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
def get_optical_psf(expid, aos=False): # set up objects. make sure I get the right mesh digestor = Digestor() PSF_Evaluator = Moment_Evaluator() mesh_name = "Science-20121120s1-v20i2_All" PSF_Interpolator = Mesh_Interpolator(mesh_name=mesh_name, directory=mesh_directory) # This will be our main wavefront WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator) # load up data expid_path = "{0:08d}/{1:08d}".format(expid - expid % 1000, expid) data_directory = base_directory + expid_path files = sorted(glob(data_directory + "/*{0}".format("_selpsfcat.fits"))) # load up all the data from an exposure. Unfortunately, pandas is stupid and # can't handle the vignet format, so we don't load those up # note that you CAN load them up by passing "do_exclude=True", which then # returns a second variable containing the vignets and aperture fluxes and # errors # data has certain columns removed, needed for processing. # unfortunately I need full_data's vignettes and other info for later steps # TODO optimize this cuz this is clearly wasteful # I'm loading an HDUlist in 2 places, but overhauling the digestor to load it once would be a challenge data = digestor.digest_fits(files[0], do_exclude=False) metaHDUList = [fits.open(files[0])] # list of HDULists #META for file in files[1:]: tmpData = digestor.digest_fits(file, do_exclude=False) data = data.append(tmpData) metaHDUList.append(fits.open(file)) fit_i = jamierod_results.loc[expid] misalignment = { "z04d": fit_i["z04d"], "z04x": fit_i["z04x"], "z04y": fit_i["z04y"], "z05d": fit_i["z05d"], "z05x": fit_i["z05x"], "z05y": fit_i["z05y"], "z06d": fit_i["z06d"], "z06x": fit_i["z06x"], "z06y": fit_i["z06y"], "z07d": fit_i["z07d"], "z07x": fit_i["z07x"], "z07y": fit_i["z07y"], "z08d": fit_i["z08d"], "z08x": fit_i["z08x"], "z08y": fit_i["z08y"], "z09d": fit_i["z09d"], "z09x": fit_i["z09x"], "z09y": fit_i["z09y"], "z10d": fit_i["z10d"], "z10x": fit_i["z10x"], "z10y": fit_i["z10y"], "rzero": fit_i["rzero"], } # print(misalignment['rzero']) # rzero needs to be adjusted to be smaller than the stars! x = 0.3 / 0.14 # 4 misalignment["rzero"] = 1 / (1 / misalignment["rzero"] - x) # print(misalignment['rzero']) # print(.14*x ) data["rzero"] = misalignment["rzero"] optPSFStamps, model = WF.draw_psf(data, misalignment=misalignment) # optPSFStamps is a numpy data cube # full_data is data frame including vignettes of the stars return optPSFStamps, metaHDUList
from WavefrontPSF.wavefront import Wavefront from WavefrontPSF.digestor import Digestor from WavefrontPSF.analytic_interpolator import DECAM_Analytic_Wavefront, r0_guess from WavefrontPSF.donutengine import DECAM_Model_Wavefront from WavefrontPSF.donutengine import generate_random_coordinates import pandas as pd mesh_directory = '/Users/cpd/Projects/WavefrontPSF/meshes/Science-20140212s2-v1i2' mesh_name = 'Science-20140212s2-v1i2_All' out_dir = mesh_directory + '/pics/' PSF_Interpolator = Other_Function_Interpolator(function=np.median, mesh_name=mesh_name, directory=mesh_directory) num_bins = 6 WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator, num_bins=num_bins) x = [] y = [] if num_bins >= 2: num_bins_make = num_bins + (num_bins-1) else: num_bins_make = num_bins for key in WF.decaminfo.infoDict.keys(): if 'F' in key: continue xi, yi = WF.decaminfo.getBounds(key, num_bins_make) xi = np.array(xi) xi = 0.5 * (xi[1:] + xi[:-1]) yi = np.array(yi) yi = 0.5 * (yi[1:] + yi[:-1])
def do_run(expid, aos=False): if aos: plot_dir = out_dir + '/plots_aos/{0:08d}'.format(expid) else: plot_dir = out_dir + '/plots/{0:08d}'.format(expid) if not path.exists(plot_dir): makedirs(plot_dir) import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from WavefrontPSF.psf_interpolator import Mesh_Interpolator from WavefrontPSF.wavefront import Wavefront from WavefrontPSF.digestor import Digestor from WavefrontPSF.psf_evaluator import Moment_Evaluator from WavefrontPSF.donutengine import DECAM_Model_Wavefront medsubkeys = ['e0', 'e1', 'e2', 'E1norm', 'E2norm', 'delta1', 'delta2', 'zeta1', 'zeta2'] rows = ['e0', 'e0_medsub', 'e1', 'e1_medsub', 'e2', 'e2_medsub', 'E1norm', 'E1norm_medsub', 'E2norm', 'E2norm_medsub', 'delta1', 'delta1_medsub', 'delta2', 'delta2_medsub', 'zeta1', 'zeta1_medsub', 'zeta2', 'zeta2_medsub'] # set up objects. make sure I get the right mesh digestor = Digestor() PSF_Evaluator = Moment_Evaluator() mesh_name = 'Science-20121120s1-v20i2_All' PSF_Interpolator = Mesh_Interpolator(mesh_name=mesh_name, directory=mesh_directory) # This will be our main wavefront WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator) # let's create a Wavefront object for the data WF_data = Wavefront(PSF_Interpolator=None, PSF_Evaluator=PSF_Evaluator) # premake coordinate list coords = [] for num_bins in xrange(6): # create coordinates x = [] y = [] if num_bins >= 2: num_bins_make = num_bins + (num_bins-1) else: num_bins_make = num_bins for key in WF.decaminfo.infoDict.keys(): if 'F' in key: continue xi, yi = WF.decaminfo.getBounds(key, num_bins_make) xi = np.array(xi) xi = 0.5 * (xi[1:] + xi[:-1]) yi = np.array(yi) yi = 0.5 * (yi[1:] + yi[:-1]) xi, yi = np.meshgrid(xi, yi) xi = xi.flatten() yi = yi.flatten() x += list(xi) y += list(yi) x = np.array(x) y = np.array(y) coords_i = pd.DataFrame({'x': x, 'y': y}) coords.append(coords_i) # load up data expid_path = '{0:08d}/{1:08d}'.format(expid - expid % 1000, expid) data_directory = base_directory + expid_path # load up all the data from an exposure. Unfortunately, pandas is stupid and # can't handle the vignet format, so we don't load those up # note that you CAN load them up by passing "do_exclude=True", which then # returns a second variable containing the vignets and aperture fluxes and # errors model = digestor.digest_directory( data_directory, file_type='_selpsfcat.fits') # cut the old data appropriately model = model[(model['SNR_WIN'] > 90) & (model['SNR_WIN'] < 400)] # create normalized moments model['E1norm'] = model['e1'] / model['e0'] model['E2norm'] = model['e2'] / model['e0'] # do med sub and add to WF_data for key in medsubkeys: model['{0}_medsub'.format(key)] = model[key] - np.median(model[key]) WF_data.data = model # set the number of bins from total number of stars if len(model) < 200: num_bins = 0 num_bins_mis = 0 num_bins_whisker = 0 elif len(model) < 1000: num_bins = 1 # num_bins_mis = 0 num_bins_mis = 1 num_bins_whisker = 1 elif len(model) < 10000: num_bins = 2 # num_bins_mis = 1 num_bins_mis = 2 num_bins_whisker = 2 else: num_bins = 3 num_bins_mis = 2 num_bins_whisker = 2 # generate optics model from fit data fit_i = jamierod_results.loc[expid] # TODO: Add ALL fit_i params that were used? # TODO: get rzero? if aos: misalignment = {'z04d': fit_i['aos_z04d'], 'z05d': fit_i['aos_z05d'], 'z05x': fit_i['aos_z05x'], 'z05y': fit_i['aos_z05y'], 'z06d': fit_i['aos_z06d'], 'z06x': fit_i['aos_z06x'], 'z06y': fit_i['aos_z06y'], 'z07d': fit_i['aos_z07d'], 'z07x': fit_i['aos_z07x'], 'z07y': fit_i['aos_z07y'], 'z08d': fit_i['aos_z08d'], 'z08x': fit_i['aos_z08x'], 'z08y': fit_i['aos_z08y'], 'z09d': fit_i['aos_z09d'], 'z10d': fit_i['aos_z10d'], 'rzero': fit_i['aos_rzero']} else: misalignment = {'z04d': fit_i['z04d'], 'z04x': fit_i['z04x'], 'z04y': fit_i['z04y'], 'z05d': fit_i['z05d'], 'z05x': fit_i['z05x'], 'z05y': fit_i['z05y'], 'z06d': fit_i['z06d'], 'z06x': fit_i['z06x'], 'z06y': fit_i['z06y'], 'z07d': fit_i['z07d'], 'z07x': fit_i['z07x'], 'z07y': fit_i['z07y'], 'z08d': fit_i['z08d'], 'z08x': fit_i['z08x'], 'z08y': fit_i['z08y'], 'z09d': fit_i['z09d'], 'z09x': fit_i['z09x'], 'z09y': fit_i['z09y'], 'z10d': fit_i['z10d'], 'z10x': fit_i['z10x'], 'z10y': fit_i['z10y'], 'rzero': fit_i['rzero']} # create model fit from donuts WF.data = coords[num_bins].copy() WF.data['rzero'] = misalignment['rzero'] WF.data = WF(WF.data, misalignment=misalignment) # add dc factors WF.data['e0'] += fit_i['e0'] WF.data['e1'] += fit_i['e1'] WF.data['e2'] += fit_i['e2'] WF.data['delta1'] += fit_i['delta1'] WF.data['delta2'] += fit_i['delta2'] WF.data['zeta1'] += fit_i['zeta1'] WF.data['zeta2'] += fit_i['zeta2'] # create normalized moments WF.data['E1norm'] = WF.data['e1'] / WF.data['e0'] WF.data['E2norm'] = WF.data['e2'] / WF.data['e0'] # update WF medsubs appropriately for key in medsubkeys: WF.data['{0}_medsub'.format(key)] = WF.data[key] - np.median(WF.data[key]) # add a couple diagnostic things WF.data['num_bins'] = num_bins WF.data['expid'] = expid # put in data into field WF.reduce(num_bins=num_bins) # create another reduced field for setting the color levels field_model, _, _ = WF.reduce_data_to_field( WF.data, xkey='x', ykey='y', reducer=np.median, num_bins=num_bins_mis) # update WF_data fields WF_data.reduce(num_bins=num_bins) # create another reduced field for setting the color levels field_data, _, _ = WF_data.reduce_data_to_field( WF_data.data, xkey='x', ykey='y', reducer=np.median, num_bins=num_bins_mis) # put in residual of data minus model for field for row_i, row in enumerate(rows): WF.field[row + '_data'] = WF_data.field[row] WF.field[row + '_residual'] = WF_data.field[row] - WF.field[row] field_model[row + '_residual'] = field_data[row] - field_model[row] # create plots for row in rows: ncols = 3 nrows = 1 fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(6*ncols, 5*nrows)) fig.suptitle('Expid: {0}, {1}'.format(expid, row)) vmin = np.nanmin((field_model[row].min(), field_data[row].min())) vmax = np.nanmax((field_data[row].max(), field_model[row].max())) vmindiff = field_model[row + '_residual'].min() vmaxdiff = field_model[row + '_residual'].max() ax = axs[0] ax.set_title('Data') WF_data.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) ax = axs[1] ax.set_title('Model') WF.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) ax = axs[2] ax.set_title('Residual') WF.plot_field(row + '_residual', fig=fig, ax=ax, a=vmindiff, b=vmaxdiff) fig.savefig(plot_dir + '/{0}_{1}.png'.format(row, expid)) fig.savefig(plot_dir + '/{0}_{1}.pdf'.format(row, expid)) # TODO: Save each axis individually as well: fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) ax.set_title('Expid: {0}, {1}'.format(expid, row)) vmin = np.nanmin((field_model[row].min(), field_data[row].min())) vmax = np.nanmax((field_data[row].max(), field_model[row].max())) vmindiff = field_model[row + '_residual'].min() vmaxdiff = field_model[row + '_residual'].max() WF_data.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) fig.savefig(plot_dir + '/{0}_{1}_data.png'.format(row, expid)) fig.savefig(plot_dir + '/{0}_{1}_data.pdf'.format(row, expid)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) ax.set_title('Expid: {0}, {1}'.format(expid, row)) WF.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) fig.savefig(plot_dir + '/{0}_{1}_model.png'.format(row, expid)) fig.savefig(plot_dir + '/{0}_{1}_model.pdf'.format(row, expid)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) ax.set_title('Expid: {0}, {1}'.format(expid, row)) WF.plot_field(row + '_residual', fig=fig, ax=ax, a=vmindiff, b=vmaxdiff) fig.savefig(plot_dir + '/{0}_{1}_residual.png'.format(row, expid)) fig.savefig(plot_dir + '/{0}_{1}_residual.pdf'.format(row, expid)) plt.close('all') # save whisker plots too # do w, e, and normalized e. # for each: data and model separate, data plus model, residual ########################################################################### # w ########################################################################### num_spokes = 2 scalefactor = 0.2 scalefactor_residual = 0.5 quiverdict = {'width': 2} # w # data fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='e1_data', e2key='e2_data', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_w_{0}_data.png'.format(expid)) fig.savefig(plot_dir + '/whisker_w_{0}_data.pdf'.format(expid)) # w # model fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='e1', e2key='e2', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_w_{0}_model.png'.format(expid)) fig.savefig(plot_dir + '/whisker_w_{0}_model.pdf'.format(expid)) # w # data + model fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='e1_data', e2key='e2_data', do_var=False, legend=False, quiverdict=quiverdict) fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=fig, ax=ax, scalefactor=scalefactor, num_spokes=num_spokes, color='red', e1key='e1', e2key='e2', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_w_{0}_blackdata_redmodel.png'.format(expid)) fig.savefig(plot_dir + '/whisker_w_{0}_blackdata_redmodel.pdf'.format(expid)) # w # residual fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor_residual, num_spokes=num_spokes, color='black', e1key='e1_residual', e2key='e2_residual', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_w_{0}_residual.png'.format(expid)) fig.savefig(plot_dir + '/whisker_w_{0}_residual.pdf'.format(expid)) ########################################################################### ########################################################################### # e ########################################################################### num_spokes = 1 scalefactor = 1 scalefactor_residual = 5 quiverdict = {'width': 2} # e # data fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='e1_data', e2key='e2_data', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_e_{0}_data.png'.format(expid)) fig.savefig(plot_dir + '/whisker_e_{0}_data.pdf'.format(expid)) # e # model fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='e1', e2key='e2', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_e_{0}_model.png'.format(expid)) fig.savefig(plot_dir + '/whisker_e_{0}_model.pdf'.format(expid)) # e # data + model fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='e1_data', e2key='e2_data', do_var=False, legend=False, quiverdict=quiverdict) fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=fig, ax=ax, scalefactor=scalefactor, num_spokes=num_spokes, color='red', e1key='e1', e2key='e2', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_e_{0}_blackdata_redmodel.png'.format(expid)) fig.savefig(plot_dir + '/whisker_e_{0}_blackdata_redmodel.pdf'.format(expid)) # e # residual fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=False, fig=None, ax=None, scalefactor=scalefactor_residual, num_spokes=num_spokes, color='black', e1key='e1_residual', e2key='e2_residual', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_e_{0}_residual.png'.format(expid)) fig.savefig(plot_dir + '/whisker_e_{0}_residual.pdf'.format(expid)) ########################################################################### ########################################################################### # E ########################################################################### num_spokes = 1 scalefactor = 1 scalefactor_residual = 5 quiverdict = {'width': 2} # E # data fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=True, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='E1norm_data', e2key='E2norm_data', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_Enorm_{0}_data.png'.format(expid)) fig.savefig(plot_dir + '/whisker_Enorm_{0}_data.pdf'.format(expid)) # E # model fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=True, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='E1norm', e2key='E2norm', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_Enorm_{0}_model.png'.format(expid)) fig.savefig(plot_dir + '/whisker_Enorm_{0}_model.pdf'.format(expid)) # E # data + model fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=True, fig=None, ax=None, scalefactor=scalefactor, num_spokes=num_spokes, color='black', e1key='E1norm_data', e2key='E2norm_data', do_var=False, legend=False, quiverdict=quiverdict) fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=True, fig=fig, ax=ax, scalefactor=scalefactor, num_spokes=num_spokes, color='red', e1key='E1norm', e2key='E2norm', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_Enorm_{0}_blackdata_redmodel.png'.format(expid)) fig.savefig(plot_dir + '/whisker_Enorm_{0}_blackdata_redmodel.pdf'.format(expid)) # E # residual fig, ax = WF.plot_whisker(WF.field, num_bins=num_bins_whisker, normalized_ellipticity=True, fig=None, ax=None, scalefactor=scalefactor_residual, num_spokes=num_spokes, color='black', e1key='E1norm_residual', e2key='E2norm_residual', do_var=False, legend=True, quiverdict=quiverdict) fig.savefig(plot_dir + '/whisker_Enorm_{0}_residual.png'.format(expid)) fig.savefig(plot_dir + '/whisker_Enorm_{0}_residual.pdf'.format(expid)) ########################################################################### # make plot of N fig, ax = plt.subplots(figsize=(6,5)) WF_data.plot_field('N', fig=fig, ax=ax) fig.savefig(plot_dir + '/N_{0}.png'.format(expid)) plt.close('all') # save summary statistics of stars to npy file for later collection if aos: field_jamie = pd.read_pickle(pkl_dir + '/{0:08d}.pkl'.format(expid)) for row in rows: WF.field[row + '_jamie'] = field_jamie[row] WF.field[row + '_jamie_residual'] = WF.field[row + '_data'] - \ WF.field[row + '_jamie'] WF.field.to_pickle(pkl_dir + '/aos_{0:08d}.pkl'.format(expid)) else: WF.field.to_pickle(pkl_dir + '/{0:08d}.pkl'.format(expid))
def meshes(mesh_i=-1): # make wavefront plots from the by_date meshes and show how they affect # params. also show difference from the nominal wavefront of all of them # combined import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from WavefrontPSF.psf_interpolator import Mesh_Interpolator, kNN_Interpolator from WavefrontPSF.wavefront import generate_one_coordinate_per_bin from WavefrontPSF.donutengine import DECAM_Model_Wavefront from glob import glob mesh_names = glob(new_mesh_directory + '/z4*Science-20140212s2-v20i2*.dat') mesh_names = [mesh_name.split('z4Mesh_')[-1].split('.dat')[0] for mesh_name in mesh_names] if mesh_i != -1: mesh_names_enumerate = [mesh_names[mesh_i]] else: mesh_names_enumerate = mesh_names rzero = 0.15 medsubkeys = ['e0', 'e1', 'e2', 'E1norm', 'E2norm', 'delta1', 'delta2', 'zeta1', 'zeta2'] medsubkeys += ['z{0}'.format(zi) for zi in xrange(4, 12)] rows = medsubkeys + [key + '_medsub' for key in medsubkeys] ########################################################################### # generate just from donuts used ########################################################################### num_bins = 2 for indx, mesh_name in enumerate(mesh_names_enumerate): plot_dir = out_dir + '/plots_meshes/{0}'.format(mesh_name) if not path.exists(plot_dir): makedirs(plot_dir) print(indx, mesh_name, len(mesh_names)) PSF_Interpolator = Mesh_Interpolator(mesh_name=mesh_name, directory=new_mesh_directory) # subtract the median z4 seeing to put this nominally in focus PSF_Interpolator.data['z4'] = PSF_Interpolator.data['z4'] - \ PSF_Interpolator.data['z4'].median() # generate mesh that contains all EXCEPT this mesh mesh_names_ex = [mesh_name_ex for mesh_name_ex in mesh_names if mesh_name_ex != mesh_name] for indx_ex, mesh_name_ex in enumerate(mesh_names_ex): PSF_Interpolator_ex = Mesh_Interpolator(mesh_name=mesh_name_ex, directory=new_mesh_directory) # subtract the median z4 seeing to put this nominally in focus PSF_Interpolator_ex.data['z4'] = PSF_Interpolator_ex.data['z4'] - \ PSF_Interpolator_ex.data['z4'].median() if indx == 0: data = PSF_Interpolator_ex.data.copy() data['mesh_name'] = mesh_name_ex else: # append PSF_Interpolator data to All_PSF_Interpolator data_i = PSF_Interpolator_ex.data.copy() data_i['mesh_name'] = mesh_name_ex data = data.append(data_i, ignore_index=True) Rest_PSF_Interpolator = kNN_Interpolator(data) print('Generating WF') # generate the two WFs WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator, num_bins=num_bins) # generate coordinates x, y = generate_one_coordinate_per_bin(num_bins) model = pd.DataFrame({'x': x, 'y': y}) model = Rest_PSF_Interpolator(model, force_interpolation=True) WF_rest = DECAM_Model_Wavefront(PSF_Interpolator=Rest_PSF_Interpolator, num_bins=num_bins) misalignment = {'rzero': rzero} # create model fit from donuts PSF_Interpolator.data['rzero'] = misalignment['rzero'] WF.data = WF(PSF_Interpolator.data, misalignment=misalignment) WF.reduce(num_bins=num_bins) model['rzero'] = misalignment['rzero'] WF_rest.data = WF_rest(model, misalignment=misalignment) WF_rest.reduce(num_bins=num_bins) # add corrections to norms in field WF.field['E1norm'] = WF.field['e1'] / WF.field['e0'] WF.field['E2norm'] = WF.field['e2'] / WF.field['e0'] WF_rest.field['E1norm'] = WF_rest.field['e1'] / WF_rest.field['e0'] WF_rest.field['E2norm'] = WF_rest.field['e2'] / WF_rest.field['e0'] # collate the medsubs for key in medsubkeys: WF.field[key + '_medsub'] = WF.field[key] - WF.field[key].median() WF_rest.field[key + '_medsub'] = WF_rest.field[key] - WF_rest.field[key].median() # now add residual to WF for row in rows: WF.field[row + '_residual'] = WF_rest.field[row] - WF.field[row] print('Making plots') # plots! for row in rows: ncols = 3 nrows = 1 fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(6*ncols, 5*nrows)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) vmin = np.nanmin((WF.field[row].min(), WF_rest.field[row].min())) vmax = np.nanmax((WF.field[row].max(), WF_rest.field[row].max())) vmindiff = WF.field[row + '_residual'].min() vmaxdiff = WF.field[row + '_residual'].max() ax = axs[0] ax.set_title('Baseline Minus Exposure') WF_rest.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) ax = axs[1] ax.set_title('Single Exposure') WF.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) ax = axs[2] ax.set_title('Residual') WF.plot_field(row + '_residual', fig=fig, ax=ax, a=vmindiff, b=vmaxdiff) fig.savefig(plot_dir + '/{0}_{1}.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/{0}_{1}.pdf'.format(row, mesh_name)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) ax.set_title('Baseline Minus Exposure') WF_rest.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) fig.savefig(plot_dir + '/{0}_{1}_baseline.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/{0}_{1}_baseline.pdf'.format(row, mesh_name)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) ax.set_title('Single Exposure') WF.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) fig.savefig(plot_dir + '/{0}_{1}_exposure.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/{0}_{1}_exposure.pdf'.format(row, mesh_name)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) ax.set_title('Residual') WF.plot_field(row + '_residual', fig=fig, ax=ax, a=vmindiff, b=vmaxdiff) fig.savefig(plot_dir + '/{0}_{1}_residual.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/{0}_{1}_residual.pdf'.format(row, mesh_name)) plt.close('all') ####################################################################### # generate interpolated model ####################################################################### print('interpolated model') num_bins = 5 # generate coordinates x, y = generate_one_coordinate_per_bin(num_bins) model = pd.DataFrame({'x': x, 'y': y}) model = PSF_Interpolator(model, force_interpolation=True) # generate the two WFs WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator, num_bins=num_bins) # generate coordinates model_rest = pd.DataFrame({'x': x, 'y': y}) model_rest = Rest_PSF_Interpolator(model_rest, force_interpolation=True) WF_rest = DECAM_Model_Wavefront(PSF_Interpolator=Rest_PSF_Interpolator, num_bins=num_bins) misalignment = {'rzero': rzero} # create model fit from donuts model['rzero'] = misalignment['rzero'] WF.data = WF(model, misalignment=misalignment) WF.reduce(num_bins=num_bins) model_rest['rzero'] = misalignment['rzero'] WF_rest.data = WF_rest(model_rest, misalignment=misalignment) WF_rest.reduce(num_bins=num_bins) # add corrections to norms in field WF.field['E1norm'] = WF.field['e1'] / WF.field['e0'] WF.field['E2norm'] = WF.sdffield['e2'] / WF.field['e0'] WF_rest.field['E1norm'] = WF_rest.field['e1'] / WF_rest.field['e0'] WF_rest.field['E2norm'] = WF_rest.field['e2'] / WF_rest.field['e0'] # collate the medsubs for key in medsubkeys: WF.field[key + '_medsub'] = WF.field[key] - WF.field[key].median() WF_rest.field[key + '_medsub'] = WF_rest.field[key] - WF_rest.field[key].median() # now add residual to WF for row in rows: WF.field[row + '_residual'] = WF_rest.field[row] - WF.field[row] print('plots') # plots! for row in rows: ncols = 3 nrows = 1 fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(6*ncols, 5*nrows)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) vmin = np.nanmin((WF.field[row].min(), WF_rest.field[row].min())) vmax = np.nanmax((WF.field[row].max(), WF_rest.field[row].max())) vmindiff = WF.field[row + '_residual'].min() vmaxdiff = WF.field[row + '_residual'].max() ax = axs[0] ax.set_title('Baseline Minus Exposure') WF_rest.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) ax = axs[1] ax.set_title('Single Exposure') WF.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) ax = axs[2] ax.set_title('Residual') WF.plot_field(row + '_residual', fig=fig, ax=ax, a=vmindiff, b=vmaxdiff) fig.savefig(plot_dir + '/interp_{0}_{1}.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/interp_{0}_{1}.pdf'.format(row, mesh_name)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) ax.set_title('Baseline Minus Exposure') WF_rest.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) fig.savefig(plot_dir + '/interp_{0}_{1}_baseline.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/interp_{0}_{1}_baseline.pdf'.format(row, mesh_name)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) ax.set_title('Single Exposure') WF.plot_field(row, fig=fig, ax=ax, a=vmin, b=vmax) fig.savefig(plot_dir + '/interp_{0}_{1}_exposure.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/interp_{0}_{1}_exposure.pdf'.format(row, mesh_name)) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 10)) fig.suptitle('{0}, {1}'.format(mesh_name, row)) ax.set_title('Residual') WF.plot_field(row + '_residual', fig=fig, ax=ax, a=vmindiff, b=vmaxdiff) fig.savefig(plot_dir + '/interp_{0}_{1}_residual.png'.format(row, mesh_name)) fig.savefig(plot_dir + '/interp_{0}_{1}_residual.pdf'.format(row, mesh_name)) plt.close('all')
from WavefrontPSF.wavefront import Wavefront from WavefrontPSF.digestor import Digestor from WavefrontPSF.psf_evaluator import Moment_Evaluator from WavefrontPSF.analytic_interpolator import DECAM_Analytic_Wavefront, r0_guess from WavefrontPSF.donutengine import DECAM_Model_Wavefront, generate_random_coordinates from WavefrontPSF.donutengine import correct_dz, correct_dz_theta digestor = Digestor() base_directory = '/nfs/slac/g/ki/ki18/des/cpd/psfex_catalogs/SVA1_FINALCUT/psfcat/' PSF_Evaluator = Moment_Evaluator() mesh_directory = '/nfs/slac/g/ki/ki18/cpd/Projects/WavefrontPSF/meshes/Science-20140212s2-v1i2' mesh_name = 'Science-20140212s2-v1i2_All' PSF_Interpolator = Mesh_Interpolator(mesh_name=mesh_name, directory=mesh_directory) # This will be our main wavefront WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator) # let's create a Wavefront object for the data WF_data = Wavefront(PSF_Interpolator=None, PSF_Evaluator=PSF_Evaluator) # premake coordinate list coords = [] for num_bins in xrange(6): # create coordinates x = [] y = [] if num_bins >= 2: num_bins_make = num_bins + (num_bins-1) else: num_bins_make = num_bins for key in WF.decaminfo.infoDict.keys(): if 'F' in key:
model = Digestor().digest_directory( '/Users/cpd/Projects/WavefrontPSF/meshes/00253794/', file_type='_selpsfcat.fits') # guess rzero model['rzero'] = r0_guess(model['e0'].min()) mesh_directory = '/Users/cpd/Projects/WavefrontPSF/meshes/Science-20140212s2-v1i2' mesh_name = 'Science-20140212s2-v1i2_All' PSF_Interpolator = Mesh_Interpolator(mesh_name=mesh_name, directory=mesh_directory) #from astropy.stats import mad_std WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator, num_bins=1, model=model)#, reducer=mad_std) # WF.plot_field('e0') # naieve = WF(WF.data) # WF.plot_colormap(naieve, 'x', 'y', 'e0', num_bins=1) # create a dummy dataset from 100 random points WF.data = model rows = np.random.choice(WF.data.index.values, 500, replace=False) test = WF.data.ix[rows].copy() # reset index test.index = np.arange(len(test)) misalignment_true = {'z09d':0.5, 'z04d':-0.1, 'z06x':0.002, 'rzero': 0.13, 'z07y': -0.003} test = WF(test, misalignment=misalignment_true) WF.data = test