| #!/usr/bin/env python | |
| from __future__ import division, print_function | |
| """ | |
| This script generates a file to use for building authorea papers, and then runs | |
| latex on them. | |
| Requires python >= 2.6 (3.x should work, too) | |
| The key assumptions are: |
| from __future__ import division, print_function | |
| """ | |
| This script takes in the URL to the JSON version of an ADS private library, and outputs a bibtex file suitable for importing into ORCiD | |
| """ | |
| import json | |
| import urllib2 | |
| from __future__ import division | |
| import os | |
| import sys | |
| import pty | |
| import select | |
| import subprocess | |
| def find_dolphot(dirstocheck=None): |
| from __future__ import (absolute_import, division, print_function, | |
| unicode_literals) | |
| import numpy as np | |
| import sys | |
| sys.path.insert(0, '/Users/bmorris/git/tmp/astropy') | |
| import astropy | |
| from astropy.time import Time | |
| from astropy.coordinates import (SkyCoord, Longitude, Latitude, GCRS, ICRS, |
| import schema_example # this schema gets created *on import* here | |
| import astropfile | |
| astropfile.engage('parfile_example') # runs all the tasks in the file | |
| parset = astropfile.edit_params('parfile_example') # pops up a GUI/Web page that can edit the file | |
| parset.engage() # actually runs the thing | |
| parset = astropfile.edit_params(schema_example.fit_gaussian_to_echelle1) # same as above, but creates a GUI with the defaults |
>>> import numpy as np >>> from astropy import units as u
Create a quantity from a pre-existing sampled distribution. The MC direction is the first dimension.
>>> parr = np.random.poisson([1, 5, 30, 400],(1000, 4)) >>> distr = u.Distribution(parr, u.kpc)