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import os
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import datetime
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import numpy
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from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter
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from schainpy.model.graphics.jroplot_base import Plot, plt
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from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot
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from schainpy.utils import log
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# libreria wradlib
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import wradlib as wrl
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EARTH_RADIUS = 6.3710e3
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def ll2xy(lat1, lon1, lat2, lon2):
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p = 0.017453292519943295
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a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \
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numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2
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r = 12742 * numpy.arcsin(numpy.sqrt(a))
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theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p)
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* numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p))
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theta = -theta + numpy.pi/2
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return r*numpy.cos(theta), r*numpy.sin(theta)
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def km2deg(km):
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'''
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Convert distance in km to degrees
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'''
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return numpy.rad2deg(km/EARTH_RADIUS)
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class SpectralMomentsPlot(SpectraPlot):
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'''
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Plot for Spectral Moments
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'''
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CODE = 'spc_moments'
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# colormap = 'jet'
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# plot_type = 'pcolor'
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class DobleGaussianPlot(SpectraPlot):
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'''
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Plot for Double Gaussian Plot
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'''
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CODE = 'gaussian_fit'
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# colormap = 'jet'
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# plot_type = 'pcolor'
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class DoubleGaussianSpectraCutPlot(SpectraCutPlot):
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'''
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Plot SpectraCut with Double Gaussian Fit
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'''
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CODE = 'cut_gaussian_fit'
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class SnrPlot(RTIPlot):
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'''
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Plot for SNR Data
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'''
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CODE = 'snr'
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colormap = 'jet'
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def update(self, dataOut):
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data = {
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'snr': 10*numpy.log10(dataOut.data_snr)
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}
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return data, {}
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class DopplerPlot(RTIPlot):
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'''
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Plot for DOPPLER Data (1st moment)
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'''
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CODE = 'dop'
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colormap = 'jet'
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def update(self, dataOut):
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data = {
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'dop': 10*numpy.log10(dataOut.data_dop)
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}
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return data, {}
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class PowerPlot(RTIPlot):
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'''
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Plot for Power Data (0 moment)
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'''
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CODE = 'pow'
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colormap = 'jet'
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def update(self, dataOut):
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data = {
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'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor)
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}
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return data, {}
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class SpectralWidthPlot(RTIPlot):
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'''
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Plot for Spectral Width Data (2nd moment)
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'''
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CODE = 'width'
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colormap = 'jet'
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def update(self, dataOut):
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data = {
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'width': dataOut.data_width
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}
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return data, {}
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class SkyMapPlot(Plot):
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'''
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Plot for meteors detection data
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'''
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CODE = 'param'
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def setup(self):
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self.ncols = 1
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self.nrows = 1
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self.width = 7.2
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self.height = 7.2
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self.nplots = 1
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self.xlabel = 'Zonal Zenith Angle (deg)'
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self.ylabel = 'Meridional Zenith Angle (deg)'
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self.polar = True
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self.ymin = -180
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self.ymax = 180
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self.colorbar = False
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def plot(self):
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arrayParameters = numpy.concatenate(self.data['param'])
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error = arrayParameters[:, -1]
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indValid = numpy.where(error == 0)[0]
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finalMeteor = arrayParameters[indValid, :]
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finalAzimuth = finalMeteor[:, 3]
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finalZenith = finalMeteor[:, 4]
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x = finalAzimuth * numpy.pi / 180
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y = finalZenith
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ax = self.axes[0]
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if ax.firsttime:
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ax.plot = ax.plot(x, y, 'bo', markersize=5)[0]
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else:
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ax.plot.set_data(x, y)
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dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S')
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dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S')
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title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1,
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dt2,
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len(x))
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self.titles[0] = title
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class GenericRTIPlot(Plot):
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'''
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Plot for data_xxxx object
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'''
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CODE = 'param'
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colormap = 'viridis'
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plot_type = 'pcolorbuffer'
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def setup(self):
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self.xaxis = 'time'
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self.ncols = 1
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self.nrows = self.data.shape('param')[0]
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self.nplots = self.nrows
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self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95})
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if not self.xlabel:
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self.xlabel = 'Time'
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self.ylabel = 'Range [km]'
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if not self.titles:
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self.titles = ['Param {}'.format(x) for x in range(self.nrows)]
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def update(self, dataOut):
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data = {
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'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0)
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}
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meta = {}
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return data, meta
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def plot(self):
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# self.data.normalize_heights()
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self.x = self.data.times
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self.y = self.data.yrange
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self.z = self.data['param']
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self.z = 10*numpy.log10(self.z)
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self.z = numpy.ma.masked_invalid(self.z)
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if self.decimation is None:
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x, y, z = self.fill_gaps(self.x, self.y, self.z)
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else:
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x, y, z = self.fill_gaps(*self.decimate())
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for n, ax in enumerate(self.axes):
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self.zmax = self.zmax if self.zmax is not None else numpy.max(
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self.z[n])
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self.zmin = self.zmin if self.zmin is not None else numpy.min(
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self.z[n])
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if ax.firsttime:
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if self.zlimits is not None:
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self.zmin, self.zmax = self.zlimits[n]
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ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
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vmin=self.zmin,
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vmax=self.zmax,
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cmap=self.cmaps[n]
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)
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else:
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if self.zlimits is not None:
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self.zmin, self.zmax = self.zlimits[n]
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ax.collections.remove(ax.collections[0])
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ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
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vmin=self.zmin,
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vmax=self.zmax,
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cmap=self.cmaps[n]
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)
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class PolarMapPlot(Plot):
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'''
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Plot for weather radar
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'''
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CODE = 'param'
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colormap = 'seismic'
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def setup(self):
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self.ncols = 1
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self.nrows = 1
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self.width = 9
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self.height = 8
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self.mode = self.data.meta['mode']
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if self.channels is not None:
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self.nplots = len(self.channels)
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self.nrows = len(self.channels)
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else:
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self.nplots = self.data.shape(self.CODE)[0]
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self.nrows = self.nplots
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self.channels = list(range(self.nplots))
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if self.mode == 'E':
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self.xlabel = 'Longitude'
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self.ylabel = 'Latitude'
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else:
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self.xlabel = 'Range (km)'
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self.ylabel = 'Height (km)'
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self.bgcolor = 'white'
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self.cb_labels = self.data.meta['units']
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self.lat = self.data.meta['latitude']
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self.lon = self.data.meta['longitude']
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self.xmin, self.xmax = float(
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km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon)
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self.ymin, self.ymax = float(
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km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat)
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# self.polar = True
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def plot(self):
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for n, ax in enumerate(self.axes):
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data = self.data['param'][self.channels[n]]
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zeniths = numpy.linspace(
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0, self.data.meta['max_range'], data.shape[1])
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if self.mode == 'E':
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azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2
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r, theta = numpy.meshgrid(zeniths, azimuths)
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x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin(
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theta)*numpy.cos(numpy.radians(self.data.meta['elevation']))
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x = km2deg(x) + self.lon
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y = km2deg(y) + self.lat
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else:
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azimuths = numpy.radians(self.data.yrange)
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r, theta = numpy.meshgrid(zeniths, azimuths)
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x, y = r*numpy.cos(theta), r*numpy.sin(theta)
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self.y = zeniths
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if ax.firsttime:
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if self.zlimits is not None:
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self.zmin, self.zmax = self.zlimits[n]
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ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
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x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
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vmin=self.zmin,
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vmax=self.zmax,
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cmap=self.cmaps[n])
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else:
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if self.zlimits is not None:
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self.zmin, self.zmax = self.zlimits[n]
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ax.collections.remove(ax.collections[0])
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ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
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x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
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vmin=self.zmin,
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vmax=self.zmax,
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cmap=self.cmaps[n])
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if self.mode == 'A':
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continue
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# plot district names
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f = open('/data/workspace/schain_scripts/distrito.csv')
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for line in f:
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label, lon, lat = [s.strip() for s in line.split(',') if s]
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lat = float(lat)
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lon = float(lon)
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# ax.plot(lon, lat, '.b', ms=2)
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ax.text(lon, lat, label.decode('utf8'), ha='center',
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va='bottom', size='8', color='black')
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# plot limites
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limites = []
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tmp = []
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for line in open('/data/workspace/schain_scripts/lima.csv'):
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if '#' in line:
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if tmp:
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limites.append(tmp)
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tmp = []
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continue
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values = line.strip().split(',')
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tmp.append((float(values[0]), float(values[1])))
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for points in limites:
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ax.add_patch(
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Polygon(points, ec='k', fc='none', ls='--', lw=0.5))
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# plot Cuencas
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for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'):
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f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca))
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values = [line.strip().split(',') for line in f]
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points = [(float(s[0]), float(s[1])) for s in values]
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ax.add_patch(Polygon(points, ec='b', fc='none'))
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# plot grid
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for r in (15, 30, 45, 60):
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ax.add_artist(plt.Circle((self.lon, self.lat),
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km2deg(r), color='0.6', fill=False, lw=0.2))
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ax.text(
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self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180),
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self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180),
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'{}km'.format(r),
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ha='center', va='bottom', size='8', color='0.6', weight='heavy')
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if self.mode == 'E':
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title = 'El={}$^\circ$'.format(self.data.meta['elevation'])
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label = 'E{:02d}'.format(int(self.data.meta['elevation']))
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else:
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title = 'Az={}$^\circ$'.format(self.data.meta['azimuth'])
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label = 'A{:02d}'.format(int(self.data.meta['azimuth']))
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self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels]
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self.titles = ['{} {}'.format(
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self.data.parameters[x], title) for x in self.channels]
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class WeatherPlot(Plot):
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CODE = 'weather'
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plot_name = 'weather'
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plot_type = 'ppistyle'
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buffering = False
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def setup(self):
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self.ncols = 1
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self.nrows = 1
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self.width =8
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self.height =8
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self.nplots= 1
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self.ylabel= 'Range [Km]'
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self.titles= ['Weather']
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self.colorbar=False
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self.ini =0
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self.len_azi =0
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self.buffer_ini = None
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self.buffer_azi = None
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self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
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self.flag =0
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self.indicador= 0
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self.last_data_azi = None
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self.val_mean = None
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def update(self, dataOut):
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data = {}
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meta = {}
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if hasattr(dataOut, 'dataPP_POWER'):
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factor = 1
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if hasattr(dataOut, 'nFFTPoints'):
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factor = dataOut.normFactor
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#print("DIME EL SHAPE PORFAVOR",dataOut.data_360.shape)
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data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
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data['azi'] = dataOut.data_azi
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data['ele'] = dataOut.data_ele
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return data, meta
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def get2List(self,angulos):
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list1=[]
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list2=[]
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for i in reversed(range(len(angulos))):
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diff_ = angulos[i]-angulos[i-1]
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if diff_ >1.5:
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list1.append(i-1)
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list2.append(diff_)
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return list(reversed(list1)),list(reversed(list2))
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def fixData360(self,list_,ang_):
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if list_[0]==-1:
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vec = numpy.where(ang_<ang_[0])
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ang_[vec] = ang_[vec]+360
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return ang_
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return ang_
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|
def fixData360HL(self,angulos):
|
|
|
vec = numpy.where(angulos>=360)
|
|
|
angulos[vec]=angulos[vec]-360
|
|
|
return angulos
|
|
|
|
|
|
def search_pos(self,pos,list_):
|
|
|
for i in range(len(list_)):
|
|
|
if pos == list_[i]:
|
|
|
return True,i
|
|
|
i=None
|
|
|
return False,i
|
|
|
|
|
|
def fixDataComp(self,ang_,list1_,list2_):
|
|
|
size = len(ang_)
|
|
|
size2 = 0
|
|
|
for i in range(len(list2_)):
|
|
|
size2=size2+round(list2_[i])-1
|
|
|
new_size= size+size2
|
|
|
ang_new = numpy.zeros(new_size)
|
|
|
ang_new2 = numpy.zeros(new_size)
|
|
|
|
|
|
tmp = 0
|
|
|
c = 0
|
|
|
for i in range(len(ang_)):
|
|
|
ang_new[tmp +c] = ang_[i]
|
|
|
ang_new2[tmp+c] = ang_[i]
|
|
|
condition , value = self.search_pos(i,list1_)
|
|
|
if condition:
|
|
|
pos = tmp + c + 1
|
|
|
for k in range(round(list2_[value])-1):
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]+1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
tmp = pos +k
|
|
|
c = 0
|
|
|
c=c+1
|
|
|
return ang_new,ang_new2
|
|
|
|
|
|
def globalCheckPED(self,angulos):
|
|
|
l1,l2 = self.get2List(angulos)
|
|
|
if len(l1)>0:
|
|
|
angulos2 = self.fixData360(list_=l1,ang_=angulos)
|
|
|
l1,l2 = self.get2List(angulos2)
|
|
|
|
|
|
ang1_,ang2_ = self.fixDataComp(ang_=angulos2,list1_=l1,list2_=l2)
|
|
|
ang1_ = self.fixData360HL(ang1_)
|
|
|
ang2_ = self.fixData360HL(ang2_)
|
|
|
else:
|
|
|
ang1_= angulos
|
|
|
ang2_= angulos
|
|
|
return ang1_,ang2_
|
|
|
|
|
|
def analizeDATA(self,data_azi):
|
|
|
list1 = []
|
|
|
list2 = []
|
|
|
dat = data_azi
|
|
|
for i in reversed(range(1,len(dat))):
|
|
|
if dat[i]>dat[i-1]:
|
|
|
diff = int(dat[i])-int(dat[i-1])
|
|
|
else:
|
|
|
diff = 360+int(dat[i])-int(dat[i-1])
|
|
|
if diff > 1:
|
|
|
list1.append(i-1)
|
|
|
list2.append(diff-1)
|
|
|
return list1,list2
|
|
|
|
|
|
def fixDATANEW(self,data_azi,data_weather):
|
|
|
list1,list2 = self.analizeDATA(data_azi)
|
|
|
if len(list1)== 0:
|
|
|
return data_azi,data_weather
|
|
|
else:
|
|
|
resize = 0
|
|
|
for i in range(len(list2)):
|
|
|
resize= resize + list2[i]
|
|
|
new_data_azi = numpy.resize(data_azi,resize)
|
|
|
new_data_weather= numpy.resize(date_weather,resize)
|
|
|
|
|
|
for i in range(len(list2)):
|
|
|
j=0
|
|
|
position=list1[i]+1
|
|
|
for j in range(list2[i]):
|
|
|
new_data_azi[position+j]=new_data_azi[position+j-1]+1
|
|
|
return new_data_azi
|
|
|
|
|
|
def fixDATA(self,data_azi):
|
|
|
data=data_azi
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data[i]=data[i-1]+1
|
|
|
return data
|
|
|
|
|
|
def replaceNAN(self,data_weather,data_azi,val):
|
|
|
data= data_azi
|
|
|
data_T= data_weather
|
|
|
if data.shape[0]> data_T.shape[0]:
|
|
|
data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
|
|
|
c = 0
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
else:
|
|
|
data_N[i,:]=data_T[c,:]
|
|
|
c=c+1
|
|
|
return data_N
|
|
|
else:
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
return data_T
|
|
|
|
|
|
def const_ploteo(self,data_weather,data_azi,step,res):
|
|
|
if self.ini==0:
|
|
|
#-------
|
|
|
n = (360/res)-len(data_azi)
|
|
|
#--------------------- new -------------------------
|
|
|
data_azi_new ,data_azi_old= self.globalCheckPED(data_azi)
|
|
|
#------------------------
|
|
|
start = data_azi_new[-1] + res
|
|
|
end = data_azi_new[0] - res
|
|
|
#------ new
|
|
|
self.last_data_azi = end
|
|
|
if start>end:
|
|
|
end = end + 360
|
|
|
azi_vacia = numpy.linspace(start,end,int(n))
|
|
|
azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia)
|
|
|
data_azi = numpy.hstack((data_azi_new,azi_vacia))
|
|
|
# RADAR
|
|
|
val_mean = numpy.mean(data_weather[:,-1])
|
|
|
self.val_mean = val_mean
|
|
|
data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
|
|
|
data_weather = numpy.vstack((data_weather,data_weather_cmp))
|
|
|
else:
|
|
|
# azimuth
|
|
|
flag=0
|
|
|
start_azi = self.res_azi[0]
|
|
|
#-----------new------------
|
|
|
data_azi ,data_azi_old= self.globalCheckPED(data_azi)
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
|
|
|
#--------------------------
|
|
|
start = data_azi[0]
|
|
|
end = data_azi[-1]
|
|
|
self.last_data_azi= end
|
|
|
if start< start_azi:
|
|
|
start = start +360
|
|
|
if end <start_azi:
|
|
|
end = end +360
|
|
|
|
|
|
pos_ini = int((start-start_azi)/res)
|
|
|
len_azi = len(data_azi)
|
|
|
if (360-pos_ini)<len_azi:
|
|
|
if pos_ini+1==360:
|
|
|
pos_ini=0
|
|
|
else:
|
|
|
flag=1
|
|
|
dif= 360-pos_ini
|
|
|
comp= len_azi-dif
|
|
|
#-----------------
|
|
|
if flag==0:
|
|
|
# AZIMUTH
|
|
|
self.res_azi[pos_ini:pos_ini+len_azi] = data_azi
|
|
|
# RADAR
|
|
|
self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather
|
|
|
else:
|
|
|
# AZIMUTH
|
|
|
self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif]
|
|
|
self.res_azi[0:comp] = data_azi[dif:]
|
|
|
# RADAR
|
|
|
self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:]
|
|
|
self.res_weather[0:comp,:] = data_weather[dif:,:]
|
|
|
flag=0
|
|
|
data_azi = self.res_azi
|
|
|
data_weather = self.res_weather
|
|
|
|
|
|
return data_weather,data_azi
|
|
|
|
|
|
def plot(self):
|
|
|
thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
|
|
|
data = self.data[-1]
|
|
|
r = self.data.yrange
|
|
|
delta_height = r[1]-r[0]
|
|
|
r_mask = numpy.where(r>=0)[0]
|
|
|
r = numpy.arange(len(r_mask))*delta_height
|
|
|
self.y = 2*r
|
|
|
# RADAR
|
|
|
#data_weather = data['weather']
|
|
|
# PEDESTAL
|
|
|
#data_azi = data['azi']
|
|
|
res = 1
|
|
|
# STEP
|
|
|
step = (360/(res*data['weather'].shape[0]))
|
|
|
|
|
|
self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res)
|
|
|
self.res_ele = numpy.mean(data['ele'])
|
|
|
################# PLOTEO ###################
|
|
|
for i,ax in enumerate(self.axes):
|
|
|
if ax.firsttime:
|
|
|
plt.clf()
|
|
|
cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=20, vmax=80)
|
|
|
else:
|
|
|
plt.clf()
|
|
|
cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=20, vmax=80)
|
|
|
caax = cgax.parasites[0]
|
|
|
paax = cgax.parasites[1]
|
|
|
cbar = plt.gcf().colorbar(pm, pad=0.075)
|
|
|
caax.set_xlabel('x_range [km]')
|
|
|
caax.set_ylabel('y_range [km]')
|
|
|
plt.text(1.0, 1.05, 'Azimuth '+str(thisDatetime)+" Step "+str(self.ini)+ " Elev: "+str(round(self.res_ele,2)), transform=caax.transAxes, va='bottom',ha='right')
|
|
|
|
|
|
self.ini= self.ini+1
|
|
|
|
|
|
|
|
|
class WeatherRHIPlot(Plot):
|
|
|
CODE = 'weather'
|
|
|
plot_name = 'weather'
|
|
|
plot_type = 'rhistyle'
|
|
|
buffering = False
|
|
|
data_ele_tmp = None
|
|
|
|
|
|
def setup(self):
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("SETUP WEATHER PLOT")
|
|
|
self.ncols = 1
|
|
|
self.nrows = 1
|
|
|
self.nplots= 1
|
|
|
self.ylabel= 'Range [Km]'
|
|
|
self.titles= ['Weather']
|
|
|
if self.channels is not None:
|
|
|
self.nplots = len(self.channels)
|
|
|
self.nrows = len(self.channels)
|
|
|
else:
|
|
|
self.nplots = self.data.shape(self.CODE)[0]
|
|
|
self.nrows = self.nplots
|
|
|
self.channels = list(range(self.nplots))
|
|
|
print("channels",self.channels)
|
|
|
print("que saldra", self.data.shape(self.CODE)[0])
|
|
|
self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
|
|
|
print("self.titles",self.titles)
|
|
|
self.colorbar=False
|
|
|
self.width =8
|
|
|
self.height =8
|
|
|
self.ini =0
|
|
|
self.len_azi =0
|
|
|
self.buffer_ini = None
|
|
|
self.buffer_ele = None
|
|
|
self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
|
|
|
self.flag =0
|
|
|
self.indicador= 0
|
|
|
self.last_data_ele = None
|
|
|
self.val_mean = None
|
|
|
|
|
|
def update(self, dataOut):
|
|
|
|
|
|
data = {}
|
|
|
meta = {}
|
|
|
if hasattr(dataOut, 'dataPP_POWER'):
|
|
|
factor = 1
|
|
|
if hasattr(dataOut, 'nFFTPoints'):
|
|
|
factor = dataOut.normFactor
|
|
|
print("dataOut",dataOut.data_360.shape)
|
|
|
#
|
|
|
data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
|
|
|
#
|
|
|
#data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
|
|
|
data['azi'] = dataOut.data_azi
|
|
|
data['ele'] = dataOut.data_ele
|
|
|
#print("UPDATE")
|
|
|
#print("data[weather]",data['weather'].shape)
|
|
|
#print("data[azi]",data['azi'])
|
|
|
return data, meta
|
|
|
|
|
|
def get2List(self,angulos):
|
|
|
list1=[]
|
|
|
list2=[]
|
|
|
for i in reversed(range(len(angulos))):
|
|
|
if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
|
|
|
diff_ = angulos[i]-angulos[i-1]
|
|
|
if abs(diff_) >1.5:
|
|
|
list1.append(i-1)
|
|
|
list2.append(diff_)
|
|
|
return list(reversed(list1)),list(reversed(list2))
|
|
|
|
|
|
def fixData90(self,list_,ang_):
|
|
|
if list_[0]==-1:
|
|
|
vec = numpy.where(ang_<ang_[0])
|
|
|
ang_[vec] = ang_[vec]+90
|
|
|
return ang_
|
|
|
return ang_
|
|
|
|
|
|
def fixData90HL(self,angulos):
|
|
|
vec = numpy.where(angulos>=90)
|
|
|
angulos[vec]=angulos[vec]-90
|
|
|
return angulos
|
|
|
|
|
|
|
|
|
def search_pos(self,pos,list_):
|
|
|
for i in range(len(list_)):
|
|
|
if pos == list_[i]:
|
|
|
return True,i
|
|
|
i=None
|
|
|
return False,i
|
|
|
|
|
|
def fixDataComp(self,ang_,list1_,list2_,tipo_case):
|
|
|
size = len(ang_)
|
|
|
size2 = 0
|
|
|
for i in range(len(list2_)):
|
|
|
size2=size2+round(abs(list2_[i]))-1
|
|
|
new_size= size+size2
|
|
|
ang_new = numpy.zeros(new_size)
|
|
|
ang_new2 = numpy.zeros(new_size)
|
|
|
|
|
|
tmp = 0
|
|
|
c = 0
|
|
|
for i in range(len(ang_)):
|
|
|
ang_new[tmp +c] = ang_[i]
|
|
|
ang_new2[tmp+c] = ang_[i]
|
|
|
condition , value = self.search_pos(i,list1_)
|
|
|
if condition:
|
|
|
pos = tmp + c + 1
|
|
|
for k in range(round(abs(list2_[value]))-1):
|
|
|
if tipo_case==0 or tipo_case==3:#subida
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]+1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
elif tipo_case==1 or tipo_case==2:#bajada
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]-1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
|
|
|
tmp = pos +k
|
|
|
c = 0
|
|
|
c=c+1
|
|
|
return ang_new,ang_new2
|
|
|
|
|
|
def globalCheckPED(self,angulos,tipo_case):
|
|
|
l1,l2 = self.get2List(angulos)
|
|
|
##print("l1",l1)
|
|
|
##print("l2",l2)
|
|
|
if len(l1)>0:
|
|
|
#angulos2 = self.fixData90(list_=l1,ang_=angulos)
|
|
|
#l1,l2 = self.get2List(angulos2)
|
|
|
ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
|
|
|
#ang1_ = self.fixData90HL(ang1_)
|
|
|
#ang2_ = self.fixData90HL(ang2_)
|
|
|
else:
|
|
|
ang1_= angulos
|
|
|
ang2_= angulos
|
|
|
return ang1_,ang2_
|
|
|
|
|
|
|
|
|
def replaceNAN(self,data_weather,data_ele,val):
|
|
|
data= data_ele
|
|
|
data_T= data_weather
|
|
|
if data.shape[0]> data_T.shape[0]:
|
|
|
data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
|
|
|
c = 0
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
else:
|
|
|
data_N[i,:]=data_T[c,:]
|
|
|
c=c+1
|
|
|
return data_N
|
|
|
else:
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
return data_T
|
|
|
|
|
|
def check_case(self,data_ele,ang_max,ang_min):
|
|
|
start = data_ele[0]
|
|
|
end = data_ele[-1]
|
|
|
number = (end-start)
|
|
|
len_ang=len(data_ele)
|
|
|
print("start",start)
|
|
|
print("end",end)
|
|
|
print("number",number)
|
|
|
|
|
|
print("len_ang",len_ang)
|
|
|
|
|
|
#exit(1)
|
|
|
|
|
|
if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida
|
|
|
return 0
|
|
|
#elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada
|
|
|
# return 1
|
|
|
elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada
|
|
|
return 1
|
|
|
elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX
|
|
|
return 2
|
|
|
elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN
|
|
|
return 3
|
|
|
|
|
|
|
|
|
def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min):
|
|
|
ang_max= ang_max
|
|
|
ang_min= ang_min
|
|
|
data_weather=data_weather
|
|
|
val_ch=val_ch
|
|
|
##print("*********************DATA WEATHER**************************************")
|
|
|
##print(data_weather)
|
|
|
if self.ini==0:
|
|
|
'''
|
|
|
print("**********************************************")
|
|
|
print("**********************************************")
|
|
|
print("***************ini**************")
|
|
|
print("**********************************************")
|
|
|
print("**********************************************")
|
|
|
'''
|
|
|
#print("data_ele",data_ele)
|
|
|
#----------------------------------------------------------
|
|
|
tipo_case = self.check_case(data_ele,ang_max,ang_min)
|
|
|
print("check_case",tipo_case)
|
|
|
#exit(1)
|
|
|
#--------------------- new -------------------------
|
|
|
data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
|
|
|
|
|
|
#-------------------------CAMBIOS RHI---------------------------------
|
|
|
start= ang_min
|
|
|
end = ang_max
|
|
|
n= (ang_max-ang_min)/res
|
|
|
#------ new
|
|
|
self.start_data_ele = data_ele_new[0]
|
|
|
self.end_data_ele = data_ele_new[-1]
|
|
|
if tipo_case==0 or tipo_case==3: # SUBIDA
|
|
|
n1= round(self.start_data_ele)- start
|
|
|
n2= end - round(self.end_data_ele)
|
|
|
print(self.start_data_ele)
|
|
|
print(self.end_data_ele)
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
print("ele1_nan",ele1_nan.shape)
|
|
|
print("data_ele_old",data_ele_old.shape)
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(self.end_data_ele+1,end,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
print("ele2_nan",ele2_nan.shape)
|
|
|
print("data_ele_old",data_ele_old.shape)
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
if tipo_case==1 or tipo_case==2: # BAJADA
|
|
|
data_ele_new = data_ele_new[::-1] # reversa
|
|
|
data_ele_old = data_ele_old[::-1]# reversa
|
|
|
data_weather = data_weather[::-1,:]# reversa
|
|
|
vec= numpy.where(data_ele_new<ang_max)
|
|
|
data_ele_new = data_ele_new[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
vec2= numpy.where(0<data_ele_new)
|
|
|
data_ele_new = data_ele_new[vec2]
|
|
|
data_ele_old = data_ele_old[vec2]
|
|
|
data_weather = data_weather[vec2[0]]
|
|
|
self.start_data_ele = data_ele_new[0]
|
|
|
self.end_data_ele = data_ele_new[-1]
|
|
|
|
|
|
n1= round(self.start_data_ele)- start
|
|
|
n2= end - round(self.end_data_ele)-1
|
|
|
print(self.start_data_ele)
|
|
|
print(self.end_data_ele)
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(self.end_data_ele+1,end,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
# RADAR
|
|
|
# NOTA data_ele y data_weather es la variable que retorna
|
|
|
val_mean = numpy.mean(data_weather[:,-1])
|
|
|
self.val_mean = val_mean
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch]= data_ele_old
|
|
|
else:
|
|
|
#print("**********************************************")
|
|
|
#print("****************VARIABLE**********************")
|
|
|
#-------------------------CAMBIOS RHI---------------------------------
|
|
|
#---------------------------------------------------------------------
|
|
|
##print("INPUT data_ele",data_ele)
|
|
|
flag=0
|
|
|
start_ele = self.res_ele[0]
|
|
|
tipo_case = self.check_case(data_ele,ang_max,ang_min)
|
|
|
#print("TIPO DE DATA",tipo_case)
|
|
|
#-----------new------------
|
|
|
data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case)
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
|
|
|
#-------------------------------NEW RHI ITERATIVO-------------------------
|
|
|
|
|
|
if tipo_case==0 : # SUBIDA
|
|
|
vec = numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
|
|
|
vec2 = numpy.where(0<data_ele)
|
|
|
data_ele= data_ele[vec2]
|
|
|
data_ele_old= data_ele_old[vec2]
|
|
|
##print(data_ele_new)
|
|
|
data_weather= data_weather[vec2[0]]
|
|
|
|
|
|
new_i_ele = int(round(data_ele[0]))
|
|
|
new_f_ele = int(round(data_ele[-1]))
|
|
|
#print(new_i_ele)
|
|
|
#print(new_f_ele)
|
|
|
#print(data_ele,len(data_ele))
|
|
|
#print(data_ele_old,len(data_ele_old))
|
|
|
if new_i_ele< 2:
|
|
|
self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old
|
|
|
self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele
|
|
|
self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==1 : #BAJADA
|
|
|
data_ele = data_ele[::-1] # reversa
|
|
|
data_ele_old = data_ele_old[::-1]# reversa
|
|
|
data_weather = data_weather[::-1,:]# reversa
|
|
|
vec= numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
vec2= numpy.where(0<data_ele)
|
|
|
data_ele = data_ele[vec2]
|
|
|
data_ele_old = data_ele_old[vec2]
|
|
|
data_weather = data_weather[vec2[0]]
|
|
|
|
|
|
|
|
|
new_i_ele = int(round(data_ele[0]))
|
|
|
new_f_ele = int(round(data_ele[-1]))
|
|
|
#print(data_ele)
|
|
|
#print(ang_max)
|
|
|
#print(data_ele_old)
|
|
|
if new_i_ele <= 1:
|
|
|
new_i_ele = 1
|
|
|
if round(data_ele[-1])>=ang_max-1:
|
|
|
self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old
|
|
|
self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele
|
|
|
self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==2: #bajada
|
|
|
vec = numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_weather= data_weather[vec[0]]
|
|
|
|
|
|
len_vec = len(vec)
|
|
|
data_ele_new = data_ele[::-1] # reversa
|
|
|
data_weather = data_weather[::-1,:]
|
|
|
new_i_ele = int(data_ele_new[0])
|
|
|
new_f_ele = int(data_ele_new[-1])
|
|
|
|
|
|
n1= new_i_ele- ang_min
|
|
|
n2= ang_max - new_f_ele-1
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
self.data_ele_tmp[val_ch] = data_ele_old
|
|
|
self.res_ele = data_ele
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==3:#subida
|
|
|
vec = numpy.where(0<data_ele)
|
|
|
data_ele= data_ele[vec]
|
|
|
data_ele_new = data_ele
|
|
|
data_ele_old= data_ele_old[vec]
|
|
|
data_weather= data_weather[vec[0]]
|
|
|
pos_ini = numpy.argmin(data_ele)
|
|
|
if pos_ini>0:
|
|
|
len_vec= len(data_ele)
|
|
|
vec3 = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int)
|
|
|
#print(vec3)
|
|
|
data_ele= data_ele[vec3]
|
|
|
data_ele_new = data_ele
|
|
|
data_ele_old= data_ele_old[vec3]
|
|
|
data_weather= data_weather[vec3]
|
|
|
|
|
|
new_i_ele = int(data_ele_new[0])
|
|
|
new_f_ele = int(data_ele_new[-1])
|
|
|
n1= new_i_ele- ang_min
|
|
|
n2= ang_max - new_f_ele-1
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
self.data_ele_tmp[val_ch] = data_ele_old
|
|
|
self.res_ele = data_ele
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
#print("self.data_ele_tmp",self.data_ele_tmp)
|
|
|
return data_weather,data_ele
|
|
|
|
|
|
|
|
|
def plot(self):
|
|
|
thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
|
|
|
data = self.data[-1]
|
|
|
r = self.data.yrange
|
|
|
delta_height = r[1]-r[0]
|
|
|
r_mask = numpy.where(r>=0)[0]
|
|
|
##print("delta_height",delta_height)
|
|
|
#print("r_mask",r_mask,len(r_mask))
|
|
|
r = numpy.arange(len(r_mask))*delta_height
|
|
|
self.y = 2*r
|
|
|
res = 1
|
|
|
###print("data['weather'].shape[0]",data['weather'].shape[0])
|
|
|
ang_max = self.ang_max
|
|
|
ang_min = self.ang_min
|
|
|
var_ang =ang_max - ang_min
|
|
|
step = (int(var_ang)/(res*data['weather'].shape[0]))
|
|
|
###print("step",step)
|
|
|
#--------------------------------------------------------
|
|
|
##print('weather',data['weather'].shape)
|
|
|
##print('ele',data['ele'].shape)
|
|
|
|
|
|
###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
|
|
|
###self.res_azi = numpy.mean(data['azi'])
|
|
|
###print("self.res_ele",self.res_ele)
|
|
|
plt.clf()
|
|
|
subplots = [121, 122]
|
|
|
if self.ini==0:
|
|
|
self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan
|
|
|
self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
|
|
|
print("SHAPE",self.data_ele_tmp.shape)
|
|
|
|
|
|
for i,ax in enumerate(self.axes):
|
|
|
self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
|
|
|
self.res_azi = numpy.mean(data['azi'])
|
|
|
if i==0:
|
|
|
print("*****************************************************************************to plot**************************",self.res_weather[i].shape)
|
|
|
if ax.firsttime:
|
|
|
#plt.clf()
|
|
|
cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
#fig=self.figures[0]
|
|
|
else:
|
|
|
#plt.clf()
|
|
|
if i==0:
|
|
|
print(self.res_weather[i])
|
|
|
print(self.res_ele)
|
|
|
cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
caax = cgax.parasites[0]
|
|
|
paax = cgax.parasites[1]
|
|
|
cbar = plt.gcf().colorbar(pm, pad=0.075)
|
|
|
caax.set_xlabel('x_range [km]')
|
|
|
caax.set_ylabel('y_range [km]')
|
|
|
plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
|
|
|
print("***************************self.ini****************************",self.ini)
|
|
|
self.ini= self.ini+1
|
|
|
|
|
|
class WeatherRHI_vRF2_Plot(Plot):
|
|
|
CODE = 'weather'
|
|
|
plot_name = 'weather'
|
|
|
plot_type = 'rhistyle'
|
|
|
buffering = False
|
|
|
data_ele_tmp = None
|
|
|
|
|
|
def setup(self):
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("SETUP WEATHER PLOT")
|
|
|
self.ncols = 1
|
|
|
self.nrows = 1
|
|
|
self.nplots= 1
|
|
|
self.ylabel= 'Range [Km]'
|
|
|
self.titles= ['Weather']
|
|
|
if self.channels is not None:
|
|
|
self.nplots = len(self.channels)
|
|
|
self.nrows = len(self.channels)
|
|
|
else:
|
|
|
self.nplots = self.data.shape(self.CODE)[0]
|
|
|
self.nrows = self.nplots
|
|
|
self.channels = list(range(self.nplots))
|
|
|
print("channels",self.channels)
|
|
|
print("que saldra", self.data.shape(self.CODE)[0])
|
|
|
self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
|
|
|
print("self.titles",self.titles)
|
|
|
self.colorbar=False
|
|
|
self.width =8
|
|
|
self.height =8
|
|
|
self.ini =0
|
|
|
self.len_azi =0
|
|
|
self.buffer_ini = None
|
|
|
self.buffer_ele = None
|
|
|
self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
|
|
|
self.flag =0
|
|
|
self.indicador= 0
|
|
|
self.last_data_ele = None
|
|
|
self.val_mean = None
|
|
|
|
|
|
def update(self, dataOut):
|
|
|
|
|
|
data = {}
|
|
|
meta = {}
|
|
|
if hasattr(dataOut, 'dataPP_POWER'):
|
|
|
factor = 1
|
|
|
if hasattr(dataOut, 'nFFTPoints'):
|
|
|
factor = dataOut.normFactor
|
|
|
print("dataOut",dataOut.data_360.shape)
|
|
|
#
|
|
|
data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
|
|
|
#
|
|
|
#data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
|
|
|
data['azi'] = dataOut.data_azi
|
|
|
data['ele'] = dataOut.data_ele
|
|
|
data['case_flag'] = dataOut.case_flag
|
|
|
#print("UPDATE")
|
|
|
#print("data[weather]",data['weather'].shape)
|
|
|
#print("data[azi]",data['azi'])
|
|
|
return data, meta
|
|
|
|
|
|
def get2List(self,angulos):
|
|
|
list1=[]
|
|
|
list2=[]
|
|
|
for i in reversed(range(len(angulos))):
|
|
|
if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
|
|
|
diff_ = angulos[i]-angulos[i-1]
|
|
|
if abs(diff_) >1.5:
|
|
|
list1.append(i-1)
|
|
|
list2.append(diff_)
|
|
|
return list(reversed(list1)),list(reversed(list2))
|
|
|
|
|
|
def fixData90(self,list_,ang_):
|
|
|
if list_[0]==-1:
|
|
|
vec = numpy.where(ang_<ang_[0])
|
|
|
ang_[vec] = ang_[vec]+90
|
|
|
return ang_
|
|
|
return ang_
|
|
|
|
|
|
def fixData90HL(self,angulos):
|
|
|
vec = numpy.where(angulos>=90)
|
|
|
angulos[vec]=angulos[vec]-90
|
|
|
return angulos
|
|
|
|
|
|
|
|
|
def search_pos(self,pos,list_):
|
|
|
for i in range(len(list_)):
|
|
|
if pos == list_[i]:
|
|
|
return True,i
|
|
|
i=None
|
|
|
return False,i
|
|
|
|
|
|
def fixDataComp(self,ang_,list1_,list2_,tipo_case):
|
|
|
size = len(ang_)
|
|
|
size2 = 0
|
|
|
for i in range(len(list2_)):
|
|
|
size2=size2+round(abs(list2_[i]))-1
|
|
|
new_size= size+size2
|
|
|
ang_new = numpy.zeros(new_size)
|
|
|
ang_new2 = numpy.zeros(new_size)
|
|
|
|
|
|
tmp = 0
|
|
|
c = 0
|
|
|
for i in range(len(ang_)):
|
|
|
ang_new[tmp +c] = ang_[i]
|
|
|
ang_new2[tmp+c] = ang_[i]
|
|
|
condition , value = self.search_pos(i,list1_)
|
|
|
if condition:
|
|
|
pos = tmp + c + 1
|
|
|
for k in range(round(abs(list2_[value]))-1):
|
|
|
if tipo_case==0 or tipo_case==3:#subida
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]+1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
elif tipo_case==1 or tipo_case==2:#bajada
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]-1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
|
|
|
tmp = pos +k
|
|
|
c = 0
|
|
|
c=c+1
|
|
|
return ang_new,ang_new2
|
|
|
|
|
|
def globalCheckPED(self,angulos,tipo_case):
|
|
|
l1,l2 = self.get2List(angulos)
|
|
|
##print("l1",l1)
|
|
|
##print("l2",l2)
|
|
|
if len(l1)>0:
|
|
|
#angulos2 = self.fixData90(list_=l1,ang_=angulos)
|
|
|
#l1,l2 = self.get2List(angulos2)
|
|
|
ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
|
|
|
#ang1_ = self.fixData90HL(ang1_)
|
|
|
#ang2_ = self.fixData90HL(ang2_)
|
|
|
else:
|
|
|
ang1_= angulos
|
|
|
ang2_= angulos
|
|
|
return ang1_,ang2_
|
|
|
|
|
|
|
|
|
def replaceNAN(self,data_weather,data_ele,val):
|
|
|
data= data_ele
|
|
|
data_T= data_weather
|
|
|
if data.shape[0]> data_T.shape[0]:
|
|
|
data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
|
|
|
c = 0
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
else:
|
|
|
data_N[i,:]=data_T[c,:]
|
|
|
c=c+1
|
|
|
return data_N
|
|
|
else:
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
return data_T
|
|
|
|
|
|
def check_case(self,data_ele,ang_max,ang_min):
|
|
|
start = data_ele[0]
|
|
|
end = data_ele[-1]
|
|
|
number = (end-start)
|
|
|
len_ang=len(data_ele)
|
|
|
print("start",start)
|
|
|
print("end",end)
|
|
|
print("number",number)
|
|
|
|
|
|
print("len_ang",len_ang)
|
|
|
|
|
|
#exit(1)
|
|
|
|
|
|
if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida
|
|
|
return 0
|
|
|
#elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada
|
|
|
# return 1
|
|
|
elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada
|
|
|
return 1
|
|
|
elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX
|
|
|
return 2
|
|
|
elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN
|
|
|
return 3
|
|
|
|
|
|
|
|
|
def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min,case_flag):
|
|
|
ang_max= ang_max
|
|
|
ang_min= ang_min
|
|
|
data_weather=data_weather
|
|
|
val_ch=val_ch
|
|
|
##print("*********************DATA WEATHER**************************************")
|
|
|
##print(data_weather)
|
|
|
if self.ini==0:
|
|
|
'''
|
|
|
print("**********************************************")
|
|
|
print("**********************************************")
|
|
|
print("***************ini**************")
|
|
|
print("**********************************************")
|
|
|
print("**********************************************")
|
|
|
'''
|
|
|
#print("data_ele",data_ele)
|
|
|
#----------------------------------------------------------
|
|
|
tipo_case = case_flag[-1]
|
|
|
#tipo_case = self.check_case(data_ele,ang_max,ang_min)
|
|
|
print("check_case",tipo_case)
|
|
|
#exit(1)
|
|
|
#--------------------- new -------------------------
|
|
|
data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
|
|
|
|
|
|
#-------------------------CAMBIOS RHI---------------------------------
|
|
|
start= ang_min
|
|
|
end = ang_max
|
|
|
n= (ang_max-ang_min)/res
|
|
|
#------ new
|
|
|
self.start_data_ele = data_ele_new[0]
|
|
|
self.end_data_ele = data_ele_new[-1]
|
|
|
if tipo_case==0 or tipo_case==3: # SUBIDA
|
|
|
n1= round(self.start_data_ele)- start
|
|
|
n2= end - round(self.end_data_ele)
|
|
|
print(self.start_data_ele)
|
|
|
print(self.end_data_ele)
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
print("ele1_nan",ele1_nan.shape)
|
|
|
print("data_ele_old",data_ele_old.shape)
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(self.end_data_ele+1,end,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
print("ele2_nan",ele2_nan.shape)
|
|
|
print("data_ele_old",data_ele_old.shape)
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
if tipo_case==1 or tipo_case==2: # BAJADA
|
|
|
data_ele_new = data_ele_new[::-1] # reversa
|
|
|
data_ele_old = data_ele_old[::-1]# reversa
|
|
|
data_weather = data_weather[::-1,:]# reversa
|
|
|
vec= numpy.where(data_ele_new<ang_max)
|
|
|
data_ele_new = data_ele_new[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
vec2= numpy.where(0<data_ele_new)
|
|
|
data_ele_new = data_ele_new[vec2]
|
|
|
data_ele_old = data_ele_old[vec2]
|
|
|
data_weather = data_weather[vec2[0]]
|
|
|
self.start_data_ele = data_ele_new[0]
|
|
|
self.end_data_ele = data_ele_new[-1]
|
|
|
|
|
|
n1= round(self.start_data_ele)- start
|
|
|
n2= end - round(self.end_data_ele)-1
|
|
|
print(self.start_data_ele)
|
|
|
print(self.end_data_ele)
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(self.end_data_ele+1,end,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
# RADAR
|
|
|
# NOTA data_ele y data_weather es la variable que retorna
|
|
|
val_mean = numpy.mean(data_weather[:,-1])
|
|
|
self.val_mean = val_mean
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
print("eleold",data_ele_old)
|
|
|
print(self.data_ele_tmp[val_ch])
|
|
|
print(data_ele_old.shape[0])
|
|
|
print(self.data_ele_tmp[val_ch].shape[0])
|
|
|
if (data_ele_old.shape[0]==91 or self.data_ele_tmp[val_ch].shape[0]==91):
|
|
|
import sys
|
|
|
print("EXIT",self.ini)
|
|
|
|
|
|
sys.exit(1)
|
|
|
self.data_ele_tmp[val_ch]= data_ele_old
|
|
|
else:
|
|
|
#print("**********************************************")
|
|
|
#print("****************VARIABLE**********************")
|
|
|
#-------------------------CAMBIOS RHI---------------------------------
|
|
|
#---------------------------------------------------------------------
|
|
|
##print("INPUT data_ele",data_ele)
|
|
|
flag=0
|
|
|
start_ele = self.res_ele[0]
|
|
|
#tipo_case = self.check_case(data_ele,ang_max,ang_min)
|
|
|
tipo_case = case_flag[-1]
|
|
|
#print("TIPO DE DATA",tipo_case)
|
|
|
#-----------new------------
|
|
|
data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case)
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
|
|
|
#-------------------------------NEW RHI ITERATIVO-------------------------
|
|
|
|
|
|
if tipo_case==0 : # SUBIDA
|
|
|
vec = numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
|
|
|
vec2 = numpy.where(0<data_ele)
|
|
|
data_ele= data_ele[vec2]
|
|
|
data_ele_old= data_ele_old[vec2]
|
|
|
##print(data_ele_new)
|
|
|
data_weather= data_weather[vec2[0]]
|
|
|
|
|
|
new_i_ele = int(round(data_ele[0]))
|
|
|
new_f_ele = int(round(data_ele[-1]))
|
|
|
#print(new_i_ele)
|
|
|
#print(new_f_ele)
|
|
|
#print(data_ele,len(data_ele))
|
|
|
#print(data_ele_old,len(data_ele_old))
|
|
|
if new_i_ele< 2:
|
|
|
self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old
|
|
|
self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele
|
|
|
self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==1 : #BAJADA
|
|
|
data_ele = data_ele[::-1] # reversa
|
|
|
data_ele_old = data_ele_old[::-1]# reversa
|
|
|
data_weather = data_weather[::-1,:]# reversa
|
|
|
vec= numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
vec2= numpy.where(0<data_ele)
|
|
|
data_ele = data_ele[vec2]
|
|
|
data_ele_old = data_ele_old[vec2]
|
|
|
data_weather = data_weather[vec2[0]]
|
|
|
|
|
|
|
|
|
new_i_ele = int(round(data_ele[0]))
|
|
|
new_f_ele = int(round(data_ele[-1]))
|
|
|
#print(data_ele)
|
|
|
#print(ang_max)
|
|
|
#print(data_ele_old)
|
|
|
if new_i_ele <= 1:
|
|
|
new_i_ele = 1
|
|
|
if round(data_ele[-1])>=ang_max-1:
|
|
|
self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old
|
|
|
self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele
|
|
|
self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==2: #bajada
|
|
|
vec = numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_weather= data_weather[vec[0]]
|
|
|
|
|
|
len_vec = len(vec)
|
|
|
data_ele_new = data_ele[::-1] # reversa
|
|
|
data_weather = data_weather[::-1,:]
|
|
|
new_i_ele = int(data_ele_new[0])
|
|
|
new_f_ele = int(data_ele_new[-1])
|
|
|
|
|
|
n1= new_i_ele- ang_min
|
|
|
n2= ang_max - new_f_ele-1
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
self.data_ele_tmp[val_ch] = data_ele_old
|
|
|
self.res_ele = data_ele
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==3:#subida
|
|
|
vec = numpy.where(0<data_ele)
|
|
|
data_ele= data_ele[vec]
|
|
|
data_ele_new = data_ele
|
|
|
data_ele_old= data_ele_old[vec]
|
|
|
data_weather= data_weather[vec[0]]
|
|
|
pos_ini = numpy.argmin(data_ele)
|
|
|
if pos_ini>0:
|
|
|
len_vec= len(data_ele)
|
|
|
vec3 = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int)
|
|
|
#print(vec3)
|
|
|
data_ele= data_ele[vec3]
|
|
|
data_ele_new = data_ele
|
|
|
data_ele_old= data_ele_old[vec3]
|
|
|
data_weather= data_weather[vec3]
|
|
|
|
|
|
new_i_ele = int(data_ele_new[0])
|
|
|
new_f_ele = int(data_ele_new[-1])
|
|
|
n1= new_i_ele- ang_min
|
|
|
n2= ang_max - new_f_ele-1
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
self.data_ele_tmp[val_ch] = data_ele_old
|
|
|
self.res_ele = data_ele
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
#print("self.data_ele_tmp",self.data_ele_tmp)
|
|
|
return data_weather,data_ele
|
|
|
|
|
|
|
|
|
def plot(self):
|
|
|
thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
|
|
|
data = self.data[-1]
|
|
|
r = self.data.yrange
|
|
|
delta_height = r[1]-r[0]
|
|
|
r_mask = numpy.where(r>=0)[0]
|
|
|
##print("delta_height",delta_height)
|
|
|
#print("r_mask",r_mask,len(r_mask))
|
|
|
r = numpy.arange(len(r_mask))*delta_height
|
|
|
self.y = 2*r
|
|
|
res = 1
|
|
|
###print("data['weather'].shape[0]",data['weather'].shape[0])
|
|
|
ang_max = self.ang_max
|
|
|
ang_min = self.ang_min
|
|
|
var_ang =ang_max - ang_min
|
|
|
step = (int(var_ang)/(res*data['weather'].shape[0]))
|
|
|
###print("step",step)
|
|
|
#--------------------------------------------------------
|
|
|
##print('weather',data['weather'].shape)
|
|
|
##print('ele',data['ele'].shape)
|
|
|
|
|
|
###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
|
|
|
###self.res_azi = numpy.mean(data['azi'])
|
|
|
###print("self.res_ele",self.res_ele)
|
|
|
plt.clf()
|
|
|
subplots = [121, 122]
|
|
|
try:
|
|
|
if self.data[-2]['ele'].max()<data['ele'].max():
|
|
|
self.ini=0
|
|
|
except:
|
|
|
pass
|
|
|
if self.ini==0:
|
|
|
self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan
|
|
|
self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
|
|
|
print("SHAPE",self.data_ele_tmp.shape)
|
|
|
|
|
|
for i,ax in enumerate(self.axes):
|
|
|
self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min,case_flag=self.data['case_flag'])
|
|
|
self.res_azi = numpy.mean(data['azi'])
|
|
|
|
|
|
if ax.firsttime:
|
|
|
#plt.clf()
|
|
|
print("Frist Plot")
|
|
|
cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
#fig=self.figures[0]
|
|
|
else:
|
|
|
#plt.clf()
|
|
|
print("ELSE PLOT")
|
|
|
cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
caax = cgax.parasites[0]
|
|
|
paax = cgax.parasites[1]
|
|
|
cbar = plt.gcf().colorbar(pm, pad=0.075)
|
|
|
caax.set_xlabel('x_range [km]')
|
|
|
caax.set_ylabel('y_range [km]')
|
|
|
plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
|
|
|
print("***************************self.ini****************************",self.ini)
|
|
|
self.ini= self.ini+1
|
|
|
|
|
|
class WeatherRHI_vRF_Plot(Plot):
|
|
|
CODE = 'weather'
|
|
|
plot_name = 'weather'
|
|
|
plot_type = 'rhistyle'
|
|
|
buffering = False
|
|
|
data_ele_tmp = None
|
|
|
|
|
|
def setup(self):
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("SETUP WEATHER PLOT")
|
|
|
self.ncols = 1
|
|
|
self.nrows = 1
|
|
|
self.nplots= 1
|
|
|
self.ylabel= 'Range [Km]'
|
|
|
self.titles= ['Weather']
|
|
|
if self.channels is not None:
|
|
|
self.nplots = len(self.channels)
|
|
|
self.nrows = len(self.channels)
|
|
|
else:
|
|
|
self.nplots = self.data.shape(self.CODE)[0]
|
|
|
self.nrows = self.nplots
|
|
|
self.channels = list(range(self.nplots))
|
|
|
print("channels",self.channels)
|
|
|
print("que saldra", self.data.shape(self.CODE)[0])
|
|
|
self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
|
|
|
print("self.titles",self.titles)
|
|
|
self.colorbar=False
|
|
|
self.width =8
|
|
|
self.height =8
|
|
|
self.ini =0
|
|
|
self.len_azi =0
|
|
|
self.buffer_ini = None
|
|
|
self.buffer_ele = None
|
|
|
self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
|
|
|
self.flag =0
|
|
|
self.indicador= 0
|
|
|
self.last_data_ele = None
|
|
|
self.val_mean = None
|
|
|
|
|
|
def update(self, dataOut):
|
|
|
|
|
|
data = {}
|
|
|
meta = {}
|
|
|
if hasattr(dataOut, 'dataPP_POWER'):
|
|
|
factor = 1
|
|
|
if hasattr(dataOut, 'nFFTPoints'):
|
|
|
factor = dataOut.normFactor
|
|
|
print("dataOut",dataOut.data_360.shape)
|
|
|
#
|
|
|
data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
|
|
|
#
|
|
|
#data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
|
|
|
data['azi'] = dataOut.data_azi
|
|
|
data['ele'] = dataOut.data_ele
|
|
|
data['case_flag'] = dataOut.case_flag
|
|
|
#print("UPDATE")
|
|
|
#print("data[weather]",data['weather'].shape)
|
|
|
#print("data[azi]",data['azi'])
|
|
|
return data, meta
|
|
|
|
|
|
def get2List(self,angulos):
|
|
|
list1=[]
|
|
|
list2=[]
|
|
|
#print(angulos)
|
|
|
#exit(1)
|
|
|
for i in reversed(range(len(angulos))):
|
|
|
if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
|
|
|
diff_ = angulos[i]-angulos[i-1]
|
|
|
if abs(diff_) >1.5:
|
|
|
list1.append(i-1)
|
|
|
list2.append(diff_)
|
|
|
return list(reversed(list1)),list(reversed(list2))
|
|
|
|
|
|
def fixData90(self,list_,ang_):
|
|
|
if list_[0]==-1:
|
|
|
vec = numpy.where(ang_<ang_[0])
|
|
|
ang_[vec] = ang_[vec]+90
|
|
|
return ang_
|
|
|
return ang_
|
|
|
|
|
|
def fixData90HL(self,angulos):
|
|
|
vec = numpy.where(angulos>=90)
|
|
|
angulos[vec]=angulos[vec]-90
|
|
|
return angulos
|
|
|
|
|
|
|
|
|
def search_pos(self,pos,list_):
|
|
|
for i in range(len(list_)):
|
|
|
if pos == list_[i]:
|
|
|
return True,i
|
|
|
i=None
|
|
|
return False,i
|
|
|
|
|
|
def fixDataComp(self,ang_,list1_,list2_,tipo_case):
|
|
|
size = len(ang_)
|
|
|
size2 = 0
|
|
|
for i in range(len(list2_)):
|
|
|
size2=size2+round(abs(list2_[i]))-1
|
|
|
new_size= size+size2
|
|
|
ang_new = numpy.zeros(new_size)
|
|
|
ang_new2 = numpy.zeros(new_size)
|
|
|
|
|
|
tmp = 0
|
|
|
c = 0
|
|
|
for i in range(len(ang_)):
|
|
|
ang_new[tmp +c] = ang_[i]
|
|
|
ang_new2[tmp+c] = ang_[i]
|
|
|
condition , value = self.search_pos(i,list1_)
|
|
|
if condition:
|
|
|
pos = tmp + c + 1
|
|
|
for k in range(round(abs(list2_[value]))-1):
|
|
|
if tipo_case==0 or tipo_case==3:#subida
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]+1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
elif tipo_case==1 or tipo_case==2:#bajada
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]-1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
|
|
|
tmp = pos +k
|
|
|
c = 0
|
|
|
c=c+1
|
|
|
return ang_new,ang_new2
|
|
|
|
|
|
def globalCheckPED(self,angulos,tipo_case):
|
|
|
l1,l2 = self.get2List(angulos)
|
|
|
print("l1",l1)
|
|
|
print("l2",l2)
|
|
|
if len(l1)>0:
|
|
|
#angulos2 = self.fixData90(list_=l1,ang_=angulos)
|
|
|
#l1,l2 = self.get2List(angulos2)
|
|
|
ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
|
|
|
#ang1_ = self.fixData90HL(ang1_)
|
|
|
#ang2_ = self.fixData90HL(ang2_)
|
|
|
else:
|
|
|
ang1_= angulos
|
|
|
ang2_= angulos
|
|
|
return ang1_,ang2_
|
|
|
|
|
|
|
|
|
def replaceNAN(self,data_weather,data_ele,val):
|
|
|
data= data_ele
|
|
|
data_T= data_weather
|
|
|
#print(data.shape[0])
|
|
|
#print(data_T.shape[0])
|
|
|
#exit(1)
|
|
|
if data.shape[0]> data_T.shape[0]:
|
|
|
data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
|
|
|
c = 0
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
else:
|
|
|
data_N[i,:]=data_T[c,:]
|
|
|
c=c+1
|
|
|
return data_N
|
|
|
else:
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
return data_T
|
|
|
|
|
|
|
|
|
def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min,case_flag):
|
|
|
ang_max= ang_max
|
|
|
ang_min= ang_min
|
|
|
data_weather=data_weather
|
|
|
val_ch=val_ch
|
|
|
##print("*********************DATA WEATHER**************************************")
|
|
|
##print(data_weather)
|
|
|
|
|
|
'''
|
|
|
print("**********************************************")
|
|
|
print("**********************************************")
|
|
|
print("***************ini**************")
|
|
|
print("**********************************************")
|
|
|
print("**********************************************")
|
|
|
'''
|
|
|
#print("data_ele",data_ele)
|
|
|
#----------------------------------------------------------
|
|
|
|
|
|
#exit(1)
|
|
|
tipo_case = case_flag[-1]
|
|
|
print("tipo_case",tipo_case)
|
|
|
#--------------------- new -------------------------
|
|
|
data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
|
|
|
|
|
|
#-------------------------CAMBIOS RHI---------------------------------
|
|
|
|
|
|
vec = numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_weather= data_weather[vec[0]]
|
|
|
|
|
|
len_vec = len(vec)
|
|
|
data_ele_new = data_ele[::-1] # reversa
|
|
|
data_weather = data_weather[::-1,:]
|
|
|
new_i_ele = int(data_ele_new[0])
|
|
|
new_f_ele = int(data_ele_new[-1])
|
|
|
|
|
|
n1= new_i_ele- ang_min
|
|
|
n2= ang_max - new_f_ele-1
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
|
|
|
print("ele shape",data_ele.shape)
|
|
|
print(data_ele)
|
|
|
|
|
|
#print("self.data_ele_tmp",self.data_ele_tmp)
|
|
|
val_mean = numpy.mean(data_weather[:,-1])
|
|
|
self.val_mean = val_mean
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch]= data_ele_old
|
|
|
|
|
|
|
|
|
print("data_weather shape",data_weather.shape)
|
|
|
print(data_weather)
|
|
|
#exit(1)
|
|
|
return data_weather,data_ele
|
|
|
|
|
|
|
|
|
def plot(self):
|
|
|
thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
|
|
|
data = self.data[-1]
|
|
|
r = self.data.yrange
|
|
|
delta_height = r[1]-r[0]
|
|
|
r_mask = numpy.where(r>=0)[0]
|
|
|
##print("delta_height",delta_height)
|
|
|
#print("r_mask",r_mask,len(r_mask))
|
|
|
r = numpy.arange(len(r_mask))*delta_height
|
|
|
self.y = 2*r
|
|
|
res = 1
|
|
|
###print("data['weather'].shape[0]",data['weather'].shape[0])
|
|
|
ang_max = self.ang_max
|
|
|
ang_min = self.ang_min
|
|
|
var_ang =ang_max - ang_min
|
|
|
step = (int(var_ang)/(res*data['weather'].shape[0]))
|
|
|
###print("step",step)
|
|
|
#--------------------------------------------------------
|
|
|
##print('weather',data['weather'].shape)
|
|
|
##print('ele',data['ele'].shape)
|
|
|
|
|
|
###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
|
|
|
###self.res_azi = numpy.mean(data['azi'])
|
|
|
###print("self.res_ele",self.res_ele)
|
|
|
plt.clf()
|
|
|
subplots = [121, 122]
|
|
|
if self.ini==0:
|
|
|
self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan
|
|
|
self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
|
|
|
print("SHAPE",self.data_ele_tmp.shape)
|
|
|
|
|
|
for i,ax in enumerate(self.axes):
|
|
|
self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min,case_flag=self.data['case_flag'])
|
|
|
self.res_azi = numpy.mean(data['azi'])
|
|
|
|
|
|
print(self.res_ele)
|
|
|
#exit(1)
|
|
|
if ax.firsttime:
|
|
|
#plt.clf()
|
|
|
cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
#fig=self.figures[0]
|
|
|
else:
|
|
|
|
|
|
#plt.clf()
|
|
|
cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
caax = cgax.parasites[0]
|
|
|
paax = cgax.parasites[1]
|
|
|
cbar = plt.gcf().colorbar(pm, pad=0.075)
|
|
|
caax.set_xlabel('x_range [km]')
|
|
|
caax.set_ylabel('y_range [km]')
|
|
|
plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
|
|
|
print("***************************self.ini****************************",self.ini)
|
|
|
self.ini= self.ini+1
|
|
|
|
|
|
class WeatherRHI_vRF3_Plot(Plot):
|
|
|
CODE = 'weather'
|
|
|
plot_name = 'weather'
|
|
|
plot_type = 'rhistyle'
|
|
|
buffering = False
|
|
|
data_ele_tmp = None
|
|
|
|
|
|
def setup(self):
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("********************")
|
|
|
print("SETUP WEATHER PLOT")
|
|
|
self.ncols = 1
|
|
|
self.nrows = 1
|
|
|
self.nplots= 1
|
|
|
self.ylabel= 'Range [Km]'
|
|
|
self.titles= ['Weather']
|
|
|
if self.channels is not None:
|
|
|
self.nplots = len(self.channels)
|
|
|
self.nrows = len(self.channels)
|
|
|
else:
|
|
|
self.nplots = self.data.shape(self.CODE)[0]
|
|
|
self.nrows = self.nplots
|
|
|
self.channels = list(range(self.nplots))
|
|
|
print("channels",self.channels)
|
|
|
print("que saldra", self.data.shape(self.CODE)[0])
|
|
|
self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
|
|
|
print("self.titles",self.titles)
|
|
|
self.colorbar=False
|
|
|
self.width =8
|
|
|
self.height =8
|
|
|
self.ini =0
|
|
|
self.len_azi =0
|
|
|
self.buffer_ini = None
|
|
|
self.buffer_ele = None
|
|
|
self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
|
|
|
self.flag =0
|
|
|
self.indicador= 0
|
|
|
self.last_data_ele = None
|
|
|
self.val_mean = None
|
|
|
|
|
|
def update(self, dataOut):
|
|
|
|
|
|
data = {}
|
|
|
meta = {}
|
|
|
if hasattr(dataOut, 'dataPP_POWER'):
|
|
|
factor = 1
|
|
|
if hasattr(dataOut, 'nFFTPoints'):
|
|
|
factor = dataOut.normFactor
|
|
|
print("dataOut",dataOut.data_360.shape)
|
|
|
#
|
|
|
data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
|
|
|
#
|
|
|
#data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
|
|
|
data['azi'] = dataOut.data_azi
|
|
|
data['ele'] = dataOut.data_ele
|
|
|
#data['case_flag'] = dataOut.case_flag
|
|
|
#print("UPDATE")
|
|
|
#print("data[weather]",data['weather'].shape)
|
|
|
#print("data[azi]",data['azi'])
|
|
|
return data, meta
|
|
|
|
|
|
def get2List(self,angulos):
|
|
|
list1=[]
|
|
|
list2=[]
|
|
|
for i in reversed(range(len(angulos))):
|
|
|
if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
|
|
|
diff_ = angulos[i]-angulos[i-1]
|
|
|
if abs(diff_) >1.5:
|
|
|
list1.append(i-1)
|
|
|
list2.append(diff_)
|
|
|
return list(reversed(list1)),list(reversed(list2))
|
|
|
|
|
|
def fixData90(self,list_,ang_):
|
|
|
if list_[0]==-1:
|
|
|
vec = numpy.where(ang_<ang_[0])
|
|
|
ang_[vec] = ang_[vec]+90
|
|
|
return ang_
|
|
|
return ang_
|
|
|
|
|
|
def fixData90HL(self,angulos):
|
|
|
vec = numpy.where(angulos>=90)
|
|
|
angulos[vec]=angulos[vec]-90
|
|
|
return angulos
|
|
|
|
|
|
|
|
|
def search_pos(self,pos,list_):
|
|
|
for i in range(len(list_)):
|
|
|
if pos == list_[i]:
|
|
|
return True,i
|
|
|
i=None
|
|
|
return False,i
|
|
|
|
|
|
def fixDataComp(self,ang_,list1_,list2_,tipo_case):
|
|
|
size = len(ang_)
|
|
|
size2 = 0
|
|
|
for i in range(len(list2_)):
|
|
|
size2=size2+round(abs(list2_[i]))-1
|
|
|
new_size= size+size2
|
|
|
ang_new = numpy.zeros(new_size)
|
|
|
ang_new2 = numpy.zeros(new_size)
|
|
|
|
|
|
tmp = 0
|
|
|
c = 0
|
|
|
for i in range(len(ang_)):
|
|
|
ang_new[tmp +c] = ang_[i]
|
|
|
ang_new2[tmp+c] = ang_[i]
|
|
|
condition , value = self.search_pos(i,list1_)
|
|
|
if condition:
|
|
|
pos = tmp + c + 1
|
|
|
for k in range(round(abs(list2_[value]))-1):
|
|
|
if tipo_case==0 or tipo_case==3:#subida
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]+1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
elif tipo_case==1 or tipo_case==2:#bajada
|
|
|
ang_new[pos+k] = ang_new[pos+k-1]-1
|
|
|
ang_new2[pos+k] = numpy.nan
|
|
|
|
|
|
tmp = pos +k
|
|
|
c = 0
|
|
|
c=c+1
|
|
|
return ang_new,ang_new2
|
|
|
|
|
|
def globalCheckPED(self,angulos,tipo_case):
|
|
|
l1,l2 = self.get2List(angulos)
|
|
|
##print("l1",l1)
|
|
|
##print("l2",l2)
|
|
|
if len(l1)>0:
|
|
|
#angulos2 = self.fixData90(list_=l1,ang_=angulos)
|
|
|
#l1,l2 = self.get2List(angulos2)
|
|
|
ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
|
|
|
#ang1_ = self.fixData90HL(ang1_)
|
|
|
#ang2_ = self.fixData90HL(ang2_)
|
|
|
else:
|
|
|
ang1_= angulos
|
|
|
ang2_= angulos
|
|
|
return ang1_,ang2_
|
|
|
|
|
|
|
|
|
def replaceNAN(self,data_weather,data_ele,val):
|
|
|
data= data_ele
|
|
|
data_T= data_weather
|
|
|
|
|
|
if data.shape[0]> data_T.shape[0]:
|
|
|
print("IF")
|
|
|
data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
|
|
|
c = 0
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
else:
|
|
|
data_N[i,:]=data_T[c,:]
|
|
|
c=c+1
|
|
|
return data_N
|
|
|
else:
|
|
|
print("else")
|
|
|
for i in range(len(data)):
|
|
|
if numpy.isnan(data[i]):
|
|
|
data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
|
|
|
return data_T
|
|
|
|
|
|
def check_case(self,data_ele,ang_max,ang_min):
|
|
|
start = data_ele[0]
|
|
|
end = data_ele[-1]
|
|
|
number = (end-start)
|
|
|
len_ang=len(data_ele)
|
|
|
print("start",start)
|
|
|
print("end",end)
|
|
|
print("number",number)
|
|
|
|
|
|
print("len_ang",len_ang)
|
|
|
|
|
|
#exit(1)
|
|
|
|
|
|
if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida
|
|
|
return 0
|
|
|
#elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada
|
|
|
# return 1
|
|
|
elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada
|
|
|
return 1
|
|
|
elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX
|
|
|
return 2
|
|
|
elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN
|
|
|
return 3
|
|
|
|
|
|
|
|
|
def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min,case_flag):
|
|
|
ang_max= ang_max
|
|
|
ang_min= ang_min
|
|
|
data_weather=data_weather
|
|
|
val_ch=val_ch
|
|
|
##print("*********************DATA WEATHER**************************************")
|
|
|
##print(data_weather)
|
|
|
if self.ini==0:
|
|
|
|
|
|
#--------------------- new -------------------------
|
|
|
data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
|
|
|
|
|
|
#-------------------------CAMBIOS RHI---------------------------------
|
|
|
start= ang_min
|
|
|
end = ang_max
|
|
|
n= (ang_max-ang_min)/res
|
|
|
#------ new
|
|
|
self.start_data_ele = data_ele_new[0]
|
|
|
self.end_data_ele = data_ele_new[-1]
|
|
|
if tipo_case==0 or tipo_case==3: # SUBIDA
|
|
|
n1= round(self.start_data_ele)- start
|
|
|
n2= end - round(self.end_data_ele)
|
|
|
print(self.start_data_ele)
|
|
|
print(self.end_data_ele)
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
print("ele1_nan",ele1_nan.shape)
|
|
|
print("data_ele_old",data_ele_old.shape)
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(self.end_data_ele+1,end,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
print("ele2_nan",ele2_nan.shape)
|
|
|
print("data_ele_old",data_ele_old.shape)
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
if tipo_case==1 or tipo_case==2: # BAJADA
|
|
|
data_ele_new = data_ele_new[::-1] # reversa
|
|
|
data_ele_old = data_ele_old[::-1]# reversa
|
|
|
data_weather = data_weather[::-1,:]# reversa
|
|
|
vec= numpy.where(data_ele_new<ang_max)
|
|
|
data_ele_new = data_ele_new[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
vec2= numpy.where(0<data_ele_new)
|
|
|
data_ele_new = data_ele_new[vec2]
|
|
|
data_ele_old = data_ele_old[vec2]
|
|
|
data_weather = data_weather[vec2[0]]
|
|
|
self.start_data_ele = data_ele_new[0]
|
|
|
self.end_data_ele = data_ele_new[-1]
|
|
|
|
|
|
n1= round(self.start_data_ele)- start
|
|
|
n2= end - round(self.end_data_ele)-1
|
|
|
print(self.start_data_ele)
|
|
|
print(self.end_data_ele)
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(self.end_data_ele+1,end,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
# RADAR
|
|
|
# NOTA data_ele y data_weather es la variable que retorna
|
|
|
val_mean = numpy.mean(data_weather[:,-1])
|
|
|
self.val_mean = val_mean
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
print("eleold",data_ele_old)
|
|
|
print(self.data_ele_tmp[val_ch])
|
|
|
print(data_ele_old.shape[0])
|
|
|
print(self.data_ele_tmp[val_ch].shape[0])
|
|
|
if (data_ele_old.shape[0]==91 or self.data_ele_tmp[val_ch].shape[0]==91):
|
|
|
import sys
|
|
|
print("EXIT",self.ini)
|
|
|
|
|
|
sys.exit(1)
|
|
|
self.data_ele_tmp[val_ch]= data_ele_old
|
|
|
else:
|
|
|
#print("**********************************************")
|
|
|
#print("****************VARIABLE**********************")
|
|
|
#-------------------------CAMBIOS RHI---------------------------------
|
|
|
#---------------------------------------------------------------------
|
|
|
##print("INPUT data_ele",data_ele)
|
|
|
flag=0
|
|
|
start_ele = self.res_ele[0]
|
|
|
#tipo_case = self.check_case(data_ele,ang_max,ang_min)
|
|
|
tipo_case = case_flag[-1]
|
|
|
#print("TIPO DE DATA",tipo_case)
|
|
|
#-----------new------------
|
|
|
data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case)
|
|
|
data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
|
|
|
#-------------------------------NEW RHI ITERATIVO-------------------------
|
|
|
|
|
|
if tipo_case==0 : # SUBIDA
|
|
|
vec = numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
|
|
|
vec2 = numpy.where(0<data_ele)
|
|
|
data_ele= data_ele[vec2]
|
|
|
data_ele_old= data_ele_old[vec2]
|
|
|
##print(data_ele_new)
|
|
|
data_weather= data_weather[vec2[0]]
|
|
|
|
|
|
new_i_ele = int(round(data_ele[0]))
|
|
|
new_f_ele = int(round(data_ele[-1]))
|
|
|
#print(new_i_ele)
|
|
|
#print(new_f_ele)
|
|
|
#print(data_ele,len(data_ele))
|
|
|
#print(data_ele_old,len(data_ele_old))
|
|
|
if new_i_ele< 2:
|
|
|
self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old
|
|
|
self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele
|
|
|
self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==1 : #BAJADA
|
|
|
data_ele = data_ele[::-1] # reversa
|
|
|
data_ele_old = data_ele_old[::-1]# reversa
|
|
|
data_weather = data_weather[::-1,:]# reversa
|
|
|
vec= numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_ele_old = data_ele_old[vec]
|
|
|
data_weather = data_weather[vec[0]]
|
|
|
vec2= numpy.where(0<data_ele)
|
|
|
data_ele = data_ele[vec2]
|
|
|
data_ele_old = data_ele_old[vec2]
|
|
|
data_weather = data_weather[vec2[0]]
|
|
|
|
|
|
|
|
|
new_i_ele = int(round(data_ele[0]))
|
|
|
new_f_ele = int(round(data_ele[-1]))
|
|
|
#print(data_ele)
|
|
|
#print(ang_max)
|
|
|
#print(data_ele_old)
|
|
|
if new_i_ele <= 1:
|
|
|
new_i_ele = 1
|
|
|
if round(data_ele[-1])>=ang_max-1:
|
|
|
self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
|
|
|
self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old
|
|
|
self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele
|
|
|
self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==2: #bajada
|
|
|
vec = numpy.where(data_ele<ang_max)
|
|
|
data_ele = data_ele[vec]
|
|
|
data_weather= data_weather[vec[0]]
|
|
|
|
|
|
len_vec = len(vec)
|
|
|
data_ele_new = data_ele[::-1] # reversa
|
|
|
data_weather = data_weather[::-1,:]
|
|
|
new_i_ele = int(data_ele_new[0])
|
|
|
new_f_ele = int(data_ele_new[-1])
|
|
|
|
|
|
n1= new_i_ele- ang_min
|
|
|
n2= ang_max - new_f_ele-1
|
|
|
if n1>0:
|
|
|
ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
|
|
|
ele1_nan= numpy.ones(n1)*numpy.nan
|
|
|
data_ele = numpy.hstack((ele1,data_ele_new))
|
|
|
data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
|
|
|
if n2>0:
|
|
|
ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
|
|
|
ele2_nan= numpy.ones(n2)*numpy.nan
|
|
|
data_ele = numpy.hstack((data_ele,ele2))
|
|
|
data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
|
|
|
|
|
|
self.data_ele_tmp[val_ch] = data_ele_old
|
|
|
self.res_ele = data_ele
|
|
|
self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
|
|
|
data_ele = self.res_ele
|
|
|
data_weather = self.res_weather[val_ch]
|
|
|
|
|
|
elif tipo_case==3:#subida
|
|
|
vec = numpy.where(0<data_ele)
|
|
|
data_ele= data_ele[vec]
|
|
|
data_ele_new = data_ele
|
|
|
data_ele_old= data_ele_old[vec]
|
|
|
data_weather= data_weather[vec[0]]
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pos_ini = numpy.argmin(data_ele)
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if pos_ini>0:
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len_vec= len(data_ele)
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vec3 = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int)
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#print(vec3)
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data_ele= data_ele[vec3]
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data_ele_new = data_ele
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data_ele_old= data_ele_old[vec3]
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data_weather= data_weather[vec3]
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new_i_ele = int(data_ele_new[0])
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new_f_ele = int(data_ele_new[-1])
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n1= new_i_ele- ang_min
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n2= ang_max - new_f_ele-1
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if n1>0:
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ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
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ele1_nan= numpy.ones(n1)*numpy.nan
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data_ele = numpy.hstack((ele1,data_ele_new))
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data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
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if n2>0:
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ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
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ele2_nan= numpy.ones(n2)*numpy.nan
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data_ele = numpy.hstack((data_ele,ele2))
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data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
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self.data_ele_tmp[val_ch] = data_ele_old
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self.res_ele = data_ele
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self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
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data_ele = self.res_ele
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data_weather = self.res_weather[val_ch]
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#print("self.data_ele_tmp",self.data_ele_tmp)
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return data_weather,data_ele
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def const_ploteo_vRF(self,val_ch,data_weather,data_ele,res,ang_max,ang_min):
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data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,1)
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data_ele = data_ele_old.copy()
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diff_1 = ang_max - data_ele[0]
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angles_1_nan = numpy.linspace(ang_max,data_ele[0]+1,int(diff_1)-1)#*numpy.nan
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diff_2 = data_ele[-1]-ang_min
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angles_2_nan = numpy.linspace(data_ele[-1]-1,ang_min,int(diff_2)-1)#*numpy.nan
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angles_filled = numpy.concatenate((angles_1_nan,data_ele,angles_2_nan))
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print(angles_filled)
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data_1_nan = numpy.ones([angles_1_nan.shape[0],len(self.r_mask)])*numpy.nan
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data_2_nan = numpy.ones([angles_2_nan.shape[0],len(self.r_mask)])*numpy.nan
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data_filled = numpy.concatenate((data_1_nan,data_weather,data_2_nan),axis=0)
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#val_mean = numpy.mean(data_weather[:,-1])
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#self.val_mean = val_mean
|
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print(data_filled)
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|
|
data_filled = self.replaceNAN(data_weather=data_filled,data_ele=angles_filled,val=numpy.nan)
|
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print(data_filled)
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print(data_filled.shape)
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|
print(angles_filled.shape)
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|
return data_filled,angles_filled
|
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|
def plot(self):
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|
thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
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|
|
data = self.data[-1]
|
|
|
r = self.data.yrange
|
|
|
delta_height = r[1]-r[0]
|
|
|
r_mask = numpy.where(r>=0)[0]
|
|
|
self.r_mask =r_mask
|
|
|
##print("delta_height",delta_height)
|
|
|
#print("r_mask",r_mask,len(r_mask))
|
|
|
r = numpy.arange(len(r_mask))*delta_height
|
|
|
self.y = 2*r
|
|
|
res = 1
|
|
|
###print("data['weather'].shape[0]",data['weather'].shape[0])
|
|
|
ang_max = self.ang_max
|
|
|
ang_min = self.ang_min
|
|
|
var_ang =ang_max - ang_min
|
|
|
step = (int(var_ang)/(res*data['weather'].shape[0]))
|
|
|
###print("step",step)
|
|
|
#--------------------------------------------------------
|
|
|
##print('weather',data['weather'].shape)
|
|
|
##print('ele',data['ele'].shape)
|
|
|
|
|
|
###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
|
|
|
###self.res_azi = numpy.mean(data['azi'])
|
|
|
###print("self.res_ele",self.res_ele)
|
|
|
|
|
|
plt.clf()
|
|
|
subplots = [121, 122]
|
|
|
#if self.ini==0:
|
|
|
#self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
|
|
|
#print("SHAPE",self.data_ele_tmp.shape)
|
|
|
|
|
|
for i,ax in enumerate(self.axes):
|
|
|
res_weather, self.res_ele = self.const_ploteo_vRF(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],res=res,ang_max=ang_max,ang_min=ang_min)
|
|
|
self.res_azi = numpy.mean(data['azi'])
|
|
|
|
|
|
if ax.firsttime:
|
|
|
#plt.clf()
|
|
|
print("Frist Plot")
|
|
|
print(data['weather'][i][:,r_mask].shape)
|
|
|
print(data['ele'].shape)
|
|
|
cgax, pm = wrl.vis.plot_rhi(res_weather,r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
#cgax, pm = wrl.vis.plot_rhi(data['weather'][i][:,r_mask],r=r,th=data['ele'],ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
gh = cgax.get_grid_helper()
|
|
|
locs = numpy.linspace(ang_min,ang_max,var_ang+1)
|
|
|
gh.grid_finder.grid_locator1 = FixedLocator(locs)
|
|
|
gh.grid_finder.tick_formatter1 = DictFormatter(dict([(i, r"${0:.0f}^\circ$".format(i)) for i in locs]))
|
|
|
|
|
|
|
|
|
#fig=self.figures[0]
|
|
|
else:
|
|
|
#plt.clf()
|
|
|
print("ELSE PLOT")
|
|
|
cgax, pm = wrl.vis.plot_rhi(res_weather,r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
#cgax, pm = wrl.vis.plot_rhi(data['weather'][i][:,r_mask],r=r,th=data['ele'],ax=subplots[i], proj='cg',vmin=20, vmax=80)
|
|
|
gh = cgax.get_grid_helper()
|
|
|
locs = numpy.linspace(ang_min,ang_max,var_ang+1)
|
|
|
gh.grid_finder.grid_locator1 = FixedLocator(locs)
|
|
|
gh.grid_finder.tick_formatter1 = DictFormatter(dict([(i, r"${0:.0f}^\circ$".format(i)) for i in locs]))
|
|
|
|
|
|
caax = cgax.parasites[0]
|
|
|
paax = cgax.parasites[1]
|
|
|
cbar = plt.gcf().colorbar(pm, pad=0.075)
|
|
|
caax.set_xlabel('x_range [km]')
|
|
|
caax.set_ylabel('y_range [km]')
|
|
|
plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
|
|
|
print("***************************self.ini****************************",self.ini)
|
|
|
self.ini= self.ini+1
|
|
|
|
