def __init__(self,local=True,channel='left',size=199,rin=30,rout=60,pathRDI='RDI_python'):
"""
Class attributes:
- pathRDI
- pathRoot
- pathSparta
- targets: a list with all targets
- pathOut_targets: a dictionary with the reduction folder of the targets
- size: the size of the images we work with
- channel: the channel we work with (left, right, sum)
- rin: the inner radius for the correlation
- rout: the outer radius for the correlation
- frames_nb: a dictionnary with the number of frames of each target
- dist_center: an array of shape (size,size) with the distance from the center
- frames_starta_index: a dictionnary where keys are target names and values are
the index at which the target starts in the master correlation matrix
- mask_correlation: an boolean array of shape (size,size) contain 1 for pixels to be used in the correlation
- total_frames: the total number of frames including all targets
- correlation_matrix: the large correlation matrix
"""
if local:
self.pathRoot = '/diskb/Data/survey_disk'
self.pathSparta = '/diskb/Data/sparta'
self.targets = self.list_targets()
else:
self.pathRoot = '/Volumes/SHARDDS_data/survey_disk'
self.pathSparta = '/Volumes/SHARDDS_data/sparta'
self.targets = ['HD60491','HD71722','HD218340','HD14082B','HD182681']
self.pathRDI = os.path.join(self.pathRoot,'../'+pathRDI)
self.size = size
self.channel=channel
self.rin=rin
self.rout=rout
self.frames_nb = {}
self.frames_start_index = {}
self.frames_start_index_list = np.ndarray((len(self.targets)),dtype=int)
self.dist_center = distance_array([self.size,self.size],verbose=False)
# self.mask_correlation = np.ndarray((self.size,self.size),dtype=int)*0
# self.mask_correlation[np.logical_and(self.dist_center<rout,self.dist_center>rin)]=1
self.mask_correlation = np.logical_and(self.dist_center<rout,self.dist_center>rin)
self.pathOut_targets = {}
total_frames = 0
for id_target,target_name in enumerate(self.targets):
pathTarget = os.path.join(self.pathRoot,target_name)
pathOut = os.path.join(pathTarget,'pipeline')
self.pathOut_targets[target_name] = pathOut
self.frames_start_index[target_name]=total_frames
self.frames_start_index_list[id_target]=total_frames
derot_angles = fits.getdata(os.path.join(pathOut,'{0:s}_derotation_angles_O.fits'.format(target_name)))
nb_frames= len(derot_angles)
total_frames = total_frames+nb_frames
self.frames_nb[target_name]=nb_frames
print('Reading files in {0:s} ({1:d} frames)'.format(target_name,nb_frames))
self.total_frames = total_frames
self.correlation_matrix = np.ndarray((total_frames,total_frames),dtype=float)*0.
def __init__(self,channel='left',size=199,rin=30,rout=60,pathRDI='RDI_python'):
"""
Constructor of the class
Input:
- size: the size of the images we work with. Default: 199
- channel: the channel we work with (left, right, sum). Defaut: left
- rin: the inner radius for the correlation. Defaut: 30
- rout: the outer radius for the correlation. Defaut: 60
- pathRDI: the relative path where the correlation matrix is saved.
Default: pathRoot/../pathRDI
"""
self.pathRoot = '/Volumes/SHARDDS/Data/survey_disk'
self.pathSparta = '/Volumes/SHARDDS/Data/sparta'
self.targets = self.list_targets()
self.pathRDI = os.path.join(self.pathRoot,'../'+pathRDI)
self.size = size
self.channel=channel
self.rin=rin
self.rout=rout
self.frames_nb = {}
self.frames_start_index = {}
self.frames_start_index_list = np.ndarray((len(self.targets)),dtype=int)
self.dist_center = distance_array([self.size,self.size],verbose=False)
self.mask_correlation = np.logical_and(self.dist_center<rout,self.dist_center>rin)
self.pathOut_targets = {}
total_frames = 0
for id_target,target_name in enumerate(self.targets):
pathTarget = os.path.join(self.pathRoot,target_name)
pathOut = os.path.join(pathTarget,'pipeline')
self.pathOut_targets[target_name] = pathOut
self.frames_start_index[target_name]=total_frames
self.frames_start_index_list[id_target]=total_frames
derot_angles = fits.getdata(os.path.join(pathOut,'{0:s}_derotation_angles_O.fits'.format(target_name)))
nb_frames= len(derot_angles)
total_frames = total_frames+nb_frames
self.frames_nb[target_name]=nb_frames
print('Reading files in {0:s} ({1:d} frames)'.format(target_name,nb_frames))
self.total_frames = total_frames
self.correlation_matrix = np.ndarray((total_frames,total_frames),dtype=float)*0.
HD22179_2nd_epoch contains only the PSF. Investigate why. I classified it in bad
"""
channels = ['left', 'right']
size_psf = 200
for id_target, target_name in enumerate(targets):
if target_name != 'HD105':
continue
pathTarget = os.path.join(pathRoot, target_name)
pathRaw = os.path.join(pathTarget, 'raw')
pathOut = os.path.join(pathTarget, 'pipeline')
#%%
size = 799
distarr = im.distance_array((size, size))
mask = np.logical_and(distarr > 100, distarr < 350)
for ichannel, channel in enumerate(channels):
cube = fits.getdata(
os.path.join(
pathOut,
'HD105_{0:d}x{0:d}_{1:s}_O.fits'.format(size, channel)))
header = fits.getheader(
os.path.join(
pathOut,
'HD105_{0:d}x{0:d}_{1:s}_O.fits'.format(size, channel)))
parang = fits.getdata(os.path.join(pathOut, 'HD105_parang_O.fits'))
derot_angles = fits.getdata(
os.path.join(pathOut, 'HD105_derotation_angles_O.fits'))
nimg = cube.shape[0]
print('You chose a 2D contrast map')
#ppf is the percent point function (inverse of cdf - percentiles)
if curve1d:
return t.ppf(confidenceLevel, nbResels-1)*np.sqrt(1.+1./nbResels)/sigma
else:
return t.ppf(confidenceLevel, nbResels-2)*np.sqrt(1.+1./(nbResels-1))/sigma
if __name__ == '__main__':
import matplotlib.pyplot as plt
from image_tools import distance_array
sigma = 3
size=10
# fake_img = np.random.normal(np.random.randint(-5,5),np.random.rand(),(size,size))
fake_img = distance_array((size,size))#,centerx=size/2.-0.5,centery=size/2.-0.5)
# fake_img = distance_array((size,size))
plt.figure(0)
plt.imshow(fake_img,origin='lower')
plt.colorbar()
# rd=Radial_data(fake_img,xvect=np.arange(-size/2,size/2.)+0.5,yvect=np.arange(-size/2,size/2.)+0.5)
rd=Radial_data(fake_img)
# example of use
plt.figure(1)
plt.plot(rd.r,rd.mean,'ro',label='Mean')
plt.plot(rd.r,rd.std,'g:',label='Std')
plt.plot([0,size/2.*np.sqrt(2)],[0,size/2.*np.sqrt(2)],'b-',label='y=x')
#ppf is the percent point function (inverse of cdf - percentiles)
if curve1d:
return t.ppf(confidenceLevel, nbResels-1)*np.sqrt(1.+1./nbResels)/sigma
else:
return t.ppf(confidenceLevel, nbResels-2)*np.sqrt(1.+1./(nbResels-1))/sigma
if __name__ == '__main__':
import matplotlib.pyplot as plt
from image_tools import distance_array
sigma = 3
size=10
# fake_img = np.random.normal(np.random.randint(-5,5),np.random.rand(),(size,size))
# fake_img = distance_array((size,size),centerx=size/2.-0.5,centery=size/2.-0.5)
fake_img = distance_array((size,size))
plt.figure(0)
plt.imshow(fake_img,origin='lower')
plt.colorbar()
# rd=Radial_data(fake_img,xvect=np.arange(-size/2,size/2.)+0.5,yvect=np.arange(-size/2,size/2.)+0.5)
rd=Radial_data(fake_img)
# example of use
plt.figure(1)
plt.plot(rd.r,rd.mean,'ro',label='Mean')
plt.plot(rd.r,rd.std,'g:',label='Std')
plt.plot([0,size/2.*np.sqrt(2)],[0,size/2.*np.sqrt(2)],'b-',label='y=x')
plt.xlabel('Separaton in px')