@@ -3279,16 +3279,16 @@ def inverse(self, value):
32793279
32803280class MultiNorm (Normalize ):
32813281"""
3282- Amixin class which contains multiple scalar norms
3282+ A class which contains multiple scalar norms
32833283 """
32843284
32853285def __init__ (self ,norms ,vmin = None ,vmax = None ,clip = False ):
32863286"""
32873287 Parameters
32883288 ----------
3289- norms :List of (str, `Normalize` or None)
3289+ norms :list of (str, `Normalize` or None)
32903290 The constituent norms. The list must have a minimum length of 2.
3291- vmin, vmax : float, None, or list of float or None
3291+ vmin, vmax : float or None or list of( float or None)
32923292 Limits of the constituent norms.
32933293 If a list, each value is assigned to each of the constituent
32943294 norms. Single values are repeated to form a list of appropriate size.
@@ -3332,14 +3332,17 @@ def __init__(self, norms, vmin=None, vmax=None, clip=False):
33323332
33333333@property
33343334def n_variables (self ):
3335+ """Number of norms held by this `MultiNorm`."""
33353336return len (self ._norms )
33363337
33373338@property
33383339def norms (self ):
3340+ """The individual norms held by this `MultiNorm`"""
33393341return self ._norms
33403342
33413343@property
33423344def vmin (self ):
3345+ """The lower limit of each constituent norm."""
33433346return tuple (n .vmin for n in self ._norms )
33443347
33453348@vmin .setter
@@ -3353,6 +3356,7 @@ def vmin(self, value):
33533356
33543357@property
33553358def vmax (self ):
3359+ """The upper limit of each constituent norm."""
33563360return tuple (n .vmax for n in self ._norms )
33573361
33583362@vmax .setter
@@ -3366,6 +3370,7 @@ def vmax(self, value):
33663370
33673371@property
33683372def clip (self ):
3373+ """The clip behaviour of each constituent norm."""
33693374return tuple (n .clip for n in self ._norms )
33703375
33713376@clip .setter
@@ -3392,17 +3397,17 @@ def __call__(self, value, clip=None):
33923397
33933398 Parameters
33943399 ----------
3395- value
3396- Data to normalize. Must be of length `n_variables` orhave adata type with
3397- `n_variables` fields.
3400+ value : array-like
3401+ Data to normalize. Must be of length `n_variables` orbe astructured
3402+ array or scalar with `n_variables` fields.
33983403 clip : list of bools or bool, optional
33993404 See the description of the parameter *clip* in Normalize.
34003405 If ``None``, defaults to ``self.clip`` (which defaults to
34013406 ``False``).
34023407
34033408 Returns
34043409 -------
3405- List
3410+ list
34063411 Normalized input values as a list of length `n_variables`
34073412
34083413 Notes
@@ -3426,8 +3431,8 @@ def inverse(self, value):
34263431 Parameters
34273432 ----------
34283433 value
3429- Normalized value. Must be of length `n_variables` orhave adata type with
3430- `n_variables` fields.
3434+ Normalized value. Must be of length `n_variables` orbe astructured array
3435+ or scalar with `n_variables` fields.
34313436 """
34323437value = self ._iterable_variates_in_data (value ,self .n_variables )
34333438result = [n .inverse (v )for n ,v in zip (self .norms ,value )]
@@ -3454,8 +3459,8 @@ def autoscale_None(self, A):
34543459 Parameters
34553460 ----------
34563461 A
3457- Data, must be of length `n_variables` orhave adata type with
3458- `n_variables` fields.
3462+ Data, must be of length `n_variables` orbe astructured array or scalar
3463+ with `n_variables` fields.
34593464 """
34603465with self .callbacks .blocked ():
34613466A = self ._iterable_variates_in_data (A ,self .n_variables )
@@ -3464,7 +3469,7 @@ def autoscale_None(self, A):
34643469self ._changed ()
34653470
34663471def scaled (self ):
3467- """Return whether both *vmin* and *vmax* are set on all constituent norms"""
3472+ """Return whether both *vmin* and *vmax* are set on all constituent norms. """
34683473return all ([n .scaled ()for n in self .norms ])
34693474
34703475@staticmethod
@@ -3490,8 +3495,8 @@ def _iterable_variates_in_data(data, n_variables):
34903495data = [data [descriptor [0 ]]for descriptor in data .dtype .descr ]
34913496if len (data )!= n_variables :
34923497raise ValueError ("The input to this `MultiNorm` must be of shape "
3493- f"({ n_variables } have a data type with "
3494- f"{ n_variables } )
3498+ f"({ n_variables } be structured array or scalar "
3499+ f"with { n_variables } )
34953500return data
34963501
34973502