utils.py
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	import numpy as np
	from random import randint
	import os
	import matplotlib.pyplot as plt
	import pandas as pd
	from prettytable import PrettyTable
	from solver import training,testing,reconstruction


	def plot_time_series(data, class_name, ax,colour,i, n_steps=10):
	time_series_df = pd.DataFrame(data)

	smooth_path = time_series_df.rolling(n_steps).mean()
	path_deviation = 3 * time_series_df.rolling(n_steps).std()

	under_line = (smooth_path - path_deviation)[0]
	over_line = (smooth_path + path_deviation)[0]

	ax.plot(smooth_path,color=colour, linewidth=3)
	ax.fill_between(
	path_deviation.index,
	under_line,
	over_line,
	alpha=.450
	)
	ax.set_title(class_name)
	ax.set_ylim([-0.1, 0.1])
	ax.set_ylabel('Amplitude (V)')
	ax.set_xlabel('Window size (μs)')

	def plot_prediction(data, neuralnet, title, ax):

	pred_losses, data,predictions = reconstruction(neuralnet=neuralnet, dataset=data)
	# predictions,pred_losses = model.ploterrortransform(data,device)
	ax.plot(data,'black' , label='Original')
	ax.plot(predictions,'r', label='GAN prediction')
	ax.set_title(f'{title} (loss: {"{:.2f}".format(pred_losses)})')
	ax.legend()



	def count_parameters(model):
	table = PrettyTable(["Modules", "Parameters"])
	total_params = 0
	for name, parameter in model.named_parameters():
	if not parameter.requires_grad: continue
	param = parameter.numel()
	table.add_row([name, param])
	total_params+=param
	print(table)
	print(f"Total Trainable Params: {total_params}")
	return total_params