Christian Di Lorenzo rcdilorenzo

To read your settings, run:

defaults read ~/Library/Containers/com.getharvest.harvestxapp/Data/Library/Preferences/group.com.getharvest.Harvest.Documents.plist

To change the time format to HH:MM, run

defaults write ~/Library/Containers/com.getharvest.harvestxapp/Data/Library/Preferences/group.com.getharvest.Harvest.Documents.plist TimeFormat hours_minutes

Restart the Harvest App, and it should now display HH:MM (I tested this)

	import numpy as np
	from sklearn.datasets import load_iris
	from sklearn.decomposition import PCA

	def covariance(M: np.ndarray) -> np.ndarray:
	"""
	Compute sample covariance matrix from data M.

	NOTE: M is assumed to be of shape (nrows, ncols).

	import numpy as np
	from typing import Callable, Optional, List
	from pydantic import BaseModel
	from tqdm.notebook import tqdm
	from math import ceil, isnan, inf

	class Activation(BaseModel):
	forward: Callable[[np.ndarray], np.ndarray] # input -> output
	backward: Callable[[np.ndarray, np.ndarray], np.ndarray] # (dA, X) -> gradients

	import * as t from 'io-ts';
	import Ajv from 'ajv';
	import addFormats from 'ajv-formats';
	import { fromNullable } from 'io-ts-types/lib/fromNullable';
	import convertToJSONSchema from './convertToJSONSchema';
	import isoString from './isoString';

	const SampleObject = t.strict(
	{
	aString: t.string,

	const puppeteer = require('puppeteer');
	const read = require('read');
	const htmlToText = require('html-to-text').fromString;
	const R = require('ramda');
	const Promise = require('bluebird');
	const fs = require('fs');
	const download = require('@jinphen/download2');
	const { CookieJar } = require('tough-cookie');

	const mapSeries = R.flip(Promise.mapSeries);

	// ========
	// Usage
	// ========
	// > const { transposeObjects } = require('./utils');
	// undefined
	// > transposeObjects([
	// ... { key1: 'object1key1', key2: 'object1key2' },
	// ... { key1: 'object2key1', key2: 'object2key2' }
	// ... ])
	// { key1: [ 'object1key1', 'object2key1' ],

	#!/bin/bash
	# resize-images.sh
	# Preprocess Stanford Cars Dataset

	# Stats for width
	# ❯ ls cars_*/.jpg \|
	# xargs -L1 identify -format "%w\n" \|
	# datamash min 1 max 1 mean 1 median 1
	#
	# 78 7800 700.49255483472 640

	hist.density <- function (data, xlab = "<x>", font.main = 1,
	main = "Histogram of data") {

	# Calculate histogram based on these values
	data.hist = hist(data, plot = FALSE)

	# Determine scaling factor
	multiplier = data.hist$counts / data.hist$density

	# Create density function of the area

	# Assumes an existing data frame called `raw`
	# that is directly imported from the URL and
	# has not converted the "?" values to NA's

	colnames(raw) <- c("Type", "CapShape", "CapSurface", "CapColor", "Bruises",
	"Odor", "GillAttachment", "GillSpacing", "GillSize",
	"GillColor", "StalkShape", "StalkRoot",
	"StalkSurfaceAboveRing", "StalkSurfaceBelowRing",
	"StalkColorAboveRing", "StalkColorBelowRing", "VeilType",
	"VeilColor", "RingNumber", "RingType", "SporePrintColor",

	from word_frequency import WordFrequency

	luke = WordFrequency('Luke', './Luke.txt')
	matthew = WordFrequency('Matt', './Matthew.txt')

	print('================================')
	print('= Frequently Occurring Phrases =')
	print('= Between Matthew & Luke (Max =')
	print('= 4 words/phrase, Min freq 15) =')
	print('================================')