@@ -3352,7 +3352,7 @@ def n_components(self):
33523352
33533353@property
33543354def norms (self ):
3355- """The individual norms held by this `MultiNorm`"""
3355+ """The individual norms held by this `MultiNorm`. """
33563356return self ._norms
33573357
33583358@property
@@ -3404,15 +3404,15 @@ def _changed(self):
34043404 """
34053405self .callbacks .process ('changed' )
34063406
3407- def __call__ (self ,value ,clip = None ,structured_output = None ):
3407+ def __call__ (self ,values ,clip = None ,structured_output = None ):
34083408"""
34093409 Normalize the data and return the normalized data.
34103410
3411- Each component of the input isassigned to the constituent norm.
3411+ Each component of the input isnormalized via the constituent norm.
34123412
34133413 Parameters
34143414 ----------
3415- value : array-like
3415+ values : array-like
34163416 Data to normalize, as tuple, scalar array or structured array.
34173417
34183418 - If tuple, must be of length `n_components`
@@ -3439,44 +3439,44 @@ def __call__(self, value, clip=None, structured_output=None):
34393439 Notes
34403440 -----
34413441 If not already initialized, ``self.vmin`` and ``self.vmax`` are
3442- initialized using ``self.autoscale_None(value )``.
3442+ initialized using ``self.autoscale_None(values )``.
34433443 """
34443444if clip is None :
34453445clip = self .clip
34463446elif not np .iterable (clip ):
34473447clip = [clip ]* self .n_components
34483448
34493449if structured_output is None :
3450- if isinstance (value ,np .ndarray )and value .dtype .fields is not None :
3450+ if isinstance (values ,np .ndarray )and values .dtype .fields is not None :
34513451structured_output = True
34523452else :
34533453structured_output = False
34543454
3455- value = self ._iterable_components_in_data (value ,self .n_components )
3455+ values = self ._iterable_components_in_data (values ,self .n_components )
34563456
3457- result = tuple (n (v ,clip = c )for n ,v ,c in zip (self .norms ,value ,clip ))
3457+ result = tuple (n (v ,clip = c )for n ,v ,c in zip (self .norms ,values ,clip ))
34583458
34593459if structured_output :
34603460result = self ._ensure_multicomponent_data (result ,self .n_components )
34613461
34623462return result
34633463
3464- def inverse (self ,value ):
3464+ def inverse (self ,values ):
34653465"""
3466- Map the normalizedvalue (i.e., index in the colormap) back toimage datavalue .
3466+ Map the normalizedvalues (i.e., index in the colormap) back to datavalues .
34673467
34683468 Parameters
34693469 ----------
3470- value
3471- Normalizedvalue , as tuple, scalar array or structured array.
3470+ values
3471+ Normalizedvalues , as tuple, scalar array or structured array.
34723472
34733473 - If tuple, must be of length `n_components`
34743474 - If scalar array, the first axis must be of length `n_components`
34753475 - If structured array, must have `n_components` fields.
34763476
34773477 """
3478- value = self ._iterable_components_in_data (value ,self .n_components )
3479- result = [n .inverse (v )for n ,v in zip (self .norms ,value )]
3478+ values = self ._iterable_components_in_data (values ,self .n_components )
3479+ result = [n .inverse (v )for n ,v in zip (self .norms ,values )]
34803480return result
34813481
34823482def autoscale (self ,A ):
@@ -3487,8 +3487,8 @@ def autoscale(self, A):
34873487 Parameters
34883488 ----------
34893489 A
3490- Data, must be of length `n_components` or be a structuredarray or scalar
3491- with `n_components` fields.
3490+ Data, must be of length `n_components` or be a structuredscalar or
3491+ structured array with `n_components` fields.
34923492 """
34933493with self .callbacks .blocked ():
34943494# Pause callbacks while we are updating so we only get
@@ -3561,7 +3561,7 @@ def _ensure_multicomponent_data(data, n_components):
35613561 Parameters
35623562 ----------
35633563 n_components : int
3564- - number of omponents in the data
3564+ Number of omponents in the data.
35653565 data : np.ndarray, PIL.Image or None
35663566
35673567 Returns