AllanLRH

AllanLRH

#Giant dictonary to hold key name and VK value
VK_CODE = {'backspace':0x08,
           'tab':0x09,
           'clear':0x0C,
           'enter':0x0D,
           'shift':0x10,
           'ctrl':0x11,
           'alt':0x12,
           'pause':0x13,
           'caps_lock':0x14,

AllanLRH

// Future versions of Hyper may add additional config options,
// which will not automatically be merged into this file.
// See https://hyper.is#cfg for all currently supported options.

module.exports = {
  config: {
    // default font size in pixels for all tabs
    fontSize: 12,

    // font family with optional fallbacks

AllanLRH

Hej Christian

Lige et par tips om Sublime Text, siden du er begyndt at bruge den mere :)

Sublime Text (ST) har en pakkemanager. Den hedder Package Control, og kan i nyeste udgave af ST installeres fra Command Palette (ctrl+shift+p) ved at vælge "Install package control". Du kan finde pakker og beskriveser på https://packgecontrol.io.

Jeg vil plejer at dele pakker op i domænespecifikke pakker, fx en linter til Python, og generelle pakker, fx noget som gør navigation i teksten nemmere. Her er de pakker som jeg er gladest for...

AllanLRH

% ****************************************************************************
% *  This demonstrate how to apply blur to an RGB-image, and create a third  *
% *    image which is composed partly of the original unblurred image, and   *
% *                       partly of the blurred image.                       *
% ****************************************************************************


% Read example image built into MATLAB
I0 = double(imread('saturn.png'));

AllanLRH

from loguru import logger

LOGGING_PATH = 'logs'
STARTED_TIME_STR = datetime.datetime.now().strftime("%m_%d_%Y__%H_%M_%S")
logger.remove()
logger.add(f"{LOGGING_PATH}/model_{STARTED_TIME_STR}.log", level='DEBUG')  # Save DEBUG level to a file
logger.add(sys.stdout, level='INFO')  # Print INFO level to stdout

AllanLRH

import imblearn
from sklearn import model_selection

rus = imblearn.under_sampling.RandomUnderSampler(sampling_strategy=1.0, random_state=42)
X, y = rus.fit_sample(df.drop('Target', axis=1), df.Target)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33, random_state=42)

AllanLRH

import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns 

AllanLRH

SELECT COLUMN_NAME
    ,IS_NULLABLE
    ,DATA_TYPE
    ,CHARACTER_MAXIMUM_LENGTH
    ,NUMERIC_PRECISION
    ,DATETIME_PRECISION
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'TABLE_NAME'

AllanLRH

b = np.arange(0, 36)
m = b[:, None, None] + b[None, :, None] + b[None, None, :]
mask = m ==35
ix = np.where(mask)
ans = np.stack(ix, axis=1)
assert (ans.sum(axis=1) == 35).all(), "Oh no! wrong resuult :'("
print(ans.shape, end='\n\n-----------\n\n')
print(*ans, sep='\n')