Meet Shah meetps

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

Installing OpenSSH 7.4 on Ubuntu 16.04

sudo apt install -y build-essential libssl-dev zlib1g-dev
wget "http://mirrors.evowise.com/pub/OpenBSD/OpenSSH/portable/openssh-7.4p1.tar.gz"
tar xfz openssh-7.4p1.tar.gz
cd openssh-7.4p1
./configure
make
sudo make install

How to add an image to a gist

Create or find a gist that you own.

Clone your gist (replace <hash> with your gist's hash):

# with ssh
git clone [email protected]:<hash>.git mygist

# with https

git clone https://gist.github.com/.git mygist

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

	import argparse
	import os
	import shutil
	import time

	import torch
	import torch.nn as nn
	import torch.nn.parallel
	import torch.backends.cudnn as cudnn
	import torch.optim

	"""Simple example on how to log scalars and images to tensorboard without tensor ops.

	License: BSD License 2.0
	"""
	__author__ = "Michael Gygli"

	import tensorflow as tf
	from StringIO import StringIO
	import matplotlib.pyplot as plt
	import numpy as np

	#!/bin/bash
	# scp-speed-test.sh
	# Author: Alec Jacobson alecjacobsonATgmailDOTcom
	# http://www.alecjacobson.com/weblog/?p=635
	#
	# Test ssh connection speed by uploading and then downloading a 10000kB test
	# file (optionally user-specified size)
	#
	# Usage:
	# ./scp-speed-test.sh user@hostname [test file size in kBs]

	import os

	descriptors = list()

	def getString():
	test_file = "nvidia-smi -q --gpu=0 \| tail -23"

	try:
	p = os.popen(test_file, 'r')
	return p.read()

	sudo pacman -S ruby ruby-rdoc gcc make

	gem update --user-install
	gem install jekyll --user-install

	# finally add $HOME/.gem/ruby/2.7.0/bin to your PATH variable

	"""Print most frequent N-grams in given file.

	Usage: python ngrams.py filename

	Problem description: Build a tool which receives a corpus of text,
	analyses it and reports the top 10 most frequent bigrams, trigrams,
	four-grams (i.e. most frequently occurring two, three and four word
	consecutive combinations).

	NOTES