Zhaozhou Li syrte

## Sersic_Einasto_index.py
from scipy.special import gammaincinv

def b(n):
    # 2 * n - 1 / 3 + 4 / (405 * n) + 46 / (25515 * n**2) + 131 / (1148175 * n**3)
    return gammaincinv(2 * n, 0.5)


def d(n):
    # 3 * n - 1 / 3 + 8 / (1215 * n) + 184 / (229635 * n**2) + 1048 / (31000725 * n**3)
    return gammaincinv(3 * n, 0.5)

## sersic_lum.py
from numpy import pi, exp
from scipy.special import gamma, gammaincinv, gammainc

# Normalisation constant
def b(n):
    return gammaincinv(2*n, 0.5)

# Total luminosity of a 2D Sérsic profile
def sersic_total_lum(Ie, re, n):
    bn = b(n)

## FFB_equations.ipynb

      
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                syrte
                / FFB_equations.ipynb
            
            
              Created
              November 12, 2023 07:29
            
          
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## mandc.py
"""
exec and load Zhao+2009 model

cf http://202.127.29.4/dhzhao/mandc.html
"""


import os
from astropy.io import ascii

## calcosmo.py
# -*- coding:utf-8 -*-
from __future__ import division
from numpy import sqrt, exp, log, abs, diff, sin, cos, linspace, interp
from numpy import empty_like, atleast_1d, asarray
from numpy.testing import assert_almost_equal
from scipy.optimize import bisect as root
from numpy import e, pi
from scipy.integrate import romberg
mid = lambda x: (x[1:] + x[:-1]) / 2.

## abmag_to_flux.py
from astropy import units as u
(0 * u.ABmag).to(u.erg/u.s/u.cm**2/u.Hz)
# 0 ABmag corresponsds to 3.6307805477010035e-20 erg / (cm2 Hz s)


## kern_smooth.py
import np


def kern_smooth(x, xp, yp, bw=1, return_std=False):
    w = np.exp(-0.5 * ((xp - x.reshape(-1, 1)) / bw)**2)
    w = w / w.sum(1, keepdims=True)
    y = (yp * w).sum(1)

    if not return_std:
        return y

## getdist_colors.py
def getdist_colors():
    "return getdist color table"
    # taken from getdist, https://github.com/cmbant/getdist
    return ['#006FED', '#E03424', 'gray', '#009966', '#000866', '#336600', '#006633', 'm', 'r']


## gaussian_fit.py
from scipy import optimize
from scipy import stats


def gaussian(x, p):
    n = len(p) // 2
    return np.add.reduce([p[i] * stats.norm.pdf(x, scale=p[n + i]) for i in range(n)])


def fit_gauss_sym(x, y, n=2, origin=True, full=True, positive=True):

## apj_short_prop.bst
%%This is often best used in a latex file in two-column mode.  To do that, do:
%%
%%\usepackage{multicol}  % before \begin{document}
%% ... document ...
%%\begin{multicols}{2}
%%\bibliography{filename}{}
%%\bibliographystyle{apj_short_prop}
%%\end{multicols}
	from scipy.special import gammaincinv

	def b(n):
	# 2 * n - 1 / 3 + 4 / (405 * n) + 46 / (25515 * n*2) + 131 / (1148175 n**3)
	return gammaincinv(2 * n, 0.5)


	def d(n):
	# 3 * n - 1 / 3 + 8 / (1215 * n) + 184 / (229635 * n*2) + 1048 / (31000725 n**3)
	return gammaincinv(3 * n, 0.5)
	from numpy import pi, exp
	from scipy.special import gamma, gammaincinv, gammainc

	# Normalisation constant
	def b(n):
	return gammaincinv(2*n, 0.5)

	# Total luminosity of a 2D Sérsic profile
	def sersic_total_lum(Ie, re, n):
	bn = b(n)
	"""
	exec and load Zhao+2009 model

	cf http://202.127.29.4/dhzhao/mandc.html
	"""


	import os
	from astropy.io import ascii
	# -- coding:utf-8 --
	from __future__ import division
	from numpy import sqrt, exp, log, abs, diff, sin, cos, linspace, interp
	from numpy import empty_like, atleast_1d, asarray
	from numpy.testing import assert_almost_equal
	from scipy.optimize import bisect as root
	from numpy import e, pi
	from scipy.integrate import romberg
	mid = lambda x: (x[1:] + x[:-1]) / 2.
	from astropy import units as u
	(0 * u.ABmag).to(u.erg/u.s/u.cm**2/u.Hz)
	# 0 ABmag corresponsds to 3.6307805477010035e-20 erg / (cm2 Hz s)
	import np


	def kern_smooth(x, xp, yp, bw=1, return_std=False):
	w = np.exp(-0.5 * ((xp - x.reshape(-1, 1)) / bw)**2)
	w = w / w.sum(1, keepdims=True)
	y = (yp * w).sum(1)

	if not return_std:
	return y
	def getdist_colors():
	"return getdist color table"
	# taken from getdist, https://github.com/cmbant/getdist
	return ['#006FED', '#E03424', 'gray', '#009966', '#000866', '#336600', '#006633', 'm', 'r']
	from scipy import optimize
	from scipy import stats


	def gaussian(x, p):
	n = len(p) // 2
	return np.add.reduce([p[i] * stats.norm.pdf(x, scale=p[n + i]) for i in range(n)])


	def fit_gauss_sym(x, y, n=2, origin=True, full=True, positive=True):
	%%This is often best used in a latex file in two-column mode. To do that, do:
	%%
	%%\usepackage{multicol} % before \begin{document}
	%% ... document ...
	%%\begin{multicols}{2}
	%%\bibliography{filename}{}
	%%\bibliographystyle{apj_short_prop}
	%%\end{multicols}