| This diff has been collapsed as it changes many lines, (525 lines changed) Show them Hide them | |||
| @@ -23,6 +23,7 class SpectraPlot(Plot): | |||
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23 | 23 | buffering = False |
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24 | 24 | |
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25 | 25 | def setup(self): |
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26 | ||
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26 | 27 | self.nplots = len(self.data.channels) |
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27 | 28 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
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28 | 29 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
| @@ -32,7 +33,7 class SpectraPlot(Plot): | |||
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32 | 33 | self.width = 4 * self.ncols |
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33 | 34 | else: |
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34 | 35 | self.width = 3.5 * self.ncols |
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35 |
self.plots_adjust.update({'wspace': 0. |
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36 | self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18}) | |
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36 | 37 | self.ylabel = 'Range [km]' |
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37 | 38 | |
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38 | 39 | def update(self, dataOut): |
| @@ -44,18 +45,16 class SpectraPlot(Plot): | |||
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44 | 45 | data['rti'] = dataOut.getPower() |
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45 | 46 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
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46 | 47 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
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47 | ||
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48 | ||
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48 | 49 | if self.CODE == 'spc_moments': |
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49 | 50 | data['moments'] = dataOut.moments |
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50 | # data['spc'] = 10*numpy.log10(dataOut.data_pre[0]/dataOut.normFactor) | |
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51 | 51 | if self.CODE == 'gaussian_fit': |
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52 | # data['moments'] = dataOut.moments | |
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53 | 52 | data['gaussfit'] = dataOut.DGauFitParams |
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54 | # data['spc'] = 10*numpy.log10(dataOut.data_pre[0]/dataOut.normFactor) | |
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55 | 53 | |
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56 |
return data, meta |
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57 | ||
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54 | return data, meta | |
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55 | ||
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58 | 56 | def plot(self): |
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57 | ||
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59 | 58 | if self.xaxis == "frequency": |
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60 | 59 | x = self.data.xrange[0] |
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61 | 60 | self.xlabel = "Frequency (kHz)" |
| @@ -80,10 +79,10 class SpectraPlot(Plot): | |||
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80 | 79 | |
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81 | 80 | for n, ax in enumerate(self.axes): |
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82 | 81 | noise = data['noise'][n] |
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82 | ||
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83 | 83 | if self.CODE == 'spc_moments': |
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84 | 84 | mean = data['moments'][n, 1] |
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85 |
if self.CODE == 'gaussian_fit': |
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86 | # mean = data['moments'][n, 1] | |
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85 | if self.CODE == 'gaussian_fit': | |
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87 | 86 | gau0 = data['gaussfit'][n][2,:,0] |
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88 | 87 | gau1 = data['gaussfit'][n][2,:,1] |
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89 | 88 | if ax.firsttime: |
| @@ -105,7 +104,6 class SpectraPlot(Plot): | |||
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105 | 104 | if self.CODE == 'spc_moments': |
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106 | 105 | ax.plt_mean = ax.plot(mean, y, color='k', lw=1)[0] |
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107 | 106 | if self.CODE == 'gaussian_fit': |
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108 | # ax.plt_mean = ax.plot(mean, y, color='k', lw=1)[0] | |
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109 | 107 | ax.plt_gau0 = ax.plot(gau0, y, color='r', lw=1)[0] |
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110 | 108 | ax.plt_gau1 = ax.plot(gau1, y, color='y', lw=1)[0] |
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111 | 109 | else: |
| @@ -116,11 +114,115 class SpectraPlot(Plot): | |||
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116 | 114 | if self.CODE == 'spc_moments': |
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117 | 115 | ax.plt_mean.set_data(mean, y) |
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118 | 116 | if self.CODE == 'gaussian_fit': |
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119 | # ax.plt_mean.set_data(mean, y) | |
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120 | 117 | ax.plt_gau0.set_data(gau0, y) |
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121 | 118 | ax.plt_gau1.set_data(gau1, y) |
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122 | 119 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
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123 | 120 | |
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121 | class SpectraObliquePlot(Plot): | |
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122 | ''' | |
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123 | Plot for Spectra data | |
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124 | ''' | |
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125 | ||
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126 | CODE = 'spc_oblique' | |
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127 | colormap = 'jet' | |
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128 | plot_type = 'pcolor' | |
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129 | ||
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130 | def setup(self): | |
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131 | self.xaxis = "oblique" | |
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132 | self.nplots = len(self.data.channels) | |
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133 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) | |
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134 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) | |
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135 | self.height = 2.6 * self.nrows | |
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136 | self.cb_label = 'dB' | |
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137 | if self.showprofile: | |
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138 | self.width = 4 * self.ncols | |
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139 | else: | |
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140 | self.width = 3.5 * self.ncols | |
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141 | self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18}) | |
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142 | self.ylabel = 'Range [km]' | |
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143 | ||
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144 | def update(self, dataOut): | |
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145 | ||
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146 | data = {} | |
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147 | meta = {} | |
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148 | ||
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149 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) | |
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150 | data['spc'] = spc | |
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151 | data['rti'] = dataOut.getPower() | |
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152 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) | |
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153 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) | |
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154 | ||
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155 | data['shift1'] = dataOut.Dop_EEJ_T1[0] | |
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156 | data['shift2'] = dataOut.Dop_EEJ_T2[0] | |
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157 | data['max_val_2'] = dataOut.Oblique_params[0,-1,:] | |
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158 | data['shift1_error'] = dataOut.Err_Dop_EEJ_T1[0] | |
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159 | data['shift2_error'] = dataOut.Err_Dop_EEJ_T2[0] | |
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160 | ||
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161 | return data, meta | |
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162 | ||
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163 | def plot(self): | |
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164 | ||
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165 | if self.xaxis == "frequency": | |
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166 | x = self.data.xrange[0] | |
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167 | self.xlabel = "Frequency (kHz)" | |
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168 | elif self.xaxis == "time": | |
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169 | x = self.data.xrange[1] | |
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170 | self.xlabel = "Time (ms)" | |
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171 | else: | |
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172 | x = self.data.xrange[2] | |
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173 | self.xlabel = "Velocity (m/s)" | |
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174 | ||
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175 | self.titles = [] | |
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176 | ||
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177 | y = self.data.yrange | |
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178 | self.y = y | |
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179 | ||
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180 | data = self.data[-1] | |
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181 | z = data['spc'] | |
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182 | ||
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183 | for n, ax in enumerate(self.axes): | |
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184 | noise = self.data['noise'][n][-1] | |
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185 | shift1 = data['shift1'] | |
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186 | shift2 = data['shift2'] | |
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187 | max_val_2 = data['max_val_2'] | |
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188 | err1 = data['shift1_error'] | |
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189 | err2 = data['shift2_error'] | |
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190 | if ax.firsttime: | |
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191 | ||
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192 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | |
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193 | self.xmin = self.xmin if self.xmin else -self.xmax | |
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194 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) | |
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195 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) | |
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196 | ax.plt = ax.pcolormesh(x, y, z[n].T, | |
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197 | vmin=self.zmin, | |
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198 | vmax=self.zmax, | |
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199 | cmap=plt.get_cmap(self.colormap) | |
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200 | ) | |
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201 | ||
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202 | if self.showprofile: | |
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203 | ax.plt_profile = self.pf_axes[n].plot( | |
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204 | self.data['rti'][n][-1], y)[0] | |
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205 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, | |
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206 | color="k", linestyle="dashed", lw=1)[0] | |
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207 | ||
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208 | self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=2.2, marker='o', linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) | |
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209 | self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) | |
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210 | self.ploterr3 = ax.errorbar(max_val_2, y, xerr=0, fmt='g^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) | |
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211 | ||
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212 | else: | |
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213 | self.ploterr1.remove() | |
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214 | self.ploterr2.remove() | |
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215 | self.ploterr3.remove() | |
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216 | ax.plt.set_array(z[n].T.ravel()) | |
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217 | if self.showprofile: | |
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218 | ax.plt_profile.set_data(self.data['rti'][n][-1], y) | |
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219 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) | |
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220 | self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=2.2, marker='o', linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) | |
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221 | self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) | |
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222 | self.ploterr3 = ax.errorbar(max_val_2, y, xerr=0, fmt='g^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) | |
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223 | ||
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224 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) | |
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225 | ||
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124 | 226 | |
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125 | 227 | class CrossSpectraPlot(Plot): |
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126 | 228 | |
| @@ -138,7 +240,7 class CrossSpectraPlot(Plot): | |||
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138 | 240 | self.nplots = len(self.data.pairs) * 2 |
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139 | 241 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
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140 | 242 | self.width = 3.1 * self.ncols |
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141 |
self.height = |
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243 | self.height = 5 * self.nrows | |
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142 | 244 | self.ylabel = 'Range [km]' |
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143 | 245 | self.showprofile = False |
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144 | 246 | self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) |
| @@ -164,8 +266,8 class CrossSpectraPlot(Plot): | |||
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164 | 266 | |
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165 | 267 | data['cspc'] = numpy.array(tmp) |
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166 | 268 | |
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167 |
return data, meta |
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168 | ||
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269 | return data, meta | |
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270 | ||
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169 | 271 | def plot(self): |
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170 | 272 | |
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171 | 273 | if self.xaxis == "frequency": |
| @@ -177,7 +279,7 class CrossSpectraPlot(Plot): | |||
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177 | 279 | else: |
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178 | 280 | x = self.data.xrange[2] |
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179 | 281 | self.xlabel = "Velocity (m/s)" |
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180 | ||
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282 | ||
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181 | 283 | self.titles = [] |
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182 | 284 | |
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183 | 285 | y = self.data.yrange |
| @@ -201,19 +303,291 class CrossSpectraPlot(Plot): | |||
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201 | 303 | ax.plt.set_array(coh.T.ravel()) |
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202 | 304 | self.titles.append( |
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203 | 305 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
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204 | ||
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306 | ||
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205 | 307 | ax = self.axes[2 * n + 1] |
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206 | 308 | if ax.firsttime: |
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207 | 309 | ax.plt = ax.pcolormesh(x, y, phase.T, |
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208 | 310 | vmin=-180, |
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209 | 311 | vmax=180, |
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210 |
cmap=plt.get_cmap(self.colormap_phase) |
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312 | cmap=plt.get_cmap(self.colormap_phase) | |
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211 | 313 | ) |
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212 | 314 | else: |
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213 | 315 | ax.plt.set_array(phase.T.ravel()) |
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214 | 316 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
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215 | 317 | |
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216 | 318 | |
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319 | class CrossSpectra4Plot(Plot): | |
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320 | ||
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321 | CODE = 'cspc' | |
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322 | colormap = 'jet' | |
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323 | plot_type = 'pcolor' | |
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324 | zmin_coh = None | |
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325 | zmax_coh = None | |
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326 | zmin_phase = None | |
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327 | zmax_phase = None | |
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328 | ||
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329 | def setup(self): | |
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330 | ||
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331 | self.ncols = 4 | |
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332 | self.nrows = len(self.data.pairs) | |
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333 | self.nplots = self.nrows * 4 | |
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334 | self.width = 3.1 * self.ncols | |
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335 | self.height = 5 * self.nrows | |
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336 | self.ylabel = 'Range [km]' | |
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337 | self.showprofile = False | |
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338 | self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) | |
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339 | ||
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340 | def plot(self): | |
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341 | ||
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342 | if self.xaxis == "frequency": | |
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343 | x = self.data.xrange[0] | |
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344 | self.xlabel = "Frequency (kHz)" | |
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345 | elif self.xaxis == "time": | |
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346 | x = self.data.xrange[1] | |
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347 | self.xlabel = "Time (ms)" | |
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348 | else: | |
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349 | x = self.data.xrange[2] | |
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350 | self.xlabel = "Velocity (m/s)" | |
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351 | ||
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352 | self.titles = [] | |
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353 | ||
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354 | ||
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355 | y = self.data.heights | |
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356 | self.y = y | |
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357 | nspc = self.data['spc'] | |
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358 | spc = self.data['cspc'][0] | |
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359 | cspc = self.data['cspc'][1] | |
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360 | ||
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361 | for n in range(self.nrows): | |
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362 | noise = self.data['noise'][:,-1] | |
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363 | pair = self.data.pairs[n] | |
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364 | ||
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365 | ax = self.axes[4 * n] | |
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366 | if ax.firsttime: | |
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367 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | |
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368 | self.xmin = self.xmin if self.xmin else -self.xmax | |
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369 | self.zmin = self.zmin if self.zmin else numpy.nanmin(nspc) | |
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370 | self.zmax = self.zmax if self.zmax else numpy.nanmax(nspc) | |
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371 | ax.plt = ax.pcolormesh(x , y , nspc[pair[0]].T, | |
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372 | vmin=self.zmin, | |
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373 | vmax=self.zmax, | |
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374 | cmap=plt.get_cmap(self.colormap) | |
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375 | ) | |
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376 | else: | |
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377 | ||
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378 | ax.plt.set_array(nspc[pair[0]].T.ravel()) | |
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379 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[0], noise[pair[0]])) | |
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380 | ||
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381 | ax = self.axes[4 * n + 1] | |
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382 | ||
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383 | if ax.firsttime: | |
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384 | ax.plt = ax.pcolormesh(x , y, numpy.flip(nspc[pair[1]],axis=0).T, | |
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385 | vmin=self.zmin, | |
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386 | vmax=self.zmax, | |
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387 | cmap=plt.get_cmap(self.colormap) | |
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388 | ) | |
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389 | else: | |
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390 | ||
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391 | ax.plt.set_array(numpy.flip(nspc[pair[1]],axis=0).T.ravel()) | |
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392 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[1], noise[pair[1]])) | |
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393 | ||
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394 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) | |
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395 | coh = numpy.abs(out) | |
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396 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi | |
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397 | ||
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398 | ax = self.axes[4 * n + 2] | |
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399 | if ax.firsttime: | |
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400 | ax.plt = ax.pcolormesh(x, y, numpy.flip(coh,axis=0).T, | |
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401 | vmin=0, | |
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402 | vmax=1, | |
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403 | cmap=plt.get_cmap(self.colormap_coh) | |
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404 | ) | |
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405 | else: | |
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406 | ax.plt.set_array(numpy.flip(coh,axis=0).T.ravel()) | |
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407 | self.titles.append( | |
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408 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) | |
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409 | ||
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410 | ax = self.axes[4 * n + 3] | |
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411 | if ax.firsttime: | |
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412 | ax.plt = ax.pcolormesh(x, y, numpy.flip(phase,axis=0).T, | |
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413 | vmin=-180, | |
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414 | vmax=180, | |
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415 | cmap=plt.get_cmap(self.colormap_phase) | |
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416 | ) | |
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417 | else: | |
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418 | ax.plt.set_array(numpy.flip(phase,axis=0).T.ravel()) | |
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419 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) | |
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420 | ||
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421 | ||
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422 | class CrossSpectra2Plot(Plot): | |
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423 | ||
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424 | CODE = 'cspc' | |
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425 | colormap = 'jet' | |
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426 | plot_type = 'pcolor' | |
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427 | zmin_coh = None | |
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428 | zmax_coh = None | |
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429 | zmin_phase = None | |
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430 | zmax_phase = None | |
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431 | ||
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432 | def setup(self): | |
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433 | ||
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434 | self.ncols = 1 | |
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435 | self.nrows = len(self.data.pairs) | |
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436 | self.nplots = self.nrows * 1 | |
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437 | self.width = 3.1 * self.ncols | |
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438 | self.height = 5 * self.nrows | |
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439 | self.ylabel = 'Range [km]' | |
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440 | self.showprofile = False | |
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441 | self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) | |
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442 | ||
|
|
443 | def plot(self): | |
|
|
444 | ||
|
|
445 | if self.xaxis == "frequency": | |
|
|
446 | x = self.data.xrange[0] | |
|
|
447 | self.xlabel = "Frequency (kHz)" | |
|
|
448 | elif self.xaxis == "time": | |
|
|
449 | x = self.data.xrange[1] | |
|
|
450 | self.xlabel = "Time (ms)" | |
|
|
451 | else: | |
|
|
452 | x = self.data.xrange[2] | |
|
|
453 | self.xlabel = "Velocity (m/s)" | |
|
|
454 | ||
|
|
455 | self.titles = [] | |
|
|
456 | ||
|
|
457 | ||
|
|
458 | y = self.data.heights | |
|
|
459 | self.y = y | |
|
|
460 | cspc = self.data['cspc'][1] | |
|
|
461 | ||
|
|
462 | for n in range(self.nrows): | |
|
|
463 | noise = self.data['noise'][:,-1] | |
|
|
464 | pair = self.data.pairs[n] | |
|
|
465 | out = cspc[n] | |
|
|
466 | cross = numpy.abs(out) | |
|
|
467 | z = cross/self.data.nFactor | |
|
|
468 | cross = 10*numpy.log10(z) | |
|
|
469 | ||
|
|
470 | ax = self.axes[1 * n] | |
|
|
471 | if ax.firsttime: | |
|
|
472 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | |
|
|
473 | self.xmin = self.xmin if self.xmin else -self.xmax | |
|
|
474 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | |
|
|
475 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | |
|
|
476 | ax.plt = ax.pcolormesh(x, y, cross.T, | |
|
|
477 | vmin=self.zmin, | |
|
|
478 | vmax=self.zmax, | |
|
|
479 | cmap=plt.get_cmap(self.colormap) | |
|
|
480 | ) | |
|
|
481 | else: | |
|
|
482 | ax.plt.set_array(cross.T.ravel()) | |
|
|
483 | self.titles.append( | |
|
|
484 | 'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1])) | |
|
|
485 | ||
|
|
486 | ||
|
|
487 | class CrossSpectra3Plot(Plot): | |
|
|
488 | ||
|
|
489 | CODE = 'cspc' | |
|
|
490 | colormap = 'jet' | |
|
|
491 | plot_type = 'pcolor' | |
|
|
492 | zmin_coh = None | |
|
|
493 | zmax_coh = None | |
|
|
494 | zmin_phase = None | |
|
|
495 | zmax_phase = None | |
|
|
496 | ||
|
|
497 | def setup(self): | |
|
|
498 | ||
|
|
499 | self.ncols = 3 | |
|
|
500 | self.nrows = len(self.data.pairs) | |
|
|
501 | self.nplots = self.nrows * 3 | |
|
|
502 | self.width = 3.1 * self.ncols | |
|
|
503 | self.height = 5 * self.nrows | |
|
|
504 | self.ylabel = 'Range [km]' | |
|
|
505 | self.showprofile = False | |
|
|
506 | self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) | |
|
|
507 | ||
|
|
508 | def plot(self): | |
|
|
509 | ||
|
|
510 | if self.xaxis == "frequency": | |
|
|
511 | x = self.data.xrange[0] | |
|
|
512 | self.xlabel = "Frequency (kHz)" | |
|
|
513 | elif self.xaxis == "time": | |
|
|
514 | x = self.data.xrange[1] | |
|
|
515 | self.xlabel = "Time (ms)" | |
|
|
516 | else: | |
|
|
517 | x = self.data.xrange[2] | |
|
|
518 | self.xlabel = "Velocity (m/s)" | |
|
|
519 | ||
|
|
520 | self.titles = [] | |
|
|
521 | ||
|
|
522 | ||
|
|
523 | y = self.data.heights | |
|
|
524 | self.y = y | |
|
|
525 | ||
|
|
526 | cspc = self.data['cspc'][1] | |
|
|
527 | ||
|
|
528 | for n in range(self.nrows): | |
|
|
529 | noise = self.data['noise'][:,-1] | |
|
|
530 | pair = self.data.pairs[n] | |
|
|
531 | out = cspc[n] | |
|
|
532 | ||
|
|
533 | cross = numpy.abs(out) | |
|
|
534 | z = cross/self.data.nFactor | |
|
|
535 | cross = 10*numpy.log10(z) | |
|
|
536 | ||
|
|
537 | out_r= out.real/self.data.nFactor | |
|
|
538 | ||
|
|
539 | out_i= out.imag/self.data.nFactor | |
|
|
540 | ||
|
|
541 | ax = self.axes[3 * n] | |
|
|
542 | if ax.firsttime: | |
|
|
543 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | |
|
|
544 | self.xmin = self.xmin if self.xmin else -self.xmax | |
|
|
545 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | |
|
|
546 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | |
|
|
547 | ax.plt = ax.pcolormesh(x, y, cross.T, | |
|
|
548 | vmin=self.zmin, | |
|
|
549 | vmax=self.zmax, | |
|
|
550 | cmap=plt.get_cmap(self.colormap) | |
|
|
551 | ) | |
|
|
552 | else: | |
|
|
553 | ax.plt.set_array(cross.T.ravel()) | |
|
|
554 | self.titles.append( | |
|
|
555 | 'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1])) | |
|
|
556 | ||
|
|
557 | ax = self.axes[3 * n + 1] | |
|
|
558 | if ax.firsttime: | |
|
|
559 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | |
|
|
560 | self.xmin = self.xmin if self.xmin else -self.xmax | |
|
|
561 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | |
|
|
562 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | |
|
|
563 | ax.plt = ax.pcolormesh(x, y, out_r.T, | |
|
|
564 | vmin=-1.e6, | |
|
|
565 | vmax=0, | |
|
|
566 | cmap=plt.get_cmap(self.colormap) | |
|
|
567 | ) | |
|
|
568 | else: | |
|
|
569 | ax.plt.set_array(out_r.T.ravel()) | |
|
|
570 | self.titles.append( | |
|
|
571 | 'Cross Spectra Real Ch{} * Ch{}'.format(pair[0], pair[1])) | |
|
|
572 | ||
|
|
573 | ax = self.axes[3 * n + 2] | |
|
|
574 | ||
|
|
575 | ||
|
|
576 | if ax.firsttime: | |
|
|
577 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | |
|
|
578 | self.xmin = self.xmin if self.xmin else -self.xmax | |
|
|
579 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | |
|
|
580 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | |
|
|
581 | ax.plt = ax.pcolormesh(x, y, out_i.T, | |
|
|
582 | vmin=-1.e6, | |
|
|
583 | vmax=1.e6, | |
|
|
584 | cmap=plt.get_cmap(self.colormap) | |
|
|
585 | ) | |
|
|
586 | else: | |
|
|
587 | ax.plt.set_array(out_i.T.ravel()) | |
|
|
588 | self.titles.append( | |
|
|
589 | 'Cross Spectra Imag Ch{} * Ch{}'.format(pair[0], pair[1])) | |
|
|
590 | ||
|
|
217 | 591 | class RTIPlot(Plot): |
|
|
218 | 592 | ''' |
|
|
219 | 593 | Plot for RTI data |
| @@ -231,7 +605,7 class RTIPlot(Plot): | |||
|
|
231 | 605 | self.ylabel = 'Range [km]' |
|
|
232 | 606 | self.xlabel = 'Time' |
|
|
233 | 607 | self.cb_label = 'dB' |
|
|
234 |
self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0. |
|
|
|
608 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95}) | |
|
|
235 | 609 | self.titles = ['{} Channel {}'.format( |
|
|
236 | 610 | self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
237 | 611 | |
| @@ -245,6 +619,7 class RTIPlot(Plot): | |||
|
|
245 | 619 | return data, meta |
|
|
246 | 620 | |
|
|
247 | 621 | def plot(self): |
|
|
622 | ||
|
|
248 | 623 | self.x = self.data.times |
|
|
249 | 624 | self.y = self.data.yrange |
|
|
250 | 625 | self.z = self.data[self.CODE] |
| @@ -256,6 +631,7 class RTIPlot(Plot): | |||
|
|
256 | 631 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
257 | 632 | |
|
|
258 | 633 | for n, ax in enumerate(self.axes): |
|
|
634 | ||
|
|
259 | 635 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
260 | 636 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
261 | 637 | data = self.data[-1] |
| @@ -282,6 +658,87 class RTIPlot(Plot): | |||
|
|
282 | 658 | ax.plot_noise.set_data(numpy.repeat( |
|
|
283 | 659 | data['noise'][n], len(self.y)), self.y) |
|
|
284 | 660 | |
|
|
661 | class SpectrogramPlot(Plot): | |
|
|
662 | ''' | |
|
|
663 | Plot for Spectrogram data | |
|
|
664 | ''' | |
|
|
665 | ||
|
|
666 | CODE = 'Spectrogram_Profile' | |
|
|
667 | colormap = 'binary' | |
|
|
668 | plot_type = 'pcolorbuffer' | |
|
|
669 | ||
|
|
670 | def setup(self): | |
|
|
671 | self.xaxis = 'time' | |
|
|
672 | self.ncols = 1 | |
|
|
673 | self.nrows = len(self.data.channels) | |
|
|
674 | self.nplots = len(self.data.channels) | |
|
|
675 | self.xlabel = 'Time' | |
|
|
676 | self.plots_adjust.update({'hspace':1.2, 'left': 0.1, 'bottom': 0.12, 'right':0.95}) | |
|
|
677 | self.titles = [] | |
|
|
678 | ||
|
|
679 | self.titles = ['{} Channel {}'.format( | |
|
|
680 | self.CODE.upper(), x) for x in range(self.nrows)] | |
|
|
681 | ||
|
|
682 | ||
|
|
683 | def update(self, dataOut): | |
|
|
684 | data = {} | |
|
|
685 | meta = {} | |
|
|
686 | ||
|
|
687 | maxHei = 1620#+12000 | |
|
|
688 | indb = numpy.where(dataOut.heightList <= maxHei) | |
|
|
689 | hei = indb[0][-1] | |
|
|
690 | ||
|
|
691 | factor = dataOut.nIncohInt | |
|
|
692 | z = dataOut.data_spc[:,:,hei] / factor | |
|
|
693 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) | |
|
|
694 | ||
|
|
695 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) | |
|
|
696 | data['Spectrogram_Profile'] = 10 * numpy.log10(z) | |
|
|
697 | ||
|
|
698 | data['hei'] = hei | |
|
|
699 | data['DH'] = (dataOut.heightList[1] - dataOut.heightList[0])/dataOut.step | |
|
|
700 | data['nProfiles'] = dataOut.nProfiles | |
|
|
701 | ||
|
|
702 | return data, meta | |
|
|
703 | ||
|
|
704 | def plot(self): | |
|
|
705 | ||
|
|
706 | self.x = self.data.times | |
|
|
707 | self.z = self.data[self.CODE] | |
|
|
708 | self.y = self.data.xrange[0] | |
|
|
709 | ||
|
|
710 | hei = self.data['hei'][-1] | |
|
|
711 | DH = self.data['DH'][-1] | |
|
|
712 | nProfiles = self.data['nProfiles'][-1] | |
|
|
713 | ||
|
|
714 | self.ylabel = "Frequency (kHz)" | |
|
|
715 | ||
|
|
716 | self.z = numpy.ma.masked_invalid(self.z) | |
|
|
717 | ||
|
|
718 | if self.decimation is None: | |
|
|
719 | x, y, z = self.fill_gaps(self.x, self.y, self.z) | |
|
|
720 | else: | |
|
|
721 | x, y, z = self.fill_gaps(*self.decimate()) | |
|
|
722 | ||
|
|
723 | for n, ax in enumerate(self.axes): | |
|
|
724 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) | |
|
|
725 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) | |
|
|
726 | data = self.data[-1] | |
|
|
727 | if ax.firsttime: | |
|
|
728 | ax.plt = ax.pcolormesh(x, y, z[n].T, | |
|
|
729 | vmin=self.zmin, | |
|
|
730 | vmax=self.zmax, | |
|
|
731 | cmap=plt.get_cmap(self.colormap) | |
|
|
732 | ) | |
|
|
733 | else: | |
|
|
734 | ax.collections.remove(ax.collections[0]) | |
|
|
735 | ax.plt = ax.pcolormesh(x, y, z[n].T, | |
|
|
736 | vmin=self.zmin, | |
|
|
737 | vmax=self.zmax, | |
|
|
738 | cmap=plt.get_cmap(self.colormap) | |
|
|
739 | ) | |
|
|
740 | ||
|
|
741 | ||
|
|
285 | 742 | |
|
|
286 | 743 | class CoherencePlot(RTIPlot): |
|
|
287 | 744 | ''' |
| @@ -335,7 +792,7 class PhasePlot(CoherencePlot): | |||
|
|
335 | 792 | |
|
|
336 | 793 | class NoisePlot(Plot): |
|
|
337 | 794 | ''' |
|
|
338 |
Plot for noise |
|
|
|
795 | Plot for noise | |
|
|
339 | 796 | ''' |
|
|
340 | 797 | |
|
|
341 | 798 | CODE = 'noise' |
| @@ -380,7 +837,6 class NoisePlot(Plot): | |||
|
|
380 | 837 | y = Y[ch] |
|
|
381 | 838 | self.axes[0].lines[ch].set_data(x, y) |
|
|
382 | 839 | |
|
|
383 | ||
|
|
384 | 840 | class PowerProfilePlot(Plot): |
|
|
385 | 841 | |
|
|
386 | 842 | CODE = 'pow_profile' |
| @@ -412,10 +868,10 class PowerProfilePlot(Plot): | |||
|
|
412 | 868 | self.y = y |
|
|
413 | 869 | |
|
|
414 | 870 | x = self.data[-1][self.CODE] |
|
|
415 | ||
|
|
871 | ||
|
|
416 | 872 | if self.xmin is None: self.xmin = numpy.nanmin(x)*0.9 |
|
|
417 | 873 | if self.xmax is None: self.xmax = numpy.nanmax(x)*1.1 |
|
|
418 | ||
|
|
874 | ||
|
|
419 | 875 | if self.axes[0].firsttime: |
|
|
420 | 876 | for ch in self.data.channels: |
|
|
421 | 877 | self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch)) |
| @@ -446,7 +902,10 class SpectraCutPlot(Plot): | |||
|
|
446 | 902 | |
|
|
447 | 903 | data = {} |
|
|
448 | 904 | meta = {} |
|
|
449 | spc = 10*numpy.log10(dataOut.data_pre[0]/dataOut.normFactor) | |
|
|
905 | try: | |
|
|
906 | spc = 10*numpy.log10(dataOut.data_pre[0]/dataOut.normFactor) | |
|
|
907 | except: | |
|
|
908 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) | |
|
|
450 | 909 | data['spc'] = spc |
|
|
451 | 910 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
452 | 911 | if self.CODE == 'cut_gaussian_fit': |
| @@ -464,7 +923,7 class SpectraCutPlot(Plot): | |||
|
|
464 | 923 | else: |
|
|
465 | 924 | x = self.data.xrange[2][:-1] |
|
|
466 | 925 | self.xlabel = "Velocity (m/s)" |
|
|
467 | ||
|
|
926 | ||
|
|
468 | 927 | if self.CODE == 'cut_gaussian_fit': |
|
|
469 | 928 | x = self.data.xrange[2][:-1] |
|
|
470 | 929 | self.xlabel = "Velocity (m/s)" |
| @@ -481,22 +940,22 class SpectraCutPlot(Plot): | |||
|
|
481 | 940 | index = numpy.arange(0, len(y), int((len(y))/9)) |
|
|
482 | 941 | |
|
|
483 | 942 | for n, ax in enumerate(self.axes): |
|
|
484 |
if self.CODE == 'cut_gaussian_fit': |
|
|
|
943 | if self.CODE == 'cut_gaussian_fit': | |
|
|
485 | 944 | gau0 = data['gauss_fit0'] |
|
|
486 | 945 | gau1 = data['gauss_fit1'] |
|
|
487 | 946 | if ax.firsttime: |
|
|
488 | 947 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
489 | 948 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
490 | self.ymin = self.ymin if self.ymin else numpy.nanmin(z) | |
|
|
491 | self.ymax = self.ymax if self.ymax else numpy.nanmax(z) | |
|
|
949 | self.ymin = self.ymin if self.ymin else numpy.nanmin(z[:,:,index]) | |
|
|
950 | self.ymax = self.ymax if self.ymax else numpy.nanmax(z[:,:,index]) | |
|
|
492 | 951 | ax.plt = ax.plot(x, z[n, :, index].T, lw=0.25) |
|
|
493 | 952 | if self.CODE == 'cut_gaussian_fit': |
|
|
494 | 953 | ax.plt_gau0 = ax.plot(x, gau0[n, :, index].T, lw=1, linestyle='-.') |
|
|
495 | 954 | for i, line in enumerate(ax.plt_gau0): |
|
|
496 |
line.set_color(ax.plt[i].get_color()) |
|
|
|
955 | line.set_color(ax.plt[i].get_color()) | |
|
|
497 | 956 | ax.plt_gau1 = ax.plot(x, gau1[n, :, index].T, lw=1, linestyle='--') |
|
|
498 | 957 | for i, line in enumerate(ax.plt_gau1): |
|
|
499 |
line.set_color(ax.plt[i].get_color()) |
|
|
|
958 | line.set_color(ax.plt[i].get_color()) | |
|
|
500 | 959 | labels = ['Range = {:2.1f}km'.format(y[i]) for i in index] |
|
|
501 | 960 | self.figures[0].legend(ax.plt, labels, loc='center right') |
|
|
502 | 961 | else: |
| @@ -600,7 +1059,7 class BeaconPhase(Plot): | |||
|
|
600 | 1059 | server=None, folder=None, username=None, password=None, |
|
|
601 | 1060 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
602 | 1061 | |
|
|
603 |
if dataOut.flagNoData: |
|
|
|
1062 | if dataOut.flagNoData: | |
|
|
604 | 1063 | return dataOut |
|
|
605 | 1064 | |
|
|
606 | 1065 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
| @@ -646,7 +1105,6 class BeaconPhase(Plot): | |||
|
|
646 | 1105 | update_figfile = False |
|
|
647 | 1106 | |
|
|
648 | 1107 | nplots = len(pairsIndexList) |
|
|
649 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) | |
|
|
650 | 1108 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
651 | 1109 | for i in range(nplots): |
|
|
652 | 1110 | pair = dataOut.pairsList[pairsIndexList[i]] |
| @@ -696,11 +1154,6 class BeaconPhase(Plot): | |||
|
|
696 | 1154 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
697 | 1155 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
698 | 1156 | self.filename_phase = os.path.join(figpath,beacon_file) |
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699 | #self.save_phase(self.filename_phase) | |
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700 | ||
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701 | ||
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702 | #store data beacon phase | |
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703 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) | |
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704 | 1157 | |
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705 | 1158 | self.setWinTitle(title) |
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706 | 1159 | |
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