def __init__(self, rzero, PSF_Interpolator=None, PSF_Evaluator=None, **kwargs): if type(PSF_Interpolator) == type(None): from WavefrontPSF.defaults import param_default_kils params = param_default_kils() PSF_Interpolator = Mesh_Interpolator(mesh_name=params['mesh_name'], directory=params['mesh_directory']) if type(PSF_Evaluator) == type(None): # translate rzero to coefficients rzeros_float = np.array([0.08 + 0.01 * i for i in xrange(15)]) rzeros = ['{0:.2f}'.format(0.08 + 0.01 * i) for i in xrange(15)] # always want the rzero one above so that we underestimate it # note that this also implies that we want a dc component for e0 in # addition to e1 and e2 rzero_i = np.searchsorted(rzeros_float, rzero) if rzero_i >= len(rzeros): # if it is too big, put it to the last entry rzero_i = len(rzeros) - 1 rzero_key = rzeros[rzero_i] from WavefrontPSF.defaults import param_default_kils params = param_default_kils() PSF_Evaluator = Zernike_Evaluator(*np.load(params['analytic_coeffs']).item()[rzero_key]) # set the drawer PSF_Drawer = Zernike_to_Misalignment_to_Pixel_Interpolator() super(DECAM_Analytic_Wavefront, self).__init__( PSF_Evaluator=PSF_Evaluator, PSF_Interpolator=PSF_Interpolator, PSF_Drawer=PSF_Drawer, **kwargs)
def drive_fit(expid, params={}, weights={}, skip_fit=False): params_default = {'analytic': True, 'number_sample': 0, 'verbose': True, 'num_bins': 1} params_default.update(param_default_kils(expid)) params_default.update(params) params = params_default model = Digestor().digest_directory( params['data_directory'], file_type=params['data_name']) if params['number_sample'] > 0: rows = np.random.choice(model.index.values, params['number_sample'], replace=False) model = model.ix[rows] # guess rzero rzero = r0_guess(model['e0'].min()) rzeros_float = np.array([0.08 + 0.01 * i for i in xrange(15)]) rzeros = ['{0:.2f}'.format(0.08 + 0.01 * i) for i in xrange(15)] # always want the rzero one above so that we underestimate it # note that this also implies that we want a dc component for e0 in # addition to e1 and e2 rzero_i = np.searchsorted(rzeros_float, rzero) rzero_key = rzeros[rzero_i] model['rzero'] = rzero # get the PSF_Interpolator PSF_Interpolator = Mesh_Interpolator(mesh_name=params['mesh_name'], directory=params['mesh_directory']) if params['analytic']: PSF_Evaluator = Zernike_Evaluator(*np.load(params['analytic_coeffs']).item()[rzero_key]) WF = DECAM_Analytic_Wavefront(rzero=rzero, PSF_Interpolator=PSF_Interpolator, PSF_Evaluator=PSF_Evaluator, num_bins=params['num_bins'], model=model) else: WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator, num_bins=params['num_bins'], model=model) misalignment = translate_misalignment_to_arguments({}) # fix some keys pop_keys = ['rzero', 'delta1', 'delta2', 'zeta1', 'zeta2'] for pop_key in pop_keys: _ = misalignment.pop(pop_key) if skip_fit: return WF minuit, chi2, plane = do_fit(WF, misalignment=misalignment, verbose=params['verbose'], weights=weights) return minuit, chi2, plane, WF
def __init__(self, PSF_Interpolator=None, **kwargs): # data here is a csv with all the zernikes if type(PSF_Interpolator) == type(None): from WavefrontPSF.defaults import param_default_kils params = param_default_kils() PSF_Interpolator = Mesh_Interpolator(mesh_name=params['mesh_name'], directory=params['mesh_directory']) PSF_Drawer = Zernike_to_Misalignment_to_Pixel_Interpolator() super(DECAM_Model_Wavefront, self).__init__( PSF_Evaluator=Moment_Evaluator(), PSF_Interpolator=PSF_Interpolator, PSF_Drawer=PSF_Drawer, **kwargs)
def stamp_collector(expid, Nmax=0, rzero=None, snr_max=400, snr_min=90): results = np.load(jamierod_dir + '/params/params_{0:08d}.npy'.format(expid)).item() keys = sorted([key for key in results.keys() if ('error' not in key and 'fix' not in key and 'limit' not in key)]) misalignment = {key: results[key] for key in keys} params = {'expid': expid, 'misalignment': misalignment, # jamie's params: 'mesh_directory': '/nfs/slac/g/ki/ki22/roodman/ComboMeshesv20', 'mesh_name': 'Science-20121120s1-v20i2_All', } params_default = {'snr_key': 'SNR_WIN', 'snr_max': snr_max, 'snr_min': snr_min, 'data_name': '_selpsfcat.fits', 'num_bins': 4 } params_default.update(param_default_kils(expid)) params_default.update(params) params = params_default fits_files = glob(params['data_directory'] + '/*{0}'.format(params['data_name'])) dig = Digestor() model, data_stamps = dig.digest_fits(fits_files[0], do_exclude=True) data_stamps = data_stamps['VIGNET'] for fits_file in fits_files[1:]: model_i, data_stamps_i = dig.digest_fits(fits_file, do_exclude=True) model = model.append(model_i, ignore_index=True) data_stamps = np.vstack((data_stamps, data_stamps_i['VIGNET'])) if params['snr_key'] in model.columns: conds = ((model[params['snr_key']] > params['snr_min']) & (model[params['snr_key']] < params['snr_max'])) data_stamps = data_stamps[conds.values] model = model[conds] if Nmax > 0: # cut out Nmax objects rows = np.random.choice(len(model), Nmax, replace=False) model = model.iloc[rows] data_stamps = data_stamps[rows] # set data_stamps to be float64 like the model stamps data_stamps = data_stamps.astype(np.float64) if type(rzero) == type(None): model['rzero'] = results['rzero'] else: model['rzero'] = rzero # get the PSF_Interpolator PSF_Interpolator = Mesh_Interpolator(mesh_name=params['mesh_name'], directory=params['mesh_directory']) WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator, num_bins=params['num_bins'], model=model) def plane(rzero, z04d, z04x, z04y, z05d, z05x, z05y, z06d, z06x, z06y, z07d, z07x, z07y, z08d, z08x, z08y, z09d, z09x, z09y, z10d, z10x, z10y, z11d, z11x, z11y, dz, dx, dy, xt, yt, e0, e1, e2, delta1, delta2, zeta1, zeta2, **kwargs): wf_misalignment = {'z04d': z04d, 'z04x': z04x, 'z04y': z04y, 'z05d': z05d, 'z05x': z05x, 'z05y': z05y, 'z06d': z06d, 'z06x': z06x, 'z06y': z06y, 'z07d': z07d, 'z07x': z07x, 'z07y': z07y, 'z08d': z08d, 'z08x': z08x, 'z08y': z08y, 'z09d': z09d, 'z09x': z09x, 'z09y': z09y, 'z10d': z10d, 'z10x': z10x, 'z10y': z10y, 'z11d': z11d, 'z11x': z11x, 'z11y': z11y, 'dz': dz, 'dx': dx, 'dy': dy, 'xt': xt, 'yt': yt} return wf_misalignment wf_misalignment = plane(**misalignment) # get evaluated PSFs stamps, eval_data = WF.draw_psf(WF.data, force_interpolation=True, misalignment=wf_misalignment) # apply shear to stamps directly! evaluated_psfs = WF.evaluate_psf(stamps, misalignment=wf_misalignment) # make sure evaluated_psfs has the right index evaluated_psfs.index = eval_data.index # combine the results from PSF_Evaluator with your input data combined_df = evaluated_psfs.combine_first(eval_data) # TODO: Deal with constant jitter terms! Currently the results are off and seem to be off related to the overall magnitude... ## # add dc factors ## # combined_df['e0'] += results['e0'] ## # combined_df['e1'] += results['e1'] ## # combined_df['e2'] += results['e2'] ## # combined_df['delta1'] += results['delta1'] ## # combined_df['delta2'] += results['delta2'] ## # combined_df['zeta1'] += results['zeta1'] ## # combined_df['zeta2'] += results['zeta2'] ## e1 = results['e1'] * 5 # empirical correction? ## e2 = results['e2'] * 5 ## Mx = combined_df['Mx'].values ## My = combined_df['My'].values ## esq = e1 * e1 + e2 * e2 ## if esq < 1e-8: ## A = 1 + esq / 8 + e1 * (0.5 + esq * 3 / 16) ## B = 1 + esq / 8 - e1 * (0.5 + esq * 3 / 16) ## C = e2 * (0.5 + esq * 3 / 16) ## else: ## temp = np.sqrt(1 - esq) ## cc = np.sqrt(0.5 * (1 + 1 / temp)) ## temp = cc * (1 - temp) / esq ## C = temp * e2 ## temp *= e1 ## A = cc + temp ## B = cc - temp ## matrix = np.array([[A, C], [C, B]]) ## matrix /= A * B - C * C ## offsetsx = -Mx * (A + C - 1) ## offsetsy = -My * (C + B - 1) ## # apply transformation ## stamps_transformed = [] ## for stamp, offsetx, offsety in zip(stamps, offsetsx, offsetsy): ## stamps_transformed_i = affine_transform(stamp, matrix, (offsetx, offsety)) ## stamps_transformed.append(stamps_transformed_i) ## stamps_transformed = np.array(stamps_transformed) ## evaluated_psfs = WF.evaluate_psf(stamps_transformed, misalignment=wf_misalignment) ## # make sure evaluated_psfs has the right index ## evaluated_psfs.index = eval_data.index ## # combine the results from PSF_Evaluator with your input data ## combined_df_trans = evaluated_psfs.combine_first(eval_data) ## e1 = combined_df['e1'] ## e2 = combined_df['e2'] ## e1_t = combined_df_trans['e1'] ## e2_t = combined_df_trans['e2'] ## print((e1 - e1_t) / (results['e1'])) ## print((e2 - e2_t) / (results['e2'])) return combined_df, stamps, data_stamps