Module deeplenstronomy.distributions
Contains all available distributions. Utilize the functions in this module
by using the DISTRIBUTION keyword in your configuration file. As an example:
seeing:
DISTRIBUTION:
NAME: uniform # function name to call
PARAMETERS:
minimum: 0 # value to set for function argument
maximim: 6 # value to set for function argument
Expand source code
"""Contains all available distributions. Utilize the functions in this module
by using the `DISTRIBUTION` keyword in your configuration file. As an example:
```
seeing:
DISTRIBUTION:
NAME: uniform # function name to call
PARAMETERS:
minimum: 0 # value to set for function argument
maximim: 6 # value to set for function argument
```
"""
import numpy as np
import random
from scipy.stats import poisson
## Single parameter sampling distributions
def uniform(minimum, maximum, bands=''):
"""
Return a samle from a uniform probability distribution
on the interval [`minimum`, `maximum`]
Args:
minimum (float or int): The minimum of the interval to sample
maximum (float or int): The maximum of the interval to sample
Returns:
A sample of the specified uniform distribution for each band in the simulation
"""
draw = random.uniform(minimum, maximum)
return [draw] * len(bands.split(','))
def uniform_int(minimum, maximum, bands=''):
"""
Return a samle from a uniform probability distribution
on the interval [`minimum`, `maximum`] rounded to the nearest integer
Args:
minimum (int): The minimum of the interval to sample
maximum (int): The maximum of the interval to sample
Returns:
A rounded sample of the specified uniform distribution for each band in the simulation
"""
draw = round(random.uniform(minimum, maximum))
return [draw] * len(bands.split(','))
def normal(mean, std, bands=''):
"""
Return a samle from a normal probability distribution
with specifeid mean and standard deviation
Args:
mean (float or int): The mean of the normal distribution to sample
std (float or int): The standard deviation of the normal distribution to sample
Returns:
A sample of the specified normal distribution for each band in the simulation
"""
draw = np.random.normal(loc=mean, scale=std)
return [draw] * len(bands.split(','))
def lognormal(mean, sigma, bands=''):
"""
Return a samle from a lognormal probability distribution
with specifeid mean and standard deviation
Args:
mean (float or int): The mean of the lognormal distribution to sample
sigma (float or int): The standard deviation of the lognormal distribution to sample
Returns:
A sample of the specified lognormal distribution for each band in the simulation
"""
draw = np.random.lognormal(mean=mean, sigma=sigma)
return [draw] * len(bands.split(','))
def delta_function(value, bands=''):
"""
Use a delta function to set a specific value. Alternatively you can directly set the
value of a parameter if it is going to be constant. This functionality is useful if
you want to avoid deleting the `DISTRIBUTION` entry for a parameter in your
configuration file.
Args:
value : The value to set for this parameter
Returns:
A list of values with one value for each band in the simulation
"""
return [value] * len(bands.split(','))
def symmetric_uniform_annulus(r1, r2, bands=''):
"""
Return a sample from a uniform probability distribtuion on the interval
[`-r2`, `-r1`] U [`r1`, `r2`]. Useful for setting `center_x`, `center_y`, `sep`, etc. while
avoiding zero values in the sample.
Args:
r1 (float or int): The minimum radius of the symmetric annulus
r2 (float or int): The maximum radius of the symmetric annulus
Returns:
A sample of the specified uniform symmetric annulus for each band in the simulation
"""
draw = random.uniform(r1, r2) * random.choice([-1.0, 1.0])
return [draw] * len(bands.split(','))
## Grid sampling distributions
def poisson_noise(shape, mean):
"""
Return a grid of values sampled form a Poisson distribution with specifed mean
Args:
shape (List[int] or int): Automatically passed based on image shape
mean: The mean value of the Poisson distirbution to sample from
Returns:
A grid of values sampled form a Poisson distribution with specifed mean
"""
return poisson.rvs(mu=mean, size=shape)
## Empirical distributions from astronomical surveys
# DES
def des_magnitude_zero_point(bands=''):
"""
Sample from the distribution of single epoch zeropoints for DES
"""
dist = {'g': 26.58, 'r': 26.78, 'i': 26.75, 'z': 26.48, 'Y': 25.40}
return [dist[b] for b in bands.split(',')]
def des_sky_brightness(bands=''):
"""
Sample from the distribution of single epoch sky brightness for DES
"""
# Figure 4 in https://arxiv.org/pdf/1801.03181.pdf
dist = {'g': {'VALUES': [21.016, 21.057, 21.106, 21.179, 21.228, 21.269, 21.326,
21.367, 21.424, 21.465, 21.522, 21.571, 21.62, 21.677,
21.717, 21.774, 21.823, 21.872, 21.921, 21.97, 22.019,
22.068, 22.117, 22.174, 22.215, 22.272, 22.321, 22.378,
22.427, 22.476],
'WEIGHTS': [0.0, 0.0, 0.001, 0.001, 0.001, 0.001, 0.002, 0.003,
0.005, 0.007, 0.009, 0.012, 0.016, 0.023, 0.034, 0.048,
0.063, 0.073, 0.081, 0.093, 0.107, 0.099, 0.087, 0.076,
0.061, 0.05, 0.027, 0.013, 0.005, 0.0]},
'r': {'VALUES': [20.16, 20.209, 20.266, 20.323, 20.372, 20.421, 20.47,
20.519, 20.576, 20.625, 20.674, 20.715, 20.772, 20.821,
20.87, 20.918, 20.976, 21.024, 21.073, 21.122, 21.171,
21.22, 21.269, 21.326, 21.375, 21.424, 21.473, 21.522,
21.571, 21.62, 21.668, 21.726],
'WEIGHTS': [0.0, 0.0, 0.001, 0.001, 0.002, 0.002, 0.005, 0.008,
0.011, 0.011, 0.012, 0.02, 0.023, 0.034, 0.043, 0.046,
0.056, 0.07, 0.075, 0.083, 0.093, 0.095, 0.092, 0.078,
0.057, 0.041, 0.024, 0.012, 0.004, 0.001, 0.0, 0.0]},
'i': {'VALUES': [18.921, 18.978, 19.027, 19.076, 19.125, 19.174, 19.223,
19.272, 19.321, 19.378, 19.418, 19.476, 19.524, 19.573,
19.622, 19.671, 19.728, 19.777, 19.826, 19.875, 19.924,
19.973, 20.022, 20.071, 20.12, 20.177, 20.226, 20.274,
20.323, 20.372, 20.421, 20.478, 20.527, 20.576, 20.617,
20.674, 20.723, 20.772, 20.829],
'WEIGHTS': [0.0, 0.0, 0.002, 0.002, 0.001, 0.002, 0.003, 0.005,
0.013, 0.017, 0.018, 0.026, 0.029, 0.035, 0.036, 0.047,
0.053, 0.067, 0.078, 0.084, 0.073, 0.073, 0.063, 0.05,
0.045, 0.039, 0.031, 0.026, 0.021, 0.018, 0.014, 0.009,
0.009, 0.003, 0.002, 0.002, 0.001, 0.0, 0.0]},
'z': {'VALUES': [17.715, 17.772, 17.804, 17.861, 17.918, 17.976, 18.024,
18.073, 18.122, 18.171, 18.228, 18.277, 18.326, 18.375,
18.424, 18.473, 18.522, 18.579, 18.628, 18.677, 18.726,
18.774, 18.823, 18.872, 18.921, 18.97, 19.019, 19.076,
19.125, 19.174, 19.231, 19.264, 19.329, 19.37, 19.427,
19.467, 19.524, 19.573, 19.63],
'WEIGHTS': [0.0, 0.0, 0.0, 0.001, 0.001, 0.004, 0.007, 0.008,
0.012, 0.014, 0.015, 0.022, 0.028, 0.028, 0.033, 0.045,
0.052, 0.058, 0.064, 0.073, 0.082, 0.078, 0.069, 0.059,
0.051, 0.044, 0.036, 0.024, 0.019, 0.018, 0.017, 0.015,
0.01, 0.005, 0.002, 0.002, 0.002, 0.001, 0.0]},
'Y': {'VALUES': [17.062, 17.128, 17.177, 17.226, 17.274, 17.323, 17.372,
17.421, 17.47, 17.527, 17.576, 17.625, 17.674, 17.723,
17.772, 17.821, 17.878, 17.927, 17.976, 18.024, 18.073,
18.13, 18.179, 18.228, 18.277, 18.326, 18.375, 18.424,
18.473, 18.53, 18.579, 18.628, 18.668, 18.726, 18.774,
18.823, 18.88, 18.929, 18.97, 19.027, 19.076],
'WEIGHTS': [0.001, 0.002, 0.002, 0.003, 0.006, 0.008, 0.011, 0.015,
0.02, 0.027, 0.032, 0.041, 0.051, 0.051, 0.05, 0.05,
0.056, 0.066, 0.072, 0.068, 0.056, 0.047, 0.042, 0.033,
0.032, 0.029, 0.024, 0.022, 0.021, 0.02, 0.014, 0.011,
0.006, 0.003, 0.002, 0.001, 0.001, 0.0, 0.002, 0.001, 0.0]}
}
return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')]
def des_exposure_time(bands=''):
"""
Sample from the single epoch exposure time for DES
"""
# https://arxiv.org/pdf/1801.03181.pdf
return [45.0 if b == 'Y' else 90.0 for b in bands.split(',')]
def des_seeing(bands=''):
"""
Sample from the single epoch seeing for DES
"""
#Figure 3 in https://arxiv.org/pdf/1801.03181.pdf
dist = {'g': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72,
0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921,
0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101,
1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282,
1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479,
1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658,
1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82],
'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.001, 0.002,
0.004, 0.005, 0.008, 0.011, 0.015, 0.02, 0.025, 0.029, 0.034,
0.038, 0.042, 0.045, 0.046, 0.047, 0.045, 0.044, 0.041, 0.04,
0.037, 0.033, 0.031, 0.028, 0.026, 0.025, 0.023, 0.021, 0.019,
0.018, 0.017, 0.016, 0.015, 0.014, 0.014, 0.013, 0.012, 0.012,
0.011, 0.01, 0.009, 0.009, 0.008, 0.007, 0.007, 0.006, 0.005,
0.004, 0.003, 0.002, 0.001, 0.001, 0.0]},
'r': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72,
0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921,
0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101,
1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282,
1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479,
1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658,
1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82],
'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.002, 0.004, 0.007, 0.012,
0.019, 0.027, 0.036, 0.043, 0.051, 0.057, 0.062, 0.063, 0.061,
0.058, 0.054, 0.048, 0.044, 0.04, 0.036, 0.032, 0.028, 0.025,
0.022, 0.019, 0.018, 0.015, 0.014, 0.012, 0.011, 0.01, 0.009,
0.008, 0.008, 0.007, 0.006, 0.005, 0.004, 0.004, 0.003, 0.003,
0.002, 0.002, 0.002, 0.002, 0.002, 0.001, 0.001, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]},
'i': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72,
0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921,
0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101,
1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282,
1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479,
1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658,
1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82],
'WEIGHTS': [0.0, 0.0, 0.0, 0.001, 0.002, 0.005, 0.01, 0.017, 0.027, 0.038,
0.049, 0.061, 0.067, 0.072, 0.076, 0.075, 0.071, 0.066, 0.058,
0.05, 0.045, 0.038, 0.032, 0.026, 0.021, 0.017, 0.014, 0.011,
0.009, 0.008, 0.007, 0.005, 0.004, 0.003, 0.003, 0.002, 0.002,
0.002, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0]},
'z': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72,
0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921,
0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101,
1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282,
1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479,
1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658,
1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82],
'WEIGHTS': [0.0, 0.0, 0.001, 0.003, 0.008, 0.016, 0.027, 0.039, 0.054,
0.066, 0.073, 0.077, 0.077, 0.073, 0.069, 0.061, 0.054, 0.045,
0.037, 0.032, 0.026, 0.022, 0.019, 0.017, 0.014, 0.013, 0.011,
0.009, 0.008, 0.007, 0.007, 0.005, 0.004, 0.003, 0.003, 0.003,
0.003, 0.002, 0.002, 0.002, 0.002, 0.001, 0.001, 0.001, 0.001,
0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0]},
'Y': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72,
0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921,
0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101,
1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282,
1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479,
1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658,
1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82],
'WEIGHTS': [0.0, 0.001, 0.001, 0.004, 0.008, 0.014, 0.023, 0.032, 0.038,
0.045, 0.049, 0.051, 0.051, 0.048, 0.046, 0.043, 0.039, 0.036,
0.033, 0.031, 0.028, 0.026, 0.023, 0.021, 0.019, 0.018, 0.017,
0.016, 0.016, 0.014, 0.013, 0.013, 0.012, 0.011, 0.01, 0.01,
0.01, 0.01, 0.009, 0.008, 0.008, 0.007, 0.008, 0.008, 0.007,
0.007, 0.007, 0.006, 0.006, 0.006, 0.006, 0.006, 0.005, 0.004,
0.003, 0.003, 0.002, 0.002, 0.001, 0.001, 0.0, 0.0]}
}
return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')]
def des_ccd_gain(bands=''):
"""
Sample from the single epoch ccd gain for DECam
"""
# Figure 2 in https://arxiv.org/pdf/1501.02802.pdf
return [5.033 if b == 'Y' else 6.083 for b in bands.split(',')]
def des_num_exposures(bands=''):
"""
Sample from the effective number of exposures for DES
"""
# Figure 5 in https://arxiv.org/pdf/1501.02802.pdf
dist = {'g': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9],
'WEIGHTS': [0.040, 0.113, 0.267, 0.311, 0.178, 0.062, 0.019, 0.007, 0.003]},
'r': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9],
'WEIGHTS': [0.041, 0.119, 0.284, 0.321, 0.167, 0.046, 0.014, 0.006, 0.002]},
'i': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9],
'WEIGHTS': [0.043, 0.121, 0.291, 0.334, 0.165, 0.033, 0.009, 0.003, 0.001]},
'z': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9],
'WEIGHTS': [0.039, 0.106, 0.272, 0.332, 0.183, 0.048, 0.013, 0.005, 0.002]},
'Y': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9],
'WEIGHTS': [0.034, 0.074, 0.195, 0.305, 0.241, 0.099, 0.035, 0.012, 0.005]}
}
return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')]
# DELVE
def delve_seeing(bands=''):
"""
Sample from the seeing distribution for DELVE observations
"""
# Erik Zaborowski and Alex Drlica-Wagner
dist = {'g': {'VALUES': [0.036, 0.107, 0.178, 0.249, 0.32, 0.392, 0.463, 0.534, 0.605, 0.676,
0.748, 0.819, 0.89, 0.961, 1.032, 1.104, 1.175, 1.246, 1.317, 1.388,
1.46, 1.531, 1.602, 1.673, 1.744, 1.816, 1.887, 1.958, 2.029, 2.1],
'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.019,
0.044, 0.076, 0.107, 0.137, 0.119, 0.126, 0.104, 0.089, 0.067, 0.049,
0.026, 0.017, 0.01, 0.003, 0.003, 0.001, 0.001]},
'r': {'VALUES': [0.811, 0.85, 0.889, 0.928, 0.967, 1.007, 1.046, 1.085, 1.124, 1.163,
1.203, 1.242, 1.281, 1.32, 1.359, 1.398, 1.438, 1.477, 1.516, 1.555,
1.594, 1.634, 1.673, 1.712, 1.751, 1.79, 1.829, 1.869, 1.908, 1.947],
'WEIGHTS': [0.005, 0.011, 0.024, 0.039, 0.062, 0.075, 0.079, 0.078, 0.082, 0.081,
0.065, 0.063, 0.055, 0.052, 0.045, 0.04, 0.033, 0.029, 0.023, 0.016,
0.013, 0.01, 0.005, 0.005, 0.002, 0.002, 0.003, 0.001, 0.001, 0.001]},
'i': {'VALUES': [0.745, 0.792, 0.839, 0.887, 0.934, 0.981, 1.028, 1.075, 1.123, 1.17,
1.217, 1.264, 1.312, 1.359, 1.406, 1.453, 1.5, 1.548, 1.595, 1.642, 1.689,
1.737, 1.784, 1.831, 1.878, 1.925, 1.973, 2.02, 2.067, 2.114],
'WEIGHTS': [0.003, 0.015, 0.033, 0.06, 0.088, 0.126, 0.127, 0.123, 0.094, 0.072, 0.058,
0.052, 0.035, 0.028, 0.023, 0.018, 0.01, 0.009, 0.007, 0.006, 0.004, 0.002,
0.002, 0.001, 0.002, 0.001, 0.0, 0.001, 0.0, 0.0]},
'z': {'VALUES': [0.754, 0.801, 0.849, 0.896, 0.944, 0.991, 1.039, 1.086, 1.134, 1.181, 1.229,
1.276, 1.324, 1.371, 1.419, 1.466, 1.514, 1.561, 1.609, 1.656, 1.704, 1.751,
1.799, 1.846, 1.894, 1.941, 1.989, 2.036, 2.084, 2.131],
'WEIGHTS': [0.009, 0.036, 0.082, 0.111, 0.105, 0.104, 0.091, 0.085, 0.087, 0.065, 0.051,
0.045, 0.035, 0.023, 0.022, 0.013, 0.009, 0.009, 0.005, 0.004, 0.003, 0.002,
0.001, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001]}
}
return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')]
def delve_sky_brightness(bands=''):
"""
Sample from the sky brightness distribution for DELVE observaitons
"""
# Erik Zaborowski and Alex Drlica-Wagner
dist = {'g': {'VALUES': [18.201, 18.362, 18.524, 18.685, 18.847, 19.008, 19.17, 19.331, 19.493, 19.654,
19.816, 19.977, 20.138, 20.3, 20.461, 20.623, 20.784, 20.946, 21.107, 21.269,
21.43, 21.592, 21.753, 21.915, 22.076, 22.238, 22.399, 22.561, 22.722, 22.884],
'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001,
0.001, 0.003, 0.007, 0.021, 0.024, 0.03, 0.075, 0.202, 0.32, 0.195, 0.099,
0.022, 0.0, 0.0, 0.0]},
'r': {'VALUES': [16.705, 17.076, 17.447, 17.818, 18.189, 18.56, 18.931, 19.302, 19.674, 20.045,
20.416, 20.787, 21.158, 21.529, 21.9, 22.271, 22.642, 23.013, 23.384, 23.755,
24.126, 24.497, 24.868, 25.239, 25.61, 25.982, 26.353, 26.724, 27.095, 27.466],
'WEIGHTS': [0.0, 0.0, 0.0, 0.001, 0.0, 0.001, 0.001, 0.001, 0.002, 0.024, 0.084, 0.312,
0.411, 0.157, 0.004, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.001, 0.001]},
'i': {'VALUES': [16.481, 16.63, 16.778, 16.927, 17.075, 17.224, 17.373, 17.521, 17.67, 17.818,
17.967, 18.116, 18.264, 18.413, 18.561, 18.71, 18.859, 19.007, 19.156, 19.304,
19.453, 19.602, 19.75, 19.899, 20.047, 20.196, 20.344, 20.493, 20.642, 20.79],
'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.003,
0.009, 0.023, 0.039, 0.057, 0.1, 0.12, 0.137, 0.138, 0.128, 0.091, 0.068, 0.05,
0.029, 0.005, 0.001]},
'z': {'VALUES': [13.205, 13.604, 14.004, 14.404, 14.804, 15.203, 15.603, 16.003, 16.403, 16.802,
17.202, 17.602, 18.001, 18.401, 18.801, 19.201, 19.6, 20.0, 20.4, 20.8, 21.199,
21.599, 21.999, 22.398, 22.798, 23.198, 23.598, 23.997, 24.397, 24.797],
'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.001, 0.001, 0.001, 0.002, 0.009, 0.043,
0.237, 0.452, 0.2, 0.038, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.005,
0.007, 0.002, 0.0]}
}
return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')]
def delve_exposure_time(bands=''):
"""
Sample from the exposure time distribtuion for DELVE observations
"""
# Erik Zaborowski and Alex Drlica-Wagner
dist = {'g': {'VALUES': [35.333, 46.0, 56.667, 67.333, 78.0, 88.667, 99.333,
110.0, 120.667, 131.333, 142.0, 152.667, 163.333, 174.0,
184.667, 195.333, 206.0, 216.667, 227.333, 238.0, 248.667,
259.333, 270.0, 280.667, 291.333, 302.0, 312.667, 323.333,
334.0, 344.667],
'WEIGHTS': [0.091, 0.031, 0.06, 0.024, 0.021, 0.4, 0.108, 0.018, 0.041,
0.015, 0.004, 0.003, 0.029, 0.015, 0.004, 0.024, 0.003, 0.0,
0.0, 0.012, 0.013, 0.003, 0.026, 0.008, 0.0, 0.044, 0.0, 0.0,
0.0, 0.003]},
'r': {'VALUES': [35.333, 46.0, 56.667, 67.333, 78.0, 88.667, 99.333, 110.0,
120.667, 131.333, 142.0, 152.667, 163.333, 174.0, 184.667,
195.333, 206.0, 216.667, 227.333, 238.0, 248.667, 259.333,
270.0, 280.667, 291.333, 302.0, 312.667, 323.333, 334.0, 344.667],
'WEIGHTS': [0.294, 0.069, 0.15, 0.033, 0.03, 0.085, 0.113, 0.007, 0.03,
0.001, 0.0, 0.085, 0.004, 0.004, 0.019, 0.02, 0.006, 0.0, 0.007,
0.0, 0.01, 0.001, 0.018, 0.0, 0.0, 0.012, 0.0, 0.0, 0.0, 0.002]},
'i': {'VALUES': [35.333, 46.0, 56.667, 67.333, 78.0, 88.667, 99.333, 110.0, 120.667,
131.333, 142.0, 152.667, 163.333, 174.0, 184.667, 195.333, 206.0,
216.667, 227.333, 238.0, 248.667, 259.333, 270.0, 280.667, 291.333,
302.0, 312.667, 323.333, 334.0, 344.667],
'WEIGHTS': [0.275, 0.029, 0.064, 0.048, 0.045, 0.241, 0.044, 0.007, 0.05, 0.007,
0.014, 0.042, 0.018, 0.0, 0.012, 0.02, 0.005, 0.005, 0.0, 0.0, 0.042,
0.0, 0.0, 0.0, 0.0, 0.021, 0.0, 0.0, 0.009, 0.002]},
'z': {'VALUES': [35.05, 45.15, 55.25, 65.35, 75.45, 85.55, 95.65, 105.75, 115.85,
125.95, 136.05, 146.15, 156.25, 166.35, 176.45, 186.55, 196.65,
206.75, 216.85, 226.95, 237.05, 247.15, 257.25, 267.35, 277.45,
287.55, 297.65, 307.75, 317.85, 327.95],
'WEIGHTS': [0.128, 0.043, 0.152, 0.062, 0.083, 0.162, 0.107, 0.022, 0.06, 0.015,
0.013, 0.019, 0.019, 0.006, 0.005, 0.005, 0.01, 0.002, 0.003, 0.001,
0.028, 0.011, 0.0, 0.01, 0.009, 0.0, 0.016, 0.0, 0.0, 0.009]}
}
return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')]
def delve_magnitude_zero_point(bands=''):
"""
Sample from the zero point distribtutions for DELVE observaitons
"""
# Erik Zaborowski and Alex Drlica-Wagner
dist = {'g': 31.550, 'r': 31.284, 'i': 31.608, 'z': 31.262}
return [dist[b] for b in bands.split(',')]
# LSST at the Vera C. Rubin Observatory
def lsst_num_exposures(bands='', coadd_years=10):
"""
Sample from the LSST number of exposures distribution
Args:
coadd_years (int): Number of years of the survey to utlize
"""
dist = {'u': 140, 'g': 200, 'r': 460, 'i': 460, 'z': 400, 'Y': 400}
return [coadd_years * dist[b] // 10 for b in bands.split(',')]
def lsst_exposure_time(bands=''):
"""
Sample from the LSST exposure time distribution
"""
dist = {'u': 15.0, 'g': 15.0, 'r': 15.0, 'i': 15.0, 'z': 15.0, 'Y': 15.0}
return [dist[b] for b in bands.split(',')]
def lsst_magnitude_zero_point(bands=''):
"""
Sample from the LSST zero point distribution
"""
dist = {'u': 26.5, 'g': 28.3, 'r': 28.13, 'i': 27.79, 'z': 27.40, 'Y': 26.58}
return [dist[b] for b in bands.split(',')]
def lsst_sky_brightness(bands=''):
"""
Sample from the LSST sky brightness distribution
"""
dist = {'u': 22.99, 'g': 22.26, 'r': 21.2, 'i': 20.48, 'z': 19.6, 'Y': 18.61}
return [dist[b] for b in bands.split(',')]
def lsst_seeing(bands=''):
"""
Sample from the LSST seeing distribution
"""
dist = {'u': 0.81, 'g': 0.77, 'r': 0.73, 'i': 0.71, 'z': 0.69, 'Y': 0.68}
return [dist[b] for b in bands.split(',')]
# ZTF
def ztf_magnitude_zero_point(bands=''):
"""
Sample from the ZTF zeropoint distribution
"""
dist = {'g': 26.325, 'r': 26.275, 'i': 25.660}
return [dist[b] for b in bands.split(',')]
def ztf_seeing(bands=''):
"""
Sample from the ZTF seeing distribution
"""
dist = {'g': 2.1, 'r': 2.0, 'i': 2.1}
return [dist[b] for b in bands.split(',')]
def ztf_sky_brightness(bands=''):
"""
Sample from the ZTF sky brightness distribution
"""
dist = {'g': 22.01, 'r': 21.15, 'i': 19.89}
return [dist[b] for b in bands.split(',')]
Functions
def delta_function(value, bands='')-
Use a delta function to set a specific value. Alternatively you can directly set the value of a parameter if it is going to be constant. This functionality is useful if you want to avoid deleting the
DISTRIBUTIONentry for a parameter in your configuration file.Args
value : The value to set for this parameter
Returns
A list of values with one value for each band in the simulation
Expand source code
def delta_function(value, bands=''): """ Use a delta function to set a specific value. Alternatively you can directly set the value of a parameter if it is going to be constant. This functionality is useful if you want to avoid deleting the `DISTRIBUTION` entry for a parameter in your configuration file. Args: value : The value to set for this parameter Returns: A list of values with one value for each band in the simulation """ return [value] * len(bands.split(',')) def delve_exposure_time(bands='')-
Sample from the exposure time distribtuion for DELVE observations
Expand source code
def delve_exposure_time(bands=''): """ Sample from the exposure time distribtuion for DELVE observations """ # Erik Zaborowski and Alex Drlica-Wagner dist = {'g': {'VALUES': [35.333, 46.0, 56.667, 67.333, 78.0, 88.667, 99.333, 110.0, 120.667, 131.333, 142.0, 152.667, 163.333, 174.0, 184.667, 195.333, 206.0, 216.667, 227.333, 238.0, 248.667, 259.333, 270.0, 280.667, 291.333, 302.0, 312.667, 323.333, 334.0, 344.667], 'WEIGHTS': [0.091, 0.031, 0.06, 0.024, 0.021, 0.4, 0.108, 0.018, 0.041, 0.015, 0.004, 0.003, 0.029, 0.015, 0.004, 0.024, 0.003, 0.0, 0.0, 0.012, 0.013, 0.003, 0.026, 0.008, 0.0, 0.044, 0.0, 0.0, 0.0, 0.003]}, 'r': {'VALUES': [35.333, 46.0, 56.667, 67.333, 78.0, 88.667, 99.333, 110.0, 120.667, 131.333, 142.0, 152.667, 163.333, 174.0, 184.667, 195.333, 206.0, 216.667, 227.333, 238.0, 248.667, 259.333, 270.0, 280.667, 291.333, 302.0, 312.667, 323.333, 334.0, 344.667], 'WEIGHTS': [0.294, 0.069, 0.15, 0.033, 0.03, 0.085, 0.113, 0.007, 0.03, 0.001, 0.0, 0.085, 0.004, 0.004, 0.019, 0.02, 0.006, 0.0, 0.007, 0.0, 0.01, 0.001, 0.018, 0.0, 0.0, 0.012, 0.0, 0.0, 0.0, 0.002]}, 'i': {'VALUES': [35.333, 46.0, 56.667, 67.333, 78.0, 88.667, 99.333, 110.0, 120.667, 131.333, 142.0, 152.667, 163.333, 174.0, 184.667, 195.333, 206.0, 216.667, 227.333, 238.0, 248.667, 259.333, 270.0, 280.667, 291.333, 302.0, 312.667, 323.333, 334.0, 344.667], 'WEIGHTS': [0.275, 0.029, 0.064, 0.048, 0.045, 0.241, 0.044, 0.007, 0.05, 0.007, 0.014, 0.042, 0.018, 0.0, 0.012, 0.02, 0.005, 0.005, 0.0, 0.0, 0.042, 0.0, 0.0, 0.0, 0.0, 0.021, 0.0, 0.0, 0.009, 0.002]}, 'z': {'VALUES': [35.05, 45.15, 55.25, 65.35, 75.45, 85.55, 95.65, 105.75, 115.85, 125.95, 136.05, 146.15, 156.25, 166.35, 176.45, 186.55, 196.65, 206.75, 216.85, 226.95, 237.05, 247.15, 257.25, 267.35, 277.45, 287.55, 297.65, 307.75, 317.85, 327.95], 'WEIGHTS': [0.128, 0.043, 0.152, 0.062, 0.083, 0.162, 0.107, 0.022, 0.06, 0.015, 0.013, 0.019, 0.019, 0.006, 0.005, 0.005, 0.01, 0.002, 0.003, 0.001, 0.028, 0.011, 0.0, 0.01, 0.009, 0.0, 0.016, 0.0, 0.0, 0.009]} } return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')] def delve_magnitude_zero_point(bands='')-
Sample from the zero point distribtutions for DELVE observaitons
Expand source code
def delve_magnitude_zero_point(bands=''): """ Sample from the zero point distribtutions for DELVE observaitons """ # Erik Zaborowski and Alex Drlica-Wagner dist = {'g': 31.550, 'r': 31.284, 'i': 31.608, 'z': 31.262} return [dist[b] for b in bands.split(',')] def delve_seeing(bands='')-
Sample from the seeing distribution for DELVE observations
Expand source code
def delve_seeing(bands=''): """ Sample from the seeing distribution for DELVE observations """ # Erik Zaborowski and Alex Drlica-Wagner dist = {'g': {'VALUES': [0.036, 0.107, 0.178, 0.249, 0.32, 0.392, 0.463, 0.534, 0.605, 0.676, 0.748, 0.819, 0.89, 0.961, 1.032, 1.104, 1.175, 1.246, 1.317, 1.388, 1.46, 1.531, 1.602, 1.673, 1.744, 1.816, 1.887, 1.958, 2.029, 2.1], 'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.019, 0.044, 0.076, 0.107, 0.137, 0.119, 0.126, 0.104, 0.089, 0.067, 0.049, 0.026, 0.017, 0.01, 0.003, 0.003, 0.001, 0.001]}, 'r': {'VALUES': [0.811, 0.85, 0.889, 0.928, 0.967, 1.007, 1.046, 1.085, 1.124, 1.163, 1.203, 1.242, 1.281, 1.32, 1.359, 1.398, 1.438, 1.477, 1.516, 1.555, 1.594, 1.634, 1.673, 1.712, 1.751, 1.79, 1.829, 1.869, 1.908, 1.947], 'WEIGHTS': [0.005, 0.011, 0.024, 0.039, 0.062, 0.075, 0.079, 0.078, 0.082, 0.081, 0.065, 0.063, 0.055, 0.052, 0.045, 0.04, 0.033, 0.029, 0.023, 0.016, 0.013, 0.01, 0.005, 0.005, 0.002, 0.002, 0.003, 0.001, 0.001, 0.001]}, 'i': {'VALUES': [0.745, 0.792, 0.839, 0.887, 0.934, 0.981, 1.028, 1.075, 1.123, 1.17, 1.217, 1.264, 1.312, 1.359, 1.406, 1.453, 1.5, 1.548, 1.595, 1.642, 1.689, 1.737, 1.784, 1.831, 1.878, 1.925, 1.973, 2.02, 2.067, 2.114], 'WEIGHTS': [0.003, 0.015, 0.033, 0.06, 0.088, 0.126, 0.127, 0.123, 0.094, 0.072, 0.058, 0.052, 0.035, 0.028, 0.023, 0.018, 0.01, 0.009, 0.007, 0.006, 0.004, 0.002, 0.002, 0.001, 0.002, 0.001, 0.0, 0.001, 0.0, 0.0]}, 'z': {'VALUES': [0.754, 0.801, 0.849, 0.896, 0.944, 0.991, 1.039, 1.086, 1.134, 1.181, 1.229, 1.276, 1.324, 1.371, 1.419, 1.466, 1.514, 1.561, 1.609, 1.656, 1.704, 1.751, 1.799, 1.846, 1.894, 1.941, 1.989, 2.036, 2.084, 2.131], 'WEIGHTS': [0.009, 0.036, 0.082, 0.111, 0.105, 0.104, 0.091, 0.085, 0.087, 0.065, 0.051, 0.045, 0.035, 0.023, 0.022, 0.013, 0.009, 0.009, 0.005, 0.004, 0.003, 0.002, 0.001, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001]} } return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')] def delve_sky_brightness(bands='')-
Sample from the sky brightness distribution for DELVE observaitons
Expand source code
def delve_sky_brightness(bands=''): """ Sample from the sky brightness distribution for DELVE observaitons """ # Erik Zaborowski and Alex Drlica-Wagner dist = {'g': {'VALUES': [18.201, 18.362, 18.524, 18.685, 18.847, 19.008, 19.17, 19.331, 19.493, 19.654, 19.816, 19.977, 20.138, 20.3, 20.461, 20.623, 20.784, 20.946, 21.107, 21.269, 21.43, 21.592, 21.753, 21.915, 22.076, 22.238, 22.399, 22.561, 22.722, 22.884], 'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.001, 0.003, 0.007, 0.021, 0.024, 0.03, 0.075, 0.202, 0.32, 0.195, 0.099, 0.022, 0.0, 0.0, 0.0]}, 'r': {'VALUES': [16.705, 17.076, 17.447, 17.818, 18.189, 18.56, 18.931, 19.302, 19.674, 20.045, 20.416, 20.787, 21.158, 21.529, 21.9, 22.271, 22.642, 23.013, 23.384, 23.755, 24.126, 24.497, 24.868, 25.239, 25.61, 25.982, 26.353, 26.724, 27.095, 27.466], 'WEIGHTS': [0.0, 0.0, 0.0, 0.001, 0.0, 0.001, 0.001, 0.001, 0.002, 0.024, 0.084, 0.312, 0.411, 0.157, 0.004, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.001]}, 'i': {'VALUES': [16.481, 16.63, 16.778, 16.927, 17.075, 17.224, 17.373, 17.521, 17.67, 17.818, 17.967, 18.116, 18.264, 18.413, 18.561, 18.71, 18.859, 19.007, 19.156, 19.304, 19.453, 19.602, 19.75, 19.899, 20.047, 20.196, 20.344, 20.493, 20.642, 20.79], 'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.003, 0.009, 0.023, 0.039, 0.057, 0.1, 0.12, 0.137, 0.138, 0.128, 0.091, 0.068, 0.05, 0.029, 0.005, 0.001]}, 'z': {'VALUES': [13.205, 13.604, 14.004, 14.404, 14.804, 15.203, 15.603, 16.003, 16.403, 16.802, 17.202, 17.602, 18.001, 18.401, 18.801, 19.201, 19.6, 20.0, 20.4, 20.8, 21.199, 21.599, 21.999, 22.398, 22.798, 23.198, 23.598, 23.997, 24.397, 24.797], 'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.001, 0.001, 0.001, 0.002, 0.009, 0.043, 0.237, 0.452, 0.2, 0.038, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.005, 0.007, 0.002, 0.0]} } return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')] def des_ccd_gain(bands='')-
Sample from the single epoch ccd gain for DECam
Expand source code
def des_ccd_gain(bands=''): """ Sample from the single epoch ccd gain for DECam """ # Figure 2 in https://arxiv.org/pdf/1501.02802.pdf return [5.033 if b == 'Y' else 6.083 for b in bands.split(',')] def des_exposure_time(bands='')-
Sample from the single epoch exposure time for DES
Expand source code
def des_exposure_time(bands=''): """ Sample from the single epoch exposure time for DES """ # https://arxiv.org/pdf/1801.03181.pdf return [45.0 if b == 'Y' else 90.0 for b in bands.split(',')] def des_magnitude_zero_point(bands='')-
Sample from the distribution of single epoch zeropoints for DES
Expand source code
def des_magnitude_zero_point(bands=''): """ Sample from the distribution of single epoch zeropoints for DES """ dist = {'g': 26.58, 'r': 26.78, 'i': 26.75, 'z': 26.48, 'Y': 25.40} return [dist[b] for b in bands.split(',')] def des_num_exposures(bands='')-
Sample from the effective number of exposures for DES
Expand source code
def des_num_exposures(bands=''): """ Sample from the effective number of exposures for DES """ # Figure 5 in https://arxiv.org/pdf/1501.02802.pdf dist = {'g': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'WEIGHTS': [0.040, 0.113, 0.267, 0.311, 0.178, 0.062, 0.019, 0.007, 0.003]}, 'r': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'WEIGHTS': [0.041, 0.119, 0.284, 0.321, 0.167, 0.046, 0.014, 0.006, 0.002]}, 'i': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'WEIGHTS': [0.043, 0.121, 0.291, 0.334, 0.165, 0.033, 0.009, 0.003, 0.001]}, 'z': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'WEIGHTS': [0.039, 0.106, 0.272, 0.332, 0.183, 0.048, 0.013, 0.005, 0.002]}, 'Y': {'VALUES': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'WEIGHTS': [0.034, 0.074, 0.195, 0.305, 0.241, 0.099, 0.035, 0.012, 0.005]} } return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')] def des_seeing(bands='')-
Sample from the single epoch seeing for DES
Expand source code
def des_seeing(bands=''): """ Sample from the single epoch seeing for DES """ #Figure 3 in https://arxiv.org/pdf/1801.03181.pdf dist = {'g': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72, 0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921, 0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101, 1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282, 1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479, 1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658, 1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82], 'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.001, 0.002, 0.004, 0.005, 0.008, 0.011, 0.015, 0.02, 0.025, 0.029, 0.034, 0.038, 0.042, 0.045, 0.046, 0.047, 0.045, 0.044, 0.041, 0.04, 0.037, 0.033, 0.031, 0.028, 0.026, 0.025, 0.023, 0.021, 0.019, 0.018, 0.017, 0.016, 0.015, 0.014, 0.014, 0.013, 0.012, 0.012, 0.011, 0.01, 0.009, 0.009, 0.008, 0.007, 0.007, 0.006, 0.005, 0.004, 0.003, 0.002, 0.001, 0.001, 0.0]}, 'r': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72, 0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921, 0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101, 1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282, 1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479, 1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658, 1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82], 'WEIGHTS': [0.0, 0.0, 0.0, 0.0, 0.0, 0.001, 0.002, 0.004, 0.007, 0.012, 0.019, 0.027, 0.036, 0.043, 0.051, 0.057, 0.062, 0.063, 0.061, 0.058, 0.054, 0.048, 0.044, 0.04, 0.036, 0.032, 0.028, 0.025, 0.022, 0.019, 0.018, 0.015, 0.014, 0.012, 0.011, 0.01, 0.009, 0.008, 0.008, 0.007, 0.006, 0.005, 0.004, 0.004, 0.003, 0.003, 0.002, 0.002, 0.002, 0.002, 0.002, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'i': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72, 0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921, 0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101, 1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282, 1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479, 1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658, 1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82], 'WEIGHTS': [0.0, 0.0, 0.0, 0.001, 0.002, 0.005, 0.01, 0.017, 0.027, 0.038, 0.049, 0.061, 0.067, 0.072, 0.076, 0.075, 0.071, 0.066, 0.058, 0.05, 0.045, 0.038, 0.032, 0.026, 0.021, 0.017, 0.014, 0.011, 0.009, 0.008, 0.007, 0.005, 0.004, 0.003, 0.003, 0.002, 0.002, 0.002, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'z': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72, 0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921, 0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101, 1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282, 1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479, 1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658, 1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82], 'WEIGHTS': [0.0, 0.0, 0.001, 0.003, 0.008, 0.016, 0.027, 0.039, 0.054, 0.066, 0.073, 0.077, 0.077, 0.073, 0.069, 0.061, 0.054, 0.045, 0.037, 0.032, 0.026, 0.022, 0.019, 0.017, 0.014, 0.013, 0.011, 0.009, 0.008, 0.007, 0.007, 0.005, 0.004, 0.003, 0.003, 0.003, 0.003, 0.002, 0.002, 0.002, 0.002, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'Y': {'VALUES': [0.56, 0.579, 0.601, 0.621, 0.642, 0.662, 0.679, 0.703, 0.72, 0.742, 0.761, 0.783, 0.822, 0.841, 0.863, 0.882, 0.902, 0.921, 0.943, 0.962, 0.982, 1.001, 1.021, 1.04, 1.062, 1.081, 1.101, 1.122, 1.139, 1.161, 1.181, 1.2, 1.219, 1.241, 1.261, 1.282, 1.302, 1.319, 1.341, 1.36, 1.379, 1.399, 1.418, 1.44, 1.479, 1.501, 1.52, 1.539, 1.559, 1.578, 1.598, 1.619, 1.639, 1.658, 1.678, 1.697, 1.719, 1.738, 1.758, 1.777, 1.799, 1.82], 'WEIGHTS': [0.0, 0.001, 0.001, 0.004, 0.008, 0.014, 0.023, 0.032, 0.038, 0.045, 0.049, 0.051, 0.051, 0.048, 0.046, 0.043, 0.039, 0.036, 0.033, 0.031, 0.028, 0.026, 0.023, 0.021, 0.019, 0.018, 0.017, 0.016, 0.016, 0.014, 0.013, 0.013, 0.012, 0.011, 0.01, 0.01, 0.01, 0.01, 0.009, 0.008, 0.008, 0.007, 0.008, 0.008, 0.007, 0.007, 0.007, 0.006, 0.006, 0.006, 0.006, 0.006, 0.005, 0.004, 0.003, 0.003, 0.002, 0.002, 0.001, 0.001, 0.0, 0.0]} } return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')] def des_sky_brightness(bands='')-
Sample from the distribution of single epoch sky brightness for DES
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def des_sky_brightness(bands=''): """ Sample from the distribution of single epoch sky brightness for DES """ # Figure 4 in https://arxiv.org/pdf/1801.03181.pdf dist = {'g': {'VALUES': [21.016, 21.057, 21.106, 21.179, 21.228, 21.269, 21.326, 21.367, 21.424, 21.465, 21.522, 21.571, 21.62, 21.677, 21.717, 21.774, 21.823, 21.872, 21.921, 21.97, 22.019, 22.068, 22.117, 22.174, 22.215, 22.272, 22.321, 22.378, 22.427, 22.476], 'WEIGHTS': [0.0, 0.0, 0.001, 0.001, 0.001, 0.001, 0.002, 0.003, 0.005, 0.007, 0.009, 0.012, 0.016, 0.023, 0.034, 0.048, 0.063, 0.073, 0.081, 0.093, 0.107, 0.099, 0.087, 0.076, 0.061, 0.05, 0.027, 0.013, 0.005, 0.0]}, 'r': {'VALUES': [20.16, 20.209, 20.266, 20.323, 20.372, 20.421, 20.47, 20.519, 20.576, 20.625, 20.674, 20.715, 20.772, 20.821, 20.87, 20.918, 20.976, 21.024, 21.073, 21.122, 21.171, 21.22, 21.269, 21.326, 21.375, 21.424, 21.473, 21.522, 21.571, 21.62, 21.668, 21.726], 'WEIGHTS': [0.0, 0.0, 0.001, 0.001, 0.002, 0.002, 0.005, 0.008, 0.011, 0.011, 0.012, 0.02, 0.023, 0.034, 0.043, 0.046, 0.056, 0.07, 0.075, 0.083, 0.093, 0.095, 0.092, 0.078, 0.057, 0.041, 0.024, 0.012, 0.004, 0.001, 0.0, 0.0]}, 'i': {'VALUES': [18.921, 18.978, 19.027, 19.076, 19.125, 19.174, 19.223, 19.272, 19.321, 19.378, 19.418, 19.476, 19.524, 19.573, 19.622, 19.671, 19.728, 19.777, 19.826, 19.875, 19.924, 19.973, 20.022, 20.071, 20.12, 20.177, 20.226, 20.274, 20.323, 20.372, 20.421, 20.478, 20.527, 20.576, 20.617, 20.674, 20.723, 20.772, 20.829], 'WEIGHTS': [0.0, 0.0, 0.002, 0.002, 0.001, 0.002, 0.003, 0.005, 0.013, 0.017, 0.018, 0.026, 0.029, 0.035, 0.036, 0.047, 0.053, 0.067, 0.078, 0.084, 0.073, 0.073, 0.063, 0.05, 0.045, 0.039, 0.031, 0.026, 0.021, 0.018, 0.014, 0.009, 0.009, 0.003, 0.002, 0.002, 0.001, 0.0, 0.0]}, 'z': {'VALUES': [17.715, 17.772, 17.804, 17.861, 17.918, 17.976, 18.024, 18.073, 18.122, 18.171, 18.228, 18.277, 18.326, 18.375, 18.424, 18.473, 18.522, 18.579, 18.628, 18.677, 18.726, 18.774, 18.823, 18.872, 18.921, 18.97, 19.019, 19.076, 19.125, 19.174, 19.231, 19.264, 19.329, 19.37, 19.427, 19.467, 19.524, 19.573, 19.63], 'WEIGHTS': [0.0, 0.0, 0.0, 0.001, 0.001, 0.004, 0.007, 0.008, 0.012, 0.014, 0.015, 0.022, 0.028, 0.028, 0.033, 0.045, 0.052, 0.058, 0.064, 0.073, 0.082, 0.078, 0.069, 0.059, 0.051, 0.044, 0.036, 0.024, 0.019, 0.018, 0.017, 0.015, 0.01, 0.005, 0.002, 0.002, 0.002, 0.001, 0.0]}, 'Y': {'VALUES': [17.062, 17.128, 17.177, 17.226, 17.274, 17.323, 17.372, 17.421, 17.47, 17.527, 17.576, 17.625, 17.674, 17.723, 17.772, 17.821, 17.878, 17.927, 17.976, 18.024, 18.073, 18.13, 18.179, 18.228, 18.277, 18.326, 18.375, 18.424, 18.473, 18.53, 18.579, 18.628, 18.668, 18.726, 18.774, 18.823, 18.88, 18.929, 18.97, 19.027, 19.076], 'WEIGHTS': [0.001, 0.002, 0.002, 0.003, 0.006, 0.008, 0.011, 0.015, 0.02, 0.027, 0.032, 0.041, 0.051, 0.051, 0.05, 0.05, 0.056, 0.066, 0.072, 0.068, 0.056, 0.047, 0.042, 0.033, 0.032, 0.029, 0.024, 0.022, 0.021, 0.02, 0.014, 0.011, 0.006, 0.003, 0.002, 0.001, 0.001, 0.0, 0.002, 0.001, 0.0]} } return [random.choices(dist[b]['VALUES'], dist[b]['WEIGHTS'])[0] for b in bands.split(',')] def lognormal(mean, sigma, bands='')-
Return a samle from a lognormal probability distribution with specifeid mean and standard deviation
Args
mean:floatorint- The mean of the lognormal distribution to sample
sigma:floatorint- The standard deviation of the lognormal distribution to sample
Returns
A sample of the specified lognormal distribution for each band in the simulation
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def lognormal(mean, sigma, bands=''): """ Return a samle from a lognormal probability distribution with specifeid mean and standard deviation Args: mean (float or int): The mean of the lognormal distribution to sample sigma (float or int): The standard deviation of the lognormal distribution to sample Returns: A sample of the specified lognormal distribution for each band in the simulation """ draw = np.random.lognormal(mean=mean, sigma=sigma) return [draw] * len(bands.split(',')) def lsst_exposure_time(bands='')-
Sample from the LSST exposure time distribution
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def lsst_exposure_time(bands=''): """ Sample from the LSST exposure time distribution """ dist = {'u': 15.0, 'g': 15.0, 'r': 15.0, 'i': 15.0, 'z': 15.0, 'Y': 15.0} return [dist[b] for b in bands.split(',')] def lsst_magnitude_zero_point(bands='')-
Sample from the LSST zero point distribution
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def lsst_magnitude_zero_point(bands=''): """ Sample from the LSST zero point distribution """ dist = {'u': 26.5, 'g': 28.3, 'r': 28.13, 'i': 27.79, 'z': 27.40, 'Y': 26.58} return [dist[b] for b in bands.split(',')] def lsst_num_exposures(bands='', coadd_years=10)-
Sample from the LSST number of exposures distribution
Args
coadd_years:int- Number of years of the survey to utlize
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def lsst_num_exposures(bands='', coadd_years=10): """ Sample from the LSST number of exposures distribution Args: coadd_years (int): Number of years of the survey to utlize """ dist = {'u': 140, 'g': 200, 'r': 460, 'i': 460, 'z': 400, 'Y': 400} return [coadd_years * dist[b] // 10 for b in bands.split(',')] def lsst_seeing(bands='')-
Sample from the LSST seeing distribution
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def lsst_seeing(bands=''): """ Sample from the LSST seeing distribution """ dist = {'u': 0.81, 'g': 0.77, 'r': 0.73, 'i': 0.71, 'z': 0.69, 'Y': 0.68} return [dist[b] for b in bands.split(',')] def lsst_sky_brightness(bands='')-
Sample from the LSST sky brightness distribution
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def lsst_sky_brightness(bands=''): """ Sample from the LSST sky brightness distribution """ dist = {'u': 22.99, 'g': 22.26, 'r': 21.2, 'i': 20.48, 'z': 19.6, 'Y': 18.61} return [dist[b] for b in bands.split(',')] def normal(mean, std, bands='')-
Return a samle from a normal probability distribution with specifeid mean and standard deviation
Args
mean:floatorint- The mean of the normal distribution to sample
std:floatorint- The standard deviation of the normal distribution to sample
Returns
A sample of the specified normal distribution for each band in the simulation
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def normal(mean, std, bands=''): """ Return a samle from a normal probability distribution with specifeid mean and standard deviation Args: mean (float or int): The mean of the normal distribution to sample std (float or int): The standard deviation of the normal distribution to sample Returns: A sample of the specified normal distribution for each band in the simulation """ draw = np.random.normal(loc=mean, scale=std) return [draw] * len(bands.split(',')) def poisson_noise(shape, mean)-
Return a grid of values sampled form a Poisson distribution with specifed mean
Args
shape:List[int]orint- Automatically passed based on image shape
mean- The mean value of the Poisson distirbution to sample from
Returns
A grid of values sampled form a Poisson distribution with specifed mean
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def poisson_noise(shape, mean): """ Return a grid of values sampled form a Poisson distribution with specifed mean Args: shape (List[int] or int): Automatically passed based on image shape mean: The mean value of the Poisson distirbution to sample from Returns: A grid of values sampled form a Poisson distribution with specifed mean """ return poisson.rvs(mu=mean, size=shape) def symmetric_uniform_annulus(r1, r2, bands='')-
Return a sample from a uniform probability distribtuion on the interval [
-r2,-r1] U [r1,r2]. Useful for settingcenter_x,center_y,sep, etc. while avoiding zero values in the sample.Args
r1:floatorint- The minimum radius of the symmetric annulus
r2:floatorint- The maximum radius of the symmetric annulus
Returns
A sample of the specified uniform symmetric annulus for each band in the simulation
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def symmetric_uniform_annulus(r1, r2, bands=''): """ Return a sample from a uniform probability distribtuion on the interval [`-r2`, `-r1`] U [`r1`, `r2`]. Useful for setting `center_x`, `center_y`, `sep`, etc. while avoiding zero values in the sample. Args: r1 (float or int): The minimum radius of the symmetric annulus r2 (float or int): The maximum radius of the symmetric annulus Returns: A sample of the specified uniform symmetric annulus for each band in the simulation """ draw = random.uniform(r1, r2) * random.choice([-1.0, 1.0]) return [draw] * len(bands.split(',')) def uniform(minimum, maximum, bands='')-
Return a samle from a uniform probability distribution on the interval [
minimum,maximum]Args
minimum:floatorint- The minimum of the interval to sample
maximum:floatorint- The maximum of the interval to sample
Returns
A sample of the specified uniform distribution for each band in the simulation
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def uniform(minimum, maximum, bands=''): """ Return a samle from a uniform probability distribution on the interval [`minimum`, `maximum`] Args: minimum (float or int): The minimum of the interval to sample maximum (float or int): The maximum of the interval to sample Returns: A sample of the specified uniform distribution for each band in the simulation """ draw = random.uniform(minimum, maximum) return [draw] * len(bands.split(',')) def uniform_int(minimum, maximum, bands='')-
Return a samle from a uniform probability distribution on the interval [
minimum,maximum] rounded to the nearest integerArgs
minimum:int- The minimum of the interval to sample
maximum:int- The maximum of the interval to sample
Returns
A rounded sample of the specified uniform distribution for each band in the simulation
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def uniform_int(minimum, maximum, bands=''): """ Return a samle from a uniform probability distribution on the interval [`minimum`, `maximum`] rounded to the nearest integer Args: minimum (int): The minimum of the interval to sample maximum (int): The maximum of the interval to sample Returns: A rounded sample of the specified uniform distribution for each band in the simulation """ draw = round(random.uniform(minimum, maximum)) return [draw] * len(bands.split(',')) def ztf_magnitude_zero_point(bands='')-
Sample from the ZTF zeropoint distribution
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def ztf_magnitude_zero_point(bands=''): """ Sample from the ZTF zeropoint distribution """ dist = {'g': 26.325, 'r': 26.275, 'i': 25.660} return [dist[b] for b in bands.split(',')] def ztf_seeing(bands='')-
Sample from the ZTF seeing distribution
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def ztf_seeing(bands=''): """ Sample from the ZTF seeing distribution """ dist = {'g': 2.1, 'r': 2.0, 'i': 2.1} return [dist[b] for b in bands.split(',')] def ztf_sky_brightness(bands='')-
Sample from the ZTF sky brightness distribution
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def ztf_sky_brightness(bands=''): """ Sample from the ZTF sky brightness distribution """ dist = {'g': 22.01, 'r': 21.15, 'i': 19.89} return [dist[b] for b in bands.split(',')]