def modelMatch(targetFitsPath, templateDir, waveRange=np.arange(4000, 7600, 1), isSave=True):
keywords, flux = astroUtil.getFitsHeaderAndFlux(targetFitsPath)
wave = astroUtil.calcWaveWithKeywords(keywords)
wave, flux = formatWaveAndFlux(wave, flux, waveRange)
templatePath, minCoeff, minChi2 = calcMinChi2(flux, templateDir)
# print "%s,%s,%s" % (targetFitsPath, templatePath, minChi2)
if isSave:
if templatePath == "":
return
templateData = np.loadtxt(templatePath)
wave_template, flux_template = formatWaveAndFlux(templateData[:, 0], templateData[:, 1], waveRange)
plt.plot(wave, flux, 'b')
plt.plot(wave_template,
flux_template * (minCoeff[0] * np.power(wave_template, 2) + minCoeff[1] * wave_template + minCoeff[2]),
'r--')
plt.ylabel('flux')
plt.xlabel('wavelength')
plt.title(templatePath)
plt.savefig(targetFitsPath + '.png')
plt.close()
return templatePath, minChi2
classCount = {"O": 0, "B": 0, "A": 0, "F": 0, "G": 0, "K": 0, "M": 0, "N": 0}
inputPath = "C:\dr2"
outFile = "data.txt"
outFilePath = util.getRelativePath(outFile)
output = open(outFilePath, "w")
dirs = os.listdir(inputPath)
count = 0
for dir in dirs:
files = os.listdir(os.path.join(inputPath, dir))
for fileName in files:
filePath = os.path.join(inputPath, dir, fileName)
try:
keywords, flux = astroUtil.getFitsHeaderAndFlux(filePath)
record = []
for fea in features:
record.append(keywords[fea])
for wl in waveLens:
record.append(astroUtil.checkLine(flux, wl))
subClass = keywords[subClassKey][0:1]
subClassTag = classMap[subClass]
classCount[subClass] += 1
record.append(subClassTag)
output.write(util.listToString(record))
output.write("\n")
count += 1
