asitang

import networkx as nx
import plotly.graph_objects as go

def plotly_DAG(nx_dag, node_description_dict):
    """
    nx_dag: a networkX directed acyclic graph
    node_description_dict: a nested dictionary that contains node attributes to show on hover 
        eg. {
            'node_A':{'property_1':'value_A_1', 'property_2':'value_A_2'}, 
            'node_B':{'property_1':'value_B_1', 'property_2':'value_B_2'}

fire1ce

THIS GIST WON'T BE UPDATED ANY MORE (24/10/18)

Follow the progress of this project here 3os.org Raspberry Pi 3 TOR Access Point Router Project

Network: Router RJ45 <--> Ethernet Port on Raspberry <--> TOR <--> Raspberry WIFI AC <--> WIFI CLIENT

# -- Download Rasbian Strech Lite from: https://www.raspberrypi.org/downloads/raspbian/

shortjared

a4b.amazonaws.com
access-analyzer.amazonaws.com
account.amazonaws.com
acm-pca.amazonaws.com
acm.amazonaws.com
airflow-env.amazonaws.com
airflow.amazonaws.com
alexa-appkit.amazon.com
alexa-connectedhome.amazon.com
amazonmq.amazonaws.com

josejimenezluna

import numpy as np
from numpy.linalg import norm, solve
from scipy.spatial.distance import cdist
from sklearn.neighbors import kneighbors_graph


def phi(l, mu):
    return (mu * (np.sqrt(l) - 1)**2)

joyrexus

collapsible markdown?

CLICK ME

yes, even hidden code blocks!

print("hello world!")

gagarine

Howto Install clamav on OSX with brew

Note: on legacy intel system the path may be /usr/local/etc/clamav instead of /opt/homebrew/etc/clamav/

$ brew install clamav
$ cd /opt/homebrew/etc/clamav/
$ cp freshclam.conf.sample freshclam.conf

0xjac

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

1. Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare git@github.com:usi-systems/easytrace.git

dannguyen

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

arfon

#!/usr/bin/ruby

# For an OO language, this is distinctly procedural. Should probably fix that.
require 'json'

details = Hash.new({})

capture_params = [
  { :name => "title", :message => "Enter project name." },
  { :name => "url", :message => "Enter the URL of the project repository." },

Uchean

Get ClamAV running on Mac OS X (using Homebrew)

The easiest way to get the ClamAV package is using Homebrew

$ brew install clamav

Before trying to start the clamd process, you'll need a copy of the ClamAV databases.

Create a freshclam.conf file and configure as so