raychorn

http://stackoverflow.com/questions/18647444/python-nested-dictionary-from-pathname

2 hours of pure entertainment...

raychorn

Rails 3.0.pre on App Engine¶ ↑

You can Rails 3 on App Engine, but it won’t be especially useful until bundler 10. You should try these instead:

• Rails 2.3.5: DataMapper version: gist.github.com/268192

• Rails 2.3.5: TinyDS version: gist.github.com/269075

Install the Development Environment¶ ↑

The gems for the development environment include a pre-release appengine-tools gem that provides a pre-release version of jruby-rack.

raychorn

#!/usr/bin/env bash
apt-get -y update
apt-get -y install build-essential zlib1g-dev libssl-dev libreadline6-dev libyaml-dev
cd /tmp
wget ftp://ftp.ruby-lang.org/pub/ruby/1.9/ruby-1.9.3-p194.tar.gz
tar -xvzf ruby-1.9.3-p194.tar.gz
cd ruby-1.9.3-p194/
./configure --prefix=/usr/local
make
make install

raychorn

#!/usr/bin/env python

import os
import xml.parsers.expat
from xml.sax.saxutils import escape
from optparse import OptionParser
from math import log10


# How much data we process at a time

raychorn

1.Create desktop config file

sudo nano /usr/share/applications/smartsvn.desktop

2. Put the text below

[Desktop Entry]
Name=SmartSVN
Name[en]=SmartSVN
Name[ru]=SmartSVN

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# Enter your code here. Read input from STDIN. Print output to STDOUT
class Node:
    def __init__(self,value,point):
        self.value = value
        self.point = point
        self.parent = None
        self.H = 0
        self.G = 0
    def move_cost(self,other):
        return 0 if self.value == '.' else 1

raychorn

package main

import (
	"github.com/qiniu/py"
	"log"
	//"path/filepath"
	"strconv"
)

func callPythonFunction(file string, function string) (int, string) {

raychorn

Problem Statement:

Use Go to augment the processing power of Python.

Solutions:

(1) Call GO from Python:

This can be done but it is a bit tedious as shown below:

raychorn

https://mungingdata.com/go/dataframes-gota-qframe/

https://github.com/go-gota/gota  <-- Minimal.

https://github.com/tobgu/qframe  <-- Stable?

https://github.com/rocketlaunchr/dataframe-go <-- Looks interesting.

Probably easier to use Python pandas to load the data then dump data into GO via JSON/Web localhost to crunch the data 
possibly faster than using Python alone. Then respond back to Python with data it can load into a Pandas dataframe, etc.

raychorn

WSL install another distro

0. Here are some default vars for the process

ISO_DIR=~/fedora;
ROOTFS_MOUNT_DIR=/mnt/contents

DISTRO_LOCATION=