jseabold

Grid 01
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import re

import numpy as np


box_slices = {
    0: slice(0, 3),
    1: slice(0, 3),
    2: slice(0, 3),
}

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import numpy as np
from scipy import optimize, sparse

board_coords = np.array([
    [0, 1, 2],
    [0, 4, 3],
    [0, 7, 4],
    [1, 0, 6],
    [1, 8, 3],
    [2, 2, 4],

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import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.stats import inter_rater


def fleiss_standard_error(table):
    # not included in statsmodels
    # only returns overall kappa value

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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)