LiamHz

#Number of villages
N = int(input())
#List of village positions
villages = list()
for i in range(N):
    villages.append(float(input()))

#List of distances between villages
distances = list()

LiamHz

// Read authentication credentials from file
var fs = require('fs');

fs.readFile('credentials.txt', 'utf8', function(err, contents) {
    var text = contents.split("\n")
    text[1] = text[1].substring(0, text[1].length - 1);
    text[2] = text[2].substring(0, text[2].length - 1);
    username = text[1];
    password = text[2];
});

LiamHz

# Trim ending characters of a filename of every file in a folder
Get-ChildItem 'FOLDER_NAME' | rename-item -newname { $_.name.substring(0, $_.name.length-N) }

# Trim beginning N characters of a filename of every file in a folder
get-childitem *.txt | rename-item -newname { [string]($_.name).substring(N) }

# Append a file extension to every file in a folder
Get-ChildItem 'FOLDER_NAME' | rename-item -NewName {$_.FullName + ".pdf"} 

# Append text to every filename BEFORE the file extension

LiamHz

from slackclient import SlackClient

slack_client = SlackClient(SLACK_OAUTH)

# Print a list of users and their respective IDs
request = slack_client.api_call("users.list")
if request['ok']:
    for item in request['members']:
        print("{}: {}".format(item['name'], item['id']))

LiamHz

# Search through current directory and all sub directories for text
dir -Recurse | Select-String -pattern "STRING"

# Recursive search and table formatting
dir -Recurse | Select-String -pattern "latest_net_G" | Select Path, LineNumber, Line | Format-Table


# Search through all files with the .EXT extension in the current directory 
# and find any lines that match with STRING

LiamHz

import json

# Read variables from disk
with open('data.json', 'r') as json_file:
    data = json.load(json_file)
    a = data['a']
    b = data['b']
    c = data['c']
    d = data['d']

LiamHz

import pickle

data = {}

# Read from disk
pickle_jar = open('data.pkl', 'rb')
data = pickle.load(pickle_jar)

# Write to disk
pickle_jar = open('data.pkl', 'wb')

LiamHz

# Calculate the average squared error between two high-level layer activations
content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])**2)

LiamHz

def gram_matrix(tensor):
    # Get the batch_size, depth, height, and width of the Tensor
    # Reshape it, so we're multiplying the features for each channel
    _, d, h, w = tensor.size()
    tensor = tensor.view(d, h*w)

    # Calculate the gram matrix by multiplying the tensor with its transpose
    gram = torch.mm(tensor, tensor.t())

    return gram

LiamHz

show_every = 400                            # Show target image every x steps
optimizer = optim.Adam([target], lr=0.003)  # Optimizer hyperparameters
steps = 2000                                # How many iterations to update content image

# Training Loop
for ii in range(steps):
    # Calculate the content loss
    target_features = get_features(target, vgg)
    content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])**2)