Brian Muhia poppingtonic

## gist:197124f557c2872c0abac43453790c78
cat dllist | parallel -j 8 rsync -aHvxz --relative user@host.domain:/path/to/files/./{} .

## raw_qs_to_qs.py
import operator

from functools import reduce

results = qs.model.objects.raw(sql, sql_params)

obj_ranks = (obj.id for obj in results)
return_objs = []
# inject rank
for obj_id, objs.items():

## kenya-countylevel-teenage-pregnancy-statistics.csv

          
            SNO
            Organisation unit/Data
            Adolescents (10-14 years) presenting with pregnancy
            Adolescents (15-19 years) presenting with pregnancy
            Total

            
              1
               Nairobi County
               2028
               9706
               11734

            
              2
               Kakamega County
               217
               6469
               6686

            
              3
               Homa Bay County
               451
               5481
               5932

            
              4
               Nakuru County
               456
               5427
               5883

            
              5
               Kajiado County
               824
               5010
               5834

            
              6
               Narok County
               310
               5275
               5585

            
              7
               Bungoma County
               151
               5426
               5577

            
              8
               Meru County
               209
               5047
               5256

            
              9
               West Pokot County
               290
               4722
               5012

## pbs.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-

# This file is part of PyBOSSA.
#
# PyBOSSA is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#

## silver map url
https://maps.googleapis.com/maps/api/staticmap?key=YOUR_API_KEY&center=47.65,-122.35000000000002&zoom=12&format=png&maptype=roadmap&style=element:geometry%7Ccolor:0xf5f5f5&style=element:labels.icon%7Cvisibility:off&style=element:labels.text.fill%7Ccolor:0x616161&style=element:labels.text.stroke%7Ccolor:0xf5f5f5&style=feature:administrative.land_parcel%7Celement:labels%7Cvisibility:off&style=feature:administrative.land_parcel%7Celement:labels.text.fill%7Ccolor:0xbdbdbd&style=feature:poi%7Celement:geometry%7Ccolor:0xeeeeee&style=feature:poi%7Celement:labels.text%7Cvisibility:off&style=feature:poi%7Celement:labels.text.fill%7Ccolor:0x757575&style=feature:poi.park%7Celement:geometry%7Ccolor:0xe5e5e5&style=feature:poi.park%7Celement:labels.text.fill%7Ccolor:0x9e9e9e&style=feature:road%7Celement:geometry%7Ccolor:0xffffff&style=feature:road.arterial%7Celement:labels.text.fill%7Ccolor:0x757575&style=feature:road.highway%7Celement:geometry%7Ccolor:0xdadada&style=feature:road.highway%7Celement:labels.text.fill%7Ccolor:

## passphrase_gen.py
#!/usr/bin/env python

# requires python 3.6 and requests
import os
import re
import secrets
import requests


wordlist_file = 'diceware.wordlist.asc'

## bot_web_server.py
# import the necessary packages
import ast
import logging
from PIL import Image
import numpy as np
import settings
import helpers
from classes import disease_classes
from quart import Quart, request, jsonify
import redis

## extract.sh

# $1 is the first argument
# remove result.txt
#rm output/result.txt

fname=`echo "$1" | awk -F "/" '{print $(NF)}'`
# convert the pdf to a group of tiffs

if [ ! -e extracted_tz_parliament/$fname.txt ];
   then

## gist:e4ebe6e68d4ba8ae7e76ac8a4a9d629d
https://docs.google.com/presentation/d/1nApyvRZSAdvomrgaWNRuP90UVAlp6OufnU7Vpo0a1Ck/edit?usp=sharing

## pretrain_lm.py
import pandas as pd
from fastai.text import *
import html

LANG='fr'

def preprocess_wiki_lm(path):
  DATA = Path(path)
  trn_df = pd.read_csv(DATA/'train.csv', header=None)
  val_df = pd.read_csv(DATA/'valid.csv', header=None)
	import operator

	from functools import reduce

	results = qs.model.objects.raw(sql, sql_params)

	obj_ranks = (obj.id for obj in results)
	return_objs = []
	# inject rank
	for obj_id, objs.items():
SNO	Organisation unit/Data	Adolescents (10-14 years) presenting with pregnancy	Adolescents (15-19 years) presenting with pregnancy	Total
1	Nairobi County	2028	9706	11734
2	Kakamega County	217	6469	6686
3	Homa Bay County	451	5481	5932
4	Nakuru County	456	5427	5883
5	Kajiado County	824	5010	5834
6	Narok County	310	5275	5585
7	Bungoma County	151	5426	5577
8	Meru County	209	5047	5256
9	West Pokot County	290	4722	5012
	#!/usr/bin/env python
	# -- coding: utf-8 --

	# This file is part of PyBOSSA.
	#
	# PyBOSSA is free software: you can redistribute it and/or modify
	# it under the terms of the GNU Affero General Public License as published by
	# the Free Software Foundation, either version 3 of the License, or
	# (at your option) any later version.
	#
	#!/usr/bin/env python

	# requires python 3.6 and requests
	import os
	import re
	import secrets
	import requests


	wordlist_file = 'diceware.wordlist.asc'
	# import the necessary packages
	import ast
	import logging
	from PIL import Image
	import numpy as np
	import settings
	import helpers
	from classes import disease_classes
	from quart import Quart, request, jsonify
	import redis

	# $1 is the first argument
	# remove result.txt
	#rm output/result.txt

	fname=`echo "$1" \| awk -F "/" '{print $(NF)}'`
	# convert the pdf to a group of tiffs

	if [ ! -e extracted_tz_parliament/$fname.txt ];
	then
	import pandas as pd
	from fastai.text import *
	import html

	LANG='fr'

	def preprocess_wiki_lm(path):
	DATA = Path(path)
	trn_df = pd.read_csv(DATA/'train.csv', header=None)
	val_df = pd.read_csv(DATA/'valid.csv', header=None)