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""" | |
This took me a while to figure out so posting for posterity. | |
plt.interactive(False) is important, if you want the grid to only show up | |
when `display`ed. Since `sns.FacetGrid` can take a few seconds depending | |
on the size of your data, this displays a spinner in the notebook cell | |
until the new graph is ready to render. | |
""" | |
import matplotlib.pyplot as plt |
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import urllib | |
import docker | |
def get_manifest_auth_token(repo): | |
# https://docs.docker.com/registry/spec/auth/token/ | |
query = urllib.parse.urlencode({ | |
'service': 'registry.docker.io', | |
'scope': 'repository:{repo}:pull'.format(repo=repo) |
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""" | |
Heavily inspired by the R package bindata. | |
""" | |
import numpy as np | |
from scipy import interpolate | |
from scipy import stats | |
def corr_to_joint(corr, marginals): | |
""" |
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#! /usr/bin/env python | |
import glob | |
import os | |
import shutil | |
import re | |
from collections import namedtuple | |
import subprocess | |
from subprocess import PIPE |
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import pandas as pd | |
from boto3 import client | |
client = client(service_name='ec2') | |
prices = client.describe_spot_price_history(InstanceTypes=["m3.medium"], | |
AvailabilityZone="us-east-1a") | |
df = pd.DataFrame(prices['SpotPriceHistory']) | |
df.set_index("Timestamp", inplace=True) | |
df["SpotPrice"] = df.SpotPrice.astype(float) |
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import numpy as np | |
from sklearn.base import TransformerMixin, BaseEstimator | |
class StandardTransformer(BaseEstimator, TransformerMixin): | |
def __init__(self, variables=[], ignore=[]): | |
self.variables = variables | |
self.ignore = ignore | |
self.transform_idx = np.asarray([True if i not in self.ignore | |
else False for i in self.variables]) |
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import os | |
import calendar | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from matplotlib.ticker import FixedLocator, FixedFormatter | |
import pandas as pd | |
import seaborn as sns | |
to_colors = lambda x : x/255. |
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from pymc import Gamma, Poisson, Normal, MCMC, deterministic | |
import numpy as np | |
dta = dict(T=73, Nsubj=430, eps=0.0, t=[1, 21, 85, 128, 129, 148, 178, 204, | |
206, 210, 211, 212, 225, 238, 241, | |
248, 259, 273, 275, 281, 286, 289, | |
301, 302, 303, 304, 313, 317, 323, | |
344, 345, 349, 350, 351, 355, 356, | |
359, 364, 385, 386, 389, 390, 391, | |
392, 394, 395, 396, 397, 398, 399, |
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