adrn

import os
import cPickle as pickle 

def some_function(another_function, overwrite=False, *args, **kwargs):
    if overwrite:
        os.remove("somefilename.pickle")
        
    if not os.path.exists("somefilename.pickle"):
        # analysis / make object
        object = another_function(*args, **kwargs)

adrn

• read_ascii()
  • Probably already included in astropy.ascii
• ascii_template()
  • Probably already included in astropy.ascii
• ntostr() (from Erin Sheldon's sdsstools)
  • It looks like this is just pure Python...
• helcorr.pro
  • This will be included in the coordinates subpackage (astropy.coordinates)
• glactc.pro
• This will be included in the coordinates subpackage (astropy.coordinates)

adrn

import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as so

def find_confidence_interval(x, pdf, confidence_level):
    return pdf[pdf > x].sum() - confidence_level

def density_contour(xdata, ydata, nbins_x, nbins_y, ax=None, **contour_kwargs):
    """ Create a density contour plot.

adrn

# coding: utf-8
import os
import random

__author__ = "adrn <adrn@astro.columbia.edu>"

def simple_data_bend(input_file, output_file, grayscale=True):
    # Open file in binary mode
    f = open(input_file, "rb")
    data = f.read()

adrn

from astropy.table import Table
from astropy.io import ascii

IDs = [1, 3, 6, 9, 100]
names = ["cat", "bat", "rat", "mat", "gnat"]
scores = [83, 51, 77, 81, 92]

t = Table([IDs, names, scores], names=["ID", "name", "score"])
ascii.write(t, delimiter=",")

adrn

"""
    - Hotfoot is Columbia's shared Astronomy-Statistics supercomputing 
        cluster. 
    - Emcee is a kick-ass MCMC black-box for sampling probability 
        distributions & parameter estimation.
    - MPI is a standardized message passing system designed for supporting 
        and running parallel code.
    
    Emcee is an implementation of an affine-invariant, ensemble sampler --
    key words here being *ensemple sampler*. The algorithm runs an ensemble

adrn

"""
    Make a figure to visualize using MCMC (in particular, the Python package
    emcee) to infer 4 parameters from a parametrized model of the Milky Way's
    dark matter halo by using tracer stars from the Sagittarius Stream.
    
    If you're unfamiliar with the jargon here (walkers, Sgr, etc.), check out:
        - Law & Majewski 2010 
            http://iopscience.iop.org/0004-637X/714/1/229/pdf/apj_714_1_229.pdf
        - emcee
            http://dan.iel.fm/emcee/

adrn

# coding: utf-8

""" Create a scatter-plot matrix using Matplotlib. """

from __future__ import division, print_function

__author__ = "adrn <adrn@astro.columbia.edu>"

# Standard library
import os, sys

adrn

import numpy as np
import matplotlib.pyplot as plt

def label_bars(rects):
    # attach some text labels to the bars
    for rect in rects:
        height = rect.get_height()
        plt.text(rect.get_x()+rect.get_width()/2., 
                 height, str(int(height)),
                 ha='center', va='bottom')

adrn

import astropy.units as u
from astropy.coordinates import ICRSCoordinates
from streams.coordinates import SgrCoordinates, OrphanCoordinates

icrs = ICRSCoordinates(15.34167, 1.53412, unit=(u.hour, u.degree))
sgr = icrs.transform_to(SgrCoordinates)
print(sgr.Lambda, sgr.Beta)

orp = sgr.transform_to(OrphanCoordinates)
print(orp.Lambda, orp.Beta)