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$ brew install fish
$ echo "/usr/local/bin/fish" | sudo tee -a /etc/shells
$ chsh -s `which fish`

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# download: remote -> local
scp user@remote_host:remote_file local_file 

# upload: local -> remote
scp local_file user@remote_host:remote_file

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# This script is designed to work with ubuntu 16.04 LTS
# with keras 1.2.2 and the latest Pytorch with CUDA 8 support 
##########################################################################
#This is used to install CUDA 8 driver for Tesla K80 
##########################################################################

#!/bin/bash
echo "Checking for CUDA and installing."
# Check for CUDA and try to install.
if ! dpkg-query -W cuda-8-0; then

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# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder

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import sys
!conda install --yes --prefix={sys.prefix} numpy

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import folium

def plot_heatmap(data):
    '''
    Args:
      takes a list of lists [[lat, long]]
    Returns:
      Folium map object with heatmap overlay
    '''
    heatmap = folium.Map(tiles="CartoDBpositron", prefer_canvas=True)

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import os
with open('../data/labels.csv','r') as labels:
    lines = [line.split(',') for line in labels.readlines()][1:]
    for line in lines:
        image = line[0]
        label = line[1].split('/')[-1].replace('\n','')
        if not os.path.exists('../data/train/'+label):
            os.mkdir('../data/train/'+label)
        os.rename('../data/train/'+image+'.jpg', '../data/train/'+label+'/'+image+'.jpg')

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from google.cloud import storage

def list_blobs(bucket, prefix):
    """
    Lists all the blobs in the bucket
    make sure prefix ends with '/'
    """
    storage_client = storage.Client()
    bucket = storage_client.get_bucket(bucket)
    

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def convert_to_datetime(df, columns):
    return df[columns].apply(pd.to_datetime, axis=1)

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from pdf2image import convert_from_path

image = convert_from_path("path_to_pdf.pdf")