Commit 26fa78ea authored by yuedong0607's avatar yuedong0607
Browse files

code reformatting

parent ff47158b

cosmology/clustering.py

+22 −19
Original line number Diff line number Diff line 
import os

import galsim

import h5py as h5

import healpy as hp

import numpy as np



import treecorr
@@ -47,6 +45,7 @@ def load_catalog_truth(file_path): 
    # return ra_arr[ind], dec_arr[ind], magr_arr[ind]

    return ra_arr, dec_arr, magr_arr





def generate_rand_radec(N, ra_range=(0, 360), dec_range=(-30, 30)):

    """

    Generate N random points uniformly distributed over RA/Dec ranges.
@@ -58,6 +57,7 @@ def generate_rand_radec(N, ra_range=(0, 360), dec_range=(-30, 30)): 
    dec = np.degrees(np.arcsin(sin_dec))

    return ra, dec





def compute_correlation(ra, dec, ra_rand, dec_rand, ra_units='deg', dec_units='deg', min_sep=0.01, max_sep=10., nbins=20, sep_units='deg'):

    cat = treecorr.Catalog(ra=ra, dec=dec, ra_units=ra_units, dec_units=dec_units)

    cat_rand = treecorr.Catalog(ra=ra_rand, dec=dec_rand, ra_units=ra_units, dec_units=dec_units)
@@ -69,12 +69,14 @@ def compute_correlation(ra, dec, ra_rand, dec_rand, ra_units='deg', dec_units='d 
    dr.process(cat, cat_rand)

    return nn, rr, dr





def get_xi(nn, rr, dr):

    xi, varxi = nn.calculateXi(rr=rr, dr=dr)

    r = np.exp(nn.meanlogr)  # in degrees

    sig = np.sqrt(varxi)

    return r, xi, sig, varxi





def plot_wtheta(r, xi, sig, min_sep=0.01, max_sep=10., color='blue', legend=' ', leg_symble=[], leg_label=[]):

    xi, varxi = nn.calculateXi(rr=rr, dr=dr)

    r = np.exp(nn.meanlogr)  # in degrees
@@ -119,6 +121,7 @@ def plot_wtheta(r, xi, sig, min_sep=0.01, max_sep=10., color='blue', legend=' ', 


#     plt.savefig('wtheta.png')





if __name__ == "__main__":

    file_path = os.path.join(gals_cat_dir, bundle_file_list[0])

    ra, dec, magr = load_catalog_truth(file_path)

cosmology/coadd_drizzle.py

+3 −2
Original line number Diff line number Diff line
@@ -294,6 +294,7 @@ def write_drizzle_output(path, sci, wht, ctx, wcs, overwrite=True): 
    fits.HDUList(hdus).writeto(path, overwrite=overwrite)

    print(f"Wrote {path}")





if __name__ == "__main__":

    exposures = [

        {

cosmology/metadetect_driver/__init__.py

+1 −1
Original line number Diff line number Diff line 
from ._base_driver import *

from .csst_mdet_driver import *
 
# from .csst_mdet_driver import *

cosmology/metadetect_driver/run_mdet_tile.py

+3 −2
Original line number Diff line number Diff line
@@ -13,6 +13,7 @@ from astropy.io.fits import Header 
from astropy.wcs import WCS

from astropy.wcs.utils import proj_plane_pixel_scales





def load_tile_from_parquet(parquet_path, tile_id):

    df = pd.read_parquet(

        parquet_path,

evaluation/aperture_noise.py

+40 −36
Original line number Diff line number Diff line
@@ -2,10 +2,9 @@ import argparse 
import numpy as np

import os

from astropy.io import fits

from astropy.stats import sigma_clip

from astropy.stats import median_absolute_deviation

from astropy.stats import mad_std





def get_all_stats(values_arrays):

    """

    """
@@ -18,6 +17,7 @@ def get_all_stats(values_arrays): 


    return stats





def define_options():

    parser = argparse.ArgumentParser()

    parser.add_argument('--data_image', dest='data_image', type=str, required=True,
@@ -42,6 +42,7 @@ def define_options(): 
                        default="./workspace", help='dir path for the output : (default: "%(default)s"')

    return parser





def create_circular_mask(h, w, center=None, radius=None):

    if center is None:  # use the middle of the image

        center = [int(w / 2), int(h / 2)]
@@ -54,6 +55,7 @@ def create_circular_mask(h, w, center=None, radius=None): 
    mask = dist_from_center <= radius

    return mask





def sampling(image_data, seg_data, flag_data, aperture, Nsample):

    wx, wy = np.where((image_data != 0) & (seg_data == 0) & (flag_data == 0))

    Nx, Ny = image_data.shape
@@ -104,6 +106,7 @@ def sampling(image_data, seg_data, flag_data, aperture, Nsample): 


    return flux_average, flux_median, x_position, y_position





def noise_statistics_aperture(fitsname, segname, flagname=None, sky_image=None, aperture_min=1, aperture_max=10, aperture_step=1, seed=None, Nsample=100, sigma_cl=10., base_name="aper", output_dir='./'):

    f = fits.open(fitsname)

    fseg = fits.open(segname)
@@ -159,6 +162,7 @@ def noise_statistics_aperture(fitsname, segname, flagname=None, sky_image=None, 


    return aperture_list, sigma_output, mad_output, mad_std_output





if __name__ == "__main__":

    args = define_options().parse_args()

    aperture_ap, sigma_ap, mad_ap, nmad_ap = noise_statistics_aperture(