"""
Generate gaussian random fields with a known power spectrum
"""
import numpy as np
import matplotlib.pyplot as plt
from astropy.units import deg
from lenstools import GaussianNoiseGenerator
#Set map side angle, and number of pixels on a side
num_pixel_side = 512
side_angle = 3.41 * deg
#Read the power spectrum (l,Pl) from an external file, and load it in numpy array format (the generator interpolates the power spectrum between bins)
l,Pl = np.loadtxt("Data/ee4e-7.txt",unpack=True)
#Instantiate the gaussian noise generator
gen = GaussianNoiseGenerator(shape=(num_pixel_side,num_pixel_side),side_angle=side_angle,label="convergence")
#Generate one random realization
gaussian_map = gen.fromConvPower(np.array([l,Pl]),seed=1,kind="linear",bounds_error=False,fill_value=0.0)
#gaussian_map is a ConvergenceMap instance
gaussian_map.visualize()
gaussian_map.savefig("example_map.png")
if (pool is not None) and not (pool.is_master()):
pool.wait()
sys.exit(0)
map_mock_ids = range(int(sys.argv[1]))
igs1_set = IGS1(
root_path=
"/Users/andreapetri/Documents/Columbia/spurious_shear/convergence_maps")
map_igs1_ids = igs1_set.getNames(z=1.0,
realizations=range(1,
int(sys.argv[1]) + 1))
gen = GaussianNoiseGenerator(shape=(2048, 2048),
side_angle=3.41 * deg,
label="convergence")
power_func = np.loadtxt("Data/ee4e-7.txt", unpack=True)
ens_mock = Ensemble.fromfilelist(map_mock_ids)
ens_igs1 = Ensemble.fromfilelist(map_igs1_ids)
ens_mock.load(callback_loader=generate_and_measure,
pool=pool,
generator=gen,
power_func=power_func)
ens_igs1.load(callback_loader=measure_from_IGS1, pool=pool)
if pool is not None:
pool.close()
import matplotlib.pyplot as plt
from astropy.units import deg
from lenstools import GaussianNoiseGenerator
#Set map side angle, and number of pixels on a side
num_pixel_side = 128
side_angle = 20 * deg
#Read the power spectrum (l,Pl) from an external file, and load it in numpy array format (the generator interpolates the power spectrum between bins)
l,Pl = np.loadtxt("./try1.dat",unpack=True)
#Instantiate the gaussian noise generator
#gen = GaussianNoiseGenerator(shape=(num_pixel_side,num_pixel_side),side_angle=side_angle,label="convergence")
gen = GaussianNoiseGenerator(shape=(num_pixel_side,num_pixel_side),side_angle=side_angle)
#Generate one random realization
gaussian_map = gen.fromConvPower(np.array([l,Pl]),seed=1,kind="linear",bounds_error=False,fill_value=0.0)
#print(dir(gen._fourierMap))
#print(gen._fourierMap.__dict__)
#print(dir(gaussian_map))
#print(gaussian_map.__dict__)
#print(getattr(gaussian_map, 'data').shape)
map_data = getattr(gaussian_map, 'data')
np.savetxt('map_data.dat', map_data)
