astrofrog

import time
import multiprocessing as mp

import numpy as np

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

astrofrog

astrofrog

import numpy as np
import numpy.ma as ma

import matplotlib.pyplot as plt
import matplotlib.colors as colors


def make_colormap(color):

    r, g, b = colors.colorConverter.to_rgb(color)

astrofrog

# Remove all but the latest N versions from wheels on Anaconda.org
# Usefult to avoid building up too many files when uploading
# developer wheels.

from binstar_client.utils import get_server_api

KEEP_N_LATEST = 10

api = get_server_api(token=<TOKEN>)
package = api.package("astropy", "astropy")

astrofrog

astrofrog

# Object-oriented API
#
# Memory usage (iteration, object count, memory size)
# 100 5637 1562216
# 200 5529 1491528
# 300 5422 1426264
# 400 5758 1587376
# 500 5422 1426288
# 600 5416 1440456
# 700 5610 1515056

astrofrog

import os
import random
import string
import tempfile
import subprocess

def random_id(length=8):
    return ''.join(random.sample(string.ascii_letters + string.digits, length))

TEMPLATE_SERIAL = """

astrofrog

import numpy as np

def merge_structured_arrays(array1, array2):
    n1 = len(array1)
    n2 = len(array2)
    array_out = array1.copy()
    array_out.resize(n1 + n2)
    array_out[n1:] = array2
    return array_out

astrofrog

# Convert 2MASS ATLAS images into MJy/sr, and subtract background value
# estimated by 2MASS pipeline.
#
# Example:
#
# >>> atlas_to_MJysr('glimpse_k_test.fits', 'glimpse_k_cal.fits')
#

import pyfits
import pywcs

astrofrog

def sample_dask_array_chunks(array, n_chunks):
    """
    Return an 1-d array which contains the data values from n_chunks randomly
    sampled from the chunks in the array (without replacement)
    """

    # Find the indices of the chunks to extract
    indices = [np.random.randint(dimsize, size=n_chunks) for dimsize in array.numblocks]

    # Determine the boundaries of chunks along each dimension