Skipper Seabold jseabold

## translate.py
# -*- coding: utf-8 -*-
"""
You need to fill in your API key from google below. Note that querying
supported languages is not implemented.

Language Code
-------- ----
Afrikaans 	af
Albanian 	sq
Arabic 	ar

## standardized_regressors.py
import statsmodels.api as sm
from statsmodels.formula.api import ols

# load some data
dta = sm.datasets.longley.load_pandas()

# make a standardized RHS formula
stand = "standardize(%s)"
std_rhs = ' + '.join([stand]*len(dta.exog_name)) % tuple(dta.exog_name)
print std_rhs

## zip_model.py
import numpy as np
from scipy import special, stats
from statsmodels.base.model import GenericLikelihoodModel

def vuong_test(p1, p2):
    r"""
    Vuong-test for non-nested models.

    Parameters
    ----------

## 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

## spirals.py
import numpy as np
import matplotlib.pyplot as plt

#As a path collection

from matplotlib.collections import LineCollection

fig, ax = plt.subplots()
r = np.arange(0, .075, 0.00001)
nverts = len(r)

## 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

## zip_fe.ado
set more off
capture program drop ZIP_FE_model

program define ZIP_FE_model
version 9.1

args todo b lnf
tempvar theta1 lambda last nonz w sln0 sln r0 r nb0 nb1 nb00 nb2 L2

local by "$MY_panel"

## tufte.py
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.

## 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)
	# -- coding: utf-8 --
	"""
	You need to fill in your API key from google below. Note that querying
	supported languages is not implemented.

	Language Code
	-------- ----
	Afrikaans af
	Albanian sq
	Arabic ar
	import statsmodels.api as sm
	from statsmodels.formula.api import ols

	# load some data
	dta = sm.datasets.longley.load_pandas()

	# make a standardized RHS formula
	stand = "standardize(%s)"
	std_rhs = ' + '.join([stand]*len(dta.exog_name)) % tuple(dta.exog_name)
	print std_rhs
	import numpy as np
	from scipy import special, stats
	from statsmodels.base.model import GenericLikelihoodModel

	def vuong_test(p1, p2):
	r"""
	Vuong-test for non-nested models.

	Parameters
	----------
	"""
	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 numpy as np
	import matplotlib.pyplot as plt

	#As a path collection

	from matplotlib.collections import LineCollection

	fig, ax = plt.subplots()
	r = np.arange(0, .075, 0.00001)
	nverts = len(r)
	#! /usr/bin/env python

	import glob
	import os
	import shutil
	import re
	from collections import namedtuple
	import subprocess
	from subprocess import PIPE
	set more off
	capture program drop ZIP_FE_model

	program define ZIP_FE_model
	version 9.1

	args todo b lnf
	tempvar theta1 lambda last nonz w sln0 sln r0 r nb0 nb1 nb00 nb2 L2

	local by "$MY_panel"
	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.
	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)