Felipe Ryan feliperyan

## gist:67c21531ddf998b762d4
def theThing():
    return "rrraaawww"

## gist:998b678d84903a8d9763
#This assumes you downloaded the PostgreSQL app,
#Python 3, the Heroku Toolbelt and Sublime Text 3.

#Create and activate virtual environment
pyvenv ~/virtualevns/wagtailenv
source ~/virtualenvs/wagtailenv/bin/activate

#Allow Sublime Text to be opened from the Terminal
ln -s "/Applications/Sublime Text.app/Contents/SharedSupport/bin/subl" /usr/local/bin/sublime

## gist:27a1347136a109cbbabf
<script type="text/javascript" src="js/twitterFetch.js"></script>

        <script>
            // ID of the Twitter List
            var id = '606429765768081409';

            // Settings for what the js library will output to the corresponding
            // element. Not an exhaustive list.
            var config5 = {
                "id": '606429765768081409',

## round_robin.py
from copy import deepcopy

def go_round_robin(list_of_lists):
  robin = deepcopy(list_of_List)

  i = len(robin)

  while len(robin) > 0:
    j = i % len(robin)
    try:

## threads_explanation.py
#!/usr/bin/python

import Queue
import threading
import time

exitFlag = 0

class myThread (threading.Thread):


## exif.py
#!/usr/local/bin/python

"""Read EXIF data from an image
https://gist.github.com/ishahid/137f8724d8cff2304dffd69f3ade26d7
"""

try:
    import os, sys
    # import json
    from PIL import Image

## get_lat_lon_exif_pil.py
from PIL import Image
from PIL.ExifTags import TAGS, GPSTAGS

def get_exif_data(image):
    """Returns a dictionary from the exif data of an PIL Image item. Also converts the GPS Tags"""
    exif_data = {}
    info = image._getexif()
    if info:
        for tag, value in info.items():
            decoded = TAGS.get(tag, tag)

## make_model.py
import pickle
import pandas as pd
import re
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import train_test_split
from time import time

## load_modle_from_pickle_v2.ipynb

      
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                feliperyan
                / load_modle_from_pickle_v2.ipynb
            
            
              Created
              February 14, 2018 00:08
            
              
                Jupyter notebook showing how to load a model and vectoriser and apply it. Results in 2 csv files one with predicted data and one with words that the model has never seen before.
              
          
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## gist:f60c7cb51f85bfcff77a3eda6df80b7f
### Keybase proof

I hereby claim:

  * I am feliperyan on github.
  * I am fryan (https://keybase.io/fryan) on keybase.
  * I have a public key ASAjRo41sVidSbYZfUdkXjsAYJPUbvEi2IuzTmr0-g0-pQo

To claim this, I am signing this object:
	#This assumes you downloaded the PostgreSQL app,
	#Python 3, the Heroku Toolbelt and Sublime Text 3.

	#Create and activate virtual environment
	pyvenv ~/virtualevns/wagtailenv
	source ~/virtualenvs/wagtailenv/bin/activate

	#Allow Sublime Text to be opened from the Terminal
	ln -s "/Applications/Sublime Text.app/Contents/SharedSupport/bin/subl" /usr/local/bin/sublime
	<script type="text/javascript" src="js/twitterFetch.js"></script>

	<script>
	// ID of the Twitter List
	var id = '606429765768081409';

	// Settings for what the js library will output to the corresponding
	// element. Not an exhaustive list.
	var config5 = {
	"id": '606429765768081409',
	from copy import deepcopy

	def go_round_robin(list_of_lists):
	robin = deepcopy(list_of_List)

	i = len(robin)

	while len(robin) > 0:
	j = i % len(robin)
	try:
	#!/usr/bin/python

	import Queue
	import threading
	import time

	exitFlag = 0

	class myThread (threading.Thread):
	#!/usr/local/bin/python

	"""Read EXIF data from an image
	https://gist.github.com/ishahid/137f8724d8cff2304dffd69f3ade26d7
	"""

	try:
	import os, sys
	# import json
	from PIL import Image
	from PIL import Image
	from PIL.ExifTags import TAGS, GPSTAGS

	def get_exif_data(image):
	"""Returns a dictionary from the exif data of an PIL Image item. Also converts the GPS Tags"""
	exif_data = {}
	info = image._getexif()
	if info:
	for tag, value in info.items():
	decoded = TAGS.get(tag, tag)
	import pickle
	import pandas as pd
	import re
	import numpy as np
	from sklearn.feature_extraction.text import CountVectorizer
	from sklearn.ensemble import RandomForestClassifier
	from sklearn import metrics
	from sklearn.model_selection import train_test_split
	from time import time
	### Keybase proof

	I hereby claim:

	* I am feliperyan on github.
	* I am fryan (https://keybase.io/fryan) on keybase.
	* I have a public key ASAjRo41sVidSbYZfUdkXjsAYJPUbvEi2IuzTmr0-g0-pQo

	To claim this, I am signing this object: