@@ -406,8 +406,10 class Plot(Operation): | |||
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406 | 406 | if self.ylabel is not None: |
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407 | 407 | ax.set_ylabel(self.ylabel) |
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408 | 408 | if self.showprofile: |
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409 | if self.zlimits is not None: | |
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410 | self.zmin, self.zmax = self.zlimits[n] | |
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409 | 411 | self.pf_axes[n].set_ylim(ymin, ymax) |
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410 |
self.pf_axes[n].set_xlim(self.zmin |
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412 | self.pf_axes[n].set_xlim(self.zmin, self.zmax) | |
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411 | 413 | self.pf_axes[n].set_xlabel('dB') |
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412 | 414 | self.pf_axes[n].grid(b=True, axis='x') |
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413 | 415 | [tick.set_visible(False) |
@@ -569,7 +571,9 class Plot(Operation): | |||
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569 | 571 | else: |
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570 | 572 | self.data.meta['colormap'] = 'Viridis' |
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571 | 573 | self.data.meta['interval'] = int(interval) |
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572 | ||
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574 | #print(last_time) | |
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575 | #print(time.time()) | |
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576 | #exit(1) | |
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573 | 577 | self.sender_queue.append(last_time) |
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574 | 578 | |
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575 | 579 | while True: |
@@ -95,18 +95,6 class SpectraPlot(Plot): | |||
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95 | 95 | |
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96 | 96 | self.CODE2 = 'spc_oblique' |
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97 | 97 | |
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98 | if not isinstance(self.zmin, collections.abc.Sequence): | |
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99 | if not self.zmin: | |
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100 | self.zmin = [numpy.min(self.z)]*len(self.axes) | |
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101 | else: | |
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102 | self.zmin = [self.zmin]*len(self.axes) | |
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103 | ||
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104 | if not isinstance(self.zmax, collections.abc.Sequence): | |
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105 | if not self.zmax: | |
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106 | self.zmax = [numpy.max(self.z)]*len(self.axes) | |
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107 | else: | |
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108 | self.zmax = [self.zmax]*len(self.axes) | |
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109 | ||
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110 | 98 | for n, ax in enumerate(self.axes): |
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111 | 99 | noise = data['noise'][n] |
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112 | 100 | if self.CODE == 'spc_moments': |
@@ -119,10 +107,12 class SpectraPlot(Plot): | |||
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119 | 107 | self.xmin = self.xmin if self.xmin else numpy.nanmin(x)#-self.xmax |
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120 | 108 | #self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
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121 | 109 | #self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
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110 | if self.zlimits is not None: | |
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111 | self.zmin, self.zmax = self.zlimits[n] | |
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122 | 112 | |
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123 | 113 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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124 |
vmin=self.zmin |
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|
125 |
vmax=self.zmax |
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114 | vmin=self.zmin, | |
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115 | vmax=self.zmax, | |
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126 | 116 | cmap=plt.get_cmap(self.colormap), |
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127 | 117 | ) |
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128 | 118 | |
@@ -137,6 +127,8 class SpectraPlot(Plot): | |||
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137 | 127 | ax.plt_gau0 = ax.plot(gau0, y, color='r', lw=1)[0] |
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138 | 128 | ax.plt_gau1 = ax.plot(gau1, y, color='y', lw=1)[0] |
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139 | 129 | else: |
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130 | if self.zlimits is not None: | |
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131 | self.zmin, self.zmax = self.zlimits[n] | |
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140 | 132 | ax.plt.set_array(z[n].T.ravel()) |
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141 | 133 | if self.showprofile: |
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142 | 134 | ax.plt_profile.set_data(data['rti'][n], y) |
@@ -725,7 +717,7 class RTIPlot(Plot): | |||
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725 | 717 | else: |
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726 | 718 | x, y, z = self.fill_gaps(*self.decimate()) |
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727 | 719 | |
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728 | ||
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720 | ''' | |
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729 | 721 | if not isinstance(self.zmin, collections.abc.Sequence): |
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730 | 722 | if not self.zmin: |
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731 | 723 | self.zmin = [numpy.min(self.z)]*len(self.axes) |
@@ -737,16 +729,18 class RTIPlot(Plot): | |||
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737 | 729 | self.zmax = [numpy.max(self.z)]*len(self.axes) |
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738 | 730 | else: |
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739 | 731 | self.zmax = [self.zmax]*len(self.axes) |
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740 | ||
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732 | ''' | |
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741 | 733 | for n, ax in enumerate(self.axes): |
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742 | 734 | |
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743 |
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744 |
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735 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) | |
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736 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) | |
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745 | 737 | |
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746 | 738 | if ax.firsttime: |
|
739 | if self.zlimits is not None: | |
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740 | self.zmin, self.zmax = self.zlimits[n] | |
|
747 | 741 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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748 |
vmin=self.zmin |
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|
749 |
vmax=self.zmax |
|
|
742 | vmin=self.zmin, | |
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743 | vmax=self.zmax, | |
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750 | 744 | cmap=plt.get_cmap(self.colormap) |
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751 | 745 | ) |
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752 | 746 | if self.showprofile: |
@@ -755,10 +749,12 class RTIPlot(Plot): | |||
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755 | 749 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(self.data['noise'][n][-1], len(self.y)), self.y, |
|
756 | 750 | color="k", linestyle="dashed", lw=1)[0] |
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757 | 751 | else: |
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752 | if self.zlimits is not None: | |
|
753 | self.zmin, self.zmax = self.zlimits[n] | |
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758 | 754 | ax.collections.remove(ax.collections[0]) |
|
759 | 755 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
760 |
vmin=self.zmin |
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|
761 |
vmax=self.zmax |
|
|
756 | vmin=self.zmin, | |
|
757 | vmax=self.zmax, | |
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762 | 758 | cmap=plt.get_cmap(self.colormap) |
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763 | 759 | ) |
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764 | 760 | if self.showprofile: |
@@ -153,24 +153,12 class RTILPPlot(RTIPlot): | |||
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153 | 153 | else: |
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154 | 154 | x, y, z = self.fill_gaps(*self.decimate()) |
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155 | 155 | |
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156 | if not isinstance(self.zmin, collections.abc.Sequence): | |
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157 | if not self.zmin: | |
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158 | self.zmin = [numpy.min(self.z)]*len(self.axes) | |
|
159 | else: | |
|
160 | self.zmin = [self.zmin]*len(self.axes) | |
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161 | ||
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162 | if not isinstance(self.zmax, collections.abc.Sequence): | |
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163 | if not self.zmax: | |
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164 | self.zmax = [numpy.max(self.z)]*len(self.axes) | |
|
165 | else: | |
|
166 | self.zmax = [self.zmax]*len(self.axes) | |
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167 | ||
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168 | 156 | for n, ax in enumerate(self.axes): |
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169 | 157 | |
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170 |
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171 |
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172 |
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173 |
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158 | self.zmax = self.zmax if self.zmax is not None else numpy.max( | |
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159 | self.z[1][0,12:40]) | |
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160 | self.zmin = self.zmin if self.zmin is not None else numpy.min( | |
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161 | self.z[1][0,12:40]) | |
|
174 | 162 | |
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175 | 163 | if ax.firsttime: |
|
176 | 164 | |
@@ -179,18 +167,18 class RTILPPlot(RTIPlot): | |||
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179 | 167 | |
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180 | 168 | |
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181 | 169 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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182 |
vmin=self.zmin |
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|
183 |
vmax=self.zmax |
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170 | vmin=self.zmin, | |
|
171 | vmax=self.zmax, | |
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184 | 172 | cmap=plt.get_cmap(self.colormap) |
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185 | 173 | ) |
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186 | 174 | |
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187 | 175 | else: |
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188 |
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189 |
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176 | if self.zlimits is not None: | |
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177 | self.zmin, self.zmax = self.zlimits[n] | |
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190 | 178 | ax.collections.remove(ax.collections[0]) |
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191 | 179 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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192 |
vmin=self.zmin |
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|
193 |
vmax=self.zmax |
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|
180 | vmin=self.zmin, | |
|
181 | vmax=self.zmax, | |
|
194 | 182 | cmap=plt.get_cmap(self.colormap) |
|
195 | 183 | ) |
|
196 | 184 |
@@ -10,6 +10,7 from schainpy.utils import log | |||
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10 | 10 | from time import time, mktime, strptime, gmtime, ctime |
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11 | 11 | from scipy.optimize import least_squares |
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12 | 12 | import datetime |
|
13 | import collections.abc | |
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13 | 14 | |
|
14 | 15 | try: |
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15 | 16 | from schainpy.model.proc import fitacf_guess |
@@ -2542,7 +2543,10 class CleanCohEchoes(Operation): | |||
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2542 | 2543 | dataOut.flagSpreadF = True |
|
2543 | 2544 | |
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2544 | 2545 | #Removing echoes greater than 35 dB |
|
2545 | maxdB = 10*numpy.log10(dataOut.pbn[0]) + 10 #Lag 0 NOise | |
|
2546 | if isinstance(dataOut.pbn, collections.abc.Sequence): | |
|
2547 | maxdB = 10*numpy.log10(dataOut.pbn[0]) + 10 #Lag 0 NOise | |
|
2548 | else: | |
|
2549 | maxdB = 10*numpy.log10(dataOut.pbn) + 10 #Lag 0 NOise | |
|
2546 | 2550 | #maxdB = 35 #DEBERÍA SER NOISE+ALGO!!!!!!!!!!!!!!!!!!!!!! |
|
2547 | 2551 | #print("noise: ",maxdB - 10) |
|
2548 | 2552 | #print(dataOut.kabxys_integrated[6][:,0,0]) |
@@ -3498,7 +3502,8 class ElectronDensityFaraday(Operation): | |||
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3498 | 3502 | #print(dataOut.phi) |
|
3499 | 3503 | #exit(1) |
|
3500 | 3504 | #''' |
|
3501 | if dataOut.flagSpreadF: | |
|
3505 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: | |
|
3506 | #if dataOut.flagSpreadF: | |
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3502 | 3507 | nanindex = numpy.argwhere(numpy.isnan(dataOut.phi)) |
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3503 | 3508 | i1 = nanindex[-1][0] |
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3504 | 3509 | #Analizar cuando SpreadF es Pluma |
@@ -3931,7 +3936,8 class NormalizeDPPowerRoberto_V2(Operation): | |||
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3931 | 3936 | dataOut.sdp2[i]/=dataOut.cf |
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3932 | 3937 | |
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3933 | 3938 | #''' |
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3934 | if dataOut.flagSpreadF: | |
|
3939 | #if dataOut.flagSpreadF: | |
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3940 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: | |
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3935 | 3941 | i2=int((620-dataOut.range1[0])/dataOut.DH) |
|
3936 | 3942 | nanindex = numpy.argwhere(numpy.isnan(dataOut.ph2)) |
|
3937 | 3943 | print("nanindex",nanindex) |
@@ -3944,7 +3950,7 class NormalizeDPPowerRoberto_V2(Operation): | |||
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3944 | 3950 | #print(dataOut.ph2[i1::]) |
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3945 | 3951 | #''' |
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3946 | 3952 | try: |
|
3947 | if dataOut.flagSpreadF and i1 > 30: | |
|
3953 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF and i1 > 30: | |
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3948 | 3954 | dataOut.cf = numpy.nan |
|
3949 | 3955 | else: |
|
3950 | 3956 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) |
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