def runMasterFlatMaker(self, event):
path = self.masterFlatPathCtrl.GetValue()
self.flatpaths = []
for impath in self.flatImagesPathCtrl.GetValue().split(','):
self.flatpaths += glob(impath)
self.flatdarkpaths = []
for dpath in self.flatDarksPathCtrl.GetValue().split(','):
self.flatdarkpaths += glob(dpath)
if not path.endswith('.fits') and not path.endswith('.fit'):
path += '.fits'
pathCorrected = path.replace('/', os.sep)
outfolder = pathCorrected[:pathCorrected.rfind(os.sep)] + os.sep + '*'
self.standardFlat = (self.flatRadioBox.GetSelection() == 0)
if pathCorrected in glob(outfolder):
OverwFlatFrame(pathCorrected,self,-1)
else:
if self.standardFlat:
oscaar.standardFlatMaker(self.flatpaths, self.flatdarkpaths, self.masterFlatPathCtrl.GetValue(), self.plotsRadioBox.GetSelection() == 0)
else:
oscaar.twilightFlatMaker(self.flatpaths, self.flatdarkpaths, self.masterFlatPathCtrl.GetValue(), self.plotsRadioBox.GetSelection() == 0)
self.Destroy()
def runMasterFlatMaker(self, event):
path = self.masterFlatPathCtrl.GetValue()
self.flatpaths = []
for impath in self.flatImagesPathCtrl.GetValue().split(','):
self.flatpaths += glob(impath)
self.flatdarkpaths = []
for dpath in self.flatDarksPathCtrl.GetValue().split(','):
self.flatdarkpaths += glob(dpath)
if not path.endswith('.fits') and not path.endswith('.fit'):
path += '.fits'
pathCorrected = path.replace('/', os.sep)
outfolder = pathCorrected[:pathCorrected.rfind(os.sep)] + os.sep + '*'
self.standardFlat = (self.flatRadioBox.GetSelection() == 0)
if pathCorrected in glob(outfolder):
OverwFlatFrame(pathCorrected,self,-1)
else:
if self.standardFlat:
oscaar.standardFlatMaker(self.flatpaths, self.flatdarkpaths, self.masterFlatPathCtrl.GetValue(), self.plotsRadioBox.GetSelection() == 0)
else:
oscaar.twilightFlatMaker(self.flatpaths, self.flatdarkpaths, self.masterFlatPathCtrl.GetValue(), self.plotsRadioBox.GetSelection() == 0)
self.Destroy()
def main():
#################################################################
## Tweak these parameters, if you like!
NdataImages = 200 ## Number of data images to generate
NdarkImages = 3 ## Number of dark frames to generate
NflatImages = 3 ## Number of flat fields to generate
flatFieldCounts = 20000 ## Approx counts per pixel in flat field
imageDimensionX = 120 ## Pixel dimensions of each image
imageDimensionY = 40
starDimensions = 4 ## Pixel dimensions of the stars
skyBackground = 500 ## Background counts from sky brightness
darkBackground = 100 ## Background counts from detector
targetFluxOOT = 10000 ## Flux (in counts) from each pixel of the unocculted target star (out-of-transit)
relativeFluxCompA = 0.85 ## Flux from comp A relative to target
relativeFluxCompB = 0.95 ## Flux from comp B relative to target
plotModel = False ## Plot the injected transit light curve
createMasterFlatNow = True ## Use oscaar to create a master flat from the freshly generated flat frames
## Isn't it nice to control the signal to noise?
#################################################################
## Delete `images` directory, if there is one, and
## make a fresh one.
if len(glob(os.path.join(os.path.dirname(__file__),'images'))) > 0: rmtree(os.path.join(os.path.dirname(__file__),'images'))
mkdir(os.path.join(os.path.dirname(__file__),'images'))
## Pixel positions of the stars (x,y)
targetX = [20-starDimensions/2,20+starDimensions/2]
compAX = [60-starDimensions/2,60+starDimensions/2]
compBX = [100-starDimensions/2,100+starDimensions/2]
starsY = [imageDimensionY/2-starDimensions/2,imageDimensionY/2+starDimensions/2]
## Set ingress/egress times, transiting system parameters
## In GD: Ingress = 2013-05-15;10:06:30; egress = 2013-05-15;11:02:35
jd0 = 2456427.88890
exposureTime = 45/(60*60*24.) ## Convert s -> hr
times = np.arange(jd0,jd0+exposureTime*NdataImages,exposureTime)
# [p,ap,P,i,gamma1,gamma2,e,longPericenter,t0]
modelParams = [ 0.1179, 14.71, 1.580400, 90.0, 0.23, \
0.30, 0.00, 0.0, np.mean(times,dtype=np.float64)]
np.savetxt(os.path.join(os.path.dirname(__file__),'modelParams.txt'),modelParams)
modelLightCurve = oscaar.occultquad(times,modelParams)
if plotModel:
fig = plt.figure()
ax1 = fig.add_subplot(111)
def format_coord(x, y):
'''Function to also give data value on mouse over with imshow.'''
return 'JD=%1.6f, Flux=%1.6f' % (x, y)
ax1.set_xlabel('Time (JD)')
ax1.set_ylabel('Relative Flux')
ax1.set_title('Injected Light Curve')
ax1.plot(times,modelLightCurve)
ax1.format_coord = format_coord
plt.show()
## For producing random arrays, initialize reference arrays with the proper shapes
imageShapedMatrix = np.zeros([imageDimensionY,imageDimensionX])
starShapedMatrix = np.zeros([starDimensions,starDimensions])
## Simulate dark frames with shot noise
for i in range(NdarkImages):
darkFrame = darkBackground + np.random.normal(imageShapedMatrix,np.sqrt(darkBackground))
darkFrame = np.require(darkFrame,dtype=int) ## Require integer counts
pyfits.writeto(os.path.join(os.path.dirname(__file__),'images/simulatedImg-'+str(i).zfill(3)+'d.fits'),darkFrame)
## Simulate ideal flat frames (perfectly flat)
for i in range(NflatImages):
## Flats will be completely flat -- ie, we're pretending that we have a
## perfect optical path with no spatial flux variations.
flatField = np.zeros([imageDimensionY,imageDimensionX]) + flatFieldCounts
flatField = np.require(flatField,dtype=int)## Require integer counts
pyfits.writeto(os.path.join(os.path.dirname(__file__),'images/simulatedImg-'+str(i).zfill(3)+'f.fits'),flatField)
## Create master flat now using oscaar's standard flat maker
if createMasterFlatNow:
flatPaths = glob(os.path.join(os.path.dirname(__file__),'images/simulatedImg-???f.fits'))
flatDarkPaths = glob(os.path.join(os.path.dirname(__file__),'images/simulatedImg-???d.fits')) ## Use the same darks
masterFlatSavePath = os.path.join(os.path.dirname(__file__),'images/masterFlat.fits') ## Where to save the master
oscaar.standardFlatMaker(flatPaths,flatDarkPaths,masterFlatSavePath,plots=False)
## Create data images
for i in range(NdataImages):
## Produce image with sky and dark background with simulated photon noise for each source
simulatedImage = darkBackground + skyBackground +\
np.random.normal(imageShapedMatrix,np.sqrt(darkBackground)) +\
np.random.normal(imageShapedMatrix,np.sqrt(skyBackground))
## Create two box-shaped stars with simulated photon noise
targetBrightness = targetFluxOOT*modelLightCurve[i] ## Scale brightness with the light curve
target = targetBrightness + np.random.normal(starShapedMatrix,np.sqrt(targetBrightness))
compBrightnessA = targetFluxOOT*relativeFluxCompA
compBrightnessB = targetFluxOOT*relativeFluxCompB
compA = compBrightnessA + np.random.normal(starShapedMatrix,np.sqrt(compBrightnessA))
compB = compBrightnessB + np.random.normal(starShapedMatrix,np.sqrt(compBrightnessB))
## Add stars onto the simulated image with some position jitter
randomPositionJitterX = np.sign(np.random.uniform(-1,1)) ## +/- 1 pixel stellar centroid position jitter
randomPositionJitterY = np.sign(np.random.uniform(-1,1)) ## +/- 1 pixel stellar centroid position jitter
simulatedImage[starsY[0]+randomPositionJitterY:starsY[1]+randomPositionJitterY,targetX[0]+randomPositionJitterX:targetX[1]+randomPositionJitterX] += target
simulatedImage[starsY[0]+randomPositionJitterY:starsY[1]+randomPositionJitterY,compAX[0]+randomPositionJitterX:compAX[1]+randomPositionJitterX] += compA
simulatedImage[starsY[0]+randomPositionJitterY:starsY[1]+randomPositionJitterY,compBX[0]+randomPositionJitterX:compBX[1]+randomPositionJitterX] += compB
## Force counts to integers, save.
simulatedImage = np.require(simulatedImage,dtype=int) ## Require integer counts before save
header = pyfits.Header()
header.append(('JD',times[i],'Simulated Time (Julian Date)'))
pyfits.writeto(os.path.join(os.path.dirname(__file__),'images/simulatedImg-'+str(i).zfill(3)+'r.fits'),simulatedImage,header=header)
def main():
#################################################################
## Tweak these parameters, if you like!
NdataImages = 200 ## Number of data images to generate
NdarkImages = 3 ## Number of dark frames to generate
NflatImages = 3 ## Number of flat fields to generate
flatFieldCounts = 20000 ## Approx counts per pixel in flat field
imageDimensionX = 120 ## Pixel dimensions of each image
imageDimensionY = 40
starDimensions = 4 ## Pixel dimensions of the stars
skyBackground = 500 ## Background counts from sky brightness
darkBackground = 100 ## Background counts from detector
targetFluxOOT = 10000 ## Flux (in counts) from each pixel of the unocculted target star (out-of-transit)
relativeFluxCompA = 0.85 ## Flux from comp A relative to target
relativeFluxCompB = 0.95 ## Flux from comp B relative to target
plotModel = False ## Plot the injected transit light curve
createMasterFlatNow = True ## Use oscaar to create a master flat from the freshly generated flat frames
## Isn't it nice to control the signal to noise?
#################################################################
## Delete `images` directory, if there is one, and
## make a fresh one.
if len(glob(os.path.join(os.path.dirname(__file__), 'images'))) > 0:
rmtree(os.path.join(os.path.dirname(__file__), 'images'))
mkdir(os.path.join(os.path.dirname(__file__), 'images'))
## Pixel positions of the stars (x,y)
targetX = [20 - starDimensions / 2, 20 + starDimensions / 2]
compAX = [60 - starDimensions / 2, 60 + starDimensions / 2]
compBX = [100 - starDimensions / 2, 100 + starDimensions / 2]
starsY = [
imageDimensionY / 2 - starDimensions / 2,
imageDimensionY / 2 + starDimensions / 2
]
## Set ingress/egress times, transiting system parameters
## In GD: Ingress = 2013-05-15;10:06:30; egress = 2013-05-15;11:02:35
jd0 = 2456427.88890
exposureTime = 45 / (60 * 60 * 24.) ## Convert s -> hr
times = np.arange(jd0, jd0 + exposureTime * NdataImages, exposureTime)
# [p,ap,P,i,gamma1,gamma2,e,longPericenter,t0]
modelParams = [ 0.1179, 14.71, 1.580400, 90.0, 0.23, \
0.30, 0.00, 0.0, np.mean(times,dtype=np.float64)]
np.savetxt(os.path.join(os.path.dirname(__file__), 'modelParams.txt'),
modelParams)
modelLightCurve = oscaar.occultquad(times, modelParams)
if plotModel:
fig = plt.figure()
ax1 = fig.add_subplot(111)
def format_coord(x, y):
'''Function to also give data value on mouse over with imshow.'''
return 'JD=%1.6f, Flux=%1.6f' % (x, y)
ax1.set_xlabel('Time (JD)')
ax1.set_ylabel('Relative Flux')
ax1.set_title('Injected Light Curve')
ax1.plot(times, modelLightCurve)
ax1.format_coord = format_coord
plt.show()
## For producing random arrays, initialize reference arrays with the proper shapes
imageShapedMatrix = np.zeros([imageDimensionY, imageDimensionX])
starShapedMatrix = np.zeros([starDimensions, starDimensions])
## Simulate dark frames with shot noise
for i in range(NdarkImages):
darkFrame = darkBackground + np.random.normal(imageShapedMatrix,
np.sqrt(darkBackground))
darkFrame = np.require(darkFrame, dtype=int) ## Require integer counts
pyfits.writeto(
os.path.join(os.path.dirname(__file__),
'images/simulatedImg-' + str(i).zfill(3) + 'd.fits'),
darkFrame)
## Simulate ideal flat frames (perfectly flat)
for i in range(NflatImages):
## Flats will be completely flat -- ie, we're pretending that we have a
## perfect optical path with no spatial flux variations.
flatField = np.zeros([imageDimensionY, imageDimensionX
]) + flatFieldCounts
flatField = np.require(flatField, dtype=int) ## Require integer counts
pyfits.writeto(
os.path.join(os.path.dirname(__file__),
'images/simulatedImg-' + str(i).zfill(3) + 'f.fits'),
flatField)
## Create master flat now using oscaar's standard flat maker
if createMasterFlatNow:
flatPaths = glob(
os.path.join(os.path.dirname(__file__),
'images/simulatedImg-???f.fits'))
flatDarkPaths = glob(
os.path.join(
os.path.dirname(__file__),
'images/simulatedImg-???d.fits')) ## Use the same darks
masterFlatSavePath = os.path.join(
os.path.dirname(__file__),
'images/masterFlat.fits') ## Where to save the master
oscaar.standardFlatMaker(flatPaths,
flatDarkPaths,
masterFlatSavePath,
plots=False)
## Create data images
for i in range(NdataImages):
## Produce image with sky and dark background with simulated photon noise for each source
simulatedImage = darkBackground + skyBackground +\
np.random.normal(imageShapedMatrix,np.sqrt(darkBackground)) +\
np.random.normal(imageShapedMatrix,np.sqrt(skyBackground))
## Create two box-shaped stars with simulated photon noise
targetBrightness = targetFluxOOT * modelLightCurve[
i] ## Scale brightness with the light curve
target = targetBrightness + np.random.normal(starShapedMatrix,
np.sqrt(targetBrightness))
compBrightnessA = targetFluxOOT * relativeFluxCompA
compBrightnessB = targetFluxOOT * relativeFluxCompB
compA = compBrightnessA + np.random.normal(starShapedMatrix,
np.sqrt(compBrightnessA))
compB = compBrightnessB + np.random.normal(starShapedMatrix,
np.sqrt(compBrightnessB))
## Add stars onto the simulated image with some position jitter
randomPositionJitterX = np.sign(np.random.uniform(
-1, 1)) ## +/- 1 pixel stellar centroid position jitter
randomPositionJitterY = np.sign(np.random.uniform(
-1, 1)) ## +/- 1 pixel stellar centroid position jitter
simulatedImage[starsY[0] + randomPositionJitterY:starsY[1] +
randomPositionJitterY,
targetX[0] + randomPositionJitterX:targetX[1] +
randomPositionJitterX] += target
simulatedImage[starsY[0] + randomPositionJitterY:starsY[1] +
randomPositionJitterY,
compAX[0] + randomPositionJitterX:compAX[1] +
randomPositionJitterX] += compA
simulatedImage[starsY[0] + randomPositionJitterY:starsY[1] +
randomPositionJitterY,
compBX[0] + randomPositionJitterX:compBX[1] +
randomPositionJitterX] += compB
## Force counts to integers, save.
simulatedImage = np.require(
simulatedImage, dtype=int) ## Require integer counts before save
header = pyfits.Header()
header.append(('JD', times[i], 'Simulated Time (Julian Date)'))
pyfits.writeto(os.path.join(
os.path.dirname(__file__),
'images/simulatedImg-' + str(i).zfill(3) + 'r.fits'),
simulatedImage,
header=header)
