
672 | 672 | |
|
|
673 | 673 | |
|
|
674 | 674 | # for i in range(len(filenameList)): |
|
|
675 | 675 | # print "[Reading] %s -> [%s]" %(filenameList[i], datetimeList[i].ctime()) |
|
|
676 | 676 | |
|
|
677 | 677 | self.filenameList = filenameList |
|
|
678 | 678 | self.datetimeList = datetimeList |
|
|
679 | 679 | |
|
|
680 | 680 | return pathList, filenameList |
|
|
681 | 681 | |
|
|
682 | 682 | def __searchFilesOnLine(self, path, expLabel = "", ext = None, walk=True, set=None): |
|
|
683 | 683 | |
|
|
684 | 684 | """ |
|
|
685 | 685 | Busca el ultimo archivo de la ultima carpeta (determinada o no por startDateTime) y |
|
|
686 | 686 | devuelve el archivo encontrado ademas de otros datos. |
|
|
687 | 687 | |
|
|
688 | 688 | Input: |
|
|
689 | 689 | path : carpeta donde estan contenidos los files que contiene data |
|
|
690 | 690 | |
|
|
691 | 691 | expLabel : Nombre del subexperimento (subfolder) |
|
|
692 | 692 | |
|
|
693 | 693 | ext : extension de los files |
|
|
694 | 694 | |
|
|
695 | 695 | walk : Si es habilitado no realiza busquedas dentro de los ubdirectorios (doypath) |
|
|
696 | 696 | |
|
|
697 | 697 | Return: |
|
|
698 | 698 | directory : eL directorio donde esta el file encontrado |
|
|
699 | 699 | filename : el ultimo file de una determinada carpeta |
|
|
700 | 700 | year : el anho |
|
|
701 | 701 | doy : el numero de dia del anho |
|
|
702 | 702 | set : el set del archivo |
|
|
703 | 703 | |
|
|
704 | 704 | |
|
|
705 | 705 | """ |
|
|
706 | 706 | if not os.path.isdir(path): |
|
|
707 | 707 | return None, None, None, None, None, None |
|
|
708 | 708 | |
|
|
709 | 709 | dirList = [] |
|
|
710 | 710 | |
|
|
711 | 711 | if not walk: |
|
|
712 | 712 | fullpath = path |
|
|
713 | 713 | foldercounter = 0 |
|
|
714 | 714 | else: |
|
|
715 | 715 | #Filtra solo los directorios |
|
|
716 | 716 | for thisPath in os.listdir(path): |
|
|
717 | 717 | if not os.path.isdir(os.path.join(path,thisPath)): |
|
|
718 | 718 | continue |
|
|
719 | 719 | if not isRadarFolder(thisPath): |
|
|
720 | 720 | continue |
|
|
721 | 721 | |
|
|
722 | 722 | dirList.append(thisPath) |
|
|
723 | 723 | |
|
|
724 | 724 | if not(dirList): |
|
|
725 | 725 | return None, None, None, None, None, None |
|
|
726 | 726 | |
|
|
727 | 727 | dirList = sorted( dirList, key=str.lower ) |
|
|
728 | 728 | |
|
|
729 | 729 | doypath = dirList[-1] |
|
|
730 | 730 | foldercounter = int(doypath.split('_')[1]) if len(doypath.split('_'))>1 else 0 |
|
|
731 | 731 | fullpath = os.path.join(path, doypath, expLabel) |
|
|
732 | 732 | |
|
|
733 | 733 | |
|
|
734 | 734 | print "[Reading] %s folder was found: " %(fullpath ) |
|
|
735 | 735 | |
|
|
736 | 736 | if set == None: |
|
|
737 | 737 | filename = getlastFileFromPath(fullpath, ext) |
|
|
738 | 738 | else: |
|
|
739 | 739 | filename = getFileFromSet(fullpath, ext, set) |
|
|
740 | 740 | |
|
|
741 | 741 | if not(filename): |
|
|
742 | 742 | return None, None, None, None, None, None |
|
|
743 | 743 | |
|
|
744 | 744 | print "[Reading] %s file was found" %(filename) |
|
|
745 | 745 | |
|
|
746 | 746 | if not(self.__verifyFile(os.path.join(fullpath, filename))): |
|
|
747 | 747 | return None, None, None, None, None, None |
|
|
748 | 748 | |
|
|
749 | 749 | year = int( filename[1:5] ) |
|
|
750 | 750 | doy = int( filename[5:8] ) |
|
|
751 | 751 | set = int( filename[8:11] ) |
|
|
752 | 752 | |
|
|
753 | 753 | return fullpath, foldercounter, filename, year, doy, set |
|
|
754 | 754 | |
|
|
755 | 755 | def __setNextFileOffline(self): |
|
|
756 | 756 | |
|
|
757 | 757 | idFile = self.fileIndex |
|
|
758 | 758 | |
|
|
759 | 759 | while (True): |
|
|
760 | 760 | idFile += 1 |
|
|
761 | 761 | if not(idFile < len(self.filenameList)): |
|
|
762 | 762 | self.flagNoMoreFiles = 1 |
|
|
763 | 763 | # print "[Reading] No more Files" |
|
|
764 | 764 | return 0 |
|
|
765 | 765 | |
|
|
766 | 766 | filename = self.filenameList[idFile] |
|
|
767 | 767 | |
|
|
768 | 768 | if not(self.__verifyFile(filename)): |
|
|
769 | 769 | continue |
|
|
770 | 770 | |
|
|
771 | 771 | fileSize = os.path.getsize(filename) |
|
|
772 | 772 | fp = open(filename,'rb') |
|
|
773 | 773 | break |
|
|
774 | 774 | |
|
|
775 | 775 | self.flagIsNewFile = 1 |
|
|
776 | 776 | self.fileIndex = idFile |
|
|
777 | 777 | self.filename = filename |
|
|
778 | 778 | self.fileSize = fileSize |
|
|
779 | 779 | self.fp = fp |
|
|
780 | 780 | |
|
|
781 | 781 | # print "[Reading] Setting the file: %s"%self.filename |
|
|
782 | 782 | |
|
|
783 | 783 | return 1 |
|
|
784 | 784 | |
|
|
785 | 785 | def __setNextFileOnline(self): |
|
|
786 | 786 | """ |
|
|
787 | 787 | Busca el siguiente file que tenga suficiente data para ser leida, dentro de un folder especifico, si |
|
|
788 | 788 | no encuentra un file valido espera un tiempo determinado y luego busca en los posibles n files |
|
|
789 | 789 | siguientes. |
|
|
790 | 790 | |
|
|
791 | 791 | Affected: |
|
|
792 | 792 | self.flagIsNewFile |
|
|
793 | 793 | self.filename |
|
|
794 | 794 | self.fileSize |
|
|
795 | 795 | self.fp |
|
|
796 | 796 | self.set |
|
|
797 | 797 | self.flagNoMoreFiles |
|
|
798 | 798 | |
|
|
799 | 799 | Return: |
|
|
800 | 800 | 0 : si luego de una busqueda del siguiente file valido este no pudo ser encontrado |
|
|
801 | 801 | 1 : si el file fue abierto con exito y esta listo a ser leido |
|
|
802 | 802 | |
|
|
803 | 803 | Excepciones: |
|
|
804 | 804 | Si un determinado file no puede ser abierto |
|
|
805 | 805 | """ |
|
|
806 | 806 | nFiles = 0 |
|
|
807 | 807 | fileOk_flag = False |
|
|
808 | 808 | firstTime_flag = True |
|
|
809 | 809 | |
|
|
810 | 810 | self.set += 1 |
|
|
811 | 811 | |
|
|
812 | 812 | if self.set > 999: |
|
|
813 | 813 | self.set = 0 |
|
|
814 | 814 | self.foldercounter += 1 |
|
|
815 | 815 | |
|
|
816 | 816 | #busca el 1er file disponible |
|
|
817 | 817 | fullfilename, filename = checkForRealPath( self.path, self.foldercounter, self.year, self.doy, self.set, self.ext ) |
|
|
818 | 818 | if fullfilename: |
|
|
819 | 819 | if self.__verifyFile(fullfilename, False): |
|
|
820 | 820 | fileOk_flag = True |
|
|
821 | 821 | |
|
|
822 | 822 | #si no encuentra un file entonces espera y vuelve a buscar |
|
|
823 | 823 | if not(fileOk_flag): |
|
|
824 | 824 | for nFiles in range(self.nFiles+1): #busco en los siguientes self.nFiles+1 files posibles |
|
|
825 | 825 | |
|
|
826 | 826 | if firstTime_flag: #si es la 1era vez entonces hace el for self.nTries veces |
|
|
827 | 827 | tries = self.nTries |
|
|
828 | 828 | else: |
|
|
829 | 829 | tries = 1 #si no es la 1era vez entonces solo lo hace una vez |
|
|
830 | 830 | |
|
|
831 | 831 | for nTries in range( tries ): |
|
|
832 | 832 | if firstTime_flag: |
|
|
833 | 833 | print "\t[Reading] Waiting %0.2f sec for the next file: \"%s\" , try %03d ..." % ( self.delay, filename, nTries+1 ) |
|
|
834 | 834 | sleep( self.delay ) |
|
|
835 | 835 | else: |
|
|
836 | 836 | print "\t[Reading] Searching the next \"%s%04d%03d%03d%s\" file ..." % (self.optchar, self.year, self.doy, self.set, self.ext) |
|
|
837 | 837 | |
|
|
838 | 838 | fullfilename, filename = checkForRealPath( self.path, self.foldercounter, self.year, self.doy, self.set, self.ext ) |
|
|
839 | 839 | if fullfilename: |
|
|
840 | 840 | if self.__verifyFile(fullfilename): |
|
|
841 | 841 | fileOk_flag = True |
|
|
842 | 842 | break |
|
|
843 | 843 | |
|
|
844 | 844 | if fileOk_flag: |
|
|
845 | 845 | break |
|
|
846 | 846 | |
|
|
847 | 847 | firstTime_flag = False |
|
|
848 | 848 | |
|
|
849 | 849 | print "\t[Reading] Skipping the file \"%s\" due to this file doesn't exist" % filename |
|
|
850 | 850 | self.set += 1 |
|
|
851 | 851 | |
|
|
852 | 852 | if nFiles == (self.nFiles-1): #si no encuentro el file buscado cambio de carpeta y busco en la siguiente carpeta |
|
|
853 | 853 | self.set = 0 |
|
|
854 | 854 | self.doy += 1 |
|
|
855 | 855 | self.foldercounter = 0 |
|
|
856 | 856 | |
|
|
857 | 857 | if fileOk_flag: |
|
|
858 | 858 | self.fileSize = os.path.getsize( fullfilename ) |
|
|
859 | 859 | self.filename = fullfilename |
|
|
860 | 860 | self.flagIsNewFile = 1 |
|
|
861 | 861 | if self.fp != None: self.fp.close() |
|
|
862 | 862 | self.fp = open(fullfilename, 'rb') |
|
|
863 | 863 | self.flagNoMoreFiles = 0 |
|
|
864 | 864 | # print '[Reading] Setting the file: %s' % fullfilename |
|
|
865 | 865 | else: |
|
|
866 | 866 | self.fileSize = 0 |
|
|
867 | 867 | self.filename = None |
|
|
868 | 868 | self.flagIsNewFile = 0 |
|
|
869 | 869 | self.fp = None |
|
|
870 | 870 | self.flagNoMoreFiles = 1 |
|
|
871 | 871 | # print '[Reading] No more files to read' |
|
|
872 | 872 | |
|
|
873 | 873 | return fileOk_flag |
|
|
874 | 874 | |
|
|
875 | 875 | def setNextFile(self): |
|
|
876 | 876 | if self.fp != None: |
|
|
877 | 877 | self.fp.close() |
|
|
878 | 878 | |
|
|
879 | 879 | if self.online: |
|
|
880 | 880 | newFile = self.__setNextFileOnline() |
|
|
881 | 881 | else: |
|
|
882 | 882 | newFile = self.__setNextFileOffline() |
|
|
883 | 883 | |
|
|
884 | 884 | if not(newFile): |
|
|
885 | 885 | print '[Reading] No more files to read' |
|
|
886 | 886 | return 0 |
|
|
887 | 887 | |
|
|
888 | 888 | if self.verbose: |
|
|
889 | 889 | print '[Reading] Setting the file: %s' % self.filename |
|
|
890 | 890 | |
|
|
891 | 891 | self.__readFirstHeader() |
|
|
892 | 892 | self.nReadBlocks = 0 |
|
|
893 | 893 | return 1 |
|
|
894 | 894 | |
|
|
895 | 895 | def __waitNewBlock(self): |
|
|
896 | 896 | """ |
|
|
897 | 897 | Return 1 si se encontro un nuevo bloque de datos, 0 de otra forma. |
|
|
898 | 898 | |
|
|
899 | 899 | Si el modo de lectura es OffLine siempre retorn 0 |
|
|
900 | 900 | """ |
|
|
901 | 901 | if not self.online: |
|
|
902 | 902 | return 0 |
|
|
903 | 903 | |
|
|
904 | 904 | if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile): |
|
|
905 | 905 | return 0 |
|
|
906 | 906 | |
|
|
907 | 907 | currentPointer = self.fp.tell() |
|
|
908 | 908 | |
|
|
909 | 909 | neededSize = self.processingHeaderObj.blockSize + self.basicHeaderSize |
|
|
910 | 910 | |
|
|
911 | 911 | for nTries in range( self.nTries ): |
|
|
912 | 912 | |
|
|
913 | 913 | self.fp.close() |
|
|
914 | 914 | self.fp = open( self.filename, 'rb' ) |
|
|
915 | 915 | self.fp.seek( currentPointer ) |
|
|
916 | 916 | |
|
|
917 | 917 | self.fileSize = os.path.getsize( self.filename ) |
|
|
918 | 918 | currentSize = self.fileSize - currentPointer |
|
|
919 | 919 | |
|
|
920 | 920 | if ( currentSize >= neededSize ): |
|
|
921 | 921 | self.basicHeaderObj.read(self.fp) |
|
|
922 | 922 | return 1 |
|
|
923 | 923 | |
|
|
924 | 924 | if self.fileSize == self.fileSizeByHeader: |
|
|
925 | 925 | # self.flagEoF = True |
|
|
926 | 926 | return 0 |
|
|
927 | 927 | |
|
|
928 | 928 | print "[Reading] Waiting %0.2f seconds for the next block, try %03d ..." % (self.delay, nTries+1) |
|
|
929 | 929 | sleep( self.delay ) |
|
|
930 | 930 | |
|
|
931 | 931 | |
|
|
932 | 932 | return 0 |
|
|
933 | 933 | |
|
|
934 | 934 | def waitDataBlock(self,pointer_location): |
|
|
935 | 935 | |
|
|
936 | 936 | currentPointer = pointer_location |
|
|
937 | 937 | |
|
|
938 | 938 | neededSize = self.processingHeaderObj.blockSize #+ self.basicHeaderSize |
|
|
939 | 939 | |
|
|
940 | 940 | for nTries in range( self.nTries ): |
|
|
941 | 941 | self.fp.close() |
|
|
942 | 942 | self.fp = open( self.filename, 'rb' ) |
|
|
943 | 943 | self.fp.seek( currentPointer ) |
|
|
944 | 944 | |
|
|
945 | 945 | self.fileSize = os.path.getsize( self.filename ) |
|
|
946 | 946 | currentSize = self.fileSize - currentPointer |
|
|
947 | 947 | |
|
|
948 | 948 | if ( currentSize >= neededSize ): |
|
|
949 | 949 | return 1 |
|
|
950 | 950 | |
|
|
951 | 951 | print "[Reading] Waiting %0.2f seconds for the next block, try %03d ..." % (self.delay, nTries+1) |
|
|
952 | 952 | sleep( self.delay ) |
|
|
953 | 953 | |
|
|
954 | 954 | return 0 |
|
|
955 | 955 | |
|
|
956 | 956 | def __jumpToLastBlock(self): |
|
|
957 | 957 | |
|
|
958 | 958 | if not(self.__isFirstTimeOnline): |
|
|
959 | 959 | return |
|
|
960 | 960 | |
|
|
961 | 961 | csize = self.fileSize - self.fp.tell() |
|
|
962 | 962 | blocksize = self.processingHeaderObj.blockSize |
|
|
963 | 963 | |
|
|
964 | 964 | #salta el primer bloque de datos |
|
|
965 | 965 | if csize > self.processingHeaderObj.blockSize: |
|
|
966 | 966 | self.fp.seek(self.fp.tell() + blocksize) |
|
|
967 | 967 | else: |
|
|
968 | 968 | return |
|
|
969 | 969 | |
|
|
970 | 970 | csize = self.fileSize - self.fp.tell() |
|
|
971 | 971 | neededsize = self.processingHeaderObj.blockSize + self.basicHeaderSize |
|
|
972 | 972 | while True: |
|
|
973 | 973 | |
|
|
974 | 974 | if self.fp.tell()<self.fileSize: |
|
|
975 | 975 | self.fp.seek(self.fp.tell() + neededsize) |
|
|
976 | 976 | else: |
|
|
977 | 977 | self.fp.seek(self.fp.tell() - neededsize) |
|
|
978 | 978 | break |
|
|
979 | 979 | |
|
|
980 | 980 | # csize = self.fileSize - self.fp.tell() |
|
|
981 | 981 | # neededsize = self.processingHeaderObj.blockSize + self.basicHeaderSize |
|
|
982 | 982 | # factor = int(csize/neededsize) |
|
|
983 | 983 | # if factor > 0: |
|
|
984 | 984 | # self.fp.seek(self.fp.tell() + factor*neededsize) |
|
|
985 | 985 | |
|
|
986 | 986 | self.flagIsNewFile = 0 |
|
|
987 | 987 | self.__isFirstTimeOnline = 0 |
|
|
988 | 988 | |
|
|
989 | 989 | def __setNewBlock(self): |
|
|
990 | 990 | #if self.server is None: |
|
|
991 | 991 | if self.fp == None: |
|
|
992 | 992 | return 0 |
|
|
993 | 993 | |
|
|
994 | 994 | # if self.online: |
|
|
995 | 995 | # self.__jumpToLastBlock() |
|
|
996 | 996 | |
|
|
997 | 997 | if self.flagIsNewFile: |
|
|
998 | 998 | self.lastUTTime = self.basicHeaderObj.utc |
|
|
999 | 999 | return 1 |
|
|
1000 | 1000 | |
|
|
1001 | 1001 | if self.realtime: |
|
|
1002 | 1002 | self.flagDiscontinuousBlock = 1 |
|
|
1003 | 1003 | if not(self.setNextFile()): |
|
|
1004 | 1004 | return 0 |
|
|
1005 | 1005 | else: |
|
|
1006 | 1006 | return 1 |
|
|
1007 | 1007 | #if self.server is None: |
|
|
1008 | 1008 | currentSize = self.fileSize - self.fp.tell() |
|
|
1009 | 1009 | neededSize = self.processingHeaderObj.blockSize + self.basicHeaderSize |
|
|
1010 | 1010 | if (currentSize >= neededSize): |
|
|
1011 | 1011 | self.basicHeaderObj.read(self.fp) |
|
|
1012 | 1012 | self.lastUTTime = self.basicHeaderObj.utc |
|
|
1013 | 1013 | return 1 |
|
|
1014 | 1014 | # else: |
|
|
1015 | 1015 | # self.basicHeaderObj.read(self.zHeader) |
|
|
1016 | 1016 | # self.lastUTTime = self.basicHeaderObj.utc |
|
|
1017 | 1017 | # return 1 |
|
|
1018 | 1018 | if self.__waitNewBlock(): |
|
|
1019 | 1019 | self.lastUTTime = self.basicHeaderObj.utc |
|
|
1020 | 1020 | return 1 |
|
|
1021 | 1021 | #if self.server is None: |
|
|
1022 | 1022 | if not(self.setNextFile()): |
|
|
1023 | 1023 | return 0 |
|
|
1024 | 1024 | |
|
|
1025 | 1025 | deltaTime = self.basicHeaderObj.utc - self.lastUTTime # |
|
|
1026 | 1026 | self.lastUTTime = self.basicHeaderObj.utc |
|
|
1027 | 1027 | |
|
|
1028 | 1028 | self.flagDiscontinuousBlock = 0 |
|
|
1029 | 1029 | |
|
|
1030 | 1030 | if deltaTime > self.maxTimeStep: |
|
|
1031 | 1031 | self.flagDiscontinuousBlock = 1 |
|
|
1032 | 1032 | |
|
|
1033 | 1033 | return 1 |
|
|
1034 | 1034 | |
|
|
1035 | 1035 | def readNextBlock(self): |
|
|
1036 | 1036 | |
|
|
1037 | 1037 | #Skip block out of startTime and endTime |
|
|
1038 | 1038 | while True: |
|
|
1039 | 1039 | if not(self.__setNewBlock()): |
|
|
1040 | 1040 | return 0 |
|
|
1041 | 1041 | |
|
|
1042 | 1042 | if not(self.readBlock()): |
|
|
1043 | 1043 | return 0 |
|
|
1044 | 1044 | |
|
|
1045 | 1045 | self.getBasicHeader() |
|
|
1046 | 1046 | |
|
|
1047 | 1047 | if not isTimeInRange(self.dataOut.datatime.time(), self.startTime, self.endTime): |
|
|
1048 | 1048 | |
|
|
1049 | 1049 | print "[Reading] Block No. %d/%d -> %s [Skipping]" %(self.nReadBlocks, |
|
|
1050 | 1050 | self.processingHeaderObj.dataBlocksPerFile, |
|
|
1051 | 1051 | self.dataOut.datatime.ctime()) |
|
|
1052 | 1052 | continue |
|
|
1053 | 1053 | |
|
|
1054 | 1054 | break |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | if self.verbose: |
|
|
1057 | 1057 | print "[Reading] Block No. %d/%d -> %s" %(self.nReadBlocks, |
|
|
1058 | 1058 | self.processingHeaderObj.dataBlocksPerFile, |
|
|
1059 | 1059 | self.dataOut.datatime.ctime()) |
|
|
1060 | 1060 | return 1 |
|
|
1061 | 1061 | |
|
|
1062 | 1062 | def __readFirstHeader(self): |
|
|
1063 | 1063 | |
|
|
1064 | 1064 | self.basicHeaderObj.read(self.fp) |
|
|
1065 | 1065 | self.systemHeaderObj.read(self.fp) |
|
|
1066 | 1066 | self.radarControllerHeaderObj.read(self.fp) |
|
|
1067 | 1067 | self.processingHeaderObj.read(self.fp) |
|
|
1068 | 1068 | |
|
|
1069 | 1069 | self.firstHeaderSize = self.basicHeaderObj.size |
|
|
1070 | 1070 | |
|
|
1071 | 1071 | datatype = int(numpy.log2((self.processingHeaderObj.processFlags & PROCFLAG.DATATYPE_MASK))-numpy.log2(PROCFLAG.DATATYPE_CHAR)) |
|
|
1072 | 1072 | if datatype == 0: |
|
|
1073 | 1073 | datatype_str = numpy.dtype([('real','<i1'),('imag','<i1')]) |
|
|
1074 | 1074 | elif datatype == 1: |
|
|
1075 | 1075 | datatype_str = numpy.dtype([('real','<i2'),('imag','<i2')]) |
|
|
1076 | 1076 | elif datatype == 2: |
|
|
1077 | 1077 | datatype_str = numpy.dtype([('real','<i4'),('imag','<i4')]) |
|
|
1078 | 1078 | elif datatype == 3: |
|
|
1079 | 1079 | datatype_str = numpy.dtype([('real','<i8'),('imag','<i8')]) |
|
|
1080 | 1080 | elif datatype == 4: |
|
|
1081 | 1081 | datatype_str = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
1082 | 1082 | elif datatype == 5: |
|
|
1083 | 1083 | datatype_str = numpy.dtype([('real','<f8'),('imag','<f8')]) |
|
|
1084 | 1084 | else: |
|
|
1085 | 1085 | raise ValueError, 'Data type was not defined' |
|
|
1086 | 1086 | |
|
|
1087 | 1087 | self.dtype = datatype_str |
|
|
1088 | 1088 | #self.ippSeconds = 2 * 1000 * self.radarControllerHeaderObj.ipp / self.c |
|
|
1089 | 1089 | self.fileSizeByHeader = self.processingHeaderObj.dataBlocksPerFile * self.processingHeaderObj.blockSize + self.firstHeaderSize + self.basicHeaderSize*(self.processingHeaderObj.dataBlocksPerFile - 1) |
|
|
1090 | 1090 | # self.dataOut.channelList = numpy.arange(self.systemHeaderObj.numChannels) |
|
|
1091 | 1091 | # self.dataOut.channelIndexList = numpy.arange(self.systemHeaderObj.numChannels) |
|
|
1092 | 1092 | self.getBlockDimension() |
|
|
1093 | 1093 | |
|
|
1094 | 1094 | def __verifyFile(self, filename, msgFlag=True): |
|
|
1095 | 1095 | |
|
|
1096 | 1096 | msg = None |
|
|
1097 | 1097 | |
|
|
1098 | 1098 | try: |
|
|
1099 | 1099 | fp = open(filename, 'rb') |
|
|
1100 | 1100 | except IOError: |
|
|
1101 | 1101 | |
|
|
1102 | 1102 | if msgFlag: |
|
|
1103 | 1103 | print "[Reading] File %s can't be opened" % (filename) |
|
|
1104 | 1104 | |
|
|
1105 | 1105 | return False |
|
|
1106 | 1106 | |
|
|
1107 | 1107 | currentPosition = fp.tell() |
|
|
1108 | 1108 | neededSize = self.processingHeaderObj.blockSize + self.firstHeaderSize |
|
|
1109 | 1109 | |
|
|
1110 | 1110 | if neededSize == 0: |
|
|
1111 | 1111 | basicHeaderObj = BasicHeader(LOCALTIME) |
|
|
1112 | 1112 | systemHeaderObj = SystemHeader() |
|
|
1113 | 1113 | radarControllerHeaderObj = RadarControllerHeader() |
|
|
1114 | 1114 | processingHeaderObj = ProcessingHeader() |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | if not( basicHeaderObj.read(fp) ): |
|
|
1117 | 1117 | fp.close() |
|
|
1118 | 1118 | return False |
|
|
1119 | 1119 | |
|
|
1120 | 1120 | if not( systemHeaderObj.read(fp) ): |
|
|
1121 | 1121 | fp.close() |
|
|
1122 | 1122 | return False |
|
|
1123 | 1123 | |
|
|
1124 | 1124 | if not( radarControllerHeaderObj.read(fp) ): |
|
|
1125 | 1125 | fp.close() |
|
|
1126 | 1126 | return False |
|
|
1127 | 1127 | |
|
|
1128 | 1128 | if not( processingHeaderObj.read(fp) ): |
|
|
1129 | 1129 | fp.close() |
|
|
1130 | 1130 | return False |
|
|
1131 | 1131 | |
|
|
1132 | 1132 | neededSize = processingHeaderObj.blockSize + basicHeaderObj.size |
|
|
1133 | 1133 | else: |
|
|
1134 | 1134 | msg = "[Reading] Skipping the file %s due to it hasn't enough data" %filename |
|
|
1135 | 1135 | |
|
|
1136 | 1136 | fp.close() |
|
|
1137 | 1137 | |
|
|
1138 | 1138 | fileSize = os.path.getsize(filename) |
|
|
1139 | 1139 | currentSize = fileSize - currentPosition |
|
|
1140 | 1140 | |
|
|
1141 | 1141 | if currentSize < neededSize: |
|
|
1142 | 1142 | if msgFlag and (msg != None): |
|
|
1143 | 1143 | print msg |
|
|
1144 | 1144 | return False |
|
|
1145 | 1145 | |
|
|
1146 | 1146 | return True |
|
|
1147 | 1147 | |
|
|
1148 | 1148 | def findDatafiles(self, path, startDate=None, endDate=None, expLabel='', ext='.r', walk=True, include_path=False): |
|
|
1149 | 1149 | |
|
|
1150 | 1150 | path_empty = True |
|
|
1151 | 1151 | |
|
|
1152 | 1152 | dateList = [] |
|
|
1153 | 1153 | pathList = [] |
|
|
1154 | 1154 | |
|
|
1155 | 1155 | multi_path = path.split(',') |
|
|
1156 | 1156 | |
|
|
1157 | 1157 | if not walk: |
|
|
1158 | 1158 | |
|
|
1159 | 1159 | for single_path in multi_path: |
|
|
1160 | 1160 | |
|
|
1161 | 1161 | if not os.path.isdir(single_path): |
|
|
1162 | 1162 | continue |
|
|
1163 | 1163 | |
|
|
1164 | 1164 | fileList = glob.glob1(single_path, "*"+ext) |
|
|
1165 | 1165 | |
|
|
1166 | 1166 | if not fileList: |
|
|
1167 | 1167 | continue |
|
|
1168 | 1168 | |
|
|
1169 | 1169 | path_empty = False |
|
|
1170 | 1170 | |
|
|
1171 | 1171 | fileList.sort() |
|
|
1172 | 1172 | |
|
|
1173 | 1173 | for thisFile in fileList: |
|
|
1174 | 1174 | |
|
|
1175 | 1175 | if not os.path.isfile(os.path.join(single_path, thisFile)): |
|
|
1176 | 1176 | continue |
|
|
1177 | 1177 | |
|
|
1178 | 1178 | if not isRadarFile(thisFile): |
|
|
1179 | 1179 | continue |
|
|
1180 | 1180 | |
|
|
1181 | 1181 | if not isFileInDateRange(thisFile, startDate, endDate): |
|
|
1182 | 1182 | continue |
|
|
1183 | 1183 | |
|
|
1184 | 1184 | thisDate = getDateFromRadarFile(thisFile) |
|
|
1185 | 1185 | |
|
|
1186 | 1186 | if thisDate in dateList: |
|
|
1187 | 1187 | continue |
|
|
1188 | 1188 | |
|
|
1189 | 1189 | dateList.append(thisDate) |
|
|
1190 | 1190 | pathList.append(single_path) |
|
|
1191 | 1191 | |
|
|
1192 | 1192 | else: |
|
|
1193 | 1193 | for single_path in multi_path: |
|
|
1194 | 1194 | |
|
|
1195 | 1195 | if not os.path.isdir(single_path): |
|
|
1196 | 1196 | continue |
|
|
1197 | 1197 | |
|
|
1198 | 1198 | dirList = [] |
|
|
1199 | 1199 | |
|
|
1200 | 1200 | for thisPath in os.listdir(single_path): |
|
|
1201 | 1201 | |
|
|
1202 | 1202 | if not os.path.isdir(os.path.join(single_path,thisPath)): |
|
|
1203 | 1203 | continue |
|
|
1204 | 1204 | |
|
|
1205 | 1205 | if not isRadarFolder(thisPath): |
|
|
1206 | 1206 | continue |
|
|
1207 | 1207 | |
|
|
1208 | 1208 | if not isFolderInDateRange(thisPath, startDate, endDate): |
|
|
1209 | 1209 | continue |
|
|
1210 | 1210 | |
|
|
1211 | 1211 | dirList.append(thisPath) |
|
|
1212 | 1212 | |
|
|
1213 | 1213 | if not dirList: |
|
|
1214 | 1214 | continue |
|
|
1215 | 1215 | |
|
|
1216 | 1216 | dirList.sort() |
|
|
1217 | 1217 | |
|
|
1218 | 1218 | for thisDir in dirList: |
|
|
1219 | 1219 | |
|
|
1220 | 1220 | datapath = os.path.join(single_path, thisDir, expLabel) |
|
|
1221 | 1221 | fileList = glob.glob1(datapath, "*"+ext) |
|
|
1222 | 1222 | |
|
|
1223 | 1223 | if not fileList: |
|
|
1224 | 1224 | continue |
|
|
1225 | 1225 | |
|
|
1226 | 1226 | path_empty = False |
|
|
1227 | 1227 | |
|
|
1228 | 1228 | thisDate = getDateFromRadarFolder(thisDir) |
|
|
1229 | 1229 | |
|
|
1230 | 1230 | pathList.append(datapath) |
|
|
1231 | 1231 | dateList.append(thisDate) |
|
|
1232 | 1232 | |
|
|
1233 | 1233 | dateList.sort() |
|
|
1234 | 1234 | |
|
|
1235 | 1235 | if walk: |
|
|
1236 | 1236 | pattern_path = os.path.join(multi_path[0], "[dYYYYDDD]", expLabel) |
|
|
1237 | 1237 | else: |
|
|
1238 | 1238 | pattern_path = multi_path[0] |
|
|
1239 | 1239 | |
|
|
1240 | 1240 | if path_empty: |
|
|
1241 | 1241 | print "[Reading] No *%s files in %s for %s to %s" %(ext, pattern_path, startDate, endDate) |
|
|
1242 | 1242 | else: |
|
|
1243 | 1243 | if not dateList: |
|
|
1244 | 1244 | print "[Reading] Date range selected invalid [%s - %s]: No *%s files in %s)" %(startDate, endDate, ext, path) |
|
|
1245 | 1245 | |
|
|
1246 | 1246 | if include_path: |
|
|
1247 | 1247 | return dateList, pathList |
|
|
1248 | 1248 | |
|
|
1249 | 1249 | return dateList |
|
|
1250 | 1250 | |
|
|
1251 | 1251 | def setup(self, |
|
|
1252 | 1252 | path=None, |
|
|
1253 | 1253 | startDate=None, |
|
|
1254 | 1254 | endDate=None, |
|
|
1255 | 1255 | startTime=datetime.time(0,0,0), |
|
|
1256 | 1256 | endTime=datetime.time(23,59,59), |
|
|
1257 | 1257 | set=None, |
|
|
1258 | 1258 | expLabel = "", |
|
|
1259 | 1259 | ext = None, |
|
|
1260 | 1260 | online = False, |
|
|
1261 | 1261 | delay = 60, |
|
|
1262 | 1262 | walk = True, |
|
|
1263 | 1263 | getblock = False, |
|
|
1264 | 1264 | nTxs = 1, |
|
|
1265 | 1265 | realtime=False, |
|
|
1266 | 1266 | blocksize=None, |
|
|
1267 | 1267 | blocktime=None, |
|
|
1268 | 1268 | skip=None, |
|
|
1269 | 1269 | cursor=None, |
|
|
1270 | 1270 | warnings=True, |
|
|
1271 | 1271 | verbose=True, |
|
|
1272 |
server=None |
|
|
|
1272 | server=None, | |
|
|
1273 | format=None, | |
|
|
1274 | oneDDict=None, | |
|
|
1275 | twoDDict=None, | |
|
|
1276 | ind2DList=None): | |
|
|
1273 | 1277 | if server is not None: |
|
|
1274 | 1278 | if 'tcp://' in server: |
|
|
1275 | 1279 | address = server |
|
|
1276 | 1280 | else: |
|
|
1277 | 1281 | address = 'ipc:///tmp/%s' % server |
|
|
1278 | 1282 | self.server = address |
|
|
1279 | 1283 | self.context = zmq.Context() |
|
|
1280 | 1284 | self.receiver = self.context.socket(zmq.PULL) |
|
|
1281 | 1285 | self.receiver.connect(self.server) |
|
|
1282 | 1286 | time.sleep(0.5) |
|
|
1283 | 1287 | print '[Starting] ReceiverData from {}'.format(self.server) |
|
|
1284 | 1288 | else: |
|
|
1285 | 1289 | self.server = None |
|
|
1286 | 1290 | if path == None: |
|
|
1287 | 1291 | raise ValueError, "[Reading] The path is not valid" |
|
|
1288 | 1292 | |
|
|
1289 | 1293 | if ext == None: |
|
|
1290 | 1294 | ext = self.ext |
|
|
1291 | 1295 | |
|
|
1292 | 1296 | if online: |
|
|
1293 | 1297 | print "[Reading] Searching files in online mode..." |
|
|
1294 | 1298 | |
|
|
1295 | 1299 | for nTries in range( self.nTries ): |
|
|
1296 | 1300 | fullpath, foldercounter, file, year, doy, set = self.__searchFilesOnLine(path=path, expLabel=expLabel, ext=ext, walk=walk, set=set) |
|
|
1297 | 1301 | |
|
|
1298 | 1302 | if fullpath: |
|
|
1299 | 1303 | break |
|
|
1300 | 1304 | |
|
|
1301 | 1305 | print '[Reading] Waiting %0.2f sec for an valid file in %s: try %02d ...' % (self.delay, path, nTries+1) |
|
|
1302 | 1306 | sleep( self.delay ) |
|
|
1303 | 1307 | |
|
|
1304 | 1308 | if not(fullpath): |
|
|
1305 | 1309 | print "[Reading] There 'isn't any valid file in %s" % path |
|
|
1306 | 1310 | return |
|
|
1307 | 1311 | |
|
|
1308 | 1312 | self.year = year |
|
|
1309 | 1313 | self.doy = doy |
|
|
1310 | 1314 | self.set = set - 1 |
|
|
1311 | 1315 | self.path = path |
|
|
1312 | 1316 | self.foldercounter = foldercounter |
|
|
1313 | 1317 | last_set = None |
|
|
1314 | 1318 | else: |
|
|
1315 | 1319 | print "[Reading] Searching files in offline mode ..." |
|
|
1316 | 1320 | pathList, filenameList = self.searchFilesOffLine(path, startDate=startDate, endDate=endDate, |
|
|
1317 | 1321 | startTime=startTime, endTime=endTime, |
|
|
1318 | 1322 | set=set, expLabel=expLabel, ext=ext, |
|
|
1319 | 1323 | walk=walk, cursor=cursor, |
|
|
1320 | 1324 | skip=skip) |
|
|
1321 | 1325 | |
|
|
1322 | 1326 | if not(pathList): |
|
|
1323 | 1327 | self.fileIndex = -1 |
|
|
1324 | 1328 | self.pathList = [] |
|
|
1325 | 1329 | self.filenameList = [] |
|
|
1326 | 1330 | return |
|
|
1327 | 1331 | |
|
|
1328 | 1332 | self.fileIndex = -1 |
|
|
1329 | 1333 | self.pathList = pathList |
|
|
1330 | 1334 | self.filenameList = filenameList |
|
|
1331 | 1335 | file_name = os.path.basename(filenameList[-1]) |
|
|
1332 | 1336 | basename, ext = os.path.splitext(file_name) |
|
|
1333 | 1337 | last_set = int(basename[-3:]) |
|
|
1334 | 1338 | |
|
|
1335 | 1339 | self.online = online |
|
|
1336 | 1340 | self.realtime = realtime |
|
|
1337 | 1341 | self.delay = delay |
|
|
1338 | 1342 | ext = ext.lower() |
|
|
1339 | 1343 | self.ext = ext |
|
|
1340 | 1344 | self.getByBlock = getblock |
|
|
1341 | 1345 | self.nTxs = nTxs |
|
|
1342 | 1346 | self.startTime = startTime |
|
|
1343 | 1347 | self.endTime = endTime |
|
|
1344 | 1348 | |
|
|
1345 | 1349 | #Added----------------- |
|
|
1346 | 1350 | self.selBlocksize = blocksize |
|
|
1347 | 1351 | self.selBlocktime = blocktime |
|
|
1348 | 1352 | |
|
|
1349 | 1353 | # Verbose----------- |
|
|
1350 | 1354 | self.verbose = verbose |
|
|
1351 | 1355 | self.warnings = warnings |
|
|
1352 | 1356 | |
|
|
1353 | 1357 | if not(self.setNextFile()): |
|
|
1354 | 1358 | if (startDate!=None) and (endDate!=None): |
|
|
1355 | 1359 | print "[Reading] No files in range: %s - %s" %(datetime.datetime.combine(startDate,startTime).ctime(), datetime.datetime.combine(endDate,endTime).ctime()) |
|
|
1356 | 1360 | elif startDate != None: |
|
|
1357 | 1361 | print "[Reading] No files in range: %s" %(datetime.datetime.combine(startDate,startTime).ctime()) |
|
|
1358 | 1362 | else: |
|
|
1359 | 1363 | print "[Reading] No files" |
|
|
1360 | 1364 | |
|
|
1361 | 1365 | self.fileIndex = -1 |
|
|
1362 | 1366 | self.pathList = [] |
|
|
1363 | 1367 | self.filenameList = [] |
|
|
1364 | 1368 | return |
|
|
1365 | 1369 | |
|
|
1366 | 1370 | # self.getBasicHeader() |
|
|
1367 | 1371 | |
|
|
1368 | 1372 | if last_set != None: |
|
|
1369 | 1373 | self.dataOut.last_block = last_set * self.processingHeaderObj.dataBlocksPerFile + self.basicHeaderObj.dataBlock |
|
|
1370 | 1374 | return |
|
|
1371 | 1375 | |
|
|
1372 | 1376 | def getBasicHeader(self): |
|
|
1373 | 1377 | |
|
|
1374 | 1378 | self.dataOut.utctime = self.basicHeaderObj.utc + self.basicHeaderObj.miliSecond/1000. + self.profileIndex * self.radarControllerHeaderObj.ippSeconds |
|
|
1375 | 1379 | |
|
|
1376 | 1380 | self.dataOut.flagDiscontinuousBlock = self.flagDiscontinuousBlock |
|
|
1377 | 1381 | |
|
|
1378 | 1382 | self.dataOut.timeZone = self.basicHeaderObj.timeZone |
|
|
1379 | 1383 | |
|
|
1380 | 1384 | self.dataOut.dstFlag = self.basicHeaderObj.dstFlag |
|
|
1381 | 1385 | |
|
|
1382 | 1386 | self.dataOut.errorCount = self.basicHeaderObj.errorCount |
|
|
1383 | 1387 | |
|
|
1384 | 1388 | self.dataOut.useLocalTime = self.basicHeaderObj.useLocalTime |
|
|
1385 | 1389 | |
|
|
1386 | 1390 | self.dataOut.ippSeconds = self.radarControllerHeaderObj.ippSeconds/self.nTxs |
|
|
1387 | 1391 | |
|
|
1388 | 1392 | # self.dataOut.nProfiles = self.processingHeaderObj.profilesPerBlock*self.nTxs |
|
|
1389 | 1393 | |
|
|
1390 | 1394 | |
|
|
1391 | 1395 | def getFirstHeader(self): |
|
|
1392 | 1396 | |
|
|
1393 | 1397 | raise NotImplementedError |
|
|
1394 | 1398 | |
|
|
1395 | 1399 | def getData(self): |
|
|
1396 | 1400 | |
|
|
1397 | 1401 | raise NotImplementedError |
|
|
1398 | 1402 | |
|
|
1399 | 1403 | def hasNotDataInBuffer(self): |
|
|
1400 | 1404 | |
|
|
1401 | 1405 | raise NotImplementedError |
|
|
1402 | 1406 | |
|
|
1403 | 1407 | def readBlock(self): |
|
|
1404 | 1408 | |
|
|
1405 | 1409 | raise NotImplementedError |
|
|
1406 | 1410 | |
|
|
1407 | 1411 | def isEndProcess(self): |
|
|
1408 | 1412 | |
|
|
1409 | 1413 | return self.flagNoMoreFiles |
|
|
1410 | 1414 | |
|
|
1411 | 1415 | def printReadBlocks(self): |
|
|
1412 | 1416 | |
|
|
1413 | 1417 | print "[Reading] Number of read blocks per file %04d" %self.nReadBlocks |
|
|
1414 | 1418 | |
|
|
1415 | 1419 | def printTotalBlocks(self): |
|
|
1416 | 1420 | |
|
|
1417 | 1421 | print "[Reading] Number of read blocks %04d" %self.nTotalBlocks |
|
|
1418 | 1422 | |
|
|
1419 | 1423 | def printNumberOfBlock(self): |
|
|
1420 | 1424 | 'SPAM!' |
|
|
1421 | 1425 | |
|
|
1422 | 1426 | # if self.flagIsNewBlock: |
|
|
1423 | 1427 | # print "[Reading] Block No. %d/%d -> %s" %(self.nReadBlocks, |
|
|
1424 | 1428 | # self.processingHeaderObj.dataBlocksPerFile, |
|
|
1425 | 1429 | # self.dataOut.datatime.ctime()) |
|
|
1426 | 1430 | |
|
|
1427 | 1431 | def printInfo(self): |
|
|
1428 | 1432 | |
|
|
1429 | 1433 | if self.__printInfo == False: |
|
|
1430 | 1434 | return |
|
|
1431 | 1435 | |
|
|
1432 | 1436 | self.basicHeaderObj.printInfo() |
|
|
1433 | 1437 | self.systemHeaderObj.printInfo() |
|
|
1434 | 1438 | self.radarControllerHeaderObj.printInfo() |
|
|
1435 | 1439 | self.processingHeaderObj.printInfo() |
|
|
1436 | 1440 | |
|
|
1437 | 1441 | self.__printInfo = False |
|
|
1438 | 1442 | |
|
|
1439 | 1443 | def run(self, |
|
|
1440 | 1444 | path=None, |
|
|
1441 | 1445 | startDate=None, |
|
|
1442 | 1446 | endDate=None, |
|
|
1443 | 1447 | startTime=datetime.time(0,0,0), |
|
|
1444 | 1448 | endTime=datetime.time(23,59,59), |
|
|
1445 | 1449 | set=None, |
|
|
1446 | 1450 | expLabel = "", |
|
|
1447 | 1451 | ext = None, |
|
|
1448 | 1452 | online = False, |
|
|
1449 | 1453 | delay = 60, |
|
|
1450 | 1454 | walk = True, |
|
|
1451 | 1455 | getblock = False, |
|
|
1452 | 1456 | nTxs = 1, |
|
|
1453 | 1457 | realtime=False, |
|
|
1454 | 1458 | blocksize=None, |
|
|
1455 | 1459 | blocktime=None, |
|
|
1456 | 1460 | skip=None, |
|
|
1457 | 1461 | cursor=None, |
|
|
1458 | 1462 | warnings=True, |
|
|
1459 | 1463 | server=None, |
|
|
1460 |
verbose=True, |
|
|
|
1464 | verbose=True, | |
|
|
1465 | format=None, | |
|
|
1466 | oneDDict=None, | |
|
|
1467 | twoDDict=None, | |
|
|
1468 | ind2DList=None, **kwargs): | |
|
|
1461 | 1469 | |
|
|
1462 | 1470 | if not(self.isConfig): |
|
|
1463 | 1471 | self.setup(path=path, |
|
|
1464 | 1472 | startDate=startDate, |
|
|
1465 | 1473 | endDate=endDate, |
|
|
1466 | 1474 | startTime=startTime, |
|
|
1467 | 1475 | endTime=endTime, |
|
|
1468 | 1476 | set=set, |
|
|
1469 | 1477 | expLabel=expLabel, |
|
|
1470 | 1478 | ext=ext, |
|
|
1471 | 1479 | online=online, |
|
|
1472 | 1480 | delay=delay, |
|
|
1473 | 1481 | walk=walk, |
|
|
1474 | 1482 | getblock=getblock, |
|
|
1475 | 1483 | nTxs=nTxs, |
|
|
1476 | 1484 | realtime=realtime, |
|
|
1477 | 1485 | blocksize=blocksize, |
|
|
1478 | 1486 | blocktime=blocktime, |
|
|
1479 | 1487 | skip=skip, |
|
|
1480 | 1488 | cursor=cursor, |
|
|
1481 | 1489 | warnings=warnings, |
|
|
1482 | 1490 | server=server, |
|
|
1483 |
verbose=verbose |
|
|
|
1491 | verbose=verbose, | |
|
|
1492 | format=format, | |
|
|
1493 | oneDDict=oneDDict, | |
|
|
1494 | twoDDict=twoDDict, | |
|
|
1495 | ind2DList=ind2DList) | |
|
|
1484 | 1496 | self.isConfig = True |
|
|
1485 | 1497 | if server is None: |
|
|
1486 | 1498 | self.getData() |
|
|
1487 | 1499 | else: |
|
|
1488 | 1500 | self.getFromServer() |
|
|
1489 | 1501 | |
|
|
1490 | 1502 | class JRODataWriter(JRODataIO): |
|
|
1491 | 1503 | |
|
|
1492 | 1504 | """ |
|
|
1493 | 1505 | Esta clase permite escribir datos a archivos procesados (.r o ,pdata). La escritura |
|
|
1494 | 1506 | de los datos siempre se realiza por bloques. |
|
|
1495 | 1507 | """ |
|
|
1496 | 1508 | |
|
|
1497 | 1509 | blockIndex = 0 |
|
|
1498 | 1510 | |
|
|
1499 | 1511 | path = None |
|
|
1500 | 1512 | |
|
|
1501 | 1513 | setFile = None |
|
|
1502 | 1514 | |
|
|
1503 | 1515 | profilesPerBlock = None |
|
|
1504 | 1516 | |
|
|
1505 | 1517 | blocksPerFile = None |
|
|
1506 | 1518 | |
|
|
1507 | 1519 | nWriteBlocks = 0 |
|
|
1508 | 1520 | |
|
|
1509 | 1521 | fileDate = None |
|
|
1510 | 1522 | |
|
|
1511 | 1523 | def __init__(self, dataOut=None): |
|
|
1512 | 1524 | raise NotImplementedError |
|
|
1513 | 1525 | |
|
|
1514 | 1526 | |
|
|
1515 | 1527 | def hasAllDataInBuffer(self): |
|
|
1516 | 1528 | raise NotImplementedError |
|
|
1517 | 1529 | |
|
|
1518 | 1530 | |
|
|
1519 | 1531 | def setBlockDimension(self): |
|
|
1520 | 1532 | raise NotImplementedError |
|
|
1521 | 1533 | |
|
|
1522 | 1534 | |
|
|
1523 | 1535 | def writeBlock(self): |
|
|
1524 | 1536 | raise NotImplementedError |
|
|
1525 | 1537 | |
|
|
1526 | 1538 | |
|
|
1527 | 1539 | def putData(self): |
|
|
1528 | 1540 | raise NotImplementedError |
|
|
1529 | 1541 | |
|
|
1530 | 1542 | |
|
|
1531 | 1543 | def getProcessFlags(self): |
|
|
1532 | 1544 | |
|
|
1533 | 1545 | processFlags = 0 |
|
|
1534 | 1546 | |
|
|
1535 | 1547 | dtype_index = get_dtype_index(self.dtype) |
|
|
1536 | 1548 | procflag_dtype = get_procflag_dtype(dtype_index) |
|
|
1537 | 1549 | |
|
|
1538 | 1550 | processFlags += procflag_dtype |
|
|
1539 | 1551 | |
|
|
1540 | 1552 | if self.dataOut.flagDecodeData: |
|
|
1541 | 1553 | processFlags += PROCFLAG.DECODE_DATA |
|
|
1542 | 1554 | |
|
|
1543 | 1555 | if self.dataOut.flagDeflipData: |
|
|
1544 | 1556 | processFlags += PROCFLAG.DEFLIP_DATA |
|
|
1545 | 1557 | |
|
|
1546 | 1558 | if self.dataOut.code is not None: |
|
|
1547 | 1559 | processFlags += PROCFLAG.DEFINE_PROCESS_CODE |
|
|
1548 | 1560 | |
|
|
1549 | 1561 | if self.dataOut.nCohInt > 1: |
|
|
1550 | 1562 | processFlags += PROCFLAG.COHERENT_INTEGRATION |
|
|
1551 | 1563 | |
|
|
1552 | 1564 | if self.dataOut.type == "Spectra": |
|
|
1553 | 1565 | if self.dataOut.nIncohInt > 1: |
|
|
1554 | 1566 | processFlags += PROCFLAG.INCOHERENT_INTEGRATION |
|
|
1555 | 1567 | |
|
|
1556 | 1568 | if self.dataOut.data_dc is not None: |
|
|
1557 | 1569 | processFlags += PROCFLAG.SAVE_CHANNELS_DC |
|
|
1558 | 1570 | |
|
|
1559 | 1571 | if self.dataOut.flagShiftFFT: |
|
|
1560 | 1572 | processFlags += PROCFLAG.SHIFT_FFT_DATA |
|
|
1561 | 1573 | |
|
|
1562 | 1574 | return processFlags |
|
|
1563 | 1575 | |
|
|
1564 | 1576 | def setBasicHeader(self): |
|
|
1565 | 1577 | |
|
|
1566 | 1578 | self.basicHeaderObj.size = self.basicHeaderSize #bytes |
|
|
1567 | 1579 | self.basicHeaderObj.version = self.versionFile |
|
|
1568 | 1580 | self.basicHeaderObj.dataBlock = self.nTotalBlocks |
|
|
1569 | 1581 | |
|
|
1570 | 1582 | utc = numpy.floor(self.dataOut.utctime) |
|
|
1571 | 1583 | milisecond = (self.dataOut.utctime - utc)* 1000.0 |
|
|
1572 | 1584 | |
|
|
1573 | 1585 | self.basicHeaderObj.utc = utc |
|
|
1574 | 1586 | self.basicHeaderObj.miliSecond = milisecond |
|
|
1575 | 1587 | self.basicHeaderObj.timeZone = self.dataOut.timeZone |
|
|
1576 | 1588 | self.basicHeaderObj.dstFlag = self.dataOut.dstFlag |
|
|
1577 | 1589 | self.basicHeaderObj.errorCount = self.dataOut.errorCount |
|
|
1578 | 1590 | |
|
|
1579 | 1591 | def setFirstHeader(self): |
|
|
1580 | 1592 | """ |
|
|
1581 | 1593 | Obtiene una copia del First Header |
|
|
1582 | 1594 | |
|
|
1583 | 1595 | Affected: |
|
|
1584 | 1596 | |
|
|
1585 | 1597 | self.basicHeaderObj |
|
|
1586 | 1598 | self.systemHeaderObj |
|
|
1587 | 1599 | self.radarControllerHeaderObj |
|
|
1588 | 1600 | self.processingHeaderObj self. |
|
|
1589 | 1601 | |
|
|
1590 | 1602 | Return: |
|
|
1591 | 1603 | None |
|
|
1592 | 1604 | """ |
|
|
1593 | 1605 | |
|
|
1594 | 1606 | raise NotImplementedError |
|
|
1595 | 1607 | |
|
|
1596 | 1608 | def __writeFirstHeader(self): |
|
|
1597 | 1609 | """ |
|
|
1598 | 1610 | Escribe el primer header del file es decir el Basic header y el Long header (SystemHeader, RadarControllerHeader, ProcessingHeader) |
|
|
1599 | 1611 | |
|
|
1600 | 1612 | Affected: |
|
|
1601 | 1613 | __dataType |
|
|
1602 | 1614 | |
|
|
1603 | 1615 | Return: |
|
|
1604 | 1616 | None |
|
|
1605 | 1617 | """ |
|
|
1606 | 1618 | |
|
|
1607 | 1619 | # CALCULAR PARAMETROS |
|
|
1608 | 1620 | |
|
|
1609 | 1621 | sizeLongHeader = self.systemHeaderObj.size + self.radarControllerHeaderObj.size + self.processingHeaderObj.size |
|
|
1610 | 1622 | self.basicHeaderObj.size = self.basicHeaderSize + sizeLongHeader |
|
|
1611 | 1623 | |
|
|
1612 | 1624 | self.basicHeaderObj.write(self.fp) |
|
|
1613 | 1625 | self.systemHeaderObj.write(self.fp) |
|
|
1614 | 1626 | self.radarControllerHeaderObj.write(self.fp) |
|
|
1615 | 1627 | self.processingHeaderObj.write(self.fp) |
|
|
1616 | 1628 | |
|
|
1617 | 1629 | def __setNewBlock(self): |
|
|
1618 | 1630 | """ |
|
|
1619 | 1631 | Si es un nuevo file escribe el First Header caso contrario escribe solo el Basic Header |
|
|
1620 | 1632 | |
|
|
1621 | 1633 | Return: |
|
|
1622 | 1634 | 0 : si no pudo escribir nada |
|
|
1623 | 1635 | 1 : Si escribio el Basic el First Header |
|
|
1624 | 1636 | """ |
|
|
1625 | 1637 | if self.fp == None: |
|
|
1626 | 1638 | self.setNextFile() |
|
|
1627 | 1639 | |
|
|
1628 | 1640 | if self.flagIsNewFile: |
|
|
1629 | 1641 | return 1 |
|
|
1630 | 1642 | |
|
|
1631 | 1643 | if self.blockIndex < self.processingHeaderObj.dataBlocksPerFile: |
|
|
1632 | 1644 | self.basicHeaderObj.write(self.fp) |
|
|
1633 | 1645 | return 1 |
|
|
1634 | 1646 | |
|
|
1635 | 1647 | if not( self.setNextFile() ): |
|
|
1636 | 1648 | return 0 |
|
|
1637 | 1649 | |
|
|
1638 | 1650 | return 1 |
|
|
1639 | 1651 | |
|
|
1640 | 1652 | |
|
|
1641 | 1653 | def writeNextBlock(self): |
|
|
1642 | 1654 | """ |
|
|
1643 | 1655 | Selecciona el bloque siguiente de datos y los escribe en un file |
|
|
1644 | 1656 | |
|
|
1645 | 1657 | Return: |
|
|
1646 | 1658 | 0 : Si no hizo pudo escribir el bloque de datos |
|
|
1647 | 1659 | 1 : Si no pudo escribir el bloque de datos |
|
|
1648 | 1660 | """ |
|
|
1649 | 1661 | if not( self.__setNewBlock() ): |
|
|
1650 | 1662 | return 0 |
|
|
1651 | 1663 | |
|
|
1652 | 1664 | self.writeBlock() |
|
|
1653 | 1665 | |
|
|
1654 | 1666 | print "[Writing] Block No. %d/%d" %(self.blockIndex, |
|
|
1655 | 1667 | self.processingHeaderObj.dataBlocksPerFile) |
|
|
1656 | 1668 | |
|
|
1657 | 1669 | return 1 |
|
|
1658 | 1670 | |
|
|
1659 | 1671 | def setNextFile(self): |
|
|
1660 | 1672 | """ |
|
|
1661 | 1673 | Determina el siguiente file que sera escrito |
|
|
1662 | 1674 | |
|
|
1663 | 1675 | Affected: |
|
|
1664 | 1676 | self.filename |
|
|
1665 | 1677 | self.subfolder |
|
|
1666 | 1678 | self.fp |
|
|
1667 | 1679 | self.setFile |
|
|
1668 | 1680 | self.flagIsNewFile |
|
|
1669 | 1681 | |
|
|
1670 | 1682 | Return: |
|
|
1671 | 1683 | 0 : Si el archivo no puede ser escrito |
|
|
1672 | 1684 | 1 : Si el archivo esta listo para ser escrito |
|
|
1673 | 1685 | """ |
|
|
1674 | 1686 | ext = self.ext |
|
|
1675 | 1687 | path = self.path |
|
|
1676 | 1688 | |
|
|
1677 | 1689 | if self.fp != None: |
|
|
1678 | 1690 | self.fp.close() |
|
|
1679 | 1691 | |
|
|
1680 | 1692 | timeTuple = time.localtime( self.dataOut.utctime) |
|
|
1681 | 1693 | subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday) |
|
|
1682 | 1694 | |
|
|
1683 | 1695 | fullpath = os.path.join( path, subfolder ) |
|
|
1684 | 1696 | setFile = self.setFile |
|
|
1685 | 1697 | |
|
|
1686 | 1698 | if not( os.path.exists(fullpath) ): |
|
|
1687 | 1699 | os.mkdir(fullpath) |
|
|
1688 | 1700 | setFile = -1 #inicializo mi contador de seteo |
|
|
1689 | 1701 | else: |
|
|
1690 | 1702 | filesList = os.listdir( fullpath ) |
|
|
1691 | 1703 | if len( filesList ) > 0: |
|
|
1692 | 1704 | filesList = sorted( filesList, key=str.lower ) |
|
|
1693 | 1705 | filen = filesList[-1] |
|
|
1694 | 1706 | # el filename debera tener el siguiente formato |
|
|
1695 | 1707 | # 0 1234 567 89A BCDE (hex) |
|
|
1696 | 1708 | # x YYYY DDD SSS .ext |
|
|
1697 | 1709 | if isNumber( filen[8:11] ): |
|
|
1698 | 1710 | setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file |
|
|
1699 | 1711 | else: |
|
|
1700 | 1712 | setFile = -1 |
|
|
1701 | 1713 | else: |
|
|
1702 | 1714 | setFile = -1 #inicializo mi contador de seteo |
|
|
1703 | 1715 | |
|
|
1704 | 1716 | setFile += 1 |
|
|
1705 | 1717 | |
|
|
1706 | 1718 | #If this is a new day it resets some values |
|
|
1707 | 1719 | if self.dataOut.datatime.date() > self.fileDate: |
|
|
1708 | 1720 | setFile = 0 |
|
|
1709 | 1721 | self.nTotalBlocks = 0 |
|
|
1710 | 1722 | |
|
|
1711 | 1723 | filen = '%s%4.4d%3.3d%3.3d%s' % (self.optchar, timeTuple.tm_year, timeTuple.tm_yday, setFile, ext ) |
|
|
1712 | 1724 | |
|
|
1713 | 1725 | filename = os.path.join( path, subfolder, filen ) |
|
|
1714 | 1726 | |
|
|
1715 | 1727 | fp = open( filename,'wb' ) |
|
|
1716 | 1728 | |
|
|
1717 | 1729 | self.blockIndex = 0 |
|
|
1718 | 1730 | |
|
|
1719 | 1731 | #guardando atributos |
|
|
1720 | 1732 | self.filename = filename |
|
|
1721 | 1733 | self.subfolder = subfolder |
|
|
1722 | 1734 | self.fp = fp |
|
|
1723 | 1735 | self.setFile = setFile |
|
|
1724 | 1736 | self.flagIsNewFile = 1 |
|
|
1725 | 1737 | self.fileDate = self.dataOut.datatime.date() |
|
|
1726 | 1738 | |
|
|
1727 | 1739 | self.setFirstHeader() |
|
|
1728 | 1740 | |
|
|
1729 | 1741 | print '[Writing] Opening file: %s'%self.filename |
|
|
1730 | 1742 | |
|
|
1731 | 1743 | self.__writeFirstHeader() |
|
|
1732 | 1744 | |
|
|
1733 | 1745 | return 1 |
|
|
1734 | 1746 | |
|
|
1735 | 1747 | def setup(self, dataOut, path, blocksPerFile, profilesPerBlock=64, set=None, ext=None, datatype=4): |
|
|
1736 | 1748 | """ |
|
|
1737 | 1749 | Setea el tipo de formato en la cual sera guardada la data y escribe el First Header |
|
|
1738 | 1750 | |
|
|
1739 | 1751 | Inputs: |
|
|
1740 | 1752 | path : directory where data will be saved |
|
|
1741 | 1753 | profilesPerBlock : number of profiles per block |
|
|
1742 | 1754 | set : initial file set |
|
|
1743 | 1755 | datatype : An integer number that defines data type: |
|
|
1744 | 1756 | 0 : int8 (1 byte) |
|
|
1745 | 1757 | 1 : int16 (2 bytes) |
|
|
1746 | 1758 | 2 : int32 (4 bytes) |
|
|
1747 | 1759 | 3 : int64 (8 bytes) |
|
|
1748 | 1760 | 4 : float32 (4 bytes) |
|
|
1749 | 1761 | 5 : double64 (8 bytes) |
|
|
1750 | 1762 | |
|
|
1751 | 1763 | Return: |
|
|
1752 | 1764 | 0 : Si no realizo un buen seteo |
|
|
1753 | 1765 | 1 : Si realizo un buen seteo |
|
|
1754 | 1766 | """ |
|
|
1755 | 1767 | |
|
|
1756 | 1768 | if ext == None: |
|
|
1757 | 1769 | ext = self.ext |
|
|
1758 | 1770 | |
|
|
1759 | 1771 | self.ext = ext.lower() |
|
|
1760 | 1772 | |
|
|
1761 | 1773 | self.path = path |
|
|
1762 | 1774 | |
|
|
1763 | 1775 | if set is None: |
|
|
1764 | 1776 | self.setFile = -1 |
|
|
1765 | 1777 | else: |
|
|
1766 | 1778 | self.setFile = set - 1 |
|
|
1767 | 1779 | |
|
|
1768 | 1780 | self.blocksPerFile = blocksPerFile |
|
|
1769 | 1781 | |
|
|
1770 | 1782 | self.profilesPerBlock = profilesPerBlock |
|
|
1771 | 1783 | |
|
|
1772 | 1784 | self.dataOut = dataOut |
|
|
1773 | 1785 | self.fileDate = self.dataOut.datatime.date() |
|
|
1774 | 1786 | #By default |
|
|
1775 | 1787 | self.dtype = self.dataOut.dtype |
|
|
1776 | 1788 | |
|
|
1777 | 1789 | if datatype is not None: |
|
|
1778 | 1790 | self.dtype = get_numpy_dtype(datatype) |
|
|
1779 | 1791 | |
|
|
1780 | 1792 | if not(self.setNextFile()): |
|
|
1781 | 1793 | print "[Writing] There isn't a next file" |
|
|
1782 | 1794 | return 0 |
|
|
1783 | 1795 | |
|
|
1784 | 1796 | self.setBlockDimension() |
|
|
1785 | 1797 | |
|
|
1786 | 1798 | return 1 |
|
|
1787 | 1799 | |
|
|
1788 | 1800 | def run(self, dataOut, path, blocksPerFile, profilesPerBlock=64, set=None, ext=None, datatype=4, **kwargs): |
|
|
1789 | 1801 | |
|
|
1790 | 1802 | if not(self.isConfig): |
|
|
1791 | 1803 | |
|
|
1792 | 1804 | self.setup(dataOut, path, blocksPerFile, profilesPerBlock=profilesPerBlock, set=set, ext=ext, datatype=datatype, **kwargs) |
|
|
1793 | 1805 | self.isConfig = True |
|
|
1794 | 1806 | |
|
|
1795 | 1807 | self.putData() |
| @@ -1,243 +1,580 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Created on Aug 1, 2017 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: Juan C. Espinoza |
|
|
5 | 5 | ''' |
|
|
6 | 6 | |
|
|
7 | 7 | import os |
|
|
8 | 8 | import sys |
|
|
9 | 9 | import time |
|
|
10 | 10 | import json |
|
|
11 | import glob | |
|
|
11 | 12 | import datetime |
|
|
12 | 13 | |
|
|
13 | 14 | import numpy |
|
|
15 | import h5py | |
|
|
14 | 16 | |
|
|
15 | 17 | try: |
|
|
16 | 18 | import madrigal |
|
|
17 | 19 | import madrigal.cedar |
|
|
18 | 20 | except: |
|
|
19 | 21 | print 'You should install "madrigal library" module if you want to read/write Madrigal data' |
|
|
20 | 22 | |
|
|
21 |
from schainpy.model. |
|
|
|
23 | from schainpy.model.io.jroIO_base import JRODataReader | |
|
|
24 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation | |
|
|
22 | 25 | from schainpy.model.data.jrodata import Parameters |
|
|
26 | from schainpy.utils import log | |
|
|
27 | ||
|
|
23 | 28 | |
|
|
24 | MISSING = -32767 | |
|
|
25 | 29 | DEF_CATALOG = { |
|
|
26 | 30 | 'principleInvestigator': 'Marco Milla', |
|
|
27 | 31 | 'expPurpose': None, |
|
|
28 | 32 | 'expMode': None, |
|
|
29 | 33 | 'cycleTime': None, |
|
|
30 | 34 | 'correlativeExp': None, |
|
|
31 | 35 | 'sciRemarks': None, |
|
|
32 | 36 | 'instRemarks': None |
|
|
33 | 37 | } |
|
|
34 | 38 | DEF_HEADER = { |
|
|
35 | 39 | 'kindatDesc': None, |
|
|
36 | 40 | 'analyst': 'Jicamarca User', |
|
|
37 | 41 | 'comments': None, |
|
|
38 | 42 | 'history': None |
|
|
39 | 43 | } |
|
|
40 | 44 | MNEMONICS = { |
|
|
41 | 45 | 10: 'jro', |
|
|
42 | 46 | 11: 'jbr', |
|
|
43 | 47 | 840: 'jul', |
|
|
44 | 48 | 13: 'jas', |
|
|
45 | 49 | 1000: 'pbr', |
|
|
46 | 50 | 1001: 'hbr', |
|
|
47 | 51 | 1002: 'obr', |
|
|
48 | 52 | } |
|
|
49 | 53 | |
|
|
54 | UT1970 = datetime.datetime(1970, 1, 1) - datetime.timedelta(seconds=time.timezone) | |
|
|
55 | ||
|
|
50 | 56 | def load_json(obj): |
|
|
51 | 57 | ''' |
|
|
52 | 58 | Parse json as string instead of unicode |
|
|
53 | 59 | ''' |
|
|
54 | 60 | |
|
|
55 | 61 | if isinstance(obj, str): |
|
|
56 |
|
|
|
|
62 | iterable = json.loads(obj) | |
|
|
63 | ||
|
|
64 | if isinstance(iterable, dict): | |
|
|
65 | return {str(k): load_json(v) if isinstance(v, dict) else str(v) if isinstance(v, unicode) else v | |
|
|
66 | for k, v in iterable.items()} | |
|
|
67 | elif isinstance(iterable, (list, tuple)): | |
|
|
68 | return [str(v) if isinstance(v, unicode) else v for v in iterable] | |
|
|
69 | ||
|
|
70 | return iterable | |
|
|
71 | ||
|
|
72 | ||
|
|
73 | class MADReader(JRODataReader, ProcessingUnit): | |
|
|
74 | ||
|
|
75 | def __init__(self, **kwargs): | |
|
|
76 | ||
|
|
77 | ProcessingUnit.__init__(self, **kwargs) | |
|
|
78 | ||
|
|
79 | self.dataOut = Parameters() | |
|
|
80 | self.counter_records = 0 | |
|
|
81 | self.nrecords = None | |
|
|
82 | self.flagNoMoreFiles = 0 | |
|
|
83 | self.isConfig = False | |
|
|
84 | self.filename = None | |
|
|
85 | self.intervals = set() | |
|
|
86 | ||
|
|
87 | def setup(self, | |
|
|
88 | path=None, | |
|
|
89 | startDate=None, | |
|
|
90 | endDate=None, | |
|
|
91 | format=None, | |
|
|
92 | startTime=datetime.time(0, 0, 0), | |
|
|
93 | endTime=datetime.time(23, 59, 59), | |
|
|
94 | **kwargs): | |
|
|
95 | ||
|
|
96 | self.started = True | |
|
|
97 | self.path = path | |
|
|
98 | self.startDate = startDate | |
|
|
99 | self.endDate = endDate | |
|
|
100 | self.startTime = startTime | |
|
|
101 | self.endTime = endTime | |
|
|
102 | self.datatime = datetime.datetime(1900,1,1) | |
|
|
103 | self.oneDDict = load_json(kwargs.get('oneDDict', | |
|
|
104 | "{\"GDLATR\":\"lat\", \"GDLONR\":\"lon\"}")) | |
|
|
105 | self.twoDDict = load_json(kwargs.get('twoDDict', | |
|
|
106 | "{\"GDALT\": \"heightList\"}")) | |
|
|
107 | self.ind2DList = load_json(kwargs.get('ind2DList', | |
|
|
108 | "[\"GDALT\"]")) | |
|
|
109 | if self.path is None: | |
|
|
110 | raise ValueError, 'The path is not valid' | |
|
|
111 | ||
|
|
112 | if format is None: | |
|
|
113 | raise ValueError, 'The format is not valid choose simple or hdf5' | |
|
|
114 | elif format.lower() in ('simple', 'txt'): | |
|
|
115 | self.ext = '.txt' | |
|
|
116 | elif format.lower() in ('cedar',): | |
|
|
117 | self.ext = '.001' | |
|
|
118 | else: | |
|
|
119 | self.ext = '.hdf5' | |
|
|
120 | ||
|
|
121 | self.search_files(self.path) | |
|
|
122 | self.fileId = 0 | |
|
|
123 | ||
|
|
124 | if not self.fileList: | |
|
|
125 | raise Warning, 'There is no files matching these date in the folder: {}. \n Check startDate and endDate'.format(path) | |
|
|
126 | ||
|
|
127 | self.setNextFile() | |
|
|
128 | ||
|
|
129 | def search_files(self, path): | |
|
|
130 | ''' | |
|
|
131 | Searching for madrigal files in path | |
|
|
132 | Creating a list of files to procces included in [startDate,endDate] | |
|
|
133 | ||
|
|
134 | Input: | |
|
|
135 | path - Path to find files | |
|
|
136 | ''' | |
|
|
137 | ||
|
|
138 | print 'Searching files {} in {} '.format(self.ext, path) | |
|
|
139 | foldercounter = 0 | |
|
|
140 | fileList0 = glob.glob1(path, '*{}'.format(self.ext)) | |
|
|
141 | fileList0.sort() | |
|
|
142 | ||
|
|
143 | self.fileList = [] | |
|
|
144 | self.dateFileList = [] | |
|
|
57 | 145 | |
|
|
58 | return {str(k): load_json(v) if isinstance(v, dict) else str(v) if isinstance(v, unicode) else v | |
|
|
59 | for k, v in obj.items()} | |
|
|
146 | startDate = self.startDate - datetime.timedelta(1) | |
|
|
147 | endDate = self.endDate + datetime.timedelta(1) | |
|
|
148 | ||
|
|
149 | for thisFile in fileList0: | |
|
|
150 | year = thisFile[3:7] | |
|
|
151 | if not year.isdigit(): | |
|
|
152 | continue | |
|
|
153 | ||
|
|
154 | month = thisFile[7:9] | |
|
|
155 | if not month.isdigit(): | |
|
|
156 | continue | |
|
|
157 | ||
|
|
158 | day = thisFile[9:11] | |
|
|
159 | if not day.isdigit(): | |
|
|
160 | continue | |
|
|
161 | ||
|
|
162 | year, month, day = int(year), int(month), int(day) | |
|
|
163 | dateFile = datetime.date(year, month, day) | |
|
|
164 | ||
|
|
165 | if (startDate > dateFile) or (endDate < dateFile): | |
|
|
166 | continue | |
|
|
167 | ||
|
|
168 | self.fileList.append(thisFile) | |
|
|
169 | self.dateFileList.append(dateFile) | |
|
|
170 | ||
|
|
171 | return | |
|
|
172 | ||
|
|
173 | def parseHeader(self): | |
|
|
174 | ''' | |
|
|
175 | ''' | |
|
|
176 | ||
|
|
177 | self.output = {} | |
|
|
178 | self.version = '2' | |
|
|
179 | s_parameters = None | |
|
|
180 | if self.ext == '.txt': | |
|
|
181 | self.parameters = [s.strip().lower() for s in self.fp.readline().strip().split(' ') if s] | |
|
|
182 | elif self.ext == '.hdf5': | |
|
|
183 | metadata = self.fp['Metadata'] | |
|
|
184 | data = self.fp['Data']['Array Layout'] | |
|
|
185 | if 'Independent Spatial Parameters' in metadata: | |
|
|
186 | s_parameters = [s[0].lower() for s in metadata['Independent Spatial Parameters']] | |
|
|
187 | self.version = '3' | |
|
|
188 | one = [s[0].lower() for s in data['1D Parameters']['Data Parameters']] | |
|
|
189 | one_d = [1 for s in one] | |
|
|
190 | two = [s[0].lower() for s in data['2D Parameters']['Data Parameters']] | |
|
|
191 | two_d = [2 for s in two] | |
|
|
192 | self.parameters = one + two | |
|
|
193 | self.parameters_d = one_d + two_d | |
|
|
194 | ||
|
|
195 | log.success('Parameters found: {}'.format(','.join(self.parameters)), | |
|
|
196 | 'MADReader') | |
|
|
197 | if s_parameters: | |
|
|
198 | log.success('Spatial parameters: {}'.format(','.join(s_parameters)), | |
|
|
199 | 'MADReader') | |
|
|
200 | ||
|
|
201 | for param in self.oneDDict.keys(): | |
|
|
202 | if param.lower() not in self.parameters: | |
|
|
203 | print('\x1b[33m[Warning]\x1b[0m Parameter \x1b[1;32m{}\x1b[0m not found will be ignored'.format( | |
|
|
204 | param | |
|
|
205 | )) | |
|
|
206 | self.oneDDict.pop(param, None) | |
|
|
207 | ||
|
|
208 | for param, value in self.twoDDict.items(): | |
|
|
209 | if param.lower() not in self.parameters: | |
|
|
210 | print('\x1b[33m[Warning]\x1b[0m Parameter \x1b[1;32m{}\x1b[0m not found will be ignored'.format( | |
|
|
211 | param | |
|
|
212 | )) | |
|
|
213 | self.twoDDict.pop(param, None) | |
|
|
214 | continue | |
|
|
215 | if isinstance(value, list): | |
|
|
216 | if value[0] not in self.output: | |
|
|
217 | self.output[value[0]] = [] | |
|
|
218 | self.output[value[0]].append(None) | |
|
|
219 | ||
|
|
220 | def parseData(self): | |
|
|
221 | ''' | |
|
|
222 | ''' | |
|
|
223 | ||
|
|
224 | if self.ext == '.txt': | |
|
|
225 | self.data = numpy.genfromtxt(self.fp, missing_values=('missing')) | |
|
|
226 | self.nrecords = self.data.shape[0] | |
|
|
227 | self.ranges = numpy.unique(self.data[:,self.parameters.index(self.ind2DList[0].lower())]) | |
|
|
228 | elif self.ext == '.hdf5': | |
|
|
229 | self.data = self.fp['Data']['Array Layout'] | |
|
|
230 | self.nrecords = len(self.data['timestamps'].value) | |
|
|
231 | self.ranges = self.data['range'].value | |
|
|
232 | ||
|
|
233 | def setNextFile(self): | |
|
|
234 | ''' | |
|
|
235 | ''' | |
|
|
236 | ||
|
|
237 | file_id = self.fileId | |
|
|
238 | ||
|
|
239 | if file_id == len(self.fileList): | |
|
|
240 | print '\nNo more files in the folder' | |
|
|
241 | print 'Total number of file(s) read : {}'.format(self.fileId) | |
|
|
242 | self.flagNoMoreFiles = 1 | |
|
|
243 | return 0 | |
|
|
244 | ||
|
|
245 | print('\x1b[32m[Info]\x1b[0m Opening: {}'.format( | |
|
|
246 | self.fileList[file_id] | |
|
|
247 | )) | |
|
|
248 | filename = os.path.join(self.path, self.fileList[file_id]) | |
|
|
249 | ||
|
|
250 | if self.filename is not None: | |
|
|
251 | self.fp.close() | |
|
|
252 | ||
|
|
253 | self.filename = filename | |
|
|
254 | self.filedate = self.dateFileList[file_id] | |
|
|
255 | ||
|
|
256 | if self.ext=='.hdf5': | |
|
|
257 | self.fp = h5py.File(self.filename, 'r') | |
|
|
258 | else: | |
|
|
259 | self.fp = open(self.filename, 'rb') | |
|
|
260 | ||
|
|
261 | self.parseHeader() | |
|
|
262 | self.parseData() | |
|
|
263 | self.sizeOfFile = os.path.getsize(self.filename) | |
|
|
264 | self.counter_records = 0 | |
|
|
265 | self.flagIsNewFile = 0 | |
|
|
266 | self.fileId += 1 | |
|
|
267 | ||
|
|
268 | return 1 | |
|
|
269 | ||
|
|
270 | def readNextBlock(self): | |
|
|
271 | ||
|
|
272 | while True: | |
|
|
273 | ||
|
|
274 | if self.flagIsNewFile: | |
|
|
275 | if not self.setNextFile(): | |
|
|
276 | return 0 | |
|
|
277 | ||
|
|
278 | self.readBlock() | |
|
|
279 | ||
|
|
280 | if (self.datatime < datetime.datetime.combine(self.startDate, self.startTime)) or \ | |
|
|
281 | (self.datatime > datetime.datetime.combine(self.endDate, self.endTime)): | |
|
|
282 | print "\x1b[32m[Reading]\x1b[0m Record No. %d/%d -> %s \x1b[33m[Skipping]\x1b[0m" %( | |
|
|
283 | self.counter_records, | |
|
|
284 | self.nrecords, | |
|
|
285 | self.datatime.ctime()) | |
|
|
286 | continue | |
|
|
287 | break | |
|
|
288 | ||
|
|
289 | print "\x1b[32m[Reading]\x1b[0m Record No. %d/%d -> %s" %( | |
|
|
290 | self.counter_records, | |
|
|
291 | self.nrecords, | |
|
|
292 | self.datatime.ctime()) | |
|
|
293 | ||
|
|
294 | return 1 | |
|
|
295 | ||
|
|
296 | def readBlock(self): | |
|
|
297 | ''' | |
|
|
298 | ''' | |
|
|
299 | dum = [] | |
|
|
300 | if self.ext == '.txt': | |
|
|
301 | dt = self.data[self.counter_records][:6].astype(int) | |
|
|
302 | self.datatime = datetime.datetime(dt[0], dt[1], dt[2], dt[3], dt[4], dt[5]) | |
|
|
303 | while True: | |
|
|
304 | dt = self.data[self.counter_records][:6].astype(int) | |
|
|
305 | datatime = datetime.datetime(dt[0], dt[1], dt[2], dt[3], dt[4], dt[5]) | |
|
|
306 | if datatime == self.datatime: | |
|
|
307 | dum.append(self.data[self.counter_records]) | |
|
|
308 | self.counter_records += 1 | |
|
|
309 | if self.counter_records == self.nrecords: | |
|
|
310 | self.flagIsNewFile = True | |
|
|
311 | break | |
|
|
312 | continue | |
|
|
313 | self.intervals.add((datatime-self.datatime).seconds) | |
|
|
314 | break | |
|
|
315 | elif self.ext == '.hdf5': | |
|
|
316 | datatime = datetime.datetime.utcfromtimestamp( | |
|
|
317 | self.data['timestamps'][self.counter_records]) | |
|
|
318 | nHeights = len(self.ranges) | |
|
|
319 | for n, param in enumerate(self.parameters): | |
|
|
320 | if self.parameters_d[n] == 1: | |
|
|
321 | dum.append(numpy.ones(nHeights)*self.data['1D Parameters'][param][self.counter_records]) | |
|
|
322 | else: | |
|
|
323 | if self.version == '2': | |
|
|
324 | dum.append(self.data['2D Parameters'][param][self.counter_records]) | |
|
|
325 | else: | |
|
|
326 | tmp = self.data['2D Parameters'][param].value.T | |
|
|
327 | dum.append(tmp[self.counter_records]) | |
|
|
328 | self.intervals.add((datatime-self.datatime).seconds) | |
|
|
329 | self.datatime = datatime | |
|
|
330 | self.counter_records += 1 | |
|
|
331 | if self.counter_records == self.nrecords: | |
|
|
332 | self.flagIsNewFile = True | |
|
|
333 | ||
|
|
334 | self.buffer = numpy.array(dum) | |
|
|
335 | return | |
|
|
336 | ||
|
|
337 | def set_output(self): | |
|
|
338 | ''' | |
|
|
339 | Storing data from buffer to dataOut object | |
|
|
340 | ''' | |
|
|
341 | ||
|
|
342 | parameters = [None for __ in self.parameters] | |
|
|
343 | ||
|
|
344 | for param, attr in self.oneDDict.items(): | |
|
|
345 | x = self.parameters.index(param.lower()) | |
|
|
346 | setattr(self.dataOut, attr, self.buffer[0][x]) | |
|
|
347 | ||
|
|
348 | for param, value in self.twoDDict.items(): | |
|
|
349 | x = self.parameters.index(param.lower()) | |
|
|
350 | if self.ext == '.txt': | |
|
|
351 | y = self.parameters.index(self.ind2DList[0].lower()) | |
|
|
352 | ranges = self.buffer[:,y] | |
|
|
353 | if self.ranges.size == ranges.size: | |
|
|
354 | continue | |
|
|
355 | index = numpy.where(numpy.in1d(self.ranges, ranges))[0] | |
|
|
356 | dummy = numpy.zeros(self.ranges.shape) + numpy.nan | |
|
|
357 | dummy[index] = self.buffer[:,x] | |
|
|
358 | else: | |
|
|
359 | ||
|
|
360 | dummy = self.buffer[x] | |
|
|
361 | ||
|
|
362 | if isinstance(value, str): | |
|
|
363 | if value not in self.ind2DList: | |
|
|
364 | setattr(self.dataOut, value, dummy.reshape(1,-1)) | |
|
|
365 | elif isinstance(value, list): | |
|
|
366 | self.output[value[0]][value[1]] = dummy | |
|
|
367 | parameters[value[1]] = param | |
|
|
368 | ||
|
|
369 | for key, value in self.output.items(): | |
|
|
370 | setattr(self.dataOut, key, numpy.array(value)) | |
|
|
371 | ||
|
|
372 | self.dataOut.parameters = [s for s in parameters if s] | |
|
|
373 | self.dataOut.heightList = self.ranges | |
|
|
374 | self.dataOut.utctime = (self.datatime - UT1970).total_seconds() | |
|
|
375 | self.dataOut.utctimeInit = self.dataOut.utctime | |
|
|
376 | self.dataOut.paramInterval = min(self.intervals) | |
|
|
377 | self.dataOut.useLocalTime = False | |
|
|
378 | self.dataOut.flagNoData = False | |
|
|
379 | self.dataOut.started = self.started | |
|
|
380 | ||
|
|
381 | def getData(self): | |
|
|
382 | ''' | |
|
|
383 | Storing data from databuffer to dataOut object | |
|
|
384 | ''' | |
|
|
385 | if self.flagNoMoreFiles: | |
|
|
386 | self.dataOut.flagNoData = True | |
|
|
387 | print 'No file left to process' | |
|
|
388 | return 0 | |
|
|
389 | ||
|
|
390 | if not self.readNextBlock(): | |
|
|
391 | self.dataOut.flagNoData = True | |
|
|
392 | return 0 | |
|
|
393 | ||
|
|
394 | self.set_output() | |
|
|
395 | ||
|
|
396 | return 1 | |
|
|
60 | 397 | |
|
|
61 | 398 | |
|
|
62 | 399 | class MAD2Writer(Operation): |
|
|
400 | ||
|
|
401 | missing = -32767 | |
|
|
402 | ext = '.dat' | |
|
|
63 | 403 | |
|
|
64 | 404 | def __init__(self, **kwargs): |
|
|
65 | 405 | |
|
|
66 | 406 | Operation.__init__(self, **kwargs) |
|
|
67 | 407 | self.dataOut = Parameters() |
|
|
68 | 408 | self.path = None |
|
|
69 | 409 | self.dataOut = None |
|
|
70 | self.ext = '.dat' | |
|
|
71 | ||
|
|
72 | return | |
|
|
73 | 410 | |
|
|
74 |
def run(self, dataOut, path, oneD |
|
|
|
411 | def run(self, dataOut, path, oneDDict, ind2DList='[]', twoDDict='{}', metadata='{}', **kwargs): | |
|
|
75 | 412 | ''' |
|
|
76 | 413 | Inputs: |
|
|
77 | 414 | path - path where files will be created |
|
|
78 |
oneD |
|
|
|
415 | oneDDict - json of one-dimensional parameters in record where keys | |
|
|
79 | 416 | are Madrigal codes (integers or mnemonics) and values the corresponding |
|
|
80 | 417 | dataOut attribute e.g: { |
|
|
81 | 418 | 'gdlatr': 'lat', |
|
|
82 | 419 | 'gdlonr': 'lon', |
|
|
83 | 420 | 'gdlat2':'lat', |
|
|
84 | 421 | 'glon2':'lon'} |
|
|
85 | twoDParam - independent parameter to get the number of rows e.g: | |
|
|
86 | heighList | |
|
|
87 |
twoD |
|
|
|
422 | ind2DList - list of independent spatial two-dimensional parameters e.g: | |
|
|
423 | ['heighList'] | |
|
|
424 | twoDDict - json of two-dimensional parameters in record where keys | |
|
|
88 | 425 | are Madrigal codes (integers or mnemonics) and values the corresponding |
|
|
89 | 426 | dataOut attribute if multidimensional array specify as tupple |
|
|
90 | 427 | ('attr', pos) e.g: { |
|
|
91 | 428 | 'gdalt': 'heightList', |
|
|
92 | 429 | 'vn1p2': ('data_output', 0), |
|
|
93 | 430 | 'vn2p2': ('data_output', 1), |
|
|
94 | 431 | 'vn3': ('data_output', 2), |
|
|
95 | 432 | 'snl': ('data_SNR', 'db') |
|
|
96 | 433 | } |
|
|
97 | 434 | metadata - json of madrigal metadata (kinst, kindat, catalog and header) |
|
|
98 | 435 | ''' |
|
|
99 | 436 | if not self.isConfig: |
|
|
100 |
self.setup(dataOut, path, oneD |
|
|
|
437 | self.setup(dataOut, path, oneDDict, ind2DList, twoDDict, metadata, **kwargs) | |
|
|
101 | 438 | self.isConfig = True |
|
|
102 | 439 | |
|
|
103 | 440 | self.putData() |
|
|
104 | 441 | return |
|
|
105 | 442 | |
|
|
106 |
def setup(self, dataOut, path, oneD |
|
|
|
443 | def setup(self, dataOut, path, oneDDict, ind2DList, twoDDict, metadata, **kwargs): | |
|
|
107 | 444 | ''' |
|
|
108 | 445 | Configure Operation |
|
|
109 | 446 | ''' |
|
|
110 | 447 | |
|
|
111 | 448 | self.dataOut = dataOut |
|
|
112 | 449 | self.nmodes = self.dataOut.nmodes |
|
|
113 | 450 | self.path = path |
|
|
114 | 451 | self.blocks = kwargs.get('blocks', None) |
|
|
115 | 452 | self.counter = 0 |
|
|
116 |
self.oneD |
|
|
|
117 |
self.twoD |
|
|
|
118 | self.twoDParam = twoDParam | |
|
|
453 | self.oneDDict = load_json(oneDDict) | |
|
|
454 | self.twoDDict = load_json(twoDDict) | |
|
|
455 | self.ind2DList = load_json(ind2DList) | |
|
|
119 | 456 | meta = load_json(metadata) |
|
|
120 | 457 | self.kinst = meta.get('kinst') |
|
|
121 | 458 | self.kindat = meta.get('kindat') |
|
|
122 | 459 | self.catalog = meta.get('catalog', DEF_CATALOG) |
|
|
123 | 460 | self.header = meta.get('header', DEF_HEADER) |
|
|
124 | 461 | |
|
|
125 | 462 | return |
|
|
126 | 463 | |
|
|
127 | 464 | def setFile(self): |
|
|
128 | 465 | ''' |
|
|
129 | 466 | Create new cedar file object |
|
|
130 | 467 | ''' |
|
|
131 | 468 | |
|
|
132 | 469 | self.mnemonic = MNEMONICS[self.kinst] #TODO get mnemonic from madrigal |
|
|
133 | 470 | date = datetime.datetime.utcfromtimestamp(self.dataOut.utctime) |
|
|
134 | 471 | |
|
|
135 | 472 | filename = '%s%s_%s%s' % (self.mnemonic, |
|
|
136 | 473 | date.strftime('%Y%m%d_%H%M%S'), |
|
|
137 | 474 | self.dataOut.mode, |
|
|
138 | 475 | self.ext) |
|
|
139 | 476 | |
|
|
140 | 477 | self.fullname = os.path.join(self.path, filename) |
|
|
141 | 478 | |
|
|
142 | 479 | if os.path.isfile(self.fullname) : |
|
|
143 | 480 | print "Destination path '%s' already exists. Previous file deleted. " %self.fullname |
|
|
144 | 481 | os.remove(self.fullname) |
|
|
145 | 482 | |
|
|
146 | 483 | try: |
|
|
147 | 484 | print '[Writing] creating file : %s' % (self.fullname) |
|
|
148 | 485 | self.cedarObj = madrigal.cedar.MadrigalCedarFile(self.fullname, True) |
|
|
149 | 486 | except ValueError, e: |
|
|
150 | 487 | print '[Error]: Impossible to create a cedar object with "madrigal.cedar.MadrigalCedarFile" ' |
|
|
151 | 488 | return |
|
|
152 | 489 | |
|
|
153 | 490 | return 1 |
|
|
154 | 491 | |
|
|
155 | 492 | def writeBlock(self): |
|
|
156 | 493 | ''' |
|
|
157 |
Add data records to cedar file taking data from oneD |
|
|
|
494 | Add data records to cedar file taking data from oneDDict and twoDDict | |
|
|
158 | 495 | attributes. |
|
|
159 | 496 | Allowed parameters in: parcodes.tab |
|
|
160 | 497 | ''' |
|
|
161 | 498 | |
|
|
162 | 499 | startTime = datetime.datetime.utcfromtimestamp(self.dataOut.utctime) |
|
|
163 | 500 | endTime = startTime + datetime.timedelta(seconds=self.dataOut.paramInterval) |
|
|
164 |
nrows = len(getattr(self.dataOut, self. |
|
|
|
501 | nrows = len(getattr(self.dataOut, self.ind2DList)) | |
|
|
165 | 502 | |
|
|
166 | 503 | rec = madrigal.cedar.MadrigalDataRecord( |
|
|
167 | 504 | self.kinst, |
|
|
168 | 505 | self.kindat, |
|
|
169 | 506 | startTime.year, |
|
|
170 | 507 | startTime.month, |
|
|
171 | 508 | startTime.day, |
|
|
172 | 509 | startTime.hour, |
|
|
173 | 510 | startTime.minute, |
|
|
174 | 511 | startTime.second, |
|
|
175 | 512 | startTime.microsecond/10000, |
|
|
176 | 513 | endTime.year, |
|
|
177 | 514 | endTime.month, |
|
|
178 | 515 | endTime.day, |
|
|
179 | 516 | endTime.hour, |
|
|
180 | 517 | endTime.minute, |
|
|
181 | 518 | endTime.second, |
|
|
182 | 519 | endTime.microsecond/10000, |
|
|
183 |
self.oneD |
|
|
|
184 |
self.twoD |
|
|
|
520 | self.oneDDict.keys(), | |
|
|
521 | self.twoDDict.keys(), | |
|
|
185 | 522 | nrows |
|
|
186 | 523 | ) |
|
|
187 | 524 | |
|
|
188 | 525 | # Setting 1d values |
|
|
189 |
for key in self.oneD |
|
|
|
190 |
rec.set1D(key, getattr(self.dataOut, self.oneD |
|
|
|
526 | for key in self.oneDDict: | |
|
|
527 | rec.set1D(key, getattr(self.dataOut, self.oneDDict[key])) | |
|
|
191 | 528 | |
|
|
192 | 529 | # Setting 2d values |
|
|
193 | 530 | invalid = numpy.isnan(self.dataOut.data_output) |
|
|
194 |
self.dataOut.data_output[invalid] = |
|
|
|
531 | self.dataOut.data_output[invalid] = self.missing | |
|
|
195 | 532 | out = {} |
|
|
196 |
for key, value in self.twoD |
|
|
|
533 | for key, value in self.twoDDict.items(): | |
|
|
197 | 534 | if isinstance(value, str): |
|
|
198 | 535 | out[key] = getattr(self.dataOut, value) |
|
|
199 | 536 | elif isinstance(value, tuple): |
|
|
200 | 537 | attr, x = value |
|
|
201 | 538 | if isinstance(x, (int, float)): |
|
|
202 | 539 | out[key] = getattr(self.dataOut, attr)[int(x)] |
|
|
203 | 540 | elif x.lower()=='db': |
|
|
204 | 541 | tmp = getattr(self.dataOut, attr) |
|
|
205 | 542 | SNRavg = numpy.average(tmp, axis=0) |
|
|
206 | 543 | out[key] = 10*numpy.log10(SNRavg) |
|
|
207 | 544 | |
|
|
208 | 545 | for n in range(nrows): |
|
|
209 | 546 | for key in out: |
|
|
210 | 547 | rec.set2D(key, n, out[key][n]) |
|
|
211 | 548 | |
|
|
212 | 549 | self.cedarObj.append(rec) |
|
|
213 | 550 | self.cedarObj.dump() |
|
|
214 | 551 | print '[Writing] Record No. {} (mode {}).'.format( |
|
|
215 | 552 | self.counter, |
|
|
216 | 553 | self.dataOut.mode |
|
|
217 | 554 | ) |
|
|
218 | 555 | |
|
|
219 | 556 | def setHeader(self): |
|
|
220 | 557 | ''' |
|
|
221 | 558 | Create an add catalog and header to cedar file |
|
|
222 | 559 | ''' |
|
|
223 | 560 | |
|
|
224 | 561 | header = madrigal.cedar.CatalogHeaderCreator(self.fullname) |
|
|
225 | 562 | header.createCatalog(**self.catalog) |
|
|
226 | 563 | header.createHeader(**self.header) |
|
|
227 | 564 | header.write() |
|
|
228 | 565 | |
|
|
229 | 566 | def putData(self): |
|
|
230 | 567 | |
|
|
231 | 568 | if self.dataOut.flagNoData: |
|
|
232 | 569 | return 0 |
|
|
233 | 570 | |
|
|
234 | 571 | if self.counter == 0: |
|
|
235 | 572 | self.setFile() |
|
|
236 | 573 | |
|
|
237 | 574 | if self.counter <= self.dataOut.nrecords: |
|
|
238 | 575 | self.writeBlock() |
|
|
239 | 576 | self.counter += 1 |
|
|
240 | 577 | |
|
|
241 | 578 | if self.counter == self.dataOut.nrecords or self.counter == self.blocks: |
|
|
242 | 579 | self.setHeader() |
|
|
243 | 580 | self.counter = 0 |
| @@ -1,707 +1,698 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Created on Jul 2, 2014 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 |
|
|
5 | 5 | ''' |
|
|
6 | 6 | import numpy |
|
|
7 | 7 | |
|
|
8 | 8 | from jroIO_base import LOCALTIME, JRODataReader, JRODataWriter |
|
|
9 | 9 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation |
|
|
10 | 10 | from schainpy.model.data.jroheaderIO import PROCFLAG, BasicHeader, SystemHeader, RadarControllerHeader, ProcessingHeader |
|
|
11 | 11 | from schainpy.model.data.jrodata import Spectra |
|
|
12 | 12 | |
|
|
13 | 13 | class SpectraReader(JRODataReader, ProcessingUnit): |
|
|
14 | 14 | |
|
|
15 | 15 | """ |
|
|
16 | 16 | Esta clase permite leer datos de espectros desde archivos procesados (.pdata). La lectura |
|
|
17 | 17 | de los datos siempre se realiza por bloques. Los datos leidos (array de 3 dimensiones) |
|
|
18 | 18 | son almacenados en tres buffer's para el Self Spectra, el Cross Spectra y el DC Channel. |
|
|
19 | 19 | |
|
|
20 | 20 | paresCanalesIguales * alturas * perfiles (Self Spectra) |
|
|
21 | 21 | paresCanalesDiferentes * alturas * perfiles (Cross Spectra) |
|
|
22 | 22 | canales * alturas (DC Channels) |
|
|
23 | 23 | |
|
|
24 | 24 | |
|
|
25 | 25 | Esta clase contiene instancias (objetos) de las clases BasicHeader, SystemHeader, |
|
|
26 | 26 | RadarControllerHeader y Spectra. Los tres primeros se usan para almacenar informacion de la |
|
|
27 | 27 | cabecera de datos (metadata), y el cuarto (Spectra) para obtener y almacenar un bloque de |
|
|
28 | 28 | datos desde el "buffer" cada vez que se ejecute el metodo "getData". |
|
|
29 | 29 | |
|
|
30 | 30 | Example: |
|
|
31 | 31 | dpath = "/home/myuser/data" |
|
|
32 | 32 | |
|
|
33 | 33 | startTime = datetime.datetime(2010,1,20,0,0,0,0,0,0) |
|
|
34 | 34 | |
|
|
35 | 35 | endTime = datetime.datetime(2010,1,21,23,59,59,0,0,0) |
|
|
36 | 36 | |
|
|
37 | 37 | readerObj = SpectraReader() |
|
|
38 | 38 | |
|
|
39 | 39 | readerObj.setup(dpath, startTime, endTime) |
|
|
40 | 40 | |
|
|
41 | 41 | while(True): |
|
|
42 | 42 | |
|
|
43 | 43 | readerObj.getData() |
|
|
44 | 44 | |
|
|
45 | 45 | print readerObj.data_spc |
|
|
46 | 46 | |
|
|
47 | 47 | print readerObj.data_cspc |
|
|
48 | 48 | |
|
|
49 | 49 | print readerObj.data_dc |
|
|
50 | 50 | |
|
|
51 | 51 | if readerObj.flagNoMoreFiles: |
|
|
52 | 52 | break |
|
|
53 | 53 | |
|
|
54 | 54 | """ |
|
|
55 | 55 | |
|
|
56 | 56 | pts2read_SelfSpectra = 0 |
|
|
57 | 57 | |
|
|
58 | 58 | pts2read_CrossSpectra = 0 |
|
|
59 | 59 | |
|
|
60 | 60 | pts2read_DCchannels = 0 |
|
|
61 | 61 | |
|
|
62 | 62 | ext = ".pdata" |
|
|
63 | 63 | |
|
|
64 | 64 | optchar = "P" |
|
|
65 | 65 | |
|
|
66 | 66 | dataOut = None |
|
|
67 | 67 | |
|
|
68 | 68 | nRdChannels = None |
|
|
69 | 69 | |
|
|
70 | 70 | nRdPairs = None |
|
|
71 | 71 | |
|
|
72 | 72 | rdPairList = [] |
|
|
73 | 73 | |
|
|
74 | 74 | def __init__(self, **kwargs): |
|
|
75 | 75 | """ |
|
|
76 | 76 | Inicializador de la clase SpectraReader para la lectura de datos de espectros. |
|
|
77 | 77 | |
|
|
78 | 78 | Inputs: |
|
|
79 | 79 | |
|
|
80 | 80 | dataOut : Objeto de la clase Spectra. Este objeto sera utilizado para |
|
|
81 | 81 | almacenar un perfil de datos cada vez que se haga un requerimiento |
|
|
82 | 82 | (getData). El perfil sera obtenido a partir del buffer de datos, |
|
|
83 | 83 | si el buffer esta vacio se hara un nuevo proceso de lectura de un |
|
|
84 | 84 | bloque de datos. |
|
|
85 | 85 | Si este parametro no es pasado se creara uno internamente. |
|
|
86 | 86 | |
|
|
87 | 87 | |
|
|
88 | 88 | Affected: |
|
|
89 | 89 | |
|
|
90 | 90 | self.dataOut |
|
|
91 | 91 | |
|
|
92 | 92 | Return : None |
|
|
93 | 93 | """ |
|
|
94 | 94 | |
|
|
95 | 95 | |
|
|
96 | 96 | #Eliminar de la base la herencia |
|
|
97 | 97 | ProcessingUnit.__init__(self, **kwargs) |
|
|
98 | 98 | |
|
|
99 | 99 | # self.isConfig = False |
|
|
100 | 100 | |
|
|
101 | 101 | self.pts2read_SelfSpectra = 0 |
|
|
102 | 102 | |
|
|
103 | 103 | self.pts2read_CrossSpectra = 0 |
|
|
104 | 104 | |
|
|
105 | 105 | self.pts2read_DCchannels = 0 |
|
|
106 | 106 | |
|
|
107 | 107 | self.datablock = None |
|
|
108 | 108 | |
|
|
109 | 109 | self.utc = None |
|
|
110 | 110 | |
|
|
111 | 111 | self.ext = ".pdata" |
|
|
112 | 112 | |
|
|
113 | 113 | self.optchar = "P" |
|
|
114 | 114 | |
|
|
115 | 115 | self.basicHeaderObj = BasicHeader(LOCALTIME) |
|
|
116 | 116 | |
|
|
117 | 117 | self.systemHeaderObj = SystemHeader() |
|
|
118 | 118 | |
|
|
119 | 119 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
120 | 120 | |
|
|
121 | 121 | self.processingHeaderObj = ProcessingHeader() |
|
|
122 | 122 | |
|
|
123 | 123 | self.online = 0 |
|
|
124 | 124 | |
|
|
125 | 125 | self.fp = None |
|
|
126 | 126 | |
|
|
127 | 127 | self.idFile = None |
|
|
128 | 128 | |
|
|
129 | 129 | self.dtype = None |
|
|
130 | 130 | |
|
|
131 | 131 | self.fileSizeByHeader = None |
|
|
132 | 132 | |
|
|
133 | 133 | self.filenameList = [] |
|
|
134 | 134 | |
|
|
135 | 135 | self.filename = None |
|
|
136 | 136 | |
|
|
137 | 137 | self.fileSize = None |
|
|
138 | 138 | |
|
|
139 | 139 | self.firstHeaderSize = 0 |
|
|
140 | 140 | |
|
|
141 | 141 | self.basicHeaderSize = 24 |
|
|
142 | 142 | |
|
|
143 | 143 | self.pathList = [] |
|
|
144 | 144 | |
|
|
145 | 145 | self.lastUTTime = 0 |
|
|
146 | 146 | |
|
|
147 | 147 | self.maxTimeStep = 30 |
|
|
148 | 148 | |
|
|
149 | 149 | self.flagNoMoreFiles = 0 |
|
|
150 | 150 | |
|
|
151 | 151 | self.set = 0 |
|
|
152 | 152 | |
|
|
153 | 153 | self.path = None |
|
|
154 | 154 | |
|
|
155 | 155 | self.delay = 60 #seconds |
|
|
156 | 156 | |
|
|
157 | 157 | self.nTries = 3 #quantity tries |
|
|
158 | 158 | |
|
|
159 | 159 | self.nFiles = 3 #number of files for searching |
|
|
160 | 160 | |
|
|
161 | 161 | self.nReadBlocks = 0 |
|
|
162 | 162 | |
|
|
163 | 163 | self.flagIsNewFile = 1 |
|
|
164 | 164 | |
|
|
165 | 165 | self.__isFirstTimeOnline = 1 |
|
|
166 | 166 | |
|
|
167 | 167 | # self.ippSeconds = 0 |
|
|
168 | 168 | |
|
|
169 | 169 | self.flagDiscontinuousBlock = 0 |
|
|
170 | 170 | |
|
|
171 | 171 | self.flagIsNewBlock = 0 |
|
|
172 | 172 | |
|
|
173 | 173 | self.nTotalBlocks = 0 |
|
|
174 | 174 | |
|
|
175 | 175 | self.blocksize = 0 |
|
|
176 | 176 | |
|
|
177 | 177 | self.dataOut = self.createObjByDefault() |
|
|
178 | 178 | |
|
|
179 | 179 | self.profileIndex = 1 #Always |
|
|
180 | 180 | |
|
|
181 | 181 | |
|
|
182 | 182 | def createObjByDefault(self): |
|
|
183 | 183 | |
|
|
184 | 184 | dataObj = Spectra() |
|
|
185 | 185 | |
|
|
186 | 186 | return dataObj |
|
|
187 | 187 | |
|
|
188 | 188 | def __hasNotDataInBuffer(self): |
|
|
189 | 189 | return 1 |
|
|
190 | 190 | |
|
|
191 | 191 | |
|
|
192 | 192 | def getBlockDimension(self): |
|
|
193 | 193 | """ |
|
|
194 | 194 | Obtiene la cantidad de puntos a leer por cada bloque de datos |
|
|
195 | 195 | |
|
|
196 | 196 | Affected: |
|
|
197 | 197 | self.nRdChannels |
|
|
198 | 198 | self.nRdPairs |
|
|
199 | 199 | self.pts2read_SelfSpectra |
|
|
200 | 200 | self.pts2read_CrossSpectra |
|
|
201 | 201 | self.pts2read_DCchannels |
|
|
202 | 202 | self.blocksize |
|
|
203 | 203 | self.dataOut.nChannels |
|
|
204 | 204 | self.dataOut.nPairs |
|
|
205 | 205 | |
|
|
206 | 206 | Return: |
|
|
207 | 207 | None |
|
|
208 | 208 | """ |
|
|
209 | 209 | self.nRdChannels = 0 |
|
|
210 | 210 | self.nRdPairs = 0 |
|
|
211 | 211 | self.rdPairList = [] |
|
|
212 | 212 | |
|
|
213 | 213 | for i in range(0, self.processingHeaderObj.totalSpectra*2, 2): |
|
|
214 | 214 | if self.processingHeaderObj.spectraComb[i] == self.processingHeaderObj.spectraComb[i+1]: |
|
|
215 | 215 | self.nRdChannels = self.nRdChannels + 1 #par de canales iguales |
|
|
216 | 216 | |
|
|
217 | 217 | else: |
|
|
218 | 218 | self.nRdPairs = self.nRdPairs + 1 #par de canales diferentes |
|
|
219 | 219 | self.rdPairList.append((self.processingHeaderObj.spectraComb[i], self.processingHeaderObj.spectraComb[i+1])) |
|
|
220 | 220 | |
|
|
221 | 221 | pts2read = self.processingHeaderObj.nHeights * self.processingHeaderObj.profilesPerBlock |
|
|
222 | 222 | |
|
|
223 | 223 | self.pts2read_SelfSpectra = int(self.nRdChannels * pts2read) |
|
|
224 | 224 | self.blocksize = self.pts2read_SelfSpectra |
|
|
225 | 225 | |
|
|
226 | 226 | if self.processingHeaderObj.flag_cspc: |
|
|
227 | 227 | self.pts2read_CrossSpectra = int(self.nRdPairs * pts2read) |
|
|
228 | 228 | self.blocksize += self.pts2read_CrossSpectra |
|
|
229 | 229 | |
|
|
230 | 230 | if self.processingHeaderObj.flag_dc: |
|
|
231 | 231 | self.pts2read_DCchannels = int(self.systemHeaderObj.nChannels * self.processingHeaderObj.nHeights) |
|
|
232 | 232 | self.blocksize += self.pts2read_DCchannels |
|
|
233 | 233 | |
|
|
234 | 234 | # self.blocksize = self.pts2read_SelfSpectra + self.pts2read_CrossSpectra + self.pts2read_DCchannels |
|
|
235 | 235 | |
|
|
236 | 236 | |
|
|
237 | 237 | def readBlock(self): |
|
|
238 | 238 | """ |
|
|
239 | 239 | Lee el bloque de datos desde la posicion actual del puntero del archivo |
|
|
240 | 240 | (self.fp) y actualiza todos los parametros relacionados al bloque de datos |
|
|
241 | 241 | (metadata + data). La data leida es almacenada en el buffer y el contador del buffer |
|
|
242 | 242 | es seteado a 0 |
|
|
243 | 243 | |
|
|
244 | 244 | Return: None |
|
|
245 | 245 | |
|
|
246 | 246 | Variables afectadas: |
|
|
247 | 247 | |
|
|
248 | 248 | |
|
|
249 | 249 | self.flagIsNewFile |
|
|
250 | 250 | self.flagIsNewBlock |
|
|
251 | 251 | self.nTotalBlocks |
|
|
252 | 252 | self.data_spc |
|
|
253 | 253 | self.data_cspc |
|
|
254 | 254 | self.data_dc |
|
|
255 | 255 | |
|
|
256 | 256 | Exceptions: |
|
|
257 | 257 | Si un bloque leido no es un bloque valido |
|
|
258 | 258 | """ |
|
|
259 | print ' ======================================================== ' | |
|
|
260 | print ' ' | |
|
|
261 | print ' ' | |
|
|
262 | print self.processingHeaderObj.totalSpectra, 'TotalSpectra', type(self.processingHeaderObj.totalSpectra) | |
|
|
263 | print self.processingHeaderObj.spectraComb, 'SpectraComb', type(self.processingHeaderObj.spectraComb) | |
|
|
264 | print ' ' | |
|
|
265 | print ' ' | |
|
|
266 | print ' ======================================================== ' | |
|
|
267 | ||
|
|
268 | 259 | |
|
|
269 | 260 | blockOk_flag = False |
|
|
270 | 261 | fpointer = self.fp.tell() |
|
|
271 | 262 | |
|
|
272 | 263 | spc = numpy.fromfile( self.fp, self.dtype[0], self.pts2read_SelfSpectra ) |
|
|
273 | 264 | spc = spc.reshape( (self.nRdChannels, self.processingHeaderObj.nHeights, self.processingHeaderObj.profilesPerBlock) ) #transforma a un arreglo 3D |
|
|
274 | 265 | |
|
|
275 | 266 | if self.processingHeaderObj.flag_cspc: |
|
|
276 | 267 | cspc = numpy.fromfile( self.fp, self.dtype, self.pts2read_CrossSpectra ) |
|
|
277 | 268 | cspc = cspc.reshape( (self.nRdPairs, self.processingHeaderObj.nHeights, self.processingHeaderObj.profilesPerBlock) ) #transforma a un arreglo 3D |
|
|
278 | 269 | |
|
|
279 | 270 | if self.processingHeaderObj.flag_dc: |
|
|
280 | 271 | dc = numpy.fromfile( self.fp, self.dtype, self.pts2read_DCchannels ) #int(self.processingHeaderObj.nHeights*self.systemHeaderObj.nChannels) ) |
|
|
281 | 272 | dc = dc.reshape( (self.systemHeaderObj.nChannels, self.processingHeaderObj.nHeights) ) #transforma a un arreglo 2D |
|
|
282 | 273 | |
|
|
283 | 274 | |
|
|
284 | 275 | if not(self.processingHeaderObj.shif_fft): |
|
|
285 | 276 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
286 | 277 | shift = int(self.processingHeaderObj.profilesPerBlock/2) |
|
|
287 | 278 | spc = numpy.roll( spc, shift , axis=2 ) |
|
|
288 | 279 | |
|
|
289 | 280 | if self.processingHeaderObj.flag_cspc: |
|
|
290 | 281 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
291 | 282 | cspc = numpy.roll( cspc, shift, axis=2 ) |
|
|
292 | 283 | |
|
|
293 | 284 | #Dimensions : nChannels, nProfiles, nSamples |
|
|
294 | 285 | spc = numpy.transpose( spc, (0,2,1) ) |
|
|
295 | 286 | self.data_spc = spc |
|
|
296 | 287 | |
|
|
297 | 288 | if self.processingHeaderObj.flag_cspc: |
|
|
298 | 289 | |
|
|
299 | 290 | cspc = numpy.transpose( cspc, (0,2,1) ) |
|
|
300 | 291 | self.data_cspc = cspc['real'] + cspc['imag']*1j |
|
|
301 | 292 | else: |
|
|
302 | 293 | self.data_cspc = None |
|
|
303 | 294 | |
|
|
304 | 295 | |
|
|
305 | 296 | if self.processingHeaderObj.flag_dc: |
|
|
306 | 297 | self.data_dc = dc['real'] + dc['imag']*1j |
|
|
307 | 298 | else: |
|
|
308 | 299 | self.data_dc = None |
|
|
309 | 300 | |
|
|
310 | 301 | self.flagIsNewFile = 0 |
|
|
311 | 302 | self.flagIsNewBlock = 1 |
|
|
312 | 303 | |
|
|
313 | 304 | self.nTotalBlocks += 1 |
|
|
314 | 305 | self.nReadBlocks += 1 |
|
|
315 | 306 | |
|
|
316 | 307 | return 1 |
|
|
317 | 308 | |
|
|
318 | 309 | def getFirstHeader(self): |
|
|
319 | 310 | |
|
|
320 | 311 | self.getBasicHeader() |
|
|
321 | 312 | |
|
|
322 | 313 | self.dataOut.systemHeaderObj = self.systemHeaderObj.copy() |
|
|
323 | 314 | |
|
|
324 | 315 | self.dataOut.radarControllerHeaderObj = self.radarControllerHeaderObj.copy() |
|
|
325 | 316 | |
|
|
326 | 317 | # self.dataOut.ippSeconds = self.ippSeconds |
|
|
327 | 318 | |
|
|
328 | 319 | # self.dataOut.timeInterval = self.radarControllerHeaderObj.ippSeconds * self.processingHeaderObj.nCohInt * self.processingHeaderObj.nIncohInt * self.processingHeaderObj.profilesPerBlock |
|
|
329 | 320 | |
|
|
330 | 321 | self.dataOut.dtype = self.dtype |
|
|
331 | 322 | |
|
|
332 | 323 | # self.dataOut.nPairs = self.nPairs |
|
|
333 | 324 | |
|
|
334 | 325 | self.dataOut.pairsList = self.rdPairList |
|
|
335 | 326 | |
|
|
336 | 327 | self.dataOut.nProfiles = self.processingHeaderObj.profilesPerBlock |
|
|
337 | 328 | |
|
|
338 | 329 | self.dataOut.nFFTPoints = self.processingHeaderObj.profilesPerBlock |
|
|
339 | 330 | |
|
|
340 | 331 | self.dataOut.nCohInt = self.processingHeaderObj.nCohInt |
|
|
341 | 332 | |
|
|
342 | 333 | self.dataOut.nIncohInt = self.processingHeaderObj.nIncohInt |
|
|
343 | 334 | |
|
|
344 | 335 | xf = self.processingHeaderObj.firstHeight + self.processingHeaderObj.nHeights*self.processingHeaderObj.deltaHeight |
|
|
345 | 336 | |
|
|
346 | 337 | self.dataOut.heightList = numpy.arange(self.processingHeaderObj.firstHeight, xf, self.processingHeaderObj.deltaHeight) |
|
|
347 | 338 | |
|
|
348 | 339 | self.dataOut.channelList = range(self.systemHeaderObj.nChannels) |
|
|
349 | 340 | |
|
|
350 | 341 | self.dataOut.flagShiftFFT = True #Data is always shifted |
|
|
351 | 342 | |
|
|
352 | 343 | self.dataOut.flagDecodeData = self.processingHeaderObj.flag_decode #asumo q la data no esta decodificada |
|
|
353 | 344 | |
|
|
354 | 345 | self.dataOut.flagDeflipData = self.processingHeaderObj.flag_deflip #asumo q la data esta sin flip |
|
|
355 | 346 | |
|
|
356 | 347 | def getData(self): |
|
|
357 | 348 | """ |
|
|
358 | 349 | First method to execute before "RUN" is called. |
|
|
359 | 350 | |
|
|
360 | 351 | Copia el buffer de lectura a la clase "Spectra", |
|
|
361 | 352 | con todos los parametros asociados a este (metadata). cuando no hay datos en el buffer de |
|
|
362 | 353 | lectura es necesario hacer una nueva lectura de los bloques de datos usando "readNextBlock" |
|
|
363 | 354 | |
|
|
364 | 355 | Return: |
|
|
365 | 356 | 0 : Si no hay mas archivos disponibles |
|
|
366 | 357 | 1 : Si hizo una buena copia del buffer |
|
|
367 | 358 | |
|
|
368 | 359 | Affected: |
|
|
369 | 360 | self.dataOut |
|
|
370 | 361 | |
|
|
371 | 362 | self.flagDiscontinuousBlock |
|
|
372 | 363 | self.flagIsNewBlock |
|
|
373 | 364 | """ |
|
|
374 | 365 | |
|
|
375 | 366 | if self.flagNoMoreFiles: |
|
|
376 | 367 | self.dataOut.flagNoData = True |
|
|
377 | 368 | print 'Process finished' |
|
|
378 | 369 | return 0 |
|
|
379 | 370 | |
|
|
380 | 371 | self.flagDiscontinuousBlock = 0 |
|
|
381 | 372 | self.flagIsNewBlock = 0 |
|
|
382 | 373 | |
|
|
383 | 374 | if self.__hasNotDataInBuffer(): |
|
|
384 | 375 | |
|
|
385 | 376 | if not( self.readNextBlock() ): |
|
|
386 | 377 | self.dataOut.flagNoData = True |
|
|
387 | 378 | return 0 |
|
|
388 | 379 | |
|
|
389 | 380 | |
|
|
390 | 381 | #data es un numpy array de 3 dmensiones (perfiles, alturas y canales) |
|
|
391 | 382 | |
|
|
392 | 383 | if self.data_spc is None: |
|
|
393 | 384 | self.dataOut.flagNoData = True |
|
|
394 | 385 | return 0 |
|
|
395 | 386 | |
|
|
396 | 387 | self.getBasicHeader() |
|
|
397 | 388 | |
|
|
398 | 389 | self.getFirstHeader() |
|
|
399 | 390 | |
|
|
400 | 391 | self.dataOut.data_spc = self.data_spc |
|
|
401 | 392 | |
|
|
402 | 393 | self.dataOut.data_cspc = self.data_cspc |
|
|
403 | 394 | |
|
|
404 | 395 | self.dataOut.data_dc = self.data_dc |
|
|
405 | 396 | |
|
|
406 | 397 | self.dataOut.flagNoData = False |
|
|
407 | 398 | |
|
|
408 | 399 | self.dataOut.realtime = self.online |
|
|
409 | 400 | |
|
|
410 | 401 | return self.dataOut.data_spc |
|
|
411 | 402 | |
|
|
412 | 403 | class SpectraWriter(JRODataWriter, Operation): |
|
|
413 | 404 | |
|
|
414 | 405 | """ |
|
|
415 | 406 | Esta clase permite escribir datos de espectros a archivos procesados (.pdata). La escritura |
|
|
416 | 407 | de los datos siempre se realiza por bloques. |
|
|
417 | 408 | """ |
|
|
418 | 409 | |
|
|
419 | 410 | ext = ".pdata" |
|
|
420 | 411 | |
|
|
421 | 412 | optchar = "P" |
|
|
422 | 413 | |
|
|
423 | 414 | shape_spc_Buffer = None |
|
|
424 | 415 | |
|
|
425 | 416 | shape_cspc_Buffer = None |
|
|
426 | 417 | |
|
|
427 | 418 | shape_dc_Buffer = None |
|
|
428 | 419 | |
|
|
429 | 420 | data_spc = None |
|
|
430 | 421 | |
|
|
431 | 422 | data_cspc = None |
|
|
432 | 423 | |
|
|
433 | 424 | data_dc = None |
|
|
434 | 425 | |
|
|
435 | 426 | # dataOut = None |
|
|
436 | 427 | |
|
|
437 | 428 | def __init__(self): |
|
|
438 | 429 | """ |
|
|
439 | 430 | Inicializador de la clase SpectraWriter para la escritura de datos de espectros. |
|
|
440 | 431 | |
|
|
441 | 432 | Affected: |
|
|
442 | 433 | |
|
|
443 | 434 | self.dataOut |
|
|
444 | 435 | self.basicHeaderObj |
|
|
445 | 436 | self.systemHeaderObj |
|
|
446 | 437 | self.radarControllerHeaderObj |
|
|
447 | 438 | self.processingHeaderObj |
|
|
448 | 439 | |
|
|
449 | 440 | Return: None |
|
|
450 | 441 | """ |
|
|
451 | 442 | |
|
|
452 | 443 | Operation.__init__(self) |
|
|
453 | 444 | |
|
|
454 | 445 | self.isConfig = False |
|
|
455 | 446 | |
|
|
456 | 447 | self.nTotalBlocks = 0 |
|
|
457 | 448 | |
|
|
458 | 449 | self.data_spc = None |
|
|
459 | 450 | |
|
|
460 | 451 | self.data_cspc = None |
|
|
461 | 452 | |
|
|
462 | 453 | |
|
|
463 | 454 | self.data_dc = None |
|
|
464 | 455 | |
|
|
465 | 456 | self.fp = None |
|
|
466 | 457 | |
|
|
467 | 458 | self.flagIsNewFile = 1 |
|
|
468 | 459 | |
|
|
469 | 460 | self.nTotalBlocks = 0 |
|
|
470 | 461 | |
|
|
471 | 462 | self.flagIsNewBlock = 0 |
|
|
472 | 463 | |
|
|
473 | 464 | self.setFile = None |
|
|
474 | 465 | |
|
|
475 | 466 | self.dtype = None |
|
|
476 | 467 | |
|
|
477 | 468 | self.path = None |
|
|
478 | 469 | |
|
|
479 | 470 | self.noMoreFiles = 0 |
|
|
480 | 471 | |
|
|
481 | 472 | self.filename = None |
|
|
482 | 473 | |
|
|
483 | 474 | self.basicHeaderObj = BasicHeader(LOCALTIME) |
|
|
484 | 475 | |
|
|
485 | 476 | self.systemHeaderObj = SystemHeader() |
|
|
486 | 477 | |
|
|
487 | 478 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
488 | 479 | |
|
|
489 | 480 | self.processingHeaderObj = ProcessingHeader() |
|
|
490 | 481 | |
|
|
491 | 482 | |
|
|
492 | 483 | def hasAllDataInBuffer(self): |
|
|
493 | 484 | return 1 |
|
|
494 | 485 | |
|
|
495 | 486 | |
|
|
496 | 487 | |
|
|
497 | 488 | def setBlockDimension(self): |
|
|
498 | 489 | """ |
|
|
499 | 490 | Obtiene las formas dimensionales del los subbloques de datos que componen un bloque |
|
|
500 | 491 | |
|
|
501 | 492 | Affected: |
|
|
502 | 493 | self.shape_spc_Buffer |
|
|
503 | 494 | self.shape_cspc_Buffer |
|
|
504 | 495 | self.shape_dc_Buffer |
|
|
505 | 496 | |
|
|
506 | 497 | Return: None |
|
|
507 | 498 | """ |
|
|
508 | 499 | self.shape_spc_Buffer = (self.dataOut.nChannels, |
|
|
509 | 500 | self.processingHeaderObj.nHeights, |
|
|
510 | 501 | self.processingHeaderObj.profilesPerBlock) |
|
|
511 | 502 | |
|
|
512 | 503 | self.shape_cspc_Buffer = (self.dataOut.nPairs, |
|
|
513 | 504 | self.processingHeaderObj.nHeights, |
|
|
514 | 505 | self.processingHeaderObj.profilesPerBlock) |
|
|
515 | 506 | |
|
|
516 | 507 | self.shape_dc_Buffer = (self.dataOut.nChannels, |
|
|
517 | 508 | self.processingHeaderObj.nHeights) |
|
|
518 | 509 | |
|
|
519 | 510 | |
|
|
520 | 511 | def writeBlock(self): |
|
|
521 | 512 | """ |
|
|
522 | 513 | Escribe el buffer en el file designado |
|
|
523 | 514 | |
|
|
524 | 515 | |
|
|
525 | 516 | Affected: |
|
|
526 | 517 | self.data_spc |
|
|
527 | 518 | self.data_cspc |
|
|
528 | 519 | self.data_dc |
|
|
529 | 520 | self.flagIsNewFile |
|
|
530 | 521 | self.flagIsNewBlock |
|
|
531 | 522 | self.nTotalBlocks |
|
|
532 | 523 | self.nWriteBlocks |
|
|
533 | 524 | |
|
|
534 | 525 | Return: None |
|
|
535 | 526 | """ |
|
|
536 | 527 | |
|
|
537 | 528 | spc = numpy.transpose( self.data_spc, (0,2,1) ) |
|
|
538 | 529 | if not( self.processingHeaderObj.shif_fft ): |
|
|
539 | 530 | spc = numpy.roll( spc, self.processingHeaderObj.profilesPerBlock/2, axis=2 ) #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
540 | 531 | data = spc.reshape((-1)) |
|
|
541 | 532 | data = data.astype(self.dtype[0]) |
|
|
542 | 533 | data.tofile(self.fp) |
|
|
543 | 534 | |
|
|
544 | 535 | if self.data_cspc is not None: |
|
|
545 | 536 | data = numpy.zeros( self.shape_cspc_Buffer, self.dtype ) |
|
|
546 | 537 | cspc = numpy.transpose( self.data_cspc, (0,2,1) ) |
|
|
547 | 538 | if not( self.processingHeaderObj.shif_fft ): |
|
|
548 | 539 | cspc = numpy.roll( cspc, self.processingHeaderObj.profilesPerBlock/2, axis=2 ) #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
549 | 540 | data['real'] = cspc.real |
|
|
550 | 541 | data['imag'] = cspc.imag |
|
|
551 | 542 | data = data.reshape((-1)) |
|
|
552 | 543 | data.tofile(self.fp) |
|
|
553 | 544 | |
|
|
554 | 545 | |
|
|
555 | 546 | if self.data_dc is not None: |
|
|
556 | 547 | data = numpy.zeros( self.shape_dc_Buffer, self.dtype ) |
|
|
557 | 548 | dc = self.data_dc |
|
|
558 | 549 | data['real'] = dc.real |
|
|
559 | 550 | data['imag'] = dc.imag |
|
|
560 | 551 | data = data.reshape((-1)) |
|
|
561 | 552 | data.tofile(self.fp) |
|
|
562 | 553 | |
|
|
563 | 554 | # self.data_spc.fill(0) |
|
|
564 | 555 | # |
|
|
565 | 556 | # if self.data_dc is not None: |
|
|
566 | 557 | # self.data_dc.fill(0) |
|
|
567 | 558 | # |
|
|
568 | 559 | # if self.data_cspc is not None: |
|
|
569 | 560 | # self.data_cspc.fill(0) |
|
|
570 | 561 | |
|
|
571 | 562 | |
|
|
572 | 563 | self.flagIsNewFile = 0 |
|
|
573 | 564 | self.flagIsNewBlock = 1 |
|
|
574 | 565 | self.nTotalBlocks += 1 |
|
|
575 | 566 | self.nWriteBlocks += 1 |
|
|
576 | 567 | self.blockIndex += 1 |
|
|
577 | 568 | |
|
|
578 | 569 | # print "[Writing] Block = %d04" %self.blockIndex |
|
|
579 | 570 | |
|
|
580 | 571 | def putData(self): |
|
|
581 | 572 | """ |
|
|
582 | 573 | Setea un bloque de datos y luego los escribe en un file |
|
|
583 | 574 | |
|
|
584 | 575 | |
|
|
585 | 576 | Affected: |
|
|
586 | 577 | self.data_spc |
|
|
587 | 578 | self.data_cspc |
|
|
588 | 579 | self.data_dc |
|
|
589 | 580 | |
|
|
590 | 581 | Return: |
|
|
591 | 582 | 0 : Si no hay data o no hay mas files que puedan escribirse |
|
|
592 | 583 | 1 : Si se escribio la data de un bloque en un file |
|
|
593 | 584 | """ |
|
|
594 | 585 | |
|
|
595 | 586 | if self.dataOut.flagNoData: |
|
|
596 | 587 | return 0 |
|
|
597 | 588 | |
|
|
598 | 589 | self.flagIsNewBlock = 0 |
|
|
599 | 590 | |
|
|
600 | 591 | if self.dataOut.flagDiscontinuousBlock: |
|
|
601 | 592 | self.data_spc.fill(0) |
|
|
602 | 593 | self.data_cspc.fill(0) |
|
|
603 | 594 | self.data_dc.fill(0) |
|
|
604 | 595 | self.setNextFile() |
|
|
605 | 596 | |
|
|
606 | 597 | if self.flagIsNewFile == 0: |
|
|
607 | 598 | self.setBasicHeader() |
|
|
608 | 599 | |
|
|
609 | 600 | self.data_spc = self.dataOut.data_spc.copy() |
|
|
610 | 601 | |
|
|
611 | 602 | if self.dataOut.data_cspc is not None: |
|
|
612 | 603 | self.data_cspc = self.dataOut.data_cspc.copy() |
|
|
613 | 604 | |
|
|
614 | 605 | if self.dataOut.data_dc is not None: |
|
|
615 | 606 | self.data_dc = self.dataOut.data_dc.copy() |
|
|
616 | 607 | |
|
|
617 | 608 | # #self.processingHeaderObj.dataBlocksPerFile) |
|
|
618 | 609 | if self.hasAllDataInBuffer(): |
|
|
619 | 610 | # self.setFirstHeader() |
|
|
620 | 611 | self.writeNextBlock() |
|
|
621 | 612 | |
|
|
622 | 613 | return 1 |
|
|
623 | 614 | |
|
|
624 | 615 | |
|
|
625 | 616 | def __getBlockSize(self): |
|
|
626 | 617 | ''' |
|
|
627 | 618 | Este metodos determina el cantidad de bytes para un bloque de datos de tipo Spectra |
|
|
628 | 619 | ''' |
|
|
629 | 620 | |
|
|
630 | 621 | dtype_width = self.getDtypeWidth() |
|
|
631 | 622 | |
|
|
632 | 623 | pts2write = self.dataOut.nHeights * self.dataOut.nFFTPoints |
|
|
633 | 624 | |
|
|
634 | 625 | pts2write_SelfSpectra = int(self.dataOut.nChannels * pts2write) |
|
|
635 | 626 | blocksize = (pts2write_SelfSpectra*dtype_width) |
|
|
636 | 627 | |
|
|
637 | 628 | if self.dataOut.data_cspc is not None: |
|
|
638 | 629 | pts2write_CrossSpectra = int(self.dataOut.nPairs * pts2write) |
|
|
639 | 630 | blocksize += (pts2write_CrossSpectra*dtype_width*2) |
|
|
640 | 631 | |
|
|
641 | 632 | if self.dataOut.data_dc is not None: |
|
|
642 | 633 | pts2write_DCchannels = int(self.dataOut.nChannels * self.dataOut.nHeights) |
|
|
643 | 634 | blocksize += (pts2write_DCchannels*dtype_width*2) |
|
|
644 | 635 | |
|
|
645 | 636 | # blocksize = blocksize #* datatypeValue * 2 #CORREGIR ESTO |
|
|
646 | 637 | |
|
|
647 | 638 | return blocksize |
|
|
648 | 639 | |
|
|
649 | 640 | def setFirstHeader(self): |
|
|
650 | 641 | |
|
|
651 | 642 | """ |
|
|
652 | 643 | Obtiene una copia del First Header |
|
|
653 | 644 | |
|
|
654 | 645 | Affected: |
|
|
655 | 646 | self.systemHeaderObj |
|
|
656 | 647 | self.radarControllerHeaderObj |
|
|
657 | 648 | self.dtype |
|
|
658 | 649 | |
|
|
659 | 650 | Return: |
|
|
660 | 651 | None |
|
|
661 | 652 | """ |
|
|
662 | 653 | |
|
|
663 | 654 | self.systemHeaderObj = self.dataOut.systemHeaderObj.copy() |
|
|
664 | 655 | self.systemHeaderObj.nChannels = self.dataOut.nChannels |
|
|
665 | 656 | self.radarControllerHeaderObj = self.dataOut.radarControllerHeaderObj.copy() |
|
|
666 | 657 | |
|
|
667 | 658 | self.processingHeaderObj.dtype = 1 # Spectra |
|
|
668 | 659 | self.processingHeaderObj.blockSize = self.__getBlockSize() |
|
|
669 | 660 | self.processingHeaderObj.profilesPerBlock = self.dataOut.nFFTPoints |
|
|
670 | 661 | self.processingHeaderObj.dataBlocksPerFile = self.blocksPerFile |
|
|
671 | 662 | self.processingHeaderObj.nWindows = 1 #podria ser 1 o self.dataOut.processingHeaderObj.nWindows |
|
|
672 | 663 | self.processingHeaderObj.nCohInt = self.dataOut.nCohInt# Se requiere para determinar el valor de timeInterval |
|
|
673 | 664 | self.processingHeaderObj.nIncohInt = self.dataOut.nIncohInt |
|
|
674 | 665 | self.processingHeaderObj.totalSpectra = self.dataOut.nPairs + self.dataOut.nChannels |
|
|
675 | 666 | self.processingHeaderObj.shif_fft = self.dataOut.flagShiftFFT |
|
|
676 | 667 | |
|
|
677 | 668 | |
|
|
678 | 669 | if self.processingHeaderObj.totalSpectra > 0: |
|
|
679 | 670 | channelList = [] |
|
|
680 | 671 | for channel in range(self.dataOut.nChannels): |
|
|
681 | 672 | channelList.append(channel) |
|
|
682 | 673 | channelList.append(channel) |
|
|
683 | 674 | |
|
|
684 | 675 | pairsList = [] |
|
|
685 | 676 | if self.dataOut.nPairs > 0: |
|
|
686 | 677 | for pair in self.dataOut.pairsList: |
|
|
687 | 678 | pairsList.append(pair[0]) |
|
|
688 | 679 | pairsList.append(pair[1]) |
|
|
689 | 680 | |
|
|
690 | 681 | spectraComb = channelList + pairsList |
|
|
691 | 682 | spectraComb = numpy.array(spectraComb, dtype="u1") |
|
|
692 | 683 | self.processingHeaderObj.spectraComb = spectraComb |
|
|
693 | 684 | |
|
|
694 | 685 | if self.dataOut.code is not None: |
|
|
695 | 686 | self.processingHeaderObj.code = self.dataOut.code |
|
|
696 | 687 | self.processingHeaderObj.nCode = self.dataOut.nCode |
|
|
697 | 688 | self.processingHeaderObj.nBaud = self.dataOut.nBaud |
|
|
698 | 689 | |
|
|
699 | 690 | if self.processingHeaderObj.nWindows != 0: |
|
|
700 | 691 | self.processingHeaderObj.firstHeight = self.dataOut.heightList[0] |
|
|
701 | 692 | self.processingHeaderObj.deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
702 | 693 | self.processingHeaderObj.nHeights = self.dataOut.nHeights |
|
|
703 | 694 | self.processingHeaderObj.samplesWin = self.dataOut.nHeights |
|
|
704 | 695 | |
|
|
705 | 696 | self.processingHeaderObj.processFlags = self.getProcessFlags() |
|
|
706 | 697 | |
|
|
707 | 698 | self.setBasicHeader() |
| @@ -1,4042 +1,4045 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import math |
|
|
3 | 3 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
4 | 4 | import scipy |
|
|
5 | 5 | import re |
|
|
6 | 6 | import datetime |
|
|
7 | 7 | import copy |
|
|
8 | 8 | import sys |
|
|
9 | 9 | import importlib |
|
|
10 | 10 | import itertools |
|
|
11 | 11 | from multiprocessing import Pool, TimeoutError |
|
|
12 | 12 | from multiprocessing.pool import ThreadPool |
|
|
13 | 13 | import copy_reg |
|
|
14 | 14 | import cPickle |
|
|
15 | 15 | import types |
|
|
16 | 16 | from functools import partial |
|
|
17 | 17 | import time |
|
|
18 | 18 | #from sklearn.cluster import KMeans |
|
|
19 | 19 | |
|
|
20 | 20 | import matplotlib.pyplot as plt |
|
|
21 | 21 | |
|
|
22 | 22 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
|
23 | 23 | from jroproc_base import ProcessingUnit, Operation |
|
|
24 | 24 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
25 | 25 | from scipy import asarray as ar,exp |
|
|
26 | 26 | from scipy.optimize import curve_fit |
|
|
27 | 27 | |
|
|
28 | 28 | import warnings |
|
|
29 | 29 | from numpy import NaN |
|
|
30 | 30 | from scipy.optimize.optimize import OptimizeWarning |
|
|
31 | 31 | warnings.filterwarnings('ignore') |
|
|
32 | 32 | |
|
|
33 | 33 | |
|
|
34 | 34 | SPEED_OF_LIGHT = 299792458 |
|
|
35 | 35 | |
|
|
36 | 36 | |
|
|
37 | 37 | '''solving pickling issue''' |
|
|
38 | 38 | |
|
|
39 | 39 | def _pickle_method(method): |
|
|
40 | 40 | func_name = method.im_func.__name__ |
|
|
41 | 41 | obj = method.im_self |
|
|
42 | 42 | cls = method.im_class |
|
|
43 | 43 | return _unpickle_method, (func_name, obj, cls) |
|
|
44 | 44 | |
|
|
45 | 45 | def _unpickle_method(func_name, obj, cls): |
|
|
46 | 46 | for cls in cls.mro(): |
|
|
47 | 47 | try: |
|
|
48 | 48 | func = cls.__dict__[func_name] |
|
|
49 | 49 | except KeyError: |
|
|
50 | 50 | pass |
|
|
51 | 51 | else: |
|
|
52 | 52 | break |
|
|
53 | 53 | return func.__get__(obj, cls) |
|
|
54 | 54 | |
|
|
55 | 55 | class ParametersProc(ProcessingUnit): |
|
|
56 | 56 | |
|
|
57 | 57 | nSeconds = None |
|
|
58 | 58 | |
|
|
59 | 59 | def __init__(self): |
|
|
60 | 60 | ProcessingUnit.__init__(self) |
|
|
61 | 61 | |
|
|
62 | 62 | # self.objectDict = {} |
|
|
63 | 63 | self.buffer = None |
|
|
64 | 64 | self.firstdatatime = None |
|
|
65 | 65 | self.profIndex = 0 |
|
|
66 | 66 | self.dataOut = Parameters() |
|
|
67 | 67 | |
|
|
68 | 68 | def __updateObjFromInput(self): |
|
|
69 | 69 | |
|
|
70 | 70 | self.dataOut.inputUnit = self.dataIn.type |
|
|
71 | 71 | |
|
|
72 | 72 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
73 | 73 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
74 | 74 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
75 | 75 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
76 | 76 | |
|
|
77 | 77 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
78 | 78 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
79 | 79 | self.dataOut.channelList = self.dataIn.channelList |
|
|
80 | 80 | self.dataOut.heightList = self.dataIn.heightList |
|
|
81 | 81 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
82 | 82 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
83 | 83 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
84 | 84 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
85 | 85 | self.dataOut.nCode = self.dataIn.nCode |
|
|
86 | 86 | self.dataOut.code = self.dataIn.code |
|
|
87 | 87 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
88 | 88 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
89 | 89 | # self.dataOut.utctime = self.firstdatatime |
|
|
90 | 90 | self.dataOut.utctime = self.dataIn.utctime |
|
|
91 | 91 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
92 | 92 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
93 | 93 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
94 | 94 | # self.dataOut.nIncohInt = 1 |
|
|
95 | 95 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
96 | 96 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
97 | 97 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
98 | 98 | self.dataOut.heightList = self.dataIn.getHeiRange() |
|
|
99 | 99 | self.dataOut.frequency = self.dataIn.frequency |
|
|
100 | 100 | # self.dataOut.noise = self.dataIn.noise |
|
|
101 | 101 | |
|
|
102 | 102 | def run(self): |
|
|
103 | 103 | |
|
|
104 | 104 | #---------------------- Voltage Data --------------------------- |
|
|
105 | 105 | |
|
|
106 | 106 | if self.dataIn.type == "Voltage": |
|
|
107 | 107 | |
|
|
108 | 108 | self.__updateObjFromInput() |
|
|
109 | 109 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
110 | 110 | self.dataOut.flagNoData = False |
|
|
111 | 111 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
112 | 112 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
113 | 113 | return |
|
|
114 | 114 | |
|
|
115 | 115 | #---------------------- Spectra Data --------------------------- |
|
|
116 | 116 | |
|
|
117 | 117 | if self.dataIn.type == "Spectra": |
|
|
118 | 118 | |
|
|
119 | 119 | self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc) |
|
|
120 | 120 | self.dataOut.data_spc = self.dataIn.data_spc |
|
|
121 | 121 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
122 | 122 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
123 | 123 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
124 | 124 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
125 | 125 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
126 | 126 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
127 | 127 | self.dataOut.spc_noise = self.dataIn.getNoise() |
|
|
128 | 128 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1)/1000. , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
|
129 | 129 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|
130 | 130 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
131 | 131 | self.dataOut.flagNoData = False |
|
|
132 | 132 | |
|
|
133 | 133 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
|
134 | 134 | self.dataOut.ChanDist = self.dataIn.ChanDist |
|
|
135 | 135 | else: self.dataOut.ChanDist = None |
|
|
136 | 136 | |
|
|
137 | 137 | if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
|
138 | 138 | self.dataOut.VelRange = self.dataIn.VelRange |
|
|
139 | 139 | else: self.dataOut.VelRange = None |
|
|
140 | 140 | |
|
|
141 | 141 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
|
142 | 142 | self.dataOut.RadarConst = self.dataIn.RadarConst |
|
|
143 | 143 | |
|
|
144 | 144 | if hasattr(self.dataIn, 'NPW'): #NPW |
|
|
145 | 145 | self.dataOut.NPW = self.dataIn.NPW |
|
|
146 | 146 | |
|
|
147 | 147 | if hasattr(self.dataIn, 'COFA'): #COFA |
|
|
148 | 148 | self.dataOut.COFA = self.dataIn.COFA |
|
|
149 | 149 | |
|
|
150 | 150 | |
|
|
151 | 151 | |
|
|
152 | 152 | #---------------------- Correlation Data --------------------------- |
|
|
153 | 153 | |
|
|
154 | 154 | if self.dataIn.type == "Correlation": |
|
|
155 | 155 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
156 | 156 | |
|
|
157 | 157 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
158 | 158 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
159 | 159 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
160 | 160 | |
|
|
161 | 161 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
162 | 162 | self.dataOut.noise = self.dataIn.noise |
|
|
163 | 163 | self.dataOut.data_SNR = self.dataIn.SNR |
|
|
164 | 164 | self.dataOut.flagNoData = False |
|
|
165 | 165 | self.dataOut.nAvg = self.dataIn.nAvg |
|
|
166 | 166 | |
|
|
167 | 167 | #---------------------- Parameters Data --------------------------- |
|
|
168 | 168 | |
|
|
169 | 169 | if self.dataIn.type == "Parameters": |
|
|
170 | 170 | self.dataOut.copy(self.dataIn) |
|
|
171 | 171 | self.dataOut.flagNoData = False |
|
|
172 | 172 | |
|
|
173 | 173 | return True |
|
|
174 | 174 | |
|
|
175 | 175 | self.__updateObjFromInput() |
|
|
176 | 176 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
177 | 177 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
178 | 178 | |
|
|
179 | 179 | return |
|
|
180 | 180 | |
|
|
181 | 181 | |
|
|
182 | 182 | def target(tups): |
|
|
183 | 183 | |
|
|
184 | 184 | obj, args = tups |
|
|
185 | 185 | #print 'TARGETTT', obj, args |
|
|
186 | 186 | return obj.FitGau(args) |
|
|
187 | 187 | |
|
|
188 | 188 | class GaussianFit(Operation): |
|
|
189 | 189 | |
|
|
190 | 190 | ''' |
|
|
191 | 191 | Function that fit of one and two generalized gaussians (gg) based |
|
|
192 | 192 | on the PSD shape across an "power band" identified from a cumsum of |
|
|
193 | 193 | the measured spectrum - noise. |
|
|
194 | 194 | |
|
|
195 | 195 | Input: |
|
|
196 | 196 | self.dataOut.data_pre : SelfSpectra |
|
|
197 | 197 | |
|
|
198 | 198 | Output: |
|
|
199 | 199 | self.dataOut.GauSPC : SPC_ch1, SPC_ch2 |
|
|
200 | 200 | |
|
|
201 | 201 | ''' |
|
|
202 | 202 | def __init__(self, **kwargs): |
|
|
203 | 203 | Operation.__init__(self, **kwargs) |
|
|
204 | 204 | self.i=0 |
|
|
205 | 205 | |
|
|
206 | 206 | |
|
|
207 | 207 | def run(self, dataOut, num_intg=7, pnoise=1., vel_arr=None, SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points |
|
|
208 | 208 | """This routine will find a couple of generalized Gaussians to a power spectrum |
|
|
209 | 209 | input: spc |
|
|
210 | 210 | output: |
|
|
211 | 211 | Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise |
|
|
212 | 212 | """ |
|
|
213 | 213 | |
|
|
214 | 214 | self.spc = dataOut.data_pre[0].copy() |
|
|
215 | 215 | |
|
|
216 | 216 | |
|
|
217 | 217 | print 'SelfSpectra Shape', numpy.asarray(self.spc).shape |
|
|
218 | 218 | |
|
|
219 | 219 | |
|
|
220 | 220 | #plt.figure(50) |
|
|
221 | 221 | #plt.subplot(121) |
|
|
222 | 222 | #plt.plot(self.spc,'k',label='spc(66)') |
|
|
223 | 223 | #plt.plot(xFrec,ySamples[1],'g',label='Ch1') |
|
|
224 | 224 | #plt.plot(xFrec,ySamples[2],'r',label='Ch2') |
|
|
225 | 225 | #plt.plot(xFrec,FitGauss,'yo:',label='fit') |
|
|
226 | 226 | #plt.legend() |
|
|
227 | 227 | #plt.title('DATOS A ALTURA DE 7500 METROS') |
|
|
228 | 228 | #plt.show() |
|
|
229 | 229 | |
|
|
230 | 230 | self.Num_Hei = self.spc.shape[2] |
|
|
231 | 231 | #self.Num_Bin = len(self.spc) |
|
|
232 | 232 | self.Num_Bin = self.spc.shape[1] |
|
|
233 | 233 | self.Num_Chn = self.spc.shape[0] |
|
|
234 | 234 | |
|
|
235 | 235 | Vrange = dataOut.abscissaList |
|
|
236 | 236 | |
|
|
237 | 237 | #print 'self.spc2', numpy.asarray(self.spc).shape |
|
|
238 | 238 | |
|
|
239 | 239 | GauSPC = numpy.empty([2,self.Num_Bin,self.Num_Hei]) |
|
|
240 | 240 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
241 | 241 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
242 | 242 | SPC_ch1[:] = numpy.NaN |
|
|
243 | 243 | SPC_ch2[:] = numpy.NaN |
|
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244 | 244 | |
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245 | 245 | |
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246 | 246 | start_time = time.time() |
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247 | 247 | |
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248 | 248 | noise_ = dataOut.spc_noise[0].copy() |
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249 | 249 | |
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250 | 250 | |
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251 | 251 | |
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252 | 252 | pool = Pool(processes=self.Num_Chn) |
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253 | 253 | args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)] |
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254 | 254 | objs = [self for __ in range(self.Num_Chn)] |
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255 | 255 | attrs = zip(objs, args) |
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256 | 256 | gauSPC = pool.map(target, attrs) |
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257 | 257 | dataOut.GauSPC = numpy.asarray(gauSPC) |
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258 | 258 | # ret = [] |
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259 | 259 | # for n in range(self.Num_Chn): |
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260 | 260 | # self.FitGau(args[n]) |
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261 | 261 | # dataOut.GauSPC = ret |
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262 | 262 | |
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263 | 263 | |
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264 | 264 | |
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265 | 265 | # for ch in range(self.Num_Chn): |
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266 | 266 | # |
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267 | 267 | # for ht in range(self.Num_Hei): |
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268 | 268 | # #print (numpy.asarray(self.spc).shape) |
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269 | 269 | # spc = numpy.asarray(self.spc)[ch,:,ht] |
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270 | 270 | # |
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271 | 271 | # ############################################# |
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272 | 272 | # # normalizing spc and noise |
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273 | 273 | # # This part differs from gg1 |
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274 | 274 | # spc_norm_max = max(spc) |
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275 | 275 | # spc = spc / spc_norm_max |
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276 | 276 | # pnoise = pnoise / spc_norm_max |
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277 | 277 | # ############################################# |
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278 | 278 | # |
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279 | 279 | # if abs(vel_arr[0])<15.0: # this switch is for spectra collected with different length IPP's |
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280 | 280 | # fatspectra=1.0 |
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281 | 281 | # else: |
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282 | 282 | # fatspectra=0.5 |
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283 | 283 | # |
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284 | 284 | # wnoise = noise_ / spc_norm_max |
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285 | 285 | # #print 'wnoise', noise_, dataOut.spc_noise[0], wnoise |
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286 | 286 | # #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
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287 | 287 | # #if wnoise>1.1*pnoise: # to be tested later |
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288 | 288 | # # wnoise=pnoise |
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289 | 289 | # noisebl=wnoise*0.9; noisebh=wnoise*1.1 |
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290 | 290 | # spc=spc-wnoise |
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291 | 291 | # |
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292 | 292 | # minx=numpy.argmin(spc) |
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293 | 293 | # spcs=numpy.roll(spc,-minx) |
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294 | 294 | # cum=numpy.cumsum(spcs) |
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295 | 295 | # tot_noise=wnoise * self.Num_Bin #64; |
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296 | 296 | # #tot_signal=sum(cum[-5:])/5.; ''' How does this line work? ''' |
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297 | 297 | # #snr=tot_signal/tot_noise |
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298 | 298 | # #snr=cum[-1]/tot_noise |
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299 | 299 | # |
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300 | 300 | # #print 'spc' , spcs[5:8] , 'tot_noise', tot_noise |
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301 | 301 | # |
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302 | 302 | # snr = sum(spcs)/tot_noise |
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303 | 303 | # snrdB=10.*numpy.log10(snr) |
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304 | 304 | # |
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305 | 305 | # #if snrdB < -9 : |
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306 | 306 | # # snrdB = numpy.NaN |
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307 | 307 | # # continue |
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308 | 308 | # |
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309 | 309 | # #print 'snr',snrdB # , sum(spcs) , tot_noise |
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310 | 310 | # |
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311 | 311 | # |
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312 | 312 | # #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
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313 | 313 | # # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
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314 | 314 | # |
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315 | 315 | # cummax=max(cum); epsi=0.08*fatspectra # cumsum to narrow down the energy region |
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316 | 316 | # cumlo=cummax*epsi; |
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317 | 317 | # cumhi=cummax*(1-epsi) |
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318 | 318 | # powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
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319 | 319 | # |
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320 | 320 | # #if len(powerindex)==1: |
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321 | 321 | # ##return [numpy.mod(powerindex[0]+minx,64),None,None,None,],[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
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322 | 322 | # #return [numpy.mod(powerindex[0]+minx, self.Num_Bin ),None,None,None,],[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
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323 | 323 | # #elif len(powerindex)<4*fatspectra: |
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324 | 324 | # #return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
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325 | 325 | # |
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326 | 326 | # if len(powerindex) < 1:# case for powerindex 0 |
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327 | 327 | # continue |
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328 | 328 | # powerlo=powerindex[0] |
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329 | 329 | # powerhi=powerindex[-1] |
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330 | 330 | # powerwidth=powerhi-powerlo |
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331 | 331 | # |
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332 | 332 | # firstpeak=powerlo+powerwidth/10.# first gaussian energy location |
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333 | 333 | # secondpeak=powerhi-powerwidth/10.#second gaussian energy location |
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334 | 334 | # midpeak=(firstpeak+secondpeak)/2. |
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335 | 335 | # firstamp=spcs[int(firstpeak)] |
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336 | 336 | # secondamp=spcs[int(secondpeak)] |
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337 | 337 | # midamp=spcs[int(midpeak)] |
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338 | 338 | # #x=numpy.spc.shape[1] |
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339 | 339 | # |
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340 | 340 | # #x=numpy.arange(64) |
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341 | 341 | # x=numpy.arange( self.Num_Bin ) |
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342 | 342 | # y_data=spc+wnoise |
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343 | 343 | # |
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344 | 344 | # # single gaussian |
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345 | 345 | # #shift0=numpy.mod(midpeak+minx,64) |
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346 | 346 | # shift0=numpy.mod(midpeak+minx, self.Num_Bin ) |
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347 | 347 | # width0=powerwidth/4.#Initialization entire power of spectrum divided by 4 |
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348 | 348 | # power0=2. |
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349 | 349 | # amplitude0=midamp |
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350 | 350 | # state0=[shift0,width0,amplitude0,power0,wnoise] |
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351 | 351 | # #bnds=((0,63),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
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352 | 352 | # bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
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353 | 353 | # #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(0.1,0.5)) |
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354 | 354 | # # bnds = range of fft, power width, amplitude, power, noise |
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355 | 355 | # lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) |
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356 | 356 | # |
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357 | 357 | # chiSq1=lsq1[1]; |
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358 | 358 | # jack1= self.y_jacobian1(x,lsq1[0]) |
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359 | 359 | # |
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|
360 | 360 | # |
|
|
361 | 361 | # try: |
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|
362 | 362 | # sigmas1=numpy.sqrt(chiSq1*numpy.diag(numpy.linalg.inv(numpy.dot(jack1.T,jack1)))) |
|
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363 | 363 | # except: |
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|
364 | 364 | # std1=32.; sigmas1=numpy.ones(5) |
|
|
365 | 365 | # else: |
|
|
366 | 366 | # std1=sigmas1[0] |
|
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367 | 367 | # |
|
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368 | 368 | # |
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369 | 369 | # if fatspectra<1.0 and powerwidth<4: |
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370 | 370 | # choice=0 |
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371 | 371 | # Amplitude0=lsq1[0][2] |
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372 | 372 | # shift0=lsq1[0][0] |
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373 | 373 | # width0=lsq1[0][1] |
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374 | 374 | # p0=lsq1[0][3] |
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375 | 375 | # Amplitude1=0. |
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376 | 376 | # shift1=0. |
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377 | 377 | # width1=0. |
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|
378 | 378 | # p1=0. |
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379 | 379 | # noise=lsq1[0][4] |
|
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380 | 380 | # #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
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381 | 381 | # # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
|
382 | 382 | # |
|
|
383 | 383 | # # two gaussians |
|
|
384 | 384 | # #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
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385 | 385 | # shift0=numpy.mod(firstpeak+minx, self.Num_Bin ); |
|
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386 | 386 | # shift1=numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
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387 | 387 | # width0=powerwidth/6.; |
|
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388 | 388 | # width1=width0 |
|
|
389 | 389 | # power0=2.; |
|
|
390 | 390 | # power1=power0 |
|
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391 | 391 | # amplitude0=firstamp; |
|
|
392 | 392 | # amplitude1=secondamp |
|
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393 | 393 | # state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
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394 | 394 | # #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
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395 | 395 | # 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)) |
|
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396 | 396 | # #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)) |
|
|
397 | 397 | # |
|
|
398 | 398 | # lsq2=fmin_l_bfgs_b(self.misfit2,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) |
|
|
399 | 399 | # |
|
|
400 | 400 | # |
|
|
401 | 401 | # chiSq2=lsq2[1]; jack2=self.y_jacobian2(x,lsq2[0]) |
|
|
402 | 402 | # |
|
|
403 | 403 | # |
|
|
404 | 404 | # try: |
|
|
405 | 405 | # sigmas2=numpy.sqrt(chiSq2*numpy.diag(numpy.linalg.inv(numpy.dot(jack2.T,jack2)))) |
|
|
406 | 406 | # except: |
|
|
407 | 407 | # std2a=32.; std2b=32.; sigmas2=numpy.ones(9) |
|
|
408 | 408 | # else: |
|
|
409 | 409 | # std2a=sigmas2[0]; std2b=sigmas2[4] |
|
|
410 | 410 | # |
|
|
411 | 411 | # |
|
|
412 | 412 | # |
|
|
413 | 413 | # 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) |
|
|
414 | 414 | # |
|
|
415 | 415 | # if snrdB>-9: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
|
416 | 416 | # if oneG: |
|
|
417 | 417 | # choice=0 |
|
|
418 | 418 | # else: |
|
|
419 | 419 | # w1=lsq2[0][1]; w2=lsq2[0][5] |
|
|
420 | 420 | # a1=lsq2[0][2]; a2=lsq2[0][6] |
|
|
421 | 421 | # p1=lsq2[0][3]; p2=lsq2[0][7] |
|
|
422 | 422 | # s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2; |
|
|
423 | 423 | # gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
|
424 | 424 | # |
|
|
425 | 425 | # if gp1>gp2: |
|
|
426 | 426 | # if a1>0.7*a2: |
|
|
427 | 427 | # choice=1 |
|
|
428 | 428 | # else: |
|
|
429 | 429 | # choice=2 |
|
|
430 | 430 | # elif gp2>gp1: |
|
|
431 | 431 | # if a2>0.7*a1: |
|
|
432 | 432 | # choice=2 |
|
|
433 | 433 | # else: |
|
|
434 | 434 | # choice=1 |
|
|
435 | 435 | # else: |
|
|
436 | 436 | # choice=numpy.argmax([a1,a2])+1 |
|
|
437 | 437 | # #else: |
|
|
438 | 438 | # #choice=argmin([std2a,std2b])+1 |
|
|
439 | 439 | # |
|
|
440 | 440 | # else: # with low SNR go to the most energetic peak |
|
|
441 | 441 | # choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
|
442 | 442 | # |
|
|
443 | 443 | # #print 'choice',choice |
|
|
444 | 444 | # |
|
|
445 | 445 | # if choice==0: # pick the single gaussian fit |
|
|
446 | 446 | # Amplitude0=lsq1[0][2] |
|
|
447 | 447 | # shift0=lsq1[0][0] |
|
|
448 | 448 | # width0=lsq1[0][1] |
|
|
449 | 449 | # p0=lsq1[0][3] |
|
|
450 | 450 | # Amplitude1=0. |
|
|
451 | 451 | # shift1=0. |
|
|
452 | 452 | # width1=0. |
|
|
453 | 453 | # p1=0. |
|
|
454 | 454 | # noise=lsq1[0][4] |
|
|
455 | 455 | # elif choice==1: # take the first one of the 2 gaussians fitted |
|
|
456 | 456 | # Amplitude0 = lsq2[0][2] |
|
|
457 | 457 | # shift0 = lsq2[0][0] |
|
|
458 | 458 | # width0 = lsq2[0][1] |
|
|
459 | 459 | # p0 = lsq2[0][3] |
|
|
460 | 460 | # Amplitude1 = lsq2[0][6] # This is 0 in gg1 |
|
|
461 | 461 | # shift1 = lsq2[0][4] # This is 0 in gg1 |
|
|
462 | 462 | # width1 = lsq2[0][5] # This is 0 in gg1 |
|
|
463 | 463 | # p1 = lsq2[0][7] # This is 0 in gg1 |
|
|
464 | 464 | # noise = lsq2[0][8] |
|
|
465 | 465 | # else: # the second one |
|
|
466 | 466 | # Amplitude0 = lsq2[0][6] |
|
|
467 | 467 | # shift0 = lsq2[0][4] |
|
|
468 | 468 | # width0 = lsq2[0][5] |
|
|
469 | 469 | # p0 = lsq2[0][7] |
|
|
470 | 470 | # Amplitude1 = lsq2[0][2] # This is 0 in gg1 |
|
|
471 | 471 | # shift1 = lsq2[0][0] # This is 0 in gg1 |
|
|
472 | 472 | # width1 = lsq2[0][1] # This is 0 in gg1 |
|
|
473 | 473 | # p1 = lsq2[0][3] # This is 0 in gg1 |
|
|
474 | 474 | # noise = lsq2[0][8] |
|
|
475 | 475 | # |
|
|
476 | 476 | # #print len(noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0) |
|
|
477 | 477 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0 |
|
|
478 | 478 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1 |
|
|
479 | 479 | # #print 'SPC_ch1.shape',SPC_ch1.shape |
|
|
480 | 480 | # #print 'SPC_ch2.shape',SPC_ch2.shape |
|
|
481 | 481 | # #dataOut.data_param = SPC_ch1 |
|
|
482 | 482 | # GauSPC[0] = SPC_ch1 |
|
|
483 | 483 | # GauSPC[1] = SPC_ch2 |
|
|
484 | 484 | |
|
|
485 | 485 | # #plt.gcf().clear() |
|
|
486 | 486 | # plt.figure(50+self.i) |
|
|
487 | 487 | # self.i=self.i+1 |
|
|
488 | 488 | # #plt.subplot(121) |
|
|
489 | 489 | # plt.plot(self.spc,'k')#,label='spc(66)') |
|
|
490 | 490 | # plt.plot(SPC_ch1[ch,ht],'b')#,label='gg1') |
|
|
491 | 491 | # #plt.plot(SPC_ch2,'r')#,label='gg2') |
|
|
492 | 492 | # #plt.plot(xFrec,ySamples[1],'g',label='Ch1') |
|
|
493 | 493 | # #plt.plot(xFrec,ySamples[2],'r',label='Ch2') |
|
|
494 | 494 | # #plt.plot(xFrec,FitGauss,'yo:',label='fit') |
|
|
495 | 495 | # plt.legend() |
|
|
496 | 496 | # plt.title('DATOS A ALTURA DE 7500 METROS') |
|
|
497 | 497 | # plt.show() |
|
|
498 | 498 | # print 'shift0', shift0 |
|
|
499 | 499 | # print 'Amplitude0', Amplitude0 |
|
|
500 | 500 | # print 'width0', width0 |
|
|
501 | 501 | # print 'p0', p0 |
|
|
502 | 502 | # print '========================' |
|
|
503 | 503 | # print 'shift1', shift1 |
|
|
504 | 504 | # print 'Amplitude1', Amplitude1 |
|
|
505 | 505 | # print 'width1', width1 |
|
|
506 | 506 | # print 'p1', p1 |
|
|
507 | 507 | # print 'noise', noise |
|
|
508 | 508 | # print 's_noise', wnoise |
|
|
509 | 509 | |
|
|
510 | 510 | print '========================================================' |
|
|
511 | 511 | print 'total_time: ', time.time()-start_time |
|
|
512 | 512 | |
|
|
513 | 513 | # re-normalizing spc and noise |
|
|
514 | 514 | # This part differs from gg1 |
|
|
515 | 515 | |
|
|
516 | 516 | |
|
|
517 | 517 | |
|
|
518 | 518 | ''' Parameters: |
|
|
519 | 519 | 1. Amplitude |
|
|
520 | 520 | 2. Shift |
|
|
521 | 521 | 3. Width |
|
|
522 | 522 | 4. Power |
|
|
523 | 523 | ''' |
|
|
524 | 524 | |
|
|
525 | 525 | |
|
|
526 | 526 | ############################################################################### |
|
|
527 | 527 | def FitGau(self, X): |
|
|
528 | 528 | |
|
|
529 | 529 | Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X |
|
|
530 | 530 | #print 'VARSSSS', ch, pnoise, noise, num_intg |
|
|
531 | 531 | |
|
|
532 | 532 | #print 'HEIGHTS', self.Num_Hei |
|
|
533 | 533 | |
|
|
534 | 534 | GauSPC = [] |
|
|
535 | 535 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
536 | 536 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
537 | 537 | SPC_ch1[:] = 0#numpy.NaN |
|
|
538 | 538 | SPC_ch2[:] = 0#numpy.NaN |
|
|
539 | 539 | |
|
|
540 | 540 | |
|
|
541 | 541 | |
|
|
542 | 542 | for ht in range(self.Num_Hei): |
|
|
543 | 543 | #print (numpy.asarray(self.spc).shape) |
|
|
544 | 544 | |
|
|
545 | 545 | #print 'TTTTT', ch , ht |
|
|
546 | 546 | #print self.spc.shape |
|
|
547 | 547 | |
|
|
548 | 548 | |
|
|
549 | 549 | spc = numpy.asarray(self.spc)[ch,:,ht] |
|
|
550 | 550 | |
|
|
551 | 551 | ############################################# |
|
|
552 | 552 | # normalizing spc and noise |
|
|
553 | 553 | # This part differs from gg1 |
|
|
554 | 554 | spc_norm_max = max(spc) |
|
|
555 | 555 | spc = spc / spc_norm_max |
|
|
556 | 556 | pnoise = pnoise / spc_norm_max |
|
|
557 | 557 | ############################################# |
|
|
558 | 558 | |
|
|
559 | 559 | fatspectra=1.0 |
|
|
560 | 560 | |
|
|
561 | 561 | wnoise = noise_ / spc_norm_max |
|
|
562 | 562 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
|
563 | 563 | #if wnoise>1.1*pnoise: # to be tested later |
|
|
564 | 564 | # wnoise=pnoise |
|
|
565 | 565 | noisebl=wnoise*0.9; noisebh=wnoise*1.1 |
|
|
566 | 566 | spc=spc-wnoise |
|
|
567 | 567 | # print 'wnoise', noise_[0], spc_norm_max, wnoise |
|
|
568 | 568 | minx=numpy.argmin(spc) |
|
|
569 | 569 | spcs=numpy.roll(spc,-minx) |
|
|
570 | 570 | cum=numpy.cumsum(spcs) |
|
|
571 | 571 | tot_noise=wnoise * self.Num_Bin #64; |
|
|
572 | 572 | #print 'spc' , spcs[5:8] , 'tot_noise', tot_noise |
|
|
573 | 573 | #tot_signal=sum(cum[-5:])/5.; ''' How does this line work? ''' |
|
|
574 | 574 | #snr=tot_signal/tot_noise |
|
|
575 | 575 | #snr=cum[-1]/tot_noise |
|
|
576 | 576 | snr = sum(spcs)/tot_noise |
|
|
577 | 577 | snrdB=10.*numpy.log10(snr) |
|
|
578 | 578 | |
|
|
579 | 579 | if snrdB < SNRlimit : |
|
|
580 | 580 | snr = numpy.NaN |
|
|
581 | 581 | SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
582 | 582 | SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
583 | 583 | GauSPC = (SPC_ch1,SPC_ch2) |
|
|
584 | 584 | continue |
|
|
585 | 585 | #print 'snr',snrdB #, sum(spcs) , tot_noise |
|
|
586 | 586 | |
|
|
587 | 587 | |
|
|
588 | 588 | |
|
|
589 | 589 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
|
590 | 590 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
|
591 | 591 | |
|
|
592 | 592 | cummax=max(cum); epsi=0.08*fatspectra # cumsum to narrow down the energy region |
|
|
593 | 593 | cumlo=cummax*epsi; |
|
|
594 | 594 | cumhi=cummax*(1-epsi) |
|
|
595 | 595 | powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
|
596 | 596 | |
|
|
597 | 597 | |
|
|
598 | 598 | if len(powerindex) < 1:# case for powerindex 0 |
|
|
599 | 599 | continue |
|
|
600 | 600 | powerlo=powerindex[0] |
|
|
601 | 601 | powerhi=powerindex[-1] |
|
|
602 | 602 | powerwidth=powerhi-powerlo |
|
|
603 | 603 | |
|
|
604 | 604 | firstpeak=powerlo+powerwidth/10.# first gaussian energy location |
|
|
605 | 605 | secondpeak=powerhi-powerwidth/10.#second gaussian energy location |
|
|
606 | 606 | midpeak=(firstpeak+secondpeak)/2. |
|
|
607 | 607 | firstamp=spcs[int(firstpeak)] |
|
|
608 | 608 | secondamp=spcs[int(secondpeak)] |
|
|
609 | 609 | midamp=spcs[int(midpeak)] |
|
|
610 | 610 | |
|
|
611 | 611 | x=numpy.arange( self.Num_Bin ) |
|
|
612 | 612 | y_data=spc+wnoise |
|
|
613 | 613 | |
|
|
614 | 614 | # single gaussian |
|
|
615 | 615 | shift0=numpy.mod(midpeak+minx, self.Num_Bin ) |
|
|
616 | 616 | width0=powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
|
617 | 617 | power0=2. |
|
|
618 | 618 | amplitude0=midamp |
|
|
619 | 619 | state0=[shift0,width0,amplitude0,power0,wnoise] |
|
|
620 | 620 | bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
621 | 621 | lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) |
|
|
622 | 622 | |
|
|
623 | 623 | chiSq1=lsq1[1]; |
|
|
624 | 624 | jack1= self.y_jacobian1(x,lsq1[0]) |
|
|
625 | 625 | |
|
|
626 | 626 | |
|
|
627 | 627 | try: |
|
|
628 | 628 | sigmas1=numpy.sqrt(chiSq1*numpy.diag(numpy.linalg.inv(numpy.dot(jack1.T,jack1)))) |
|
|
629 | 629 | except: |
|
|
630 | 630 | std1=32.; sigmas1=numpy.ones(5) |
|
|
631 | 631 | else: |
|
|
632 | 632 | std1=sigmas1[0] |
|
|
633 | 633 | |
|
|
634 | 634 | |
|
|
635 | 635 | if fatspectra<1.0 and powerwidth<4: |
|
|
636 | 636 | choice=0 |
|
|
637 | 637 | Amplitude0=lsq1[0][2] |
|
|
638 | 638 | shift0=lsq1[0][0] |
|
|
639 | 639 | width0=lsq1[0][1] |
|
|
640 | 640 | p0=lsq1[0][3] |
|
|
641 | 641 | Amplitude1=0. |
|
|
642 | 642 | shift1=0. |
|
|
643 | 643 | width1=0. |
|
|
644 | 644 | p1=0. |
|
|
645 | 645 | noise=lsq1[0][4] |
|
|
646 | 646 | #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
|
647 | 647 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
|
648 | 648 | |
|
|
649 | 649 | # two gaussians |
|
|
650 | 650 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
|
651 | 651 | shift0=numpy.mod(firstpeak+minx, self.Num_Bin ); |
|
|
652 | 652 | shift1=numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
|
653 | 653 | width0=powerwidth/6.; |
|
|
654 | 654 | width1=width0 |
|
|
655 | 655 | power0=2.; |
|
|
656 | 656 | power1=power0 |
|
|
657 | 657 | amplitude0=firstamp; |
|
|
658 | 658 | amplitude1=secondamp |
|
|
659 | 659 | state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
|
660 | 660 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
661 | 661 | 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)) |
|
|
662 | 662 | #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)) |
|
|
663 | 663 | |
|
|
664 | 664 | lsq2=fmin_l_bfgs_b(self.misfit2,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) |
|
|
665 | 665 | |
|
|
666 | 666 | |
|
|
667 | 667 | chiSq2=lsq2[1]; jack2=self.y_jacobian2(x,lsq2[0]) |
|
|
668 | 668 | |
|
|
669 | 669 | |
|
|
670 | 670 | try: |
|
|
671 | 671 | sigmas2=numpy.sqrt(chiSq2*numpy.diag(numpy.linalg.inv(numpy.dot(jack2.T,jack2)))) |
|
|
672 | 672 | except: |
|
|
673 | 673 | std2a=32.; std2b=32.; sigmas2=numpy.ones(9) |
|
|
674 | 674 | else: |
|
|
675 | 675 | std2a=sigmas2[0]; std2b=sigmas2[4] |
|
|
676 | 676 | |
|
|
677 | 677 | |
|
|
678 | 678 | |
|
|
679 | 679 | 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) |
|
|
680 | 680 | |
|
|
681 | 681 | if snrdB>-6: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
|
682 | 682 | if oneG: |
|
|
683 | 683 | choice=0 |
|
|
684 | 684 | else: |
|
|
685 | 685 | w1=lsq2[0][1]; w2=lsq2[0][5] |
|
|
686 | 686 | a1=lsq2[0][2]; a2=lsq2[0][6] |
|
|
687 | 687 | p1=lsq2[0][3]; p2=lsq2[0][7] |
|
|
688 | 688 | s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; |
|
|
689 | 689 | s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2; |
|
|
690 | 690 | gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
|
691 | 691 | |
|
|
692 | 692 | if gp1>gp2: |
|
|
693 | 693 | if a1>0.7*a2: |
|
|
694 | 694 | choice=1 |
|
|
695 | 695 | else: |
|
|
696 | 696 | choice=2 |
|
|
697 | 697 | elif gp2>gp1: |
|
|
698 | 698 | if a2>0.7*a1: |
|
|
699 | 699 | choice=2 |
|
|
700 | 700 | else: |
|
|
701 | 701 | choice=1 |
|
|
702 | 702 | else: |
|
|
703 | 703 | choice=numpy.argmax([a1,a2])+1 |
|
|
704 | 704 | #else: |
|
|
705 | 705 | #choice=argmin([std2a,std2b])+1 |
|
|
706 | 706 | |
|
|
707 | 707 | else: # with low SNR go to the most energetic peak |
|
|
708 | 708 | choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
|
709 | 709 | |
|
|
710 | 710 | |
|
|
711 | 711 | shift0=lsq2[0][0]; vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0]) |
|
|
712 | 712 | shift1=lsq2[0][4]; vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0]) |
|
|
713 | 713 | |
|
|
714 | 714 | max_vel = 20 |
|
|
715 | 715 | |
|
|
716 | 716 | #first peak will be 0, second peak will be 1 |
|
|
717 | 717 | if vel0 > 0 and vel0 < max_vel : #first peak is in the correct range |
|
|
718 | 718 | shift0=lsq2[0][0] |
|
|
719 | 719 | width0=lsq2[0][1] |
|
|
720 | 720 | Amplitude0=lsq2[0][2] |
|
|
721 | 721 | p0=lsq2[0][3] |
|
|
722 | 722 | |
|
|
723 | 723 | shift1=lsq2[0][4] |
|
|
724 | 724 | width1=lsq2[0][5] |
|
|
725 | 725 | Amplitude1=lsq2[0][6] |
|
|
726 | 726 | p1=lsq2[0][7] |
|
|
727 | 727 | noise=lsq2[0][8] |
|
|
728 | 728 | else: |
|
|
729 | 729 | shift1=lsq2[0][0] |
|
|
730 | 730 | width1=lsq2[0][1] |
|
|
731 | 731 | Amplitude1=lsq2[0][2] |
|
|
732 | 732 | p1=lsq2[0][3] |
|
|
733 | 733 | |
|
|
734 | 734 | shift0=lsq2[0][4] |
|
|
735 | 735 | width0=lsq2[0][5] |
|
|
736 | 736 | Amplitude0=lsq2[0][6] |
|
|
737 | 737 | p0=lsq2[0][7] |
|
|
738 | 738 | noise=lsq2[0][8] |
|
|
739 | 739 | |
|
|
740 | 740 | if Amplitude0<0.1: # in case the peak is noise |
|
|
741 | 741 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] |
|
|
742 | 742 | if Amplitude1<0.1: |
|
|
743 | 743 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] |
|
|
744 | 744 | |
|
|
745 | 745 | |
|
|
746 | 746 | # if choice==0: # pick the single gaussian fit |
|
|
747 | 747 | # Amplitude0=lsq1[0][2] |
|
|
748 | 748 | # shift0=lsq1[0][0] |
|
|
749 | 749 | # width0=lsq1[0][1] |
|
|
750 | 750 | # p0=lsq1[0][3] |
|
|
751 | 751 | # Amplitude1=0. |
|
|
752 | 752 | # shift1=0. |
|
|
753 | 753 | # width1=0. |
|
|
754 | 754 | # p1=0. |
|
|
755 | 755 | # noise=lsq1[0][4] |
|
|
756 | 756 | # elif choice==1: # take the first one of the 2 gaussians fitted |
|
|
757 | 757 | # Amplitude0 = lsq2[0][2] |
|
|
758 | 758 | # shift0 = lsq2[0][0] |
|
|
759 | 759 | # width0 = lsq2[0][1] |
|
|
760 | 760 | # p0 = lsq2[0][3] |
|
|
761 | 761 | # Amplitude1 = lsq2[0][6] # This is 0 in gg1 |
|
|
762 | 762 | # shift1 = lsq2[0][4] # This is 0 in gg1 |
|
|
763 | 763 | # width1 = lsq2[0][5] # This is 0 in gg1 |
|
|
764 | 764 | # p1 = lsq2[0][7] # This is 0 in gg1 |
|
|
765 | 765 | # noise = lsq2[0][8] |
|
|
766 | 766 | # else: # the second one |
|
|
767 | 767 | # Amplitude0 = lsq2[0][6] |
|
|
768 | 768 | # shift0 = lsq2[0][4] |
|
|
769 | 769 | # width0 = lsq2[0][5] |
|
|
770 | 770 | # p0 = lsq2[0][7] |
|
|
771 | 771 | # Amplitude1 = lsq2[0][2] # This is 0 in gg1 |
|
|
772 | 772 | # shift1 = lsq2[0][0] # This is 0 in gg1 |
|
|
773 | 773 | # width1 = lsq2[0][1] # This is 0 in gg1 |
|
|
774 | 774 | # p1 = lsq2[0][3] # This is 0 in gg1 |
|
|
775 | 775 | # noise = lsq2[0][8] |
|
|
776 | 776 | |
|
|
777 | 777 | #print len(noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0) |
|
|
778 | 778 | SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0 |
|
|
779 | 779 | SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1 |
|
|
780 | 780 | #print 'SPC_ch1.shape',SPC_ch1.shape |
|
|
781 | 781 | #print 'SPC_ch2.shape',SPC_ch2.shape |
|
|
782 | 782 | #dataOut.data_param = SPC_ch1 |
|
|
783 | 783 | GauSPC = (SPC_ch1,SPC_ch2) |
|
|
784 | 784 | #GauSPC[1] = SPC_ch2 |
|
|
785 | 785 | |
|
|
786 | 786 | # print 'shift0', shift0 |
|
|
787 | 787 | # print 'Amplitude0', Amplitude0 |
|
|
788 | 788 | # print 'width0', width0 |
|
|
789 | 789 | # print 'p0', p0 |
|
|
790 | 790 | # print '========================' |
|
|
791 | 791 | # print 'shift1', shift1 |
|
|
792 | 792 | # print 'Amplitude1', Amplitude1 |
|
|
793 | 793 | # print 'width1', width1 |
|
|
794 | 794 | # print 'p1', p1 |
|
|
795 | 795 | # print 'noise', noise |
|
|
796 | 796 | # print 's_noise', wnoise |
|
|
797 | 797 | |
|
|
798 | 798 | return GauSPC |
|
|
799 | 799 | |
|
|
800 | 800 | |
|
|
801 | 801 | def y_jacobian1(self,x,state): # This function is for further analysis of generalized Gaussians, it is not too importan for the signal discrimination. |
|
|
802 | 802 | y_model=self.y_model1(x,state) |
|
|
803 | 803 | s0,w0,a0,p0,n=state |
|
|
804 | 804 | e0=((x-s0)/w0)**2; |
|
|
805 | 805 | |
|
|
806 | 806 | e0u=((x-s0-self.Num_Bin)/w0)**2; |
|
|
807 | 807 | |
|
|
808 | 808 | e0d=((x-s0+self.Num_Bin)/w0)**2 |
|
|
809 | 809 | m0=numpy.exp(-0.5*e0**(p0/2.)); |
|
|
810 | 810 | m0u=numpy.exp(-0.5*e0u**(p0/2.)); |
|
|
811 | 811 | m0d=numpy.exp(-0.5*e0d**(p0/2.)) |
|
|
812 | 812 | JA=m0+m0u+m0d |
|
|
813 | 813 | JP=(-1/4.)*a0*m0*e0**(p0/2.)*numpy.log(e0)+(-1/4.)*a0*m0u*e0u**(p0/2.)*numpy.log(e0u)+(-1/4.)*a0*m0d*e0d**(p0/2.)*numpy.log(e0d) |
|
|
814 | 814 | |
|
|
815 | 815 | JS=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0) |
|
|
816 | 816 | |
|
|
817 | 817 | JW=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)**2+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)**2+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)**2 |
|
|
818 | 818 | jack1=numpy.sqrt(7)*numpy.array([JS/y_model,JW/y_model,JA/y_model,JP/y_model,1./y_model]) |
|
|
819 | 819 | return jack1.T |
|
|
820 | 820 | |
|
|
821 | 821 | def y_jacobian2(self,x,state): |
|
|
822 | 822 | y_model=self.y_model2(x,state) |
|
|
823 | 823 | s0,w0,a0,p0,s1,w1,a1,p1,n=state |
|
|
824 | 824 | e0=((x-s0)/w0)**2; |
|
|
825 | 825 | |
|
|
826 | 826 | e0u=((x-s0- self.Num_Bin )/w0)**2; |
|
|
827 | 827 | |
|
|
828 | 828 | e0d=((x-s0+ self.Num_Bin )/w0)**2 |
|
|
829 | 829 | e1=((x-s1)/w1)**2; |
|
|
830 | 830 | |
|
|
831 | 831 | e1u=((x-s1- self.Num_Bin )/w1)**2; |
|
|
832 | 832 | |
|
|
833 | 833 | e1d=((x-s1+ self.Num_Bin )/w1)**2 |
|
|
834 | 834 | m0=numpy.exp(-0.5*e0**(p0/2.)); |
|
|
835 | 835 | m0u=numpy.exp(-0.5*e0u**(p0/2.)); |
|
|
836 | 836 | m0d=numpy.exp(-0.5*e0d**(p0/2.)) |
|
|
837 | 837 | m1=numpy.exp(-0.5*e1**(p1/2.)); |
|
|
838 | 838 | m1u=numpy.exp(-0.5*e1u**(p1/2.)); |
|
|
839 | 839 | m1d=numpy.exp(-0.5*e1d**(p1/2.)) |
|
|
840 | 840 | JA=m0+m0u+m0d |
|
|
841 | 841 | JA1=m1+m1u+m1d |
|
|
842 | 842 | JP=(-1/4.)*a0*m0*e0**(p0/2.)*numpy.log(e0)+(-1/4.)*a0*m0u*e0u**(p0/2.)*numpy.log(e0u)+(-1/4.)*a0*m0d*e0d**(p0/2.)*numpy.log(e0d) |
|
|
843 | 843 | JP1=(-1/4.)*a1*m1*e1**(p1/2.)*numpy.log(e1)+(-1/4.)*a1*m1u*e1u**(p1/2.)*numpy.log(e1u)+(-1/4.)*a1*m1d*e1d**(p1/2.)*numpy.log(e1d) |
|
|
844 | 844 | |
|
|
845 | 845 | JS=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0) |
|
|
846 | 846 | |
|
|
847 | 847 | JS1=(p1/w1/2.)*a1*m1*e1**(p1/2.-1)*((x-s1)/w1)+(p1/w1/2.)*a1*m1u*e1u**(p1/2.-1)*((x-s1- self.Num_Bin )/w1)+(p1/w1/2.)*a1*m1d*e1d**(p1/2.-1)*((x-s1+ self.Num_Bin )/w1) |
|
|
848 | 848 | |
|
|
849 | 849 | JW=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)**2+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)**2+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)**2 |
|
|
850 | 850 | |
|
|
851 | 851 | JW1=(p1/w1/2.)*a1*m1*e1**(p1/2.-1)*((x-s1)/w1)**2+(p1/w1/2.)*a1*m1u*e1u**(p1/2.-1)*((x-s1- self.Num_Bin )/w1)**2+(p1/w1/2.)*a1*m1d*e1d**(p1/2.-1)*((x-s1+ self.Num_Bin )/w1)**2 |
|
|
852 | 852 | jack2=numpy.sqrt(7)*numpy.array([JS/y_model,JW/y_model,JA/y_model,JP/y_model,JS1/y_model,JW1/y_model,JA1/y_model,JP1/y_model,1./y_model]) |
|
|
853 | 853 | return jack2.T |
|
|
854 | 854 | |
|
|
855 | 855 | def y_model1(self,x,state): |
|
|
856 | 856 | shift0,width0,amplitude0,power0,noise=state |
|
|
857 | 857 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
858 | 858 | |
|
|
859 | 859 | model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
860 | 860 | |
|
|
861 | 861 | model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
862 | 862 | return model0+model0u+model0d+noise |
|
|
863 | 863 | |
|
|
864 | 864 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
|
865 | 865 | shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state |
|
|
866 | 866 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
867 | 867 | |
|
|
868 | 868 | model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
869 | 869 | |
|
|
870 | 870 | model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
871 | 871 | model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1) |
|
|
872 | 872 | |
|
|
873 | 873 | model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1) |
|
|
874 | 874 | |
|
|
875 | 875 | model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1) |
|
|
876 | 876 | return model0+model0u+model0d+model1+model1u+model1d+noise |
|
|
877 | 877 | |
|
|
878 | 878 | 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. |
|
|
879 | 879 | |
|
|
880 | 880 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
|
881 | 881 | |
|
|
882 | 882 | def misfit2(self,state,y_data,x,num_intg): |
|
|
883 | 883 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
|
884 | 884 | |
|
|
885 | 885 | |
|
|
886 | 886 | class PrecipitationProc(Operation): |
|
|
887 | 887 | |
|
|
888 | 888 | ''' |
|
|
889 | 889 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
|
890 | 890 | |
|
|
891 | 891 | Input: |
|
|
892 | 892 | self.dataOut.data_pre : SelfSpectra |
|
|
893 | 893 | |
|
|
894 | 894 | Output: |
|
|
895 | 895 | |
|
|
896 | 896 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
|
897 | 897 | |
|
|
898 | 898 | |
|
|
899 | 899 | Parameters affected: |
|
|
900 | 900 | ''' |
|
|
901 | 901 | |
|
|
902 | 902 | |
|
|
903 | 903 | def run(self, dataOut, radar=None, Pt=None, Gt=None, Gr=None, Lambda=None, aL=None, |
|
|
904 | 904 | tauW=None, ThetaT=None, ThetaR=None, Km = 0.93, Altitude=None): |
|
|
905 | 905 | |
|
|
906 | 906 | self.spc = dataOut.data_pre[0].copy() |
|
|
907 | 907 | self.Num_Hei = self.spc.shape[2] |
|
|
908 | 908 | self.Num_Bin = self.spc.shape[1] |
|
|
909 | 909 | self.Num_Chn = self.spc.shape[0] |
|
|
910 | 910 | |
|
|
911 | 911 | Velrange = dataOut.abscissaList |
|
|
912 | 912 | |
|
|
913 | 913 | if radar == "MIRA35C" : |
|
|
914 | 914 | |
|
|
915 | 915 | Ze = self.dBZeMODE2(dataOut) |
|
|
916 | 916 | |
|
|
917 | 917 | else: |
|
|
918 | 918 | |
|
|
919 | 919 | self.Pt = Pt |
|
|
920 | 920 | self.Gt = Gt |
|
|
921 | 921 | self.Gr = Gr |
|
|
922 | 922 | self.Lambda = Lambda |
|
|
923 | 923 | self.aL = aL |
|
|
924 | 924 | self.tauW = tauW |
|
|
925 | 925 | self.ThetaT = ThetaT |
|
|
926 | 926 | self.ThetaR = ThetaR |
|
|
927 | 927 | |
|
|
928 | 928 | RadarConstant = GetRadarConstant() |
|
|
929 | 929 | SPCmean = numpy.mean(self.spc,0) |
|
|
930 | 930 | ETA = numpy.zeros(self.Num_Hei) |
|
|
931 | 931 | Pr = numpy.sum(SPCmean,0) |
|
|
932 | 932 | |
|
|
933 | 933 | #for R in range(self.Num_Hei): |
|
|
934 | 934 | # ETA[R] = RadarConstant * Pr[R] * R**2 #Reflectivity (ETA) |
|
|
935 | 935 | |
|
|
936 | 936 | D_range = numpy.zeros(self.Num_Hei) |
|
|
937 | 937 | EqSec = numpy.zeros(self.Num_Hei) |
|
|
938 | 938 | del_V = numpy.zeros(self.Num_Hei) |
|
|
939 | 939 | |
|
|
940 | 940 | for R in range(self.Num_Hei): |
|
|
941 | 941 | ETA[R] = RadarConstant * Pr[R] * R**2 #Reflectivity (ETA) |
|
|
942 | 942 | |
|
|
943 | 943 | h = R + Altitude #Range from ground to radar pulse altitude |
|
|
944 | 944 | del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2 #Density change correction for velocity |
|
|
945 | 945 | |
|
|
946 | 946 | D_range[R] = numpy.log( (9.65 - (Velrange[R]/del_V[R])) / 10.3 ) / -0.6 #Range of Diameter of drops related to velocity |
|
|
947 | 947 | SIGMA[R] = numpy.pi**5 / Lambda**4 * Km * D_range[R]**6 #Equivalent Section of drops (sigma) |
|
|
948 | 948 | |
|
|
949 | 949 | N_dist[R] = ETA[R] / SIGMA[R] |
|
|
950 | 950 | |
|
|
951 | 951 | Ze = (ETA * Lambda**4) / (numpy.pi * Km) |
|
|
952 | 952 | Z = numpy.sum( N_dist * D_range**6 ) |
|
|
953 | 953 | RR = 6*10**-4*numpy.pi * numpy.sum( D_range**3 * N_dist * Velrange ) #Rainfall rate |
|
|
954 | 954 | |
|
|
955 | 955 | |
|
|
956 | 956 | RR = (Ze/200)**(1/1.6) |
|
|
957 | 957 | dBRR = 10*numpy.log10(RR) |
|
|
958 | 958 | |
|
|
959 | 959 | dBZe = 10*numpy.log10(Ze) |
|
|
960 | 960 | dataOut.data_output = Ze |
|
|
961 | 961 | dataOut.data_param = numpy.ones([2,self.Num_Hei]) |
|
|
962 | 962 | dataOut.channelList = [0,1] |
|
|
963 | 963 | print 'channelList', dataOut.channelList |
|
|
964 | 964 | dataOut.data_param[0]=dBZe |
|
|
965 | 965 | dataOut.data_param[1]=dBRR |
|
|
966 | 966 | print 'RR SHAPE', dBRR.shape |
|
|
967 | 967 | print 'Ze SHAPE', dBZe.shape |
|
|
968 | 968 | print 'dataOut.data_param SHAPE', dataOut.data_param.shape |
|
|
969 | 969 | |
|
|
970 | 970 | |
|
|
971 | 971 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
|
972 | 972 | |
|
|
973 | 973 | NPW = dataOut.NPW |
|
|
974 | 974 | COFA = dataOut.COFA |
|
|
975 | 975 | |
|
|
976 | 976 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
|
977 | 977 | RadarConst = dataOut.RadarConst |
|
|
978 | 978 | #frequency = 34.85*10**9 |
|
|
979 | 979 | |
|
|
980 | 980 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
|
981 | 981 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
|
982 | 982 | |
|
|
983 | 983 | ETA = numpy.sum(SNR,1) |
|
|
984 | 984 | print 'ETA' , ETA |
|
|
985 | 985 | ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN) |
|
|
986 | 986 | |
|
|
987 | 987 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
|
988 | 988 | |
|
|
989 | 989 | for r in range(self.Num_Hei): |
|
|
990 | 990 | |
|
|
991 | 991 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
|
992 | 992 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
|
993 | 993 | |
|
|
994 | 994 | return Ze |
|
|
995 | 995 | |
|
|
996 | 996 | def GetRadarConstant(self): |
|
|
997 | 997 | |
|
|
998 | 998 | """ |
|
|
999 | 999 | Constants: |
|
|
1000 | 1000 | |
|
|
1001 | 1001 | Pt: Transmission Power dB |
|
|
1002 | 1002 | Gt: Transmission Gain dB |
|
|
1003 | 1003 | Gr: Reception Gain dB |
|
|
1004 | 1004 | Lambda: Wavelenght m |
|
|
1005 | 1005 | aL: Attenuation loses dB |
|
|
1006 | 1006 | tauW: Width of transmission pulse s |
|
|
1007 | 1007 | ThetaT: Transmission antenna bean angle rad |
|
|
1008 | 1008 | ThetaR: Reception antenna beam angle rad |
|
|
1009 | 1009 | |
|
|
1010 | 1010 | """ |
|
|
1011 | 1011 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
1012 | 1012 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
|
1013 | 1013 | RadarConstant = Numerator / Denominator |
|
|
1014 | 1014 | |
|
|
1015 | 1015 | return RadarConstant |
|
|
1016 | 1016 | |
|
|
1017 | 1017 | |
|
|
1018 | 1018 | |
|
|
1019 | 1019 | class FullSpectralAnalysis(Operation): |
|
|
1020 | 1020 | |
|
|
1021 | 1021 | """ |
|
|
1022 | 1022 | Function that implements Full Spectral Analisys technique. |
|
|
1023 | 1023 | |
|
|
1024 | 1024 | Input: |
|
|
1025 | 1025 | self.dataOut.data_pre : SelfSpectra and CrossSPectra data |
|
|
1026 | 1026 | self.dataOut.groupList : Pairlist of channels |
|
|
1027 | 1027 | self.dataOut.ChanDist : Physical distance between receivers |
|
|
1028 | 1028 | |
|
|
1029 | 1029 | |
|
|
1030 | 1030 | Output: |
|
|
1031 | 1031 | |
|
|
1032 | 1032 | self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind |
|
|
1033 | 1033 | |
|
|
1034 | 1034 | |
|
|
1035 | 1035 | Parameters affected: Winds, height range, SNR |
|
|
1036 | 1036 | |
|
|
1037 | 1037 | """ |
|
|
1038 | 1038 | def run(self, dataOut, E01=None, E02=None, E12=None, N01=None, N02=None, N12=None, SNRlimit=7): |
|
|
1039 | 1039 | |
|
|
1040 | 1040 | spc = dataOut.data_pre[0].copy() |
|
|
1041 | 1041 | cspc = dataOut.data_pre[1].copy() |
|
|
1042 | 1042 | |
|
|
1043 | 1043 | nChannel = spc.shape[0] |
|
|
1044 | 1044 | nProfiles = spc.shape[1] |
|
|
1045 | 1045 | nHeights = spc.shape[2] |
|
|
1046 | 1046 | |
|
|
1047 | 1047 | pairsList = dataOut.groupList |
|
|
1048 | 1048 | if dataOut.ChanDist is not None : |
|
|
1049 | 1049 | ChanDist = dataOut.ChanDist |
|
|
1050 | 1050 | else: |
|
|
1051 | 1051 | ChanDist = numpy.array([[E01, N01],[E02,N02],[E12,N12]]) |
|
|
1052 | 1052 | |
|
|
1053 | 1053 | #print 'ChanDist', ChanDist |
|
|
1054 | 1054 | |
|
|
1055 | 1055 | if dataOut.VelRange is not None: |
|
|
1056 | 1056 | VelRange= dataOut.VelRange |
|
|
1057 | 1057 | else: |
|
|
1058 | 1058 | VelRange= dataOut.abscissaList |
|
|
1059 | 1059 | |
|
|
1060 | 1060 | ySamples=numpy.ones([nChannel,nProfiles]) |
|
|
1061 | 1061 | phase=numpy.ones([nChannel,nProfiles]) |
|
|
1062 | 1062 | CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_) |
|
|
1063 | 1063 | coherence=numpy.ones([nChannel,nProfiles]) |
|
|
1064 | 1064 | PhaseSlope=numpy.ones(nChannel) |
|
|
1065 | 1065 | PhaseInter=numpy.ones(nChannel) |
|
|
1066 | 1066 | dataSNR = dataOut.data_SNR |
|
|
1067 | 1067 | |
|
|
1068 | 1068 | |
|
|
1069 | 1069 | |
|
|
1070 | 1070 | data = dataOut.data_pre |
|
|
1071 | 1071 | noise = dataOut.noise |
|
|
1072 | 1072 | print 'noise',noise |
|
|
1073 | 1073 | #SNRdB = 10*numpy.log10(dataOut.data_SNR) |
|
|
1074 | 1074 | |
|
|
1075 | 1075 | FirstMoment = numpy.average(dataOut.data_param[:,1,:],0) |
|
|
1076 | 1076 | #SNRdBMean = [] |
|
|
1077 | 1077 | |
|
|
1078 | 1078 | |
|
|
1079 | 1079 | #for j in range(nHeights): |
|
|
1080 | 1080 | # FirstMoment = numpy.append(FirstMoment,numpy.mean([dataOut.data_param[0,1,j],dataOut.data_param[1,1,j],dataOut.data_param[2,1,j]])) |
|
|
1081 | 1081 | # SNRdBMean = numpy.append(SNRdBMean,numpy.mean([SNRdB[0,j],SNRdB[1,j],SNRdB[2,j]])) |
|
|
1082 | 1082 | |
|
|
1083 | 1083 | data_output=numpy.ones([3,spc.shape[2]])*numpy.NaN |
|
|
1084 | 1084 | |
|
|
1085 | 1085 | velocityX=[] |
|
|
1086 | 1086 | velocityY=[] |
|
|
1087 | 1087 | velocityV=[] |
|
|
1088 | 1088 | |
|
|
1089 | 1089 | dbSNR = 10*numpy.log10(dataSNR) |
|
|
1090 | 1090 | dbSNR = numpy.average(dbSNR,0) |
|
|
1091 | 1091 | for Height in range(nHeights): |
|
|
1092 | 1092 | |
|
|
1093 | 1093 | [Vzon,Vmer,Vver, GaussCenter]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, VelRange, dbSNR[Height], SNRlimit) |
|
|
1094 | 1094 | |
|
|
1095 | 1095 | if abs(Vzon)<100. and abs(Vzon)> 0.: |
|
|
1096 | 1096 | velocityX=numpy.append(velocityX, Vzon)#Vmag |
|
|
1097 | 1097 | |
|
|
1098 | 1098 | else: |
|
|
1099 | 1099 | print 'Vzon',Vzon |
|
|
1100 | 1100 | velocityX=numpy.append(velocityX, numpy.NaN) |
|
|
1101 | 1101 | |
|
|
1102 | 1102 | if abs(Vmer)<100. and abs(Vmer) > 0.: |
|
|
1103 | 1103 | velocityY=numpy.append(velocityY, Vmer)#Vang |
|
|
1104 | 1104 | |
|
|
1105 | 1105 | else: |
|
|
1106 | 1106 | print 'Vmer',Vmer |
|
|
1107 | 1107 | velocityY=numpy.append(velocityY, numpy.NaN) |
|
|
1108 | 1108 | |
|
|
1109 | 1109 | if dbSNR[Height] > SNRlimit: |
|
|
1110 | 1110 | velocityV=numpy.append(velocityV, FirstMoment[Height]) |
|
|
1111 | 1111 | else: |
|
|
1112 | 1112 | velocityV=numpy.append(velocityV, numpy.NaN) |
|
|
1113 | 1113 | #FirstMoment[Height]= numpy.NaN |
|
|
1114 | 1114 | # if SNRdBMean[Height] <12: |
|
|
1115 | 1115 | # FirstMoment[Height] = numpy.NaN |
|
|
1116 | 1116 | # velocityX[Height] = numpy.NaN |
|
|
1117 | 1117 | # velocityY[Height] = numpy.NaN |
|
|
1118 | 1118 | |
|
|
1119 | 1119 | |
|
|
1120 | 1120 | data_output[0]=numpy.array(velocityX) |
|
|
1121 | 1121 | data_output[1]=numpy.array(velocityY) |
|
|
1122 | 1122 | data_output[2]=-velocityV#FirstMoment |
|
|
1123 | 1123 | |
|
|
1124 | 1124 | print ' ' |
|
|
1125 | 1125 | #print 'FirstMoment' |
|
|
1126 | 1126 | #print FirstMoment |
|
|
1127 | 1127 | print 'velocityX',data_output[0] |
|
|
1128 | 1128 | print ' ' |
|
|
1129 | 1129 | print 'velocityY',data_output[1] |
|
|
1130 | 1130 | #print numpy.array(velocityY) |
|
|
1131 | 1131 | print ' ' |
|
|
1132 | 1132 | #print 'SNR' |
|
|
1133 | 1133 | #print 10*numpy.log10(dataOut.data_SNR) |
|
|
1134 | 1134 | #print numpy.shape(10*numpy.log10(dataOut.data_SNR)) |
|
|
1135 | 1135 | print ' ' |
|
|
1136 | 1136 | |
|
|
1137 | 1137 | |
|
|
1138 | 1138 | dataOut.data_output=data_output |
|
|
1139 | 1139 | return |
|
|
1140 | 1140 | |
|
|
1141 | 1141 | |
|
|
1142 | 1142 | def moving_average(self,x, N=2): |
|
|
1143 | 1143 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
|
1144 | 1144 | |
|
|
1145 | 1145 | def gaus(self,xSamples,a,x0,sigma): |
|
|
1146 | 1146 | return a*numpy.exp(-(xSamples-x0)**2/(2*sigma**2)) |
|
|
1147 | 1147 | |
|
|
1148 | 1148 | def Find(self,x,value): |
|
|
1149 | 1149 | for index in range(len(x)): |
|
|
1150 | 1150 | if x[index]==value: |
|
|
1151 | 1151 | return index |
|
|
1152 | 1152 | |
|
|
1153 | 1153 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, VelRange, dbSNR, SNRlimit): |
|
|
1154 | 1154 | |
|
|
1155 | 1155 | ySamples=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
1156 | 1156 | phase=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
1157 | 1157 | CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) |
|
|
1158 | 1158 | coherence=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
1159 | 1159 | PhaseSlope=numpy.ones(spc.shape[0]) |
|
|
1160 | 1160 | PhaseInter=numpy.ones(spc.shape[0]) |
|
|
1161 | 1161 | xFrec=VelRange |
|
|
1162 | 1162 | |
|
|
1163 | 1163 | '''Getting Eij and Nij''' |
|
|
1164 | 1164 | |
|
|
1165 | 1165 | E01=ChanDist[0][0] |
|
|
1166 | 1166 | N01=ChanDist[0][1] |
|
|
1167 | 1167 | |
|
|
1168 | 1168 | E02=ChanDist[1][0] |
|
|
1169 | 1169 | N02=ChanDist[1][1] |
|
|
1170 | 1170 | |
|
|
1171 | 1171 | E12=ChanDist[2][0] |
|
|
1172 | 1172 | N12=ChanDist[2][1] |
|
|
1173 | 1173 | |
|
|
1174 | 1174 | z = spc.copy() |
|
|
1175 | 1175 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1176 | 1176 | |
|
|
1177 | 1177 | for i in range(spc.shape[0]): |
|
|
1178 | 1178 | |
|
|
1179 | 1179 | '''****** Line of Data SPC ******''' |
|
|
1180 | 1180 | zline=z[i,:,Height] |
|
|
1181 | 1181 | |
|
|
1182 | 1182 | '''****** SPC is normalized ******''' |
|
|
1183 | 1183 | FactNorm= (zline.copy()-noise[i]) / numpy.sum(zline.copy()) |
|
|
1184 | 1184 | FactNorm= FactNorm/numpy.sum(FactNorm) |
|
|
1185 | 1185 | |
|
|
1186 | 1186 | SmoothSPC=self.moving_average(FactNorm,N=3) |
|
|
1187 | 1187 | |
|
|
1188 | 1188 | xSamples = ar(range(len(SmoothSPC))) |
|
|
1189 | 1189 | ySamples[i] = SmoothSPC |
|
|
1190 | 1190 | |
|
|
1191 | 1191 | #dbSNR=10*numpy.log10(dataSNR) |
|
|
1192 | 1192 | print ' ' |
|
|
1193 | 1193 | print ' ' |
|
|
1194 | 1194 | print ' ' |
|
|
1195 | 1195 | |
|
|
1196 | 1196 | #print 'dataSNR', dbSNR.shape, dbSNR[0,40:120] |
|
|
1197 | 1197 | print 'SmoothSPC', SmoothSPC.shape, SmoothSPC[0:20] |
|
|
1198 | 1198 | print 'noise',noise |
|
|
1199 | 1199 | print 'zline',zline.shape, zline[0:20] |
|
|
1200 | 1200 | print 'FactNorm',FactNorm.shape, FactNorm[0:20] |
|
|
1201 | 1201 | print 'FactNorm suma', numpy.sum(FactNorm) |
|
|
1202 | 1202 | |
|
|
1203 | 1203 | for i in range(spc.shape[0]): |
|
|
1204 | 1204 | |
|
|
1205 | 1205 | '''****** Line of Data CSPC ******''' |
|
|
1206 | 1206 | cspcLine=cspc[i,:,Height].copy() |
|
|
1207 | 1207 | |
|
|
1208 | 1208 | '''****** CSPC is normalized ******''' |
|
|
1209 | 1209 | chan_index0 = pairsList[i][0] |
|
|
1210 | 1210 | chan_index1 = pairsList[i][1] |
|
|
1211 | 1211 | CSPCFactor= abs(numpy.sum(ySamples[chan_index0]) * numpy.sum(ySamples[chan_index1])) # |
|
|
1212 | 1212 | |
|
|
1213 | 1213 | CSPCNorm = (cspcLine.copy() -noise[i]) / numpy.sqrt(CSPCFactor) |
|
|
1214 | 1214 | |
|
|
1215 | 1215 | CSPCSamples[i] = CSPCNorm |
|
|
1216 | 1216 | coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor) |
|
|
1217 | 1217 | |
|
|
1218 | 1218 | coherence[i]= self.moving_average(coherence[i],N=2) |
|
|
1219 | 1219 | |
|
|
1220 | 1220 | phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi |
|
|
1221 | 1221 | |
|
|
1222 | 1222 | print 'cspcLine', cspcLine.shape, cspcLine[0:20] |
|
|
1223 | 1223 | print 'CSPCFactor', CSPCFactor#, CSPCFactor[0:20] |
|
|
1224 | 1224 | print numpy.sum(ySamples[chan_index0]), numpy.sum(ySamples[chan_index1]), -noise[i] |
|
|
1225 | 1225 | print 'CSPCNorm', CSPCNorm.shape, CSPCNorm[0:20] |
|
|
1226 | 1226 | print 'CSPCNorm suma', numpy.sum(CSPCNorm) |
|
|
1227 | 1227 | print 'CSPCSamples', CSPCSamples.shape, CSPCSamples[0,0:20] |
|
|
1228 | 1228 | |
|
|
1229 | 1229 | '''****** Getting fij width ******''' |
|
|
1230 | 1230 | |
|
|
1231 | 1231 | yMean=[] |
|
|
1232 | 1232 | yMean2=[] |
|
|
1233 | 1233 | |
|
|
1234 | 1234 | for j in range(len(ySamples[1])): |
|
|
1235 | 1235 | yMean=numpy.append(yMean,numpy.mean([ySamples[0,j],ySamples[1,j],ySamples[2,j]])) |
|
|
1236 | 1236 | |
|
|
1237 | 1237 | '''******* Getting fitting Gaussian ******''' |
|
|
1238 | 1238 | meanGauss=sum(xSamples*yMean) / len(xSamples) |
|
|
1239 | 1239 | sigma=sum(yMean*(xSamples-meanGauss)**2) / len(xSamples) |
|
|
1240 | 1240 | |
|
|
1241 | 1241 | print '****************************' |
|
|
1242 | 1242 | print 'len(xSamples): ',len(xSamples) |
|
|
1243 | 1243 | print 'yMean: ', yMean.shape, yMean[0:20] |
|
|
1244 | 1244 | print 'ySamples', ySamples.shape, ySamples[0,0:20] |
|
|
1245 | 1245 | print 'xSamples: ',xSamples.shape, xSamples[0:20] |
|
|
1246 | 1246 | |
|
|
1247 | 1247 | print 'meanGauss',meanGauss |
|
|
1248 | 1248 | print 'sigma',sigma |
|
|
1249 | 1249 | |
|
|
1250 | 1250 | #if (abs(meanGauss/sigma**2) > 0.0001) : #0.000000001): |
|
|
1251 | 1251 | if dbSNR > SNRlimit : |
|
|
1252 | 1252 | try: |
|
|
1253 | 1253 | popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=[1,meanGauss,sigma]) |
|
|
1254 | 1254 | |
|
|
1255 | 1255 | if numpy.amax(popt)>numpy.amax(yMean)*0.3: |
|
|
1256 | 1256 | FitGauss=self.gaus(xSamples,*popt) |
|
|
1257 | 1257 | |
|
|
1258 | 1258 | else: |
|
|
1259 | 1259 | FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) |
|
|
1260 | 1260 | print 'Verificador: Dentro', Height |
|
|
1261 | 1261 | except :#RuntimeError: |
|
|
1262 | 1262 | FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) |
|
|
1263 | 1263 | |
|
|
1264 | 1264 | |
|
|
1265 | 1265 | else: |
|
|
1266 | 1266 | FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) |
|
|
1267 | 1267 | |
|
|
1268 | 1268 | Maximun=numpy.amax(yMean) |
|
|
1269 | 1269 | eMinus1=Maximun*numpy.exp(-1)#*0.8 |
|
|
1270 | 1270 | |
|
|
1271 | 1271 | HWpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-eMinus1))) |
|
|
1272 | 1272 | HalfWidth= xFrec[HWpos] |
|
|
1273 | 1273 | GCpos=self.Find(FitGauss, numpy.amax(FitGauss)) |
|
|
1274 | 1274 | Vpos=self.Find(FactNorm, numpy.amax(FactNorm)) |
|
|
1275 | 1275 | |
|
|
1276 | 1276 | #Vpos=FirstMoment[] |
|
|
1277 | 1277 | |
|
|
1278 | 1278 | '''****** Getting Fij ******''' |
|
|
1279 | 1279 | |
|
|
1280 | 1280 | GaussCenter=xFrec[GCpos] |
|
|
1281 | 1281 | if (GaussCenter<0 and HalfWidth>0) or (GaussCenter>0 and HalfWidth<0): |
|
|
1282 | 1282 | Fij=abs(GaussCenter)+abs(HalfWidth)+0.0000001 |
|
|
1283 | 1283 | else: |
|
|
1284 | 1284 | Fij=abs(GaussCenter-HalfWidth)+0.0000001 |
|
|
1285 | 1285 | |
|
|
1286 | 1286 | '''****** Getting Frecuency range of significant data ******''' |
|
|
1287 | 1287 | |
|
|
1288 | 1288 | Rangpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-Maximun*0.10))) |
|
|
1289 | 1289 | |
|
|
1290 | 1290 | if Rangpos<GCpos: |
|
|
1291 | 1291 | Range=numpy.array([Rangpos,2*GCpos-Rangpos]) |
|
|
1292 | 1292 | elif Rangpos< ( len(xFrec)- len(xFrec)*0.1): |
|
|
1293 | 1293 | Range=numpy.array([2*GCpos-Rangpos,Rangpos]) |
|
|
1294 | 1294 | else: |
|
|
1295 | 1295 | Range = numpy.array([0,0]) |
|
|
1296 | 1296 | |
|
|
1297 | 1297 | print ' ' |
|
|
1298 | 1298 | print 'GCpos',GCpos, ( len(xFrec)- len(xFrec)*0.1) |
|
|
1299 | 1299 | print 'Rangpos',Rangpos |
|
|
1300 | 1300 | print 'RANGE: ', Range |
|
|
1301 | 1301 | FrecRange=xFrec[Range[0]:Range[1]] |
|
|
1302 | 1302 | |
|
|
1303 | 1303 | '''****** Getting SCPC Slope ******''' |
|
|
1304 | 1304 | |
|
|
1305 | 1305 | for i in range(spc.shape[0]): |
|
|
1306 | 1306 | |
|
|
1307 | 1307 | if len(FrecRange)>5 and len(FrecRange)<spc.shape[1]*0.5: |
|
|
1308 | 1308 | PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=3) |
|
|
1309 | 1309 | |
|
|
1310 | 1310 | print 'FrecRange', len(FrecRange) , FrecRange |
|
|
1311 | 1311 | print 'PhaseRange', len(PhaseRange), PhaseRange |
|
|
1312 | 1312 | print ' ' |
|
|
1313 | 1313 | if len(FrecRange) == len(PhaseRange): |
|
|
1314 | 1314 | slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange,PhaseRange) |
|
|
1315 | 1315 | PhaseSlope[i]=slope |
|
|
1316 | 1316 | PhaseInter[i]=intercept |
|
|
1317 | 1317 | else: |
|
|
1318 | 1318 | PhaseSlope[i]=0 |
|
|
1319 | 1319 | PhaseInter[i]=0 |
|
|
1320 | 1320 | else: |
|
|
1321 | 1321 | PhaseSlope[i]=0 |
|
|
1322 | 1322 | PhaseInter[i]=0 |
|
|
1323 | 1323 | |
|
|
1324 | 1324 | '''Getting constant C''' |
|
|
1325 | 1325 | cC=(Fij*numpy.pi)**2 |
|
|
1326 | 1326 | |
|
|
1327 | 1327 | '''****** Getting constants F and G ******''' |
|
|
1328 | 1328 | MijEijNij=numpy.array([[E02,N02], [E12,N12]]) |
|
|
1329 | 1329 | MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi) |
|
|
1330 | 1330 | MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi) |
|
|
1331 | 1331 | MijResults=numpy.array([MijResult0,MijResult1]) |
|
|
1332 | 1332 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
|
1333 | 1333 | |
|
|
1334 | 1334 | '''****** Getting constants A, B and H ******''' |
|
|
1335 | 1335 | W01=numpy.amax(coherence[0]) |
|
|
1336 | 1336 | W02=numpy.amax(coherence[1]) |
|
|
1337 | 1337 | W12=numpy.amax(coherence[2]) |
|
|
1338 | 1338 | |
|
|
1339 | 1339 | WijResult0=((cF*E01+cG*N01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC)) |
|
|
1340 | 1340 | WijResult1=((cF*E02+cG*N02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC)) |
|
|
1341 | 1341 | WijResult2=((cF*E12+cG*N12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC)) |
|
|
1342 | 1342 | |
|
|
1343 | 1343 | WijResults=numpy.array([WijResult0, WijResult1, WijResult2]) |
|
|
1344 | 1344 | |
|
|
1345 | 1345 | WijEijNij=numpy.array([ [E01**2, N01**2, 2*E01*N01] , [E02**2, N02**2, 2*E02*N02] , [E12**2, N12**2, 2*E12*N12] ]) |
|
|
1346 | 1346 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
|
1347 | 1347 | |
|
|
1348 | 1348 | VxVy=numpy.array([[cA,cH],[cH,cB]]) |
|
|
1349 | 1349 | |
|
|
1350 | 1350 | VxVyResults=numpy.array([-cF,-cG]) |
|
|
1351 | 1351 | (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) |
|
|
1352 | 1352 | |
|
|
1353 | 1353 | Vzon = Vy |
|
|
1354 | 1354 | Vmer = Vx |
|
|
1355 | 1355 | Vmag=numpy.sqrt(Vzon**2+Vmer**2) |
|
|
1356 | 1356 | Vang=numpy.arctan2(Vmer,Vzon) |
|
|
1357 | 1357 | Vver=xFrec[Vpos] |
|
|
1358 | 1358 | print 'vzon y vmer', Vzon, Vmer |
|
|
1359 | 1359 | return Vzon, Vmer, Vver, GaussCenter |
|
|
1360 | 1360 | |
|
|
1361 | 1361 | class SpectralMoments(Operation): |
|
|
1362 | 1362 | |
|
|
1363 | 1363 | ''' |
|
|
1364 | 1364 | Function SpectralMoments() |
|
|
1365 | 1365 | |
|
|
1366 | 1366 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
1367 | 1367 | |
|
|
1368 | 1368 | Type of dataIn: Spectra |
|
|
1369 | 1369 | |
|
|
1370 | 1370 | Configuration Parameters: |
|
|
1371 | 1371 | |
|
|
1372 | 1372 | dirCosx : Cosine director in X axis |
|
|
1373 | 1373 | dirCosy : Cosine director in Y axis |
|
|
1374 | 1374 | |
|
|
1375 | 1375 | elevation : |
|
|
1376 | 1376 | azimuth : |
|
|
1377 | 1377 | |
|
|
1378 | 1378 | Input: |
|
|
1379 | 1379 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
1380 | 1380 | self.dataOut.data_pre : Spectral data |
|
|
1381 | 1381 | self.dataOut.abscissaList : List of frequencies |
|
|
1382 | 1382 | self.dataOut.noise : Noise level per channel |
|
|
1383 | 1383 | |
|
|
1384 | 1384 | Affected: |
|
|
1385 | 1385 | self.dataOut.data_param : Parameters per channel |
|
|
1386 | 1386 | self.dataOut.data_SNR : SNR per channel |
|
|
1387 | 1387 | |
|
|
1388 | 1388 | ''' |
|
|
1389 | 1389 | |
|
|
1390 | 1390 | def run(self, dataOut): |
|
|
1391 | 1391 | |
|
|
1392 | 1392 | #dataOut.data_pre = dataOut.data_pre[0] |
|
|
1393 | 1393 | data = dataOut.data_pre[0] |
|
|
1394 | 1394 | absc = dataOut.abscissaList[:-1] |
|
|
1395 | 1395 | noise = dataOut.noise |
|
|
1396 | 1396 | nChannel = data.shape[0] |
|
|
1397 | 1397 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
1398 | 1398 | |
|
|
1399 | 1399 | for ind in range(nChannel): |
|
|
1400 | 1400 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) |
|
|
1401 | 1401 | |
|
|
1402 | 1402 | dataOut.data_param = data_param[:,1:,:] |
|
|
1403 | 1403 | dataOut.data_SNR = data_param[:,0] |
|
|
1404 | dataOut.data_DOP = data_param[:,1] | |
|
|
1405 | dataOut.data_MEAN = data_param[:,2] | |
|
|
1406 | dataOut.data_STD = data_param[:,3] | |
|
|
1404 | 1407 | return |
|
|
1405 | 1408 | |
|
|
1406 | 1409 | def __calculateMoments(self, oldspec, oldfreq, n0, |
|
|
1407 | 1410 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
1408 | 1411 | |
|
|
1409 | 1412 | if (nicoh == None): nicoh = 1 |
|
|
1410 | 1413 | if (graph == None): graph = 0 |
|
|
1411 | 1414 | if (smooth == None): smooth = 0 |
|
|
1412 | 1415 | elif (self.smooth < 3): smooth = 0 |
|
|
1413 | 1416 | |
|
|
1414 | 1417 | if (type1 == None): type1 = 0 |
|
|
1415 | 1418 | if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
1416 | 1419 | if (snrth == None): snrth = -3 |
|
|
1417 | 1420 | if (dc == None): dc = 0 |
|
|
1418 | 1421 | if (aliasing == None): aliasing = 0 |
|
|
1419 | 1422 | if (oldfd == None): oldfd = 0 |
|
|
1420 | 1423 | if (wwauto == None): wwauto = 0 |
|
|
1421 | 1424 | |
|
|
1422 | 1425 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
1423 | 1426 | |
|
|
1424 | 1427 | freq = oldfreq |
|
|
1425 | 1428 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
1426 | 1429 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
1427 | 1430 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
1428 | 1431 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
1429 | 1432 | |
|
|
1430 | 1433 | for ind in range(oldspec.shape[1]): |
|
|
1431 | 1434 | |
|
|
1432 | 1435 | spec = oldspec[:,ind] |
|
|
1433 | 1436 | aux = spec*fwindow |
|
|
1434 | 1437 | max_spec = aux.max() |
|
|
1435 | 1438 | m = list(aux).index(max_spec) |
|
|
1436 | 1439 | |
|
|
1437 | 1440 | #Smooth |
|
|
1438 | 1441 | if (smooth == 0): spec2 = spec |
|
|
1439 | 1442 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
1440 | 1443 | |
|
|
1441 | 1444 | # Calculo de Momentos |
|
|
1442 | 1445 | bb = spec2[range(m,spec2.size)] |
|
|
1443 | 1446 | bb = (bb<n0).nonzero() |
|
|
1444 | 1447 | bb = bb[0] |
|
|
1445 | 1448 | |
|
|
1446 | 1449 | ss = spec2[range(0,m + 1)] |
|
|
1447 | 1450 | ss = (ss<n0).nonzero() |
|
|
1448 | 1451 | ss = ss[0] |
|
|
1449 | 1452 | |
|
|
1450 | 1453 | if (bb.size == 0): |
|
|
1451 | 1454 | bb0 = spec.size - 1 - m |
|
|
1452 | 1455 | else: |
|
|
1453 | 1456 | bb0 = bb[0] - 1 |
|
|
1454 | 1457 | if (bb0 < 0): |
|
|
1455 | 1458 | bb0 = 0 |
|
|
1456 | 1459 | |
|
|
1457 | 1460 | if (ss.size == 0): ss1 = 1 |
|
|
1458 | 1461 | else: ss1 = max(ss) + 1 |
|
|
1459 | 1462 | |
|
|
1460 | 1463 | if (ss1 > m): ss1 = m |
|
|
1461 | 1464 | |
|
|
1462 | 1465 | valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
|
|
1463 | 1466 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
|
1464 | 1467 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
|
1465 | 1468 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
|
1466 | 1469 | snr = (spec2.mean()-n0)/n0 |
|
|
1467 | 1470 | |
|
|
1468 | 1471 | if (snr < 1.e-20) : |
|
|
1469 | 1472 | snr = 1.e-20 |
|
|
1470 | 1473 | |
|
|
1471 | 1474 | vec_power[ind] = power |
|
|
1472 | 1475 | vec_fd[ind] = fd |
|
|
1473 | 1476 | vec_w[ind] = w |
|
|
1474 | 1477 | vec_snr[ind] = snr |
|
|
1475 | 1478 | |
|
|
1476 | 1479 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
1477 | 1480 | return moments |
|
|
1478 | 1481 | |
|
|
1479 | 1482 | #------------------ Get SA Parameters -------------------------- |
|
|
1480 | 1483 | |
|
|
1481 | 1484 | def GetSAParameters(self): |
|
|
1482 | 1485 | #SA en frecuencia |
|
|
1483 | 1486 | pairslist = self.dataOut.groupList |
|
|
1484 | 1487 | num_pairs = len(pairslist) |
|
|
1485 | 1488 | |
|
|
1486 | 1489 | vel = self.dataOut.abscissaList |
|
|
1487 | 1490 | spectra = self.dataOut.data_pre |
|
|
1488 | 1491 | cspectra = self.dataIn.data_cspc |
|
|
1489 | 1492 | delta_v = vel[1] - vel[0] |
|
|
1490 | 1493 | |
|
|
1491 | 1494 | #Calculating the power spectrum |
|
|
1492 | 1495 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
1493 | 1496 | #Normalizing Spectra |
|
|
1494 | 1497 | norm_spectra = spectra/spc_pow |
|
|
1495 | 1498 | #Calculating the norm_spectra at peak |
|
|
1496 | 1499 | max_spectra = numpy.max(norm_spectra, 3) |
|
|
1497 | 1500 | |
|
|
1498 | 1501 | #Normalizing Cross Spectra |
|
|
1499 | 1502 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
|
1500 | 1503 | |
|
|
1501 | 1504 | for i in range(num_chan): |
|
|
1502 | 1505 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
|
1503 | 1506 | |
|
|
1504 | 1507 | max_cspectra = numpy.max(norm_cspectra,2) |
|
|
1505 | 1508 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
1506 | 1509 | |
|
|
1507 | 1510 | for i in range(num_pairs): |
|
|
1508 | 1511 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
1509 | 1512 | #------------------- Get Lags ---------------------------------- |
|
|
1510 | 1513 | |
|
|
1511 | 1514 | class SALags(Operation): |
|
|
1512 | 1515 | ''' |
|
|
1513 | 1516 | Function GetMoments() |
|
|
1514 | 1517 | |
|
|
1515 | 1518 | Input: |
|
|
1516 | 1519 | self.dataOut.data_pre |
|
|
1517 | 1520 | self.dataOut.abscissaList |
|
|
1518 | 1521 | self.dataOut.noise |
|
|
1519 | 1522 | self.dataOut.normFactor |
|
|
1520 | 1523 | self.dataOut.data_SNR |
|
|
1521 | 1524 | self.dataOut.groupList |
|
|
1522 | 1525 | self.dataOut.nChannels |
|
|
1523 | 1526 | |
|
|
1524 | 1527 | Affected: |
|
|
1525 | 1528 | self.dataOut.data_param |
|
|
1526 | 1529 | |
|
|
1527 | 1530 | ''' |
|
|
1528 | 1531 | def run(self, dataOut): |
|
|
1529 | 1532 | data_acf = dataOut.data_pre[0] |
|
|
1530 | 1533 | data_ccf = dataOut.data_pre[1] |
|
|
1531 | 1534 | normFactor_acf = dataOut.normFactor[0] |
|
|
1532 | 1535 | normFactor_ccf = dataOut.normFactor[1] |
|
|
1533 | 1536 | pairs_acf = dataOut.groupList[0] |
|
|
1534 | 1537 | pairs_ccf = dataOut.groupList[1] |
|
|
1535 | 1538 | |
|
|
1536 | 1539 | nHeights = dataOut.nHeights |
|
|
1537 | 1540 | absc = dataOut.abscissaList |
|
|
1538 | 1541 | noise = dataOut.noise |
|
|
1539 | 1542 | SNR = dataOut.data_SNR |
|
|
1540 | 1543 | nChannels = dataOut.nChannels |
|
|
1541 | 1544 | # pairsList = dataOut.groupList |
|
|
1542 | 1545 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1543 | 1546 | |
|
|
1544 | 1547 | for l in range(len(pairs_acf)): |
|
|
1545 | 1548 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
|
1546 | 1549 | |
|
|
1547 | 1550 | for l in range(len(pairs_ccf)): |
|
|
1548 | 1551 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
|
1549 | 1552 | |
|
|
1550 | 1553 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
|
1551 | 1554 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
1552 | 1555 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
1553 | 1556 | return |
|
|
1554 | 1557 | |
|
|
1555 | 1558 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1556 | 1559 | # |
|
|
1557 | 1560 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1558 | 1561 | # |
|
|
1559 | 1562 | # for l in range(len(pairsList)): |
|
|
1560 | 1563 | # firstChannel = pairsList[l][0] |
|
|
1561 | 1564 | # secondChannel = pairsList[l][1] |
|
|
1562 | 1565 | # |
|
|
1563 | 1566 | # #Obteniendo pares de Autocorrelacion |
|
|
1564 | 1567 | # if firstChannel == secondChannel: |
|
|
1565 | 1568 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1566 | 1569 | # |
|
|
1567 | 1570 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1568 | 1571 | # |
|
|
1569 | 1572 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1570 | 1573 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1571 | 1574 | # |
|
|
1572 | 1575 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1573 | 1576 | |
|
|
1574 | 1577 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
1575 | 1578 | |
|
|
1576 | 1579 | lag0 = data_acf.shape[1]/2 |
|
|
1577 | 1580 | #Funcion de Autocorrelacion |
|
|
1578 | 1581 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
1579 | 1582 | |
|
|
1580 | 1583 | #Obtencion Indice de TauCross |
|
|
1581 | 1584 | ind_ccf = data_ccf.argmax(axis = 1) |
|
|
1582 | 1585 | #Obtencion Indice de TauAuto |
|
|
1583 | 1586 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
1584 | 1587 | ccf_lag0 = data_ccf[:,lag0,:] |
|
|
1585 | 1588 | |
|
|
1586 | 1589 | for i in range(ccf_lag0.shape[0]): |
|
|
1587 | 1590 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
1588 | 1591 | |
|
|
1589 | 1592 | #Obtencion de TauCross y TauAuto |
|
|
1590 | 1593 | tau_ccf = lagRange[ind_ccf] |
|
|
1591 | 1594 | tau_acf = lagRange[ind_acf] |
|
|
1592 | 1595 | |
|
|
1593 | 1596 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
1594 | 1597 | |
|
|
1595 | 1598 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
1596 | 1599 | tau_acf[Nan1,Nan2] = numpy.nan |
|
|
1597 | 1600 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
1598 | 1601 | |
|
|
1599 | 1602 | return tau |
|
|
1600 | 1603 | |
|
|
1601 | 1604 | def __calculateLag1Phase(self, data, lagTRange): |
|
|
1602 | 1605 | data1 = stats.nanmean(data, axis = 0) |
|
|
1603 | 1606 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
1604 | 1607 | |
|
|
1605 | 1608 | phase = numpy.angle(data1[lag1,:]) |
|
|
1606 | 1609 | |
|
|
1607 | 1610 | return phase |
|
|
1608 | 1611 | |
|
|
1609 | 1612 | class SpectralFitting(Operation): |
|
|
1610 | 1613 | ''' |
|
|
1611 | 1614 | Function GetMoments() |
|
|
1612 | 1615 | |
|
|
1613 | 1616 | Input: |
|
|
1614 | 1617 | Output: |
|
|
1615 | 1618 | Variables modified: |
|
|
1616 | 1619 | ''' |
|
|
1617 | 1620 | |
|
|
1618 | 1621 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
1619 | 1622 | |
|
|
1620 | 1623 | |
|
|
1621 | 1624 | if path != None: |
|
|
1622 | 1625 | sys.path.append(path) |
|
|
1623 | 1626 | self.dataOut.library = importlib.import_module(file) |
|
|
1624 | 1627 | |
|
|
1625 | 1628 | #To be inserted as a parameter |
|
|
1626 | 1629 | groupArray = numpy.array(groupList) |
|
|
1627 | 1630 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1628 | 1631 | self.dataOut.groupList = groupArray |
|
|
1629 | 1632 | |
|
|
1630 | 1633 | nGroups = groupArray.shape[0] |
|
|
1631 | 1634 | nChannels = self.dataIn.nChannels |
|
|
1632 | 1635 | nHeights=self.dataIn.heightList.size |
|
|
1633 | 1636 | |
|
|
1634 | 1637 | #Parameters Array |
|
|
1635 | 1638 | self.dataOut.data_param = None |
|
|
1636 | 1639 | |
|
|
1637 | 1640 | #Set constants |
|
|
1638 | 1641 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1639 | 1642 | self.dataOut.constants = constants |
|
|
1640 | 1643 | M = self.dataIn.normFactor |
|
|
1641 | 1644 | N = self.dataIn.nFFTPoints |
|
|
1642 | 1645 | ippSeconds = self.dataIn.ippSeconds |
|
|
1643 | 1646 | K = self.dataIn.nIncohInt |
|
|
1644 | 1647 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1645 | 1648 | |
|
|
1646 | 1649 | #List of possible combinations |
|
|
1647 | 1650 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1648 | 1651 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1649 | 1652 | |
|
|
1650 | 1653 | if getSNR: |
|
|
1651 | 1654 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1652 | 1655 | listChannels.sort() |
|
|
1653 | 1656 | noise = self.dataIn.getNoise() |
|
|
1654 | 1657 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1655 | 1658 | |
|
|
1656 | 1659 | for i in range(nGroups): |
|
|
1657 | 1660 | coord = groupArray[i,:] |
|
|
1658 | 1661 | |
|
|
1659 | 1662 | #Input data array |
|
|
1660 | 1663 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1661 | 1664 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1662 | 1665 | |
|
|
1663 | 1666 | #Cross Spectra data array for Covariance Matrixes |
|
|
1664 | 1667 | ind = 0 |
|
|
1665 | 1668 | for pairs in listComb: |
|
|
1666 | 1669 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1667 | 1670 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1668 | 1671 | ind += 1 |
|
|
1669 | 1672 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1670 | 1673 | dataCross = dataCross**2/K |
|
|
1671 | 1674 | |
|
|
1672 | 1675 | for h in range(nHeights): |
|
|
1673 | 1676 | # print self.dataOut.heightList[h] |
|
|
1674 | 1677 | |
|
|
1675 | 1678 | #Input |
|
|
1676 | 1679 | d = data[:,h] |
|
|
1677 | 1680 | |
|
|
1678 | 1681 | #Covariance Matrix |
|
|
1679 | 1682 | D = numpy.diag(d**2/K) |
|
|
1680 | 1683 | ind = 0 |
|
|
1681 | 1684 | for pairs in listComb: |
|
|
1682 | 1685 | #Coordinates in Covariance Matrix |
|
|
1683 | 1686 | x = pairs[0] |
|
|
1684 | 1687 | y = pairs[1] |
|
|
1685 | 1688 | #Channel Index |
|
|
1686 | 1689 | S12 = dataCross[ind,:,h] |
|
|
1687 | 1690 | D12 = numpy.diag(S12) |
|
|
1688 | 1691 | #Completing Covariance Matrix with Cross Spectras |
|
|
1689 | 1692 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1690 | 1693 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1691 | 1694 | ind += 1 |
|
|
1692 | 1695 | Dinv=numpy.linalg.inv(D) |
|
|
1693 | 1696 | L=numpy.linalg.cholesky(Dinv) |
|
|
1694 | 1697 | LT=L.T |
|
|
1695 | 1698 | |
|
|
1696 | 1699 | dp = numpy.dot(LT,d) |
|
|
1697 | 1700 | |
|
|
1698 | 1701 | #Initial values |
|
|
1699 | 1702 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1700 | 1703 | |
|
|
1701 | 1704 | if (h>0)and(error1[3]<5): |
|
|
1702 | 1705 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1703 | 1706 | else: |
|
|
1704 | 1707 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1705 | 1708 | |
|
|
1706 | 1709 | try: |
|
|
1707 | 1710 | #Least Squares |
|
|
1708 | 1711 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1709 | 1712 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1710 | 1713 | #Chi square error |
|
|
1711 | 1714 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1712 | 1715 | #Error with Jacobian |
|
|
1713 | 1716 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1714 | 1717 | except: |
|
|
1715 | 1718 | minp = p0*numpy.nan |
|
|
1716 | 1719 | error0 = numpy.nan |
|
|
1717 | 1720 | error1 = p0*numpy.nan |
|
|
1718 | 1721 | |
|
|
1719 | 1722 | #Save |
|
|
1720 | 1723 | if self.dataOut.data_param == None: |
|
|
1721 | 1724 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1722 | 1725 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1723 | 1726 | |
|
|
1724 | 1727 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1725 | 1728 | self.dataOut.data_param[i,:,h] = minp |
|
|
1726 | 1729 | return |
|
|
1727 | 1730 | |
|
|
1728 | 1731 | def __residFunction(self, p, dp, LT, constants): |
|
|
1729 | 1732 | |
|
|
1730 | 1733 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1731 | 1734 | fmp=numpy.dot(LT,fm) |
|
|
1732 | 1735 | |
|
|
1733 | 1736 | return dp-fmp |
|
|
1734 | 1737 | |
|
|
1735 | 1738 | def __getSNR(self, z, noise): |
|
|
1736 | 1739 | |
|
|
1737 | 1740 | avg = numpy.average(z, axis=1) |
|
|
1738 | 1741 | SNR = (avg.T-noise)/noise |
|
|
1739 | 1742 | SNR = SNR.T |
|
|
1740 | 1743 | return SNR |
|
|
1741 | 1744 | |
|
|
1742 | 1745 | def __chisq(p,chindex,hindex): |
|
|
1743 | 1746 | #similar to Resid but calculates CHI**2 |
|
|
1744 | 1747 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1745 | 1748 | dp=numpy.dot(LT,d) |
|
|
1746 | 1749 | fmp=numpy.dot(LT,fm) |
|
|
1747 | 1750 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1748 | 1751 | return chisq |
|
|
1749 | 1752 | |
|
|
1750 | 1753 | class WindProfiler(Operation): |
|
|
1751 | 1754 | |
|
|
1752 | 1755 | __isConfig = False |
|
|
1753 | 1756 | |
|
|
1754 | 1757 | __initime = None |
|
|
1755 | 1758 | __lastdatatime = None |
|
|
1756 | 1759 | __integrationtime = None |
|
|
1757 | 1760 | |
|
|
1758 | 1761 | __buffer = None |
|
|
1759 | 1762 | |
|
|
1760 | 1763 | __dataReady = False |
|
|
1761 | 1764 | |
|
|
1762 | 1765 | __firstdata = None |
|
|
1763 | 1766 | |
|
|
1764 | 1767 | n = None |
|
|
1765 | 1768 | |
|
|
1766 | 1769 | def __init__(self): |
|
|
1767 | 1770 | Operation.__init__(self) |
|
|
1768 | 1771 | |
|
|
1769 | 1772 | def __calculateCosDir(self, elev, azim): |
|
|
1770 | 1773 | zen = (90 - elev)*numpy.pi/180 |
|
|
1771 | 1774 | azim = azim*numpy.pi/180 |
|
|
1772 | 1775 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1773 | 1776 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1774 | 1777 | |
|
|
1775 | 1778 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1776 | 1779 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1777 | 1780 | |
|
|
1778 | 1781 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1779 | 1782 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1780 | 1783 | return cosDirX, cosDirY |
|
|
1781 | 1784 | |
|
|
1782 | 1785 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1783 | 1786 | |
|
|
1784 | 1787 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1785 | 1788 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1786 | 1789 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1787 | 1790 | |
|
|
1788 | 1791 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1789 | 1792 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1790 | 1793 | |
|
|
1791 | 1794 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1792 | 1795 | |
|
|
1793 | 1796 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1794 | 1797 | |
|
|
1795 | 1798 | # |
|
|
1796 | 1799 | if horOnly: |
|
|
1797 | 1800 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1798 | 1801 | else: |
|
|
1799 | 1802 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1800 | 1803 | A = numpy.asmatrix(A) |
|
|
1801 | 1804 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1802 | 1805 | |
|
|
1803 | 1806 | return A1 |
|
|
1804 | 1807 | |
|
|
1805 | 1808 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1806 | 1809 | listPhi = phi.tolist() |
|
|
1807 | 1810 | maxid = listPhi.index(max(listPhi)) |
|
|
1808 | 1811 | minid = listPhi.index(min(listPhi)) |
|
|
1809 | 1812 | |
|
|
1810 | 1813 | rango = range(len(phi)) |
|
|
1811 | 1814 | # rango = numpy.delete(rango,maxid) |
|
|
1812 | 1815 | |
|
|
1813 | 1816 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1814 | 1817 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1815 | 1818 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1816 | 1819 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1817 | 1820 | |
|
|
1818 | 1821 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1819 | 1822 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1820 | 1823 | |
|
|
1821 | 1824 | for i in rango: |
|
|
1822 | 1825 | x = heiRang*math.cos(phi[i]) |
|
|
1823 | 1826 | y1 = velRadial[i,:] |
|
|
1824 | 1827 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1825 | 1828 | |
|
|
1826 | 1829 | x1 = heiRang1 |
|
|
1827 | 1830 | y11 = f1(x1) |
|
|
1828 | 1831 | |
|
|
1829 | 1832 | y2 = SNR[i,:] |
|
|
1830 | 1833 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1831 | 1834 | y21 = f2(x1) |
|
|
1832 | 1835 | |
|
|
1833 | 1836 | velRadial1[i,:] = y11 |
|
|
1834 | 1837 | SNR1[i,:] = y21 |
|
|
1835 | 1838 | |
|
|
1836 | 1839 | return heiRang1, velRadial1, SNR1 |
|
|
1837 | 1840 | |
|
|
1838 | 1841 | def __calculateVelUVW(self, A, velRadial): |
|
|
1839 | 1842 | |
|
|
1840 | 1843 | #Operacion Matricial |
|
|
1841 | 1844 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1842 | 1845 | # for ind in range(velRadial.shape[1]): |
|
|
1843 | 1846 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1844 | 1847 | # velUVW = velUVW.transpose() |
|
|
1845 | 1848 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1846 | 1849 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1847 | 1850 | |
|
|
1848 | 1851 | |
|
|
1849 | 1852 | return velUVW |
|
|
1850 | 1853 | |
|
|
1851 | 1854 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1852 | 1855 | |
|
|
1853 | 1856 | def techniqueDBS(self, kwargs): |
|
|
1854 | 1857 | """ |
|
|
1855 | 1858 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1856 | 1859 | |
|
|
1857 | 1860 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1858 | 1861 | Direction correction (if necessary), Ranges and SNR |
|
|
1859 | 1862 | |
|
|
1860 | 1863 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1861 | 1864 | |
|
|
1862 | 1865 | Parameters affected: Winds, height range, SNR |
|
|
1863 | 1866 | """ |
|
|
1864 | 1867 | velRadial0 = kwargs['velRadial'] |
|
|
1865 | 1868 | heiRang = kwargs['heightList'] |
|
|
1866 | 1869 | SNR0 = kwargs['SNR'] |
|
|
1867 | 1870 | |
|
|
1868 | 1871 | if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
1869 | 1872 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1870 | 1873 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1871 | 1874 | else: |
|
|
1872 | 1875 | elev = numpy.array(kwargs['elevation']) |
|
|
1873 | 1876 | azim = numpy.array(kwargs['azimuth']) |
|
|
1874 | 1877 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1875 | 1878 | azimuth = kwargs['correctAzimuth'] |
|
|
1876 | 1879 | if kwargs.has_key('horizontalOnly'): |
|
|
1877 | 1880 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1878 | 1881 | else: horizontalOnly = False |
|
|
1879 | 1882 | if kwargs.has_key('correctFactor'): |
|
|
1880 | 1883 | correctFactor = kwargs['correctFactor'] |
|
|
1881 | 1884 | else: correctFactor = 1 |
|
|
1882 | 1885 | if kwargs.has_key('channelList'): |
|
|
1883 | 1886 | channelList = kwargs['channelList'] |
|
|
1884 | 1887 | if len(channelList) == 2: |
|
|
1885 | 1888 | horizontalOnly = True |
|
|
1886 | 1889 | arrayChannel = numpy.array(channelList) |
|
|
1887 | 1890 | param = param[arrayChannel,:,:] |
|
|
1888 | 1891 | theta_x = theta_x[arrayChannel] |
|
|
1889 | 1892 | theta_y = theta_y[arrayChannel] |
|
|
1890 | 1893 | |
|
|
1891 | 1894 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
1892 | 1895 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
1893 | 1896 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1894 | 1897 | |
|
|
1895 | 1898 | #Calculo de Componentes de la velocidad con DBS |
|
|
1896 | 1899 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1897 | 1900 | |
|
|
1898 | 1901 | return winds, heiRang1, SNR1 |
|
|
1899 | 1902 | |
|
|
1900 | 1903 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
1901 | 1904 | |
|
|
1902 | 1905 | nPairs = len(pairs_ccf) |
|
|
1903 | 1906 | posx = numpy.asarray(posx) |
|
|
1904 | 1907 | posy = numpy.asarray(posy) |
|
|
1905 | 1908 | |
|
|
1906 | 1909 | #Rotacion Inversa para alinear con el azimuth |
|
|
1907 | 1910 | if azimuth!= None: |
|
|
1908 | 1911 | azimuth = azimuth*math.pi/180 |
|
|
1909 | 1912 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1910 | 1913 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1911 | 1914 | else: |
|
|
1912 | 1915 | posx1 = posx |
|
|
1913 | 1916 | posy1 = posy |
|
|
1914 | 1917 | |
|
|
1915 | 1918 | #Calculo de Distancias |
|
|
1916 | 1919 | distx = numpy.zeros(nPairs) |
|
|
1917 | 1920 | disty = numpy.zeros(nPairs) |
|
|
1918 | 1921 | dist = numpy.zeros(nPairs) |
|
|
1919 | 1922 | ang = numpy.zeros(nPairs) |
|
|
1920 | 1923 | |
|
|
1921 | 1924 | for i in range(nPairs): |
|
|
1922 | 1925 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
1923 | 1926 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
1924 | 1927 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1925 | 1928 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1926 | 1929 | |
|
|
1927 | 1930 | return distx, disty, dist, ang |
|
|
1928 | 1931 | #Calculo de Matrices |
|
|
1929 | 1932 | # nPairs = len(pairs) |
|
|
1930 | 1933 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1931 | 1934 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1932 | 1935 | # |
|
|
1933 | 1936 | # for j in range(nPairs): |
|
|
1934 | 1937 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1935 | 1938 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1936 | 1939 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1937 | 1940 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1938 | 1941 | # |
|
|
1939 | 1942 | # return distx,disty, dist1,ang1 |
|
|
1940 | 1943 | |
|
|
1941 | 1944 | |
|
|
1942 | 1945 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1943 | 1946 | |
|
|
1944 | 1947 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1945 | 1948 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1946 | 1949 | |
|
|
1947 | 1950 | return velW |
|
|
1948 | 1951 | |
|
|
1949 | 1952 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1950 | 1953 | nPairs = tau1.shape[0] |
|
|
1951 | 1954 | nHeights = tau1.shape[1] |
|
|
1952 | 1955 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
1953 | 1956 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
1954 | 1957 | |
|
|
1955 | 1958 | angCos = numpy.cos(ang) |
|
|
1956 | 1959 | angSin = numpy.sin(ang) |
|
|
1957 | 1960 | |
|
|
1958 | 1961 | vel0 = dist1*tau1/(2*tau2**2) |
|
|
1959 | 1962 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1960 | 1963 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1961 | 1964 | |
|
|
1962 | 1965 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1963 | 1966 | vel[ind] = numpy.nan |
|
|
1964 | 1967 | |
|
|
1965 | 1968 | return vel |
|
|
1966 | 1969 | |
|
|
1967 | 1970 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1968 | 1971 | # |
|
|
1969 | 1972 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1970 | 1973 | # |
|
|
1971 | 1974 | # for l in range(len(pairsList)): |
|
|
1972 | 1975 | # firstChannel = pairsList[l][0] |
|
|
1973 | 1976 | # secondChannel = pairsList[l][1] |
|
|
1974 | 1977 | # |
|
|
1975 | 1978 | # #Obteniendo pares de Autocorrelacion |
|
|
1976 | 1979 | # if firstChannel == secondChannel: |
|
|
1977 | 1980 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1978 | 1981 | # |
|
|
1979 | 1982 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1980 | 1983 | # |
|
|
1981 | 1984 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1982 | 1985 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1983 | 1986 | # |
|
|
1984 | 1987 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1985 | 1988 | |
|
|
1986 | 1989 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1987 | 1990 | def techniqueSA(self, kwargs): |
|
|
1988 | 1991 | |
|
|
1989 | 1992 | """ |
|
|
1990 | 1993 | Function that implements Spaced Antenna (SA) technique. |
|
|
1991 | 1994 | |
|
|
1992 | 1995 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1993 | 1996 | Direction correction (if necessary), Ranges and SNR |
|
|
1994 | 1997 | |
|
|
1995 | 1998 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1996 | 1999 | |
|
|
1997 | 2000 | Parameters affected: Winds |
|
|
1998 | 2001 | """ |
|
|
1999 | 2002 | position_x = kwargs['positionX'] |
|
|
2000 | 2003 | position_y = kwargs['positionY'] |
|
|
2001 | 2004 | azimuth = kwargs['azimuth'] |
|
|
2002 | 2005 | |
|
|
2003 | 2006 | if kwargs.has_key('correctFactor'): |
|
|
2004 | 2007 | correctFactor = kwargs['correctFactor'] |
|
|
2005 | 2008 | else: |
|
|
2006 | 2009 | correctFactor = 1 |
|
|
2007 | 2010 | |
|
|
2008 | 2011 | groupList = kwargs['groupList'] |
|
|
2009 | 2012 | pairs_ccf = groupList[1] |
|
|
2010 | 2013 | tau = kwargs['tau'] |
|
|
2011 | 2014 | _lambda = kwargs['_lambda'] |
|
|
2012 | 2015 | |
|
|
2013 | 2016 | #Cross Correlation pairs obtained |
|
|
2014 | 2017 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
2015 | 2018 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
2016 | 2019 | # pairsSelArray = numpy.array(pairsSelected) |
|
|
2017 | 2020 | # pairs = [] |
|
|
2018 | 2021 | # |
|
|
2019 | 2022 | # #Wind estimation pairs obtained |
|
|
2020 | 2023 | # for i in range(pairsSelArray.shape[0]/2): |
|
|
2021 | 2024 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
2022 | 2025 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
2023 | 2026 | # pairs.append((ind1,ind2)) |
|
|
2024 | 2027 | |
|
|
2025 | 2028 | indtau = tau.shape[0]/2 |
|
|
2026 | 2029 | tau1 = tau[:indtau,:] |
|
|
2027 | 2030 | tau2 = tau[indtau:-1,:] |
|
|
2028 | 2031 | # tau1 = tau1[pairs,:] |
|
|
2029 | 2032 | # tau2 = tau2[pairs,:] |
|
|
2030 | 2033 | phase1 = tau[-1,:] |
|
|
2031 | 2034 | |
|
|
2032 | 2035 | #--------------------------------------------------------------------- |
|
|
2033 | 2036 | #Metodo Directo |
|
|
2034 | 2037 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
2035 | 2038 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
2036 | 2039 | winds = stats.nanmean(winds, axis=0) |
|
|
2037 | 2040 | #--------------------------------------------------------------------- |
|
|
2038 | 2041 | #Metodo General |
|
|
2039 | 2042 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
2040 | 2043 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
2041 | 2044 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
2042 | 2045 | # #Calculo de Velocidades |
|
|
2043 | 2046 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
2044 | 2047 | |
|
|
2045 | 2048 | #--------------------------------------------------------------------- |
|
|
2046 | 2049 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
2047 | 2050 | winds = correctFactor*winds |
|
|
2048 | 2051 | return winds |
|
|
2049 | 2052 | |
|
|
2050 | 2053 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
2051 | 2054 | |
|
|
2052 | 2055 | dataTime = currentTime + paramInterval |
|
|
2053 | 2056 | deltaTime = dataTime - self.__initime |
|
|
2054 | 2057 | |
|
|
2055 | 2058 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
2056 | 2059 | self.__dataReady = True |
|
|
2057 | 2060 | return |
|
|
2058 | 2061 | |
|
|
2059 | 2062 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
2060 | 2063 | ''' |
|
|
2061 | 2064 | Function that implements winds estimation technique with detected meteors. |
|
|
2062 | 2065 | |
|
|
2063 | 2066 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
2064 | 2067 | |
|
|
2065 | 2068 | Output: Winds estimation (Zonal and Meridional) |
|
|
2066 | 2069 | |
|
|
2067 | 2070 | Parameters affected: Winds |
|
|
2068 | 2071 | ''' |
|
|
2069 | 2072 | # print arrayMeteor.shape |
|
|
2070 | 2073 | #Settings |
|
|
2071 | 2074 | nInt = (heightMax - heightMin)/2 |
|
|
2072 | 2075 | # print nInt |
|
|
2073 | 2076 | nInt = int(nInt) |
|
|
2074 | 2077 | # print nInt |
|
|
2075 | 2078 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
2076 | 2079 | |
|
|
2077 | 2080 | #Filter errors |
|
|
2078 | 2081 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
2079 | 2082 | finalMeteor = arrayMeteor[error,:] |
|
|
2080 | 2083 | |
|
|
2081 | 2084 | #Meteor Histogram |
|
|
2082 | 2085 | finalHeights = finalMeteor[:,2] |
|
|
2083 | 2086 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
2084 | 2087 | nMeteorsPerI = hist[0] |
|
|
2085 | 2088 | heightPerI = hist[1] |
|
|
2086 | 2089 | |
|
|
2087 | 2090 | #Sort of meteors |
|
|
2088 | 2091 | indSort = finalHeights.argsort() |
|
|
2089 | 2092 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
2090 | 2093 | |
|
|
2091 | 2094 | # Calculating winds |
|
|
2092 | 2095 | ind1 = 0 |
|
|
2093 | 2096 | ind2 = 0 |
|
|
2094 | 2097 | |
|
|
2095 | 2098 | for i in range(nInt): |
|
|
2096 | 2099 | nMet = nMeteorsPerI[i] |
|
|
2097 | 2100 | ind1 = ind2 |
|
|
2098 | 2101 | ind2 = ind1 + nMet |
|
|
2099 | 2102 | |
|
|
2100 | 2103 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
2101 | 2104 | |
|
|
2102 | 2105 | if meteorAux.shape[0] >= meteorThresh: |
|
|
2103 | 2106 | vel = meteorAux[:, 6] |
|
|
2104 | 2107 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
2105 | 2108 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
2106 | 2109 | |
|
|
2107 | 2110 | n = numpy.cos(zen) |
|
|
2108 | 2111 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
2109 | 2112 | # l = m*numpy.tan(azim) |
|
|
2110 | 2113 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
2111 | 2114 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
2112 | 2115 | |
|
|
2113 | 2116 | A = numpy.vstack((l, m)).transpose() |
|
|
2114 | 2117 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
2115 | 2118 | windsAux = numpy.dot(A1, vel) |
|
|
2116 | 2119 | |
|
|
2117 | 2120 | winds[0,i] = windsAux[0] |
|
|
2118 | 2121 | winds[1,i] = windsAux[1] |
|
|
2119 | 2122 | |
|
|
2120 | 2123 | return winds, heightPerI[:-1] |
|
|
2121 | 2124 | |
|
|
2122 | 2125 | def techniqueNSM_SA(self, **kwargs): |
|
|
2123 | 2126 | metArray = kwargs['metArray'] |
|
|
2124 | 2127 | heightList = kwargs['heightList'] |
|
|
2125 | 2128 | timeList = kwargs['timeList'] |
|
|
2126 | 2129 | |
|
|
2127 | 2130 | rx_location = kwargs['rx_location'] |
|
|
2128 | 2131 | groupList = kwargs['groupList'] |
|
|
2129 | 2132 | azimuth = kwargs['azimuth'] |
|
|
2130 | 2133 | dfactor = kwargs['dfactor'] |
|
|
2131 | 2134 | k = kwargs['k'] |
|
|
2132 | 2135 | |
|
|
2133 | 2136 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
2134 | 2137 | d = dist*dfactor |
|
|
2135 | 2138 | #Phase calculation |
|
|
2136 | 2139 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
2137 | 2140 | |
|
|
2138 | 2141 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
2139 | 2142 | |
|
|
2140 | 2143 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2141 | 2144 | azimuth1 = azimuth1*numpy.pi/180 |
|
|
2142 | 2145 | |
|
|
2143 | 2146 | for i in range(heightList.size): |
|
|
2144 | 2147 | h = heightList[i] |
|
|
2145 | 2148 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
2146 | 2149 | metHeight = metArray1[indH,:] |
|
|
2147 | 2150 | if metHeight.shape[0] >= 2: |
|
|
2148 | 2151 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
2149 | 2152 | iazim = metHeight[:,1].astype(int) |
|
|
2150 | 2153 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
2151 | 2154 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
2152 | 2155 | A = numpy.asmatrix(A) |
|
|
2153 | 2156 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
2154 | 2157 | velHor = numpy.dot(A1,velAux) |
|
|
2155 | 2158 | |
|
|
2156 | 2159 | velEst[i,:] = numpy.squeeze(velHor) |
|
|
2157 | 2160 | return velEst |
|
|
2158 | 2161 | |
|
|
2159 | 2162 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
2160 | 2163 | meteorList = [] |
|
|
2161 | 2164 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
2162 | 2165 | #Putting back together the meteor matrix |
|
|
2163 | 2166 | utctime = metArray[:,0] |
|
|
2164 | 2167 | uniqueTime = numpy.unique(utctime) |
|
|
2165 | 2168 | |
|
|
2166 | 2169 | phaseDerThresh = 0.5 |
|
|
2167 | 2170 | ippSeconds = timeList[1] - timeList[0] |
|
|
2168 | 2171 | sec = numpy.where(timeList>1)[0][0] |
|
|
2169 | 2172 | nPairs = metArray.shape[1] - 6 |
|
|
2170 | 2173 | nHeights = len(heightList) |
|
|
2171 | 2174 | |
|
|
2172 | 2175 | for t in uniqueTime: |
|
|
2173 | 2176 | metArray1 = metArray[utctime==t,:] |
|
|
2174 | 2177 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
2175 | 2178 | tmet = metArray1[:,1].astype(int) |
|
|
2176 | 2179 | hmet = metArray1[:,2].astype(int) |
|
|
2177 | 2180 | |
|
|
2178 | 2181 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
2179 | 2182 | metPhase[:,:] = numpy.nan |
|
|
2180 | 2183 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
2181 | 2184 | |
|
|
2182 | 2185 | #Delete short trails |
|
|
2183 | 2186 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
2184 | 2187 | heightVect = numpy.sum(metBool, axis = 1) |
|
|
2185 | 2188 | metBool[heightVect<sec,:] = False |
|
|
2186 | 2189 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
2187 | 2190 | |
|
|
2188 | 2191 | #Derivative |
|
|
2189 | 2192 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
2190 | 2193 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
2191 | 2194 | metPhase[phDerAux] = numpy.nan |
|
|
2192 | 2195 | |
|
|
2193 | 2196 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
|
2194 | 2197 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
2195 | 2198 | |
|
|
2196 | 2199 | for p in numpy.arange(nPairs): |
|
|
2197 | 2200 | phase = metPhase[p,:,:] |
|
|
2198 | 2201 | phDer = metDer[p,:,:] |
|
|
2199 | 2202 | |
|
|
2200 | 2203 | for h in indMet: |
|
|
2201 | 2204 | height = heightList[h] |
|
|
2202 | 2205 | phase1 = phase[h,:] #82 |
|
|
2203 | 2206 | phDer1 = phDer[h,:] |
|
|
2204 | 2207 | |
|
|
2205 | 2208 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
2206 | 2209 | |
|
|
2207 | 2210 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
2208 | 2211 | initMet = indValid[0] |
|
|
2209 | 2212 | endMet = 0 |
|
|
2210 | 2213 | |
|
|
2211 | 2214 | for i in range(len(indValid)-1): |
|
|
2212 | 2215 | |
|
|
2213 | 2216 | #Time difference |
|
|
2214 | 2217 | inow = indValid[i] |
|
|
2215 | 2218 | inext = indValid[i+1] |
|
|
2216 | 2219 | idiff = inext - inow |
|
|
2217 | 2220 | #Phase difference |
|
|
2218 | 2221 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
2219 | 2222 | |
|
|
2220 | 2223 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
2221 | 2224 | sizeTrail = inow - initMet + 1 |
|
|
2222 | 2225 | if sizeTrail>3*sec: #Too short meteors |
|
|
2223 | 2226 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
2224 | 2227 | y = phase1[initMet:inow+1] |
|
|
2225 | 2228 | ynnan = ~numpy.isnan(y) |
|
|
2226 | 2229 | x = x[ynnan] |
|
|
2227 | 2230 | y = y[ynnan] |
|
|
2228 | 2231 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
2229 | 2232 | ylin = x*slope + intercept |
|
|
2230 | 2233 | rsq = r_value**2 |
|
|
2231 | 2234 | if rsq > 0.5: |
|
|
2232 | 2235 | vel = slope#*height*1000/(k*d) |
|
|
2233 | 2236 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
2234 | 2237 | meteorList.append(estAux) |
|
|
2235 | 2238 | initMet = inext |
|
|
2236 | 2239 | metArray2 = numpy.array(meteorList) |
|
|
2237 | 2240 | |
|
|
2238 | 2241 | return metArray2 |
|
|
2239 | 2242 | |
|
|
2240 | 2243 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
2241 | 2244 | |
|
|
2242 | 2245 | azimuth1 = numpy.zeros(len(pairslist)) |
|
|
2243 | 2246 | dist = numpy.zeros(len(pairslist)) |
|
|
2244 | 2247 | |
|
|
2245 | 2248 | for i in range(len(rx_location)): |
|
|
2246 | 2249 | ch0 = pairslist[i][0] |
|
|
2247 | 2250 | ch1 = pairslist[i][1] |
|
|
2248 | 2251 | |
|
|
2249 | 2252 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
2250 | 2253 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
2251 | 2254 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
2252 | 2255 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
2253 | 2256 | |
|
|
2254 | 2257 | azimuth1 -= azimuth0 |
|
|
2255 | 2258 | return azimuth1, dist |
|
|
2256 | 2259 | |
|
|
2257 | 2260 | def techniqueNSM_DBS(self, **kwargs): |
|
|
2258 | 2261 | metArray = kwargs['metArray'] |
|
|
2259 | 2262 | heightList = kwargs['heightList'] |
|
|
2260 | 2263 | timeList = kwargs['timeList'] |
|
|
2261 | 2264 | azimuth = kwargs['azimuth'] |
|
|
2262 | 2265 | theta_x = numpy.array(kwargs['theta_x']) |
|
|
2263 | 2266 | theta_y = numpy.array(kwargs['theta_y']) |
|
|
2264 | 2267 | |
|
|
2265 | 2268 | utctime = metArray[:,0] |
|
|
2266 | 2269 | cmet = metArray[:,1].astype(int) |
|
|
2267 | 2270 | hmet = metArray[:,3].astype(int) |
|
|
2268 | 2271 | SNRmet = metArray[:,4] |
|
|
2269 | 2272 | vmet = metArray[:,5] |
|
|
2270 | 2273 | spcmet = metArray[:,6] |
|
|
2271 | 2274 | |
|
|
2272 | 2275 | nChan = numpy.max(cmet) + 1 |
|
|
2273 | 2276 | nHeights = len(heightList) |
|
|
2274 | 2277 | |
|
|
2275 | 2278 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
2276 | 2279 | hmet = heightList[hmet] |
|
|
2277 | 2280 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
|
2278 | 2281 | |
|
|
2279 | 2282 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2280 | 2283 | |
|
|
2281 | 2284 | for i in range(nHeights - 1): |
|
|
2282 | 2285 | hmin = heightList[i] |
|
|
2283 | 2286 | hmax = heightList[i + 1] |
|
|
2284 | 2287 | |
|
|
2285 | 2288 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
|
2286 | 2289 | indthisH = numpy.where(thisH) |
|
|
2287 | 2290 | |
|
|
2288 | 2291 | if numpy.size(indthisH) > 3: |
|
|
2289 | 2292 | |
|
|
2290 | 2293 | vel_aux = vmet[thisH] |
|
|
2291 | 2294 | chan_aux = cmet[thisH] |
|
|
2292 | 2295 | cosu_aux = dir_cosu[chan_aux] |
|
|
2293 | 2296 | cosv_aux = dir_cosv[chan_aux] |
|
|
2294 | 2297 | cosw_aux = dir_cosw[chan_aux] |
|
|
2295 | 2298 | |
|
|
2296 | 2299 | nch = numpy.size(numpy.unique(chan_aux)) |
|
|
2297 | 2300 | if nch > 1: |
|
|
2298 | 2301 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
|
2299 | 2302 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
|
2300 | 2303 | |
|
|
2301 | 2304 | return velEst |
|
|
2302 | 2305 | |
|
|
2303 | 2306 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
|
2304 | 2307 | |
|
|
2305 | 2308 | param = dataOut.data_param |
|
|
2306 | 2309 | if dataOut.abscissaList != None: |
|
|
2307 | 2310 | absc = dataOut.abscissaList[:-1] |
|
|
2308 | 2311 | # noise = dataOut.noise |
|
|
2309 | 2312 | heightList = dataOut.heightList |
|
|
2310 | 2313 | SNR = dataOut.data_SNR |
|
|
2311 | 2314 | |
|
|
2312 | 2315 | if technique == 'DBS': |
|
|
2313 | 2316 | |
|
|
2314 | 2317 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
2315 | 2318 | kwargs['heightList'] = heightList |
|
|
2316 | 2319 | kwargs['SNR'] = SNR |
|
|
2317 | 2320 | |
|
|
2318 | 2321 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function |
|
|
2319 | 2322 | dataOut.utctimeInit = dataOut.utctime |
|
|
2320 | 2323 | dataOut.outputInterval = dataOut.paramInterval |
|
|
2321 | 2324 | |
|
|
2322 | 2325 | elif technique == 'SA': |
|
|
2323 | 2326 | |
|
|
2324 | 2327 | #Parameters |
|
|
2325 | 2328 | # position_x = kwargs['positionX'] |
|
|
2326 | 2329 | # position_y = kwargs['positionY'] |
|
|
2327 | 2330 | # azimuth = kwargs['azimuth'] |
|
|
2328 | 2331 | # |
|
|
2329 | 2332 | # if kwargs.has_key('crosspairsList'): |
|
|
2330 | 2333 | # pairs = kwargs['crosspairsList'] |
|
|
2331 | 2334 | # else: |
|
|
2332 | 2335 | # pairs = None |
|
|
2333 | 2336 | # |
|
|
2334 | 2337 | # if kwargs.has_key('correctFactor'): |
|
|
2335 | 2338 | # correctFactor = kwargs['correctFactor'] |
|
|
2336 | 2339 | # else: |
|
|
2337 | 2340 | # correctFactor = 1 |
|
|
2338 | 2341 | |
|
|
2339 | 2342 | # tau = dataOut.data_param |
|
|
2340 | 2343 | # _lambda = dataOut.C/dataOut.frequency |
|
|
2341 | 2344 | # pairsList = dataOut.groupList |
|
|
2342 | 2345 | # nChannels = dataOut.nChannels |
|
|
2343 | 2346 | |
|
|
2344 | 2347 | kwargs['groupList'] = dataOut.groupList |
|
|
2345 | 2348 | kwargs['tau'] = dataOut.data_param |
|
|
2346 | 2349 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
2347 | 2350 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
2348 | 2351 | dataOut.data_output = self.techniqueSA(kwargs) |
|
|
2349 | 2352 | dataOut.utctimeInit = dataOut.utctime |
|
|
2350 | 2353 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2351 | 2354 | |
|
|
2352 | 2355 | elif technique == 'Meteors': |
|
|
2353 | 2356 | dataOut.flagNoData = True |
|
|
2354 | 2357 | self.__dataReady = False |
|
|
2355 | 2358 | |
|
|
2356 | 2359 | if kwargs.has_key('nHours'): |
|
|
2357 | 2360 | nHours = kwargs['nHours'] |
|
|
2358 | 2361 | else: |
|
|
2359 | 2362 | nHours = 1 |
|
|
2360 | 2363 | |
|
|
2361 | 2364 | if kwargs.has_key('meteorsPerBin'): |
|
|
2362 | 2365 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
2363 | 2366 | else: |
|
|
2364 | 2367 | meteorThresh = 6 |
|
|
2365 | 2368 | |
|
|
2366 | 2369 | if kwargs.has_key('hmin'): |
|
|
2367 | 2370 | hmin = kwargs['hmin'] |
|
|
2368 | 2371 | else: hmin = 70 |
|
|
2369 | 2372 | if kwargs.has_key('hmax'): |
|
|
2370 | 2373 | hmax = kwargs['hmax'] |
|
|
2371 | 2374 | else: hmax = 110 |
|
|
2372 | 2375 | |
|
|
2373 | 2376 | dataOut.outputInterval = nHours*3600 |
|
|
2374 | 2377 | |
|
|
2375 | 2378 | if self.__isConfig == False: |
|
|
2376 | 2379 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2377 | 2380 | #Get Initial LTC time |
|
|
2378 | 2381 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2379 | 2382 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2380 | 2383 | |
|
|
2381 | 2384 | self.__isConfig = True |
|
|
2382 | 2385 | |
|
|
2383 | 2386 | if self.__buffer == None: |
|
|
2384 | 2387 | self.__buffer = dataOut.data_param |
|
|
2385 | 2388 | self.__firstdata = copy.copy(dataOut) |
|
|
2386 | 2389 | |
|
|
2387 | 2390 | else: |
|
|
2388 | 2391 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2389 | 2392 | |
|
|
2390 | 2393 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2391 | 2394 | |
|
|
2392 | 2395 | if self.__dataReady: |
|
|
2393 | 2396 | dataOut.utctimeInit = self.__initime |
|
|
2394 | 2397 | |
|
|
2395 | 2398 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2396 | 2399 | |
|
|
2397 | 2400 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
2398 | 2401 | dataOut.flagNoData = False |
|
|
2399 | 2402 | self.__buffer = None |
|
|
2400 | 2403 | |
|
|
2401 | 2404 | elif technique == 'Meteors1': |
|
|
2402 | 2405 | dataOut.flagNoData = True |
|
|
2403 | 2406 | self.__dataReady = False |
|
|
2404 | 2407 | |
|
|
2405 | 2408 | if kwargs.has_key('nMins'): |
|
|
2406 | 2409 | nMins = kwargs['nMins'] |
|
|
2407 | 2410 | else: nMins = 20 |
|
|
2408 | 2411 | if kwargs.has_key('rx_location'): |
|
|
2409 | 2412 | rx_location = kwargs['rx_location'] |
|
|
2410 | 2413 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
2411 | 2414 | if kwargs.has_key('azimuth'): |
|
|
2412 | 2415 | azimuth = kwargs['azimuth'] |
|
|
2413 | 2416 | else: azimuth = 51.06 |
|
|
2414 | 2417 | if kwargs.has_key('dfactor'): |
|
|
2415 | 2418 | dfactor = kwargs['dfactor'] |
|
|
2416 | 2419 | if kwargs.has_key('mode'): |
|
|
2417 | 2420 | mode = kwargs['mode'] |
|
|
2418 | 2421 | if kwargs.has_key('theta_x'): |
|
|
2419 | 2422 | theta_x = kwargs['theta_x'] |
|
|
2420 | 2423 | if kwargs.has_key('theta_y'): |
|
|
2421 | 2424 | theta_y = kwargs['theta_y'] |
|
|
2422 | 2425 | else: mode = 'SA' |
|
|
2423 | 2426 | |
|
|
2424 | 2427 | #Borrar luego esto |
|
|
2425 | 2428 | if dataOut.groupList == None: |
|
|
2426 | 2429 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
2427 | 2430 | groupList = dataOut.groupList |
|
|
2428 | 2431 | C = 3e8 |
|
|
2429 | 2432 | freq = 50e6 |
|
|
2430 | 2433 | lamb = C/freq |
|
|
2431 | 2434 | k = 2*numpy.pi/lamb |
|
|
2432 | 2435 | |
|
|
2433 | 2436 | timeList = dataOut.abscissaList |
|
|
2434 | 2437 | heightList = dataOut.heightList |
|
|
2435 | 2438 | |
|
|
2436 | 2439 | if self.__isConfig == False: |
|
|
2437 | 2440 | dataOut.outputInterval = nMins*60 |
|
|
2438 | 2441 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2439 | 2442 | #Get Initial LTC time |
|
|
2440 | 2443 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2441 | 2444 | minuteAux = initime.minute |
|
|
2442 | 2445 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
2443 | 2446 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2444 | 2447 | |
|
|
2445 | 2448 | self.__isConfig = True |
|
|
2446 | 2449 | |
|
|
2447 | 2450 | if self.__buffer == None: |
|
|
2448 | 2451 | self.__buffer = dataOut.data_param |
|
|
2449 | 2452 | self.__firstdata = copy.copy(dataOut) |
|
|
2450 | 2453 | |
|
|
2451 | 2454 | else: |
|
|
2452 | 2455 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2453 | 2456 | |
|
|
2454 | 2457 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2455 | 2458 | |
|
|
2456 | 2459 | if self.__dataReady: |
|
|
2457 | 2460 | dataOut.utctimeInit = self.__initime |
|
|
2458 | 2461 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2459 | 2462 | |
|
|
2460 | 2463 | metArray = self.__buffer |
|
|
2461 | 2464 | if mode == 'SA': |
|
|
2462 | 2465 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
2463 | 2466 | elif mode == 'DBS': |
|
|
2464 | 2467 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
|
2465 | 2468 | dataOut.data_output = dataOut.data_output.T |
|
|
2466 | 2469 | dataOut.flagNoData = False |
|
|
2467 | 2470 | self.__buffer = None |
|
|
2468 | 2471 | |
|
|
2469 | 2472 | return |
|
|
2470 | 2473 | |
|
|
2471 | 2474 | class EWDriftsEstimation(Operation): |
|
|
2472 | 2475 | |
|
|
2473 | 2476 | def __init__(self): |
|
|
2474 | 2477 | Operation.__init__(self) |
|
|
2475 | 2478 | |
|
|
2476 | 2479 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
2477 | 2480 | listPhi = phi.tolist() |
|
|
2478 | 2481 | maxid = listPhi.index(max(listPhi)) |
|
|
2479 | 2482 | minid = listPhi.index(min(listPhi)) |
|
|
2480 | 2483 | |
|
|
2481 | 2484 | rango = range(len(phi)) |
|
|
2482 | 2485 | # rango = numpy.delete(rango,maxid) |
|
|
2483 | 2486 | |
|
|
2484 | 2487 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
2485 | 2488 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
2486 | 2489 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
2487 | 2490 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
2488 | 2491 | |
|
|
2489 | 2492 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2490 | 2493 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2491 | 2494 | |
|
|
2492 | 2495 | for i in rango: |
|
|
2493 | 2496 | x = heiRang*math.cos(phi[i]) |
|
|
2494 | 2497 | y1 = velRadial[i,:] |
|
|
2495 | 2498 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
2496 | 2499 | |
|
|
2497 | 2500 | x1 = heiRang1 |
|
|
2498 | 2501 | y11 = f1(x1) |
|
|
2499 | 2502 | |
|
|
2500 | 2503 | y2 = SNR[i,:] |
|
|
2501 | 2504 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
2502 | 2505 | y21 = f2(x1) |
|
|
2503 | 2506 | |
|
|
2504 | 2507 | velRadial1[i,:] = y11 |
|
|
2505 | 2508 | SNR1[i,:] = y21 |
|
|
2506 | 2509 | |
|
|
2507 | 2510 | return heiRang1, velRadial1, SNR1 |
|
|
2508 | 2511 | |
|
|
2509 | 2512 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
2510 | 2513 | heiRang = dataOut.heightList |
|
|
2511 | 2514 | velRadial = dataOut.data_param[:,3,:] |
|
|
2512 | 2515 | SNR = dataOut.data_SNR |
|
|
2513 | 2516 | |
|
|
2514 | 2517 | zenith = numpy.array(zenith) |
|
|
2515 | 2518 | zenith -= zenithCorrection |
|
|
2516 | 2519 | zenith *= numpy.pi/180 |
|
|
2517 | 2520 | |
|
|
2518 | 2521 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
2519 | 2522 | |
|
|
2520 | 2523 | alp = zenith[0] |
|
|
2521 | 2524 | bet = zenith[1] |
|
|
2522 | 2525 | |
|
|
2523 | 2526 | w_w = velRadial1[0,:] |
|
|
2524 | 2527 | w_e = velRadial1[1,:] |
|
|
2525 | 2528 | |
|
|
2526 | 2529 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
2527 | 2530 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
2528 | 2531 | |
|
|
2529 | 2532 | winds = numpy.vstack((u,w)) |
|
|
2530 | 2533 | |
|
|
2531 | 2534 | dataOut.heightList = heiRang1 |
|
|
2532 | 2535 | dataOut.data_output = winds |
|
|
2533 | 2536 | dataOut.data_SNR = SNR1 |
|
|
2534 | 2537 | |
|
|
2535 | 2538 | dataOut.utctimeInit = dataOut.utctime |
|
|
2536 | 2539 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2537 | 2540 | return |
|
|
2538 | 2541 | |
|
|
2539 | 2542 | #--------------- Non Specular Meteor ---------------- |
|
|
2540 | 2543 | |
|
|
2541 | 2544 | class NonSpecularMeteorDetection(Operation): |
|
|
2542 | 2545 | |
|
|
2543 | 2546 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
2544 | 2547 | data_acf = dataOut.data_pre[0] |
|
|
2545 | 2548 | data_ccf = dataOut.data_pre[1] |
|
|
2546 | 2549 | pairsList = dataOut.groupList[1] |
|
|
2547 | 2550 | |
|
|
2548 | 2551 | lamb = dataOut.C/dataOut.frequency |
|
|
2549 | 2552 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
|
2550 | 2553 | paramInterval = dataOut.paramInterval |
|
|
2551 | 2554 | |
|
|
2552 | 2555 | nChannels = data_acf.shape[0] |
|
|
2553 | 2556 | nLags = data_acf.shape[1] |
|
|
2554 | 2557 | nProfiles = data_acf.shape[2] |
|
|
2555 | 2558 | nHeights = dataOut.nHeights |
|
|
2556 | 2559 | nCohInt = dataOut.nCohInt |
|
|
2557 | 2560 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
|
2558 | 2561 | heightList = dataOut.heightList |
|
|
2559 | 2562 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
|
2560 | 2563 | utctime = dataOut.utctime |
|
|
2561 | 2564 | |
|
|
2562 | 2565 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
2563 | 2566 | |
|
|
2564 | 2567 | #------------------------ SNR -------------------------------------- |
|
|
2565 | 2568 | power = data_acf[:,0,:,:].real |
|
|
2566 | 2569 | noise = numpy.zeros(nChannels) |
|
|
2567 | 2570 | SNR = numpy.zeros(power.shape) |
|
|
2568 | 2571 | for i in range(nChannels): |
|
|
2569 | 2572 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
2570 | 2573 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
2571 | 2574 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
2572 | 2575 | SNRdB = 10*numpy.log10(SNR) |
|
|
2573 | 2576 | |
|
|
2574 | 2577 | if mode == 'SA': |
|
|
2575 | 2578 | dataOut.groupList = dataOut.groupList[1] |
|
|
2576 | 2579 | nPairs = data_ccf.shape[0] |
|
|
2577 | 2580 | #---------------------- Coherence and Phase -------------------------- |
|
|
2578 | 2581 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2579 | 2582 | # phase1 = numpy.copy(phase) |
|
|
2580 | 2583 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2581 | 2584 | |
|
|
2582 | 2585 | for p in range(nPairs): |
|
|
2583 | 2586 | ch0 = pairsList[p][0] |
|
|
2584 | 2587 | ch1 = pairsList[p][1] |
|
|
2585 | 2588 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
2586 | 2589 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
2587 | 2590 | # phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
2588 | 2591 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
2589 | 2592 | # coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
2590 | 2593 | coh = numpy.nanmax(coh1, axis = 0) |
|
|
2591 | 2594 | # struc = numpy.ones((5,1)) |
|
|
2592 | 2595 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
2593 | 2596 | #---------------------- Radial Velocity ---------------------------- |
|
|
2594 | 2597 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
2595 | 2598 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
2596 | 2599 | |
|
|
2597 | 2600 | if allData: |
|
|
2598 | 2601 | boolMetFin = ~numpy.isnan(SNRm) |
|
|
2599 | 2602 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2600 | 2603 | else: |
|
|
2601 | 2604 | #------------------------ Meteor mask --------------------------------- |
|
|
2602 | 2605 | # #SNR mask |
|
|
2603 | 2606 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
2604 | 2607 | # |
|
|
2605 | 2608 | # #Erase small objects |
|
|
2606 | 2609 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
2607 | 2610 | # |
|
|
2608 | 2611 | # auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
2609 | 2612 | # indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
2610 | 2613 | # indEEJ = numpy.where(indOver)[0] |
|
|
2611 | 2614 | # indNEEJ = numpy.where(~indOver)[0] |
|
|
2612 | 2615 | # |
|
|
2613 | 2616 | # boolMetFin = boolMet1 |
|
|
2614 | 2617 | # |
|
|
2615 | 2618 | # if indEEJ.size > 0: |
|
|
2616 | 2619 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
2617 | 2620 | # |
|
|
2618 | 2621 | # boolMet2 = coh > cohThresh |
|
|
2619 | 2622 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
2620 | 2623 | # |
|
|
2621 | 2624 | # #Final Meteor mask |
|
|
2622 | 2625 | # boolMetFin = boolMet1|boolMet2 |
|
|
2623 | 2626 | |
|
|
2624 | 2627 | #Coherence mask |
|
|
2625 | 2628 | boolMet1 = coh > 0.75 |
|
|
2626 | 2629 | struc = numpy.ones((30,1)) |
|
|
2627 | 2630 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
2628 | 2631 | |
|
|
2629 | 2632 | #Derivative mask |
|
|
2630 | 2633 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2631 | 2634 | boolMet2 = derPhase < 0.2 |
|
|
2632 | 2635 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
2633 | 2636 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
2634 | 2637 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
2635 | 2638 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
2636 | 2639 | # #Final mask |
|
|
2637 | 2640 | # boolMetFin = boolMet2 |
|
|
2638 | 2641 | boolMetFin = boolMet1&boolMet2 |
|
|
2639 | 2642 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
2640 | 2643 | #Creating data_param |
|
|
2641 | 2644 | coordMet = numpy.where(boolMetFin) |
|
|
2642 | 2645 | |
|
|
2643 | 2646 | tmet = coordMet[0] |
|
|
2644 | 2647 | hmet = coordMet[1] |
|
|
2645 | 2648 | |
|
|
2646 | 2649 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
2647 | 2650 | data_param[:,0] = utctime |
|
|
2648 | 2651 | data_param[:,1] = tmet |
|
|
2649 | 2652 | data_param[:,2] = hmet |
|
|
2650 | 2653 | data_param[:,3] = SNRm[tmet,hmet] |
|
|
2651 | 2654 | data_param[:,4] = velRad[tmet,hmet] |
|
|
2652 | 2655 | data_param[:,5] = coh[tmet,hmet] |
|
|
2653 | 2656 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
2654 | 2657 | |
|
|
2655 | 2658 | elif mode == 'DBS': |
|
|
2656 | 2659 | dataOut.groupList = numpy.arange(nChannels) |
|
|
2657 | 2660 | |
|
|
2658 | 2661 | #Radial Velocities |
|
|
2659 | 2662 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
2660 | 2663 | # phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2661 | 2664 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
2662 | 2665 | |
|
|
2663 | 2666 | #Spectral width |
|
|
2664 | 2667 | # acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2665 | 2668 | # acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
2666 | 2669 | acf1 = data_acf[:,1,:,:] |
|
|
2667 | 2670 | acf2 = data_acf[:,2,:,:] |
|
|
2668 | 2671 | |
|
|
2669 | 2672 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
2670 | 2673 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
2671 | 2674 | if allData: |
|
|
2672 | 2675 | boolMetFin = ~numpy.isnan(SNRdB) |
|
|
2673 | 2676 | else: |
|
|
2674 | 2677 | #SNR |
|
|
2675 | 2678 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
2676 | 2679 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
2677 | 2680 | |
|
|
2678 | 2681 | #Radial velocity |
|
|
2679 | 2682 | boolMet2 = numpy.abs(velRad) < 20 |
|
|
2680 | 2683 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
2681 | 2684 | |
|
|
2682 | 2685 | #Spectral Width |
|
|
2683 | 2686 | boolMet3 = spcWidth < 30 |
|
|
2684 | 2687 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
2685 | 2688 | # boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
2686 | 2689 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
2687 | 2690 | |
|
|
2688 | 2691 | #Creating data_param |
|
|
2689 | 2692 | coordMet = numpy.where(boolMetFin) |
|
|
2690 | 2693 | |
|
|
2691 | 2694 | cmet = coordMet[0] |
|
|
2692 | 2695 | tmet = coordMet[1] |
|
|
2693 | 2696 | hmet = coordMet[2] |
|
|
2694 | 2697 | |
|
|
2695 | 2698 | data_param = numpy.zeros((tmet.size, 7)) |
|
|
2696 | 2699 | data_param[:,0] = utctime |
|
|
2697 | 2700 | data_param[:,1] = cmet |
|
|
2698 | 2701 | data_param[:,2] = tmet |
|
|
2699 | 2702 | data_param[:,3] = hmet |
|
|
2700 | 2703 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
2701 | 2704 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
2702 | 2705 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
2703 | 2706 | |
|
|
2704 | 2707 | # self.dataOut.data_param = data_int |
|
|
2705 | 2708 | if len(data_param) == 0: |
|
|
2706 | 2709 | dataOut.flagNoData = True |
|
|
2707 | 2710 | else: |
|
|
2708 | 2711 | dataOut.data_param = data_param |
|
|
2709 | 2712 | |
|
|
2710 | 2713 | def __erase_small(self, binArray, threshX, threshY): |
|
|
2711 | 2714 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
2712 | 2715 | binArray1 = numpy.copy(binArray) |
|
|
2713 | 2716 | |
|
|
2714 | 2717 | for i in range(1,numfeat + 1): |
|
|
2715 | 2718 | auxBin = (labarray==i) |
|
|
2716 | 2719 | auxSize = auxBin.sum() |
|
|
2717 | 2720 | |
|
|
2718 | 2721 | x,y = numpy.where(auxBin) |
|
|
2719 | 2722 | widthX = x.max() - x.min() |
|
|
2720 | 2723 | widthY = y.max() - y.min() |
|
|
2721 | 2724 | |
|
|
2722 | 2725 | #width X: 3 seg -> 12.5*3 |
|
|
2723 | 2726 | #width Y: |
|
|
2724 | 2727 | |
|
|
2725 | 2728 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
2726 | 2729 | binArray1[auxBin] = False |
|
|
2727 | 2730 | |
|
|
2728 | 2731 | return binArray1 |
|
|
2729 | 2732 | |
|
|
2730 | 2733 | #--------------- Specular Meteor ---------------- |
|
|
2731 | 2734 | |
|
|
2732 | 2735 | class SMDetection(Operation): |
|
|
2733 | 2736 | ''' |
|
|
2734 | 2737 | Function DetectMeteors() |
|
|
2735 | 2738 | Project developed with paper: |
|
|
2736 | 2739 | HOLDSWORTH ET AL. 2004 |
|
|
2737 | 2740 | |
|
|
2738 | 2741 | Input: |
|
|
2739 | 2742 | self.dataOut.data_pre |
|
|
2740 | 2743 | |
|
|
2741 | 2744 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
2742 | 2745 | |
|
|
2743 | 2746 | hei_ref: Height reference for the Beacon signal extraction |
|
|
2744 | 2747 | tauindex: |
|
|
2745 | 2748 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
2746 | 2749 | |
|
|
2747 | 2750 | cohDetection: Whether to user Coherent detection or not |
|
|
2748 | 2751 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
2749 | 2752 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
2750 | 2753 | |
|
|
2751 | 2754 | noise_timeStep: Noise calculation time step |
|
|
2752 | 2755 | noise_multiple: Noise multiple to define signal threshold |
|
|
2753 | 2756 | |
|
|
2754 | 2757 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
2755 | 2758 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
2756 | 2759 | |
|
|
2757 | 2760 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
2758 | 2761 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
2759 | 2762 | |
|
|
2760 | 2763 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
2761 | 2764 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
2762 | 2765 | azimuth: Azimuth angle correction |
|
|
2763 | 2766 | |
|
|
2764 | 2767 | Affected: |
|
|
2765 | 2768 | self.dataOut.data_param |
|
|
2766 | 2769 | |
|
|
2767 | 2770 | Rejection Criteria (Errors): |
|
|
2768 | 2771 | 0: No error; analysis OK |
|
|
2769 | 2772 | 1: SNR < SNR threshold |
|
|
2770 | 2773 | 2: angle of arrival (AOA) ambiguously determined |
|
|
2771 | 2774 | 3: AOA estimate not feasible |
|
|
2772 | 2775 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2773 | 2776 | 5: echo at start or end of time series |
|
|
2774 | 2777 | 6: echo less than 5 examples long; too short for analysis |
|
|
2775 | 2778 | 7: echo rise exceeds 0.3s |
|
|
2776 | 2779 | 8: echo decay time less than twice rise time |
|
|
2777 | 2780 | 9: large power level before echo |
|
|
2778 | 2781 | 10: large power level after echo |
|
|
2779 | 2782 | 11: poor fit to amplitude for estimation of decay time |
|
|
2780 | 2783 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
2781 | 2784 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
2782 | 2785 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
2783 | 2786 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2784 | 2787 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
2785 | 2788 | |
|
|
2786 | 2789 | 17: phase difference in meteor Reestimation |
|
|
2787 | 2790 | |
|
|
2788 | 2791 | Data Storage: |
|
|
2789 | 2792 | Meteors for Wind Estimation (8): |
|
|
2790 | 2793 | Utc Time | Range Height |
|
|
2791 | 2794 | Azimuth Zenith errorCosDir |
|
|
2792 | 2795 | VelRad errorVelRad |
|
|
2793 | 2796 | Phase0 Phase1 Phase2 Phase3 |
|
|
2794 | 2797 | TypeError |
|
|
2795 | 2798 | |
|
|
2796 | 2799 | ''' |
|
|
2797 | 2800 | |
|
|
2798 | 2801 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
2799 | 2802 | phaseOffsets = None, |
|
|
2800 | 2803 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
2801 | 2804 | noise_timeStep = 4, noise_multiple = 4, |
|
|
2802 | 2805 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
2803 | 2806 | phaseThresh = 20, SNRThresh = 5, |
|
|
2804 | 2807 | hmin = 50, hmax=150, azimuth = 0, |
|
|
2805 | 2808 | channelPositions = None) : |
|
|
2806 | 2809 | |
|
|
2807 | 2810 | |
|
|
2808 | 2811 | #Getting Pairslist |
|
|
2809 | 2812 | if channelPositions == None: |
|
|
2810 | 2813 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2811 | 2814 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2812 | 2815 | meteorOps = SMOperations() |
|
|
2813 | 2816 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2814 | 2817 | heiRang = dataOut.getHeiRange() |
|
|
2815 | 2818 | #Get Beacon signal - No Beacon signal anymore |
|
|
2816 | 2819 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
2817 | 2820 | # |
|
|
2818 | 2821 | # if hei_ref != None: |
|
|
2819 | 2822 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
2820 | 2823 | # |
|
|
2821 | 2824 | |
|
|
2822 | 2825 | |
|
|
2823 | 2826 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
2824 | 2827 | # see if the user put in pre defined phase shifts |
|
|
2825 | 2828 | voltsPShift = dataOut.data_pre.copy() |
|
|
2826 | 2829 | |
|
|
2827 | 2830 | # if predefinedPhaseShifts != None: |
|
|
2828 | 2831 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
2829 | 2832 | # |
|
|
2830 | 2833 | # # elif beaconPhaseShifts: |
|
|
2831 | 2834 | # # #get hardware phase shifts using beacon signal |
|
|
2832 | 2835 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
2833 | 2836 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
2834 | 2837 | # |
|
|
2835 | 2838 | # else: |
|
|
2836 | 2839 | # hardwarePhaseShifts = numpy.zeros(5) |
|
|
2837 | 2840 | # |
|
|
2838 | 2841 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
2839 | 2842 | # for i in range(self.dataOut.data_pre.shape[0]): |
|
|
2840 | 2843 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
2841 | 2844 | |
|
|
2842 | 2845 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
2843 | 2846 | |
|
|
2844 | 2847 | #Remove DC |
|
|
2845 | 2848 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
2846 | 2849 | voltsDC = numpy.mean(voltsDC,1) |
|
|
2847 | 2850 | for i in range(voltsDC.shape[0]): |
|
|
2848 | 2851 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
2849 | 2852 | |
|
|
2850 | 2853 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
2851 | 2854 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
2852 | 2855 | |
|
|
2853 | 2856 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
2854 | 2857 | #Coherent Detection |
|
|
2855 | 2858 | if cohDetection: |
|
|
2856 | 2859 | #use coherent detection to get the net power |
|
|
2857 | 2860 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
2858 | 2861 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
2859 | 2862 | |
|
|
2860 | 2863 | #Non-coherent detection! |
|
|
2861 | 2864 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
2862 | 2865 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
2863 | 2866 | |
|
|
2864 | 2867 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
2865 | 2868 | #Get noise |
|
|
2866 | 2869 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
2867 | 2870 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
2868 | 2871 | #Get signal threshold |
|
|
2869 | 2872 | signalThresh = noise_multiple*noise |
|
|
2870 | 2873 | #Meteor echoes detection |
|
|
2871 | 2874 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
2872 | 2875 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
2873 | 2876 | |
|
|
2874 | 2877 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
2875 | 2878 | #Parameters |
|
|
2876 | 2879 | heiRange = dataOut.getHeiRange() |
|
|
2877 | 2880 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
2878 | 2881 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
2879 | 2882 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
2880 | 2883 | #Multiple detection removals |
|
|
2881 | 2884 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
2882 | 2885 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
2883 | 2886 | |
|
|
2884 | 2887 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
2885 | 2888 | #Parameters |
|
|
2886 | 2889 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
2887 | 2890 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
2888 | 2891 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
2889 | 2892 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
2890 | 2893 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
2891 | 2894 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
2892 | 2895 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
2893 | 2896 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
2894 | 2897 | |
|
|
2895 | 2898 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
2896 | 2899 | #Calculating Radial Velocity (Error N 15) |
|
|
2897 | 2900 | radialStdThresh = 10 |
|
|
2898 | 2901 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
2899 | 2902 | |
|
|
2900 | 2903 | if len(listMeteors4) > 0: |
|
|
2901 | 2904 | #Setting New Array |
|
|
2902 | 2905 | date = dataOut.utctime |
|
|
2903 | 2906 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
2904 | 2907 | |
|
|
2905 | 2908 | #Correcting phase offset |
|
|
2906 | 2909 | if phaseOffsets != None: |
|
|
2907 | 2910 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2908 | 2911 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2909 | 2912 | |
|
|
2910 | 2913 | #Second Pairslist |
|
|
2911 | 2914 | pairsList = [] |
|
|
2912 | 2915 | pairx = (0,1) |
|
|
2913 | 2916 | pairy = (2,3) |
|
|
2914 | 2917 | pairsList.append(pairx) |
|
|
2915 | 2918 | pairsList.append(pairy) |
|
|
2916 | 2919 | |
|
|
2917 | 2920 | jph = numpy.array([0,0,0,0]) |
|
|
2918 | 2921 | h = (hmin,hmax) |
|
|
2919 | 2922 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2920 | 2923 | |
|
|
2921 | 2924 | # #Calculate AOA (Error N 3, 4) |
|
|
2922 | 2925 | # #JONES ET AL. 1998 |
|
|
2923 | 2926 | # error = arrayParameters[:,-1] |
|
|
2924 | 2927 | # AOAthresh = numpy.pi/8 |
|
|
2925 | 2928 | # phases = -arrayParameters[:,9:13] |
|
|
2926 | 2929 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
2927 | 2930 | # |
|
|
2928 | 2931 | # #Calculate Heights (Error N 13 and 14) |
|
|
2929 | 2932 | # error = arrayParameters[:,-1] |
|
|
2930 | 2933 | # Ranges = arrayParameters[:,2] |
|
|
2931 | 2934 | # zenith = arrayParameters[:,5] |
|
|
2932 | 2935 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2933 | 2936 | # error = arrayParameters[:,-1] |
|
|
2934 | 2937 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
2935 | 2938 | |
|
|
2936 | 2939 | #***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
2937 | 2940 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
2938 | 2941 | dataOut.data_param = arrayParameters |
|
|
2939 | 2942 | |
|
|
2940 | 2943 | if arrayParameters == None: |
|
|
2941 | 2944 | dataOut.flagNoData = True |
|
|
2942 | 2945 | else: |
|
|
2943 | 2946 | dataOut.flagNoData = True |
|
|
2944 | 2947 | |
|
|
2945 | 2948 | return |
|
|
2946 | 2949 | |
|
|
2947 | 2950 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
2948 | 2951 | |
|
|
2949 | 2952 | minIndex = min(newheis[0]) |
|
|
2950 | 2953 | maxIndex = max(newheis[0]) |
|
|
2951 | 2954 | |
|
|
2952 | 2955 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
2953 | 2956 | nLength = voltage.shape[1]/n |
|
|
2954 | 2957 | nMin = 0 |
|
|
2955 | 2958 | nMax = 0 |
|
|
2956 | 2959 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
2957 | 2960 | |
|
|
2958 | 2961 | for i in range(n): |
|
|
2959 | 2962 | nMax += nLength |
|
|
2960 | 2963 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
2961 | 2964 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
2962 | 2965 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
2963 | 2966 | nMin = nMax |
|
|
2964 | 2967 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
2965 | 2968 | |
|
|
2966 | 2969 | #Remove Outliers |
|
|
2967 | 2970 | factor = 2 |
|
|
2968 | 2971 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
2969 | 2972 | dw = numpy.std(wt,axis = 1) |
|
|
2970 | 2973 | dw = dw.reshape((dw.size,1)) |
|
|
2971 | 2974 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
2972 | 2975 | phaseOffset[ind] = numpy.nan |
|
|
2973 | 2976 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
2974 | 2977 | |
|
|
2975 | 2978 | return phaseOffset |
|
|
2976 | 2979 | |
|
|
2977 | 2980 | def __shiftPhase(self, data, phaseShift): |
|
|
2978 | 2981 | #this will shift the phase of a complex number |
|
|
2979 | 2982 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
2980 | 2983 | return dataShifted |
|
|
2981 | 2984 | |
|
|
2982 | 2985 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
2983 | 2986 | nChannel = array.shape[0] |
|
|
2984 | 2987 | nHeights = array.shape[2] |
|
|
2985 | 2988 | numPairs = len(pairslist) |
|
|
2986 | 2989 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
2987 | 2990 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
2988 | 2991 | |
|
|
2989 | 2992 | #Correct phases |
|
|
2990 | 2993 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
2991 | 2994 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
2992 | 2995 | |
|
|
2993 | 2996 | if indDer[0].shape[0] > 0: |
|
|
2994 | 2997 | for i in range(indDer[0].shape[0]): |
|
|
2995 | 2998 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
2996 | 2999 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
2997 | 3000 | |
|
|
2998 | 3001 | # for j in range(numSides): |
|
|
2999 | 3002 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
3000 | 3003 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
3001 | 3004 | # |
|
|
3002 | 3005 | #Linear |
|
|
3003 | 3006 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
3004 | 3007 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
3005 | 3008 | for j in range(numPairs): |
|
|
3006 | 3009 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
3007 | 3010 | phaseInt[j] = fit[1] |
|
|
3008 | 3011 | #Phase Differences |
|
|
3009 | 3012 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
3010 | 3013 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
3011 | 3014 | |
|
|
3012 | 3015 | #Dealias |
|
|
3013 | 3016 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
3014 | 3017 | # indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
3015 | 3018 | # phaseArrival[indAlias] -= 2*numpy.pi |
|
|
3016 | 3019 | # indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
3017 | 3020 | # phaseArrival[indAlias] += 2*numpy.pi |
|
|
3018 | 3021 | |
|
|
3019 | 3022 | return phaseDiff, phaseArrival |
|
|
3020 | 3023 | |
|
|
3021 | 3024 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
3022 | 3025 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
3023 | 3026 | #find the phase shifts of each channel over 1 second intervals |
|
|
3024 | 3027 | #only look at ranges below the beacon signal |
|
|
3025 | 3028 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
3026 | 3029 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
3027 | 3030 | numHeights = volts.shape[2] |
|
|
3028 | 3031 | nChannel = volts.shape[0] |
|
|
3029 | 3032 | voltsCohDet = volts.copy() |
|
|
3030 | 3033 | |
|
|
3031 | 3034 | pairsarray = numpy.array(pairslist) |
|
|
3032 | 3035 | indSides = pairsarray[:,1] |
|
|
3033 | 3036 | # indSides = numpy.array(range(nChannel)) |
|
|
3034 | 3037 | # indSides = numpy.delete(indSides, indCenter) |
|
|
3035 | 3038 | # |
|
|
3036 | 3039 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
3037 | 3040 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
3038 | 3041 | |
|
|
3039 | 3042 | startInd = 0 |
|
|
3040 | 3043 | endInd = 0 |
|
|
3041 | 3044 | |
|
|
3042 | 3045 | for i in range(numBlocks): |
|
|
3043 | 3046 | startInd = endInd |
|
|
3044 | 3047 | endInd = endInd + listBlocks[i].shape[1] |
|
|
3045 | 3048 | |
|
|
3046 | 3049 | arrayBlock = listBlocks[i] |
|
|
3047 | 3050 | # arrayBlockCenter = listCenter[i] |
|
|
3048 | 3051 | |
|
|
3049 | 3052 | #Estimate the Phase Difference |
|
|
3050 | 3053 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
3051 | 3054 | #Phase Difference RMS |
|
|
3052 | 3055 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
3053 | 3056 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
3054 | 3057 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
3055 | 3058 | #Shifting |
|
|
3056 | 3059 | if indPhase[0].shape[0] > 0: |
|
|
3057 | 3060 | for j in range(indSides.size): |
|
|
3058 | 3061 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
3059 | 3062 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
3060 | 3063 | |
|
|
3061 | 3064 | return voltsCohDet |
|
|
3062 | 3065 | |
|
|
3063 | 3066 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
3064 | 3067 | |
|
|
3065 | 3068 | nHeights = volts.shape[2] |
|
|
3066 | 3069 | nPoints = volts.shape[1] |
|
|
3067 | 3070 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
3068 | 3071 | |
|
|
3069 | 3072 | for i in range(len(pairslist)): |
|
|
3070 | 3073 | volts1 = volts[pairslist[i][0]] |
|
|
3071 | 3074 | volts2 = volts[pairslist[i][1]] |
|
|
3072 | 3075 | |
|
|
3073 | 3076 | for t in range(len(laglist)): |
|
|
3074 | 3077 | idxT = laglist[t] |
|
|
3075 | 3078 | if idxT >= 0: |
|
|
3076 | 3079 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
3077 | 3080 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
3078 | 3081 | else: |
|
|
3079 | 3082 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
3080 | 3083 | volts2[:(nPoints + idxT),:])) |
|
|
3081 | 3084 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
3082 | 3085 | |
|
|
3083 | 3086 | vStacked = None |
|
|
3084 | 3087 | return voltsCCF |
|
|
3085 | 3088 | |
|
|
3086 | 3089 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
3087 | 3090 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
3088 | 3091 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
3089 | 3092 | numHeights = power.shape[1] |
|
|
3090 | 3093 | |
|
|
3091 | 3094 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
3092 | 3095 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
3093 | 3096 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
3094 | 3097 | |
|
|
3095 | 3098 | startInd = 0 |
|
|
3096 | 3099 | endInd = 0 |
|
|
3097 | 3100 | |
|
|
3098 | 3101 | for i in range(numBlocks): #split por canal |
|
|
3099 | 3102 | startInd = endInd |
|
|
3100 | 3103 | endInd = endInd + listPower[i].shape[0] |
|
|
3101 | 3104 | |
|
|
3102 | 3105 | arrayBlock = listPower[i] |
|
|
3103 | 3106 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
3104 | 3107 | # noiseAux = numpy.median(noiseAux) |
|
|
3105 | 3108 | # noiseAux = numpy.mean(arrayBlock) |
|
|
3106 | 3109 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
3107 | 3110 | |
|
|
3108 | 3111 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
3109 | 3112 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
3110 | 3113 | |
|
|
3111 | 3114 | return noise, noise1 |
|
|
3112 | 3115 | |
|
|
3113 | 3116 | def __findMeteors(self, power, thresh): |
|
|
3114 | 3117 | nProf = power.shape[0] |
|
|
3115 | 3118 | nHeights = power.shape[1] |
|
|
3116 | 3119 | listMeteors = [] |
|
|
3117 | 3120 | |
|
|
3118 | 3121 | for i in range(nHeights): |
|
|
3119 | 3122 | powerAux = power[:,i] |
|
|
3120 | 3123 | threshAux = thresh[:,i] |
|
|
3121 | 3124 | |
|
|
3122 | 3125 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
3123 | 3126 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
3124 | 3127 | |
|
|
3125 | 3128 | j = 0 |
|
|
3126 | 3129 | |
|
|
3127 | 3130 | while (j < indUPthresh.size - 2): |
|
|
3128 | 3131 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
3129 | 3132 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
3130 | 3133 | indDNthresh = indDNthresh[indDNAux] |
|
|
3131 | 3134 | |
|
|
3132 | 3135 | if (indDNthresh.size > 0): |
|
|
3133 | 3136 | indEnd = indDNthresh[0] - 1 |
|
|
3134 | 3137 | indInit = indUPthresh[j] |
|
|
3135 | 3138 | |
|
|
3136 | 3139 | meteor = powerAux[indInit:indEnd + 1] |
|
|
3137 | 3140 | indPeak = meteor.argmax() + indInit |
|
|
3138 | 3141 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
3139 | 3142 | |
|
|
3140 | 3143 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
3141 | 3144 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
3142 | 3145 | else: j+=1 |
|
|
3143 | 3146 | else: j+=1 |
|
|
3144 | 3147 | |
|
|
3145 | 3148 | return listMeteors |
|
|
3146 | 3149 | |
|
|
3147 | 3150 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
3148 | 3151 | |
|
|
3149 | 3152 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
3150 | 3153 | listMeteors1 = [] |
|
|
3151 | 3154 | |
|
|
3152 | 3155 | while arrayMeteors.shape[0] > 0: |
|
|
3153 | 3156 | FLAs = arrayMeteors[:,4] |
|
|
3154 | 3157 | maxFLA = FLAs.argmax() |
|
|
3155 | 3158 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
3156 | 3159 | |
|
|
3157 | 3160 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
3158 | 3161 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
3159 | 3162 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
3160 | 3163 | |
|
|
3161 | 3164 | #Check neighborhood |
|
|
3162 | 3165 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
3163 | 3166 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
3164 | 3167 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
3165 | 3168 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
3166 | 3169 | |
|
|
3167 | 3170 | #Check Heights |
|
|
3168 | 3171 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
3169 | 3172 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
3170 | 3173 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
3171 | 3174 | |
|
|
3172 | 3175 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
3173 | 3176 | |
|
|
3174 | 3177 | return listMeteors1 |
|
|
3175 | 3178 | |
|
|
3176 | 3179 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
3177 | 3180 | numHeights = volts.shape[2] |
|
|
3178 | 3181 | nChannel = volts.shape[0] |
|
|
3179 | 3182 | |
|
|
3180 | 3183 | thresholdPhase = thresh[0] |
|
|
3181 | 3184 | thresholdNoise = thresh[1] |
|
|
3182 | 3185 | thresholdDB = float(thresh[2]) |
|
|
3183 | 3186 | |
|
|
3184 | 3187 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
3185 | 3188 | pairsarray = numpy.array(pairslist) |
|
|
3186 | 3189 | indSides = pairsarray[:,1] |
|
|
3187 | 3190 | |
|
|
3188 | 3191 | pairslist1 = list(pairslist) |
|
|
3189 | 3192 | pairslist1.append((0,1)) |
|
|
3190 | 3193 | pairslist1.append((3,4)) |
|
|
3191 | 3194 | |
|
|
3192 | 3195 | listMeteors1 = [] |
|
|
3193 | 3196 | listPowerSeries = [] |
|
|
3194 | 3197 | listVoltageSeries = [] |
|
|
3195 | 3198 | #volts has the war data |
|
|
3196 | 3199 | |
|
|
3197 | 3200 | if frequency == 30e6: |
|
|
3198 | 3201 | timeLag = 45*10**-3 |
|
|
3199 | 3202 | else: |
|
|
3200 | 3203 | timeLag = 15*10**-3 |
|
|
3201 | 3204 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3202 | 3205 | |
|
|
3203 | 3206 | for i in range(len(listMeteors)): |
|
|
3204 | 3207 | |
|
|
3205 | 3208 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
3206 | 3209 | meteorAux = numpy.zeros(16) |
|
|
3207 | 3210 | |
|
|
3208 | 3211 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
3209 | 3212 | mHeight = listMeteors[i][0] |
|
|
3210 | 3213 | mStart = listMeteors[i][1] |
|
|
3211 | 3214 | mPeak = listMeteors[i][2] |
|
|
3212 | 3215 | mEnd = listMeteors[i][3] |
|
|
3213 | 3216 | |
|
|
3214 | 3217 | #get the volt data between the start and end times of the meteor |
|
|
3215 | 3218 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
3216 | 3219 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3217 | 3220 | |
|
|
3218 | 3221 | #3.6. Phase Difference estimation |
|
|
3219 | 3222 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
3220 | 3223 | |
|
|
3221 | 3224 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
3222 | 3225 | #meteorVolts0.- all Channels, all Profiles |
|
|
3223 | 3226 | meteorVolts0 = volts[:,:,mHeight] |
|
|
3224 | 3227 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
3225 | 3228 | meteorNoise = noise[:,mHeight] |
|
|
3226 | 3229 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
3227 | 3230 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
3228 | 3231 | |
|
|
3229 | 3232 | #Times reestimation |
|
|
3230 | 3233 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
3231 | 3234 | if mStart1.size > 0: |
|
|
3232 | 3235 | mStart1 = mStart1[-1] + 1 |
|
|
3233 | 3236 | |
|
|
3234 | 3237 | else: |
|
|
3235 | 3238 | mStart1 = mPeak |
|
|
3236 | 3239 | |
|
|
3237 | 3240 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
3238 | 3241 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
3239 | 3242 | if mEndDecayTime1.size == 0: |
|
|
3240 | 3243 | mEndDecayTime1 = powerNet0.size |
|
|
3241 | 3244 | else: |
|
|
3242 | 3245 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
3243 | 3246 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
3244 | 3247 | |
|
|
3245 | 3248 | #meteorVolts1.- all Channels, from start to end |
|
|
3246 | 3249 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
3247 | 3250 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
3248 | 3251 | if meteorVolts2.shape[1] == 0: |
|
|
3249 | 3252 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
3250 | 3253 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
3251 | 3254 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
3252 | 3255 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
3253 | 3256 | |
|
|
3254 | 3257 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
3255 | 3258 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3256 | 3259 | if meteorVolts2.shape[1] > 0: |
|
|
3257 | 3260 | #Phase Difference re-estimation |
|
|
3258 | 3261 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
3259 | 3262 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
3260 | 3263 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
3261 | 3264 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
3262 | 3265 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
3263 | 3266 | |
|
|
3264 | 3267 | #Phase Difference RMS |
|
|
3265 | 3268 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
3266 | 3269 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
3267 | 3270 | #Data from Meteor |
|
|
3268 | 3271 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
3269 | 3272 | mPeakPower1 = powerNet1.max() |
|
|
3270 | 3273 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
3271 | 3274 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
3272 | 3275 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
3273 | 3276 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
3274 | 3277 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
3275 | 3278 | #Vectorize |
|
|
3276 | 3279 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
3277 | 3280 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
3278 | 3281 | |
|
|
3279 | 3282 | #Rejection Criterions |
|
|
3280 | 3283 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
3281 | 3284 | meteorAux[-1] = 17 |
|
|
3282 | 3285 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
3283 | 3286 | meteorAux[-1] = 1 |
|
|
3284 | 3287 | |
|
|
3285 | 3288 | |
|
|
3286 | 3289 | else: |
|
|
3287 | 3290 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
3288 | 3291 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3289 | 3292 | PowerSeries = 0 |
|
|
3290 | 3293 | |
|
|
3291 | 3294 | listMeteors1.append(meteorAux) |
|
|
3292 | 3295 | listPowerSeries.append(PowerSeries) |
|
|
3293 | 3296 | listVoltageSeries.append(meteorVolts1) |
|
|
3294 | 3297 | |
|
|
3295 | 3298 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
3296 | 3299 | |
|
|
3297 | 3300 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
3298 | 3301 | |
|
|
3299 | 3302 | threshError = 10 |
|
|
3300 | 3303 | #Depending if it is 30 or 50 MHz |
|
|
3301 | 3304 | if frequency == 30e6: |
|
|
3302 | 3305 | timeLag = 45*10**-3 |
|
|
3303 | 3306 | else: |
|
|
3304 | 3307 | timeLag = 15*10**-3 |
|
|
3305 | 3308 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3306 | 3309 | |
|
|
3307 | 3310 | listMeteors1 = [] |
|
|
3308 | 3311 | |
|
|
3309 | 3312 | for i in range(len(listMeteors)): |
|
|
3310 | 3313 | meteorPower = listPower[i] |
|
|
3311 | 3314 | meteorAux = listMeteors[i] |
|
|
3312 | 3315 | |
|
|
3313 | 3316 | if meteorAux[-1] == 0: |
|
|
3314 | 3317 | |
|
|
3315 | 3318 | try: |
|
|
3316 | 3319 | indmax = meteorPower.argmax() |
|
|
3317 | 3320 | indlag = indmax + lag |
|
|
3318 | 3321 | |
|
|
3319 | 3322 | y = meteorPower[indlag:] |
|
|
3320 | 3323 | x = numpy.arange(0, y.size)*timeLag |
|
|
3321 | 3324 | |
|
|
3322 | 3325 | #first guess |
|
|
3323 | 3326 | a = y[0] |
|
|
3324 | 3327 | tau = timeLag |
|
|
3325 | 3328 | #exponential fit |
|
|
3326 | 3329 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
3327 | 3330 | y1 = self.__exponential_function(x, *popt) |
|
|
3328 | 3331 | #error estimation |
|
|
3329 | 3332 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
3330 | 3333 | |
|
|
3331 | 3334 | decayTime = popt[1] |
|
|
3332 | 3335 | riseTime = indmax*timeInterval |
|
|
3333 | 3336 | meteorAux[11:13] = [decayTime, error] |
|
|
3334 | 3337 | |
|
|
3335 | 3338 | #Table items 7, 8 and 11 |
|
|
3336 | 3339 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
3337 | 3340 | meteorAux[-1] = 7 |
|
|
3338 | 3341 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
3339 | 3342 | meteorAux[-1] = 8 |
|
|
3340 | 3343 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
3341 | 3344 | meteorAux[-1] = 11 |
|
|
3342 | 3345 | |
|
|
3343 | 3346 | |
|
|
3344 | 3347 | except: |
|
|
3345 | 3348 | meteorAux[-1] = 11 |
|
|
3346 | 3349 | |
|
|
3347 | 3350 | |
|
|
3348 | 3351 | listMeteors1.append(meteorAux) |
|
|
3349 | 3352 | |
|
|
3350 | 3353 | return listMeteors1 |
|
|
3351 | 3354 | |
|
|
3352 | 3355 | #Exponential Function |
|
|
3353 | 3356 | |
|
|
3354 | 3357 | def __exponential_function(self, x, a, tau): |
|
|
3355 | 3358 | y = a*numpy.exp(-x/tau) |
|
|
3356 | 3359 | return y |
|
|
3357 | 3360 | |
|
|
3358 | 3361 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
3359 | 3362 | |
|
|
3360 | 3363 | pairslist1 = list(pairslist) |
|
|
3361 | 3364 | pairslist1.append((0,1)) |
|
|
3362 | 3365 | pairslist1.append((3,4)) |
|
|
3363 | 3366 | numPairs = len(pairslist1) |
|
|
3364 | 3367 | #Time Lag |
|
|
3365 | 3368 | timeLag = 45*10**-3 |
|
|
3366 | 3369 | c = 3e8 |
|
|
3367 | 3370 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3368 | 3371 | freq = 30e6 |
|
|
3369 | 3372 | |
|
|
3370 | 3373 | listMeteors1 = [] |
|
|
3371 | 3374 | |
|
|
3372 | 3375 | for i in range(len(listMeteors)): |
|
|
3373 | 3376 | meteorAux = listMeteors[i] |
|
|
3374 | 3377 | if meteorAux[-1] == 0: |
|
|
3375 | 3378 | mStart = listMeteors[i][1] |
|
|
3376 | 3379 | mPeak = listMeteors[i][2] |
|
|
3377 | 3380 | mLag = mPeak - mStart + lag |
|
|
3378 | 3381 | |
|
|
3379 | 3382 | #get the volt data between the start and end times of the meteor |
|
|
3380 | 3383 | meteorVolts = listVolts[i] |
|
|
3381 | 3384 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3382 | 3385 | |
|
|
3383 | 3386 | #Get CCF |
|
|
3384 | 3387 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
3385 | 3388 | |
|
|
3386 | 3389 | #Method 2 |
|
|
3387 | 3390 | slopes = numpy.zeros(numPairs) |
|
|
3388 | 3391 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
3389 | 3392 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
3390 | 3393 | |
|
|
3391 | 3394 | #Correct phases |
|
|
3392 | 3395 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
3393 | 3396 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
3394 | 3397 | |
|
|
3395 | 3398 | if indDer[0].shape[0] > 0: |
|
|
3396 | 3399 | for i in range(indDer[0].shape[0]): |
|
|
3397 | 3400 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
3398 | 3401 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
3399 | 3402 | |
|
|
3400 | 3403 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
3401 | 3404 | for j in range(numPairs): |
|
|
3402 | 3405 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
3403 | 3406 | slopes[j] = fit[0] |
|
|
3404 | 3407 | |
|
|
3405 | 3408 | #Remove Outlier |
|
|
3406 | 3409 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3407 | 3410 | # slopes = numpy.delete(slopes,indOut) |
|
|
3408 | 3411 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3409 | 3412 | # slopes = numpy.delete(slopes,indOut) |
|
|
3410 | 3413 | |
|
|
3411 | 3414 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3412 | 3415 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3413 | 3416 | meteorAux[-2] = radialError |
|
|
3414 | 3417 | meteorAux[-3] = radialVelocity |
|
|
3415 | 3418 | |
|
|
3416 | 3419 | #Setting Error |
|
|
3417 | 3420 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
3418 | 3421 | if numpy.abs(radialVelocity) > 200: |
|
|
3419 | 3422 | meteorAux[-1] = 15 |
|
|
3420 | 3423 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
3421 | 3424 | elif radialError > radialStdThresh: |
|
|
3422 | 3425 | meteorAux[-1] = 12 |
|
|
3423 | 3426 | |
|
|
3424 | 3427 | listMeteors1.append(meteorAux) |
|
|
3425 | 3428 | return listMeteors1 |
|
|
3426 | 3429 | |
|
|
3427 | 3430 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
3428 | 3431 | |
|
|
3429 | 3432 | #New arrays |
|
|
3430 | 3433 | arrayMeteors = numpy.array(listMeteors) |
|
|
3431 | 3434 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
3432 | 3435 | |
|
|
3433 | 3436 | #Date inclusion |
|
|
3434 | 3437 | # date = re.findall(r'\((.*?)\)', date) |
|
|
3435 | 3438 | # date = date[0].split(',') |
|
|
3436 | 3439 | # date = map(int, date) |
|
|
3437 | 3440 | # |
|
|
3438 | 3441 | # if len(date)<6: |
|
|
3439 | 3442 | # date.append(0) |
|
|
3440 | 3443 | # |
|
|
3441 | 3444 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
3442 | 3445 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
3443 | 3446 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
3444 | 3447 | |
|
|
3445 | 3448 | #Meteor array |
|
|
3446 | 3449 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
3447 | 3450 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
3448 | 3451 | |
|
|
3449 | 3452 | #Parameters Array |
|
|
3450 | 3453 | arrayParameters[:,0] = arrayDate #Date |
|
|
3451 | 3454 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
3452 | 3455 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
3453 | 3456 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
3454 | 3457 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
3455 | 3458 | |
|
|
3456 | 3459 | |
|
|
3457 | 3460 | return arrayParameters |
|
|
3458 | 3461 | |
|
|
3459 | 3462 | class CorrectSMPhases(Operation): |
|
|
3460 | 3463 | |
|
|
3461 | 3464 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
3462 | 3465 | |
|
|
3463 | 3466 | arrayParameters = dataOut.data_param |
|
|
3464 | 3467 | pairsList = [] |
|
|
3465 | 3468 | pairx = (0,1) |
|
|
3466 | 3469 | pairy = (2,3) |
|
|
3467 | 3470 | pairsList.append(pairx) |
|
|
3468 | 3471 | pairsList.append(pairy) |
|
|
3469 | 3472 | jph = numpy.zeros(4) |
|
|
3470 | 3473 | |
|
|
3471 | 3474 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
3472 | 3475 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
3473 | 3476 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
3474 | 3477 | |
|
|
3475 | 3478 | meteorOps = SMOperations() |
|
|
3476 | 3479 | if channelPositions == None: |
|
|
3477 | 3480 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3478 | 3481 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3479 | 3482 | |
|
|
3480 | 3483 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3481 | 3484 | h = (hmin,hmax) |
|
|
3482 | 3485 | |
|
|
3483 | 3486 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
3484 | 3487 | |
|
|
3485 | 3488 | dataOut.data_param = arrayParameters |
|
|
3486 | 3489 | return |
|
|
3487 | 3490 | |
|
|
3488 | 3491 | class SMPhaseCalibration(Operation): |
|
|
3489 | 3492 | |
|
|
3490 | 3493 | __buffer = None |
|
|
3491 | 3494 | |
|
|
3492 | 3495 | __initime = None |
|
|
3493 | 3496 | |
|
|
3494 | 3497 | __dataReady = False |
|
|
3495 | 3498 | |
|
|
3496 | 3499 | __isConfig = False |
|
|
3497 | 3500 | |
|
|
3498 | 3501 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
3499 | 3502 | |
|
|
3500 | 3503 | dataTime = currentTime + paramInterval |
|
|
3501 | 3504 | deltaTime = dataTime - initTime |
|
|
3502 | 3505 | |
|
|
3503 | 3506 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
3504 | 3507 | return True |
|
|
3505 | 3508 | |
|
|
3506 | 3509 | return False |
|
|
3507 | 3510 | |
|
|
3508 | 3511 | def __getGammas(self, pairs, d, phases): |
|
|
3509 | 3512 | gammas = numpy.zeros(2) |
|
|
3510 | 3513 | |
|
|
3511 | 3514 | for i in range(len(pairs)): |
|
|
3512 | 3515 | |
|
|
3513 | 3516 | pairi = pairs[i] |
|
|
3514 | 3517 | |
|
|
3515 | 3518 | phip3 = phases[:,pairi[0]] |
|
|
3516 | 3519 | d3 = d[pairi[0]] |
|
|
3517 | 3520 | phip2 = phases[:,pairi[1]] |
|
|
3518 | 3521 | d2 = d[pairi[1]] |
|
|
3519 | 3522 | #Calculating gamma |
|
|
3520 | 3523 | # jdcos = alp1/(k*d1) |
|
|
3521 | 3524 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
3522 | 3525 | jgamma = -phip2*d3/d2 - phip3 |
|
|
3523 | 3526 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
3524 | 3527 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
3525 | 3528 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
3526 | 3529 | |
|
|
3527 | 3530 | #Revised distribution |
|
|
3528 | 3531 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
3529 | 3532 | |
|
|
3530 | 3533 | #Histogram |
|
|
3531 | 3534 | nBins = 64 |
|
|
3532 | 3535 | rmin = -0.5*numpy.pi |
|
|
3533 | 3536 | rmax = 0.5*numpy.pi |
|
|
3534 | 3537 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
3535 | 3538 | |
|
|
3536 | 3539 | meteorsY = phaseHisto[0] |
|
|
3537 | 3540 | phasesX = phaseHisto[1][:-1] |
|
|
3538 | 3541 | width = phasesX[1] - phasesX[0] |
|
|
3539 | 3542 | phasesX += width/2 |
|
|
3540 | 3543 | |
|
|
3541 | 3544 | #Gaussian aproximation |
|
|
3542 | 3545 | bpeak = meteorsY.argmax() |
|
|
3543 | 3546 | peak = meteorsY.max() |
|
|
3544 | 3547 | jmin = bpeak - 5 |
|
|
3545 | 3548 | jmax = bpeak + 5 + 1 |
|
|
3546 | 3549 | |
|
|
3547 | 3550 | if jmin<0: |
|
|
3548 | 3551 | jmin = 0 |
|
|
3549 | 3552 | jmax = 6 |
|
|
3550 | 3553 | elif jmax > meteorsY.size: |
|
|
3551 | 3554 | jmin = meteorsY.size - 6 |
|
|
3552 | 3555 | jmax = meteorsY.size |
|
|
3553 | 3556 | |
|
|
3554 | 3557 | x0 = numpy.array([peak,bpeak,50]) |
|
|
3555 | 3558 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
3556 | 3559 | |
|
|
3557 | 3560 | #Gammas |
|
|
3558 | 3561 | gammas[i] = coeff[0][1] |
|
|
3559 | 3562 | |
|
|
3560 | 3563 | return gammas |
|
|
3561 | 3564 | |
|
|
3562 | 3565 | def __residualFunction(self, coeffs, y, t): |
|
|
3563 | 3566 | |
|
|
3564 | 3567 | return y - self.__gauss_function(t, coeffs) |
|
|
3565 | 3568 | |
|
|
3566 | 3569 | def __gauss_function(self, t, coeffs): |
|
|
3567 | 3570 | |
|
|
3568 | 3571 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
3569 | 3572 | |
|
|
3570 | 3573 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
3571 | 3574 | meteorOps = SMOperations() |
|
|
3572 | 3575 | nchan = 4 |
|
|
3573 | 3576 | pairx = pairsList[0] #x es 0 |
|
|
3574 | 3577 | pairy = pairsList[1] #y es 1 |
|
|
3575 | 3578 | center_xangle = 0 |
|
|
3576 | 3579 | center_yangle = 0 |
|
|
3577 | 3580 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
3578 | 3581 | ntimes = len(range_angle) |
|
|
3579 | 3582 | |
|
|
3580 | 3583 | nstepsx = 20 |
|
|
3581 | 3584 | nstepsy = 20 |
|
|
3582 | 3585 | |
|
|
3583 | 3586 | for iz in range(ntimes): |
|
|
3584 | 3587 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
3585 | 3588 | max_xangle = range_angle[iz]/2 + center_xangle |
|
|
3586 | 3589 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
3587 | 3590 | max_yangle = range_angle[iz]/2 + center_yangle |
|
|
3588 | 3591 | |
|
|
3589 | 3592 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
3590 | 3593 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
3591 | 3594 | |
|
|
3592 | 3595 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
3593 | 3596 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
3594 | 3597 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
3595 | 3598 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
3596 | 3599 | jph = numpy.zeros(nchan) |
|
|
3597 | 3600 | |
|
|
3598 | 3601 | # Iterations looking for the offset |
|
|
3599 | 3602 | for iy in range(int(nstepsy)): |
|
|
3600 | 3603 | for ix in range(int(nstepsx)): |
|
|
3601 | 3604 | d3 = d[pairsList[1][0]] |
|
|
3602 | 3605 | d2 = d[pairsList[1][1]] |
|
|
3603 | 3606 | d5 = d[pairsList[0][0]] |
|
|
3604 | 3607 | d4 = d[pairsList[0][1]] |
|
|
3605 | 3608 | |
|
|
3606 | 3609 | alp2 = alpha_y[iy] #gamma 1 |
|
|
3607 | 3610 | alp4 = alpha_x[ix] #gamma 0 |
|
|
3608 | 3611 | |
|
|
3609 | 3612 | alp3 = -alp2*d3/d2 - gammas[1] |
|
|
3610 | 3613 | alp5 = -alp4*d5/d4 - gammas[0] |
|
|
3611 | 3614 | # jph[pairy[1]] = alpha_y[iy] |
|
|
3612 | 3615 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
3613 | 3616 | |
|
|
3614 | 3617 | # jph[pairx[1]] = alpha_x[ix] |
|
|
3615 | 3618 | # jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
3616 | 3619 | jph[pairsList[0][1]] = alp4 |
|
|
3617 | 3620 | jph[pairsList[0][0]] = alp5 |
|
|
3618 | 3621 | jph[pairsList[1][0]] = alp3 |
|
|
3619 | 3622 | jph[pairsList[1][1]] = alp2 |
|
|
3620 | 3623 | jph_array[:,ix,iy] = jph |
|
|
3621 | 3624 | # d = [2.0,2.5,2.5,2.0] |
|
|
3622 | 3625 | #falta chequear si va a leer bien los meteoros |
|
|
3623 | 3626 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
3624 | 3627 | error = meteorsArray1[:,-1] |
|
|
3625 | 3628 | ind1 = numpy.where(error==0)[0] |
|
|
3626 | 3629 | penalty[ix,iy] = ind1.size |
|
|
3627 | 3630 | |
|
|
3628 | 3631 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
3629 | 3632 | phOffset = jph_array[:,i,j] |
|
|
3630 | 3633 | |
|
|
3631 | 3634 | center_xangle = phOffset[pairx[1]] |
|
|
3632 | 3635 | center_yangle = phOffset[pairy[1]] |
|
|
3633 | 3636 | |
|
|
3634 | 3637 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
3635 | 3638 | phOffset = phOffset*180/numpy.pi |
|
|
3636 | 3639 | return phOffset |
|
|
3637 | 3640 | |
|
|
3638 | 3641 | |
|
|
3639 | 3642 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
3640 | 3643 | |
|
|
3641 | 3644 | dataOut.flagNoData = True |
|
|
3642 | 3645 | self.__dataReady = False |
|
|
3643 | 3646 | dataOut.outputInterval = nHours*3600 |
|
|
3644 | 3647 | |
|
|
3645 | 3648 | if self.__isConfig == False: |
|
|
3646 | 3649 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
3647 | 3650 | #Get Initial LTC time |
|
|
3648 | 3651 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
3649 | 3652 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
3650 | 3653 | |
|
|
3651 | 3654 | self.__isConfig = True |
|
|
3652 | 3655 | |
|
|
3653 | 3656 | if self.__buffer == None: |
|
|
3654 | 3657 | self.__buffer = dataOut.data_param.copy() |
|
|
3655 | 3658 | |
|
|
3656 | 3659 | else: |
|
|
3657 | 3660 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
3658 | 3661 | |
|
|
3659 | 3662 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
3660 | 3663 | |
|
|
3661 | 3664 | if self.__dataReady: |
|
|
3662 | 3665 | dataOut.utctimeInit = self.__initime |
|
|
3663 | 3666 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
3664 | 3667 | |
|
|
3665 | 3668 | freq = dataOut.frequency |
|
|
3666 | 3669 | c = dataOut.C #m/s |
|
|
3667 | 3670 | lamb = c/freq |
|
|
3668 | 3671 | k = 2*numpy.pi/lamb |
|
|
3669 | 3672 | azimuth = 0 |
|
|
3670 | 3673 | h = (hmin, hmax) |
|
|
3671 | 3674 | # pairs = ((0,1),(2,3)) #Estrella |
|
|
3672 | 3675 | # pairs = ((1,0),(2,3)) #T |
|
|
3673 | 3676 | |
|
|
3674 | 3677 | if channelPositions is None: |
|
|
3675 | 3678 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3676 | 3679 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3677 | 3680 | meteorOps = SMOperations() |
|
|
3678 | 3681 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3679 | 3682 | |
|
|
3680 | 3683 | #Checking correct order of pairs |
|
|
3681 | 3684 | pairs = [] |
|
|
3682 | 3685 | if distances[1] > distances[0]: |
|
|
3683 | 3686 | pairs.append((1,0)) |
|
|
3684 | 3687 | else: |
|
|
3685 | 3688 | pairs.append((0,1)) |
|
|
3686 | 3689 | |
|
|
3687 | 3690 | if distances[3] > distances[2]: |
|
|
3688 | 3691 | pairs.append((3,2)) |
|
|
3689 | 3692 | else: |
|
|
3690 | 3693 | pairs.append((2,3)) |
|
|
3691 | 3694 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
3692 | 3695 | |
|
|
3693 | 3696 | meteorsArray = self.__buffer |
|
|
3694 | 3697 | error = meteorsArray[:,-1] |
|
|
3695 | 3698 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
3696 | 3699 | ind1 = numpy.where(boolError)[0] |
|
|
3697 | 3700 | meteorsArray = meteorsArray[ind1,:] |
|
|
3698 | 3701 | meteorsArray[:,-1] = 0 |
|
|
3699 | 3702 | phases = meteorsArray[:,8:12] |
|
|
3700 | 3703 | |
|
|
3701 | 3704 | #Calculate Gammas |
|
|
3702 | 3705 | gammas = self.__getGammas(pairs, distances, phases) |
|
|
3703 | 3706 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
3704 | 3707 | #Calculate Phases |
|
|
3705 | 3708 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
3706 | 3709 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
3707 | 3710 | dataOut.data_output = -phasesOff |
|
|
3708 | 3711 | dataOut.flagNoData = False |
|
|
3709 | 3712 | self.__buffer = None |
|
|
3710 | 3713 | |
|
|
3711 | 3714 | |
|
|
3712 | 3715 | return |
|
|
3713 | 3716 | |
|
|
3714 | 3717 | class SMOperations(): |
|
|
3715 | 3718 | |
|
|
3716 | 3719 | def __init__(self): |
|
|
3717 | 3720 | |
|
|
3718 | 3721 | return |
|
|
3719 | 3722 | |
|
|
3720 | 3723 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
3721 | 3724 | |
|
|
3722 | 3725 | arrayParameters = arrayParameters0.copy() |
|
|
3723 | 3726 | hmin = h[0] |
|
|
3724 | 3727 | hmax = h[1] |
|
|
3725 | 3728 | |
|
|
3726 | 3729 | #Calculate AOA (Error N 3, 4) |
|
|
3727 | 3730 | #JONES ET AL. 1998 |
|
|
3728 | 3731 | AOAthresh = numpy.pi/8 |
|
|
3729 | 3732 | error = arrayParameters[:,-1] |
|
|
3730 | 3733 | phases = -arrayParameters[:,8:12] + jph |
|
|
3731 | 3734 | # phases = numpy.unwrap(phases) |
|
|
3732 | 3735 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
3733 | 3736 | |
|
|
3734 | 3737 | #Calculate Heights (Error N 13 and 14) |
|
|
3735 | 3738 | error = arrayParameters[:,-1] |
|
|
3736 | 3739 | Ranges = arrayParameters[:,1] |
|
|
3737 | 3740 | zenith = arrayParameters[:,4] |
|
|
3738 | 3741 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
3739 | 3742 | |
|
|
3740 | 3743 | #----------------------- Get Final data ------------------------------------ |
|
|
3741 | 3744 | # error = arrayParameters[:,-1] |
|
|
3742 | 3745 | # ind1 = numpy.where(error==0)[0] |
|
|
3743 | 3746 | # arrayParameters = arrayParameters[ind1,:] |
|
|
3744 | 3747 | |
|
|
3745 | 3748 | return arrayParameters |
|
|
3746 | 3749 | |
|
|
3747 | 3750 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
3748 | 3751 | |
|
|
3749 | 3752 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3750 | 3753 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
3751 | 3754 | |
|
|
3752 | 3755 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3753 | 3756 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3754 | 3757 | arrayAOA[:,2] = cosDirError |
|
|
3755 | 3758 | |
|
|
3756 | 3759 | azimuthAngle = arrayAOA[:,0] |
|
|
3757 | 3760 | zenithAngle = arrayAOA[:,1] |
|
|
3758 | 3761 | |
|
|
3759 | 3762 | #Setting Error |
|
|
3760 | 3763 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
3761 | 3764 | error[indError] = 0 |
|
|
3762 | 3765 | #Number 3: AOA not fesible |
|
|
3763 | 3766 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3764 | 3767 | error[indInvalid] = 3 |
|
|
3765 | 3768 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3766 | 3769 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3767 | 3770 | error[indInvalid] = 4 |
|
|
3768 | 3771 | return arrayAOA, error |
|
|
3769 | 3772 | |
|
|
3770 | 3773 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
3771 | 3774 | |
|
|
3772 | 3775 | #Initializing some variables |
|
|
3773 | 3776 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3774 | 3777 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3775 | 3778 | |
|
|
3776 | 3779 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3777 | 3780 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3778 | 3781 | |
|
|
3779 | 3782 | |
|
|
3780 | 3783 | for i in range(2): |
|
|
3781 | 3784 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
3782 | 3785 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
3783 | 3786 | d0 = distances[pairsList[i][0]] |
|
|
3784 | 3787 | d1 = distances[pairsList[i][1]] |
|
|
3785 | 3788 | |
|
|
3786 | 3789 | ph0_aux = ph0 + ph1 |
|
|
3787 | 3790 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
3788 | 3791 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
3789 | 3792 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
3790 | 3793 | #First Estimation |
|
|
3791 | 3794 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
3792 | 3795 | |
|
|
3793 | 3796 | #Most-Accurate Second Estimation |
|
|
3794 | 3797 | phi1_aux = ph0 - ph1 |
|
|
3795 | 3798 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3796 | 3799 | #Direction Cosine 1 |
|
|
3797 | 3800 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
3798 | 3801 | |
|
|
3799 | 3802 | #Searching the correct Direction Cosine |
|
|
3800 | 3803 | cosdir0_aux = cosdir0[:,i] |
|
|
3801 | 3804 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3802 | 3805 | #Minimum Distance |
|
|
3803 | 3806 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3804 | 3807 | indcos = cosDiff.argmin(axis = 1) |
|
|
3805 | 3808 | #Saving Value obtained |
|
|
3806 | 3809 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3807 | 3810 | |
|
|
3808 | 3811 | return cosdir0, cosdir |
|
|
3809 | 3812 | |
|
|
3810 | 3813 | def __calculateAOA(self, cosdir, azimuth): |
|
|
3811 | 3814 | cosdirX = cosdir[:,0] |
|
|
3812 | 3815 | cosdirY = cosdir[:,1] |
|
|
3813 | 3816 | |
|
|
3814 | 3817 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3815 | 3818 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
3816 | 3819 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3817 | 3820 | |
|
|
3818 | 3821 | return angles |
|
|
3819 | 3822 | |
|
|
3820 | 3823 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3821 | 3824 | |
|
|
3822 | 3825 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3823 | 3826 | Re = 6371 #Earth Radius |
|
|
3824 | 3827 | heights = numpy.zeros(Ranges.shape) |
|
|
3825 | 3828 | |
|
|
3826 | 3829 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
3827 | 3830 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3828 | 3831 | |
|
|
3829 | 3832 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3830 | 3833 | |
|
|
3831 | 3834 | Ri = Ranges + R_aux |
|
|
3832 | 3835 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3833 | 3836 | |
|
|
3834 | 3837 | #Check if there is a height between 70 and 110 km |
|
|
3835 | 3838 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3836 | 3839 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
3837 | 3840 | |
|
|
3838 | 3841 | hCorr = hi[ind_h, :] |
|
|
3839 | 3842 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3840 | 3843 | |
|
|
3841 | 3844 | hCorr = hi[ind_hCorr][:len(ind_h)] |
|
|
3842 | 3845 | heights[ind_h] = hCorr |
|
|
3843 | 3846 | |
|
|
3844 | 3847 | #Setting Error |
|
|
3845 | 3848 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3846 | 3849 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3847 | 3850 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
3848 | 3851 | error[indError] = 0 |
|
|
3849 | 3852 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3850 | 3853 | error[indInvalid2] = 14 |
|
|
3851 | 3854 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3852 | 3855 | error[indInvalid1] = 13 |
|
|
3853 | 3856 | |
|
|
3854 | 3857 | return heights, error |
|
|
3855 | 3858 | |
|
|
3856 | 3859 | def getPhasePairs(self, channelPositions): |
|
|
3857 | 3860 | chanPos = numpy.array(channelPositions) |
|
|
3858 | 3861 | listOper = list(itertools.combinations(range(5),2)) |
|
|
3859 | 3862 | |
|
|
3860 | 3863 | distances = numpy.zeros(4) |
|
|
3861 | 3864 | axisX = [] |
|
|
3862 | 3865 | axisY = [] |
|
|
3863 | 3866 | distX = numpy.zeros(3) |
|
|
3864 | 3867 | distY = numpy.zeros(3) |
|
|
3865 | 3868 | ix = 0 |
|
|
3866 | 3869 | iy = 0 |
|
|
3867 | 3870 | |
|
|
3868 | 3871 | pairX = numpy.zeros((2,2)) |
|
|
3869 | 3872 | pairY = numpy.zeros((2,2)) |
|
|
3870 | 3873 | |
|
|
3871 | 3874 | for i in range(len(listOper)): |
|
|
3872 | 3875 | pairi = listOper[i] |
|
|
3873 | 3876 | |
|
|
3874 | 3877 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
3875 | 3878 | |
|
|
3876 | 3879 | if posDif[0] == 0: |
|
|
3877 | 3880 | axisY.append(pairi) |
|
|
3878 | 3881 | distY[iy] = posDif[1] |
|
|
3879 | 3882 | iy += 1 |
|
|
3880 | 3883 | elif posDif[1] == 0: |
|
|
3881 | 3884 | axisX.append(pairi) |
|
|
3882 | 3885 | distX[ix] = posDif[0] |
|
|
3883 | 3886 | ix += 1 |
|
|
3884 | 3887 | |
|
|
3885 | 3888 | for i in range(2): |
|
|
3886 | 3889 | if i==0: |
|
|
3887 | 3890 | dist0 = distX |
|
|
3888 | 3891 | axis0 = axisX |
|
|
3889 | 3892 | else: |
|
|
3890 | 3893 | dist0 = distY |
|
|
3891 | 3894 | axis0 = axisY |
|
|
3892 | 3895 | |
|
|
3893 | 3896 | side = numpy.argsort(dist0)[:-1] |
|
|
3894 | 3897 | axis0 = numpy.array(axis0)[side,:] |
|
|
3895 | 3898 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
3896 | 3899 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
3897 | 3900 | side = axis1[axis1 != chanC] |
|
|
3898 | 3901 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
3899 | 3902 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
3900 | 3903 | if diff1<0: |
|
|
3901 | 3904 | chan2 = side[0] |
|
|
3902 | 3905 | d2 = numpy.abs(diff1) |
|
|
3903 | 3906 | chan1 = side[1] |
|
|
3904 | 3907 | d1 = numpy.abs(diff2) |
|
|
3905 | 3908 | else: |
|
|
3906 | 3909 | chan2 = side[1] |
|
|
3907 | 3910 | d2 = numpy.abs(diff2) |
|
|
3908 | 3911 | chan1 = side[0] |
|
|
3909 | 3912 | d1 = numpy.abs(diff1) |
|
|
3910 | 3913 | |
|
|
3911 | 3914 | if i==0: |
|
|
3912 | 3915 | chanCX = chanC |
|
|
3913 | 3916 | chan1X = chan1 |
|
|
3914 | 3917 | chan2X = chan2 |
|
|
3915 | 3918 | distances[0:2] = numpy.array([d1,d2]) |
|
|
3916 | 3919 | else: |
|
|
3917 | 3920 | chanCY = chanC |
|
|
3918 | 3921 | chan1Y = chan1 |
|
|
3919 | 3922 | chan2Y = chan2 |
|
|
3920 | 3923 | distances[2:4] = numpy.array([d1,d2]) |
|
|
3921 | 3924 | # axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
3922 | 3925 | # |
|
|
3923 | 3926 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
3924 | 3927 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
3925 | 3928 | # |
|
|
3926 | 3929 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
3927 | 3930 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
3928 | 3931 | # channel25X = int(pairX[0,ind25X]) |
|
|
3929 | 3932 | # channel20X = int(pairX[1,ind20X]) |
|
|
3930 | 3933 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
3931 | 3934 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
3932 | 3935 | # channel25Y = int(pairY[0,ind25Y]) |
|
|
3933 | 3936 | # channel20Y = int(pairY[1,ind20Y]) |
|
|
3934 | 3937 | |
|
|
3935 | 3938 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
3936 | 3939 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
3937 | 3940 | |
|
|
3938 | 3941 | return pairslist, distances |
|
|
3939 | 3942 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
3940 | 3943 | # |
|
|
3941 | 3944 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3942 | 3945 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
3943 | 3946 | # |
|
|
3944 | 3947 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3945 | 3948 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3946 | 3949 | # arrayAOA[:,2] = cosDirError |
|
|
3947 | 3950 | # |
|
|
3948 | 3951 | # azimuthAngle = arrayAOA[:,0] |
|
|
3949 | 3952 | # zenithAngle = arrayAOA[:,1] |
|
|
3950 | 3953 | # |
|
|
3951 | 3954 | # #Setting Error |
|
|
3952 | 3955 | # #Number 3: AOA not fesible |
|
|
3953 | 3956 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3954 | 3957 | # error[indInvalid] = 3 |
|
|
3955 | 3958 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3956 | 3959 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3957 | 3960 | # error[indInvalid] = 4 |
|
|
3958 | 3961 | # return arrayAOA, error |
|
|
3959 | 3962 | # |
|
|
3960 | 3963 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
3961 | 3964 | # |
|
|
3962 | 3965 | # #Initializing some variables |
|
|
3963 | 3966 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3964 | 3967 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3965 | 3968 | # |
|
|
3966 | 3969 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3967 | 3970 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3968 | 3971 | # |
|
|
3969 | 3972 | # |
|
|
3970 | 3973 | # for i in range(2): |
|
|
3971 | 3974 | # #First Estimation |
|
|
3972 | 3975 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
3973 | 3976 | # #Dealias |
|
|
3974 | 3977 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
3975 | 3978 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
|
3976 | 3979 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
3977 | 3980 | # phi0_aux[indcsi] += 2*numpy.pi |
|
|
3978 | 3981 | # #Direction Cosine 0 |
|
|
3979 | 3982 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
3980 | 3983 | # |
|
|
3981 | 3984 | # #Most-Accurate Second Estimation |
|
|
3982 | 3985 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
3983 | 3986 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3984 | 3987 | # #Direction Cosine 1 |
|
|
3985 | 3988 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
3986 | 3989 | # |
|
|
3987 | 3990 | # #Searching the correct Direction Cosine |
|
|
3988 | 3991 | # cosdir0_aux = cosdir0[:,i] |
|
|
3989 | 3992 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3990 | 3993 | # #Minimum Distance |
|
|
3991 | 3994 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3992 | 3995 | # indcos = cosDiff.argmin(axis = 1) |
|
|
3993 | 3996 | # #Saving Value obtained |
|
|
3994 | 3997 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3995 | 3998 | # |
|
|
3996 | 3999 | # return cosdir0, cosdir |
|
|
3997 | 4000 | # |
|
|
3998 | 4001 | # def __calculateAOA(self, cosdir, azimuth): |
|
|
3999 | 4002 | # cosdirX = cosdir[:,0] |
|
|
4000 | 4003 | # cosdirY = cosdir[:,1] |
|
|
4001 | 4004 | # |
|
|
4002 | 4005 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
4003 | 4006 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
4004 | 4007 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
4005 | 4008 | # |
|
|
4006 | 4009 | # return angles |
|
|
4007 | 4010 | # |
|
|
4008 | 4011 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
4009 | 4012 | # |
|
|
4010 | 4013 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
|
4011 | 4014 | # Re = 6371 #Earth Radius |
|
|
4012 | 4015 | # heights = numpy.zeros(Ranges.shape) |
|
|
4013 | 4016 | # |
|
|
4014 | 4017 | # R_aux = numpy.array([0,1,2])*Ramb |
|
|
4015 | 4018 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
|
4016 | 4019 | # |
|
|
4017 | 4020 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
|
4018 | 4021 | # |
|
|
4019 | 4022 | # Ri = Ranges + R_aux |
|
|
4020 | 4023 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
4021 | 4024 | # |
|
|
4022 | 4025 | # #Check if there is a height between 70 and 110 km |
|
|
4023 | 4026 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
4024 | 4027 | # ind_h = numpy.where(h_bool == 1)[0] |
|
|
4025 | 4028 | # |
|
|
4026 | 4029 | # hCorr = hi[ind_h, :] |
|
|
4027 | 4030 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
4028 | 4031 | # |
|
|
4029 | 4032 | # hCorr = hi[ind_hCorr] |
|
|
4030 | 4033 | # heights[ind_h] = hCorr |
|
|
4031 | 4034 | # |
|
|
4032 | 4035 | # #Setting Error |
|
|
4033 | 4036 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
4034 | 4037 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
4035 | 4038 | # |
|
|
4036 | 4039 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
4037 | 4040 | # error[indInvalid2] = 14 |
|
|
4038 | 4041 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
4039 | 4042 | # error[indInvalid1] = 13 |
|
|
4040 | 4043 | # |
|
|
4041 | 4044 | # return heights, error |
|
|
4042 | 4045 | No newline at end of file |
| @@ -1,604 +1,607 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | @author: Juan C. Espinoza |
|
|
3 | 3 | ''' |
|
|
4 | 4 | |
|
|
5 | 5 | import time |
|
|
6 | 6 | import json |
|
|
7 | 7 | import numpy |
|
|
8 | 8 | import paho.mqtt.client as mqtt |
|
|
9 | 9 | import zmq |
|
|
10 | 10 | import datetime |
|
|
11 | 11 | from zmq.utils.monitor import recv_monitor_message |
|
|
12 | 12 | from functools import wraps |
|
|
13 | 13 | from threading import Thread |
|
|
14 | 14 | from multiprocessing import Process |
|
|
15 | 15 | |
|
|
16 | 16 | from schainpy.model.proc.jroproc_base import Operation, ProcessingUnit |
|
|
17 | 17 | from schainpy.model.data.jrodata import JROData |
|
|
18 | 18 | from schainpy.utils import log |
|
|
19 | 19 | |
|
|
20 | 20 | MAXNUMX = 100 |
|
|
21 | 21 | MAXNUMY = 100 |
|
|
22 | 22 | |
|
|
23 | 23 | class PrettyFloat(float): |
|
|
24 | 24 | def __repr__(self): |
|
|
25 | 25 | return '%.2f' % self |
|
|
26 | 26 | |
|
|
27 | 27 | def roundFloats(obj): |
|
|
28 | 28 | if isinstance(obj, list): |
|
|
29 | 29 | return map(roundFloats, obj) |
|
|
30 | 30 | elif isinstance(obj, float): |
|
|
31 | 31 | return round(obj, 2) |
|
|
32 | 32 | |
|
|
33 | 33 | def decimate(z, MAXNUMY): |
|
|
34 | 34 | dy = int(len(z[0])/MAXNUMY) + 1 |
|
|
35 | 35 | |
|
|
36 | 36 | return z[::, ::dy] |
|
|
37 | 37 | |
|
|
38 | 38 | class throttle(object): |
|
|
39 | 39 | ''' |
|
|
40 | 40 | Decorator that prevents a function from being called more than once every |
|
|
41 | 41 | time period. |
|
|
42 | 42 | To create a function that cannot be called more than once a minute, but |
|
|
43 | 43 | will sleep until it can be called: |
|
|
44 | 44 | @throttle(minutes=1) |
|
|
45 | 45 | def foo(): |
|
|
46 | 46 | pass |
|
|
47 | 47 | |
|
|
48 | 48 | for i in range(10): |
|
|
49 | 49 | foo() |
|
|
50 | 50 | print "This function has run %s times." % i |
|
|
51 | 51 | ''' |
|
|
52 | 52 | |
|
|
53 | 53 | def __init__(self, seconds=0, minutes=0, hours=0): |
|
|
54 | 54 | self.throttle_period = datetime.timedelta( |
|
|
55 | 55 | seconds=seconds, minutes=minutes, hours=hours |
|
|
56 | 56 | ) |
|
|
57 | 57 | |
|
|
58 | 58 | self.time_of_last_call = datetime.datetime.min |
|
|
59 | 59 | |
|
|
60 | 60 | def __call__(self, fn): |
|
|
61 | 61 | @wraps(fn) |
|
|
62 | 62 | def wrapper(*args, **kwargs): |
|
|
63 | 63 | now = datetime.datetime.now() |
|
|
64 | 64 | time_since_last_call = now - self.time_of_last_call |
|
|
65 | 65 | time_left = self.throttle_period - time_since_last_call |
|
|
66 | 66 | |
|
|
67 | 67 | if time_left > datetime.timedelta(seconds=0): |
|
|
68 | 68 | return |
|
|
69 | 69 | |
|
|
70 | 70 | self.time_of_last_call = datetime.datetime.now() |
|
|
71 | 71 | return fn(*args, **kwargs) |
|
|
72 | 72 | |
|
|
73 | 73 | return wrapper |
|
|
74 | 74 | |
|
|
75 | 75 | class Data(object): |
|
|
76 | 76 | ''' |
|
|
77 | 77 | Object to hold data to be plotted |
|
|
78 | 78 | ''' |
|
|
79 | 79 | |
|
|
80 | 80 | def __init__(self, plottypes, throttle_value): |
|
|
81 | 81 | self.plottypes = plottypes |
|
|
82 | 82 | self.throttle = throttle_value |
|
|
83 | 83 | self.ended = False |
|
|
84 | 84 | self.__times = [] |
|
|
85 | 85 | |
|
|
86 | 86 | def __str__(self): |
|
|
87 | 87 | dum = ['{}{}'.format(key, self.shape(key)) for key in self.data] |
|
|
88 | 88 | return 'Data[{}][{}]'.format(';'.join(dum), len(self.__times)) |
|
|
89 | 89 | |
|
|
90 | 90 | def __len__(self): |
|
|
91 | 91 | return len(self.__times) |
|
|
92 | 92 | |
|
|
93 | 93 | def __getitem__(self, key): |
|
|
94 | 94 | if key not in self.data: |
|
|
95 | 95 | raise KeyError(log.error('Missing key: {}'.format(key))) |
|
|
96 | 96 | |
|
|
97 | 97 | if 'spc' in key: |
|
|
98 | 98 | ret = self.data[key] |
|
|
99 | 99 | else: |
|
|
100 | 100 | ret = numpy.array([self.data[key][x] for x in self.times]) |
|
|
101 | 101 | if ret.ndim > 1: |
|
|
102 | 102 | ret = numpy.swapaxes(ret, 0, 1) |
|
|
103 | 103 | return ret |
|
|
104 | 104 | |
|
|
105 | 105 | def setup(self): |
|
|
106 | 106 | ''' |
|
|
107 | 107 | Configure object |
|
|
108 | 108 | ''' |
|
|
109 | 109 | |
|
|
110 | 110 | self.ended = False |
|
|
111 | 111 | self.data = {} |
|
|
112 | 112 | self.__times = [] |
|
|
113 | 113 | self.__heights = [] |
|
|
114 | 114 | self.__all_heights = set() |
|
|
115 | 115 | for plot in self.plottypes: |
|
|
116 | if 'snr' in plot: | |
|
|
117 | plot = 'snr' | |
|
|
116 | 118 | self.data[plot] = {} |
|
|
117 | 119 | |
|
|
118 | 120 | def shape(self, key): |
|
|
119 | 121 | ''' |
|
|
120 | 122 | Get the shape of the one-element data for the given key |
|
|
121 | 123 | ''' |
|
|
122 | 124 | |
|
|
123 | 125 | if len(self.data[key]): |
|
|
124 | 126 | if 'spc' in key: |
|
|
125 | 127 | return self.data[key].shape |
|
|
126 | 128 | return self.data[key][self.__times[0]].shape |
|
|
127 | 129 | return (0,) |
|
|
128 | 130 | |
|
|
129 | 131 | def update(self, dataOut): |
|
|
130 | 132 | ''' |
|
|
131 | 133 | Update data object with new dataOut |
|
|
132 | 134 | ''' |
|
|
133 | 135 | |
|
|
134 | 136 | tm = dataOut.utctime |
|
|
135 | 137 | if tm in self.__times: |
|
|
136 | 138 | return |
|
|
137 | 139 | |
|
|
138 | 140 | self.parameters = getattr(dataOut, 'parameters', []) |
|
|
139 | 141 | self.pairs = dataOut.pairsList |
|
|
140 | 142 | self.channels = dataOut.channelList |
|
|
141 | self.xrange = (dataOut.getFreqRange(1)/1000. , dataOut.getAcfRange(1) , dataOut.getVelRange(1)) | |
|
|
142 | 143 | self.interval = dataOut.getTimeInterval() |
|
|
144 | if 'spc' in self.plottypes or 'cspc' in self.plottypes: | |
|
|
145 | self.xrange = (dataOut.getFreqRange(1)/1000. , dataOut.getAcfRange(1) , dataOut.getVelRange(1)) | |
|
|
143 | 146 | self.__heights.append(dataOut.heightList) |
|
|
144 | 147 | self.__all_heights.update(dataOut.heightList) |
|
|
145 | 148 | self.__times.append(tm) |
|
|
146 | 149 | |
|
|
147 | 150 | for plot in self.plottypes: |
|
|
148 | 151 | if plot == 'spc': |
|
|
149 | 152 | z = dataOut.data_spc/dataOut.normFactor |
|
|
150 | 153 | self.data[plot] = 10*numpy.log10(z) |
|
|
151 | 154 | if plot == 'cspc': |
|
|
152 | 155 | self.data[plot] = dataOut.data_cspc |
|
|
153 | 156 | if plot == 'noise': |
|
|
154 | 157 | self.data[plot][tm] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
|
|
155 | 158 | if plot == 'rti': |
|
|
156 | 159 | self.data[plot][tm] = dataOut.getPower() |
|
|
157 | 160 | if plot == 'snr_db': |
|
|
158 | 161 | self.data['snr'][tm] = dataOut.data_SNR |
|
|
159 | 162 | if plot == 'snr': |
|
|
160 | 163 | self.data[plot][tm] = 10*numpy.log10(dataOut.data_SNR) |
|
|
161 | 164 | if plot == 'dop': |
|
|
162 | 165 | self.data[plot][tm] = 10*numpy.log10(dataOut.data_DOP) |
|
|
163 | 166 | if plot == 'mean': |
|
|
164 | 167 | self.data[plot][tm] = dataOut.data_MEAN |
|
|
165 | 168 | if plot == 'std': |
|
|
166 | 169 | self.data[plot][tm] = dataOut.data_STD |
|
|
167 | 170 | if plot == 'coh': |
|
|
168 | 171 | self.data[plot][tm] = dataOut.getCoherence() |
|
|
169 | 172 | if plot == 'phase': |
|
|
170 | 173 | self.data[plot][tm] = dataOut.getCoherence(phase=True) |
|
|
171 | 174 | if plot == 'output': |
|
|
172 | 175 | self.data[plot][tm] = dataOut.data_output |
|
|
173 | 176 | if plot == 'param': |
|
|
174 | 177 | self.data[plot][tm] = dataOut.data_param |
|
|
175 | 178 | |
|
|
176 | 179 | def normalize_heights(self): |
|
|
177 | 180 | ''' |
|
|
178 | 181 | Ensure same-dimension of the data for different heighList |
|
|
179 | 182 | ''' |
|
|
180 | 183 | |
|
|
181 | 184 | H = numpy.array(list(self.__all_heights)) |
|
|
182 | 185 | H.sort() |
|
|
183 | 186 | for key in self.data: |
|
|
184 | 187 | shape = self.shape(key)[:-1] + H.shape |
|
|
185 | 188 | for tm, obj in self.data[key].items(): |
|
|
186 | 189 | h = self.__heights[self.__times.index(tm)] |
|
|
187 | 190 | if H.size == h.size: |
|
|
188 | 191 | continue |
|
|
189 | 192 | index = numpy.where(numpy.in1d(H, h))[0] |
|
|
190 | 193 | dummy = numpy.zeros(shape) + numpy.nan |
|
|
191 | 194 | if len(shape) == 2: |
|
|
192 | 195 | dummy[:, index] = obj |
|
|
193 | 196 | else: |
|
|
194 | 197 | dummy[index] = obj |
|
|
195 | 198 | self.data[key][tm] = dummy |
|
|
196 | 199 | |
|
|
197 | 200 | self.__heights = [H for tm in self.__times] |
|
|
198 | 201 | |
|
|
199 | 202 | def jsonify(self, decimate=False): |
|
|
200 | 203 | ''' |
|
|
201 | 204 | Convert data to json |
|
|
202 | 205 | ''' |
|
|
203 | 206 | |
|
|
204 | 207 | ret = {} |
|
|
205 | 208 | tm = self.times[-1] |
|
|
206 | 209 | |
|
|
207 | 210 | for key, value in self.data: |
|
|
208 | 211 | if key in ('spc', 'cspc'): |
|
|
209 | 212 | ret[key] = roundFloats(self.data[key].to_list()) |
|
|
210 | 213 | else: |
|
|
211 | 214 | ret[key] = roundFloats(self.data[key][tm].to_list()) |
|
|
212 | 215 | |
|
|
213 | 216 | ret['timestamp'] = tm |
|
|
214 | 217 | ret['interval'] = self.interval |
|
|
215 | 218 | |
|
|
216 | 219 | @property |
|
|
217 | 220 | def times(self): |
|
|
218 | 221 | ''' |
|
|
219 | 222 | Return the list of times of the current data |
|
|
220 | 223 | ''' |
|
|
221 | 224 | |
|
|
222 | 225 | ret = numpy.array(self.__times) |
|
|
223 | 226 | ret.sort() |
|
|
224 | 227 | return ret |
|
|
225 | 228 | |
|
|
226 | 229 | @property |
|
|
227 | 230 | def heights(self): |
|
|
228 | 231 | ''' |
|
|
229 | 232 | Return the list of heights of the current data |
|
|
230 | 233 | ''' |
|
|
231 | 234 | |
|
|
232 | 235 | return numpy.array(self.__heights[-1]) |
|
|
233 | 236 | |
|
|
234 | 237 | class PublishData(Operation): |
|
|
235 | 238 | ''' |
|
|
236 | 239 | Operation to send data over zmq. |
|
|
237 | 240 | ''' |
|
|
238 | 241 | |
|
|
239 | 242 | def __init__(self, **kwargs): |
|
|
240 | 243 | """Inicio.""" |
|
|
241 | 244 | Operation.__init__(self, **kwargs) |
|
|
242 | 245 | self.isConfig = False |
|
|
243 | 246 | self.client = None |
|
|
244 | 247 | self.zeromq = None |
|
|
245 | 248 | self.mqtt = None |
|
|
246 | 249 | |
|
|
247 | 250 | def on_disconnect(self, client, userdata, rc): |
|
|
248 | 251 | if rc != 0: |
|
|
249 | 252 | log.warning('Unexpected disconnection.') |
|
|
250 | 253 | self.connect() |
|
|
251 | 254 | |
|
|
252 | 255 | def connect(self): |
|
|
253 | 256 | log.warning('trying to connect') |
|
|
254 | 257 | try: |
|
|
255 | 258 | self.client.connect( |
|
|
256 | 259 | host=self.host, |
|
|
257 | 260 | port=self.port, |
|
|
258 | 261 | keepalive=60*10, |
|
|
259 | 262 | bind_address='') |
|
|
260 | 263 | self.client.loop_start() |
|
|
261 | 264 | # self.client.publish( |
|
|
262 | 265 | # self.topic + 'SETUP', |
|
|
263 | 266 | # json.dumps(setup), |
|
|
264 | 267 | # retain=True |
|
|
265 | 268 | # ) |
|
|
266 | 269 | except: |
|
|
267 | 270 | log.error('MQTT Conection error.') |
|
|
268 | 271 | self.client = False |
|
|
269 | 272 | |
|
|
270 | 273 | def setup(self, port=1883, username=None, password=None, clientId="user", zeromq=1, verbose=True, **kwargs): |
|
|
271 | 274 | self.counter = 0 |
|
|
272 | 275 | self.topic = kwargs.get('topic', 'schain') |
|
|
273 | 276 | self.delay = kwargs.get('delay', 0) |
|
|
274 | 277 | self.plottype = kwargs.get('plottype', 'spectra') |
|
|
275 | 278 | self.host = kwargs.get('host', "10.10.10.82") |
|
|
276 | 279 | self.port = kwargs.get('port', 3000) |
|
|
277 | 280 | self.clientId = clientId |
|
|
278 | 281 | self.cnt = 0 |
|
|
279 | 282 | self.zeromq = zeromq |
|
|
280 | 283 | self.mqtt = kwargs.get('plottype', 0) |
|
|
281 | 284 | self.client = None |
|
|
282 | 285 | self.verbose = verbose |
|
|
283 | 286 | setup = [] |
|
|
284 | 287 | if mqtt is 1: |
|
|
285 | 288 | self.client = mqtt.Client( |
|
|
286 | 289 | client_id=self.clientId + self.topic + 'SCHAIN', |
|
|
287 | 290 | clean_session=True) |
|
|
288 | 291 | self.client.on_disconnect = self.on_disconnect |
|
|
289 | 292 | self.connect() |
|
|
290 | 293 | for plot in self.plottype: |
|
|
291 | 294 | setup.append({ |
|
|
292 | 295 | 'plot': plot, |
|
|
293 | 296 | 'topic': self.topic + plot, |
|
|
294 | 297 | 'title': getattr(self, plot + '_' + 'title', False), |
|
|
295 | 298 | 'xlabel': getattr(self, plot + '_' + 'xlabel', False), |
|
|
296 | 299 | 'ylabel': getattr(self, plot + '_' + 'ylabel', False), |
|
|
297 | 300 | 'xrange': getattr(self, plot + '_' + 'xrange', False), |
|
|
298 | 301 | 'yrange': getattr(self, plot + '_' + 'yrange', False), |
|
|
299 | 302 | 'zrange': getattr(self, plot + '_' + 'zrange', False), |
|
|
300 | 303 | }) |
|
|
301 | 304 | if zeromq is 1: |
|
|
302 | 305 | context = zmq.Context() |
|
|
303 | 306 | self.zmq_socket = context.socket(zmq.PUSH) |
|
|
304 | 307 | server = kwargs.get('server', 'zmq.pipe') |
|
|
305 | 308 | |
|
|
306 | 309 | if 'tcp://' in server: |
|
|
307 | 310 | address = server |
|
|
308 | 311 | else: |
|
|
309 | 312 | address = 'ipc:///tmp/%s' % server |
|
|
310 | 313 | |
|
|
311 | 314 | self.zmq_socket.connect(address) |
|
|
312 | 315 | time.sleep(1) |
|
|
313 | 316 | |
|
|
314 | 317 | |
|
|
315 | 318 | def publish_data(self): |
|
|
316 | 319 | self.dataOut.finished = False |
|
|
317 | 320 | if self.mqtt is 1: |
|
|
318 | 321 | yData = self.dataOut.heightList[:2].tolist() |
|
|
319 | 322 | if self.plottype == 'spectra': |
|
|
320 | 323 | data = getattr(self.dataOut, 'data_spc') |
|
|
321 | 324 | z = data/self.dataOut.normFactor |
|
|
322 | 325 | zdB = 10*numpy.log10(z) |
|
|
323 | 326 | xlen, ylen = zdB[0].shape |
|
|
324 | 327 | dx = int(xlen/MAXNUMX) + 1 |
|
|
325 | 328 | dy = int(ylen/MAXNUMY) + 1 |
|
|
326 | 329 | Z = [0 for i in self.dataOut.channelList] |
|
|
327 | 330 | for i in self.dataOut.channelList: |
|
|
328 | 331 | Z[i] = zdB[i][::dx, ::dy].tolist() |
|
|
329 | 332 | payload = { |
|
|
330 | 333 | 'timestamp': self.dataOut.utctime, |
|
|
331 | 334 | 'data': roundFloats(Z), |
|
|
332 | 335 | 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList], |
|
|
333 | 336 | 'interval': self.dataOut.getTimeInterval(), |
|
|
334 | 337 | 'type': self.plottype, |
|
|
335 | 338 | 'yData': yData |
|
|
336 | 339 | } |
|
|
337 | 340 | |
|
|
338 | 341 | elif self.plottype in ('rti', 'power'): |
|
|
339 | 342 | data = getattr(self.dataOut, 'data_spc') |
|
|
340 | 343 | z = data/self.dataOut.normFactor |
|
|
341 | 344 | avg = numpy.average(z, axis=1) |
|
|
342 | 345 | avgdB = 10*numpy.log10(avg) |
|
|
343 | 346 | xlen, ylen = z[0].shape |
|
|
344 | 347 | dy = numpy.floor(ylen/self.__MAXNUMY) + 1 |
|
|
345 | 348 | AVG = [0 for i in self.dataOut.channelList] |
|
|
346 | 349 | for i in self.dataOut.channelList: |
|
|
347 | 350 | AVG[i] = avgdB[i][::dy].tolist() |
|
|
348 | 351 | payload = { |
|
|
349 | 352 | 'timestamp': self.dataOut.utctime, |
|
|
350 | 353 | 'data': roundFloats(AVG), |
|
|
351 | 354 | 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList], |
|
|
352 | 355 | 'interval': self.dataOut.getTimeInterval(), |
|
|
353 | 356 | 'type': self.plottype, |
|
|
354 | 357 | 'yData': yData |
|
|
355 | 358 | } |
|
|
356 | 359 | elif self.plottype == 'noise': |
|
|
357 | 360 | noise = self.dataOut.getNoise()/self.dataOut.normFactor |
|
|
358 | 361 | noisedB = 10*numpy.log10(noise) |
|
|
359 | 362 | payload = { |
|
|
360 | 363 | 'timestamp': self.dataOut.utctime, |
|
|
361 | 364 | 'data': roundFloats(noisedB.reshape(-1, 1).tolist()), |
|
|
362 | 365 | 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList], |
|
|
363 | 366 | 'interval': self.dataOut.getTimeInterval(), |
|
|
364 | 367 | 'type': self.plottype, |
|
|
365 | 368 | 'yData': yData |
|
|
366 | 369 | } |
|
|
367 | 370 | elif self.plottype == 'snr': |
|
|
368 | 371 | data = getattr(self.dataOut, 'data_SNR') |
|
|
369 | 372 | avgdB = 10*numpy.log10(data) |
|
|
370 | 373 | |
|
|
371 | 374 | ylen = data[0].size |
|
|
372 | 375 | dy = numpy.floor(ylen/self.__MAXNUMY) + 1 |
|
|
373 | 376 | AVG = [0 for i in self.dataOut.channelList] |
|
|
374 | 377 | for i in self.dataOut.channelList: |
|
|
375 | 378 | AVG[i] = avgdB[i][::dy].tolist() |
|
|
376 | 379 | payload = { |
|
|
377 | 380 | 'timestamp': self.dataOut.utctime, |
|
|
378 | 381 | 'data': roundFloats(AVG), |
|
|
379 | 382 | 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList], |
|
|
380 | 383 | 'type': self.plottype, |
|
|
381 | 384 | 'yData': yData |
|
|
382 | 385 | } |
|
|
383 | 386 | else: |
|
|
384 | 387 | print "Tipo de grafico invalido" |
|
|
385 | 388 | payload = { |
|
|
386 | 389 | 'data': 'None', |
|
|
387 | 390 | 'timestamp': 'None', |
|
|
388 | 391 | 'type': None |
|
|
389 | 392 | } |
|
|
390 | 393 | |
|
|
391 | 394 | self.client.publish(self.topic + self.plottype, json.dumps(payload), qos=0) |
|
|
392 | 395 | |
|
|
393 | 396 | if self.zeromq is 1: |
|
|
394 | 397 | if self.verbose: |
|
|
395 | 398 | log.log( |
|
|
396 | 399 | '{} - {}'.format(self.dataOut.type, self.dataOut.datatime), |
|
|
397 | 400 | 'Sending' |
|
|
398 | 401 | ) |
|
|
399 | 402 | self.zmq_socket.send_pyobj(self.dataOut) |
|
|
400 | 403 | |
|
|
401 | 404 | def run(self, dataOut, **kwargs): |
|
|
402 | 405 | self.dataOut = dataOut |
|
|
403 | 406 | if not self.isConfig: |
|
|
404 | 407 | self.setup(**kwargs) |
|
|
405 | 408 | self.isConfig = True |
|
|
406 | 409 | |
|
|
407 | 410 | self.publish_data() |
|
|
408 | 411 | time.sleep(self.delay) |
|
|
409 | 412 | |
|
|
410 | 413 | def close(self): |
|
|
411 | 414 | if self.zeromq is 1: |
|
|
412 | 415 | self.dataOut.finished = True |
|
|
413 | 416 | self.zmq_socket.send_pyobj(self.dataOut) |
|
|
414 | 417 | time.sleep(0.1) |
|
|
415 | 418 | self.zmq_socket.close() |
|
|
416 | 419 | if self.client: |
|
|
417 | 420 | self.client.loop_stop() |
|
|
418 | 421 | self.client.disconnect() |
|
|
419 | 422 | |
|
|
420 | 423 | |
|
|
421 | 424 | class ReceiverData(ProcessingUnit): |
|
|
422 | 425 | |
|
|
423 | 426 | def __init__(self, **kwargs): |
|
|
424 | 427 | |
|
|
425 | 428 | ProcessingUnit.__init__(self, **kwargs) |
|
|
426 | 429 | |
|
|
427 | 430 | self.isConfig = False |
|
|
428 | 431 | server = kwargs.get('server', 'zmq.pipe') |
|
|
429 | 432 | if 'tcp://' in server: |
|
|
430 | 433 | address = server |
|
|
431 | 434 | else: |
|
|
432 | 435 | address = 'ipc:///tmp/%s' % server |
|
|
433 | 436 | |
|
|
434 | 437 | self.address = address |
|
|
435 | 438 | self.dataOut = JROData() |
|
|
436 | 439 | |
|
|
437 | 440 | def setup(self): |
|
|
438 | 441 | |
|
|
439 | 442 | self.context = zmq.Context() |
|
|
440 | 443 | self.receiver = self.context.socket(zmq.PULL) |
|
|
441 | 444 | self.receiver.bind(self.address) |
|
|
442 | 445 | time.sleep(0.5) |
|
|
443 | 446 | log.success('ReceiverData from {}'.format(self.address)) |
|
|
444 | 447 | |
|
|
445 | 448 | |
|
|
446 | 449 | def run(self): |
|
|
447 | 450 | |
|
|
448 | 451 | if not self.isConfig: |
|
|
449 | 452 | self.setup() |
|
|
450 | 453 | self.isConfig = True |
|
|
451 | 454 | |
|
|
452 | 455 | self.dataOut = self.receiver.recv_pyobj() |
|
|
453 | 456 | log.log('{} - {}'.format(self.dataOut.type, |
|
|
454 | 457 | self.dataOut.datatime.ctime(),), |
|
|
455 | 458 | 'Receiving') |
|
|
456 | 459 | |
|
|
457 | 460 | |
|
|
458 | 461 | class PlotterReceiver(ProcessingUnit, Process): |
|
|
459 | 462 | |
|
|
460 | 463 | throttle_value = 5 |
|
|
461 | 464 | |
|
|
462 | 465 | def __init__(self, **kwargs): |
|
|
463 | 466 | |
|
|
464 | 467 | ProcessingUnit.__init__(self, **kwargs) |
|
|
465 | 468 | Process.__init__(self) |
|
|
466 | 469 | self.mp = False |
|
|
467 | 470 | self.isConfig = False |
|
|
468 | 471 | self.isWebConfig = False |
|
|
469 | 472 | self.connections = 0 |
|
|
470 | 473 | server = kwargs.get('server', 'zmq.pipe') |
|
|
471 | 474 | plot_server = kwargs.get('plot_server', 'zmq.web') |
|
|
472 | 475 | if 'tcp://' in server: |
|
|
473 | 476 | address = server |
|
|
474 | 477 | else: |
|
|
475 | 478 | address = 'ipc:///tmp/%s' % server |
|
|
476 | 479 | |
|
|
477 | 480 | if 'tcp://' in plot_server: |
|
|
478 | 481 | plot_address = plot_server |
|
|
479 | 482 | else: |
|
|
480 | 483 | plot_address = 'ipc:///tmp/%s' % plot_server |
|
|
481 | 484 | |
|
|
482 | 485 | self.address = address |
|
|
483 | 486 | self.plot_address = plot_address |
|
|
484 | 487 | self.plottypes = [s.strip() for s in kwargs.get('plottypes', 'rti').split(',')] |
|
|
485 | 488 | self.realtime = kwargs.get('realtime', False) |
|
|
486 | 489 | self.throttle_value = kwargs.get('throttle', 5) |
|
|
487 | 490 | self.sendData = self.initThrottle(self.throttle_value) |
|
|
488 | 491 | self.dates = [] |
|
|
489 | 492 | self.setup() |
|
|
490 | 493 | |
|
|
491 | 494 | def setup(self): |
|
|
492 | 495 | |
|
|
493 | 496 | self.data = Data(self.plottypes, self.throttle_value) |
|
|
494 | 497 | self.isConfig = True |
|
|
495 | 498 | |
|
|
496 | 499 | def event_monitor(self, monitor): |
|
|
497 | 500 | |
|
|
498 | 501 | events = {} |
|
|
499 | 502 | |
|
|
500 | 503 | for name in dir(zmq): |
|
|
501 | 504 | if name.startswith('EVENT_'): |
|
|
502 | 505 | value = getattr(zmq, name) |
|
|
503 | 506 | events[value] = name |
|
|
504 | 507 | |
|
|
505 | 508 | while monitor.poll(): |
|
|
506 | 509 | evt = recv_monitor_message(monitor) |
|
|
507 | 510 | if evt['event'] == 32: |
|
|
508 | 511 | self.connections += 1 |
|
|
509 | 512 | if evt['event'] == 512: |
|
|
510 | 513 | pass |
|
|
511 | 514 | |
|
|
512 | 515 | evt.update({'description': events[evt['event']]}) |
|
|
513 | 516 | |
|
|
514 | 517 | if evt['event'] == zmq.EVENT_MONITOR_STOPPED: |
|
|
515 | 518 | break |
|
|
516 | 519 | monitor.close() |
|
|
517 | 520 | print('event monitor thread done!') |
|
|
518 | 521 | |
|
|
519 | 522 | def initThrottle(self, throttle_value): |
|
|
520 | 523 | |
|
|
521 | 524 | @throttle(seconds=throttle_value) |
|
|
522 | 525 | def sendDataThrottled(fn_sender, data): |
|
|
523 | 526 | fn_sender(data) |
|
|
524 | 527 | |
|
|
525 | 528 | return sendDataThrottled |
|
|
526 | 529 | |
|
|
527 | 530 | def send(self, data): |
|
|
528 | 531 | log.success('Sending {}'.format(data), self.name) |
|
|
529 | 532 | self.sender.send_pyobj(data) |
|
|
530 | 533 | |
|
|
531 | 534 | def run(self): |
|
|
532 | 535 | |
|
|
533 | 536 | log.success( |
|
|
534 | 537 | 'Starting from {}'.format(self.address), |
|
|
535 | 538 | self.name |
|
|
536 | 539 | ) |
|
|
537 | 540 | |
|
|
538 | 541 | self.context = zmq.Context() |
|
|
539 | 542 | self.receiver = self.context.socket(zmq.PULL) |
|
|
540 | 543 | self.receiver.bind(self.address) |
|
|
541 | 544 | monitor = self.receiver.get_monitor_socket() |
|
|
542 | 545 | self.sender = self.context.socket(zmq.PUB) |
|
|
543 | 546 | if self.realtime: |
|
|
544 | 547 | self.sender_web = self.context.socket(zmq.PUB) |
|
|
545 | 548 | self.sender_web.connect(self.plot_address) |
|
|
546 | 549 | time.sleep(1) |
|
|
547 | 550 | |
|
|
548 | 551 | if 'server' in self.kwargs: |
|
|
549 | 552 | self.sender.bind("ipc:///tmp/{}.plots".format(self.kwargs['server'])) |
|
|
550 | 553 | else: |
|
|
551 | 554 | self.sender.bind("ipc:///tmp/zmq.plots") |
|
|
552 | 555 | |
|
|
553 | 556 | time.sleep(2) |
|
|
554 | 557 | |
|
|
555 | 558 | t = Thread(target=self.event_monitor, args=(monitor,)) |
|
|
556 | 559 | t.start() |
|
|
557 | 560 | |
|
|
558 | 561 | while True: |
|
|
559 | 562 | dataOut = self.receiver.recv_pyobj() |
|
|
560 | 563 | dt = datetime.datetime.fromtimestamp(dataOut.utctime).date() |
|
|
561 | 564 | sended = False |
|
|
562 | 565 | if dt not in self.dates: |
|
|
563 | 566 | if self.data: |
|
|
564 | 567 | self.data.ended = True |
|
|
565 | 568 | self.send(self.data) |
|
|
566 | 569 | sended = True |
|
|
567 | 570 | self.data.setup() |
|
|
568 | 571 | self.dates.append(dt) |
|
|
569 | 572 | |
|
|
570 | 573 | self.data.update(dataOut) |
|
|
571 | 574 | |
|
|
572 | 575 | if dataOut.finished is True: |
|
|
573 | 576 | self.connections -= 1 |
|
|
574 | 577 | if self.connections == 0 and dt in self.dates: |
|
|
575 | 578 | self.data.ended = True |
|
|
576 | 579 | self.send(self.data) |
|
|
577 | 580 | self.data.setup() |
|
|
578 | 581 | else: |
|
|
579 | 582 | if self.realtime: |
|
|
580 | 583 | self.send(self.data) |
|
|
581 | 584 | # self.sender_web.send_string(self.data.jsonify()) |
|
|
582 | 585 | else: |
|
|
583 | 586 | if not sended: |
|
|
584 | 587 | self.sendData(self.send, self.data) |
|
|
585 | 588 | |
|
|
586 | 589 | return |
|
|
587 | 590 | |
|
|
588 | 591 | def sendToWeb(self): |
|
|
589 | 592 | |
|
|
590 | 593 | if not self.isWebConfig: |
|
|
591 | 594 | context = zmq.Context() |
|
|
592 | 595 | sender_web_config = context.socket(zmq.PUB) |
|
|
593 | 596 | if 'tcp://' in self.plot_address: |
|
|
594 | 597 | dum, address, port = self.plot_address.split(':') |
|
|
595 | 598 | conf_address = '{}:{}:{}'.format(dum, address, int(port)+1) |
|
|
596 | 599 | else: |
|
|
597 | 600 | conf_address = self.plot_address + '.config' |
|
|
598 | 601 | sender_web_config.bind(conf_address) |
|
|
599 | 602 | time.sleep(1) |
|
|
600 | 603 | for kwargs in self.operationKwargs.values(): |
|
|
601 | 604 | if 'plot' in kwargs: |
|
|
602 | 605 | log.success('[Sending] Config data to web for {}'.format(kwargs['code'].upper())) |
|
|
603 | 606 | sender_web_config.send_string(json.dumps(kwargs)) |
|
|
604 | 607 | self.isWebConfig = True No newline at end of file |
| @@ -1,34 +0,0 | |||
|
|
1 | from schainpy.controller import Project | |
|
|
2 | ||
|
|
3 | desc = "A schain project" | |
|
|
4 | ||
|
|
5 | controller = Project() | |
|
|
6 | controller.setup(id='191', name="project", description=desc) | |
|
|
7 | ||
|
|
8 | readUnitConf = controller.addReadUnit(datatype='VoltageReader', | |
|
|
9 | path="/home/nanosat/schain/schainpy", | |
|
|
10 | startDate="1970/01/01", | |
|
|
11 | endDate="2017/12/31", | |
|
|
12 | startTime="00:00:00", | |
|
|
13 | endTime="23:59:59", | |
|
|
14 | online=0, | |
|
|
15 | verbose=1, | |
|
|
16 | walk=1, | |
|
|
17 | ) | |
|
|
18 | ||
|
|
19 | procUnitConf1 = controller.addProcUnit(datatype='VoltageProc', inputId=readUnitConf.getId()) | |
|
|
20 | ||
|
|
21 | opObj11 = procUnitConf1.addOperation(name='ProfileSelector', optype='other') | |
|
|
22 | opObj11.addParameter(name='profileRangeList', value='120,183', format='intlist') | |
|
|
23 | ||
|
|
24 | opObj11 = procUnitConf1.addOperation(name='RTIPlot', optype='other') | |
|
|
25 | opObj11.addParameter(name='wintitle', value='Jicamarca Radio Observatory', format='str') | |
|
|
26 | opObj11.addParameter(name='showprofile', value='0', format='int') | |
|
|
27 | opObj11.addParameter(name='xmin', value='0', format='int') | |
|
|
28 | opObj11.addParameter(name='xmax', value='24', format='int') | |
|
|
29 | opObj11.addParameter(name='figpath', value="/home/nanosat/schain/schainpy/figs", format='str') | |
|
|
30 | opObj11.addParameter(name='wr_period', value='5', format='int') | |
|
|
31 | opObj11.addParameter(name='exp_code', value='22', format='int') | |
|
|
32 | ||
|
|
33 | ||
|
|
34 | controller.start() | |
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