def plotSpecVsWLen():
wLen = getWavelengthArr(specFile, 0)
spec = getImageData(specFile, 0)
# wLen = range(len(spec))
for i in range(len(wLen)):
print('wLen = ', wLen[i], ', spec = ', spec[i])
fig, ax = plt.subplots()
fig.set_size_inches(18.5, 10.5)
ax.plot(wLen, spec)
#plt.show()
ax.set_xlabel('Wavelength [$\mathrm{\AA}$]')
ax.set_ylabel(
'$\mathrm{F}_\lambda$ [$\mathrm{erg}$ $\mathrm{s^{-1}} \mathrm{cm^{-2}} \mathrm{\AA^{-1}}]$'
)
#ax.set_xlim([0,2000])
yLim = 1e6 #ax.get_ylim()[1]
ax.set_ylim([0, yLim * 1.3])
lineid_plot.plot_line_ids(wLen,
spec,
linePos,
lineLabel,
ax=ax,
arrow_tip=yLim * 1.1,
box_loc=yLim * 1.27)
plt.title = specFile[specFile.rfind('/') + 1:]
plt.savefig(specFile[:specFile.rfind('.')] + '.pdf', bbox_inches='tight')
plt.show()
def plotAug2013():
spec = getImageData(aug2013Arc, 0)
wLen = getWavelengthArr(aug2013Arc, 0)
fig, ax = plt.subplots()
fig.set_size_inches(18.5, 10.5)
ax.plot(wLen, spec)
ax.set_xlabel('Wavelength [$\mathrm{\AA}$]')
ax.set_ylabel(
'$\mathrm{F}_\lambda$ [$\mathrm{erg}$ $\mathrm{s^{-1}} \mathrm{cm^{-2}} \mathrm{\AA^{-1}}]$'
)
#ax.set_xlim([0,2000])
yLim = ax.get_ylim()[1]
ax.set_ylim([0, yLim * 1.4])
lineid_plot.plot_line_ids(wLen,
spec, [float(l) for l in lineLabel],
lineLabel,
ax=ax,
arrow_tip=yLim * 1.1,
box_loc=yLim * 1.27)
plt.savefig(aug2013Arc[:aug2013Arc.rfind('.')] + '.pdf',
bbox_inches='tight')
plt.show()
line = line.strip().split()
print('line = ', line)
linePosRef.append(float(line[0]))
lineLabelRef.append(line[1])
with open(lineListFile, 'r') as f:
lines = f.readlines()
linePos = []
lineLabel = []
for line in lines:
line = line.strip().split()
print('line = ', line)
linePos.append(float(line[0]))
lineLabel.append(line[1])
refSpec = getImageData(refSpecFile, 0)
def plotRefSpec(lam, flux, title='refArc_spupnic_may2007_d.fits'):
fig, ax = plt.subplots()
fig.set_size_inches(18.5, 10.5)
#ax.plot(wLenArr[0:len(wLenArr)-1],spec)
ax.plot(lam, flux)
ax.set_xlabel('Wavelength [$\mathrm{\AA}$]')
ax.set_ylabel(
'$\mathrm{F}_\lambda$ [$\mathrm{erg}$ $\mathrm{s^{-1}} \mathrm{cm^{-2}} \mathrm{\AA^{-1}}]$'
)
#ax.set_xlim([0,2000])
yLim = ax.get_ylim()[1]
ax.set_ylim([0, yLim * 1.4])
import numpy as np
from drUtils import xCor, getImageData, xCorFindMinimum
inputLineList = '/Users/azuri/stella/referenceFiles/spupnic/saao_refspec_gr7_angle16_3_lines_identified_good.dat'
outputLineList = '/Users/azuri/stella/referenceFiles/spupnic/saao_refspec_gr7_angle16_3_may2020_lines_identified_good.dat'
oldArcSpec = '/Users/azuri/spectra/saao/saao_sep2019/20190907/refArc_spupnic_gr7_16_30_otzxfifEc.fits'
newArcSpec = '/Users/azuri/spectra/saao/saao_may2020/20200519/refARC_spupnic_grat7_16_30_otzxf_may2020iEc.fits'
oldSpec = getImageData(oldArcSpec, 0)
oldSpec /= np.mean(oldSpec)
newSpec = getImageData(newArcSpec, 0)
newSpec /= np.mean(newSpec)
oldX = np.arange(0, len(oldSpec), 1)
newX = np.arange(0, len(newSpec), 1)
static = [oldX, oldSpec]
moving = [newX[40:newX.shape[0] - 40], newSpec[40:newX.shape[0] - 40]]
print('static[0][1] = ', static[0][1])
print('moving[0][1] = ', moving[0][1])
result = xCor(static, moving)
print('result = ', result)
print('result[0] = ', result[0])
print('result[1] = ', result[1])
minPos = xCorFindMinimum(result[0], result[1])
print('minPos = ', minPos)
def getSigmaVsWLen(spectrumName, nPix=20, show=False):
wLen = getWavelengthArr(spectrumName, 0)
print(spectrumName, ': wLen = ', wLen)
arrayLength = wLen.shape[0]
spec = getImageData(spectrumName, 0)
if show:
plt.plot(wLen, spec)
plt.show()
sigmas = np.zeros(arrayLength)
for i in range(arrayLength):
if i < int(nPix / 2.):
sigmas[i] = np.std(spec[:nPix])
elif i > arrayLength - int(nPix / 2.):
sigmas[i] = np.std(spec[:-nPix])
else:
sigmas[i] = np.std(spec[i - int(nPix / 2.):i + int(nPix / 2.) + 1])
sigmaSpecFileName = os.path.join(
spectrumName[:spectrumName.rfind('/')], 'test',
'sigma' + spectrumName[spectrumName.rfind('/') + 1:])
sigmaFitSpecFileName = os.path.join(
spectrumName[:spectrumName.rfind('/')], 'test',
'sigmaFit' + spectrumName[spectrumName.rfind('/') + 1:])
writeFits1D(
sigmas,
sigmaSpecFileName,
wavelength=None,
header=spectrumName,
CRVAL1=getHeaderValue(spectrumName, 'CRVAL1'),
CRPIX1=getHeaderValue(spectrumName, 'CRPIX1'),
CDELT1=getHeaderValue(spectrumName, 'CDELT1'),
)
fittingFunction = np.polynomial.legendre.legfit
evalFunction = np.polynomial.legendre.legval
order = 4
nIterReject = 2
nIterFit = 2
lowReject = 3.
highReject = 1.
useMean = False
continuum(sigmaSpecFileName,
sigmaFitSpecFileName,
fittingFunction,
evalFunction,
order,
nIterReject,
nIterFit,
lowReject,
highReject,
type='fit',
adjustSigLevels=False,
useMean=useMean,
display=show)
fit = getImageData(sigmaFitSpecFileName, 0)
if show:
plt.plot(wLen, spec)
plt.plot(wLen, sigmas)
plt.plot(wLen, fit)
plt.show()
return [wLen, fit]
def calculateErrors(spectrumFileName, idPNMain, csvLinesFileName, show=False):
print('spectrumFileName = <' + spectrumFileName + '>')
wLen, sigmaFit = getSigmaVsWLen(spectrumFileName, show=False)
print('len(wLen) = ', len(wLen), ', len(sigmaFit) = ', len(sigmaFit))
flux = getImageData(spectrumFileName, 0)
print('len(flux) = ', len(flux))
csvLines = csvFree.readCSVFile(csvLinesFileName, '\t', False)
csvVRad = csvFree.readCSVFile(
os.path.join(imPath[:imPath.rfind('/')], 'vrad.csv'))
print('csvVRad.header = ', csvVRad.header)
filenames = csvVRad.getData('fileName')
print('filenames = ', filenames)
vradPos = csvVRad.find('fileName',
spectrumFileName[spectrumFileName.rfind('/') + 1:],
0)[0]
if vradPos < 0:
print('error: did not find spectrumFileName <' + spectrumFileName +
'>')
#STOP
else:
vrad = float(csvVRad.getData('vrad', vradPos))
print('vrad = ', type(vrad), ': ', vrad)
wLen = applyVRadCorrection(wLen, vrad)
print('vradPos = ', vradPos)
header = csvLines.header
keys = list(linesOfInterest.keys())
for i in range(csvLines.size()):
if idPNMain == csvLines.getData('NAME', i):
for iLine in range(len(linesOfInterest)):
area = float(csvLines.getData(keys[iLine], i))
print('key = ', keys[iLine], ': area = ', area)
if area > 0.:
x0 = linesOfInterest[keys[iLine]]
x = wLen[np.where(np.abs(wLen - x0) < 20.)[0]]
thisFlux = flux[np.where(np.abs(wLen - x0) < 3.)[0]]
maxFlux = np.max(thisFlux)
sigma = area / (maxFlux * 2.13 *
np.sqrt(2. * np.log(2.)))
print('x = ', x0, ', a = ', maxFlux, ', sigma = ',
sigma)
thisFlux = flux[np.where(np.abs(wLen - x0) < 20.)[0]]
thisSDev = sigmaFit[np.where(
np.abs(wLen - x0) < 20.)[0]]
gaussFit = gauss(x, maxFlux, x0, sigma)
if show:
plt.plot(x, thisFlux, label='flux')
plt.plot(x, thisSDev, label='sigma')
plt.plot(x, gaussFit, label='fit')
plt.legend()
plt.show()
newArea = getAreaGauss(x,
thisFlux,
maxFlux,
x0,
sigma,
addOnBothSidesOfX=0.,
show=False,
save=None)
print('old area = ', area, ', newly fitted area = ',
newArea)
if show:
plt.plot(x, gaussFit, label='fit')
plt.plot(x, thisFlux, label='flux')
newAreas = []
for iRun in range(100):
thisFluxWithErr = np.zeros(x.shape,
dtype='float32')
for thisFluxPos in range(x.shape[0]):
thisFluxWithErr[thisFluxPos] = gaussFit[
thisFluxPos] + np.random.normal(
0., np.abs(thisSDev[thisFluxPos]))
if show:
plt.plot(x,
thisFluxWithErr,
label='%d' % (iRun))
try:
newAreas.append(
getAreaGauss(x,
thisFluxWithErr,
maxFlux,
x0,
sigma,
addOnBothSidesOfX=0.,
show=False,
save=None)[0])
except Exception as e:
plt.plot(x, thisFlux, label='original')
plt.plot(x,
thisFluxWithErr,
label='with errors')
plt.show()
newAreas.append(area)
STOP
if show:
plt.legend()
plt.show()
newAreas = np.array(newAreas)
print('newAreas = ', len(newAreas), ': ', newAreas)
if show:
plt.hist(newAreas)
plt.show()
sDev = np.std(newAreas)
print('sDev = ', sDev)
csvLines.setData(keys[iLine] + 'e', i, '%.3E' % (sDev))
csvFree.writeCSVFile(csvLines, csvLinesFileName, '\t')
import astropy.io.fits as pyfits
import lineid_plot
import matplotlib
matplotlib.use('tkagg')
import matplotlib.pyplot as plt
import numpy as np
from drUtils import getWavelengthArr, getImageData, readMyPNLineList
import csvFree, csvData
csFileName = '/Users/azuri/daten/uni/HKU/IPHAS-GTC/hash_PNMain_tbCSCoords_240222.csv'
fitsFileName = '/Users/azuri/daten/uni/HKU/IPHAS-GTC/IPHASGTC_Ju1_GT110616_id4408.fits' #IPHASGTC_KK26_GT090316_id4393.fits'
lineListName = '/Users/azuri/entwicklung/python/data_reduction/pnLineList_Ju1.dat' #KK26.dat'
wLen = getWavelengthArr(fitsFileName, 0)
spec = getImageData(fitsFileName, 0)
lineWave, lineLabel = readMyPNLineList(lineListName)
fig, ax = plt.subplots()
ax.plot(wLen, spec)
ax.set_xlabel('Wavelength [$\mathrm{\AA}$]')
ax.set_ylabel(
'$\mathrm{F}_\lambda$ [$\mathrm{erg}$ $\mathrm{s^{-1}} \mathrm{cm^{-2}} \mathrm{\AA^{-1}}]$'
)
ax.set_xlim([4000, 7200])
ax.set_ylim([-0.1e-15, 3.1e-15])
lineid_plot.plot_line_ids(wLen,
spec,
lineWave,