""" save the label features table as .fits file """ path_dir = os.path.dirname(path) if not os.path.exists(path_dir): os.makedirs(path_dir) ft.write(path, table, clobber=True) if __name__ == '__main__': from argparse import ArgumentParser ap = ArgumentParser(description='Prepare eBOSS data for NN regression') ap.add_argument('-d', '--data_path', type=str, required=True) ap.add_argument('-r', '--randoms_path', type=str, required=True) ap.add_argument('-s', '--templates_path', type=str, required=True) ap.add_argument('-o', '--output_path', type=str, required=True) ap.add_argument('--label', type=str, default='ngal') ap.add_argument('-n', '--nside', type=int, default=512) ap.add_argument('-sl', '--slices', type=str, nargs='*', default=['main', 'highz']) config = ap.parse_args() setup_logging('info') prepare_table(config)
cls_list = get_cl(ngal, nran, mask, selection_fn=selection_fn, systematics=sysm, njack=0) if comm.rank == 0: output_dir = os.path.dirname(args.output_path) if not os.path.exists(output_dir): print(f'creating {output_dir}') os.makedirs(output_dir) np.save(args.output_path, cls_list) if __name__ == '__main__': setup_logging("info") # turn on logging to screen comm = CurrentMPIComm.get() if comm.rank == 0: print(f'hi from {comm.rank}') from argparse import ArgumentParser ap = ArgumentParser(description='Angular Clustering') ap.add_argument('-d', '--data_path', required=True) ap.add_argument('-o', '--output_path', required=True) ap.add_argument('-s', '--selection', default=None) ns = ap.parse_args() for (key, value) in ns.__dict__.items(): print(f'{key:15s} : {value}') else:
import sys sys.path.append('/Users/mehdi/github/lssutils') from lssutils.lab import * from lssutils import setup_logging setup_logging("info") biases = [1.0, 2.0, 3.0, 4.0] # initialize the task manager to run the tasks with TaskManager(cpus_per_task=1, use_all_cpus=True) as tm: # set up the linear power spectrum # iterate through the bias values for bias in tm.iterate(biases): print(2 * bias)
def main(mockid, my_cols=cols_dr8_rand): setup_logging('info') logger = logging.getLogger('RegressionPrep') # --- input parameters nside = 256 dataframe = '/home/mehdi/data/templates/pixweight-dr8-0.31.1.h5' random = f'/B/Shared/mehdi/mocksys/FA_EZmock_desi_ELG_v0_rand_00to2.hp{nside}.fits' output_dir = '/B/Shared/mehdi/mocksys/regression/' zcuts = {'low': [0.7, 1.0], 'high': [1.0, 1.5], 'all': [0.7, 1.5]} #--- # start #--- logger.info(f'ouput : {output_dir}') # --- templates df = pd.read_hdf(dataframe, key='templates') logger.info(f'read {dataframe}') # --- random hprandom = hp.read_map(random, verbose=False) logger.info(f'read {random}') # --- data data = ft.read( f'/B/Shared/Shadab/FA_LSS/FA_EZmock_desi_ELG_v0_{mockid}.fits') mask = ft.read( f'/B/Shared/Shadab/FA_LSS/EZmock_desi_v0.0_{mockid}/bool_index.fits' )['bool_index'] data = data[mask] z_rsd = data['Z_COSMO'] + data['DZ_RSD'] logger.info(f'read mock-{mockid}') for i, key_i in enumerate(zcuts): logger.info('split based on {}'.format(zcuts[key_i])) # --- prepare the names for the output files hpcat = None #output_dir + f'/galmap_{mockid}_{key_i}_{nside}.hp.fits' hpmask = None #output_dir + f'/mask_{mockid}_{key_i}_{nside}.hp.fits' fracgood = None #output_dir + f'/frac_{mockid}_{key_i}_{nside}.hp.fits' fitname = None #output_dir + f'/ngal_features_{mockid}_{key_i}_{nside}.fits' fitkfold = output_dir + f'ngal_features_{mockid}_{key_i}_{nside}.5r.npy' good = (z_rsd >= zcuts[key_i][0]) & (z_rsd < zcuts[key_i][1]) logger.info(f'total # : {good.sum()}') hpdata = hpixsum(nside, data[good]['RA'], data[good]['DEC']) # --- append the galaxy and random density dataframe_i = df.copy() dataframe_i['ngal'] = hpdata dataframe_i['nran'] = hprandom dataframe_i['nran'][hprandom == 0] = np.nan dataframe_i.replace() dataframe_i.replace([np.inf, -np.inf], value=np.nan, inplace=True) # replace inf dataframe_i.dropna(inplace=True) logger.info('df shape : {}'.format(dataframe_i.shape)) logger.info('columns : {}'.format(my_cols)) for column in dataframe_i.columns: logger.info( f'{column}: {np.percentile(dataframe_i[column], [0,1,99, 100])}' ) # --- write hd5_2_fits(dataframe_i, my_cols, fitname, hpmask, fracgood, fitkfold, res=nside, k=5)
default=[i for i in range(18)]) ap.add_argument('--nbin', default=8, type=int) ap.add_argument('--njack', default=20, type=int) ap.add_argument('--nside', default=256, type=int) ap.add_argument('--lmax', default=512, type=int) ap.add_argument('--smooth', action='store_true') ap.add_argument('--verbose', action='store_true') ns = ap.parse_args() if not os.path.exists(ns.oudir):os.makedirs(ns.oudir) logfile = ''.join([ns.oudir, ns.log]) if ns.log!='none' else None if logfile is not None:print(f'log in {logfile}') setup_logging('info', logfile=logfile) else: ns = None ns = comm.bcast(ns, root=0) #--- run engine = PhotData(ns) engine.read() if ns.nnbar != 'none': engine.run_nnbar() # mean density if ns.clfile != 'none':