def apply_displ_bias(self):
logging.info('Applying displacement bias...')
ax_slice, az_slice = ifunc.get_ax_az_slice(self.clip)
displ_x_clip = self.displ['X']['img']['full'][ax_slice, az_slice]
min_displ_x = np.percentile(displ_x_clip, 0.5)
max_displ_x = np.percentile(displ_x_clip, 99.5)
logging.info(' min_displ_x = {}'.format(min_displ_x))
bias = -min_displ_x + (max_displ_x - min_displ_x) * self.bias_mult
self.displ['X']['img']['full'] += bias
def apply_displ_bias(self):
logging.info("Applying displacement bias...")
ax_slice, az_slice = ifunc.get_ax_az_slice(self.clip)
displ_x_clip = self.displ["X"]["img"]["full"][ax_slice, az_slice]
min_displ_x = np.percentile(displ_x_clip, 0.5)
max_displ_x = np.percentile(displ_x_clip, 99.5)
logging.info(" min_displ_x = {}".format(min_displ_x))
bias = -min_displ_x + (max_displ_x - min_displ_x) * self.bias_mult
self.displ["X"]["img"]["full"] += bias
def calc_adj(self):
for corr in self.corr_axes:
if corr not in self.coeffs:
self.coeffs[corr] = self.calc_coeffs(corr)
ax_slice, az_slice = ifunc.get_ax_az_slice(self.clip)
for axis in self.displ_axes:
logging.info("Computing adj[{}][{}][full,clip]".format(axis, corr))
adj = ifunc.calc_adj_displ(self.ifuncs[axis], self.coeffs[corr], adj_clip=self.adj_clip)
self.adj[axis][corr]["full"] = adj
self.adj[axis][corr]["clip"] = adj[ax_slice, az_slice]
def calc_adj(self):
for corr in self.corr_axes:
if corr not in self.coeffs:
self.coeffs[corr] = self.calc_coeffs(corr)
ax_slice, az_slice = ifunc.get_ax_az_slice(self.clip)
for axis in self.displ_axes:
logging.info("Computing adj[{}][{}][full,clip]".format(
axis, corr))
adj = ifunc.calc_adj_displ(self.ifuncs[axis],
self.coeffs[corr],
adj_clip=self.adj_clip)
self.adj[axis][corr]['full'] = adj
self.adj[axis][corr]['clip'] = adj[ax_slice, az_slice]
class AdjOpticsCase(object):
"""Provide the infrastructure to handle an analysis case for adjustable
optics including a set of influence functions and displacements.
:param ifuncs: dict to load ifuncs or define ifuncs
:param displ: dict to load or define displacements
:param case_id: case identifier string for report naming
:param adj_clip: number of ifunc actuator rows, cols from edge to clip (scalar)
:param clip: number of ax / az pixels from edge to clip (scalar or tuple)
:param n_ss: sub-sample period (use 1 out of n_ss rows/columns)
:param n_strips: number of axial strips for scatter calculation
:param node_sep: node separation (microns)
:param units: units
"""
def __init__(self,
ifuncs=None,
displ=None,
case_id='10+2_exemplar',
subcase_id=1,
adj_clip=None,
clip=20,
bias_mult=None,
n_ss=5,
piston_tilt=True,
node_sep=500,
units='um',
displ_axes=None,
corr_axes=None,
n_proc=4,
n_strips=9):
case = CASES[case_id]
ifuncs = case['ifuncs']
displ = case['displ']
self.title = case['title']
self.case_id = case_id
self.subcase_id = subcase_id
self.ifuncs = dict()
self.displ = AutoDict()
self.piston_tilt = piston_tilt
self.ifuncs_kwargs = ifuncs['kwargs']
self.displ_kwargs = displ['kwargs']
self.node_sep = node_sep
self.units = units
self.displ_axes = displ_axes or AXES
self.corr_axes = corr_axes or AXES
self.n_proc = n_proc
self.n_strips = n_strips
self.n_ss = n_ss
self.adj_clip = adj_clip
self.clip = clip
self.bias_mult = bias_mult
# Check if input ifuncs already has X and RY keys
if all(axis in ifuncs for axis in AXES):
for axis in AXES:
self.ifuncs[axis] = ifuncs[axis].copy()
else: # load ifuncs
logging.info('Loading ifuncs X...')
self.ifuncs['X'] = ifuncs['load_func'](axis='X',
**ifuncs['kwargs'])
logging.info('Computing ifuncs RY...')
n_ax, n_az = self.ifuncs['X'].shape[-2:]
self.n_coeffs_ax, self.n_coeffs_az = self.ifuncs['X'].shape[:2]
ifx = self.ifuncs['X'] = self.ifuncs['X'].reshape(-1, n_ax, n_az)
ifry = self.ifuncs['RY'] = np.empty_like(ifx)
for i in range(ifx.shape[0]):
ifry[i] = np.gradient(ifx[i], node_sep)[0] * RAD2ARCSEC
self.n_ax, self.n_az = self.ifuncs['X'].shape[-2:]
# Check if input displ already has X and RY keys
if all(axis in displ for axis in AXES):
for axis in AXES:
self.displ[axis]['img']['full'] = \
displ[axis]['img']['full'].copy()
else: # load displacements
logging.info('Loading displ ...')
self.displ['X']['img']['full'], self.displ['RY']['img']['full'] = \
displ['load_func'](self.n_ax, self.n_az, **displ['kwargs'])
if self.bias_mult is not None:
self.apply_displ_bias()
self.displ['RY']['img']['full'] *= RAD2ARCSEC
# Provide clipped displacements
ax_slice, az_slice = ifunc.get_ax_az_slice(self.clip)
for axis in AXES:
self.displ[axis]['img']['clip'] = \
self.displ[axis]['img']['full'][ax_slice, az_slice]
self.coeffs = AutoDict() # [corr_axis]
self.adj = AutoDict() # [axis][corr_axis]
self.resid = AutoDict() # [clip][type] for clip=('clip'|'full')
# type = ('img'|'std'|'mean')
self.scatter = AutoDict()
def __init__(
self,
ifuncs=None,
displ=None,
case_id="10+2_exemplar",
subcase_id=1,
adj_clip=None,
clip=20,
bias_mult=None,
n_ss=5,
piston_tilt=True,
node_sep=500,
units="um",
displ_axes=None,
corr_axes=None,
n_proc=4,
n_strips=9,
):
case = CASES[case_id]
ifuncs = case["ifuncs"]
displ = case["displ"]
self.title = case["title"]
self.case_id = case_id
self.subcase_id = subcase_id
self.ifuncs = dict()
self.displ = AutoDict()
self.piston_tilt = piston_tilt
self.ifuncs_kwargs = ifuncs["kwargs"]
self.displ_kwargs = displ["kwargs"]
self.node_sep = node_sep
self.units = units
self.displ_axes = displ_axes or AXES
self.corr_axes = corr_axes or AXES
self.n_proc = n_proc
self.n_strips = n_strips
self.n_ss = n_ss
self.adj_clip = adj_clip
self.clip = clip
self.bias_mult = bias_mult
# Check if input ifuncs already has X and RY keys
if all(axis in ifuncs for axis in AXES):
for axis in AXES:
self.ifuncs[axis] = ifuncs[axis].copy()
else: # load ifuncs
logging.info("Loading ifuncs X...")
self.ifuncs["X"] = ifuncs["load_func"](axis="X", **ifuncs["kwargs"])
logging.info("Computing ifuncs RY...")
n_ax, n_az = self.ifuncs["X"].shape[-2:]
self.n_coeffs_ax, self.n_coeffs_az = self.ifuncs["X"].shape[:2]
ifx = self.ifuncs["X"] = self.ifuncs["X"].reshape(-1, n_ax, n_az)
ifry = self.ifuncs["RY"] = np.empty_like(ifx)
for i in range(ifx.shape[0]):
ifry[i] = np.gradient(ifx[i], node_sep)[0] * RAD2ARCSEC
self.n_ax, self.n_az = self.ifuncs["X"].shape[-2:]
# Check if input displ already has X and RY keys
if all(axis in displ for axis in AXES):
for axis in AXES:
self.displ[axis]["img"]["full"] = displ[axis]["img"]["full"].copy()
else: # load displacements
logging.info("Loading displ ...")
self.displ["X"]["img"]["full"], self.displ["RY"]["img"]["full"] = displ["load_func"](
self.n_ax, self.n_az, **displ["kwargs"]
)
if self.bias_mult is not None:
self.apply_displ_bias()
self.displ["RY"]["img"]["full"] *= RAD2ARCSEC
# Provide clipped displacements
ax_slice, az_slice = ifunc.get_ax_az_slice(self.clip)
for axis in AXES:
self.displ[axis]["img"]["clip"] = self.displ[axis]["img"]["full"][ax_slice, az_slice]
self.coeffs = AutoDict() # [corr_axis]
self.adj = AutoDict() # [axis][corr_axis]
self.resid = AutoDict() # [clip][type] for clip=('clip'|'full')
# type = ('img'|'std'|'mean')
self.scatter = AutoDict()
