esprengle

# Pythonista Markdown Action Menu
# By: EJM Software ---- http://ejm.cloudvent.net
import ui, console, editor, markdown

def format_italic():
    s = editor.get_selection()
    if not s==None and s[1]-s[0]>0:
        editor.replace_text(s[0], s[1], '*%s*'%editor.get_text()[s[0]:s[1]])

def format_bold():

esprengle

const log_el = document.getElementById('log')

function log(...messages) {
  console.log(...messages)
  log_el.innerText += '\n' + messages.map(m => JSON.stringify(m, null, 2)).join(' ')
}

function error(message) {
  console.error(message)
  log_el.innerText += '\n' + message

esprengle

class Graph:
  def __init__(self):
    self.nodes = set()
    self.edges = defaultdict(list)
    self.distances = {}

  def add_node(self, value):
    self.nodes.add(value)

  def add_edge(self, from_node, to_node, distance):

esprengle

# coding=UTF-8
from __future__ import division
import re

# This is a naive text summarization algorithm
# Created by Shlomi Babluki
# April, 2013


class SummaryTool(object):

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'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

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# extracted from http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip
# under public domain terms

country_bounding_boxes = {
    'AF': ('Afghanistan', (60.5284298033, 29.318572496, 75.1580277851, 38.4862816432)),
    'AO': ('Angola', (11.6400960629, -17.9306364885, 24.0799052263, -4.43802336998)),
    'AL': ('Albania', (19.3044861183, 39.624997667, 21.0200403175, 42.6882473822)),
    'AE': ('United Arab Emirates', (51.5795186705, 22.4969475367, 56.3968473651, 26.055464179)),
    'AR': ('Argentina', (-73.4154357571, -55.25, -53.628348965, -21.8323104794)),
    'AM': ('Armenia', (43.5827458026, 38.7412014837, 46.5057198423, 41.2481285671)),

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Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

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VSCode: Python Sort Imports on File Save (isort)

Problem

VSCode does have support to sort imports inside a .py file using isort. The recommended way to turn this on is via the following settings:

"[python]": {
    "editor.formatOnSave": true,
 "editor.codeActionsOnSave": {

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Hyperlinks (a.k.a. HTML-like anchors) in terminal emulators

[ Update 2020-05-31: I won't be maintaining this page or responding to comments anymore. The list of supporting software reflects the known state as of this date. ]

---

Most of the terminal emulators auto-detect when a URL appears onscreen and allow to conveniently open them (e.g. via Ctrl+click or Cmd+click, or the right click menu).

It was, however, not possible until now for arbitrary text to point to URLs, just as on webpages.

esprengle

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD