@@ -319,7 +319,7 @@ def spike_regressors(
319319 spikes = np .zeros ((max (indices ) + 1 , len (mask )))
320320 for i , m in enumerate (sorted (mask )):
321321 spikes [m , i ] = 1
322- header = ["{:s}{ :02d}". format ( header_prefix , vol ) for vol in range (len (mask ))]
322+ header = [f" { header_prefix } { vol :02d} " for vol in range (len (mask ))]
323323 spikes = pd .DataFrame (data = spikes , columns = header )
324324 if concatenate :
325325 return pd .concat ((data , spikes ), axis = 1 )
@@ -360,7 +360,7 @@ def temporal_derivatives(order, variables, data):
360360 variables_deriv [0 ] = variables
361361 order = set (order ) - set ([0 ])
362362 for o in order :
363- variables_deriv [o ] = ["{ }_derivative{}" . format ( v , o ) for v in variables ]
363+ variables_deriv [o ] = [f" { v } _derivative{ o } " for v in variables ]
364364 data_deriv [o ] = np .tile (np .nan , data [variables ].shape )
365365 data_deriv [o ][o :, :] = np .diff (data [variables ], n = o , axis = 0 )
366366 variables_deriv = reduce (operator .add , variables_deriv .values ())
@@ -403,7 +403,7 @@ def exponential_terms(order, variables, data):
403403 variables_exp [1 ] = variables
404404 order = set (order ) - set ([1 ])
405405 for o in order :
406- variables_exp [o ] = ["{ }_power{}" . format ( v , o ) for v in variables ]
406+ variables_exp [o ] = [f" { v } _power{ o } " for v in variables ]
407407 data_exp [o ] = data [variables ] ** o
408408 variables_exp = reduce (operator .add , variables_exp .values ())
409409 data_exp = pd .DataFrame (
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