Skipper Seabold jseabold

## p096_sudoku.txt
Grid 01
003020600
900305001
001806400
008102900
700000008
006708200
002609500
800203009
005010300

## sudoku_backtracking.py
import re

import numpy as np


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

## sudoku.py
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],

## inter_rater.py
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

## Difference_in_Differences_Differences.ipynb

      
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                jseabold
                / Difference_in_Differences_Differences.ipynb
            
            
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              March 14, 2021 18:13
            
              
                Working out some differences between R and Python for Causal Inference: The Mixtape 
              
          
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## Notebook Plot with Spinner.py
"""
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

## docker_manifest.py
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)

## binary_rng.py
"""
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):
    """

## git_find_big.py
#! /usr/bin/env python

import glob
import os
import shutil
import re
from collections import namedtuple
import subprocess
from subprocess import PIPE

## spot_pricing.py
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)
	Grid 01
	003020600
	900305001
	001806400
	008102900
	700000008
	006708200
	002609500
	800203009
	005010300
	import re

	import numpy as np


	box_slices = {
	0: slice(0, 3),
	1: slice(0, 3),
	2: slice(0, 3),
	}
	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],
	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
	"""
	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
	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)
	"""
	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):
	"""
	#! /usr/bin/env python

	import glob
	import os
	import shutil
	import re
	from collections import namedtuple
	import subprocess
	from subprocess import PIPE
	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)