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question,correct_answers,incorrect_answer,difficulty,topic,explanation
Identify the incorrect statement about basic properties of arithmetic operations.,"['The commutative property states that the order in which two numbers are added does not affect the sum.', 'The associative property states that when three or more numbers are added, the way in which the numbers are grouped does not affect the sum.', 'The distributive property allows one to multiply a sum by multiplying each addend individually and then adding the products together.']","Subtraction is associative, which means the way numbers are grouped in subtraction does not change the result.",3,math,"The incorrect statement claims that subtraction is associative, which is false. The associative property applies to addition and multiplication, but not to subtraction or division. In subtraction, the grouping of numbers (i.e., the placement of parentheses) can change the result. For example, (5 - 3) - 2 equals 0, whereas 5 - (3 - 2) equals 4. This differenc

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from datetime import datetime
from enum import Enum
from dateutil.relativedelta import relativedelta
from typing import Tuple, List



def get_today() -> datetime:
    tz = pytz.timezone('America/New_York')
    return datetime.now(tz)

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{"abbey-white": "#EBE5D0", "abyss": "#DFE6EA", "acapulco-cliffs": "#4E9AA8", "acceleration": "#808EA1", "accolade": "#F1EEE2", "acorn-squash": "#FFDF38", "admiralty": "#404E61", "adobe-white": "#F2ECD9", "adorable": "#E3BEB0", "adventure": "#34788C", "after-the-storm": "#919FA1", "afternoon-tea": "#594E40", "ageless": "#ECECDF", "airy": "#DAE6E9", "alaskan-blue": "#7E9EC2", "alaskan-mist": "#ECF0E5", "aldabra": "#AAA492", "all-about-olive": "#676C58", "allegro": "#B28959", "alley-cat": "#656874", "allspice": "#CC974D", "almond-brittle": "#B5865A", "almond-cream": "#E1CFB2", "almond-paste": "#E5DBC5", "almond-roca": "#B89068", "almost-aloe": "#BFE5B1", "almost-aqua": "#98DDC5", "aloe-essence": "#ECF1E2", "aloe-vera": "#DBE5B9", "aloha": "#1DB394", "aloof": "#D4E2E6", "alpaca-wool": "#E4D5BC", "alpine-blue": "#DBE4E5", "always-almond": "#EBE5D2", "always-apple": "#A0A667", "always-blue": "#A2BACB", "alyssa": "#F4E2D6", "amaretto": "#AB6F60", "amaretto-sours": "#C09856", "ambrosia": "#F4DED3", "american-anthem":

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Creating a Cloud Optimised Geotiffs (COGs) for raster photo imagery

This process outlines the process for creating Cloud Optimised Geotiffs suitable for hosting in services such as AWS S3. COGs enables more efficient workflows use cases such as fast access from Functions as a Services (E.g AWS Lambda), or comsumption into client desktop GIS systems (e.g QGIS). For more details on COGs please see https://www.cogeo.org/in-depth.html

1. Create a mosaic

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import React, { Component } from 'react';
import './Fetch.css';
import 'abortcontroller-polyfill/dist/polyfill-patch-fetch';

const AbortController = window.AbortController;
const controller = new AbortController();
const signal = controller.signal;

class Fetch extends Component {

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def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.
    
    Parameters
    ----------
    array : array_like
        Array to which the rolling window is applied.
    window : int or tuple

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name: point-clouds
channels:
- menpo
- conda-forge
- defaults
dependencies:
- libiconv=1.14=4
- libxslt=1.1.29=3
- pyside=1.2.4=py27_6
- configobj=5.0.6=py27_0

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from functools import partial
import numpy as np
from scipy.spatial import cKDTree
from dask.delayed import delayed
import dask.bag as db

class DaskKDTree(object):
    """
    
    Usage Example:

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from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing

import pandas as pd
import numpy as np

from functools import partial


def encode_data(df, cols):

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from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing

import pandas as pd
import numpy as np

from functools import partial


def encode_data(df, cols):