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from os.path import basename
from IPython import get_ipython
shell = get_ipython()
print(basename(shell.kernel.config.IPKernelApp.connection_file))

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from pyspark.sql import DataFrame
from functools import wraps
import hashlib
from os import path, remove
import logging
from sys import stderr

def getLogger(name, debug=False, loglevel=None):
    logger = logging.getLogger(name)
    if not logger.handlers:

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from sklearn.feature_extraction import DictVectorizer


class DataFrameVectorizer(DictVectorizer):
    '''Scikit-learn transformer for DataFrames. Categorical features become one-hot encoded.
    
    >>> df = pd.DataFrame({'school': ['a','b','c'], 'gpa': [2,3,4]})
    >>> dv = DataFrameVectorizer()
    >>> transformed = dv.fit_transform(df)
    >>> dv.inverse_transform(transformed)

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#!/usr/bin/env python
# Install a matplotlib style file
from __future__ import print_function
import matplotlib
from matplotlib import style as mplstyle
import os
import sys
import shutil
import argparse
import requests

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def arrays_equal_nan(a, b):
    'Arrays compare True if they are equal or both NaN, else False'
    return np.all((a==b) | ((a!=a) & (b!=b)))

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%load_ext autoreload
%autoreload 1
%matplotlib inline
%config InlineBackend.figure_format='retina' 

from __future__ import division, print_function
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

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def by_n(iterable, n=2):
    """Return an sliding window iterable of tuples of size `n`

    E.g.
        >>> w = by_n(range(10), n=2)
        >>> next(w)
         (0,1)
        >>> next(w)
         (1,2)
         """

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#!/usr/bin/env python
"""Docstring
"""

def main(args=None):
  # Pipe into head/tail w/o IOErrors
  from signal import signal, SIGPIPE, SIG_DFL 
  signal(SIGPIPE,SIG_DFL) 
  
  parsed_args = _parse_args(args)

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from sklearn.base import BaseEstimator,TransformerMixin

class LogTransformer(BaseEstimator,TransformerMixin):
    def __init__(self, constant=1, base='e'):
        from numpy import log,log10
        if base == 'e' or base == np.e:
            self.log = log
        elif base == '10' or base == 10:
            self.log = log10
        else:

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from __future__ import print_function
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.spines import Spine
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projection
import numpy as np

def radar_plot(data, axes=[], colors=[], labels=[], ax=None, frame='circle', **kwargs):
    '''Create radar plot from 2D array. Each row is a closed line; each column is a feature/axis.