import os import datetime import numpy from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter from schainpy.model.graphics.jroplot_base import Plot, plt from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot from schainpy.utils import log import wradlib.georef as georef EARTH_RADIUS = 6.3710e3 def ll2xy(lat1, lon1, lat2, lon2): p = 0.017453292519943295 a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 r = 12742 * numpy.arcsin(numpy.sqrt(a)) theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) theta = -theta + numpy.pi/2 return r*numpy.cos(theta), r*numpy.sin(theta) def km2deg(km): ''' Convert distance in km to degrees ''' return numpy.rad2deg(km/EARTH_RADIUS) class SpectralMomentsPlot(SpectraPlot): ''' Plot for Spectral Moments ''' CODE = 'spc_moments' # colormap = 'jet' # plot_type = 'pcolor' class DobleGaussianPlot(SpectraPlot): ''' Plot for Double Gaussian Plot ''' CODE = 'gaussian_fit' # colormap = 'jet' # plot_type = 'pcolor' class DoubleGaussianSpectraCutPlot(SpectraCutPlot): ''' Plot SpectraCut with Double Gaussian Fit ''' CODE = 'cut_gaussian_fit' class SnrPlot(RTIPlot): ''' Plot for SNR Data ''' CODE = 'snr' colormap = 'jet' def update(self, dataOut): data = { 'snr': 10*numpy.log10(dataOut.data_snr) } return data, {} class DopplerPlot(RTIPlot): ''' Plot for DOPPLER Data (1st moment) ''' CODE = 'dop' colormap = 'jet' def update(self, dataOut): data = { 'dop': 10*numpy.log10(dataOut.data_dop) } return data, {} class PowerPlot(RTIPlot): ''' Plot for Power Data (0 moment) ''' CODE = 'pow' colormap = 'jet' def update(self, dataOut): data = { 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor) } return data, {} class SpectralWidthPlot(RTIPlot): ''' Plot for Spectral Width Data (2nd moment) ''' CODE = 'width' colormap = 'jet' def update(self, dataOut): data = { 'width': dataOut.data_width } return data, {} class SkyMapPlot(Plot): ''' Plot for meteors detection data ''' CODE = 'param' def setup(self): self.ncols = 1 self.nrows = 1 self.width = 7.2 self.height = 7.2 self.nplots = 1 self.xlabel = 'Zonal Zenith Angle (deg)' self.ylabel = 'Meridional Zenith Angle (deg)' self.polar = True self.ymin = -180 self.ymax = 180 self.colorbar = False def plot(self): arrayParameters = numpy.concatenate(self.data['param']) error = arrayParameters[:, -1] indValid = numpy.where(error == 0)[0] finalMeteor = arrayParameters[indValid, :] finalAzimuth = finalMeteor[:, 3] finalZenith = finalMeteor[:, 4] x = finalAzimuth * numpy.pi / 180 y = finalZenith ax = self.axes[0] if ax.firsttime: ax.plot = ax.plot(x, y, 'bo', markersize=5)[0] else: ax.plot.set_data(x, y) dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S') dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S') title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, dt2, len(x)) self.titles[0] = title class GenericRTIPlot(Plot): ''' Plot for data_xxxx object ''' CODE = 'param' colormap = 'viridis' plot_type = 'pcolorbuffer' def setup(self): self.xaxis = 'time' self.ncols = 1 self.nrows = self.data.shape('param')[0] self.nplots = self.nrows self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95}) if not self.xlabel: self.xlabel = 'Time' self.ylabel = 'Range [km]' if not self.titles: self.titles = ['Param {}'.format(x) for x in range(self.nrows)] def update(self, dataOut): data = { 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0) } meta = {} return data, meta def plot(self): # self.data.normalize_heights() self.x = self.data.times self.y = self.data.yrange self.z = self.data['param'] self.z = 10*numpy.log10(self.z) self.z = numpy.ma.masked_invalid(self.z) if self.decimation is None: x, y, z = self.fill_gaps(self.x, self.y, self.z) else: x, y, z = self.fill_gaps(*self.decimate()) for n, ax in enumerate(self.axes): self.zmax = self.zmax if self.zmax is not None else numpy.max( self.z[n]) self.zmin = self.zmin if self.zmin is not None else numpy.min( self.z[n]) if ax.firsttime: if self.zlimits is not None: self.zmin, self.zmax = self.zlimits[n] ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], vmin=self.zmin, vmax=self.zmax, cmap=self.cmaps[n] ) else: if self.zlimits is not None: self.zmin, self.zmax = self.zlimits[n] ax.collections.remove(ax.collections[0]) ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], vmin=self.zmin, vmax=self.zmax, cmap=self.cmaps[n] ) class PolarMapPlot(Plot): ''' Plot for weather radar ''' CODE = 'param' colormap = 'seismic' def setup(self): self.ncols = 1 self.nrows = 1 self.width = 9 self.height = 8 self.mode = self.data.meta['mode'] 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)) if self.mode == 'E': self.xlabel = 'Longitude' self.ylabel = 'Latitude' else: self.xlabel = 'Range (km)' self.ylabel = 'Height (km)' self.bgcolor = 'white' self.cb_labels = self.data.meta['units'] self.lat = self.data.meta['latitude'] self.lon = self.data.meta['longitude'] self.xmin, self.xmax = float( km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon) self.ymin, self.ymax = float( km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat) # self.polar = True def plot(self): for n, ax in enumerate(self.axes): data = self.data['param'][self.channels[n]] zeniths = numpy.linspace( 0, self.data.meta['max_range'], data.shape[1]) if self.mode == 'E': azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2 r, theta = numpy.meshgrid(zeniths, azimuths) x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin( theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])) x = km2deg(x) + self.lon y = km2deg(y) + self.lat else: azimuths = numpy.radians(self.data.yrange) r, theta = numpy.meshgrid(zeniths, azimuths) x, y = r*numpy.cos(theta), r*numpy.sin(theta) self.y = zeniths if ax.firsttime: if self.zlimits is not None: self.zmin, self.zmax = self.zlimits[n] ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), x, y, numpy.ma.array(data, mask=numpy.isnan(data)), vmin=self.zmin, vmax=self.zmax, cmap=self.cmaps[n]) else: if self.zlimits is not None: self.zmin, self.zmax = self.zlimits[n] ax.collections.remove(ax.collections[0]) ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), x, y, numpy.ma.array(data, mask=numpy.isnan(data)), vmin=self.zmin, vmax=self.zmax, cmap=self.cmaps[n]) if self.mode == 'A': continue # plot district names f = open('/data/workspace/schain_scripts/distrito.csv') for line in f: label, lon, lat = [s.strip() for s in line.split(',') if s] lat = float(lat) lon = float(lon) # ax.plot(lon, lat, '.b', ms=2) ax.text(lon, lat, label.decode('utf8'), ha='center', va='bottom', size='8', color='black') # plot limites limites = [] tmp = [] for line in open('/data/workspace/schain_scripts/lima.csv'): if '#' in line: if tmp: limites.append(tmp) tmp = [] continue values = line.strip().split(',') tmp.append((float(values[0]), float(values[1]))) for points in limites: ax.add_patch( Polygon(points, ec='k', fc='none', ls='--', lw=0.5)) # plot Cuencas for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'): f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca)) values = [line.strip().split(',') for line in f] points = [(float(s[0]), float(s[1])) for s in values] ax.add_patch(Polygon(points, ec='b', fc='none')) # plot grid for r in (15, 30, 45, 60): ax.add_artist(plt.Circle((self.lon, self.lat), km2deg(r), color='0.6', fill=False, lw=0.2)) ax.text( self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180), self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180), '{}km'.format(r), ha='center', va='bottom', size='8', color='0.6', weight='heavy') if self.mode == 'E': title = 'El={}$^\circ$'.format(self.data.meta['elevation']) label = 'E{:02d}'.format(int(self.data.meta['elevation'])) else: title = 'Az={}$^\circ$'.format(self.data.meta['azimuth']) label = 'A{:02d}'.format(int(self.data.meta['azimuth'])) self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels] self.titles = ['{} {}'.format( self.data.parameters[x], title) for x in self.channels] class WeatherParamsPlot(Plot): #CODE = 'RHI' #plot_name = 'RHI' plot_type = 'scattermap' buffering = False def setup(self): self.ncols = 1 self.nrows = 1 self.nplots= 1 self.ylabel= 'Range [km]' self.xlabel= 'Range [km]' self.polar = True self.grid = True 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)) self.colorbar=True 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): vars = { 'S' : 0, 'V' : 1, 'W' : 2, 'SNR' : 3, 'Z' : 4, 'D' : 5, 'P' : 6, 'R' : 7, } data = {} meta = {} if hasattr(dataOut, 'nFFTPoints'): factor = dataOut.normFactor else: factor = 1 if 'S' in self.attr_data[0]: tmp = 10*numpy.log10(10.0*getattr(dataOut, 'data_param')[:,0,:]/(factor)) else: tmp = getattr(dataOut, 'data_param')[:,vars[self.attr_data[0]],:] if self.mask: mask = dataOut.data_param[:,3,:] < self.mask tmp = numpy.ma.masked_array(tmp, mask=mask) r = dataOut.heightList delta_height = r[1]-r[0] valid = numpy.where(r>=0)[0] data['r'] = numpy.arange(len(valid))*delta_height try: data['data'] = tmp[self.channels[0]][:,valid] except: data['data'] = tmp[0][:,valid] if dataOut.mode_op == 'PPI': self.CODE = 'PPI' self.title = self.CODE elif dataOut.mode_op == 'RHI': self.CODE = 'RHI' self.title = self.CODE data['azi'] = dataOut.data_azi data['ele'] = dataOut.data_ele data['mode_op'] = dataOut.mode_op var = data['data'].flatten() r = numpy.tile(data['r'], data['data'].shape[0]).reshape(data['data'].shape)*1000 lla = georef.spherical_to_proj(r, data['azi'], data['ele'], (-75.295893, -12.040436, 3379.2147)) if self.mask: meta['lat'] = lla[:,:,1].flatten()[var.mask==False] meta['lon'] = lla[:,:,0].flatten()[var.mask==False] data['var'] = numpy.array([var[var.mask==False]]) else: meta['lat'] = lla[:,:,1].flatten() meta['lon'] = lla[:,:,0].flatten() data['var'] = numpy.array([var]) return data, meta def plot(self): data = self.data[-1] z = data['data'] r = data['r'] self.titles = [] self.ymax = self.ymax if self.ymax else numpy.nanmax(r) self.ymin = self.ymin if self.ymin else numpy.nanmin(r) self.zmax = self.zmax if self.zmax else numpy.nanmax(z) self.zmin = self.zmin if self.zmin is not None else numpy.nanmin(z) if data['mode_op'] == 'RHI': try: if self.data['mode_op'][-2] == 'PPI': self.ang_min = None self.ang_max = None except: pass self.ang_min = self.ang_min if self.ang_min else 0 self.ang_max = self.ang_max if self.ang_max else 90 r, theta = numpy.meshgrid(r, numpy.radians(data['ele']) ) elif data['mode_op'] == 'PPI': try: if self.data['mode_op'][-2] == 'RHI': self.ang_min = None self.ang_max = None except: pass self.ang_min = self.ang_min if self.ang_min else 0 self.ang_max = self.ang_max if self.ang_max else 360 r, theta = numpy.meshgrid(r, numpy.radians(data['azi']) ) self.clear_figures() for i,ax in enumerate(self.axes): if ax.firsttime: ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) if data['mode_op'] == 'PPI': ax.set_theta_direction(-1) ax.set_theta_offset(numpy.pi/2) else: ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) if data['mode_op'] == 'PPI': ax.set_theta_direction(-1) ax.set_theta_offset(numpy.pi/2) ax.grid(True) if data['mode_op'] == 'RHI': len_aux = int(data['azi'].shape[0]/4) mean = numpy.mean(data['azi'][len_aux:-len_aux]) if len(self.channels) !=1: self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in range(self.nrows)] else: self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])] elif data['mode_op'] == 'PPI': len_aux = int(data['ele'].shape[0]/4) mean = numpy.mean(data['ele'][len_aux:-len_aux]) if len(self.channels) !=1: self.titles = ['PPI {} at EL: {} CH {}'.format(self.self.labels[x], str(round(mean,1)), x) for x in range(self.nrows)] else: self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])]