samuelsmal

# 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

samuelsmal

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):

samuelsmal

steps

1. connect kobo to your device
2. open a file browser and navigate to the mounted kobo folder
3. navigate to the hidden folder .kobo/Kobo
4. open the file Kobo eReader.conf
5. navigate to the section [FeatureSettings], if it doesn't exist, create it.
6. add the line ExportHighlights=true and save
7. disconnect the device
8. 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.

samuelsmal

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'
    

samuelsmal

# 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

samuelsmal

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

samuelsmal

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):

samuelsmal

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)

samuelsmal

// 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);

samuelsmal

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