HarshTrivedi

import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

xgfs

xgfs_normal6 = [(64, 83, 211), (221, 179, 16), (181, 29, 20), (0, 190, 255), (251, 73, 176), (0, 178, 93), (202, 202, 202)]
xgfs_normal12 = [(235, 172, 35), (184, 0, 88), (0, 140, 249), (0, 110, 0), (0, 187, 173), (209, 99, 230), (178, 69, 2), (255, 146, 135), (89, 84, 214), (0, 198, 248), (135, 133, 0), (0, 167, 108), (189, 189, 189)]
xgfs_bright6 = [(239, 230, 69), (233, 53, 161), (0, 227, 255), (225, 86, 44), (83, 126, 255), (0, 203, 133), (238, 238, 238)]
xgfs_dark6 = [(0, 89, 0), (0, 0, 120), (73, 13, 0), (138, 3, 79), (0, 90, 138), (68, 53, 0), (88, 88, 88)]
xgfs_fancy6 = [(86, 100, 26), (192, 175, 251), (230, 161, 118), (0, 103, 138), (152, 68, 100), (94, 204, 171), (205, 205, 205)]
xgfs_tarnish6 = [(39, 77, 82), (199, 162, 166), (129, 139, 112), (96, 78, 60), (140, 159, 183), (121, 104, 128), (192, 192, 192)]

Digital2Slave

import matplotlib.pyplot as plt
import numpy as np

def show_images(images, cols = 1, titles = None):
    """Display a list of images in a single figure with matplotlib.
    
    Parameters
    ---------
    images: List of np.arrays compatible with plt.imshow.