def simTOT(self, oImg, tImg, subImgNum=1000):
mchFile, nmhFile = self.runSelfMatch(self.objectImgCatNew, 24)
self.osn32 = nmhFile
osn16s = selectTempOTs(self.osn16, self.tmpDir)
tdata = np.loadtxt("%s/%s" % (self.tmpDir, osn16s))
if len(tdata.shape) < 2 or tdata.shape[0] < 100:
print("%s has too little stars, break this run" % (oImg))
return np.array([]), np.array([])
osn16sf = filtOTs(osn16s, self.tmpDir)
tdata = np.loadtxt("%s/%s" % (self.tmpDir, osn16sf))
if len(tdata.shape) < 2 or tdata.shape[0] < 45:
print("%s has too little stars, break this run" % (oImg))
return np.array([]), np.array([])
osn32f = filtOTs(self.osn32, self.tmpDir)
totalTOT = subImgNum
subImgBuffer = []
objS2NBuffer = []
tnum = 0
imgSimClass = ImageSimulation()
ii = 1
sexConf = [
'-DETECT_MINAREA', '3', '-DETECT_THRESH', '2.5',
'-ANALYSIS_THRESH', '2.5'
]
while tnum < totalTOT:
simFile, simPosFile, simDeltaXYA, tmpOtImgs = imgSimClass.simulateImage1(
osn32f, self.newImageName, osn16sf, self.newImageName)
self.objectImgSim = simFile
self.objectImgSimAdd = simPosFile
self.runSextractor(self.objectImgSim)
self.simTmpResi = self.runHotpants(self.objectImgSim,
self.templateImg)
self.simTmpResiCat = self.runSextractor(self.simTmpResi, sexConf)
simTmpResiCatEf = filtByEllipticity(self.simTmpResiCat,
self.tmpDir,
maxEllip=0.5)
mchFile, nmhFile = self.runSelfMatch(simTmpResiCatEf, 16)
simTmpResiCatEf_sn32 = nmhFile
#simTmpResiCatEf_sn32 = simTmpResiCatEf
mchFile, nmhFile, mchPair = self.runCrossMatch(
simTmpResiCatEf_sn32, self.objectImgSimAdd, 1)
tdata1 = np.loadtxt("%s/%s" % (self.tmpDir, mchFile))
poslist = tdata1[:, 0:2]
size = 100
subImgs = self.getWindowImgs(self.objectImgSim, self.templateImg,
self.simTmpResi, poslist, size)
tnum = tnum + len(subImgs)
ts2n = tdata1[:, 3]
subImgBuffer.extend(subImgs)
objS2NBuffer.extend(ts2n.tolist())
self.log.info("\n******************")
self.log.info("run %d, total sub image %d" % (ii, tnum))
if ii > 6:
break
ii = ii + 1
#break
subImgs = np.array(subImgBuffer)
objS2NBuffer = np.array(objS2NBuffer)
print(subImgs.shape)
print(objS2NBuffer.shape)
return subImgs, objS2NBuffer
def simTOT(self, oImg, tImg, subImgNum=1000):
mchFile, nmhFile = self.runSelfMatch(self.objectImgCat, 24)
self.osn32 = nmhFile
osn16s = selectTempOTs(self.osn16, self.tmpDir)
tdata = np.loadtxt("%s/%s" % (self.tmpDir, osn16s))
if len(tdata.shape) < 2 or tdata.shape[0] < 100:
print("%s has too little stars, break this run" % (oImg))
return np.array([]), np.array([])
osn16sf = filtOTs(osn16s, self.tmpDir)
tdata = np.loadtxt("%s/%s" % (self.tmpDir, osn16sf))
if len(tdata.shape) < 2 or tdata.shape[0] < 45:
print("%s has too little stars, break this run" % (oImg))
return np.array([]), np.array([])
osn32f = filtOTs(self.osn32, self.tmpDir)
mchFile, nmhFile, mchPair = self.runCrossMatch(osn32f, self.tsn16, 5)
osn32s_tsn5_cm5 = mchFile
osn32s_tsn5_cm5_pair = mchPair
totalTOT = subImgNum
subImgBuffer = []
objS2NBuffer = []
tnum = 0
imgSimClass = ImageSimulation()
ii = 1
sexConf = [
'-DETECT_MINAREA', '3', '-DETECT_THRESH', '2.5',
'-ANALYSIS_THRESH', '2.5'
]
while tnum < totalTOT:
simFile, simPosFile, simDeltaXYA, psfImgs = imgSimClass.simulateImage1(
osn32f, self.objectImg, osn16sf, self.objectImg)
self.objectImgSim = simFile
self.objectImgSimAdd = simPosFile
self.runSextractor(self.objectImgSim)
self.simTmpResi = self.runHotpants(self.objectImgSim,
self.templateImg)
self.simTmpResiCat = self.runSextractor(self.simTmpResi, sexConf)
simTmpResiCatEf = filtByEllipticity(self.simTmpResiCat,
self.tmpDir,
maxEllip=0.5)
mchFile, nmhFile = self.runSelfMatch(simTmpResiCatEf, 16)
simTmpResiCatEf_sn32 = nmhFile
#simTmpResiCatEf_sn32 = simTmpResiCatEf
mchFile, nmhFile, mchPair = self.runCrossMatch(
self.objectImgSimAdd, simTmpResiCatEf_sn32, self.r5)
str_oisa_cm5 = mchFile
str_oisa_cm5_pair = mchPair
tIdx1 = np.loadtxt(
"%s/%s" % (self.tmpDir, osn32s_tsn5_cm5_pair)).astype(np.int)
tIdx2 = np.loadtxt("%s/%s" %
(self.tmpDir, str_oisa_cm5_pair)).astype(np.int)
tIdx1 = tIdx1 - 1
tIdx2 = tIdx2 - 1
self.log.debug(
"objectCat matched data %d, ResiCat matched data %d" %
(tIdx1.shape[0], tIdx2.shape[0]))
tnames1 = ['objId', 'tmpId']
tnames2 = ['objId', 'resiId']
#unionIdx = np.intersect1d(tIdx1[:,0], tIdx2[:,0]) #union1d
#self.log.debug("intersect objectCat and templateCat matched data: %d"%(unionIdx.shape[0]))
df1 = pd.DataFrame(data=tIdx1, columns=tnames1)
df2 = pd.DataFrame(data=tIdx2, columns=tnames2)
unionIdx = pd.merge(df1, df2, how='inner', on=['objId'])
self.log.debug(
"innerjoin objectCat and templateCat matched data %d" %
(unionIdx.shape[0]))
tdata1 = np.loadtxt("%s/%s" % (self.tmpDir, self.objectImgSimAdd))
tdata2 = np.loadtxt("%s/%s" % (self.tmpDir, self.tsn16))
tdata3 = np.loadtxt("%s/%s" % (self.tmpDir, simTmpResiCatEf_sn32))
simDeltaXYA = np.array(simDeltaXYA)
tdeltaXYA = simDeltaXYA[unionIdx["objId"].values]
tdata11 = tdata1[unionIdx["objId"].values]
tdata12 = tdata2[unionIdx["tmpId"].values]
tdata22 = tdata3[unionIdx["resiId"].values]
poslist = np.concatenate(([tdata11[:, 0]], [tdata11[:, 1]], [
tdata12[:, 3] + tdeltaXYA[:, 0]
], [tdata12[:, 4] + tdeltaXYA[:, 1]], [tdata22[:, 0]
], [tdata22[:, 1]]),
axis=0).transpose()
#print(poslist)
#genFinalOTDs9Reg('tot', self.tmpDir, poslist)
#size = self.subImgSize
size = 100
subImgs = self.getWindowImgs(self.objectImgSim, self.templateImg,
self.simTmpResi, poslist, size)
#subImgs = self.getWindowImgs(self.objectImgSimSubBkg, self.templateImgSubBkg, self.simTmpResi, poslist, size)
tnum = tnum + len(subImgs)
subImgBuffer.extend(subImgs)
objS2NBuffer.extend(tdata22[:, 3].tolist())
self.log.info("\n******************")
self.log.info("run %d, total sub image %d" % (ii, tnum))
if ii > 6:
break
ii = ii + 1
#break
subImgs = np.array(subImgBuffer)
objS2NBuffer = np.array(objS2NBuffer)
print(subImgs.shape)
print(objS2NBuffer.shape)
return subImgs, objS2NBuffer
def simTOT(self, oImg, tImg, subImgNum=100):
mchFile, nmhFile = self.runSelfMatch(self.objectImgCat, 24)
self.osn32 = nmhFile
osn16s = selectTempOTs(self.osn16, self.tmpDir)
osn16sf = filtOTs(osn16s, self.tmpDir)
osn32f = filtOTs(self.osn32, self.tmpDir)
mchFile, nmhFile, mchPair = self.runCrossMatch(osn32f, self.tsn5, self.r5)
osn32s_tsn5_cm5 = mchFile
osn32s_tsn5_cm5_pair = mchPair
totalTOT = subImgNum
subImgBuffer = []
tnum = 0
imgSimClass = ImageSimulation()
ii = 1
sexConf=['-DETECT_MINAREA','3','-DETECT_THRESH','2.5','-ANALYSIS_THRESH','2.5']
while tnum<totalTOT:
simFile, simPosFile, simDeltaXYA = imgSimClass.simulateImage1(osn32f, self.objectImg, osn16sf, self.objectImg)
self.objectImgSim = simFile
self.objectImgSimAdd = simPosFile
self.simTmpResi = self.runHotpants(self.objectImgSim, self.templateImg)
objectImgSimCat = self.runSextractor(self.simTmpResi, sexConf)
tdata0 = np.loadtxt("%s/%s"%(self.tmpDir, objectImgSimCat))
print(tdata0.shape)
simTmpResiCatEf = filtByEllipticity(objectImgSimCat, self.tmpDir, maxEllip=0.5)
mchFile, nmhFile = self.runSelfMatch(simTmpResiCatEf, self.r5)
simTmpResiCatEf_sn32 = nmhFile
#simTmpResiCatEf_sn32 = simTmpResiCatEf
mchFile, nmhFile, mchPair = self.runCrossMatch(self.objectImgSimAdd, simTmpResiCatEf_sn32, self.r5)
str_oisa_cm5 = mchFile
str_oisa_cm5_pair = mchPair
tIdx1 = np.loadtxt("%s/%s"%(self.tmpDir, osn32s_tsn5_cm5_pair)).astype(np.int)
tIdx2 = np.loadtxt("%s/%s"%(self.tmpDir, str_oisa_cm5_pair)).astype(np.int)
tIdx1 = tIdx1 - 1
tIdx2 = tIdx2 - 1
self.log.debug("objectCat matched data %d, templateCat matched data %d"%(tIdx1.shape[0], tIdx2.shape[0]))
tnames1 = ['objId', 'tmpId']
tnames2 = ['objId', 'resiId']
#unionIdx = np.intersect1d(tIdx1[:,0], tIdx2[:,0]) #union1d
#self.log.debug("intersect objectCat and templateCat matched data: %d"%(unionIdx.shape[0]))
df1 = pd.DataFrame(data=tIdx1, columns=tnames1)
df2 = pd.DataFrame(data=tIdx2, columns=tnames2)
unionIdx=pd.merge(df1, df2, how='inner', on=['objId'])
self.log.debug("innerjoin objectCat and templateCat matched data %d"%(unionIdx.shape[0]))
tdata1 = np.loadtxt("%s/%s"%(self.tmpDir, self.objectImgSimAdd))
tdata2 = np.loadtxt("%s/%s"%(self.tmpDir, self.tsn5))
tdata3 = np.loadtxt("%s/%s"%(self.tmpDir, simTmpResiCatEf_sn32))
simDeltaXYA = np.array(simDeltaXYA)
tdeltaXYA = simDeltaXYA[unionIdx["objId"].values]
tdata11 = tdata1[unionIdx["objId"].values]
tdata12 = tdata2[unionIdx["tmpId"].values]
tdata22 = tdata3[unionIdx["resiId"].values]
print(tdata22.shape)
print("sim star %d, matched %d"%(tdata1.shape[0],tIdx2.shape[0]))
magerr = tdata22[:,3]
print(magerr[:10])
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
xmajorLocator = MultipleLocator(2)
xminorLocator = MultipleLocator(1)
plt.figure(figsize = (16, 8))
ax = plt.subplot(111)
plt.hist(magerr, bins=20, range=(0,20), normed=False,
weights=None, cumulative=False, bottom=None,
histtype=u'bar', align=u'left', orientation=u'vertical',
rwidth=0.8, log=False, color=None, label=None, stacked=False,
hold=None)
ax.xaxis.set_major_locator(xmajorLocator)
ax.xaxis.set_minor_locator(xminorLocator)
plt.show()
self.log.info("\n******************")
self.log.info("run %d, total sub image %d"%(ii, tnum))
if ii>0:
break
ii = ii + 1
rsts = np.array(subImgBuffer)
print(rsts.shape)
return rsts