syrte

from __future__ import division, print_function, absolute_import
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Arc
from matplotlib.collections import PatchCollection

__all__ = ['arcs']

def arcs(x, y, w, h=None, rot=0.0, theta1=0.0, theta2=360.0,
         c='b', vmin=None, vmax=None, **kwargs):

syrte

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)

syrte

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)

syrte

"""
A handy script for querying the new parsec website of padova isochrones.

author: syrte (http://github.com/syrte)

## Features
  - Easy to use
  - Flexibility
  - Friendly error prompts
  - Support latest also previous versions of parsec website

syrte

import glob
import numpy as np
import healpy
from astropy.io import fits


LEVEL_HPIX = 12
NSIDE_HPIX = 2**LEVEL_HPIX

syrte

syrte

"""
Add exponentiated variance (EV) acquisition function to skopt.

Author:
    Zhaozhou Li (lizz.astro@gmail.com)
"""

import numpy as np
import warnings
import skopt.acquisition

syrte

#!/usr/bin/env python
import sys
import json

for file in sys.argv[1:]:
    print('# file: %s' % file)
    print('# vi: filetype=python')
    print('')
    code = json.load(open(file))

syrte

"""
exec and load Zhao+2009 model

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


import os
from astropy.io import ascii

syrte

# -*- 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.