PF:Sterne eines Sternhaufens automatisch zählen

In Arbeit.

Codebeispiel

from astropy.io import fits
from astropy.table import Table
from photutils.detection import IRAFStarFinder
from photutils.psf import IntegratedGaussianPRF, DAOGroup
from photutils.background import MMMBackground,MADStdBackgroundRMS
from astropy.modeling.fitting import LevMarLSQFitter
from astropy.stats import gaussian_sigma_to_fwhm
from photutils.psf import IterativelySubtractedPSFPhotometry
from photutils.psf import BasicPSFPhotometry
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import const # Lädt die Konstanten XMIN, XMAX, YMIN, YMAX
,(relevanter Bildausschnitt)
def photometry(file, fixedStars):
image = fits.open(file)[0].data[const.YMIN:const.YMAX,
,→ const.XMIN:const.XMAX]
# Auslesen der Clear-Sterndaten für B- und V-Bilder
if fixedStars:
 clear = pd.read_csv("clear.csv")
 clear = clear[clear['x_fit'] < const.XMAX - const.XMIN]
 clear = clear[clear['y_fit'] < const.YMAX - const.YMIN]
 clear = clear[clear['x_fit'] >= 0]
 clear = clear[clear['y_fit'] >= 0]
 pos = Table(names=['x_0', 'y_0'], data=[clear['x_fit'],clear['y_fit']])
sigma_psf = 2.0
bkgrms = MADStdBackgroundRMS()
std = bkgrms(image)
# Bei kleineren Bildausschnitten ist eine kleinere
,→ Standardabweichung über Rauschen nötig, da bei größeren
,→ Bildern mehr Nicht-Stern-Hintergrund sichtbar ist
iraffind = IRAFStarFinder(threshold=2.2*std,
fwhm=sigma_psf *
,→ gaussian_sigma_to_fwhm,
minsep_fwhm=0.01,
roundhi=5.0, roundlo=-5.0,
sharplo=0.0, sharphi=2.0)
daogroup = DAOGroup(2.0 * sigma_psf *
,→ gaussian_sigma_to_fwhm)
mmm_bkg = MMMBackground()
fitter = LevMarLSQFitter()
psf_model = IntegratedGaussianPRF(sigma=sigma_psf)
# Für B- oder V-Bilder benutze bereits existierende
,→ Sternpositionen aus Clear
if fixedStars:
psf_model.x_0.fixed = True
psf_model.y_0.fixed = True
photometry = BasicPSFPhotometry(group_maker=daogroup,
bkg_estimator=mmm_bkg,
psf_model=psf_model,
fitter=LevMarLSQFitter(),
fitshape=(7, 7))
# Finde die Sternpositionen im Clear-Fall
else:
photometry =
,→ IterativelySubtractedPSFPhotometry(finder=iraffind,
group_maker=daogroup,
bkg_estimator=mmm_bkg,
psf_model=psf_model,
fitter=LevMarLSQFitter(),
niters=1,
fitshape=(7, 7))
if fixedStars:
return image, photometry(image=image, init_guesses=pos)
else:
return image, photometry(image=image)
# Bilder laden und Ergebnisse speichern
img1, res1 = photometry('30_06/M13/clear_final.fit', False)
res1.write('clear.csv', format='csv', overwrite=True)
img2, res2 = photometry('30_06/M13/b_final.fit', True)
res2.write('b.csv', format='csv', overwrite=True)
img3, res3 = photometry('30_06/M13/v_final.fit', True)
res3.write('v.csv', format='csv', overwrite=True)

Universität Stuttgart, 5. Physikalisches Institut, AG Physik und ihre Didaktik, lizenziert unter CC BY-NC-SA 4.0