syrte

import numpy as np


def gaussian_kernel_regression(
    x, y, sigma, yerr=None, w=None, x_new=None, niter=20, atol=1e-6, rtol=1e-3
):
    """
    Iterative Gaussian kernel smoothing.

    Parameters

syrte

import numpy as np
import matplotlib.pyplot as plt


def fit_gp(x, y, y_err, y_avg=None, x_pred=None, scale=1):
    from sklearn.gaussian_process import GaussianProcessRegressor
    from sklearn.gaussian_process.kernels import RBF, ConstantKernel, Matern, WhiteKernel
    from sklearn.exceptions import ConvergenceWarning
    import warnings

syrte

import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import griddata


def add_label(x, y, x0=None, y0=None, dx=0, dy=0, s='', **kwargs):
    "add label on the curve (x, y) at location specified by (x0, y0)"
    if x0 is not None:
        if x[-1] > x[0]:
            y0 = np.interp(x0, x, y)

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

import astropy.units as u
import numpy as np
from matplotlib.pylab import *

import agama
agama.setUnits(length=1, velocity=1, mass=1)


def sigmoid(x, fmax=1, scale=0.5):
    """

syrte

%%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}

syrte

#!/bin/bash

for image_file in $(ls figs6/)
do
if grep $image_file */*.log -c > 1
then
        echo "File $image_file is in use."
else
        echo "File $image_file is not in use."
        mv "figs6/$image_file" "figs6/moved.$image_file" # or any other action

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