autocorr

# Here we import the numpy module to use arrays, and matplotlib for plotting.
# Pyplot is a sub-module of matplotlib designed to be a good interface for
# interactive plotting, matplotlib in general contains many sub-modules defining
# all sorts of behavior, from colors, fonts, to how tickmarks are rendered. We
# use the "as" keyword to give the modules an alias, so we don't import literally
# thousands of function names into the scope of the program (in IPython just type
# "np.<tab>" to see how many symbols are defined.
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

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#!/usr/bin/env python3

import argparse
import difflib
import subprocess


EMOJI_UNICODE = {
   '+1': '\U0001F44D',
   '-1': '\U0001F44E',

autocorr

from __future__ import print_function
import glob
from os import path
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from astropy import units as u
from astropy import constants as c

autocorr

import pandas as pd
from astropy import table

# use the astropy table reader method to read the file and convert
# to a `pandas.DataFrame`
df = table.Table.read('table1.txt', format='ascii.cds').to_pandas()
df.to_csv('table1.csv', index=False)

# when reading the files, remember to set the index column for
# matching and other things

autocorr

" settings
let barposition = "bottom"
let scrollstep = 150
let mapleader = ","

" options
set nosmoothscroll
set scalehints
set linkanimations
set numerichints

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#!/usr/bin/env python2


def is_prime(x):
    assert type(x) == int
    assert x > 0
    if x == 2:
        return True
    for n in xrange(2, x / 2 + 1):
        if not x % n:

autocorr

def make_dark(infilen, targetfilen, outfilen='masterdark'):
    # step 1: median stack the darks
    darklist = open(infilen,'r')
    darklist = darklist.readlines()
    n = len(darklist)

    fits = pyfits.open(darklist[0])
    darkheader = fits[0].header
    imdark = fits[0].data

autocorr

# if you haven't setup a ~/.gitconfig, make one with these lines:
git config --global user.name "Your Name"
git config --global user.email "you@email.com"

# first enter the directory you want to version-control and initialize the repository
git init

# git works by storing the changes to files rather than the files themselves,
# so you first have to add the files, or start tracking them
# add all files in the current directory:

autocorr

class BaseFitter(object):
    # Put everything common to all fitting routines in this class
    def __init__(self, *args):
        # handle the arguments
        pass

    def interpolate(self):
        pass

    def calc_covariance(self):

autocorr

"""
An example of a class and a factory function.
"""

import model_fit  # fit_func lives here


class Fitter(object):
    # This would be the starting point for your fit routine,
    # and the different methods could do different parts. You could