def handle(self, fitsInfo):
keywords = fitsInfo[0].header
flux = fitsInfo[0].data[0]
ra = astroUtil.getKeyword(keywords, 'RA')
dec = astroUtil.getKeyword(keywords, 'DEC')
if ra == 0 or dec == 0:
return
self.output.write(ra + " " + dec)
self.output.write("\n")
self.count += 1
def getCatchData(ra, dec, cache):
r1 = int(ra / 5) * 5
r2 = r1 + 5
d1 = int(dec / 5) * 5
if dec < 0:
if d1 <= 0:
d1 -= 5
d2 = d1 + 5
fileName = "ucal_magsqw_r%d-%d_d%d-%d.fits" % (r1, r2, d1, d2)
data = getCache(cache, fileName)
if data is not None:
return data
filePath = os.path.join(pastarrDir, fileName)
if not os.path.exists(filePath):
return None
fitsInfo = astroUtil.getFitsInfo(filePath)
data = fitsInfo[1].data
cacheValue = []
for row in data:
mags = row[6]
rowValue = [row[0], row[1], mags[0], mags[1], mags[2], mags[3], mags[4]]
cacheValue.append(rowValue)
addCache(cache, fileName, cacheValue)
return cacheValue
def M31RegionSQLGen():
sql_select = "SELECT * FROM dr3.M31_Coordinate"
sql_select_vo = "SELECT objID,ra,dec,petroMag_u,petroMag_g,petroMag_r,petroMag_i,petroMag_z FROM BestDR12.dbo.PhotoObjALl WHERE "
conn, data = sqlHelper.getFetchMany(sql_select)
dic_radec = util.getRaDecMap(data)
sql_whereList = []
for ra in dic_radec.keys():
minDec = min(dic_radec[ra])
maxDec = max(dic_radec[ra])
sql_whereList.append(genSQL(ra, minDec, maxDec))
sql_where = util.listToString(sql_whereList, "or")
sql_select_vo += sql_where
print sql_select_vo
def getDistance(x1, y1, x2, y2):
solution = astroUtil.solution
if abs(x1 - x2) > solution or abs(y1 - y2) > solution:
return 100
distance = astroUtil.getStarDistance(x1, y1, x2, y2)
if distance <= solution:
return distance
else:
return 100
def fitsToDatabase(self, strategy):
strategy.preHandle()
filePaths = util.listDirFiles(strategy.dirPath, ".fits")
for filePath in filePaths:
try:
fitsInfo = astroUtil.getFitsInfo(filePath)
strategy.handle(fitsInfo)
except Exception, ex:
exUtil.printTagMessage(ex, "Fits handle error", filePath)
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
def PanstarrsCrossUpdate(specid, ra, dec, conn, cache):
data = getCatchData(ra, dec, cache)
if data is None:
print "failed [file not exist]:" + specid
return False
for row in data:
ra_panstarrs = row[0]
dec_panstarrs = row[1]
if astroUtil.checkDuplicate(ra, dec, ra_panstarrs, dec_panstarrs):
# mags = row[6]
# values = [mags[0], mags[1], mags[2], mags[3], mags[4], specid]
values = [row[2], row[3], row[4], row[5], row[6], specid]
sqlHelper.execWithValues(conn, sql_update, values)
print "succeed :" + specid
return True
print "failed :" + specid
return False
def importLamostData(sql_select, sql_insert):
conn = sqlHelper.getMySQLConn()
conn45 = sqlHelper.getMySQL45Conn()
count = 0
data = sqlHelper.getFetchManyByConn(conn45, sql_select)
for info in data:
sqlRecord = []
colIndex = 1
try:
for col in info:
if colIndex == 13:
classType = info[10]
subClass = info[11]
mainClass = astroUtil.getMainClass(classType, subClass)
sqlRecord.append(mainClass)
sqlRecord.append(col)
colIndex += 1
sqlHelper.execWithValues(conn, sql_insert, sqlRecord)
count += 1
except Exception, ex:
exUtil.printMessage(ex, "Migration")
def getCoordinateDic(tableName):
sql_select = "SELECT * FROM " + tableName
data = sqlHelper.getFetchAll(sql_select)
return astroUtil.getCoordinateCountMap(data)
# -*- coding:utf-8 -*- import Astronomy.Util.CommonUtil as astroUtil import os # waveLens = [1184,1452,2023,2480,3262] waveLens = [1188, 2023, 2217, 2356, 2491, 3613, 3262, 3635, 3696] scale = 15 fitsPath = '/media/zdwdong/my/fits/test/spec-56609-HD004201N404117V01_sp04-217.fits' # files = os.listdir(os.path.join(dr2Dir, subDir)) # for fileName in files: # LamostFuns.imageExtract(dr2Dir, subDir, fileName, waveLens, waveType, False) astroUtil.imageExtract(fitsPath, waveLens, True)
def handle(self, fitsInfo):
keywords = fitsInfo[0].header
classType = astroUtil.getKeyword(keywords, "CLASS", "1D_CLASS")
if classType == "SKY":
return
ra = astroUtil.getKeyword(keywords, 'RA')
dec = astroUtil.getKeyword(keywords, 'DEC')
subClass = astroUtil.getKeyword(keywords, "SUBCLASS", "1D_SUBCL")
mainClass = astroUtil.getMainClass(classType, subClass)
date = astroUtil.getKeyword(keywords, "DATE-OBS")[0:10]
planID = astroUtil.getKeyword(keywords, "PLANID")
z = astroUtil.getKeyword(keywords, "Z", "1D_Z")
z_err = astroUtil.getKeyword(keywords, "1D_Z_ERR", "Z_ERR")
version_idl = astroUtil.getKeyword(keywords, "VERSIDL")
version_ppl = astroUtil.getKeyword(keywords, "VERS2D", "VERSPIPE")
SN_U = astroUtil.getKeyword(keywords, "SN_U")
SN_G = astroUtil.getKeyword(keywords, "SN_G")
SN_R = astroUtil.getKeyword(keywords, "SN_R")
SN_I = astroUtil.getKeyword(keywords, "SN_I")
SN_Z = astroUtil.getKeyword(keywords, "SN_Z")
sqlRecord = [planID, version_idl, version_ppl, date, ra, dec,
classType, mainClass, subClass, z, z_err, SN_U, SN_G, SN_R, SN_I, SN_Z]
sqlHelper.execWithValues(self.conn, self.sql_insert, sqlRecord)
self.count += 1
import numpy as np
import CodeLib.Util.CommonUtil as util
import Astronomy.Util.CommonUtil as astroUtil
from CodeLib.Util import MathUtil
inputFile = "data_v4.txt"
data = util.loadTxtData(inputFile)
m, n = data.shape
testCount = int(m * 0.2)
testData = data[0:testCount, :]
trainData = data[testCount:m, :]
subClassData, subClassCount = astroUtil.formatClassifyData(trainData)
classMeanVar = {}
for key in subClassData.keys():
classMeanVar[key] = []
for i in range(len(subClassData[key])):
if i < 3:
mean, var, num = MathUtil.calcMeanVarCount(subClassData[key][i])
classMeanVar[key].append([mean, var, num])
else:
classMeanVar[key].append(np.sum(subClassData[key][i]))
def testData(data):
m, n = data.shape
tags = data[:, n - 1]
errorCount = 0
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
