twolodzko

import numpy as np

def pcor(X, rowvar=False):
    """
    Partial correlation
    
    Implemented as in pcor::pcor function in R.
    
    Kim, S. (2015) ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients.
    Communications for Statistical Applications and Methods, 22(6), 665-674. 

twolodzko

import matplotlib.pyplot as plt

def identity_line(ax=None, ls='--', *args, **kwargs):
    # see: https://stackoverflow.com/q/22104256/3986320
    ax = ax or plt.gca()
    identity, = ax.plot([], [], ls=ls, *args, **kwargs)
    def callback(axes):
        low_x, high_x = ax.get_xlim()
        low_y, high_y = ax.get_ylim()
        low = min(low_x, low_y)

twolodzko

import numpy as np
import matplotlib.pyplot as plt

class ECDF:
    def __init__(self, arr: np.ndarray, max_points: Optional[int] = None) -> None:
        self.arr = np.sort(arr)
        n = len(self.arr)
        if max_points and n > max_points:
            self.arr = self.arr[:: (n // max_points)]

twolodzko

twolodzko

"""
This is a pure python implementation of t-digest algorithm 1. Parts of the code are borrowed
from https://github.com/CamDavidsonPilon/tdigest

Code is released under The MIT License (MIT).
"""

import math
from typing import List, Tuple, Union, Optional

twolodzko

Conda

# new env:
conda create --name myenv python=3.7 ipykernel numpy pandas

# remove:
conda remove --name myenv --all

# export:

twolodzko

import numpy as np
import scipy.stats as sp

C = np.array([
    [3, 2, 3],
    [2, 4, 2],
    [3, 2, 3]
])

n = 100_000

twolodzko

#!/bin/bash
set -e

git fetch

BRANCH=$(git rev-parse --abbrev-ref HEAD)
NEWBRANCH="$BRANCH-pr"

if [[ -n $(git rev-parse --verify $NEWBRANCH) ]]; then
    read -p "'$NEWBRANCH' exists, do you want to overwrite it? (y/[n])? " CONTINUE

twolodzko

twolodzko