def smooth(x, window_len=<NUM_LIT:11>, window='<STR_LIT>'):
if x.ndim != <NUM_LIT:1>:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>if x.size < window_len:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>if window_len < <NUM_LIT:3>:<EOL><INDENT>return x<EOL><DEDENT>if not window in ['<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>']:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>s = np.r_[<NUM_LIT:2> * x[<NUM_LIT:0>] - x[window_len - <NUM_LIT:1>::-<NUM_LIT:1>],<EOL>x, <NUM_LIT:2> * x[-<NUM_LIT:1>] - x[-<NUM_LIT:1>:-window_len:-<NUM_LIT:1>]]<EOL>if window == '<STR_LIT>':  <EOL><INDENT>w = np.ones(window_len, '<STR_LIT:d>')<EOL><DEDENT>else:<EOL><INDENT>w = eval('<STR_LIT>' + window + '<STR_LIT>')<EOL><DEDENT>y = np.convolve(w / w.sum(), s, mode='<STR_LIT>')<EOL>return y[window_len:-window_len + <NUM_LIT:1>]<EOL>
Smoothen a time series.