]
i=1
for obs in tab:
image_file = os.path.join('/Volumes/obiwan/azuri/spectra/IPHAS_GTC/',obs['FName'])
hdulist = pyfits.open(image_file)
# print 'type(hdulist) = ',type(hdulist)
header = hdulist[0].header
if header['OBJECT'] != '':
obs['OName'] = header['OBJECT']
try:
print('airmass = ',header['AIRMASS'])
except:
"""do nothing"""
print(obs)
if True:
wavelength = getWavelength(header)
image_data = fits.getdata(image_file)
# print 'type(image_data) = ',type(image_data)
# print 'type(image_data[1000,1000]) = ',type(image_data[1000,1000])
# print 'len(image_data) = ',len(image_data)
# print 'image_data.shape = ',image_data.shape
# print 'image_data[1551,',obs['ObjectAreas'][0][0]+1,'] = ',image_data[1551,obs['ObjectAreas'][0][0]+1]
imageMinusSky, imageSky = subtractSky(image_data,obs['SkyLeft'],obs['SkyRight'])
# print 'imageMinusSky[1551,',obs['ObjectAreas'][0][0]+1,':',obs['ObjectAreas'][0][0]+5,'] = ',imageMinusSky[1551,obs['ObjectAreas'][0][0]+1:obs['ObjectAreas'][0][0]+5]
obsCols = populateObjectArray(imageMinusSky,obs['ObjectAreas'])
# print 'obsCols[1551,1:5] = ',obsCols[1551,1:5]
# print 'obsCols[1000,0] = %.3e' % (obsCols[1000,0])
# print 'sum(obsCols[1551,:]) = ',np.sum(obsCols[1551,:])
obsColsSmoothed = boxCarMedianSmooth(obsCols, 0, 15)
from datetime import datetime as dt
import math
import numpy as np
from matplotlib import pyplot as plt
from myUtils import getWavelength, smooth, toYearFraction
from Pa30_LBT import readLBTFiles
photometry_file = '/Users/azuri/daten/uni/HKU/Pa30/variability/pa30_photometry_visual_1.txt'
gtc_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/Pa30_GT080716_cal_sum_cleaned.fits"
wiyn_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/Pa30_WN151014_cal_sum_cleaned_t.fits"
gvaramadze_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/gvaramadze.fits"
gvaramadze_hdulist = pyfits.open(gvaramadze_file)
gvaramadze_header = gvaramadze_hdulist[0].header
gvaramadze_wavelength = np.array(getWavelength(gvaramadze_header,
1)) * unit['AA']
gvaramadze_spectrum = np.array(
pyfits.getdata(gvaramadze_file)) * unit['erg/s/cm**2/AA']
#gvaramadze_wavelength,gvaramadze_spectrum = readGvaramadzeFile()
garnavich_wavelength, garnavich_spectrum = readLBTFiles()
garnavich_wavelength = np.array(garnavich_wavelength)
garnavich_spectrum = np.array(garnavich_spectrum)
garnavich_spectrum = garnavich_spectrum[garnavich_wavelength < 5455.]
garnavich_wavelength = garnavich_wavelength[
garnavich_wavelength < 5455.] * unit['AA']
#garnavich_spectrum_smoothed = smooth(garnavich_spectrum,9)#scipy.ndimage.mean_filter(garnavich_spectrum, 7)#boxCarMeanSmooth(somme_spectrum, 0, 21)
garnavich_spectrum = garnavich_spectrum * unit['erg/s/cm**2/AA']
gtc_hdulist = pyfits.open(gtc_file)
wiyn_hdulist = pyfits.open(wiyn_file)
skyOutMedian = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/pa30_zdtsEcndr_skyMedian.fits'
componentsOutRoot = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/pa30_zdtsEcndr_PCA'
if standardStar:
cubePlusSky = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/kwb_141015_114545_ori_zdtsEcn1dr.fits'
cubeMinusSkyOut = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/kwb_141015_114545_ori_zdtsEcn1dr-skyMean.fits'
cubeMinusSkyOutMedian = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/kwb_141015_114545_ori_zdtsEcn1dr-skyMedian.fits'
skyOutMean = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/kwb_141015_114545_ori_zdtsEcn1dr_skyMean.fits'
skyOutMedian = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/kwb_141015_114545_ori_zdtsEcn1dr_skyMedian.fits'
componentsOutRoot = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/kwb_141015_114545_ori_zdtsEcn1dr_PCA'
hdulistCubePlusSky = pyfits.open(cubePlusSky)
wavelength = getWavelength(hdulistCubePlusSky[0].header, 1)
print("wavelength = ",wavelength)
dataCubePlusSky = hdulistCubePlusSky[0].data
# create the PCA instance
nComponents = 10
pca = PCA(nComponents)
# fit on data
pca.fit(dataCubePlusSky)
# access values and vectors
print('pca.components_ = ',pca.components_.shape,': ',pca.components_)
print('pca_explained_variance_ = ',pca.explained_variance_.shape,': ',pca.explained_variance_)
# transform data
B = pca.transform(dataCubePlusSky)
print('B = ',B.shape,': ',B)
from myUtils import getWavelength
#execfile("/Users/azuri/entwicklung/python/myUtils.py")# import getDate, findClosestDate,...
#ebv = 1.206
ebv = 0.79 # 3D map Bayestar15
#ebv = 0.99 # 3D map Bayestar17
R_V = 3.1 # (Fitzpatrick and Massa 2007)
#R_V = 3.1
A_V = 2.4 #Claire
ebv = A_V / R_V
somme_file = "/Users/azuri/daten/uni/HKU/Pa30/_pa30_somme6_scaled.fits"
somme_hdulist = pyfits.open(somme_file)
somme_header = somme_hdulist[0].header
somme_wavelength = getWavelength(somme_header, 1)
somme_spectrum = fits.getdata(somme_file)
print('somme_wavelength = ', somme_wavelength)
somme_spectrum_smoothed = ndimage.median_filter(somme_spectrum, size=21)
somme_spectrum_smoothed_dereddened = pyasl.unred(somme_wavelength,
somme_spectrum_smoothed,
ebv=ebv,
R_V=R_V)
plt.plot(somme_wavelength,
np.log10(somme_spectrum_smoothed),
'm-',
label='Buil scaled and smoothed')
plt.plot(somme_wavelength, np.log10(somme_spectrum_smoothed_dereddened),
'm-') #, label='Somme scaled, smoothed, and dereddened')
plt.legend()
lines_names = [
'O VI', 'O VI', 'O VIII', 'O VIII', 'O VIII', 'C V', 'O VI', 'O VI',
'O VII', 'C IV', 'O VIII', 'C VI'
]
lines_wave = [
3811, 3834, 4340, 4501, 4658, 4944, 5270, 5291, 5670, 5808, 6068, 6202
]
leDu_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/_pa30_20181009_941_PLeDu.fits"
somme_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/_pa30_somme6.fits"
gtc_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/Pa30_GT080716_cal_sum_cleaned.fits"
wiyn_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/Pa30_WN151014_cal_sum_cleaned_t.fits"
gvaramadze_file = "/Users/azuri/daten/uni/HKU/Pa30/variability/gvaramadze.fits"
gvaramadze_hdulist = pyfits.open(gvaramadze_file)
gvaramadze_header = gvaramadze_hdulist[0].header
gvaramadze_wavelength = getWavelength(gvaramadze_header, 1)
gvaramadze_spectrum = fits.getdata(gvaramadze_file)
#gvaramadze_wavelength,gvaramadze_spectrum = readGvaramadzeFile()
gvaramadze_wavelength = np.array(gvaramadze_wavelength)
gvaramadze_spectrum = np.array(gvaramadze_spectrum)
garnavich_wavelength, garnavich_spectrum = readLBTFiles()
garnavich_wavelength = np.array(garnavich_wavelength)
garnavich_spectrum = np.array(garnavich_spectrum)
garnavich_spectrum = garnavich_spectrum[garnavich_wavelength < 5455.]
garnavich_wavelength = garnavich_wavelength[garnavich_wavelength < 5455.]
garnavich_spectrum_smoothed = smooth(
garnavich_spectrum, 9
) #scipy.ndimage.mean_filter(garnavich_spectrum, 7)#boxCarMeanSmooth(somme_spectrum, 0, 21)
def plot_lines():
import astropy.io.fits as pyfits
import matplotlib.pyplot as plt
import numpy as np
from myUtils import getWavelength
calibratedSpectraFile = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/pa30_zdtsEcndr-skyMedian_cal.fits'
calibratedSpectraFile = '/Users/azuri/daten/uni/HKU/Pa30/sparsepak/spectra/pa30_zd_Ecmsndr-skyMedian_cal.fits'
starSpectra = [0,4,5,6,9,18,20,21,30,33,]
hdulist = pyfits.open(calibratedSpectraFile)
header = hdulist[0].header
wavelength = getWavelength(header,1)
spectrum = hdulist[0].data
print('spectrum = ',spectrum.shape,': ',spectrum)
print('wavelength = ',wavelength.shape,': ',wavelength)
coadd = np.zeros(wavelength.shape[0])
nSpec = 0
for iSpec in np.arange(0,spectrum.shape[0],1):
specMean = np.mean(spectrum[iSpec,:])
print('mean of spectrum ',iSpec,' = ',specMean)
if abs(specMean) < 2.0e-16:
print('using spectrum ',iSpec)
coadd += spectrum[iSpec,:]
nSpec += 1
print('mean of ',nSpec,' spectra in coadd = ',np.mean(coadd))
plt.plot(wavelength,coadd)
plt.show()