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import numpy as np
from scipy.stats import t, zscore


def grubbs(X, test='two-tailed', alpha=0.05):

    '''
    Performs Grubbs' test for outliers recursively until the null hypothesis is
    true.

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import numpy as np
import matplotlib.pyplot as plt 

N = 100 
x = np.zeros(N)
x[50] = 1 

# compute the dct
X = np.zeros(N)
for k in range(N):

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"""
Computes the two dimensional discrete cosine transform, first over rows, than over columns
"""

import numpy as np
import matplotlib.pyplot as plt

# create input image
N1, N2 = 100, 100
x = np.zeros((N1, N2))

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from bokeh.plotting import figure, show
from bokeh.io import output_file
import numpy as np
import pandas as pd

output_file('n_filings.html')

# read the filing data
filings = pd.read_csv('filings.csv')

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CREATE DATABASE campaign_expenditures;
USE `campaign_expenditures`;

# create the filing table
CREATE TABLE filings (
    filing_id INT,
    fec_committee_id VARCHAR(255),
    committee_name VARCHAR(255),
    PRIMARY KEY (filing_id)
);

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import numpy as np
import pandas as pd
import time

N = int(1e7)
M = 100 

counter = 0 

df = pd.DataFrame({'y1': np.zeros(N)})

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