vmonaco

"""
Segment an acceleration or gyroscopic CSV file into motion/non-motion segments
using a 2-state HMM and Savitzky–Golay filter as preprocessing
"""
import sys
import pandas as pd
import matplotlib.pyplot as plt
from hmmlearn.hmm import GaussianHMM
from scipy.signal import savgol_filter

stevecheckoway

\NeedsTeXFormat{LaTeX2e}
\ProvidesPackage{fa}
  [2018/03/09 v0.3 Construct finite automata and Turing machines]

\RequirePackage{tikz}
\RequirePackage{etoolbox}

\usetikzlibrary{arrows.meta,automata,calc,chains,positioning}

% Define \emptystring to \varepsilon

vmonaco

'''
Created on May 26, 2015

@author: vinnie, vincent@vmonaco.com

Power-law results from:
"DATA FORENSIC TECHNIQUES USING BENFORD’S LAW AND ZIPF’S LAW FOR KEYSTROKE
DYNAMICS", Aamo Iorliam, Anthony T.S. Ho, Norman Poh, Santosh Tirunagari, 
and Patrick Bours. IWBF 2015.

tylerl

#!/usr/bin/env python

# This example demonstrates RSA public-key cryptography in an
# easy-to-follow manner. It works on integers alone, and uses much smaller numbers
# for the sake of clarity. 

#####################################################################
# First we pick our primes. These will determine our keys.
#####################################################################

vmonaco

"""
Multivariate Wald-Wolfowitz test for two samples in separate CSV files.

See:
  Friedman, Jerome H., and Lawrence C. Rafsky.
  "Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests."
  The Annals of Statistics (1979): 697-717.

Given multivariate sample X of length m and sample Y of length n, test the null hypothesis:

dmyersturnbull

import pandas as pd
import itertools
import time
import multiprocessing
from typing import Callable, Tuple, Union

def groupby_parallel(
  groupby_df: pd.core.groupby.DataFrameGroupBy,
  func: Callable[[Tuple[str, pd.DataFrame]], Union[pd.DataFrame, pd.Series]],
  num_cpus: int = multiprocessing.cpu_count() - 1,