import time import matplotlib.pyplot as plt import dill import numpy as np current_milli_time = lambda: int(round(time.time() * 1000)) def uniquefilename(name): if '.' in name: tmp = name.split('.') return tmp[0] + str(current_milli_time()) + '.' + tmp[1] else: return name + str(current_milli_time()) def showAndSaveImage(fig,file,flag,lgd=None): """ Show and image and save on file :param fig: Matplotlib Figure object :param file: filename to save the picture :param flag: if True the image will be saved :param lgd: legend """ if flag: plt.show() if lgd is not None: fig.savefig(file, additional_artists=lgd,bbox_inches='tight') #bbox_extra_artists=(lgd,), ) else: fig.savefig(file) plt.close(fig) def enumerate2(xs, start=0, step=1): for x in xs: yield (start, x) start += step def sliding_window(data, windowsize, train=0.8, inc=0.1): """ Sliding window method of cross validation for time series :param data: the entire dataset :param windowsize: window size :param train: percentual of the window size will be used for training the models :param inc: percentual of data used for slide the window :return: window count, training set, test set """ l = len(data) ttrain = int(round(windowsize * train, 0)) ic = int(round(windowsize * inc, 0)) for count in np.arange(0,l-windowsize+ic,ic): if count + windowsize > l: _end = l else: _end = count + windowsize yield (count, data[count : count + ttrain], data[count + ttrain : _end] ) def persist_obj(obj, file): """ Persist an object on filesystem. This function depends on Dill package :param obj: object on memory :param file: file name to store the object """ with open(file, 'wb') as _file: dill.dump(obj, _file) def load_obj(file): """ Load to memory an object stored filesystem. This function depends on Dill package :param file: file name where the object is stored :return: object """ with open(file, 'rb') as _file: obj = dill.load(_file) return obj def persist_env(file): """ Persist an entire environment on file. This function depends on Dill package :param file: file name to store the environment """ dill.dump_session(file) def load_env(file): dill.load_session(file)