Plot posteriors#

[1]:

import os
default_n_threads = 1
os.environ['OPENBLAS_NUM_THREADS'] = f"{default_n_threads}"

# Disable GPU memory pre-allocation
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'

import lsdb
import pandas as pd
import numpy as np

from astropy import units as u
from astropy.coordinates import SkyCoord
from photod.bayes import makeBayesPosteriors3d
from photod.locus import LSSTsimsLocus, subsampleLocusData, get3DmodelList
from photod.parameters import GlobalParams
from photod.plotting import plotStars

[2]:

s82StripeUrl = "/mnt/beegfs/scratch/data/S82_standards/S82_hats/S82_hats_fixed"
s82StripeCatalog = lsdb.read_hats(s82StripeUrl)
s82StripeCatalog

[2]:

lsdb Catalog S82_fixed:

	CALIBSTARS	ra	dec	RArms	Decrms	Ntot	Ar	uNobs	umag	ummu	uErr	umrms	umchi2	gNobs	gmag	gmmu	gErr	gmrms	gmchi2	rNobs	rmag	rmmu	rErr	rmrms	rmchi2	iNobs	imag	immu	iErr	imrms	imchi2	zNobs	zmag	zmmu	zErr	zmrms	zmchi2	Norder	Dir	Npix	Mr	FeH	MrEst	MrEstUnc	FeHEst	ug	gr	gi	ri	iz	ugErr	grErr	giErr	riErr	izErr	glon	glat
npartitions=7
Order: 4, Pixel: 0	string[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	double[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int8[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]
Order: 4, Pixel: 768	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 4, Pixel: 2303	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 4, Pixel: 3071	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...

The catalog has been loaded lazily, meaning no data has been read, only the catalog schema

[3]:

priorMapUrl = "/mnt/beegfs/scratch/data/priors/hats/s82_priors"
priorMapCatalog = lsdb.read_hats(priorMapUrl)
priorMapCatalog

[3]:

lsdb Catalog s82_priors:

	rmag	kde	xGrid	yGrid	Norder	Dir	Npix
npartitions=207
Order: 5, Pixel: 0	double[pyarrow]	binary[pyarrow]	binary[pyarrow]	binary[pyarrow]	uint8[pyarrow]	uint64[pyarrow]	uint64[pyarrow]
Order: 5, Pixel: 1	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...
Order: 5, Pixel: 12286	...	...	...	...	...	...	...
Order: 5, Pixel: 12287	...	...	...	...	...	...	...

The catalog has been loaded lazily, meaning no data has been read, only the catalog schema

[4]:

locusPath = "../../data/MSandRGBcolors_v1.3.txt"
fitColors = ("ug", "gr", "ri", "iz")
LSSTlocus = LSSTsimsLocus(fixForStripe82=False, datafile=locusPath)
OKlocus = LSSTlocus[(LSSTlocus["gi"] > 0.2) & (LSSTlocus["gi"] < 3.55)]
locusData = subsampleLocusData(OKlocus, kMr=10, kFeH=2)
ArGridList, locus3DList = get3DmodelList(locusData, fitColors)
globalParams = GlobalParams(fitColors, locusData, ArGridList, locus3DList)

subsampled locus 2D grid in FeH and Mr from 51 1559 to: 25 155

[5]:

# Select one random star
starDf = s82StripeCatalog.partitions[3].compute()[193:195]
starDf

[5]:

	CALIBSTARS	ra	dec	RArms	Decrms	Ntot	Ar	uNobs	umag	ummu	...	gi	ri	iz	ugErr	grErr	giErr	riErr	izErr	glon	glat
_healpix_29
1633320273864372627	CALIBSTARS_0901350	45.495008	0.176704	0.0272	0.0272	24	0.264	2	24.012	24.027	...	2.642	1.085	0.594	0.544111	0.012083	0.011705	0.006403	0.007211	177.28692	-48.42306
1633300043542236272	CALIBSTARS_0901352	45.571676	0.023538	0.0259	0.0259	23	0.28	6	23.951	23.915	...	2.313	0.836	0.449	0.361168	0.012083	0.012083	0.007071	0.010296	177.533874	-48.473121

2 rows × 57 columns

[6]:

bayesResults = makeBayesPosteriors3d(starDf, priorMapCatalog, globalParams)

[7]:

# Plot the stars. We provide a DataFrame with the star data, the results
# object and a set of parameters that derive from the global parameters.
plotStars(starDf, bayesResults, *globalParams.getPlottingArgs())

Mr1d= -1.0 14.4 155
MrBright, MrFaint= -1.0 14.4
Plotting star 0...
pts: 25 155

../../_images/pre_executed_plotting_plot_stars_7_1.png

../../_images/pre_executed_plotting_plot_stars_7_2.png

../../_images/pre_executed_plotting_plot_stars_7_3.png

../../_images/pre_executed_plotting_plot_stars_7_4.png

10.286956 0.26310575
Plotting star 1...
pts: 25 155

../../_images/pre_executed_plotting_plot_stars_7_6.png

../../_images/pre_executed_plotting_plot_stars_7_7.png

../../_images/pre_executed_plotting_plot_stars_7_8.png

../../_images/pre_executed_plotting_plot_stars_7_9.png

9.448109 0.3383043

Let’s try with yet a different star. We will construct the DataFrame and provide it to the plotting API.

[8]:

cols = "glon       glat      comp   logg    FeH      Mr      DM      Ar  rmagObs0   ug0     gr0     ri0     iz0    rmag   ugObs   grObs   riObs   izObs   uErr   gErr   rErr   iErr   zErr    ugSL    grSL    riSL    izSL ugErrSL grErrSL riErrSL izErrSL".split()
vals = "66.88731266 -49.16282181    3    4.50  -2.00    5.12   17.85   0.150   23.12   0.777   0.309   0.100   0.046   23.13   0.838   0.369   0.136   0.075  0.059  0.012  0.010  0.012  0.019   0.860   0.380   0.142   0.050   0.061   0.016   0.016   0.023".split()

_ = pd.DataFrame.from_dict({c: [float(v)] for c, v in zip(cols, vals)})

# rmag, ugObs... = generirane iz modela, sa šumom i ekstinkcijom
# greške za boje *Obs treba odrediti iz magnituda

c = SkyCoord(l=_['glon'].to_numpy()*u.degree, b=_['glat'].to_numpy()*u.degree, frame='galactic').transform_to('icrs')
_['ra'] = c.ra
_['dec'] = c.dec

_['ugErr'] = np.sqrt(_['uErr']**2+_['gErr']**2)
_['grErr'] = np.sqrt(_['gErr']**2+_['rErr']**2)
_['riErr'] = np.sqrt(_['rErr']**2+_['iErr']**2)
_['izErr'] = np.sqrt(_['iErr']**2+_['zErr']**2)

_.rename(columns={"ugObs":"ug", "grObs":"gr", "riObs":"ri", "izObs":"iz"}, inplace=True)

star = _["glon glat ra dec FeH Mr DM Ar rmag ug gr ri iz ugErr grErr riErr izErr".split()]
star

[8]:

	glon	glat	ra	dec	FeH	Mr	DM	Ar	rmag	ug	gr	ri	iz	ugErr	grErr	riErr	izErr
0	66.887313	-49.162822	340.004837	-1.141657	-2.0	5.12	17.85	0.15	23.13	0.838	0.369	0.136	0.075	0.060208	0.01562	0.01562	0.022472

[9]:

bayesResults = makeBayesPosteriors3d(star, priorMapCatalog, globalParams)

[10]:

plotStars(star, bayesResults, *globalParams.getPlottingArgs())

Mr1d= -1.0 14.4 155
MrBright, MrFaint= -1.0 14.4
Plotting star 0...
pts: 25 155

../../_images/pre_executed_plotting_plot_stars_11_1.png

../../_images/pre_executed_plotting_plot_stars_11_2.png

../../_images/pre_executed_plotting_plot_stars_11_3.png

../../_images/pre_executed_plotting_plot_stars_11_4.png

4.8340087 0.59558743