Simple notebook to compare the Signal-to-noise between different input catalogs

Standard imports

[1]:

import hpmcm
import tables_io
import glob
import os
import numpy as np
import matplotlib.pyplot as plt

Configuration data

[2]:

DATADIR = "test_data"
shear_st = "0p01"

tract = 10463
SOURCE_TABLEFILES = sorted(glob.glob(os.path.join(DATADIR, f"shear_*_{shear_st}_cleaned_{tract}_ns.pq")))
SOURCE_TABLEFILES.append(os.path.join(DATADIR, f"object_{tract}.pq"))
SOURCE_TABLEFILES.reverse()

Read the input files

[3]:

dd = {i:tables_io.read(file_) for i, file_ in enumerate(SOURCE_TABLEFILES)}

column_list None
column_list None

Plot the SNR for the various catalgos

[4]:

#for i, key in enumerate(['Object', 'wmom', 'pgauss', 'gauss']):
for i, key in enumerate(['Object', 'wmom']):
    mask = dd[i].snr > 1
    _ = plt.hist(dd[i].snr[mask], bins=np.logspace(0, 4, 101), label=key, alpha=0.5)
_ = plt.legend()
_ = plt.xscale('log')
_ = plt.xlabel("Signal-to-noise [r-band]")
_ = plt.ylabel("Objects [per 0.04 dex]")
#_ = plt.yscale('log')

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