@@ -318,7 +318,7 @@ def spike_regressors(
318318 spikes = np .zeros ((max (indices ) + 1 , len (mask )))
319319 for i , m in enumerate (sorted (mask )):
320320 spikes [m , i ] = 1
321- header = ["{:s}{ :02d}". format ( header_prefix , vol ) for vol in range (len (mask ))]
321+ header = [f" { header_prefix } { vol :02d} " for vol in range (len (mask ))]
322322 spikes = pd .DataFrame (data = spikes , columns = header )
323323 if concatenate :
324324 return pd .concat ((data , spikes ), axis = 1 )
@@ -359,7 +359,7 @@ def temporal_derivatives(order, variables, data):
359359 variables_deriv [0 ] = variables
360360 order = set (order ) - set ([0 ])
361361 for o in order :
362- variables_deriv [o ] = ["{ }_derivative{}" . format ( v , o ) for v in variables ]
362+ variables_deriv [o ] = [f" { v } _derivative{ o } " for v in variables ]
363363 data_deriv [o ] = np .tile (np .nan , data [variables ].shape )
364364 data_deriv [o ][o :, :] = np .diff (data [variables ], n = o , axis = 0 )
365365 variables_deriv = reduce ((lambda x , y : x + y ), variables_deriv .values ())
@@ -402,7 +402,7 @@ def exponential_terms(order, variables, data):
402402 variables_exp [1 ] = variables
403403 order = set (order ) - set ([1 ])
404404 for o in order :
405- variables_exp [o ] = ["{ }_power{}" . format ( v , o ) for v in variables ]
405+ variables_exp [o ] = [f" { v } _power{ o } " for v in variables ]
406406 data_exp [o ] = data [variables ] ** o
407407 variables_exp = reduce ((lambda x , y : x + y ), variables_exp .values ())
408408 data_exp = pd .DataFrame (
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