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def contextify(cls):
    @contextmanager
    def wrapper(*args, **kwargs):
        yield cls(*args, **kwargs)
    return wrapper

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# content for ipython notebook
%matplotlib inline
execfile('/Users/rob/pandasSetup.py')
import mpld3
mpld3.enable_notebook()
from pandas_datareader.data import Options

options = Options('spy', 'yahoo')
expiry_dates = options.expiry_dates
df = options.get_options_data(expiry=expiry_dates[0]).reset_index()

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from sys import maxint
def queryset_chunker(queryset, chunksize=10000):
    pk = -maxint
    last_pk = queryset.order_by('-pk')[0][0]
    queryset = queryset.order_by('pk')
    while pk < last_pk:
        for row in queryset.filter(pk__gt=pk)[:chunksize]:
            pk = row[0]
            yield row

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def values_list_queryset_chunker(queryset, chunk_size=10000):
    """
    Yields batched querysets from a values_list queryset.
    First element of each record must always be pk
    """
    queryset = queryset.order_by('pk')
    pk = queryset.order_by('pk')[0][0] - 1
    batch = queryset.filter(pk__gt=pk)[:chunk_size]
    while batch.count() > 0:
        chunk_size = min([chunk_size, batch.count()])

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def chunk_values_queryset(queryset, chunksize):
    if queryset.count() > 0:
        ending_pk = queryset.aggregate(Min('id'))['id__min'] - 1
        out = ['dummy']
        while len(out) > 0:
            out = list(queryset.filter(pk__gt=ending_pk).order_by('id')[:chunksize])
            if out:
                ending_pk = out[-1]['id']
                yield out

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"""
This collection of functions provides convenience abstractions
for working with lognormal distributions.  Sums of lognormals result
in a distribution with no closed form solution.  The functions provided
here assume the Fenton-Wilkinson approximation for such sums.

Running this google search should get you a good reference paper:
  cobb "Approximating the Distribution of a Sum of Log-normal Random Variables"
  
Note that this package looks like an interesting add-on to the scipy

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cat ~/.ssh/id_rsa.pub | ssh user@host 'cat - >> ~/.ssh/authorized_keys'

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# create new environment
conda create -n <env-name> [<package_name=version>] [anaconda]

# list current environments
conda info -e

# activate an environment
. activate <env_name> 

# deactivate environment

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# list images
docker images

# list only image ids
docker images -q

# build an image from a dockerfile with a tag name
docker build -t image_tag_name

# list running containers

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# Here is a simple version
def make_plots_simple():
    import pandas as pd
    import pylab as pl
    import matplotlib.dates as mdates
    x = pd.date_range('4/1/2020', '4/20/2020', freq='H')
    y = [n for n in range(len(x))]

    fig, ax = pl.subplots()