import os import datetime import warnings import numpy from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter from matplotlib.patches import Circle import cartopy.crs as ccrs from cartopy.feature import ShapelyFeature import cartopy.io.shapereader as shpreader 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 from schainpy.model.graphics.plotting_codes import cb_tables EARTH_RADIUS = 6.3710e3 def antenna_to_cartesian(ranges, azimuths, elevations): """ Return Cartesian coordinates from antenna coordinates. Parameters ---------- ranges : array Distances to the center of the radar gates (bins) in kilometers. azimuths : array Azimuth angle of the radar in degrees. elevations : array Elevation angle of the radar in degrees. Returns ------- x, y, z : array Cartesian coordinates in meters from the radar. Notes ----- The calculation for Cartesian coordinate is adapted from equations 2.28(b) and 2.28(c) of Doviak and Zrnic [1]_ assuming a standard atmosphere (4/3 Earth's radius model). .. math:: z = \\sqrt{r^2+R^2+2*r*R*sin(\\theta_e)} - R s = R * arcsin(\\frac{r*cos(\\theta_e)}{R+z}) x = s * sin(\\theta_a) y = s * cos(\\theta_a) Where r is the distance from the radar to the center of the gate, :math:`\\theta_a` is the azimuth angle, :math:`\\theta_e` is the elevation angle, s is the arc length, and R is the effective radius of the earth, taken to be 4/3 the mean radius of earth (6371 km). References ---------- .. [1] Doviak and Zrnic, Doppler Radar and Weather Observations, Second Edition, 1993, p. 21. """ theta_e = numpy.deg2rad(elevations) # elevation angle in radians. theta_a = numpy.deg2rad(azimuths) # azimuth angle in radians. R = 6371.0 * 1000.0 * 4.0 / 3.0 # effective radius of earth in meters. r = ranges * 1000.0 # distances to gates in meters. z = (r ** 2 + R ** 2 + 2.0 * r * R * numpy.sin(theta_e)) ** 0.5 - R s = R * numpy.arcsin(r * numpy.cos(theta_e) / (R + z)) # arc length in m. x = s * numpy.sin(theta_a) y = s * numpy.cos(theta_a) return x, y, z def cartesian_to_geographic_aeqd(x, y, lon_0, lat_0, R=EARTH_RADIUS): """ Azimuthal equidistant Cartesian to geographic coordinate transform. Transform a set of Cartesian/Cartographic coordinates (x, y) to geographic coordinate system (lat, lon) using a azimuthal equidistant map projection [1]_. .. math:: lat = \\arcsin(\\cos(c) * \\sin(lat_0) + (y * \\sin(c) * \\cos(lat_0) / \\rho)) lon = lon_0 + \\arctan2( x * \\sin(c), \\rho * \\cos(lat_0) * \\cos(c) - y * \\sin(lat_0) * \\sin(c)) \\rho = \\sqrt(x^2 + y^2) c = \\rho / R Where x, y are the Cartesian position from the center of projection; lat, lon the corresponding latitude and longitude; lat_0, lon_0 are the latitude and longitude of the center of the projection; R is the radius of the earth (defaults to ~6371 km). lon is adjusted to be between -180 and 180. Parameters ---------- x, y : array-like Cartesian coordinates in the same units as R, typically meters. lon_0, lat_0 : float Longitude and latitude, in degrees, of the center of the projection. R : float, optional Earth radius in the same units as x and y. The default value is in units of meters. Returns ------- lon, lat : array Longitude and latitude of Cartesian coordinates in degrees. References ---------- .. [1] Snyder, J. P. Map Projections--A Working Manual. U. S. Geological Survey Professional Paper 1395, 1987, pp. 191-202. """ x = numpy.atleast_1d(numpy.asarray(x)) y = numpy.atleast_1d(numpy.asarray(y)) lat_0_rad = numpy.deg2rad(lat_0) lon_0_rad = numpy.deg2rad(lon_0) rho = numpy.sqrt(x*x + y*y) c = rho / R with warnings.catch_warnings(): # division by zero may occur here but is properly addressed below so # the warnings can be ignored warnings.simplefilter("ignore", RuntimeWarning) lat_rad = numpy.arcsin(numpy.cos(c) * numpy.sin(lat_0_rad) + y * numpy.sin(c) * numpy.cos(lat_0_rad) / rho) lat_deg = numpy.rad2deg(lat_rad) # fix cases where the distance from the center of the projection is zero lat_deg[rho == 0] = lat_0 x1 = x * numpy.sin(c) x2 = rho*numpy.cos(lat_0_rad)*numpy.cos(c) - y*numpy.sin(lat_0_rad)*numpy.sin(c) lon_rad = lon_0_rad + numpy.arctan2(x1, x2) lon_deg = numpy.rad2deg(lon_rad) # Longitudes should be from -180 to 180 degrees lon_deg[lon_deg > 180] -= 360. lon_deg[lon_deg < -180] += 360. return lon_deg, lat_deg def antenna_to_geographic(ranges, azimuths, elevations, site): x, y, z = antenna_to_cartesian(numpy.array(ranges), numpy.array(azimuths), numpy.array(elevations)) lon, lat = cartesian_to_geographic_aeqd(x, y, site[0], site[1], R=6370997.) return lon, lat 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 projection = ccrs.PlateCarree() def setup(self): self.ncols = 1 self.nrows = 1 self.nplots= 1 self.ylabel= 'Height [km]' self.xlabel= 'Distance from radar [km]' if self.channels is not None: self.nplots = len(self.channels) self.ncols = len(self.channels) else: self.nplots = self.data.shape(self.CODE)[0] self.ncols = self.nplots self.channels = list(range(self.nplots)) self.colorbar=True if len(self.channels)>1: self.width = 12 else: self.width =8 self.height =7 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': 0.1}) 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 hasattr(dataOut, 'dparam'): tmp = getattr(dataOut, 'data_param') else: 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 data['data'] = [0, 0] try: data['data'][0] = tmp[0][:,valid] data['data'][1] = tmp[1][:,valid] except: data['data'][0] = tmp[0][:,valid] data['data'][1] = 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 self.mode = dataOut.mode_op 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 isinstance(data['mode_op'], bytes): data['mode_op'] = data['mode_op'].decode() if data['mode_op'] == 'RHI': r, theta = numpy.meshgrid(r, numpy.radians(data['ele'])) len_aux = int(data['azi'].shape[0]/4) mean = numpy.mean(data['azi'][len_aux:-len_aux]) x, y = r*numpy.cos(theta), r*numpy.sin(theta) elif data['mode_op'] == 'PPI': r, theta = numpy.meshgrid(r, -numpy.radians(data['azi'])+numpy.pi/2) len_aux = int(data['ele'].shape[0]/4) mean = numpy.mean(data['ele'][len_aux:-len_aux]) x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(mean)), r*numpy.sin( theta)*numpy.cos(numpy.radians(mean)) x = km2deg(x) + -75.295893 y = km2deg(y) + -12.040436 self.clear_figures() if data['mode_op'] == 'PPI': axes = self.axes['PPI'] else: axes = self.axes['RHI'] if self.colormap in cb_tables: norm = cb_tables[self.colormap]['norm'] else: norm = None for i, ax in enumerate(axes): if data['mode_op'] == 'PPI': ax.set_extent([-75.745893, -74.845893, -12.490436, -11.590436]) if norm is None: ax.plt = ax.pcolormesh(x, y, z[i], cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) else: ax.plt = ax.pcolormesh(x, y, z[i], cmap=self.colormap, norm=norm) 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 self.channels] 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.labels[x], str(round(mean,1)), x) for x in self.channels] else: self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])] self.mode_value = round(mean,1) if data['mode_op'] == 'PPI': gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--') gl.xlabel_style = {'size': 8} gl.ylabel_style = {'size': 8} gl.xlabels_top = False gl.ylabels_right = False shape_p = os.path.join(self.shapes,'PER_ADM2/PER_ADM2.shp') shape_d = os.path.join(self.shapes,'PER_ADM1/PER_ADM1.shp') capitales = os.path.join(self.shapes,'CAPITALES/cap_provincia.shp') vias = os.path.join(self.shapes,'Carreteras/VIAS_NACIONAL_250000.shp') reader_d = shpreader.BasicReader(shape_p, encoding='latin1') reader_p = shpreader.BasicReader(shape_d, encoding='latin1') reader_c = shpreader.BasicReader(capitales, encoding='latin1') reader_v = shpreader.BasicReader(vias, encoding='latin1') caps = [x for x in reader_c.records() if x.attributes["Departa"] in ("JUNIN", "LIMA", "AYACUCHO", "HUANCAVELICA")] districts = [x for x in reader_d.records() if x.attributes["Name"] in ("JUNÍN", "CHANCHAMAYO", "CHUPACA", "CONCEPCIÓN", "HUANCAYO", "JAUJA", "SATIPO", "TARMA", "YAUYOS", "HUAROCHIRÍ", "CANTA", "HUANTA", "TAYACAJA")] provs = [x for x in reader_p.records() if x.attributes["NAME"] in ("Junín", "Lima")] vias = [x for x in reader_v.records() if x.attributes["DEP"] in ("JUNIN", "LIMA")] # Display limits and streets shape_feature = ShapelyFeature([x.geometry for x in districts], ccrs.PlateCarree(), facecolor="none", edgecolor='grey', lw=0.5) ax.add_feature(shape_feature) shape_feature = ShapelyFeature([x.geometry for x in provs], ccrs.PlateCarree(), facecolor="none", edgecolor='white', lw=1) ax.add_feature(shape_feature) shape_feature = ShapelyFeature([x.geometry for x in vias], ccrs.PlateCarree(), facecolor="none", edgecolor='yellow', lw=1) ax.add_feature(shape_feature) for cap in caps: if cap.attributes['Nombre'] in ("LA OROYA", "CONCEPCIÓN", "HUANCAYO", "JAUJA", "CHUPACA", "YAUYOS", "HUANTA", "PAMPAS"): ax.text(cap.attributes['X'], cap.attributes['Y'], cap.attributes['Nombre'].title(), size=7, color='white') ax.text(-75.052003, -11.915552, 'Huaytapallana', size=7, color='cyan') ax.plot(-75.052003, -11.915552, '*') for R in (10, 20, 30 , 40, 50): circle = Circle((-75.295893, -12.040436), km2deg(R), facecolor='none', edgecolor='skyblue', linewidth=1, alpha=0.5) ax.add_patch(circle) ax.text(km2deg(R)*numpy.cos(numpy.radians(45))-75.295893, km2deg(R)*numpy.sin(numpy.radians(45))-12.040436, '{}km'.format(R), color='skyblue', size=7) elif data['mode_op'] == 'RHI': ax.grid(color='grey', alpha=0.5, linestyle='--', linewidth=1)