Muoki Caleb muokicaleb

## gist:072be9d85beebd8977a00e70486efd02

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                muokicaleb
                / gist:072be9d85beebd8977a00e70486efd02
            
            
              Created
              June 21, 2019 10:43
                — forked from CristinaSolana/gist:1885435
            
              
                Keeping a fork up to date
              
          
    1. Clone your fork:

git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream


## autocommit.sh
# a script that automatically commits when the file is saved
# you need to install inotify-tools
inotifywait -q -m -e CLOSE_WRITE --format="git commit -m 'autocommit ' %w" *.* | sh

## fastai_setup_windows.sh
git clone https://github.com/fastai/fastai

cd fastai

conda create -n fastai python=3.6 anaconda

conda env update

activate fastai
i

## missing_val
'''
Make the assumption that weight value is missing in the df but
 Looking at the columns we'll notice that there is Item_Identifier. Similar items have the same identifier.
 We can have a dictionary of the weigts and identifier. Then look up the missing values for each.
'''
# first create a pivot table

item_avg_weight = df.pivot_table(values='Item_Weight', index='Item_Identifier')

'''

## common_df.functions
# get sum of all unique values in all columns
df.apply(lambda x: len(x.unique()))

# SUm null values
df.isnull().sum()

#apply to rows based on a row

df['Name_entity'] = df.apply (lambda df: name_entity_recognition(df['clean_tweet']), axis=1)

## nginx.conf
# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

## tweet_dumper.py
#!/usr/bin/env python
# encoding: utf-8

import tweepy #https://github.com/tweepy/tweepy
import csv

#Twitter API credentials
consumer_key = ""
consumer_secret = ""
access_key = ""

## node_exporter_conf.yaml
basic_auth_users:
  prometheus: $2y$12$UR3iyrDxfWsahCIg0ikKquNBjV3xwdoJZiuz.vhHgpvQrjLhmqmE6s

## node-exporter-no-password.yaml
version: '3.7'

services:
  node_exporter:
    image: quay.io/prometheus/node-exporter:latest
    command:
      - '--path.rootfs=/host'
    network_mode: host
    pid: host
    restart: unless-stopped

## docker-compose-node-exporter.yaml
version: '3.7'

services:
  node_exporter:
    image: quay.io/prometheus/node-exporter:latest
    command:
      - '--path.rootfs=/host'
      - '--web.config=/node_exporter_conf.yaml'
    network_mode: host
    pid: host
	# a script that automatically commits when the file is saved
	# you need to install inotify-tools
	inotifywait -q -m -e CLOSE_WRITE --format="git commit -m 'autocommit ' %w" . \| sh
	git clone https://github.com/fastai/fastai

	cd fastai

	conda create -n fastai python=3.6 anaconda

	conda env update

	activate fastai
	i
	'''
	Make the assumption that weight value is missing in the df but
	Looking at the columns we'll notice that there is Item_Identifier. Similar items have the same identifier.
	We can have a dictionary of the weigts and identifier. Then look up the missing values for each.
	'''
	# first create a pivot table

	item_avg_weight = df.pivot_table(values='Item_Weight', index='Item_Identifier')

	'''
	# get sum of all unique values in all columns
	df.apply(lambda x: len(x.unique()))

	# SUm null values
	df.isnull().sum()

	#apply to rows based on a row

	df['Name_entity'] = df.apply (lambda df: name_entity_recognition(df['clean_tweet']), axis=1)
	# to generate your dhparam.pem file, run in the terminal
	openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048
	#!/usr/bin/env python
	# encoding: utf-8

	import tweepy #https://github.com/tweepy/tweepy
	import csv

	#Twitter API credentials
	consumer_key = ""
	consumer_secret = ""
	access_key = ""
	basic_auth_users:
	prometheus: $2y$12$UR3iyrDxfWsahCIg0ikKquNBjV3xwdoJZiuz.vhHgpvQrjLhmqmE6s
	version: '3.7'

	services:
	node_exporter:
	image: quay.io/prometheus/node-exporter:latest
	command:
	- '--path.rootfs=/host'
	network_mode: host
	pid: host
	restart: unless-stopped