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tutorials/euclid/euclid_clusters_tutorial.md

@@ -188,6 +188,7 @@ To explore a different control field, change the seed value or remove the seed e
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 def check_mer_tile_requirement(coord, search_radius=2.0):
     """Check whether a sky coordinate is covered by exactly 4 Euclid MER science images.
@@ -230,6 +231,7 @@ def check_mer_tile_requirement(coord, search_radius=2.0):
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 def find_control_field(cluster_df, cluster_ra, cluster_dec, min_distance_arcmin=15, max_attempts=100):
     """Find a random control field offset from the known cluster catalog.
@@ -342,6 +344,7 @@ Because the four photometric bands are independent of each other, `download_and_
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 def _download_band(band, mer_images, field_coord, field_id, cache_dir, s3):
     """Download one photometric band from S3 and write a cutout FITS to the local cache.
@@ -401,6 +404,7 @@ def _download_band(band, mer_images, field_coord, field_id, cache_dir, s3):
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 def download_and_cache_field(mer_images, field_name, field_coord, field_id):
     """Stream cutout FITS images from S3 and cache them locally.
@@ -482,6 +486,7 @@ Displaying the cluster and control fields side by side at the same stretch gives
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 def normalize_with_consistent_stretch(cluster_cutouts, control_cutouts, lower_percentile=1, upper_percentile=99):
     """Normalize cluster and control cutouts using a shared percentile stretch.
@@ -538,6 +543,7 @@ def normalize_with_consistent_stretch(cluster_cutouts, control_cutouts, lower_pe
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 def downsample(arr, factor=4):
     """Block-average a 2-D or 3-D image array by an integer factor for faster display.
@@ -646,6 +652,7 @@ Then we show part of the cluster dataframe to see what information we have avail
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 def query_galaxies_for_field(ra, dec, field_name, redshift_center, redshift_width=0.1):
     """Query galaxies within a redshift slice around a sky position.
@@ -781,6 +788,7 @@ The function prints the number of galaxies within bounds as a diagnostic, if a l
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 def apply_dbscan_clustering(galaxy_df, wcs, rgb_image, field_name, eps=500, min_samples=18):
     """Apply DBSCAN to detect galaxy overdensities in a redshift-selected sample. DBSCAN operates on the 2-D projected
@@ -938,6 +946,7 @@ Combining field galaxies from both the cluster and control fields gives us a lar
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 def identify_cluster_members(galaxy_df, labels, galaxy_coords, field_name):
     """Separate a galaxy sample into cluster members and field galaxies.
@@ -1058,6 +1067,7 @@ We convert the uniform-aperture fluxes in the photo-z catalog to AB magnitudes a
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 def calculate_color_magnitude(df):
     """Convert uniform-aperture fluxes to AB magnitudes and compute Y-H color.
@@ -1102,6 +1112,7 @@ field_cmd = calculate_color_magnitude(all_field_galaxies[['flux_y_unif', 'flux_h
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 def remove_outliers_bounds(df, h_min=17, h_max=25, yh_min=-0.5, yh_max=1.5):
     """Filter a colour-magnitude table to a physically motivated range.
@@ -1242,6 +1253,7 @@ Ten spectra per group is sufficient to show whether the cluster and field popula
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 def get_n_spectra(obj_ids, n=10):
     """
@@ -1434,6 +1446,7 @@ The three helper functions below handle this: `preprocess_spectrum` continuum-su
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 def preprocess_spectrum(spec):
     """Continuum-remove + robust-normalize a spectrum for visualization."""
@@ -1487,6 +1500,7 @@ def preprocess_spectrum(spec):
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 def build_stack(spectra_dict, w_grid):
     """Interpolate processed spectra onto a common grid and return matrix [nobj, ngrid]."""
@@ -1514,6 +1528,7 @@ def build_stack(spectra_dict, w_grid):
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 def lines_in_window(wmin, wmax, z):
     """Return dict of lines whose observed wavelength falls in [wmin, wmax]."""
@@ -1553,6 +1568,7 @@ field_stack   = build_stack(field_spectra,   w_grid)
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 def label_emission_line(ax, x, label, y=0.92, rotation=90, color='k'):
     """Place a vertical emission-line label at wavelength x."""
@@ -1639,6 +1655,7 @@ This provides an independent check of cluster membership using spectroscopic red
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 def search_ned_field(ra, dec, z_min, z_max, label, radius_arcmin=3, max_retries=3):
     """Search NED within radius_arcmin of (ra, dec), filter to z_min–z_max, and print a summary.
@@ -1731,6 +1748,7 @@ We now overlay the NED-catalogued objects on the cluster and control field image
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 def overlay_ned_sources(
     cluster_objects, control_objects,

tutorials/euclid/merged-objects-hats-catalog/4-euclid-q1-hats-magnitudes.md

@@ -232,6 +232,7 @@ Since the template-fit photometry is recommended for extended objects, we'll sep
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 # Galaxy + any. Star + galaxy. QSO + galaxy.
 classes = {"Galaxy": (2, 3, 6, 7), "Star": (1, 3), "QSO": (4, 6)}
@@ -304,6 +305,7 @@ This figure is inspired by Romelli Fig. 6 (top panel).
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 # Only consider objects within these mag and mag difference limits.
 mag_limits, mag_diff_limits = (16, 24), (-1, 1)

tutorials/simulated-data/OpenUniverse2024/openuniverse2024_SED_fit.md

@@ -110,6 +110,7 @@ list out some basic information including column names
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 def inspect_parquet_columns(s3_path, *, region='us-east-1', max_rows=0):
     """
@@ -176,6 +177,7 @@ Next we merge the SN sample with the host galaxy fluxes and physical properties.
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 def assemble_SN_data(sn_flux_file, galaxy_flux_file, galaxy_info_file, *, region='us-east-1'):
     """
@@ -251,6 +253,7 @@ A tight correlation along the 1:1 line indicates successful matching.
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 def plot_redshift_comparison(df):
     """
@@ -349,6 +352,7 @@ roman_bands = {
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 def scale_rubin_to_roman(df, rubin_bands, roman_bands, match_wave=0.87, verbose=False):
     """
@@ -445,6 +449,7 @@ def scale_rubin_to_roman(df, rubin_bands, roman_bands, match_wave=0.87, verbose=
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 def photons_to_maggies_with_filter(photon_flux, filt):
     """
@@ -493,6 +498,7 @@ def photons_to_maggies_with_filter(photon_flux, filt):
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 def add_maggie_columns(df):
     """
@@ -545,6 +551,7 @@ def add_maggie_columns(df):
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 def plot_single_sed(df, rubin_bands, roman_bands, galaxy_index=0, loglog=False):
     """
@@ -636,6 +643,7 @@ plot_single_sed(df_scaled, rubin_bands, roman_bands, 1, loglog=True)
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 def plot_many_seds(df, rubin_bands, roman_bands, n_galaxies=10, loglog=False):
     """
@@ -744,6 +752,7 @@ Prospector has its own built in functions to do writing and reading, but
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 def df_to_all_obs(df, flux_err_frac=0.1):
     """
@@ -801,6 +810,7 @@ def df_to_all_obs(df, flux_err_frac=0.1):
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 def process_output(output, model, obs, sps):
     """
@@ -903,6 +913,7 @@ def process_output(output, model, obs, sps):
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 def run_fit(obs, model_params, sps, lnprobfn, noise_model, fitting_kwargs):
     """
@@ -965,6 +976,7 @@ def run_fit(obs, model_params, sps, lnprobfn, noise_model, fitting_kwargs):
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 def run_fit_star(args):
     """
@@ -1007,6 +1019,7 @@ def run_fit_star(args):
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 def fit_sample_parallel(obs_list, model_params, sps, lnprobfn, noise_model=(None,None),
                         fitting_kwargs=None, nproc=None):
@@ -1057,6 +1070,7 @@ def fit_sample_parallel(obs_list, model_params, sps, lnprobfn, noise_model=(None
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 def save_outputs_hdf5(outputs, obs_list, filename="all_galaxies.h5"):
     """
@@ -1240,6 +1254,7 @@ use this section to quickly load the fitted results instead of re-running Prospe
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 def download_results_hdf5_from_gdrive(file_id, dest_path="./sn_fits.h5"):
     """
@@ -1272,6 +1287,7 @@ def download_results_hdf5_from_gdrive(file_id, dest_path="./sn_fits.h5"):
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 def load_outputs_hdf5(filename="sn_fits.h5",
                       file_id="1BuymtEXJsxbd8PqwkIEmff-76JGGa650"):
@@ -1353,6 +1369,7 @@ After fitting, we can visualize the results by comparing observed and modeled SE
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 def plot_SED_fit(outputs, obs_list, galaxy_id):
     """
@@ -1464,6 +1481,7 @@ for gid in galaxy_ids[:5]:
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 def plot_corner(outputs, obs_list, galaxy_id):
     """
@@ -1556,6 +1574,7 @@ Agreement along the 1:1 line indicates successful recovery of physical parameter
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 def plot_stellar_mass_verification(df, outputs):
     """
@@ -1642,6 +1661,7 @@ We compare the distribution of Prospector-derived stellar masses for the two SN
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 def plot_1a_vs_cc_mass(df_sn, outputs):
     """

tutorials/simulated-data/OpenUniverse2024/openuniverse2024_TDE_light_curve.md

@@ -143,6 +143,7 @@ Let's take a look at a few images to see what we are dealing with.
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 def show_gallery(files, max_images=9):
     """
@@ -292,6 +293,7 @@ OU_RUBIN_SIA_COLLECTION = 'simulated_rubin_openuniverse2024'
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 def get_s3_fpath(cloud_access):
     cloud_info = json.loads(cloud_access) # converts str to dict
@@ -307,6 +309,7 @@ First, we find the filenames of the images in the Roman TDS survey which include
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 def Roman_TDS_image_search(host_galaxy, radius, bandname):
     """
@@ -360,6 +363,7 @@ Since there are > 100 TDS images per band, we will:
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 def select_images_by_mjd_quantiles(images, n_select=9):
     """
@@ -426,6 +430,7 @@ The two plotting functions then compile these measurements into time-ordered plo
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 def run_aperture_photometry(host_galaxy, bandname, image_column="image_filenames", aperture_radius=1.0):
     """
@@ -525,6 +530,7 @@ host_galaxy
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 def plot_single_band_light_curve(df, bandname, start_mjd):
     """
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 def plot_multiband_light_curve(df, bands, start_mjd):
     """
@@ -685,6 +692,7 @@ We follow the example in this [tutorial](https://caltech-ipac.github.io/irsa-tut
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 def make_cutout(fname, ra, dec, size=100):
     """
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 def cutout_gallery(image_filenames, mjd_list, ra, dec, aperture_radius_pix_list, size=100, ncols=4,
                    galaxy_id=None):