autocorr

#!/usr/bin/env python
# encoding: utf-8

from os.path import splitext
from argparse import ArgumentParser
from markdown import markdown
from weasyprint import HTML


parser = ArgumentParser(description='Write a markdown file to PDF.')

autocorr

wl = [(3, 'Fizz'),
      (5, 'Buzz'),
      (7, 'Qux')]

for i in range(0, 101):
    print ''.join([w for n, w in wl if not i % n]) or i

autocorr

!!! write spectra to fits
@p:header

!!! read in and select data
file in class_022.smt
set source "RhoOphE"
set line "HTCOP"
set telescope SMT-FQM-*
find

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

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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

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

# 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:

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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

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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

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

" options
set nosmoothscroll
set scalehints
set linkanimations
set numerichints

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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