sam samuelsmal

Steps

connect kobo to your device
open a file browser and navigate to the mounted kobo folder
navigate to the hidden folder .kobo/Kobo
open the file Kobo eReader.conf
navigate to the section [FeatureSettings], if it doesn't exist, create it.
add the line ExportHighlights=true and save
disconnect the device
now you can long press on a book and export the note file into a .txt file which will be saved alongside your books. to get it connect your ereader.

As I just begun reading and learning Scala somethings might be outdated. If that is so, please leave a comment below. The order reflects the importance - or just my way of learning a new language.

Basically it is: overview, syntax, some usage, pitfalls, best practices.

Wikipedia entry Gives a simple overview, syntax, classification of the language, ...
ScalaTutorial Basic introduction, some knowledge in Java might be helpful.
Collections overview
About the uniform return type thingy
Pitfalls

	# Simple Config class, basically a dict with some enhancements
	#
	# Best used if you read your config only a couple of times. This version will read in the values everytime.

	from functools import reduce
	import os.path as path
	import yaml

	class ConfigValueError(ValueError):
	pass

	import warnings
	import imageio

	def gen_frames(data):
	bandpower_over_time_index = data.index

	frames = []

	# loop over your images
	for idx, time_index in enumerate(times_index_to_plot):

	def tf_clean_variable_name(var_name):
	"""
	Usage example (and an example of how to log a histogram in eager mode)

	with gradients_writer.as_default(), tfc.summary.always_record_summaries():
	for g, var_name in zip(gradients, [tf_clean_variable_name(v.name) for v in model.trainable_variables]):
	tfc.summary.histogram(f'gradient_{var_name}', g, step=epoch)
	"""
	# example of a var_name: 'inf_dense_0_23/kernel:0'

	# jupyter config options go here
	# usually goes into ~/.jupyter/jupyter_notebook_config.py
	# use `$(jupyter --config-dir)/jupyter_notebook_config.py` to check


	#-------
	# custom code
	#-------
	import sys
	import json

	from pyspark.sql.functions import udf
	from pyspark.sql.types import BooleanType

	def regex_filter(x):
	regexs = ['.ALLYOURBASEBELONGTOUS.']

	if x and x.strip():
	for r in regexs:
	if re.match(r, x, re.IGNORECASE):
	return True

	import operator
	import matplotlib.patches as mpatches
	from itertools import groupby
	import re
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt


	def cm2inch(*tupl):

	import os
	from subprocess import check_call

	c = get_config()

	def post_save(model, os_path, contents_manager):
	"""post-save hook for converting notebooks to .py scripts"""
	if model['type'] != 'notebook':
	return # only do this for notebooks
	d, fname = os.path.split(os_path)

	// p is a point
	const auto pv = p.getVector3fMap();
	const uint32_t rgb = reinterpret_cast<const int>(&p.rgb);
	int r = int((rgb >> 16) & 0x0000ff);
	int g = int((rgb >> 8) & 0x0000ff);
	int b = int((rgb) & 0x0000ff);