def testg_initCustomShape():
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH,
"sh_8x8.yaml"))
config.sim.pupilSize = 64
config.sim.simSize = 66
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.ShackHartmann(config, mask=mask)
dm = DM.KarhunenLoeve(config, n_dm=1, wfss=[wfs], mask=mask)
customShape = dm.iMatShapes
# saving temporary shapes
fname = os.path.dirname(
os.path.abspath(__file__)) + '/tmp_CustomDmShapes.fits'
fits.writeto(fname, customShape, overwrite=True)
# change size to ensure it tests interpolation
config.sim.pupilSize = 100
config.sim.simSize = 104
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.ShackHartmann(config, mask=mask)
config.dms[1].dmShapesFilename = fname
dm = DM.CustomShapes(config, n_dm=1, wfss=[wfs], mask=mask)
# remove temporary shapes
os.remove(fname)
def make_pupil_mask(mask, n_subap, nx_subap, obs_diam, tel_diam):
"""Generates a SOAPY pupil mask
- code has been adapted from SOAPY make_mask.py
Parameters:
mask (str): must be 'circle'
nx_subap (int): number of sub-apertures across telescope's pupil.
obs_diam (float): diameter of central obscuration.
tel_diam (float): diameter of telescope pupil.
Returns:
ndarray: mask of SHWFS sub-apertures within the telescope's pupil."""
if mask == "circle":
pupil_mask = aotools.circle(nx_subap / 2., nx_subap)
if obs_diam != None:
pupil_mask -= aotools.circle(
nx_subap * ((obs_diam / tel_diam) / 2.), nx_subap)
else:
raise Exception(
'Only circlular pupil masks have been integrated (sorry).')
if n_subap[0] != int(numpy.sum(pupil_mask)):
raise Exception(
'Error in the number of sub-apertures within pupil mask.')
return pupil_mask.astype(int)
def test_slopecovmat_makecovmat_uneven():
threads = 1
n_wfs = 3
telescope_diameter = 8.
nx_subaps = 10
n_layers = 3
layer_altitudes = numpy.linspace(0, 20000, n_layers)
layer_r0s = [1] * n_layers
layer_L0s = [25.] * n_layers
asterism_radius = 10
# What if all WFSs don't have the same number of subaps?
pupil_masks = [aotools.circle(4.5, nx_subaps), aotools.circle(nx_subaps / 2., nx_subaps), aotools.circle(nx_subaps / 2., nx_subaps)]
subap_diameters = [telescope_diameter / nx_subaps] * n_wfs
gs_altitudes = [90000] * n_wfs
gs_positions = [
[asterism_radius, 0],
[numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-asterism_radius, 0]]
wfs_magnifications = [1.] * n_wfs
pupil_offsets = [[0, 0]] * n_wfs
wfs_rotations = [0] * n_wfs
wfs_wavelengths = [550e-9] * n_wfs
cov_mat = aotools.CovarianceMatrix(n_wfs, pupil_masks, telescope_diameter, subap_diameters, gs_altitudes, gs_positions,
wfs_wavelengths,
n_layers, layer_altitudes, layer_r0s, layer_L0s, threads)
covariance_matrix = cov_mat.make_covariance_matrix()
def test_slopecovmat_makecovmat_uneven():
threads = 1
n_wfs = 3
telescope_diameter = 8.
nx_subaps = 10
n_layers = 3
layer_altitudes = numpy.linspace(0, 20000, n_layers)
layer_r0s = [1] * n_layers
layer_L0s = [25.] * n_layers
asterism_radius = 10
# What if all WFSs don't have the same number of subaps?
pupil_masks = [
aotools.circle(4.5, nx_subaps),
aotools.circle(nx_subaps / 2., nx_subaps),
aotools.circle(nx_subaps / 2., nx_subaps)
]
subap_diameters = [telescope_diameter / nx_subaps] * n_wfs
gs_altitudes = [90000] * n_wfs
gs_positions = [[asterism_radius, 0],
[
numpy.sin(numpy.pi / 3.) * asterism_radius,
numpy.cos(numpy.pi / 3.) * asterism_radius
],
[
numpy.sin(numpy.pi / 3.) * asterism_radius,
-numpy.cos(numpy.pi / 3.) * asterism_radius
],
[
-numpy.sin(numpy.pi / 3.) * asterism_radius,
numpy.cos(numpy.pi / 3.) * asterism_radius
],
[
-numpy.sin(numpy.pi / 3.) * asterism_radius,
-numpy.cos(numpy.pi / 3.) * asterism_radius
], [-asterism_radius, 0]]
wfs_magnifications = [1.] * n_wfs
pupil_offsets = [[0, 0]] * n_wfs
wfs_rotations = [0] * n_wfs
wfs_wavelengths = [550e-9] * n_wfs
cov_mat = aotools.CovarianceMatrix(n_wfs, pupil_masks, telescope_diameter,
subap_diameters, gs_altitudes,
gs_positions, wfs_wavelengths, n_layers,
layer_altitudes, layer_r0s, layer_L0s,
threads)
covariance_matrix = cov_mat.make_covariance_matrix()
def test_chop_subaps_mask_threads():
"""
Tests that the numba routing chops phase into sub-apertures in the same way
as using numpy indices
Runs with multiple threads many times to detectect potential intermittant errors
"""
nx_phase = 12
nx_subap_size = 3
nx_subaps = nx_phase // nx_subap_size
subap_array = numpy.zeros((nx_subaps * nx_subaps, nx_subap_size,
nx_subap_size)).astype("complex64")
numpy_subap_array = subap_array.copy()
mask = aotools.circle(nx_phase / 2., nx_phase)
x_coords, y_coords = numpy.meshgrid(
numpy.arange(0, nx_phase, nx_subap_size),
numpy.arange(0, nx_phase, nx_subap_size))
subap_coords = numpy.array([y_coords.flatten(), x_coords.flatten()]).T
for i in range(50):
phase = (numpy.random.random(
(nx_phase, nx_phase)) + 1j * numpy.random.random(
(nx_phase, nx_phase))).astype("complex64")
numpy_chop(phase, subap_coords, nx_subap_size, numpy_subap_array, mask)
thread_pool = numbalib.ThreadPool(1)
numbalib.wfs.chop_subaps_mask_pool(phase, subap_coords, nx_subap_size,
subap_array, mask, thread_pool)
assert numpy.array_equal(numpy_subap_array, subap_array)
def test_chop_subaps_mask():
"""
Tests that the numba routing chops phase into sub-apertures in the same way
as using numpy indices
"""
nx_phase = 12
nx_subap_size = 3
nx_subaps = nx_phase // nx_subap_size
phase = (numpy.random.random(
(nx_phase, nx_phase)) + 1j * numpy.random.random(
(nx_phase, nx_phase))).astype("complex64")
subap_array = numpy.zeros((nx_subaps * nx_subaps, nx_subap_size,
nx_subap_size)).astype("complex64")
numpy_subap_array = subap_array.copy()
mask = aotools.circle(nx_phase / 2., nx_phase)
x_coords, y_coords = numpy.meshgrid(
numpy.arange(0, nx_phase, nx_subap_size),
numpy.arange(0, nx_phase, nx_subap_size))
subap_coords = numpy.array([x_coords.flatten(), y_coords.flatten()]).T
numpy_chop(phase, subap_coords, nx_subap_size, numpy_subap_array, mask)
numbalib.wfslib.chop_subaps_mask(phase, subap_coords, nx_subap_size,
subap_array, mask)
assert numpy.array_equal(numpy_subap_array, subap_array)
def testd_LegacySHWfsFrame(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.ShackHartmannLegacy(config, mask=mask)
wfs.frame(numpy.zeros((config.atmos.scrnNo, config.sim.simSize, config.sim.simSize)))
def testd_initPhysLgs(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
config.wfss[1].lgs.propagationMode = "Physical"
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
lgs = LGS.LGS_Physical(config.wfss[1], config)
def calcInitParams(self):
"""
Calculate some useful paramters to be used later
"""
self.lgsPupilPxls = int(
round(self.config.pupilDiam * self.simConfig.pxlScale))
if self.outPxlScale is None:
self.outPxlScale_m = 1. / self.simConfig.pxlScale
else:
# The pixel scale in metres per pixel at the LGS altitude
self.outPxlScale_m = (self.outPxlScale / 3600.) * (
numpy.pi / 180.) * self.config.height
# Get the angular scale in radians of the output array
self.outPxlScale_rad = self.outPxlScale_m / self.config.height
# The number of pixels required across the LGS image
if self.nOutPxls is None:
self.nOutPxls = self.simConfig.simSize
# Field of fov of the requested LGS PSF image
self.fov = (self.nOutPxls * self.outPxlScale_rad) * RAD2ASEC
# The number of points required to get the correct FOV after the FFT
fov_rad = self.fov / RAD2ASEC
self.nFovPxls = int(
round(fov_rad * self.config.pupilDiam / self.config.wavelength))
# The mask to apply before geometric FFTing
self.mask = aotools.circle(self.nFovPxls / 2., self.nFovPxls)
self.losNOutPxls = self.lgsPupilPxls
self.losOutPxlScale = self.config.pupilDiam / self.lgsPupilPxls
def testc_initGradWfs(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
config.sim.pupilSize = 10*config.wfss[0].nxSubaps
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
wfs = WFS.Gradient(config, mask=mask)
def __init__ (self, soapy_config, n_dm=0, wfss=None, mask=None):
# Sort out some required attributes
self.soapy_config = soapy_config
self.n_dm = n_dm
self.simConfig = self.soapy_config.sim
self.config = self.dmConfig = self.soapy_config.dms[n_dm]
self.pupil_size = self.soapy_config.sim.pupilSize
self.sim_size = self.soapy_config.sim.simSize
self.scrn_size = self.soapy_config.sim.scrnSize
self.altitude = self.config.altitude
self.diameter = self.config.diameter
self.telescope_diameter = self.soapy_config.tel.telDiam
self.wfss = wfss
# If supplied use the mask
if numpy.any(mask):
self.mask = mask
# Else we'll just make a circle
else:
self.mask = aotools.circle(
self.pupil_size/2., self.sim_size,
)
# the number of phase elements at the DM altitude
self.nx_dm_elements = int(round(self.pupil_size * self.diameter / self.telescope_diameter))
self.dm_frame = numpy.zeros((self.nx_dm_elements, self.nx_dm_elements))
# An array of phase screen size to be observed by a line of sight
self.dm_screen = numpy.zeros((self.scrn_size, self.scrn_size))
# Coordinate required to fit dm size back into screen
self.screen_coord = int(round((self.scrn_size - self.nx_dm_elements)/2.))
self.n_acts = self.getActiveActs()
self.actCoeffs = numpy.zeros((self.n_acts))
# Sort out which WFS(s) observes the DM (for iMat making)
if self.dmConfig.wfs!=None:
try:
# Make sure the specifed WFS actually exists
self.wfss = [wfss[self.dmConfig.wfs]]
self.wfs = self.wfss[0]
except KeyError:
raise KeyError("DM attached to WFS {}, but that WFS is not specifed in config".format(self.dmConfig.wfs))
else:
self.wfss = wfss
logger.info("Making DM Influence Functions...")
self.makeIMatShapes()
# If using imatshapes, sclae by imat value
if hasattr(self, "iMatShapes"):
self.iMatShapes *= self.config.iMatValue
# An array of values for each actuator. 1 if actuator is valid, 0 if not
self._valid_actuators = numpy.ones((self.n_acts))
def test_Piezo_valid_actuators():
"""
Tests that when you set the "valid actuators", the DM doesn't use actuators marked 'invalid'
"""
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.ShackHartmann(config, mask=mask)
dm = DM.FastPiezo(config, n_dm=1, wfss=[wfs], mask=mask)
act_coord1 = dm.valid_act_coords[0]
act_coord_last = dm.valid_act_coords[-1]
act_coord2 = dm.valid_act_coords[1]
valid_actuators = numpy.ones(dm.n_acts, dtype=int)
valid_actuators[0] = valid_actuators[-1] = 0
dm.valid_actuators = valid_actuators
assert dm.n_valid_actuators == (dm.n_acts - 2)
assert not numpy.array_equal(dm.valid_act_coords[0], act_coord1)
assert not numpy.array_equal(dm.valid_act_coords[-1], act_coord_last)
assert numpy.array_equal(dm.valid_act_coords[0], act_coord2)
def test_chop_subaps_mask():
"""
Tests that the numba routing chops phase into sub-apertures in the same way
as using numpy indices
"""
nx_phase = 12
nx_subap_size = 3
nx_subaps = nx_phase // nx_subap_size
phase = (numpy.random.random((nx_phase, nx_phase))
+ 1j * numpy.random.random((nx_phase, nx_phase))
).astype("complex64")
subap_array = numpy.zeros((nx_subaps * nx_subaps, nx_subap_size, nx_subap_size)).astype("complex64")
numpy_subap_array = subap_array.copy()
mask = aotools.circle(nx_phase/2., nx_phase)
x_coords, y_coords = numpy.meshgrid(
numpy.arange(0, nx_phase, nx_subap_size),
numpy.arange(0, nx_phase, nx_subap_size))
subap_coords = numpy.array([x_coords.flatten(), y_coords.flatten()]).T
numpy_chop(phase, subap_coords, nx_subap_size, numpy_subap_array, mask)
thread_pool = numbalib.ThreadPool(1)
numbalib.wfs.chop_subaps_mask_pool(
phase, subap_coords, nx_subap_size, subap_array, mask, thread_pool)
assert numpy.array_equal(numpy_subap_array, subap_array)
def __init__ (self, soapy_config, n_dm=0, wfss=None, mask=None):
# Sort out some required attributes
self.soapy_config = soapy_config
self.n_dm = n_dm
self.simConfig = self.soapy_config.sim
self.config = self.dmConfig = self.soapy_config.dms[n_dm]
self.pupil_size = self.soapy_config.sim.pupilSize
self.sim_size = self.soapy_config.sim.simSize
self.scrn_size = self.soapy_config.sim.scrnSize
self.altitude = self.config.altitude
self.diameter = self.config.diameter
self.telescope_diameter = self.soapy_config.tel.telDiam
self.wfss = wfss
# If supplied use the mask
if numpy.any(mask):
self.mask = mask
# Else we'll just make a circle
else:
self.mask = aotools.circle(
self.pupil_size/2., self.sim_size,
)
# the number of phase elements at the DM altitude
self.nx_dm_elements = int(round(self.pupil_size * self.diameter / self.telescope_diameter))
self.dm_frame = numpy.zeros((self.nx_dm_elements, self.nx_dm_elements))
# An array of phase screen size to be observed by a line of sight
self.dm_screen = numpy.zeros((self.scrn_size, self.scrn_size))
# Coordinate required to fit dm size back into screen
self.screen_coord = int(round((self.scrn_size - self.nx_dm_elements)/2.))
self.n_acts = self.getActiveActs()
self.actCoeffs = numpy.zeros((self.n_acts))
# Sort out which WFS(s) observes the DM (for iMat making)
if self.dmConfig.wfs!=None:
try:
# Make sure the specifed WFS actually exists
self.wfss = [wfss[self.dmConfig.wfs]]
self.wfs = self.wfss[0]
except KeyError:
raise KeyError("DM attached to WFS {}, but that WFS is not specifed in config".format(self.dmConfig.wfs))
else:
self.wfss = wfss
logger.info("Making DM Influence Functions...")
self.makeIMatShapes()
# If using imatshapes, sclae by imat value
if hasattr(self, "iMatShapes"):
self.iMatShapes *= self.config.iMatValue
# An array of values for each actuator. 1 if actuator is valid, 0 if not
self._valid_actuators = numpy.ones((self.n_acts))
def test_findActiveSubaps_with_returnFill():
subapertures = 10
mask = circle(4, 10)
threshold = .6
active_subapertures, fill_factors = wfs.findActiveSubaps(subapertures, mask, threshold, returnFill=True)
assert active_subapertures.shape == (52, 2)
assert len(fill_factors) == 52
def updateAnimation(f, axarr, error, phase, phaseEst, psf, timer):
f.suptitle('Algorithm time: {0:.5}s'.format(timer))
cmap = plt.cm.jet
error = np.array(error)
im1 = axarr[0, 0].plot(error[:, 1], linewidth=2.5)
axarr[0, 0].grid(color='lightgrey', linestyle='--')
axarr[0, 0].set_title("Wavefront error")
axarr[0, 0].set_xlabel('iterations')
axarr[0, 0].set_ylabel('RMSE')
im2 = axarr[0, 1].imshow(psf**(1/3), cmap=cmap)
cb2 = plt.colorbar(im2, ax=axarr[0, 1], fraction=0.046)
axarr[0, 1].set_title("Point Spread function (strehl={0:.5f})".format(utils.strehl(phase-phaseEst)))
axarr[0, 1].set_axis_off()
mask=aotools.circle(64, 128).astype(np.float64)
phase[mask<0.1]=None
phaseEst[mask<0.1]=None
im3 = axarr[1, 0].imshow(phase, cmap=cmap)
im3.set_clim(-np.pi,np.pi)
cb3 = plt.colorbar(im3, ax=axarr[1, 0], fraction=0.046)
axarr[1, 0].set_title("Exact Phase")
axarr[1, 0].set_axis_off()
im4 = axarr[1, 1].imshow(phaseEst, cmap=cmap)
im4.set_clim(-np.pi, np.pi)
axarr[1, 1].set_title("Recovered phase")
axarr[1, 1].set_axis_off()
cb4 = plt.colorbar(im4, ax=axarr[1, 1], fraction=0.046)
plt.pause(1e-5)
axarr[0, 0].cla()
cb2.remove()
cb3.remove()
cb4.remove()
phase[mask<0.1]=0
phaseEst[mask<0.1]=0
def testa_initLgs(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
lgs = LGS.LGS(config.wfss[1], config)
def testc_initLegacySHWfs(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.ShackHartmannLegacy(config, mask=mask)
def test_chop_subaps_mask_threads():
"""
Tests that the numba routing chops phase into sub-apertures in the same way
as using numpy indices
Runs with multiple threads many times to detectect potential intermittant errors
"""
nx_phase = 12
nx_subap_size = 3
nx_subaps = nx_phase // nx_subap_size
subap_array = numpy.zeros((nx_subaps * nx_subaps, nx_subap_size, nx_subap_size)).astype("complex64")
numpy_subap_array = subap_array.copy()
mask = aotools.circle(nx_phase/2., nx_phase)
x_coords, y_coords = numpy.meshgrid(
numpy.arange(0, nx_phase, nx_subap_size),
numpy.arange(0, nx_phase, nx_subap_size))
subap_coords = numpy.array([y_coords.flatten(),x_coords.flatten()]).T
for i in range(50):
phase = (numpy.random.random((nx_phase, nx_phase))
+ 1j * numpy.random.random((nx_phase, nx_phase))
).astype("complex64")
numpy_chop(phase, subap_coords, nx_subap_size, numpy_subap_array, mask)
thread_pool = numbalib.ThreadPool(1)
numbalib.wfs.chop_subaps_mask_pool(
phase, subap_coords, nx_subap_size, subap_array, mask, thread_pool)
assert numpy.array_equal(numpy_subap_array, subap_array)
def testa_initWfs(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
wfs = WFS.WFS(config, mask=mask)
def testa_initDM():
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
wfs = WFS.ShackHartmann(config, mask=mask)
dm = DM.DM(config, wfss=[wfs], mask=mask)
def testd_initPhysLgs(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
config.wfss[1].lgs.propagationMode = "Physical"
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
lgs = LGS.LGS_Physical(config.wfss[1], config)
def testa_initLgs(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
lgs = LGS.LGS(config.wfss[1], config)
def testc_geoLgsPsf(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
config.wfss[1].lgs.propagationMode = "Geometric"
lgs = LGS.LGS_Geometric(config.wfss[1], config)
psf = lgs.getLgsPsf(
numpy.zeros((config.atmos.scrnNo, config.sim.simSize, config.sim.simSize)))
def testa_initWfs(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.WFS(config, mask=mask)
def testc_initGradWfs(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
config.sim.pupilSize = 10 * config.wfss[0].nxSubaps
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.Gradient(config, mask=mask)
def testd_SHWfsFrame(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.ShackHartmann(config, mask=mask)
wfs.frame(numpy.zeros((config.sim.simSize, config.sim.simSize)))
def testb_wfsFrame(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
wfs = WFS.WFS(config, mask=mask)
wfs.frame(numpy.zeros((config.sim.simSize, config.sim.simSize)))
def test_fibreInit(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
sci = SCI.singleModeFibre(config, 0, mask)
def test_sciInit(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
sci = scienceinstrument.PSFCamera(config, 0, mask)
def loadConfig(self):
"""
Load the Soapy config file
"""
self.config = confParse.YAML_Configurator(self.configfile)
self.config.loadSimParams()
self.mask = aotools.circle(
self.config.sim.pupilSize/2., self.config.sim.simSize)
def teste_physLgsPsf(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
config.wfss[1].lgs.propagationMode = "Physical"
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
lgs = LGS.LGS_Physical(config.wfss[1], config, nOutPxls=10)
psf = lgs.getLgsPsf(
numpy.zeros((config.atmos.scrnNo, config.sim.simSize, config.sim.simSize)))
def testd_GradWfsFrame(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
config.sim.pupilSize = 10*config.wfss[0].nxSubaps
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
wfs = WFS.Gradient(config, mask=mask)
wfs.frame(numpy.zeros((config.sim.simSize, config.sim.simSize)))
def test_PhysWfs(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
config.wfss[0].propagationMode = "Physical"
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
wfs = WFS.WFS(config, mask=mask)
wfs.frame(numpy.zeros((config.atmos.scrnNo, config.sim.scrnSize, config.sim.scrnSize)))
def testd_GradWfsFrame(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
config.sim.pupilSize = 10 * config.wfss[0].nxSubaps
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.Gradient(config, mask=mask)
wfs.frame(numpy.zeros((config.sim.simSize, config.sim.simSize)))
def testc_geoLgsPsf(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
config.wfss[1].lgs.propagationMode = "Geometric"
lgs = LGS.LGS_Geometric(config.wfss[1], config)
psf = lgs.getLgsPsf(
numpy.zeros(
(config.atmos.scrnNo, config.sim.simSize, config.sim.simSize)))
def test_fibreFrame(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
sci = SCI.singleModeFibre(config, 0, mask)
sci.frame(
numpy.ones((config.atmos.scrnNo, config.sim.scrnSize,
config.sim.scrnSize)))
def make_mask(config):
"""
Generates a Soapy pupil mask
Parameters:
config (SoapyConfig): Config object describing Soapy simulation
Returns:
ndarray: 2-d pupil mask
"""
if config.tel.mask == "circle":
mask = aotools.circle(config.sim.pupilSize / 2.,
config.sim.simSize)
if config.tel.obsDiam != None:
mask -= aotools.circle(
config.tel.obsDiam * config.sim.pxlScale / 2.,
config.sim.simSize
)
elif isinstance(config.tel.mask, str):
maskHDUList = fits.open(config.tel.mask)
mask = maskHDUList[0].data.copy()
maskHDUList.close()
logger.info('load mask "{}", of size: {}'.format(config.tel.mask, mask.shape))
if not numpy.array_equal(mask.shape, (config.sim.pupilSize,) * 2):
# interpolate mask to pupilSize if not that size already
mask = numpy.round(interp.zoom(mask, config.sim.pupilSize))
else:
mask = config.tel.mask.copy()
# Check its size is compatible. If its the pupil size, pad to sim size
if (not numpy.array_equal(mask.shape, (config.sim.pupilSize,)*2)
and not numpy.array_equal(mask.shape, (config.sim.simSize,)*2) ):
raise ValueError("Mask Shape {} not compatible. Should be either `pupilSize` or `simSize`".format(mask.shape))
if mask.shape != (config.sim.simSize, )*2:
mask = numpy.pad(
mask, config.sim.simPad, mode="constant")
return mask
def make_mask(config):
"""
Generates a Soapy pupil mask
Parameters:
config (SoapyConfig): Config object describing Soapy simulation
Returns:
ndarray: 2-d pupil mask
"""
if config.tel.mask == "circle":
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
if config.tel.obsDiam != None:
mask -= aotools.circle(
config.tel.obsDiam * config.sim.pxlScale / 2.,
config.sim.simSize)
elif isinstance(config.tel.mask, str):
maskHDUList = fits.open(config.tel.mask)
mask = maskHDUList[0].data.copy()
maskHDUList.close()
logger.info('load mask "{}", of size: {}'.format(
config.tel.mask, mask.shape))
if not numpy.array_equal(mask.shape, (config.sim.pupilSize, ) * 2):
# interpolate mask to pupilSize if not that size already
mask = numpy.round(interp.zoom(mask, config.sim.pupilSize))
else:
mask = config.tel.mask.copy()
# Check its size is compatible. If its the pupil size, pad to sim size
if (not numpy.array_equal(mask.shape, (config.sim.pupilSize, ) * 2)
and not numpy.array_equal(mask.shape, (config.sim.simSize, ) * 2)):
raise ValueError(
"Mask Shape {} not compatible. Should be either `pupilSize` or `simSize`"
.format(mask.shape))
if mask.shape != (config.sim.simSize, ) * 2:
mask = numpy.pad(mask, config.sim.simPad, mode="constant")
return mask
def teste_physLgsPsf(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8_lgs-uplink.yaml"))
config.wfss[1].lgs.propagationMode = "Physical"
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
lgs = LGS.LGS_Physical(config.wfss[1], config, nOutPxls=10)
psf = lgs.getLgsPsf(
numpy.zeros(
(config.atmos.scrnNo, config.sim.simSize, config.sim.simSize)))
def test_sciFrame(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
sci = scienceinstrument.PSFCamera(config, 0, mask)
sci.frame(
numpy.ones((config.atmos.scrnNo, config.sim.scrnSize,
config.sim.scrnSize)))
def strehl(phase):
mask = aotools.circle(64, 128)
N = 0.0
phase_mean = 0.0
for i in range(128):
for j in range(128):
if (mask[i, j] >= 0.001):
N += 1
phase_mean += phase[i, j]
phase_mean = phase_mean / N
strehl = np.abs(np.mean(np.exp(1j * (phase - phase_mean))))**2
return strehl
def test_propagation_conserves_intensity():
screen = phasescreen.ft_phase_screen(0.16, 512, 4.2 / 512, 100, 0.01)
# Input E Field
E = numpy.exp(1j * screen)
Em = E * circle(150, 512)
sum1 = (abs(Em) ** 2).sum()
prop1 = opticalpropagation.angularSpectrum(Em, 500e-9, 4.2 / 512, 4.2 / 512, 10000.)
sum2 = (abs(prop1) ** 2).sum()
assert numpy.allclose(sum1, sum2)
def test_slopecovmat_makecovmat_multithreaded():
threads = 2
n_wfs = 3
telescope_diameter = 8.
nx_subaps = 10
n_layers = 3
layer_altitudes = numpy.linspace(0, 20000, n_layers)
layer_r0s = [1] * n_layers
layer_L0s = [25.] * n_layers
asterism_radius = 10
subap_diameters = [telescope_diameter / nx_subaps] * n_wfs
pupil_masks = [aotools.circle(nx_subaps / 2., nx_subaps)] * n_wfs
gs_altitudes = [90000] * n_wfs
gs_positions = [[asterism_radius, 0],
[
numpy.sin(numpy.pi / 3.) * asterism_radius,
numpy.cos(numpy.pi / 3.) * asterism_radius
],
[
numpy.sin(numpy.pi / 3.) * asterism_radius,
-numpy.cos(numpy.pi / 3.) * asterism_radius
],
[
-numpy.sin(numpy.pi / 3.) * asterism_radius,
numpy.cos(numpy.pi / 3.) * asterism_radius
],
[
-numpy.sin(numpy.pi / 3.) * asterism_radius,
-numpy.cos(numpy.pi / 3.) * asterism_radius
], [-asterism_radius, 0]]
wfs_magnifications = [1.] * n_wfs
pupil_offsets = [[0, 0]] * n_wfs
wfs_rotations = [0] * n_wfs
wfs_wavelengths = [550e-9] * n_wfs
cov_mat = aotools.CovarianceMatrix(n_wfs, pupil_masks, telescope_diameter,
subap_diameters, gs_altitudes,
gs_positions, wfs_wavelengths, n_layers,
layer_altitudes, layer_r0s, layer_L0s,
threads)
covariance_matrix_multithread = cov_mat.make_covariance_matrix()
# Check consistant with single thread implementation
cov_mat.threads = 1
covariance_matrix = cov_mat.make_covariance_matrix()
assert numpy.array_equal(covariance_matrix, covariance_matrix_multithread)
def test_sciStrehl(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
sci = SCI.PSF(config, 0, mask)
sci.frame(
numpy.ones((config.atmos.scrnNo, config.sim.scrnSize,
config.sim.scrnSize)))
self.assertTrue(numpy.allclose(sci.instStrehl, 1.))
def test_PhysWfs(self):
config = confParse.loadSoapyConfig(
os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
config.wfss[0].propagationMode = "Physical"
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.WFS(config, mask=mask)
wfs.frame(
numpy.zeros((config.atmos.scrnNo, config.sim.scrnSize,
config.sim.scrnSize)))
def __init__(
self, soapy_config, n_wfs=0, mask=None):
self.soapy_config = soapy_config
self.config = self.wfsConfig = soapy_config.wfss[n_wfs] # For compatability
self.lgsConfig = self.config.lgs
# Sort out some required, static, parameters
self.pupil_size = self.soapy_config.sim.pupilSize
self.sim_size = self.soapy_config.sim.simSize
self.phase_scale = 1./self.soapy_config.sim.pxlScale
self.sim_pad = self.soapy_config.sim.simPad
self.screen_size = self.soapy_config.sim.scrnSize
self.telescope_diameter = self.soapy_config.tel.telDiam
self.wavelength = self.config.wavelength
self.threads = self.soapy_config.sim.threads
# If supplied use the mask
if numpy.any(mask):
self.mask = mask
# Else we'll just make a circle
else:
self.mask = aotools.circle(self.pupil_size/2., self.sim_size)
self.iMat = False
# Init the line of sight
self.initLos()
self.calcInitParams()
# If GS not at infinity, find meta-pupil radii for each layer
# if self.config.GSHeight != 0:
# self.radii = self.los.findMetaPupilSizes(self.config.GSHeight)
# else:
# self.radii = None
# Init LGS, FFTs and allocate some data arrays
self.initFFTs()
if self.lgsConfig and self.config.lgs:
self.initLGS()
self.allocDataArrays()
self.calcTiltCorrect()
self.getStatic()
# base WFS makes no measurements....
self.n_measurements = 0
def setMask(self, mask):
"""
Sets the pupil mask as seen by the WFS.
This method can be called during a simulation
"""
# If supplied use the mask
if numpy.any(mask):
self.mask = mask
else:
self.mask = aotools.circle(
self.pupil_size/2., self.sim_size,
)
def test_set_valid_actuators():
"""
Tests that when you set the "valid actuators", the DM computes how many valid actuators there are correctly
"""
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize/2., config.sim.simSize)
wfs = WFS.ShackHartmann(config, mask=mask)
dm = DM.DM(config, n_dm=1, wfss=[wfs], mask=mask)
valid_actuators = numpy.ones(dm.n_acts, dtype=int)
valid_actuators[0] = valid_actuators[-1] = 0
dm.valid_actuators = valid_actuators
assert dm.n_valid_actuators == (dm.n_acts - 2)
def test_slopecovmat_makecovmat_multithreaded():
threads = 2
n_wfs = 3
telescope_diameter = 8.
nx_subaps = 10
n_layers = 3
layer_altitudes = numpy.linspace(0, 20000, n_layers)
layer_r0s = [1] * n_layers
layer_L0s = [25.] * n_layers
asterism_radius = 10
subap_diameters = [telescope_diameter / nx_subaps] * n_wfs
pupil_masks = [aotools.circle(nx_subaps / 2., nx_subaps)] * n_wfs
gs_altitudes = [90000] * n_wfs
gs_positions = [
[asterism_radius, 0],
[numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-asterism_radius, 0]]
wfs_magnifications = [1.] * n_wfs
pupil_offsets = [[0, 0]] * n_wfs
wfs_rotations = [0] * n_wfs
wfs_wavelengths = [550e-9] * n_wfs
cov_mat = aotools.CovarianceMatrix(n_wfs, pupil_masks, telescope_diameter, subap_diameters, gs_altitudes, gs_positions,
wfs_wavelengths,
n_layers, layer_altitudes, layer_r0s, layer_L0s, threads)
covariance_matrix_multithread = cov_mat.make_covariance_matrix()
# Check consistant with single thread implementation
cov_mat.threads = 1
covariance_matrix = cov_mat.make_covariance_matrix()
assert numpy.array_equal(covariance_matrix, covariance_matrix_multithread)
def test_covtomorecon():
threads = 1
n_wfs = 3
telescope_diameter = 8.
nx_subaps = 10
n_layers = 3
layer_altitudes = numpy.linspace(0, 20000, n_layers)
layer_r0s = [1] * n_layers
layer_L0s = [25.] * n_layers
asterism_radius = 10
subap_diameters = [telescope_diameter / nx_subaps] * n_wfs
pupil_masks = [aotools.circle(nx_subaps / 2., nx_subaps)] * n_wfs
gs_altitudes = [90000] * n_wfs
gs_positions = [
[asterism_radius, 0],
[numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-asterism_radius, 0]]
wfs_magnifications = [1.] * n_wfs
pupil_offsets = [[0, 0]] * n_wfs
wfs_rotations = [0] * n_wfs
wfs_wavelengths = [550e-9] * n_wfs
cov_mat = aotools.CovarianceMatrix(n_wfs, pupil_masks, telescope_diameter, subap_diameters, gs_altitudes, gs_positions,
wfs_wavelengths,
n_layers, layer_altitudes, layer_r0s, layer_L0s, threads)
cov_mat.make_covariance_matrix()
tomo_recon = cov_mat.make_tomographic_reconstructor()
def testc_initLegacySHWfs(self):
config = confParse.loadSoapyConfig(os.path.join(CONFIG_PATH, "sh_8x8.yaml"))
mask = aotools.circle(config.sim.pupilSize / 2., config.sim.simSize)
wfs = WFS.ShackHartmannLegacy(config, mask=mask)
N = 1
threads = 20
n_wfs = 6
telescope_diameter = 8.
nx_subaps = 10
n_layers = 3
layer_altitudes = numpy.linspace(0, 20000, n_layers)
layer_r0s = [1] * n_layers
layer_L0s = [25.] * n_layers
asterism_radius = 10
subap_diameters = [telescope_diameter / nx_subaps] * n_wfs
pupil_masks = [aotools.circle(nx_subaps / 2., nx_subaps)] * n_wfs
gs_altitudes = [90000] * n_wfs
gs_positions = [
[asterism_radius, 0],
[numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, numpy.cos(numpy.pi / 3.) * asterism_radius],
[-numpy.sin(numpy.pi / 3.) * asterism_radius, -numpy.cos(numpy.pi / 3.) * asterism_radius],
[-asterism_radius, 0]]
wfs_magnifications = [1.] * n_wfs
pupil_offsets = [[0, 0]] * n_wfs
wfs_rotations = [0] * n_wfs
wfs_wavelengths = [550e-9] * n_wfs
t1 = time.time()
for i in range(N):
def test_photons_per_mag():
mask = circle(2, 5)
photons = astronomy.photons_per_mag(5.56, mask, 0.5, 0.3, 10)
assert type(photons) == float
def test_photons_per_band():
photons = astronomy.photons_per_band(5.56, circle(2, 5), 0.5, 0.001)
assert type(photons) == float
