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#include <IRremote.h>

/*

 Send infrared commands from the Arduino to the iRobot Roomba
 by probono
 
 2013-03-17 Initial release
 

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import http.server
import cgi
import base64
import json
from urllib.parse import urlparse, parse_qs


class CustomServerHandler(http.server.BaseHTTPRequestHandler):

    def do_HEAD(self):

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## Delete a remote branch
$ git push origin --delete <branch> # Git version 1.7.0 or newer 
$ git push origin :<branch> # Git versions older than 1.7.0

## Delete a local branch
$ git branch --delete <branch>
$ git branch -d <branch> # Shorter version
$ git branch -D <branch> # Force delete un-merged branches

## Delete a local remote-tracking branch

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//
// Adafruit Feather M0 Express ADC data logging
//
// Based on the Adafruit SdFat_circuitpython example
//
// The files can be accessed by double pressing the reset button and
// copying the circuitpython uf2 file (https://circuitpython.org/board/feather_m0_express/)
// into the featherboot drive (must be connected to the computer USB port :P)
// If you want to keep your arduino code, save the CURRENT.UF2 file before copying
// the circuitpython one from the link above.

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import storage
storage.remount("/", readonly=True) # to enable the code to save data to the flash this needs to be False

# After the system is reset with the above line set to False, it will not allow to update 
# anything using the virtual USB drive. Therefore it's necessary to connect using the serial USB (minicom),
# enter the REPL (contr+c to cancel code.py if it's running and any key) and paste the line below:
# import os; os.remove("/boot.py"); fs=open("/boot.py", "w"); fs.write("import storage\nstorage.remount(\"/\", readonly=True)"); fs.close()

#
# Usage

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def ransac_polyfit(x, y, order=3, n=20, k=100, t=0.1, d=100, f=0.8):
  # Thanks https://en.wikipedia.org/wiki/Random_sample_consensus
  
  # n – minimum number of data points required to fit the model
  # k – maximum number of iterations allowed in the algorithm
  # t – threshold value to determine when a data point fits a model
  # d – number of close data points required to assert that a model fits well to data
  # f – fraction of close data points required
  
  besterr = np.inf

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# from : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/graph_editor/examples/edit_graph_example.py
import numpy as np

import tensorflow as tf
from tensorflow.contrib import graph_editor as ge

# create a graph
g = tf.Graph()

with g.as_default():

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