Remove deprecated view_* methods from docs materials

docs/developers/architecture/app_model.md

@@ -437,7 +437,8 @@ Our provider was context dependent. Only when we have an active viewer with a
 points layer, can it be provided:
 
 ```python
->>> viewer = napari.view_points(name='Some Points')
+>>> viewer = napari.Viewer()
+>>> viewer.add_points(name='Some Points')
 
 >>> injected_func()
 Some Points

docs/developers/contributing/documentation/index.md

@@ -775,7 +775,7 @@ from skimage import data
 import napari
 
 # create the viewer with an image
-viewer = napari.view_image(data.astronaut(), rgb=True)
+viewer, layer = napari.imshow(data.astronaut(), rgb=True)
 
 if __name__ == '__main__':
     napari.run()

docs/developers/contributing/performance/benchmarks.md

@@ -67,7 +67,7 @@ class ViewImageSuite:
 
     def time_view_image(self):
         """Time to view an image."""
-        self.viewer = napari.view_image(self.data)
+        self.viewer, _ = napari.imshow(self.data)
 ```
 
 Here, the creation of the image is completed in the ``setup`` method, and not

docs/guides/layers.md

@@ -188,9 +188,11 @@ existing layer using the `scale` as a keyword argument or property respectively.
 
 ```python
 # scaling while creating the image layer
-napari.view_image(retina, name='retina', scale=[1,10,1,1])
-# scaling an existing layer
+viewer, layer = napari.imshow(retina, name='retina', scale=[1,10,1,1])
+# scaling an existing layer by accessing from the layer list
 viewer.layers['retina'].scale = [1,10,1,1]
+# alternatively using the returned layer variable
+layer.scale = [1,10,1,1]
 ```
 
 ```{raw} html

docs/howtos/extending/magicgui.md

@@ -143,7 +143,7 @@ def threshold_magic_widget(
     return img_as_float(img_layer.data) > threshold
 
 # Create the viewer and add an image
-viewer = napari.view_image(data.camera())
+viewer, _ = napari.imshow(data.camera())
 # Add widget to viewer
 viewer.window.add_dock_widget(threshold_magic_widget)
 ```
@@ -303,7 +303,7 @@ from napari.layers import Image
 def my_widget(image: Image):
     ...
 
-viewer = napari.view_image(np.random.rand(64, 64), name="My Image")
+viewer, _ = napari.imshow(np.random.rand(64, 64), name="My Image")
 viewer.window.add_dock_widget(my_widget)
 ```
 *Note the widget on the right side with "My Image" as the currently selected option*
@@ -508,7 +508,7 @@ def threshold(image: ImageData, threshold: int = 75) -> LabelsData:
     """Threshold an image and return a mask."""
     return (image > threshold).astype(int)
 
-viewer = napari.view_image(np.random.randint(0, 100, (64, 64)))
+viewer, _ = napari.imshow(np.random.randint(0, 100, (64, 64)))
 viewer.window.add_dock_widget(threshold)
 threshold()  # "call the widget" to call the function, so it shows in the
              # screenshot below.

docs/howtos/layers/image.md

@@ -100,10 +100,10 @@ The GUI controls may be adjusted as follows:
 
 ### A simple example
 
-Create a new viewer and add an image in one go using the {func}`napari.view_image`
+Create a new viewer and add an image in one go using the {func}`napari.imshow`
 function, or if you already have an existing viewer, add an image to it using
 `viewer.add_image`. The API for both methods is the same. In these examples
-we'll mainly use `view_image`.
+we'll mainly use `imshow`.
 
 A simple example of viewing an image is as follows:
 
@@ -112,7 +112,7 @@ import napari
 from skimage import data
 
 cells = data.cells3d()[30, 1]  # grab some data
-viewer = napari.view_image(cells, colormap='magma')
+viewer, _ = napari.imshow(cells, colormap='magma')
 ```
 
 ```{code-cell} python
@@ -129,15 +129,15 @@ nbscreenshot(viewer, alt_text="Cells")
 viewer.close()
 ```
 
-## Arguments of `view_image` and `add_image`
+## Arguments of `imshow` and `add_image`
 
-{meth}`~napari.view_layers.view_image` and {meth}`~napari.Viewer.add_image`
+{func}`napari.imshow` and {meth}`~napari.Viewer.add_image`
 accept the same layer-creation parameters.
 
 ```{code-cell} python
 :tags: [hide-output]
 
-help(napari.view_image)
+help(napari.imshow)
 ```
 
 ## Image data and NumPy-like arrays
@@ -234,10 +234,10 @@ from skimage import data
 cells = data.cells3d() #ZCYX image data
 
 # load multichannel image in one line
-viewer = napari.view_image(cells, channel_axis=1)
+viewer, layers = napari.imshow(cells, channel_axis=1)
 
 # load multichannel image in one line, with additional options
-viewer = napari.view_image(
+viewer, layers = napari.imshow(
     cells,
     channel_axis=1,
     name=["membrane", "nuclei"],
@@ -266,7 +266,7 @@ In this example, the `rgb` keyword is explicitly set to `True` because we know
 we are working with an `rgb` image:
 
 ```{code-cell} python
-viewer = napari.view_image(data.astronaut(), rgb=True)
+viewer, layer = napari.imshow(data.astronaut(), rgb=True)
 ```
 
 ```{code-cell} python
@@ -307,7 +307,7 @@ Pass any of these strings to set the image colormap as shown below:
 
 
 ```{code-cell} python
-viewer = napari.view_image(data.moon(), colormap='red')
+viewer, layer = napari.imshow(data.moon(), colormap='red')
 ```
 
 You can also access the current colormap through the `layer.colormap` property
@@ -331,7 +331,7 @@ from vispy.color import Colormap
 cmap = Colormap([[1, 0, 0], [0, 0, 0], [0, 0, 1]])
 image = cell()
 
-viewer = napari.view_image(image, colormap=('diverging', cmap))
+viewer, layer = napari.imshow(image, colormap=('diverging', cmap))
 ```
 
 ```{code-cell} python
@@ -374,7 +374,7 @@ an existing layer using the `contrast_limits` keyword argument or property,
 respectively.
 
 ```{code-cell} python
-viewer = napari.view_image(data.moon(), name='moon')
+viewer, layer = napari.imshow(data.moon(), name='moon')
 viewer.layers['moon'].contrast_limits=(100, 175)
 ```
 

docs/howtos/layers/labels.md

@@ -413,10 +413,8 @@ cause the undo history to be reset.
 ## Controlling the `labels` layer from the console
 ### A simple example
 
-Create a new viewer and add a labels image in one go using the
-{meth}`napari.view_labels` method. If you already have an existing viewer, you
-can add a `Labels` image to it using `viewer.add_labels`. The API for both
-methods is the same. In these examples we'll mainly use `add_labels` to overlay
+Create a new viewer with `napari.Viewer()` and then add a labels image in one go using the {meth}`~napari.Viewer.add_labels` method.
+In these examples we'll mainly use `add_labels` to overlay
 a `Labels` layer onto on image.
 
 In this example of instance segmentation, we will find and segment each of the
@@ -441,7 +439,7 @@ cleared = remove_small_objects(clear_border(bw), 20)
 label_image = label(cleared)
 
 # create the viewer and add the coins image
-viewer = napari.view_image(coins, name='coins')
+viewer, _ = napari.imshow(coins, name='coins')
 # add the labels
 labels_layer = viewer.add_labels(label_image, name='segmentation')
 ```
@@ -460,15 +458,15 @@ nbscreenshot(viewer, alt_text="Segmentation of coins in an image, displayed usin
 viewer.close()
 ```
 
-### Arguments of `view_labels` and `add_labels`
+### Arguments of `add_labels`
 
-{meth}`~napari.view_layers.view_labels` and {meth}`~napari.Viewer.add_labels`
-accept the same layer-creation parameters.
+{meth}`~napari.Viewer.add_labels`
+accepts the following layer-creation parameters.
 
 ```{code-cell} python
 :tags: [hide-output]
 
-help(napari.view_labels)
+help(napari.Viewer.add_labels)
 ```
 
 ### Labels data
@@ -534,7 +532,7 @@ from skimage import data
 from scipy import ndimage as ndi
 
 blobs = data.binary_blobs(length=128, volume_fraction=0.1, n_dim=3)
-viewer = napari.view_image(blobs.astype(float), name='blobs')
+viewer, _ = napari.imshow(blobs.astype(float), name='blobs')
 labeled = ndi.label(blobs)[0]
 labels_layer = viewer.add_labels(labeled, name='blob ID')
 viewer.dims.ndisplay = 3

docs/howtos/layers/points.md

@@ -217,11 +217,8 @@ layer:
 ## Controlling the `points` layer programmatically
 ### A simple example
 
-You can create a new viewer and add a set of points in one go using the
-{meth}`napari.view_points` method, or if you already have an existing viewer,
-you can add points to it using `viewer.add_points`. The API of both methods is
-the same. In these examples we'll mainly use `add_points` to overlay points onto
-on an existing image.
+You can create a new viewer with `napari.Viewer()` and add a set of points with the `viewer.add_points` method.
+In these examples we'll mainly use `add_points` to overlay points onto an existing image.
 
 Each data point can have annotations associated with it using the
 `Points.features` table. These features can be used to set the face and
@@ -238,7 +235,7 @@ import napari
 import numpy as np
 from skimage import data
 
-viewer = napari.view_image(data.astronaut(), rgb=True)
+viewer, _ = napari.imshow(data.astronaut(), rgb=True)
 points = np.array([[100, 100], [200, 200], [300, 100]])
 
 points_layer = viewer.add_points(points, size=30)
@@ -258,15 +255,15 @@ nbscreenshot(viewer, alt_text="3 points overlaid on an astronaut image")
 viewer.close()
 ```
 
-### Arguments of `view_points` and `add_points`
+### Arguments of `add_points`
 
-{meth}`~napari.view_layers.view_points` and {meth}`~napari.Viewer.add_points`
-accept the same layer-creation parameters.
+{meth}`~napari.Viewer.add_points`
+accepts the following layer-creation parameters.
 
 ```{code-cell} python
 :tags: [hide-output]
 
-help(napari.view_points)
+help(napari.Viewer.add_points)
 ```
 
 ### Points data
@@ -403,7 +400,7 @@ To do the same for a face color, substitute `face_color` for `border_color` in t
 example snippet below.
 
 ```{code-cell} python
-viewer = napari.view_image(data.astronaut(), rgb=True)
+viewer, _ = napari.imshow(data.astronaut(), rgb=True)
 points = np.array([[100, 100], [200, 200], [300, 100]])
 point_features = {
     'good_point': [True, True, False],
@@ -448,7 +445,7 @@ colormap on a feature. To do the same for a border color, substitute `face` for
 `border`.
 
 ```{code-cell} python
-viewer = napari.view_image(data.astronaut(), rgb=True)
+viewer, _ = napari.imshow(data.astronaut(), rgb=True)
 points = np.array([[100, 100], [200, 200], [300, 100]])
 point_features = {
     'good_point': [True, True, False],

docs/howtos/layers/shapes.md

@@ -340,11 +340,9 @@ are used. i.e. You can't remove a vertex before you have created a shape.
 ## Controlling the shapes layer programmatically
 ### A simple example
 
-You can create a new viewer and add a list of shapes in one go using the
-{meth}`napari.view_shapes` method, or if you already have an existing viewer,
-you can add shapes to it using `viewer.add_shapes`. The API of both methods is
-the same. In these examples we'll mainly use `add_shapes` to overlay shapes onto
-an existing image.
+You can create a new viewer with `napari.Viewer()` and add a list of shapes with the
+`viewer.add_shapes` method.
+In these examples we'll mainly use `add_shapes` to overlay shapes onto an existing image.
 
 In this example, we will overlay shapes on the image of a photographer:
 
@@ -369,7 +367,7 @@ building = np.array([[310, 382], [229, 381], [209, 401], [221, 411],
 polygons = [triangle, person, building]
 
 # add the image
-viewer = napari.view_image(data.camera(), name='photographer')
+viewer, _ = napari.imshow(data.camera(), name='photographer')
 
 # add the polygons
 shapes_layer = viewer.add_shapes(
@@ -395,15 +393,15 @@ nbscreenshot(viewer, alt_text="Shapes overlaid on image")
 viewer.close()
 ```
 
-### Arguments of `view_shapes` and `add_shapes`
+### Arguments of `add_shapes`
 
-{meth}`~napari.view_layers.view_shapes` and {meth}`~napari.Viewer.add_shapes`
-accept the same layer-creation parameters.
+{meth}`~napari.Viewer.add_shapes`
+accepts the following layer-creation parameters.
 
 ```{code-cell} python
 :tags: [hide-output]
 
-help(napari.view_shapes)
+help(napari.Viewer.add_shapes)
 ```
 
 ### Shapes data
@@ -465,7 +463,7 @@ import numpy as np
 from skimage import data
 
 # add the image
-viewer = napari.view_image(data.camera(), name='photographer')
+viewer, _ = napari.imshow(data.camera(), name='photographer')
 
 # create a triangle
 triangle = np.array([[11, 13], [111, 113], [22, 246]])
@@ -513,7 +511,7 @@ import numpy as np
 from skimage import data
 
 # add the image
-viewer = napari.view_image(data.camera(), name='photographer')
+viewer, _ = napari.imshow(data.camera(), name='photographer')
 
 # create some ellipses
 ellipse = np.array([[59, 222], [110, 289], [170, 243], [119, 176]])

docs/howtos/layers/surface.md

@@ -39,10 +39,8 @@ colormap.
 
 ## A simple example
 
-You can create a new viewer and add a surface in one go using the
-{meth}`napari.view_surface` method, or if you already have an existing viewer,
-you can add an image to it using `viewer.add_surface`. The API of both methods
-is the same. In these examples we'll mainly use `view_surface`.
+You can create a new viewer with `napari.Viewer()` and add a surface using the
+{meth}`~napari.Viewer.add_surface` method.
 
 A simple example of viewing a surface follows. You can copy and paste these
 statements into the napari console to see how they work:
@@ -56,7 +54,8 @@ faces = np.array([[0, 1, 2], [1, 2, 3]])
 values = np.linspace(0, 1, len(vertices))
 surface = (vertices, faces, values)
 
-viewer = napari.view_surface(surface)  # add the surface
+viewer = napari.Viewer()
+viewer.add_surface(surface)  # add the surface
 ```
 
 ```{code-cell} python
@@ -102,15 +101,14 @@ controls are available in the viewer:
     section of _Layers at a glance_ for an explanation of each type of blending.
   * Shading - Choose `none`, `flat`, or `smooth` from the dropdown.
 
-## Arguments of `view_surface` and `add_surface`
+## Arguments of `add_surface`
 
-{meth}`~napari.view_layers.view_surface` and {meth}`~napari.Viewer.add_surface`
-accept the same layer-creation parameters.
+{meth}`~napari.Viewer.add_surface` accepts the following layer-creation parameters.
 
 ```{code-cell} python
 :tags: [hide-output]
 
-help(napari.view_surface)
+help(napari.Viewer.add_surface)
 ```
 
 ## Surface data