import pandas as pd import numpy as np

add FAST system noise on the blur map to simulate true observed map

sigma = 800 # \mu Jy, Standard Deviation of noise

read map

filepath = '/home/wangjiaxin/final/blurred/flux_blurred_all_gal_300f.hdf5' with pd.HDFStore(filepath, mode='r') as store: S_df = store['S'] xbin_df = store['x_bin_edge'] ybin_df = store['y_bin_edge'] fbin_df = store['nu_bin_edge']

add noise

noise = np.random.normal(0, sigma, S_df.shape) newdata = S_df + noise

save

output = '/home/dyliu/Filament/TNG_100/flux_blurred_all_gal_300f_800n.hdf5' with pd.HDFStore(output, mode='w') as store: store['S'] = newdata store['x_bin_edge'] = xbin_df store['y_bin_edge'] = ybin_df store['nu_bin_edge'] = fbin_df

This code demonstrates how to add realistic system noise from the FAST telescope to a blurred astronomical map, simulating a true observed map.

The code uses the Pandas library and HDF5 file format for efficient data handling.

The pd.HDFStore function is used to read and write data from the HDF5 file.

The np.random.normal function is used to generate random noise with the specified standard deviation.

The code adds the noise to the blurred map and then saves the new data to a new HDF5 file.

This code can be used to simulate realistic observations of astronomical sources, which can then be used to test different data analysis techniques.