def remakedonut(self):
"""Remake make_donut
Notes
-----
No input or return.
"""
self.make_donut = makedonut(**self.makedonut_dict)
return
def __init__(self, **kwargs):
y_keys = ['stamp']
x_keys = ['x', 'y', 'rzero',
'z1', 'z2', 'z3',
'z4', 'z5', 'z6',
'z7', 'z8',
'z9', 'z10',
'z11']
super(Zernike_to_Pixel_Interpolator, self).__init__(
y_keys=y_keys, x_keys=x_keys, **kwargs)
# set up makedonut
self.makedonut_dict = {'nbin': 256,
'nPixels': 32,
'pixelOverSample': 8,
'scaleFactor': 1,
'randomFlag': 0}
for kwarg in kwargs:
if kwarg in self.makedonut_dict:
self.makedonut_dict[kwarg] = kwargs[kwarg]
self.make_donut = makedonut(**self.makedonut_dict)
'nbin': 512,
'nPixels': 64,
'pixelOverSample': 8,
'scaleFactor': 1.,
'rzero': 0.125,
'nEle': 1.0e6,
'background': 4000.,
'randomFlag': True,
'randomSeed': 2314809,
'ZernikeArray': zarray,
'gain': 4.5,
'xDECam': xdecam,
'yDECam': ydecam
}
m = makedonut(**inputDict)
donut1 = m.make()
## fit them
fitinitDict = {
"nZernikeTerms":
37,
"fixedParamArray1": [
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
"fixedParamArray2": [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
def makeTheDonuts(donut1Name, donut2Name, z4, z5, z6, z7, z8, z9, z10, z11,
z12, z13, z14, z15, z16, z17, z18, z19, z20, z21, z22, iNr):
# Define Zernike coefficients of the donuts.
# The first 3 terms are set to 0.
z1 = 0.0
z2 = 0.0
z3 = 0.0
# In order to create defocussed images, we add 40 micrometer to z4 for donut image 1 and
# subtract 40 micrometer for donut image 2.
#z4 = z4 + 40.
# Define the background.
bkgr = 0.0
# Define the x,y position of the camera.
xCam = 0.00
yCam = 0.00
# Set the random number used in the simulation
# rndSeed = 2314809
rndSeed = np.random.randint(1, np.iinfo(np.int32).max)
# Create the input dictionary for donut production (donut 1
inputDict = {'writeToFits':True,'outputPrefix':donut1Name,'iTelescope':3,'nZernikeTerms':37,\
'nbin':1792,'nPixels':224,'pixelOverSample':8,'scaleFactor':1.,'rzero':0.125,\
'nEle':1.0e8, 'background':bkgr, 'randomFlag':False, 'randomSeed':rndSeed,\
'ZernikeArray':[z1,z2,z3,z4,z5,z6,z7,z8,z9,z10,z11,z12,z13,z14,z15,z16,z17,z18,z19,z20,z21,z22],\
'xDECam':xCam,'yDECam':yCam,'printLevel':5}
#,'xDECam':xCam,'yDECam':yCam}
# Create the image of donut 1
m = makedonut(**inputDict)
donut1 = m.make()
# Update the disctionary for donut 2 and create the image of donut 2.
# Note: we also change the value of z4 by -80 micrometer to reflect that we want to look at the second
# out-of-focus sensor. The out-of-focus sensors are assumed to be defocussed by +40 micrometer and
# -40 micrometer. We added 40 micrometer to z4 when we generated donut 1; we now have to subtract
# 80 micrometer in order to create donut 2.
#z4 = z4 - 80.
newDict = {'outputPrefix':donut2Name,\
'ZernikeArray':[z1,z2,z3,z4,z5,z6,z7,z8,z9,z10,z11,z12,z13,z14,z15,z16,z17,z18,z19,z20,z21,z22]}
donut2 = m.make(**newDict)
# Print current file names.
print donut1Name
print donut2Name
# Done making donuts.
# We now create the images we need for learning:
# donut 1
# donut 2
# donut difference = donut 1 - donut 2
# These images are written in png and hdf5 format.
# Open the data files.
#hdu1 = pyfits.open(donut1Name+'.stamp.fits')
#hdu2 = pyfits.open(donut2Name+'.stamp.fits')
# Get the image data.
#donut1Data = hdu1[0].data
#donut2Data = hdu2[0].data
# Calculate the difference between donut 1 and donut 2
#donutDiffData = donut1Data - donut2Data
# Calculate the name of the image file for the donut difference.
#diffName = donut1Name[0:22]+'12'+donut1Name[24:]
# Write images in png format.
#imageio.imwrite(donut1Name+'.png', donut1Data)
#imageio.imwrite(donut2Name+'.png', donut2Data)
#imageio.imwrite(diffName+'.png', donutDiffData)
# Write image in hdf5 format.
#h5f = h5py.File(donut1Name+'.h5', 'w')
#h5f.create_dataset('Donut 1', data=donut1Data)
#h5f.close()
#h5f = h5py.File(donut2Name+'.h5', 'w')
#h5f.create_dataset('Donut 2', data=donut2Data)
#h5f.close()
#h5f = h5py.File(diffName+'.h5', 'w')
#h5f.create_dataset('Donut 1 - Donut 2', data=donutDiffData)
#h5f.close()
return
from donutlib.makedonut import makedonut
from donutlib.donutfit import donutfit
# make donuts
z4 = 10.
z5 = 0.2
z6 = 0.2
z7 = -0.15
z8 = .05
z9 = 0.3
z10 = -.05
z11 = .2
inputDict = {'writeToFits':True,'outputPrefix':'unittest.0001','iTelescope':0,'nZernikeTerms':37,'nbin':512,'nPixels':64,'pixelOverSample':8,'scaleFactor':1.,'rzero':0.125, 'nEle':1.0e6, 'background':4000., 'randomFlag':True, 'randomSeed':2314809, 'ZernikeArray':[0.,0.,0.,z4,z5,z6,z7,z8,z9,z10,z11]}
m = makedonut(**inputDict)
donut1 = m.make()
z4 = 10.5
z9 = -0.05
z10 = 0.3
newDict = {'outputPrefix':'unittest.0002','ZernikeArray':[0.,0.,0.,z4,z5,z6,z7,z8,z9,z10,z11]}
donut2 = m.make(**newDict)
## fit them
fitinitDict = {"nZernikeTerms":15,"fixedParamArray1":[0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1],"fixedParamArray2":[0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0],"nFits":2,"nPixels":64,"nbin":512,"scaleFactor":1.0,"pixelOverSample":8,"iTelescope":0,"inputrzero":0.15,"debugFlag":False}
df = donutfit(**fitinitDict)
# fit first donut
fitDict = {}
fitDict["inputFile"] = 'unittest.0001.stamp.fits'
