andycasey

import os
import cPickle as pickle
import matplotlib.pyplot as plt

import ads

max_retries = 5
N_top_papers = 1000

andycasey

"""
Retrieve all articles authored by ANU RSAA researchers in the previous month.
"""

__author__ = 'Andy Casey <arc@ast.cam.ac.uk>'

# You will also need PyPDF2 (use `pip install pypdf2`) for this example in order
# to manipulate PDFs.

# Example usage:    python stromlo.py           (to run previous month)

andycasey

import numpy as np
from astropy.table import Table

import thecannon as tc

# The Ness et al. (2015) data is accessible from [URL]
labelled_set = Table.read("Ness_2015_labelled.csv")
normalized_flux = np.memmap("Ness_2015_normalized_flux.memmap",
    mode="r", dtype=float)
normalized_ivar = np.memmap("Ness_2015_normalized_ivar.memmap",

andycasey

In [30]: ok = image[1].data["physical"]

In [31]: elements = [_ for _ in image[1].data.dtype.names if _.endswith("_H") and _ not in ("SRC_H", "PARAM_M_H")]

In [32]: for element in elements:
    print(element, np.sum(np.isfinite(image[1].data[element][ok])))
   ....:     
('AL_H', 99127)
('CA_H', 99127)
('C_H', 99127)

andycasey

import os
import numpy as np
from astropy.table import Table
from astropy.io import fits

import AnniesLasso as tc


training_set_path = "example_DR10/reference_labels.csv"

andycasey

import numpy as np


def distance_to_nearest_major_city(database, postcodes, 
    major_city_postcodes=None):
    """
    Calculate the estimate distance between a postcode and the nearest major
    city.

andycasey

# Script to monitor and restart wireless access point when needed

maxPloss=10 #Maximum percent packet loss before a restart

restart_networking() {
        # Add any commands need to get network back up and running
        /etc/init.d/networking restart

        #only needed if your running a wireless ap

andycasey

wget https://data.sdss.org/sas/dr14/apogee/spectro/redux/r8/stars/l31c/l31c.2/cannon/allStarCannon-l31c.2.fits
wget https://data.sdss.org/sas/dr14/apogee/spectro/redux/r8/stars/l31c/l31c.2/allStar-l31c.2.fits

andycasey

import numpy as np
from scipy.spatial import Delaunay

training_set_labels = #.... from somewhere else

test_set_labels = np.atleast_2d(test_set_labels)

# construct convex hull of the training set using all labels
hull = Delaunay(training_set_labels)

andycasey

import numpy as np
from astroquery.gaia import Gaia
import matplotlib.pyplot as plt

def astrometry_covariance_matrix(source):
    """
    Construct a covariance matrix for a 5-parameter astrometric solution 
    measured by Gaia for a single source.