ax2 = fig.add_axes([left, bottom, width, height])
## hardwired column names and file paths here. Remember to change.
cluster = pyfits.open(
'/home/s1/ynzhang/redmapper_centering/data_dump_June21/SDSS-aug-30-2017-highsnr-peak-sample-noastro.fits'
)[1].data
lambdas = cluster['lambda']
rdmp_ra = cluster['redMaPPer_ra']
rdmp_dec = cluster['redMaPPer_dec']
xray_ra = cluster['x_ray_peak_ra']
xray_dec = cluster['x_ray_peak_dec']
centr = SkyCoord(rdmp_ra, rdmp_dec, frame='icrs', unit='deg')
coord = SkyCoord(xray_ra, xray_dec, frame='icrs', unit='deg')
sep = centr.separation(coord).arcminute * u.arcmin
psep = sep * cosmo.kpc_proper_per_arcmin(cluster['Redshift']).to(
u.Mpc / u.arcmin)
roffs = psep.value
rlmds = (0.01 * lambdas)**0.2 / 0.7
######################
lmd_bins = [20]
lmd_bins_max = [300]
for ii in np.arange(0, len(lmd_bins)):
lmd_min = lmd_bins[ii]
lmd_max = lmd_bins_max[ii]
ind, = np.where((lambdas < lmd_max) & (lambdas >= lmd_min)
& (rlmds > 0))
r_offset = roffs[ind]
lmd = lambdas[ind]
rlmd = rlmds[ind]
'/home/s1/ynzhang/redmapper_centering/data_dump_June21/RM_SDSS_YUANYUAN-oct11.fits'
)[1].data
lambdas = cluster['LAMBDA_OPTICAL']
zs = cluster['Z_LAMBDA_OPTICAL']
rdmp_ra = cluster['RM ra']
rdmp_dec = cluster['RM dec']
xray_ra = cluster['peak ra']
xray_dec = cluster['peak dec']
#####################
# calculate separations
centr = SkyCoord(rdmp_ra, rdmp_dec, frame='icrs', unit='deg')
coord = SkyCoord(xray_ra, xray_dec, frame='icrs', unit='deg')
sep = centr.separation(coord).arcminute * u.arcmin
psep = sep * cosmo.kpc_proper_per_arcmin(cluster['Z_LAMBDA_OPTICAL']).to(
u.Mpc / u.arcmin)
roffs = psep.value
rlmds = (0.01 * lambdas)**0.2 / 0.7
lmd_bins = [20]
lmd_bins_max = [1000]
for ii in np.arange(0, len(lmd_bins)):
# some additional purging and binning
lmd_min = lmd_bins[ii]
lmd_max = lmd_bins_max[ii]
ind, = np.where((rlmds > 0) & (cluster['Z_LAMBDA_OPTICAL'] < 0.35)
& (cluster['Z_LAMBDA_OPTICAL'] > 0.1)
& (lambdas >= lmd_min) & (lambdas < lmd_max))
r_offset = roffs[ind]
lmd = lambdas[ind]
rlmd = rlmds[ind]
