Coverage for python/lsst/sims/coordUtils/LsstZernikeFitter.py : 11%
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""" Convert Observed RA, Dec into pupil coordinates
Parameters ---------- ra_obs is the observed RA in radians
dec_obs is the observed Dec in radians
ra0 is the RA of the boresite in radians
dec0 is the Dec of the boresite in radians
rotSkyPos is in radians
Returns -------- A numpy array whose first row is the x coordinate on the pupil in radians and whose second row is the y coordinate in radians """
are_arrays = _validate_inputs([ra_obs, dec_obs], ['ra_obs', 'dec_obs'], "pupilCoordsFromObserved")
theta = -1.0*rotSkyPos
ra_pointing = ra0 dec_pointing = dec0
# palpy.ds2tp performs the gnomonic projection on ra_in and dec_in # with a tangent point at (pointingRA, pointingDec) # if not are_arrays: try: x, y = palpy.ds2tp(ra_obs, dec_obs, ra_pointing, dec_pointing) except: x = np.NaN y = np.NaN else: try: x, y = palpy.ds2tpVector(ra_obs, dec_obs, ra_pointing, dec_pointing) except: # apparently, one of your ra/dec values was improper; we will have to do this # element-wise, putting NaN in the place of the bad values x = [] y = [] for rr, dd in zip(ra_obs, dec_obs): try: xx, yy = palpy.ds2tp(rr, dd, ra_pointing, dec_pointing) except: xx = np.NaN yy = np.NaN x.append(xx) y.append(yy) x = np.array(x) y = np.array(y)
# rotate the result by rotskypos (rotskypos being "the angle of the sky relative to # camera coordinates" according to phoSim documentation) to account for # the rotation of the focal plane about the telescope pointing
x_out = x*np.cos(theta) - y*np.sin(theta) y_out = x*np.sin(theta) + y*np.cos(theta)
return np.array([x_out, y_out])
""" This class will fit and then apply the Zernike polynomials needed to correct the FIELD_ANGLE to FOCAL_PLANE transformation for the filter-dependent part. """
self._camera = lsst_camera() self._pixel_transformer = DMtoCameraPixelTransformer() self._z_gen = ZernikePolynomialGenerator()
self._rr = 500.0 # radius in mm of circle containing LSST focal plane; # make it a little bigger to accommodate any slop in # the conversion from focal plane coordinates back to # pupil coordinates (in case the optical distortions # cause points to cross over the actual boundary of # the focal plane)
self._band_to_int = {} self._band_to_int['u'] = 0 self._band_to_int['g'] = 1 self._band_to_int['r'] = 2 self._band_to_int['i'] = 3 self._band_to_int['z'] = 4 self._band_to_int['y'] = 5
self._int_to_band = 'ugrizy'
self._n_grid = [] self._m_grid = []
# 2018 May 8 # During development, I found that there was negligible # improvement in the fit when allowing n>4, so I am # hard-coding the limit of n=4 here. for n in range(4): for m in range(-n, n+1, 2): self._n_grid.append(n) self._m_grid.append(m)
self._build_transformations()
""" Get the coefficients of the best fit Zernike Polynomial expansion that transforms from x_in, y_in to x_out, y_out.
Returns numpy arrays of the Zernike Polynomial expansion coefficients in x and y. Zernike Polynomials correspond to the radial and angular orders stored in self._n_grid and self._m_grid. """
polynomials = {} for n, m in zip(self._n_grid, self._m_grid): values = self._z_gen.evaluate_xy(x_in/self._rr, y_in/self._rr, n, m) polynomials[(n,m)] = values
poly_keys = list(polynomials.keys())
dx = x_out - x_in dy = y_out - y_in
b = np.array([(dx*polynomials[k]).sum() for k in poly_keys]) m = np.array([[(polynomials[k1]*polynomials[k2]).sum() for k1 in poly_keys] for k2 in poly_keys])
alpha_x_ = np.linalg.solve(m, b) alpha_x = {} for ii, kk in enumerate(poly_keys): alpha_x[kk] = alpha_x_[ii]
b = np.array([(dy*polynomials[k]).sum() for k in poly_keys]) m = np.array([[(polynomials[k1]*polynomials[k2]).sum() for k1 in poly_keys] for k2 in poly_keys])
alpha_y_ = np.linalg.solve(m, b) alpha_y = {} for ii, kk in enumerate(poly_keys): alpha_y[kk] = alpha_y_[ii]
return alpha_x, alpha_y
""" Solve for and store the coefficients of the Zernike polynomial expansion of the difference between the naive and the bandpass-dependent optical distortions in the LSST camera. """ catsim_dir = os.path.join(getPackageDir('sims_data'), 'FocalPlaneData', 'CatSimData')
phosim_dir = os.path.join(getPackageDir('sims_data'), 'FocalPlaneData', 'PhoSimData')
# the file which contains the input sky positions of the objects # that were given to PhoSim catsim_catalog = os.path.join(catsim_dir,'predicted_positions.txt')
with open(catsim_catalog, 'r') as input_file: header = input_file.readline() params = header.strip().split() ra0 = np.radians(float(params[2])) dec0 = np.radians(float(params[4])) rotSkyPos = np.radians(float(params[6]))
catsim_dtype = np.dtype([('id', int), ('xmm_old', float), ('ymm_old', float), ('xpup', float), ('ypup', float), ('raObs', float), ('decObs', float)])
catsim_data = np.genfromtxt(catsim_catalog, dtype=catsim_dtype)
sorted_dex = np.argsort(catsim_data['id']) catsim_data=catsim_data[sorted_dex]
# convert from RA, Dec to pupil coors/FIELD_ANGLE x_field, y_field = _rawPupilCoordsFromObserved(np.radians(catsim_data['raObs']), np.radians(catsim_data['decObs']), ra0, dec0, rotSkyPos)
# convert from FIELD_ANGLE to FOCAL_PLANE without attempting to model # the optical distortions in the telescope field_to_focal = self._camera.getTransform(FIELD_ANGLE, FOCAL_PLANE) catsim_xmm = np.zeros(len(x_field), dtype=float) catsim_ymm = np.zeros(len(y_field), dtype=float) for ii, (xx, yy) in enumerate(zip(x_field, y_field)): focal_pt = field_to_focal.applyForward(geom.Point2D(xx, yy)) catsim_xmm[ii] = focal_pt.getX() catsim_ymm[ii] = focal_pt.getY()
phosim_dtype = np.dtype([('id', int), ('phot', float), ('xpix', float), ('ypix', float)])
self._pupil_to_focal = {} self._focal_to_pupil = {}
for i_filter in range(6): self._pupil_to_focal[self._int_to_band[i_filter]] = {} self._focal_to_pupil[self._int_to_band[i_filter]] = {} phosim_xmm = np.zeros(len(catsim_data['ypup']), dtype=float) phosim_ymm = np.zeros(len(catsim_data['ypup']), dtype=float)
for det in self._camera: if det.getType() != DetectorType.SCIENCE: continue pixels_to_focal = det.getTransform(PIXELS, FOCAL_PLANE) det_name = det.getName() bbox = det.getBBox() det_name_m = det_name.replace(':','').replace(',','').replace(' ','_')
# read in the actual pixel positions of the sources as realized # by PhoSim centroid_name = 'centroid_lsst_e_2_f%d_%s_E000.txt' % (i_filter, det_name_m) full_name = os.path.join(phosim_dir, centroid_name) phosim_data = np.genfromtxt(full_name, dtype=phosim_dtype, skip_header=1)
# make sure that the data we are fitting to is not too close # to the edge of the detector assert phosim_data['xpix'].min() > bbox.getMinY() + 50.0 assert phosim_data['xpix'].max() < bbox.getMaxY() - 50.0 assert phosim_data['ypix'].min() > bbox.getMinX() + 50.0 assert phosim_data['ypix'].max() < bbox.getMaxX() - 50.0
xpix, ypix = self._pixel_transformer.dmPixFromCameraPix(phosim_data['xpix'], phosim_data['ypix'], det_name) xmm = np.zeros(len(xpix), dtype=float) ymm = np.zeros(len(ypix), dtype=float) for ii in range(len(xpix)): focal_pt = pixels_to_focal.applyForward(geom.Point2D(xpix[ii], ypix[ii])) xmm[ii] = focal_pt.getX() ymm[ii] = focal_pt.getY() phosim_xmm[phosim_data['id']-1] = xmm phosim_ymm[phosim_data['id']-1] = ymm
# solve for the coefficients of the Zernike expansions # necessary to model the optical transformations and go # from the naive focal plane positions (catsim_xmm, catsim_ymm) # to the PhoSim realized focal plane positions alpha_x, alpha_y = self._get_coeffs(catsim_xmm, catsim_ymm, phosim_xmm, phosim_ymm)
self._pupil_to_focal[self._int_to_band[i_filter]]['x'] = alpha_x self._pupil_to_focal[self._int_to_band[i_filter]]['y'] = alpha_y
# solve for the coefficients to the Zernike expansions # necessary to go back from the PhoSim realized focal plane # positions to the naive CatSim predicted focal plane # positions alpha_x, alpha_y = self._get_coeffs(phosim_xmm, phosim_ymm, catsim_xmm, catsim_ymm)
self._focal_to_pupil[self._int_to_band[i_filter]]['x'] = alpha_x self._focal_to_pupil[self._int_to_band[i_filter]]['y'] = alpha_y
""" Parameters ---------- tranformation_dict -- a dict containing the coefficients of the Zernike decomposition to be applied
xmm -- the input x position in mm
ymm -- the input y position in mm
band -- the filter in which we are operating (can be either a string or an int; 0=u, 1=g, 2=r, etc.)
Returns ------- dx -- the x offset resulting from the transformation
dy -- the y offset resulting from the transformation """ if isinstance(band, int): band = self._int_to_band[band]
if isinstance(xmm, numbers.Number): dx = 0.0 dy = 0.0 else: dx = np.zeros(len(xmm), dtype=float) dy = np.zeros(len(ymm), dtype=float)
for kk in self._pupil_to_focal[band]['x']: values = self._z_gen.evaluate_xy(xmm/self._rr, ymm/self._rr, kk[0], kk[1]) dx += transformation_dict[band]['x'][kk]*values dy += transformation_dict[band]['y'][kk]*values
return dx, dy
""" Apply the transformation necessary when going from pupil coordinates to focal plane coordinates.
The recipe to correctly use this method is
xf0, yf0 = focalPlaneCoordsFromPupilCoords(xpupil, ypupil, camera=lsst_camera())
dx, dy = LsstZernikeFitter().dxdy(xf0, yf0, band=band)
xf = xf0 + dx yf = yf0 + dy
xf and yf are now the actual position in millimeters on the LSST focal plane corresponding to xpupil, ypupil
Parameters ---------- xmm -- the naive x focal plane position in mm
ymm -- the naive y focal plane position in mm
band -- the filter in which we are operating (can be either a string or an int; 0=u, 1=g, 2=r, etc.)
Returns ------- dx -- the offset in the x focal plane position in mm
dy -- the offset in the y focal plane position in mm """ return self._apply_transformation(self._pupil_to_focal, xmm, ymm, band)
""" Apply the transformation necessary when going from focal plane coordinates to pupil coordinates.
The recipe to correctly use this method is
dx, dy = LsstZernikeFitter().dxdy_inverse(xf, yf, band=band)
xp, yp = pupilCoordsFromFocalPlaneCoords(xf+dx, yf+dy, camera=lsst_camera())
xp and yp are now the actual position in radians on the pupil corresponding to the focal plane coordinates xf, yf
Parameters ---------- xmm -- the naive x focal plane position in mm
ymm -- the naive y focal plane position in mm
band -- the filter in which we are operating (can be either a string or an int; 0=u, 1=g, 2=r, etc.)
Returns ------- dx -- the offset in the x focal plane position in mm
dy -- the offset in the y focal plane position in mm """ return self._apply_transformation(self._focal_to_pupil, xmm, ymm, band) |