leoluyi

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

leoluyi

-- Adapted from these sources:
-- http://peterdowns.com/posts/open-iterm-finder-service.html
-- https://gist.github.com/cowboy/905546
-- 
-- Modified to work with files as well, cd-ing to their container folder
on run {input, parameters}
  tell application "Finder"
		set my_file to first item of input
		set filetype to (kind of (info for my_file))
		-- Treats OS X applications as files.  To treat them as folders, integrate this SO answer:

leoluyi

on run {input, parameters}
    tell application "Finder"
        set dir_path to quoted form of (POSIX path of (folder of the front window as alias))
    end tell
    CD_to(dir_path)
end run

on CD_to(theDir)
    tell application "iTerm"
        activate

leoluyi

#! /bin/bash
#
# backup_redmine.sh
# modified by ronan@lespolypodes.com
# Inspiration: https://gist.github.com/gabrielkfr/6432185
#
# Distributed under terms of the MIT license.

# -- VARS
DAY=`date +"%Y%m%d"`

leoluyi

<script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>

2016-09-16 更新
最近內政部釋出 20 m DTM，志展(aka. Rex)收集了資料並做了許多處理應用(參見應用內政部20公尺網格數值地形模型資料)，綬草北三也提到幾個參數轉換的更正，一併整理如下：

MillerLiu's comments:

「關於坐標轉換參數請務必更正, 引述參數文章關於轉換部分恰好是我多年前蒐集及撰寫的， OSG1是TWD67轉TWD97、OSG2則是Hu-Tzu-Shan轉TWD97、OSG3則是Bessel轉TWD97，

leoluyi

library(shiny)
library(datasets)
Logged = FALSE;
PASSWORD <- data.frame(Brukernavn = "withr", Passord = "25d55ad283aa400af464c76d713c07ad")
# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
  source("www/Login.R",  local = TRUE)
  
  observe({
    if (USER$Logged == TRUE) {

leoluyi

//
//                       _oo0oo_
//                      o8888888o
//                      88" . "88
//                      (| -_- |)
//                      0\  =  /0
//                    ___/`---'\___
//                  .' \\|     |// '.
//                 / \\|||  :  |||// \
//                / _||||| -:- |||||- \

leoluyi

# Installing TOR on mac: brew install tor
# Run TOR on custom port: tor --SOCKSPort 9050

# Check the 'origin' field in the response to verify TOR is working.
library(httr)
GET("https://httpbin.org/get", use_proxy("socks5://localhost:9050"))

# Set proxy in curl
library(curl)
h <- new_handle(proxy = "socks5://localhost:9050")

leoluyi

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

leoluyi

import fiona
import fiona.crs

def convert(f_in, f_out):
    with fiona.open(f_in) as source:
        with fiona.open(
                f_out,
                'w',
                driver='GeoJSON',
                crs = fiona.crs.from_epsg(4326),