def combineLists():
keepColumns = ['NOM', 'AD:(J2000)', 'DEC (J2000)']
print('keepColumns = ', keepColumns)
csvAll = None
for iList in [longList, shortList]:
csv = readFRAFile(iList)
print('csv.header = ', csv.header)
# print('csv.data = ',csv.data)
# STOP
for keyword in csv.header:
print('checking keyword <' + keyword + '>')
if keyword not in keepColumns:
print('keyword <' + keyword +
'> not found in keepColumns => removing column')
csv.removeColumn(keyword)
print('csv.header = ', csv.header)
print('csv.data = ', csv.data)
for i in np.arange(0, csv.size(), 1):
print('changing ', csv.getData('NOM', i))
csv.setData('NOM', i, csv.getData('NOM', i).replace(' ', ''))
print('... to ', csv.getData('NOM', i))
if not csvAll:
csvAll = csv
else:
csvAll.append(csv)
print('csvAll.size() = ', csvAll.size())
csvFree.writeCSVFile(csvAll, outList)
def changePAs(inFileNameData, morphClassHeader, morphClass, GPAHeader,
angleMean, angleSDev, percentage, outFileName):
csv = csvFree.readCSVFile(inFileNameData)
morphClasses = csv.getData(morphClassHeader)
for iM in range(len(morphClass)):
nPNeWithMorphClass = 0
pNeWithMorphClass = []
for i in range(csv.size()):
if morphClasses[i] == morphClass[iM]:
pNeWithMorphClass.append(i)
nPNeWithMorphClass += 1
randomIDs = []
nPNeToChangeGPA = int(nPNeWithMorphClass * percentage / 100)
while len(randomIDs) < nPNeToChangeGPA:
rand = randint(0, nPNeToChangeGPA - 1)
if rand not in randomIDs:
randomIDs.append(rand)
print('nPNeToChangeGPA = ', nPNeToChangeGPA)
print('randomIDs = ', len(randomIDs), ': ', randomIDs)
# for the circular problem rescale sigma and use random range [-1,1]
a, b = -1., 1.
mu, sigma = 0., angleSDev[iM] / 90.
dist = stats.truncnorm((a - mu) / sigma, (b - mu) / sigma,
loc=mu,
scale=sigma)
randAngles = angleMean[iM] + (dist.rvs(nPNeToChangeGPA) * 90.)
for iR in range(len(randAngles)):
if randAngles[iR] < 0.:
randAngles[iR] += 180.
if randAngles[iR] >= 180.:
randAngles[iR] -= 180.
plt.hist(randAngles)
plt.show()
randAngles = np.array(randAngles)
print('mean(randAngles) = ', randAngles.mean(),
', sDev(randAngles) = ', randAngles.std())
for i in range(len(randomIDs)):
print(
'i = ', i, ': HASH ID = ',
csv.getData("idPNMain",
pNeWithMorphClass[randomIDs[i]]), ', mainClass=',
csv.getData(morphClassHeader, pNeWithMorphClass[randomIDs[i]]),
', GPA = ',
csv.getData(GPAHeader, pNeWithMorphClass[randomIDs[i]]))
csv.setData(GPAHeader, pNeWithMorphClass[randomIDs[i]],
str(randAngles[i]))
print(
'i = ', i, ': HASH ID = ',
csv.getData("idPNMain",
pNeWithMorphClass[randomIDs[i]]), ', mainClass=',
csv.getData(morphClassHeader, pNeWithMorphClass[randomIDs[i]]),
', GPA = ',
csv.getData(GPAHeader, pNeWithMorphClass[randomIDs[i]]))
csvFree.writeCSVFile(csv, outFileName)
def checkDistances(inFileName, angDiamFileName, outFileName):
hashSearch = csvFree.readCSVFile(
hashSearchFileName[:hashSearchFileName.rfind('.')] +
'_with_header.csv')
hashFound = csvFree.readCSVFile(inFileName)
angDiams = csvFree.readCSVFile(angDiamFileName)
csvOut = csvData.CSVData()
csvOut.header = hashFound.header
nNotFound = 0
ra_3238 = hmsToDeg('17:59:45.20')
dec_3238 = dmsToDeg('-33:21:13.00')
toDelete = []
for i in range(hashFound.size()):
name = hashFound.getData('id', i)
idPNMain = hashFound.getData('pndb', i)
if idPNMain == '':
csvOut.append(hashFound.getData(i))
else:
dist = float(hashFound.getData('dist[arcsec]', i))
found = False
for j in range(angDiams.size()):
if angDiams.getData('idPNMain', j) == idPNMain:
if angDiams.getData('InUse', j) == '1':
found = True
if dist > float(angDiams.getData('MajDiam', j)):
csvOut.append([name, '', ''])
else:
csvOut.append(hashFound.getData(i))
if not found:
nNotFound += 1
print(
'Problem: did not find an angular diameter for <' + name +
'>: idPNMain = ', idPNMain, ', dist = ', dist)
if dist > 50.:
csvOut.append([name, '', ''])
else:
csvOut.append(hashFound.getData(i))
for j in range(hashSearch.size()):
if hashSearch.getData('id',
j) == csvOut.getData('id',
csvOut.size() - 1):
ra = hmsToDeg(hashSearch.getData('ra', j))
dec = dmsToDeg(hashSearch.getData('dec', j))
angDist = angularDistance(ra, dec, ra_3238, dec_3238)
print('ra = ', ra, ', dec = ', dec, ': angDist = ', angDist)
angDistPyAsl = pyasl.getAngDist(ra, dec, ra_3238,
dec_3238) * 3600.
if angDist < 800:
if csvOut.getData('pndb', csvOut.size() - 1) != '3238':
toDelete.append([
csvOut.getData('pndb',
csvOut.size() - 1), ra, dec,
angDist, angDistPyAsl
])
csvOut.setData('pndb', csvOut.size() - 1, '3238')
csvFree.writeCSVFile(csvOut, outFileName)
for i in toDelete:
print('toDelete : ', i)
def randomizePAs(inFileNameData, GPAHeader, outFileName):
seed(1)
csv = csvFree.readCSVFile(inFileNameData)
sequence = [i for i in range(180)]
shuffle(sequence)
for i in range(csv.size()):
rand = choice(sequence) #random()
print('rand = ', rand)
csv.setData(GPAHeader, i, str(int(rand)))
csvFree.writeCSVFile(csv, outFileName)
def findClosestMatch(hashFoundFile, outFileName):
hashFound = csvFree.readCSVFile(hashFoundFile)
closestMatch = csvData.CSVData()
closestMatch.header = hashFound.header
hashFoundNames = np.array([str(n) for n in hashFound.getData('id')])
print('hashFoundNames = ', hashFoundNames)
nDoubles = 0
for name in hashFoundNames:
name = str(name)
idx = np.where(hashFoundNames == name)[0]
if len(idx) == 0:
arr = bytes(name, 'utf-8')
arr2 = bytes(hashFoundNames[0], 'utf-8')
for byte in arr:
print(byte, end=' ')
print("\n")
for byte in arr2:
print(byte, end=' ')
print("\n")
if name == hashFoundNames[0]:
print('both are the same')
else:
print('both are still not the same')
if str(name) == str(hashFoundNames[0]):
print('both are the same if using unicode')
else:
print('both are still not the same if using unicode')
print('found name <' + name + '> ', len(idx), ' times in indices ',
idx)
lineToAdd = ''
if len(idx) == 1:
lineToAdd = hashFound.getData(idx[0])
else:
dists = []
for i in range(len(idx)):
dist = hashFound.getData('dist[arcsec]', idx[i])
if dist != '':
dists.append(float(dist))
if len(dists) > 0:
minId = np.where(dists == np.min(dists))[0][0]
else:
minId = 0
print('idx[', minId, '] = ', idx[minId])
lineToAdd = hashFound.getData(idx[minId])
closestMatchNames = closestMatch.getData('id')
if name not in closestMatchNames:
print('lineToAdd = ', lineToAdd)
closestMatch.append(lineToAdd)
else:
print('name <' + name + '> already in closestMatch')
nDoubles += 1
print('nDoubles = ', nDoubles)
csvFree.writeCSVFile(closestMatch, outFileName)
def checkCombinedDiscrepancies(combinedDiscrepancies, allHASHobjects, hashCommonNames):
csvOut = csvData.CSVData()
csvOut.header = ['Name','idPNMain','HASH common names', 'RA FRA', 'RA HASH', 'DEC FRA', 'DEC HASH', 'angular distance [arcsec]']
csvOut.data = []
emptyData = ['','','', '', '', '', '', '']
for i in range(combinedDiscrepancies.size()):
ra = combinedDiscrepancies.getData('RA',i)
dec = combinedDiscrepancies.getData('DEC',i)
raDeg = hmsToDeg(ra)
decDeg = dmsToDeg(dec)
name = getName(combinedDiscrepancies,'Name',i)
print('name = <'+name+'>')
found = False
for j in range(allHASHobjects.size()):
if name == allHASHobjects.getData('Name',j).replace(' ',''):
hashID = allHASHobjects.getData('idPNMain',j)
angDist = degToArcsec(angularDistancePyAsl(raDeg,decDeg,hmsToDeg(allHASHobjects.getData('RAJ2000',j)),dmsToDeg(allHASHobjects.getData('DECJ2000',j))))
commonNames = getCommonNames(hashCommonNames,'idPNMain',hashID)
print('Name = '+name+': HASH ID = ',hashID,': commonNames = ',commonNames,': ra = ',ra,', RA = ',allHASHobjects.getData('RAJ2000',j),', dec = ',dec,', DEC = ',allHASHobjects.getData('DECJ2000',j),', angDist = ',angDist)
found = True
if not found:
print('ERROR: object with name <'+name+'> not found in HASH')
for j in range(allHASHobjects.size()):
angDist = degToArcsec(angularDistancePyAsl(raDeg,decDeg,hmsToDeg(allHASHobjects.getData('RAJ2000',j)),dmsToDeg(allHASHobjects.getData('DECJ2000',j))))
if angDist < maxAngularDistance:
hashID = allHASHobjects.getData('idPNMain',j)
commonNames = getCommonNames(hashCommonNames,'idPNMain',hashID)
if name in commonNames:
print('Name = '+name+': HASH ID = ',hashID,': commonNames = ',commonNames,': ra = ',ra,', RA = ',allHASHobjects.getData('RAJ2000',j),', dec = ',dec,', DEC = ',allHASHobjects.getData('DECJ2000',j),', angDist = ',angDist)
found = True
else:
print('Name = <'+name+'>: object found within ',maxAngularDistance,' arcsec: angDist = ',angDist,': ra = ',ra,', RA = ',allHASHobjects.getData('RAJ2000',j),', dec = ',dec,', DEC = ',allHASHobjects.getData('DECJ2000',j),', HASH name = <'+allHASHobjects.getData('Name',j))
csvOut.append(emptyData)
csvOut.setData('Name',csvOut.size()-1,name)
csvOut.setData('idPNMain',csvOut.size()-1,hashID)
cNames = commonNames[0]
for k in np.arange(1,len(commonNames),1):
cNames += ';'+commonNames[k]
csvOut.setData('HASH common names',csvOut.size()-1,cNames)
csvOut.setData('RA FRA',csvOut.size()-1,ra)
csvOut.setData('RA HASH',csvOut.size()-1,allHASHobjects.getData('RAJ2000',j))
csvOut.setData('DEC FRA',csvOut.size()-1,dec)
csvOut.setData('DEC HASH',csvOut.size()-1,allHASHobjects.getData('DECJ2000',j))
csvOut.setData('angular distance [arcsec]',csvOut.size()-1,str(angDist))
writeCSVFile(csvOut,disrepanciesOutFileName)
inFileName = '/Users/azuri/daten/uni/HKU/HASH/hash-no-show.csv'
inData = csvFree.readCSVFile(inFileName)
print('inData.size() = ',inData.size())
pnStat = inData.getData('PNstat')
print('pnStat = ',pnStat)
tlps = csvData.CSVData()
tlps.header = inData.header
newCandidates = csvData.CSVData()
newCandidates.header = inData.header
others = csvData.CSVData()
others.header = inData.header
for i in range(inData.size()):
pnStat = inData.getData('PNstat',i)
if pnStat in ['T','L','P']:
tlps.append(inData.getData(i))
elif pnStat == 'c':
newCandidates.append(inData.getData(i))
else:
others.append(inData.getData(i))
csvFree.writeCSVFile(tlps,inFileName[:-4]+'_TLPs.csv')
csvFree.writeCSVFile(newCandidates,inFileName[:-4]+'_newCandidates.csv')
csvFree.writeCSVFile(others,inFileName[:-4]+'_others.csv')
for i in range(csvEventRegistration.size()):
name = csvEventRegistration.getData(
'First Name (Billing)', i) + ' ' + csvEventRegistration.getData(
'Last Name (Billing)', i) + ' Wild Wood Guest'
print('name = ', name)
if name not in names:
print(name + ' not found')
names.append(name)
csvContactsToImport.append(newLine)
csvContactsToImport.setData('Name', iContact,
name + ' Wild Wood ' + 'Guest')
csvContactsToImport.setData('Group Membership', iContact,
'* myContacts')
csvContactsToImport.setData('Phone 1 - Type', iContact, 'Mobile')
csvContactsToImport.setData(
'Phone 1 - Value', iContact,
csvEventRegistration.getData('Phone (Billing)', i))
iContact += 1
print('csvContactsToImport.size() = ', csvContactsToImport.size())
else:
print(name + ' found')
print('csvContactsToImport.size() = ', csvContactsToImport.size())
#for i in np.arange(csvContactsToImport.size()-1,-1,-1):
# print('checking contact ',i)
# if existingContacts.find('Phone 1 - Value',csvContactsToImport.getData('Phone 1 - Value',i),0)[0] > -1:
# print('found ',csvContactsToImport.getData('Name',i))
# csvContactsToImport.removeRow(i)
csvFree.writeCSVFile(csvContactsToImport, contactsToImport)
data = []
lines[iLine] = lines[iLine].replace('\r', '')
lines[iLine] = lines[iLine].replace('\n', '')
lines[iLine] = lines[iLine].replace('}', '+-')
lines[iLine] = lines[iLine].replace(' v ', ' v')
lines[iLine] = lines[iLine].replace(' r ', ' r')
lines[iLine] = lines[iLine].replace(' b ', ' b')
lines[iLine] = lines[iLine].replace(' i ', ' i')
data = lines[iLine].split(' ')
if len(data) < len(csv2.header):
data.insert(3, ' ')
print('line[', iLine, '] = ', lines[iLine])
print('data = ', data)
csv2.append(data)
print('csv2.size() = ', csv2.size())
csvFree.writeCSVFile(csv2, f2name[:-3] + 'csv')
csv1 = csvFree.readCSVFile(f1name)
print('csv1 = ', csv1)
print(csv1.header)
head = csv1.header
print('head[', len(head) - 1, '] = ', head[len(head) - 1])
head[len(head) -
1] = 'Ref. spectra' #csv1.header[len(csv1.header)-1].strip('\\r')
csv1.header = head #csv1.header[len(csv1.header)-1].strip('\\r')
print('csv1.header[',
len(csv1.header) - 1, '] = ', csv1.header[len(csv1.header) - 1])
print(csv1.header)
for i in range(csv1.size()):
csv1.setData('Ref. spectra', i,
csv1.getData('Ref. spectra', i).replace('\r', ''))
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')
def findPNeNotInHASH():
csvHASH = csvFree.readCSVFile(hashOutList)
print('csvAll.size() = ', csvHASH.size())
print('csvAll.header = ', csvHASH.header)
found = []
for i in np.arange(0, csvHASH.size(), 1):
if csvHASH.getData('pndb', i) != '':
found.append(i)
print('found = ', found)
found.reverse()
print('found = ', found)
for i in found:
csvHASH.removeRow(i)
print('csvAll.size() = ', csvHASH.size())
keepColumns = [
"NOM", "AD:(J2000)", "DEC (J2000)", "Dimension en minute d'arc (')",
"Coordonnées galactiques"
]
print('keepColumns = ', keepColumns)
csvOut = None
for iList in [longList, shortList]:
csv = readFRAFile(iList)
for keyword in csv.header:
print('checking keyword <' + keyword + '>')
if keyword not in keepColumns:
print('keyword <' + keyword +
'> not found in keepColumns => removing column')
csv.removeColumn(keyword)
print('csv.header = ', csv.header)
print('csv.data = ', csv.data)
hashNames = csvHASH.getData('id')
remove = []
for i in np.arange(0, csv.size(), 1):
if csv.getData('NOM', i).replace(' ', '') not in hashNames:
remove.append(i)
remove.reverse()
for i in remove:
csv.removeRow(i)
if "Coordonnées galactiques" not in csv.header:
csv.addColumn("Coordonnées galactiques")
for i in np.arange(0, csv.size(), 1):
lon, lat = raDecToLonLat(hmsToDeg(csv.getData("AD:(J2000)", i)),
dmsToDeg(csv.getData("DEC (J2000)", i)))
print('lon=', lon, ', lat=', lat)
png = ''
if lon < 100:
png += '0'
if lon < 10:
png += '0'
png += str(lon)
png = png[:png.rfind('.') + 2]
# print('png = <'+png+'>')
if lat >= 0.:
png += '+'
# print('png = <'+png+'>')
png += str(lat)
# print('png = <'+png+'>')
png = png[:png.rfind('.') + 2]
# print('png = <'+png+'>')
if (lat < 10.) and (lat >= 0.):
png = png[:png.rfind('+') + 1] + '0' + png[png.rfind('+') + 1:]
if (lat > -10.) and (lat < 0.):
png = png[:png.rfind('-') + 1] + '0' + png[png.rfind('-') + 1:]
# print('png = <'+png+'>')
print('PNG ' + png)
csv.setData("Coordonnées galactiques", i, png)
if not csvOut:
csvOut = csv
else:
csvOut.append(csv)
# convert diameters from arcmin to arcsec
for i in np.arange(0, csvOut.size(), 1):
diamStr = csvOut.getData("Dimension en minute d'arc (')",
i).rstrip(' ')
print('diamStr = <' + diamStr + '>')
diamStrOut = ''
if 'x' in diamStr:
diamA = float(diamStr[:diamStr.find(' ')]) * 60.
diamB = float(diamStr[diamStr.rfind(' ') + 1:]) * 60.
diamStrOut = '%.1f x %.1f' % (diamA, diamB)
else:
diamStrOut = '%.1f' % (float(diamStr) * 60.)
csvOut.setData("Dimension en minute d'arc (')", i, diamStrOut)
# write output
csvFree.writeCSVFile(
csvOut, hashOutList[:hashOutList.rfind('.')] + '_not_in_HASH.csv')
csvA = csvFree.readCSVFile(
'/Users/azuri/daten/uni/HKU/HASH/ziggy_Calern_PN_candidates_May2019.csv')
csvA.append(
csvFree.readCSVFile(
'/Users/azuri/daten/uni/HKU/HASH/ziggy_Calern_PN_candidates_May2019_II.csv'
))
csvA.append(
csvFree.readCSVFile(
'/Users/azuri/daten/uni/HKU/HASH/ziggy_Calern_PN_candidates_May2019_III.csv'
))
sorted = np.sort(
csvFree.convertStringVectorToUnsignedVector(csvA.getData('idPNMain')))
print('sorted = ', sorted)
fNameOut = '/Users/azuri/daten/uni/HKU/HASH/ziggy_Calern_PN_candidates_May2019_out.csv'
csvOut = csvData.CSVData()
csvOut.header = csvA.header
for i in np.arange(0, len(sorted), 1):
found = csvA.find('idPNMain', str(sorted[i]))
print('i = ', i, ': found = ', found)
csvOut.append(csvA.getData(found))
print('csvOut.size() = ', csvOut.size())
csvFree.writeCSVFile(csvOut, fNameOut)
{'fiber': 71, 'centerDistanceX' : -9.85, 'centerDistanceY' : -16.89, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6697.64,6347.0,6.761], 'SII6731a' : [6711.26,2755.0,4.769], 'SII6716b' : [0.0,0.0,0.0], 'SII6731b' : [0.0,0.0,0.0], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 72, 'centerDistanceX' : -29.56, 'centerDistanceY' : -33.77, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6701.61,2018.0,4.635], 'SII6731a' : [6714.72,1485.0,3.686], 'SII6716b' : [0.0,0.0,0.0], 'SII6731b' : [0.0,0.0,0.0], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 73, 'centerDistanceX' : -29.56, 'centerDistanceY' : 0.0, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [0.0,0.0,0.0], 'SII6731a' : [0.0,0.0,0.0], 'SII6716b' : [6738.43,9211.0,3.504], 'SII6731b' : [6752.36,5277.0,4.732], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 74, 'centerDistanceX' : -34.49, 'centerDistanceY' : -8.44, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6701.5,5182.0,6.961], 'SII6731a' : [6717.77,5797.0,7.725], 'SII6716b' : [0.0,0.0,0.0], 'SII6731b' : [0.0,0.0,0.0], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 75, 'centerDistanceX' : -29.56, 'centerDistanceY' : 16.89, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6702.62,2895.0,4.994], 'SII6731a' : [6716.83,5538.0,6.046], 'SII6716b' : [6741.37,722.0,2.374], 'SII6731b' : [6754.7,2541.0,5.616], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 76, 'centerDistanceX' : -34.49, 'centerDistanceY' : 8.44, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6698.82,8039.0,6.714], 'SII6731a' : [6713.97,7082.0,5.429], 'SII6716b' : [6740.03,7786.0,6.309], 'SII6731b' : [6755.38,8260.0,7.74], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 77, 'centerDistanceX' : -29.56, 'centerDistanceY' : -16.89, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [0.0,0.0,0.0], 'SII6731a' : [0.0,0.0,0.0], 'SII6716b' : [0.0,0.0,0.0], 'SII6731b' : [0.0,0.0,0.0], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 78, 'centerDistanceX' : -34.49, 'centerDistanceY' : -25.33, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6702.77,1952.0,2.199], 'SII6731a' : [6709.09,1772.0,2.871], 'SII6716b' : [6737.29,6487.0,4.756], 'SII6731b' : [0.0,0.0,0.0], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 79, 'centerDistanceX' : 0.0, 'centerDistanceY' : -16.89, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6702.74,1318.0,3.651], 'SII6731a' : [6718.44,2504.0,3.964], 'SII6716b' : [6738.8,1210.0,2.705], 'SII6731b' : [6753.06,823.0,2.725], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 80, 'centerDistanceX' : -64.05, 'centerDistanceY' : 2.81, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [6705.44,7949.0,7.032], 'SII6731a' : [6721.96,16228.0,8.417], 'SII6716b' : [0.0,0.0,0.0], 'SII6731b' : [0.0,0.0,0.0], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 81, 'centerDistanceX' : -24.64, 'centerDistanceY' : -25.33, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [0.0,0.0,0.0], 'SII6731a' : [0.0,0.0,0.0], 'SII6716b' : [6732.72,6155.0,7.314], 'SII6731b' : [6747.07,8696.0,10.28], 'ArIII7136' : [0.0,0.0,0.0]},
{'fiber': 82, 'centerDistanceX' : -19.71, 'centerDistanceY' : 0.0, 'Halpha6563a': [0.0,0.0,0.0], 'Halpha6563b': [0.0,0.0,0.0], 'OIII5007a': [0.0,0.0,0.0], 'OIII5007b': [0.0,0.0,0.0], 'SII6716a': [0.0,0.0,0.0], 'SII6731a' : [0.0,0.0,0.0], 'SII6716b' : [0.0,0.0,0.0], 'SII6731b' : [0.0,0.0,0.0], 'ArIII7136' : [0.0,0.0,0.0]},
]
for a in tab:
data = [str(a['fiber'])]
data.append('%.2f' % a['centerDistanceX'])
data.append('%.2f' % a['centerDistanceY'])
data.append('%.2f' % a['Halpha6563a'][0])
data.append('%.2f' % a['Halpha6563b'][0])
data.append('%.2f' % a['OIII5007a'][0])
data.append('%.2f' % a['OIII5007b'][0])
data.append('%.2f' % a['SII6716a'][0])
data.append('%.2f' % a['SII6731a'][0])
data.append('%.2f' % a['SII6716b'][0])
data.append('%.2f' % a['SII6731b'][0])
data.append('%.2f' % a['ArIII7136'][0])
csv.append(data)
csvFree.writeCSVFile(csv,'/Users/azuri/daten/uni/HKU/Pa30/pa30_sparsepak_data.csv')
import os
import csvFree,csvData
path = '/Users/azuri/daten/uni/HKU/PN alignment'
fNameIn = 'HASH_bipolar_likely_PN.csv'
csv = csvFree.readCSVFile(os.path.join(path,fNameIn))
csvOut = csvData.CSVData()
csvOut.header = csv.header
for i in range(csv.size()):
if csv.getData('PNstat',i) == 'L':
lon = float(csv.getData('Glon',i))
lat = float(csv.getData('Glat',i))
if ((lon < 10.) or (lon > 350.)) and (((lat > 0.) and (lat < 10.)) or ((lat < 0.) and (lat) > -10.)):
print('lon = ',lon,', lat = ',lat)
csvOut.append(csv.getData(i))
csvFree.writeCSVFile(csvOut,os.path.join(path,fNameIn[:-4]+'_bulge.csv'))
for i in np.arange(0, len(rzPNGs), 1):
lineOut = []
p = rzPNGs[i]
found = False
if rzMorph[i] in ['Bipolar']:
for j in np.arange(0, len(myPNGs), 1):
if p == myPNGs[j]:
print('PNG ' + p + ' found in my data')
found = True
nFound += 1
lineOut.append(myHashData.getData('idPNMain', j))
lineOut.append(rzData.getData('PA', i))
lineOut.append('1')
lineOut.append(rzData.getData('Telescope', i))
lineOut.append(rzData.getData('GPA', i).split('±')[0])
lineOut.append(myHashData.getData('DRAJ2000', j))
lineOut.append(myHashData.getData('DDECJ2000', j))
lineOut.append(myHashData.getData('Glon', j))
lineOut.append(myHashData.getData('Glat', j))
lineOut.append(rzData.getData('GPA', i))
lineOut.append('')
csvOut.append(lineOut)
if not found:
print('PNG ' + p + ' not found in my data')
nNotFound += 1
print('found ', nFound, ' PNe in my data')
print('did not find ', nNotFound, ' PNe in my data')
csvFree.writeCSVFile(csvOut, dataFileOut)
