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# Reshaping the array to 4-dims so that it can work with the Keras API
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
# Making sure that the values are float so that we can get decimal points after division
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# Normalizing the RGB codes by dividing it to the max RGB value.
x_train /= 255
x_test /= 255

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import glob # The glob module is used for Unix style pathname pattern expansion.
import imageio # The library that provides an easy interface to read and write a wide range of image data

anim_file = 'dcgan.gif'

with imageio.get_writer(anim_file, mode='I') as writer:
  filenames = glob.glob('image*.png')
  filenames = sorted(filenames)
  for filename in filenames:
    image = imageio.imread(filename)

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def convert_data_to_examples(train, test, DATA_COLUMN, LABEL_COLUMN): 
  train_InputExamples = train.apply(lambda x: InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this case
                                                          text_a = x[DATA_COLUMN], 
                                                          text_b = None,
                                                          label = x[LABEL_COLUMN]), axis = 1)

  validation_InputExamples = test.apply(lambda x: InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this case
                                                          text_a = x[DATA_COLUMN], 
                                                          text_b = None,
                                                          label = x[LABEL_COLUMN]), axis = 1)

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# tf.function annotation causes the function 
# to be "compiled" as part of the training
@tf.function
def train_step(images):
  
    # 1 - Create a random noise to feed it into the model
    # for the image generation
    noise = tf.random.normal([BATCH_SIZE, noise_dim])
    
    # 2 - Generate images and calculate loss values

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x_train = x_train[…, tf.newaxis]
x_test = x_test[…, tf.newaxis]

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import requests,json,numpy as np,pandas as pd

# https://docs.coinranking.com/

def hist_price_dl(coin_id=1335,timeframe = "5y",coinID = "Qwsogvtv82FCd", APIKey="YOUR-COINRANKING-API-KEY"):
    '''It accepts coin_id, timeframe, and currency parameters to clean the historic coin data taken from COINRANKING.COM
    It returns a Pandas Series with daily mean values of the selected coin in which the date is set as the index'''
    r = requests.get("https://api.coinranking.com/v2/coin/" + coinID + "/history?timePeriod=" + timeframe + "&x-access-token="+APIKey)
    coin = json.loads(r.text)['data']['history'] # Reading in json and cleaning the irrelevant parts
    df = pd.DataFrame(coin)

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def price_matrix_creator(data, seq_len=30):
    '''
    It converts the series into a nested list where every item of the list contains historic prices of 30 days
    '''
    price_matrix = []
    for index in range(len(data)-seq_len+1):
        price_matrix.append(data[index:index+seq_len])
    return price_matrix

def normalize_windows(window_data):

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#Adding an existing project to GitHub using the command line

Simple steps to add existing project to Github.

1. Create a new repository on GitHub.

In Terminal, change the current working directory to your local project.

##2. Initialize the local directory as a Git repository.

git init