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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() |
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