def interpret_header(self): """ Read pertinent information from the image headers, especially location and radius of the Sun to calculate the default thematic map :return: setes self.date, self.cy, self.cx, and self.sun_radius_pixel """ # handle special cases since date-obs field changed names if 'DATE_OBS' in self.header: self.date = self.header['DATE_OBS'] elif 'DATE-OBS' in self.header: self.date = self.header['DATE-OBS'] else: raise Exception("Image does not have a DATE_OBS or DATE-OBS field") self.cy, self.cx = self.header['CRPIX1'], self.header['CRPIX2'] sun_radius_angular = sun.solar_semidiameter_angular_size(t=time.parse_time(self.date)).arcsec arcsec_per_pixel = self.header['CDELT1'] self.sun_radius_pixel = (sun_radius_angular / arcsec_per_pixel)
def plt_qlook_image(imres, figdir=None, verbose=True, synoptic=False): from matplotlib import pyplot as plt from sunpy import map as smap from sunpy import sun from matplotlib import colors import astropy.units as u from suncasa.utils import plot_mapX as pmX # from matplotlib import gridspec as gridspec if not figdir: figdir = './' nspw = len(set(imres['Spw'])) plttimes = list(set(imres['BeginTime'])) ntime = len(plttimes) # sort the imres according to time images = np.array(imres['ImageName']) btimes = Time(imres['BeginTime']) etimes = Time(imres['EndTime']) spws = np.array(imres['Spw']) suc = np.array(imres['Succeeded']) inds = btimes.argsort() images_sort = images[inds].reshape(ntime, nspw) btimes_sort = btimes[inds].reshape(ntime, nspw) suc_sort = suc[inds].reshape(ntime, nspw) if verbose: print('{0:d} figures to plot'.format(ntime)) plt.ioff() fig = plt.figure(figsize=(8, 8)) plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) axs = [] ims = [] pltst = 0 for i in range(ntime): plt.ioff() plttime = btimes_sort[i, 0] tofd = plttime.mjd - np.fix(plttime.mjd) suci = suc_sort[i] if not synoptic: if tofd < 16. / 24. or sum( suci ) < nspw - 2: # if time of the day is before 16 UT (and 24 UT), skip plotting (because the old antennas are not tracking) continue else: if pltst == 0: i0 = i pltst = 1 else: if pltst == 0: i0 = i pltst = 1 if i == i0: if synoptic: timetext = fig.text(0.01, 0.98, plttime.iso[:10], color='w', fontweight='bold', fontsize=12, ha='left') else: timetext = fig.text(0.01, 0.98, plttime.iso[:19], color='w', fontweight='bold', fontsize=12, ha='left') else: if synoptic: timetext.set_text(plttime.iso[:10]) else: timetext.set_text(plttime.iso[:19]) if verbose: print('Plotting image at: ', plttime.iso) for n in range(nspw): plt.ioff() if i == i0: if nspw == 1: ax = fig.add_subplot(111) else: ax = fig.add_subplot(nspw / 2, 2, n + 1) axs.append(ax) else: ax = axs[n] image = images_sort[i, n] if suci[n] or os.path.exists(image): try: eomap = smap.Map(image) except: continue data = eomap.data sz = data.shape if len(sz) == 4: data = data.reshape((sz[2], sz[3])) data[np.isnan(data)] = 0.0 # add a basin flux to the image to avoid negative values data = data + 0.8e5 data[data < 0] = 0.0 data = np.sqrt(data) eomap = smap.Map(data, eomap.meta) # resample the image for plotting dim = u.Quantity([256, 256], u.pixel) eomap = eomap.resample(dim) else: # make an empty map data = np.zeros((256, 256)) header = { "DATE-OBS": plttime.isot, "EXPTIME": 0., "CDELT1": 10., "NAXIS1": 256, "CRVAL1": 0., "CRPIX1": 128.5, "CUNIT1": "arcsec", "CTYPE1": "HPLN-TAN", "CDELT2": 10., "NAXIS2": 256, "CRVAL2": 0., "CRPIX2": 128.5, "CUNIT2": "arcsec", "CTYPE2": "HPLT-TAN", "HGLT_OBS": sun.heliographic_solar_center(plttime)[1].value, "HGLN_OBS": 0., "RSUN_OBS": sun.solar_semidiameter_angular_size(plttime).value, "RSUN_REF": sun.constants.radius.value, "DSUN_OBS": sun.sunearth_distance(plttime).to(u.meter).value, } eomap = smap.Map(data, header) if i == i0: eomap_ = pmX.Sunmap(eomap) # im = eomap_.imshow(axes=ax, cmap='jet', norm=colors.LogNorm(vmin=0.1, vmax=1e8)) im = eomap_.imshow(axes=ax, cmap='jet', norm=colors.Normalize(vmin=150, vmax=700)) ims.append(im) if not synoptic: eomap_.draw_limb(axes=ax) eomap_.draw_grid(axes=ax) ax.set_xlim([-1080, 1080]) ax.set_ylim([-1080, 1080]) try: cfreq = eomap.meta['crval3'] / 1.0e9 bdwid = eomap.meta['cdelt3'] / 1.0e9 ax.text(0.98, 0.01, '{0:.1f} - {1:.1f} GHz'.format( cfreq - bdwid / 2.0, cfreq + bdwid / 2.0), color='w', transform=ax.transAxes, fontweight='bold', ha='right') except: pass ax.set_title(' ') ax.set_xlabel('') ax.set_ylabel('') ax.set_xticklabels(['']) ax.set_yticklabels(['']) else: ims[n].set_data(eomap.data) fig_tdt = plttime.to_datetime() if synoptic: fig_subdir = fig_tdt.strftime("%Y/") figname = 'eovsa_qlimg_' + plttime.iso[:10].replace('-', '') + '.png' else: fig_subdir = fig_tdt.strftime("%Y/%m/%d/") figname = 'eovsa_qlimg_' + plttime.isot.replace(':', '').replace( '-', '')[:15] + '.png' figdir_ = figdir + fig_subdir if not os.path.exists(figdir_): os.makedirs(figdir_) if verbose: print('Saving plot to :' + figdir_ + figname) plt.savefig(figdir_ + figname) plt.close(fig)
def map_hpc_to_hg_rotate(m, epi_lon=0*u.degree, epi_lat=90*u.degree, lon_bin=1*u.degree, lat_bin=1*u.degree, lon_num=None, lat_num=None, **kwargs): """ Transform raw data in HPC coordinates to HG' coordinates HG' = HG, except center at wave epicenter """ x, y = wcs.convert_pixel_to_data([m.data.shape[1], m.data.shape[0]], [m.scale.x.value, m.scale.y.value], [m.reference_pixel.x.value, m.reference_pixel.y.value], [m.reference_coordinate.x.value, m.reference_coordinate.y.value]) hccx, hccy, hccz = wcs.convert_hpc_hcc(x, y, angle_units='arcsec', dsun_meters=m.dsun.to('meter').value, z=True) rot_hccz, rot_hccx, rot_hccy = euler_zyz((hccz, hccx, hccy), (0., epi_lat.to('degree').value-90., -epi_lon.to('degree').value)) lon_map, lat_map = wcs.convert_hcc_hg(rot_hccx, rot_hccy, b0_deg=m.heliographic_latitude.to('degree').value, l0_deg=m.heliographic_longitude.to('degree').value, z=rot_hccz) lon_range = (np.nanmin(lon_map), np.nanmax(lon_map)) lat_range = (np.nanmin(lat_map), np.nanmax(lat_map)) # This method results in a set of lons and lats that in general does not # exactly span the range of the data. # lon = np.arange(lon_range[0], lon_range[1], lon_bin) # lat = np.arange(lat_range[0], lat_range[1], lat_bin) # This method gives a set of lons and lats that exactly spans the range of # the data at the expense of having to define values of cdelt1 and cdelt2 if lon_num is None: cdelt1 = lon_bin.to('degree').value lon = np.arange(lon_range[0], lon_range[1], cdelt1) else: nlon = lon_num.to('pixel').value cdelt1 = (lon_range[1] - lon_range[0]) / (1.0*nlon - 1.0) lon = np.linspace(lon_range[0], lon_range[1], num=nlon) if lat_num is None: cdelt2 = lat_bin.to('degree').value lat = np.arange(lat_range[0], lat_range[1], cdelt2) else: nlat = lat_num.to('pixel').value cdelt2 = (lat_range[1] - lat_range[0]) / (1.0*nlat - 1.0) lat = np.linspace(lat_range[0], lat_range[1], num=nlat) # Create the grid x_grid, y_grid = np.meshgrid(lon, lat) ng_xyz = wcs.convert_hg_hcc(x_grid, y_grid, b0_deg=m.heliographic_latitude.to('degree').value, l0_deg=m.heliographic_longitude.to('degree').value, z=True) ng_zp, ng_xp, ng_yp = euler_zyz((ng_xyz[2], ng_xyz[0], ng_xyz[1]), (epi_lon.to('degree').value, 90.-epi_lat.to('degree').value, 0.)) # The function ravel flattens the data into a 1D array points = np.vstack((lon_map.ravel(), lat_map.ravel())).T values = np.array(m.data).ravel() # Get rid of all of the bad (nan) indices (i.e. those off of the sun) index = np.isfinite(points[:, 0]) * np.isfinite(points[:, 1]) # points = np.vstack((points[index,0], points[index,1])).T points = points[index] values = values[index] newdata = griddata(points, values, (x_grid, y_grid), **kwargs) newdata[ng_zp < 0] = np.nan try: rsun = m.rsun_obs.to(u.arcsec).value except: rsun = solar_semidiameter_angular_size(t=m.meta['date-obs']).to(u.arcsec).value dict_header = { 'CDELT1': cdelt1, 'NAXIS1': len(lon), 'CRVAL1': lon.min(), 'CRPIX1': crpix12_value_for_HG, 'CRPIX2': crpix12_value_for_HG, 'CUNIT1': "deg", 'CTYPE1': "HG", 'CDELT2': cdelt2, 'NAXIS2': len(lat), 'CRVAL2': lat.min(), 'CUNIT2': "deg", 'CTYPE2': "HG", 'DATE_OBS': m.meta['date-obs'], 'DSUN_OBS': m.dsun.to('m').value, "CRLN_OBS": m.carrington_longitude.to('degree').value, "HGLT_OBS": m.heliographic_latitude.to('degree').value, "HGLN_OBS": m.heliographic_longitude.to('degree').value, 'EXPTIME': m.exposure_time.to('s').value, 'RSUN': rsun } # Find out where the non-finites are mask = np.logical_not(np.isfinite(newdata)) # Return a masked array is appropriate if mask is None: hg = Map(newdata, dict_header) else: hg = Map(ma.array(newdata, mask=mask), dict_header) hg.plot_settings = m.plot_settings return hg
def map_hg_to_hpc_rotate(m, epi_lon=90*u.degree, epi_lat=0*u.degree, xbin=2.4*u.arcsec, ybin=2.4*u.arcsec, xnum=None, ynum=None, solar_information=None, **kwargs): """ Transform raw data in HG' coordinates to HPC coordinates HG' = HG, except center at wave epicenter """ # Origin grid, HG' lon_grid, lat_grid = wcs.convert_pixel_to_data([m.data.shape[1], m.data.shape[0]], [m.scale.x.value, m.scale.y.value], [m.reference_pixel.x.value, m.reference_pixel.y.value], [m.reference_coordinate.x.value, m.reference_coordinate.y.value]) # Origin grid, HG' to HCC' # HCC' = HCC, except centered at wave epicenter x, y, z = wcs.convert_hg_hcc(lon_grid, lat_grid, b0_deg=m.heliographic_latitude.to('degree').value, l0_deg=m.carrington_longitude.to('degree').value, z=True) # Origin grid, HCC' to HCC'' # Moves the wave epicenter to initial conditions # HCC'' = HCC, except assuming that HGLT_OBS = 0 zpp, xpp, ypp = euler_zyz((z, x, y), (epi_lon.to('degree').value, 90.-epi_lat.to('degree').value, 0.)) # Add in a solar rotation. Useful when creating simulated HPC data from # HG data. This code was adapted from the wave simulation code of the # AWARE project. if solar_information is not None: hglt_obs = solar_information['hglt_obs'].to('degree').value solar_rotation_value = solar_information['angle_rotated'].to('degree').value #print(hglt_obs, solar_rotation_value) #print('before', zpp, xpp, ypp) zpp, xpp, ypp = euler_zyz((zpp, xpp, ypp), (0., hglt_obs, solar_rotation_value)) #print('after', zpp, xpp, ypp) # Origin grid, HCC to HPC (arcsec) # xx, yy = wcs.convert_hcc_hpc(current_wave_map.header, xpp, ypp) xx, yy = wcs.convert_hcc_hpc(xpp, ypp, dsun_meters=m.dsun.to('meter').value) # Destination HPC grid hpcx_range = (np.nanmin(xx), np.nanmax(xx)) hpcy_range = (np.nanmin(yy), np.nanmax(yy)) if xnum is None: cdelt1 = xbin.to('arcsec').value hpcx = np.arange(hpcx_range[0], hpcx_range[1], cdelt1) else: nx = xnum.to('pixel').value cdelt1 = (hpcx_range[1] - hpcx_range[0]) / (1.0*nx - 1.0) hpcx = np.linspace(hpcx_range[1], hpcx_range[0], num=nx) if ynum is None: cdelt2 = ybin.to('arcsec').value hpcy = np.arange(hpcy_range[0], hpcy_range[1], cdelt2) else: ny = ynum.to('pixel').value cdelt2 = (hpcy_range[1] - hpcy_range[0]) / (1.0*ny - 1.0) hpcy = np.linspace(hpcy_range[1], hpcy_range[0], num=ny) # Calculate the grid mesh newgrid_x, newgrid_y = np.meshgrid(hpcx, hpcy) # # CRVAL1,2 and CRPIX1,2 are calculated so that the co-ordinate system is # at the center of the image # Note that crpix[] counts pixels starting at 1 try: rsun = m.rsun_obs.to(u.arcsec).value except: rsun = solar_semidiameter_angular_size(t=m.meta['date-obs']).to(u.arcsec).value crpix1 = hpcx.size // 2 crval1 = 0.0#hpcx[crpix1 - 1] crpix2 = hpcy.size // 2 crval2 = 0.0#hpcy[crpix2 - 1] dict_header = { "CDELT1": cdelt1, "NAXIS1": len(hpcx), "CRVAL1": crval1, "CRPIX1": crpix1, "CUNIT1": "arcsec", "CTYPE1": "HPLN-TAN", "CDELT2": cdelt2, "NAXIS2": len(hpcy), "CRVAL2": crval2, "CRPIX2": crpix2, "CUNIT2": "arcsec", "CTYPE2": "HPLT-TAN", "HGLT_OBS": m.heliographic_latitude.to('degree').value, # 0.0 # "HGLN_OBS": 0.0, "CRLN_OBS": m.carrington_longitude.to('degree').value, # 0.0 'DATE_OBS': m.meta['date-obs'], 'DSUN_OBS': m.dsun.to('m').value, 'EXPTIME': m.exposure_time.to('s').value, 'RSUN': rsun } # Coordinate positions (HPC) with corresponding map data points = np.vstack((xx.ravel(), yy.ravel())).T values = np.asarray(deepcopy(m.data)).ravel() # Solar rotation can push the points off disk and into areas that have # nans. if this is the case, then griddata fails # Two solutions # 1 - replace all the nans with zeros, in order to get the code to run # 2 - the initial condition of zpp.ravel() >= 0 should be extended # to make sure that only finite points are used. # 2D interpolation from origin grid to destination grid xy_condition = np.logical_and(np.isfinite(points[:, 0]), np.isfinite(points[:, 1])) valid_points = np.logical_and(zpp.ravel() >= 0, xy_condition) # 2D interpolation from origin grid to destination grid grid = griddata(points[valid_points], values[valid_points], (newgrid_x, newgrid_y), **kwargs) # Find out where the non-finites are mask = np.logical_not(np.isfinite(grid)) # Return a masked array is appropriate if mask is None: hpc = Map(grid, dict_header) else: hpc = Map(ma.array(grid, mask=mask), dict_header) hpc.plot_settings = m.plot_settings return hpc
def imreg(vis=None, ephem=None, msinfo=None, imagefile=None, timerange=None, reftime=None, fitsfile=None, beamfile=None, offsetfile=None, toTb=None, sclfactor=1.0, verbose=False, p_ang=False, overwrite=True, usephacenter=True, deletehistory=False, subregion=[], docompress=False): ''' main routine to register CASA images Required Inputs: vis: STRING. CASA measurement set from which the image is derived imagefile: STRING or LIST. name of the input CASA image timerange: STRING or LIST. timerange used to generate the CASA image, must have the same length as the input images. Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00~2012/03/03/13:00:00' Optional Inputs: msinfo: DICTIONARY. CASA MS information, output from read_msinfo. If not provided, generate one from the supplied vis ephem: DICTIONARY. solar ephem, output from read_horizons. If not provided, query JPL Horizons based on time info of the vis (internet connection required) fitsfile: STRING or LIST. name of the output registered fits files reftime: STRING or LIST. Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00' offsetfile: optionally provide an offset with a series of solar x and y offsets with timestamps toTb: Bool. Convert the default Jy/beam to brightness temperature? sclfactor: scale the image values up by its value (to compensate VLA 20 dB attenuator) verbose: Bool. Show more diagnostic info if True. usephacenter: Bool -- if True, correct for the RA and DEC in the ms file based on solar empheris. Otherwise assume the phasecenter is correctly pointed to the solar disk center (EOVSA case) subregion: Region selection. See 'help par.region' for details. Usage: >>> from suncasa.utils import helioimage2fits as hf >>> hf.imreg(vis='mydata.ms', imagefile='myimage.image', fitsfile='myimage.fits', timerange='2017/08/21/20:21:10~2017/08/21/20:21:18') The output fits file is 'myimage.fits' History: BC (sometime in 2014): function was first wrote, followed by a number of edits by BC and SY BC (2019-07-16): Added checks for stokes parameter. Verified that for converting from Jy/beam to brightness temperature, the convention of 2*k_b*T should always be used. I.e., for unpolarized source, stokes I, RR, LL, XX, YY, etc. in the output CASA images from (t)clean should all have same values of radio intensity (in Jy/beam) and brightness temperature (in K). ''' if deletehistory: ms_clearhistory(vis) if not imagefile: raise ValueError('Please specify input image') if not timerange: raise ValueError('Please specify timerange of the input image') if type(imagefile) == str: imagefile = [imagefile] if type(timerange) == str: timerange = [timerange] if not fitsfile: fitsfile = [img + '.fits' for img in imagefile] if type(fitsfile) == str: fitsfile = [fitsfile] nimg = len(imagefile) if len(timerange) != nimg: raise ValueError( 'Number of input images does not equal to number of timeranges!') if len(fitsfile) != nimg: raise ValueError( 'Number of input images does not equal to number of output fits files!' ) nimg = len(imagefile) if verbose: print(str(nimg) + ' images to process...') if reftime: # use as reference time to find solar disk RA and DEC to register the image, but not the actual timerange associated with the image if type(reftime) == str: reftime = [reftime] * nimg if len(reftime) != nimg: raise ValueError( 'Number of reference times does not match that of input images!' ) helio = ephem_to_helio(vis, ephem=ephem, msinfo=msinfo, reftime=reftime, usephacenter=usephacenter) else: # use the supplied timerange to register the image helio = ephem_to_helio(vis, ephem=ephem, msinfo=msinfo, reftime=timerange, usephacenter=usephacenter) if toTb: (bmajs, bmins, bpas, beamunits, bpaunits) = getbeam(imagefile=imagefile, beamfile=beamfile) for n, img in enumerate(imagefile): if verbose: print('processing image #' + str(n) + ' ' + img) fitsf = fitsfile[n] timeran = timerange[n] # obtain duration of the image as FITS header exptime try: [tbg0, tend0] = timeran.split('~') tbg_d = qa.getvalue(qa.convert(qa.totime(tbg0), 'd'))[0] tend_d = qa.getvalue(qa.convert(qa.totime(tend0), 'd'))[0] tdur_s = (tend_d - tbg_d) * 3600. * 24. dateobs = qa.time(qa.quantity(tbg_d, 'd'), form='fits', prec=10)[0] except: print('Error in converting the input timerange: ' + str(timeran) + '. Proceeding to the next image...') continue hel = helio[n] if not os.path.exists(img): warnings.warn('{} does not existed!'.format(img)) else: if os.path.exists(fitsf) and not overwrite: raise ValueError( 'Specified fits file already exists and overwrite is set to False. Aborting...' ) else: p0 = hel['p0'] tb.open(img + '/logtable', nomodify=False) nobs = tb.nrows() tb.removerows([i + 1 for i in range(nobs - 1)]) tb.close() ia.open(img) imr = ia.rotate(pa=str(-p0) + 'deg') if subregion is not []: imr = imr.subimage(region=subregion) imr.tofits(fitsf, history=False, overwrite=overwrite) imr.close() imsum = ia.summary() ia.close() ia.done() # construct the standard fits header # RA and DEC of the reference pixel crpix1 and crpix2 (imra, imdec) = (imsum['refval'][0], imsum['refval'][1]) # find out the difference of the image center to the CASA phase center # RA and DEC difference in arcseconds ddec = degrees((imdec - hel['dec_fld'])) * 3600. dra = degrees((imra - hel['ra_fld']) * cos(hel['dec_fld'])) * 3600. # Convert into image heliocentric offsets prad = -radians(hel['p0']) dx = (-dra) * cos(prad) - ddec * sin(prad) dy = (-dra) * sin(prad) + ddec * cos(prad) if offsetfile: try: offset = np.load(offsetfile) except: raise ValueError( 'The specified offsetfile does not exist!') reftimes_d = offset['reftimes_d'] xoffs = offset['xoffs'] yoffs = offset['yoffs'] timg_d = hel['reftime'] ind = bisect.bisect_left(reftimes_d, timg_d) xoff = xoffs[ind - 1] yoff = yoffs[ind - 1] else: xoff = hel['refx'] yoff = hel['refy'] if verbose: print( 'offset of image phase center to visibility phase center (arcsec): dx={0:.2f}, dy={1:.2f}' .format(dx, dy)) print( 'offset of visibility phase center to solar disk center (arcsec): dx={0:.2f}, dy={1:.2f}' .format(xoff, yoff)) (crval1, crval2) = (xoff + dx, yoff + dy) # update the fits header to heliocentric coordinates hdu = pyfits.open(fitsf, mode='update') hdu[0].verify('fix') header = hdu[0].header dshape = hdu[0].data.shape ndim = hdu[0].data.ndim (cdelt1, cdelt2) = (-header['cdelt1'] * 3600., header['cdelt2'] * 3600. ) # Original CDELT1, 2 are for RA and DEC in degrees header['cdelt1'] = cdelt1 header['cdelt2'] = cdelt2 header['cunit1'] = 'arcsec' header['cunit2'] = 'arcsec' header['crval1'] = crval1 header['crval2'] = crval2 header['ctype1'] = 'HPLN-TAN' header['ctype2'] = 'HPLT-TAN' header['date-obs'] = dateobs # begin time of the image if not p_ang: hel['p0'] = 0 try: # this works for pyfits version of CASA 4.7.0 but not CASA 4.6.0 if tdur_s: header.set('exptime', tdur_s) else: header.set('exptime', 1.) header.set('p_angle', hel['p0']) header.set('hgln_obs', 0.) header.set('rsun_ref', sun.constants.radius.value) if sunpyver <= 1: header.set( 'dsun_obs', sun.sunearth_distance(Time(dateobs)).to(u.meter).value) header.set( 'rsun_obs', sun.solar_semidiameter_angular_size( Time(dateobs)).value) header.set( 'hglt_obs', sun.heliographic_solar_center(Time(dateobs))[1].value) else: header.set( 'dsun_obs', sun.earth_distance(Time(dateobs)).to(u.meter).value) header.set('rsun_obs', sun.angular_radius(Time(dateobs)).value) header.set('hglt_obs', sun.L0(Time(dateobs)).value) except: # this works for astropy.io.fits if tdur_s: header.append(('exptime', tdur_s)) else: header.append(('exptime', 1.)) header.append(('p_angle', hel['p0'])) header.append(('hgln_obs', 0.)) header.append(('rsun_ref', sun.constants.radius.value)) if sunpyver <= 1: header.append( ('dsun_obs', sun.sunearth_distance(Time(dateobs)).to( u.meter).value)) header.append(('rsun_obs', sun.solar_semidiameter_angular_size( Time(dateobs)).value)) header.append(('hglt_obs', sun.heliographic_solar_center( Time(dateobs))[1].value)) else: header.append( ('dsun_obs', sun.earth_distance(Time(dateobs)).to(u.meter).value)) header.append( ('rsun_obs', sun.angular_radius(Time(dateobs)).value)) header.append(('hglt_obs', sun.L0(Time(dateobs)).value)) # check if stokes parameter exist exist_stokes = False stokes_mapper = { 'I': 1, 'Q': 2, 'U': 3, 'V': 4, 'RR': -1, 'LL': -2, 'RL': -3, 'LR': -4, 'XX': -5, 'YY': -6, 'XY': -7, 'YX': -8 } if 'CRVAL3' in header.keys(): if header['CTYPE3'] == 'STOKES': stokenum = header['CRVAL3'] exist_stokes = True if 'CRVAL4' in header.keys(): if header['CTYPE4'] == 'STOKES': stokenum = header['CRVAL4'] exist_stokes = True if exist_stokes: if stokenum in stokes_mapper.values(): stokesstr = list(stokes_mapper.keys())[list( stokes_mapper.values()).index(stokenum)] else: print('Stokes parameter {0:d} not recognized'.format( stokenum)) if verbose: print('This image is in Stokes ' + stokesstr) else: print( 'STOKES Information does not seem to exist! Assuming Stokes I' ) stokenum = 1 # intensity units to brightness temperature if toTb: # get restoring beam info bmaj = bmajs[n] bmin = bmins[n] beamunit = beamunits[n] data = hdu[ 0].data # remember the data order is reversed due to the FITS convension keys = list(header.keys()) values = list(header.values()) # which axis is frequency? faxis = keys[values.index('FREQ')][-1] faxis_ind = ndim - int(faxis) # find out the polarization of this image k_b = qa.constants('k')['value'] c_l = qa.constants('c')['value'] # Always use 2*kb for all polarizations const = 2. * k_b / c_l**2 if header['BUNIT'].lower() == 'jy/beam': header['BUNIT'] = 'K' header['BTYPE'] = 'Brightness Temperature' for i in range(dshape[faxis_ind]): nu = header['CRVAL' + faxis] + header['CDELT' + faxis] * ( i + 1 - header['CRPIX' + faxis]) if header['CUNIT' + faxis] == 'KHz': nu *= 1e3 if header['CUNIT' + faxis] == 'MHz': nu *= 1e6 if header['CUNIT' + faxis] == 'GHz': nu *= 1e9 if len(bmaj) > 1: # multiple (per-plane) beams bmajtmp = bmaj[i] bmintmp = bmin[i] else: # one single beam bmajtmp = bmaj[0] bmintmp = bmin[0] if beamunit == 'arcsec': bmaj0 = np.radians(bmajtmp / 3600.) bmin0 = np.radians(bmintmp / 3600.) if beamunit == 'arcmin': bmaj0 = np.radians(bmajtmp / 60.) bmin0 = np.radians(bmintmp / 60.) if beamunit == 'deg': bmaj0 = np.radians(bmajtmp) bmin0 = np.radians(bmintmp) if beamunit == 'rad': bmaj0 = bmajtmp bmin0 = bmintmp beam_area = bmaj0 * bmin0 * np.pi / (4. * log(2.)) factor = const * nu**2 # SI unit jy_to_si = 1e-26 # print(nu/1e9, beam_area, factor) factor2 = sclfactor # if sclfactor: # factor2 = 100. if faxis == '3': data[:, i, :, :] *= jy_to_si / beam_area / factor * factor2 if faxis == '4': data[ i, :, :, :] *= jy_to_si / beam_area / factor * factor2 header = fu.headerfix(header) hdu.flush() hdu.close() if ndim - np.count_nonzero(np.array(dshape) == 1) > 3: docompress = False ''' Caveat: only 1D, 2D, or 3D images are currently supported by the astropy fits compression. If a n-dimensional image data array does not have at least n-3 single-dimensional entries, force docompress to be False ''' print( 'warning: The fits data contains more than 3 non squeezable dimensions. Skipping fits compression..' ) if docompress: fitsftmp = fitsf + ".tmp.fits" os.system("mv {} {}".format(fitsf, fitsftmp)) hdu = pyfits.open(fitsftmp) hdu[0].verify('fix') header = hdu[0].header data = hdu[0].data fu.write_compressed_image_fits(fitsf, data, header, compression_type='RICE_1', quantize_level=4.0) os.system("rm -rf {}".format(fitsftmp)) if deletehistory: ms_restorehistory(vis) return fitsfile
def plot_filament_track(self): good, = np.where(self.dat['track_id'].values == self.tid) #get start time of track for filename # ofname = '{0}_track{1:6d}'.format(dat['event_starttime'][good[0]],i).replace(' ','0').replace(':','_') self.ofile = self.ifile.split('/')[-1].replace('fits.fz','png') sun = pyfits.open(self.ifile) #Solar Halpha data sundat = sun[1].data #Set up image properties sc = 1 dpi = 100*sc #get image extent (i.e. physical coordinates) x0 = sun[1].header['CRVAL1']-sun[1].header['CRPIX1'] y0 = sun[1].header['CRVAL2']-sun[1].header['CRPIX2'] dx = 1.#assumed approximate dy = 1.#assumed approximate #Correct for Halpha images automatic scaling of solar radius to 900`` try: asr = solar_semidiameter_angular_size(self.dat['event_starttime'].values[good[0]]) except: asr = solar_semidiameter_angular_size(self.dat['event_starttime'].values[good]) #store the scale factor sf = asr.value/900. sx, sy = np.shape(sundat) #add to stoptime (i.e. observed time) self.stop = datetime.datetime.strptime(self.ofile[:-6],'%Y%m%d%H%M%S') #create figure and add sun self.fig, self.ax = plt.subplots(figsize=(sc*float(sx)/dpi,sc*float(sy)/dpi),dpi=dpi) #set sun to fill entire range self.fig.subplots_adjust(left=0,bottom=0,right=1,top=1) #Turn off axis self.ax.set_axis_off() self.ax.imshow(sundat,cmap=plt.cm.gray,extent=[sf*x0,sf*(x0+dx*sx),sf*y0,sf*(y0+dy*sy)],origin='lower') #text offset poff = 0.01 self.ax.text(sf*x0+poff*sf*(dx*sx-x0),sf*y0+poff*sf*(dy*sy-y0),sun[1].header['DATE-OBS'],color='white',fontsize=38,fontweight='bold') # rs = plt.Circle((0.,0.),radius=1000.,color='gray',fill=False,linewidth=5,zorder=0) # ax.add_patch(rs) #plot track polygons for given id for j in good: inc = 'red' if self.dat['obs_observatory'].values[j] == 'HA2': inc='blue' poly = plt.Polygon(loads(self.dat['hpc_bbox'].values[j]).exterior,color=inc,linewidth=0.5,fill=None) self.ax.add_patch(poly) #over plot rotation track if good.size > 1: #array of time differences between obs and filament track td = np.abs(self.dat['event_starttime_dt'][good]-self.stop) nearest, = np.where(td == td.min()) roplot = good[nearest][0] #nearest filament trace in time else: roplot = good[0] #plot rotation of nearest filament placement self.plot_rotation(self.dat['event_starttime_dt'][roplot],self.dat['hpc_bbox'].values[roplot],color='green') #Setup plots # ticks = [-1000.,-500.,0.,500.,1000.] lim = [sf*x0,sf*(x0+sx*dx)] self.ax.set_xlim(lim) self.ax.set_ylim(lim) #remove axis labels just make an image # self.ax.set_xticks(ticks) # self.ax.set_yticks(ticks) # self.ax.set_xlabel('Solar X [arcsec]') # self.ax.set_ylabel('Solar Y [arcsec]') #save fig self.fig.savefig(self.pdir+self.ofile,bbox_inches=0,dpi=dpi) self.fig.clear() plt.close()
def plt_qlook_image(imres, figdir=None, verbose=True, synoptic=False): from matplotlib import pyplot as plt from sunpy import map as smap from sunpy import sun from matplotlib import colors import astropy.units as u if not figdir: figdir = './' nspw = len(set(imres['Spw'])) plttimes = list(set(imres['BeginTime'])) ntime = len(plttimes) # sort the imres according to time images = np.array(imres['ImageName']) btimes = Time(imres['BeginTime']) etimes = Time(imres['EndTime']) spws = np.array(imres['Spw']) suc = np.array(imres['Succeeded']) inds = btimes.argsort() images_sort = images[inds].reshape(ntime, nspw) btimes_sort = btimes[inds].reshape(ntime, nspw) suc_sort = suc[inds].reshape(ntime, nspw) spws_sort = spws[inds].reshape(ntime, nspw) if verbose: print '{0:d} figures to plot'.format(ntime) plt.ioff() fig = plt.figure(figsize=(8, 8)) plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) for i in range(ntime): plt.ioff() plt.clf() plttime = btimes_sort[i, 0] tofd = plttime.mjd - np.fix(plttime.mjd) suci = suc_sort[i] if not synoptic: if tofd < 16. / 24. or sum( suci ) < nspw - 2: # if time of the day is before 16 UT (and 24 UT), skip plotting (because the old antennas are not tracking) continue #fig=plt.figure(figsize=(9,6)) #fig.suptitle('EOVSA @ '+plttime.iso[:19]) if synoptic: fig.text(0.01, 0.98, plttime.iso[:10], color='w', fontweight='bold', fontsize=12, ha='left') else: fig.text(0.01, 0.98, plttime.iso[:19], color='w', fontweight='bold', fontsize=12, ha='left') if verbose: print 'Plotting image at: ', plttime.iso for n in range(nspw): plt.ioff() image = images_sort[i, n] #fig.add_subplot(nspw/3, 3, n+1) fig.add_subplot(nspw / 2, 2, n + 1) if suci[n]: try: eomap = smap.Map(image) except: continue sz = eomap.data.shape if len(sz) == 4: eomap.data = eomap.data.reshape((sz[2], sz[3])) eomap.data[np.isnan(eomap.data)] = 0.0 #resample the image for plotting dim = u.Quantity([256, 256], u.pixel) eomap = eomap.resample(dim) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot_settings['norm'] = colors.Normalize(vmin=-1e5, vmax=1e6) eomap.plot() if not synoptic: eomap.draw_limb() eomap.draw_grid() ax = plt.gca() ax.set_xlim([-1080, 1080]) ax.set_ylim([-1080, 1080]) spwran = spws_sort[i, n] freqran = [int(s) * 0.5 + 2.9 for s in spwran.split('~')] ax.text(0.98, 0.01, '{0:.1f} - {1:.1f} GHz'.format(freqran[0], freqran[1]), color='w', transform=ax.transAxes, fontweight='bold', ha='right') ax.set_title(' ') #ax.set_title('spw '+spws_sort[i,n]) #ax.text(0.01,0.02, plttime.isot,transform=ax.transAxes,color='white') ax.set_xlabel('') ax.set_ylabel('') ax.set_xticklabels(['']) ax.set_yticklabels(['']) else: #make an empty map data = np.zeros((512, 512)) header = { "DATE-OBS": plttime.isot, "EXPTIME": 0., "CDELT1": 5., "NAXIS1": 512, "CRVAL1": 0., "CRPIX1": 257, "CUNIT1": "arcsec", "CTYPE1": "HPLN-TAN", "CDELT2": 5., "NAXIS2": 512, "CRVAL2": 0., "CRPIX2": 257, "CUNIT2": "arcsec", "CTYPE2": "HPLT-TAN", "HGLT_OBS": sun.heliographic_solar_center(plttime)[1].value, "HGLN_OBS": 0., "RSUN_OBS": sun.solar_semidiameter_angular_size(plttime).value, "RSUN_REF": sun.constants.radius.value, "DSUN_OBS": sun.sunearth_distance(plttime).to(u.meter).value, } eomap = smap.Map(data, header) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot_settings['norm'] = colors.Normalize(vmin=-1e5, vmax=1e6) eomap.plot() if not synoptic: eomap.draw_limb() eomap.draw_grid() ax = plt.gca() ax.set_xlim([-1080, 1080]) ax.set_ylim([-1080, 1080]) #ax.set_title('spw '+spwran+'( )')) spwran = spws_sort[i, n] freqran = [int(s) * 0.5 + 2.9 for s in spwran.split('~')] spwran = spws_sort[i, n] #ax.set_title('{0:.1f} - {1:.1f} GHz'.format(freqran[0],freqran[1])) ax.text(0.98, 0.01, '{0:.1f} - {1:.1f} GHz'.format(freqran[0], freqran[1]), color='w', transform=ax.transAxes, fontweight='bold', ha='right') ax.set_title(' ') #ax.text(0.01,0.02, plttime.isot,transform=ax.transAxes,color='white') ax.set_xlabel('') ax.set_ylabel('') ax.set_xticklabels(['']) ax.set_yticklabels(['']) fig_tdt = plttime.to_datetime() if synoptic: fig_subdir = fig_tdt.strftime("%Y/") figname = 'eovsa_qlimg_' + plttime.iso[:10].replace('-', '') + '.png' else: fig_subdir = fig_tdt.strftime("%Y/%m/%d/") figname = 'eovsa_qlimg_' + plttime.isot.replace(':', '').replace( '-', '')[:15] + '.png' figdir_ = figdir + fig_subdir if not os.path.exists(figdir_): os.makedirs(figdir_) if verbose: print 'Saving plot to :' + figdir_ + figname plt.savefig(figdir_ + figname) plt.close(fig)
def imreg(vis=None, ephem=None, msinfo=None, reftime=None, imagefile=None, fitsfile=None, beamfile=None, \ offsetfile=None, toTb=None, scl100=None, verbose=False, p_ang = False, overwrite = True, usephacenter=False): ia = iatool() if not imagefile: raise ValueError, 'Please specify input image' if not reftime: raise ValueError, 'Please specify reference time corresponding to the input image' if not fitsfile: fitsfile = [img + '.fits' for img in imagefile] if len(imagefile) != len(reftime): raise ValueError, 'Number of input images does not equal to number of helio coord headers!' if len(imagefile) != len(fitsfile): raise ValueError, 'Number of input images does not equal to number of output fits files!' nimg = len(imagefile) if verbose: print str(nimg) + ' images to process...' helio = ephem_to_helio(vis, ephem=ephem, msinfo=msinfo, reftime=reftime, usephacenter=usephacenter) for n, img in enumerate(imagefile): if verbose: print 'processing image #' + str(n) fitsf = fitsfile[n] hel = helio[n] if not os.path.exists(img): raise ValueError, 'Please specify input image' if os.path.exists(fitsf) and not overwrite: raise ValueError, 'Specified fits file already exists and overwrite is set to False. Aborting...' else: p0 = hel['p0'] ia.open(img) imr = ia.rotate(pa=str(-p0) + 'deg') imr.tofits(fitsf, history=False, overwrite=overwrite) imr.close() sum = ia.summary() ia.close() # construct the standard fits header # RA and DEC of the reference pixel crpix1 and crpix2 (imra, imdec) = (sum['refval'][0], sum['refval'][1]) # find out the difference of the image center to the CASA phase center # RA and DEC difference in arcseconds ddec = degrees((imdec - hel['dec_fld'])) * 3600. dra = degrees((imra - hel['ra_fld']) * cos(hel['dec_fld'])) * 3600. # Convert into image heliocentric offsets prad = -radians(hel['p0']) dx = (-dra) * cos(prad) - ddec * sin(prad) dy = (-dra) * sin(prad) + ddec * cos(prad) if offsetfile: try: offset = np.load(offsetfile) except: raise ValueError, 'The specified offsetfile does not exist!' reftimes_d = offset['reftimes_d'] xoffs = offset['xoffs'] yoffs = offset['yoffs'] timg_d = hel['reftime'] ind = bisect.bisect_left(reftimes_d, timg_d) xoff = xoffs[ind - 1] yoff = yoffs[ind - 1] else: xoff = hel['refx'] yoff = hel['refy'] if verbose: print 'offset of image phase center to visibility phase center (arcsec): ', dx, dy print 'offset of visibility phase center to solar disk center (arcsec): ', xoff, yoff (crval1, crval2) = (xoff + dx, yoff + dy) # update the fits header to heliocentric coordinates hdu = pyfits.open(fitsf, mode='update') header = hdu[0].header (cdelt1, cdelt2) = (-header['cdelt1'] * 3600., header['cdelt2'] * 3600. ) # Original CDELT1, 2 are for RA and DEC in degrees header['cdelt1'] = cdelt1 header['cdelt2'] = cdelt2 header['cunit1'] = 'arcsec' header['cunit2'] = 'arcsec' header['crval1'] = crval1 header['crval2'] = crval2 header['ctype1'] = 'HPLN-TAN' header['ctype2'] = 'HPLT-TAN' header['date-obs'] = hel['date-obs'] #begin time of the image if not p_ang: hel['p0'] = 0 try: # this works for pyfits version of CASA 4.7.0 but not CASA 4.6.0 header.update('exptime', hel['exptime']) header.update('p_angle', hel['p0']) header.update( 'dsun_obs', sun.sunearth_distance(Time(hel['date-obs'])).to(u.meter).value) header.update( 'rsun_obs', sun.solar_semidiameter_angular_size(Time( hel['date-obs'])).value) header.update('rsun_ref', sun.constants.radius.value) header.update('hgln_obs', 0.) header.update( 'hglt_obs', sun.heliographic_solar_center(Time(hel['date-obs']))[1].value) except: # this works for astropy.io.fits header.append(('exptime', hel['exptime'])) header.append(('p_angle', hel['p0'])) header.append( ('dsun_obs', sun.sunearth_distance(Time(hel['date-obs'])).to( u.meter).value)) header.append(('rsun_obs', sun.solar_semidiameter_angular_size( Time(hel['date-obs'])).value)) header.append(('rsun_ref', sun.constants.radius.value)) header.append(('hgln_obs', 0.)) header.append(('hglt_obs', sun.heliographic_solar_center(Time( hel['date-obs']))[1].value)) # header.update('comment', 'Fits header updated to heliocentric coordinates by Bin Chen') # update intensity units, i.e. to brightness temperature? if toTb: # get restoring beam info (bmajs, bmins, bpas, beamunits, bpaunits) = getbeam(imagefile=imagefile, beamfile=beamfile) bmaj = bmajs[n] bmin = bmins[n] beamunit = beamunits[n] data = hdu[ 0].data # remember the data order is reversed due to the FITS convension dim = data.ndim sz = data.shape keys = header.keys() values = header.values() # which axis is frequency? faxis = keys[values.index('FREQ')][-1] faxis_ind = dim - int(faxis) if header['BUNIT'].lower() == 'jy/beam': header['BUNIT'] = 'K' for i in range(sz[faxis_ind]): nu = header['CRVAL' + faxis] + header['CDELT' + faxis] * \ (i + 1 - header['CRPIX' + faxis]) if header['CUNIT' + faxis] == 'KHz': nu *= 1e3 if header['CUNIT' + faxis] == 'MHz': nu *= 1e6 if header['CUNIT' + faxis] == 'GHz': nu *= 1e9 if len(bmaj) > 1: # multiple (per-plane) beams bmajtmp = bmaj[i] bmintmp = bmin[i] else: # one single beam bmajtmp = bmaj[0] bmintmp = bmin[0] if beamunit == 'arcsec': bmaj0 = np.radians(bmajtmp / 3600.) bmin0 = np.radians(bmajtmp / 3600.) if beamunit == 'arcmin': bmaj0 = np.radians(bmajtmp / 60.) bmin0 = np.radians(bmintmp / 60.) if beamunit == 'deg': bmaj0 = np.radians(bmajtmp) bmin0 = np.radians(bmintmp) if beamunit == 'rad': bmaj0 = bmajtmp bmin0 = bmintmp beam_area = bmaj0 * bmin0 * np.pi / (4. * log(2.)) k_b = qa.constants('k')['value'] c_l = qa.constants('c')['value'] factor = 2. * k_b * nu**2 / c_l**2 # SI unit jy_to_si = 1e-26 # print nu/1e9, beam_area, factor factor2 = 1. if scl100: factor2 = 100. if faxis == '3': data[:, i, :, :] *= jy_to_si / beam_area / factor * factor2 if faxis == '4': data[ i, :, :, :] *= jy_to_si / beam_area / factor * factor2 hdu.flush() hdu.close()
def make_sunpy(evtdata, hdr, norm_map=False): """ Make a sunpy map based on the NuSTAR data. Parameters ---------- evtdata: FITS data structure This should be an hdu.data structure from a NuSTAR FITS file. hdr: FITS header containing the astrometric information Optional keywords norm_map: Normalise the map data by the exposure (live) time, so units of DN/s. Defaults to "False" and DN Returns ------- nustar_map: A sunpy map objecct """ from sunpy.coordinates import get_sunearth_distance, get_sun_B0 # Parse Header keywords for field in hdr.keys(): if field.find('TYPE') != -1: if hdr[field] == 'X': print(hdr[field][5:8]) xval = field[5:8] if hdr[field] == 'Y': print(hdr[field][5:8]) yval = field[5:8] min_x= hdr['TLMIN'+xval] min_y= hdr['TLMIN'+yval] max_x= hdr['TLMAX'+xval] max_y= hdr['TLMAX'+yval] delx = abs(hdr['TCDLT'+xval]) dely = abs(hdr['TCDLT'+yval]) x = evtdata['X'][:] y = evtdata['Y'][:] met = evtdata['TIME'][:]*u.s mjdref=hdr['MJDREFI'] mid_obs_time = astropy.time.Time(mjdref*u.d+met.mean(), format = 'mjd') # Add in the exposure time (or livetime), just a number not units of seconds exp_time=hdr['EXPOSURE'] # Use the native binning for now # Assume X and Y are the same size resample = 1.0 scale = delx * resample bins = (max_x - min_x) / (resample) H, yedges, xedges = np.histogram2d(y, x, bins=bins, range = [[min_y,max_y], [min_x, max_x]]) #Normalise the data with the exposure (or live) time? if norm_map is True: H=H/exp_time pixluname='DN/s' else: pixluname='DN' dict_header = { "DATE-OBS": mid_obs_time.iso, "EXPTIME": exp_time, "CDELT1": scale, "NAXIS1": bins, "CRVAL1": 0., "CRPIX1": bins*0.5, "CUNIT1": "arcsec", "CTYPE1": "HPLN-TAN", "CDELT2": scale, "NAXIS2": bins, "CRVAL2": 0., "CRPIX2": bins*0.5 + 0.5, "CUNIT2": "arcsec", "CTYPE2": "HPLT-TAN", "PIXLUNIT": pixluname, "HGLT_OBS": get_sun_B0(mid_obs_time), "HGLN_OBS": 0, "RSUN_OBS": sun.solar_semidiameter_angular_size(mid_obs_time).value, "RSUN_REF": sun.constants.radius.value, # Assumes dsun_obs in m if don't specify the units, so give units "DSUN_OBS": get_sunearth_distance(mid_obs_time).value*u.astrophys.au } # For some reason the DSUN_OBS crashed the save... # header = sunpy.map.MapMeta(dict_header) header = sunpy.util.MetaDict(dict_header) nustar_map = sunpy.map.Map(H, header) return nustar_map
def imreg(vis=None, ephem=None, msinfo=None, imagefile=None, timerange=None, reftime=None, fitsfile=None, beamfile=None, offsetfile=None, toTb=None, scl100=None, verbose=False, p_ang=False, overwrite=True, usephacenter=True, deletehistory=False): ''' main routine to register CASA images Required Inputs: vis: STRING. CASA measurement set from which the image is derived imagefile: STRING or LIST. name of the input CASA image timerange: STRING or LIST. timerange used to generate the CASA image, must have the same length as the input images. Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00~2012/03/03/13:00:00' Optional Inputs: msinfo: DICTIONARY. CASA MS information, output from read_msinfo. If not provided, generate one from the supplied vis ephem: DICTIONARY. solar ephem, output from read_horizons. If not provided, query JPL Horizons based on time info of the vis (internet connection required) fitsfile: STRING or LIST. name of the output registered fits files reftime: STRING or LIST. Each element should be in CASA standard time format, e.g., '2012/03/03/12:00:00' offsetfile: optionally provide an offset with a series of solar x and y offsets with timestamps toTb: Bool. Convert the default Jy/beam to brightness temperature? scl100: Bool. If True, scale the image values up by 100 (to compensate VLA 20 dB attenuator) verbose: Bool. Show more diagnostic info if True. usephacenter: Bool -- if True, correct for the RA and DEC in the ms file based on solar empheris. Otherwise assume the phasecenter is correctly pointed to the solar disk center (EOVSA case) ''' ia = iatool() if deletehistory: msclearhistory(vis) if verbose: import time t0 = time.time() prtidx = 1 print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 if not imagefile: raise ValueError, 'Please specify input image' if not timerange: raise ValueError, 'Please specify timerange of the input image' if type(imagefile) == str: imagefile = [imagefile] if type(timerange) == str: timerange = [timerange] if not fitsfile: fitsfile = [img + '.fits' for img in imagefile] if type(fitsfile) == str: fitsfile = [fitsfile] nimg = len(imagefile) if len(timerange) != nimg: raise ValueError, 'Number of input images does not equal to number of timeranges!' if len(fitsfile) != nimg: raise ValueError, 'Number of input images does not equal to number of output fits files!' nimg = len(imagefile) if verbose: print str(nimg) + ' images to process...' if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 if reftime: # use as reference time to find solar disk RA and DEC to register the image, but not the actual timerange associated with the image if type(reftime) == str: reftime = [reftime] * nimg if len(reftime) != nimg: raise ValueError, 'Number of reference times does not match that of input images!' helio = ephem_to_helio(vis, ephem=ephem, msinfo=msinfo, reftime=reftime, usephacenter=usephacenter) else: # use the supplied timerange to register the image helio = ephem_to_helio(vis, ephem=ephem, msinfo=msinfo, reftime=timerange, usephacenter=usephacenter) if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 for n, img in enumerate(imagefile): if verbose: print 'processing image #' + str(n) fitsf = fitsfile[n] timeran = timerange[n] # obtain duration of the image as FITS header exptime try: [tbg0, tend0] = timeran.split('~') tbg_d = qa.getvalue(qa.convert(qa.totime(tbg0), 'd'))[0] tend_d = qa.getvalue(qa.convert(qa.totime(tend0), 'd'))[0] tdur_s = (tend_d - tbg_d) * 3600. * 24. dateobs = qa.time(qa.quantity(tbg_d, 'd'), form='fits', prec=10)[0] except: print 'Error in converting the input timerange: ' + str( timeran) + '. Proceeding to the next image...' continue if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 hel = helio[n] if not os.path.exists(img): raise ValueError, 'Please specify input image' if os.path.exists(fitsf) and not overwrite: raise ValueError, 'Specified fits file already exists and overwrite is set to False. Aborting...' else: p0 = hel['p0'] ia.open(img) imr = ia.rotate(pa=str(-p0) + 'deg') imr.tofits(fitsf, history=False, overwrite=overwrite) imr.close() imsum = ia.summary() ia.close() if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 # construct the standard fits header # RA and DEC of the reference pixel crpix1 and crpix2 (imra, imdec) = (imsum['refval'][0], imsum['refval'][1]) # find out the difference of the image center to the CASA phase center # RA and DEC difference in arcseconds ddec = degrees((imdec - hel['dec_fld'])) * 3600. dra = degrees((imra - hel['ra_fld']) * cos(hel['dec_fld'])) * 3600. # Convert into image heliocentric offsets prad = -radians(hel['p0']) dx = (-dra) * cos(prad) - ddec * sin(prad) dy = (-dra) * sin(prad) + ddec * cos(prad) if offsetfile: try: offset = np.load(offsetfile) except: raise ValueError, 'The specified offsetfile does not exist!' reftimes_d = offset['reftimes_d'] xoffs = offset['xoffs'] yoffs = offset['yoffs'] timg_d = hel['reftime'] ind = bisect.bisect_left(reftimes_d, timg_d) xoff = xoffs[ind - 1] yoff = yoffs[ind - 1] else: xoff = hel['refx'] yoff = hel['refy'] if verbose: print 'offset of image phase center to visibility phase center (arcsec): ', dx, dy print 'offset of visibility phase center to solar disk center (arcsec): ', xoff, yoff (crval1, crval2) = (xoff + dx, yoff + dy) # update the fits header to heliocentric coordinates if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 hdu = pyfits.open(fitsf, mode='update') if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 header = hdu[0].header (cdelt1, cdelt2) = (-header['cdelt1'] * 3600., header['cdelt2'] * 3600. ) # Original CDELT1, 2 are for RA and DEC in degrees header['cdelt1'] = cdelt1 header['cdelt2'] = cdelt2 header['cunit1'] = 'arcsec' header['cunit2'] = 'arcsec' header['crval1'] = crval1 header['crval2'] = crval2 header['ctype1'] = 'HPLN-TAN' header['ctype2'] = 'HPLT-TAN' header['date-obs'] = dateobs # begin time of the image if not p_ang: hel['p0'] = 0 try: # this works for pyfits version of CASA 4.7.0 but not CASA 4.6.0 if tdur_s: header.set('exptime', tdur_s) else: header.set('exptime', 1.) header.set('p_angle', hel['p0']) header.set('dsun_obs', sun.sunearth_distance(Time(dateobs)).to(u.meter).value) header.set( 'rsun_obs', sun.solar_semidiameter_angular_size(Time(dateobs)).value) header.set('rsun_ref', sun.constants.radius.value) header.set('hgln_obs', 0.) header.set('hglt_obs', sun.heliographic_solar_center(Time(dateobs))[1].value) except: # this works for astropy.io.fits if tdur_s: header.append(('exptime', tdur_s)) else: header.append(('exptime', 1.)) header.append(('p_angle', hel['p0'])) header.append( ('dsun_obs', sun.sunearth_distance(Time(dateobs)).to(u.meter).value)) header.append( ('rsun_obs', sun.solar_semidiameter_angular_size(Time(dateobs)).value)) header.append(('rsun_ref', sun.constants.radius.value)) header.append(('hgln_obs', 0.)) header.append( ('hglt_obs', sun.heliographic_solar_center(Time(dateobs))[1].value)) if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 # update intensity units, i.e. to brightness temperature? if toTb: # get restoring beam info (bmajs, bmins, bpas, beamunits, bpaunits) = getbeam(imagefile=imagefile, beamfile=beamfile) bmaj = bmajs[n] bmin = bmins[n] beamunit = beamunits[n] data = hdu[ 0].data # remember the data order is reversed due to the FITS convension dim = data.ndim sz = data.shape keys = header.keys() values = header.values() # which axis is frequency? faxis = keys[values.index('FREQ')][-1] faxis_ind = dim - int(faxis) if header['BUNIT'].lower() == 'jy/beam': header['BUNIT'] = 'K' header['BTYPE'] = 'Brightness Temperature' for i in range(sz[faxis_ind]): nu = header['CRVAL' + faxis] + header['CDELT' + faxis] * ( i + 1 - header['CRPIX' + faxis]) if header['CUNIT' + faxis] == 'KHz': nu *= 1e3 if header['CUNIT' + faxis] == 'MHz': nu *= 1e6 if header['CUNIT' + faxis] == 'GHz': nu *= 1e9 if len(bmaj) > 1: # multiple (per-plane) beams bmajtmp = bmaj[i] bmintmp = bmin[i] else: # one single beam bmajtmp = bmaj[0] bmintmp = bmin[0] if beamunit == 'arcsec': bmaj0 = np.radians(bmajtmp / 3600.) bmin0 = np.radians(bmajtmp / 3600.) if beamunit == 'arcmin': bmaj0 = np.radians(bmajtmp / 60.) bmin0 = np.radians(bmintmp / 60.) if beamunit == 'deg': bmaj0 = np.radians(bmajtmp) bmin0 = np.radians(bmintmp) if beamunit == 'rad': bmaj0 = bmajtmp bmin0 = bmintmp beam_area = bmaj0 * bmin0 * np.pi / (4. * log(2.)) k_b = qa.constants('k')['value'] c_l = qa.constants('c')['value'] factor = 2. * k_b * nu**2 / c_l**2 # SI unit jy_to_si = 1e-26 # print nu/1e9, beam_area, factor factor2 = 1. if scl100: factor2 = 100. if faxis == '3': data[:, i, :, :] *= jy_to_si / beam_area / factor * factor2 if faxis == '4': data[ i, :, :, :] *= jy_to_si / beam_area / factor * factor2 if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1 hdu.flush() hdu.close() if verbose: print('point {}: {}'.format(prtidx, time.time() - t0)) prtidx += 1
def plt_qlook_image(imres, figdir=None, specdata=None, verbose=True, stokes='I,V', fov=None): from matplotlib import pyplot as plt from sunpy import map as smap from sunpy import sun import astropy.units as u if not figdir: figdir = './' observatory = 'EOVSA' polmap = {'RR': 0, 'LL': 1, 'I': 0, 'V': 1} pols = stokes.split(',') npols = len(pols) # SRL = set(['RR', 'LL']) # SXY = set(['XX', 'YY', 'XY', 'YX']) Spw = sorted(list(set(imres['Spw']))) nspw = len(Spw) # Freq = set(imres['Freq']) ## list is an unhashable type Freq = sorted(uniq(imres['Freq'])) plttimes = list(set(imres['BeginTime'])) ntime = len(plttimes) # sort the imres according to time images = np.array(imres['ImageName']) btimes = Time(imres['BeginTime']) etimes = Time(imres['EndTime']) spws = np.array(imres['Spw']) suc = np.array(imres['Succeeded']) inds = btimes.argsort() images_sort = images[inds].reshape(ntime, nspw) btimes_sort = btimes[inds].reshape(ntime, nspw) suc_sort = suc[inds].reshape(ntime, nspw) spws_sort = spws[inds].reshape(ntime, nspw) if verbose: print '{0:d} figures to plot'.format(ntime) plt.ioff() import matplotlib.gridspec as gridspec spec = specdata['spec'] (npol, nbl, nfreq, ntim) = spec.shape tidx = range(ntim) fidx = range(nfreq) tim = specdata['tim'] freq = specdata['freq'] freqghz = freq / 1e9 pol = ''.join(pols) spec_tim = Time(specdata['tim'] / 3600. / 24., format='mjd') timstrr = spec_tim.plot_date if npols == 1: if pol == 'RR': spec_plt = spec[0, 0, :, :] elif pol == 'LL': spec_plt = spec[1, 0, :, :] elif pol == 'I': spec_plt = (spec[0, 0, :, :] + spec[1, 0, :, :]) / 2. elif pol == 'V': spec_plt = (spec[0, 0, :, :] - spec[1, 0, :, :]) / 2. spec_plt = [spec_plt] print 'plot the dynamic spectrum in pol ' + pol # ax1 = fig.add_subplot(211) hnspw = nspw / 2 ncols = hnspw nrows = 2 + 2 # 1 image: 1x1, 1 dspec:2x4 fig = plt.figure(figsize=(8, 8)) gs = gridspec.GridSpec(nrows, ncols) axs = [plt.subplot(gs[0, 0])] for ll in range(1, nspw): axs.append(plt.subplot(gs[ll / hnspw, ll % hnspw], sharex=axs[0], sharey=axs[0])) for ll in range(nspw): axs.append(plt.subplot(gs[ll / hnspw + 2, ll % hnspw], sharex=axs[0], sharey=axs[0])) axs_dspec = [plt.subplot(gs[2:, :])] cmaps = ['jet'] elif npols == 2: R_plot = np.absolute(spec[0, 0, :, :]) L_plot = np.absolute(spec[1, 0, :, :]) if pol == 'RRLL': spec_plt = [R_plot, L_plot] polstr = ['RR', 'LL'] cmaps = ['jet'] * 2 if pol == 'IV': I_plot = (R_plot + L_plot) / 2. V_plot = (R_plot - L_plot) / 2. spec_plt = [I_plot, V_plot] polstr = ['I', 'V'] cmaps = ['jet', 'RdBu'] print 'plot the dynamic spectrum in pol ' + pol hnspw = nspw / 2 ncols = hnspw + 2 # 1 image: 1x1, 1 dspec:2x2 nrows = 2 + 2 fig = plt.figure(figsize=(12, 8)) gs = gridspec.GridSpec(nrows, ncols) axs = [plt.subplot(gs[0, 0])] for ll in range(1, nspw): axs.append(plt.subplot(gs[ll / hnspw, ll % hnspw], sharex=axs[0], sharey=axs[0])) for ll in range(nspw): axs.append(plt.subplot(gs[ll / hnspw + 2, ll % hnspw], sharex=axs[0], sharey=axs[0])) axs_dspec = [plt.subplot(gs[:2, hnspw:])] axs_dspec.append(plt.subplot(gs[2:, hnspw:], sharex=axs_dspec[0], sharey=axs_dspec[0])) fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) timetext = fig.text(0.01, 0.98, '', color='w', fontweight='bold', fontsize=12, ha='left', va='top') for i in range(ntime): plt.ioff() # plt.clf() for ax in axs: ax.cla() plttime = btimes_sort[i, 0] # tofd = plttime.mjd - np.fix(plttime.mjd) suci = suc_sort[i] # if tofd < 16. / 24. or sum( # suci) < nspw - 2: # if time of the day is before 16 UT (and 24 UT), skip plotting (because the old antennas are not tracking) # continue # fig=plt.figure(figsize=(9,6)) # fig.suptitle('EOVSA @ '+plttime.iso[:19]) timetext.set_text(plttime.iso[:19]) if verbose: print 'Plotting image at: ', plttime.iso if i == 0: dspecvspans = [] for pol in range(npols): ax = axs_dspec[pol] ax.pcolormesh(timstrr, freqghz, spec_plt[pol], cmap=cmaps[pol]) ax.xaxis_date() ax.xaxis.set_major_formatter(DateFormatter("%H:%M:%S")) # plt.xticks(rotation=45) ax.set_xlim(timstrr[tidx[0]], timstrr[tidx[-1]]) ax.set_ylim(freqghz[fidx[0]], freqghz[fidx[-1]]) ax.set_xlabel('Time [UT]') ax.set_ylabel('Frequency [GHz]') for idx, freq in enumerate(Freq): ax.axhspan(freq[0], freq[1], linestyle='dotted', edgecolor='w', alpha=0.7, facecolor='none') xtext, ytext = ax.transAxes.inverted().transform(ax.transData.transform([timstrr[tidx[0]], np.mean(freq)])) ax.text(xtext + 0.01, ytext, 'spw ' + Spw[idx], color='w', transform=ax.transAxes, fontweight='bold', ha='left', va='center', fontsize=8, alpha=0.5) ax.text(0.01, 0.98, 'Stokes ' + pols[pol], color='w', transform=ax.transAxes, fontweight='bold', ha='left', va='top') dspecvspans.append(ax.axvspan(btimes[i].plot_date, etimes[i].plot_date, color='w', alpha=0.4)) ax_pos = ax.get_position().extents x0, y0, x1, y1 = ax_pos h, v = x1 - x0, y1 - y0 x0_new = x0 + 0.15 * h y0_new = y0 + 0.15 * v x1_new = x1 - 0.05 * h y1_new = y1 - 0.05 * v ax.set_position(mpl.transforms.Bbox([[x0_new, y0_new], [x1_new, y1_new]])) else: for pol in range(npols): xy = dspecvspans[pol].get_xy() xy[:, 0][np.array([0, 1, 4])] = btimes[i].plot_date xy[:, 0][np.array([2, 3])] = etimes[i].plot_date dspecvspans[pol].set_xy(xy) for n in range(nspw): image = images_sort[i, n] # fig.add_subplot(nspw/3, 3, n+1) # fig.add_subplot(2, nspw / 2, n + 1) for pol in range(npols): if suci[n]: try: eomap = smap.Map(image) except: continue sz = eomap.data.shape if len(sz) == 4: eomap.data = eomap.data[min(polmap[pols[pol]], eomap.meta['naxis4'] - 1), 0, :, :].reshape((sz[2], sz[3])) # resample the image for plotting if fov is not None: fov = [np.array(ll) for ll in fov] pad = max(np.diff(fov[0])[0], np.diff(fov[1])[0]) eomap = eomap.submap((fov[0] + np.array([-1.0, 1.0]) * pad) * u.arcsec, (fov[1] + np.array([-1.0, 1.0]) * pad) * u.arcsec) else: dim = u.Quantity([256, 256], u.pixel) eomap = eomap.resample(dim) eomap.plot_settings['cmap'] = plt.get_cmap(cmaps[pol]) # import pdb # pdb.set_trace() eomap.plot(axes=axs[n + nspw * pol]) eomap.draw_limb() eomap.draw_grid() ax = plt.gca() ax.set_autoscale_on(False) if fov: # pass ax.set_xlim(fov[0]) ax.set_ylim(fov[1]) else: ax.set_xlim([-1080, 1080]) ax.set_ylim([-1080, 1080]) spwran = spws_sort[i, n] # freqran = [int(s) * 0.5 + 2.9 for s in spwran.split('~')] # if len(freqran) == 1: # ax.text(0.98, 0.01, '{0:.1f} GHz'.format(freqran[0]), color='w', # transform=ax.transAxes, fontweight='bold', ha='right') # else: # ax.text(0.98, 0.01, '{0:.1f} - {1:.1f} GHz'.format(freqran[0], freqran[1]), color='w', # transform=ax.transAxes, fontweight='bold', ha='right') ax.text(0.98, 0.01, 'Stokes {1} @ {0:.3f} GHz'.format(eomap.meta['crval3'] / 1e9, pols[pol]), color='w', transform=ax.transAxes, fontweight='bold', ha='right') ax.set_title(' ') # ax.set_title('spw '+spws_sort[i,n]) # ax.text(0.01,0.02, plttime.isot,transform=ax.transAxes,color='white') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) else: # make an empty map data = np.zeros((512, 512)) header = {"DATE-OBS": plttime.isot, "EXPTIME": 0., "CDELT1": 5., "NAXIS1": 512, "CRVAL1": 0., "CRPIX1": 257, "CUNIT1": "arcsec", "CTYPE1": "HPLN-TAN", "CDELT2": 5., "NAXIS2": 512, "CRVAL2": 0., "CRPIX2": 257, "CUNIT2": "arcsec", "CTYPE2": "HPLT-TAN", "HGLT_OBS": sun.heliographic_solar_center(plttime)[1].value, "HGLN_OBS": 0., "RSUN_OBS": sun.solar_semidiameter_angular_size(plttime).value, "RSUN_REF": sun.constants.radius.value, "DSUN_OBS": sun.sunearth_distance(plttime).to(u.meter).value, } eomap = smap.Map(data, header) # resample the image for plotting if fov: fov = [np.array(ll) for ll in fov] pad = max(np.diff(fov[0])[0], np.diff(fov[1])[0]) try: eomap = eomap.submap((fov[0] + np.array([-1.0, 1.0]) * pad) * u.arcsec, (fov[1] + np.array([-1.0, 1.0]) * pad) * u.arcsec) except: x0, x1 = fov[0] + np.array([-1.0, 1.0]) * pad y0, y1 = fov[1] + np.array([-1.0, 1.0]) * pad bl = SkyCoord(x0 * u.arcsec, y0 * u.arcsec, frame=eomap.coordinate_frame) tr = SkyCoord(x1 * u.arcsec, y1 * u.arcsec, frame=eomap.coordinate_frame) eomap = eomap.submap(bl, tr) else: dim = u.Quantity([256, 256], u.pixel) eomap = eomap.resample(dim) eomap.plot_settings['cmap'] = plt.get_cmap(cmaps[pol]) eomap.plot(axes=axs[n + nspw * pol]) eomap.draw_limb() eomap.draw_grid() ax = plt.gca() ax.set_autoscale_on(False) if fov: # pass ax.set_xlim(fov[0]) ax.set_ylim(fov[1]) else: ax.set_xlim([-1080, 1080]) ax.set_ylim([-1080, 1080]) # ax.set_title('spw '+spwran+'( )')) spwran = spws_sort[i, n] freqran = [int(s) * 0.5 + 2.9 for s in spwran.split('~')] spwran = spws_sort[i, n] # ax.set_title('{0:.1f} - {1:.1f} GHz'.format(freqran[0],freqran[1])) # ax.text(0.98, 0.01, '{0:.1f} - {1:.1f} GHz'.format(freqran[0], freqran[1]), color='w', # transform=ax.transAxes, fontweight='bold', ha='right') ax.text(0.98, 0.01, 'Stokes {1} @ {0:.3f} GHz'.format(0., pols[pol]), color='w', transform=ax.transAxes, fontweight='bold', ha='right') ax.set_title(' ') # ax.text(0.01,0.02, plttime.isot,transform=ax.transAxes,color='white') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) figname = observatory + '_qlimg_' + plttime.isot.replace(':', '').replace('-', '')[:19] + '.png' fig_tdt = plttime.to_datetime() # fig_subdir = fig_tdt.strftime("%Y/%m/%d/") figdir_ = figdir # + fig_subdir if not os.path.exists(figdir_): os.makedirs(figdir_) if verbose: print 'Saving plot to: ' + os.path.join(figdir_, figname) plt.savefig(os.path.join(figdir_, figname)) plt.close(fig) DButil.img2html_movie(figdir_)
#track ID to plot track_id = 12963 track_id = 12397 track_id = 14122 track_sp = 6840 fil = pd.read_pickle('filament_categories_hgs_mean_l.pic') pfil = fil.loc[track_id, :] #another track for the south pol sfil = fil.loc[track_sp, :] sr = sun.solar_semidiameter_angular_size() r1 = plt.Circle((0, 0), radius=sr.value, color='lightgray', fill=True, linewidth=5, zorder=1) fig, ax = plt.subplots(figsize=(10, 10), dpi=300) #draw reference lines draw_lines(sr, ax) #plot all polygons for j, i in enumerate(pfil['hpc_bbox'].values):
def plot_filament_track(self): """ Function to create H alpha GONG plots with tracks overplotted plot_filament_tracks creates png files of GONG halpha data with all observed H alpha filament tracks visible on the sun at that time. The function can only be called after initialization of the halpha_plot object. Parameters ---------- self Returns ------- H alpha with with tracks overplotted """ #get start time of track for filename # ofname = '{0}_track{1:6d}'.format(dat['event_starttime'][good[0]],i).replace(' ','0').replace(':','_') #replaced with named extension 2018/04/02 J. Prchlik self.ofile = self.ifile.split('/')[-1].replace('fits.fz', self.ext) try: sun = pyfits.open(self.ifile) # observed time of GONG halpha image self.stop = datetime.datetime.strptime(self.ofile[:-6], '%Y%m%d%H%M%S') #Solar Halpha data sundat = sun[1].data #Set up image properties sc = self.dpi / 100. dpi = self.dpi #get image extent (i.e. physical coordinates) x0 = sun[1].header['CRVAL1'] - sun[1].header['CRPIX1'] y0 = sun[1].header['CRVAL2'] - sun[1].header['CRPIX2'] dx = 1. #assumed approximate dy = 1. #assumed approximate #Correct for Halpha images automatic scaling of solar radius to 900`` # try: # asr = solar_semidiameter_angular_size(self.dat['event_starttime'].values[good[0]]) # except: # asr = solar_semidiameter_angular_size(self.dat['event_starttime'].values[good]) self.asr = solar_semidiameter_angular_size( self.stop.strftime('%Y/%m/%d %H:%M:%S')) #store the scale factor sf = self.asr.value / 900. sx, sy = np.shape(sundat) #get tracks visible on the surface using a given time frame good, = np.where((self.dat['total_event_start'] <= self.stop) & (self.dat['total_event_end'] >= self.stop)) #create figure and add sun self.fig, self.ax = plt.subplots(figsize=(sc * float(sx) / dpi, sc * float(sy) / dpi), dpi=dpi) #set sun to fill entire range self.fig.subplots_adjust(left=0, bottom=0, right=1, top=1) #Turn off axis self.ax.set_axis_off() stat_max = 4300. stat_min = 75. self.ax.imshow(sundat, cmap=plt.cm.gray, extent=[ sf * x0, sf * (x0 + dx * sx), sf * y0, sf * (y0 + dy * sy) ], origin='lower', vmin=stat_min, vmax=stat_max) #text offset poff = 0.01 self.ax.text(sf * x0 + poff * sf * (dx * sx - x0), sf * y0 + poff * sf * (dy * sy - y0), sun[1].header['DATE-OBS'], color='white', fontsize=38, fontweight='bold') # rs = plt.Circle((0.,0.),radius=1000.,color='gray',fill=False,linewidth=5,zorder=0) # ax.add_patch(rs) if self.lref: self.draw_lines() #remove identifying plot ######## #plot track polygons for given id ######## for j in good: ######## inc = 'red' ######## if self.dat['obs_observatory'].values[j] == 'HA2': inc='blue' ######## poly = plt.Polygon(loads(self.dat['hpc_bbox'].values[j]).exterior,color=inc,linewidth=0.5,fill=None) ######## self.ax.add_patch(poly) ######## xx, yy = self.calc_poly_values(self.dat['hpc_bbox'].values[j]) ######## self.ax.text(np.mean(xx),np.max(yy),self.dat['track_id'].values[j],alpha=.5,color=inc) ######## #list of track ids ltid = np.unique(self.dat['track_id'].values[good]) #loop over track ids to find the closest rotational track for tid in ltid: #over plot rotation track idmatch, = np.where(self.dat['track_id'].values == tid) td = np.abs(self.dat['event_starttime_dt'][idmatch] - self.stop) nearest, = np.where(td == td.min()) roplot = idmatch[nearest] #### if idmatch.size < -100: #####array of time differences between obs and filament track #### td = np.abs(self.dat['event_starttime_dt'][idmatch]-self.stop) #### nearest, = np.where(td == td.min()) #### roplot = idmatch[nearest]#[0] #nearest filament trace in time #### else: #### roplot = idmatch[0] roplot = roplot.tolist() #plot rotation of nearest filament placement #fix hpc_bbox to hpc_boundcc because columns used to be off in Dustin's file for k in roplot: self.plot_rotation(self.dat['event_starttime_dt'][k], self.dat['hpc_boundcc'].values[k], color='teal', ids=self.dat['track_id'].values[k]) #Setup plots # ticks = [-1000.,-500.,0.,500.,1000.] lim = [sf * x0, sf * (x0 + sx * dx)] self.ax.set_xlim(lim) self.ax.set_ylim(lim) #Add AIA plot 2018/05/02 J. Prchlik if self.add_aia: self.add_aia_image() #remove axis labels just make an image # self.ax.set_xticks(ticks) # self.ax.set_yticks(ticks) # self.ax.set_xlabel('Solar X [arcsec]') # self.ax.set_ylabel('Solar Y [arcsec]') #save fig self.fig.savefig(self.pdir + self.ofile, bbox_inches=0, dpi=dpi) self.fig.clear() plt.close() except: print 'Unable to create image' print sys.exc_info()
def plt_qlook_image(imres, figdir=None, verbose=True): from matplotlib import pyplot as plt from sunpy import map as smap from sunpy import sun import astropy.units as u if not figdir: figdir = './' nspw = len(set(imres['Spw'])) plttimes = list(set(imres['BeginTime'])) ntime = len(plttimes) # sort the imres according to time images = np.array(imres['ImageName']) btimes = Time(imres['BeginTime']) etimes = Time(imres['EndTime']) spws = np.array(imres['Spw']) suc = np.array(imres['Succeeded']) inds = btimes.argsort() images_sort = images[inds].reshape(ntime, nspw) btimes_sort = btimes[inds].reshape(ntime, nspw) suc_sort = suc[inds].reshape(ntime, nspw) spws_sort = spws[inds].reshape(ntime, nspw) if verbose: print '{0:d} figures to plot'.format(ntime) for i in range(ntime): #for i in range(1): plt.ioff() fig = plt.figure(figsize=(11, 6)) plttime = btimes_sort[i, 0] fig.suptitle('EOVSA @ ' + plttime.iso[:19]) if verbose: print 'Plotting image at: ', plttime.iso suci = suc_sort[i] for n in range(nspw): plt.ioff() image = images_sort[i, n] fig.add_subplot(nspw / 3, 3, n + 1) if suci[n]: try: eomap = smap.Map(image) except: continue sz = eomap.data.shape if len(sz) == 4: eomap.data = eomap.data.reshape((sz[2], sz[3])) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot() eomap.draw_limb() eomap.draw_grid() ax = plt.gca() ax.set_xlim([-1050, 1050]) ax.set_ylim([-1050, 1050]) ax.set_title('spw ' + spws_sort[i, n]) #ax.text(0.01,0.02, plttime.isot,transform=ax.transAxes,color='white') if n != nspw - 3: ax.set_xlabel('') ax.set_ylabel('') ax.set_xticklabels(['']) ax.set_yticklabels(['']) else: #make an empty map data = np.zeros((512, 512)) header = { "DATE-OBS": plttime.isot, "EXPTIME": 0., "CDELT1": 5., "NAXIS1": 512, "CRVAL1": 0., "CRPIX1": 257, "CUNIT1": "arcsec", "CTYPE1": "HPLN-TAN", "CDELT2": 5., "NAXIS2": 512, "CRVAL2": 0., "CRPIX2": 257, "CUNIT2": "arcsec", "CTYPE2": "HPLT-TAN", "HGLT_OBS": sun.heliographic_solar_center(plttime)[1].value, "HGLN_OBS": 0., "RSUN_OBS": sun.solar_semidiameter_angular_size(plttime).value, "RSUN_REF": sun.constants.radius.value, "DSUN_OBS": sun.sunearth_distance(plttime).to(u.meter).value, } eomap = smap.Map(data, header) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot() eomap.draw_limb() eomap.draw_grid() ax = plt.gca() ax.set_xlim([-1050, 1050]) ax.set_ylim([-1050, 1050]) ax.set_title('spw ' + spws_sort[i, n]) #ax.text(0.01,0.02, plttime.isot,transform=ax.transAxes,color='white') if n != 6: ax.set_xlabel('') ax.set_ylabel('') ax.set_xticklabels(['']) ax.set_yticklabels(['']) figname = 'eovsa_qlimg_' + plttime.isot.replace(':', '').replace( '-', '')[:15] + '.png' fig_tdt = plttime.to_datetime() fig_subdir = fig_tdt.strftime("%Y/%m/%d/") figdir_ = figdir + fig_subdir if not os.path.exists(figdir_): os.makedirs(figdir_) if verbose: print 'Saving plot to :' + figdir_ + figname plt.savefig(figdir_ + figname) plt.close(fig)
#get where pandas dataframe equals track id track, = np.where(dat['track_id'].values == i) #which element to grab from track for k in track: t1str = dat['event_starttime_dt'].values[k] #grab data from first element in track t132b = datetime.utcfromtimestamp(t1str.tolist() / 1e9) t1posx, t1posy = calc_poly_values(dat['hpc_bbox'].values[k]) t1meanx = dat['meanx'].values[k] t1meany = dat['meany'].values[k] maxr = solar_semidiameter_angular_size( t132b).value - 10. #10 arcsec of the limb curr = np.sqrt(t1meanx**2. + t1meany**2.) p = 1 #find when filament goes over the limb while curr < maxr: maxt = t132b + timedelta(minutes=20 * p) #remove deprecated fucntion J. Prchlik 2017/11/03 #curx, cury = solar_rotation.rot_hpc(t1meanx*u.arcsec,t1meany*u.arcsec,t132b,maxt,rot_type=rot_type) #current position curx, cury = rot_hpc(t1meanx * u.arcsec, t1meany * u.arcsec, t132b, maxt, rot_type=rot_type) #current position