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# Run this as
# python -3 MODULE TESTFILE NOSETESTS_OPTIONS
# where:
#     MODULE: module that you want to check for python3 compatibility
#     TESTFILE: file with tests
#     NOSETESTS OPTIONS: other options that you would give to nosetests (e.g., -v, -x, --nocapture)

import sys
import warnings
import nose

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Usage

The Python script nonaxi_velocity_field.py can be used to compute the velocity distribution function using the backwards-integration method of Dehnen (2000), as implemented in galpy (Bovy 2015). You can use the nonaxi_velocity_field.py code in a few different ways:

python nonaxi_velocity_field.py -h

gives information on the various options. Possible ways to run the code are:

python nonaxi_velocity_field.py bar_altrect_vr.ps \

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This gist contains some code used in Meszaros et al. (2015) pertaining to the analysis of multiple populations in globular clusters using the extreme deconvolution algorithm. The necessary data files are available upon request.

The basic use of the code is to run:

IDL> analyze_gc, gc=2, ngauss=2, outfilename='m2pho_2comp.fits'

This runs the analysis on a given globular cluster (see read_gc.pro for the mapping between gc and globular cluster). The produced output file is a fits file that has the ID, the component that the XD code assigns (xdpop), the probability of that assignment (logp, actually the natural log of the probability), and the population that was in the original data file from a preliminary analysis of membership probability. Information on the best-fit amplitudes, means, and covariances of the populations is printed directly.

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import sys
import numpy
from galpy.util import bovy_plot
import apogee.tools.read as apread
from matplotlib import pyplot
_DISTTAG= 'DISO_GAL'
def plot_distance_cohorts(plotfilename,region='inner'):
    #Read the data
    data= apread.allStar(adddist=True,distredux='v402',main=True)
    #Cut the data to the relevant region

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These Python scripts can be used to reproduce the figures in Bovy & Tremaine (2012): On the local dark matter density.

Figure 1 is produced by:

python plot_dvcapprox.py

while Figure 2 is produced by:

python plot_sigmaz.py

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#Module to read and process the Koposov et al. (2010) data
import os
import numpy
from galpy.util import bovy_coords
_DATADIR='../data/'
# Koposov et al. (2010) transformation matrix
_TKOP= numpy.zeros((3,3))
_TKOP[0,:]= [-0.4776303088,-0.1738432154,0.8611897727]
_TKOP[1,:]= [0.510844589,-0.8524449229,0.111245042]
_TKOP[2,:]= [0.7147776536,0.4930681392,0.4959603976]

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#!/bin/sh

# Post-commit hook for revision-awsm-ness

function gettempfilename()
{
    tempfilename=$1-$RANDOM$RANDOM.tex
    if [ -e $tempfilename ]
    then
        tempfilename=$(gettempfilename)

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