def test283(image, pscale=None, step=None):
''' Return roc on 80 mm spatial scale (thresh = 0.05)
Parameters
----------
image: masked array
robust image for the analysis
Other Parameters
----------------
step: int
distance between patches
Returns
-------
roc: float
roc at threshold 0.05
'''
mask = np.invert(image.mask).astype(int)
x, y, r, xx, yy = geo.qpupil(mask)
if pscale is None:
pscale = r * (1 / 0.17) #pscale[px/m]
else:
pscale = pscale
print(pscale)
roc, list_ima, result_vect = patches_analysis(image, 0.04, pscale, step)
return roc
def test243(image, radius_m, pscale=None, step=None, n_patches=None):
''' Return rms at the interactuator scale 31 mm or 150 mm (thresh = 0.95)
Parameters
----------
image: masked array
robust image for the analysis
radius_m: int
radius of circular patch in meters
Other Parameters
----------------
step: int
distance between patches
n_patches: int
number of patches for the second cut
(if it is None sw creates a single crop in the center of the image)
Returns
-------
rms: float
rms at threshold 0.95
'''
# radius_m = 0.015 or 0.1
mask = np.invert(image.mask).astype(int)
x, y, r, xx, yy = geo.qpupil(mask)
if pscale is None:
pscale = r * (1 / 0.17)
else:
pscale = pscale
rms, list_ima, result_vect = patches_analysis(image, radius_m, pscale,
step, n_patches)
return rms
def test242(image, pscale=None):
'''
Parameters
----------
image: masked array
robust image for the analysis
Returns
-------
rms: float
rms slope in arcsec
'''
mask = np.invert(image.mask).astype(int)
x, y, r, xx, yy = geo.qpupil(mask)
if pscale is None:
pscale = r * (1 / 0.17)
else:
pscale = pscale
print(pscale)
sp = slope(image, pscale) #pscale [px/m]
# sp in pixel * fattore di conversione da rad ad arcsec
slope_arcsec = sp * 206265
rms = slope_arcsec.std()
return rms
def create_zmat(self, file_name):
'''
Returns
-------
zmat: numpy array
'''
# file_name = '/Users/rm/Desktop/Arcetri/M4/ProvaCodice/OTT/ott_mask.fits'
hduList = pyfits.open(file_name)
final_mask = np.invert(hduList[0].data.astype(bool))
prova = np.ma.masked_array(np.ones(((2 * OttParameters.parab_radius * OttParameters.pscale).astype(int),
(2 * OttParameters.parab_radius * OttParameters.pscale).astype(int))),
mask=final_mask)
zernike_mode = np.arange(10) + 1
mm = np.where(final_mask == False)
x, y, r, xx, yy = geo.qpupil(final_mask)
zmat = zernike._getZernike(xx[mm], yy[mm], zernike_mode)
return zmat
def zernikeFit(img, zernike_index_vector):
'''
Parameters
----------
img: numpy masked array
image for zernike fit
zernike_index_vector: numpy array
vector containing the index of Zernike modes to be fitted starting from 1
Returns
-------
coeff: numpy array [m]
vector of zernike coefficients
mat: numpy array
'''
img1 = img.data
mask = np.invert(img.mask).astype(int)
x, y, r, xx, yy = geo.qpupil(mask)
mm = (mask==1)
coeff = _surf_fit(xx[mm], yy[mm], img1[mm], zernike_index_vector)
mat = _getZernike(xx[mm], yy[mm], zernike_index_vector)
return coeff, mat
def testGeometry(self):
img = np.random.rand(500, 500)
masked_ima = geo.draw_mask(img, 250, 250, 50)
geo.qpupil(masked_ima)
geo.rotate(img, 30)