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from collections import Counter, deque
from copy import copy
from dataclasses import dataclass, asdict
import datetime
from itertools import islice
import json
import os
from pathlib import Path
import sys
import time

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import os
import time
from itertools import repeat
import random
from deap import creator, base, tools, algorithms

import numpy as np
from joblib import Parallel, delayed
from joblib import dump, load

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from pathlib import Path

import pandas as pd


@pd.api.extensions.register_series_accessor("path")
@pd.api.extensions.register_index_accessor("path")
class PathAccessor:
    def __init__(self, pandas_obj):
        self._validate(pandas_obj)

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{
	"name": "Default color scheme for shell prompts",
	"groups": {
		"hostname":         { "fg": "brightyellow", "bg": "mediumorange", "attrs": [] },
		"environment":      { "fg": "white", "bg": "darkestgreen", "attrs": [] },
		"virtualenv":      { "fg": "gray2", "bg": "solarized:green", "attrs": [] },
		"mode":             { "fg": "darkestgreen", "bg": "brightgreen", "attrs": ["bold"] },
		"attached_clients": { "fg": "white", "bg": "darkestgreen", "attrs": [] },
		"gitstatus":                 { "fg": "gray8",           "bg": "gray2", "attrs": [] },
	    "gitstatus_branch":          { "fg": "gray8",           "bg": "gray2", "attrs": [] },

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find . *.ipynb -maxdepth 0 -type f -exec jupyter nbconvert --ExecutePreprocessor.timeout=-1 --to notebook {} --execute --output {} \;

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import matplotlib as mpl
import matplotlib.pyplot as plt

# whatever the data is
a = np.arange(5, 15)

# get colors for data from colormap (viridis in this case)
norm = mpl.colors.Normalize(vmin=a.min(), vmax=a.max())
rgba_color = plt.cm.viridis([norm(v) for v in a]) 

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import subprocess

path = ROOT_DIR
result = (subprocess.check_output(['tree', '--dirsfirst', path])
                    .decode("utf-8", "strict"))

file_list = result.split('\n')

root = file_list[0]
file_list = file_list[1:-3]

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from sklearn.base import BaseEstimator, TransformerMixin
from scipy import sparse
from itertools import combinations


class SparseInteractions(BaseEstimator, TransformerMixin):
    def __init__(self, degree=2, feature_name_separator="_"):
        self.degree = degree
        self.feature_name_separator = feature_name_separator