lzkelley

def some_task(catalog, ...):
    ...
    data, changed = catalog.load_url(url, path)
    
    # If the data is not changed, and we are updating... we're done
    if not changed and catalog.args.update:
        return

    # Continue processing, update data blah blah blah

lzkelley

def label_line(ax, line, label, color='0.5', fs=14, halign='left'):
    """Add an annotation to the given line with appropriate placement and rotation.

    Based on code from:
        [How to rotate matplotlib annotation to match a line?]
        (http://stackoverflow.com/a/18800233/230468)
        User: [Adam](http://stackoverflow.com/users/321772/adam)

    Arguments
    ---------

lzkelley

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=[10, 6])
ax.set(xscale='log', yscale='log')
ax.axis('off')

DURATION = 20.0
CADENCE = 0.1

lzkelley

#!/bin/bash

# Detect which `ls` flavor is in use
if ls --color > /dev/null 2>&1; then # GNU `ls`
	colorflag="--color"
else # OS X `ls`
	colorflag="-G"
fi

# List all files colorized in long format

lzkelley

import sys
import os

import numpy as np
import scipy as sp
import scipy.stats
import matplotlib as mpl
import matplotlib.pyplot as plt

# Silence annoying numpy errors

lzkelley

# Initialize Auto-Reloading Magic
%reload_ext autoreload
%autoreload 2

import os
import sys
from importlib import reload

import astropy as ap
import numpy as np

lzkelley

EVOLVE_ACCRETION_FLAG = True


def main():

  mbhb, sets = run_init()

  # ... do some check and logging and whatever ...

  if EVOLVE_ACCRETION_FLAG:

lzkelley

import numpy as np
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=[8, 5])
ax.set_xscale('log')

# Construct a log-normal distribution
dist = sp.stats.lognorm(1)

lzkelley

import numpy as np
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt


NUM = 100
xx = np.random.uniform(-1.0, 1.0, NUM)

plt.hist(xx)

lzkelley

import numpy as np
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt


def sf_rad(mm, aa, bb):
    rs = aa + bb * (np.log10(mm) - 10.0)
    return np.power(10.0, rs)