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Klipper mesh on print area only install guide

What this macro do

• This macro will dymanic change the bed_mesh area based on the size of the printed part. The fw will only probe on the area that the part will be printed (plus mesh_area_offset value)

Setup guide

• (1) Add the following macrro to your printer config, this will replace the default BED_MESH_CALIBRATE command.

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

# Add your freebase key here
# If you don't have one, register at https://code.google.com/apis/console
FREEBASE_KEY = ""

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"""
An implementation of the `Local reparameterization trick`
from Kingma & Wellings and 
Bayesian RNN 
from Fortunato, Blundell & Vinyals
"""
import os
import time
import copy
from os.path import join as pjoin

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Elasticsearch CLI

Experimental CLI interface for the helpers in the `python library`_.

Main purpose is to expose the bulk functionality to enable rapid loading of data into an elasticsearch cluster. Combined with the scan command it can also be used to reindex data from elasticsearch into a different index or cluster.

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data {
    int<lower=0> J; // number of schools
    real y[J]; // estimated treatment effects
    real<lower=0> sigma[J]; // s.e. of effect estimates
}
parameters {
    real mu;
    real<lower=0> tau;
    real eta[J];
}

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#!/usr/bin/python

# This work is licensed under the Creative Commons Attribution 3.0 United 
# States License. To view a copy of this license, visit 
# http://creativecommons.org/licenses/by/3.0/us/ or send a letter to Creative
# Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.

# from http://oranlooney.com/make-css-sprites-python-image-library/ 
# Orignial Author Oran Looney <[email protected]>

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#!/bin/bash

ROS_DISTRO=${ROS_DISTRO:-kinetic}


/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

brew update
brew install cmake

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package com.databricks.spark.jira

import scala.io.Source

import org.apache.spark.rdd.RDD

import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.sources.{TableScan, BaseRelation, RelationProvider}

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import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.conf.Configuration 

  case class S3File(path: String, isDir: Boolean, size: Long) {
    def children = listFiles(path)
  }

  def listFiles(path: String): Seq[S3File] = {
    val fs = FileSystem.get(new java.net.URI(path), new Configuration())
    fs.listStatus(new Path(path)).map(s => S3File(s.getPath.toString, s.isDir, s.getLen))

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class AC_Network():
    def __init__(self,s_size,a_size,scope,trainer):
        with tf.variable_scope(scope):
            #Input and visual encoding layers
            self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32)
            self.imageIn = tf.reshape(self.inputs,shape=[-1,84,84,1])
            self.conv1 = slim.conv2d(activation_fn=tf.nn.elu,
                inputs=self.imageIn,num_outputs=16,
                kernel_size=[8,8],stride=[4,4],padding='VALID')
            self.conv2 = slim.conv2d(activation_fn=tf.nn.elu,