Jeremy Mason-Herr jmasonherr

## Collect Parler Metadata.ipynb

      
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                jmasonherr
                / Collect Parler Metadata.ipynb
            
            
              Created
              January 12, 2021 17:53
                — forked from kylemcdonald/Collect Parler Metadata.ipynb
            
              
                Collect video URLs and GPS data for Parler videos.
              
          
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## recursive_sql_example.sql
-- A bottom-up recursive Postgres query on a table named 'family'
-- with three columns: id, parent_id, child_id

WITH RECURSIVE bottom_up_family (
  parent_id, child_id, depth, path
) AS (
SELECT
fam_initial.parent_id,
fam_initial.child_id,
  1,

## middleware.py
from __future__ import absolute_import, division, print_function

try:
    from threading import local
except ImportError:
    from django.utils._threading_local import local

_thread_locals = local()

## ubiome2fastq.py
"""
No dependency Python script for converting uBiome's raw data .zip
into a single FASTQ file.  Based on
https://gist.github.com/boydgreenfield/805ac27e0a6b9a5adea7

Usage:

>>>python ubiome2fastq.py ubiomedatafile.zip

"""

## statclassify.py
# -*- coding: utf-8 -*-
import math


"""

    Statistical classification classes based on Toby Segarin's Programming Collective Intelligence.  I wanted a version that was a little easier to read and didn't talk to SQLite.  Its meant to be trained and tested on blocks of text.

    This is good for separating things of discrete classes.  Spam detection is used as the example in the book

## connect_tutorial_python.py
# coding: utf-8
import json
import stripe
import datetime

# Required for OAuth flow
from rauth import OAuth2Service

# Our secret key from stripe
STRIPE_SECRET_KEY = 'sk_test_xxxxxxxxxxxxx'

## url_to_image_service.py
# Requires poster https://pypi.python.org/pypi/poster/
# Requires webapp2 on App Engine for demo

# Example usage:

# import requests
# 'http://upload.wikimedia.org/math/4/7/9/479d9d21b183eb546b771447fcf69ddf.png'
# response = requests.post('http://myapp.appspot.com/i/post_from_url', {'url': wikipediaimg})

import json
	-- A bottom-up recursive Postgres query on a table named 'family'
	-- with three columns: id, parent_id, child_id

	WITH RECURSIVE bottom_up_family (
	parent_id, child_id, depth, path
	) AS (
	SELECT
	fam_initial.parent_id,
	fam_initial.child_id,
	1,
	from __future__ import absolute_import, division, print_function

	try:
	from threading import local
	except ImportError:
	from django.utils._threading_local import local

	_thread_locals = local()
	"""
	No dependency Python script for converting uBiome's raw data .zip
	into a single FASTQ file. Based on
	https://gist.github.com/boydgreenfield/805ac27e0a6b9a5adea7

	Usage:

	>>>python ubiome2fastq.py ubiomedatafile.zip

	"""
	# -- coding: utf-8 --
	import math


	"""

	Statistical classification classes based on Toby Segarin's Programming Collective Intelligence. I wanted a version that was a little easier to read and didn't talk to SQLite. Its meant to be trained and tested on blocks of text.

	This is good for separating things of discrete classes. Spam detection is used as the example in the book
	# coding: utf-8
	import json
	import stripe
	import datetime

	# Required for OAuth flow
	from rauth import OAuth2Service

	# Our secret key from stripe
	STRIPE_SECRET_KEY = 'sk_test_xxxxxxxxxxxxx'
	# Requires poster https://pypi.python.org/pypi/poster/
	# Requires webapp2 on App Engine for demo

	# Example usage:

	# import requests
	# 'http://upload.wikimedia.org/math/4/7/9/479d9d21b183eb546b771447fcf69ddf.png'
	# response = requests.post('http://myapp.appspot.com/i/post_from_url', {'url': wikipediaimg})

	import json