def setUpClass(cls): cls.scratchDir = os.path.join(getPackageDir('sims_GalSimInterface'), 'tests', 'scratchSpace') cls.obs = ObservationMetaData(pointingRA=122.0, pointingDec=-29.1, mjd=57381.2, rotSkyPos=43.2) cls.camera = camTestUtils.CameraWrapper().camera cls.dbFileName = os.path.join(cls.scratchDir, 'allowed_chips_test_db.txt') if os.path.exists(cls.dbFileName): os.unlink(cls.dbFileName) cls.controlSed = Sed() cls.controlSed.readSED_flambda(os.path.join(getPackageDir('sims_sed_library'), 'flatSED','sed_flat.txt.gz')) cls.magNorm = 18.1 imsim = Bandpass() imsim.imsimBandpass() ff = cls.controlSed.calcFluxNorm(cls.magNorm, imsim) cls.controlSed.multiplyFluxNorm(ff) a_x, b_x = cls.controlSed.setupCCMab() cls.controlSed.addCCMDust(a_x, b_x, A_v=0.1, R_v=3.1) bpd = BandpassDict.loadTotalBandpassesFromFiles() pp = PhotometricParameters() cls.controlADU = cls.controlSed.calcADU(bpd['u'], pp) cls.countSigma = np.sqrt(cls.controlADU/pp.gain) cls.x_pix = 50 cls.y_pix = 50 x_list = [] y_list = [] name_list = [] for dd in cls.camera: x_list.append(cls.x_pix) y_list.append(cls.y_pix) name_list.append(dd.getName()) x_list = np.array(x_list) y_list = np.array(y_list) ra_list, dec_list = raDecFromPixelCoords(x_list, y_list, name_list, camera=cls.camera, obs_metadata=cls.obs, epoch=2000.0) dra_list = 3600.0*(ra_list-cls.obs.pointingRA) ddec_list = 3600.0*(dec_list-cls.obs.pointingDec) create_text_catalog(cls.obs, cls.dbFileName, dra_list, ddec_list, mag_norm=[cls.magNorm]*len(dra_list)) cls.db = allowedChipsFileDBObj(cls.dbFileName, runtable='test')
def raDecFromPixelCoords(self, xPix, yPix, chipName, obs_metadata, epoch=2000.0, includeDistortion=True): """ Convert pixel coordinates into RA, Dec Parameters ---------- xPix is the x pixel coordinate. It can be either a float or a numpy array. yPix is the y pixel coordinate. It can be either a float or a numpy array. chipName is the name of the chip(s) on which the pixel coordinates are defined. This can be a list (in which case there should be one chip name for each (xPix, yPix) coordinate pair), or a single value (in which case, all of the (xPix, yPix) points will be reckoned on that chip). obs_metadata is an ObservationMetaData defining the pointing epoch is the mean epoch in years of the celestial coordinate system. Default is 2000. includeDistortion is a boolean. If True (default), then this method will expect the true pixel coordinates with optical distortion included. If False, this method will expect TAN_PIXEL coordinates, which are the pixel coordinates with estimated optical distortion removed. See the documentation in afw.cameraGeom for more details. Returns ------- a 2-D numpy array in which the first row is the RA coordinate and the second row is the Dec coordinate (both in degrees; in the International Celestial Reference System) WARNING: This method does not account for apparent motion due to parallax. This method is only useful for mapping positions on a theoretical focal plane to positions on the celestial sphere. """ return coordUtils.raDecFromPixelCoords(xPix, yPix, chipName, camera=self._camera, obs_metadata=obs_metadata, epoch=2000.0, includeDistortion=True)
def setUpClass(cls): cls.scratchDir = tempfile.mkdtemp(dir=ROOT, prefix='allowedChipsTest-') cls.obs = ObservationMetaData(pointingRA=122.0, pointingDec=-29.1, mjd=57381.2, rotSkyPos=43.2, bandpassName='r') cls.camera = camTestUtils.CameraWrapper().camera cls.dbFileName = os.path.join(cls.scratchDir, 'allowed_chips_test_db.txt') if os.path.exists(cls.dbFileName): os.unlink(cls.dbFileName) cls.controlSed = Sed() cls.controlSed.readSED_flambda(os.path.join(getPackageDir('sims_sed_library'), 'flatSED', 'sed_flat.txt.gz')) cls.magNorm = 18.1 imsim = Bandpass() imsim.imsimBandpass() ff = cls.controlSed.calcFluxNorm(cls.magNorm, imsim) cls.controlSed.multiplyFluxNorm(ff) a_x, b_x = cls.controlSed.setupCCMab() cls.controlSed.addCCMDust(a_x, b_x, A_v=0.1, R_v=3.1) bpd = BandpassDict.loadTotalBandpassesFromFiles() pp = PhotometricParameters() cls.controlADU = cls.controlSed.calcADU(bpd['u'], pp) cls.countSigma = np.sqrt(cls.controlADU/pp.gain) cls.x_pix = 50 cls.y_pix = 50 x_list = [] y_list = [] name_list = [] for dd in cls.camera: x_list.append(cls.x_pix) y_list.append(cls.y_pix) name_list.append(dd.getName()) x_list = np.array(x_list) y_list = np.array(y_list) ra_list, dec_list = raDecFromPixelCoords(x_list, y_list, name_list, camera=cls.camera, obs_metadata=cls.obs, epoch=2000.0) dra_list = 3600.0*(ra_list-cls.obs.pointingRA) ddec_list = 3600.0*(dec_list-cls.obs.pointingDec) create_text_catalog(cls.obs, cls.dbFileName, dra_list, ddec_list, mag_norm=[cls.magNorm]*len(dra_list)) cls.db = allowedChipsFileDBObj(cls.dbFileName, runtable='test')
def chip_center_coords(chip_name, obs_md, camera): """ The coordinates of the center of the specified chip. Parameters ---------- chip_name : str The chip name, e.g., "R:2,2 S:1,1". obs_md : lsst.sims.utils.ObservationMetaData Obsevation metadata extracted from the phosim commands. camera : lsst.afw.cameraGeom.camera.Camera The camera instance from lsst.obs.lsstSim.LsstSimMapper(). """ corner_pixels = coordUtils.getCornerPixels(chip_name, camera) xmid = (corner_pixels[-1][0] - corner_pixels[0][0] + 1) / 2 ymid = (corner_pixels[-1][1] - corner_pixels[0][1] + 1) / 2 return tuple( coordUtils.raDecFromPixelCoords(ymid, xmid, chip_name, camera=camera, obs_metadata=obs_md))
(args.obs, filter_name), 'w') as truth_file: truth_file.write( '# i_obj ra_icrs dec_icrs ra_deprecessed dec_deprecessed x_dm y_dm x_f y_f x_cam y_cam\n' ) for det_name in det_name_list: det_name_m = det_name.replace(':', '').replace(',', '').replace( ' ', '_') dm_xpix_in, dm_ypix_in = camera_wrapper.dmPixFromCameraPix( cam_xpix_in, cam_ypix_in, det_name) ra_icrs, dec_icrs = raDecFromPixelCoords(dm_xpix_in, dm_ypix_in, det_name, camera=lsst_camera(), obs_metadata=obs) ra_obs, dec_obs = observedFromICRS(ra_icrs, dec_icrs, obs_metadata=obs, epoch=2000.0, includeRefraction=False) (ra_deprecessed, dec_deprecessed) = de_precessor._dePrecess( np.radians(ra_obs), np.radians(dec_obs), obs) x_f, y_f = focalPlaneCoordsFromRaDec(ra_icrs, dec_icrs, obs_metadata=obs,
def test_generic_camera_wrapper(self): """ Test that GalSimCameraWrapper wraps its methods as expected. This is mostly to catch changes in afw API. """ camera = camTestUtils.CameraWrapper().camera camera_wrapper = GalSimCameraWrapper(camera) obs_mjd = ObservationMetaData(mjd=60000.0) ra, dec = raDecFromAltAz(35.0, 112.0, obs_mjd) obs = ObservationMetaData(pointingRA=ra, pointingDec=dec, mjd=obs_mjd.mjd, rotSkyPos=22.4) rng = np.random.RandomState(8124) for detector in camera: name = detector.getName() bbox = camera[name].getBBox() bbox_wrapper = camera_wrapper.getBBox(name) self.assertEqual(bbox.getMinX(), bbox_wrapper.getMinX()) self.assertEqual(bbox.getMaxX(), bbox_wrapper.getMaxX()) self.assertEqual(bbox.getMinY(), bbox_wrapper.getMinY()) self.assertEqual(bbox.getMaxY(), bbox_wrapper.getMaxY()) center_point = camera[name].getCenter(FOCAL_PLANE) pixel_system = camera[name].makeCameraSys(PIXELS) center_pix = camera.transform(center_point, FOCAL_PLANE, pixel_system) center_pix_wrapper = camera_wrapper.getCenterPixel(name) self.assertEqual(center_pix.getX(), center_pix_wrapper.getX()) self.assertEqual(center_pix.getY(), center_pix_wrapper.getY()) pupil_system = camera[name].makeCameraSys(FIELD_ANGLE) center_pupil = camera.transform(center_point, FOCAL_PLANE, pupil_system) center_pupil_wrapper = camera_wrapper.getCenterPupil(name) self.assertEqual(center_pupil.getX(), center_pupil_wrapper.getX()) self.assertEqual(center_pupil.getY(), center_pupil_wrapper.getY()) corner_pupil_wrapper = camera_wrapper.getCornerPupilList(name) corner_point_list = camera[name].getCorners(FOCAL_PLANE) for point in corner_point_list: point_pupil = camera.transform(point, FOCAL_PLANE, pupil_system) dd_min = 1.0e10 for wrapper_point in corner_pupil_wrapper: dd = np.sqrt( (point_pupil.getX() - wrapper_point.getX())**2 + (point_pupil.getY() - wrapper_point.getY())**2) if dd < dd_min: dd_min = dd self.assertLess(dd_min, 1.0e-20) xpix_min = None xpix_max = None ypix_min = None ypix_max = None focal_to_tan_pix = camera[name].getTransform( FOCAL_PLANE, TAN_PIXELS) for point in corner_point_list: pixel_point = focal_to_tan_pix.applyForward(point) xx = pixel_point.getX() yy = pixel_point.getY() if xpix_min is None or xx < xpix_min: xpix_min = xx if ypix_min is None or yy < ypix_min: ypix_min = yy if xpix_max is None or xx > xpix_max: xpix_max = xx if ypix_max is None or yy > ypix_max: ypix_max = yy pix_bounds_wrapper = camera_wrapper.getTanPixelBounds(name) self.assertEqual(pix_bounds_wrapper[0], xpix_min) self.assertEqual(pix_bounds_wrapper[1], xpix_max) self.assertEqual(pix_bounds_wrapper[2], ypix_min) self.assertEqual(pix_bounds_wrapper[3], ypix_max) x_pup = rng.random_sample(10) * 0.005 - 0.01 y_pup = rng.random_sample(10) * 0.005 - 0.01 x_pix, y_pix = pixelCoordsFromPupilCoords(x_pup, y_pup, chipName=name, camera=camera) (x_pix_wrapper, y_pix_wrapper) = camera_wrapper.pixelCoordsFromPupilCoords( x_pup, y_pup, name, obs) nan_x = np.where(np.isnan(x_pix)) self.assertEqual(len(nan_x[0]), 0) np.testing.assert_array_equal(x_pix, x_pix_wrapper) np.testing.assert_array_equal(y_pix, y_pix_wrapper) x_pix = rng.random_sample(10) * 100.0 - 200.0 y_pix = rng.random_sample(10) * 100.0 - 200.0 x_pup, y_pup = pupilCoordsFromPixelCoords(x_pix, y_pix, chipName=name, camera=camera) (x_pup_wrapper, y_pup_wrapper) = camera_wrapper.pupilCoordsFromPixelCoords( x_pix, y_pix, name, obs) nan_x = np.where(np.isnan(x_pup)) self.assertEqual(len(nan_x[0]), 0) np.testing.assert_array_equal(x_pup, x_pup_wrapper) np.testing.assert_array_equal(y_pup, y_pup_wrapper) ra, dec = raDecFromPixelCoords(x_pix, y_pix, name, camera=camera, obs_metadata=obs) (ra_wrapper, dec_wrapper) = camera_wrapper.raDecFromPixelCoords( x_pix, y_pix, name, obs) nan_ra = np.where(np.isnan(ra)) self.assertEqual(len(nan_ra[0]), 0) np.testing.assert_array_equal(ra, ra_wrapper) np.testing.assert_array_equal(dec, dec_wrapper) ra, dec = _raDecFromPixelCoords(x_pix, y_pix, name, camera=camera, obs_metadata=obs) (ra_wrapper, dec_wrapper) = camera_wrapper._raDecFromPixelCoords( x_pix, y_pix, name, obs) nan_ra = np.where(np.isnan(ra)) self.assertEqual(len(nan_ra[0]), 0) np.testing.assert_array_equal(ra, ra_wrapper) np.testing.assert_array_equal(dec, dec_wrapper) ra = obs.pointingRA + (rng.random_sample(10) * 150.0 - 100.0) / 160.0 dec = obs.pointingDec + (rng.random_sample(10) * 150.0 - 100.0) / 160.0 x_pix, y_pix = pixelCoordsFromRaDec(ra, dec, chipName=name, camera=camera, obs_metadata=obs) (x_pix_wrapper, y_pix_wrapper) = camera_wrapper.pixelCoordsFromRaDec( ra, dec, chipName=name, obs_metadata=obs) nan_x = np.where(np.isnan(x_pix)) self.assertEqual(len(nan_x[0]), 0) np.testing.assert_array_equal(x_pix, x_pix_wrapper) np.testing.assert_array_equal(y_pix, y_pix_wrapper) ra = np.radians(ra) dec = np.radians(dec) x_pix, y_pix = _pixelCoordsFromRaDec(ra, dec, chipName=name, camera=camera, obs_metadata=obs) (x_pix_wrapper, y_pix_wrapper) = camera_wrapper._pixelCoordsFromRaDec( ra, dec, chipName=name, obs_metadata=obs) nan_x = np.where(np.isnan(x_pix)) self.assertEqual(len(nan_x[0]), 0) np.testing.assert_array_equal(x_pix, x_pix_wrapper) np.testing.assert_array_equal(y_pix, y_pix_wrapper) del camera
def approximateWcs(wcs, bbox, camera=None, detector=None, obs_metadata=None, order=3, nx=20, ny=20, iterations=3, skyTolerance=0.001 * afwGeom.arcseconds, pixelTolerance=0.02, useTanWcs=False): """Approximate an existing WCS as a TAN-SIP WCS The fit is performed by evaluating the WCS at a uniform grid of points within a bounding box. @param[in] wcs wcs to approximate @param[in] bbox the region over which the WCS will be fit @param[in] camera is an instantiation of afw.cameraGeom.camera @param[in] detector is a detector from camera @param[in] obs_metadata is an ObservationMetaData characterizing the telescope pointing @param[in] order order of SIP fit @param[in] nx number of grid points along x @param[in] ny number of grid points along y @param[in] iterations number of times to iterate over fitting @param[in] skyTolerance maximum allowed difference in world coordinates between input wcs and approximate wcs (default is 0.001 arcsec) @param[in] pixelTolerance maximum allowed difference in pixel coordinates between input wcs and approximate wcs (default is 0.02 pixels) @param[in] useTanWcs send a TAN version of wcs to the fitter? It is documented to require that, but I don't think the fitter actually cares @return the fit TAN-SIP WCS """ if useTanWcs: crCoord = wcs.getSkyOrigin() crPix = wcs.getPixelOrigin() cdMat = wcs.getCDMatrix() tanWcs = afwImage.makeWcs(crCoord, crPix, cdMat[0, 0], cdMat[0, 1], cdMat[1, 0], cdMat[1, 1]) else: tanWcs = wcs # create a matchList consisting of a grid of points covering the bbox refSchema = afwTable.SimpleTable.makeMinimalSchema() refCoordKey = afwTable.CoordKey(refSchema["coord"]) refCat = afwTable.SimpleCatalog(refSchema) sourceSchema = afwTable.SourceTable.makeMinimalSchema() SingleFrameMeasurementTask(schema=sourceSchema) # expand the schema sourceCentroidKey = afwTable.Point2DKey(sourceSchema["slot_Centroid"]) sourceCat = afwTable.SourceCatalog(sourceSchema) # 20 March 2017 # the 'try' block is how it works in swig; # the 'except' block is how it works in pybind11 try: matchList = afwTable.ReferenceMatchVector() except AttributeError: matchList = [] bboxd = afwGeom.Box2D(bbox) for x in np.linspace(bboxd.getMinX(), bboxd.getMaxX(), nx): for y in np.linspace(bboxd.getMinY(), bboxd.getMaxY(), ny): pixelPos = afwGeom.Point2D(x, y) ra, dec = raDecFromPixelCoords(np.array([x]), np.array([y]), [detector.getName()], camera=camera, obs_metadata=obs_metadata, epoch=2000.0, includeDistortion=True) skyCoord = afwCoord.Coord(afwGeom.Point2D(ra[0], dec[0])) refObj = refCat.addNew() refObj.set(refCoordKey, skyCoord) source = sourceCat.addNew() source.set(sourceCentroidKey, pixelPos) matchList.append(afwTable.ReferenceMatch(refObj, source, 0.0)) # The TAN-SIP fitter is fitting x and y separately, so we have to iterate to make it converge for indx in range(iterations): sipObject = makeCreateWcsWithSip(matchList, tanWcs, order, bbox) tanWcs = sipObject.getNewWcs() fitWcs = sipObject.getNewWcs() return fitWcs
def approximateWcs(wcs, bbox, camera=None, detector=None, obs_metadata=None, order=3, nx=20, ny=20, iterations=3, skyTolerance=0.001*afwGeom.arcseconds, pixelTolerance=0.02, useTanWcs=False): """Approximate an existing WCS as a TAN-SIP WCS The fit is performed by evaluating the WCS at a uniform grid of points within a bounding box. @param[in] wcs wcs to approximate @param[in] bbox the region over which the WCS will be fit @param[in] camera is an instantiation of afw.cameraGeom.camera @param[in] detector is a detector from camera @param[in] obs_metadata is an ObservationMetaData characterizing the telescope pointing @param[in] order order of SIP fit @param[in] nx number of grid points along x @param[in] ny number of grid points along y @param[in] iterations number of times to iterate over fitting @param[in] skyTolerance maximum allowed difference in world coordinates between input wcs and approximate wcs (default is 0.001 arcsec) @param[in] pixelTolerance maximum allowed difference in pixel coordinates between input wcs and approximate wcs (default is 0.02 pixels) @param[in] useTanWcs send a TAN version of wcs to the fitter? It is documented to require that, but I don't think the fitter actually cares @return the fit TAN-SIP WCS """ if useTanWcs: crCoord = wcs.getSkyOrigin() crPix = wcs.getPixelOrigin() cdMat = wcs.getCDMatrix() tanWcs = afwImage.makeWcs(crCoord, crPix, cdMat[0,0], cdMat[0,1], cdMat[1,0], cdMat[1,1]) else: tanWcs = wcs # create a matchList consisting of a grid of points covering the bbox refSchema = afwTable.SimpleTable.makeMinimalSchema() refCoordKey = afwTable.CoordKey(refSchema["coord"]) refCat = afwTable.SimpleCatalog(refSchema) sourceSchema = afwTable.SourceTable.makeMinimalSchema() SingleFrameMeasurementTask(schema=sourceSchema) # expand the schema sourceCentroidKey = afwTable.Point2DKey(sourceSchema["slot_Centroid"]) sourceCat = afwTable.SourceCatalog(sourceSchema) matchList = afwTable.ReferenceMatchVector() bboxd = afwGeom.Box2D(bbox) for x in np.linspace(bboxd.getMinX(), bboxd.getMaxX(), nx): for y in np.linspace(bboxd.getMinY(), bboxd.getMaxY(), ny): pixelPos = afwGeom.Point2D(x, y) ra, dec = raDecFromPixelCoords(np.array([x]), np.array([y]), [detector.getName()], camera=camera, obs_metadata=obs_metadata, epoch=2000.0, includeDistortion=True) skyCoord = afwCoord.Coord(afwGeom.Point2D(ra[0], dec[0])) refObj = refCat.addNew() refObj.set(refCoordKey, skyCoord) source = sourceCat.addNew() source.set(sourceCentroidKey, pixelPos) matchList.append(afwTable.ReferenceMatch(refObj, source, 0.0)) # The TAN-SIP fitter is fitting x and y separately, so we have to iterate to make it converge for indx in range(iterations) : sipObject = makeCreateWcsWithSip(matchList, tanWcs, order, bbox) tanWcs = sipObject.getNewWcs() fitWcs = sipObject.getNewWcs() return fitWcs
def test_LSST_camera_wrapper(self): """ Test that LSSTCameraWrapper wraps its methods as expected. Recall that the LSSTCameraWrapper applies the 90 degree rotation to go from DM pixel coordinates to Camera team pixel coordinates. Namely, Camera +y = DM +x Camera +x = DM -y """ camera = lsst_camera() camera_wrapper = LSSTCameraWrapper() obs_mjd = ObservationMetaData(mjd=60000.0) ra, dec = raDecFromAltAz(35.0, 112.0, obs_mjd) obs = ObservationMetaData(pointingRA=ra, pointingDec=dec, mjd=obs_mjd.mjd, rotSkyPos=22.4) rng = np.random.RandomState(8124) for detector in camera: name = detector.getName() bbox = camera[name].getBBox() bbox_wrapper = camera_wrapper.getBBox(name) self.assertEqual(bbox.getMinX(), bbox_wrapper.getMinY()) self.assertEqual(bbox.getMaxX(), bbox_wrapper.getMaxY()) self.assertEqual(bbox.getMinY(), bbox_wrapper.getMinX()) self.assertEqual(bbox.getMaxY(), bbox_wrapper.getMaxX()) self.assertGreater(bbox_wrapper.getMaxY()-bbox_wrapper.getMinY(), bbox_wrapper.getMaxX()-bbox_wrapper.getMinX()) center_point = camera[name].getCenter(FOCAL_PLANE) pixel_system = camera[name].makeCameraSys(PIXELS) center_pix = camera.transform(center_point, FOCAL_PLANE, pixel_system) center_pix_wrapper = camera_wrapper.getCenterPixel(name) self.assertEqual(center_pix.getX(), center_pix_wrapper.getY()) self.assertEqual(center_pix.getY(), center_pix_wrapper.getX()) # Note that DM and the Camera team agree on the orientation # of the pupil coordinate/field angle axes pupil_system = camera[name].makeCameraSys(FIELD_ANGLE) center_pupil = camera.transform(center_point, FOCAL_PLANE, pupil_system) center_pupil_wrapper = camera_wrapper.getCenterPupil(name) self.assertEqual(center_pupil.getX(), center_pupil_wrapper.getX()) self.assertEqual(center_pupil.getY(), center_pupil_wrapper.getY()) corner_pupil_wrapper = camera_wrapper.getCornerPupilList(name) corner_point_list = camera[name].getCorners(FOCAL_PLANE) for point in corner_point_list: point_pupil = camera.transform(point, FOCAL_PLANE, pupil_system) dd_min = 1.0e10 for wrapper_point in corner_pupil_wrapper: dd = np.sqrt((point_pupil.getX()-wrapper_point.getX())**2 + (point_pupil.getY()-wrapper_point.getY())**2) if dd < dd_min: dd_min = dd self.assertLess(dd_min, 1.0e-20) xpix_min = None xpix_max = None ypix_min = None ypix_max = None focal_to_tan_pix = camera[name].getTransform(FOCAL_PLANE, TAN_PIXELS) for point in corner_point_list: pixel_point = focal_to_tan_pix.applyForward(point) xx = pixel_point.getX() yy = pixel_point.getY() if xpix_min is None or xx<xpix_min: xpix_min = xx if ypix_min is None or yy<ypix_min: ypix_min = yy if xpix_max is None or xx>xpix_max: xpix_max = xx if ypix_max is None or yy>ypix_max: ypix_max = yy pix_bounds_wrapper = camera_wrapper.getTanPixelBounds(name) self.assertEqual(pix_bounds_wrapper[0], ypix_min) self.assertEqual(pix_bounds_wrapper[1], ypix_max) self.assertEqual(pix_bounds_wrapper[2], xpix_min) self.assertEqual(pix_bounds_wrapper[3], xpix_max) # generate some random pupil coordinates; # verify that the relationship between the DM and Camera team # pixel coordinates corresponding to those pupil coordinates # is as expected x_pup = rng.random_sample(10)*0.005-0.01 y_pup = rng.random_sample(10)*0.005-0.01 x_pix, y_pix = pixelCoordsFromPupilCoords(x_pup, y_pup, chipName=name, camera=camera) (x_pix_wrapper, y_pix_wrapper) = camera_wrapper.pixelCoordsFromPupilCoords(x_pup, y_pup, chipName=name) nan_x = np.where(np.isnan(x_pix)) self.assertEqual(len(nan_x[0]), 0) np.testing.assert_allclose(x_pix-center_pix.getX(), y_pix_wrapper-center_pix_wrapper.getY(), atol=1.0e-10, rtol=0.0) np.testing.assert_allclose(y_pix-center_pix.getY(), center_pix_wrapper.getX()-x_pix_wrapper, atol=1.0e-10, rtol=0.0) # use camera_wrapper.pupilCoordsFromPixelCoords to go back to pupil # coordinates from x_pix_wrapper, y_pix_wrapper; make sure you get # the original pupil coordinates back out (x_pup_wrapper, y_pup_wrapper) = camera_wrapper.pupilCoordsFromPixelCoords(x_pix_wrapper, y_pix_wrapper, chipName=name) msg = 'worst error %e' % np.abs(x_pup-x_pup_wrapper).max() np.testing.assert_allclose(x_pup, x_pup_wrapper, atol=1.0e-10, rtol=0.0, err_msg=msg) msg = 'worst error %e' % np.abs(y_pup-y_pup_wrapper).max() np.testing.assert_allclose(y_pup, y_pup_wrapper, atol=1.0e-10, rtol=0.0, err_msg=msg) # generate some random sky coordinates; verify that the methods that # convert between (RA, Dec) and pixel coordinates behave as expected. # NOTE: x_pix, y_pix will be in DM pixel coordinate convention x_pix = bbox.getMinX() + rng.random_sample(10)*(bbox.getMaxX()-bbox.getMinX()) y_pix = bbox.getMinY() + rng.random_sample(10)*(bbox.getMaxY()-bbox.getMinY()) ra, dec = raDecFromPixelCoords(x_pix, y_pix, name, camera=camera, obs_metadata=obs) (ra_wrapper, dec_wrapper) = camera_wrapper.raDecFromPixelCoords(2.0*center_pix.getY()-y_pix, x_pix, name, obs) nan_ra = np.where(np.isnan(ra)) self.assertEqual(len(nan_ra[0]), 0) np.testing.assert_allclose(ra, ra_wrapper, atol=1.0e-10, rtol=0.0) np.testing.assert_allclose(dec, dec_wrapper, atol=1.0e-10, rtol=0.0) # make sure that the method that returns RA, Dec in radians agrees with # the method that returns RA, Dec in degrees (ra_rad, dec_rad) = camera_wrapper._raDecFromPixelCoords(2.0*center_pix.getY()-y_pix, x_pix, name, obs) np.testing.assert_allclose(np.radians(ra_wrapper), ra_rad, atol=1.0e-10, rtol=0.0) np.testing.assert_allclose(np.radians(dec_wrapper), dec_rad, atol=1.0e-10, rtol=0.0) # Go back to pixel coordinates with pixelCoordsFromRaDec; verify that # the result relates to the original DM pixel coordinates as expected # (x_pix_inv, y_pix_inv will be in Camera pixel coordinates) (x_pix_inv, y_pix_inv) = camera_wrapper.pixelCoordsFromRaDec(ra_wrapper, dec_wrapper, chipName=name, obs_metadata=obs) np.testing.assert_allclose(y_pix_inv, x_pix, atol=1.0e-5, rtol=0.0) np.testing.assert_allclose(x_pix_inv, 2.0*center_pix.getY()-y_pix, atol=1.0e-5, rtol=0.0) ra = np.radians(ra_wrapper) dec = np.radians(dec_wrapper) # check that the the method that accepts RA, Dec in radians agrees with the # method that accepts RA, Dec in degrees (x_pix_wrapper, y_pix_wrapper) = camera_wrapper._pixelCoordsFromRaDec(ra, dec, chipName=name, obs_metadata=obs) np.testing.assert_allclose(x_pix_inv, x_pix_wrapper, atol=1.0e-10, rtol=0.0) np.testing.assert_allclose(y_pix_inv, y_pix_wrapper, atol=1.0e-10, rtol=0.0) del camera del camera_wrapper del lsst_camera._lsst_camera