def __init__(self, sky_catalog, pointing, orientation):
''' This class generates a Camera Catalog object, which is just the
Survey Catalog transfered into focal plane coordinates with the given
telescope pointing and orientation information.
Input
-----
sky_catalog : object
The sky catalog
pointing : numpy array [alpha, beta]
The telescope pointing coordinates in degrees
orientation : float
The telescope orientation in degrees
'''
self.k = sky_catalog.k
self.mag = sky_catalog.mag
self.alpha = sky_catalog.alpha
self.beta = sky_catalog.beta
self.is_in_analysis_region = sky_catalog.is_in_analysis_region
self.x, self.y = \
transformations.sky2fp(sky_catalog.alpha, sky_catalog.beta,\
pointing, orientation)
self.size = sky_catalog.size
self.flux = transformations.mag2flux(self.mag)
def __init__(self, sky_catalog, pointing, orientation):
''' This class generates a Camera Catalog object, which is just the
Survey Catalog transfered into focal plane coordinates with the given
telescope pointing and orientation information.
Input
-----
sky_catalog : object
The sky catalog
pointing : numpy array [alpha, beta]
The telescope pointing coordinates in degrees
orientation : float
The telescope orientation in degrees
'''
self.k = sky_catalog.k
self.mag = sky_catalog.mag
self.alpha = sky_catalog.alpha
self.beta = sky_catalog.beta
self.is_in_analysis_region = sky_catalog.is_in_analysis_region
self.x, self.y = \
transformations.sky2fp(sky_catalog.alpha, sky_catalog.beta,\
pointing, orientation)
self.size = sky_catalog.size
self.flux = transformations.mag2flux(self.mag)
def __init__(self, sky_limits, density_of_stars, m_min, m_max, A,
analysis_limits):
''' This class generates the Source Catalog object
Input
-----
sky_limits : float array
The area of sky to generate sources in
[alpha_min, alpha_max, beta_min, beta_max]
density_of_stars : int
The maximum number of sources (all magnitude) per unit area
to generate for the self-calibration simulations
m_min : float
The saturation limit of the simulated imager
m_max : float
The 10-sigma detection limit of the simulated imager
A : numpy array
The parameters describing the magnitude distribution of the sources
in the sky, according to: log10(dN/dm) = A + B * mag + C * mag ** 2
analysis_limits : list
Sky region for rms calculation.
'''
area = (sky_limits[1] - sky_limits[0]) \
* (sky_limits[3] - sky_limits[2])
self.size = int(density_of_stars * area)
self.k = np.arange(self.size).astype(int)
self.alpha = np.random.uniform(low=sky_limits[0], \
high=sky_limits[1], size=self.size)
self.beta = np.random.uniform(low=sky_limits[2], \
high=sky_limits[3], size=self.size)
self.mag = self.generate_magnitudes(m_min, m_max, A, area, self.size)
self.flux = transformations.mag2flux(self.mag)
self.is_in_analysis_region = (
(self.alpha > analysis_limits[0]) *
(self.alpha < analysis_limits[1]) *
(self.beta > analysis_limits[2]) *
(self.beta < analysis_limits[3])).astype(bool)
def __init__(self, sky_limits, density_of_stars,
m_min, m_max, A, analysis_limits):
''' This class generates the Source Catalog object
Input
-----
sky_limits : float array
The area of sky to generate sources in
[alpha_min, alpha_max, beta_min, beta_max]
density_of_stars : int
The maximum number of sources (all magnitude) per unit area
to generate for the self-calibration simulations
m_min : float
The saturation limit of the simulated imager
m_max : float
The 10-sigma detection limit of the simulated imager
A : numpy array
The parameters describing the magnitude distribution of the sources
in the sky, according to: log10(dN/dm) = A + B * mag + C * mag ** 2
analysis_limits : list
Sky region for rms calculation.
'''
area = (sky_limits[1] - sky_limits[0]) \
* (sky_limits[3] - sky_limits[2])
self.size = int(density_of_stars * area)
self.k = np.arange(self.size).astype(int)
self.alpha = np.random.uniform(low=sky_limits[0], \
high=sky_limits[1], size=self.size)
self.beta = np.random.uniform(low=sky_limits[2], \
high=sky_limits[3], size=self.size)
self.mag = self.generate_magnitudes(m_min, m_max, A, area, self.size)
self.flux = transformations.mag2flux(self.mag)
self.is_in_analysis_region = ((self.alpha > analysis_limits[0])
* (self.alpha < analysis_limits[1])
* (self.beta > analysis_limits[2])
* (self.beta < analysis_limits[3])).astype(bool)