astro313

from matplotlib import pyplot as plt
import numpy as np
import matplotlib
from astropy.io import fits
from astropy.convolution import Gaussian2DKernel
from scipy import ndimage, stats, interpolate, signal
import random

plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.size'] = 18

astro313

pro readdata, max
 
readcol, 'data.txt', name, KIC, Kp, t_0, t_0_unc, P, P_unc, Rp, a, T_eq, Dur, Depth, a_to_Rstar, a_to_Rstar_unc, r_to_Rstar, r_to_Rstar_unc, b, b_unc, SNR, chi, T_eff, log_g, Rad2, f_T_eff, /silent ;read in the given file with all the information
  
BJD=2456069                     ; May 21th 2012, initial start time for the plot
t=findgen(1000000)/10000+BJD          ;ends August 29 2012
dist=500                        ;pc distance from earth to the objects
f=4.84e-6                       ;convert AU to pc
ff=2.254e-8                     ;conert solar R to pc
 

astro313

import numpy as np
# from statsmodels.tsa.stattools import ccf
import scipy
from scipy import optimize


def template_fun(x, x_0, W_FWHM, A):
    return A * np.exp(-0.5 * (x - x_0)**2 * (2.35/W_FWHM)**2)

astro313

'''

Just integrating a model SED in observed frame from mbb & RP's output

Last Modified: 05 May 2016

'''

from astropy.cosmology import WMAP9
import numpy as np

astro313

#!/bin/bash
#####################################################
# Name: Bash CheatSheet for Mac OSX
# 
# A little overlook of the Bash basics
#
# Usage:
#
# Author: J. Le Coupanec
# Date: 2014/11/04

astro313

#!/usr/bin/python

'''
from http://apple.stackexchange.com/questions/4286/is-there-a-mac-os-x-terminal-version-of-the-free-command-in-linux-systems
'''

import subprocess
import re

# Get process info

astro313

import random

peeps = [
         'Dominik', 
         'Martha', 
         'Gordon', 
         'Thomas',
         'Henrik',
         'Tyler', 
         'Luke',

astro313

import emcee
import numpy as np

# TODO:
# write something using sampler.sample() instead of predefined niter to ensure we have enough uncorrelated sample after run..
nindependent = 0
nind = 500
niter = 1000
attempts = 10
counter = 0

astro313

#! /usr/bin/env python

'''

Reads in image data from a FITS file. It transforms the image using FFT.

Plot the original image, the real part and the imaginary part of the transform, and the inverse transform -- real part, which should be equal to the original image, and a imaginary part.

The last panel = residual, and shows the quality of the FFT = original image - real part of the inverse FFT image.

astro313

'''Convert between R_E to vdisp and M_E

one z_lens, one z_source, one R_E at a time


'''

import numpy

#HELMS59