Matt Ball yosemitebandit

## crop_to_path.py
"""Crop a polygonal selection from an image."""
import numpy as np
from PIL import Image
from shapely.geometry import Point
from shapely.geometry import Polygon


im = Image.open('bird.jpg').convert('RGBA')
pixels = np.array(im)
im_copy = np.array(im)

## yupyupnope.conf
server {
	listen 80;
	server_name www.yupyupnope.com;
	rewrite ^/(.*) https://yupyupnope.com/$1 permanent;
}

server {
	listen 80;
	server_name yupyupnope.com;
	rewrite ^/(.*) https://yupyupnope.com/$1 permanent;

## classify.py
''' classify
after training, classify a sample image with K-Nearest Neighbor

annotated from Abid Rahman's post: http://stackoverflow.com/a/9620295/232638
'''
import cv2
import numpy as np

# load the data we generated previously
samples = np.loadtxt('general-samples.data', np.float32)

## udev.txt
watch -n 1 "ls /dev"
watch -n 1 "ls /dev | grep unitek"
udevadm monitor

sudo service udev restart
udevadm info -a -p  $(udevadm info -q path -n /dev/ttyACM0)

udevadm info --name=/dev/ttyACM0 --attribute-walk
udevadm info --name=/dev/ttyACM0 --attribute-walk | grep KERNELS

## 3_regularization.ipynb

      
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                / 3_regularization.ipynb
            
            
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              June 8, 2017 01:49
            
              
                udacity neural network course -- assignment 3.4, 3-layer NN with regularization and dropout
              
          
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## sample.ino
/* Sample sketch for review.
*/
#define USB Serial

const int usbBaud = 57600;
const int ledPin = 13;

const int commandBufferLength = 80;
static char commandBuffer[commandBufferLength];
char *commandMode;

## 1_notmnist.ipynb

      
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              March 7, 2017 03:20
            
              
                assignment two from the Udacity Deep Learning course
              
          
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## notes.md

      
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              November 27, 2016 09:41
            
              
                live streaming notes
              
          
    youtube


create an event and setup custom ingestion so you get an rtmp url,
then use something like to stream from a mac's webcam:

ffmpeg -f avfoundation -i "default" -re -ar 44100 -ac 2 -acodec pcm_s16le -f s16le -ac 2 -i /dev/zero -f h264 -acodec aac -ab 128k -g 50 -strict experimental -f flv -s 426x240 "<rtmp_path>/<stream_id>"

zencoder


## gene_expression_plotter.py
"""Plotting gene expression data from a csv"""
import os

import matplotlib
import matplotlib.pyplot as plt
import pylab


# Load data.
path = 'murine-placenta-gene-1-2.csv'

## two-player-go.html
<!doctype html>
<html>

  <head>
    <style>
      body, html, table {
        margin: 0;
        padding: 0;
      }
      body {
	"""Crop a polygonal selection from an image."""
	import numpy as np
	from PIL import Image
	from shapely.geometry import Point
	from shapely.geometry import Polygon


	im = Image.open('bird.jpg').convert('RGBA')
	pixels = np.array(im)
	im_copy = np.array(im)
	server {
	listen 80;
	server_name www.yupyupnope.com;
	rewrite ^/(.*) https://yupyupnope.com/$1 permanent;
	}

	server {
	listen 80;
	server_name yupyupnope.com;
	rewrite ^/(.*) https://yupyupnope.com/$1 permanent;
	''' classify
	after training, classify a sample image with K-Nearest Neighbor

	annotated from Abid Rahman's post: http://stackoverflow.com/a/9620295/232638
	'''
	import cv2
	import numpy as np

	# load the data we generated previously
	samples = np.loadtxt('general-samples.data', np.float32)
	watch -n 1 "ls /dev"
	watch -n 1 "ls /dev \| grep unitek"
	udevadm monitor

	sudo service udev restart
	udevadm info -a -p $(udevadm info -q path -n /dev/ttyACM0)

	udevadm info --name=/dev/ttyACM0 --attribute-walk
	udevadm info --name=/dev/ttyACM0 --attribute-walk \| grep KERNELS
	/* Sample sketch for review.
	*/
	#define USB Serial

	const int usbBaud = 57600;
	const int ledPin = 13;

	const int commandBufferLength = 80;
	static char commandBuffer[commandBufferLength];
	char *commandMode;
	"""Plotting gene expression data from a csv"""
	import os

	import matplotlib
	import matplotlib.pyplot as plt
	import pylab


	# Load data.
	path = 'murine-placenta-gene-1-2.csv'
	<!doctype html>
	<html>

	<head>
	<style>
	body, html, table {
	margin: 0;
	padding: 0;
	}
	body {