keflavich

setup: function() {
  // code to run on startup
  flotoptions.yaxis.max = prompt("Enter a maximum graph height percentage, such as '50'");$.plot($("#graph"),$W.data,flotoptions);
},
draw: function() {
  // code to run every frame

}

keflavich

setup: function() {
  // code to run on startup
 
smooth_complete = function(row) {
  var orig_img = $('#image')[0]
  $('body').append("<canvas id='tmp_canvas'></canvas>")
  var img = new Image()
  img.src = orig_img.src
  var canvas = $('#tmp_canvas')[0]
  canvas.width = img.width

keflavich

import numpy as np
from astropy.io import fits

header1 = """
SIMPLE  =                    T / conforms to FITS standard
BITPIX  =                  -64 / array data type
NAXIS   =                    2 / number of array dimensions
NAXIS1  =                  128
NAXIS2  =                  128
CRVAL1  =                  0.0 / Value at ref. pixel on axis 1

keflavich

import functools
 
class class_or_instance(object):
    def __init__(self, fn):
        self.fn = fn
    def __get__(self, obj, cls):
        if obj is not None:
            return lambda *args, **kwds: self.fn(obj, *args, **kwds)
        else:
            return lambda *args, **kwds: self.fn(cls, *args, **kwds)

keflavich

import functools
 
class class_or_instance(object):
    def __init__(self, fn):
        self.fn = fn
    def __get__(self, obj, cls):
        if obj is not None:
            return lambda *args, **kwds: self.fn(obj, *args, **kwds)
        else:
            return lambda *args, **kwds: self.fn(cls, *args, **kwds)

keflavich

import numpy as np
import fftw3
import pyfftw
import multiprocessing
import matplotlib
import matplotlib.pyplot as pl
import time


def fft_comparison_tests(size=2048, dtype=np.complex128, byte_align=False):

keflavich

import pymc as pm
import numpy as np

# FIXME: Need to store duplicates too, when jumps are rejected. That means some mechanism
# for making sure the history is full-rank needs to be employed.

class HistoryCovarianceStepper(pm.StepMethod):
    
    _state = ['n_points','history','tally','verbose']
    

keflavich

"""
Match two sets of on-sky coordinates to each other.
I.e., find nearest neighbor of one that's in the other.

Similar in purpose to IDL's spherematch, but totally different implementation.

Requires numpy and scipy.
"""
from __future__ import division
import numpy as np

keflavich

SIMPLE  =                    T / file does conform to FITS standard             BITPIX  =                   32 / number of bits per data pixel                  NAXIS   =                    2 / number of data axes                            NAXIS1  =                  341 / length of data axis 1                          NAXIS2  =                  291 / length of data axis 2                          EXTEND  =                    T / FITS dataset may contain extensions            COMMENT   FITS (Flexible Image Transport System) format is defined in 'AstronomyCOMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H EXTNAME = 'On      '                                                            HDUNAME = 'On      '                                                            CTYPE1  = 'GLON-CAR'           / Type of co-ordinate on axis 1                  CTYPE2  = 'GLAT-CAR'           / Type of co-ordinate on axis 2                  RADESYS = '        '           / Reference frame for RA/DEC valu

keflavich

require 'rubygems'
require 'nokogiri'
require 'fileutils'
require 'date'
require 'uri'

# usage: ruby import.rb my-blog.xml
# my-blog.xml is a file from Settings -> Basic -> Export in blogger.

data = File.read ARGV[0]