Ch/psmodel#82
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distributed point sources
ljgray
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Jul 25, 2025
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I have no intuition about the constants defined here, but it looks good overall to me. Just some comments about removing commented code and adding docstrings in a couple of places.
| k1 : scalar | ||
| The amplitude of the luminosity function (number of sources / | ||
| Jy / deg^2 at the pivot). | ||
| The amplitude of the sourc count function (number of sources / |
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| The amplitude of the sourc count function (number of sources / | |
| The amplitude of the source count function (number of sources / |
| powerlaw_2 = self.k2 * (flux / self.S_0) ** (-self.gamma2) | ||
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| return self.k1 / (s**self.gamma1 + s**self.gamma2) | ||
| # return self.k1 / (s**self.gamma1 + s**self.gamma2) |
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| # return self.k1 / (s**self.gamma1 + s**self.gamma2) |
| poisson = True | ||
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| def __init__(self): | ||
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| # use np.where and use vector integrator (?) | ||
| # if flux <= self.source_pivot: | ||
| # return self.k1 * (flux / self.S_0) ** (-self.gamma1) | ||
| # else: | ||
| # return self.k2 * (flux / self.S_0) ** (-self.gamma2) |
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Can probably remove this if it isn't needed anymore
| # return self.k2 * (flux / self.S_0) ** (-self.gamma2) | ||
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| def angular_ps(self, larray): | ||
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| # Calculate the background intensity in units of sr^{-1} Jy^{-1} | ||
| background_intensity = integrate.quad(integrand, 0.0, self.flux_max) | ||
| # background_intensity = integrate.quad_vec(integrand, 0.0, self.flux_max) |
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| # background_intensity = integrate.quad_vec(integrand, 0.0, self.flux_max) |
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| # Calculate the power due to poisson noise | ||
| poisson_noise = integrate.quad(integrand, 0.0, self.flux_max) | ||
| # poisson_noise = integrate.quad_vec(integrand, 0.0, self.flux_max) |
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| # poisson_noise = integrate.quad_vec(integrand, 0.0, self.flux_max) |
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This is actually some of my "old" work with the point source model by DiMatteo and bug fixes. More importantly, the LOFAR_NCP class is Gaussian style foreground with parameters informed by LOFAR data.